diff --git a/previews/PR441/404.html b/previews/PR441/404.html index 6152dcf15..8d8b66ffc 100644 --- a/previews/PR441/404.html +++ b/previews/PR441/404.html @@ -9,7 +9,7 @@ - + @@ -19,7 +19,7 @@
- + \ No newline at end of file diff --git a/previews/PR441/api/affine.html b/previews/PR441/api/affine.html index f4ae0a13a..022e25d6e 100644 --- a/previews/PR441/api/affine.html +++ b/previews/PR441/api/affine.html @@ -9,11 +9,11 @@ - + - + - + @@ -26,12 +26,12 @@ %1 = affine.apply #map10 (%s, %t) // Inline example. -%2 = affine.apply affine_map<(i)[s0] -> (i+s0)> (%42)[%n]

source

Reactant.MLIR.Dialects.affine.delinearize_index Method

delinearize_index

The affine.delinearize_index operation takes a single index value and calculates the multi-index according to the given basis.

Example

%indices:3 = affine.delinearize_index %linear_index into (%c16, %c224, %c224) : index, index, index

In the above example, %indices:3 conceptually holds the following:

#map0 = affine_map<()[s0] -> (s0 floordiv 50176)>
+%2 = affine.apply affine_map<(i)[s0] -> (i+s0)> (%42)[%n]

source

Reactant.MLIR.Dialects.affine.delinearize_index Method

delinearize_index

The affine.delinearize_index operation takes a single index value and calculates the multi-index according to the given basis.

Example

%indices:3 = affine.delinearize_index %linear_index into (%c16, %c224, %c224) : index, index, index

In the above example, %indices:3 conceptually holds the following:

#map0 = affine_map<()[s0] -> (s0 floordiv 50176)>
 #map1 = affine_map<()[s0] -> ((s0 mod 50176) floordiv 224)>
 #map2 = affine_map<()[s0] -> (s0 mod 224)>
 %indices_0 = affine.apply #map0()[%linear_index]
 %indices_1 = affine.apply #map1()[%linear_index]
-%indices_2 = affine.apply #map2()[%linear_index]

The basis may either contain N or N-1 elements, where N is the number of results. If there are N basis elements, the first one will not be used during computations, but may be used during analysis and canonicalization to eliminate terms from the affine.delinearize_index or to enable conclusions about the total size of %linear_index.

If the basis is fully provided, the delinearize_index operation is said to "have an outer bound". The builders assume that an affine.delinearize_index has an outer bound by default, as this is how the operation was initially defined.

That is, the example above could also have been written

mlir
%0:3 = affine.delinearize_index %linear_index into (244, 244) : index, index

Note that, due to the constraints of affine maps, all the basis elements must be strictly positive. A dynamic basis element being 0 or negative causes undefined behavior.

source

Reactant.MLIR.Dialects.affine.for_ Method

for_

Syntax

operation   ::= `affine.for` ssa-id `=` lower-bound `to` upper-bound
+%indices_2 = affine.apply #map2()[%linear_index]

The basis may either contain N or N-1 elements, where N is the number of results. If there are N basis elements, the first one will not be used during computations, but may be used during analysis and canonicalization to eliminate terms from the affine.delinearize_index or to enable conclusions about the total size of %linear_index.

If the basis is fully provided, the delinearize_index operation is said to "have an outer bound". The builders assume that an affine.delinearize_index has an outer bound by default, as this is how the operation was initially defined.

That is, the example above could also have been written

mlir
%0:3 = affine.delinearize_index %linear_index into (244, 244) : index, index

Note that, due to the constraints of affine maps, all the basis elements must be strictly positive. A dynamic basis element being 0 or negative causes undefined behavior.

source

Reactant.MLIR.Dialects.affine.for_ Method

for_

Syntax

operation   ::= `affine.for` ssa-id `=` lower-bound `to` upper-bound
                 (`step` integer-literal)? `{` op* `}`
 
 lower-bound ::= `max`? affine-map-attribute dim-and-symbol-use-list | shorthand-bound
@@ -66,7 +66,7 @@
   %y0 = arith.addi %arg0, %c1 : index
   %y1 = arith.addi %arg1, %c2 : index
   affine.yield %y0, %y1 : index, index
-}

If the affine.for defines any values, a yield terminator must be explicitly present. The number and types of the "affine.for" results must match the initial values in the iter_args binding and the yield operands.

source

Reactant.MLIR.Dialects.affine.if_ Method

if_

Syntax

operation  ::= `affine.if` if-op-cond `{` op* `}` (`else` `{` op* `}`)?
+}

If the affine.for defines any values, a yield terminator must be explicitly present. The number and types of the "affine.for" results must match the initial values in the iter_args binding and the yield operands.

source

Reactant.MLIR.Dialects.affine.if_ Method

if_

Syntax

operation  ::= `affine.if` if-op-cond `{` op* `}` (`else` `{` op* `}`)?
 if-op-cond ::= integer-set-attr dim-and-symbol-use-list

The affine.if operation restricts execution to a subset of the loop iteration space defined by an integer set (a conjunction of affine constraints). A single affine.if may end with an optional else clause.

The condition of the affine.if is represented by an integer set (a conjunction of affine constraints), and the SSA values bound to the dimensions and symbols in the integer set. The same restrictions hold for these SSA values as for all bindings of SSA values to dimensions and symbols.

The affine.if operation contains two regions for the "then" and "else" clauses. affine.if may return results that are defined in its regions. The values defined are determined by which execution path is taken. Each region of the affine.if must contain a single block with no arguments, and be terminated by affine.yield. If affine.if defines no values, the affine.yield can be left out, and will be inserted implicitly. Otherwise, it must be explicit. If no values are defined, the else block may be empty (i.e. contain no blocks).

Example

mlir
#set = affine_set<(d0, d1)[s0]: (d0 - 10 >= 0, s0 - d0 - 9 >= 0,
                                  d1 - 10 >= 0, s0 - d1 - 9 >= 0)>
 func.func @reduced_domain_example(%A, %X, %N) : (memref<10xi32>, i32, i32) {
@@ -95,10 +95,10 @@
     affine.store %1, %O[%i, %j] : memref<12x12xf32>
   }
   return %O
-}

source

Reactant.MLIR.Dialects.affine.linearize_index Method

linearize_index

The affine.linearize_index operation takes a sequence of index values and a basis of the same length and linearizes the indices using that basis.

That is, for indices %idx_0 to %idx_{N-1} and basis elements b_0 (or b_1) up to b_{N-1} it computes

sum(i = 0 to N-1) %idx_i * product(j = i + 1 to N-1) B_j

The basis may either have N or N-1 elements, where N is the number of inputs to linearize_index. If N inputs are provided, the first one is not used in computation, but may be used during analysis or canonicalization as a bound on %idx_0.

If all N basis elements are provided, the linearize_index operation is said to "have an outer bound".

If the disjoint property is present, this is an optimization hint that, for all i, 0 <= %idx_i < B_i - that is, no index affects any other index, except that %idx_0 may be negative to make the index as a whole negative.

Note that the outputs of affine.delinearize_index are, by definition, disjoint.

Example

mlir
%linear_index = affine.linearize_index [%index_0, %index_1, %index_2] by (2, 3, 5) : index
+}

source

Reactant.MLIR.Dialects.affine.linearize_index Method

linearize_index

The affine.linearize_index operation takes a sequence of index values and a basis of the same length and linearizes the indices using that basis.

That is, for indices %idx_0 to %idx_{N-1} and basis elements b_0 (or b_1) up to b_{N-1} it computes

sum(i = 0 to N-1) %idx_i * product(j = i + 1 to N-1) B_j

The basis may either have N or N-1 elements, where N is the number of inputs to linearize_index. If N inputs are provided, the first one is not used in computation, but may be used during analysis or canonicalization as a bound on %idx_0.

If all N basis elements are provided, the linearize_index operation is said to "have an outer bound".

If the disjoint property is present, this is an optimization hint that, for all i, 0 <= %idx_i < B_i - that is, no index affects any other index, except that %idx_0 may be negative to make the index as a whole negative.

Note that the outputs of affine.delinearize_index are, by definition, disjoint.

Example

mlir
%linear_index = affine.linearize_index [%index_0, %index_1, %index_2] by (2, 3, 5) : index
 // Same effect
 %linear_index = affine.linearize_index [%index_0, %index_1, %index_2] by (3, 5) : index

In the above example, %linear_index conceptually holds the following:

mlir
#map = affine_map<()[s0, s1, s2] -> (s0 * 15 + s1 * 5 + s2)>
-%linear_index = affine.apply #map()[%index_0, %index_1, %index_2]

source

Reactant.MLIR.Dialects.affine.load Method

load

Syntax

operation ::= ssa-id `=` `affine.load` ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.load op reads an element from a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The output of affine.load is a new value with the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
%1 = affine.load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

Reactant.MLIR.Dialects.affine.max Method

max

The affine.max operation computes the maximum value result from a multi-result affine map.

Example

mlir
%0 = affine.max (d0) -> (1000, d0 + 512) (%i0) : index

source

Reactant.MLIR.Dialects.affine.min Method

min

Syntax

operation ::= ssa-id `=` `affine.min` affine-map-attribute dim-and-symbol-use-list

The affine.min operation applies an affine mapping to a list of SSA values, and returns the minimum value of all result expressions. The number of dimension and symbol arguments to affine.min must be equal to the respective number of dimensional and symbolic inputs to the affine mapping; the affine.min operation always returns one value. The input operands and result must all have 'index' type.

Example

mlir
%0 = affine.min affine_map<(d0)[s0] -> (1000, d0 + 512, s0)> (%arg0)[%arg1]

source

Reactant.MLIR.Dialects.affine.parallel Method

parallel

The affine.parallel operation represents a hyper-rectangular affine parallel band, defining zero or more SSA values for its induction variables. It has one region capturing the parallel band body. The induction variables are represented as arguments of this region. These SSA values always have type index, which is the size of the machine word. The strides, represented by steps, are positive constant integers which defaults to "1" if not present. The lower and upper bounds specify a half-open range: the range includes the lower bound but does not include the upper bound. The body region must contain exactly one block that terminates with affine.yield.

The lower and upper bounds of a parallel operation are represented as an application of an affine mapping to a list of SSA values passed to the map. The same restrictions hold for these SSA values as for all bindings of SSA values to dimensions and symbols. The list of expressions in each map is interpreted according to the respective bounds group attribute. If a single expression belongs to the group, then the result of this expression is taken as a lower(upper) bound of the corresponding loop induction variable. If multiple expressions belong to the group, then the lower(upper) bound is the max(min) of these values obtained from these expressions. The loop band has as many loops as elements in the group bounds attributes.

Each value yielded by affine.yield will be accumulated/reduced via one of the reduction methods defined in the AtomicRMWKind enum. The order of reduction is unspecified, and lowering may produce any valid ordering. Loops with a 0 trip count will produce as a result the identity value associated with each reduction (i.e. 0.0 for addf, 1.0 for mulf). Assign reductions for loops with a trip count != 1 produces undefined results.

Note: Calling AffineParallelOp::build will create the required region and block, and insert the required terminator if it is trivial (i.e. no values are yielded). Parsing will also create the required region, block, and terminator, even when they are missing from the textual representation.

Example (3x3 valid convolution):

mlir
func.func @conv_2d(%D : memref<100x100xf32>, %K : memref<3x3xf32>) -> (memref<98x98xf32>) {
+%linear_index = affine.apply #map()[%index_0, %index_1, %index_2]

source

Reactant.MLIR.Dialects.affine.load Method

load

Syntax

operation ::= ssa-id `=` `affine.load` ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.load op reads an element from a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The output of affine.load is a new value with the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
%1 = affine.load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

Reactant.MLIR.Dialects.affine.max Method

max

The affine.max operation computes the maximum value result from a multi-result affine map.

Example

mlir
%0 = affine.max (d0) -> (1000, d0 + 512) (%i0) : index

source

Reactant.MLIR.Dialects.affine.min Method

min

Syntax

operation ::= ssa-id `=` `affine.min` affine-map-attribute dim-and-symbol-use-list

The affine.min operation applies an affine mapping to a list of SSA values, and returns the minimum value of all result expressions. The number of dimension and symbol arguments to affine.min must be equal to the respective number of dimensional and symbolic inputs to the affine mapping; the affine.min operation always returns one value. The input operands and result must all have 'index' type.

Example

mlir
%0 = affine.min affine_map<(d0)[s0] -> (1000, d0 + 512, s0)> (%arg0)[%arg1]

source

Reactant.MLIR.Dialects.affine.parallel Method

parallel

The affine.parallel operation represents a hyper-rectangular affine parallel band, defining zero or more SSA values for its induction variables. It has one region capturing the parallel band body. The induction variables are represented as arguments of this region. These SSA values always have type index, which is the size of the machine word. The strides, represented by steps, are positive constant integers which defaults to "1" if not present. The lower and upper bounds specify a half-open range: the range includes the lower bound but does not include the upper bound. The body region must contain exactly one block that terminates with affine.yield.

The lower and upper bounds of a parallel operation are represented as an application of an affine mapping to a list of SSA values passed to the map. The same restrictions hold for these SSA values as for all bindings of SSA values to dimensions and symbols. The list of expressions in each map is interpreted according to the respective bounds group attribute. If a single expression belongs to the group, then the result of this expression is taken as a lower(upper) bound of the corresponding loop induction variable. If multiple expressions belong to the group, then the lower(upper) bound is the max(min) of these values obtained from these expressions. The loop band has as many loops as elements in the group bounds attributes.

Each value yielded by affine.yield will be accumulated/reduced via one of the reduction methods defined in the AtomicRMWKind enum. The order of reduction is unspecified, and lowering may produce any valid ordering. Loops with a 0 trip count will produce as a result the identity value associated with each reduction (i.e. 0.0 for addf, 1.0 for mulf). Assign reductions for loops with a trip count != 1 produces undefined results.

Note: Calling AffineParallelOp::build will create the required region and block, and insert the required terminator if it is trivial (i.e. no values are yielded). Parsing will also create the required region, block, and terminator, even when they are missing from the textual representation.

Example (3x3 valid convolution):

mlir
func.func @conv_2d(%D : memref<100x100xf32>, %K : memref<3x3xf32>) -> (memref<98x98xf32>) {
   %O = memref.alloc() : memref<98x98xf32>
   affine.parallel (%x, %y) = (0, 0) to (98, 98) {
     %0 = affine.parallel (%kx, %ky) = (0, 0) to (2, 2) reduce ("addf") -> f32 {
@@ -115,8 +115,8 @@
                           to (min(%ii + 32, %N), min(%jj + 32, %M)) {
     call @f(%i, %j) : (index, index) -> ()
   }
-}

source

Reactant.MLIR.Dialects.affine.prefetch Method

prefetch

The affine.prefetch op prefetches data from a memref location described with an affine subscript similar to affine.load, and has three attributes: a read/write specifier, a locality hint, and a cache type specifier as shown below:

mlir
affine.prefetch %0[%i, %j + 5], read, locality<3>, data : memref<400x400xi32>

The read/write specifier is either 'read' or 'write', the locality hint specifier ranges from locality<0> (no locality) to locality<3> (extremely local keep in cache). The cache type specifier is either 'data' or 'instr' and specifies whether the prefetch is performed on data cache or on instruction cache.

source

Reactant.MLIR.Dialects.affine.store Method

store

Syntax

operation ::= `affine.store` ssa-use, ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.store op writes an element to a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The affine.store op stores a new value which is the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
affine.store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
affine.store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

Reactant.MLIR.Dialects.affine.vector_load Method

vector_load

The affine.vector_load is the vector counterpart of affine.load. It reads a slice from a MemRef, supplied as its first operand, into a vector of the same base elemental type. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the read within the memref. The shape of the return vector type determines the shape of the slice read from the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector loads will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector load.

mlir
%1 = affine.vector_load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector load. Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.vector_load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector load.

mlir
%1 = affine.vector_load %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_read).

source

Reactant.MLIR.Dialects.affine.vector_store Method

vector_store

The affine.vector_store is the vector counterpart of affine.store. It writes a vector, supplied as its first operand, into a slice within a MemRef of the same base elemental type, supplied as its second operand. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the write within the memref. The shape of th input vector determines the shape of the slice written to the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector stores will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector store.

mlir
affine.vector_store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector store. Uses symbol keyword for symbols %n and %m.

mlir
affine.vector_store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector store.

mlir
affine.vector_store %v0, %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_write).

source

Reactant.MLIR.Dialects.affine.yield Method

yield

The affine.yield yields zero or more SSA values from an affine op region and terminates the region. The semantics of how the values yielded are used is defined by the parent operation. If affine.yield has any operands, the operands must match the parent operation's results. If the parent operation defines no values, then the affine.yield may be left out in the custom syntax and the builders will insert one implicitly. Otherwise, it has to be present in the syntax to indicate which values are yielded.

source

- +}

source

Reactant.MLIR.Dialects.affine.prefetch Method

prefetch

The affine.prefetch op prefetches data from a memref location described with an affine subscript similar to affine.load, and has three attributes: a read/write specifier, a locality hint, and a cache type specifier as shown below:

mlir
affine.prefetch %0[%i, %j + 5], read, locality<3>, data : memref<400x400xi32>

The read/write specifier is either 'read' or 'write', the locality hint specifier ranges from locality<0> (no locality) to locality<3> (extremely local keep in cache). The cache type specifier is either 'data' or 'instr' and specifies whether the prefetch is performed on data cache or on instruction cache.

source

Reactant.MLIR.Dialects.affine.store Method

store

Syntax

operation ::= `affine.store` ssa-use, ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.store op writes an element to a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The affine.store op stores a new value which is the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
affine.store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
affine.store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

Reactant.MLIR.Dialects.affine.vector_load Method

vector_load

The affine.vector_load is the vector counterpart of affine.load. It reads a slice from a MemRef, supplied as its first operand, into a vector of the same base elemental type. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the read within the memref. The shape of the return vector type determines the shape of the slice read from the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector loads will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector load.

mlir
%1 = affine.vector_load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector load. Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.vector_load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector load.

mlir
%1 = affine.vector_load %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_read).

source

Reactant.MLIR.Dialects.affine.vector_store Method

vector_store

The affine.vector_store is the vector counterpart of affine.store. It writes a vector, supplied as its first operand, into a slice within a MemRef of the same base elemental type, supplied as its second operand. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the write within the memref. The shape of th input vector determines the shape of the slice written to the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector stores will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector store.

mlir
affine.vector_store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector store. Uses symbol keyword for symbols %n and %m.

mlir
affine.vector_store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector store.

mlir
affine.vector_store %v0, %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_write).

source

Reactant.MLIR.Dialects.affine.yield Method

yield

The affine.yield yields zero or more SSA values from an affine op region and terminates the region. The semantics of how the values yielded are used is defined by the parent operation. If affine.yield has any operands, the operands must match the parent operation's results. If the parent operation defines no values, then the affine.yield may be left out in the custom syntax and the builders will insert one implicitly. Otherwise, it has to be present in the syntax to indicate which values are yielded.

source

+ \ No newline at end of file diff --git a/previews/PR441/api/api.html b/previews/PR441/api/api.html index 263f9f5bc..5609bb428 100644 --- a/previews/PR441/api/api.html +++ b/previews/PR441/api/api.html @@ -9,11 +9,11 @@ - + - + - + @@ -21,9 +21,9 @@ -
Reactant.jl

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Reactant.Compiler.@compile Macro
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@compile f(args...)

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Reactant.Compiler.@jit Macro
julia
@jit f(args...)
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Reactant.jl
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 Trusted by 
Scientific Computing
Lux.jl
Machine Learning
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Tenet.jl
Tensor Networks
Core Reactant API ​
Compile API ​
Reactant.Compiler.@compile Macro
julia
@compile f(args...)
source
Reactant.Compiler.@jit Macro
julia
@jit f(args...)
 
-Run @compile f(args..) then immediately execute it
source
ReactantCore API ​
ReactantCore.@trace Macro
julia
@trace <expr>
Converts certain expressions like control flow into a Reactant friendly form. Importantly, if no traced value is found inside the expression, then there is no overhead.
Currently Supported
  • if conditions (with elseif and other niceties) (@trace if ...)
  • if statements with a preceeding assignment (@trace a = if ...) (note the positioning of the macro needs to be before the assignment and not before the if)
  • for statements with a single induction variable iterating over a syntactic StepRange of integers.
Special Considerations
  • Apply @trace only at the outermost if. Nested if statements will be automatically expanded into the correct form.
Extended Help
Caveats (Deviations from Core Julia Semantics)
New variables introduced
julia
@trace if x > 0
+Run @compile f(args..) then immediately execute it
source
ReactantCore API ​
ReactantCore.@trace Macro
julia
@trace <expr>
Converts certain expressions like control flow into a Reactant friendly form. Importantly, if no traced value is found inside the expression, then there is no overhead.
Currently Supported
  • if conditions (with elseif and other niceties) (@trace if ...)
  • if statements with a preceeding assignment (@trace a = if ...) (note the positioning of the macro needs to be before the assignment and not before the if)
  • for statements with a single induction variable iterating over a syntactic StepRange of integers.
Special Considerations
  • Apply @trace only at the outermost if. Nested if statements will be automatically expanded into the correct form.
Extended Help
Caveats (Deviations from Core Julia Semantics)
New variables introduced
julia
@trace if x > 0
     y = x + 1
     p = 1
 else
@@ -47,8 +47,8 @@
         y = 2.0
     end
     return y, nothing
-end
source
Inspect Generated HLO ​
Reactant.Compiler.@code_hlo Macro
julia
@code_hlo [optimize = ...] f(args...)
source

Internal Functionality ​
Private
These functions are not part of the public API and are subject to change at any time.
Reactant.Compiler.codegen_unflatten! Function
julia
codegen_unflatten!
Generate Julia code to wrap the XLA buffers back into the output result datatypes. The name is due to its similarity to the unflatten function in jax.tree_util.register_pytree_node.
source
Reactant.Compiler.codegen_flatten! Function
julia
codegen_flatten!
Generate Julia code to extract the XLA buffers from input arguments. The name is due to its similarity to the flatten function in jax.tree_util.register_pytree_node.
Arguments
  • linear_args: A list of arguments to be flattened.
Returns
  • flatten_names: A list of Symbols representing the names of the flattened arguments.
  • flatten_code: A list of Exprs to extract the XLA buffers from the input arguments.
Note
The linearized arguments do not directly refer to the are the arguments that have been flattened into a single list.
source
Reactant.Compiler.codegen_xla_call Function
julia
codegen_xla_call
Generate Julia code to call the XLA executable.
Arguments
  • exec: The XLA executable to call.
  • flatten_names: A list of Symbols representing the names of the flattened linear arguments.
  • donated_args_mask: A list of UInt8s representing whether the argument is donated.
  • nresults: The number of results to expect.
source

-    
+end

source

Inspect Generated HLO ​

Reactant.Compiler.@code_hlo Macro
julia
@code_hlo [optimize = ...] f(args...)

source


Internal Functionality ​

Private

These functions are not part of the public API and are subject to change at any time.

Reactant.Compiler.codegen_unflatten! Function
julia
codegen_unflatten!

Generate Julia code to wrap the XLA buffers back into the output result datatypes. The name is due to its similarity to the unflatten function in jax.tree_util.register_pytree_node.

source

Reactant.Compiler.codegen_flatten! Function
julia
codegen_flatten!

Generate Julia code to extract the XLA buffers from input arguments. The name is due to its similarity to the flatten function in jax.tree_util.register_pytree_node.

Arguments

  • linear_args: A list of arguments to be flattened.

Returns

  • flatten_names: A list of Symbols representing the names of the flattened arguments.

  • flatten_code: A list of Exprs to extract the XLA buffers from the input arguments.

Note

The linearized arguments do not directly refer to the are the arguments that have been flattened into a single list.

source

Reactant.Compiler.codegen_xla_call Function
julia
codegen_xla_call

Generate Julia code to call the XLA executable.

Arguments

  • exec: The XLA executable to call.

  • flatten_names: A list of Symbols representing the names of the flattened linear arguments.

  • donated_args_mask: A list of UInt8s representing whether the argument is donated.

  • nresults: The number of results to expect.

source

+ \ No newline at end of file diff --git a/previews/PR441/api/arith.html b/previews/PR441/api/arith.html index 000692844..f46b1f666 100644 --- a/previews/PR441/api/arith.html +++ b/previews/PR441/api/arith.html @@ -9,11 +9,11 @@ - + - + - + @@ -28,7 +28,7 @@ %f = arith.addf %g, %h : vector<4xf32> // Tensor addition. -%x = arith.addf %y, %z : tensor<4x?xbf16>

TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.

source

Reactant.MLIR.Dialects.arith.addi Method

addi

Performs N-bit addition on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the addition modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.

The addi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.

This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.

Example

mlir
// Scalar addition.
+%x = arith.addf %y, %z : tensor<4x?xbf16>

TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.

source

Reactant.MLIR.Dialects.arith.addi Method

addi

Performs N-bit addition on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the addition modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.

The addi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.

This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.

Example

mlir
// Scalar addition.
 %a = arith.addi %b, %c : i64
 
 // Scalar addition with overflow flags.
@@ -38,25 +38,25 @@
 %f = arith.addi %g, %h : vector<4xi32>
 
 // Tensor element-wise addition.
-%x = arith.addi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.addui_extended Method

addui_extended

Performs (N+1)-bit addition on zero-extended operands. Returns two results: the N-bit sum (same type as both operands), and the overflow bit (boolean-like), where 1 indicates unsigned addition overflow, while 0 indicates no overflow.

Example

mlir
// Scalar addition.
+%x = arith.addi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.addui_extended Method

addui_extended

Performs (N+1)-bit addition on zero-extended operands. Returns two results: the N-bit sum (same type as both operands), and the overflow bit (boolean-like), where 1 indicates unsigned addition overflow, while 0 indicates no overflow.

Example

mlir
// Scalar addition.
 %sum, %overflow = arith.addui_extended %b, %c : i64, i1
 
 // Vector element-wise addition.
 %d:2 = arith.addui_extended %e, %f : vector<4xi32>, vector<4xi1>
 
 // Tensor element-wise addition.
-%x:2 = arith.addui_extended %y, %z : tensor<4x?xi8>, tensor<4x?xi1>

source

Reactant.MLIR.Dialects.arith.andi Method

andi

The andi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.

Example

mlir
// Scalar integer bitwise and.
+%x:2 = arith.addui_extended %y, %z : tensor<4x?xi8>, tensor<4x?xi1>

source

Reactant.MLIR.Dialects.arith.andi Method

andi

The andi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.

Example

mlir
// Scalar integer bitwise and.
 %a = arith.andi %b, %c : i64
 
 // SIMD vector element-wise bitwise integer and.
 %f = arith.andi %g, %h : vector<4xi32>
 
 // Tensor element-wise bitwise integer and.
-%x = arith.andi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.bitcast Method

bitcast

Bitcast an integer or floating point value to an integer or floating point value of equal bit width. When operating on vectors, casts elementwise.

Note that this implements a logical bitcast independent of target endianness. This allows constant folding without target information and is consitent with the bitcast constant folders in LLVM (see https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168) For targets where the source and target type have the same endianness (which is the standard), this cast will also change no bits at runtime, but it may still require an operation, for example if the machine has different floating point and integer register files. For targets that have a different endianness for the source and target types (e.g. float is big-endian and integer is little-endian) a proper lowering would add operations to swap the order of words in addition to the bitcast.

source

Reactant.MLIR.Dialects.arith.ceildivsi Method

ceildivsi

Signed integer division. Rounds towards positive infinity, i.e. 7 / -2 = -3.

Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.

Example

mlir
// Scalar signed integer division.
-%a = arith.ceildivsi %b, %c : i64

source

Reactant.MLIR.Dialects.arith.ceildivui Method

ceildivui

Unsigned integer division. Rounds towards positive infinity. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 1.

Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.

Example

mlir
// Scalar unsigned integer division.
-%a = arith.ceildivui %b, %c : i64

source

Reactant.MLIR.Dialects.arith.cmpf Method

cmpf

The cmpf operation compares its two operands according to the float comparison rules and the predicate specified by the respective attribute. The predicate defines the type of comparison: (un)orderedness, (in)equality and signed less/greater than (or equal to) as well as predicates that are always true or false. The operands must have the same type, and this type must be a float type, or a vector or tensor thereof. The result is an i1, or a vector/tensor thereof having the same shape as the inputs. Unlike cmpi, the operands are always treated as signed. The u prefix indicates unordered comparison, not unsigned comparison, so "une" means unordered or not equal. For the sake of readability by humans, custom assembly form for the operation uses a string-typed attribute for the predicate. The value of this attribute corresponds to lower-cased name of the predicate constant, e.g., "one" means "ordered not equal". The string representation of the attribute is merely a syntactic sugar and is converted to an integer attribute by the parser.

Example

mlir
%r1 = arith.cmpf oeq, %0, %1 : f32
+%x = arith.andi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.bitcast Method

bitcast

Bitcast an integer or floating point value to an integer or floating point value of equal bit width. When operating on vectors, casts elementwise.

Note that this implements a logical bitcast independent of target endianness. This allows constant folding without target information and is consitent with the bitcast constant folders in LLVM (see https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168) For targets where the source and target type have the same endianness (which is the standard), this cast will also change no bits at runtime, but it may still require an operation, for example if the machine has different floating point and integer register files. For targets that have a different endianness for the source and target types (e.g. float is big-endian and integer is little-endian) a proper lowering would add operations to swap the order of words in addition to the bitcast.

source

Reactant.MLIR.Dialects.arith.ceildivsi Method

ceildivsi

Signed integer division. Rounds towards positive infinity, i.e. 7 / -2 = -3.

Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.

Example

mlir
// Scalar signed integer division.
+%a = arith.ceildivsi %b, %c : i64

source

Reactant.MLIR.Dialects.arith.ceildivui Method

ceildivui

Unsigned integer division. Rounds towards positive infinity. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 1.

Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.

Example

mlir
// Scalar unsigned integer division.
+%a = arith.ceildivui %b, %c : i64

source

Reactant.MLIR.Dialects.arith.cmpf Method

cmpf

The cmpf operation compares its two operands according to the float comparison rules and the predicate specified by the respective attribute. The predicate defines the type of comparison: (un)orderedness, (in)equality and signed less/greater than (or equal to) as well as predicates that are always true or false. The operands must have the same type, and this type must be a float type, or a vector or tensor thereof. The result is an i1, or a vector/tensor thereof having the same shape as the inputs. Unlike cmpi, the operands are always treated as signed. The u prefix indicates unordered comparison, not unsigned comparison, so "une" means unordered or not equal. For the sake of readability by humans, custom assembly form for the operation uses a string-typed attribute for the predicate. The value of this attribute corresponds to lower-cased name of the predicate constant, e.g., "one" means "ordered not equal". The string representation of the attribute is merely a syntactic sugar and is converted to an integer attribute by the parser.

Example

mlir
%r1 = arith.cmpf oeq, %0, %1 : f32
 %r2 = arith.cmpf ult, %0, %1 : tensor<42x42xf64>
-%r3 = "arith.cmpf"(%0, %1) {predicate: 0} : (f8, f8) -> i1

source

Reactant.MLIR.Dialects.arith.cmpi Method

cmpi

The cmpi operation is a generic comparison for integer-like types. Its two arguments can be integers, vectors or tensors thereof as long as their types match. The operation produces an i1 for the former case, a vector or a tensor of i1 with the same shape as inputs in the other cases.

Its first argument is an attribute that defines which type of comparison is performed. The following comparisons are supported:

The result is 1 if the comparison is true and 0 otherwise. For vector or tensor operands, the comparison is performed elementwise and the element of the result indicates whether the comparison is true for the operand elements with the same indices as those of the result.

Note: while the custom assembly form uses strings, the actual underlying attribute has integer type (or rather enum class in C++ code) as seen from the generic assembly form. String literals are used to improve readability of the IR by humans.

This operation only applies to integer-like operands, but not floats. The main reason being that comparison operations have diverging sets of attributes: integers require sign specification while floats require various floating point-related particularities, e.g., -ffast-math behavior, IEEE754 compliance, etc (rationale). The type of comparison is specified as attribute to avoid introducing ten similar operations, taking into account that they are often implemented using the same operation downstream (rationale). The separation between signed and unsigned order comparisons is necessary because of integers being signless. The comparison operation must know how to interpret values with the foremost bit being set: negatives in two's complement or large positives (rationale).

Example

mlir
// Custom form of scalar "signed less than" comparison.
+%r3 = "arith.cmpf"(%0, %1) {predicate: 0} : (f8, f8) -> i1

source

Reactant.MLIR.Dialects.arith.cmpi Method

cmpi

The cmpi operation is a generic comparison for integer-like types. Its two arguments can be integers, vectors or tensors thereof as long as their types match. The operation produces an i1 for the former case, a vector or a tensor of i1 with the same shape as inputs in the other cases.

Its first argument is an attribute that defines which type of comparison is performed. The following comparisons are supported:

The result is 1 if the comparison is true and 0 otherwise. For vector or tensor operands, the comparison is performed elementwise and the element of the result indicates whether the comparison is true for the operand elements with the same indices as those of the result.

Note: while the custom assembly form uses strings, the actual underlying attribute has integer type (or rather enum class in C++ code) as seen from the generic assembly form. String literals are used to improve readability of the IR by humans.

This operation only applies to integer-like operands, but not floats. The main reason being that comparison operations have diverging sets of attributes: integers require sign specification while floats require various floating point-related particularities, e.g., -ffast-math behavior, IEEE754 compliance, etc (rationale). The type of comparison is specified as attribute to avoid introducing ten similar operations, taking into account that they are often implemented using the same operation downstream (rationale). The separation between signed and unsigned order comparisons is necessary because of integers being signless. The comparison operation must know how to interpret values with the foremost bit being set: negatives in two's complement or large positives (rationale).

Example

mlir
// Custom form of scalar "signed less than" comparison.
 %x = arith.cmpi slt, %lhs, %rhs : i32
 
 // Generic form of the same operation.
@@ -67,47 +67,47 @@
 
 // Generic form of the same operation.
 %x = "arith.cmpi"(%lhs, %rhs) {predicate = 0 : i64}
-    : (vector<4xi64>, vector<4xi64>) -> vector<4xi1>

source

Reactant.MLIR.Dialects.arith.constant Method

constant

The constant operation produces an SSA value equal to some integer or floating-point constant specified by an attribute. This is the way MLIR forms simple integer and floating point constants.

Example

// Integer constant
+    : (vector<4xi64>, vector<4xi64>) -> vector<4xi1>

source

Reactant.MLIR.Dialects.arith.constant Method

constant

The constant operation produces an SSA value equal to some integer or floating-point constant specified by an attribute. This is the way MLIR forms simple integer and floating point constants.

Example

// Integer constant
 %1 = arith.constant 42 : i32
 
 // Equivalent generic form
-%1 = "arith.constant"() {value = 42 : i32} : () -> i32

source

Reactant.MLIR.Dialects.arith.divsi Method

divsi

Signed integer division. Rounds towards zero. Treats the leading bit as sign, i.e. 6 / -2 = -3.

Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.

Example

mlir
// Scalar signed integer division.
+%1 = "arith.constant"() {value = 42 : i32} : () -> i32

source

Reactant.MLIR.Dialects.arith.divsi Method

divsi

Signed integer division. Rounds towards zero. Treats the leading bit as sign, i.e. 6 / -2 = -3.

Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.

Example

mlir
// Scalar signed integer division.
 %a = arith.divsi %b, %c : i64
 
 // SIMD vector element-wise division.
 %f = arith.divsi %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division.
-%x = arith.divsi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.divui Method

divui

Unsigned integer division. Rounds towards zero. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 0.

Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.

Example

mlir
// Scalar unsigned integer division.
+%x = arith.divsi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.divui Method

divui

Unsigned integer division. Rounds towards zero. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 0.

Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.

Example

mlir
// Scalar unsigned integer division.
 %a = arith.divui %b, %c : i64
 
 // SIMD vector element-wise division.
 %f = arith.divui %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division.
-%x = arith.divui %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.extf Method

extf

Cast a floating-point value to a larger floating-point-typed value. The destination type must to be strictly wider than the source type. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.extsi Method

extsi

The integer sign extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with copies of the most-significant bit of the input.

Example

mlir
%1 = arith.constant 5 : i3      // %1 is 0b101
+%x = arith.divui %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.extf Method

extf

Cast a floating-point value to a larger floating-point-typed value. The destination type must to be strictly wider than the source type. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.extsi Method

extsi

The integer sign extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with copies of the most-significant bit of the input.

Example

mlir
%1 = arith.constant 5 : i3      // %1 is 0b101
 %2 = arith.extsi %1 : i3 to i6  // %2 is 0b111101
 %3 = arith.constant 2 : i3      // %3 is 0b010
 %4 = arith.extsi %3 : i3 to i6  // %4 is 0b000010
 
-%5 = arith.extsi %0 : vector<2 x i32> to vector<2 x i64>

source

Reactant.MLIR.Dialects.arith.extui Method

extui

The integer zero extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with zeros.

Example

mlir
  %1 = arith.constant 5 : i3      // %1 is 0b101
+%5 = arith.extsi %0 : vector<2 x i32> to vector<2 x i64>

source

Reactant.MLIR.Dialects.arith.extui Method

extui

The integer zero extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with zeros.

Example

mlir
  %1 = arith.constant 5 : i3      // %1 is 0b101
   %2 = arith.extui %1 : i3 to i6  // %2 is 0b000101
   %3 = arith.constant 2 : i3      // %3 is 0b010
   %4 = arith.extui %3 : i3 to i6  // %4 is 0b000010
 
-  %5 = arith.extui %0 : vector<2 x i32> to vector<2 x i64>

source

Reactant.MLIR.Dialects.arith.floordivsi Method

floordivsi

Signed integer division. Rounds towards negative infinity, i.e. 5 / -2 = -3.

Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.

Example

mlir
// Scalar signed integer division.
-%a = arith.floordivsi %b, %c : i64

source

Reactant.MLIR.Dialects.arith.fptosi Method

fptosi

Cast from a value interpreted as floating-point to the nearest (rounding towards zero) signed integer value. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.fptoui Method

fptoui

Cast from a value interpreted as floating-point to the nearest (rounding towards zero) unsigned integer value. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.index_cast Method

index_cast

Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is sign-extended. If casting to a narrower integer, the value is truncated.

source

Reactant.MLIR.Dialects.arith.index_castui Method

index_castui

Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is zero-extended. If casting to a narrower integer, the value is truncated.

source

Reactant.MLIR.Dialects.arith.maximumf Method

maximumf

Returns the maximum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.

Example

mlir
// Scalar floating-point maximum.
-%a = arith.maximumf %b, %c : f64

source

Reactant.MLIR.Dialects.arith.maxnumf Method

maxnumf

Returns the maximum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.

Example

mlir
// Scalar floating-point maximum.
-%a = arith.maxnumf %b, %c : f64

source

Reactant.MLIR.Dialects.arith.minimumf Method

minimumf

Returns the minimum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.

Example

mlir
// Scalar floating-point minimum.
-%a = arith.minimumf %b, %c : f64

source

Reactant.MLIR.Dialects.arith.minnumf Method

minnumf

Returns the minimum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.

Example

mlir
// Scalar floating-point minimum.
-%a = arith.minnumf %b, %c : f64

source

Reactant.MLIR.Dialects.arith.mulf Method

mulf

The mulf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.

Example

mlir
// Scalar multiplication.
+  %5 = arith.extui %0 : vector<2 x i32> to vector<2 x i64>

source

Reactant.MLIR.Dialects.arith.floordivsi Method

floordivsi

Signed integer division. Rounds towards negative infinity, i.e. 5 / -2 = -3.

Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.

Example

mlir
// Scalar signed integer division.
+%a = arith.floordivsi %b, %c : i64

source

Reactant.MLIR.Dialects.arith.fptosi Method

fptosi

Cast from a value interpreted as floating-point to the nearest (rounding towards zero) signed integer value. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.fptoui Method

fptoui

Cast from a value interpreted as floating-point to the nearest (rounding towards zero) unsigned integer value. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.index_cast Method

index_cast

Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is sign-extended. If casting to a narrower integer, the value is truncated.

source

Reactant.MLIR.Dialects.arith.index_castui Method

index_castui

Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is zero-extended. If casting to a narrower integer, the value is truncated.

source

Reactant.MLIR.Dialects.arith.maximumf Method

maximumf

Returns the maximum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.

Example

mlir
// Scalar floating-point maximum.
+%a = arith.maximumf %b, %c : f64

source

Reactant.MLIR.Dialects.arith.maxnumf Method

maxnumf

Returns the maximum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.

Example

mlir
// Scalar floating-point maximum.
+%a = arith.maxnumf %b, %c : f64

source

Reactant.MLIR.Dialects.arith.minimumf Method

minimumf

Returns the minimum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.

Example

mlir
// Scalar floating-point minimum.
+%a = arith.minimumf %b, %c : f64

source

Reactant.MLIR.Dialects.arith.minnumf Method

minnumf

Returns the minimum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.

Example

mlir
// Scalar floating-point minimum.
+%a = arith.minnumf %b, %c : f64

source

Reactant.MLIR.Dialects.arith.mulf Method

mulf

The mulf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.

Example

mlir
// Scalar multiplication.
 %a = arith.mulf %b, %c : f64
 
 // SIMD pointwise vector multiplication, e.g. for Intel SSE.
 %f = arith.mulf %g, %h : vector<4xf32>
 
 // Tensor pointwise multiplication.
-%x = arith.mulf %y, %z : tensor<4x?xbf16>

TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.

source

Reactant.MLIR.Dialects.arith.muli Method

muli

Performs N-bit multiplication on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the multiplication modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.

The muli operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.

This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.

Example

mlir
// Scalar multiplication.
+%x = arith.mulf %y, %z : tensor<4x?xbf16>

TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.

source

Reactant.MLIR.Dialects.arith.muli Method

muli

Performs N-bit multiplication on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the multiplication modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.

The muli operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.

This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.

Example

mlir
// Scalar multiplication.
 %a = arith.muli %b, %c : i64
 
 // Scalar multiplication with overflow flags.
@@ -117,49 +117,49 @@
 %f = arith.muli %g, %h : vector<4xi32>
 
 // Tensor element-wise multiplication.
-%x = arith.muli %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.mulsi_extended Method

mulsi_extended

Performs (2*N)-bit multiplication on sign-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.

Example

mlir
// Scalar multiplication.
+%x = arith.muli %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.mulsi_extended Method

mulsi_extended

Performs (2*N)-bit multiplication on sign-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.

Example

mlir
// Scalar multiplication.
 %low, %high = arith.mulsi_extended %a, %b : i32
 
 // Vector element-wise multiplication.
 %c:2 = arith.mulsi_extended %d, %e : vector<4xi32>
 
 // Tensor element-wise multiplication.
-%x:2 = arith.mulsi_extended %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.mului_extended Method

mului_extended

Performs (2*N)-bit multiplication on zero-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.

Example

mlir
// Scalar multiplication.
+%x:2 = arith.mulsi_extended %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.mului_extended Method

mului_extended

Performs (2*N)-bit multiplication on zero-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.

Example

mlir
// Scalar multiplication.
 %low, %high = arith.mului_extended %a, %b : i32
 
 // Vector element-wise multiplication.
 %c:2 = arith.mului_extended %d, %e : vector<4xi32>
 
 // Tensor element-wise multiplication.
-%x:2 = arith.mului_extended %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.negf Method

negf

The negf operation computes the negation of a given value. It takes one operand and returns one result of the same type. This type may be a float scalar type, a vector whose element type is float, or a tensor of floats. It has no standard attributes.

Example

mlir
// Scalar negation value.
+%x:2 = arith.mului_extended %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.negf Method

negf

The negf operation computes the negation of a given value. It takes one operand and returns one result of the same type. This type may be a float scalar type, a vector whose element type is float, or a tensor of floats. It has no standard attributes.

Example

mlir
// Scalar negation value.
 %a = arith.negf %b : f64
 
 // SIMD vector element-wise negation value.
 %f = arith.negf %g : vector<4xf32>
 
 // Tensor element-wise negation value.
-%x = arith.negf %y : tensor<4x?xf8>

source

Reactant.MLIR.Dialects.arith.ori Method

ori

The ori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.

Example

mlir
// Scalar integer bitwise or.
+%x = arith.negf %y : tensor<4x?xf8>

source

Reactant.MLIR.Dialects.arith.ori Method

ori

The ori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.

Example

mlir
// Scalar integer bitwise or.
 %a = arith.ori %b, %c : i64
 
 // SIMD vector element-wise bitwise integer or.
 %f = arith.ori %g, %h : vector<4xi32>
 
 // Tensor element-wise bitwise integer or.
-%x = arith.ori %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.remf Method

remf

Returns the floating point division remainder. The remainder has the same sign as the dividend (lhs operand).

source

Reactant.MLIR.Dialects.arith.remsi Method

remsi

Signed integer division remainder. Treats the leading bit as sign, i.e. 6 % -2 = 0.

Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.

Example

mlir
// Scalar signed integer division remainder.
+%x = arith.ori %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.remf Method

remf

Returns the floating point division remainder. The remainder has the same sign as the dividend (lhs operand).

source

Reactant.MLIR.Dialects.arith.remsi Method

remsi

Signed integer division remainder. Treats the leading bit as sign, i.e. 6 % -2 = 0.

Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.

Example

mlir
// Scalar signed integer division remainder.
 %a = arith.remsi %b, %c : i64
 
 // SIMD vector element-wise division remainder.
 %f = arith.remsi %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division remainder.
-%x = arith.remsi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.remui Method

remui

Unsigned integer division remainder. Treats the leading bit as the most significant, i.e. for i16, 6 % -2 = 6 % (2^16 - 2) = 6.

Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.

Example

mlir
// Scalar unsigned integer division remainder.
+%x = arith.remsi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.remui Method

remui

Unsigned integer division remainder. Treats the leading bit as the most significant, i.e. for i16, 6 % -2 = 6 % (2^16 - 2) = 6.

Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.

Example

mlir
// Scalar unsigned integer division remainder.
 %a = arith.remui %b, %c : i64
 
 // SIMD vector element-wise division remainder.
 %f = arith.remui %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division remainder.
-%x = arith.remui %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.select Method

select

The arith.select operation chooses one value based on a binary condition supplied as its first operand.

If the value of the first operand (the condition) is 1, then the second operand is returned, and the third operand is ignored, even if it was poison.

If the value of the first operand (the condition) is 0, then the third operand is returned, and the second operand is ignored, even if it was poison.

If the value of the first operand (the condition) is poison, then the operation returns poison.

The operation applies to vectors and tensors elementwise given the shape of all operands is identical. The choice is made for each element individually based on the value at the same position as the element in the condition operand. If an i1 is provided as the condition, the entire vector or tensor is chosen.

Example

mlir
// Custom form of scalar selection.
+%x = arith.remui %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.select Method

select

The arith.select operation chooses one value based on a binary condition supplied as its first operand.

If the value of the first operand (the condition) is 1, then the second operand is returned, and the third operand is ignored, even if it was poison.

If the value of the first operand (the condition) is 0, then the third operand is returned, and the second operand is ignored, even if it was poison.

If the value of the first operand (the condition) is poison, then the operation returns poison.

The operation applies to vectors and tensors elementwise given the shape of all operands is identical. The choice is made for each element individually based on the value at the same position as the element in the condition operand. If an i1 is provided as the condition, the entire vector or tensor is chosen.

Example

mlir
// Custom form of scalar selection.
 %x = arith.select %cond, %true, %false : i32
 
 // Generic form of the same operation.
@@ -169,23 +169,23 @@
 %vx = arith.select %vcond, %vtrue, %vfalse : vector<42xi1>, vector<42xf32>
 
 // Full vector selection.
-%vx = arith.select %cond, %vtrue, %vfalse : vector<42xf32>

source

Reactant.MLIR.Dialects.arith.shli Method

shli

The shli operation shifts the integer value of the first operand to the left by the integer value of the second operand. The second operand is interpreted as unsigned. The low order bits are filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.

This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.

Example

mlir
%1 = arith.constant 5 : i8  // %1 is 0b00000101
+%vx = arith.select %cond, %vtrue, %vfalse : vector<42xf32>

source

Reactant.MLIR.Dialects.arith.shli Method

shli

The shli operation shifts the integer value of the first operand to the left by the integer value of the second operand. The second operand is interpreted as unsigned. The low order bits are filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.

This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.

Example

mlir
%1 = arith.constant 5 : i8  // %1 is 0b00000101
 %2 = arith.constant 3 : i8
 %3 = arith.shli %1, %2 : i8 // %3 is 0b00101000
-%4 = arith.shli %1, %2 overflow<nsw, nuw> : i8

source

Reactant.MLIR.Dialects.arith.shrsi Method

shrsi

The shrsi operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as signed, and the second operand is interpreter as unsigned. The high order bits in the output are filled with copies of the most-significant bit of the shifted value (which means that the sign of the value is preserved). If the value of the second operand is greater or equal than bitwidth of the first operand, then the operation returns poison.

Example

mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
+%4 = arith.shli %1, %2 overflow<nsw, nuw> : i8

source

Reactant.MLIR.Dialects.arith.shrsi Method

shrsi

The shrsi operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as signed, and the second operand is interpreter as unsigned. The high order bits in the output are filled with copies of the most-significant bit of the shifted value (which means that the sign of the value is preserved). If the value of the second operand is greater or equal than bitwidth of the first operand, then the operation returns poison.

Example

mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
 %2 = arith.constant 3 : i8
 %3 = arith.shrsi %1, %2 : (i8, i8) -> i8   // %3 is 0b11110100
 %4 = arith.constant 96 : i8                   // %4 is 0b01100000
-%5 = arith.shrsi %4, %2 : (i8, i8) -> i8   // %5 is 0b00001100

source

Reactant.MLIR.Dialects.arith.shrui Method

shrui

The shrui operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as unsigned, and the second operand is interpreted as unsigned. The high order bits are always filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.

Example

mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
+%5 = arith.shrsi %4, %2 : (i8, i8) -> i8   // %5 is 0b00001100

source

Reactant.MLIR.Dialects.arith.shrui Method

shrui

The shrui operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as unsigned, and the second operand is interpreted as unsigned. The high order bits are always filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.

Example

mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
 %2 = arith.constant 3 : i8
-%3 = arith.shrui %1, %2 : (i8, i8) -> i8   // %3 is 0b00010100

source

Reactant.MLIR.Dialects.arith.sitofp Method

sitofp

Cast from a value interpreted as a signed integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.subf Method

subf

The subf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.

Example

mlir
// Scalar subtraction.
+%3 = arith.shrui %1, %2 : (i8, i8) -> i8   // %3 is 0b00010100

source

Reactant.MLIR.Dialects.arith.sitofp Method

sitofp

Cast from a value interpreted as a signed integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.subf Method

subf

The subf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.

Example

mlir
// Scalar subtraction.
 %a = arith.subf %b, %c : f64
 
 // SIMD vector subtraction, e.g. for Intel SSE.
 %f = arith.subf %g, %h : vector<4xf32>
 
 // Tensor subtraction.
-%x = arith.subf %y, %z : tensor<4x?xbf16>

TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.

source

Reactant.MLIR.Dialects.arith.subi Method

subi

Performs N-bit subtraction on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the subtraction modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.

The subi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.

This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.

Example

mlir
// Scalar subtraction.
+%x = arith.subf %y, %z : tensor<4x?xbf16>

TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.

source

Reactant.MLIR.Dialects.arith.subi Method

subi

Performs N-bit subtraction on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the subtraction modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.

The subi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.

This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.

Example

mlir
// Scalar subtraction.
 %a = arith.subi %b, %c : i64
 
 // Scalar subtraction with overflow flags.
@@ -195,19 +195,19 @@
 %f = arith.subi %g, %h : vector<4xi32>
 
 // Tensor element-wise subtraction.
-%x = arith.subi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.truncf Method

truncf

Truncate a floating-point value to a smaller floating-point-typed value. The destination type must be strictly narrower than the source type. If the value cannot be exactly represented, it is rounded using the provided rounding mode or the default one if no rounding mode is provided. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.trunci Method

trunci

The integer truncation operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be smaller than the input bit-width (N < M). The top-most (N - M) bits of the input are discarded.

Example

mlir
  %1 = arith.constant 21 : i5     // %1 is 0b10101
+%x = arith.subi %y, %z : tensor<4x?xi8>

source

Reactant.MLIR.Dialects.arith.truncf Method

truncf

Truncate a floating-point value to a smaller floating-point-typed value. The destination type must be strictly narrower than the source type. If the value cannot be exactly represented, it is rounded using the provided rounding mode or the default one if no rounding mode is provided. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.trunci Method

trunci

The integer truncation operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be smaller than the input bit-width (N < M). The top-most (N - M) bits of the input are discarded.

Example

mlir
  %1 = arith.constant 21 : i5     // %1 is 0b10101
   %2 = arith.trunci %1 : i5 to i4 // %2 is 0b0101
   %3 = arith.trunci %1 : i5 to i3 // %3 is 0b101
 
-  %5 = arith.trunci %0 : vector<2 x i32> to vector<2 x i16>

source

Reactant.MLIR.Dialects.arith.uitofp Method

uitofp

Cast from a value interpreted as unsigned integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.xori Method

xori

The xori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.

Example

mlir
// Scalar integer bitwise xor.
+  %5 = arith.trunci %0 : vector<2 x i32> to vector<2 x i16>

source

Reactant.MLIR.Dialects.arith.uitofp Method

uitofp

Cast from a value interpreted as unsigned integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.

source

Reactant.MLIR.Dialects.arith.xori Method

xori

The xori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.

Example

mlir
// Scalar integer bitwise xor.
 %a = arith.xori %b, %c : i64
 
 // SIMD vector element-wise bitwise integer xor.
 %f = arith.xori %g, %h : vector<4xi32>
 
 // Tensor element-wise bitwise integer xor.
-%x = arith.xori %y, %z : tensor<4x?xi8>

source

- +%x = arith.xori %y, %z : tensor<4x?xi8>

source

+ \ No newline at end of file diff --git a/previews/PR441/api/builtin.html b/previews/PR441/api/builtin.html index 971c16121..b9fd13f15 100644 --- a/previews/PR441/api/builtin.html +++ b/previews/PR441/api/builtin.html @@ -9,11 +9,11 @@ - + - + - + @@ -23,7 +23,7 @@
Reactant.jl

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Builtin Dialect ​

Refer to the official documentation for more details.

Reactant.MLIR.Dialects.builtin.module_ Method

module_

A module represents a top-level container operation. It contains a single graph region containing a single block which can contain any operations and does not have a terminator. Operations within this region cannot implicitly capture values defined outside the module, i.e. Modules are IsolatedFromAbove. Modules have an optional symbol name which can be used to refer to them in operations.

Example

mlir
module {
   func.func @foo()
-}

source

Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast Method

unrealized_conversion_cast

An unrealized_conversion_cast operation represents an unrealized conversion from one set of types to another, that is used to enable the inter-mixing of different type systems. This operation should not be attributed any special representational or execution semantics, and is generally only intended to be used to satisfy the temporary intermixing of type systems during the conversion of one type system to another.

This operation may produce results of arity 1-N, and accept as input operands of arity 0-N.

Example

mlir
// An unrealized 0-1 conversion. These types of conversions are useful in
+}

source

Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast Method

unrealized_conversion_cast

An unrealized_conversion_cast operation represents an unrealized conversion from one set of types to another, that is used to enable the inter-mixing of different type systems. This operation should not be attributed any special representational or execution semantics, and is generally only intended to be used to satisfy the temporary intermixing of type systems during the conversion of one type system to another.

This operation may produce results of arity 1-N, and accept as input operands of arity 0-N.

Example

mlir
// An unrealized 0-1 conversion. These types of conversions are useful in
 // cases where a type is removed from the type system, but not all uses have
 // been converted. For example, imagine we have a tuple type that is
 // expanded to its element types. If only some uses of an empty tuple type
@@ -38,8 +38,8 @@
 %results2:2 = unrealized_conversion_cast %tuple_operand : !foo.tuple_type<!foo.type, !foo.type> to !foo.type, !foo.type
 
 // An unrealized N-1 conversion.
-%result3 = unrealized_conversion_cast %operand, %operand : !foo.type, !foo.type to !bar.tuple_type<!foo.type, !foo.type>

source

- +%result3 = unrealized_conversion_cast %operand, %operand : !foo.type, !foo.type to !bar.tuple_type<!foo.type, !foo.type>

source

+ \ No newline at end of file diff --git a/previews/PR441/api/chlo.html b/previews/PR441/api/chlo.html index 964ba5679..9100e7bc8 100644 --- a/previews/PR441/api/chlo.html +++ b/previews/PR441/api/chlo.html @@ -9,11 +9,11 @@ - + - + - + @@ -21,8 +21,8 @@ -
Reactant.jl

Appearance


Trusted by

Scientific Computing

Lux.jl

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Tensor Networks

CHLO Dialect ​

Refer to the official documentation for more details.

Reactant.MLIR.Dialects.chlo._asin_acos_kernel Method

_asin_acos_kernel

Returns AsinAcosKernel(operand) element-wise.

If w = _asin_acos_kernel(z) w' = _asin_acos_kernel(I * z) then asin(z) = complex(atan2(z.real, w.real), sign(z.imag) * w.imag) acos(z) = complex(atan2(w.real, z.real), -sign(z.imag) * w.imag) asinh(z) = complex(sign(z.real) * w'.imag, atan2(z.imag, w'.real)) acosh(z) = complex(w.imag, sign(z.imag) * atan2(w.real, z.real))

This op is used as an intermediate value in decompositions and should never be constructed directly by frameworks or consumed by backends.

source

Reactant.MLIR.Dialects.chlo.acos Method

acos

Returns Acos(operand) element-wise.

$

\acos(x) = 2 * \atan(\sqrt(1 - x^2) / (1 + x)) if x != -1 = pi if x == -1 $

source

Reactant.MLIR.Dialects.chlo.acosh Method

acosh

Returns Acosh(operand) element-wise.

$

\acosh(x) = log(x + sqrt(x^2 - 1)) if x >= -1 \acosh(x) = nan if x < -1 $

source

Reactant.MLIR.Dialects.chlo.asin Method

asin

Returns Asin(operand) element-wise.

$

\asin(x) = 2 * atan(x / (1 + sqrt(1 - x^2))) $

source

Reactant.MLIR.Dialects.chlo.asinh Method

asinh

Returns Asinh(operand) element-wise.

$

\asinh(x) = log(x + sqrt(x^2 + 1)) $

source

Reactant.MLIR.Dialects.chlo.atan Method

atan

Returns Atan(operand) element-wise.

$

\atan(x) = \atan2(x, 1) $

source

Reactant.MLIR.Dialects.chlo.atanh Method

atanh

Returns Atanh(operand) element-wise.

$

\atanh(x) = 0.5 * log((1 + x) / (1 - x)) if abs(x) <= 1 = nan otherwise $

source

Reactant.MLIR.Dialects.chlo.bessel_i1e Method

bessel_i1e

Returns bessel_i1e(operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.broadcast_add Method

broadcast_add

Returns lhs + rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_and Method

broadcast_and

Returns logical_and(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_atan2 Method

broadcast_atan2

Returns atan2(lhs/rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_compare Method

broadcast_compare

Compares lhs and rhs elementwise according to comparison_direction and compare_type. If unspecified, compare_type is FLOAT for float element types, SIGNED for signed element types and UNSIGNED for unsigned element types.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_comparison_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_complex Method

broadcast_complex

Performs element-wise conversion of a pair of real and imaginary values to a complex value.

source

Reactant.MLIR.Dialects.chlo.broadcast_divide Method

broadcast_divide

Returns lhs / rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_maximum Method

broadcast_maximum

Returns max(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_minimum Method

broadcast_minimum

Returns min(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_multiply Method

broadcast_multiply

Returns lhs * rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_next_after Method

broadcast_next_after

Returns the next representable value of lhs in the direction of rhs, element-wise. It can also return a subnormal number.

Equivalent to the C++ std::nextafter function.

source

Reactant.MLIR.Dialects.chlo.broadcast_or Method

broadcast_or

Returns logical_or(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_polygamma Method

broadcast_polygamma

Returns Polygamma(operand, operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.broadcast_power Method

broadcast_power

Returns lhs ^ rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_remainder Method

broadcast_remainder

Returns lhs % rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_select Method

broadcast_select

Constructs an output array from elements of two input arrays, based on the values of a predicate array.

See https://www.tensorflow.org/xla/operation_semantics#select

source

Reactant.MLIR.Dialects.chlo.broadcast_shift_left Method

broadcast_shift_left

Returns lhs << rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_shift_right_arithmetic Method

broadcast_shift_right_arithmetic

Returns lhs >> rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_shift_right_logical Method

broadcast_shift_right_logical

Returns lhs >> rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_subtract Method

broadcast_subtract

Returns lhs - rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_xor Method

broadcast_xor

Returns logical_xor(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_zeta Method

broadcast_zeta

Returns Zeta(operand, operand) element-wise.

$

(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}) $

source

Reactant.MLIR.Dialects.chlo.conj Method

conj

Returns Conj(operand) element-wise.

$

\conj(x) = (\real(x), \neg(\imag(x))) $

source

Reactant.MLIR.Dialects.chlo.constant Method

constant

Represents a constant value.

source

Reactant.MLIR.Dialects.chlo.constant_like Method

constant_like

Returns a splat constant of the same shape as the operand.

source

Reactant.MLIR.Dialects.chlo.cosh Method

cosh

Returns Cosh(operand) element-wise.

$

\cosh(x) = (e^x + e^-x) / 2 $

source

Reactant.MLIR.Dialects.chlo.digamma Method

digamma

Returns Digamma(operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.erf Method

erf

Computes the Gauss error function of x element-wise.

erf(x) = erf_impl(x) if |x| < 1 = 1 - erfc_impl(x) otherwise

source

Reactant.MLIR.Dialects.chlo.erf_inv Method

erf_inv

Returns ErfInv(operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.erfc Method

erfc

Computes an approximation of the error function complement (1 - erf(x)).

erfc(x) = erfc_impl(x) if |x| > 1 = 1 - erf_impl(x) otherwise

source

Reactant.MLIR.Dialects.chlo.is_inf Method

is_inf

Returns if a value is +/-inf element-wise.

source

Reactant.MLIR.Dialects.chlo.is_neg_inf Method

is_neg_inf

Returns if a value is -inf element-wise.

source

Reactant.MLIR.Dialects.chlo.is_pos_inf Method

is_pos_inf

Returns if a value is +inf element-wise.

source

Reactant.MLIR.Dialects.chlo.lgamma Method

lgamma

Returns Lgamma(operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.next_after Method

next_after

Returns the next representable value of x in the direction of y, element-wise. It can also return a subnormal number.

Equivalent to the C++ std::nextafter function.

source

Reactant.MLIR.Dialects.chlo.polygamma Method

polygamma

Returns Polygamma(operand, operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.ragged_dot Method

ragged_dot

This operation takes three tensor args–-lhs, rhs, and group_sizes–-and a "ragged_dot_dimension_numbers" attribute. Like dot_general, the lhs and rhs are allowed arbitrary batch and contracting dimensions. Additionally, the lhs is required to have one ragged dimension, and the rhs may have at most one group dimension. The op has three modes, depending on the kind of the lhs ragged dimension.

In mode 1, the shape-signature is [b,m,k], [g,b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs non-contracting dimension (m). The dimensions b and k represent batch and contracting dimensions respectively. The rhs is required to have a group dimension (g).

In mode 2, the shape-signature is [b,m,k], [b,k,n], [g] -> [g,b,m,n]. Here the ragged dimension is an lhs/rhs contracting dimension (k).

In mode 3, the shape-signature is [b,m,k], [b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs/rhs batch dimension (b).

source

Reactant.MLIR.Dialects.chlo.sinh Method

sinh

Returns Sinh(operand) element-wise.

$

\sinh(x) = (e^x - e^-x) / 2 if |x| < 1 = e^(x + log(1/2)) - e^(-x + log(1/2)) otherwise. $

source

Reactant.MLIR.Dialects.chlo.square Method

square

Returns Square(operand) element-wise.

$

\square(x) = complex((x.real - x.imag) * (x.real + x.imag), x.real * x.imag * 2) if x is a complex number = x * x otherwise $

source

Reactant.MLIR.Dialects.chlo.tan Method

tan

Returns Tan(operand) element-wise.

$

\tan(x) = \sin(x) / \cos(x) $

source

Reactant.MLIR.Dialects.chlo.top_k Method

top_k

If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j].

For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus,

values.shape = indices.shape = input.shape[:-1] + [k]

If two elements are equal, the lower-index element appears first.

source

Reactant.MLIR.Dialects.chlo.zeta Method

zeta

Returns Zeta(operand, operand) element-wise.

$

(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}) $

source

- +
Reactant.jl

Appearance


Trusted by

Scientific Computing

Lux.jl

Machine Learning

Quantum Simulation

Tenet.jl

Tensor Networks

CHLO Dialect ​

Refer to the official documentation for more details.

Reactant.MLIR.Dialects.chlo._asin_acos_kernel Method

_asin_acos_kernel

Returns AsinAcosKernel(operand) element-wise.

If w = _asin_acos_kernel(z) w' = _asin_acos_kernel(I * z) then asin(z) = complex(atan2(z.real, w.real), sign(z.imag) * w.imag) acos(z) = complex(atan2(w.real, z.real), -sign(z.imag) * w.imag) asinh(z) = complex(sign(z.real) * w'.imag, atan2(z.imag, w'.real)) acosh(z) = complex(w.imag, sign(z.imag) * atan2(w.real, z.real))

This op is used as an intermediate value in decompositions and should never be constructed directly by frameworks or consumed by backends.

source

Reactant.MLIR.Dialects.chlo.acos Method

acos

Returns Acos(operand) element-wise.

$

\acos(x) = 2 * \atan(\sqrt(1 - x^2) / (1 + x)) if x != -1 = pi if x == -1 $

source

Reactant.MLIR.Dialects.chlo.acosh Method

acosh

Returns Acosh(operand) element-wise.

$

\acosh(x) = log(x + sqrt(x^2 - 1)) if x >= -1 \acosh(x) = nan if x < -1 $

source

Reactant.MLIR.Dialects.chlo.asin Method

asin

Returns Asin(operand) element-wise.

$

\asin(x) = 2 * atan(x / (1 + sqrt(1 - x^2))) $

source

Reactant.MLIR.Dialects.chlo.asinh Method

asinh

Returns Asinh(operand) element-wise.

$

\asinh(x) = log(x + sqrt(x^2 + 1)) $

source

Reactant.MLIR.Dialects.chlo.atan Method

atan

Returns Atan(operand) element-wise.

$

\atan(x) = \atan2(x, 1) $

source

Reactant.MLIR.Dialects.chlo.atanh Method

atanh

Returns Atanh(operand) element-wise.

$

\atanh(x) = 0.5 * log((1 + x) / (1 - x)) if abs(x) <= 1 = nan otherwise $

source

Reactant.MLIR.Dialects.chlo.bessel_i1e Method

bessel_i1e

Returns bessel_i1e(operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.broadcast_add Method

broadcast_add

Returns lhs + rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_and Method

broadcast_and

Returns logical_and(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_atan2 Method

broadcast_atan2

Returns atan2(lhs/rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_compare Method

broadcast_compare

Compares lhs and rhs elementwise according to comparison_direction and compare_type. If unspecified, compare_type is FLOAT for float element types, SIGNED for signed element types and UNSIGNED for unsigned element types.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_comparison_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_complex Method

broadcast_complex

Performs element-wise conversion of a pair of real and imaginary values to a complex value.

source

Reactant.MLIR.Dialects.chlo.broadcast_divide Method

broadcast_divide

Returns lhs / rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_maximum Method

broadcast_maximum

Returns max(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_minimum Method

broadcast_minimum

Returns min(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_multiply Method

broadcast_multiply

Returns lhs * rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_next_after Method

broadcast_next_after

Returns the next representable value of lhs in the direction of rhs, element-wise. It can also return a subnormal number.

Equivalent to the C++ std::nextafter function.

source

Reactant.MLIR.Dialects.chlo.broadcast_or Method

broadcast_or

Returns logical_or(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_polygamma Method

broadcast_polygamma

Returns Polygamma(operand, operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.broadcast_power Method

broadcast_power

Returns lhs ^ rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_remainder Method

broadcast_remainder

Returns lhs % rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_select Method

broadcast_select

Constructs an output array from elements of two input arrays, based on the values of a predicate array.

See https://www.tensorflow.org/xla/operation_semantics#select

source

Reactant.MLIR.Dialects.chlo.broadcast_shift_left Method

broadcast_shift_left

Returns lhs << rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_shift_right_arithmetic Method

broadcast_shift_right_arithmetic

Returns lhs >> rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_shift_right_logical Method

broadcast_shift_right_logical

Returns lhs >> rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_subtract Method

broadcast_subtract

Returns lhs - rhs element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_xor Method

broadcast_xor

Returns logical_xor(lhs, rhs) element-wise.

See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations.

source

Reactant.MLIR.Dialects.chlo.broadcast_zeta Method

broadcast_zeta

Returns Zeta(operand, operand) element-wise.

$

(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}) $

source

Reactant.MLIR.Dialects.chlo.conj Method

conj

Returns Conj(operand) element-wise.

$

\conj(x) = (\real(x), \neg(\imag(x))) $

source

Reactant.MLIR.Dialects.chlo.constant Method

constant

Represents a constant value.

source

Reactant.MLIR.Dialects.chlo.constant_like Method

constant_like

Returns a splat constant of the same shape as the operand.

source

Reactant.MLIR.Dialects.chlo.cosh Method

cosh

Returns Cosh(operand) element-wise.

$

\cosh(x) = (e^x + e^-x) / 2 $

source

Reactant.MLIR.Dialects.chlo.digamma Method

digamma

Returns Digamma(operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.erf Method

erf

Computes the Gauss error function of x element-wise.

erf(x) = erf_impl(x) if |x| < 1 = 1 - erfc_impl(x) otherwise

source

Reactant.MLIR.Dialects.chlo.erf_inv Method

erf_inv

Returns ErfInv(operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.erfc Method

erfc

Computes an approximation of the error function complement (1 - erf(x)).

erfc(x) = erfc_impl(x) if |x| > 1 = 1 - erf_impl(x) otherwise

source

Reactant.MLIR.Dialects.chlo.is_inf Method

is_inf

Returns if a value is +/-inf element-wise.

source

Reactant.MLIR.Dialects.chlo.is_neg_inf Method

is_neg_inf

Returns if a value is -inf element-wise.

source

Reactant.MLIR.Dialects.chlo.is_pos_inf Method

is_pos_inf

Returns if a value is +inf element-wise.

source

Reactant.MLIR.Dialects.chlo.lgamma Method

lgamma

Returns Lgamma(operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.next_after Method

next_after

Returns the next representable value of x in the direction of y, element-wise. It can also return a subnormal number.

Equivalent to the C++ std::nextafter function.

source

Reactant.MLIR.Dialects.chlo.polygamma Method

polygamma

Returns Polygamma(operand, operand) element-wise.

source

Reactant.MLIR.Dialects.chlo.ragged_dot Method

ragged_dot

This operation takes three tensor args–-lhs, rhs, and group_sizes–-and a "ragged_dot_dimension_numbers" attribute. Like dot_general, the lhs and rhs are allowed arbitrary batch and contracting dimensions. Additionally, the lhs is required to have one ragged dimension, and the rhs may have at most one group dimension. The op has three modes, depending on the kind of the lhs ragged dimension.

In mode 1, the shape-signature is [b,m,k], [g,b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs non-contracting dimension (m). The dimensions b and k represent batch and contracting dimensions respectively. The rhs is required to have a group dimension (g).

In mode 2, the shape-signature is [b,m,k], [b,k,n], [g] -> [g,b,m,n]. Here the ragged dimension is an lhs/rhs contracting dimension (k).

In mode 3, the shape-signature is [b,m,k], [b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs/rhs batch dimension (b).

source

Reactant.MLIR.Dialects.chlo.sinh Method

sinh

Returns Sinh(operand) element-wise.

$

\sinh(x) = (e^x - e^-x) / 2 if |x| < 1 = e^(x + log(1/2)) - e^(-x + log(1/2)) otherwise. $

source

Reactant.MLIR.Dialects.chlo.square Method

square

Returns Square(operand) element-wise.

$

\square(x) = complex((x.real - x.imag) * (x.real + x.imag), x.real * x.imag * 2) if x is a complex number = x * x otherwise $

source

Reactant.MLIR.Dialects.chlo.tan Method

tan

Returns Tan(operand) element-wise.

$

\tan(x) = \sin(x) / \cos(x) $

source

Reactant.MLIR.Dialects.chlo.top_k Method

top_k

If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j].

For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus,

values.shape = indices.shape = input.shape[:-1] + [k]

If two elements are equal, the lower-index element appears first.

source

Reactant.MLIR.Dialects.chlo.zeta Method

zeta

Returns Zeta(operand, operand) element-wise.

$

(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}) $

source

+ \ No newline at end of file diff --git a/previews/PR441/api/enzyme.html b/previews/PR441/api/enzyme.html index 14f10e786..e2b5af0a3 100644 --- a/previews/PR441/api/enzyme.html +++ b/previews/PR441/api/enzyme.html @@ -9,11 +9,11 @@ - + - + - + @@ -21,8 +21,8 @@ -
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Enzyme Dialect ​

Reactant.MLIR.Dialects.enzyme.addTo Method

addTo

TODO

source

Reactant.MLIR.Dialects.enzyme.broadcast Method

broadcast

Broadcast the operand by adding extra dimensions with sizes provided by the shape attribute to the front. For scalar operands, ranked tensor is created.

NOTE: Only works for scalar and ranked tensor operands for now.

source

- +
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Enzyme Dialect ​

Reactant.MLIR.Dialects.enzyme.addTo Method

addTo

TODO

source

Reactant.MLIR.Dialects.enzyme.broadcast Method

broadcast

Broadcast the operand by adding extra dimensions with sizes provided by the shape attribute to the front. For scalar operands, ranked tensor is created.

NOTE: Only works for scalar and ranked tensor operands for now.

source

+ \ No newline at end of file diff --git a/previews/PR441/api/func.html b/previews/PR441/api/func.html index 5d45e9648..a43315312 100644 --- a/previews/PR441/api/func.html +++ b/previews/PR441/api/func.html @@ -9,11 +9,11 @@ - + - + - + @@ -21,12 +21,12 @@ -
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Func Dialect ​

Refer to the official documentation for more details.

Reactant.MLIR.Dialects.func.call Method

call

The func.call operation represents a direct call to a function that is within the same symbol scope as the call. The operands and result types of the call must match the specified function type. The callee is encoded as a symbol reference attribute named "callee".

Example

mlir
%2 = func.call @my_add(%0, %1) : (f32, f32) -> f32

source

Reactant.MLIR.Dialects.func.call_indirect Method

call_indirect

The func.call_indirect operation represents an indirect call to a value of function type. The operands and result types of the call must match the specified function type.

Function values can be created with the func.constant operation.

Example

mlir
%func = func.constant @my_func : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>
-%result = func.call_indirect %func(%0, %1) : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>

source

Reactant.MLIR.Dialects.func.constant Method

constant

The func.constant operation produces an SSA value from a symbol reference to a func.func operation

Example

mlir
// Reference to function @myfn.
+    
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Func Dialect ​
Refer to the official documentation for more details.
Reactant.MLIR.Dialects.func.call Method
call
The func.call operation represents a direct call to a function that is within the same symbol scope as the call. The operands and result types of the call must match the specified function type. The callee is encoded as a symbol reference attribute named "callee".
Example
mlir
%2 = func.call @my_add(%0, %1) : (f32, f32) -> f32
source
Reactant.MLIR.Dialects.func.call_indirect Method
call_indirect
The func.call_indirect operation represents an indirect call to a value of function type. The operands and result types of the call must match the specified function type.
Function values can be created with the func.constant operation.
Example
mlir
%func = func.constant @my_func : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>
+%result = func.call_indirect %func(%0, %1) : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>
source
Reactant.MLIR.Dialects.func.constant Method
constant
The func.constant operation produces an SSA value from a symbol reference to a func.func operation
Example
mlir
// Reference to function @myfn.
 %2 = func.constant @myfn : (tensor<16xf32>, f32) -> tensor<16xf32>
 
 // Equivalent generic forms
-%2 = "func.constant"() { value = @myfn } : () -> ((tensor<16xf32>, f32) -> tensor<16xf32>)
MLIR does not allow direct references to functions in SSA operands because the compiler is multithreaded, and disallowing SSA values to directly reference a function simplifies this (rationale).
source
Reactant.MLIR.Dialects.func.func_ Method
func_
Operations within the function cannot implicitly capture values defined outside of the function, i.e. Functions are IsolatedFromAbove. All external references must use function arguments or attributes that establish a symbolic connection (e.g. symbols referenced by name via a string attribute like SymbolRefAttr). An external function declaration (used when referring to a function declared in some other module) has no body. While the MLIR textual form provides a nice inline syntax for function arguments, they are internally represented as “block arguments” to the first block in the region.
Only dialect attribute names may be specified in the attribute dictionaries for function arguments, results, or the function itself.
Example
mlir
// External function definitions.
+%2 = "func.constant"() { value = @myfn } : () -> ((tensor<16xf32>, f32) -> tensor<16xf32>)
MLIR does not allow direct references to functions in SSA operands because the compiler is multithreaded, and disallowing SSA values to directly reference a function simplifies this (rationale).
source
Reactant.MLIR.Dialects.func.func_ Method
func_
Operations within the function cannot implicitly capture values defined outside of the function, i.e. Functions are IsolatedFromAbove. All external references must use function arguments or attributes that establish a symbolic connection (e.g. symbols referenced by name via a string attribute like SymbolRefAttr). An external function declaration (used when referring to a function declared in some other module) has no body. While the MLIR textual form provides a nice inline syntax for function arguments, they are internally represented as “block arguments” to the first block in the region.
Only dialect attribute names may be specified in the attribute dictionaries for function arguments, results, or the function itself.
Example
mlir
// External function definitions.
 func.func private @abort()
 func.func private @scribble(i32, i64, memref<? x 128 x f32, #layout_map0>) -> f64
 
@@ -43,11 +43,11 @@
 func.func private @example_fn_result() -> (f64 {dialectName.attrName = 0 : i64})
 
 // A function with an attribute
-func.func private @example_fn_attr() attributes {dialectName.attrName = false}
source
Reactant.MLIR.Dialects.func.return_ Method
return_
The func.return operation represents a return operation within a function. The operation takes variable number of operands and produces no results. The operand number and types must match the signature of the function that contains the operation.
Example
mlir
func.func @foo() : (i32, f8) {
+func.func private @example_fn_attr() attributes {dialectName.attrName = false}
source
Reactant.MLIR.Dialects.func.return_ Method
return_
The func.return operation represents a return operation within a function. The operation takes variable number of operands and produces no results. The operand number and types must match the signature of the function that contains the operation.
Example
mlir
func.func @foo() : (i32, f8) {
   ...
   return %0, %1 : i32, f8
-}
source

-    
+}

source

+ \ No newline at end of file diff --git a/previews/PR441/api/internal.html b/previews/PR441/api/internal.html index ddc681728..99e99d7c4 100644 --- a/previews/PR441/api/internal.html +++ b/previews/PR441/api/internal.html @@ -9,11 +9,11 @@ - + - + - + @@ -21,8 +21,8 @@ -
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Internal API ​

These functions are not part of the public API and are subject to change at any time.

Reactant.REDUB_ARGUMENTS_NAME Constant
julia
Reactant.REDUB_ARGUMENTS_NAME

The variable name bound to call_with_reactant's tuple of arguments in its @generated method definition.

This binding can be used to manually reference/destructure call_with_reactants arguments

This is required because user arguments could have a name which clashes with whatever name we choose for our argument. Thus we gensym to create it.

This originates from https://github.com/JuliaLabs/Cassette.jl/blob/c29b237c1ec0deda3a1037ec519eebe216952bfe/src/overdub.jl#L154

source

Reactant.within_reactant_interpreter Function
julia
within_reactant_interpreter()

Returns true if we are currently inside the ReactantInterpreter.

source

- +
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Internal API ​

These functions are not part of the public API and are subject to change at any time.

Reactant.REDUB_ARGUMENTS_NAME Constant
julia
Reactant.REDUB_ARGUMENTS_NAME

The variable name bound to call_with_reactant's tuple of arguments in its @generated method definition.

This binding can be used to manually reference/destructure call_with_reactants arguments

This is required because user arguments could have a name which clashes with whatever name we choose for our argument. Thus we gensym to create it.

This originates from https://github.com/JuliaLabs/Cassette.jl/blob/c29b237c1ec0deda3a1037ec519eebe216952bfe/src/overdub.jl#L154

source

Reactant.within_reactant_interpreter Function
julia
within_reactant_interpreter()

Returns true if we are currently inside the ReactantInterpreter.

source

+ \ No newline at end of file diff --git a/previews/PR441/api/mlirc.html b/previews/PR441/api/mlirc.html index 8979b6eec..f99e02e3d 100644 --- a/previews/PR441/api/mlirc.html +++ b/previews/PR441/api/mlirc.html @@ -9,11 +9,11 @@ - + - + - + @@ -21,16 +21,16 @@ -
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Higher level API ​

Core.Bool Method
julia
Bool(attr)

Returns the value stored in the given bool attribute.

source

Core.Float64 Method
julia
Float64(attr)

Returns the value stored in the given floating point attribute, interpreting the value as double.

source

Core.Int64 Method
julia
Int64(attr)

Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.

source

Core.String Method
julia
String(attr)

Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.

source

Core.String Method
julia
String(ident)

Gets the string value of the identifier.

source

Core.UInt64 Method
julia
UInt64(attr)

Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.

source

Reactant.MLIR.IR.AffineMap Method
julia
AffineMap(ndims, nsymbols, affineExprs; context=context())

Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.

source

Reactant.MLIR.IR.AffineMap Method
julia
AffineMap(ndims, nsymbols; context=context())

Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.

source

Reactant.MLIR.IR.AffineMap Method
julia
AffineMap(attr)

Returns the affine map wrapped in the given affine map attribute.

source

Reactant.MLIR.IR.AffineMap Method
julia
AffineMap(; context=context())

Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(str; context=context())

Creates a string attribute in the given context containing the given string.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(value; context=context())

Creates a bool attribute in the given context with the given value.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(elements; context=context())

Creates a dictionary attribute containing the given list of elements in the provided context.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(affineMap)

Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(type, str)

Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(type)

Creates a type attribute wrapping the given type in the same context as the type.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(float; context=context(), location=Location(), check=false)

Creates a floating point attribute in the given context with the given double value and double-precision FP semantics. If check=true, emits appropriate diagnostics on illegal arguments.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(complex; context=context(), location=Location(), check=false)

Creates a complex attribute in the given context with the given complex value and double-precision FP semantics.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(elements; context=context())

Creates an array element containing the given list of elements in the given context.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute()

Returns an empty attribute.

source

Reactant.MLIR.IR.Attribute Method
julia
Attribute(int)

Creates an integer attribute of the given type with the given integer value.

source

Reactant.MLIR.IR.Block Method
julia
Block(args, locs)

Creates a new empty block with the given argument types and transfers ownership to the caller.

source

Reactant.MLIR.IR.BlockIterator Type
julia
BlockIterator(region::Region)

Iterates over all blocks in the given region.

source

Reactant.MLIR.IR.Context Method
julia
Context()

Creates an MLIR context and transfers its ownership to the caller.

source

Reactant.MLIR.IR.ExecutionEngine Type
julia
ExecutionEngine(op, optLevel, sharedlibs = [])

Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.

source

Reactant.MLIR.IR.Identifier Method
julia
Identifier(context, str)

Gets an identifier with the given string value.

source

Reactant.MLIR.IR.IntegerSet Method
julia
IntegerSet(ndims, nsymbols, constraints, eqflags; context=context())

Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqflags is 1) or inequalities (eqflags is 0). Both constraints and eqflags need to be arrays of the same length.

source

Reactant.MLIR.IR.IntegerSet Method
julia
Integerset(ndims, nsymbols; context=context())

Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.

source

Reactant.MLIR.IR.LogicalResult Type
julia
LogicalResult

A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of LogicalResult must only be inspected using the associated functions.

source

Reactant.MLIR.IR.Module Type
julia
Module(location=Location())

Creates a new, empty module and transfers ownership to the caller.

source

Reactant.MLIR.IR.NamedAttribute Method
julia
NamedAttribute(name, attr)

Associates an attribute with the name. Takes ownership of neither.

source

Reactant.MLIR.IR.OpPassManager Method
julia
OpPassManager(opPassManager, operationName)

Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.

source

Reactant.MLIR.IR.OpPassManager Method
julia
OpPassManager(passManager, operationName)

Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.

source

Reactant.MLIR.IR.OpPassManager Method
julia
OpPassManager(passManager)

Cast a top-level PassManager to a generic OpPassManager.

source

Reactant.MLIR.IR.Operation Method
julia
Operation(module)

Views the module as a generic operation.

source

Reactant.MLIR.IR.OperationIterator Type
julia
OperationIterator(block::Block)

Iterates over all operations for the given block.

source

Reactant.MLIR.IR.PassManager Method
julia
PassManager(anchorOp; context=context())

Create a new top-level PassManager anchored on anchorOp.

source

Reactant.MLIR.IR.PassManager Method
julia
PassManager(; context=context())

Create a new top-level PassManager.

source

Reactant.MLIR.IR.Region Method
julia
Region()

Creates a new empty region and transfers ownership to the caller.

source

Reactant.MLIR.IR.RegionIterator Type
julia
RegionIterator(::Operation)

Iterates over all sub-regions for the given operation.

source

Reactant.MLIR.IR.SymbolTable Method
julia
mlirSymbolTableCreate(operation)

Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.

source

Reactant.MLIR.IR.Type Method
julia
Type(attr)

Returns the type stored in the given type attribute.

source

Reactant.MLIR.IR.Type Method
julia
Type(T::Core.Type{<:Integer}; context=context()

Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.

source

Reactant.MLIR.IR.Type Method
julia
Type(T::Core.Type{<:Signed}; context=context()

Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.

source

Reactant.MLIR.IR.Type Method
julia
Type(T::Core.Type{<:Unsigned}; context=context()

Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.

source

Reactant.MLIR.IR.Type Method
julia
Type(T::Core.Type{Bool}; context=context()

Creates a 1-bit signless integer type in the context. The type is owned by the context.

source

Reactant.MLIR.IR.Type Method
julia
Type(::Core.Type{Float16}; context=context())

Creates an f16 type in the given context. The type is owned by the context.

source

Reactant.MLIR.IR.Type Method
julia
Type(Core.Type{Float32}; context=context())

Creates an f32 type in the given context. The type is owned by the context.

source

Reactant.MLIR.IR.Type Method
julia
Type(Core.Type{Float64}; context=context())

Creates a f64 type in the given context. The type is owned by the context.

source

Reactant.MLIR.IR.Type Method
julia
Type(::Core.Type{Nothing}; context=context())

Creates a None type in the given context. The type is owned by the context.

source

Reactant.MLIR.IR.Type Method
julia
Type(elements; context=context())
-Type(::Core.Type{<:Tuple{T...}}; context=context())

Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.

source

Reactant.MLIR.IR.Type Method
julia
Type(Complex{T}) where {T}

Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.

source

Base.:* Method
julia
*(lhs, rhs)

Creates an affine mul expression with 'lhs' and 'rhs'.

source

Base.:+ Method
julia
+(lhs, rhs)

Creates an affine add expression with 'lhs' and 'rhs'.

source

Base.:== Method
julia
==(a, b)

Returns true if the two affine expressions are equal.

source

Base.:== Method
julia
==(a, b)

Checks if two affine maps are equal.

source

Base.:== Method
julia
==(a1, a2)

Checks if two attributes are equal.

source

Base.:== Method
julia
==(block, other)

Checks whether two blocks handles point to the same block. This does not perform deep comparison.

source

Base.:== Method
julia
==(ident, other)

Checks whether two identifiers are the same.

source

Base.:== Method
julia
==(s1, s2)

Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.

source

Base.:== Method
julia
==(region, other)

Checks whether two region handles point to the same region. This does not perform deep comparison.

source

Base.:== Method
julia
==(t1, t2)

Checks if two types are equal.

source

Base.:== Method
julia
==(typeID1, typeID2)

Checks if two type ids are equal.

source

Base.:== Method
julia
==(value1, value2)

Returns 1 if two values are equal, 0 otherwise.

source

Base.cld Method
julia
cld(lhs, rhs)

Creates an affine ceildiv expression with 'lhs' and 'rhs'.

source

Base.copy Method
julia
copy(op)

Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.

source

Base.div Method
julia
div(lhs, rhs)
+    
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Higher level API ​
Core.Bool Method
julia
Bool(attr)
Returns the value stored in the given bool attribute.
source
Core.Float64 Method
julia
Float64(attr)
Returns the value stored in the given floating point attribute, interpreting the value as double.
source
Core.Int64 Method
julia
Int64(attr)
Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.
source
Core.String Method
julia
String(attr)
Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.
source
Core.String Method
julia
String(ident)
Gets the string value of the identifier.
source
Core.UInt64 Method
julia
UInt64(attr)
Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.
source
Reactant.MLIR.IR.AffineMap Method
julia
AffineMap(ndims, nsymbols, affineExprs; context=context())
Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.
source
Reactant.MLIR.IR.AffineMap Method
julia
AffineMap(ndims, nsymbols; context=context())
Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.
source
Reactant.MLIR.IR.AffineMap Method
julia
AffineMap(attr)
Returns the affine map wrapped in the given affine map attribute.
source
Reactant.MLIR.IR.AffineMap Method
julia
AffineMap(; context=context())
Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(str; context=context())
Creates a string attribute in the given context containing the given string.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(value; context=context())
Creates a bool attribute in the given context with the given value.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(elements; context=context())
Creates a dictionary attribute containing the given list of elements in the provided context.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(affineMap)
Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(type, str)
Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(type)
Creates a type attribute wrapping the given type in the same context as the type.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(float; context=context(), location=Location(), check=false)
Creates a floating point attribute in the given context with the given double value and double-precision FP semantics. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(complex; context=context(), location=Location(), check=false)
Creates a complex attribute in the given context with the given complex value and double-precision FP semantics.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(elements; context=context())
Creates an array element containing the given list of elements in the given context.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute()
Returns an empty attribute.
source
Reactant.MLIR.IR.Attribute Method
julia
Attribute(int)
Creates an integer attribute of the given type with the given integer value.
source
Reactant.MLIR.IR.Block Method
julia
Block(args, locs)
Creates a new empty block with the given argument types and transfers ownership to the caller.
source
Reactant.MLIR.IR.BlockIterator Type
julia
BlockIterator(region::Region)
Iterates over all blocks in the given region.
source
Reactant.MLIR.IR.Context Method
julia
Context()
Creates an MLIR context and transfers its ownership to the caller.
source
Reactant.MLIR.IR.ExecutionEngine Type
julia
ExecutionEngine(op, optLevel, sharedlibs = [])
Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.
source
Reactant.MLIR.IR.Identifier Method
julia
Identifier(context, str)
Gets an identifier with the given string value.
source
Reactant.MLIR.IR.IntegerSet Method
julia
IntegerSet(ndims, nsymbols, constraints, eqflags; context=context())
Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqflags is 1) or inequalities (eqflags is 0). Both constraints and eqflags need to be arrays of the same length.
source
Reactant.MLIR.IR.IntegerSet Method
julia
Integerset(ndims, nsymbols; context=context())
Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.
source
Reactant.MLIR.IR.LogicalResult Type
julia
LogicalResult
A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of LogicalResult must only be inspected using the associated functions.
source
Reactant.MLIR.IR.Module Type
julia
Module(location=Location())
Creates a new, empty module and transfers ownership to the caller.
source
Reactant.MLIR.IR.NamedAttribute Method
julia
NamedAttribute(name, attr)
Associates an attribute with the name. Takes ownership of neither.
source
Reactant.MLIR.IR.OpPassManager Method
julia
OpPassManager(opPassManager, operationName)
Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.
source
Reactant.MLIR.IR.OpPassManager Method
julia
OpPassManager(passManager, operationName)
Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.
source
Reactant.MLIR.IR.OpPassManager Method
julia
OpPassManager(passManager)
Cast a top-level PassManager to a generic OpPassManager.
source
Reactant.MLIR.IR.Operation Method
julia
Operation(module)
Views the module as a generic operation.
source
Reactant.MLIR.IR.OperationIterator Type
julia
OperationIterator(block::Block)
Iterates over all operations for the given block.
source
Reactant.MLIR.IR.PassManager Method
julia
PassManager(anchorOp; context=context())
Create a new top-level PassManager anchored on anchorOp.
source
Reactant.MLIR.IR.PassManager Method
julia
PassManager(; context=context())
Create a new top-level PassManager.
source
Reactant.MLIR.IR.Region Method
julia
Region()
Creates a new empty region and transfers ownership to the caller.
source
Reactant.MLIR.IR.RegionIterator Type
julia
RegionIterator(::Operation)
Iterates over all sub-regions for the given operation.
source
Reactant.MLIR.IR.SymbolTable Method
julia
mlirSymbolTableCreate(operation)
Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.
source
Reactant.MLIR.IR.Type Method
julia
Type(attr)
Returns the type stored in the given type attribute.
source
Reactant.MLIR.IR.Type Method
julia
Type(T::Core.Type{<:Integer}; context=context()
Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.
source
Reactant.MLIR.IR.Type Method
julia
Type(T::Core.Type{<:Signed}; context=context()
Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.
source
Reactant.MLIR.IR.Type Method
julia
Type(T::Core.Type{<:Unsigned}; context=context()
Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.
source
Reactant.MLIR.IR.Type Method
julia
Type(T::Core.Type{Bool}; context=context()
Creates a 1-bit signless integer type in the context. The type is owned by the context.
source
Reactant.MLIR.IR.Type Method
julia
Type(::Core.Type{Float16}; context=context())
Creates an f16 type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.Type Method
julia
Type(Core.Type{Float32}; context=context())
Creates an f32 type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.Type Method
julia
Type(Core.Type{Float64}; context=context())
Creates a f64 type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.Type Method
julia
Type(::Core.Type{Nothing}; context=context())
Creates a None type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.Type Method
julia
Type(elements; context=context())
+Type(::Core.Type{<:Tuple{T...}}; context=context())
Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.
source
Reactant.MLIR.IR.Type Method
julia
Type(Complex{T}) where {T}
Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.
source
Base.:* Method
julia
*(lhs, rhs)
Creates an affine mul expression with 'lhs' and 'rhs'.
source
Base.:+ Method
julia
+(lhs, rhs)
Creates an affine add expression with 'lhs' and 'rhs'.
source
Base.:== Method
julia
==(a, b)
Returns true if the two affine expressions are equal.
source
Base.:== Method
julia
==(a, b)
Checks if two affine maps are equal.
source
Base.:== Method
julia
==(a1, a2)
Checks if two attributes are equal.
source
Base.:== Method
julia
==(block, other)
Checks whether two blocks handles point to the same block. This does not perform deep comparison.
source
Base.:== Method
julia
==(ident, other)
Checks whether two identifiers are the same.
source
Base.:== Method
julia
==(s1, s2)
Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.
source
Base.:== Method
julia
==(region, other)
Checks whether two region handles point to the same region. This does not perform deep comparison.
source
Base.:== Method
julia
==(t1, t2)
Checks if two types are equal.
source
Base.:== Method
julia
==(typeID1, typeID2)
Checks if two type ids are equal.
source
Base.:== Method
julia
==(value1, value2)
Returns 1 if two values are equal, 0 otherwise.
source
Base.cld Method
julia
cld(lhs, rhs)
Creates an affine ceildiv expression with 'lhs' and 'rhs'.
source
Base.copy Method
julia
copy(op)
Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.
source
Base.div Method
julia
div(lhs, rhs)
 ÷(lhs, rhs)
-fld(lhs, rhs)
Creates an affine floordiv expression with 'lhs' and 'rhs'.
source
Base.fill Method
julia
fill(attr, shapedType)
Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).
source
Base.gcd Method
julia
gcd(affineExpr)
Returns the greatest known integral divisor of this affine expression. The result is always positive.
source
Base.hash Method
julia
hash(typeID)
Returns the hash value of the type id.
source
Base.insert! Method
julia
insert!(block, index, operation)
Takes an operation owned by the caller and inserts it as index to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.
source
Base.insert! Method
julia
insert!(region, index, block)
Takes a block owned by the caller and inserts it at index to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.
source
Base.isempty Method
julia
isempty(affineMap)
Checks whether the given affine map is an empty affine map.
source
Base.isperm Method
julia
isperm(affineMap)
Checks whether the given affine map represents a symbol-less permutation map.
source
Base.mod Method
julia
mod(lhs, rhs)
Creates an affine mod expression with 'lhs' and 'rhs'.
source
Base.ndims Method
julia
ndims(affineMap)
Returns the number of dimensions of the given affine map.
source
Base.ndims Method
julia
ndims(set)
Returns the number of dimensions in the given set.
source
Base.ndims Method
julia
ndims(type)
Returns the rank of the given ranked shaped type.
source
Base.parse Method
julia
parse(passManager, pipeline)
Parse a textual MLIR pass pipeline and add it to the provided OpPassManager.
source
Base.parse Method
julia
parse(::Core.Type{Attribute}, str; context=context())
Parses an attribute. The attribute is owned by the context.
source
Base.parse Method
julia
parse(::Type{Module}, module; context=context())
Parses a module from the string and transfers ownership to the caller.
source
Base.parse Method
julia
parse(type; context=context())
Parses a type. The type is owned by the context.
source
Base.push! Method
julia
push!(block, operation)
Takes an operation owned by the caller and appends it to the block.
source
Base.push! Method
julia
push!(region, block)
Takes a block owned by the caller and appends it to the given region.
source
Base.push! Method
julia
push!(symboltable, operation)
Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.
source
Base.replace Method
julia
mlirAffineMapReplace(affineMap, expression => replacement, numResultDims, numResultSyms)
Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.
source
Base.replace Method
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)
Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.
source
Base.reshape Method
julia
Base.reshape(attr, shapedType)
Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.
source
Base.size Method
julia
size(type, i)
Returns the i-th dimension of the given ranked shaped type.
source
Base.write Method
julia
write(fileName, jit)
Dump as an object in fileName.
source
Reactant.MLIR.IR.AffineDimensionExpr Method
julia
AffineDimensionExpr(position; context=context)
Creates an affine dimension expression with 'position' in the context.
source
Reactant.MLIR.IR.BFloat16Type Method
BFloat16Type(; context=context())
Creates a bf16 type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.ConstantAffineMap Method
julia
ConstantAffineMap(val; context=context())
Creates a single constant result affine map in the context. The affine map is owned by the context.
source
Reactant.MLIR.IR.ConstantExpr Method
julia
ConstantExpr(constant::Int; context=context())
Creates an affine constant expression with 'constant' in the context.
source
Reactant.MLIR.IR.DenseElementsAttribute Method
julia
DenseElementsAttribute(array::AbstractArray)
Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.
source
Reactant.MLIR.IR.DenseElementsAttribute Method
julia
DenseElementsAttribute(array::AbstractArray{String})
Creates a dense elements attribute with the given shaped type from string elements.
source
Reactant.MLIR.IR.DenseElementsAttribute Method
julia
DenseElementsAttribute(shapedType, elements)
Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.
source
Reactant.MLIR.IR.FlatSymbolRefAttribute Method
julia
FlatSymbolRefAttribute(ctx, symbol)
Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.
source
Reactant.MLIR.IR.Float8E4M3FN Method
julia
Float8E4M3FN(; context=context())
Creates an f8E4M3FN type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.Float8E5M2 Method
julia
Float8E5M2(; context=context())
Creates an f8E5M2 type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.FunctionType Method
julia
FunctionType(inputs, results; context=context())
Creates a function type, mapping a list of input types to result types.
source
Reactant.MLIR.IR.IdentityAffineMap Method
julia
IdentityAffineMap(ndims; context=context())
Creates an affine map with 'ndims' identity in the context. The affine map is owned by the context.
source
Reactant.MLIR.IR.IndexType Method
julia
IndexType(; context=context())
Creates an index type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.MemRefType Method
julia
MemRefType(elementType, rank, shape, layout, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.MemRefType Method
julia
MemRefType(elementType, rank, shape, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.MemRefType Method
julia
MemRefType(elementType, memorySpace)
Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.MinorIdentityAffineMap Method
julia
MinorIdentityAffineMap(ndims, nresults; context=context())
Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.
source
Reactant.MLIR.IR.OpaqueAttribute Method
julia
OpaqueAttribute(dialectNamespace, dataLength, data, type; context=context())
Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).
source
Reactant.MLIR.IR.OpaqueType Method
julia
OpaqueType(dialectNamespace, typeData; context=context())
Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).
source
Reactant.MLIR.IR.PermutationAffineMap Method
julia
PermutationAffineMap(permutation; context=context())
Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid invalid permutation). The affine map is owned by the context.
source
Reactant.MLIR.IR.SymbolExpr Method
julia
SymbolExpr(position; context=context())
Creates an affine symbol expression with 'position' in the context.
source
Reactant.MLIR.IR.SymbolRefAttribute Method
julia
SymbolRefAttribute(symbol, references; context=context())
Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.
source
Reactant.MLIR.IR.TensorType Function
julia
TensorType(shape, elementType, encoding=Attribute(); location=Location(), check=false)
Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull to this parameter. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.TensorType Method
julia
TensorType(elementType)
Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.UnitAttribute Method
julia
UnitAttribute(; context=context())
Creates a unit attribute in the given context.
source
Reactant.MLIR.IR.VectorType Method
julia
VectorType(rank, shape, elementType; location=Location(), check=false)
Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.add_owned_pass! Method
julia
add_owned_pass!(opPassManager, pass)
Add a pass and transfer ownership to the provided OpPassManager. If the pass is not a generic operation pass or matching the type of the provided OpPassManager, a new OpPassManager is implicitly nested under the provided OpPassManager.
source
Reactant.MLIR.IR.add_owned_pass! Method
julia
add_owned_pass!(passManager, pass)
Add a pass and transfer ownership to the provided top-level PassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.
source
Reactant.MLIR.IR.add_pipeline! Method
julia
add_pipeline!(passManager, pipelineElements, callback, userData)
Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
Reactant.MLIR.IR.affinemap Method
julia
affinemap(type)
Returns the affine map of the given MemRef type.
source
Reactant.MLIR.IR.argument Method
julia
argument(block, i)
Returns i-th argument of the block.
source
Reactant.MLIR.IR.attr! Method
julia
attr!(op, name, attr)
Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise.
source
Reactant.MLIR.IR.attr Method
julia
attr(op, name)
Returns an attribute attached to the operation given its name.
source
Reactant.MLIR.IR.attr Method
julia
attr(op, i)
Return i-th attribute of the operation.
source
Reactant.MLIR.IR.bitwidth Method
julia
bitwidth(type)
Returns the bitwidth of an integer type.
source
Reactant.MLIR.IR.block Method
julia
block(op)
Gets the block that owns this operation, returning null if the operation is not owned.
source
Reactant.MLIR.IR.block_arg_num Method
julia
block_arg_num(value)
Returns the position of the value in the argument list of its block.
source
Reactant.MLIR.IR.block_owner Method
julia
block_owner(value)
Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.
source
Reactant.MLIR.IR.body Method
julia
body(module)
Gets the body of the module, i.e. the only block it contains.
source
Reactant.MLIR.IR.compose Method
julia
compose(affineExpr, affineMap)
Composes the given map with the given expression.
source
Reactant.MLIR.IR.constraint Method
julia
mlirIntegerSetGetConstraint(set, i)
Returns i-th constraint of the set.
source
Reactant.MLIR.IR.context Method
julia
context(affineExpr)
Gets the context that owns the affine expression.
source
Reactant.MLIR.IR.context Method
julia
context(affineMap)
Gets the context that the given affine map was created with.
source
Reactant.MLIR.IR.context Method
julia
context(attribute)
Gets the context that an attribute was created with.
source
Reactant.MLIR.IR.context Method
julia
context(ident)
Returns the context associated with this identifier
source
Reactant.MLIR.IR.context Method
julia
context(set)
Gets the context in which the given integer set lives.
source
Reactant.MLIR.IR.context Method
julia
context(module)
Gets the context that a module was created with.
source
Reactant.MLIR.IR.context Method
julia
context(op)
Gets the context this operation is associated with.
source
Reactant.MLIR.IR.context Method
julia
context(type)
Gets the context that a type was created with.
source
Reactant.MLIR.IR.data Method
julia
data(attr)
Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.IR.data Method
julia
mlirOpaqueTypeGetData(type)
Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.
source
Reactant.MLIR.IR.delete! Method
julia
delete!(symboltable, operation)
Removes the given operation from the symbol table and erases it.
source
Reactant.MLIR.IR.dynsize Method
julia
dynsize()
Returns the value indicating a dynamic size in a shaped type. Prefer isdynsize to direct comparisons with this value.
source
Reactant.MLIR.IR.dynstrideoroffset Method
julia
mlirShapedTypeGetDynamicStrideOrOffset()
Returns the value indicating a dynamic stride or offset in a shaped type. Prefer isdynstrideoroffset to direct comparisons with this value.
source
Reactant.MLIR.IR.enable_ir_printing! Method
julia
enable_ir_printing!(passManager)
Enable mlir-print-ir-after-all.
source
Reactant.MLIR.IR.enable_verifier! Function
julia
enable_verifier!(passManager, enable)
Enable / disable verify-each.
source
Reactant.MLIR.IR.encoding Method
julia
encoding(type)
Gets the 'encoding' attribute from the ranked tensor type, returning a nothing if none.
source
Reactant.MLIR.IR.failure Method
julia
failure()
Creates a logical result representing a failure.
source
Reactant.MLIR.IR.first_block Method
julia
first_block(region)
Gets the first block in the region.
source
Reactant.MLIR.IR.first_op Method
julia
first_op(block)
Returns the first operation in the block or nothing if empty.
source
Reactant.MLIR.IR.first_use Method
julia
first_use(value)
Returns an OpOperand representing the first use of the value, or a nothing if there are no uses.
source
Reactant.MLIR.IR.flatsymbol Method
julia
flatsymbol(attr)
Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.IR.hasrank Method
julia
hasrank(type)
Checks whether the given shaped type is ranked.
source
Reactant.MLIR.IR.hasstaticshape Method
julia
hasstaticshape(type)
Checks whether the given shaped type has a static shape.
source
Reactant.MLIR.IR.input Method
julia
input(type, i)
Returns the i-th input type.
source
Reactant.MLIR.IR.insert_after! Method
julia
insert_after!(block, reference, operation)
Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.
source
Reactant.MLIR.IR.insert_after! Method
julia
insert_after!(region, reference, block)
Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.
source
Reactant.MLIR.IR.insert_before! Method
julia
insert_before!(block, reference, operation)
Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.
source
Reactant.MLIR.IR.insert_before! Method
julia
insert_before!(region, reference, block)
Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.
source
Reactant.MLIR.IR.is_block_arg Method
julia
is_block_arg(value)
Returns 1 if the value is a block argument, 0 otherwise.
source
Reactant.MLIR.IR.is_op_res Method
julia
is_op_res(value)
Returns 1 if the value is an operation result, 0 otherwise.
source
Reactant.MLIR.IR.is_pure_affine Method
julia
is_pure_affine(affineExpr)
Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.
source
Reactant.MLIR.IR.is_registered Method
julia
is_registered(name; context=context())
Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.
source
Reactant.MLIR.IR.is_symbolic_or_constant Method
julia
is_symbolic_or_constant(affineExpr)
Checks whether the given affine expression is made out of only symbols and constants.
source
Reactant.MLIR.IR.isadd Method
julia
isadd(affineExpr)
Checks whether the given affine expression is an add expression.
source
Reactant.MLIR.IR.isaffinemap Method
julia
isaffinemap(attr)
Checks whether the given attribute is an affine map attribute.
source
Reactant.MLIR.IR.isarray Method
julia
isarray(attr)
Checks whether the given attribute is an array attribute.
source
Reactant.MLIR.IR.isbf16 Method
julia
isbf16(type)
Checks whether the given type is a bf16 type.
source
Reactant.MLIR.IR.isbinary Method
julia
isbinary(affineExpr)
Checks whether the given affine expression is binary.
source
Reactant.MLIR.IR.isbool Method
julia
isbool(attr)
Checks whether the given attribute is a bool attribute.
source
Reactant.MLIR.IR.isceildiv Method
julia
isceildiv(affineExpr)
Checks whether the given affine expression is an ceildiv expression.
source
Reactant.MLIR.IR.iscomplex Method
julia
iscomplex(type)
Checks whether the given type is a Complex type.
source
Reactant.MLIR.IR.isconstantexpr Method
julia
isconstantexpr(affineExpr)
Checks whether the given affine expression is a constant expression.
source
Reactant.MLIR.IR.isconstrainteq Method
julia
mlirIntegerSetIsConstraintEq(set, i)
Returns true of the i-th constraint of the set is an equality constraint, false otherwise.
source
Reactant.MLIR.IR.isdenseelements Method
julia
isdenseelements(attr)
Checks whether the given attribute is a dense elements attribute.
source
Reactant.MLIR.IR.isdict Method
julia
isdict(attr)
Checks whether the given attribute is a dictionary attribute.
source
Reactant.MLIR.IR.isdimexpr Method
julia
isdimexpr(affineExpr)
Checks whether the given affine expression is a dimension expression.
source
Reactant.MLIR.IR.isdyndim Method
julia
isdyndim(type, i)
Checks wither the i-th dimension of the given shaped type is dynamic.
source
Reactant.MLIR.IR.isdynsize Method
julia
isdynsize(size)
Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.
source
Reactant.MLIR.IR.isdynstrideoroffset Method
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)
Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.
source
Reactant.MLIR.IR.iselements Method
julia
iselements(attr)
Checks whether the given attribute is an elements attribute.
source
Reactant.MLIR.IR.isempty Method
julia
isempty(set)
Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.
source
Reactant.MLIR.IR.isf16 Method
julia
isf16(type)
Checks whether the given type is an f16 type.
source
Reactant.MLIR.IR.isf32 Method
julia
isf32(type)
Checks whether the given type is an f32 type.
source
Reactant.MLIR.IR.isf64 Method
julia
isf64(type)
Checks whether the given type is an f64 type.
source
Reactant.MLIR.IR.isf8e4m3fn Method
julia
isf8e4m3fn(type)
Checks whether the given type is an f8E4M3FN type.
source
Reactant.MLIR.IR.isf8e5m2 Method
julia
isf8e5m2(type)
Checks whether the given type is an f8E5M2 type.
source
Reactant.MLIR.IR.isfailure Method
julia
isfailure(res)
Checks if the given logical result represents a failure.
source
Reactant.MLIR.IR.isflatsymbolref Method
julia
isflatsymbolref(attr)
Checks whether the given attribute is a flat symbol reference attribute.
source
Reactant.MLIR.IR.isfloat Method
julia
isfloat(attr)
Checks whether the given attribute is a floating point attribute.
source
Reactant.MLIR.IR.isfloordiv Method
julia
isfloordiv(affineExpr)
Checks whether the given affine expression is an floordiv expression.
source
Reactant.MLIR.IR.isfunction Method
julia
isfunction(type)
Checks whether the given type is a function type.
source
Reactant.MLIR.IR.isfunctionofdimexpr Method
julia
isfunctionofdimexpr(affineExpr, position)
Checks whether the given affine expression involves AffineDimExpr 'position'.
source
Reactant.MLIR.IR.isidentity Method
julia
isidentity(affineMap)
Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.
source
Reactant.MLIR.IR.isindex Method
julia
isindex(type)
Checks whether the given type is an index type.
source
Reactant.MLIR.IR.isinteger Method
julia
isinteger(attr)
Checks whether the given attribute is an integer attribute.
source
Reactant.MLIR.IR.isinteger Method
julia
isinteger(type)
Checks whether the given type is an integer type.
source
Reactant.MLIR.IR.isintegerset Method
julia
isintegerset(attr)
Checks whether the given attribute is an integer set attribute.
source
Reactant.MLIR.IR.ismemref Method
julia
ismemref(type)
Checks whether the given type is a MemRef type.
source
Reactant.MLIR.IR.isminoridentity Method
julia
isminoridentity(affineMap)
Checks whether the given affine map is a minor identity affine map.
source
Reactant.MLIR.IR.ismod Method
julia
ismod(affineExpr)
Checks whether the given affine expression is an mod expression.
source
Reactant.MLIR.IR.ismul Method
julia
ismul(affineExpr)
Checks whether the given affine expression is an mul expression.
source
Reactant.MLIR.IR.ismultipleof Method
julia
ismultipleof(affineExpr, factor)
Checks whether the given affine expression is a multiple of 'factor'.
source
Reactant.MLIR.IR.isnone Method
julia
mlirTypeIsANone(type)
Checks whether the given type is a None type.
source
Reactant.MLIR.IR.isopaque Method
julia
isopaque(attr)
Checks whether the given attribute is an opaque attribute.
source
Reactant.MLIR.IR.isopaque Method
julia
isopaque(type)
Checks whether the given type is an opaque type.
source
Reactant.MLIR.IR.isprojperm Method
julia
isprojperm(affineMap)
Checks whether the given affine map represents a subset of a symbol-less permutation map.
source
Reactant.MLIR.IR.isrankedtensor Method
julia
isrankedtensor(type)
Checks whether the given type is a ranked tensor type.
source
Reactant.MLIR.IR.isshaped Method
julia
isshaped(type)
Checks whether the given type is a Shaped type.
source
Reactant.MLIR.IR.issigned Method
julia
issigned(type)
Checks whether the given integer type is signed.
source
Reactant.MLIR.IR.issignless Method
julia
issignless(type)
Checks whether the given integer type is signless.
source
Reactant.MLIR.IR.issingleconstant Method
julia
issingleconstant(affineMap)
Checks whether the given affine map is a single result constant affine map.
source
Reactant.MLIR.IR.issparseelements Method
julia
issparseelements(attr)
Checks whether the given attribute is a sparse elements attribute.
source
Reactant.MLIR.IR.issplat Method
julia
issplat(attr)
Checks whether the given dense elements attribute contains a single replicated value (splat).
source
Reactant.MLIR.IR.isstring Method
julia
isstring(attr)
Checks whether the given attribute is a string attribute.
source
Reactant.MLIR.IR.issuccess Method
julia
issuccess(res)
Checks if the given logical result represents a success.
source
Reactant.MLIR.IR.issymbolexpr Method
julia
issymbolexpr(affineExpr)
Checks whether the given affine expression is a symbol expression.
source
Reactant.MLIR.IR.issymbolref Method
julia
issymbolref(attr)
Checks whether the given attribute is a symbol reference attribute.
source
Reactant.MLIR.IR.istensor Method
julia
istensor(type)
Checks whether the given type is a Tensor type.
source
Reactant.MLIR.IR.istuple Method
julia
istuple(type)
Checks whether the given type is a tuple type.
source
Reactant.MLIR.IR.istype Method
julia
istype(attr)
Checks whether the given attribute is a type attribute.
source
Reactant.MLIR.IR.isunit Method
julia
isunit(attr)
Checks whether the given attribute is a unit attribute.
source
Reactant.MLIR.IR.isunrankedmemref Method
julia
mlirTypeIsAUnrankedMemRef(type)
Checks whether the given type is an UnrankedMemRef type.
source
Reactant.MLIR.IR.isunrankedtensor Method
julia
isunrankedtensor(type)
Checks whether the given type is an unranked tensor type.
source
Reactant.MLIR.IR.isunsigned Method
julia
isunsigned(type)
Checks whether the given integer type is unsigned.
source
Reactant.MLIR.IR.isvector Method
julia
isvector(type)
Checks whether the given type is a Vector type.
source
Reactant.MLIR.IR.layout Method
julia
layout(type)
Returns the layout of the given MemRef type.
source
Reactant.MLIR.IR.leafref Method
julia
leafref(attr)
Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.IR.lhs Method
julia
lhs(affineExpr)
Returns the left hand side affine expression of the given affine binary operation expression.
source
Reactant.MLIR.IR.location Method
julia
location(op)
Gets the location of the operation.
source
Reactant.MLIR.IR.lookup Method
julia
lookup(jit, name)
Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.
source
Reactant.MLIR.IR.lookup Method
julia
lookup(symboltable, name)
Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.
source
Reactant.MLIR.IR.majorsubmap Method
julia
majorsubmap(affineMap, nresults)
Returns the affine map consisting of the most major nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
source
Reactant.MLIR.IR.memspace Method
julia
mlirMemRefTypeGetMemorySpace(type)
Returns the memory space of the given MemRef type.
source
Reactant.MLIR.IR.minorsubmap Method
julia
minorsubmap(affineMap, nresults)
Returns the affine map consisting of the most minor nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
source
Reactant.MLIR.IR.move_after! Method
julia
move_after!(op, other)
Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
Reactant.MLIR.IR.move_before! Method
julia
move_before!(op, other)
Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
Reactant.MLIR.IR.name Method
julia
name(op)
Gets the name of the operation as an identifier.
source
Reactant.MLIR.IR.namespace Method
julia
mlirOpaqueAttrGetDialectNamespace(attr)
Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.
source
Reactant.MLIR.IR.namespace Method
julia
mlirOpaqueTypeGetDialectNamespace(type)
Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.
source
Reactant.MLIR.IR.nargs Method
julia
nargs(block)
Returns the number of arguments of the block.
source
Reactant.MLIR.IR.nattrs Method
julia
nattrs(op)
Returns the number of attributes attached to the operation.
source
Reactant.MLIR.IR.nconstraints Method
julia
nconstraints(set)
Returns the number of constraints (equalities + inequalities) in the given set.
source
Reactant.MLIR.IR.nequalities Method
julia
nequalities(set)
Returns the number of equalities in the given set.
source
Reactant.MLIR.IR.next Method
julia
next(block)
Returns the block immediately following the given block in its parent region or nothing if last.
source
Reactant.MLIR.IR.next Method
julia
next(opOperand)
Returns an op operand representing the next use of the value, or nothing if there is no next use.
source
Reactant.MLIR.IR.ninequalities Method
julia
ninequalities(set)
Returns the number of inequalities in the given set.
source
Reactant.MLIR.IR.ninputs Method
julia
ninputs(affineMap)
Returns the number of inputs (dimensions + symbols) of the given affine map.
source
Reactant.MLIR.IR.ninputs Method
julia
ninputs(set)
Returns the number of inputs (dimensions + symbols) in the given set.
source
Reactant.MLIR.IR.ninputs Method
julia
ninputs(type)
Returns the number of input types.
source
Reactant.MLIR.IR.nnestedrefs Method
julia
nnestedrefs(attr)
Returns the number of references nested in the given symbol reference attribute.
source
Reactant.MLIR.IR.noperands Method
julia
noperands(op)
Returns the number of operands of the operation.
source
Reactant.MLIR.IR.nregions Method
julia
nregions(op)
Returns the number of regions attached to the given operation.
source
Reactant.MLIR.IR.nresults Method
julia
nresults(affineMap)
Returns the number of results of the given affine map.
source
Reactant.MLIR.IR.nresults Method
julia
nresults(op)
Returns the number of results of the operation.
source
Reactant.MLIR.IR.nresults Method
julia
nresults(type)
Returns the number of result types.
source
Reactant.MLIR.IR.nsuccessors Method
julia
nsuccessors(op)
Returns the number of successor blocks of the operation.
source
Reactant.MLIR.IR.nsymbols Method
julia
nsymbols(affineMap)
Returns the number of symbols of the given affine map.
source
Reactant.MLIR.IR.nsymbols Method
julia
nsymbols(set)
Returns the number of symbols in the given set.
source
Reactant.MLIR.IR.op_owner Method
julia
op_owner(value)
Returns an operation that produced this value as its result. Asserts if the value is not an op result.
source
Reactant.MLIR.IR.op_res_num Method
julia
op_res_num(value)
Returns the position of the value in the list of results of the operation that produced it.
source
Reactant.MLIR.IR.operand Function
julia
operand(op, i)
Returns i-th operand of the operation.
source
Reactant.MLIR.IR.operand! Method
julia
operand!(op, i, value)
Sets the i-th operand of the operation.
source
Reactant.MLIR.IR.operandindex Method
julia
operandindex(opOperand)
Returns the operand number of an op operand.
source
Reactant.MLIR.IR.owner Method
julia
owner(opOperand)
Returns the owner operation of an op operand.
source
Reactant.MLIR.IR.parent_op Method
julia
parent_op(block)
Returns the closest surrounding operation that contains this block.
source
Reactant.MLIR.IR.parent_op Method
julia
parent_op(op)
Gets the operation that owns this operation, returning null if the operation is not owned.
source
Reactant.MLIR.IR.parent_region Method
julia
parent_region(block)
Returns the region that contains this block.
source
Reactant.MLIR.IR.position Method
julia
position(affineExpr)
Returns the position of the given affine dimension expression, affine symbol expression or ...
source
Reactant.MLIR.IR.push_argument! Method
julia
push_argument!(block, type; location=Location())
Appends an argument of the specified type to the block. Returns the newly added argument.
source
Reactant.MLIR.IR.region Method
julia
region(op, i)
Returns i-th region attached to the operation.
source
Reactant.MLIR.IR.result Function
julia
result(op, i)
Returns i-th result of the operation.
source
Reactant.MLIR.IR.result Function
julia
result(type, i)
Returns the i-th result type.
source
Reactant.MLIR.IR.result Method
julia
result(affineMap, pos)
Returns the result at the given position.
source
Reactant.MLIR.IR.result Method
julia
result(affineMap)
Returns the constant result of the given affine map. The function asserts that the map has a single constant result.
source
Reactant.MLIR.IR.rhs Method
julia
rhs(affineExpr)
Returns the right hand side affine expression of the given affine binary operation expression.
source
Reactant.MLIR.IR.rmattr! Method
julia
rmattr!(op, name)
Removes an attribute by name. Returns false if the attribute was not found and true if removed.
source
Reactant.MLIR.IR.rmfromparent! Method
julia
rmfromparent!(op)
Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.
source
Reactant.MLIR.IR.rootref Method
julia
rootref(attr)
Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.IR.run! Method
julia
run!(passManager, module)
Run the provided passManager on the given module.
source
Reactant.MLIR.IR.submap Method
julia
submap(affineMap, positions)
Returns the affine map consisting of the positions subset.
source
Reactant.MLIR.IR.success Method
julia
success()
Creates a logical result representing a success.
source
Reactant.MLIR.IR.successor Method
julia
successor(op, i)
Returns i-th successor of the operation.
source
Reactant.MLIR.IR.terminator Method
julia
terminator(block)
Returns the terminator operation in the block or nothing if no terminator.
source
Reactant.MLIR.IR.type! Method
julia
set_type!(value, type)
Sets the type of the block argument to the given type.
source
Reactant.MLIR.IR.type Method
julia
type(attribute)
Gets the type of this attribute.
source
Reactant.MLIR.IR.type Method
julia
type(value)
Returns the type of the value.
source
Reactant.MLIR.IR.typeid Method
julia
typeid(attribute)
Gets the type id of the attribute.
source
Reactant.MLIR.IR.typeid Method
julia
typeid(op)
Gets the type id of the operation. Returns null if the operation does not have a registered operation description.
source
Reactant.MLIR.IR.typeid Method
julia
typeid(type)
Gets the type ID of the type.
source
Reactant.MLIR.IR.value Method
julia
value(affineExpr)
Returns the value of the given affine constant expression.
source
Reactant.MLIR.IR.verify Method
julia
verify(op)
Verify the operation and return true if it passes, false if it fails.
source
Reactant.MLIR.IR.verifyall Method
julia
verifyall(operation; debug=false)
Prints the operations which could not be verified.
source
Reactant.MLIR.IR.@affinemap Macro
julia
@affinemap (d1, d2, d3, ...)[s1, s2, ...] -> (d0 + d1, ...)
Returns an affine map from the provided Julia expression. On the right hand side are allowed the following function calls:
  • +, *, ÷, %, fld, cld
The rhs can only contains dimensions and symbols present on the left hand side or integer literals.
julia
julia> using Reactant.MLIR: IR
+fld(lhs, rhs)
Creates an affine floordiv expression with 'lhs' and 'rhs'.
source
Base.fill Method
julia
fill(attr, shapedType)
Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).
source
Base.gcd Method
julia
gcd(affineExpr)
Returns the greatest known integral divisor of this affine expression. The result is always positive.
source
Base.hash Method
julia
hash(typeID)
Returns the hash value of the type id.
source
Base.insert! Method
julia
insert!(block, index, operation)
Takes an operation owned by the caller and inserts it as index to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.
source
Base.insert! Method
julia
insert!(region, index, block)
Takes a block owned by the caller and inserts it at index to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.
source
Base.isempty Method
julia
isempty(affineMap)
Checks whether the given affine map is an empty affine map.
source
Base.isperm Method
julia
isperm(affineMap)
Checks whether the given affine map represents a symbol-less permutation map.
source
Base.mod Method
julia
mod(lhs, rhs)
Creates an affine mod expression with 'lhs' and 'rhs'.
source
Base.ndims Method
julia
ndims(affineMap)
Returns the number of dimensions of the given affine map.
source
Base.ndims Method
julia
ndims(set)
Returns the number of dimensions in the given set.
source
Base.ndims Method
julia
ndims(type)
Returns the rank of the given ranked shaped type.
source
Base.parse Method
julia
parse(passManager, pipeline)
Parse a textual MLIR pass pipeline and add it to the provided OpPassManager.
source
Base.parse Method
julia
parse(::Core.Type{Attribute}, str; context=context())
Parses an attribute. The attribute is owned by the context.
source
Base.parse Method
julia
parse(::Type{Module}, module; context=context())
Parses a module from the string and transfers ownership to the caller.
source
Base.parse Method
julia
parse(type; context=context())
Parses a type. The type is owned by the context.
source
Base.push! Method
julia
push!(block, operation)
Takes an operation owned by the caller and appends it to the block.
source
Base.push! Method
julia
push!(region, block)
Takes a block owned by the caller and appends it to the given region.
source
Base.push! Method
julia
push!(symboltable, operation)
Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.
source
Base.replace Method
julia
mlirAffineMapReplace(affineMap, expression => replacement, numResultDims, numResultSyms)
Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.
source
Base.replace Method
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)
Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.
source
Base.reshape Method
julia
Base.reshape(attr, shapedType)
Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.
source
Base.size Method
julia
size(type, i)
Returns the i-th dimension of the given ranked shaped type.
source
Base.write Method
julia
write(fileName, jit)
Dump as an object in fileName.
source
Reactant.MLIR.IR.AffineDimensionExpr Method
julia
AffineDimensionExpr(position; context=context)
Creates an affine dimension expression with 'position' in the context.
source
Reactant.MLIR.IR.BFloat16Type Method
BFloat16Type(; context=context())
Creates a bf16 type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.ConstantAffineMap Method
julia
ConstantAffineMap(val; context=context())
Creates a single constant result affine map in the context. The affine map is owned by the context.
source
Reactant.MLIR.IR.ConstantExpr Method
julia
ConstantExpr(constant::Int; context=context())
Creates an affine constant expression with 'constant' in the context.
source
Reactant.MLIR.IR.DenseElementsAttribute Method
julia
DenseElementsAttribute(array::AbstractArray)
Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.
source
Reactant.MLIR.IR.DenseElementsAttribute Method
julia
DenseElementsAttribute(array::AbstractArray{String})
Creates a dense elements attribute with the given shaped type from string elements.
source
Reactant.MLIR.IR.DenseElementsAttribute Method
julia
DenseElementsAttribute(shapedType, elements)
Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.
source
Reactant.MLIR.IR.FlatSymbolRefAttribute Method
julia
FlatSymbolRefAttribute(ctx, symbol)
Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.
source
Reactant.MLIR.IR.Float8E4M3FN Method
julia
Float8E4M3FN(; context=context())
Creates an f8E4M3FN type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.Float8E5M2 Method
julia
Float8E5M2(; context=context())
Creates an f8E5M2 type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.FunctionType Method
julia
FunctionType(inputs, results; context=context())
Creates a function type, mapping a list of input types to result types.
source
Reactant.MLIR.IR.IdentityAffineMap Method
julia
IdentityAffineMap(ndims; context=context())
Creates an affine map with 'ndims' identity in the context. The affine map is owned by the context.
source
Reactant.MLIR.IR.IndexType Method
julia
IndexType(; context=context())
Creates an index type in the given context. The type is owned by the context.
source
Reactant.MLIR.IR.MemRefType Method
julia
MemRefType(elementType, rank, shape, layout, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.MemRefType Method
julia
MemRefType(elementType, rank, shape, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.MemRefType Method
julia
MemRefType(elementType, memorySpace)
Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.MinorIdentityAffineMap Method
julia
MinorIdentityAffineMap(ndims, nresults; context=context())
Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.
source
Reactant.MLIR.IR.OpaqueAttribute Method
julia
OpaqueAttribute(dialectNamespace, dataLength, data, type; context=context())
Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).
source
Reactant.MLIR.IR.OpaqueType Method
julia
OpaqueType(dialectNamespace, typeData; context=context())
Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).
source
Reactant.MLIR.IR.PermutationAffineMap Method
julia
PermutationAffineMap(permutation; context=context())
Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid invalid permutation). The affine map is owned by the context.
source
Reactant.MLIR.IR.SymbolExpr Method
julia
SymbolExpr(position; context=context())
Creates an affine symbol expression with 'position' in the context.
source
Reactant.MLIR.IR.SymbolRefAttribute Method
julia
SymbolRefAttribute(symbol, references; context=context())
Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.
source
Reactant.MLIR.IR.TensorType Function
julia
TensorType(shape, elementType, encoding=Attribute(); location=Location(), check=false)
Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull to this parameter. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.TensorType Method
julia
TensorType(elementType)
Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.UnitAttribute Method
julia
UnitAttribute(; context=context())
Creates a unit attribute in the given context.
source
Reactant.MLIR.IR.VectorType Method
julia
VectorType(rank, shape, elementType; location=Location(), check=false)
Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
Reactant.MLIR.IR.add_owned_pass! Method
julia
add_owned_pass!(opPassManager, pass)
Add a pass and transfer ownership to the provided OpPassManager. If the pass is not a generic operation pass or matching the type of the provided OpPassManager, a new OpPassManager is implicitly nested under the provided OpPassManager.
source
Reactant.MLIR.IR.add_owned_pass! Method
julia
add_owned_pass!(passManager, pass)
Add a pass and transfer ownership to the provided top-level PassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.
source
Reactant.MLIR.IR.add_pipeline! Method
julia
add_pipeline!(passManager, pipelineElements, callback, userData)
Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
Reactant.MLIR.IR.affinemap Method
julia
affinemap(type)
Returns the affine map of the given MemRef type.
source
Reactant.MLIR.IR.argument Method
julia
argument(block, i)
Returns i-th argument of the block.
source
Reactant.MLIR.IR.attr! Method
julia
attr!(op, name, attr)
Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise.
source
Reactant.MLIR.IR.attr Method
julia
attr(op, name)
Returns an attribute attached to the operation given its name.
source
Reactant.MLIR.IR.attr Method
julia
attr(op, i)
Return i-th attribute of the operation.
source
Reactant.MLIR.IR.bitwidth Method
julia
bitwidth(type)
Returns the bitwidth of an integer type.
source
Reactant.MLIR.IR.block Method
julia
block(op)
Gets the block that owns this operation, returning null if the operation is not owned.
source
Reactant.MLIR.IR.block_arg_num Method
julia
block_arg_num(value)
Returns the position of the value in the argument list of its block.
source
Reactant.MLIR.IR.block_owner Method
julia
block_owner(value)
Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.
source
Reactant.MLIR.IR.body Method
julia
body(module)
Gets the body of the module, i.e. the only block it contains.
source
Reactant.MLIR.IR.compose Method
julia
compose(affineExpr, affineMap)
Composes the given map with the given expression.
source
Reactant.MLIR.IR.constraint Method
julia
mlirIntegerSetGetConstraint(set, i)
Returns i-th constraint of the set.
source
Reactant.MLIR.IR.context Method
julia
context(affineExpr)
Gets the context that owns the affine expression.
source
Reactant.MLIR.IR.context Method
julia
context(affineMap)
Gets the context that the given affine map was created with.
source
Reactant.MLIR.IR.context Method
julia
context(attribute)
Gets the context that an attribute was created with.
source
Reactant.MLIR.IR.context Method
julia
context(ident)
Returns the context associated with this identifier
source
Reactant.MLIR.IR.context Method
julia
context(set)
Gets the context in which the given integer set lives.
source
Reactant.MLIR.IR.context Method
julia
context(module)
Gets the context that a module was created with.
source
Reactant.MLIR.IR.context Method
julia
context(op)
Gets the context this operation is associated with.
source
Reactant.MLIR.IR.context Method
julia
context(type)
Gets the context that a type was created with.
source
Reactant.MLIR.IR.data Method
julia
data(attr)
Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.IR.data Method
julia
mlirOpaqueTypeGetData(type)
Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.
source
Reactant.MLIR.IR.delete! Method
julia
delete!(symboltable, operation)
Removes the given operation from the symbol table and erases it.
source
Reactant.MLIR.IR.dynsize Method
julia
dynsize()
Returns the value indicating a dynamic size in a shaped type. Prefer isdynsize to direct comparisons with this value.
source
Reactant.MLIR.IR.dynstrideoroffset Method
julia
mlirShapedTypeGetDynamicStrideOrOffset()
Returns the value indicating a dynamic stride or offset in a shaped type. Prefer isdynstrideoroffset to direct comparisons with this value.
source
Reactant.MLIR.IR.enable_ir_printing! Method
julia
enable_ir_printing!(passManager)
Enable mlir-print-ir-after-all.
source
Reactant.MLIR.IR.enable_verifier! Function
julia
enable_verifier!(passManager, enable)
Enable / disable verify-each.
source
Reactant.MLIR.IR.encoding Method
julia
encoding(type)
Gets the 'encoding' attribute from the ranked tensor type, returning a nothing if none.
source
Reactant.MLIR.IR.failure Method
julia
failure()
Creates a logical result representing a failure.
source
Reactant.MLIR.IR.first_block Method
julia
first_block(region)
Gets the first block in the region.
source
Reactant.MLIR.IR.first_op Method
julia
first_op(block)
Returns the first operation in the block or nothing if empty.
source
Reactant.MLIR.IR.first_use Method
julia
first_use(value)
Returns an OpOperand representing the first use of the value, or a nothing if there are no uses.
source
Reactant.MLIR.IR.flatsymbol Method
julia
flatsymbol(attr)
Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.IR.hasrank Method
julia
hasrank(type)
Checks whether the given shaped type is ranked.
source
Reactant.MLIR.IR.hasstaticshape Method
julia
hasstaticshape(type)
Checks whether the given shaped type has a static shape.
source
Reactant.MLIR.IR.input Method
julia
input(type, i)
Returns the i-th input type.
source
Reactant.MLIR.IR.insert_after! Method
julia
insert_after!(block, reference, operation)
Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.
source
Reactant.MLIR.IR.insert_after! Method
julia
insert_after!(region, reference, block)
Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.
source
Reactant.MLIR.IR.insert_before! Method
julia
insert_before!(block, reference, operation)
Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.
source
Reactant.MLIR.IR.insert_before! Method
julia
insert_before!(region, reference, block)
Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.
source
Reactant.MLIR.IR.is_block_arg Method
julia
is_block_arg(value)
Returns 1 if the value is a block argument, 0 otherwise.
source
Reactant.MLIR.IR.is_op_res Method
julia
is_op_res(value)
Returns 1 if the value is an operation result, 0 otherwise.
source
Reactant.MLIR.IR.is_pure_affine Method
julia
is_pure_affine(affineExpr)
Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.
source
Reactant.MLIR.IR.is_registered Method
julia
is_registered(name; context=context())
Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.
source
Reactant.MLIR.IR.is_symbolic_or_constant Method
julia
is_symbolic_or_constant(affineExpr)
Checks whether the given affine expression is made out of only symbols and constants.
source
Reactant.MLIR.IR.isadd Method
julia
isadd(affineExpr)
Checks whether the given affine expression is an add expression.
source
Reactant.MLIR.IR.isaffinemap Method
julia
isaffinemap(attr)
Checks whether the given attribute is an affine map attribute.
source
Reactant.MLIR.IR.isarray Method
julia
isarray(attr)
Checks whether the given attribute is an array attribute.
source
Reactant.MLIR.IR.isbf16 Method
julia
isbf16(type)
Checks whether the given type is a bf16 type.
source
Reactant.MLIR.IR.isbinary Method
julia
isbinary(affineExpr)
Checks whether the given affine expression is binary.
source
Reactant.MLIR.IR.isbool Method
julia
isbool(attr)
Checks whether the given attribute is a bool attribute.
source
Reactant.MLIR.IR.isceildiv Method
julia
isceildiv(affineExpr)
Checks whether the given affine expression is an ceildiv expression.
source
Reactant.MLIR.IR.iscomplex Method
julia
iscomplex(type)
Checks whether the given type is a Complex type.
source
Reactant.MLIR.IR.isconstantexpr Method
julia
isconstantexpr(affineExpr)
Checks whether the given affine expression is a constant expression.
source
Reactant.MLIR.IR.isconstrainteq Method
julia
mlirIntegerSetIsConstraintEq(set, i)
Returns true of the i-th constraint of the set is an equality constraint, false otherwise.
source
Reactant.MLIR.IR.isdenseelements Method
julia
isdenseelements(attr)
Checks whether the given attribute is a dense elements attribute.
source
Reactant.MLIR.IR.isdict Method
julia
isdict(attr)
Checks whether the given attribute is a dictionary attribute.
source
Reactant.MLIR.IR.isdimexpr Method
julia
isdimexpr(affineExpr)
Checks whether the given affine expression is a dimension expression.
source
Reactant.MLIR.IR.isdyndim Method
julia
isdyndim(type, i)
Checks wither the i-th dimension of the given shaped type is dynamic.
source
Reactant.MLIR.IR.isdynsize Method
julia
isdynsize(size)
Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.
source
Reactant.MLIR.IR.isdynstrideoroffset Method
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)
Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.
source
Reactant.MLIR.IR.iselements Method
julia
iselements(attr)
Checks whether the given attribute is an elements attribute.
source
Reactant.MLIR.IR.isempty Method
julia
isempty(set)
Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.
source
Reactant.MLIR.IR.isf16 Method
julia
isf16(type)
Checks whether the given type is an f16 type.
source
Reactant.MLIR.IR.isf32 Method
julia
isf32(type)
Checks whether the given type is an f32 type.
source
Reactant.MLIR.IR.isf64 Method
julia
isf64(type)
Checks whether the given type is an f64 type.
source
Reactant.MLIR.IR.isf8e4m3fn Method
julia
isf8e4m3fn(type)
Checks whether the given type is an f8E4M3FN type.
source
Reactant.MLIR.IR.isf8e5m2 Method
julia
isf8e5m2(type)
Checks whether the given type is an f8E5M2 type.
source
Reactant.MLIR.IR.isfailure Method
julia
isfailure(res)
Checks if the given logical result represents a failure.
source
Reactant.MLIR.IR.isflatsymbolref Method
julia
isflatsymbolref(attr)
Checks whether the given attribute is a flat symbol reference attribute.
source
Reactant.MLIR.IR.isfloat Method
julia
isfloat(attr)
Checks whether the given attribute is a floating point attribute.
source
Reactant.MLIR.IR.isfloordiv Method
julia
isfloordiv(affineExpr)
Checks whether the given affine expression is an floordiv expression.
source
Reactant.MLIR.IR.isfunction Method
julia
isfunction(type)
Checks whether the given type is a function type.
source
Reactant.MLIR.IR.isfunctionofdimexpr Method
julia
isfunctionofdimexpr(affineExpr, position)
Checks whether the given affine expression involves AffineDimExpr 'position'.
source
Reactant.MLIR.IR.isidentity Method
julia
isidentity(affineMap)
Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.
source
Reactant.MLIR.IR.isindex Method
julia
isindex(type)
Checks whether the given type is an index type.
source
Reactant.MLIR.IR.isinteger Method
julia
isinteger(attr)
Checks whether the given attribute is an integer attribute.
source
Reactant.MLIR.IR.isinteger Method
julia
isinteger(type)
Checks whether the given type is an integer type.
source
Reactant.MLIR.IR.isintegerset Method
julia
isintegerset(attr)
Checks whether the given attribute is an integer set attribute.
source
Reactant.MLIR.IR.ismemref Method
julia
ismemref(type)
Checks whether the given type is a MemRef type.
source
Reactant.MLIR.IR.isminoridentity Method
julia
isminoridentity(affineMap)
Checks whether the given affine map is a minor identity affine map.
source
Reactant.MLIR.IR.ismod Method
julia
ismod(affineExpr)
Checks whether the given affine expression is an mod expression.
source
Reactant.MLIR.IR.ismul Method
julia
ismul(affineExpr)
Checks whether the given affine expression is an mul expression.
source
Reactant.MLIR.IR.ismultipleof Method
julia
ismultipleof(affineExpr, factor)
Checks whether the given affine expression is a multiple of 'factor'.
source
Reactant.MLIR.IR.isnone Method
julia
mlirTypeIsANone(type)
Checks whether the given type is a None type.
source
Reactant.MLIR.IR.isopaque Method
julia
isopaque(attr)
Checks whether the given attribute is an opaque attribute.
source
Reactant.MLIR.IR.isopaque Method
julia
isopaque(type)
Checks whether the given type is an opaque type.
source
Reactant.MLIR.IR.isprojperm Method
julia
isprojperm(affineMap)
Checks whether the given affine map represents a subset of a symbol-less permutation map.
source
Reactant.MLIR.IR.isrankedtensor Method
julia
isrankedtensor(type)
Checks whether the given type is a ranked tensor type.
source
Reactant.MLIR.IR.isshaped Method
julia
isshaped(type)
Checks whether the given type is a Shaped type.
source
Reactant.MLIR.IR.issigned Method
julia
issigned(type)
Checks whether the given integer type is signed.
source
Reactant.MLIR.IR.issignless Method
julia
issignless(type)
Checks whether the given integer type is signless.
source
Reactant.MLIR.IR.issingleconstant Method
julia
issingleconstant(affineMap)
Checks whether the given affine map is a single result constant affine map.
source
Reactant.MLIR.IR.issparseelements Method
julia
issparseelements(attr)
Checks whether the given attribute is a sparse elements attribute.
source
Reactant.MLIR.IR.issplat Method
julia
issplat(attr)
Checks whether the given dense elements attribute contains a single replicated value (splat).
source
Reactant.MLIR.IR.isstring Method
julia
isstring(attr)
Checks whether the given attribute is a string attribute.
source
Reactant.MLIR.IR.issuccess Method
julia
issuccess(res)
Checks if the given logical result represents a success.
source
Reactant.MLIR.IR.issymbolexpr Method
julia
issymbolexpr(affineExpr)
Checks whether the given affine expression is a symbol expression.
source
Reactant.MLIR.IR.issymbolref Method
julia
issymbolref(attr)
Checks whether the given attribute is a symbol reference attribute.
source
Reactant.MLIR.IR.istensor Method
julia
istensor(type)
Checks whether the given type is a Tensor type.
source
Reactant.MLIR.IR.istuple Method
julia
istuple(type)
Checks whether the given type is a tuple type.
source
Reactant.MLIR.IR.istype Method
julia
istype(attr)
Checks whether the given attribute is a type attribute.
source
Reactant.MLIR.IR.isunit Method
julia
isunit(attr)
Checks whether the given attribute is a unit attribute.
source
Reactant.MLIR.IR.isunrankedmemref Method
julia
mlirTypeIsAUnrankedMemRef(type)
Checks whether the given type is an UnrankedMemRef type.
source
Reactant.MLIR.IR.isunrankedtensor Method
julia
isunrankedtensor(type)
Checks whether the given type is an unranked tensor type.
source
Reactant.MLIR.IR.isunsigned Method
julia
isunsigned(type)
Checks whether the given integer type is unsigned.
source
Reactant.MLIR.IR.isvector Method
julia
isvector(type)
Checks whether the given type is a Vector type.
source
Reactant.MLIR.IR.layout Method
julia
layout(type)
Returns the layout of the given MemRef type.
source
Reactant.MLIR.IR.leafref Method
julia
leafref(attr)
Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.IR.lhs Method
julia
lhs(affineExpr)
Returns the left hand side affine expression of the given affine binary operation expression.
source
Reactant.MLIR.IR.location Method
julia
location(op)
Gets the location of the operation.
source
Reactant.MLIR.IR.lookup Method
julia
lookup(jit, name)
Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.
source
Reactant.MLIR.IR.lookup Method
julia
lookup(symboltable, name)
Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.
source
Reactant.MLIR.IR.majorsubmap Method
julia
majorsubmap(affineMap, nresults)
Returns the affine map consisting of the most major nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
source
Reactant.MLIR.IR.memspace Method
julia
mlirMemRefTypeGetMemorySpace(type)
Returns the memory space of the given MemRef type.
source
Reactant.MLIR.IR.minorsubmap Method
julia
minorsubmap(affineMap, nresults)
Returns the affine map consisting of the most minor nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
source
Reactant.MLIR.IR.move_after! Method
julia
move_after!(op, other)
Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
Reactant.MLIR.IR.move_before! Method
julia
move_before!(op, other)
Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
Reactant.MLIR.IR.name Method
julia
name(op)
Gets the name of the operation as an identifier.
source
Reactant.MLIR.IR.namespace Method
julia
mlirOpaqueAttrGetDialectNamespace(attr)
Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.
source
Reactant.MLIR.IR.namespace Method
julia
mlirOpaqueTypeGetDialectNamespace(type)
Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.
source
Reactant.MLIR.IR.nargs Method
julia
nargs(block)
Returns the number of arguments of the block.
source
Reactant.MLIR.IR.nattrs Method
julia
nattrs(op)
Returns the number of attributes attached to the operation.
source
Reactant.MLIR.IR.nconstraints Method
julia
nconstraints(set)
Returns the number of constraints (equalities + inequalities) in the given set.
source
Reactant.MLIR.IR.nequalities Method
julia
nequalities(set)
Returns the number of equalities in the given set.
source
Reactant.MLIR.IR.next Method
julia
next(block)
Returns the block immediately following the given block in its parent region or nothing if last.
source
Reactant.MLIR.IR.next Method
julia
next(opOperand)
Returns an op operand representing the next use of the value, or nothing if there is no next use.
source
Reactant.MLIR.IR.ninequalities Method
julia
ninequalities(set)
Returns the number of inequalities in the given set.
source
Reactant.MLIR.IR.ninputs Method
julia
ninputs(affineMap)
Returns the number of inputs (dimensions + symbols) of the given affine map.
source
Reactant.MLIR.IR.ninputs Method
julia
ninputs(set)
Returns the number of inputs (dimensions + symbols) in the given set.
source
Reactant.MLIR.IR.ninputs Method
julia
ninputs(type)
Returns the number of input types.
source
Reactant.MLIR.IR.nnestedrefs Method
julia
nnestedrefs(attr)
Returns the number of references nested in the given symbol reference attribute.
source
Reactant.MLIR.IR.noperands Method
julia
noperands(op)
Returns the number of operands of the operation.
source
Reactant.MLIR.IR.nregions Method
julia
nregions(op)
Returns the number of regions attached to the given operation.
source
Reactant.MLIR.IR.nresults Method
julia
nresults(affineMap)
Returns the number of results of the given affine map.
source
Reactant.MLIR.IR.nresults Method
julia
nresults(op)
Returns the number of results of the operation.
source
Reactant.MLIR.IR.nresults Method
julia
nresults(type)
Returns the number of result types.
source
Reactant.MLIR.IR.nsuccessors Method
julia
nsuccessors(op)
Returns the number of successor blocks of the operation.
source
Reactant.MLIR.IR.nsymbols Method
julia
nsymbols(affineMap)
Returns the number of symbols of the given affine map.
source
Reactant.MLIR.IR.nsymbols Method
julia
nsymbols(set)
Returns the number of symbols in the given set.
source
Reactant.MLIR.IR.op_owner Method
julia
op_owner(value)
Returns an operation that produced this value as its result. Asserts if the value is not an op result.
source
Reactant.MLIR.IR.op_res_num Method
julia
op_res_num(value)
Returns the position of the value in the list of results of the operation that produced it.
source
Reactant.MLIR.IR.operand Function
julia
operand(op, i)
Returns i-th operand of the operation.
source
Reactant.MLIR.IR.operand! Method
julia
operand!(op, i, value)
Sets the i-th operand of the operation.
source
Reactant.MLIR.IR.operandindex Method
julia
operandindex(opOperand)
Returns the operand number of an op operand.
source
Reactant.MLIR.IR.owner Method
julia
owner(opOperand)
Returns the owner operation of an op operand.
source
Reactant.MLIR.IR.parent_op Method
julia
parent_op(block)
Returns the closest surrounding operation that contains this block.
source
Reactant.MLIR.IR.parent_op Method
julia
parent_op(op)
Gets the operation that owns this operation, returning null if the operation is not owned.
source
Reactant.MLIR.IR.parent_region Method
julia
parent_region(block)
Returns the region that contains this block.
source
Reactant.MLIR.IR.position Method
julia
position(affineExpr)
Returns the position of the given affine dimension expression, affine symbol expression or ...
source
Reactant.MLIR.IR.push_argument! Method
julia
push_argument!(block, type; location=Location())
Appends an argument of the specified type to the block. Returns the newly added argument.
source
Reactant.MLIR.IR.region Method
julia
region(op, i)
Returns i-th region attached to the operation.
source
Reactant.MLIR.IR.result Function
julia
result(type, i)
Returns the i-th result type.
source
Reactant.MLIR.IR.result Function
julia
result(op, i)
Returns i-th result of the operation.
source
Reactant.MLIR.IR.result Method
julia
result(affineMap, pos)
Returns the result at the given position.
source
Reactant.MLIR.IR.result Method
julia
result(affineMap)
Returns the constant result of the given affine map. The function asserts that the map has a single constant result.
source
Reactant.MLIR.IR.rhs Method
julia
rhs(affineExpr)
Returns the right hand side affine expression of the given affine binary operation expression.
source
Reactant.MLIR.IR.rmattr! Method
julia
rmattr!(op, name)
Removes an attribute by name. Returns false if the attribute was not found and true if removed.
source
Reactant.MLIR.IR.rmfromparent! Method
julia
rmfromparent!(op)
Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.
source
Reactant.MLIR.IR.rootref Method
julia
rootref(attr)
Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.IR.run! Method
julia
run!(passManager, module)
Run the provided passManager on the given module.
source
Reactant.MLIR.IR.submap Method
julia
submap(affineMap, positions)
Returns the affine map consisting of the positions subset.
source
Reactant.MLIR.IR.success Method
julia
success()
Creates a logical result representing a success.
source
Reactant.MLIR.IR.successor Method
julia
successor(op, i)
Returns i-th successor of the operation.
source
Reactant.MLIR.IR.terminator Method
julia
terminator(block)
Returns the terminator operation in the block or nothing if no terminator.
source
Reactant.MLIR.IR.type! Method
julia
set_type!(value, type)
Sets the type of the block argument to the given type.
source
Reactant.MLIR.IR.type Method
julia
type(attribute)
Gets the type of this attribute.
source
Reactant.MLIR.IR.type Method
julia
type(value)
Returns the type of the value.
source
Reactant.MLIR.IR.typeid Method
julia
typeid(attribute)
Gets the type id of the attribute.
source
Reactant.MLIR.IR.typeid Method
julia
typeid(op)
Gets the type id of the operation. Returns null if the operation does not have a registered operation description.
source
Reactant.MLIR.IR.typeid Method
julia
typeid(type)
Gets the type ID of the type.
source
Reactant.MLIR.IR.value Method
julia
value(affineExpr)
Returns the value of the given affine constant expression.
source
Reactant.MLIR.IR.verify Method
julia
verify(op)
Verify the operation and return true if it passes, false if it fails.
source
Reactant.MLIR.IR.verifyall Method
julia
verifyall(operation; debug=false)
Prints the operations which could not be verified.
source
Reactant.MLIR.IR.@affinemap Macro
julia
@affinemap (d1, d2, d3, ...)[s1, s2, ...] -> (d0 + d1, ...)
Returns an affine map from the provided Julia expression. On the right hand side are allowed the following function calls:
  • +, *, ÷, %, fld, cld
The rhs can only contains dimensions and symbols present on the left hand side or integer literals.
julia
julia> using Reactant.MLIR: IR
 
 julia> IR.context!(IR.Context()) do
            IR.@affinemap (d1, d2)[s0] -> (d1 + s0, d2 % 10)
        end
-MLIR.IR.AffineMap(#= (d0, d1)[s0] -> (d0 + s0, d1 mod 10) =#)
source
MLIR C API ​
Reactant.MLIR.API.LLVMAttributeRef Type
Used to represent an attributes.
See also
llvm::Attribute
source
Reactant.MLIR.API.LLVMBasicBlockRef Type
Represents a basic block of instructions in LLVM IR.
This models llvm::BasicBlock.
source
Reactant.MLIR.API.LLVMBinaryRef Type
See also
llvm::object::Binary
source
Reactant.MLIR.API.LLVMBool Type
LLVMCSupportTypes Types and Enumerations
@{
source
Reactant.MLIR.API.LLVMBuilderRef Type
Represents an LLVM basic block builder.
This models llvm::IRBuilder.
source
Reactant.MLIR.API.LLVMComdatRef Type
See also
llvm::Comdat
source
Reactant.MLIR.API.LLVMContextRef Type
The top-level container for all LLVM global data. See the LLVMContext class.
source
Reactant.MLIR.API.LLVMDIBuilderRef Type
Represents an LLVM debug info builder.
This models llvm::DIBuilder.
source
Reactant.MLIR.API.LLVMDbgRecordRef Type
See also
llvm::DbgRecord
source
Reactant.MLIR.API.LLVMDiagnosticInfoRef Type
See also
llvm::DiagnosticInfo
source
Reactant.MLIR.API.LLVMJITEventListenerRef Type
See also
llvm::JITEventListener
source
Reactant.MLIR.API.LLVMMemoryBufferRef Type
Used to pass regions of memory through LLVM interfaces.
See also
llvm::MemoryBuffer
source
Reactant.MLIR.API.LLVMMetadataRef Type
Represents an LLVM Metadata.
This models llvm::Metadata.
source
Reactant.MLIR.API.LLVMModuleFlagEntry Type
See also
llvm::Module::ModuleFlagEntry
source
Reactant.MLIR.API.LLVMModuleProviderRef Type
Interface used to provide a module to JIT or interpreter. This is now just a synonym for llvm::Module, but we have to keep using the different type to keep binary compatibility.
source
Reactant.MLIR.API.LLVMModuleRef Type
The top-level container for all other LLVM Intermediate Representation (IR) objects.
See also
llvm::Module
source
Reactant.MLIR.API.LLVMNamedMDNodeRef Type
Represents an LLVM Named Metadata Node.
This models llvm::NamedMDNode.
source
Reactant.MLIR.API.LLVMOperandBundleRef Type
See also
llvm::OperandBundleDef
source
Reactant.MLIR.API.LLVMPassManagerRef Type
See also
llvm::PassManagerBase
source
Reactant.MLIR.API.LLVMTypeRef Type
Each value in the LLVM IR has a type, an LLVMTypeRef.
See also
llvm::Type
source
Reactant.MLIR.API.LLVMUseRef Type
Used to get the users and usees of a Value.
See also
llvm::Use
source
Reactant.MLIR.API.LLVMValueMetadataEntry Type
Represents an entry in a Global Object's metadata attachments.
This models std::pair<unsigned, MDNode *>
source
Reactant.MLIR.API.LLVMValueRef Type
Represents an individual value in LLVM IR.
This models llvm::Value.
source
Reactant.MLIR.API.MlirDiagnostic Type
julia
MlirDiagnostic
An opaque reference to a diagnostic, always owned by the diagnostics engine (context). Must not be stored outside of the diagnostic handler.
source
Reactant.MLIR.API.MlirDiagnosticHandler Type
Diagnostic handler type. Accepts a reference to a diagnostic, which is only guaranteed to be live during the call. The handler is passed the userData that was provided when the handler was attached to a context. If the handler processed the diagnostic completely, it is expected to return success. Otherwise, it is expected to return failure to indicate that other handlers should attempt to process the diagnostic.
source
Reactant.MLIR.API.MlirDiagnosticHandlerID Type
Opaque identifier of a diagnostic handler, useful to detach a handler.
source
Reactant.MLIR.API.MlirDiagnosticSeverity Type
julia
MlirDiagnosticSeverity
Severity of a diagnostic.
source
Reactant.MLIR.API.MlirExternalPassCallbacks Type
julia
MlirExternalPassCallbacks
Structure of external MlirPass callbacks. All callbacks are required to be set unless otherwise specified.
FieldNote
constructThis callback is called from the pass is created. This is analogous to a C++ pass constructor.
destructThis callback is called when the pass is destroyed This is analogous to a C++ pass destructor.
initializeThis callback is optional. The callback is called before the pass is run, allowing a chance to initialize any complex state necessary for running the pass. See Pass::initialize(MLIRContext *).
cloneThis callback is called when the pass is cloned. See Pass::clonePass().
runThis callback is called when the pass is run. See Pass::runOnOperation().
source
Reactant.MLIR.API.MlirLlvmThreadPool Type
julia
MlirLlvmThreadPool
Re-export llvm::ThreadPool so as to avoid including the LLVM C API directly.
source
Reactant.MLIR.API.MlirLogicalResult Type
julia
MlirLogicalResult
A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of MlirLogicalResult must only be inspected using the associated functions.
source
Reactant.MLIR.API.MlirNamedAttribute Type
julia
MlirNamedAttribute
Named MLIR attribute.
A named attribute is essentially a (name, attribute) pair where the name is a string.
source
Reactant.MLIR.API.MlirOperationState Type
julia
MlirOperationState
An auxiliary class for constructing operations.
This class contains all the information necessary to construct the operation. It owns the MlirRegions it has pointers to and does not own anything else. By default, the state can be constructed from a name and location, the latter being also used to access the context, and has no other components. These components can be added progressively until the operation is constructed. Users are not expected to rely on the internals of this class and should use mlirOperationState* functions instead.
source
Reactant.MLIR.API.MlirOperationWalkCallback Type
Operation walker type. The handler is passed an (opaque) reference to an operation and a pointer to a userData.
source
Reactant.MLIR.API.MlirShapedTypeComponentsCallback Type
These callbacks are used to return multiple shaped type components from functions while transferring ownership to the caller. The first argument is the has rank boolean followed by the the rank and a pointer to the shape (if applicable). The next argument is the element type, then the attribute. The last argument is an opaque pointer forwarded to the callback by the caller. This callback will be called potentially multiple times for each shaped type components.
source
Reactant.MLIR.API.MlirSparseTensorLevelType Type
Dimension level types (and properties) that define sparse tensors. See the documentation in SparseTensorAttrDefs.td for their meaning.
These correspond to SparseTensorEncodingAttr::LevelType in the C++ API. If updating, keep them in sync and update the static_assert in the impl file.
source
Reactant.MLIR.API.MlirStringCallback Type
A callback for returning string references.
This function is called back by the functions that need to return a reference to the portion of the string with the following arguments: - an MlirStringRef representing the current portion of the string - a pointer to user data forwarded from the printing call.
source
Reactant.MLIR.API.MlirStringRef Type
julia
MlirStringRef
A pointer to a sized fragment of a string, not necessarily null-terminated. Does not own the underlying string. This is equivalent to llvm::StringRef.
FieldNote
dataPointer to the first symbol.
lengthLength of the fragment.
source
Reactant.MLIR.API.MlirTypesCallback Type
These callbacks are used to return multiple types from functions while transferring ownership to the caller. The first argument is the number of consecutive elements pointed to by the second argument. The third argument is an opaque pointer forwarded to the callback by the caller.
source
Reactant.MLIR.API.MlirWalkOrder Type
julia
MlirWalkOrder
Traversal order for operation walk.
source
Reactant.MLIR.API.MlirWalkResult Type
julia
MlirWalkResult
Operation walk result.
source
Reactant.MLIR.API.LLVMAddSymbol Method
julia
LLVMAddSymbol(symbolName, symbolValue)
This functions permanently adds the symbol symbolName with the value symbolValue. These symbols are searched before any libraries.
See also
sys::DynamicLibrary::AddSymbol()
source
Reactant.MLIR.API.LLVMLoadLibraryPermanently Method
julia
LLVMLoadLibraryPermanently(Filename)
This function permanently loads the dynamic library at the given path. It is safe to call this function multiple times for the same library.
See also
sys::DynamicLibrary::LoadLibraryPermanently()
source
Reactant.MLIR.API.LLVMParseCommandLineOptions Method
julia
LLVMParseCommandLineOptions(argc, argv, Overview)
This function parses the given arguments using the LLVM command line parser. Note that the only stable thing about this function is its signature; you cannot rely on any particular set of command line arguments being interpreted the same way across LLVM versions.
See also
llvm:🆑:ParseCommandLineOptions()
source
Reactant.MLIR.API.LLVMSearchForAddressOfSymbol Method
julia
LLVMSearchForAddressOfSymbol(symbolName)
This function will search through all previously loaded dynamic libraries for the symbol symbolName. If it is found, the address of that symbol is returned. If not, null is returned.
See also
sys::DynamicLibrary::SearchForAddressOfSymbol()
source
Reactant.MLIR.API.mlirAffineAddExprGet Method
julia
mlirAffineAddExprGet(lhs, rhs)
Creates an affine add expression with 'lhs' and 'rhs'.
source
Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS Method
julia
mlirAffineBinaryOpExprGetLHS(affineExpr)
Returns the left hand side affine expression of the given affine binary operation expression.
source
Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS Method
julia
mlirAffineBinaryOpExprGetRHS(affineExpr)
Returns the right hand side affine expression of the given affine binary operation expression.
source
Reactant.MLIR.API.mlirAffineCeilDivExprGet Method
julia
mlirAffineCeilDivExprGet(lhs, rhs)
Creates an affine ceildiv expression with 'lhs' and 'rhs'.
source
Reactant.MLIR.API.mlirAffineConstantExprGet Method
julia
mlirAffineConstantExprGet(ctx, constant)
Creates an affine constant expression with 'constant' in the context.
source
Reactant.MLIR.API.mlirAffineConstantExprGetValue Method
julia
mlirAffineConstantExprGetValue(affineExpr)
Returns the value of the given affine constant expression.
source
Reactant.MLIR.API.mlirAffineDimExprGet Method
julia
mlirAffineDimExprGet(ctx, position)
Creates an affine dimension expression with 'position' in the context.
source
Reactant.MLIR.API.mlirAffineDimExprGetPosition Method
julia
mlirAffineDimExprGetPosition(affineExpr)
Returns the position of the given affine dimension expression.
source
Reactant.MLIR.API.mlirAffineExprCompose Method
julia
mlirAffineExprCompose(affineExpr, affineMap)
Composes the given map with the given expression.
source
Reactant.MLIR.API.mlirAffineExprDump Method
julia
mlirAffineExprDump(affineExpr)
Prints the affine expression to the standard error stream.
source
Reactant.MLIR.API.mlirAffineExprEqual Method
julia
mlirAffineExprEqual(lhs, rhs)
Returns true if the two affine expressions are equal.
source
Reactant.MLIR.API.mlirAffineExprGetContext Method
julia
mlirAffineExprGetContext(affineExpr)
Gets the context that owns the affine expression.
source
Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor Method
julia
mlirAffineExprGetLargestKnownDivisor(affineExpr)
Returns the greatest known integral divisor of this affine expression. The result is always positive.
source
Reactant.MLIR.API.mlirAffineExprIsAAdd Method
julia
mlirAffineExprIsAAdd(affineExpr)
Checks whether the given affine expression is an add expression.
source
Reactant.MLIR.API.mlirAffineExprIsABinary Method
julia
mlirAffineExprIsABinary(affineExpr)
Checks whether the given affine expression is binary.
source
Reactant.MLIR.API.mlirAffineExprIsACeilDiv Method
julia
mlirAffineExprIsACeilDiv(affineExpr)
Checks whether the given affine expression is an ceildiv expression.
source
Reactant.MLIR.API.mlirAffineExprIsAConstant Method
julia
mlirAffineExprIsAConstant(affineExpr)
Checks whether the given affine expression is a constant expression.
source
Reactant.MLIR.API.mlirAffineExprIsADim Method
julia
mlirAffineExprIsADim(affineExpr)
Checks whether the given affine expression is a dimension expression.
source
Reactant.MLIR.API.mlirAffineExprIsAFloorDiv Method
julia
mlirAffineExprIsAFloorDiv(affineExpr)
Checks whether the given affine expression is an floordiv expression.
source
Reactant.MLIR.API.mlirAffineExprIsAMod Method
julia
mlirAffineExprIsAMod(affineExpr)
Checks whether the given affine expression is an mod expression.
source
Reactant.MLIR.API.mlirAffineExprIsAMul Method
julia
mlirAffineExprIsAMul(affineExpr)
Checks whether the given affine expression is an mul expression.
source
Reactant.MLIR.API.mlirAffineExprIsASymbol Method
julia
mlirAffineExprIsASymbol(affineExpr)
Checks whether the given affine expression is a symbol expression.
source
Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim Method
julia
mlirAffineExprIsFunctionOfDim(affineExpr, position)
Checks whether the given affine expression involves AffineDimExpr 'position'.
source
Reactant.MLIR.API.mlirAffineExprIsMultipleOf Method
julia
mlirAffineExprIsMultipleOf(affineExpr, factor)
Checks whether the given affine expression is a multiple of 'factor'.
source
Reactant.MLIR.API.mlirAffineExprIsNull Method
julia
mlirAffineExprIsNull(affineExpr)
Returns true if the given affine expression is a null expression. Note constant zero is not a null expression.
source
Reactant.MLIR.API.mlirAffineExprIsPureAffine Method
julia
mlirAffineExprIsPureAffine(affineExpr)
Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.
source
Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant Method
julia
mlirAffineExprIsSymbolicOrConstant(affineExpr)
Checks whether the given affine expression is made out of only symbols and constants.
source
Reactant.MLIR.API.mlirAffineExprPrint Method
julia
mlirAffineExprPrint(affineExpr, callback, userData)
Prints an affine expression by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
Reactant.MLIR.API.mlirAffineFloorDivExprGet Method
julia
mlirAffineFloorDivExprGet(lhs, rhs)
Creates an affine floordiv expression with 'lhs' and 'rhs'.
source
Reactant.MLIR.API.mlirAffineMapAttrGet Method
julia
mlirAffineMapAttrGet(map)
Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.
source
Reactant.MLIR.API.mlirAffineMapAttrGetTypeID Method
julia
mlirAffineMapAttrGetTypeID()
Returns the typeID of an AffineMap attribute.
source
Reactant.MLIR.API.mlirAffineMapAttrGetValue Method
julia
mlirAffineMapAttrGetValue(attr)
Returns the affine map wrapped in the given affine map attribute.
source
Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols Method
julia
mlirAffineMapCompressUnusedSymbols(affineMaps, size, result, populateResult)
Returns the simplified affine map resulting from dropping the symbols that do not appear in any of the individual maps in affineMaps. Asserts that all maps in affineMaps are normalized to the same number of dims and symbols. Takes a callback populateResult to fill the res container with value m at entry idx. This allows returning without worrying about ownership considerations.
source
Reactant.MLIR.API.mlirAffineMapConstantGet Method
julia
mlirAffineMapConstantGet(ctx, val)
Creates a single constant result affine map in the context. The affine map is owned by the context.
source
Reactant.MLIR.API.mlirAffineMapDump Method
julia
mlirAffineMapDump(affineMap)
Prints the affine map to the standard error stream.
source
Reactant.MLIR.API.mlirAffineMapEmptyGet Method
julia
mlirAffineMapEmptyGet(ctx)
Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.
source
Reactant.MLIR.API.mlirAffineMapEqual Method
julia
mlirAffineMapEqual(a1, a2)
Checks if two affine maps are equal.
source
Reactant.MLIR.API.mlirAffineMapGet Method
julia
mlirAffineMapGet(ctx, dimCount, symbolCount, nAffineExprs, affineExprs)
Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.
source
Reactant.MLIR.API.mlirAffineMapGetContext Method
julia
mlirAffineMapGetContext(affineMap)
Gets the context that the given affine map was created with
source
Reactant.MLIR.API.mlirAffineMapGetMajorSubMap Method
julia
mlirAffineMapGetMajorSubMap(affineMap, numResults)
Returns the affine map consisting of the most major numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.
source
Reactant.MLIR.API.mlirAffineMapGetMinorSubMap Method
julia
mlirAffineMapGetMinorSubMap(affineMap, numResults)
Returns the affine map consisting of the most minor numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.
source
Reactant.MLIR.API.mlirAffineMapGetNumDims Method
julia
mlirAffineMapGetNumDims(affineMap)
Returns the number of dimensions of the given affine map.
source
Reactant.MLIR.API.mlirAffineMapGetNumInputs Method
julia
mlirAffineMapGetNumInputs(affineMap)
Returns the number of inputs (dimensions + symbols) of the given affine map.
source
Reactant.MLIR.API.mlirAffineMapGetNumResults Method
julia
mlirAffineMapGetNumResults(affineMap)
Returns the number of results of the given affine map.
source
Reactant.MLIR.API.mlirAffineMapGetNumSymbols Method
julia
mlirAffineMapGetNumSymbols(affineMap)
Returns the number of symbols of the given affine map.
source
Reactant.MLIR.API.mlirAffineMapGetResult Method
julia
mlirAffineMapGetResult(affineMap, pos)
Returns the result at the given position.
source
Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult Method
julia
mlirAffineMapGetSingleConstantResult(affineMap)
Returns the constant result of the given affine map. The function asserts that the map has a single constant result.
source
Reactant.MLIR.API.mlirAffineMapGetSubMap Method
julia
mlirAffineMapGetSubMap(affineMap, size, resultPos)
Returns the affine map consisting of the resultPos subset.
source
Reactant.MLIR.API.mlirAffineMapIsEmpty Method
julia
mlirAffineMapIsEmpty(affineMap)
Checks whether the given affine map is an empty affine map.
source
Reactant.MLIR.API.mlirAffineMapIsIdentity Method
julia
mlirAffineMapIsIdentity(affineMap)
Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.
source
Reactant.MLIR.API.mlirAffineMapIsMinorIdentity Method
julia
mlirAffineMapIsMinorIdentity(affineMap)
Checks whether the given affine map is a minor identity affine map.
source
Reactant.MLIR.API.mlirAffineMapIsNull Method
julia
mlirAffineMapIsNull(affineMap)
Checks whether an affine map is null.
source
Reactant.MLIR.API.mlirAffineMapIsPermutation Method
julia
mlirAffineMapIsPermutation(affineMap)
Checks whether the given affine map represents a symbol-less permutation map.
source
Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation Method
julia
mlirAffineMapIsProjectedPermutation(affineMap)
Checks whether the given affine map represents a subset of a symbol-less permutation map.
source
Reactant.MLIR.API.mlirAffineMapIsSingleConstant Method
julia
mlirAffineMapIsSingleConstant(affineMap)
Checks whether the given affine map is a single result constant affine map.
source
Reactant.MLIR.API.mlirAffineMapMinorIdentityGet Method
julia
mlirAffineMapMinorIdentityGet(ctx, dims, results)
Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.
source
Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet Method
julia
mlirAffineMapMultiDimIdentityGet(ctx, numDims)
Creates an affine map with 'numDims' identity in the context. The affine map is owned by the context.
source
Reactant.MLIR.API.mlirAffineMapPermutationGet Method
julia
mlirAffineMapPermutationGet(ctx, size, permutation)
Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid permutation.) The affine map is owned by the context.
source
Reactant.MLIR.API.mlirAffineMapPrint Method
julia
mlirAffineMapPrint(affineMap, callback, userData)
Prints an affine map by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
Reactant.MLIR.API.mlirAffineMapReplace Method
julia
mlirAffineMapReplace(affineMap, expression, replacement, numResultDims, numResultSyms)
Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.
source
Reactant.MLIR.API.mlirAffineMapZeroResultGet Method
julia
mlirAffineMapZeroResultGet(ctx, dimCount, symbolCount)
Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.
source
Reactant.MLIR.API.mlirAffineModExprGet Method
julia
mlirAffineModExprGet(lhs, rhs)
Creates an affine mod expression with 'lhs' and 'rhs'.
source
Reactant.MLIR.API.mlirAffineMulExprGet Method
julia
mlirAffineMulExprGet(lhs, rhs)
Creates an affine mul expression with 'lhs' and 'rhs'.
source
Reactant.MLIR.API.mlirAffineSymbolExprGet Method
julia
mlirAffineSymbolExprGet(ctx, position)
Creates an affine symbol expression with 'position' in the context.
source
Reactant.MLIR.API.mlirAffineSymbolExprGetPosition Method
julia
mlirAffineSymbolExprGetPosition(affineExpr)
Returns the position of the given affine symbol expression.
source
Reactant.MLIR.API.mlirAnyQuantizedTypeGet Method
julia
mlirAnyQuantizedTypeGet(flags, storageType, expressedType, storageTypeMin, storageTypeMax)
Creates an instance of AnyQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.
source
Reactant.MLIR.API.mlirArrayAttrGet Method
julia
mlirArrayAttrGet(ctx, numElements, elements)
Creates an array element containing the given list of elements in the given context.
source
Reactant.MLIR.API.mlirArrayAttrGetElement Method
julia
mlirArrayAttrGetElement(attr, pos)
Returns pos-th element stored in the given array attribute.
source
Reactant.MLIR.API.mlirArrayAttrGetNumElements Method
julia
mlirArrayAttrGetNumElements(attr)
Returns the number of elements stored in the given array attribute.
source
Reactant.MLIR.API.mlirArrayAttrGetTypeID Method
julia
mlirArrayAttrGetTypeID()
Returns the typeID of an Array attribute.
source
Reactant.MLIR.API.mlirAsmStateCreateForOperation Method
julia
mlirAsmStateCreateForOperation(op, flags)
Creates new AsmState, as with AsmState the IR should not be mutated in-between using this state. Must be freed with a call to mlirAsmStateDestroy().
source
Reactant.MLIR.API.mlirAsmStateCreateForValue Method
julia
mlirAsmStateCreateForValue(value, flags)
Creates new AsmState from value. Must be freed with a call to mlirAsmStateDestroy().
source
Reactant.MLIR.API.mlirAsmStateDestroy Method
julia
mlirAsmStateDestroy(state)
Destroys printing flags created with mlirAsmStateCreate.
source
Reactant.MLIR.API.mlirAttributeDump Method
julia
mlirAttributeDump(attr)
Prints the attribute to the standard error stream.
source
Reactant.MLIR.API.mlirAttributeEqual Method
julia
mlirAttributeEqual(a1, a2)
Checks if two attributes are equal.
source
Reactant.MLIR.API.mlirAttributeGetContext Method
julia
mlirAttributeGetContext(attribute)
Gets the context that an attribute was created with.
source
Reactant.MLIR.API.mlirAttributeGetDialect Method
julia
mlirAttributeGetDialect(attribute)
Gets the dialect of the attribute.
source
Reactant.MLIR.API.mlirAttributeGetNull Method
julia
mlirAttributeGetNull()
Returns an empty attribute.
source
Reactant.MLIR.API.mlirAttributeGetType Method
julia
mlirAttributeGetType(attribute)
Gets the type of this attribute.
source
Reactant.MLIR.API.mlirAttributeGetTypeID Method
julia
mlirAttributeGetTypeID(attribute)
Gets the type id of the attribute.
source
Reactant.MLIR.API.mlirAttributeIsAAffineMap Method
julia
mlirAttributeIsAAffineMap(attr)
Checks whether the given attribute is an affine map attribute.
source
Reactant.MLIR.API.mlirAttributeIsAArray Method
julia
mlirAttributeIsAArray(attr)
Checks whether the given attribute is an array attribute.
source
Reactant.MLIR.API.mlirAttributeIsABool Method
julia
mlirAttributeIsABool(attr)
Checks whether the given attribute is a bool attribute.
source
Reactant.MLIR.API.mlirAttributeIsADenseBoolArray Method
julia
mlirAttributeIsADenseBoolArray(attr)
Checks whether the given attribute is a dense array attribute.
source
Reactant.MLIR.API.mlirAttributeIsADenseElements Method
julia
mlirAttributeIsADenseElements(attr)
Checks whether the given attribute is a dense elements attribute.
source
Reactant.MLIR.API.mlirAttributeIsADictionary Method
julia
mlirAttributeIsADictionary(attr)
Checks whether the given attribute is a dictionary attribute.
source
Reactant.MLIR.API.mlirAttributeIsAElements Method
julia
mlirAttributeIsAElements(attr)
Checks whether the given attribute is an elements attribute.
source
Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef Method
julia
mlirAttributeIsAFlatSymbolRef(attr)
Checks whether the given attribute is a flat symbol reference attribute.
source
Reactant.MLIR.API.mlirAttributeIsAFloat Method
julia
mlirAttributeIsAFloat(attr)
Checks whether the given attribute is a floating point attribute.
source
Reactant.MLIR.API.mlirAttributeIsAInteger Method
julia
mlirAttributeIsAInteger(attr)
Checks whether the given attribute is an integer attribute.
source
Reactant.MLIR.API.mlirAttributeIsAIntegerSet Method
julia
mlirAttributeIsAIntegerSet(attr)
Checks whether the given attribute is an integer set attribute.
source
Reactant.MLIR.API.mlirAttributeIsAOpaque Method
julia
mlirAttributeIsAOpaque(attr)
Checks whether the given attribute is an opaque attribute.
source
Reactant.MLIR.API.mlirAttributeIsASparseElements Method
julia
mlirAttributeIsASparseElements(attr)
Checks whether the given attribute is a sparse elements attribute.
source
Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr Method
julia
mlirAttributeIsASparseTensorEncodingAttr(attr)
Checks whether the given attribute is a sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirAttributeIsAString Method
julia
mlirAttributeIsAString(attr)
Checks whether the given attribute is a string attribute.
source
Reactant.MLIR.API.mlirAttributeIsASymbolRef Method
julia
mlirAttributeIsASymbolRef(attr)
Checks whether the given attribute is a symbol reference attribute.
source
Reactant.MLIR.API.mlirAttributeIsAType Method
julia
mlirAttributeIsAType(attr)
Checks whether the given attribute is a type attribute.
source
Reactant.MLIR.API.mlirAttributeIsAUnit Method
julia
mlirAttributeIsAUnit(attr)
Checks whether the given attribute is a unit attribute.
source
Reactant.MLIR.API.mlirAttributeIsNull Method
julia
mlirAttributeIsNull(attr)
Checks whether an attribute is null.
source
Reactant.MLIR.API.mlirAttributeParseGet Method
julia
mlirAttributeParseGet(context, attr)
Parses an attribute. The attribute is owned by the context.
source
Reactant.MLIR.API.mlirAttributePrint Method
julia
mlirAttributePrint(attr, callback, userData)
Prints an attribute by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
Reactant.MLIR.API.mlirBF16TypeGet Method
julia
mlirBF16TypeGet(ctx)
Creates a bf16 type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirBFloat16TypeGetTypeID Method
julia
mlirBFloat16TypeGetTypeID()
Returns the typeID of an BFloat16 type.
source
Reactant.MLIR.API.mlirBlockAddArgument Method
julia
mlirBlockAddArgument(block, type, loc)
Appends an argument of the specified type to the block. Returns the newly added argument.
source
Reactant.MLIR.API.mlirBlockAppendOwnedOperation Method
julia
mlirBlockAppendOwnedOperation(block, operation)
Takes an operation owned by the caller and appends it to the block.
source
Reactant.MLIR.API.mlirBlockArgumentGetArgNumber Method
julia
mlirBlockArgumentGetArgNumber(value)
Returns the position of the value in the argument list of its block.
source
Reactant.MLIR.API.mlirBlockArgumentGetOwner Method
julia
mlirBlockArgumentGetOwner(value)
Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.
source
Reactant.MLIR.API.mlirBlockArgumentSetType Method
julia
mlirBlockArgumentSetType(value, type)
Sets the type of the block argument to the given type.
source
Reactant.MLIR.API.mlirBlockCreate Method
julia
mlirBlockCreate(nArgs, args, locs)
Creates a new empty block with the given argument types and transfers ownership to the caller.
source
Reactant.MLIR.API.mlirBlockDestroy Method
julia
mlirBlockDestroy(block)
Takes a block owned by the caller and destroys it.
source
Reactant.MLIR.API.mlirBlockDetach Method
julia
mlirBlockDetach(block)
Detach a block from the owning region and assume ownership.
source
Reactant.MLIR.API.mlirBlockEqual Method
julia
mlirBlockEqual(block, other)
Checks whether two blocks handles point to the same block. This does not perform deep comparison.
source
Reactant.MLIR.API.mlirBlockEraseArgument Method
julia
mlirBlockEraseArgument(block, index)
Erase the argument at 'index' and remove it from the argument list.
source
Reactant.MLIR.API.mlirBlockGetArgument Method
julia
mlirBlockGetArgument(block, pos)
Returns pos-th argument of the block.
source
Reactant.MLIR.API.mlirBlockGetFirstOperation Method
julia
mlirBlockGetFirstOperation(block)
Returns the first operation in the block.
source
Reactant.MLIR.API.mlirBlockGetNextInRegion Method
julia
mlirBlockGetNextInRegion(block)
Returns the block immediately following the given block in its parent region.
source
Reactant.MLIR.API.mlirBlockGetNumArguments Method
julia
mlirBlockGetNumArguments(block)
Returns the number of arguments of the block.
source
Reactant.MLIR.API.mlirBlockGetParentOperation Method
julia
mlirBlockGetParentOperation(arg1)
Returns the closest surrounding operation that contains this block.
source
Reactant.MLIR.API.mlirBlockGetParentRegion Method
julia
mlirBlockGetParentRegion(block)
Returns the region that contains this block.
source
Reactant.MLIR.API.mlirBlockGetTerminator Method
julia
mlirBlockGetTerminator(block)
Returns the terminator operation in the block or null if no terminator.
source
Reactant.MLIR.API.mlirBlockInsertArgument Method
julia
mlirBlockInsertArgument(block, pos, type, loc)
Inserts an argument of the specified type at a specified index to the block. Returns the newly added argument.
source
Reactant.MLIR.API.mlirBlockInsertOwnedOperation Method
julia
mlirBlockInsertOwnedOperation(block, pos, operation)
Takes an operation owned by the caller and inserts it as pos to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.
source
Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter Method
julia
mlirBlockInsertOwnedOperationAfter(block, reference, operation)
Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.
source
Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore Method
julia
mlirBlockInsertOwnedOperationBefore(block, reference, operation)
Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.
source
Reactant.MLIR.API.mlirBlockIsNull Method
julia
mlirBlockIsNull(block)
Checks whether a block is null.
source
Reactant.MLIR.API.mlirBlockPrint Method
julia
mlirBlockPrint(block, callback, userData)
Prints a block by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
Reactant.MLIR.API.mlirBoolAttrGet Method
julia
mlirBoolAttrGet(ctx, value)
Creates a bool attribute in the given context with the given value.
source
Reactant.MLIR.API.mlirBoolAttrGetValue Method
julia
mlirBoolAttrGetValue(attr)
Returns the value stored in the given bool attribute.
source
Reactant.MLIR.API.mlirBytecodeWriterConfigCreate Method
julia
mlirBytecodeWriterConfigCreate()
Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirBytecodeWriterConfigDestroy().
source
Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion Method
julia
mlirBytecodeWriterConfigDesiredEmitVersion(flags, version)
Sets the version to emit in the writer config.
source
Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy Method
julia
mlirBytecodeWriterConfigDestroy(config)
Destroys printing flags created with mlirBytecodeWriterConfigCreate.
source
Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet Method
julia
mlirCalibratedQuantizedTypeGet(expressedType, min, max)
Creates an instance of CalibratedQuantizedType with the given parameters in the same context as expressedType and returns it. The instance is owned by the context.
source
Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax Method
julia
mlirCalibratedQuantizedTypeGetMax(type)
Returns the max value of the given calibrated quantized type.
source
Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin Method
julia
mlirCalibratedQuantizedTypeGetMin(type)
Returns the min value of the given calibrated quantized type.
source
Reactant.MLIR.API.mlirComplexTypeGet Method
julia
mlirComplexTypeGet(elementType)
Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.
source
Reactant.MLIR.API.mlirComplexTypeGetElementType Method
julia
mlirComplexTypeGetElementType(type)
Returns the element type of the given complex type.
source
Reactant.MLIR.API.mlirComplexTypeGetTypeID Method
julia
mlirComplexTypeGetTypeID()
Returns the typeID of an Complex type.
source
Reactant.MLIR.API.mlirContextAppendDialectRegistry Method
julia
mlirContextAppendDialectRegistry(ctx, registry)
Append the contents of the given dialect registry to the registry associated with the context.
source
Reactant.MLIR.API.mlirContextAttachDiagnosticHandler Method
julia
mlirContextAttachDiagnosticHandler(context, handler, userData, deleteUserData)
Attaches the diagnostic handler to the context. Handlers are invoked in the reverse order of attachment until one of them processes the diagnostic completely. When a handler is invoked it is passed the userData that was provided when it was attached. If non-NULL, deleteUserData is called once the system no longer needs to call the handler (for instance after the handler is detached or the context is destroyed). Returns an identifier that can be used to detach the handler.
source
Reactant.MLIR.API.mlirContextCreate Method
julia
mlirContextCreate()
Creates an MLIR context and transfers its ownership to the caller. This sets the default multithreading option (enabled).
source
Reactant.MLIR.API.mlirContextCreateWithRegistry Method
julia
mlirContextCreateWithRegistry(registry, threadingEnabled)
Creates an MLIR context, setting the multithreading setting explicitly and pre-loading the dialects from the provided DialectRegistry.
source
Reactant.MLIR.API.mlirContextCreateWithThreading Method
julia
mlirContextCreateWithThreading(threadingEnabled)
Creates an MLIR context with an explicit setting of the multithreading setting and transfers its ownership to the caller.
source
Reactant.MLIR.API.mlirContextDestroy Method
julia
mlirContextDestroy(context)
Takes an MLIR context owned by the caller and destroys it.
source
Reactant.MLIR.API.mlirContextDetachDiagnosticHandler Method
julia
mlirContextDetachDiagnosticHandler(context, id)
Detaches an attached diagnostic handler from the context given its identifier.
source
Reactant.MLIR.API.mlirContextEnableMultithreading Method
julia
mlirContextEnableMultithreading(context, enable)
Set threading mode (must be set to false to mlir-print-ir-after-all).
source
Reactant.MLIR.API.mlirContextEqual Method
julia
mlirContextEqual(ctx1, ctx2)
Checks if two contexts are equal.
source
Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects Method
julia
mlirContextGetAllowUnregisteredDialects(context)
Returns whether the context allows unregistered dialects.
source
Reactant.MLIR.API.mlirContextGetNumLoadedDialects Method
julia
mlirContextGetNumLoadedDialects(context)
Returns the number of dialects loaded by the context.
source
Reactant.MLIR.API.mlirContextGetNumRegisteredDialects Method
julia
mlirContextGetNumRegisteredDialects(context)
Returns the number of dialects registered with the given context. A registered dialect will be loaded if needed by the parser.
source
Reactant.MLIR.API.mlirContextGetOrLoadDialect Method
julia
mlirContextGetOrLoadDialect(context, name)
Gets the dialect instance owned by the given context using the dialect namespace to identify it, loads (i.e., constructs the instance of) the dialect if necessary. If the dialect is not registered with the context, returns null. Use mlirContextLoad<Name>Dialect to load an unregistered dialect.
source
Reactant.MLIR.API.mlirContextIsNull Method
julia
mlirContextIsNull(context)
Checks whether a context is null.
source
Reactant.MLIR.API.mlirContextIsRegisteredOperation Method
julia
mlirContextIsRegisteredOperation(context, name)
Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.
source
Reactant.MLIR.API.mlirContextLoadAllAvailableDialects Method
julia
mlirContextLoadAllAvailableDialects(context)
Eagerly loads all available dialects registered with a context, making them available for use for IR construction.
source
Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects Method
julia
mlirContextSetAllowUnregisteredDialects(context, allow)
Sets whether unregistered dialects are allowed in this context.
source
Reactant.MLIR.API.mlirContextSetThreadPool Method
julia
mlirContextSetThreadPool(context, threadPool)
Sets the thread pool of the context explicitly, enabling multithreading in the process. This API should be used to avoid re-creating thread pools in long-running applications that perform multiple compilations, see the C++ documentation for MLIRContext for details.
source
Reactant.MLIR.API.mlirCreateExternalPass Method
julia
mlirCreateExternalPass(passID, name, argument, description, opName, nDependentDialects, dependentDialects, callbacks, userData)
Creates an external MlirPass that calls the supplied callbacks using the supplied userData. If opName is empty, the pass is a generic operation pass. Otherwise it is an operation pass specific to the specified pass name.
source
Reactant.MLIR.API.mlirDenseArrayGetNumElements Method
julia
mlirDenseArrayGetNumElements(attr)
Get the size of a dense array.
source
Reactant.MLIR.API.mlirDenseBoolArrayGet Method
julia
mlirDenseBoolArrayGet(ctx, size, values)
Create a dense array attribute with the given elements.
source
Reactant.MLIR.API.mlirDenseBoolArrayGetElement Method
julia
mlirDenseBoolArrayGetElement(attr, pos)
Get an element of a dense array.
source
Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue Method
julia
mlirDenseBoolResourceElementsAttrGetValue(attr, pos)
Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense resource elements attribute.
source
Reactant.MLIR.API.mlirDenseElementsAttrBoolGet Method
julia
mlirDenseElementsAttrBoolGet(shapedType, numElements, elements)
Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.
source
Reactant.MLIR.API.mlirDenseElementsAttrGet Method
julia
mlirDenseElementsAttrGet(shapedType, numElements, elements)
Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.
source
Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue Method
julia
mlirDenseElementsAttrGetBoolValue(attr, pos)
Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense elements attribute.
source
Reactant.MLIR.API.mlirDenseElementsAttrGetRawData Method
julia
mlirDenseElementsAttrGetRawData(attr)
Returns the raw data of the given dense elements attribute.
source
Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue Method
julia
mlirDenseElementsAttrGetSplatValue(attr)
Returns the single replicated value (splat) of a specific type contained by the given dense elements attribute.
source
Reactant.MLIR.API.mlirDenseElementsAttrIsSplat Method
julia
mlirDenseElementsAttrIsSplat(attr)
Checks whether the given dense elements attribute contains a single replicated value (splat).
source
Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet Method
julia
mlirDenseElementsAttrRawBufferGet(shapedType, rawBufferSize, rawBuffer)
Creates a dense elements attribute with the given Shaped type and elements populated from a packed, row-major opaque buffer of contents.
The format of the raw buffer is a densely packed array of values that can be bitcast to the storage format of the element type specified. Types that are not byte aligned will be: - For bitwidth > 1: Rounded up to the next byte. - For bitwidth = 1: Packed into 8bit bytes with bits corresponding to the linear order of the shape type from MSB to LSB, padded to on the right.
A raw buffer of a single element (or for 1-bit, a byte of value 0 or 255) will be interpreted as a splat. User code should be prepared for additional, conformant patterns to be identified as splats in the future.
source
Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet Method
julia
mlirDenseElementsAttrReshapeGet(attr, shapedType)
Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.
source
Reactant.MLIR.API.mlirDenseElementsAttrSplatGet Method
julia
mlirDenseElementsAttrSplatGet(shapedType, element)
Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).
source
Reactant.MLIR.API.mlirDenseElementsAttrStringGet Method
julia
mlirDenseElementsAttrStringGet(shapedType, numElements, strs)
Creates a dense elements attribute with the given shaped type from string elements.
source
Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID Method
julia
mlirDenseIntOrFPElementsAttrGetTypeID()
Returns the typeID of an DenseIntOrFPElements attribute.
source
Reactant.MLIR.API.mlirDiagnosticGetLocation Method
julia
mlirDiagnosticGetLocation(diagnostic)
Returns the location at which the diagnostic is reported.
source
Reactant.MLIR.API.mlirDiagnosticGetNote Method
julia
mlirDiagnosticGetNote(diagnostic, pos)
Returns pos-th note attached to the diagnostic. Expects pos to be a valid zero-based index into the list of notes.
source
Reactant.MLIR.API.mlirDiagnosticGetNumNotes Method
julia
mlirDiagnosticGetNumNotes(diagnostic)
Returns the number of notes attached to the diagnostic.
source
Reactant.MLIR.API.mlirDiagnosticGetSeverity Method
julia
mlirDiagnosticGetSeverity(diagnostic)
Returns the severity of the diagnostic.
source
Reactant.MLIR.API.mlirDiagnosticPrint Method
julia
mlirDiagnosticPrint(diagnostic, callback, userData)
Prints a diagnostic using the provided callback.
source
Reactant.MLIR.API.mlirDialectEqual Method
julia
mlirDialectEqual(dialect1, dialect2)
Checks if two dialects that belong to the same context are equal. Dialects from different contexts will not compare equal.
source
Reactant.MLIR.API.mlirDialectGetContext Method
julia
mlirDialectGetContext(dialect)
Returns the context that owns the dialect.
source
Reactant.MLIR.API.mlirDialectGetNamespace Method
julia
mlirDialectGetNamespace(dialect)
Returns the namespace of the given dialect.
source
Reactant.MLIR.API.mlirDialectHandleGetNamespace Method
julia
mlirDialectHandleGetNamespace(arg1)
Returns the namespace associated with the provided dialect handle.
source
Reactant.MLIR.API.mlirDialectHandleInsertDialect Method
julia
mlirDialectHandleInsertDialect(arg1, arg2)
Inserts the dialect associated with the provided dialect handle into the provided dialect registry
source
Reactant.MLIR.API.mlirDialectHandleLoadDialect Method
julia
mlirDialectHandleLoadDialect(arg1, arg2)
Loads the dialect associated with the provided dialect handle.
source
Reactant.MLIR.API.mlirDialectHandleRegisterDialect Method
julia
mlirDialectHandleRegisterDialect(arg1, arg2)
Registers the dialect associated with the provided dialect handle.
source
Reactant.MLIR.API.mlirDialectIsNull Method
julia
mlirDialectIsNull(dialect)
Checks if the dialect is null.
source
Reactant.MLIR.API.mlirDialectRegistryCreate Method
julia
mlirDialectRegistryCreate()
Creates a dialect registry and transfers its ownership to the caller.
source
Reactant.MLIR.API.mlirDialectRegistryDestroy Method
julia
mlirDialectRegistryDestroy(registry)
Takes a dialect registry owned by the caller and destroys it.
source
Reactant.MLIR.API.mlirDialectRegistryIsNull Method
julia
mlirDialectRegistryIsNull(registry)
Checks if the dialect registry is null.
source
Reactant.MLIR.API.mlirDictionaryAttrGet Method
julia
mlirDictionaryAttrGet(ctx, numElements, elements)
Creates a dictionary attribute containing the given list of elements in the provided context.
source
Reactant.MLIR.API.mlirDictionaryAttrGetElement Method
julia
mlirDictionaryAttrGetElement(attr, pos)
Returns pos-th element of the given dictionary attribute.
source
Reactant.MLIR.API.mlirDictionaryAttrGetElementByName Method
julia
mlirDictionaryAttrGetElementByName(attr, name)
Returns the dictionary attribute element with the given name or NULL if the given name does not exist in the dictionary.
source
Reactant.MLIR.API.mlirDictionaryAttrGetNumElements Method
julia
mlirDictionaryAttrGetNumElements(attr)
Returns the number of attributes contained in a dictionary attribute.
source
Reactant.MLIR.API.mlirDictionaryAttrGetTypeID Method
julia
mlirDictionaryAttrGetTypeID()
Returns the typeID of a Dictionary attribute.
source
Reactant.MLIR.API.mlirDisctinctAttrCreate Method
julia
mlirDisctinctAttrCreate(referencedAttr)
Creates a DisctinctAttr with the referenced attribute.
source
Reactant.MLIR.API.mlirElementsAttrGetNumElements Method
julia
mlirElementsAttrGetNumElements(attr)
Gets the total number of elements in the given elements attribute. In order to iterate over the attribute, obtain its type, which must be a statically shaped type and use its sizes to build a multi-dimensional index.
source
Reactant.MLIR.API.mlirElementsAttrGetValue Method
julia
mlirElementsAttrGetValue(attr, rank, idxs)
Returns the element at the given rank-dimensional index.
source
Reactant.MLIR.API.mlirElementsAttrIsValidIndex Method
julia
mlirElementsAttrIsValidIndex(attr, rank, idxs)
Checks whether the given rank-dimensional index is valid in the given elements attribute.
source
Reactant.MLIR.API.mlirEmitError Method
julia
mlirEmitError(location, message)
Emits an error at the given location through the diagnostics engine. Used for testing purposes.
source
Reactant.MLIR.API.mlirEnableGlobalDebug Method
julia
mlirEnableGlobalDebug(enable)
Sets the global debugging flag.
source
Reactant.MLIR.API.mlirExecutionEngineCreate Method
julia
mlirExecutionEngineCreate(op, optLevel, numPaths, sharedLibPaths, enableObjectDump)
Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.
source
Reactant.MLIR.API.mlirExecutionEngineDestroy Method
julia
mlirExecutionEngineDestroy(jit)
Destroy an ExecutionEngine instance.
source
Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile Method
julia
mlirExecutionEngineDumpToObjectFile(jit, fileName)
Dump as an object in fileName.
source
Reactant.MLIR.API.mlirExecutionEngineInvokePacked Method
julia
mlirExecutionEngineInvokePacked(jit, name, arguments)
Invoke a native function in the execution engine by name with the arguments and result of the invoked function passed as an array of pointers. The function must have been tagged with the llvm.emit\_c\_interface attribute. Returns a failure if the execution fails for any reason (the function name can't be resolved for instance).
source
Reactant.MLIR.API.mlirExecutionEngineIsNull Method
julia
mlirExecutionEngineIsNull(jit)
Checks whether an execution engine is null.
source
Reactant.MLIR.API.mlirExecutionEngineLookup Method
julia
mlirExecutionEngineLookup(jit, name)
Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.
source
Reactant.MLIR.API.mlirExecutionEngineLookupPacked Method
julia
mlirExecutionEngineLookupPacked(jit, name)
Lookup the wrapper of the native function in the execution engine with the given name, returns nullptr if the function can't be looked-up.
source
Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol Method
julia
mlirExecutionEngineRegisterSymbol(jit, name, sym)
Register a symbol with the jit: this symbol will be accessible to the jitted code.
source
Reactant.MLIR.API.mlirExternalPassSignalFailure Method
julia
mlirExternalPassSignalFailure(pass)
This signals that the pass has failed. This is only valid to call during the run callback of MlirExternalPassCallbacks. See Pass::signalPassFailure().
source
Reactant.MLIR.API.mlirF16TypeGet Method
julia
mlirF16TypeGet(ctx)
Creates an f16 type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirF32TypeGet Method
julia
mlirF32TypeGet(ctx)
Creates an f32 type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirF64TypeGet Method
julia
mlirF64TypeGet(ctx)
Creates a f64 type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFlatSymbolRefAttrGet Method
julia
mlirFlatSymbolRefAttrGet(ctx, symbol)
Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.
source
Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue Method
julia
mlirFlatSymbolRefAttrGetValue(attr)
Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.API.mlirFloat16TypeGetTypeID Method
julia
mlirFloat16TypeGetTypeID()
Returns the typeID of an Float16 type.
source
Reactant.MLIR.API.mlirFloat32TypeGetTypeID Method
julia
mlirFloat32TypeGetTypeID()
Returns the typeID of an Float32 type.
source
Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet Method
julia
mlirFloat4E2M1FNTypeGet(ctx)
Creates an f4E2M1FN type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID Method
julia
mlirFloat4E2M1FNTypeGetTypeID()
Returns the typeID of an Float4E2M1FN type.
source
Reactant.MLIR.API.mlirFloat64TypeGetTypeID Method
julia
mlirFloat64TypeGetTypeID()
Returns the typeID of an Float64 type.
source
Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet Method
julia
mlirFloat6E2M3FNTypeGet(ctx)
Creates an f6E2M3FN type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID Method
julia
mlirFloat6E2M3FNTypeGetTypeID()
Returns the typeID of an Float6E2M3FN type.
source
Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet Method
julia
mlirFloat6E3M2FNTypeGet(ctx)
Creates an f6E3M2FN type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID Method
julia
mlirFloat6E3M2FNTypeGetTypeID()
Returns the typeID of an Float6E3M2FN type.
source
Reactant.MLIR.API.mlirFloat8E3M4TypeGet Method
julia
mlirFloat8E3M4TypeGet(ctx)
Creates an f8E3M4 type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID Method
julia
mlirFloat8E3M4TypeGetTypeID()
Returns the typeID of an Float8E3M4 type.
source
Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGet Method
julia
mlirFloat8E4M3B11FNUZTypeGet(ctx)
Creates an f8E4M3B11FNUZ type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGetTypeID Method
julia
mlirFloat8E4M3B11FNUZTypeGetTypeID()
Returns the typeID of an Float8E4M3B11FNUZ type.
source
Reactant.MLIR.API.mlirFloat8E4M3FNTypeGet Method
julia
mlirFloat8E4M3FNTypeGet(ctx)
Creates an f8E4M3FN type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID Method
julia
mlirFloat8E4M3FNTypeGetTypeID()
Returns the typeID of an Float8E4M3FN type.
source
Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet Method
julia
mlirFloat8E4M3FNUZTypeGet(ctx)
Creates an f8E4M3FNUZ type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID Method
julia
mlirFloat8E4M3FNUZTypeGetTypeID()
Returns the typeID of an Float8E4M3FNUZ type.
source
Reactant.MLIR.API.mlirFloat8E4M3TypeGet Method
julia
mlirFloat8E4M3TypeGet(ctx)
Creates an f8E4M3 type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID Method
julia
mlirFloat8E4M3TypeGetTypeID()
Returns the typeID of an Float8E4M3 type.
source
Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGet Method
julia
mlirFloat8E5M2FNUZTypeGet(ctx)
Creates an f8E5M2FNUZ type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID Method
julia
mlirFloat8E5M2FNUZTypeGetTypeID()
Returns the typeID of an Float8E5M2FNUZ type.
source
Reactant.MLIR.API.mlirFloat8E5M2TypeGet Method
julia
mlirFloat8E5M2TypeGet(ctx)
Creates an f8E5M2 type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID Method
julia
mlirFloat8E5M2TypeGetTypeID()
Returns the typeID of an Float8E5M2 type.
source
Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet Method
julia
mlirFloat8E8M0FNUTypeGet(ctx)
Creates an f8E8M0FNU type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID Method
julia
mlirFloat8E8M0FNUTypeGetTypeID()
Returns the typeID of an Float8E8M0FNU type.
source
Reactant.MLIR.API.mlirFloatAttrDoubleGet Method
julia
mlirFloatAttrDoubleGet(ctx, type, value)
Creates a floating point attribute in the given context with the given double value and double-precision FP semantics.
source
Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked Method
julia
mlirFloatAttrDoubleGetChecked(loc, type, value)
Same as "mlirFloatAttrDoubleGet", but if the type is not valid for a construction of a FloatAttr, returns a null MlirAttribute.
source
Reactant.MLIR.API.mlirFloatAttrGetTypeID Method
julia
mlirFloatAttrGetTypeID()
Returns the typeID of a Float attribute.
source
Reactant.MLIR.API.mlirFloatAttrGetValueDouble Method
julia
mlirFloatAttrGetValueDouble(attr)
Returns the value stored in the given floating point attribute, interpreting the value as double.
source
Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID Method
julia
mlirFloatTF32TypeGetTypeID()
Returns the typeID of a TF32 type.
source
Reactant.MLIR.API.mlirFloatTypeGetWidth Method
julia
mlirFloatTypeGetWidth(type)
Returns the bitwidth of a floating-point type.
source
Reactant.MLIR.API.mlirFreezeRewritePattern Method
julia
mlirFreezeRewritePattern(op)
FrozenRewritePatternSet API
source
Reactant.MLIR.API.mlirFuncSetArgAttr Method
julia
mlirFuncSetArgAttr(op, pos, name, attr)
Sets the argument attribute 'name' of an argument at index 'pos'. Asserts that the operation is a FuncOp.
source
Reactant.MLIR.API.mlirFunctionTypeGet Method
julia
mlirFunctionTypeGet(ctx, numInputs, inputs, numResults, results)
Creates a function type, mapping a list of input types to result types.
source
Reactant.MLIR.API.mlirFunctionTypeGetInput Method
julia
mlirFunctionTypeGetInput(type, pos)
Returns the pos-th input type.
source
Reactant.MLIR.API.mlirFunctionTypeGetNumInputs Method
julia
mlirFunctionTypeGetNumInputs(type)
Returns the number of input types.
source
Reactant.MLIR.API.mlirFunctionTypeGetNumResults Method
julia
mlirFunctionTypeGetNumResults(type)
Returns the number of result types.
source
Reactant.MLIR.API.mlirFunctionTypeGetResult Method
julia
mlirFunctionTypeGetResult(type, pos)
Returns the pos-th result type.
source
Reactant.MLIR.API.mlirFunctionTypeGetTypeID Method
julia
mlirFunctionTypeGetTypeID()
Returns the typeID of an Function type.
source
Reactant.MLIR.API.mlirIRRewriterCreate Method
julia
mlirIRRewriterCreate(context)
Create an IRRewriter and transfer ownership to the caller.
source
Reactant.MLIR.API.mlirIRRewriterCreateFromOp Method
julia
mlirIRRewriterCreateFromOp(op)
Create an IRRewriter and transfer ownership to the caller. Additionally set the insertion point before the operation.
source
Reactant.MLIR.API.mlirIRRewriterDestroy Method
julia
mlirIRRewriterDestroy(rewriter)
Takes an IRRewriter owned by the caller and destroys it. It is the responsibility of the user to only pass an IRRewriter class.
source
Reactant.MLIR.API.mlirIdentifierEqual Method
julia
mlirIdentifierEqual(ident, other)
Checks whether two identifiers are the same.
source
Reactant.MLIR.API.mlirIdentifierGet Method
julia
mlirIdentifierGet(context, str)
Gets an identifier with the given string value.
source
Reactant.MLIR.API.mlirIdentifierGetContext Method
julia
mlirIdentifierGetContext(arg1)
Returns the context associated with this identifier
source
Reactant.MLIR.API.mlirIdentifierStr Method
julia
mlirIdentifierStr(ident)
Gets the string value of the identifier.
source
Reactant.MLIR.API.mlirIndexTypeGet Method
julia
mlirIndexTypeGet(ctx)
Creates an index type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirIndexTypeGetTypeID Method
julia
mlirIndexTypeGetTypeID()
Returns the typeID of an Index type.
source
Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes Method
julia
mlirInferShapedTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)
Infers the return shaped type components of the operation. Calls callback with the types of inferred arguments on success. Returns failure otherwise.
source
Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID Method
julia
mlirInferShapedTypeOpInterfaceTypeID()
Returns the interface TypeID of the InferShapedTypeOpInterface.
source
Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes Method
julia
mlirInferTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)
Infers the return types of the operation identified by its canonical given the arguments that will be supplied to its generic builder. Calls callback with the types of inferred arguments, potentially several times, on success. Returns failure otherwise.
source
Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID Method
julia
mlirInferTypeOpInterfaceTypeID()
Returns the interface TypeID of the InferTypeOpInterface.
source
Reactant.MLIR.API.mlirIntegerAttrGet Method
julia
mlirIntegerAttrGet(type, value)
Creates an integer attribute of the given type with the given integer value.
source
Reactant.MLIR.API.mlirIntegerAttrGetTypeID Method
julia
mlirIntegerAttrGetTypeID()
Returns the typeID of an Integer attribute.
source
Reactant.MLIR.API.mlirIntegerAttrGetValueInt Method
julia
mlirIntegerAttrGetValueInt(attr)
Returns the value stored in the given integer attribute, assuming the value is of signless type and fits into a signed 64-bit integer.
source
Reactant.MLIR.API.mlirIntegerAttrGetValueSInt Method
julia
mlirIntegerAttrGetValueSInt(attr)
Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.
source
Reactant.MLIR.API.mlirIntegerAttrGetValueUInt Method
julia
mlirIntegerAttrGetValueUInt(attr)
Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.
source
Reactant.MLIR.API.mlirIntegerSetAttrGet Method
julia
mlirIntegerSetAttrGet(set)
Creates an integer set attribute wrapping the given set. The attribute belongs to the same context as the integer set.
source
Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID Method
julia
mlirIntegerSetAttrGetTypeID()
Returns the typeID of an IntegerSet attribute.
source
Reactant.MLIR.API.mlirIntegerSetAttrGetValue Method
julia
mlirIntegerSetAttrGetValue(attr)
Returns the integer set wrapped in the given integer set attribute.
source
Reactant.MLIR.API.mlirIntegerSetDump Method
julia
mlirIntegerSetDump(set)
Prints an integer set to the standard error stream.
source
Reactant.MLIR.API.mlirIntegerSetEmptyGet Method
julia
mlirIntegerSetEmptyGet(context, numDims, numSymbols)
Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.
source
Reactant.MLIR.API.mlirIntegerSetEqual Method
julia
mlirIntegerSetEqual(s1, s2)
Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.
source
Reactant.MLIR.API.mlirIntegerSetGet Method
julia
mlirIntegerSetGet(context, numDims, numSymbols, numConstraints, constraints, eqFlags)
Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqFlags is 1) or inequalities (eqFlags is 0). Both constraints and eqFlags are expected to point to at least numConstraint consecutive values.
source
Reactant.MLIR.API.mlirIntegerSetGetConstraint Method
julia
mlirIntegerSetGetConstraint(set, pos)
Returns pos-th constraint of the set.
source
Reactant.MLIR.API.mlirIntegerSetGetContext Method
julia
mlirIntegerSetGetContext(set)
Gets the context in which the given integer set lives.
source
Reactant.MLIR.API.mlirIntegerSetGetNumConstraints Method
julia
mlirIntegerSetGetNumConstraints(set)
Returns the number of constraints (equalities + inequalities) in the given set.
source
Reactant.MLIR.API.mlirIntegerSetGetNumDims Method
julia
mlirIntegerSetGetNumDims(set)
Returns the number of dimensions in the given set.
source
Reactant.MLIR.API.mlirIntegerSetGetNumEqualities Method
julia
mlirIntegerSetGetNumEqualities(set)
Returns the number of equalities in the given set.
source
Reactant.MLIR.API.mlirIntegerSetGetNumInequalities Method
julia
mlirIntegerSetGetNumInequalities(set)
Returns the number of inequalities in the given set.
source
Reactant.MLIR.API.mlirIntegerSetGetNumInputs Method
julia
mlirIntegerSetGetNumInputs(set)
Returns the number of inputs (dimensions + symbols) in the given set.
source
Reactant.MLIR.API.mlirIntegerSetGetNumSymbols Method
julia
mlirIntegerSetGetNumSymbols(set)
Returns the number of symbols in the given set.
source
Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty Method
julia
mlirIntegerSetIsCanonicalEmpty(set)
Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.
source
Reactant.MLIR.API.mlirIntegerSetIsConstraintEq Method
julia
mlirIntegerSetIsConstraintEq(set, pos)
Returns true of the pos-th constraint of the set is an equality constraint, false otherwise.
source
Reactant.MLIR.API.mlirIntegerSetIsNull Method
julia
mlirIntegerSetIsNull(set)
Checks whether an integer set is a null object.
source
Reactant.MLIR.API.mlirIntegerSetPrint Method
julia
mlirIntegerSetPrint(set, callback, userData)
Prints an integer set by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
Reactant.MLIR.API.mlirIntegerSetReplaceGet Method
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)
Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.
source
Reactant.MLIR.API.mlirIntegerTypeGet Method
julia
mlirIntegerTypeGet(ctx, bitwidth)
Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.
source
Reactant.MLIR.API.mlirIntegerTypeGetTypeID Method
julia
mlirIntegerTypeGetTypeID()
Returns the typeID of an Integer type.
source
Reactant.MLIR.API.mlirIntegerTypeGetWidth Method
julia
mlirIntegerTypeGetWidth(type)
Returns the bitwidth of an integer type.
source
Reactant.MLIR.API.mlirIntegerTypeIsSigned Method
julia
mlirIntegerTypeIsSigned(type)
Checks whether the given integer type is signed.
source
Reactant.MLIR.API.mlirIntegerTypeIsSignless Method
julia
mlirIntegerTypeIsSignless(type)
Checks whether the given integer type is signless.
source
Reactant.MLIR.API.mlirIntegerTypeIsUnsigned Method
julia
mlirIntegerTypeIsUnsigned(type)
Checks whether the given integer type is unsigned.
source
Reactant.MLIR.API.mlirIntegerTypeSignedGet Method
julia
mlirIntegerTypeSignedGet(ctx, bitwidth)
Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.
source
Reactant.MLIR.API.mlirIntegerTypeUnsignedGet Method
julia
mlirIntegerTypeUnsignedGet(ctx, bitwidth)
Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.
source
Reactant.MLIR.API.mlirIsCurrentDebugType Method
julia
mlirIsCurrentDebugType(type)
Checks if type is set as the current debug type.
source
Reactant.MLIR.API.mlirIsGlobalDebugEnabled Method
julia
mlirIsGlobalDebugEnabled()
Retuns true if the global debugging flag is set, false otherwise.
source
Reactant.MLIR.API.mlirLLVMArrayTypeGet Method
julia
mlirLLVMArrayTypeGet(elementType, numElements)
Creates an llvm.array type.
source
Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType Method
julia
mlirLLVMArrayTypeGetElementType(type)
Returns the element type of the llvm.array type.
source
Reactant.MLIR.API.mlirLLVMCConvAttrGet Method
julia
mlirLLVMCConvAttrGet(ctx, cconv)
Creates a LLVM CConv attribute.
source
Reactant.MLIR.API.mlirLLVMComdatAttrGet Method
julia
mlirLLVMComdatAttrGet(ctx, comdat)
Creates a LLVM Comdat attribute.
source
Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet Method
julia
mlirLLVMDIAnnotationAttrGet(ctx, name, value)
Creates a LLVM DIAnnotation attribute.
source
Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet Method
julia
mlirLLVMDIBasicTypeAttrGet(ctx, tag, name, sizeInBits, encoding)
Creates a LLVM DIBasicType attribute.
source
Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet Method
julia
mlirLLVMDICompileUnitAttrGet(ctx, id, sourceLanguage, file, producer, isOptimized, emissionKind, nameTableKind)
Creates a LLVM DICompileUnit attribute.
source
Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet Method
julia
mlirLLVMDICompositeTypeAttrGet(ctx, recId, isRecSelf, tag, name, file, line, scope, baseType, flags, sizeInBits, alignInBits, nElements, elements, dataLocation, rank, allocated, associated)
Creates a LLVM DICompositeType attribute.
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Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf Method
julia
mlirLLVMDICompositeTypeAttrGetRecSelf(recId)
Creates a self-referencing LLVM DICompositeType attribute.
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Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet Method
julia
mlirLLVMDIDerivedTypeAttrGet(ctx, tag, name, baseType, sizeInBits, alignInBits, offsetInBits, dwarfAddressSpace, extraData)
Creates a LLVM DIDerivedType attribute. Note that dwarfAddressSpace is an optional field, where MLIR_CAPI_DWARF_ADDRESS_SPACE_NULL indicates null and non-negative values indicate a value present.
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Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType Method
julia
mlirLLVMDIDerivedTypeAttrGetBaseType(diDerivedType)
Gets the base type from a LLVM DIDerivedType attribute.
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Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet Method
julia
mlirLLVMDIExpressionAttrGet(ctx, nOperations, operations)
Creates a LLVM DIExpression attribute.
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Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet Method
julia
mlirLLVMDIExpressionElemAttrGet(ctx, opcode, nArguments, arguments)
Creates a LLVM DIExpressionElem attribute.
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Reactant.MLIR.API.mlirLLVMDIFileAttrGet Method
julia
mlirLLVMDIFileAttrGet(ctx, name, directory)
Creates a LLVM DIFileAttr attribute.
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Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet Method
julia
mlirLLVMDIFlagsAttrGet(ctx, value)
Creates a LLVM DIFlags attribute.
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Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet Method
julia
mlirLLVMDIImportedEntityAttrGet(ctx, tag, scope, entity, file, line, name, nElements, elements)
Creates a LLVM DIImportedEntityAttr attribute.
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Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet Method
julia
mlirLLVMDILexicalBlockAttrGet(ctx, scope, file, line, column)
Creates a LLVM DILexicalBlock attribute.
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Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet Method
julia
mlirLLVMDILexicalBlockFileAttrGet(ctx, scope, file, discriminator)
Creates a LLVM DILexicalBlockFile attribute.
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Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet Method
julia
mlirLLVMDILocalVariableAttrGet(ctx, scope, name, diFile, line, arg, alignInBits, diType, flags)
Creates a LLVM DILocalVariableAttr attribute.
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Reactant.MLIR.API.mlirLLVMDIModuleAttrGet Method
julia
mlirLLVMDIModuleAttrGet(ctx, file, scope, name, configMacros, includePath, apinotes, line, isDecl)
Creates a LLVM DIModuleAttr attribute.
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Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope Method
julia
mlirLLVMDIModuleAttrGetScope(diModule)
Gets the scope of this DIModuleAttr.
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Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet Method
julia
mlirLLVMDINullTypeAttrGet(ctx)
Creates a LLVM DINullType attribute.
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Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet Method
julia
mlirLLVMDISubprogramAttrGet(ctx, recId, isRecSelf, id, compileUnit, scope, name, linkageName, file, line, scopeLine, subprogramFlags, type, nRetainedNodes, retainedNodes, nAnnotations, annotations)
Creates a LLVM DISubprogramAttr attribute.
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Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit Method
julia
mlirLLVMDISubprogramAttrGetCompileUnit(diSubprogram)
Gets the compile unit from this DISubprogram.
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Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile Method
julia
mlirLLVMDISubprogramAttrGetFile(diSubprogram)
Gets the file from this DISubprogramAttr.
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Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine Method
julia
mlirLLVMDISubprogramAttrGetLine(diSubprogram)
Gets the line from this DISubprogramAttr.
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Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf Method
julia
mlirLLVMDISubprogramAttrGetRecSelf(recId)
Creates a self-referencing LLVM DISubprogramAttr attribute.
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Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope Method
julia
mlirLLVMDISubprogramAttrGetScope(diSubprogram)
Gets the scope from this DISubprogramAttr.
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Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine Method
julia
mlirLLVMDISubprogramAttrGetScopeLine(diSubprogram)
Gets the scope line from this DISubprogram.
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Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType Method
julia
mlirLLVMDISubprogramAttrGetType(diSubprogram)
Gets the type from this DISubprogramAttr.
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Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet Method
julia
mlirLLVMDISubroutineTypeAttrGet(ctx, callingConvention, nTypes, types)
Creates a LLVM DISubroutineTypeAttr attribute.
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Reactant.MLIR.API.mlirLLVMFunctionTypeGet Method
julia
mlirLLVMFunctionTypeGet(resultType, nArgumentTypes, argumentTypes, isVarArg)
Creates an llvm.func type.
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Reactant.MLIR.API.mlirLLVMLinkageAttrGet Method
julia
mlirLLVMLinkageAttrGet(ctx, linkage)
Creates a LLVM Linkage attribute.
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Reactant.MLIR.API.mlirLLVMPointerTypeGet Method
julia
mlirLLVMPointerTypeGet(ctx, addressSpace)
Creates an llvm.ptr type.
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Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace Method
julia
mlirLLVMPointerTypeGetAddressSpace(pointerType)
Returns address space of llvm.ptr
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Reactant.MLIR.API.mlirLLVMStructTypeGetElementType Method
julia
mlirLLVMStructTypeGetElementType(type, position)
Returns the positions-th field of the struct. Asserts if the struct is opaque, not yet initialized or if the position is out of range.
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Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier Method
julia
mlirLLVMStructTypeGetIdentifier(type)
Returns the identifier of the identified struct. Asserts that the struct is identified, i.e., not literal.
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Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes Method
julia
mlirLLVMStructTypeGetNumElementTypes(type)
Returns the number of fields in the struct. Asserts if the struct is opaque or not yet initialized.
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Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet Method
julia
mlirLLVMStructTypeIdentifiedGet(ctx, name)
Creates an LLVM identified struct type with no body. If a struct type with this name already exists in the context, returns that type. Use mlirLLVMStructTypeIdentifiedNewGet to create a fresh struct type, potentially renaming it. The body should be set separatelty by calling mlirLLVMStructTypeSetBody, if it isn't set already.
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Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet Method
julia
mlirLLVMStructTypeIdentifiedNewGet(ctx, name, nFieldTypes, fieldTypes, isPacked)
Creates an LLVM identified struct type with no body and a name starting with the given prefix. If a struct with the exact name as the given prefix already exists, appends an unspecified suffix to the name so that the name is unique in context.
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Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral Method
julia
mlirLLVMStructTypeIsLiteral(type)
Returns true if the type is a literal (unnamed) LLVM struct type.
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Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque Method
julia
mlirLLVMStructTypeIsOpaque(type)
Returns true is the struct is explicitly opaque (will not have a body) or uninitialized (will eventually have a body).
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Reactant.MLIR.API.mlirLLVMStructTypeIsPacked Method
julia
mlirLLVMStructTypeIsPacked(type)
Returns true if the struct is packed.
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Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet Method
julia
mlirLLVMStructTypeLiteralGet(ctx, nFieldTypes, fieldTypes, isPacked)
Creates an LLVM literal (unnamed) struct type. This may assert if the fields have types not compatible with the LLVM dialect. For a graceful failure, use the checked version.
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Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked Method
julia
mlirLLVMStructTypeLiteralGetChecked(loc, nFieldTypes, fieldTypes, isPacked)
Creates an LLVM literal (unnamed) struct type if possible. Emits a diagnostic at the given location and returns null otherwise.
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Reactant.MLIR.API.mlirLLVMStructTypeSetBody Method
julia
mlirLLVMStructTypeSetBody(structType, nFieldTypes, fieldTypes, isPacked)
Sets the body of the identified struct if it hasn't been set yet. Returns whether the operation was successful.
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Reactant.MLIR.API.mlirLLVMVoidTypeGet Method
julia
mlirLLVMVoidTypeGet(ctx)
Creates an llmv.void type.
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Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion Method
julia
mlirLinalgFillBuiltinNamedOpRegion(mlirOp)
Apply the special region builder for the builtin named Linalg op. Assert that mlirOp is a builtin named Linalg op.
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Reactant.MLIR.API.mlirLlvmThreadPoolCreate Method
julia
mlirLlvmThreadPoolCreate()
Create an LLVM thread pool. This is reexported here to avoid directly pulling in the LLVM headers directly.
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Reactant.MLIR.API.mlirLlvmThreadPoolDestroy Method
julia
mlirLlvmThreadPoolDestroy(pool)
Destroy an LLVM thread pool.
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Reactant.MLIR.API.mlirLoadIRDLDialects Method
julia
mlirLoadIRDLDialects(_module)
Loads all IRDL dialects in the provided module, registering the dialects in the module's associated context.
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Reactant.MLIR.API.mlirLocationCallSiteGet Method
julia
mlirLocationCallSiteGet(callee, caller)
Creates a call site location with a callee and a caller.
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Reactant.MLIR.API.mlirLocationEqual Method
julia
mlirLocationEqual(l1, l2)
Checks if two locations are equal.
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Reactant.MLIR.API.mlirLocationFileLineColGet Method
julia
mlirLocationFileLineColGet(context, filename, line, col)
Creates an File/Line/Column location owned by the given context.
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Reactant.MLIR.API.mlirLocationFromAttribute Method
julia
mlirLocationFromAttribute(attribute)
Creates a location from a location attribute.
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Reactant.MLIR.API.mlirLocationFusedGet Method
julia
mlirLocationFusedGet(ctx, nLocations, locations, metadata)
Creates a fused location with an array of locations and metadata.
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Reactant.MLIR.API.mlirLocationGetAttribute Method
julia
mlirLocationGetAttribute(location)
Returns the underlying location attribute of this location.
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Reactant.MLIR.API.mlirLocationGetContext Method
julia
mlirLocationGetContext(location)
Gets the context that a location was created with.
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Reactant.MLIR.API.mlirLocationIsNull Method
julia
mlirLocationIsNull(location)
Checks if the location is null.
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Reactant.MLIR.API.mlirLocationNameGet Method
julia
mlirLocationNameGet(context, name, childLoc)
Creates a name location owned by the given context. Providing null location for childLoc is allowed and if childLoc is null location, then the behavior is the same as having unknown child location.
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Reactant.MLIR.API.mlirLocationPrint Method
julia
mlirLocationPrint(location, callback, userData)
Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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Reactant.MLIR.API.mlirLocationUnknownGet Method
julia
mlirLocationUnknownGet(context)
Creates a location with unknown position owned by the given context.
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Reactant.MLIR.API.mlirLogicalResultFailure Method
julia
mlirLogicalResultFailure()
Creates a logical result representing a failure.
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Reactant.MLIR.API.mlirLogicalResultIsFailure Method
julia
mlirLogicalResultIsFailure(res)
Checks if the given logical result represents a failure.
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Reactant.MLIR.API.mlirLogicalResultIsSuccess Method
julia
mlirLogicalResultIsSuccess(res)
Checks if the given logical result represents a success.
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Reactant.MLIR.API.mlirLogicalResultSuccess Method
julia
mlirLogicalResultSuccess()
Creates a logical result representing a success.
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Reactant.MLIR.API.mlirMemRefTypeContiguousGet Method
julia
mlirMemRefTypeContiguousGet(elementType, rank, shape, memorySpace)
Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context.
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Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked Method
julia
mlirMemRefTypeContiguousGetChecked(loc, elementType, rank, shape, memorySpace)
Same as "mlirMemRefTypeContiguousGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
Reactant.MLIR.API.mlirMemRefTypeGet Method
julia
mlirMemRefTypeGet(elementType, rank, shape, layout, memorySpace)
Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context.
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Reactant.MLIR.API.mlirMemRefTypeGetAffineMap Method
julia
mlirMemRefTypeGetAffineMap(type)
Returns the affine map of the given MemRef type.
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Reactant.MLIR.API.mlirMemRefTypeGetChecked Method
julia
mlirMemRefTypeGetChecked(loc, elementType, rank, shape, layout, memorySpace)
Same as "mlirMemRefTypeGet" but returns a nullptr-wrapping MlirType o illegal arguments, emitting appropriate diagnostics.
source
Reactant.MLIR.API.mlirMemRefTypeGetLayout Method
julia
mlirMemRefTypeGetLayout(type)
Returns the layout of the given MemRef type.
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Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace Method
julia
mlirMemRefTypeGetMemorySpace(type)
Returns the memory space of the given MemRef type.
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Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset Method
julia
mlirMemRefTypeGetStridesAndOffset(type, strides, offset)
Returns the strides of the MemRef if the layout map is in strided form. Both strides and offset are out params. strides must point to pre-allocated memory of length equal to the rank of the memref.
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Reactant.MLIR.API.mlirMemRefTypeGetTypeID Method
julia
mlirMemRefTypeGetTypeID()
Returns the typeID of an MemRef type.
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Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone Method
julia
mlirMergeSymbolsIntoFromClone(target, other)
Merge the symbols from other into target, potentially renaming them to avoid conflicts. Private symbols may be renamed during the merge, public symbols must have at most one declaration. A name conflict in public symbols is reported as an error before returning a failure.
Note that this clones the other operation unlike the C++ counterpart that takes ownership.
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Reactant.MLIR.API.mlirModuleCreateEmpty Method
julia
mlirModuleCreateEmpty(location)
Creates a new, empty module and transfers ownership to the caller.
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Reactant.MLIR.API.mlirModuleCreateParse Method
julia
mlirModuleCreateParse(context, _module)
Parses a module from the string and transfers ownership to the caller.
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Reactant.MLIR.API.mlirModuleDestroy Method
julia
mlirModuleDestroy(_module)
Takes a module owned by the caller and deletes it.
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Reactant.MLIR.API.mlirModuleFromOperation Method
julia
mlirModuleFromOperation(op)
Views the generic operation as a module. The returned module is null when the input operation was not a ModuleOp.
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Reactant.MLIR.API.mlirModuleGetBody Method
julia
mlirModuleGetBody(_module)
Gets the body of the module, i.e. the only block it contains.
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Reactant.MLIR.API.mlirModuleGetContext Method
julia
mlirModuleGetContext(_module)
Gets the context that a module was created with.
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Reactant.MLIR.API.mlirModuleGetOperation Method
julia
mlirModuleGetOperation(_module)
Views the module as a generic operation.
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Reactant.MLIR.API.mlirModuleIsNull Method
julia
mlirModuleIsNull(_module)
Checks whether a module is null.
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Reactant.MLIR.API.mlirNamedAttributeGet Method
julia
mlirNamedAttributeGet(name, attr)
Associates an attribute with the name. Takes ownership of neither.
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Reactant.MLIR.API.mlirNoneTypeGet Method
julia
mlirNoneTypeGet(ctx)
Creates a None type in the given context. The type is owned by the context.
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Reactant.MLIR.API.mlirNoneTypeGetTypeID Method
julia
mlirNoneTypeGetTypeID()
Returns the typeID of an None type.
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Reactant.MLIR.API.mlirOpOperandGetNextUse Method
julia
mlirOpOperandGetNextUse(opOperand)
Returns an op operand representing the next use of the value, or a null op operand if there is no next use.
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Reactant.MLIR.API.mlirOpOperandGetOperandNumber Method
julia
mlirOpOperandGetOperandNumber(opOperand)
Returns the operand number of an op operand.
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Reactant.MLIR.API.mlirOpOperandGetOwner Method
julia
mlirOpOperandGetOwner(opOperand)
Returns the owner operation of an op operand.
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Reactant.MLIR.API.mlirOpOperandGetValue Method
julia
mlirOpOperandGetValue(opOperand)
Returns the value of an op operand.
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Reactant.MLIR.API.mlirOpOperandIsNull Method
julia
mlirOpOperandIsNull(opOperand)
Returns whether the op operand is null.
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Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass Method
julia
mlirOpPassManagerAddOwnedPass(passManager, pass)
Add a pass and transfer ownership to the provided mlirOpPassManager. If the pass is not a generic operation pass or matching the type of the provided PassManager, a new OpPassManager is implicitly nested under the provided PassManager.
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Reactant.MLIR.API.mlirOpPassManagerAddPipeline Method
julia
mlirOpPassManagerAddPipeline(passManager, pipelineElements, callback, userData)
Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
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Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder Method
julia
mlirOpPassManagerGetNestedUnder(passManager, operationName)
Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.
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Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified Method
julia
mlirOpPrintingFlagsAssumeVerified(flags)
Do not verify the operation when using custom operation printers.
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Reactant.MLIR.API.mlirOpPrintingFlagsCreate Method
julia
mlirOpPrintingFlagsCreate()
Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirOpPrintingFlagsDestroy().
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Reactant.MLIR.API.mlirOpPrintingFlagsDestroy Method
julia
mlirOpPrintingFlagsDestroy(flags)
Destroys printing flags created with mlirOpPrintingFlagsCreate.
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Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs Method
julia
mlirOpPrintingFlagsElideLargeElementsAttrs(flags, largeElementLimit)
Enables the elision of large elements attributes by printing a lexically valid but otherwise meaningless form instead of the element data. The largeElementLimit is used to configure what is considered to be a "large" ElementsAttr by providing an upper limit to the number of elements.
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Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString Method
julia
mlirOpPrintingFlagsElideLargeResourceString(flags, largeResourceLimit)
Enables the elision of large resources strings by omitting them from the dialect_resources section. The largeResourceLimit is used to configure what is considered to be a "large" resource by providing an upper limit to the string size.
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Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo Method
julia
mlirOpPrintingFlagsEnableDebugInfo(flags, enable, prettyForm)
Enable or disable printing of debug information (based on enable). If 'prettyForm' is set to true, debug information is printed in a more readable 'pretty' form. Note: The IR generated with 'prettyForm' is not parsable.
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Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm Method
julia
mlirOpPrintingFlagsPrintGenericOpForm(flags)
Always print operations in the generic form.
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Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions Method
julia
mlirOpPrintingFlagsSkipRegions(flags)
Skip printing regions.
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Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope Method
julia
mlirOpPrintingFlagsUseLocalScope(flags)
Use local scope when printing the operation. This allows for using the printer in a more localized and thread-safe setting, but may not necessarily be identical to what the IR will look like when dumping the full module.
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Reactant.MLIR.API.mlirOpResultGetOwner Method
julia
mlirOpResultGetOwner(value)
Returns an operation that produced this value as its result. Asserts if the value is not an op result.
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Reactant.MLIR.API.mlirOpResultGetResultNumber Method
julia
mlirOpResultGetResultNumber(value)
Returns the position of the value in the list of results of the operation that produced it.
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Reactant.MLIR.API.mlirOpaqueAttrGet Method
julia
mlirOpaqueAttrGet(ctx, dialectNamespace, dataLength, data, type)
Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).
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Reactant.MLIR.API.mlirOpaqueAttrGetData Method
julia
mlirOpaqueAttrGetData(attr)
Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.
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Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace Method
julia
mlirOpaqueAttrGetDialectNamespace(attr)
Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.
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Reactant.MLIR.API.mlirOpaqueAttrGetTypeID Method
julia
mlirOpaqueAttrGetTypeID()
Returns the typeID of an Opaque attribute.
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Reactant.MLIR.API.mlirOpaqueTypeGet Method
julia
mlirOpaqueTypeGet(ctx, dialectNamespace, typeData)
Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).
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Reactant.MLIR.API.mlirOpaqueTypeGetData Method
julia
mlirOpaqueTypeGetData(type)
Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.
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Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace Method
julia
mlirOpaqueTypeGetDialectNamespace(type)
Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.
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Reactant.MLIR.API.mlirOpaqueTypeGetTypeID Method
julia
mlirOpaqueTypeGetTypeID()
Returns the typeID of an Opaque type.
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Reactant.MLIR.API.mlirOperationClone Method
julia
mlirOperationClone(op)
Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.
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Reactant.MLIR.API.mlirOperationCreate Method
julia
mlirOperationCreate(state)
Creates an operation and transfers ownership to the caller. Note that caller owned child objects are transferred in this call and must not be further used. Particularly, this applies to any regions added to the state (the implementation may invalidate any such pointers).
This call can fail under the following conditions, in which case, it will return a null operation and emit diagnostics: - Result type inference is enabled and cannot be performed.
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Reactant.MLIR.API.mlirOperationCreateParse Method
julia
mlirOperationCreateParse(context, sourceStr, sourceName)
Parses an operation, giving ownership to the caller. If parsing fails a null operation will be returned, and an error diagnostic emitted.
sourceStr may be either the text assembly format, or binary bytecode format. sourceName is used as the file name of the source; any IR without locations will get a FileLineColLoc location with sourceName as the file name.
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Reactant.MLIR.API.mlirOperationDestroy Method
julia
mlirOperationDestroy(op)
Takes an operation owned by the caller and destroys it.
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Reactant.MLIR.API.mlirOperationDump Method
julia
mlirOperationDump(op)
Prints an operation to stderr.
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Reactant.MLIR.API.mlirOperationEqual Method
julia
mlirOperationEqual(op, other)
Checks whether two operation handles point to the same operation. This does not perform deep comparison.
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Reactant.MLIR.API.mlirOperationGetAttribute Method
julia
mlirOperationGetAttribute(op, pos)
Return pos-th attribute of the operation. Deprecated, please use mlirOperationGetInherentAttribute or mlirOperationGetDiscardableAttribute.
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Reactant.MLIR.API.mlirOperationGetAttributeByName Method
julia
mlirOperationGetAttributeByName(op, name)
Returns an attribute attached to the operation given its name. Deprecated, please use mlirOperationGetInherentAttributeByName or mlirOperationGetDiscardableAttributeByName.
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Reactant.MLIR.API.mlirOperationGetBlock Method
julia
mlirOperationGetBlock(op)
Gets the block that owns this operation, returning null if the operation is not owned.
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Reactant.MLIR.API.mlirOperationGetContext Method
julia
mlirOperationGetContext(op)
Gets the context this operation is associated with
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Reactant.MLIR.API.mlirOperationGetDiscardableAttribute Method
julia
mlirOperationGetDiscardableAttribute(op, pos)
Return pos-th discardable attribute of the operation.
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Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName Method
julia
mlirOperationGetDiscardableAttributeByName(op, name)
Returns a discardable attribute attached to the operation given its name.
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Reactant.MLIR.API.mlirOperationGetFirstRegion Method
julia
mlirOperationGetFirstRegion(op)
Returns first region attached to the operation.
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Reactant.MLIR.API.mlirOperationGetInherentAttributeByName Method
julia
mlirOperationGetInherentAttributeByName(op, name)
Returns an inherent attribute attached to the operation given its name.
source
Reactant.MLIR.API.mlirOperationGetLocation Method
julia
mlirOperationGetLocation(op)
Gets the location of the operation.
source
Reactant.MLIR.API.mlirOperationGetName Method
julia
mlirOperationGetName(op)
Gets the name of the operation as an identifier.
source
Reactant.MLIR.API.mlirOperationGetNextInBlock Method
julia
mlirOperationGetNextInBlock(op)
Returns an operation immediately following the given operation it its enclosing block.
source
Reactant.MLIR.API.mlirOperationGetNumAttributes Method
julia
mlirOperationGetNumAttributes(op)
Returns the number of attributes attached to the operation. Deprecated, please use mlirOperationGetNumInherentAttributes or mlirOperationGetNumDiscardableAttributes.
source
Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes Method
julia
mlirOperationGetNumDiscardableAttributes(op)
Returns the number of discardable attributes attached to the operation.
source
Reactant.MLIR.API.mlirOperationGetNumOperands Method
julia
mlirOperationGetNumOperands(op)
Returns the number of operands of the operation.
source
Reactant.MLIR.API.mlirOperationGetNumRegions Method
julia
mlirOperationGetNumRegions(op)
Returns the number of regions attached to the given operation.
source
Reactant.MLIR.API.mlirOperationGetNumResults Method
julia
mlirOperationGetNumResults(op)
Returns the number of results of the operation.
source
Reactant.MLIR.API.mlirOperationGetNumSuccessors Method
julia
mlirOperationGetNumSuccessors(op)
Returns the number of successor blocks of the operation.
source
Reactant.MLIR.API.mlirOperationGetOperand Method
julia
mlirOperationGetOperand(op, pos)
Returns pos-th operand of the operation.
source
Reactant.MLIR.API.mlirOperationGetParentOperation Method
julia
mlirOperationGetParentOperation(op)
Gets the operation that owns this operation, returning null if the operation is not owned.
source
Reactant.MLIR.API.mlirOperationGetRegion Method
julia
mlirOperationGetRegion(op, pos)
Returns pos-th region attached to the operation.
source
Reactant.MLIR.API.mlirOperationGetResult Method
julia
mlirOperationGetResult(op, pos)
Returns pos-th result of the operation.
source
Reactant.MLIR.API.mlirOperationGetSuccessor Method
julia
mlirOperationGetSuccessor(op, pos)
Returns pos-th successor of the operation.
source
Reactant.MLIR.API.mlirOperationGetTypeID Method
julia
mlirOperationGetTypeID(op)
Gets the type id of the operation. Returns null if the operation does not have a registered operation description.
source
Reactant.MLIR.API.mlirOperationHasInherentAttributeByName Method
julia
mlirOperationHasInherentAttributeByName(op, name)
Returns true if this operation defines an inherent attribute with this name. Note: the attribute can be optional, so mlirOperationGetInherentAttributeByName can still return a null attribute.
source
Reactant.MLIR.API.mlirOperationImplementsInterface Method
julia
mlirOperationImplementsInterface(operation, interfaceTypeID)
Returns true if the given operation implements an interface identified by its TypeID.
source
Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic Method
julia
mlirOperationImplementsInterfaceStatic(operationName, context, interfaceTypeID)
Returns true if the operation identified by its canonical string name implements the interface identified by its TypeID in the given context. Note that interfaces may be attached to operations in some contexts and not others.
source
Reactant.MLIR.API.mlirOperationIsNull Method
julia
mlirOperationIsNull(op)
Checks whether the underlying operation is null.
source
Reactant.MLIR.API.mlirOperationMoveAfter Method
julia
mlirOperationMoveAfter(op, other)
Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
Reactant.MLIR.API.mlirOperationMoveBefore Method
julia
mlirOperationMoveBefore(op, other)
Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
Reactant.MLIR.API.mlirOperationPrint Method
julia
mlirOperationPrint(op, callback, userData)
Prints an operation by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
Reactant.MLIR.API.mlirOperationPrintWithFlags Method
julia
mlirOperationPrintWithFlags(op, flags, callback, userData)
Same as mlirOperationPrint but accepts flags controlling the printing behavior.
source
Reactant.MLIR.API.mlirOperationPrintWithState Method
julia
mlirOperationPrintWithState(op, state, callback, userData)
Same as mlirOperationPrint but accepts AsmState controlling the printing behavior as well as caching computed names.
source
Reactant.MLIR.API.mlirOperationRemoveAttributeByName Method
julia
mlirOperationRemoveAttributeByName(op, name)
Removes an attribute by name. Returns false if the attribute was not found and true if removed. Deprecated, please use mlirOperationRemoveInherentAttributeByName or mlirOperationRemoveDiscardableAttributeByName.
source
Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName Method
julia
mlirOperationRemoveDiscardableAttributeByName(op, name)
Removes a discardable attribute by name. Returns false if the attribute was not found and true if removed.
source
Reactant.MLIR.API.mlirOperationRemoveFromParent Method
julia
mlirOperationRemoveFromParent(op)
Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.
source
Reactant.MLIR.API.mlirOperationSetAttributeByName Method
julia
mlirOperationSetAttributeByName(op, name, attr)
Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise. Deprecated, please use mlirOperationSetInherentAttributeByName or mlirOperationSetDiscardableAttributeByName.
source
Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName Method
julia
mlirOperationSetDiscardableAttributeByName(op, name, attr)
Sets a discardable attribute by name, replacing the existing if it exists or adding a new one otherwise. The new attr Attribute is not allowed to be null, use mlirOperationRemoveDiscardableAttributeByName to remove an Attribute instead.
source
Reactant.MLIR.API.mlirOperationSetInherentAttributeByName Method
julia
mlirOperationSetInherentAttributeByName(op, name, attr)
Sets an inherent attribute by name, replacing the existing if it exists. This has no effect if "name" does not match an inherent attribute.
source
Reactant.MLIR.API.mlirOperationSetOperand Method
julia
mlirOperationSetOperand(op, pos, newValue)
Sets the pos-th operand of the operation.
source
Reactant.MLIR.API.mlirOperationSetOperands Method
julia
mlirOperationSetOperands(op, nOperands, operands)
Replaces the operands of the operation.
source
Reactant.MLIR.API.mlirOperationSetSuccessor Method
julia
mlirOperationSetSuccessor(op, pos, block)
Set pos-th successor of the operation.
source
Reactant.MLIR.API.mlirOperationStateAddResults Method
julia
mlirOperationStateAddResults(state, n, results)
Adds a list of components to the operation state.
source
Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference Method
julia
mlirOperationStateEnableResultTypeInference(state)
Enables result type inference for the operation under construction. If enabled, then the caller must not have called mlirOperationStateAddResults(). Note that if enabled, the mlirOperationCreate() call is failable: it will return a null operation on inference failure and will emit diagnostics.
source
Reactant.MLIR.API.mlirOperationStateGet Method
julia
mlirOperationStateGet(name, loc)
Constructs an operation state from a name and a location.
source
Reactant.MLIR.API.mlirOperationVerify Method
julia
mlirOperationVerify(op)
Verify the operation and return true if it passes, false if it fails.
source
Reactant.MLIR.API.mlirOperationWalk Method
julia
mlirOperationWalk(op, callback, userData, walkOrder)
Walks operation op in walkOrder and calls callback on that operation. *userData is passed to the callback as well and can be used to tunnel some context or other data into the callback.
source
Reactant.MLIR.API.mlirOperationWriteBytecode Method
julia
mlirOperationWriteBytecode(op, callback, userData)
Same as mlirOperationPrint but writing the bytecode format.
source
Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig Method
julia
mlirOperationWriteBytecodeWithConfig(op, config, callback, userData)
Same as mlirOperationWriteBytecode but with writer config and returns failure only if desired bytecode could not be honored.
source
Reactant.MLIR.API.mlirParsePassPipeline Method
julia
mlirParsePassPipeline(passManager, pipeline, callback, userData)
Parse a textual MLIR pass pipeline and assign it to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
Reactant.MLIR.API.mlirPassManagerAddOwnedPass Method
julia
mlirPassManagerAddOwnedPass(passManager, pass)
Add a pass and transfer ownership to the provided top-level mlirPassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.
source
Reactant.MLIR.API.mlirPassManagerCreate Method
julia
mlirPassManagerCreate(ctx)
Create a new top-level PassManager with the default anchor.
source
Reactant.MLIR.API.mlirPassManagerCreateOnOperation Method
julia
mlirPassManagerCreateOnOperation(ctx, anchorOp)
Create a new top-level PassManager anchored on anchorOp.
source
Reactant.MLIR.API.mlirPassManagerDestroy Method
julia
mlirPassManagerDestroy(passManager)
Destroy the provided PassManager.
source
Reactant.MLIR.API.mlirPassManagerEnableIRPrinting Method
julia
mlirPassManagerEnableIRPrinting(passManager, printBeforeAll, printAfterAll, printModuleScope, printAfterOnlyOnChange, printAfterOnlyOnFailure, flags, treePrintingPath)
Enable IR printing. The treePrintingPath argument is an optional path to a directory where the dumps will be produced. If it isn't provided then dumps are produced to stderr.
source
Reactant.MLIR.API.mlirPassManagerEnableVerifier Method
julia
mlirPassManagerEnableVerifier(passManager, enable)
Enable / disable verify-each.
source
Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager Method
julia
mlirPassManagerGetAsOpPassManager(passManager)
Cast a top-level PassManager to a generic OpPassManager.
source
Reactant.MLIR.API.mlirPassManagerGetNestedUnder Method
julia
mlirPassManagerGetNestedUnder(passManager, operationName)
Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.
source
Reactant.MLIR.API.mlirPassManagerIsNull Method
julia
mlirPassManagerIsNull(passManager)
Checks if a PassManager is null.
source
Reactant.MLIR.API.mlirPassManagerRunOnOp Method
julia
mlirPassManagerRunOnOp(passManager, op)
Run the provided passManager on the given op.
source
Reactant.MLIR.API.mlirPrintPassPipeline Method
julia
mlirPrintPassPipeline(passManager, callback, userData)
Print a textual MLIR pass pipeline by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType Method
julia
mlirQuantizedTypeCastExpressedToStorageType(type, candidate)
Casts from a type based on the expressed type of the given quantized type to equivalent type based on storage type of the same quantized type.
source
Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType Method
julia
mlirQuantizedTypeCastFromExpressedType(type, candidate)
Casts from a type based on the expressed type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.
source
Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType Method
julia
mlirQuantizedTypeCastFromStorageType(type, candidate)
Casts from a type based on the storage type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.
source
Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType Method
julia
mlirQuantizedTypeCastToExpressedType(type)
Casts from a type based on a quantized type to a corresponding typed based on the expressed type. Returns a null type if the cast is not valid.
source
Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType Method
julia
mlirQuantizedTypeCastToStorageType(type)
Casts from a type based on a quantized type to a corresponding typed based on the storage type. Returns a null type if the cast is not valid.
source
Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger Method
julia
mlirQuantizedTypeGetDefaultMaximumForInteger(isSigned, integralWidth)
Returns the maximum possible value stored by a quantized type.
source
Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger Method
julia
mlirQuantizedTypeGetDefaultMinimumForInteger(isSigned, integralWidth)
Returns the minimum possible value stored by a quantized type.
source
Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType Method
julia
mlirQuantizedTypeGetExpressedType(type)
Gets the original type approximated by the given quantized type.
source
Reactant.MLIR.API.mlirQuantizedTypeGetFlags Method
julia
mlirQuantizedTypeGetFlags(type)
Gets the flags associated with the given quantized type.
source
Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType Method
julia
mlirQuantizedTypeGetQuantizedElementType(type)
Returns the element type of the given quantized type as another quantized type.
source
Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag Method
julia
mlirQuantizedTypeGetSignedFlag()
Returns the bit flag used to indicate signedness of a quantized type.
source
Reactant.MLIR.API.mlirQuantizedTypeGetStorageType Method
julia
mlirQuantizedTypeGetStorageType(type)
Returns the underlying type used to store the values.
source
Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth Method
julia
mlirQuantizedTypeGetStorageTypeIntegralWidth(type)
Returns the integral bitwidth that the storage type of the given quantized type can represent exactly.
source
Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax Method
julia
mlirQuantizedTypeGetStorageTypeMax(type)
Returns the maximum value that the storage type of the given quantized type can take.
source
Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin Method
julia
mlirQuantizedTypeGetStorageTypeMin(type)
Returns the minimum value that the storage type of the given quantized type can take.
source
Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType Method
julia
mlirQuantizedTypeIsCompatibleExpressedType(type, candidate)
Returns true if the candidate type is compatible with the given quantized type.
source
Reactant.MLIR.API.mlirQuantizedTypeIsSigned Method
julia
mlirQuantizedTypeIsSigned(type)
Returns true if the given type is signed, false otherwise.
source
Reactant.MLIR.API.mlirRankedTensorTypeGet Method
julia
mlirRankedTensorTypeGet(rank, shape, elementType, encoding)
Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull() to this parameter.
source
Reactant.MLIR.API.mlirRankedTensorTypeGetChecked Method
julia
mlirRankedTensorTypeGetChecked(loc, rank, shape, elementType, encoding)
Same as "mlirRankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding Method
julia
mlirRankedTensorTypeGetEncoding(type)
Gets the 'encoding' attribute from the ranked tensor type, returning a null attribute if none.
source
Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID Method
julia
mlirRankedTensorTypeGetTypeID()
Returns the typeID of an RankedTensor type.
source
Reactant.MLIR.API.mlirRegionAppendOwnedBlock Method
julia
mlirRegionAppendOwnedBlock(region, block)
Takes a block owned by the caller and appends it to the given region.
source
Reactant.MLIR.API.mlirRegionCreate Method
julia
mlirRegionCreate()
Creates a new empty region and transfers ownership to the caller.
source
Reactant.MLIR.API.mlirRegionDestroy Method
julia
mlirRegionDestroy(region)
Takes a region owned by the caller and destroys it.
source
Reactant.MLIR.API.mlirRegionEqual Method
julia
mlirRegionEqual(region, other)
Checks whether two region handles point to the same region. This does not perform deep comparison.
source
Reactant.MLIR.API.mlirRegionGetFirstBlock Method
julia
mlirRegionGetFirstBlock(region)
Gets the first block in the region.
source
Reactant.MLIR.API.mlirRegionGetNextInOperation Method
julia
mlirRegionGetNextInOperation(region)
Returns the region immediately following the given region in its parent operation.
source
Reactant.MLIR.API.mlirRegionInsertOwnedBlock Method
julia
mlirRegionInsertOwnedBlock(region, pos, block)
Takes a block owned by the caller and inserts it at pos to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.
source
Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter Method
julia
mlirRegionInsertOwnedBlockAfter(region, reference, block)
Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.
source
Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore Method
julia
mlirRegionInsertOwnedBlockBefore(region, reference, block)
Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.
source
Reactant.MLIR.API.mlirRegionIsNull Method
julia
mlirRegionIsNull(region)
Checks whether a region is null.
source
Reactant.MLIR.API.mlirRegionTakeBody Method
julia
mlirRegionTakeBody(target, source)
Moves the entire content of the source region to the target region.
source
Reactant.MLIR.API.mlirRegisterAllDialects Method
julia
mlirRegisterAllDialects(registry)
Appends all upstream dialects and extensions to the dialect registry.
source
Reactant.MLIR.API.mlirRegisterAllLLVMTranslations Method
julia
mlirRegisterAllLLVMTranslations(context)
Register all translations to LLVM IR for dialects that can support it.
source
Reactant.MLIR.API.mlirRegisterAllPasses Method
julia
mlirRegisterAllPasses()
Register all compiler passes of MLIR.
source
Reactant.MLIR.API.mlirRewriterBaseCancelOpModification Method
julia
mlirRewriterBaseCancelOpModification(rewriter, op)
This method cancels a pending in-place modification. This can only be called on operations that were provided to a call to startOpModification.
source
Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint Method
julia
mlirRewriterBaseClearInsertionPoint(rewriter)
Reset the insertion point to no location. Creating an operation without a set insertion point is an error, but this can still be useful when the current insertion point a builder refers to is being removed.
source
Reactant.MLIR.API.mlirRewriterBaseClone Method
julia
mlirRewriterBaseClone(rewriter, op)
Creates a deep copy of the specified operation.
source
Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore Method
julia
mlirRewriterBaseCloneRegionBefore(rewriter, region, before)
Clone the blocks that belong to "region" before the given position in another region "parent".
source
Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions Method
julia
mlirRewriterBaseCloneWithoutRegions(rewriter, op)
Creates a deep copy of this operation but keep the operation regions empty.
source
Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore Method
julia
mlirRewriterBaseCreateBlockBefore(rewriter, insertBefore, nArgTypes, argTypes, locations)
Add new block with 'argTypes' arguments and set the insertion point to the end of it. The block is placed before 'insertBefore'. locs contains the locations of the inserted arguments, and should match the size of argTypes.
source
Reactant.MLIR.API.mlirRewriterBaseEraseBlock Method
julia
mlirRewriterBaseEraseBlock(rewriter, block)
Erases a block along with all operations inside it.
source
Reactant.MLIR.API.mlirRewriterBaseEraseOp Method
julia
mlirRewriterBaseEraseOp(rewriter, op)
Erases an operation that is known to have no uses.
source
Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification Method
julia
mlirRewriterBaseFinalizeOpModification(rewriter, op)
This method is used to signal the end of an in-place modification of the given operation. This can only be called on operations that were provided to a call to startOpModification.
source
Reactant.MLIR.API.mlirRewriterBaseGetBlock Method
julia
mlirRewriterBaseGetBlock(rewriter)
Returns the current block of the rewriter.
source
Reactant.MLIR.API.mlirRewriterBaseGetContext Method
julia
mlirRewriterBaseGetContext(rewriter)
Get the MLIR context referenced by the rewriter.
source
Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock Method
julia
mlirRewriterBaseGetInsertionBlock(rewriter)
Return the block the current insertion point belongs to. Note that the insertion point is not necessarily the end of the block.
source
Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore Method
julia
mlirRewriterBaseInlineBlockBefore(rewriter, source, op, nArgValues, argValues)
Inline the operations of block 'source' before the operation 'op'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'
The source block must have no successors. Otherwise, the resulting IR would have unreachable operations.
source
Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore Method
julia
mlirRewriterBaseInlineRegionBefore(rewriter, region, before)
Move the blocks that belong to "region" before the given position in another region "parent". The two regions must be different. The caller is responsible for creating or updating the operation transferring flow of control to the region and passing it the correct block arguments.
source
Reactant.MLIR.API.mlirRewriterBaseInsert Method
julia
mlirRewriterBaseInsert(rewriter, op)
Insert the given operation at the current insertion point and return it.
source
Reactant.MLIR.API.mlirRewriterBaseMergeBlocks Method
julia
mlirRewriterBaseMergeBlocks(rewriter, source, dest, nArgValues, argValues)
Inline the operations of block 'source' into the end of block 'dest'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'
The dest block must have no successors. Otherwise, the resulting IR would have unreachable operation.
source
Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore Method
julia
mlirRewriterBaseMoveBlockBefore(rewriter, block, existingBlock)
Unlink this block and insert it right before existingBlock.
source
Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter Method
julia
mlirRewriterBaseMoveOpAfter(rewriter, op, existingOp)
Unlink this operation from its current block and insert it right after existingOp which may be in the same or another block in the same function.
source
Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore Method
julia
mlirRewriterBaseMoveOpBefore(rewriter, op, existingOp)
Unlink this operation from its current block and insert it right before existingOp which may be in the same or another block in the same function.
source
Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation Method
julia
mlirRewriterBaseReplaceAllOpUsesWithOperation(rewriter, from, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.
source
Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange Method
julia
mlirRewriterBaseReplaceAllOpUsesWithValueRange(rewriter, from, nTo, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.
source
Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept Method
julia
mlirRewriterBaseReplaceAllUsesExcept(rewriter, from, to, exceptedUser)
Find uses of from and replace them with to except if the user is exceptedUser. Also notify the listener about every in-place op modification (for every use that was replaced).
source
Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith Method
julia
mlirRewriterBaseReplaceAllUsesWith(rewriter, from, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).
source
Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith Method
julia
mlirRewriterBaseReplaceAllValueRangeUsesWith(rewriter, nValues, from, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).
source
Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock Method
julia
mlirRewriterBaseReplaceOpUsesWithinBlock(rewriter, op, nNewValues, newValues, block)
Find uses of from within block and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced). The optional allUsesReplaced flag is set to "true" if all uses were replaced.
source
Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation Method
julia
mlirRewriterBaseReplaceOpWithOperation(rewriter, op, newOp)
Replace the results of the given (original) operation with the specified new op (replacement). The result types of the two ops must match. The original op is erased.
source
Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues Method
julia
mlirRewriterBaseReplaceOpWithValues(rewriter, op, nValues, values)
Replace the results of the given (original) operation with the specified list of values (replacements). The result types of the given op and the replacements must match. The original op is erased.
source
Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter Method
julia
mlirRewriterBaseSetInsertionPointAfter(rewriter, op)
Sets the insertion point to the node after the specified operation, which will cause subsequent insertions to go right after it.
source
Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue Method
julia
mlirRewriterBaseSetInsertionPointAfterValue(rewriter, value)
Sets the insertion point to the node after the specified value. If value has a defining operation, sets the insertion point to the node after such defining operation. This will cause subsequent insertions to go right after it. Otherwise, value is a BlockArgument. Sets the insertion point to the start of its block.
source
Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore Method
julia
mlirRewriterBaseSetInsertionPointBefore(rewriter, op)
Sets the insertion point to the specified operation, which will cause subsequent insertions to go right before it.
source
Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd Method
julia
mlirRewriterBaseSetInsertionPointToEnd(rewriter, block)
Sets the insertion point to the end of the specified block.
source
Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart Method
julia
mlirRewriterBaseSetInsertionPointToStart(rewriter, block)
Sets the insertion point to the start of the specified block.
source
Reactant.MLIR.API.mlirRewriterBaseStartOpModification Method
julia
mlirRewriterBaseStartOpModification(rewriter, op)
This method is used to notify the rewriter that an in-place operation modification is about to happen. A call to this function must be followed by a call to either finalizeOpModification or cancelOpModification. This is a minor efficiency win (it avoids creating a new operation and removing the old one) but also often allows simpler code in the client.
source
Reactant.MLIR.API.mlirSetGlobalDebugType Method
julia
mlirSetGlobalDebugType(type)
Sets the current debug type, similarly to -debug-only=type in the command-line tools. Note that global debug should be enabled for any output to be produced.
source
Reactant.MLIR.API.mlirSetGlobalDebugTypes Method
julia
mlirSetGlobalDebugTypes(types, n)
Sets multiple current debug types, similarly to `-debug-only=type1,type2" in the command-line tools. Note that global debug should be enabled for any output to be produced.
source
Reactant.MLIR.API.mlirShapedTypeGetDimSize Method
julia
mlirShapedTypeGetDimSize(type, dim)
Returns the dim-th dimension of the given ranked shaped type.
source
Reactant.MLIR.API.mlirShapedTypeGetDynamicSize Method
julia
mlirShapedTypeGetDynamicSize()
Returns the value indicating a dynamic size in a shaped type. Prefer mlirShapedTypeIsDynamicSize to direct comparisons with this value.
source
Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset Method
julia
mlirShapedTypeGetDynamicStrideOrOffset()
Returns the value indicating a dynamic stride or offset in a shaped type. Prefer mlirShapedTypeGetDynamicStrideOrOffset to direct comparisons with this value.
source
Reactant.MLIR.API.mlirShapedTypeGetElementType Method
julia
mlirShapedTypeGetElementType(type)
Returns the element type of the shaped type.
source
Reactant.MLIR.API.mlirShapedTypeGetRank Method
julia
mlirShapedTypeGetRank(type)
Returns the rank of the given ranked shaped type.
source
Reactant.MLIR.API.mlirShapedTypeHasRank Method
julia
mlirShapedTypeHasRank(type)
Checks whether the given shaped type is ranked.
source
Reactant.MLIR.API.mlirShapedTypeHasStaticShape Method
julia
mlirShapedTypeHasStaticShape(type)
Checks whether the given shaped type has a static shape.
source
Reactant.MLIR.API.mlirShapedTypeIsDynamicDim Method
julia
mlirShapedTypeIsDynamicDim(type, dim)
Checks wither the dim-th dimension of the given shaped type is dynamic.
source
Reactant.MLIR.API.mlirShapedTypeIsDynamicSize Method
julia
mlirShapedTypeIsDynamicSize(size)
Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.
source
Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset Method
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)
Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.
source
Reactant.MLIR.API.mlirSparseElementsAttrGetIndices Method
julia
mlirSparseElementsAttrGetIndices(attr)
Returns the dense elements attribute containing 64-bit integer indices of non-null elements in the given sparse elements attribute.
source
Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID Method
julia
mlirSparseElementsAttrGetTypeID()
Returns the typeID of a SparseElements attribute.
source
Reactant.MLIR.API.mlirSparseElementsAttrGetValues Method
julia
mlirSparseElementsAttrGetValues(attr)
Returns the dense elements attribute containing the non-null elements in the given sparse elements attribute.
source
Reactant.MLIR.API.mlirSparseElementsAttribute Method
julia
mlirSparseElementsAttribute(shapedType, denseIndices, denseValues)
Creates a sparse elements attribute of the given shape from a list of indices and a list of associated values. Both lists are expected to be dense elements attributes with the same number of elements. The list of indices is expected to contain 64-bit integers. The attribute is created in the same context as the type.
source
Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet Method
julia
mlirSparseTensorEncodingAttrGet(ctx, lvlRank, lvlTypes, dimToLvl, lvlTodim, posWidth, crdWidth, explicitVal, implicitVal)
Creates a sparse\_tensor.encoding attribute with the given parameters.
source
Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth Method
julia
mlirSparseTensorEncodingAttrGetCrdWidth(attr)
Returns the coordinate bitwidth of the sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl Method
julia
mlirSparseTensorEncodingAttrGetDimToLvl(attr)
Returns the dimension-to-level mapping of the sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal Method
julia
mlirSparseTensorEncodingAttrGetExplicitVal(attr)
Returns the explicit value of the sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal Method
julia
mlirSparseTensorEncodingAttrGetImplicitVal(attr)
Returns the implicit value of the sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt Method
julia
mlirSparseTensorEncodingAttrGetLvlFmt(attr, lvl)
Returns a specified level-format of the sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim Method
julia
mlirSparseTensorEncodingAttrGetLvlToDim(attr)
Returns the level-to-dimension mapping of the sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType Method
julia
mlirSparseTensorEncodingAttrGetLvlType(attr, lvl)
Returns a specified level-type of the sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth Method
julia
mlirSparseTensorEncodingAttrGetPosWidth(attr)
Returns the position bitwidth of the sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank Method
julia
mlirSparseTensorEncodingGetLvlRank(attr)
Returns the level-rank of the sparse\_tensor.encoding attribute.
source
Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID Method
julia
mlirStridedLayoutAttrGetTypeID()
Returns the typeID of a StridedLayout attribute.
source
Reactant.MLIR.API.mlirStringAttrGet Method
julia
mlirStringAttrGet(ctx, str)
Creates a string attribute in the given context containing the given string.
source
Reactant.MLIR.API.mlirStringAttrGetTypeID Method
julia
mlirStringAttrGetTypeID()
Returns the typeID of a String attribute.
source
Reactant.MLIR.API.mlirStringAttrGetValue Method
julia
mlirStringAttrGetValue(attr)
Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.API.mlirStringAttrTypedGet Method
julia
mlirStringAttrTypedGet(type, str)
Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.
source
Reactant.MLIR.API.mlirStringRefCreate Method
julia
mlirStringRefCreate(str, length)
Constructs a string reference from the pointer and length. The pointer need not reference to a null-terminated string.
source
Reactant.MLIR.API.mlirStringRefCreateFromCString Method
julia
mlirStringRefCreateFromCString(str)
Constructs a string reference from a null-terminated C string. Prefer mlirStringRefCreate if the length of the string is known.
source
Reactant.MLIR.API.mlirStringRefEqual Method
julia
mlirStringRefEqual(string, other)
Returns true if two string references are equal, false otherwise.
source
Reactant.MLIR.API.mlirSymbolRefAttrGet Method
julia
mlirSymbolRefAttrGet(ctx, symbol, numReferences, references)
Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.
source
Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference Method
julia
mlirSymbolRefAttrGetLeafReference(attr)
Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference Method
julia
mlirSymbolRefAttrGetNestedReference(attr, pos)
Returns pos-th reference nested in the given symbol reference attribute.
source
Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences Method
julia
mlirSymbolRefAttrGetNumNestedReferences(attr)
Returns the number of references nested in the given symbol reference attribute.
source
Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference Method
julia
mlirSymbolRefAttrGetRootReference(attr)
Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.
source
Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID Method
julia
mlirSymbolRefAttrGetTypeID()
Returns the typeID of an SymbolRef attribute.
source
Reactant.MLIR.API.mlirSymbolTableCreate Method
julia
mlirSymbolTableCreate(operation)
Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.
source
Reactant.MLIR.API.mlirSymbolTableDestroy Method
julia
mlirSymbolTableDestroy(symbolTable)
Destroys the symbol table created with mlirSymbolTableCreate. This does not affect the operations in the table.
source
Reactant.MLIR.API.mlirSymbolTableErase Method
julia
mlirSymbolTableErase(symbolTable, operation)
Removes the given operation from the symbol table and erases it.
source
Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName Method
julia
mlirSymbolTableGetSymbolAttributeName()
Returns the name of the attribute used to store symbol names compatible with symbol tables.
source
Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName Method
julia
mlirSymbolTableGetVisibilityAttributeName()
Returns the name of the attribute used to store symbol visibility.
source
Reactant.MLIR.API.mlirSymbolTableInsert Method
julia
mlirSymbolTableInsert(symbolTable, operation)
Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.
source
Reactant.MLIR.API.mlirSymbolTableIsNull Method
julia
mlirSymbolTableIsNull(symbolTable)
Returns true if the symbol table is null.
source
Reactant.MLIR.API.mlirSymbolTableLookup Method
julia
mlirSymbolTableLookup(symbolTable, name)
Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.
source
Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses Method
julia
mlirSymbolTableReplaceAllSymbolUses(oldSymbol, newSymbol, from)
Attempt to replace all uses that are nested within the given operation of the given symbol 'oldSymbol' with the provided 'newSymbol'. This does not traverse into nested symbol tables. Will fail atomically if there are any unknown operations that may be potential symbol tables.
source
Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables Method
julia
mlirSymbolTableWalkSymbolTables(from, allSymUsesVisible, callback, userData)
Walks all symbol table operations nested within, and including, op. For each symbol table operation, the provided callback is invoked with the op and a boolean signifying if the symbols within that symbol table can be treated as if all uses within the IR are visible to the caller. allSymUsesVisible identifies whether all of the symbol uses of symbols within op are visible.
source
Reactant.MLIR.API.mlirTF32TypeGet Method
julia
mlirTF32TypeGet(ctx)
Creates a TF32 type in the given context. The type is owned by the context.
source
Reactant.MLIR.API.mlirTransformApplyNamedSequence Method
julia
mlirTransformApplyNamedSequence(payload, transformRoot, transformModule, transformOptions)
Applies the transformation script starting at the given transform root operation to the given payload operation. The module containing the transform root as well as the transform options should be provided. The transform operation must implement TransformOpInterface and the module must be a ModuleOp. Returns the status of the application.
source
Reactant.MLIR.API.mlirTransformOptionsCreate Method
julia
mlirTransformOptionsCreate()
Creates a default-initialized transform options object.
source
Reactant.MLIR.API.mlirTransformOptionsDestroy Method
julia
mlirTransformOptionsDestroy(transformOptions)
Destroys a transform options object previously created by mlirTransformOptionsCreate.
source
Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks Method
julia
mlirTransformOptionsEnableExpensiveChecks(transformOptions, enable)
Enables or disables expensive checks in transform options.
source
Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp Method
julia
mlirTransformOptionsEnforceSingleTopLevelTransformOp(transformOptions, enable)
Enables or disables the enforcement of the top-level transform op being single in transform options.
source
Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp Method
julia
mlirTransformOptionsGetEnforceSingleTopLevelTransformOp(transformOptions)
Returns true if the enforcement of the top-level transform op being single is enabled in transform options.
source
Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled Method
julia
mlirTransformOptionsGetExpensiveChecksEnabled(transformOptions)
Returns true if expensive checks are enabled in transform options.
source
Reactant.MLIR.API.mlirTranslateModuleToLLVMIR Method
julia
mlirTranslateModuleToLLVMIR(_module, context)
Translate operation that satisfies LLVM dialect module requirements into an LLVM IR module living in the given context. This translates operations from any dilalect that has a registered implementation of LLVMTranslationDialectInterface.
Returns
the generated LLVM IR Module from the translated MLIR module, it is owned by the caller.
source
Reactant.MLIR.API.mlirTupleTypeGet Method
julia
mlirTupleTypeGet(ctx, numElements, elements)
Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.
source
Reactant.MLIR.API.mlirTupleTypeGetNumTypes Method
julia
mlirTupleTypeGetNumTypes(type)
Returns the number of types contained in a tuple.
source
Reactant.MLIR.API.mlirTupleTypeGetType Method
julia
mlirTupleTypeGetType(type, pos)
Returns the pos-th type in the tuple type.
source
Reactant.MLIR.API.mlirTupleTypeGetTypeID Method
julia
mlirTupleTypeGetTypeID()
Returns the typeID of an Tuple type.
source
Reactant.MLIR.API.mlirTypeAttrGet Method
julia
mlirTypeAttrGet(type)
Creates a type attribute wrapping the given type in the same context as the type.
source
Reactant.MLIR.API.mlirTypeAttrGetTypeID Method
julia
mlirTypeAttrGetTypeID()
Returns the typeID of a Type attribute.
source
Reactant.MLIR.API.mlirTypeAttrGetValue Method
julia
mlirTypeAttrGetValue(attr)
Returns the type stored in the given type attribute.
source
Reactant.MLIR.API.mlirTypeDump Method
julia
mlirTypeDump(type)
Prints the type to the standard error stream.
source
Reactant.MLIR.API.mlirTypeEqual Method
julia
mlirTypeEqual(t1, t2)
Checks if two types are equal.
source
Reactant.MLIR.API.mlirTypeGetContext Method
julia
mlirTypeGetContext(type)
Gets the context that a type was created with.
source
Reactant.MLIR.API.mlirTypeGetDialect Method
julia
mlirTypeGetDialect(type)
Gets the dialect a type belongs to.
source
Reactant.MLIR.API.mlirTypeGetTypeID Method
julia
mlirTypeGetTypeID(type)
Gets the type ID of the type.
source
Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID Method
julia
mlirTypeIDAllocatorAllocateTypeID(allocator)
Allocates a type id that is valid for the lifetime of the allocator
source
Reactant.MLIR.API.mlirTypeIDAllocatorCreate Method
julia
mlirTypeIDAllocatorCreate()
Creates a type id allocator for dynamic type id creation
source
Reactant.MLIR.API.mlirTypeIDAllocatorDestroy Method
julia
mlirTypeIDAllocatorDestroy(allocator)
Deallocates the allocator and all allocated type ids
source
Reactant.MLIR.API.mlirTypeIDCreate Method
julia
mlirTypeIDCreate(ptr)
ptr must be 8 byte aligned and unique to a type valid for the duration of the returned type id's usage
source
Reactant.MLIR.API.mlirTypeIDEqual Method
julia
mlirTypeIDEqual(typeID1, typeID2)
Checks if two type ids are equal.
source
Reactant.MLIR.API.mlirTypeIDHashValue Method
julia
mlirTypeIDHashValue(typeID)
Returns the hash value of the type id.
source
Reactant.MLIR.API.mlirTypeIDIsNull Method
julia
mlirTypeIDIsNull(typeID)
Checks whether a type id is null.
source
Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType Method
julia
mlirTypeIsAAnyQuantizedType(type)
Returns true if the given type is an AnyQuantizedType.
source
Reactant.MLIR.API.mlirTypeIsABF16 Method
julia
mlirTypeIsABF16(type)
Checks whether the given type is a bf16 type.
source
Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType Method
julia
mlirTypeIsACalibratedQuantizedType(type)
Returns true if the given type is a CalibratedQuantizedType.
source
Reactant.MLIR.API.mlirTypeIsAComplex Method
julia
mlirTypeIsAComplex(type)
Checks whether the given type is a Complex type.
source
Reactant.MLIR.API.mlirTypeIsAF16 Method
julia
mlirTypeIsAF16(type)
Checks whether the given type is an f16 type.
source
Reactant.MLIR.API.mlirTypeIsAF32 Method
julia
mlirTypeIsAF32(type)
Checks whether the given type is an f32 type.
source
Reactant.MLIR.API.mlirTypeIsAF64 Method
julia
mlirTypeIsAF64(type)
Checks whether the given type is an f64 type.
source
Reactant.MLIR.API.mlirTypeIsAFloat Method
julia
mlirTypeIsAFloat(type)
Checks whether the given type is a floating-point type.
source
Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN Method
julia
mlirTypeIsAFloat4E2M1FN(type)
Checks whether the given type is an f4E2M1FN type.
source
Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN Method
julia
mlirTypeIsAFloat6E2M3FN(type)
Checks whether the given type is an f6E2M3FN type.
source
Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN Method
julia
mlirTypeIsAFloat6E3M2FN(type)
Checks whether the given type is an f6E3M2FN type.
source
Reactant.MLIR.API.mlirTypeIsAFloat8E3M4 Method
julia
mlirTypeIsAFloat8E3M4(type)
Checks whether the given type is an f8E3M4 type.
source
Reactant.MLIR.API.mlirTypeIsAFloat8E4M3 Method
julia
mlirTypeIsAFloat8E4M3(type)
Checks whether the given type is an f8E4M3 type.
source
Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ Method
julia
mlirTypeIsAFloat8E4M3B11FNUZ(type)
Checks whether the given type is an f8E4M3B11FNUZ type.
source
Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN Method
julia
mlirTypeIsAFloat8E4M3FN(type)
Checks whether the given type is an f8E4M3FN type.
source
Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ Method
julia
mlirTypeIsAFloat8E4M3FNUZ(type)
Checks whether the given type is an f8E4M3FNUZ type.
source
Reactant.MLIR.API.mlirTypeIsAFloat8E5M2 Method
julia
mlirTypeIsAFloat8E5M2(type)
Checks whether the given type is an f8E5M2 type.
source
Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ Method
julia
mlirTypeIsAFloat8E5M2FNUZ(type)
Checks whether the given type is an f8E5M2FNUZ type.
source
Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU Method
julia
mlirTypeIsAFloat8E8M0FNU(type)
Checks whether the given type is an f8E8M0FNU type.
source
Reactant.MLIR.API.mlirTypeIsAFunction Method
julia
mlirTypeIsAFunction(type)
Checks whether the given type is a function type.
source
Reactant.MLIR.API.mlirTypeIsAIndex Method
julia
mlirTypeIsAIndex(type)
Checks whether the given type is an index type.
source
Reactant.MLIR.API.mlirTypeIsAInteger Method
julia
mlirTypeIsAInteger(type)
Checks whether the given type is an integer type.
source
Reactant.MLIR.API.mlirTypeIsALLVMPointerType Method
julia
mlirTypeIsALLVMPointerType(type)
Returns true if the type is an LLVM dialect pointer type.
source
Reactant.MLIR.API.mlirTypeIsALLVMStructType Method
julia
mlirTypeIsALLVMStructType(type)
Returns true if the type is an LLVM dialect struct type.
source
Reactant.MLIR.API.mlirTypeIsAMemRef Method
julia
mlirTypeIsAMemRef(type)
Checks whether the given type is a MemRef type.
source
Reactant.MLIR.API.mlirTypeIsANone Method
julia
mlirTypeIsANone(type)
Checks whether the given type is a None type.
source
Reactant.MLIR.API.mlirTypeIsAOpaque Method
julia
mlirTypeIsAOpaque(type)
Checks whether the given type is an opaque type.
source
Reactant.MLIR.API.mlirTypeIsAQuantizedType Method
julia
mlirTypeIsAQuantizedType(type)
Returns true if the given type is a quantization dialect type.
source
Reactant.MLIR.API.mlirTypeIsARankedTensor Method
julia
mlirTypeIsARankedTensor(type)
Checks whether the given type is a ranked tensor type.
source
Reactant.MLIR.API.mlirTypeIsAShaped Method
julia
mlirTypeIsAShaped(type)
Checks whether the given type is a Shaped type.
source
Reactant.MLIR.API.mlirTypeIsATF32 Method
julia
mlirTypeIsATF32(type)
Checks whether the given type is an TF32 type.
source
Reactant.MLIR.API.mlirTypeIsATensor Method
julia
mlirTypeIsATensor(type)
Checks whether the given type is a Tensor type.
source
Reactant.MLIR.API.mlirTypeIsATuple Method
julia
mlirTypeIsATuple(type)
Checks whether the given type is a tuple type.
source
Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType Method
julia
mlirTypeIsAUniformQuantizedPerAxisType(type)
Returns true if the given type is a UniformQuantizedPerAxisType.
source
Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType Method
julia
mlirTypeIsAUniformQuantizedType(type)
Returns true if the given type is a UniformQuantizedType.
source
Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef Method
julia
mlirTypeIsAUnrankedMemRef(type)
Checks whether the given type is an UnrankedMemRef type.
source
Reactant.MLIR.API.mlirTypeIsAUnrankedTensor Method
julia
mlirTypeIsAUnrankedTensor(type)
Checks whether the given type is an unranked tensor type.
source
Reactant.MLIR.API.mlirTypeIsAVector Method
julia
mlirTypeIsAVector(type)
Checks whether the given type is a Vector type.
source
Reactant.MLIR.API.mlirTypeIsNull Method
julia
mlirTypeIsNull(type)
Checks whether a type is null.
source
Reactant.MLIR.API.mlirTypeParseGet Method
julia
mlirTypeParseGet(context, type)
Parses a type. The type is owned by the context.
source
Reactant.MLIR.API.mlirTypePrint Method
julia
mlirTypePrint(type, callback, userData)
Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet Method
julia
mlirUniformQuantizedPerAxisTypeGet(flags, storageType, expressedType, nDims, scales, zeroPoints, quantizedDimension, storageTypeMin, storageTypeMax)
Creates an instance of UniformQuantizedPerAxisType with the given parameters in the same context as storageType and returns it. scales and zeroPoints point to nDims number of elements. The instance is owned by the context.
source
Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims Method
julia
mlirUniformQuantizedPerAxisTypeGetNumDims(type)
Returns the number of axes in the given quantized per-axis type.
source
Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension Method
julia
mlirUniformQuantizedPerAxisTypeGetQuantizedDimension(type)
Returns the index of the quantized dimension in the given quantized per-axis type.
source
Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale Method
julia
mlirUniformQuantizedPerAxisTypeGetScale(type, pos)
Returns pos-th scale of the given quantized per-axis type.
source
Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint Method
julia
mlirUniformQuantizedPerAxisTypeGetZeroPoint(type, pos)
Returns pos-th zero point of the given quantized per-axis type.
source
Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint Method
julia
mlirUniformQuantizedPerAxisTypeIsFixedPoint(type)
Returns true if the given uniform quantized per-axis type is fixed-point.
source
Reactant.MLIR.API.mlirUniformQuantizedTypeGet Method
julia
mlirUniformQuantizedTypeGet(flags, storageType, expressedType, scale, zeroPoint, storageTypeMin, storageTypeMax)
Creates an instance of UniformQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.
source
Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale Method
julia
mlirUniformQuantizedTypeGetScale(type)
Returns the scale of the given uniform quantized type.
source
Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint Method
julia
mlirUniformQuantizedTypeGetZeroPoint(type)
Returns the zero point of the given uniform quantized type.
source
Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint Method
julia
mlirUniformQuantizedTypeIsFixedPoint(type)
Returns true if the given uniform quantized type is fixed-point.
source
Reactant.MLIR.API.mlirUnitAttrGet Method
julia
mlirUnitAttrGet(ctx)
Creates a unit attribute in the given context.
source
Reactant.MLIR.API.mlirUnitAttrGetTypeID Method
julia
mlirUnitAttrGetTypeID()
Returns the typeID of a Unit attribute.
source
Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet Method
julia
mlirUnmanagedDenseResourceElementsAttrGet(shapedType, name, data, dataLength, dataAlignment, dataIsMutable, deleter, userData)
Unlike the typed accessors below, constructs the attribute with a raw data buffer and no type/alignment checking. Use a more strongly typed accessor if possible. If dataIsMutable is false, then an immutable AsmResourceBlob will be created and that passed data contents will be treated as const. If the deleter is non NULL, then it will be called when the data buffer can no longer be accessed (passing userData to it).
source
Reactant.MLIR.API.mlirUnrankedMemRefTypeGet Method
julia
mlirUnrankedMemRefTypeGet(elementType, memorySpace)
Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type.
source
Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked Method
julia
mlirUnrankedMemRefTypeGetChecked(loc, elementType, memorySpace)
Same as "mlirUnrankedMemRefTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID Method
julia
mlirUnrankedMemRefTypeGetTypeID()
Returns the typeID of an UnrankedMemRef type.
source
Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace Method
julia
mlirUnrankedMemrefGetMemorySpace(type)
Returns the memory spcae of the given Unranked MemRef type.
source
Reactant.MLIR.API.mlirUnrankedTensorTypeGet Method
julia
mlirUnrankedTensorTypeGet(elementType)
Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context.
source
Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked Method
julia
mlirUnrankedTensorTypeGetChecked(loc, elementType)
Same as "mlirUnrankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID Method
julia
mlirUnrankedTensorTypeGetTypeID()
Returns the typeID of an UnrankedTensor type.
source
Reactant.MLIR.API.mlirValueDump Method
julia
mlirValueDump(value)
Prints the value to the standard error stream.
source
Reactant.MLIR.API.mlirValueEqual Method
julia
mlirValueEqual(value1, value2)
Returns 1 if two values are equal, 0 otherwise.
source
Reactant.MLIR.API.mlirValueGetFirstUse Method
julia
mlirValueGetFirstUse(value)
Returns an op operand representing the first use of the value, or a null op operand if there are no uses.
source
Reactant.MLIR.API.mlirValueGetType Method
julia
mlirValueGetType(value)
Returns the type of the value.
source
Reactant.MLIR.API.mlirValueIsABlockArgument Method
julia
mlirValueIsABlockArgument(value)
Returns 1 if the value is a block argument, 0 otherwise.
source
Reactant.MLIR.API.mlirValueIsAOpResult Method
julia
mlirValueIsAOpResult(value)
Returns 1 if the value is an operation result, 0 otherwise.
source
Reactant.MLIR.API.mlirValueIsNull Method
julia
mlirValueIsNull(value)
Returns whether the value is null.
source
Reactant.MLIR.API.mlirValuePrint Method
julia
mlirValuePrint(value, callback, userData)
Prints a value by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
Reactant.MLIR.API.mlirValuePrintAsOperand Method
julia
mlirValuePrintAsOperand(value, state, callback, userData)
Prints a value as an operand (i.e., the ValueID).
source
Reactant.MLIR.API.mlirValueReplaceAllUsesExcept Method
julia
mlirValueReplaceAllUsesExcept(of, with, numExceptions, exceptions)
Replace all uses of 'of' value with 'with' value, updating anything in the IR that uses 'of' to use 'with' instead, except if the user is listed in 'exceptions'. The 'exceptions' parameter is an array of MlirOperation pointers with a length of 'numExceptions'.
source
Reactant.MLIR.API.mlirValueReplaceAllUsesOfWith Method
julia
mlirValueReplaceAllUsesOfWith(of, with)
Replace all uses of 'of' value with the 'with' value, updating anything in the IR that uses 'of' to use the other value instead. When this returns there are zero uses of 'of'.
source
Reactant.MLIR.API.mlirValueSetType Method
julia
mlirValueSetType(value, type)
Set the type of the value.
source
Reactant.MLIR.API.mlirVectorTypeGet Method
julia
mlirVectorTypeGet(rank, shape, elementType)
Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context.
source
Reactant.MLIR.API.mlirVectorTypeGetChecked Method
julia
mlirVectorTypeGetChecked(loc, rank, shape, elementType)
Same as "mlirVectorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
Reactant.MLIR.API.mlirVectorTypeGetScalable Method
julia
mlirVectorTypeGetScalable(rank, shape, scalable, elementType)
Creates a scalable vector type with the shape identified by its rank and dimensions. A subset of dimensions may be marked as scalable via the corresponding flag list, which is expected to have as many entries as the rank of the vector. The vector is created in the same context as the element type.
source
Reactant.MLIR.API.mlirVectorTypeGetScalableChecked Method
julia
mlirVectorTypeGetScalableChecked(loc, rank, shape, scalable, elementType)
Same as "mlirVectorTypeGetScalable" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
Reactant.MLIR.API.mlirVectorTypeGetTypeID Method
julia
mlirVectorTypeGetTypeID()
Returns the typeID of an Vector type.
source
Reactant.MLIR.API.mlirVectorTypeIsDimScalable Method
julia
mlirVectorTypeIsDimScalable(type, dim)
Checks whether the "dim"-th dimension of the given vector is scalable.
source
Reactant.MLIR.API.mlirVectorTypeIsScalable Method
julia
mlirVectorTypeIsScalable(type)
Checks whether the given vector type is scalable, i.e., has at least one scalable dimension.
source
Other Functions ​

-    
+MLIR.IR.AffineMap(#= (d0, d1)[s0] -> (d0 + s0, d1 mod 10) =#)

source

MLIR C API ​

Reactant.MLIR.API.LLVMAttributeRef Type

Used to represent an attributes.

See also

llvm::Attribute

source

Reactant.MLIR.API.LLVMBasicBlockRef Type

Represents a basic block of instructions in LLVM IR.

This models llvm::BasicBlock.

source

Reactant.MLIR.API.LLVMBinaryRef Type

See also

llvm::object::Binary

source

Reactant.MLIR.API.LLVMBool Type

LLVMCSupportTypes Types and Enumerations

@{

source

Reactant.MLIR.API.LLVMBuilderRef Type

Represents an LLVM basic block builder.

This models llvm::IRBuilder.

source

Reactant.MLIR.API.LLVMComdatRef Type

See also

llvm::Comdat

source

Reactant.MLIR.API.LLVMContextRef Type

The top-level container for all LLVM global data. See the LLVMContext class.

source

Reactant.MLIR.API.LLVMDIBuilderRef Type

Represents an LLVM debug info builder.

This models llvm::DIBuilder.

source

Reactant.MLIR.API.LLVMDbgRecordRef Type

See also

llvm::DbgRecord

source

Reactant.MLIR.API.LLVMDiagnosticInfoRef Type

See also

llvm::DiagnosticInfo

source

Reactant.MLIR.API.LLVMJITEventListenerRef Type

See also

llvm::JITEventListener

source

Reactant.MLIR.API.LLVMMemoryBufferRef Type

Used to pass regions of memory through LLVM interfaces.

See also

llvm::MemoryBuffer

source

Reactant.MLIR.API.LLVMMetadataRef Type

Represents an LLVM Metadata.

This models llvm::Metadata.

source

Reactant.MLIR.API.LLVMModuleFlagEntry Type

See also

llvm::Module::ModuleFlagEntry

source

Reactant.MLIR.API.LLVMModuleProviderRef Type

Interface used to provide a module to JIT or interpreter. This is now just a synonym for llvm::Module, but we have to keep using the different type to keep binary compatibility.

source

Reactant.MLIR.API.LLVMModuleRef Type

The top-level container for all other LLVM Intermediate Representation (IR) objects.

See also

llvm::Module

source

Reactant.MLIR.API.LLVMNamedMDNodeRef Type

Represents an LLVM Named Metadata Node.

This models llvm::NamedMDNode.

source

Reactant.MLIR.API.LLVMOperandBundleRef Type

See also

llvm::OperandBundleDef

source

Reactant.MLIR.API.LLVMPassManagerRef Type

See also

llvm::PassManagerBase

source

Reactant.MLIR.API.LLVMTypeRef Type

Each value in the LLVM IR has a type, an LLVMTypeRef.

See also

llvm::Type

source

Reactant.MLIR.API.LLVMUseRef Type

Used to get the users and usees of a Value.

See also

llvm::Use

source

Reactant.MLIR.API.LLVMValueMetadataEntry Type

Represents an entry in a Global Object's metadata attachments.

This models std::pair<unsigned, MDNode *>

source

Reactant.MLIR.API.LLVMValueRef Type

Represents an individual value in LLVM IR.

This models llvm::Value.

source

Reactant.MLIR.API.MlirDiagnostic Type
julia
MlirDiagnostic

An opaque reference to a diagnostic, always owned by the diagnostics engine (context). Must not be stored outside of the diagnostic handler.

source

Reactant.MLIR.API.MlirDiagnosticHandler Type

Diagnostic handler type. Accepts a reference to a diagnostic, which is only guaranteed to be live during the call. The handler is passed the userData that was provided when the handler was attached to a context. If the handler processed the diagnostic completely, it is expected to return success. Otherwise, it is expected to return failure to indicate that other handlers should attempt to process the diagnostic.

source

Reactant.MLIR.API.MlirDiagnosticHandlerID Type

Opaque identifier of a diagnostic handler, useful to detach a handler.

source

Reactant.MLIR.API.MlirDiagnosticSeverity Type
julia
MlirDiagnosticSeverity

Severity of a diagnostic.

source

Reactant.MLIR.API.MlirExternalPassCallbacks Type
julia
MlirExternalPassCallbacks

Structure of external MlirPass callbacks. All callbacks are required to be set unless otherwise specified.

FieldNote
constructThis callback is called from the pass is created. This is analogous to a C++ pass constructor.
destructThis callback is called when the pass is destroyed This is analogous to a C++ pass destructor.
initializeThis callback is optional. The callback is called before the pass is run, allowing a chance to initialize any complex state necessary for running the pass. See Pass::initialize(MLIRContext *).
cloneThis callback is called when the pass is cloned. See Pass::clonePass().
runThis callback is called when the pass is run. See Pass::runOnOperation().

source

Reactant.MLIR.API.MlirLlvmThreadPool Type
julia
MlirLlvmThreadPool

Re-export llvm::ThreadPool so as to avoid including the LLVM C API directly.

source

Reactant.MLIR.API.MlirLogicalResult Type
julia
MlirLogicalResult

A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of MlirLogicalResult must only be inspected using the associated functions.

source

Reactant.MLIR.API.MlirNamedAttribute Type
julia
MlirNamedAttribute

Named MLIR attribute.

A named attribute is essentially a (name, attribute) pair where the name is a string.

source

Reactant.MLIR.API.MlirOperationState Type
julia
MlirOperationState

An auxiliary class for constructing operations.

This class contains all the information necessary to construct the operation. It owns the MlirRegions it has pointers to and does not own anything else. By default, the state can be constructed from a name and location, the latter being also used to access the context, and has no other components. These components can be added progressively until the operation is constructed. Users are not expected to rely on the internals of this class and should use mlirOperationState* functions instead.

source

Reactant.MLIR.API.MlirOperationWalkCallback Type

Operation walker type. The handler is passed an (opaque) reference to an operation and a pointer to a userData.

source

Reactant.MLIR.API.MlirShapedTypeComponentsCallback Type

These callbacks are used to return multiple shaped type components from functions while transferring ownership to the caller. The first argument is the has rank boolean followed by the the rank and a pointer to the shape (if applicable). The next argument is the element type, then the attribute. The last argument is an opaque pointer forwarded to the callback by the caller. This callback will be called potentially multiple times for each shaped type components.

source

Reactant.MLIR.API.MlirSparseTensorLevelType Type

Dimension level types (and properties) that define sparse tensors. See the documentation in SparseTensorAttrDefs.td for their meaning.

These correspond to SparseTensorEncodingAttr::LevelType in the C++ API. If updating, keep them in sync and update the static_assert in the impl file.

source

Reactant.MLIR.API.MlirStringCallback Type

A callback for returning string references.

This function is called back by the functions that need to return a reference to the portion of the string with the following arguments: - an MlirStringRef representing the current portion of the string - a pointer to user data forwarded from the printing call.

source

Reactant.MLIR.API.MlirStringRef Type
julia
MlirStringRef

A pointer to a sized fragment of a string, not necessarily null-terminated. Does not own the underlying string. This is equivalent to llvm::StringRef.

FieldNote
dataPointer to the first symbol.
lengthLength of the fragment.

source

Reactant.MLIR.API.MlirTypesCallback Type

These callbacks are used to return multiple types from functions while transferring ownership to the caller. The first argument is the number of consecutive elements pointed to by the second argument. The third argument is an opaque pointer forwarded to the callback by the caller.

source

Reactant.MLIR.API.MlirWalkOrder Type
julia
MlirWalkOrder

Traversal order for operation walk.

source

Reactant.MLIR.API.MlirWalkResult Type
julia
MlirWalkResult

Operation walk result.

source

Reactant.MLIR.API.LLVMAddSymbol Method
julia
LLVMAddSymbol(symbolName, symbolValue)

This functions permanently adds the symbol symbolName with the value symbolValue. These symbols are searched before any libraries.

See also

sys::DynamicLibrary::AddSymbol()

source

Reactant.MLIR.API.LLVMLoadLibraryPermanently Method
julia
LLVMLoadLibraryPermanently(Filename)

This function permanently loads the dynamic library at the given path. It is safe to call this function multiple times for the same library.

See also

sys::DynamicLibrary::LoadLibraryPermanently()

source

Reactant.MLIR.API.LLVMParseCommandLineOptions Method
julia
LLVMParseCommandLineOptions(argc, argv, Overview)

This function parses the given arguments using the LLVM command line parser. Note that the only stable thing about this function is its signature; you cannot rely on any particular set of command line arguments being interpreted the same way across LLVM versions.

See also

llvm:🆑:ParseCommandLineOptions()

source

Reactant.MLIR.API.LLVMSearchForAddressOfSymbol Method
julia
LLVMSearchForAddressOfSymbol(symbolName)

This function will search through all previously loaded dynamic libraries for the symbol symbolName. If it is found, the address of that symbol is returned. If not, null is returned.

See also

sys::DynamicLibrary::SearchForAddressOfSymbol()

source

Reactant.MLIR.API.mlirAffineAddExprGet Method
julia
mlirAffineAddExprGet(lhs, rhs)

Creates an affine add expression with 'lhs' and 'rhs'.

source

Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS Method
julia
mlirAffineBinaryOpExprGetLHS(affineExpr)

Returns the left hand side affine expression of the given affine binary operation expression.

source

Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS Method
julia
mlirAffineBinaryOpExprGetRHS(affineExpr)

Returns the right hand side affine expression of the given affine binary operation expression.

source

Reactant.MLIR.API.mlirAffineCeilDivExprGet Method
julia
mlirAffineCeilDivExprGet(lhs, rhs)

Creates an affine ceildiv expression with 'lhs' and 'rhs'.

source

Reactant.MLIR.API.mlirAffineConstantExprGet Method
julia
mlirAffineConstantExprGet(ctx, constant)

Creates an affine constant expression with 'constant' in the context.

source

Reactant.MLIR.API.mlirAffineConstantExprGetValue Method
julia
mlirAffineConstantExprGetValue(affineExpr)

Returns the value of the given affine constant expression.

source

Reactant.MLIR.API.mlirAffineDimExprGet Method
julia
mlirAffineDimExprGet(ctx, position)

Creates an affine dimension expression with 'position' in the context.

source

Reactant.MLIR.API.mlirAffineDimExprGetPosition Method
julia
mlirAffineDimExprGetPosition(affineExpr)

Returns the position of the given affine dimension expression.

source

Reactant.MLIR.API.mlirAffineExprCompose Method
julia
mlirAffineExprCompose(affineExpr, affineMap)

Composes the given map with the given expression.

source

Reactant.MLIR.API.mlirAffineExprDump Method
julia
mlirAffineExprDump(affineExpr)

Prints the affine expression to the standard error stream.

source

Reactant.MLIR.API.mlirAffineExprEqual Method
julia
mlirAffineExprEqual(lhs, rhs)

Returns true if the two affine expressions are equal.

source

Reactant.MLIR.API.mlirAffineExprGetContext Method
julia
mlirAffineExprGetContext(affineExpr)

Gets the context that owns the affine expression.

source

Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor Method
julia
mlirAffineExprGetLargestKnownDivisor(affineExpr)

Returns the greatest known integral divisor of this affine expression. The result is always positive.

source

Reactant.MLIR.API.mlirAffineExprIsAAdd Method
julia
mlirAffineExprIsAAdd(affineExpr)

Checks whether the given affine expression is an add expression.

source

Reactant.MLIR.API.mlirAffineExprIsABinary Method
julia
mlirAffineExprIsABinary(affineExpr)

Checks whether the given affine expression is binary.

source

Reactant.MLIR.API.mlirAffineExprIsACeilDiv Method
julia
mlirAffineExprIsACeilDiv(affineExpr)

Checks whether the given affine expression is an ceildiv expression.

source

Reactant.MLIR.API.mlirAffineExprIsAConstant Method
julia
mlirAffineExprIsAConstant(affineExpr)

Checks whether the given affine expression is a constant expression.

source

Reactant.MLIR.API.mlirAffineExprIsADim Method
julia
mlirAffineExprIsADim(affineExpr)

Checks whether the given affine expression is a dimension expression.

source

Reactant.MLIR.API.mlirAffineExprIsAFloorDiv Method
julia
mlirAffineExprIsAFloorDiv(affineExpr)

Checks whether the given affine expression is an floordiv expression.

source

Reactant.MLIR.API.mlirAffineExprIsAMod Method
julia
mlirAffineExprIsAMod(affineExpr)

Checks whether the given affine expression is an mod expression.

source

Reactant.MLIR.API.mlirAffineExprIsAMul Method
julia
mlirAffineExprIsAMul(affineExpr)

Checks whether the given affine expression is an mul expression.

source

Reactant.MLIR.API.mlirAffineExprIsASymbol Method
julia
mlirAffineExprIsASymbol(affineExpr)

Checks whether the given affine expression is a symbol expression.

source

Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim Method
julia
mlirAffineExprIsFunctionOfDim(affineExpr, position)

Checks whether the given affine expression involves AffineDimExpr 'position'.

source

Reactant.MLIR.API.mlirAffineExprIsMultipleOf Method
julia
mlirAffineExprIsMultipleOf(affineExpr, factor)

Checks whether the given affine expression is a multiple of 'factor'.

source

Reactant.MLIR.API.mlirAffineExprIsNull Method
julia
mlirAffineExprIsNull(affineExpr)

Returns true if the given affine expression is a null expression. Note constant zero is not a null expression.

source

Reactant.MLIR.API.mlirAffineExprIsPureAffine Method
julia
mlirAffineExprIsPureAffine(affineExpr)

Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.

source

Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant Method
julia
mlirAffineExprIsSymbolicOrConstant(affineExpr)

Checks whether the given affine expression is made out of only symbols and constants.

source

Reactant.MLIR.API.mlirAffineExprPrint Method
julia
mlirAffineExprPrint(affineExpr, callback, userData)

Prints an affine expression by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirAffineFloorDivExprGet Method
julia
mlirAffineFloorDivExprGet(lhs, rhs)

Creates an affine floordiv expression with 'lhs' and 'rhs'.

source

Reactant.MLIR.API.mlirAffineMapAttrGet Method
julia
mlirAffineMapAttrGet(map)

Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.

source

Reactant.MLIR.API.mlirAffineMapAttrGetTypeID Method
julia
mlirAffineMapAttrGetTypeID()

Returns the typeID of an AffineMap attribute.

source

Reactant.MLIR.API.mlirAffineMapAttrGetValue Method
julia
mlirAffineMapAttrGetValue(attr)

Returns the affine map wrapped in the given affine map attribute.

source

Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols Method
julia
mlirAffineMapCompressUnusedSymbols(affineMaps, size, result, populateResult)

Returns the simplified affine map resulting from dropping the symbols that do not appear in any of the individual maps in affineMaps. Asserts that all maps in affineMaps are normalized to the same number of dims and symbols. Takes a callback populateResult to fill the res container with value m at entry idx. This allows returning without worrying about ownership considerations.

source

Reactant.MLIR.API.mlirAffineMapConstantGet Method
julia
mlirAffineMapConstantGet(ctx, val)

Creates a single constant result affine map in the context. The affine map is owned by the context.

source

Reactant.MLIR.API.mlirAffineMapDump Method
julia
mlirAffineMapDump(affineMap)

Prints the affine map to the standard error stream.

source

Reactant.MLIR.API.mlirAffineMapEmptyGet Method
julia
mlirAffineMapEmptyGet(ctx)

Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.

source

Reactant.MLIR.API.mlirAffineMapEqual Method
julia
mlirAffineMapEqual(a1, a2)

Checks if two affine maps are equal.

source

Reactant.MLIR.API.mlirAffineMapGet Method
julia
mlirAffineMapGet(ctx, dimCount, symbolCount, nAffineExprs, affineExprs)

Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.

source

Reactant.MLIR.API.mlirAffineMapGetContext Method
julia
mlirAffineMapGetContext(affineMap)

Gets the context that the given affine map was created with

source

Reactant.MLIR.API.mlirAffineMapGetMajorSubMap Method
julia
mlirAffineMapGetMajorSubMap(affineMap, numResults)

Returns the affine map consisting of the most major numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.

source

Reactant.MLIR.API.mlirAffineMapGetMinorSubMap Method
julia
mlirAffineMapGetMinorSubMap(affineMap, numResults)

Returns the affine map consisting of the most minor numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.

source

Reactant.MLIR.API.mlirAffineMapGetNumDims Method
julia
mlirAffineMapGetNumDims(affineMap)

Returns the number of dimensions of the given affine map.

source

Reactant.MLIR.API.mlirAffineMapGetNumInputs Method
julia
mlirAffineMapGetNumInputs(affineMap)

Returns the number of inputs (dimensions + symbols) of the given affine map.

source

Reactant.MLIR.API.mlirAffineMapGetNumResults Method
julia
mlirAffineMapGetNumResults(affineMap)

Returns the number of results of the given affine map.

source

Reactant.MLIR.API.mlirAffineMapGetNumSymbols Method
julia
mlirAffineMapGetNumSymbols(affineMap)

Returns the number of symbols of the given affine map.

source

Reactant.MLIR.API.mlirAffineMapGetResult Method
julia
mlirAffineMapGetResult(affineMap, pos)

Returns the result at the given position.

source

Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult Method
julia
mlirAffineMapGetSingleConstantResult(affineMap)

Returns the constant result of the given affine map. The function asserts that the map has a single constant result.

source

Reactant.MLIR.API.mlirAffineMapGetSubMap Method
julia
mlirAffineMapGetSubMap(affineMap, size, resultPos)

Returns the affine map consisting of the resultPos subset.

source

Reactant.MLIR.API.mlirAffineMapIsEmpty Method
julia
mlirAffineMapIsEmpty(affineMap)

Checks whether the given affine map is an empty affine map.

source

Reactant.MLIR.API.mlirAffineMapIsIdentity Method
julia
mlirAffineMapIsIdentity(affineMap)

Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.

source

Reactant.MLIR.API.mlirAffineMapIsMinorIdentity Method
julia
mlirAffineMapIsMinorIdentity(affineMap)

Checks whether the given affine map is a minor identity affine map.

source

Reactant.MLIR.API.mlirAffineMapIsNull Method
julia
mlirAffineMapIsNull(affineMap)

Checks whether an affine map is null.

source

Reactant.MLIR.API.mlirAffineMapIsPermutation Method
julia
mlirAffineMapIsPermutation(affineMap)

Checks whether the given affine map represents a symbol-less permutation map.

source

Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation Method
julia
mlirAffineMapIsProjectedPermutation(affineMap)

Checks whether the given affine map represents a subset of a symbol-less permutation map.

source

Reactant.MLIR.API.mlirAffineMapIsSingleConstant Method
julia
mlirAffineMapIsSingleConstant(affineMap)

Checks whether the given affine map is a single result constant affine map.

source

Reactant.MLIR.API.mlirAffineMapMinorIdentityGet Method
julia
mlirAffineMapMinorIdentityGet(ctx, dims, results)

Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.

source

Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet Method
julia
mlirAffineMapMultiDimIdentityGet(ctx, numDims)

Creates an affine map with 'numDims' identity in the context. The affine map is owned by the context.

source

Reactant.MLIR.API.mlirAffineMapPermutationGet Method
julia
mlirAffineMapPermutationGet(ctx, size, permutation)

Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid permutation.) The affine map is owned by the context.

source

Reactant.MLIR.API.mlirAffineMapPrint Method
julia
mlirAffineMapPrint(affineMap, callback, userData)

Prints an affine map by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirAffineMapReplace Method
julia
mlirAffineMapReplace(affineMap, expression, replacement, numResultDims, numResultSyms)

Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.

source

Reactant.MLIR.API.mlirAffineMapZeroResultGet Method
julia
mlirAffineMapZeroResultGet(ctx, dimCount, symbolCount)

Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.

source

Reactant.MLIR.API.mlirAffineModExprGet Method
julia
mlirAffineModExprGet(lhs, rhs)

Creates an affine mod expression with 'lhs' and 'rhs'.

source

Reactant.MLIR.API.mlirAffineMulExprGet Method
julia
mlirAffineMulExprGet(lhs, rhs)

Creates an affine mul expression with 'lhs' and 'rhs'.

source

Reactant.MLIR.API.mlirAffineSymbolExprGet Method
julia
mlirAffineSymbolExprGet(ctx, position)

Creates an affine symbol expression with 'position' in the context.

source

Reactant.MLIR.API.mlirAffineSymbolExprGetPosition Method
julia
mlirAffineSymbolExprGetPosition(affineExpr)

Returns the position of the given affine symbol expression.

source

Reactant.MLIR.API.mlirAnyQuantizedTypeGet Method
julia
mlirAnyQuantizedTypeGet(flags, storageType, expressedType, storageTypeMin, storageTypeMax)

Creates an instance of AnyQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.

source

Reactant.MLIR.API.mlirArrayAttrGet Method
julia
mlirArrayAttrGet(ctx, numElements, elements)

Creates an array element containing the given list of elements in the given context.

source

Reactant.MLIR.API.mlirArrayAttrGetElement Method
julia
mlirArrayAttrGetElement(attr, pos)

Returns pos-th element stored in the given array attribute.

source

Reactant.MLIR.API.mlirArrayAttrGetNumElements Method
julia
mlirArrayAttrGetNumElements(attr)

Returns the number of elements stored in the given array attribute.

source

Reactant.MLIR.API.mlirArrayAttrGetTypeID Method
julia
mlirArrayAttrGetTypeID()

Returns the typeID of an Array attribute.

source

Reactant.MLIR.API.mlirAsmStateCreateForOperation Method
julia
mlirAsmStateCreateForOperation(op, flags)

Creates new AsmState, as with AsmState the IR should not be mutated in-between using this state. Must be freed with a call to mlirAsmStateDestroy().

source

Reactant.MLIR.API.mlirAsmStateCreateForValue Method
julia
mlirAsmStateCreateForValue(value, flags)

Creates new AsmState from value. Must be freed with a call to mlirAsmStateDestroy().

source

Reactant.MLIR.API.mlirAsmStateDestroy Method
julia
mlirAsmStateDestroy(state)

Destroys printing flags created with mlirAsmStateCreate.

source

Reactant.MLIR.API.mlirAttributeDump Method
julia
mlirAttributeDump(attr)

Prints the attribute to the standard error stream.

source

Reactant.MLIR.API.mlirAttributeEqual Method
julia
mlirAttributeEqual(a1, a2)

Checks if two attributes are equal.

source

Reactant.MLIR.API.mlirAttributeGetContext Method
julia
mlirAttributeGetContext(attribute)

Gets the context that an attribute was created with.

source

Reactant.MLIR.API.mlirAttributeGetDialect Method
julia
mlirAttributeGetDialect(attribute)

Gets the dialect of the attribute.

source

Reactant.MLIR.API.mlirAttributeGetNull Method
julia
mlirAttributeGetNull()

Returns an empty attribute.

source

Reactant.MLIR.API.mlirAttributeGetType Method
julia
mlirAttributeGetType(attribute)

Gets the type of this attribute.

source

Reactant.MLIR.API.mlirAttributeGetTypeID Method
julia
mlirAttributeGetTypeID(attribute)

Gets the type id of the attribute.

source

Reactant.MLIR.API.mlirAttributeIsAAffineMap Method
julia
mlirAttributeIsAAffineMap(attr)

Checks whether the given attribute is an affine map attribute.

source

Reactant.MLIR.API.mlirAttributeIsAArray Method
julia
mlirAttributeIsAArray(attr)

Checks whether the given attribute is an array attribute.

source

Reactant.MLIR.API.mlirAttributeIsABool Method
julia
mlirAttributeIsABool(attr)

Checks whether the given attribute is a bool attribute.

source

Reactant.MLIR.API.mlirAttributeIsADenseBoolArray Method
julia
mlirAttributeIsADenseBoolArray(attr)

Checks whether the given attribute is a dense array attribute.

source

Reactant.MLIR.API.mlirAttributeIsADenseElements Method
julia
mlirAttributeIsADenseElements(attr)

Checks whether the given attribute is a dense elements attribute.

source

Reactant.MLIR.API.mlirAttributeIsADictionary Method
julia
mlirAttributeIsADictionary(attr)

Checks whether the given attribute is a dictionary attribute.

source

Reactant.MLIR.API.mlirAttributeIsAElements Method
julia
mlirAttributeIsAElements(attr)

Checks whether the given attribute is an elements attribute.

source

Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef Method
julia
mlirAttributeIsAFlatSymbolRef(attr)

Checks whether the given attribute is a flat symbol reference attribute.

source

Reactant.MLIR.API.mlirAttributeIsAFloat Method
julia
mlirAttributeIsAFloat(attr)

Checks whether the given attribute is a floating point attribute.

source

Reactant.MLIR.API.mlirAttributeIsAInteger Method
julia
mlirAttributeIsAInteger(attr)

Checks whether the given attribute is an integer attribute.

source

Reactant.MLIR.API.mlirAttributeIsAIntegerSet Method
julia
mlirAttributeIsAIntegerSet(attr)

Checks whether the given attribute is an integer set attribute.

source

Reactant.MLIR.API.mlirAttributeIsAOpaque Method
julia
mlirAttributeIsAOpaque(attr)

Checks whether the given attribute is an opaque attribute.

source

Reactant.MLIR.API.mlirAttributeIsASparseElements Method
julia
mlirAttributeIsASparseElements(attr)

Checks whether the given attribute is a sparse elements attribute.

source

Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr Method
julia
mlirAttributeIsASparseTensorEncodingAttr(attr)

Checks whether the given attribute is a sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirAttributeIsAString Method
julia
mlirAttributeIsAString(attr)

Checks whether the given attribute is a string attribute.

source

Reactant.MLIR.API.mlirAttributeIsASymbolRef Method
julia
mlirAttributeIsASymbolRef(attr)

Checks whether the given attribute is a symbol reference attribute.

source

Reactant.MLIR.API.mlirAttributeIsAType Method
julia
mlirAttributeIsAType(attr)

Checks whether the given attribute is a type attribute.

source

Reactant.MLIR.API.mlirAttributeIsAUnit Method
julia
mlirAttributeIsAUnit(attr)

Checks whether the given attribute is a unit attribute.

source

Reactant.MLIR.API.mlirAttributeIsNull Method
julia
mlirAttributeIsNull(attr)

Checks whether an attribute is null.

source

Reactant.MLIR.API.mlirAttributeParseGet Method
julia
mlirAttributeParseGet(context, attr)

Parses an attribute. The attribute is owned by the context.

source

Reactant.MLIR.API.mlirAttributePrint Method
julia
mlirAttributePrint(attr, callback, userData)

Prints an attribute by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirBF16TypeGet Method
julia
mlirBF16TypeGet(ctx)

Creates a bf16 type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirBFloat16TypeGetTypeID Method
julia
mlirBFloat16TypeGetTypeID()

Returns the typeID of an BFloat16 type.

source

Reactant.MLIR.API.mlirBlockAddArgument Method
julia
mlirBlockAddArgument(block, type, loc)

Appends an argument of the specified type to the block. Returns the newly added argument.

source

Reactant.MLIR.API.mlirBlockAppendOwnedOperation Method
julia
mlirBlockAppendOwnedOperation(block, operation)

Takes an operation owned by the caller and appends it to the block.

source

Reactant.MLIR.API.mlirBlockArgumentGetArgNumber Method
julia
mlirBlockArgumentGetArgNumber(value)

Returns the position of the value in the argument list of its block.

source

Reactant.MLIR.API.mlirBlockArgumentGetOwner Method
julia
mlirBlockArgumentGetOwner(value)

Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.

source

Reactant.MLIR.API.mlirBlockArgumentSetType Method
julia
mlirBlockArgumentSetType(value, type)

Sets the type of the block argument to the given type.

source

Reactant.MLIR.API.mlirBlockCreate Method
julia
mlirBlockCreate(nArgs, args, locs)

Creates a new empty block with the given argument types and transfers ownership to the caller.

source

Reactant.MLIR.API.mlirBlockDestroy Method
julia
mlirBlockDestroy(block)

Takes a block owned by the caller and destroys it.

source

Reactant.MLIR.API.mlirBlockDetach Method
julia
mlirBlockDetach(block)

Detach a block from the owning region and assume ownership.

source

Reactant.MLIR.API.mlirBlockEqual Method
julia
mlirBlockEqual(block, other)

Checks whether two blocks handles point to the same block. This does not perform deep comparison.

source

Reactant.MLIR.API.mlirBlockEraseArgument Method
julia
mlirBlockEraseArgument(block, index)

Erase the argument at 'index' and remove it from the argument list.

source

Reactant.MLIR.API.mlirBlockGetArgument Method
julia
mlirBlockGetArgument(block, pos)

Returns pos-th argument of the block.

source

Reactant.MLIR.API.mlirBlockGetFirstOperation Method
julia
mlirBlockGetFirstOperation(block)

Returns the first operation in the block.

source

Reactant.MLIR.API.mlirBlockGetNextInRegion Method
julia
mlirBlockGetNextInRegion(block)

Returns the block immediately following the given block in its parent region.

source

Reactant.MLIR.API.mlirBlockGetNumArguments Method
julia
mlirBlockGetNumArguments(block)

Returns the number of arguments of the block.

source

Reactant.MLIR.API.mlirBlockGetParentOperation Method
julia
mlirBlockGetParentOperation(arg1)

Returns the closest surrounding operation that contains this block.

source

Reactant.MLIR.API.mlirBlockGetParentRegion Method
julia
mlirBlockGetParentRegion(block)

Returns the region that contains this block.

source

Reactant.MLIR.API.mlirBlockGetTerminator Method
julia
mlirBlockGetTerminator(block)

Returns the terminator operation in the block or null if no terminator.

source

Reactant.MLIR.API.mlirBlockInsertArgument Method
julia
mlirBlockInsertArgument(block, pos, type, loc)

Inserts an argument of the specified type at a specified index to the block. Returns the newly added argument.

source

Reactant.MLIR.API.mlirBlockInsertOwnedOperation Method
julia
mlirBlockInsertOwnedOperation(block, pos, operation)

Takes an operation owned by the caller and inserts it as pos to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.

source

Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter Method
julia
mlirBlockInsertOwnedOperationAfter(block, reference, operation)

Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.

source

Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore Method
julia
mlirBlockInsertOwnedOperationBefore(block, reference, operation)

Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.

source

Reactant.MLIR.API.mlirBlockIsNull Method
julia
mlirBlockIsNull(block)

Checks whether a block is null.

source

Reactant.MLIR.API.mlirBlockPrint Method
julia
mlirBlockPrint(block, callback, userData)

Prints a block by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirBoolAttrGet Method
julia
mlirBoolAttrGet(ctx, value)

Creates a bool attribute in the given context with the given value.

source

Reactant.MLIR.API.mlirBoolAttrGetValue Method
julia
mlirBoolAttrGetValue(attr)

Returns the value stored in the given bool attribute.

source

Reactant.MLIR.API.mlirBytecodeWriterConfigCreate Method
julia
mlirBytecodeWriterConfigCreate()

Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirBytecodeWriterConfigDestroy().

source

Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion Method
julia
mlirBytecodeWriterConfigDesiredEmitVersion(flags, version)

Sets the version to emit in the writer config.

source

Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy Method
julia
mlirBytecodeWriterConfigDestroy(config)

Destroys printing flags created with mlirBytecodeWriterConfigCreate.

source

Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet Method
julia
mlirCalibratedQuantizedTypeGet(expressedType, min, max)

Creates an instance of CalibratedQuantizedType with the given parameters in the same context as expressedType and returns it. The instance is owned by the context.

source

Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax Method
julia
mlirCalibratedQuantizedTypeGetMax(type)

Returns the max value of the given calibrated quantized type.

source

Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin Method
julia
mlirCalibratedQuantizedTypeGetMin(type)

Returns the min value of the given calibrated quantized type.

source

Reactant.MLIR.API.mlirComplexTypeGet Method
julia
mlirComplexTypeGet(elementType)

Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.

source

Reactant.MLIR.API.mlirComplexTypeGetElementType Method
julia
mlirComplexTypeGetElementType(type)

Returns the element type of the given complex type.

source

Reactant.MLIR.API.mlirComplexTypeGetTypeID Method
julia
mlirComplexTypeGetTypeID()

Returns the typeID of an Complex type.

source

Reactant.MLIR.API.mlirContextAppendDialectRegistry Method
julia
mlirContextAppendDialectRegistry(ctx, registry)

Append the contents of the given dialect registry to the registry associated with the context.

source

Reactant.MLIR.API.mlirContextAttachDiagnosticHandler Method
julia
mlirContextAttachDiagnosticHandler(context, handler, userData, deleteUserData)

Attaches the diagnostic handler to the context. Handlers are invoked in the reverse order of attachment until one of them processes the diagnostic completely. When a handler is invoked it is passed the userData that was provided when it was attached. If non-NULL, deleteUserData is called once the system no longer needs to call the handler (for instance after the handler is detached or the context is destroyed). Returns an identifier that can be used to detach the handler.

source

Reactant.MLIR.API.mlirContextCreate Method
julia
mlirContextCreate()

Creates an MLIR context and transfers its ownership to the caller. This sets the default multithreading option (enabled).

source

Reactant.MLIR.API.mlirContextCreateWithRegistry Method
julia
mlirContextCreateWithRegistry(registry, threadingEnabled)

Creates an MLIR context, setting the multithreading setting explicitly and pre-loading the dialects from the provided DialectRegistry.

source

Reactant.MLIR.API.mlirContextCreateWithThreading Method
julia
mlirContextCreateWithThreading(threadingEnabled)

Creates an MLIR context with an explicit setting of the multithreading setting and transfers its ownership to the caller.

source

Reactant.MLIR.API.mlirContextDestroy Method
julia
mlirContextDestroy(context)

Takes an MLIR context owned by the caller and destroys it.

source

Reactant.MLIR.API.mlirContextDetachDiagnosticHandler Method
julia
mlirContextDetachDiagnosticHandler(context, id)

Detaches an attached diagnostic handler from the context given its identifier.

source

Reactant.MLIR.API.mlirContextEnableMultithreading Method
julia
mlirContextEnableMultithreading(context, enable)

Set threading mode (must be set to false to mlir-print-ir-after-all).

source

Reactant.MLIR.API.mlirContextEqual Method
julia
mlirContextEqual(ctx1, ctx2)

Checks if two contexts are equal.

source

Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects Method
julia
mlirContextGetAllowUnregisteredDialects(context)

Returns whether the context allows unregistered dialects.

source

Reactant.MLIR.API.mlirContextGetNumLoadedDialects Method
julia
mlirContextGetNumLoadedDialects(context)

Returns the number of dialects loaded by the context.

source

Reactant.MLIR.API.mlirContextGetNumRegisteredDialects Method
julia
mlirContextGetNumRegisteredDialects(context)

Returns the number of dialects registered with the given context. A registered dialect will be loaded if needed by the parser.

source

Reactant.MLIR.API.mlirContextGetOrLoadDialect Method
julia
mlirContextGetOrLoadDialect(context, name)

Gets the dialect instance owned by the given context using the dialect namespace to identify it, loads (i.e., constructs the instance of) the dialect if necessary. If the dialect is not registered with the context, returns null. Use mlirContextLoad<Name>Dialect to load an unregistered dialect.

source

Reactant.MLIR.API.mlirContextIsNull Method
julia
mlirContextIsNull(context)

Checks whether a context is null.

source

Reactant.MLIR.API.mlirContextIsRegisteredOperation Method
julia
mlirContextIsRegisteredOperation(context, name)

Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.

source

Reactant.MLIR.API.mlirContextLoadAllAvailableDialects Method
julia
mlirContextLoadAllAvailableDialects(context)

Eagerly loads all available dialects registered with a context, making them available for use for IR construction.

source

Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects Method
julia
mlirContextSetAllowUnregisteredDialects(context, allow)

Sets whether unregistered dialects are allowed in this context.

source

Reactant.MLIR.API.mlirContextSetThreadPool Method
julia
mlirContextSetThreadPool(context, threadPool)

Sets the thread pool of the context explicitly, enabling multithreading in the process. This API should be used to avoid re-creating thread pools in long-running applications that perform multiple compilations, see the C++ documentation for MLIRContext for details.

source

Reactant.MLIR.API.mlirCreateExternalPass Method
julia
mlirCreateExternalPass(passID, name, argument, description, opName, nDependentDialects, dependentDialects, callbacks, userData)

Creates an external MlirPass that calls the supplied callbacks using the supplied userData. If opName is empty, the pass is a generic operation pass. Otherwise it is an operation pass specific to the specified pass name.

source

Reactant.MLIR.API.mlirDenseArrayGetNumElements Method
julia
mlirDenseArrayGetNumElements(attr)

Get the size of a dense array.

source

Reactant.MLIR.API.mlirDenseBoolArrayGet Method
julia
mlirDenseBoolArrayGet(ctx, size, values)

Create a dense array attribute with the given elements.

source

Reactant.MLIR.API.mlirDenseBoolArrayGetElement Method
julia
mlirDenseBoolArrayGetElement(attr, pos)

Get an element of a dense array.

source

Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue Method
julia
mlirDenseBoolResourceElementsAttrGetValue(attr, pos)

Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense resource elements attribute.

source

Reactant.MLIR.API.mlirDenseElementsAttrBoolGet Method
julia
mlirDenseElementsAttrBoolGet(shapedType, numElements, elements)

Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.

source

Reactant.MLIR.API.mlirDenseElementsAttrGet Method
julia
mlirDenseElementsAttrGet(shapedType, numElements, elements)

Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.

source

Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue Method
julia
mlirDenseElementsAttrGetBoolValue(attr, pos)

Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense elements attribute.

source

Reactant.MLIR.API.mlirDenseElementsAttrGetRawData Method
julia
mlirDenseElementsAttrGetRawData(attr)

Returns the raw data of the given dense elements attribute.

source

Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue Method
julia
mlirDenseElementsAttrGetSplatValue(attr)

Returns the single replicated value (splat) of a specific type contained by the given dense elements attribute.

source

Reactant.MLIR.API.mlirDenseElementsAttrIsSplat Method
julia
mlirDenseElementsAttrIsSplat(attr)

Checks whether the given dense elements attribute contains a single replicated value (splat).

source

Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet Method
julia
mlirDenseElementsAttrRawBufferGet(shapedType, rawBufferSize, rawBuffer)

Creates a dense elements attribute with the given Shaped type and elements populated from a packed, row-major opaque buffer of contents.

The format of the raw buffer is a densely packed array of values that can be bitcast to the storage format of the element type specified. Types that are not byte aligned will be: - For bitwidth > 1: Rounded up to the next byte. - For bitwidth = 1: Packed into 8bit bytes with bits corresponding to the linear order of the shape type from MSB to LSB, padded to on the right.

A raw buffer of a single element (or for 1-bit, a byte of value 0 or 255) will be interpreted as a splat. User code should be prepared for additional, conformant patterns to be identified as splats in the future.

source

Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet Method
julia
mlirDenseElementsAttrReshapeGet(attr, shapedType)

Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.

source

Reactant.MLIR.API.mlirDenseElementsAttrSplatGet Method
julia
mlirDenseElementsAttrSplatGet(shapedType, element)

Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).

source

Reactant.MLIR.API.mlirDenseElementsAttrStringGet Method
julia
mlirDenseElementsAttrStringGet(shapedType, numElements, strs)

Creates a dense elements attribute with the given shaped type from string elements.

source

Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID Method
julia
mlirDenseIntOrFPElementsAttrGetTypeID()

Returns the typeID of an DenseIntOrFPElements attribute.

source

Reactant.MLIR.API.mlirDiagnosticGetLocation Method
julia
mlirDiagnosticGetLocation(diagnostic)

Returns the location at which the diagnostic is reported.

source

Reactant.MLIR.API.mlirDiagnosticGetNote Method
julia
mlirDiagnosticGetNote(diagnostic, pos)

Returns pos-th note attached to the diagnostic. Expects pos to be a valid zero-based index into the list of notes.

source

Reactant.MLIR.API.mlirDiagnosticGetNumNotes Method
julia
mlirDiagnosticGetNumNotes(diagnostic)

Returns the number of notes attached to the diagnostic.

source

Reactant.MLIR.API.mlirDiagnosticGetSeverity Method
julia
mlirDiagnosticGetSeverity(diagnostic)

Returns the severity of the diagnostic.

source

Reactant.MLIR.API.mlirDiagnosticPrint Method
julia
mlirDiagnosticPrint(diagnostic, callback, userData)

Prints a diagnostic using the provided callback.

source

Reactant.MLIR.API.mlirDialectEqual Method
julia
mlirDialectEqual(dialect1, dialect2)

Checks if two dialects that belong to the same context are equal. Dialects from different contexts will not compare equal.

source

Reactant.MLIR.API.mlirDialectGetContext Method
julia
mlirDialectGetContext(dialect)

Returns the context that owns the dialect.

source

Reactant.MLIR.API.mlirDialectGetNamespace Method
julia
mlirDialectGetNamespace(dialect)

Returns the namespace of the given dialect.

source

Reactant.MLIR.API.mlirDialectHandleGetNamespace Method
julia
mlirDialectHandleGetNamespace(arg1)

Returns the namespace associated with the provided dialect handle.

source

Reactant.MLIR.API.mlirDialectHandleInsertDialect Method
julia
mlirDialectHandleInsertDialect(arg1, arg2)

Inserts the dialect associated with the provided dialect handle into the provided dialect registry

source

Reactant.MLIR.API.mlirDialectHandleLoadDialect Method
julia
mlirDialectHandleLoadDialect(arg1, arg2)

Loads the dialect associated with the provided dialect handle.

source

Reactant.MLIR.API.mlirDialectHandleRegisterDialect Method
julia
mlirDialectHandleRegisterDialect(arg1, arg2)

Registers the dialect associated with the provided dialect handle.

source

Reactant.MLIR.API.mlirDialectIsNull Method
julia
mlirDialectIsNull(dialect)

Checks if the dialect is null.

source

Reactant.MLIR.API.mlirDialectRegistryCreate Method
julia
mlirDialectRegistryCreate()

Creates a dialect registry and transfers its ownership to the caller.

source

Reactant.MLIR.API.mlirDialectRegistryDestroy Method
julia
mlirDialectRegistryDestroy(registry)

Takes a dialect registry owned by the caller and destroys it.

source

Reactant.MLIR.API.mlirDialectRegistryIsNull Method
julia
mlirDialectRegistryIsNull(registry)

Checks if the dialect registry is null.

source

Reactant.MLIR.API.mlirDictionaryAttrGet Method
julia
mlirDictionaryAttrGet(ctx, numElements, elements)

Creates a dictionary attribute containing the given list of elements in the provided context.

source

Reactant.MLIR.API.mlirDictionaryAttrGetElement Method
julia
mlirDictionaryAttrGetElement(attr, pos)

Returns pos-th element of the given dictionary attribute.

source

Reactant.MLIR.API.mlirDictionaryAttrGetElementByName Method
julia
mlirDictionaryAttrGetElementByName(attr, name)

Returns the dictionary attribute element with the given name or NULL if the given name does not exist in the dictionary.

source

Reactant.MLIR.API.mlirDictionaryAttrGetNumElements Method
julia
mlirDictionaryAttrGetNumElements(attr)

Returns the number of attributes contained in a dictionary attribute.

source

Reactant.MLIR.API.mlirDictionaryAttrGetTypeID Method
julia
mlirDictionaryAttrGetTypeID()

Returns the typeID of a Dictionary attribute.

source

Reactant.MLIR.API.mlirDisctinctAttrCreate Method
julia
mlirDisctinctAttrCreate(referencedAttr)

Creates a DisctinctAttr with the referenced attribute.

source

Reactant.MLIR.API.mlirElementsAttrGetNumElements Method
julia
mlirElementsAttrGetNumElements(attr)

Gets the total number of elements in the given elements attribute. In order to iterate over the attribute, obtain its type, which must be a statically shaped type and use its sizes to build a multi-dimensional index.

source

Reactant.MLIR.API.mlirElementsAttrGetValue Method
julia
mlirElementsAttrGetValue(attr, rank, idxs)

Returns the element at the given rank-dimensional index.

source

Reactant.MLIR.API.mlirElementsAttrIsValidIndex Method
julia
mlirElementsAttrIsValidIndex(attr, rank, idxs)

Checks whether the given rank-dimensional index is valid in the given elements attribute.

source

Reactant.MLIR.API.mlirEmitError Method
julia
mlirEmitError(location, message)

Emits an error at the given location through the diagnostics engine. Used for testing purposes.

source

Reactant.MLIR.API.mlirEnableGlobalDebug Method
julia
mlirEnableGlobalDebug(enable)

Sets the global debugging flag.

source

Reactant.MLIR.API.mlirExecutionEngineCreate Method
julia
mlirExecutionEngineCreate(op, optLevel, numPaths, sharedLibPaths, enableObjectDump)

Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.

source

Reactant.MLIR.API.mlirExecutionEngineDestroy Method
julia
mlirExecutionEngineDestroy(jit)

Destroy an ExecutionEngine instance.

source

Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile Method
julia
mlirExecutionEngineDumpToObjectFile(jit, fileName)

Dump as an object in fileName.

source

Reactant.MLIR.API.mlirExecutionEngineInvokePacked Method
julia
mlirExecutionEngineInvokePacked(jit, name, arguments)

Invoke a native function in the execution engine by name with the arguments and result of the invoked function passed as an array of pointers. The function must have been tagged with the llvm.emit\_c\_interface attribute. Returns a failure if the execution fails for any reason (the function name can't be resolved for instance).

source

Reactant.MLIR.API.mlirExecutionEngineIsNull Method
julia
mlirExecutionEngineIsNull(jit)

Checks whether an execution engine is null.

source

Reactant.MLIR.API.mlirExecutionEngineLookup Method
julia
mlirExecutionEngineLookup(jit, name)

Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.

source

Reactant.MLIR.API.mlirExecutionEngineLookupPacked Method
julia
mlirExecutionEngineLookupPacked(jit, name)

Lookup the wrapper of the native function in the execution engine with the given name, returns nullptr if the function can't be looked-up.

source

Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol Method
julia
mlirExecutionEngineRegisterSymbol(jit, name, sym)

Register a symbol with the jit: this symbol will be accessible to the jitted code.

source

Reactant.MLIR.API.mlirExternalPassSignalFailure Method
julia
mlirExternalPassSignalFailure(pass)

This signals that the pass has failed. This is only valid to call during the run callback of MlirExternalPassCallbacks. See Pass::signalPassFailure().

source

Reactant.MLIR.API.mlirF16TypeGet Method
julia
mlirF16TypeGet(ctx)

Creates an f16 type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirF32TypeGet Method
julia
mlirF32TypeGet(ctx)

Creates an f32 type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirF64TypeGet Method
julia
mlirF64TypeGet(ctx)

Creates a f64 type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFlatSymbolRefAttrGet Method
julia
mlirFlatSymbolRefAttrGet(ctx, symbol)

Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.

source

Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue Method
julia
mlirFlatSymbolRefAttrGetValue(attr)

Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.

source

Reactant.MLIR.API.mlirFloat16TypeGetTypeID Method
julia
mlirFloat16TypeGetTypeID()

Returns the typeID of an Float16 type.

source

Reactant.MLIR.API.mlirFloat32TypeGetTypeID Method
julia
mlirFloat32TypeGetTypeID()

Returns the typeID of an Float32 type.

source

Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet Method
julia
mlirFloat4E2M1FNTypeGet(ctx)

Creates an f4E2M1FN type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID Method
julia
mlirFloat4E2M1FNTypeGetTypeID()

Returns the typeID of an Float4E2M1FN type.

source

Reactant.MLIR.API.mlirFloat64TypeGetTypeID Method
julia
mlirFloat64TypeGetTypeID()

Returns the typeID of an Float64 type.

source

Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet Method
julia
mlirFloat6E2M3FNTypeGet(ctx)

Creates an f6E2M3FN type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID Method
julia
mlirFloat6E2M3FNTypeGetTypeID()

Returns the typeID of an Float6E2M3FN type.

source

Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet Method
julia
mlirFloat6E3M2FNTypeGet(ctx)

Creates an f6E3M2FN type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID Method
julia
mlirFloat6E3M2FNTypeGetTypeID()

Returns the typeID of an Float6E3M2FN type.

source

Reactant.MLIR.API.mlirFloat8E3M4TypeGet Method
julia
mlirFloat8E3M4TypeGet(ctx)

Creates an f8E3M4 type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID Method
julia
mlirFloat8E3M4TypeGetTypeID()

Returns the typeID of an Float8E3M4 type.

source

Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGet Method
julia
mlirFloat8E4M3B11FNUZTypeGet(ctx)

Creates an f8E4M3B11FNUZ type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGetTypeID Method
julia
mlirFloat8E4M3B11FNUZTypeGetTypeID()

Returns the typeID of an Float8E4M3B11FNUZ type.

source

Reactant.MLIR.API.mlirFloat8E4M3FNTypeGet Method
julia
mlirFloat8E4M3FNTypeGet(ctx)

Creates an f8E4M3FN type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID Method
julia
mlirFloat8E4M3FNTypeGetTypeID()

Returns the typeID of an Float8E4M3FN type.

source

Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet Method
julia
mlirFloat8E4M3FNUZTypeGet(ctx)

Creates an f8E4M3FNUZ type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID Method
julia
mlirFloat8E4M3FNUZTypeGetTypeID()

Returns the typeID of an Float8E4M3FNUZ type.

source

Reactant.MLIR.API.mlirFloat8E4M3TypeGet Method
julia
mlirFloat8E4M3TypeGet(ctx)

Creates an f8E4M3 type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID Method
julia
mlirFloat8E4M3TypeGetTypeID()

Returns the typeID of an Float8E4M3 type.

source

Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGet Method
julia
mlirFloat8E5M2FNUZTypeGet(ctx)

Creates an f8E5M2FNUZ type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID Method
julia
mlirFloat8E5M2FNUZTypeGetTypeID()

Returns the typeID of an Float8E5M2FNUZ type.

source

Reactant.MLIR.API.mlirFloat8E5M2TypeGet Method
julia
mlirFloat8E5M2TypeGet(ctx)

Creates an f8E5M2 type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID Method
julia
mlirFloat8E5M2TypeGetTypeID()

Returns the typeID of an Float8E5M2 type.

source

Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet Method
julia
mlirFloat8E8M0FNUTypeGet(ctx)

Creates an f8E8M0FNU type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID Method
julia
mlirFloat8E8M0FNUTypeGetTypeID()

Returns the typeID of an Float8E8M0FNU type.

source

Reactant.MLIR.API.mlirFloatAttrDoubleGet Method
julia
mlirFloatAttrDoubleGet(ctx, type, value)

Creates a floating point attribute in the given context with the given double value and double-precision FP semantics.

source

Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked Method
julia
mlirFloatAttrDoubleGetChecked(loc, type, value)

Same as "mlirFloatAttrDoubleGet", but if the type is not valid for a construction of a FloatAttr, returns a null MlirAttribute.

source

Reactant.MLIR.API.mlirFloatAttrGetTypeID Method
julia
mlirFloatAttrGetTypeID()

Returns the typeID of a Float attribute.

source

Reactant.MLIR.API.mlirFloatAttrGetValueDouble Method
julia
mlirFloatAttrGetValueDouble(attr)

Returns the value stored in the given floating point attribute, interpreting the value as double.

source

Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID Method
julia
mlirFloatTF32TypeGetTypeID()

Returns the typeID of a TF32 type.

source

Reactant.MLIR.API.mlirFloatTypeGetWidth Method
julia
mlirFloatTypeGetWidth(type)

Returns the bitwidth of a floating-point type.

source

Reactant.MLIR.API.mlirFreezeRewritePattern Method
julia
mlirFreezeRewritePattern(op)

FrozenRewritePatternSet API

source

Reactant.MLIR.API.mlirFuncSetArgAttr Method
julia
mlirFuncSetArgAttr(op, pos, name, attr)

Sets the argument attribute 'name' of an argument at index 'pos'. Asserts that the operation is a FuncOp.

source

Reactant.MLIR.API.mlirFunctionTypeGet Method
julia
mlirFunctionTypeGet(ctx, numInputs, inputs, numResults, results)

Creates a function type, mapping a list of input types to result types.

source

Reactant.MLIR.API.mlirFunctionTypeGetInput Method
julia
mlirFunctionTypeGetInput(type, pos)

Returns the pos-th input type.

source

Reactant.MLIR.API.mlirFunctionTypeGetNumInputs Method
julia
mlirFunctionTypeGetNumInputs(type)

Returns the number of input types.

source

Reactant.MLIR.API.mlirFunctionTypeGetNumResults Method
julia
mlirFunctionTypeGetNumResults(type)

Returns the number of result types.

source

Reactant.MLIR.API.mlirFunctionTypeGetResult Method
julia
mlirFunctionTypeGetResult(type, pos)

Returns the pos-th result type.

source

Reactant.MLIR.API.mlirFunctionTypeGetTypeID Method
julia
mlirFunctionTypeGetTypeID()

Returns the typeID of an Function type.

source

Reactant.MLIR.API.mlirIRRewriterCreate Method
julia
mlirIRRewriterCreate(context)

Create an IRRewriter and transfer ownership to the caller.

source

Reactant.MLIR.API.mlirIRRewriterCreateFromOp Method
julia
mlirIRRewriterCreateFromOp(op)

Create an IRRewriter and transfer ownership to the caller. Additionally set the insertion point before the operation.

source

Reactant.MLIR.API.mlirIRRewriterDestroy Method
julia
mlirIRRewriterDestroy(rewriter)

Takes an IRRewriter owned by the caller and destroys it. It is the responsibility of the user to only pass an IRRewriter class.

source

Reactant.MLIR.API.mlirIdentifierEqual Method
julia
mlirIdentifierEqual(ident, other)

Checks whether two identifiers are the same.

source

Reactant.MLIR.API.mlirIdentifierGet Method
julia
mlirIdentifierGet(context, str)

Gets an identifier with the given string value.

source

Reactant.MLIR.API.mlirIdentifierGetContext Method
julia
mlirIdentifierGetContext(arg1)

Returns the context associated with this identifier

source

Reactant.MLIR.API.mlirIdentifierStr Method
julia
mlirIdentifierStr(ident)

Gets the string value of the identifier.

source

Reactant.MLIR.API.mlirIndexTypeGet Method
julia
mlirIndexTypeGet(ctx)

Creates an index type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirIndexTypeGetTypeID Method
julia
mlirIndexTypeGetTypeID()

Returns the typeID of an Index type.

source

Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes Method
julia
mlirInferShapedTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)

Infers the return shaped type components of the operation. Calls callback with the types of inferred arguments on success. Returns failure otherwise.

source

Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID Method
julia
mlirInferShapedTypeOpInterfaceTypeID()

Returns the interface TypeID of the InferShapedTypeOpInterface.

source

Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes Method
julia
mlirInferTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)

Infers the return types of the operation identified by its canonical given the arguments that will be supplied to its generic builder. Calls callback with the types of inferred arguments, potentially several times, on success. Returns failure otherwise.

source

Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID Method
julia
mlirInferTypeOpInterfaceTypeID()

Returns the interface TypeID of the InferTypeOpInterface.

source

Reactant.MLIR.API.mlirIntegerAttrGet Method
julia
mlirIntegerAttrGet(type, value)

Creates an integer attribute of the given type with the given integer value.

source

Reactant.MLIR.API.mlirIntegerAttrGetTypeID Method
julia
mlirIntegerAttrGetTypeID()

Returns the typeID of an Integer attribute.

source

Reactant.MLIR.API.mlirIntegerAttrGetValueInt Method
julia
mlirIntegerAttrGetValueInt(attr)

Returns the value stored in the given integer attribute, assuming the value is of signless type and fits into a signed 64-bit integer.

source

Reactant.MLIR.API.mlirIntegerAttrGetValueSInt Method
julia
mlirIntegerAttrGetValueSInt(attr)

Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.

source

Reactant.MLIR.API.mlirIntegerAttrGetValueUInt Method
julia
mlirIntegerAttrGetValueUInt(attr)

Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.

source

Reactant.MLIR.API.mlirIntegerSetAttrGet Method
julia
mlirIntegerSetAttrGet(set)

Creates an integer set attribute wrapping the given set. The attribute belongs to the same context as the integer set.

source

Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID Method
julia
mlirIntegerSetAttrGetTypeID()

Returns the typeID of an IntegerSet attribute.

source

Reactant.MLIR.API.mlirIntegerSetAttrGetValue Method
julia
mlirIntegerSetAttrGetValue(attr)

Returns the integer set wrapped in the given integer set attribute.

source

Reactant.MLIR.API.mlirIntegerSetDump Method
julia
mlirIntegerSetDump(set)

Prints an integer set to the standard error stream.

source

Reactant.MLIR.API.mlirIntegerSetEmptyGet Method
julia
mlirIntegerSetEmptyGet(context, numDims, numSymbols)

Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.

source

Reactant.MLIR.API.mlirIntegerSetEqual Method
julia
mlirIntegerSetEqual(s1, s2)

Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.

source

Reactant.MLIR.API.mlirIntegerSetGet Method
julia
mlirIntegerSetGet(context, numDims, numSymbols, numConstraints, constraints, eqFlags)

Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqFlags is 1) or inequalities (eqFlags is 0). Both constraints and eqFlags are expected to point to at least numConstraint consecutive values.

source

Reactant.MLIR.API.mlirIntegerSetGetConstraint Method
julia
mlirIntegerSetGetConstraint(set, pos)

Returns pos-th constraint of the set.

source

Reactant.MLIR.API.mlirIntegerSetGetContext Method
julia
mlirIntegerSetGetContext(set)

Gets the context in which the given integer set lives.

source

Reactant.MLIR.API.mlirIntegerSetGetNumConstraints Method
julia
mlirIntegerSetGetNumConstraints(set)

Returns the number of constraints (equalities + inequalities) in the given set.

source

Reactant.MLIR.API.mlirIntegerSetGetNumDims Method
julia
mlirIntegerSetGetNumDims(set)

Returns the number of dimensions in the given set.

source

Reactant.MLIR.API.mlirIntegerSetGetNumEqualities Method
julia
mlirIntegerSetGetNumEqualities(set)

Returns the number of equalities in the given set.

source

Reactant.MLIR.API.mlirIntegerSetGetNumInequalities Method
julia
mlirIntegerSetGetNumInequalities(set)

Returns the number of inequalities in the given set.

source

Reactant.MLIR.API.mlirIntegerSetGetNumInputs Method
julia
mlirIntegerSetGetNumInputs(set)

Returns the number of inputs (dimensions + symbols) in the given set.

source

Reactant.MLIR.API.mlirIntegerSetGetNumSymbols Method
julia
mlirIntegerSetGetNumSymbols(set)

Returns the number of symbols in the given set.

source

Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty Method
julia
mlirIntegerSetIsCanonicalEmpty(set)

Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.

source

Reactant.MLIR.API.mlirIntegerSetIsConstraintEq Method
julia
mlirIntegerSetIsConstraintEq(set, pos)

Returns true of the pos-th constraint of the set is an equality constraint, false otherwise.

source

Reactant.MLIR.API.mlirIntegerSetIsNull Method
julia
mlirIntegerSetIsNull(set)

Checks whether an integer set is a null object.

source

Reactant.MLIR.API.mlirIntegerSetPrint Method
julia
mlirIntegerSetPrint(set, callback, userData)

Prints an integer set by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirIntegerSetReplaceGet Method
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)

Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.

source

Reactant.MLIR.API.mlirIntegerTypeGet Method
julia
mlirIntegerTypeGet(ctx, bitwidth)

Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.

source

Reactant.MLIR.API.mlirIntegerTypeGetTypeID Method
julia
mlirIntegerTypeGetTypeID()

Returns the typeID of an Integer type.

source

Reactant.MLIR.API.mlirIntegerTypeGetWidth Method
julia
mlirIntegerTypeGetWidth(type)

Returns the bitwidth of an integer type.

source

Reactant.MLIR.API.mlirIntegerTypeIsSigned Method
julia
mlirIntegerTypeIsSigned(type)

Checks whether the given integer type is signed.

source

Reactant.MLIR.API.mlirIntegerTypeIsSignless Method
julia
mlirIntegerTypeIsSignless(type)

Checks whether the given integer type is signless.

source

Reactant.MLIR.API.mlirIntegerTypeIsUnsigned Method
julia
mlirIntegerTypeIsUnsigned(type)

Checks whether the given integer type is unsigned.

source

Reactant.MLIR.API.mlirIntegerTypeSignedGet Method
julia
mlirIntegerTypeSignedGet(ctx, bitwidth)

Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.

source

Reactant.MLIR.API.mlirIntegerTypeUnsignedGet Method
julia
mlirIntegerTypeUnsignedGet(ctx, bitwidth)

Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.

source

Reactant.MLIR.API.mlirIsCurrentDebugType Method
julia
mlirIsCurrentDebugType(type)

Checks if type is set as the current debug type.

source

Reactant.MLIR.API.mlirIsGlobalDebugEnabled Method
julia
mlirIsGlobalDebugEnabled()

Retuns true if the global debugging flag is set, false otherwise.

source

Reactant.MLIR.API.mlirLLVMArrayTypeGet Method
julia
mlirLLVMArrayTypeGet(elementType, numElements)

Creates an llvm.array type.

source

Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType Method
julia
mlirLLVMArrayTypeGetElementType(type)

Returns the element type of the llvm.array type.

source

Reactant.MLIR.API.mlirLLVMCConvAttrGet Method
julia
mlirLLVMCConvAttrGet(ctx, cconv)

Creates a LLVM CConv attribute.

source

Reactant.MLIR.API.mlirLLVMComdatAttrGet Method
julia
mlirLLVMComdatAttrGet(ctx, comdat)

Creates a LLVM Comdat attribute.

source

Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet Method
julia
mlirLLVMDIAnnotationAttrGet(ctx, name, value)

Creates a LLVM DIAnnotation attribute.

source

Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet Method
julia
mlirLLVMDIBasicTypeAttrGet(ctx, tag, name, sizeInBits, encoding)

Creates a LLVM DIBasicType attribute.

source

Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet Method
julia
mlirLLVMDICompileUnitAttrGet(ctx, id, sourceLanguage, file, producer, isOptimized, emissionKind, nameTableKind)

Creates a LLVM DICompileUnit attribute.

source

Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet Method
julia
mlirLLVMDICompositeTypeAttrGet(ctx, recId, isRecSelf, tag, name, file, line, scope, baseType, flags, sizeInBits, alignInBits, nElements, elements, dataLocation, rank, allocated, associated)

Creates a LLVM DICompositeType attribute.

source

Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf Method
julia
mlirLLVMDICompositeTypeAttrGetRecSelf(recId)

Creates a self-referencing LLVM DICompositeType attribute.

source

Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet Method
julia
mlirLLVMDIDerivedTypeAttrGet(ctx, tag, name, baseType, sizeInBits, alignInBits, offsetInBits, dwarfAddressSpace, extraData)

Creates a LLVM DIDerivedType attribute. Note that dwarfAddressSpace is an optional field, where MLIR_CAPI_DWARF_ADDRESS_SPACE_NULL indicates null and non-negative values indicate a value present.

source

Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType Method
julia
mlirLLVMDIDerivedTypeAttrGetBaseType(diDerivedType)

Gets the base type from a LLVM DIDerivedType attribute.

source

Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet Method
julia
mlirLLVMDIExpressionAttrGet(ctx, nOperations, operations)

Creates a LLVM DIExpression attribute.

source

Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet Method
julia
mlirLLVMDIExpressionElemAttrGet(ctx, opcode, nArguments, arguments)

Creates a LLVM DIExpressionElem attribute.

source

Reactant.MLIR.API.mlirLLVMDIFileAttrGet Method
julia
mlirLLVMDIFileAttrGet(ctx, name, directory)

Creates a LLVM DIFileAttr attribute.

source

Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet Method
julia
mlirLLVMDIFlagsAttrGet(ctx, value)

Creates a LLVM DIFlags attribute.

source

Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet Method
julia
mlirLLVMDIImportedEntityAttrGet(ctx, tag, scope, entity, file, line, name, nElements, elements)

Creates a LLVM DIImportedEntityAttr attribute.

source

Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet Method
julia
mlirLLVMDILexicalBlockAttrGet(ctx, scope, file, line, column)

Creates a LLVM DILexicalBlock attribute.

source

Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet Method
julia
mlirLLVMDILexicalBlockFileAttrGet(ctx, scope, file, discriminator)

Creates a LLVM DILexicalBlockFile attribute.

source

Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet Method
julia
mlirLLVMDILocalVariableAttrGet(ctx, scope, name, diFile, line, arg, alignInBits, diType, flags)

Creates a LLVM DILocalVariableAttr attribute.

source

Reactant.MLIR.API.mlirLLVMDIModuleAttrGet Method
julia
mlirLLVMDIModuleAttrGet(ctx, file, scope, name, configMacros, includePath, apinotes, line, isDecl)

Creates a LLVM DIModuleAttr attribute.

source

Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope Method
julia
mlirLLVMDIModuleAttrGetScope(diModule)

Gets the scope of this DIModuleAttr.

source

Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet Method
julia
mlirLLVMDINullTypeAttrGet(ctx)

Creates a LLVM DINullType attribute.

source

Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet Method
julia
mlirLLVMDISubprogramAttrGet(ctx, recId, isRecSelf, id, compileUnit, scope, name, linkageName, file, line, scopeLine, subprogramFlags, type, nRetainedNodes, retainedNodes, nAnnotations, annotations)

Creates a LLVM DISubprogramAttr attribute.

source

Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit Method
julia
mlirLLVMDISubprogramAttrGetCompileUnit(diSubprogram)

Gets the compile unit from this DISubprogram.

source

Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile Method
julia
mlirLLVMDISubprogramAttrGetFile(diSubprogram)

Gets the file from this DISubprogramAttr.

source

Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine Method
julia
mlirLLVMDISubprogramAttrGetLine(diSubprogram)

Gets the line from this DISubprogramAttr.

source

Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf Method
julia
mlirLLVMDISubprogramAttrGetRecSelf(recId)

Creates a self-referencing LLVM DISubprogramAttr attribute.

source

Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope Method
julia
mlirLLVMDISubprogramAttrGetScope(diSubprogram)

Gets the scope from this DISubprogramAttr.

source

Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine Method
julia
mlirLLVMDISubprogramAttrGetScopeLine(diSubprogram)

Gets the scope line from this DISubprogram.

source

Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType Method
julia
mlirLLVMDISubprogramAttrGetType(diSubprogram)

Gets the type from this DISubprogramAttr.

source

Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet Method
julia
mlirLLVMDISubroutineTypeAttrGet(ctx, callingConvention, nTypes, types)

Creates a LLVM DISubroutineTypeAttr attribute.

source

Reactant.MLIR.API.mlirLLVMFunctionTypeGet Method
julia
mlirLLVMFunctionTypeGet(resultType, nArgumentTypes, argumentTypes, isVarArg)

Creates an llvm.func type.

source

Reactant.MLIR.API.mlirLLVMLinkageAttrGet Method
julia
mlirLLVMLinkageAttrGet(ctx, linkage)

Creates a LLVM Linkage attribute.

source

Reactant.MLIR.API.mlirLLVMPointerTypeGet Method
julia
mlirLLVMPointerTypeGet(ctx, addressSpace)

Creates an llvm.ptr type.

source

Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace Method
julia
mlirLLVMPointerTypeGetAddressSpace(pointerType)

Returns address space of llvm.ptr

source

Reactant.MLIR.API.mlirLLVMStructTypeGetElementType Method
julia
mlirLLVMStructTypeGetElementType(type, position)

Returns the positions-th field of the struct. Asserts if the struct is opaque, not yet initialized or if the position is out of range.

source

Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier Method
julia
mlirLLVMStructTypeGetIdentifier(type)

Returns the identifier of the identified struct. Asserts that the struct is identified, i.e., not literal.

source

Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes Method
julia
mlirLLVMStructTypeGetNumElementTypes(type)

Returns the number of fields in the struct. Asserts if the struct is opaque or not yet initialized.

source

Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet Method
julia
mlirLLVMStructTypeIdentifiedGet(ctx, name)

Creates an LLVM identified struct type with no body. If a struct type with this name already exists in the context, returns that type. Use mlirLLVMStructTypeIdentifiedNewGet to create a fresh struct type, potentially renaming it. The body should be set separatelty by calling mlirLLVMStructTypeSetBody, if it isn't set already.

source

Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet Method
julia
mlirLLVMStructTypeIdentifiedNewGet(ctx, name, nFieldTypes, fieldTypes, isPacked)

Creates an LLVM identified struct type with no body and a name starting with the given prefix. If a struct with the exact name as the given prefix already exists, appends an unspecified suffix to the name so that the name is unique in context.

source

Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral Method
julia
mlirLLVMStructTypeIsLiteral(type)

Returns true if the type is a literal (unnamed) LLVM struct type.

source

Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque Method
julia
mlirLLVMStructTypeIsOpaque(type)

Returns true is the struct is explicitly opaque (will not have a body) or uninitialized (will eventually have a body).

source

Reactant.MLIR.API.mlirLLVMStructTypeIsPacked Method
julia
mlirLLVMStructTypeIsPacked(type)

Returns true if the struct is packed.

source

Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet Method
julia
mlirLLVMStructTypeLiteralGet(ctx, nFieldTypes, fieldTypes, isPacked)

Creates an LLVM literal (unnamed) struct type. This may assert if the fields have types not compatible with the LLVM dialect. For a graceful failure, use the checked version.

source

Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked Method
julia
mlirLLVMStructTypeLiteralGetChecked(loc, nFieldTypes, fieldTypes, isPacked)

Creates an LLVM literal (unnamed) struct type if possible. Emits a diagnostic at the given location and returns null otherwise.

source

Reactant.MLIR.API.mlirLLVMStructTypeSetBody Method
julia
mlirLLVMStructTypeSetBody(structType, nFieldTypes, fieldTypes, isPacked)

Sets the body of the identified struct if it hasn't been set yet. Returns whether the operation was successful.

source

Reactant.MLIR.API.mlirLLVMVoidTypeGet Method
julia
mlirLLVMVoidTypeGet(ctx)

Creates an llmv.void type.

source

Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion Method
julia
mlirLinalgFillBuiltinNamedOpRegion(mlirOp)

Apply the special region builder for the builtin named Linalg op. Assert that mlirOp is a builtin named Linalg op.

source

Reactant.MLIR.API.mlirLlvmThreadPoolCreate Method
julia
mlirLlvmThreadPoolCreate()

Create an LLVM thread pool. This is reexported here to avoid directly pulling in the LLVM headers directly.

source

Reactant.MLIR.API.mlirLlvmThreadPoolDestroy Method
julia
mlirLlvmThreadPoolDestroy(pool)

Destroy an LLVM thread pool.

source

Reactant.MLIR.API.mlirLoadIRDLDialects Method
julia
mlirLoadIRDLDialects(_module)

Loads all IRDL dialects in the provided module, registering the dialects in the module's associated context.

source

Reactant.MLIR.API.mlirLocationCallSiteGet Method
julia
mlirLocationCallSiteGet(callee, caller)

Creates a call site location with a callee and a caller.

source

Reactant.MLIR.API.mlirLocationEqual Method
julia
mlirLocationEqual(l1, l2)

Checks if two locations are equal.

source

Reactant.MLIR.API.mlirLocationFileLineColGet Method
julia
mlirLocationFileLineColGet(context, filename, line, col)

Creates an File/Line/Column location owned by the given context.

source

Reactant.MLIR.API.mlirLocationFromAttribute Method
julia
mlirLocationFromAttribute(attribute)

Creates a location from a location attribute.

source

Reactant.MLIR.API.mlirLocationFusedGet Method
julia
mlirLocationFusedGet(ctx, nLocations, locations, metadata)

Creates a fused location with an array of locations and metadata.

source

Reactant.MLIR.API.mlirLocationGetAttribute Method
julia
mlirLocationGetAttribute(location)

Returns the underlying location attribute of this location.

source

Reactant.MLIR.API.mlirLocationGetContext Method
julia
mlirLocationGetContext(location)

Gets the context that a location was created with.

source

Reactant.MLIR.API.mlirLocationIsNull Method
julia
mlirLocationIsNull(location)

Checks if the location is null.

source

Reactant.MLIR.API.mlirLocationNameGet Method
julia
mlirLocationNameGet(context, name, childLoc)

Creates a name location owned by the given context. Providing null location for childLoc is allowed and if childLoc is null location, then the behavior is the same as having unknown child location.

source

Reactant.MLIR.API.mlirLocationPrint Method
julia
mlirLocationPrint(location, callback, userData)

Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirLocationUnknownGet Method
julia
mlirLocationUnknownGet(context)

Creates a location with unknown position owned by the given context.

source

Reactant.MLIR.API.mlirLogicalResultFailure Method
julia
mlirLogicalResultFailure()

Creates a logical result representing a failure.

source

Reactant.MLIR.API.mlirLogicalResultIsFailure Method
julia
mlirLogicalResultIsFailure(res)

Checks if the given logical result represents a failure.

source

Reactant.MLIR.API.mlirLogicalResultIsSuccess Method
julia
mlirLogicalResultIsSuccess(res)

Checks if the given logical result represents a success.

source

Reactant.MLIR.API.mlirLogicalResultSuccess Method
julia
mlirLogicalResultSuccess()

Creates a logical result representing a success.

source

Reactant.MLIR.API.mlirMemRefTypeContiguousGet Method
julia
mlirMemRefTypeContiguousGet(elementType, rank, shape, memorySpace)

Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context.

source

Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked Method
julia
mlirMemRefTypeContiguousGetChecked(loc, elementType, rank, shape, memorySpace)

Same as "mlirMemRefTypeContiguousGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

Reactant.MLIR.API.mlirMemRefTypeGet Method
julia
mlirMemRefTypeGet(elementType, rank, shape, layout, memorySpace)

Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context.

source

Reactant.MLIR.API.mlirMemRefTypeGetAffineMap Method
julia
mlirMemRefTypeGetAffineMap(type)

Returns the affine map of the given MemRef type.

source

Reactant.MLIR.API.mlirMemRefTypeGetChecked Method
julia
mlirMemRefTypeGetChecked(loc, elementType, rank, shape, layout, memorySpace)

Same as "mlirMemRefTypeGet" but returns a nullptr-wrapping MlirType o illegal arguments, emitting appropriate diagnostics.

source

Reactant.MLIR.API.mlirMemRefTypeGetLayout Method
julia
mlirMemRefTypeGetLayout(type)

Returns the layout of the given MemRef type.

source

Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace Method
julia
mlirMemRefTypeGetMemorySpace(type)

Returns the memory space of the given MemRef type.

source

Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset Method
julia
mlirMemRefTypeGetStridesAndOffset(type, strides, offset)

Returns the strides of the MemRef if the layout map is in strided form. Both strides and offset are out params. strides must point to pre-allocated memory of length equal to the rank of the memref.

source

Reactant.MLIR.API.mlirMemRefTypeGetTypeID Method
julia
mlirMemRefTypeGetTypeID()

Returns the typeID of an MemRef type.

source

Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone Method
julia
mlirMergeSymbolsIntoFromClone(target, other)

Merge the symbols from other into target, potentially renaming them to avoid conflicts. Private symbols may be renamed during the merge, public symbols must have at most one declaration. A name conflict in public symbols is reported as an error before returning a failure.

Note that this clones the other operation unlike the C++ counterpart that takes ownership.

source

Reactant.MLIR.API.mlirModuleCreateEmpty Method
julia
mlirModuleCreateEmpty(location)

Creates a new, empty module and transfers ownership to the caller.

source

Reactant.MLIR.API.mlirModuleCreateParse Method
julia
mlirModuleCreateParse(context, _module)

Parses a module from the string and transfers ownership to the caller.

source

Reactant.MLIR.API.mlirModuleDestroy Method
julia
mlirModuleDestroy(_module)

Takes a module owned by the caller and deletes it.

source

Reactant.MLIR.API.mlirModuleFromOperation Method
julia
mlirModuleFromOperation(op)

Views the generic operation as a module. The returned module is null when the input operation was not a ModuleOp.

source

Reactant.MLIR.API.mlirModuleGetBody Method
julia
mlirModuleGetBody(_module)

Gets the body of the module, i.e. the only block it contains.

source

Reactant.MLIR.API.mlirModuleGetContext Method
julia
mlirModuleGetContext(_module)

Gets the context that a module was created with.

source

Reactant.MLIR.API.mlirModuleGetOperation Method
julia
mlirModuleGetOperation(_module)

Views the module as a generic operation.

source

Reactant.MLIR.API.mlirModuleIsNull Method
julia
mlirModuleIsNull(_module)

Checks whether a module is null.

source

Reactant.MLIR.API.mlirNamedAttributeGet Method
julia
mlirNamedAttributeGet(name, attr)

Associates an attribute with the name. Takes ownership of neither.

source

Reactant.MLIR.API.mlirNoneTypeGet Method
julia
mlirNoneTypeGet(ctx)

Creates a None type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirNoneTypeGetTypeID Method
julia
mlirNoneTypeGetTypeID()

Returns the typeID of an None type.

source

Reactant.MLIR.API.mlirOpOperandGetNextUse Method
julia
mlirOpOperandGetNextUse(opOperand)

Returns an op operand representing the next use of the value, or a null op operand if there is no next use.

source

Reactant.MLIR.API.mlirOpOperandGetOperandNumber Method
julia
mlirOpOperandGetOperandNumber(opOperand)

Returns the operand number of an op operand.

source

Reactant.MLIR.API.mlirOpOperandGetOwner Method
julia
mlirOpOperandGetOwner(opOperand)

Returns the owner operation of an op operand.

source

Reactant.MLIR.API.mlirOpOperandGetValue Method
julia
mlirOpOperandGetValue(opOperand)

Returns the value of an op operand.

source

Reactant.MLIR.API.mlirOpOperandIsNull Method
julia
mlirOpOperandIsNull(opOperand)

Returns whether the op operand is null.

source

Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass Method
julia
mlirOpPassManagerAddOwnedPass(passManager, pass)

Add a pass and transfer ownership to the provided mlirOpPassManager. If the pass is not a generic operation pass or matching the type of the provided PassManager, a new OpPassManager is implicitly nested under the provided PassManager.

source

Reactant.MLIR.API.mlirOpPassManagerAddPipeline Method
julia
mlirOpPassManagerAddPipeline(passManager, pipelineElements, callback, userData)

Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.

source

Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder Method
julia
mlirOpPassManagerGetNestedUnder(passManager, operationName)

Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.

source

Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified Method
julia
mlirOpPrintingFlagsAssumeVerified(flags)

Do not verify the operation when using custom operation printers.

source

Reactant.MLIR.API.mlirOpPrintingFlagsCreate Method
julia
mlirOpPrintingFlagsCreate()

Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirOpPrintingFlagsDestroy().

source

Reactant.MLIR.API.mlirOpPrintingFlagsDestroy Method
julia
mlirOpPrintingFlagsDestroy(flags)

Destroys printing flags created with mlirOpPrintingFlagsCreate.

source

Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs Method
julia
mlirOpPrintingFlagsElideLargeElementsAttrs(flags, largeElementLimit)

Enables the elision of large elements attributes by printing a lexically valid but otherwise meaningless form instead of the element data. The largeElementLimit is used to configure what is considered to be a "large" ElementsAttr by providing an upper limit to the number of elements.

source

Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString Method
julia
mlirOpPrintingFlagsElideLargeResourceString(flags, largeResourceLimit)

Enables the elision of large resources strings by omitting them from the dialect_resources section. The largeResourceLimit is used to configure what is considered to be a "large" resource by providing an upper limit to the string size.

source

Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo Method
julia
mlirOpPrintingFlagsEnableDebugInfo(flags, enable, prettyForm)

Enable or disable printing of debug information (based on enable). If 'prettyForm' is set to true, debug information is printed in a more readable 'pretty' form. Note: The IR generated with 'prettyForm' is not parsable.

source

Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm Method
julia
mlirOpPrintingFlagsPrintGenericOpForm(flags)

Always print operations in the generic form.

source

Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions Method
julia
mlirOpPrintingFlagsSkipRegions(flags)

Skip printing regions.

source

Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope Method
julia
mlirOpPrintingFlagsUseLocalScope(flags)

Use local scope when printing the operation. This allows for using the printer in a more localized and thread-safe setting, but may not necessarily be identical to what the IR will look like when dumping the full module.

source

Reactant.MLIR.API.mlirOpResultGetOwner Method
julia
mlirOpResultGetOwner(value)

Returns an operation that produced this value as its result. Asserts if the value is not an op result.

source

Reactant.MLIR.API.mlirOpResultGetResultNumber Method
julia
mlirOpResultGetResultNumber(value)

Returns the position of the value in the list of results of the operation that produced it.

source

Reactant.MLIR.API.mlirOpaqueAttrGet Method
julia
mlirOpaqueAttrGet(ctx, dialectNamespace, dataLength, data, type)

Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).

source

Reactant.MLIR.API.mlirOpaqueAttrGetData Method
julia
mlirOpaqueAttrGetData(attr)

Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.

source

Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace Method
julia
mlirOpaqueAttrGetDialectNamespace(attr)

Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.

source

Reactant.MLIR.API.mlirOpaqueAttrGetTypeID Method
julia
mlirOpaqueAttrGetTypeID()

Returns the typeID of an Opaque attribute.

source

Reactant.MLIR.API.mlirOpaqueTypeGet Method
julia
mlirOpaqueTypeGet(ctx, dialectNamespace, typeData)

Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).

source

Reactant.MLIR.API.mlirOpaqueTypeGetData Method
julia
mlirOpaqueTypeGetData(type)

Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.

source

Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace Method
julia
mlirOpaqueTypeGetDialectNamespace(type)

Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.

source

Reactant.MLIR.API.mlirOpaqueTypeGetTypeID Method
julia
mlirOpaqueTypeGetTypeID()

Returns the typeID of an Opaque type.

source

Reactant.MLIR.API.mlirOperationClone Method
julia
mlirOperationClone(op)

Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.

source

Reactant.MLIR.API.mlirOperationCreate Method
julia
mlirOperationCreate(state)

Creates an operation and transfers ownership to the caller. Note that caller owned child objects are transferred in this call and must not be further used. Particularly, this applies to any regions added to the state (the implementation may invalidate any such pointers).

This call can fail under the following conditions, in which case, it will return a null operation and emit diagnostics: - Result type inference is enabled and cannot be performed.

source

Reactant.MLIR.API.mlirOperationCreateParse Method
julia
mlirOperationCreateParse(context, sourceStr, sourceName)

Parses an operation, giving ownership to the caller. If parsing fails a null operation will be returned, and an error diagnostic emitted.

sourceStr may be either the text assembly format, or binary bytecode format. sourceName is used as the file name of the source; any IR without locations will get a FileLineColLoc location with sourceName as the file name.

source

Reactant.MLIR.API.mlirOperationDestroy Method
julia
mlirOperationDestroy(op)

Takes an operation owned by the caller and destroys it.

source

Reactant.MLIR.API.mlirOperationDump Method
julia
mlirOperationDump(op)

Prints an operation to stderr.

source

Reactant.MLIR.API.mlirOperationEqual Method
julia
mlirOperationEqual(op, other)

Checks whether two operation handles point to the same operation. This does not perform deep comparison.

source

Reactant.MLIR.API.mlirOperationGetAttribute Method
julia
mlirOperationGetAttribute(op, pos)

Return pos-th attribute of the operation. Deprecated, please use mlirOperationGetInherentAttribute or mlirOperationGetDiscardableAttribute.

source

Reactant.MLIR.API.mlirOperationGetAttributeByName Method
julia
mlirOperationGetAttributeByName(op, name)

Returns an attribute attached to the operation given its name. Deprecated, please use mlirOperationGetInherentAttributeByName or mlirOperationGetDiscardableAttributeByName.

source

Reactant.MLIR.API.mlirOperationGetBlock Method
julia
mlirOperationGetBlock(op)

Gets the block that owns this operation, returning null if the operation is not owned.

source

Reactant.MLIR.API.mlirOperationGetContext Method
julia
mlirOperationGetContext(op)

Gets the context this operation is associated with

source

Reactant.MLIR.API.mlirOperationGetDiscardableAttribute Method
julia
mlirOperationGetDiscardableAttribute(op, pos)

Return pos-th discardable attribute of the operation.

source

Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName Method
julia
mlirOperationGetDiscardableAttributeByName(op, name)

Returns a discardable attribute attached to the operation given its name.

source

Reactant.MLIR.API.mlirOperationGetFirstRegion Method
julia
mlirOperationGetFirstRegion(op)

Returns first region attached to the operation.

source

Reactant.MLIR.API.mlirOperationGetInherentAttributeByName Method
julia
mlirOperationGetInherentAttributeByName(op, name)

Returns an inherent attribute attached to the operation given its name.

source

Reactant.MLIR.API.mlirOperationGetLocation Method
julia
mlirOperationGetLocation(op)

Gets the location of the operation.

source

Reactant.MLIR.API.mlirOperationGetName Method
julia
mlirOperationGetName(op)

Gets the name of the operation as an identifier.

source

Reactant.MLIR.API.mlirOperationGetNextInBlock Method
julia
mlirOperationGetNextInBlock(op)

Returns an operation immediately following the given operation it its enclosing block.

source

Reactant.MLIR.API.mlirOperationGetNumAttributes Method
julia
mlirOperationGetNumAttributes(op)

Returns the number of attributes attached to the operation. Deprecated, please use mlirOperationGetNumInherentAttributes or mlirOperationGetNumDiscardableAttributes.

source

Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes Method
julia
mlirOperationGetNumDiscardableAttributes(op)

Returns the number of discardable attributes attached to the operation.

source

Reactant.MLIR.API.mlirOperationGetNumOperands Method
julia
mlirOperationGetNumOperands(op)

Returns the number of operands of the operation.

source

Reactant.MLIR.API.mlirOperationGetNumRegions Method
julia
mlirOperationGetNumRegions(op)

Returns the number of regions attached to the given operation.

source

Reactant.MLIR.API.mlirOperationGetNumResults Method
julia
mlirOperationGetNumResults(op)

Returns the number of results of the operation.

source

Reactant.MLIR.API.mlirOperationGetNumSuccessors Method
julia
mlirOperationGetNumSuccessors(op)

Returns the number of successor blocks of the operation.

source

Reactant.MLIR.API.mlirOperationGetOperand Method
julia
mlirOperationGetOperand(op, pos)

Returns pos-th operand of the operation.

source

Reactant.MLIR.API.mlirOperationGetParentOperation Method
julia
mlirOperationGetParentOperation(op)

Gets the operation that owns this operation, returning null if the operation is not owned.

source

Reactant.MLIR.API.mlirOperationGetRegion Method
julia
mlirOperationGetRegion(op, pos)

Returns pos-th region attached to the operation.

source

Reactant.MLIR.API.mlirOperationGetResult Method
julia
mlirOperationGetResult(op, pos)

Returns pos-th result of the operation.

source

Reactant.MLIR.API.mlirOperationGetSuccessor Method
julia
mlirOperationGetSuccessor(op, pos)

Returns pos-th successor of the operation.

source

Reactant.MLIR.API.mlirOperationGetTypeID Method
julia
mlirOperationGetTypeID(op)

Gets the type id of the operation. Returns null if the operation does not have a registered operation description.

source

Reactant.MLIR.API.mlirOperationHasInherentAttributeByName Method
julia
mlirOperationHasInherentAttributeByName(op, name)

Returns true if this operation defines an inherent attribute with this name. Note: the attribute can be optional, so mlirOperationGetInherentAttributeByName can still return a null attribute.

source

Reactant.MLIR.API.mlirOperationImplementsInterface Method
julia
mlirOperationImplementsInterface(operation, interfaceTypeID)

Returns true if the given operation implements an interface identified by its TypeID.

source

Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic Method
julia
mlirOperationImplementsInterfaceStatic(operationName, context, interfaceTypeID)

Returns true if the operation identified by its canonical string name implements the interface identified by its TypeID in the given context. Note that interfaces may be attached to operations in some contexts and not others.

source

Reactant.MLIR.API.mlirOperationIsNull Method
julia
mlirOperationIsNull(op)

Checks whether the underlying operation is null.

source

Reactant.MLIR.API.mlirOperationMoveAfter Method
julia
mlirOperationMoveAfter(op, other)

Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.

source

Reactant.MLIR.API.mlirOperationMoveBefore Method
julia
mlirOperationMoveBefore(op, other)

Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.

source

Reactant.MLIR.API.mlirOperationPrint Method
julia
mlirOperationPrint(op, callback, userData)

Prints an operation by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirOperationPrintWithFlags Method
julia
mlirOperationPrintWithFlags(op, flags, callback, userData)

Same as mlirOperationPrint but accepts flags controlling the printing behavior.

source

Reactant.MLIR.API.mlirOperationPrintWithState Method
julia
mlirOperationPrintWithState(op, state, callback, userData)

Same as mlirOperationPrint but accepts AsmState controlling the printing behavior as well as caching computed names.

source

Reactant.MLIR.API.mlirOperationRemoveAttributeByName Method
julia
mlirOperationRemoveAttributeByName(op, name)

Removes an attribute by name. Returns false if the attribute was not found and true if removed. Deprecated, please use mlirOperationRemoveInherentAttributeByName or mlirOperationRemoveDiscardableAttributeByName.

source

Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName Method
julia
mlirOperationRemoveDiscardableAttributeByName(op, name)

Removes a discardable attribute by name. Returns false if the attribute was not found and true if removed.

source

Reactant.MLIR.API.mlirOperationRemoveFromParent Method
julia
mlirOperationRemoveFromParent(op)

Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.

source

Reactant.MLIR.API.mlirOperationSetAttributeByName Method
julia
mlirOperationSetAttributeByName(op, name, attr)

Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise. Deprecated, please use mlirOperationSetInherentAttributeByName or mlirOperationSetDiscardableAttributeByName.

source

Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName Method
julia
mlirOperationSetDiscardableAttributeByName(op, name, attr)

Sets a discardable attribute by name, replacing the existing if it exists or adding a new one otherwise. The new attr Attribute is not allowed to be null, use mlirOperationRemoveDiscardableAttributeByName to remove an Attribute instead.

source

Reactant.MLIR.API.mlirOperationSetInherentAttributeByName Method
julia
mlirOperationSetInherentAttributeByName(op, name, attr)

Sets an inherent attribute by name, replacing the existing if it exists. This has no effect if "name" does not match an inherent attribute.

source

Reactant.MLIR.API.mlirOperationSetOperand Method
julia
mlirOperationSetOperand(op, pos, newValue)

Sets the pos-th operand of the operation.

source

Reactant.MLIR.API.mlirOperationSetOperands Method
julia
mlirOperationSetOperands(op, nOperands, operands)

Replaces the operands of the operation.

source

Reactant.MLIR.API.mlirOperationSetSuccessor Method
julia
mlirOperationSetSuccessor(op, pos, block)

Set pos-th successor of the operation.

source

Reactant.MLIR.API.mlirOperationStateAddResults Method
julia
mlirOperationStateAddResults(state, n, results)

Adds a list of components to the operation state.

source

Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference Method
julia
mlirOperationStateEnableResultTypeInference(state)

Enables result type inference for the operation under construction. If enabled, then the caller must not have called mlirOperationStateAddResults(). Note that if enabled, the mlirOperationCreate() call is failable: it will return a null operation on inference failure and will emit diagnostics.

source

Reactant.MLIR.API.mlirOperationStateGet Method
julia
mlirOperationStateGet(name, loc)

Constructs an operation state from a name and a location.

source

Reactant.MLIR.API.mlirOperationVerify Method
julia
mlirOperationVerify(op)

Verify the operation and return true if it passes, false if it fails.

source

Reactant.MLIR.API.mlirOperationWalk Method
julia
mlirOperationWalk(op, callback, userData, walkOrder)

Walks operation op in walkOrder and calls callback on that operation. *userData is passed to the callback as well and can be used to tunnel some context or other data into the callback.

source

Reactant.MLIR.API.mlirOperationWriteBytecode Method
julia
mlirOperationWriteBytecode(op, callback, userData)

Same as mlirOperationPrint but writing the bytecode format.

source

Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig Method
julia
mlirOperationWriteBytecodeWithConfig(op, config, callback, userData)

Same as mlirOperationWriteBytecode but with writer config and returns failure only if desired bytecode could not be honored.

source

Reactant.MLIR.API.mlirParsePassPipeline Method
julia
mlirParsePassPipeline(passManager, pipeline, callback, userData)

Parse a textual MLIR pass pipeline and assign it to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.

source

Reactant.MLIR.API.mlirPassManagerAddOwnedPass Method
julia
mlirPassManagerAddOwnedPass(passManager, pass)

Add a pass and transfer ownership to the provided top-level mlirPassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.

source

Reactant.MLIR.API.mlirPassManagerCreate Method
julia
mlirPassManagerCreate(ctx)

Create a new top-level PassManager with the default anchor.

source

Reactant.MLIR.API.mlirPassManagerCreateOnOperation Method
julia
mlirPassManagerCreateOnOperation(ctx, anchorOp)

Create a new top-level PassManager anchored on anchorOp.

source

Reactant.MLIR.API.mlirPassManagerDestroy Method
julia
mlirPassManagerDestroy(passManager)

Destroy the provided PassManager.

source

Reactant.MLIR.API.mlirPassManagerEnableIRPrinting Method
julia
mlirPassManagerEnableIRPrinting(passManager, printBeforeAll, printAfterAll, printModuleScope, printAfterOnlyOnChange, printAfterOnlyOnFailure, flags, treePrintingPath)

Enable IR printing. The treePrintingPath argument is an optional path to a directory where the dumps will be produced. If it isn't provided then dumps are produced to stderr.

source

Reactant.MLIR.API.mlirPassManagerEnableVerifier Method
julia
mlirPassManagerEnableVerifier(passManager, enable)

Enable / disable verify-each.

source

Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager Method
julia
mlirPassManagerGetAsOpPassManager(passManager)

Cast a top-level PassManager to a generic OpPassManager.

source

Reactant.MLIR.API.mlirPassManagerGetNestedUnder Method
julia
mlirPassManagerGetNestedUnder(passManager, operationName)

Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.

source

Reactant.MLIR.API.mlirPassManagerIsNull Method
julia
mlirPassManagerIsNull(passManager)

Checks if a PassManager is null.

source

Reactant.MLIR.API.mlirPassManagerRunOnOp Method
julia
mlirPassManagerRunOnOp(passManager, op)

Run the provided passManager on the given op.

source

Reactant.MLIR.API.mlirPrintPassPipeline Method
julia
mlirPrintPassPipeline(passManager, callback, userData)

Print a textual MLIR pass pipeline by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType Method
julia
mlirQuantizedTypeCastExpressedToStorageType(type, candidate)

Casts from a type based on the expressed type of the given quantized type to equivalent type based on storage type of the same quantized type.

source

Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType Method
julia
mlirQuantizedTypeCastFromExpressedType(type, candidate)

Casts from a type based on the expressed type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.

source

Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType Method
julia
mlirQuantizedTypeCastFromStorageType(type, candidate)

Casts from a type based on the storage type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.

source

Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType Method
julia
mlirQuantizedTypeCastToExpressedType(type)

Casts from a type based on a quantized type to a corresponding typed based on the expressed type. Returns a null type if the cast is not valid.

source

Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType Method
julia
mlirQuantizedTypeCastToStorageType(type)

Casts from a type based on a quantized type to a corresponding typed based on the storage type. Returns a null type if the cast is not valid.

source

Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger Method
julia
mlirQuantizedTypeGetDefaultMaximumForInteger(isSigned, integralWidth)

Returns the maximum possible value stored by a quantized type.

source

Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger Method
julia
mlirQuantizedTypeGetDefaultMinimumForInteger(isSigned, integralWidth)

Returns the minimum possible value stored by a quantized type.

source

Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType Method
julia
mlirQuantizedTypeGetExpressedType(type)

Gets the original type approximated by the given quantized type.

source

Reactant.MLIR.API.mlirQuantizedTypeGetFlags Method
julia
mlirQuantizedTypeGetFlags(type)

Gets the flags associated with the given quantized type.

source

Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType Method
julia
mlirQuantizedTypeGetQuantizedElementType(type)

Returns the element type of the given quantized type as another quantized type.

source

Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag Method
julia
mlirQuantizedTypeGetSignedFlag()

Returns the bit flag used to indicate signedness of a quantized type.

source

Reactant.MLIR.API.mlirQuantizedTypeGetStorageType Method
julia
mlirQuantizedTypeGetStorageType(type)

Returns the underlying type used to store the values.

source

Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth Method
julia
mlirQuantizedTypeGetStorageTypeIntegralWidth(type)

Returns the integral bitwidth that the storage type of the given quantized type can represent exactly.

source

Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax Method
julia
mlirQuantizedTypeGetStorageTypeMax(type)

Returns the maximum value that the storage type of the given quantized type can take.

source

Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin Method
julia
mlirQuantizedTypeGetStorageTypeMin(type)

Returns the minimum value that the storage type of the given quantized type can take.

source

Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType Method
julia
mlirQuantizedTypeIsCompatibleExpressedType(type, candidate)

Returns true if the candidate type is compatible with the given quantized type.

source

Reactant.MLIR.API.mlirQuantizedTypeIsSigned Method
julia
mlirQuantizedTypeIsSigned(type)

Returns true if the given type is signed, false otherwise.

source

Reactant.MLIR.API.mlirRankedTensorTypeGet Method
julia
mlirRankedTensorTypeGet(rank, shape, elementType, encoding)

Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull() to this parameter.

source

Reactant.MLIR.API.mlirRankedTensorTypeGetChecked Method
julia
mlirRankedTensorTypeGetChecked(loc, rank, shape, elementType, encoding)

Same as "mlirRankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding Method
julia
mlirRankedTensorTypeGetEncoding(type)

Gets the 'encoding' attribute from the ranked tensor type, returning a null attribute if none.

source

Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID Method
julia
mlirRankedTensorTypeGetTypeID()

Returns the typeID of an RankedTensor type.

source

Reactant.MLIR.API.mlirRegionAppendOwnedBlock Method
julia
mlirRegionAppendOwnedBlock(region, block)

Takes a block owned by the caller and appends it to the given region.

source

Reactant.MLIR.API.mlirRegionCreate Method
julia
mlirRegionCreate()

Creates a new empty region and transfers ownership to the caller.

source

Reactant.MLIR.API.mlirRegionDestroy Method
julia
mlirRegionDestroy(region)

Takes a region owned by the caller and destroys it.

source

Reactant.MLIR.API.mlirRegionEqual Method
julia
mlirRegionEqual(region, other)

Checks whether two region handles point to the same region. This does not perform deep comparison.

source

Reactant.MLIR.API.mlirRegionGetFirstBlock Method
julia
mlirRegionGetFirstBlock(region)

Gets the first block in the region.

source

Reactant.MLIR.API.mlirRegionGetNextInOperation Method
julia
mlirRegionGetNextInOperation(region)

Returns the region immediately following the given region in its parent operation.

source

Reactant.MLIR.API.mlirRegionInsertOwnedBlock Method
julia
mlirRegionInsertOwnedBlock(region, pos, block)

Takes a block owned by the caller and inserts it at pos to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.

source

Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter Method
julia
mlirRegionInsertOwnedBlockAfter(region, reference, block)

Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.

source

Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore Method
julia
mlirRegionInsertOwnedBlockBefore(region, reference, block)

Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.

source

Reactant.MLIR.API.mlirRegionIsNull Method
julia
mlirRegionIsNull(region)

Checks whether a region is null.

source

Reactant.MLIR.API.mlirRegionTakeBody Method
julia
mlirRegionTakeBody(target, source)

Moves the entire content of the source region to the target region.

source

Reactant.MLIR.API.mlirRegisterAllDialects Method
julia
mlirRegisterAllDialects(registry)

Appends all upstream dialects and extensions to the dialect registry.

source

Reactant.MLIR.API.mlirRegisterAllLLVMTranslations Method
julia
mlirRegisterAllLLVMTranslations(context)

Register all translations to LLVM IR for dialects that can support it.

source

Reactant.MLIR.API.mlirRegisterAllPasses Method
julia
mlirRegisterAllPasses()

Register all compiler passes of MLIR.

source

Reactant.MLIR.API.mlirRewriterBaseCancelOpModification Method
julia
mlirRewriterBaseCancelOpModification(rewriter, op)

This method cancels a pending in-place modification. This can only be called on operations that were provided to a call to startOpModification.

source

Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint Method
julia
mlirRewriterBaseClearInsertionPoint(rewriter)

Reset the insertion point to no location. Creating an operation without a set insertion point is an error, but this can still be useful when the current insertion point a builder refers to is being removed.

source

Reactant.MLIR.API.mlirRewriterBaseClone Method
julia
mlirRewriterBaseClone(rewriter, op)

Creates a deep copy of the specified operation.

source

Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore Method
julia
mlirRewriterBaseCloneRegionBefore(rewriter, region, before)

Clone the blocks that belong to "region" before the given position in another region "parent".

source

Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions Method
julia
mlirRewriterBaseCloneWithoutRegions(rewriter, op)

Creates a deep copy of this operation but keep the operation regions empty.

source

Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore Method
julia
mlirRewriterBaseCreateBlockBefore(rewriter, insertBefore, nArgTypes, argTypes, locations)

Add new block with 'argTypes' arguments and set the insertion point to the end of it. The block is placed before 'insertBefore'. locs contains the locations of the inserted arguments, and should match the size of argTypes.

source

Reactant.MLIR.API.mlirRewriterBaseEraseBlock Method
julia
mlirRewriterBaseEraseBlock(rewriter, block)

Erases a block along with all operations inside it.

source

Reactant.MLIR.API.mlirRewriterBaseEraseOp Method
julia
mlirRewriterBaseEraseOp(rewriter, op)

Erases an operation that is known to have no uses.

source

Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification Method
julia
mlirRewriterBaseFinalizeOpModification(rewriter, op)

This method is used to signal the end of an in-place modification of the given operation. This can only be called on operations that were provided to a call to startOpModification.

source

Reactant.MLIR.API.mlirRewriterBaseGetBlock Method
julia
mlirRewriterBaseGetBlock(rewriter)

Returns the current block of the rewriter.

source

Reactant.MLIR.API.mlirRewriterBaseGetContext Method
julia
mlirRewriterBaseGetContext(rewriter)

Get the MLIR context referenced by the rewriter.

source

Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock Method
julia
mlirRewriterBaseGetInsertionBlock(rewriter)

Return the block the current insertion point belongs to. Note that the insertion point is not necessarily the end of the block.

source

Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore Method
julia
mlirRewriterBaseInlineBlockBefore(rewriter, source, op, nArgValues, argValues)

Inline the operations of block 'source' before the operation 'op'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'

The source block must have no successors. Otherwise, the resulting IR would have unreachable operations.

source

Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore Method
julia
mlirRewriterBaseInlineRegionBefore(rewriter, region, before)

Move the blocks that belong to "region" before the given position in another region "parent". The two regions must be different. The caller is responsible for creating or updating the operation transferring flow of control to the region and passing it the correct block arguments.

source

Reactant.MLIR.API.mlirRewriterBaseInsert Method
julia
mlirRewriterBaseInsert(rewriter, op)

Insert the given operation at the current insertion point and return it.

source

Reactant.MLIR.API.mlirRewriterBaseMergeBlocks Method
julia
mlirRewriterBaseMergeBlocks(rewriter, source, dest, nArgValues, argValues)

Inline the operations of block 'source' into the end of block 'dest'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'

The dest block must have no successors. Otherwise, the resulting IR would have unreachable operation.

source

Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore Method
julia
mlirRewriterBaseMoveBlockBefore(rewriter, block, existingBlock)

Unlink this block and insert it right before existingBlock.

source

Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter Method
julia
mlirRewriterBaseMoveOpAfter(rewriter, op, existingOp)

Unlink this operation from its current block and insert it right after existingOp which may be in the same or another block in the same function.

source

Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore Method
julia
mlirRewriterBaseMoveOpBefore(rewriter, op, existingOp)

Unlink this operation from its current block and insert it right before existingOp which may be in the same or another block in the same function.

source

Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation Method
julia
mlirRewriterBaseReplaceAllOpUsesWithOperation(rewriter, from, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.

source

Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange Method
julia
mlirRewriterBaseReplaceAllOpUsesWithValueRange(rewriter, from, nTo, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.

source

Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept Method
julia
mlirRewriterBaseReplaceAllUsesExcept(rewriter, from, to, exceptedUser)

Find uses of from and replace them with to except if the user is exceptedUser. Also notify the listener about every in-place op modification (for every use that was replaced).

source

Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith Method
julia
mlirRewriterBaseReplaceAllUsesWith(rewriter, from, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).

source

Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith Method
julia
mlirRewriterBaseReplaceAllValueRangeUsesWith(rewriter, nValues, from, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).

source

Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock Method
julia
mlirRewriterBaseReplaceOpUsesWithinBlock(rewriter, op, nNewValues, newValues, block)

Find uses of from within block and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced). The optional allUsesReplaced flag is set to "true" if all uses were replaced.

source

Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation Method
julia
mlirRewriterBaseReplaceOpWithOperation(rewriter, op, newOp)

Replace the results of the given (original) operation with the specified new op (replacement). The result types of the two ops must match. The original op is erased.

source

Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues Method
julia
mlirRewriterBaseReplaceOpWithValues(rewriter, op, nValues, values)

Replace the results of the given (original) operation with the specified list of values (replacements). The result types of the given op and the replacements must match. The original op is erased.

source

Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter Method
julia
mlirRewriterBaseSetInsertionPointAfter(rewriter, op)

Sets the insertion point to the node after the specified operation, which will cause subsequent insertions to go right after it.

source

Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue Method
julia
mlirRewriterBaseSetInsertionPointAfterValue(rewriter, value)

Sets the insertion point to the node after the specified value. If value has a defining operation, sets the insertion point to the node after such defining operation. This will cause subsequent insertions to go right after it. Otherwise, value is a BlockArgument. Sets the insertion point to the start of its block.

source

Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore Method
julia
mlirRewriterBaseSetInsertionPointBefore(rewriter, op)

Sets the insertion point to the specified operation, which will cause subsequent insertions to go right before it.

source

Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd Method
julia
mlirRewriterBaseSetInsertionPointToEnd(rewriter, block)

Sets the insertion point to the end of the specified block.

source

Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart Method
julia
mlirRewriterBaseSetInsertionPointToStart(rewriter, block)

Sets the insertion point to the start of the specified block.

source

Reactant.MLIR.API.mlirRewriterBaseStartOpModification Method
julia
mlirRewriterBaseStartOpModification(rewriter, op)

This method is used to notify the rewriter that an in-place operation modification is about to happen. A call to this function must be followed by a call to either finalizeOpModification or cancelOpModification. This is a minor efficiency win (it avoids creating a new operation and removing the old one) but also often allows simpler code in the client.

source

Reactant.MLIR.API.mlirSetGlobalDebugType Method
julia
mlirSetGlobalDebugType(type)

Sets the current debug type, similarly to -debug-only=type in the command-line tools. Note that global debug should be enabled for any output to be produced.

source

Reactant.MLIR.API.mlirSetGlobalDebugTypes Method
julia
mlirSetGlobalDebugTypes(types, n)

Sets multiple current debug types, similarly to `-debug-only=type1,type2" in the command-line tools. Note that global debug should be enabled for any output to be produced.

source

Reactant.MLIR.API.mlirShapedTypeGetDimSize Method
julia
mlirShapedTypeGetDimSize(type, dim)

Returns the dim-th dimension of the given ranked shaped type.

source

Reactant.MLIR.API.mlirShapedTypeGetDynamicSize Method
julia
mlirShapedTypeGetDynamicSize()

Returns the value indicating a dynamic size in a shaped type. Prefer mlirShapedTypeIsDynamicSize to direct comparisons with this value.

source

Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset Method
julia
mlirShapedTypeGetDynamicStrideOrOffset()

Returns the value indicating a dynamic stride or offset in a shaped type. Prefer mlirShapedTypeGetDynamicStrideOrOffset to direct comparisons with this value.

source

Reactant.MLIR.API.mlirShapedTypeGetElementType Method
julia
mlirShapedTypeGetElementType(type)

Returns the element type of the shaped type.

source

Reactant.MLIR.API.mlirShapedTypeGetRank Method
julia
mlirShapedTypeGetRank(type)

Returns the rank of the given ranked shaped type.

source

Reactant.MLIR.API.mlirShapedTypeHasRank Method
julia
mlirShapedTypeHasRank(type)

Checks whether the given shaped type is ranked.

source

Reactant.MLIR.API.mlirShapedTypeHasStaticShape Method
julia
mlirShapedTypeHasStaticShape(type)

Checks whether the given shaped type has a static shape.

source

Reactant.MLIR.API.mlirShapedTypeIsDynamicDim Method
julia
mlirShapedTypeIsDynamicDim(type, dim)

Checks wither the dim-th dimension of the given shaped type is dynamic.

source

Reactant.MLIR.API.mlirShapedTypeIsDynamicSize Method
julia
mlirShapedTypeIsDynamicSize(size)

Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.

source

Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset Method
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)

Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.

source

Reactant.MLIR.API.mlirSparseElementsAttrGetIndices Method
julia
mlirSparseElementsAttrGetIndices(attr)

Returns the dense elements attribute containing 64-bit integer indices of non-null elements in the given sparse elements attribute.

source

Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID Method
julia
mlirSparseElementsAttrGetTypeID()

Returns the typeID of a SparseElements attribute.

source

Reactant.MLIR.API.mlirSparseElementsAttrGetValues Method
julia
mlirSparseElementsAttrGetValues(attr)

Returns the dense elements attribute containing the non-null elements in the given sparse elements attribute.

source

Reactant.MLIR.API.mlirSparseElementsAttribute Method
julia
mlirSparseElementsAttribute(shapedType, denseIndices, denseValues)

Creates a sparse elements attribute of the given shape from a list of indices and a list of associated values. Both lists are expected to be dense elements attributes with the same number of elements. The list of indices is expected to contain 64-bit integers. The attribute is created in the same context as the type.

source

Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet Method
julia
mlirSparseTensorEncodingAttrGet(ctx, lvlRank, lvlTypes, dimToLvl, lvlTodim, posWidth, crdWidth, explicitVal, implicitVal)

Creates a sparse\_tensor.encoding attribute with the given parameters.

source

Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth Method
julia
mlirSparseTensorEncodingAttrGetCrdWidth(attr)

Returns the coordinate bitwidth of the sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl Method
julia
mlirSparseTensorEncodingAttrGetDimToLvl(attr)

Returns the dimension-to-level mapping of the sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal Method
julia
mlirSparseTensorEncodingAttrGetExplicitVal(attr)

Returns the explicit value of the sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal Method
julia
mlirSparseTensorEncodingAttrGetImplicitVal(attr)

Returns the implicit value of the sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt Method
julia
mlirSparseTensorEncodingAttrGetLvlFmt(attr, lvl)

Returns a specified level-format of the sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim Method
julia
mlirSparseTensorEncodingAttrGetLvlToDim(attr)

Returns the level-to-dimension mapping of the sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType Method
julia
mlirSparseTensorEncodingAttrGetLvlType(attr, lvl)

Returns a specified level-type of the sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth Method
julia
mlirSparseTensorEncodingAttrGetPosWidth(attr)

Returns the position bitwidth of the sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank Method
julia
mlirSparseTensorEncodingGetLvlRank(attr)

Returns the level-rank of the sparse\_tensor.encoding attribute.

source

Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID Method
julia
mlirStridedLayoutAttrGetTypeID()

Returns the typeID of a StridedLayout attribute.

source

Reactant.MLIR.API.mlirStringAttrGet Method
julia
mlirStringAttrGet(ctx, str)

Creates a string attribute in the given context containing the given string.

source

Reactant.MLIR.API.mlirStringAttrGetTypeID Method
julia
mlirStringAttrGetTypeID()

Returns the typeID of a String attribute.

source

Reactant.MLIR.API.mlirStringAttrGetValue Method
julia
mlirStringAttrGetValue(attr)

Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.

source

Reactant.MLIR.API.mlirStringAttrTypedGet Method
julia
mlirStringAttrTypedGet(type, str)

Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.

source

Reactant.MLIR.API.mlirStringRefCreate Method
julia
mlirStringRefCreate(str, length)

Constructs a string reference from the pointer and length. The pointer need not reference to a null-terminated string.

source

Reactant.MLIR.API.mlirStringRefCreateFromCString Method
julia
mlirStringRefCreateFromCString(str)

Constructs a string reference from a null-terminated C string. Prefer mlirStringRefCreate if the length of the string is known.

source

Reactant.MLIR.API.mlirStringRefEqual Method
julia
mlirStringRefEqual(string, other)

Returns true if two string references are equal, false otherwise.

source

Reactant.MLIR.API.mlirSymbolRefAttrGet Method
julia
mlirSymbolRefAttrGet(ctx, symbol, numReferences, references)

Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.

source

Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference Method
julia
mlirSymbolRefAttrGetLeafReference(attr)

Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.

source

Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference Method
julia
mlirSymbolRefAttrGetNestedReference(attr, pos)

Returns pos-th reference nested in the given symbol reference attribute.

source

Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences Method
julia
mlirSymbolRefAttrGetNumNestedReferences(attr)

Returns the number of references nested in the given symbol reference attribute.

source

Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference Method
julia
mlirSymbolRefAttrGetRootReference(attr)

Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.

source

Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID Method
julia
mlirSymbolRefAttrGetTypeID()

Returns the typeID of an SymbolRef attribute.

source

Reactant.MLIR.API.mlirSymbolTableCreate Method
julia
mlirSymbolTableCreate(operation)

Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.

source

Reactant.MLIR.API.mlirSymbolTableDestroy Method
julia
mlirSymbolTableDestroy(symbolTable)

Destroys the symbol table created with mlirSymbolTableCreate. This does not affect the operations in the table.

source

Reactant.MLIR.API.mlirSymbolTableErase Method
julia
mlirSymbolTableErase(symbolTable, operation)

Removes the given operation from the symbol table and erases it.

source

Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName Method
julia
mlirSymbolTableGetSymbolAttributeName()

Returns the name of the attribute used to store symbol names compatible with symbol tables.

source

Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName Method
julia
mlirSymbolTableGetVisibilityAttributeName()

Returns the name of the attribute used to store symbol visibility.

source

Reactant.MLIR.API.mlirSymbolTableInsert Method
julia
mlirSymbolTableInsert(symbolTable, operation)

Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.

source

Reactant.MLIR.API.mlirSymbolTableIsNull Method
julia
mlirSymbolTableIsNull(symbolTable)

Returns true if the symbol table is null.

source

Reactant.MLIR.API.mlirSymbolTableLookup Method
julia
mlirSymbolTableLookup(symbolTable, name)

Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.

source

Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses Method
julia
mlirSymbolTableReplaceAllSymbolUses(oldSymbol, newSymbol, from)

Attempt to replace all uses that are nested within the given operation of the given symbol 'oldSymbol' with the provided 'newSymbol'. This does not traverse into nested symbol tables. Will fail atomically if there are any unknown operations that may be potential symbol tables.

source

Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables Method
julia
mlirSymbolTableWalkSymbolTables(from, allSymUsesVisible, callback, userData)

Walks all symbol table operations nested within, and including, op. For each symbol table operation, the provided callback is invoked with the op and a boolean signifying if the symbols within that symbol table can be treated as if all uses within the IR are visible to the caller. allSymUsesVisible identifies whether all of the symbol uses of symbols within op are visible.

source

Reactant.MLIR.API.mlirTF32TypeGet Method
julia
mlirTF32TypeGet(ctx)

Creates a TF32 type in the given context. The type is owned by the context.

source

Reactant.MLIR.API.mlirTransformApplyNamedSequence Method
julia
mlirTransformApplyNamedSequence(payload, transformRoot, transformModule, transformOptions)

Applies the transformation script starting at the given transform root operation to the given payload operation. The module containing the transform root as well as the transform options should be provided. The transform operation must implement TransformOpInterface and the module must be a ModuleOp. Returns the status of the application.

source

Reactant.MLIR.API.mlirTransformOptionsCreate Method
julia
mlirTransformOptionsCreate()

Creates a default-initialized transform options object.

source

Reactant.MLIR.API.mlirTransformOptionsDestroy Method
julia
mlirTransformOptionsDestroy(transformOptions)

Destroys a transform options object previously created by mlirTransformOptionsCreate.

source

Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks Method
julia
mlirTransformOptionsEnableExpensiveChecks(transformOptions, enable)

Enables or disables expensive checks in transform options.

source

Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp Method
julia
mlirTransformOptionsEnforceSingleTopLevelTransformOp(transformOptions, enable)

Enables or disables the enforcement of the top-level transform op being single in transform options.

source

Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp Method
julia
mlirTransformOptionsGetEnforceSingleTopLevelTransformOp(transformOptions)

Returns true if the enforcement of the top-level transform op being single is enabled in transform options.

source

Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled Method
julia
mlirTransformOptionsGetExpensiveChecksEnabled(transformOptions)

Returns true if expensive checks are enabled in transform options.

source

Reactant.MLIR.API.mlirTranslateModuleToLLVMIR Method
julia
mlirTranslateModuleToLLVMIR(_module, context)

Translate operation that satisfies LLVM dialect module requirements into an LLVM IR module living in the given context. This translates operations from any dilalect that has a registered implementation of LLVMTranslationDialectInterface.

Returns

the generated LLVM IR Module from the translated MLIR module, it is owned by the caller.

source

Reactant.MLIR.API.mlirTupleTypeGet Method
julia
mlirTupleTypeGet(ctx, numElements, elements)

Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.

source

Reactant.MLIR.API.mlirTupleTypeGetNumTypes Method
julia
mlirTupleTypeGetNumTypes(type)

Returns the number of types contained in a tuple.

source

Reactant.MLIR.API.mlirTupleTypeGetType Method
julia
mlirTupleTypeGetType(type, pos)

Returns the pos-th type in the tuple type.

source

Reactant.MLIR.API.mlirTupleTypeGetTypeID Method
julia
mlirTupleTypeGetTypeID()

Returns the typeID of an Tuple type.

source

Reactant.MLIR.API.mlirTypeAttrGet Method
julia
mlirTypeAttrGet(type)

Creates a type attribute wrapping the given type in the same context as the type.

source

Reactant.MLIR.API.mlirTypeAttrGetTypeID Method
julia
mlirTypeAttrGetTypeID()

Returns the typeID of a Type attribute.

source

Reactant.MLIR.API.mlirTypeAttrGetValue Method
julia
mlirTypeAttrGetValue(attr)

Returns the type stored in the given type attribute.

source

Reactant.MLIR.API.mlirTypeDump Method
julia
mlirTypeDump(type)

Prints the type to the standard error stream.

source

Reactant.MLIR.API.mlirTypeEqual Method
julia
mlirTypeEqual(t1, t2)

Checks if two types are equal.

source

Reactant.MLIR.API.mlirTypeGetContext Method
julia
mlirTypeGetContext(type)

Gets the context that a type was created with.

source

Reactant.MLIR.API.mlirTypeGetDialect Method
julia
mlirTypeGetDialect(type)

Gets the dialect a type belongs to.

source

Reactant.MLIR.API.mlirTypeGetTypeID Method
julia
mlirTypeGetTypeID(type)

Gets the type ID of the type.

source

Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID Method
julia
mlirTypeIDAllocatorAllocateTypeID(allocator)

Allocates a type id that is valid for the lifetime of the allocator

source

Reactant.MLIR.API.mlirTypeIDAllocatorCreate Method
julia
mlirTypeIDAllocatorCreate()

Creates a type id allocator for dynamic type id creation

source

Reactant.MLIR.API.mlirTypeIDAllocatorDestroy Method
julia
mlirTypeIDAllocatorDestroy(allocator)

Deallocates the allocator and all allocated type ids

source

Reactant.MLIR.API.mlirTypeIDCreate Method
julia
mlirTypeIDCreate(ptr)

ptr must be 8 byte aligned and unique to a type valid for the duration of the returned type id's usage

source

Reactant.MLIR.API.mlirTypeIDEqual Method
julia
mlirTypeIDEqual(typeID1, typeID2)

Checks if two type ids are equal.

source

Reactant.MLIR.API.mlirTypeIDHashValue Method
julia
mlirTypeIDHashValue(typeID)

Returns the hash value of the type id.

source

Reactant.MLIR.API.mlirTypeIDIsNull Method
julia
mlirTypeIDIsNull(typeID)

Checks whether a type id is null.

source

Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType Method
julia
mlirTypeIsAAnyQuantizedType(type)

Returns true if the given type is an AnyQuantizedType.

source

Reactant.MLIR.API.mlirTypeIsABF16 Method
julia
mlirTypeIsABF16(type)

Checks whether the given type is a bf16 type.

source

Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType Method
julia
mlirTypeIsACalibratedQuantizedType(type)

Returns true if the given type is a CalibratedQuantizedType.

source

Reactant.MLIR.API.mlirTypeIsAComplex Method
julia
mlirTypeIsAComplex(type)

Checks whether the given type is a Complex type.

source

Reactant.MLIR.API.mlirTypeIsAF16 Method
julia
mlirTypeIsAF16(type)

Checks whether the given type is an f16 type.

source

Reactant.MLIR.API.mlirTypeIsAF32 Method
julia
mlirTypeIsAF32(type)

Checks whether the given type is an f32 type.

source

Reactant.MLIR.API.mlirTypeIsAF64 Method
julia
mlirTypeIsAF64(type)

Checks whether the given type is an f64 type.

source

Reactant.MLIR.API.mlirTypeIsAFloat Method
julia
mlirTypeIsAFloat(type)

Checks whether the given type is a floating-point type.

source

Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN Method
julia
mlirTypeIsAFloat4E2M1FN(type)

Checks whether the given type is an f4E2M1FN type.

source

Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN Method
julia
mlirTypeIsAFloat6E2M3FN(type)

Checks whether the given type is an f6E2M3FN type.

source

Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN Method
julia
mlirTypeIsAFloat6E3M2FN(type)

Checks whether the given type is an f6E3M2FN type.

source

Reactant.MLIR.API.mlirTypeIsAFloat8E3M4 Method
julia
mlirTypeIsAFloat8E3M4(type)

Checks whether the given type is an f8E3M4 type.

source

Reactant.MLIR.API.mlirTypeIsAFloat8E4M3 Method
julia
mlirTypeIsAFloat8E4M3(type)

Checks whether the given type is an f8E4M3 type.

source

Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ Method
julia
mlirTypeIsAFloat8E4M3B11FNUZ(type)

Checks whether the given type is an f8E4M3B11FNUZ type.

source

Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN Method
julia
mlirTypeIsAFloat8E4M3FN(type)

Checks whether the given type is an f8E4M3FN type.

source

Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ Method
julia
mlirTypeIsAFloat8E4M3FNUZ(type)

Checks whether the given type is an f8E4M3FNUZ type.

source

Reactant.MLIR.API.mlirTypeIsAFloat8E5M2 Method
julia
mlirTypeIsAFloat8E5M2(type)

Checks whether the given type is an f8E5M2 type.

source

Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ Method
julia
mlirTypeIsAFloat8E5M2FNUZ(type)

Checks whether the given type is an f8E5M2FNUZ type.

source

Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU Method
julia
mlirTypeIsAFloat8E8M0FNU(type)

Checks whether the given type is an f8E8M0FNU type.

source

Reactant.MLIR.API.mlirTypeIsAFunction Method
julia
mlirTypeIsAFunction(type)

Checks whether the given type is a function type.

source

Reactant.MLIR.API.mlirTypeIsAIndex Method
julia
mlirTypeIsAIndex(type)

Checks whether the given type is an index type.

source

Reactant.MLIR.API.mlirTypeIsAInteger Method
julia
mlirTypeIsAInteger(type)

Checks whether the given type is an integer type.

source

Reactant.MLIR.API.mlirTypeIsALLVMPointerType Method
julia
mlirTypeIsALLVMPointerType(type)

Returns true if the type is an LLVM dialect pointer type.

source

Reactant.MLIR.API.mlirTypeIsALLVMStructType Method
julia
mlirTypeIsALLVMStructType(type)

Returns true if the type is an LLVM dialect struct type.

source

Reactant.MLIR.API.mlirTypeIsAMemRef Method
julia
mlirTypeIsAMemRef(type)

Checks whether the given type is a MemRef type.

source

Reactant.MLIR.API.mlirTypeIsANone Method
julia
mlirTypeIsANone(type)

Checks whether the given type is a None type.

source

Reactant.MLIR.API.mlirTypeIsAOpaque Method
julia
mlirTypeIsAOpaque(type)

Checks whether the given type is an opaque type.

source

Reactant.MLIR.API.mlirTypeIsAQuantizedType Method
julia
mlirTypeIsAQuantizedType(type)

Returns true if the given type is a quantization dialect type.

source

Reactant.MLIR.API.mlirTypeIsARankedTensor Method
julia
mlirTypeIsARankedTensor(type)

Checks whether the given type is a ranked tensor type.

source

Reactant.MLIR.API.mlirTypeIsAShaped Method
julia
mlirTypeIsAShaped(type)

Checks whether the given type is a Shaped type.

source

Reactant.MLIR.API.mlirTypeIsATF32 Method
julia
mlirTypeIsATF32(type)

Checks whether the given type is an TF32 type.

source

Reactant.MLIR.API.mlirTypeIsATensor Method
julia
mlirTypeIsATensor(type)

Checks whether the given type is a Tensor type.

source

Reactant.MLIR.API.mlirTypeIsATuple Method
julia
mlirTypeIsATuple(type)

Checks whether the given type is a tuple type.

source

Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType Method
julia
mlirTypeIsAUniformQuantizedPerAxisType(type)

Returns true if the given type is a UniformQuantizedPerAxisType.

source

Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType Method
julia
mlirTypeIsAUniformQuantizedType(type)

Returns true if the given type is a UniformQuantizedType.

source

Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef Method
julia
mlirTypeIsAUnrankedMemRef(type)

Checks whether the given type is an UnrankedMemRef type.

source

Reactant.MLIR.API.mlirTypeIsAUnrankedTensor Method
julia
mlirTypeIsAUnrankedTensor(type)

Checks whether the given type is an unranked tensor type.

source

Reactant.MLIR.API.mlirTypeIsAVector Method
julia
mlirTypeIsAVector(type)

Checks whether the given type is a Vector type.

source

Reactant.MLIR.API.mlirTypeIsNull Method
julia
mlirTypeIsNull(type)

Checks whether a type is null.

source

Reactant.MLIR.API.mlirTypeParseGet Method
julia
mlirTypeParseGet(context, type)

Parses a type. The type is owned by the context.

source

Reactant.MLIR.API.mlirTypePrint Method
julia
mlirTypePrint(type, callback, userData)

Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet Method
julia
mlirUniformQuantizedPerAxisTypeGet(flags, storageType, expressedType, nDims, scales, zeroPoints, quantizedDimension, storageTypeMin, storageTypeMax)

Creates an instance of UniformQuantizedPerAxisType with the given parameters in the same context as storageType and returns it. scales and zeroPoints point to nDims number of elements. The instance is owned by the context.

source

Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims Method
julia
mlirUniformQuantizedPerAxisTypeGetNumDims(type)

Returns the number of axes in the given quantized per-axis type.

source

Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension Method
julia
mlirUniformQuantizedPerAxisTypeGetQuantizedDimension(type)

Returns the index of the quantized dimension in the given quantized per-axis type.

source

Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale Method
julia
mlirUniformQuantizedPerAxisTypeGetScale(type, pos)

Returns pos-th scale of the given quantized per-axis type.

source

Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint Method
julia
mlirUniformQuantizedPerAxisTypeGetZeroPoint(type, pos)

Returns pos-th zero point of the given quantized per-axis type.

source

Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint Method
julia
mlirUniformQuantizedPerAxisTypeIsFixedPoint(type)

Returns true if the given uniform quantized per-axis type is fixed-point.

source

Reactant.MLIR.API.mlirUniformQuantizedTypeGet Method
julia
mlirUniformQuantizedTypeGet(flags, storageType, expressedType, scale, zeroPoint, storageTypeMin, storageTypeMax)

Creates an instance of UniformQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.

source

Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale Method
julia
mlirUniformQuantizedTypeGetScale(type)

Returns the scale of the given uniform quantized type.

source

Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint Method
julia
mlirUniformQuantizedTypeGetZeroPoint(type)

Returns the zero point of the given uniform quantized type.

source

Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint Method
julia
mlirUniformQuantizedTypeIsFixedPoint(type)

Returns true if the given uniform quantized type is fixed-point.

source

Reactant.MLIR.API.mlirUnitAttrGet Method
julia
mlirUnitAttrGet(ctx)

Creates a unit attribute in the given context.

source

Reactant.MLIR.API.mlirUnitAttrGetTypeID Method
julia
mlirUnitAttrGetTypeID()

Returns the typeID of a Unit attribute.

source

Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet Method
julia
mlirUnmanagedDenseResourceElementsAttrGet(shapedType, name, data, dataLength, dataAlignment, dataIsMutable, deleter, userData)

Unlike the typed accessors below, constructs the attribute with a raw data buffer and no type/alignment checking. Use a more strongly typed accessor if possible. If dataIsMutable is false, then an immutable AsmResourceBlob will be created and that passed data contents will be treated as const. If the deleter is non NULL, then it will be called when the data buffer can no longer be accessed (passing userData to it).

source

Reactant.MLIR.API.mlirUnrankedMemRefTypeGet Method
julia
mlirUnrankedMemRefTypeGet(elementType, memorySpace)

Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type.

source

Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked Method
julia
mlirUnrankedMemRefTypeGetChecked(loc, elementType, memorySpace)

Same as "mlirUnrankedMemRefTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID Method
julia
mlirUnrankedMemRefTypeGetTypeID()

Returns the typeID of an UnrankedMemRef type.

source

Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace Method
julia
mlirUnrankedMemrefGetMemorySpace(type)

Returns the memory spcae of the given Unranked MemRef type.

source

Reactant.MLIR.API.mlirUnrankedTensorTypeGet Method
julia
mlirUnrankedTensorTypeGet(elementType)

Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context.

source

Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked Method
julia
mlirUnrankedTensorTypeGetChecked(loc, elementType)

Same as "mlirUnrankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID Method
julia
mlirUnrankedTensorTypeGetTypeID()

Returns the typeID of an UnrankedTensor type.

source

Reactant.MLIR.API.mlirValueDump Method
julia
mlirValueDump(value)

Prints the value to the standard error stream.

source

Reactant.MLIR.API.mlirValueEqual Method
julia
mlirValueEqual(value1, value2)

Returns 1 if two values are equal, 0 otherwise.

source

Reactant.MLIR.API.mlirValueGetFirstUse Method
julia
mlirValueGetFirstUse(value)

Returns an op operand representing the first use of the value, or a null op operand if there are no uses.

source

Reactant.MLIR.API.mlirValueGetType Method
julia
mlirValueGetType(value)

Returns the type of the value.

source

Reactant.MLIR.API.mlirValueIsABlockArgument Method
julia
mlirValueIsABlockArgument(value)

Returns 1 if the value is a block argument, 0 otherwise.

source

Reactant.MLIR.API.mlirValueIsAOpResult Method
julia
mlirValueIsAOpResult(value)

Returns 1 if the value is an operation result, 0 otherwise.

source

Reactant.MLIR.API.mlirValueIsNull Method
julia
mlirValueIsNull(value)

Returns whether the value is null.

source

Reactant.MLIR.API.mlirValuePrint Method
julia
mlirValuePrint(value, callback, userData)

Prints a value by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

Reactant.MLIR.API.mlirValuePrintAsOperand Method
julia
mlirValuePrintAsOperand(value, state, callback, userData)

Prints a value as an operand (i.e., the ValueID).

source

Reactant.MLIR.API.mlirValueReplaceAllUsesExcept Method
julia
mlirValueReplaceAllUsesExcept(of, with, numExceptions, exceptions)

Replace all uses of 'of' value with 'with' value, updating anything in the IR that uses 'of' to use 'with' instead, except if the user is listed in 'exceptions'. The 'exceptions' parameter is an array of MlirOperation pointers with a length of 'numExceptions'.

source

Reactant.MLIR.API.mlirValueReplaceAllUsesOfWith Method
julia
mlirValueReplaceAllUsesOfWith(of, with)

Replace all uses of 'of' value with the 'with' value, updating anything in the IR that uses 'of' to use the other value instead. When this returns there are zero uses of 'of'.

source

Reactant.MLIR.API.mlirValueSetType Method
julia
mlirValueSetType(value, type)

Set the type of the value.

source

Reactant.MLIR.API.mlirVectorTypeGet Method
julia
mlirVectorTypeGet(rank, shape, elementType)

Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context.

source

Reactant.MLIR.API.mlirVectorTypeGetChecked Method
julia
mlirVectorTypeGetChecked(loc, rank, shape, elementType)

Same as "mlirVectorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

Reactant.MLIR.API.mlirVectorTypeGetScalable Method
julia
mlirVectorTypeGetScalable(rank, shape, scalable, elementType)

Creates a scalable vector type with the shape identified by its rank and dimensions. A subset of dimensions may be marked as scalable via the corresponding flag list, which is expected to have as many entries as the rank of the vector. The vector is created in the same context as the element type.

source

Reactant.MLIR.API.mlirVectorTypeGetScalableChecked Method
julia
mlirVectorTypeGetScalableChecked(loc, rank, shape, scalable, elementType)

Same as "mlirVectorTypeGetScalable" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

Reactant.MLIR.API.mlirVectorTypeGetTypeID Method
julia
mlirVectorTypeGetTypeID()

Returns the typeID of an Vector type.

source

Reactant.MLIR.API.mlirVectorTypeIsDimScalable Method
julia
mlirVectorTypeIsDimScalable(type, dim)

Checks whether the "dim"-th dimension of the given vector is scalable.

source

Reactant.MLIR.API.mlirVectorTypeIsScalable Method
julia
mlirVectorTypeIsScalable(type)

Checks whether the given vector type is scalable, i.e., has at least one scalable dimension.

source

Other Functions ​

+ \ No newline at end of file diff --git a/previews/PR441/api/ops.html b/previews/PR441/api/ops.html index 47cf12ecb..7d0ed06ed 100644 --- a/previews/PR441/api/ops.html +++ b/previews/PR441/api/ops.html @@ -9,11 +9,11 @@ - + - + - + @@ -21,7 +21,7 @@ -
Reactant.jl

Appearance


Trusted by

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Reactant.Ops API ​

Reactant.Ops module provides a high-level API to construct MLIR operations without having to directly interact with the different dialects.

Currently we haven't documented all the functions in Reactant.Ops.

Reactant.Ops.gather_getindex Method
julia
gather_getindex(src, gather_indices)

Uses MLIR.Dialects.stablehlo.gather to get the values of src at the indices specified by gather_indices. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_slice instead.

source

Reactant.Ops.hlo_call Method
julia
Ops.hlo_call(mlir_code::String, args::Vararg{AnyTracedRArray}...; func_name::String="main") -> NTuple{N, AnyTracedRArray}

Given a MLIR module given as a string, calls the function identified by the func_name keyword parameter (default "main") with the provided arguments and return a tuple for each result of the call.

julia
julia> Reactant.@jit(
+    
Reactant.jl
Appearance

 Trusted by 
Scientific Computing
Lux.jl
Machine Learning
Quantum Simulation
Tenet.jl
Tensor Networks
Reactant.Ops API ​
Reactant.Ops module provides a high-level API to construct MLIR operations without having to directly interact with the different dialects.
Currently we haven't documented all the functions in Reactant.Ops.
Reactant.Ops.gather_getindex Method
julia
gather_getindex(src, gather_indices)
Uses MLIR.Dialects.stablehlo.gather to get the values of src at the indices specified by gather_indices. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_slice instead.
source
Reactant.Ops.hlo_call Method
julia
Ops.hlo_call(mlir_code::String, args::Vararg{AnyTracedRArray}...; func_name::String="main") -> NTuple{N, AnyTracedRArray}
Given a MLIR module given as a string, calls the function identified by the func_name keyword parameter (default "main") with the provided arguments and return a tuple for each result of the call.
julia
julia> Reactant.@jit(
           Ops.hlo_call(
               """
               module {
@@ -35,26 +35,26 @@
               Reactant.to_rarray(Float32[1, 2, 3]),
           )
        )
-(ConcreteRArray{Float32, 1}(Float32[2.0, 4.0, 6.0]),)
source
Reactant.Ops.randexp Method
julia
randexp(
+(ConcreteRArray{Float32, 1}(Float32[2.0, 4.0, 6.0]),)
source
Reactant.Ops.randexp Method
julia
randexp(
     ::Type{T},
     seed::TracedRArray{UInt64,1},
     shape;
     algorithm::String="DEFAULT",
     location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
-)
Generate a random array of type T with the given shape and seed from an exponential distribution with rate 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
Reactant.Ops.randn Method
julia
randn(
+)
Generate a random array of type T with the given shape and seed from an exponential distribution with rate 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
Reactant.Ops.randn Method
julia
randn(
     ::Type{T},
     seed::TracedRArray{UInt64,1},
     shape;
     algorithm::String="DEFAULT",
     location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
-)
Generate a random array of type T with the given shape and seed from a standard normal distribution of mean 0 and standard deviation 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
Reactant.Ops.rng_bit_generator Method
julia
rng_bit_generator(
+)
Generate a random array of type T with the given shape and seed from a standard normal distribution of mean 0 and standard deviation 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
Reactant.Ops.rng_bit_generator Method
julia
rng_bit_generator(
     ::Type{T},
     seed::TracedRArray{UInt64,1},
     shape;
     algorithm::String="DEFAULT",
     location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
-)
Generate a random array of type T with the given shape and seed from a uniform random distribution between 0 and 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
Reactant.Ops.scatter_setindex Method
julia
scatter_setindex(dest, scatter_indices, updates)
Uses MLIR.Dialects.stablehlo.scatter to set the values of dest at the indices specified by scatter_indices to the values in updates. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_update_slice instead.
source

-    
+)

Generate a random array of type T with the given shape and seed from a uniform random distribution between 0 and 1. Returns a NamedTuple with the following fields:

  • output_state: The state of the random number generator after the operation.

  • output: The generated array.

Arguments

  • T: The type of the generated array.

  • seed: The seed for the random number generator.

  • shape: The shape of the generated array.

  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".

source

Reactant.Ops.scatter_setindex Method
julia
scatter_setindex(dest, scatter_indices, updates)

Uses MLIR.Dialects.stablehlo.scatter to set the values of dest at the indices specified by scatter_indices to the values in updates. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_update_slice instead.

source

+ \ No newline at end of file diff --git a/previews/PR441/api/stablehlo.html b/previews/PR441/api/stablehlo.html index dabb5d2df..b14659f63 100644 --- a/previews/PR441/api/stablehlo.html +++ b/previews/PR441/api/stablehlo.html @@ -9,11 +9,11 @@ - + - + - + @@ -21,51 +21,51 @@ -
Reactant.jl

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StableHLO Dialect ​

Refer to the official documentation for more details.

Reactant.MLIR.Dialects.stablehlo.abs Method

abs

Performs element-wise abs operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#abs

Example

mlir
%result = stablehlo.abs %operand : tensor<3xi32>

source

Reactant.MLIR.Dialects.stablehlo.add Method

add

Performs element-wise addition of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#add

Example

mlir
%result = stablehlo.add %lhs, %rhs : tensor<2x2xi32>

source

Reactant.MLIR.Dialects.stablehlo.after_all Method

after_all

Ensures that the operations producing the inputs are executed before any operations that depend on result.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all

Example

mlir
%result = stablehlo.after_all %input0, %input1 : !stablehlo.token

source

Reactant.MLIR.Dialects.stablehlo.all_gather Method

all_gather

Within each process group in the process grid, concatenates the values of the operand tensor from each process along all_gather_dim and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_gather

Example

mlir
%result:2 = "stablehlo.all_gather"(%operand0, %operand1) {
+    
Reactant.jl
Appearance

 Trusted by 
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StableHLO Dialect ​
Refer to the official documentation for more details.
Reactant.MLIR.Dialects.stablehlo.abs Method
abs
Performs element-wise abs operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#abs
Example
mlir
%result = stablehlo.abs %operand : tensor<3xi32>
source
Reactant.MLIR.Dialects.stablehlo.add Method
add
Performs element-wise addition of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#add
Example
mlir
%result = stablehlo.add %lhs, %rhs : tensor<2x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.after_all Method
after_all
Ensures that the operations producing the inputs are executed before any operations that depend on result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all
Example
mlir
%result = stablehlo.after_all %input0, %input1 : !stablehlo.token
source
Reactant.MLIR.Dialects.stablehlo.all_gather Method
all_gather
Within each process group in the process grid, concatenates the values of the operand tensor from each process along all_gather_dim and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_gather
Example
mlir
%result:2 = "stablehlo.all_gather"(%operand0, %operand1) {
   all_gather_dim = 1 : i64,
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<2x2xi64>, tensor<2x2xi64>) -> (tensor<2x4xi64>, tensor<2x4xi64>)
source
Reactant.MLIR.Dialects.stablehlo.all_reduce Method
all_reduce
Within each process group in the process grid, applies a reduction function computation to the values of the operand tensor from each process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result:2 = "stablehlo.all_reduce"(%operand0, %operand0) ({
+} : (tensor<2x2xi64>, tensor<2x2xi64>) -> (tensor<2x4xi64>, tensor<2x4xi64>)
source
Reactant.MLIR.Dialects.stablehlo.all_reduce Method
all_reduce
Within each process group in the process grid, applies a reduction function computation to the values of the operand tensor from each process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result:2 = "stablehlo.all_reduce"(%operand0, %operand0) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
   %0 = "stablehlo.add"(%arg0, %arg1) : (tensor<i64>, tensor<i64>) -> tensor<i64>
   "stablehlo.return"(%0) : (tensor<i64>) -> ()
 }) {
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<4xi64>, tensor<4xi64>) -> (tensor<4xi64>, tensor<4xi64>)
source
Reactant.MLIR.Dialects.stablehlo.all_to_all Method
all_to_all
Within each process group in the process grid, splits the values of the operand tensor along split_dimension into parts, scatters the split parts between the processes, concatenates the scattered parts along concat_dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_to_all
Example
mlir
%result:2 = "stablehlo.all_to_all"(%operand1, %operand2) {
+} : (tensor<4xi64>, tensor<4xi64>) -> (tensor<4xi64>, tensor<4xi64>)
source
Reactant.MLIR.Dialects.stablehlo.all_to_all Method
all_to_all
Within each process group in the process grid, splits the values of the operand tensor along split_dimension into parts, scatters the split parts between the processes, concatenates the scattered parts along concat_dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_to_all
Example
mlir
%result:2 = "stablehlo.all_to_all"(%operand1, %operand2) {
   split_dimension = 1 : i64,
   concat_dimension = 0 : i64,
   split_count = 2 : i64,
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
-} : (tensor<2x4xi64>, tensor<2x4xi64>) -> (tensor<4x2xi64>, tensor<4x2xi64>)
source
Reactant.MLIR.Dialects.stablehlo.and Method
and
Performs element-wise AND of two tensors lhs and rhs and produces a result tensor
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#and
Example
mlir
%result = stablehlo.and %lhs, %rhs : tensor<2x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.atan2 Method
atan2
Performs element-wise atan2 operation on lhs and rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#atan2
Example
mlir
%result = stablehlo.atan2 %lhs, %rhs : tensor<3xf64>
source
Reactant.MLIR.Dialects.stablehlo.batch_norm_grad Method
batch_norm_grad
Computes gradients of several inputs of BatchNormTrainingOp backpropagating from grad_output, and produces grad_operand, grad_scale and grad_offset tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_grad
Example
mlir
%grad_operand, %grad_scale, %grad_offset =
+} : (tensor<2x4xi64>, tensor<2x4xi64>) -> (tensor<4x2xi64>, tensor<4x2xi64>)
source
Reactant.MLIR.Dialects.stablehlo.and Method
and
Performs element-wise AND of two tensors lhs and rhs and produces a result tensor
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#and
Example
mlir
%result = stablehlo.and %lhs, %rhs : tensor<2x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.atan2 Method
atan2
Performs element-wise atan2 operation on lhs and rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#atan2
Example
mlir
%result = stablehlo.atan2 %lhs, %rhs : tensor<3xf64>
source
Reactant.MLIR.Dialects.stablehlo.batch_norm_grad Method
batch_norm_grad
Computes gradients of several inputs of BatchNormTrainingOp backpropagating from grad_output, and produces grad_operand, grad_scale and grad_offset tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_grad
Example
mlir
%grad_operand, %grad_scale, %grad_offset =
 "stablehlo.batch_norm_grad"(%operand, %scale, %mean, %variance, %grad_output) {
   epsilon = 0.0 : f32,
   feature_index = 2 : i64
 } : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>,
-     tensor<2x2x2xf64>) -> (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
Reactant.MLIR.Dialects.stablehlo.batch_norm_inference Method
batch_norm_inference
Normalizes the operand tensor across all dimensions except for the feature_index dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_inference
Example
mlir
%result = "stablehlo.batch_norm_inference"(%operand, %scale, %offset, %mean, %variance) {
+     tensor<2x2x2xf64>) -> (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
Reactant.MLIR.Dialects.stablehlo.batch_norm_inference Method
batch_norm_inference
Normalizes the operand tensor across all dimensions except for the feature_index dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_inference
Example
mlir
%result = "stablehlo.batch_norm_inference"(%operand, %scale, %offset, %mean, %variance) {
   epsilon = 0.0 : f32,
   feature_index = 2 : i64
-} : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>) -> tensor<2x2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.batch_norm_training Method
batch_norm_training
Computes mean and variance across batch and spatial dimensions and normalizes the operand tensor, for each feature in the feature_index dimension and produces output, batch_mean and batch_var tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_training
Example
mlir
%output, %batch_mean, %batch_var = "stablehlo.batch_norm_training"(%operand, %scale, %offset) {
+} : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>) -> tensor<2x2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.batch_norm_training Method
batch_norm_training
Computes mean and variance across batch and spatial dimensions and normalizes the operand tensor, for each feature in the feature_index dimension and produces output, batch_mean and batch_var tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_training
Example
mlir
%output, %batch_mean, %batch_var = "stablehlo.batch_norm_training"(%operand, %scale, %offset) {
   epsilon = 0.0 : f32,
   feature_index = 2 : i64
 } : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>) ->
-    (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
Reactant.MLIR.Dialects.stablehlo.bitcast_convert Method
bitcast_convert
Performs a bitcast operation on operand tensor and produces a result tensor where the bits of the entire operand tensor are reinterpreted using the type of the result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#bitcast_convert
Example
mlir
%result = stablehlo.bitcast_convert %operand : (tensor<f64>) -> tensor<4xf16>
source
Reactant.MLIR.Dialects.stablehlo.broadcast Method
broadcast
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Broadcast: https://www.tensorflow.org/xla/operation_semantics#broadcast
Example
mlir
%result = stablehlo.broadcast %operand, sizes = [1, 2] : (tensor<3xi32>) -> tensor<1x2x3xi32>
source
Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim Method
broadcast_in_dim
Expands the dimensions and/or rank of an input tensor by duplicating the data in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#broadcast_in_dim
Example
mlir
%result = stablehlo.broadcast_in_dim %operand, dims = [2, 1] : (tensor<1x3xi32>) -> tensor<2x3x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.case Method
case
Produces the output from executing exactly one function from branches depending on the value of index.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#case
Example
mlir
%result0, %result1 = "stablehlo.case"(%index) ({
+    (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
Reactant.MLIR.Dialects.stablehlo.bitcast_convert Method
bitcast_convert
Performs a bitcast operation on operand tensor and produces a result tensor where the bits of the entire operand tensor are reinterpreted using the type of the result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#bitcast_convert
Example
mlir
%result = stablehlo.bitcast_convert %operand : (tensor<f64>) -> tensor<4xf16>
source
Reactant.MLIR.Dialects.stablehlo.broadcast Method
broadcast
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Broadcast: https://www.tensorflow.org/xla/operation_semantics#broadcast
Example
mlir
%result = stablehlo.broadcast %operand, sizes = [1, 2] : (tensor<3xi32>) -> tensor<1x2x3xi32>
source
Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim Method
broadcast_in_dim
Expands the dimensions and/or rank of an input tensor by duplicating the data in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#broadcast_in_dim
Example
mlir
%result = stablehlo.broadcast_in_dim %operand, dims = [2, 1] : (tensor<1x3xi32>) -> tensor<2x3x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.case Method
case
Produces the output from executing exactly one function from branches depending on the value of index.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#case
Example
mlir
%result0, %result1 = "stablehlo.case"(%index) ({
   stablehlo.return %result_branch0, %result_branch0 : tensor<2xi64>, tensor<2xi64>
 }, {
   stablehlo.return %result_branch1, %result_branch1 : tensor<2xi64>, tensor<2xi64>
-}) : (tensor<i32>) -> (tensor<2xi64>, tensor<2xi64>)
source
Reactant.MLIR.Dialects.stablehlo.cbrt Method
cbrt
Performs element-wise cubic root operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cbrt
Example
mlir
%result = stablehlo.cbrt %operand : tensor<4xf64>
source
Reactant.MLIR.Dialects.stablehlo.ceil Method
ceil
Performs element-wise ceil of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#ceil
Example
mlir
%result = stablehlo.ceil %operand : tensor<5xf32>
source
Reactant.MLIR.Dialects.stablehlo.cholesky Method
cholesky
Computes the Cholesky decomposition of a batch of matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cholesky
Example
mlir
%result = stablehlo.cholesky %a, lower = true : tensor<3x3xf64>
source
Reactant.MLIR.Dialects.stablehlo.clamp Method
clamp
Clamps every element of the operand tensor between a minimum and maximum value and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#clamp
Example
mlir
%result = stablehlo.clamp %min, %operand, %max : tensor<3xi32>
source
Reactant.MLIR.Dialects.stablehlo.collective_broadcast Method
collective_broadcast
Within each process group in the process grid, send the value of the operand tensor from the source process to the target processes and produce a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_broadcast
Example
mlir
%result = "stablehlo.collective_broadcast"(%operand) {
+}) : (tensor<i32>) -> (tensor<2xi64>, tensor<2xi64>)
source
Reactant.MLIR.Dialects.stablehlo.cbrt Method
cbrt
Performs element-wise cubic root operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cbrt
Example
mlir
%result = stablehlo.cbrt %operand : tensor<4xf64>
source
Reactant.MLIR.Dialects.stablehlo.ceil Method
ceil
Performs element-wise ceil of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#ceil
Example
mlir
%result = stablehlo.ceil %operand : tensor<5xf32>
source
Reactant.MLIR.Dialects.stablehlo.cholesky Method
cholesky
Computes the Cholesky decomposition of a batch of matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cholesky
Example
mlir
%result = stablehlo.cholesky %a, lower = true : tensor<3x3xf64>
source
Reactant.MLIR.Dialects.stablehlo.clamp Method
clamp
Clamps every element of the operand tensor between a minimum and maximum value and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#clamp
Example
mlir
%result = stablehlo.clamp %min, %operand, %max : tensor<3xi32>
source
Reactant.MLIR.Dialects.stablehlo.collective_broadcast Method
collective_broadcast
Within each process group in the process grid, send the value of the operand tensor from the source process to the target processes and produce a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_broadcast
Example
mlir
%result = "stablehlo.collective_broadcast"(%operand) {
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<1x2xi64>) -> tensor<1x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.collective_permute Method
collective_permute
Within each process group in the process grid, sends the value of the operand tensor from the source process to the target process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_permute
Example
mlir
%result = "stablehlo.collective_permute"(%operand) {
+} : (tensor<1x2xi64>) -> tensor<1x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.collective_permute Method
collective_permute
Within each process group in the process grid, sends the value of the operand tensor from the source process to the target process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_permute
Example
mlir
%result = "stablehlo.collective_permute"(%operand) {
   source_target_pairs = dense<[[0, 1], [1, 2]]> : tensor<2x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<2x2xi64>) -> tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.compare Method
compare
Performs element-wise comparison of lhs and rhs tensors according to comparison_direction and compare_type, and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#compare
Example
mlir
%result = stablehlo.compare LT, %lhs, %rhs, FLOAT : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1>
source
Reactant.MLIR.Dialects.stablehlo.complex Method
complex
Performs element-wise conversion to a complex value from a pair of real and imaginary values, lhs and rhs, and produces a result tensor. See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#complex
Example
mlir
%result = stablehlo.complex %lhs, %rhs : tensor<2xcomplex<f64>>
source
Reactant.MLIR.Dialects.stablehlo.composite Method
composite
Encapsulates an operation made up (composed) of other StableHLO operations, taking inputs and composite_attributes and producing results. The semantics of the op are implemented by the decomposition attribute. The composite op can be replaced with its decomposition without changing program semantics. In cases where inlining the decomposition does not provide the same op semantics, prefer using custom_call.
The version field (defaults to 0) is used to denote when a composite's semantics change.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#composite
Example
mlir
%results = stablehlo.composite "my.op" %input0, %input1 {
+} : (tensor<2x2xi64>) -> tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.compare Method
compare
Performs element-wise comparison of lhs and rhs tensors according to comparison_direction and compare_type, and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#compare
Example
mlir
%result = stablehlo.compare LT, %lhs, %rhs, FLOAT : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1>
source
Reactant.MLIR.Dialects.stablehlo.complex Method
complex
Performs element-wise conversion to a complex value from a pair of real and imaginary values, lhs and rhs, and produces a result tensor. See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#complex
Example
mlir
%result = stablehlo.complex %lhs, %rhs : tensor<2xcomplex<f64>>
source
Reactant.MLIR.Dialects.stablehlo.composite Method
composite
Encapsulates an operation made up (composed) of other StableHLO operations, taking inputs and composite_attributes and producing results. The semantics of the op are implemented by the decomposition attribute. The composite op can be replaced with its decomposition without changing program semantics. In cases where inlining the decomposition does not provide the same op semantics, prefer using custom_call.
The version field (defaults to 0) is used to denote when a composite's semantics change.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#composite
Example
mlir
%results = stablehlo.composite "my.op" %input0, %input1 {
   composite_attributes = {
     my_attribute = "my_value"
   },
   decomposition = @my_op,
   version = 1 : i32
-} : (tensor<f32>, tensor<f32>) -> tensor<f32>
source
Reactant.MLIR.Dialects.stablehlo.concatenate Method
concatenate
Concatenates a variadic number of tensors in inputs along dimension dimension in the same order as the given arguments and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#concatenate
Example
mlir
%result = stablehlo.concatenate %input0, %input1, dim = 0 : (tensor<3x2xi64>, tensor<1x2xi64>) -> tensor<4x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.constant Method
constant
Produces an output tensor from a constant value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#constant
Example
mlir
%output = stablehlo.constant dense<[[0.0, 1.0], [2.0, 3.0]]> : tensor<2x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.convert Method
convert
Performs an element-wise conversion from one element type to another on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convert
Example
mlir
%result = stablehlo.convert %operand : (tensor<3xi64>) -> tensor<3xcomplex<f64>>
source
Reactant.MLIR.Dialects.stablehlo.convolution Method
convolution
Computes dot products between windows of lhs and slices of rhs and produces result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convolution
Example
mlir
%result = stablehlo.convolution(%lhs, %rhs)
+} : (tensor<f32>, tensor<f32>) -> tensor<f32>
source
Reactant.MLIR.Dialects.stablehlo.concatenate Method
concatenate
Concatenates a variadic number of tensors in inputs along dimension dimension in the same order as the given arguments and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#concatenate
Example
mlir
%result = stablehlo.concatenate %input0, %input1, dim = 0 : (tensor<3x2xi64>, tensor<1x2xi64>) -> tensor<4x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.constant Method
constant
Produces an output tensor from a constant value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#constant
Example
mlir
%output = stablehlo.constant dense<[[0.0, 1.0], [2.0, 3.0]]> : tensor<2x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.convert Method
convert
Performs an element-wise conversion from one element type to another on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convert
Example
mlir
%result = stablehlo.convert %operand : (tensor<3xi64>) -> tensor<3xcomplex<f64>>
source
Reactant.MLIR.Dialects.stablehlo.convolution Method
convolution
Computes dot products between windows of lhs and slices of rhs and produces result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convolution
Example
mlir
%result = stablehlo.convolution(%lhs, %rhs)
   dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
   window = {
     stride = [4, 4],
@@ -78,24 +78,24 @@
     batch_group_count = 1 : i64,
     precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
   } :
-(tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>) -> tensor<1x2x2x1xi64>
source
Reactant.MLIR.Dialects.stablehlo.cosine Method
cosine
Performs element-wise cosine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cosine
Example
mlir
%result = stablehlo.cosine %operand : tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.count_leading_zeros Method
count_leading_zeros
Performs element-wise count of the number of leading zero bits in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#count_leading_zeros
Example
mlir
%result = stablehlo.count_leading_zeros %operand : tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.create_token Method
create_token
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AfterAllOp with 0 inputs: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all
Example
mlir
%output = stablehlo.create_token : !stablehlo.token
source
Reactant.MLIR.Dialects.stablehlo.cross_replica_sum Method
cross_replica_sum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AllReduceOp with channel_id = 0, use_global_device_ids = false and computation implementing addition: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result = "stablehlo.cross-replica-sum"(%operand) {
+(tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>) -> tensor<1x2x2x1xi64>
source
Reactant.MLIR.Dialects.stablehlo.cosine Method
cosine
Performs element-wise cosine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cosine
Example
mlir
%result = stablehlo.cosine %operand : tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.count_leading_zeros Method
count_leading_zeros
Performs element-wise count of the number of leading zero bits in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#count_leading_zeros
Example
mlir
%result = stablehlo.count_leading_zeros %operand : tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.create_token Method
create_token
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AfterAllOp with 0 inputs: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all
Example
mlir
%output = stablehlo.create_token : !stablehlo.token
source
Reactant.MLIR.Dialects.stablehlo.cross_replica_sum Method
cross_replica_sum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AllReduceOp with channel_id = 0, use_global_device_ids = false and computation implementing addition: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result = "stablehlo.cross-replica-sum"(%operand) {
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
-} : (tensor<4xf32>) -> tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.custom_call Method
custom_call
Encapsulates an implementation-defined operation call_target_name that takes inputs and called_computations and produces results.
Depending on the API version there are two ways to pass extra bits of static information to the external function:
  1. Use API_VERSION_TYPED_FFI which allows passing a dictionary attribute.
  2. Use a previous API version with a StringAttr to encode backend config.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#custom_call
Example
mlir
%results = stablehlo.custom_call @foo(%input0) {
+} : (tensor<4xf32>) -> tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.custom_call Method
custom_call
Encapsulates an implementation-defined operation call_target_name that takes inputs and called_computations and produces results.
Depending on the API version there are two ways to pass extra bits of static information to the external function:
  1. Use API_VERSION_TYPED_FFI which allows passing a dictionary attribute.
  2. Use a previous API version with a StringAttr to encode backend config.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#custom_call
Example
mlir
%results = stablehlo.custom_call @foo(%input0) {
   backend_config = {bar = 42 : i32},
   api_version = 4 : i32,
   called_computations = [@foo]
-} : (tensor<f64>) -> tensor<f64>
source
Reactant.MLIR.Dialects.stablehlo.divide Method
divide
Performs element-wise division of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#divide
Example
mlir
%result = stablehlo.divide %lhs, %rhs : tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.dot Method
dot
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Dot: https://www.tensorflow.org/xla/operation_semantics#dot
Example
mlir
%0 = stablehlo.dot %arg0, %arg1 : (tensor<1x2xi32>, tensor<2x1xi32>) -> tensor<1x1xi32>
source
Reactant.MLIR.Dialects.stablehlo.dot_general Method
dot_general
Computes dot products between slices of lhs and slices of rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dot_general
Example
mlir
%result = stablehlo.dot_general %lhs, %rhs,
+} : (tensor<f64>) -> tensor<f64>
source
Reactant.MLIR.Dialects.stablehlo.divide Method
divide
Performs element-wise division of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#divide
Example
mlir
%result = stablehlo.divide %lhs, %rhs : tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.dot Method
dot
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Dot: https://www.tensorflow.org/xla/operation_semantics#dot
Example
mlir
%0 = stablehlo.dot %arg0, %arg1 : (tensor<1x2xi32>, tensor<2x1xi32>) -> tensor<1x1xi32>
source
Reactant.MLIR.Dialects.stablehlo.dot_general Method
dot_general
Computes dot products between slices of lhs and slices of rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dot_general
Example
mlir
%result = stablehlo.dot_general %lhs, %rhs,
   batching_dims = [0] x [0],
   contracting_dims = [2] x [1],
   precision = [DEFAULT, DEFAULT],
   algorithm = <lhs_precision_type = tf32, rhs_precision_type = tf32, accumulation_type = f32, lhs_component_count = 1, rhs_component_count = 1, num_primitive_operations = 1, allow_imprecise_accumulation = false>
-  : (tensor<2x2x2xi64>, tensor<2x2x2xi64>) -> tensor<2x2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim Method
dynamic_broadcast_in_dim
This operation is functionally identical to broadcast_in_dim op, but the result shape is specified dynamically via output_dimensions.
It also accepts optional attributes to express static knowledge about the expanding behavior of dimensions. If not specified, all dimensions are assumed to be possibly expanding. The sets of dimensions that are known to be expanding and the set of dimensions that are known to be non-expanding must be disjoint and they must be a subset of the operand's dimensions.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_broadcast_in_dim
Example
mlir
%operand = stablehlo.constant dense<[[1, 2, 3]]> : tensor<1x3xi64>
+  : (tensor<2x2x2xi64>, tensor<2x2x2xi64>) -> tensor<2x2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim Method
dynamic_broadcast_in_dim
This operation is functionally identical to broadcast_in_dim op, but the result shape is specified dynamically via output_dimensions.
It also accepts optional attributes to express static knowledge about the expanding behavior of dimensions. If not specified, all dimensions are assumed to be possibly expanding. The sets of dimensions that are known to be expanding and the set of dimensions that are known to be non-expanding must be disjoint and they must be a subset of the operand's dimensions.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_broadcast_in_dim
Example
mlir
%operand = stablehlo.constant dense<[[1, 2, 3]]> : tensor<1x3xi64>
 %output_dimensions = stablehlo.constant dense<[2, 3, 2]> : tensor<3xi64>
 %result = "stablehlo.dynamic_broadcast_in_dim"(%operand, %output_dimensions) {
   broadcast_dimensions = array<i64: 2, 1>,
   known_expanding_dimensions = array<i64: 0>,
   known_nonexpanding_dimensions = array<i64: 1>
-} : (tensor<1x3xi64>, tensor<3xi64>) -> tensor<2x3x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_conv Method
dynamic_conv
This operation is functionally identical to convolution op, but the padding is specified dynamically via padding.
Example
mlir
%padding = stablehlo.constant dense<2> : tensor<2x2xi64>
+} : (tensor<1x3xi64>, tensor<3xi64>) -> tensor<2x3x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_conv Method
dynamic_conv
This operation is functionally identical to convolution op, but the padding is specified dynamically via padding.
Example
mlir
%padding = stablehlo.constant dense<2> : tensor<2x2xi64>
 %result = "stablehlo.dynamic_conv"(%lhs, %rhs, %padding) {
   window_strides = array<i64: 4, 4>,
   lhs_dilation = array<i64: 2, 2>,
@@ -105,7 +105,7 @@
   batch_group_count = 1 : i64,
   feature_group_count = 1 : i64,
   precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
-} : (tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>, tensor<2x2xi64>) -> tensor<1x2x2x1xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_gather Method
dynamic_gather
This operation is functionally identical to gather op, with the slice_sizes specified dynamically as an operand.
Example
mlir
%slice_sizes = stablehlo.constant dense<[1, 2, 2]> : tensor<3xi64>
+} : (tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>, tensor<2x2xi64>) -> tensor<1x2x2x1xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_gather Method
dynamic_gather
This operation is functionally identical to gather op, with the slice_sizes specified dynamically as an operand.
Example
mlir
%slice_sizes = stablehlo.constant dense<[1, 2, 2]> : tensor<3xi64>
 %result = "stablehlo.dynamic_gather"(%operand, %start_indices, %slice_sizes) {
   dimension_numbers = #stablehlo.gather<
     offset_dims = [2, 3],
@@ -113,18 +113,18 @@
     start_index_map = [0, 2],
     index_vector_dim = 2>,
   indices_are_sorted = false
-} : (tensor<3x4x2xi64>, tensor<2x3x2xi64>, tensor<3xi64>) -> tensor<2x3x2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_iota Method
dynamic_iota
This operation is functionally identical to iota op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_iota
Example
mlir
%output_shape = stablehlo.constant dense<[4, 5]> : tensor<2xi64>
-%0 = stablehlo.dynamic_iota %output_shape, dim = 0 : (tensor<2xi64>) -> tensor<4x5xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_pad Method
dynamic_pad
This operation is functionally identical to pad https://github.com/openxla/stablehlo/pull/2306#discussion_r1595669709 op, but with edge_padding_low,edge_padding_highandinterior_padding specified dynamically as values.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_pad
Example
mlir
%edge_padding_low = stablehlo.constant dense<[0, 1]> : tensor<2xi32>
+} : (tensor<3x4x2xi64>, tensor<2x3x2xi64>, tensor<3xi64>) -> tensor<2x3x2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_iota Method
dynamic_iota
This operation is functionally identical to iota op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_iota
Example
mlir
%output_shape = stablehlo.constant dense<[4, 5]> : tensor<2xi64>
+%0 = stablehlo.dynamic_iota %output_shape, dim = 0 : (tensor<2xi64>) -> tensor<4x5xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_pad Method
dynamic_pad
This operation is functionally identical to pad https://github.com/openxla/stablehlo/pull/2306#discussion_r1595669709 op, but with edge_padding_low,edge_padding_highandinterior_padding specified dynamically as values.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_pad
Example
mlir
%edge_padding_low = stablehlo.constant dense<[0, 1]> : tensor<2xi32>
 %edge_padding_high = stablehlo.constant dense<[2, 1]> : tensor<2xi32>
 %interior_padding = stablehlo.constant dense<[1, 2]> : tensor<2xi32>
 %result = stablehlo.dynamic_pad %operand, %padding_value,
             %edge_padding_low, %edge_padding_high, %interior_padding
-            : (tensor<2x3xi64>, tensor<i64>, tensor<2xi64>, tensor<2xi64>, tensor<2xi64>) -> tensor<5x9xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_reshape Method
dynamic_reshape
This operation is functionally identical to reshape op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_reshape
Example
mlir
%output_shape = stablehlo.constant dense<[3, 2]> : tensor<2xi64>
-%result = stablehlo.dynamic_reshape %operand, %output_shape : (tensor<2x3xi64>, tensor<2xi64>) -> tensor<3x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_slice Method
dynamic_slice
Extracts a slice from the operand using dynamically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_slice
Example
mlir
%result = stablehlo.dynamic_slice %operand, %start_indices0, %start_indices1, sizes = [2, 2]
-  : (tensor<4x4xi32>, tensor<i64>, tensor<i64>) -> tensor<2x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice Method
dynamic_update_slice
Produces a result tensor which is equal to the operand tensor except that the slice starting at start_indices is updated with the values in update.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_update_slice
Example
mlir
%result = stablehlo.dynamic_update_slice %operand, %update, %start_indices0, %start_indices1
-  : (tensor<4x4xi32>, tensor<2x2xi32>, tensor<i64>, tensor<i64>) -> tensor<4x4xi32>
source
Reactant.MLIR.Dialects.stablehlo.einsum Method
einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.einsum"(%lhs, %rhs) {
+            : (tensor<2x3xi64>, tensor<i64>, tensor<2xi64>, tensor<2xi64>, tensor<2xi64>) -> tensor<5x9xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_reshape Method
dynamic_reshape
This operation is functionally identical to reshape op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_reshape
Example
mlir
%output_shape = stablehlo.constant dense<[3, 2]> : tensor<2xi64>
+%result = stablehlo.dynamic_reshape %operand, %output_shape : (tensor<2x3xi64>, tensor<2xi64>) -> tensor<3x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_slice Method
dynamic_slice
Extracts a slice from the operand using dynamically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_slice
Example
mlir
%result = stablehlo.dynamic_slice %operand, %start_indices0, %start_indices1, sizes = [2, 2]
+  : (tensor<4x4xi32>, tensor<i64>, tensor<i64>) -> tensor<2x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice Method
dynamic_update_slice
Produces a result tensor which is equal to the operand tensor except that the slice starting at start_indices is updated with the values in update.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_update_slice
Example
mlir
%result = stablehlo.dynamic_update_slice %operand, %update, %start_indices0, %start_indices1
+  : (tensor<4x4xi32>, tensor<2x2xi32>, tensor<i64>, tensor<i64>) -> tensor<4x4xi32>
source
Reactant.MLIR.Dialects.stablehlo.einsum Method
einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.einsum"(%lhs, %rhs) {
   einsum_config = "ab,bc->ac"
-} : (tensor<4x16xf32>, tensor<16x4xf32>) -> tensor<4x4xf32>
source
Reactant.MLIR.Dialects.stablehlo.exponential Method
exponential
Performs element-wise exponential operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential
Example
mlir
%result = stablehlo.exponential %operand : tensor<2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.exponential_minus_one Method
exponential_minus_one
Performs element-wise exponential minus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential_minus_one
Example
mlir
%result = stablehlo.exponential_minus_one %operand : tensor<2xf64>
source
Reactant.MLIR.Dialects.stablehlo.fft Method
fft
Performs the forward and inverse Fourier transforms for real and complex inputs/outputs.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#fft
Example
mlir
%result = stablehlo.fft %operand, type = FFT, length = [4] : (tensor<4xcomplex<f32>>) -> tensor<4xcomplex<f32>>
source
Reactant.MLIR.Dialects.stablehlo.floor Method
floor
Performs element-wise floor of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#floor
Example
mlir
%result = stablehlo.floor %operand : tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.gather Method
gather
Gathers slices from operand tensor from offsets specified in start_indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather
Example
mlir
%result = "stablehlo.gather"(%operand, %start_indices) {
+} : (tensor<4x16xf32>, tensor<16x4xf32>) -> tensor<4x4xf32>
source
Reactant.MLIR.Dialects.stablehlo.exponential Method
exponential
Performs element-wise exponential operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential
Example
mlir
%result = stablehlo.exponential %operand : tensor<2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.exponential_minus_one Method
exponential_minus_one
Performs element-wise exponential minus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential_minus_one
Example
mlir
%result = stablehlo.exponential_minus_one %operand : tensor<2xf64>
source
Reactant.MLIR.Dialects.stablehlo.fft Method
fft
Performs the forward and inverse Fourier transforms for real and complex inputs/outputs.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#fft
Example
mlir
%result = stablehlo.fft %operand, type = FFT, length = [4] : (tensor<4xcomplex<f32>>) -> tensor<4xcomplex<f32>>
source
Reactant.MLIR.Dialects.stablehlo.floor Method
floor
Performs element-wise floor of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#floor
Example
mlir
%result = stablehlo.floor %operand : tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.gather Method
gather
Gathers slices from operand tensor from offsets specified in start_indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather
Example
mlir
%result = "stablehlo.gather"(%operand, %start_indices) {
   dimension_numbers = #stablehlo.gather<
     offset_dims = [3, 4],
     collapsed_slice_dims = [1],
@@ -134,32 +134,32 @@
     index_vector_dim = 3>,
   slice_sizes = array<i64: 1, 1, 2, 2>,
   indices_are_sorted = false
-} : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>) -> tensor<2x2x3x2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.get_dimension_size Method
get_dimension_size
Produces the size of the given dimension of the operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_dimension_size
Example
mlir
%result = stablehlo.get_dimension_size %operand, dim = 1 : (tensor<2x3xi64>) -> tensor<i32>
source
Reactant.MLIR.Dialects.stablehlo.get_tuple_element Method
get_tuple_element
Extracts element at index position of the operand tuple and produces a result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_tuple_element
Example
mlir
%result = stablehlo.get_tuple_element %operand[0] : (tuple<tensor<2xf64>, tuple<tensor<i64>>>) -> tensor<2xf64>
source
Reactant.MLIR.Dialects.stablehlo.if_ Method
if_
Produces the output from executing exactly one branch from true_branch or false_branch depending on the value of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#if
Example
%result = "stablehlo.if"(%pred) ({ "stablehlo.return"(%result_true_branch) : (tensor<i32>) -> () }, { "stablehlo.return"(%result_false_branch) : (tensor<i32>) -> () }) : (tensor<i1>) -> tensor<i32>
source
Reactant.MLIR.Dialects.stablehlo.imag Method
imag
Extracts the imaginary part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#imag
Example
mlir
%result = stablehlo.imag %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.infeed Method
infeed
Reads data from the infeed and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#infeed
Example
mlir
%results0:2 = "stablehlo.infeed"(%token) :
-    (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
Reactant.MLIR.Dialects.stablehlo.iota Method
iota
Fills an output tensor with values in increasing order starting from zero along the iota_dimension dimension.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#iota
Example
mlir
%output = stablehlo.iota dim = 0 : tensor<4x5xi32>
source
Reactant.MLIR.Dialects.stablehlo.is_finite Method
is_finite
Performs element-wise check whether the value in x is finite (i.e. is neither +Inf, -Inf, nor NaN) and produces a y tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#is_finite
Example
mlir
%y = stablehlo.is_finite %x : (tensor<7xf64>) -> tensor<7xi1>
source
Reactant.MLIR.Dialects.stablehlo.log Method
log
Performs element-wise logarithm operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log
Example
mlir
%result = stablehlo.log %operand : tensor<2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.log_plus_one Method
log_plus_one
Performs element-wise logarithm plus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log_plus_one
Example
mlir
%result = stablehlo.log_plus_one %operand : tensor<5xf64>
source
Reactant.MLIR.Dialects.stablehlo.logistic Method
logistic
Performs element-wise logistic operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#logistic
Example
mlir
%result = stablehlo.logistic %operand : tensor<2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.map Method
map
Applies a map function computation to inputs along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#map
Example
mlir
%result = "stablehlo.map"(%input0, %input1) ({
+} : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>) -> tensor<2x2x3x2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.get_dimension_size Method
get_dimension_size
Produces the size of the given dimension of the operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_dimension_size
Example
mlir
%result = stablehlo.get_dimension_size %operand, dim = 1 : (tensor<2x3xi64>) -> tensor<i32>
source
Reactant.MLIR.Dialects.stablehlo.get_tuple_element Method
get_tuple_element
Extracts element at index position of the operand tuple and produces a result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_tuple_element
Example
mlir
%result = stablehlo.get_tuple_element %operand[0] : (tuple<tensor<2xf64>, tuple<tensor<i64>>>) -> tensor<2xf64>
source
Reactant.MLIR.Dialects.stablehlo.if_ Method
if_
Produces the output from executing exactly one branch from true_branch or false_branch depending on the value of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#if
Example
%result = "stablehlo.if"(%pred) ({ "stablehlo.return"(%result_true_branch) : (tensor<i32>) -> () }, { "stablehlo.return"(%result_false_branch) : (tensor<i32>) -> () }) : (tensor<i1>) -> tensor<i32>
source
Reactant.MLIR.Dialects.stablehlo.imag Method
imag
Extracts the imaginary part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#imag
Example
mlir
%result = stablehlo.imag %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.infeed Method
infeed
Reads data from the infeed and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#infeed
Example
mlir
%results0:2 = "stablehlo.infeed"(%token) :
+    (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
Reactant.MLIR.Dialects.stablehlo.iota Method
iota
Fills an output tensor with values in increasing order starting from zero along the iota_dimension dimension.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#iota
Example
mlir
%output = stablehlo.iota dim = 0 : tensor<4x5xi32>
source
Reactant.MLIR.Dialects.stablehlo.is_finite Method
is_finite
Performs element-wise check whether the value in x is finite (i.e. is neither +Inf, -Inf, nor NaN) and produces a y tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#is_finite
Example
mlir
%y = stablehlo.is_finite %x : (tensor<7xf64>) -> tensor<7xi1>
source
Reactant.MLIR.Dialects.stablehlo.log Method
log
Performs element-wise logarithm operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log
Example
mlir
%result = stablehlo.log %operand : tensor<2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.log_plus_one Method
log_plus_one
Performs element-wise logarithm plus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log_plus_one
Example
mlir
%result = stablehlo.log_plus_one %operand : tensor<5xf64>
source
Reactant.MLIR.Dialects.stablehlo.logistic Method
logistic
Performs element-wise logistic operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#logistic
Example
mlir
%result = stablehlo.logistic %operand : tensor<2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.map Method
map
Applies a map function computation to inputs along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#map
Example
mlir
%result = "stablehlo.map"(%input0, %input1) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.multiply %arg0, %arg1 : tensor<i64>
     stablehlo.return %0 : tensor<i64>
 }) {
   dimensions = array<i64: 0, 1>
-} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.maximum Method
maximum
Performs element-wise max operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#maximum
Example
mlir
%result = stablehlo.maximum %lhs, %rhs : tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.minimum Method
minimum
Performs element-wise min operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#minimum
Example
mlir
%result = stablehlo.minimum %lhs, %rhs : tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.multiply Method
multiply
Performs element-wise product of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#multiply
Example
mlir
%result = stablehlo.multiply %lhs, %rhs : tensor<2xi32>
source
Reactant.MLIR.Dialects.stablehlo.negate Method
negate
Performs element-wise negation of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#negate
Example
mlir
%result = stablehlo.negate %operand : tensor<2x3xi32>
source
Reactant.MLIR.Dialects.stablehlo.not Method
not
Performs element-wise NOT of tensor operand of type integer and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#not
Example
mlir
%result = stablehlo.not %operand : tensor<5x3x1xi1>
source
Reactant.MLIR.Dialects.stablehlo.optimization_barrier Method
optimization_barrier
Ensures that the operations that produce the operand are executed before any operations that depend on the result and prevents compiler transformations from moving operations across the barrier. Other than that, the operation is an identity, i.e. result = operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#optimization_barrier
Example
mlir
%result0, %result1 = stablehlo.optimization_barrier %operand0, %operand1 : tensor<f32>, tensor<f32>
source
Reactant.MLIR.Dialects.stablehlo.or Method
or
Performs element-wise OR of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#or
Example
mlir
%result = stablehlo.or %lhs, %rhs : tensor<2xi1>
source
Reactant.MLIR.Dialects.stablehlo.outfeed Method
outfeed
Writes inputs to the outfeed and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#outfeed
Example
mlir
%result = "stablehlo.outfeed"(%input0, %token) :
-    (tensor<2x2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
Reactant.MLIR.Dialects.stablehlo.pad Method
pad
Expands operand by padding around the tensor as well as between the elements of the tensor with the given padding_value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#pad
Example
mlir
%0 = stablehlo.pad %arg0, %arg1, low = [0, 1], high = [2, 1], interior = [1, 2]
-  : (tensor<2x3xi32>, tensor<i32>) -> tensor<5x9xi32>
source
Reactant.MLIR.Dialects.stablehlo.partition_id Method
partition_id
Produces partition_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#partition_id
Example
mlir
%result = stablehlo.partition_id : tensor<ui32>
source
Reactant.MLIR.Dialects.stablehlo.popcnt Method
popcnt
Performs element-wise count of the number of bits set in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#popcnt
Example
mlir
%result = stablehlo.popcnt %operand : tensor<4xi64>
source
Reactant.MLIR.Dialects.stablehlo.power Method
power
Performs element-wise exponentiation of lhs tensor by rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#power
Example
mlir
%result = stablehlo.power %lhs, %rhs : tensor<6xf64>
source
Reactant.MLIR.Dialects.stablehlo.real Method
real
Extracts the real part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#real
Example
mlir
%result = stablehlo.real %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice Method
real_dynamic_slice
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as SliceOp except that start_indices, limit_indices and strides are specified dynamically: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.real_dynamic_slice %operand,
+} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.maximum Method
maximum
Performs element-wise max operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#maximum
Example
mlir
%result = stablehlo.maximum %lhs, %rhs : tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.minimum Method
minimum
Performs element-wise min operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#minimum
Example
mlir
%result = stablehlo.minimum %lhs, %rhs : tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.multiply Method
multiply
Performs element-wise product of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#multiply
Example
mlir
%result = stablehlo.multiply %lhs, %rhs : tensor<2xi32>
source
Reactant.MLIR.Dialects.stablehlo.negate Method
negate
Performs element-wise negation of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#negate
Example
mlir
%result = stablehlo.negate %operand : tensor<2x3xi32>
source
Reactant.MLIR.Dialects.stablehlo.not Method
not
Performs element-wise NOT of tensor operand of type integer and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#not
Example
mlir
%result = stablehlo.not %operand : tensor<5x3x1xi1>
source
Reactant.MLIR.Dialects.stablehlo.optimization_barrier Method
optimization_barrier
Ensures that the operations that produce the operand are executed before any operations that depend on the result and prevents compiler transformations from moving operations across the barrier. Other than that, the operation is an identity, i.e. result = operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#optimization_barrier
Example
mlir
%result0, %result1 = stablehlo.optimization_barrier %operand0, %operand1 : tensor<f32>, tensor<f32>
source
Reactant.MLIR.Dialects.stablehlo.or Method
or
Performs element-wise OR of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#or
Example
mlir
%result = stablehlo.or %lhs, %rhs : tensor<2xi1>
source
Reactant.MLIR.Dialects.stablehlo.outfeed Method
outfeed
Writes inputs to the outfeed and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#outfeed
Example
mlir
%result = "stablehlo.outfeed"(%input0, %token) :
+    (tensor<2x2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
Reactant.MLIR.Dialects.stablehlo.pad Method
pad
Expands operand by padding around the tensor as well as between the elements of the tensor with the given padding_value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#pad
Example
mlir
%0 = stablehlo.pad %arg0, %arg1, low = [0, 1], high = [2, 1], interior = [1, 2]
+  : (tensor<2x3xi32>, tensor<i32>) -> tensor<5x9xi32>
source
Reactant.MLIR.Dialects.stablehlo.partition_id Method
partition_id
Produces partition_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#partition_id
Example
mlir
%result = stablehlo.partition_id : tensor<ui32>
source
Reactant.MLIR.Dialects.stablehlo.popcnt Method
popcnt
Performs element-wise count of the number of bits set in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#popcnt
Example
mlir
%result = stablehlo.popcnt %operand : tensor<4xi64>
source
Reactant.MLIR.Dialects.stablehlo.power Method
power
Performs element-wise exponentiation of lhs tensor by rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#power
Example
mlir
%result = stablehlo.power %lhs, %rhs : tensor<6xf64>
source
Reactant.MLIR.Dialects.stablehlo.real Method
real
Extracts the real part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#real
Example
mlir
%result = stablehlo.real %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice Method
real_dynamic_slice
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as SliceOp except that start_indices, limit_indices and strides are specified dynamically: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.real_dynamic_slice %operand,
             %start_indices, %limit_indices, %strides
-       : (tensor<256x?xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<256x?xf32>
source
Reactant.MLIR.Dialects.stablehlo.recv Method
recv
Receives data from a channel with channel_id and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#recv
Example
mlir
%results:2 = "stablehlo.recv"(%token) {
+       : (tensor<256x?xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<256x?xf32>
source
Reactant.MLIR.Dialects.stablehlo.recv Method
recv
Receives data from a channel with channel_id and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#recv
Example
mlir
%results:2 = "stablehlo.recv"(%token) {
   channel_handle = #stablehlo.channel_handle<handle = 1, type = 3>,
   is_host_transfer = true
-} : (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
Reactant.MLIR.Dialects.stablehlo.reduce Method
reduce
Applies a reduction function body to inputs and init_values along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce
Example
mlir
%result = "stablehlo.reduce"(%input, %init_value) ({
+} : (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
Reactant.MLIR.Dialects.stablehlo.reduce Method
reduce
Applies a reduction function body to inputs and init_values along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce
Example
mlir
%result = "stablehlo.reduce"(%input, %init_value) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
     stablehlo.return %0 : tensor<i64>
 }) {
   dimensions = array<i64: 1>
-} : (tensor<1x6xi64>, tensor<i64>) -> tensor<1xi64>
source
Reactant.MLIR.Dialects.stablehlo.reduce_precision Method
reduce_precision
Performs element-wise conversion of operand to another floating-point type that uses exponent_bits and mantissa_bits and back to the original floating-point type and produces an output tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_precision
Example
mlir
%output = stablehlo.reduce_precision %operand, format = e5m10 : tensor<6xf64>
source
Reactant.MLIR.Dialects.stablehlo.reduce_scatter Method
reduce_scatter
Within each process group in the process grid, performs reduction, using computations, over the values of the operand tensor from each process, splits the reduction result along scatter_dimension into parts, and scatters the split parts between the processes to produce the result.
See:
+} : (tensor<1x6xi64>, tensor<i64>) -> tensor<1xi64>
source
Reactant.MLIR.Dialects.stablehlo.reduce_precision Method
reduce_precision
Performs element-wise conversion of operand to another floating-point type that uses exponent_bits and mantissa_bits and back to the original floating-point type and produces an output tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_precision
Example
mlir
%output = stablehlo.reduce_precision %operand, format = e5m10 : tensor<6xf64>
source
Reactant.MLIR.Dialects.stablehlo.reduce_scatter Method
reduce_scatter
Within each process group in the process grid, performs reduction, using computations, over the values of the operand tensor from each process, splits the reduction result along scatter_dimension into parts, and scatters the split parts between the processes to produce the result.
See:
 https://github.com/openxla/stablehlo/blob/main/docs/spec#reduce_scatter
 
 Example:
 ```mlir
-%result = "stablehlo.reduce_scatter"(%operand) ({
^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension = 1 : i64, replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>, channel_handle = #stablehlo.channel_handle<handle = 0, type = 0> } : (tensor<2x4xi64>) -> tensor<2x2xi64> ```
source
Reactant.MLIR.Dialects.stablehlo.reduce_window Method
reduce_window
Applies a reduction function body to windows of inputs and init_values and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_window
Example
mlir
%result = "stablehlo.reduce_window"(%input, %init_value) ({
+%result = "stablehlo.reduce_scatter"(%operand) ({
^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension = 1 : i64, replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>, channel_handle = #stablehlo.channel_handle<handle = 0, type = 0> } : (tensor<2x4xi64>) -> tensor<2x2xi64> ```
source
Reactant.MLIR.Dialects.stablehlo.reduce_window Method
reduce_window
Applies a reduction function body to windows of inputs and init_values and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_window
Example
mlir
%result = "stablehlo.reduce_window"(%input, %init_value) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
     stablehlo.return %0 : tensor<i64>
@@ -169,7 +169,7 @@
   base_dilations = array<i64: 2, 1>,
   window_dilations = array<i64: 3, 1>,
   padding = dense<[[2, 1], [0, 0]]> : tensor<2x2xi64>
-} : (tensor<3x2xi64>, tensor<i64>) -> tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.remainder Method
remainder
Performs element-wise remainder of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#remainder
Example
mlir
%result = stablehlo.remainder %lhs, %rhs : tensor<4xi64>
source
Reactant.MLIR.Dialects.stablehlo.replica_id Method
replica_id
Produces replica_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#replica_id
Example
mlir
%result = stablehlo.replica_id : tensor<ui32>
source
Reactant.MLIR.Dialects.stablehlo.reshape Method
reshape
Performs reshape of operand tensor to a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reshape
Example
mlir
%result = stablehlo.reshape %operand : (tensor<2xf32>) -> tensor<1x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.reverse Method
reverse
Reverses the order of elements in the operand along the specified dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reverse
Example
mlir
%result = stablehlo.reverse %operand, dims = [1] : tensor<3x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.rng Method
rng
Generates random numbers using the rng_distribution algorithm and produces a result tensor of a given shape shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng
Example
mlir
%result = stablehlo.rng %a, %b, %shape, distribution = NORMAL : (tensor<i32>, tensor<i32>, tensor<2xi64>) -> tensor<3x3xi32>
source
Reactant.MLIR.Dialects.stablehlo.rng_bit_generator Method
rng_bit_generator
Returns an output filled with uniform random data and an updated output state output_state given an initial state initial_state using the pseudorandom number generator algorithm rng_algorithm.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng_bit_generator
Example
mlir
%output_state, %output = stablehlo.rng_bit_generator %initial_state, algorithm = THREE_FRY : (tensor<2xui64>) -> (tensor<2xui64>, tensor<2x2xui64>)
source
Reactant.MLIR.Dialects.stablehlo.round_nearest_afz Method
round_nearest_afz
Performs element-wise rounding towards the nearest integer, breaking ties away from zero, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_afz
Example
mlir
%result = stablehlo.round_nearest_afz %operand : tensor<5xf64>
source
Reactant.MLIR.Dialects.stablehlo.round_nearest_even Method
round_nearest_even
Performs element-wise rounding towards the nearest integer, breaking ties towards the even integer, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_even
Example
mlir
%result = stablehlo.round_nearest_even %operand : tensor<5xf64>
source
Reactant.MLIR.Dialects.stablehlo.rsqrt Method
rsqrt
Performs element-wise reciprocal square root operation on operand tensor and produces a result tensor, implementing the rSqrt operation from the IEEE-754 specification.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rsqrt
Example
mlir
%result = stablehlo.rsqrt %operand : tensor<2x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.scatter Method
scatter
Produces results tensors which are equal to inputs tensors except that several slices specified by scatter_indices are updated with the values updates using update_computation.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter
Example: mlir %result = "stablehlo.scatter"(%input, %scatter_indices, %update) ({ ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension_numbers = #stablehlo.scatter< update_window_dims = [3, 4], inserted_window_dims = [1], input_batching_dims = [0], scatter_indices_batching_dims = [1], scatter_dims_to_operand_dims = [2, 1], index_vector_dim = 3>, indices_are_sorted = false, unique_indices = false } : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>, tensor<2x2x3x2x2xi64>) -> tensor<2x3x4x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.select Method
select
Produces a result tensor where each element is selected from on_true or on_false tensor based on the value of the corresponding element of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select
Example
mlir
%result = stablehlo.select %pred, %on_true, %on_false : tensor<2x2xi1>, tensor<2x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.select_and_scatter Method
select_and_scatter
Scatters the values from the source tensor using scatter based on the outcome of reduce_window of the input tensor using select and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select_and_scatter
Example
mlir
%result = "stablehlo.select_and_scatter"(%operand, %source, %init_value) ({
+} : (tensor<3x2xi64>, tensor<i64>) -> tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.remainder Method
remainder
Performs element-wise remainder of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#remainder
Example
mlir
%result = stablehlo.remainder %lhs, %rhs : tensor<4xi64>
source
Reactant.MLIR.Dialects.stablehlo.replica_id Method
replica_id
Produces replica_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#replica_id
Example
mlir
%result = stablehlo.replica_id : tensor<ui32>
source
Reactant.MLIR.Dialects.stablehlo.reshape Method
reshape
Performs reshape of operand tensor to a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reshape
Example
mlir
%result = stablehlo.reshape %operand : (tensor<2xf32>) -> tensor<1x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.reverse Method
reverse
Reverses the order of elements in the operand along the specified dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reverse
Example
mlir
%result = stablehlo.reverse %operand, dims = [1] : tensor<3x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.rng Method
rng
Generates random numbers using the rng_distribution algorithm and produces a result tensor of a given shape shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng
Example
mlir
%result = stablehlo.rng %a, %b, %shape, distribution = NORMAL : (tensor<i32>, tensor<i32>, tensor<2xi64>) -> tensor<3x3xi32>
source
Reactant.MLIR.Dialects.stablehlo.rng_bit_generator Method
rng_bit_generator
Returns an output filled with uniform random data and an updated output state output_state given an initial state initial_state using the pseudorandom number generator algorithm rng_algorithm.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng_bit_generator
Example
mlir
%output_state, %output = stablehlo.rng_bit_generator %initial_state, algorithm = THREE_FRY : (tensor<2xui64>) -> (tensor<2xui64>, tensor<2x2xui64>)
source
Reactant.MLIR.Dialects.stablehlo.round_nearest_afz Method
round_nearest_afz
Performs element-wise rounding towards the nearest integer, breaking ties away from zero, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_afz
Example
mlir
%result = stablehlo.round_nearest_afz %operand : tensor<5xf64>
source
Reactant.MLIR.Dialects.stablehlo.round_nearest_even Method
round_nearest_even
Performs element-wise rounding towards the nearest integer, breaking ties towards the even integer, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_even
Example
mlir
%result = stablehlo.round_nearest_even %operand : tensor<5xf64>
source
Reactant.MLIR.Dialects.stablehlo.rsqrt Method
rsqrt
Performs element-wise reciprocal square root operation on operand tensor and produces a result tensor, implementing the rSqrt operation from the IEEE-754 specification.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rsqrt
Example
mlir
%result = stablehlo.rsqrt %operand : tensor<2x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.scatter Method
scatter
Produces results tensors which are equal to inputs tensors except that several slices specified by scatter_indices are updated with the values updates using update_computation.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter
Example: mlir %result = "stablehlo.scatter"(%input, %scatter_indices, %update) ({ ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension_numbers = #stablehlo.scatter< update_window_dims = [3, 4], inserted_window_dims = [1], input_batching_dims = [0], scatter_indices_batching_dims = [1], scatter_dims_to_operand_dims = [2, 1], index_vector_dim = 3>, indices_are_sorted = false, unique_indices = false } : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>, tensor<2x2x3x2x2xi64>) -> tensor<2x3x4x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.select Method
select
Produces a result tensor where each element is selected from on_true or on_false tensor based on the value of the corresponding element of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select
Example
mlir
%result = stablehlo.select %pred, %on_true, %on_false : tensor<2x2xi1>, tensor<2x2xi32>
source
Reactant.MLIR.Dialects.stablehlo.select_and_scatter Method
select_and_scatter
Scatters the values from the source tensor using scatter based on the outcome of reduce_window of the input tensor using select and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select_and_scatter
Example
mlir
%result = "stablehlo.select_and_scatter"(%operand, %source, %init_value) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.compare GE, %arg0, %arg1 : (tensor<i64>, tensor<i64>) -> tensor<i1>
     stablehlo.return %0 : tensor<i1>
@@ -181,10 +181,10 @@
   window_dimensions = dense<[3, 1]> : tensor<2xi64>,
   window_strides = dense<[2, 1]> : tensor<2xi64>,
   padding = dense<[[0, 1], [0, 0]]> : tensor<2x2xi64>
-} : (tensor<4x2xi64>, tensor<2x2xi64>, tensor<i64>) -> tensor<4x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.send Method
send
Sends inputs to a channel channel_id and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#send
Example
mlir
%result = "stablehlo.send"(%operand, %token) {
+} : (tensor<4x2xi64>, tensor<2x2xi64>, tensor<i64>) -> tensor<4x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.send Method
send
Sends inputs to a channel channel_id and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#send
Example
mlir
%result = "stablehlo.send"(%operand, %token) {
   channel_handle = #stablehlo.channel_handle<handle = 1, type = 2>,
   is_host_transfer = true
-} : (tensor<2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
Reactant.MLIR.Dialects.stablehlo.set_dimension_size Method
set_dimension_size
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as XLA's SetDimensionSize: https://www.tensorflow.org/xla/operation_semantics#setdimensionsize
Example
mlir
%0 = stablehlo.set_dimension_size %arg0, %arg1, dim = 1 : (tensor<4x2xf32>, tensor<i32>) -> tensor<4x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.shift_left Method
shift_left
Performs element-wise left-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_left
Example
mlir
%result = stablehlo.shift_left %lhs, %rhs : tensor<3xi64>
source
Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic Method
shift_right_arithmetic
Performs element-wise arithmetic right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_arithmetic
Example
mlir
%result = stablehlo.shift_right_arithmetic %lhs, %rhs : tensor<3xi64>
source
Reactant.MLIR.Dialects.stablehlo.shift_right_logical Method
shift_right_logical
Performs element-wise logical right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_logical
Example
mlir
%result = stablehlo.shift_right_logical %lhs, %rhs : tensor<3xi64>
source
Reactant.MLIR.Dialects.stablehlo.sign Method
sign
Returns the sign of the operand element-wise and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sign
Example
mlir
%result = stablehlo.sign %operand : tensor<5xf64>
source
Reactant.MLIR.Dialects.stablehlo.sine Method
sine
Performs element-wise sine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sine
Example
mlir
%result = stablehlo.sine %operand : tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.slice Method
slice
Extracts a slice from the operand using statically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.slice %operand [1:3, 4:8:2]
+} : (tensor<2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
Reactant.MLIR.Dialects.stablehlo.set_dimension_size Method
set_dimension_size
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as XLA's SetDimensionSize: https://www.tensorflow.org/xla/operation_semantics#setdimensionsize
Example
mlir
%0 = stablehlo.set_dimension_size %arg0, %arg1, dim = 1 : (tensor<4x2xf32>, tensor<i32>) -> tensor<4x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.shift_left Method
shift_left
Performs element-wise left-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_left
Example
mlir
%result = stablehlo.shift_left %lhs, %rhs : tensor<3xi64>
source
Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic Method
shift_right_arithmetic
Performs element-wise arithmetic right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_arithmetic
Example
mlir
%result = stablehlo.shift_right_arithmetic %lhs, %rhs : tensor<3xi64>
source
Reactant.MLIR.Dialects.stablehlo.shift_right_logical Method
shift_right_logical
Performs element-wise logical right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_logical
Example
mlir
%result = stablehlo.shift_right_logical %lhs, %rhs : tensor<3xi64>
source
Reactant.MLIR.Dialects.stablehlo.sign Method
sign
Returns the sign of the operand element-wise and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sign
Example
mlir
%result = stablehlo.sign %operand : tensor<5xf64>
source
Reactant.MLIR.Dialects.stablehlo.sine Method
sine
Performs element-wise sine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sine
Example
mlir
%result = stablehlo.sine %operand : tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.slice Method
slice
Extracts a slice from the operand using statically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.slice %operand [1:3, 4:8:2]
    : (tensor<3x8xi64>) -> tensor<2x2xi64>
 
 // Same in generic form: the `1:3` above is mapped to the first entry in
@@ -195,9 +195,9 @@
   start_indices = array<i64: 1, 4>,
   limit_indices = array<i64: 3, 8>,
   strides = array<i64: 1, 2>
-} : (tensor<3x8xi64>) -> tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.sort Method
sort
Sorts a variadic number of tensors in inputs together, according to a custom comparator, along the given dimension and produces a variadic number of tensors as results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sort
Example
mlir

+} : (tensor<3x8xi64>) -> tensor<2x2xi64>
source
Reactant.MLIR.Dialects.stablehlo.sort Method
sort
Sorts a variadic number of tensors in inputs together, according to a custom comparator, along the given dimension and produces a variadic number of tensors as results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sort
Example
mlir

 
-[source](https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/StableHLO.jl#L4195-L4215)
+[source](https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/StableHLO.jl#L4195-L4215)
 
 </details>
 
@@ -215,17 +215,17 @@
 **Example**
 
 ```mlir
-%result = stablehlo.sqrt %operand : tensor<2x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.subtract Method
subtract
Performs element-wise subtraction of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#subtract
Example
mlir
%result = stablehlo.subtract %lhs, %rhs : tensor<2xi32>
source
Reactant.MLIR.Dialects.stablehlo.tan Method
tan
Performs element-wise tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tan
Example
mlir
%result = stablehlo.tan %operand : tensor<2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.tanh Method
tanh
Performs element-wise hyperbolic tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tanh
Example
mlir
%result = stablehlo.tanh %operand : tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.torch_index_select Method
torch_index_select
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as PyTorch's index_select, augmented with support for batch dimensions: https://pytorch.org/docs/stable/generated/torch.index_select.html.
The batch_dims attribute specifies the number of major batch dimensions (0 or more) that act like a multidimensional loop over both the operand and the index.
Example
mlir
%result = "stablehlo.torch_index_select"(%operand, %index) {
+%result = stablehlo.sqrt %operand : tensor<2x2xf32>
source
Reactant.MLIR.Dialects.stablehlo.subtract Method
subtract
Performs element-wise subtraction of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#subtract
Example
mlir
%result = stablehlo.subtract %lhs, %rhs : tensor<2xi32>
source
Reactant.MLIR.Dialects.stablehlo.tan Method
tan
Performs element-wise tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tan
Example
mlir
%result = stablehlo.tan %operand : tensor<2x2xf64>
source
Reactant.MLIR.Dialects.stablehlo.tanh Method
tanh
Performs element-wise hyperbolic tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tanh
Example
mlir
%result = stablehlo.tanh %operand : tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.torch_index_select Method
torch_index_select
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as PyTorch's index_select, augmented with support for batch dimensions: https://pytorch.org/docs/stable/generated/torch.index_select.html.
The batch_dims attribute specifies the number of major batch dimensions (0 or more) that act like a multidimensional loop over both the operand and the index.
Example
mlir
%result = "stablehlo.torch_index_select"(%operand, %index) {
   dim = 2 : i64,
   batch_dims = 1 : i64
-} : (tensor<8x128x3072x64xf32>, tensor<8x16x1024xi32>) -> tensor<8x128x16x1024x64xf32>
source
Reactant.MLIR.Dialects.stablehlo.transpose Method
transpose
Permutes the dimensions of operand tensor using permutation and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#transpose
Example
mlir
%0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<1x2x3xi32>) -> tensor<3x2x1xi32>
source
Reactant.MLIR.Dialects.stablehlo.triangular_solve Method
triangular_solve
Solves batches of systems of linear equations with lower or upper triangular coefficient matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#triangular_solve
Example
mlir
%result = "stablehlo.triangular_solve"(%a, %b) {
+} : (tensor<8x128x3072x64xf32>, tensor<8x16x1024xi32>) -> tensor<8x128x16x1024x64xf32>
source
Reactant.MLIR.Dialects.stablehlo.transpose Method
transpose
Permutes the dimensions of operand tensor using permutation and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#transpose
Example
mlir
%0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<1x2x3xi32>) -> tensor<3x2x1xi32>
source
Reactant.MLIR.Dialects.stablehlo.triangular_solve Method
triangular_solve
Solves batches of systems of linear equations with lower or upper triangular coefficient matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#triangular_solve
Example
mlir
%result = "stablehlo.triangular_solve"(%a, %b) {
   left_side = true,
   lower = true,
   unit_diagonal = false,
   transpose_a = #stablehlo<transpose NO_TRANSPOSE>
-} : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
source
Reactant.MLIR.Dialects.stablehlo.tuple Method
tuple
Produces a result tuple from values val.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tuple
Example
mlir
%result = stablehlo.tuple %val0, %val1 : tuple<tensor<2xf64>, tuple<tensor<i64>>>
source
Reactant.MLIR.Dialects.stablehlo.unary_einsum Method
unary_einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.unary_einsum"(%operand) {
+} : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
source
Reactant.MLIR.Dialects.stablehlo.tuple Method
tuple
Produces a result tuple from values val.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tuple
Example
mlir
%result = stablehlo.tuple %val0, %val1 : tuple<tensor<2xf64>, tuple<tensor<i64>>>
source
Reactant.MLIR.Dialects.stablehlo.unary_einsum Method
unary_einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.unary_einsum"(%operand) {
   einsum_config = "ab->a"
-} : (tensor<4x16xf32>) -> tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.uniform_dequantize Method
uniform_dequantize
Performs element-wise conversion of quantized tensor operand to a floating-point tensor result according to the quantization parameters defined by the operand type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_dequantize
Example
mlir
%result = stablehlo.uniform_dequantize %operand : (tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>) -> tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.uniform_quantize Method
uniform_quantize
Performs element-wise conversion of floating-point tensor or quantized tensor operand to a quantized tensor result according to the quantization parameters defined by the result type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_quantize
Example
mlir
%result = stablehlo.uniform_quantize %operand : (tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>
source
Reactant.MLIR.Dialects.stablehlo.while_ Method
while_
Produces the output from executing body function 0 or more times while the cond function outputs true.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#while
Example
mlir
%results0, %results1 = stablehlo.while(%arg0 = %init_i, %arg1 = %init_sum) : tensor<i64>, tensor<i64>
+} : (tensor<4x16xf32>) -> tensor<4xf32>
source
Reactant.MLIR.Dialects.stablehlo.uniform_dequantize Method
uniform_dequantize
Performs element-wise conversion of quantized tensor operand to a floating-point tensor result according to the quantization parameters defined by the operand type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_dequantize
Example
mlir
%result = stablehlo.uniform_dequantize %operand : (tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>) -> tensor<2xf32>
source
Reactant.MLIR.Dialects.stablehlo.uniform_quantize Method
uniform_quantize
Performs element-wise conversion of floating-point tensor or quantized tensor operand to a quantized tensor result according to the quantization parameters defined by the result type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_quantize
Example
mlir
%result = stablehlo.uniform_quantize %operand : (tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>
source
Reactant.MLIR.Dialects.stablehlo.while_ Method
while_
Produces the output from executing body function 0 or more times while the cond function outputs true.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#while
Example
mlir
%results0, %results1 = stablehlo.while(%arg0 = %init_i, %arg1 = %init_sum) : tensor<i64>, tensor<i64>
 cond {
   %cond = stablehlo.compare LT, %arg0, %ten : (tensor<i64>, tensor<i64>) -> tensor<i1>
   stablehlo.return %cond : tensor<i1>
@@ -233,8 +233,8 @@
   %new_sum = stablehlo.add %arg1, %one : tensor<i64>
   %new_i = stablehlo.add %arg0, %one : tensor<i64>
   stablehlo.return %new_i, %new_sum : tensor<i64>, tensor<i64>
-}
source
Reactant.MLIR.Dialects.stablehlo.xor Method
xor
Performs element-wise XOR of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#xor
Example
mlir
%result = stablehlo.xor %lhs, %rhs : tensor<2xi32>
source

-    
+}

source

Reactant.MLIR.Dialects.stablehlo.xor Method

xor

Performs element-wise XOR of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#xor

Example

mlir
%result = stablehlo.xor %lhs, %rhs : tensor<2xi32>

source

+ \ No newline at end of file diff --git a/previews/PR441/api/vhlo.html b/previews/PR441/api/vhlo.html index 9a878d67d..b6eee1530 100644 --- a/previews/PR441/api/vhlo.html +++ b/previews/PR441/api/vhlo.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@
Reactant.jl

Appearance

- + \ No newline at end of file diff --git a/previews/PR441/api/xla.html b/previews/PR441/api/xla.html index 16bbcdf1d..73445a64f 100644 --- a/previews/PR441/api/xla.html +++ b/previews/PR441/api/xla.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@
Reactant.jl

Appearance

- + \ No newline at end of file diff --git a/previews/PR441/assets/api_affine.md.DDDiaunG.js b/previews/PR441/assets/api_affine.md.BDXHCMIg.js similarity index 96% rename from previews/PR441/assets/api_affine.md.DDDiaunG.js rename to previews/PR441/assets/api_affine.md.BDXHCMIg.js index 73126912d..473cb57c7 100644 --- a/previews/PR441/assets/api_affine.md.DDDiaunG.js +++ b/previews/PR441/assets/api_affine.md.BDXHCMIg.js @@ -3,12 +3,12 @@ import{_ as o,c as l,j as a,a as s,G as t,a2 as i,B as p,o as r}from"./chunks/fr %1 = affine.apply #map10 (%s, %t) // Inline example. -%2 = affine.apply affine_map<(i)[s0] -> (i+s0)> (%42)[%n]

source

`,5))]),a("details",f,[a("summary",null,[e[3]||(e[3]=a("a",{id:"Reactant.MLIR.Dialects.affine.delinearize_index-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.delinearize_index-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.delinearize_index")],-1)),e[4]||(e[4]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=i(`

delinearize_index

The affine.delinearize_index operation takes a single index value and calculates the multi-index according to the given basis.

Example

%indices:3 = affine.delinearize_index %linear_index into (%c16, %c224, %c224) : index, index, index

In the above example, %indices:3 conceptually holds the following:

#map0 = affine_map<()[s0] -> (s0 floordiv 50176)>
+%2 = affine.apply affine_map<(i)[s0] -> (i+s0)> (%42)[%n]

source

`,5))]),a("details",f,[a("summary",null,[e[3]||(e[3]=a("a",{id:"Reactant.MLIR.Dialects.affine.delinearize_index-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.delinearize_index-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.delinearize_index")],-1)),e[4]||(e[4]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=i(`

delinearize_index

The affine.delinearize_index operation takes a single index value and calculates the multi-index according to the given basis.

Example

%indices:3 = affine.delinearize_index %linear_index into (%c16, %c224, %c224) : index, index, index

In the above example, %indices:3 conceptually holds the following:

#map0 = affine_map<()[s0] -> (s0 floordiv 50176)>
 #map1 = affine_map<()[s0] -> ((s0 mod 50176) floordiv 224)>
 #map2 = affine_map<()[s0] -> (s0 mod 224)>
 %indices_0 = affine.apply #map0()[%linear_index]
 %indices_1 = affine.apply #map1()[%linear_index]
-%indices_2 = affine.apply #map2()[%linear_index]

The basis may either contain N or N-1 elements, where N is the number of results. If there are N basis elements, the first one will not be used during computations, but may be used during analysis and canonicalization to eliminate terms from the affine.delinearize_index or to enable conclusions about the total size of %linear_index.

If the basis is fully provided, the delinearize_index operation is said to "have an outer bound". The builders assume that an affine.delinearize_index has an outer bound by default, as this is how the operation was initially defined.

That is, the example above could also have been written

mlir
%0:3 = affine.delinearize_index %linear_index into (244, 244) : index, index

Note that, due to the constraints of affine maps, all the basis elements must be strictly positive. A dynamic basis element being 0 or negative causes undefined behavior.

source

`,12))]),a("details",m,[a("summary",null,[e[6]||(e[6]=a("a",{id:"Reactant.MLIR.Dialects.affine.for_-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.for_-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.for_")],-1)),e[7]||(e[7]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=i(`

for_

Syntax

operation   ::= \`affine.for\` ssa-id \`=\` lower-bound \`to\` upper-bound
+%indices_2 = affine.apply #map2()[%linear_index]

The basis may either contain N or N-1 elements, where N is the number of results. If there are N basis elements, the first one will not be used during computations, but may be used during analysis and canonicalization to eliminate terms from the affine.delinearize_index or to enable conclusions about the total size of %linear_index.

If the basis is fully provided, the delinearize_index operation is said to "have an outer bound". The builders assume that an affine.delinearize_index has an outer bound by default, as this is how the operation was initially defined.

That is, the example above could also have been written

mlir
%0:3 = affine.delinearize_index %linear_index into (244, 244) : index, index

Note that, due to the constraints of affine maps, all the basis elements must be strictly positive. A dynamic basis element being 0 or negative causes undefined behavior.

source

`,12))]),a("details",m,[a("summary",null,[e[6]||(e[6]=a("a",{id:"Reactant.MLIR.Dialects.affine.for_-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.for_-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.for_")],-1)),e[7]||(e[7]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=i(`

for_

Syntax

operation   ::= \`affine.for\` ssa-id \`=\` lower-bound \`to\` upper-bound
                 (\`step\` integer-literal)? \`{\` op* \`}\`
 
 lower-bound ::= \`max\`? affine-map-attribute dim-and-symbol-use-list | shorthand-bound
@@ -43,7 +43,7 @@ import{_ as o,c as l,j as a,a as s,G as t,a2 as i,B as p,o as r}from"./chunks/fr
   %y0 = arith.addi %arg0, %c1 : index
   %y1 = arith.addi %arg1, %c2 : index
   affine.yield %y0, %y1 : index, index
-}

If the affine.for defines any values, a yield terminator must be explicitly present. The number and types of the "affine.for" results must match the initial values in the iter_args binding and the yield operands.

source

`,17))]),a("details",u,[a("summary",null,[e[9]||(e[9]=a("a",{id:"Reactant.MLIR.Dialects.affine.if_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.if_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.if_")],-1)),e[10]||(e[10]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=i(`

if_

Syntax

operation  ::= \`affine.if\` if-op-cond \`{\` op* \`}\` (\`else\` \`{\` op* \`}\`)?
+}

If the affine.for defines any values, a yield terminator must be explicitly present. The number and types of the "affine.for" results must match the initial values in the iter_args binding and the yield operands.

source

`,17))]),a("details",u,[a("summary",null,[e[9]||(e[9]=a("a",{id:"Reactant.MLIR.Dialects.affine.if_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.if_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.if_")],-1)),e[10]||(e[10]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=i(`

if_

Syntax

operation  ::= \`affine.if\` if-op-cond \`{\` op* \`}\` (\`else\` \`{\` op* \`}\`)?
 if-op-cond ::= integer-set-attr dim-and-symbol-use-list

The affine.if operation restricts execution to a subset of the loop iteration space defined by an integer set (a conjunction of affine constraints). A single affine.if may end with an optional else clause.

The condition of the affine.if is represented by an integer set (a conjunction of affine constraints), and the SSA values bound to the dimensions and symbols in the integer set. The same restrictions hold for these SSA values as for all bindings of SSA values to dimensions and symbols.

The affine.if operation contains two regions for the "then" and "else" clauses. affine.if may return results that are defined in its regions. The values defined are determined by which execution path is taken. Each region of the affine.if must contain a single block with no arguments, and be terminated by affine.yield. If affine.if defines no values, the affine.yield can be left out, and will be inserted implicitly. Otherwise, it must be explicit. If no values are defined, the else block may be empty (i.e. contain no blocks).

Example

mlir
#set = affine_set<(d0, d1)[s0]: (d0 - 10 >= 0, s0 - d0 - 9 >= 0,
                                  d1 - 10 >= 0, s0 - d1 - 9 >= 0)>
 func.func @reduced_domain_example(%A, %X, %N) : (memref<10xi32>, i32, i32) {
@@ -72,10 +72,10 @@ import{_ as o,c as l,j as a,a as s,G as t,a2 as i,B as p,o as r}from"./chunks/fr
     affine.store %1, %O[%i, %j] : memref<12x12xf32>
   }
   return %O
-}

source

`,11))]),a("details",h,[a("summary",null,[e[12]||(e[12]=a("a",{id:"Reactant.MLIR.Dialects.affine.linearize_index-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.linearize_index-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.linearize_index")],-1)),e[13]||(e[13]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=i(`

linearize_index

The affine.linearize_index operation takes a sequence of index values and a basis of the same length and linearizes the indices using that basis.

That is, for indices %idx_0 to %idx_{N-1} and basis elements b_0 (or b_1) up to b_{N-1} it computes

sum(i = 0 to N-1) %idx_i * product(j = i + 1 to N-1) B_j

The basis may either have N or N-1 elements, where N is the number of inputs to linearize_index. If N inputs are provided, the first one is not used in computation, but may be used during analysis or canonicalization as a bound on %idx_0.

If all N basis elements are provided, the linearize_index operation is said to "have an outer bound".

If the disjoint property is present, this is an optimization hint that, for all i, 0 <= %idx_i < B_i - that is, no index affects any other index, except that %idx_0 may be negative to make the index as a whole negative.

Note that the outputs of affine.delinearize_index are, by definition, disjoint.

Example

mlir
%linear_index = affine.linearize_index [%index_0, %index_1, %index_2] by (2, 3, 5) : index
+}

source

`,11))]),a("details",h,[a("summary",null,[e[12]||(e[12]=a("a",{id:"Reactant.MLIR.Dialects.affine.linearize_index-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.linearize_index-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.linearize_index")],-1)),e[13]||(e[13]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=i(`

linearize_index

The affine.linearize_index operation takes a sequence of index values and a basis of the same length and linearizes the indices using that basis.

That is, for indices %idx_0 to %idx_{N-1} and basis elements b_0 (or b_1) up to b_{N-1} it computes

sum(i = 0 to N-1) %idx_i * product(j = i + 1 to N-1) B_j

The basis may either have N or N-1 elements, where N is the number of inputs to linearize_index. If N inputs are provided, the first one is not used in computation, but may be used during analysis or canonicalization as a bound on %idx_0.

If all N basis elements are provided, the linearize_index operation is said to "have an outer bound".

If the disjoint property is present, this is an optimization hint that, for all i, 0 <= %idx_i < B_i - that is, no index affects any other index, except that %idx_0 may be negative to make the index as a whole negative.

Note that the outputs of affine.delinearize_index are, by definition, disjoint.

Example

mlir
%linear_index = affine.linearize_index [%index_0, %index_1, %index_2] by (2, 3, 5) : index
 // Same effect
 %linear_index = affine.linearize_index [%index_0, %index_1, %index_2] by (3, 5) : index

In the above example, %linear_index conceptually holds the following:

mlir
#map = affine_map<()[s0, s1, s2] -> (s0 * 15 + s1 * 5 + s2)>
-%linear_index = affine.apply #map()[%index_0, %index_1, %index_2]

source

`,13))]),a("details",b,[a("summary",null,[e[15]||(e[15]=a("a",{id:"Reactant.MLIR.Dialects.affine.load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.load")],-1)),e[16]||(e[16]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=i('

load

Syntax

operation ::= ssa-id `=` `affine.load` ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.load op reads an element from a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The output of affine.load is a new value with the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
%1 = affine.load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

',9))]),a("details",g,[a("summary",null,[e[18]||(e[18]=a("a",{id:"Reactant.MLIR.Dialects.affine.max-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.max-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.max")],-1)),e[19]||(e[19]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=i('

max

The affine.max operation computes the maximum value result from a multi-result affine map.

Example

mlir
%0 = affine.max (d0) -> (1000, d0 + 512) (%i0) : index

source

',5))]),a("details",y,[a("summary",null,[e[21]||(e[21]=a("a",{id:"Reactant.MLIR.Dialects.affine.min-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.min-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.min")],-1)),e[22]||(e[22]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=i('

min

Syntax

operation ::= ssa-id `=` `affine.min` affine-map-attribute dim-and-symbol-use-list

The affine.min operation applies an affine mapping to a list of SSA values, and returns the minimum value of all result expressions. The number of dimension and symbol arguments to affine.min must be equal to the respective number of dimensional and symbolic inputs to the affine mapping; the affine.min operation always returns one value. The input operands and result must all have 'index' type.

Example

mlir
%0 = affine.min affine_map<(d0)[s0] -> (1000, d0 + 512, s0)> (%arg0)[%arg1]

source

',7))]),a("details",v,[a("summary",null,[e[24]||(e[24]=a("a",{id:"Reactant.MLIR.Dialects.affine.parallel-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.parallel-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.parallel")],-1)),e[25]||(e[25]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=i(`

parallel

The affine.parallel operation represents a hyper-rectangular affine parallel band, defining zero or more SSA values for its induction variables. It has one region capturing the parallel band body. The induction variables are represented as arguments of this region. These SSA values always have type index, which is the size of the machine word. The strides, represented by steps, are positive constant integers which defaults to "1" if not present. The lower and upper bounds specify a half-open range: the range includes the lower bound but does not include the upper bound. The body region must contain exactly one block that terminates with affine.yield.

The lower and upper bounds of a parallel operation are represented as an application of an affine mapping to a list of SSA values passed to the map. The same restrictions hold for these SSA values as for all bindings of SSA values to dimensions and symbols. The list of expressions in each map is interpreted according to the respective bounds group attribute. If a single expression belongs to the group, then the result of this expression is taken as a lower(upper) bound of the corresponding loop induction variable. If multiple expressions belong to the group, then the lower(upper) bound is the max(min) of these values obtained from these expressions. The loop band has as many loops as elements in the group bounds attributes.

Each value yielded by affine.yield will be accumulated/reduced via one of the reduction methods defined in the AtomicRMWKind enum. The order of reduction is unspecified, and lowering may produce any valid ordering. Loops with a 0 trip count will produce as a result the identity value associated with each reduction (i.e. 0.0 for addf, 1.0 for mulf). Assign reductions for loops with a trip count != 1 produces undefined results.

Note: Calling AffineParallelOp::build will create the required region and block, and insert the required terminator if it is trivial (i.e. no values are yielded). Parsing will also create the required region, block, and terminator, even when they are missing from the textual representation.

Example (3x3 valid convolution):

mlir
func.func @conv_2d(%D : memref<100x100xf32>, %K : memref<3x3xf32>) -> (memref<98x98xf32>) {
+%linear_index = affine.apply #map()[%index_0, %index_1, %index_2]

source

`,13))]),a("details",b,[a("summary",null,[e[15]||(e[15]=a("a",{id:"Reactant.MLIR.Dialects.affine.load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.load")],-1)),e[16]||(e[16]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=i('

load

Syntax

operation ::= ssa-id `=` `affine.load` ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.load op reads an element from a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The output of affine.load is a new value with the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
%1 = affine.load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

',9))]),a("details",g,[a("summary",null,[e[18]||(e[18]=a("a",{id:"Reactant.MLIR.Dialects.affine.max-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.max-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.max")],-1)),e[19]||(e[19]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=i('

max

The affine.max operation computes the maximum value result from a multi-result affine map.

Example

mlir
%0 = affine.max (d0) -> (1000, d0 + 512) (%i0) : index

source

',5))]),a("details",y,[a("summary",null,[e[21]||(e[21]=a("a",{id:"Reactant.MLIR.Dialects.affine.min-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.min-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.min")],-1)),e[22]||(e[22]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=i('

min

Syntax

operation ::= ssa-id `=` `affine.min` affine-map-attribute dim-and-symbol-use-list

The affine.min operation applies an affine mapping to a list of SSA values, and returns the minimum value of all result expressions. The number of dimension and symbol arguments to affine.min must be equal to the respective number of dimensional and symbolic inputs to the affine mapping; the affine.min operation always returns one value. The input operands and result must all have 'index' type.

Example

mlir
%0 = affine.min affine_map<(d0)[s0] -> (1000, d0 + 512, s0)> (%arg0)[%arg1]

source

',7))]),a("details",v,[a("summary",null,[e[24]||(e[24]=a("a",{id:"Reactant.MLIR.Dialects.affine.parallel-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.parallel-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.parallel")],-1)),e[25]||(e[25]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=i(`

parallel

The affine.parallel operation represents a hyper-rectangular affine parallel band, defining zero or more SSA values for its induction variables. It has one region capturing the parallel band body. The induction variables are represented as arguments of this region. These SSA values always have type index, which is the size of the machine word. The strides, represented by steps, are positive constant integers which defaults to "1" if not present. The lower and upper bounds specify a half-open range: the range includes the lower bound but does not include the upper bound. The body region must contain exactly one block that terminates with affine.yield.

The lower and upper bounds of a parallel operation are represented as an application of an affine mapping to a list of SSA values passed to the map. The same restrictions hold for these SSA values as for all bindings of SSA values to dimensions and symbols. The list of expressions in each map is interpreted according to the respective bounds group attribute. If a single expression belongs to the group, then the result of this expression is taken as a lower(upper) bound of the corresponding loop induction variable. If multiple expressions belong to the group, then the lower(upper) bound is the max(min) of these values obtained from these expressions. The loop band has as many loops as elements in the group bounds attributes.

Each value yielded by affine.yield will be accumulated/reduced via one of the reduction methods defined in the AtomicRMWKind enum. The order of reduction is unspecified, and lowering may produce any valid ordering. Loops with a 0 trip count will produce as a result the identity value associated with each reduction (i.e. 0.0 for addf, 1.0 for mulf). Assign reductions for loops with a trip count != 1 produces undefined results.

Note: Calling AffineParallelOp::build will create the required region and block, and insert the required terminator if it is trivial (i.e. no values are yielded). Parsing will also create the required region, block, and terminator, even when they are missing from the textual representation.

Example (3x3 valid convolution):

mlir
func.func @conv_2d(%D : memref<100x100xf32>, %K : memref<3x3xf32>) -> (memref<98x98xf32>) {
   %O = memref.alloc() : memref<98x98xf32>
   affine.parallel (%x, %y) = (0, 0) to (98, 98) {
     %0 = affine.parallel (%kx, %ky) = (0, 0) to (2, 2) reduce ("addf") -> f32 {
@@ -92,4 +92,4 @@ import{_ as o,c as l,j as a,a as s,G as t,a2 as i,B as p,o as r}from"./chunks/fr
                           to (min(%ii + 32, %N), min(%jj + 32, %M)) {
     call @f(%i, %j) : (index, index) -> ()
   }
-}

source

`,10))]),a("details",x,[a("summary",null,[e[27]||(e[27]=a("a",{id:"Reactant.MLIR.Dialects.affine.prefetch-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.prefetch-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.prefetch")],-1)),e[28]||(e[28]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=i('

prefetch

The affine.prefetch op prefetches data from a memref location described with an affine subscript similar to affine.load, and has three attributes: a read/write specifier, a locality hint, and a cache type specifier as shown below:

mlir
affine.prefetch %0[%i, %j + 5], read, locality<3>, data : memref<400x400xi32>

The read/write specifier is either 'read' or 'write', the locality hint specifier ranges from locality<0> (no locality) to locality<3> (extremely local keep in cache). The cache type specifier is either 'data' or 'instr' and specifies whether the prefetch is performed on data cache or on instruction cache.

source

',5))]),a("details",R,[a("summary",null,[e[30]||(e[30]=a("a",{id:"Reactant.MLIR.Dialects.affine.store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.store")],-1)),e[31]||(e[31]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=i('

store

Syntax

operation ::= `affine.store` ssa-use, ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.store op writes an element to a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The affine.store op stores a new value which is the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
affine.store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
affine.store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

',9))]),a("details",I,[a("summary",null,[e[33]||(e[33]=a("a",{id:"Reactant.MLIR.Dialects.affine.vector_load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.vector_load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.vector_load")],-1)),e[34]||(e[34]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=i('

vector_load

The affine.vector_load is the vector counterpart of affine.load. It reads a slice from a MemRef, supplied as its first operand, into a vector of the same base elemental type. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the read within the memref. The shape of the return vector type determines the shape of the slice read from the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector loads will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector load.

mlir
%1 = affine.vector_load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector load. Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.vector_load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector load.

mlir
%1 = affine.vector_load %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_read).

source

',12))]),a("details",k,[a("summary",null,[e[36]||(e[36]=a("a",{id:"Reactant.MLIR.Dialects.affine.vector_store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.vector_store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.vector_store")],-1)),e[37]||(e[37]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=i('

vector_store

The affine.vector_store is the vector counterpart of affine.store. It writes a vector, supplied as its first operand, into a slice within a MemRef of the same base elemental type, supplied as its second operand. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the write within the memref. The shape of th input vector determines the shape of the slice written to the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector stores will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector store.

mlir
affine.vector_store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector store. Uses symbol keyword for symbols %n and %m.

mlir
affine.vector_store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector store.

mlir
affine.vector_store %v0, %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_write).

source

',12))]),a("details",j,[a("summary",null,[e[39]||(e[39]=a("a",{id:"Reactant.MLIR.Dialects.affine.yield-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.yield-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.yield")],-1)),e[40]||(e[40]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=a("p",null,[a("code",null,"yield")],-1)),e[42]||(e[42]=a("p",null,[s("The "),a("code",null,"affine.yield"),s(" yields zero or more SSA values from an affine op region and terminates the region. The semantics of how the values yielded are used is defined by the parent operation. If "),a("code",null,"affine.yield"),s(" has any operands, the operands must match the parent operation's results. If the parent operation defines no values, then the "),a("code",null,"affine.yield"),s(" may be left out in the custom syntax and the builders will insert one implicitly. Otherwise, it has to be present in the syntax to indicate which values are yielded.")],-1)),e[43]||(e[43]=a("p",null,[a("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Affine.jl#L897-L909",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const C=o(d,[["render",M]]);export{_ as __pageData,C as default}; +}

source

`,10))]),a("details",x,[a("summary",null,[e[27]||(e[27]=a("a",{id:"Reactant.MLIR.Dialects.affine.prefetch-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.prefetch-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.prefetch")],-1)),e[28]||(e[28]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=i('

prefetch

The affine.prefetch op prefetches data from a memref location described with an affine subscript similar to affine.load, and has three attributes: a read/write specifier, a locality hint, and a cache type specifier as shown below:

mlir
affine.prefetch %0[%i, %j + 5], read, locality<3>, data : memref<400x400xi32>

The read/write specifier is either 'read' or 'write', the locality hint specifier ranges from locality<0> (no locality) to locality<3> (extremely local keep in cache). The cache type specifier is either 'data' or 'instr' and specifies whether the prefetch is performed on data cache or on instruction cache.

source

',5))]),a("details",R,[a("summary",null,[e[30]||(e[30]=a("a",{id:"Reactant.MLIR.Dialects.affine.store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.store")],-1)),e[31]||(e[31]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=i('

store

Syntax

operation ::= `affine.store` ssa-use, ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.store op writes an element to a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The affine.store op stores a new value which is the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
affine.store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
affine.store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

',9))]),a("details",I,[a("summary",null,[e[33]||(e[33]=a("a",{id:"Reactant.MLIR.Dialects.affine.vector_load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.vector_load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.vector_load")],-1)),e[34]||(e[34]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=i('

vector_load

The affine.vector_load is the vector counterpart of affine.load. It reads a slice from a MemRef, supplied as its first operand, into a vector of the same base elemental type. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the read within the memref. The shape of the return vector type determines the shape of the slice read from the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector loads will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector load.

mlir
%1 = affine.vector_load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector load. Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.vector_load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector load.

mlir
%1 = affine.vector_load %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_read).

source

',12))]),a("details",k,[a("summary",null,[e[36]||(e[36]=a("a",{id:"Reactant.MLIR.Dialects.affine.vector_store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.vector_store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.vector_store")],-1)),e[37]||(e[37]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=i('

vector_store

The affine.vector_store is the vector counterpart of affine.store. It writes a vector, supplied as its first operand, into a slice within a MemRef of the same base elemental type, supplied as its second operand. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the write within the memref. The shape of th input vector determines the shape of the slice written to the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector stores will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector store.

mlir
affine.vector_store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector store. Uses symbol keyword for symbols %n and %m.

mlir
affine.vector_store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector store.

mlir
affine.vector_store %v0, %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_write).

source

',12))]),a("details",j,[a("summary",null,[e[39]||(e[39]=a("a",{id:"Reactant.MLIR.Dialects.affine.yield-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.yield-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.yield")],-1)),e[40]||(e[40]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=a("p",null,[a("code",null,"yield")],-1)),e[42]||(e[42]=a("p",null,[s("The "),a("code",null,"affine.yield"),s(" yields zero or more SSA values from an affine op region and terminates the region. The semantics of how the values yielded are used is defined by the parent operation. If "),a("code",null,"affine.yield"),s(" has any operands, the operands must match the parent operation's results. If the parent operation defines no values, then the "),a("code",null,"affine.yield"),s(" may be left out in the custom syntax and the builders will insert one implicitly. Otherwise, it has to be present in the syntax to indicate which values are yielded.")],-1)),e[43]||(e[43]=a("p",null,[a("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Affine.jl#L897-L909",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const C=o(d,[["render",M]]);export{_ as __pageData,C as default}; diff --git a/previews/PR441/assets/api_affine.md.DDDiaunG.lean.js b/previews/PR441/assets/api_affine.md.BDXHCMIg.lean.js similarity index 96% rename from previews/PR441/assets/api_affine.md.DDDiaunG.lean.js rename to previews/PR441/assets/api_affine.md.BDXHCMIg.lean.js index 73126912d..473cb57c7 100644 --- a/previews/PR441/assets/api_affine.md.DDDiaunG.lean.js +++ b/previews/PR441/assets/api_affine.md.BDXHCMIg.lean.js @@ -3,12 +3,12 @@ import{_ as o,c as l,j as a,a as s,G as t,a2 as i,B as p,o as r}from"./chunks/fr %1 = affine.apply #map10 (%s, %t) // Inline example. -%2 = affine.apply affine_map<(i)[s0] -> (i+s0)> (%42)[%n]

source

`,5))]),a("details",f,[a("summary",null,[e[3]||(e[3]=a("a",{id:"Reactant.MLIR.Dialects.affine.delinearize_index-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.delinearize_index-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.delinearize_index")],-1)),e[4]||(e[4]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=i(`

delinearize_index

The affine.delinearize_index operation takes a single index value and calculates the multi-index according to the given basis.

Example

%indices:3 = affine.delinearize_index %linear_index into (%c16, %c224, %c224) : index, index, index

In the above example, %indices:3 conceptually holds the following:

#map0 = affine_map<()[s0] -> (s0 floordiv 50176)>
+%2 = affine.apply affine_map<(i)[s0] -> (i+s0)> (%42)[%n]

source

`,5))]),a("details",f,[a("summary",null,[e[3]||(e[3]=a("a",{id:"Reactant.MLIR.Dialects.affine.delinearize_index-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.delinearize_index-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.delinearize_index")],-1)),e[4]||(e[4]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=i(`

delinearize_index

The affine.delinearize_index operation takes a single index value and calculates the multi-index according to the given basis.

Example

%indices:3 = affine.delinearize_index %linear_index into (%c16, %c224, %c224) : index, index, index

In the above example, %indices:3 conceptually holds the following:

#map0 = affine_map<()[s0] -> (s0 floordiv 50176)>
 #map1 = affine_map<()[s0] -> ((s0 mod 50176) floordiv 224)>
 #map2 = affine_map<()[s0] -> (s0 mod 224)>
 %indices_0 = affine.apply #map0()[%linear_index]
 %indices_1 = affine.apply #map1()[%linear_index]
-%indices_2 = affine.apply #map2()[%linear_index]

The basis may either contain N or N-1 elements, where N is the number of results. If there are N basis elements, the first one will not be used during computations, but may be used during analysis and canonicalization to eliminate terms from the affine.delinearize_index or to enable conclusions about the total size of %linear_index.

If the basis is fully provided, the delinearize_index operation is said to "have an outer bound". The builders assume that an affine.delinearize_index has an outer bound by default, as this is how the operation was initially defined.

That is, the example above could also have been written

mlir
%0:3 = affine.delinearize_index %linear_index into (244, 244) : index, index

Note that, due to the constraints of affine maps, all the basis elements must be strictly positive. A dynamic basis element being 0 or negative causes undefined behavior.

source

`,12))]),a("details",m,[a("summary",null,[e[6]||(e[6]=a("a",{id:"Reactant.MLIR.Dialects.affine.for_-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.for_-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.for_")],-1)),e[7]||(e[7]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=i(`

for_

Syntax

operation   ::= \`affine.for\` ssa-id \`=\` lower-bound \`to\` upper-bound
+%indices_2 = affine.apply #map2()[%linear_index]

The basis may either contain N or N-1 elements, where N is the number of results. If there are N basis elements, the first one will not be used during computations, but may be used during analysis and canonicalization to eliminate terms from the affine.delinearize_index or to enable conclusions about the total size of %linear_index.

If the basis is fully provided, the delinearize_index operation is said to "have an outer bound". The builders assume that an affine.delinearize_index has an outer bound by default, as this is how the operation was initially defined.

That is, the example above could also have been written

mlir
%0:3 = affine.delinearize_index %linear_index into (244, 244) : index, index

Note that, due to the constraints of affine maps, all the basis elements must be strictly positive. A dynamic basis element being 0 or negative causes undefined behavior.

source

`,12))]),a("details",m,[a("summary",null,[e[6]||(e[6]=a("a",{id:"Reactant.MLIR.Dialects.affine.for_-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.for_-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.for_")],-1)),e[7]||(e[7]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=i(`

for_

Syntax

operation   ::= \`affine.for\` ssa-id \`=\` lower-bound \`to\` upper-bound
                 (\`step\` integer-literal)? \`{\` op* \`}\`
 
 lower-bound ::= \`max\`? affine-map-attribute dim-and-symbol-use-list | shorthand-bound
@@ -43,7 +43,7 @@ import{_ as o,c as l,j as a,a as s,G as t,a2 as i,B as p,o as r}from"./chunks/fr
   %y0 = arith.addi %arg0, %c1 : index
   %y1 = arith.addi %arg1, %c2 : index
   affine.yield %y0, %y1 : index, index
-}

If the affine.for defines any values, a yield terminator must be explicitly present. The number and types of the "affine.for" results must match the initial values in the iter_args binding and the yield operands.

source

`,17))]),a("details",u,[a("summary",null,[e[9]||(e[9]=a("a",{id:"Reactant.MLIR.Dialects.affine.if_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.if_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.if_")],-1)),e[10]||(e[10]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=i(`

if_

Syntax

operation  ::= \`affine.if\` if-op-cond \`{\` op* \`}\` (\`else\` \`{\` op* \`}\`)?
+}

If the affine.for defines any values, a yield terminator must be explicitly present. The number and types of the "affine.for" results must match the initial values in the iter_args binding and the yield operands.

source

`,17))]),a("details",u,[a("summary",null,[e[9]||(e[9]=a("a",{id:"Reactant.MLIR.Dialects.affine.if_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.if_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.if_")],-1)),e[10]||(e[10]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=i(`

if_

Syntax

operation  ::= \`affine.if\` if-op-cond \`{\` op* \`}\` (\`else\` \`{\` op* \`}\`)?
 if-op-cond ::= integer-set-attr dim-and-symbol-use-list

The affine.if operation restricts execution to a subset of the loop iteration space defined by an integer set (a conjunction of affine constraints). A single affine.if may end with an optional else clause.

The condition of the affine.if is represented by an integer set (a conjunction of affine constraints), and the SSA values bound to the dimensions and symbols in the integer set. The same restrictions hold for these SSA values as for all bindings of SSA values to dimensions and symbols.

The affine.if operation contains two regions for the "then" and "else" clauses. affine.if may return results that are defined in its regions. The values defined are determined by which execution path is taken. Each region of the affine.if must contain a single block with no arguments, and be terminated by affine.yield. If affine.if defines no values, the affine.yield can be left out, and will be inserted implicitly. Otherwise, it must be explicit. If no values are defined, the else block may be empty (i.e. contain no blocks).

Example

mlir
#set = affine_set<(d0, d1)[s0]: (d0 - 10 >= 0, s0 - d0 - 9 >= 0,
                                  d1 - 10 >= 0, s0 - d1 - 9 >= 0)>
 func.func @reduced_domain_example(%A, %X, %N) : (memref<10xi32>, i32, i32) {
@@ -72,10 +72,10 @@ import{_ as o,c as l,j as a,a as s,G as t,a2 as i,B as p,o as r}from"./chunks/fr
     affine.store %1, %O[%i, %j] : memref<12x12xf32>
   }
   return %O
-}

source

`,11))]),a("details",h,[a("summary",null,[e[12]||(e[12]=a("a",{id:"Reactant.MLIR.Dialects.affine.linearize_index-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.linearize_index-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.linearize_index")],-1)),e[13]||(e[13]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=i(`

linearize_index

The affine.linearize_index operation takes a sequence of index values and a basis of the same length and linearizes the indices using that basis.

That is, for indices %idx_0 to %idx_{N-1} and basis elements b_0 (or b_1) up to b_{N-1} it computes

sum(i = 0 to N-1) %idx_i * product(j = i + 1 to N-1) B_j

The basis may either have N or N-1 elements, where N is the number of inputs to linearize_index. If N inputs are provided, the first one is not used in computation, but may be used during analysis or canonicalization as a bound on %idx_0.

If all N basis elements are provided, the linearize_index operation is said to "have an outer bound".

If the disjoint property is present, this is an optimization hint that, for all i, 0 <= %idx_i < B_i - that is, no index affects any other index, except that %idx_0 may be negative to make the index as a whole negative.

Note that the outputs of affine.delinearize_index are, by definition, disjoint.

Example

mlir
%linear_index = affine.linearize_index [%index_0, %index_1, %index_2] by (2, 3, 5) : index
+}

source

`,11))]),a("details",h,[a("summary",null,[e[12]||(e[12]=a("a",{id:"Reactant.MLIR.Dialects.affine.linearize_index-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.linearize_index-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.linearize_index")],-1)),e[13]||(e[13]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=i(`

linearize_index

The affine.linearize_index operation takes a sequence of index values and a basis of the same length and linearizes the indices using that basis.

That is, for indices %idx_0 to %idx_{N-1} and basis elements b_0 (or b_1) up to b_{N-1} it computes

sum(i = 0 to N-1) %idx_i * product(j = i + 1 to N-1) B_j

The basis may either have N or N-1 elements, where N is the number of inputs to linearize_index. If N inputs are provided, the first one is not used in computation, but may be used during analysis or canonicalization as a bound on %idx_0.

If all N basis elements are provided, the linearize_index operation is said to "have an outer bound".

If the disjoint property is present, this is an optimization hint that, for all i, 0 <= %idx_i < B_i - that is, no index affects any other index, except that %idx_0 may be negative to make the index as a whole negative.

Note that the outputs of affine.delinearize_index are, by definition, disjoint.

Example

mlir
%linear_index = affine.linearize_index [%index_0, %index_1, %index_2] by (2, 3, 5) : index
 // Same effect
 %linear_index = affine.linearize_index [%index_0, %index_1, %index_2] by (3, 5) : index

In the above example, %linear_index conceptually holds the following:

mlir
#map = affine_map<()[s0, s1, s2] -> (s0 * 15 + s1 * 5 + s2)>
-%linear_index = affine.apply #map()[%index_0, %index_1, %index_2]

source

`,13))]),a("details",b,[a("summary",null,[e[15]||(e[15]=a("a",{id:"Reactant.MLIR.Dialects.affine.load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.load")],-1)),e[16]||(e[16]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=i('

load

Syntax

operation ::= ssa-id `=` `affine.load` ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.load op reads an element from a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The output of affine.load is a new value with the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
%1 = affine.load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

',9))]),a("details",g,[a("summary",null,[e[18]||(e[18]=a("a",{id:"Reactant.MLIR.Dialects.affine.max-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.max-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.max")],-1)),e[19]||(e[19]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=i('

max

The affine.max operation computes the maximum value result from a multi-result affine map.

Example

mlir
%0 = affine.max (d0) -> (1000, d0 + 512) (%i0) : index

source

',5))]),a("details",y,[a("summary",null,[e[21]||(e[21]=a("a",{id:"Reactant.MLIR.Dialects.affine.min-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.min-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.min")],-1)),e[22]||(e[22]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=i('

min

Syntax

operation ::= ssa-id `=` `affine.min` affine-map-attribute dim-and-symbol-use-list

The affine.min operation applies an affine mapping to a list of SSA values, and returns the minimum value of all result expressions. The number of dimension and symbol arguments to affine.min must be equal to the respective number of dimensional and symbolic inputs to the affine mapping; the affine.min operation always returns one value. The input operands and result must all have 'index' type.

Example

mlir
%0 = affine.min affine_map<(d0)[s0] -> (1000, d0 + 512, s0)> (%arg0)[%arg1]

source

',7))]),a("details",v,[a("summary",null,[e[24]||(e[24]=a("a",{id:"Reactant.MLIR.Dialects.affine.parallel-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.parallel-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.parallel")],-1)),e[25]||(e[25]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=i(`

parallel

The affine.parallel operation represents a hyper-rectangular affine parallel band, defining zero or more SSA values for its induction variables. It has one region capturing the parallel band body. The induction variables are represented as arguments of this region. These SSA values always have type index, which is the size of the machine word. The strides, represented by steps, are positive constant integers which defaults to "1" if not present. The lower and upper bounds specify a half-open range: the range includes the lower bound but does not include the upper bound. The body region must contain exactly one block that terminates with affine.yield.

The lower and upper bounds of a parallel operation are represented as an application of an affine mapping to a list of SSA values passed to the map. The same restrictions hold for these SSA values as for all bindings of SSA values to dimensions and symbols. The list of expressions in each map is interpreted according to the respective bounds group attribute. If a single expression belongs to the group, then the result of this expression is taken as a lower(upper) bound of the corresponding loop induction variable. If multiple expressions belong to the group, then the lower(upper) bound is the max(min) of these values obtained from these expressions. The loop band has as many loops as elements in the group bounds attributes.

Each value yielded by affine.yield will be accumulated/reduced via one of the reduction methods defined in the AtomicRMWKind enum. The order of reduction is unspecified, and lowering may produce any valid ordering. Loops with a 0 trip count will produce as a result the identity value associated with each reduction (i.e. 0.0 for addf, 1.0 for mulf). Assign reductions for loops with a trip count != 1 produces undefined results.

Note: Calling AffineParallelOp::build will create the required region and block, and insert the required terminator if it is trivial (i.e. no values are yielded). Parsing will also create the required region, block, and terminator, even when they are missing from the textual representation.

Example (3x3 valid convolution):

mlir
func.func @conv_2d(%D : memref<100x100xf32>, %K : memref<3x3xf32>) -> (memref<98x98xf32>) {
+%linear_index = affine.apply #map()[%index_0, %index_1, %index_2]

source

`,13))]),a("details",b,[a("summary",null,[e[15]||(e[15]=a("a",{id:"Reactant.MLIR.Dialects.affine.load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.load")],-1)),e[16]||(e[16]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=i('

load

Syntax

operation ::= ssa-id `=` `affine.load` ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.load op reads an element from a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The output of affine.load is a new value with the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
%1 = affine.load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

',9))]),a("details",g,[a("summary",null,[e[18]||(e[18]=a("a",{id:"Reactant.MLIR.Dialects.affine.max-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.max-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.max")],-1)),e[19]||(e[19]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=i('

max

The affine.max operation computes the maximum value result from a multi-result affine map.

Example

mlir
%0 = affine.max (d0) -> (1000, d0 + 512) (%i0) : index

source

',5))]),a("details",y,[a("summary",null,[e[21]||(e[21]=a("a",{id:"Reactant.MLIR.Dialects.affine.min-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.min-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.min")],-1)),e[22]||(e[22]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=i('

min

Syntax

operation ::= ssa-id `=` `affine.min` affine-map-attribute dim-and-symbol-use-list

The affine.min operation applies an affine mapping to a list of SSA values, and returns the minimum value of all result expressions. The number of dimension and symbol arguments to affine.min must be equal to the respective number of dimensional and symbolic inputs to the affine mapping; the affine.min operation always returns one value. The input operands and result must all have 'index' type.

Example

mlir
%0 = affine.min affine_map<(d0)[s0] -> (1000, d0 + 512, s0)> (%arg0)[%arg1]

source

',7))]),a("details",v,[a("summary",null,[e[24]||(e[24]=a("a",{id:"Reactant.MLIR.Dialects.affine.parallel-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.parallel-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.parallel")],-1)),e[25]||(e[25]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=i(`

parallel

The affine.parallel operation represents a hyper-rectangular affine parallel band, defining zero or more SSA values for its induction variables. It has one region capturing the parallel band body. The induction variables are represented as arguments of this region. These SSA values always have type index, which is the size of the machine word. The strides, represented by steps, are positive constant integers which defaults to "1" if not present. The lower and upper bounds specify a half-open range: the range includes the lower bound but does not include the upper bound. The body region must contain exactly one block that terminates with affine.yield.

The lower and upper bounds of a parallel operation are represented as an application of an affine mapping to a list of SSA values passed to the map. The same restrictions hold for these SSA values as for all bindings of SSA values to dimensions and symbols. The list of expressions in each map is interpreted according to the respective bounds group attribute. If a single expression belongs to the group, then the result of this expression is taken as a lower(upper) bound of the corresponding loop induction variable. If multiple expressions belong to the group, then the lower(upper) bound is the max(min) of these values obtained from these expressions. The loop band has as many loops as elements in the group bounds attributes.

Each value yielded by affine.yield will be accumulated/reduced via one of the reduction methods defined in the AtomicRMWKind enum. The order of reduction is unspecified, and lowering may produce any valid ordering. Loops with a 0 trip count will produce as a result the identity value associated with each reduction (i.e. 0.0 for addf, 1.0 for mulf). Assign reductions for loops with a trip count != 1 produces undefined results.

Note: Calling AffineParallelOp::build will create the required region and block, and insert the required terminator if it is trivial (i.e. no values are yielded). Parsing will also create the required region, block, and terminator, even when they are missing from the textual representation.

Example (3x3 valid convolution):

mlir
func.func @conv_2d(%D : memref<100x100xf32>, %K : memref<3x3xf32>) -> (memref<98x98xf32>) {
   %O = memref.alloc() : memref<98x98xf32>
   affine.parallel (%x, %y) = (0, 0) to (98, 98) {
     %0 = affine.parallel (%kx, %ky) = (0, 0) to (2, 2) reduce ("addf") -> f32 {
@@ -92,4 +92,4 @@ import{_ as o,c as l,j as a,a as s,G as t,a2 as i,B as p,o as r}from"./chunks/fr
                           to (min(%ii + 32, %N), min(%jj + 32, %M)) {
     call @f(%i, %j) : (index, index) -> ()
   }
-}

source

`,10))]),a("details",x,[a("summary",null,[e[27]||(e[27]=a("a",{id:"Reactant.MLIR.Dialects.affine.prefetch-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.prefetch-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.prefetch")],-1)),e[28]||(e[28]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=i('

prefetch

The affine.prefetch op prefetches data from a memref location described with an affine subscript similar to affine.load, and has three attributes: a read/write specifier, a locality hint, and a cache type specifier as shown below:

mlir
affine.prefetch %0[%i, %j + 5], read, locality<3>, data : memref<400x400xi32>

The read/write specifier is either 'read' or 'write', the locality hint specifier ranges from locality<0> (no locality) to locality<3> (extremely local keep in cache). The cache type specifier is either 'data' or 'instr' and specifies whether the prefetch is performed on data cache or on instruction cache.

source

',5))]),a("details",R,[a("summary",null,[e[30]||(e[30]=a("a",{id:"Reactant.MLIR.Dialects.affine.store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.store")],-1)),e[31]||(e[31]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=i('

store

Syntax

operation ::= `affine.store` ssa-use, ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.store op writes an element to a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The affine.store op stores a new value which is the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
affine.store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
affine.store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

',9))]),a("details",I,[a("summary",null,[e[33]||(e[33]=a("a",{id:"Reactant.MLIR.Dialects.affine.vector_load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.vector_load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.vector_load")],-1)),e[34]||(e[34]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=i('

vector_load

The affine.vector_load is the vector counterpart of affine.load. It reads a slice from a MemRef, supplied as its first operand, into a vector of the same base elemental type. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the read within the memref. The shape of the return vector type determines the shape of the slice read from the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector loads will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector load.

mlir
%1 = affine.vector_load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector load. Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.vector_load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector load.

mlir
%1 = affine.vector_load %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_read).

source

',12))]),a("details",k,[a("summary",null,[e[36]||(e[36]=a("a",{id:"Reactant.MLIR.Dialects.affine.vector_store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.vector_store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.vector_store")],-1)),e[37]||(e[37]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=i('

vector_store

The affine.vector_store is the vector counterpart of affine.store. It writes a vector, supplied as its first operand, into a slice within a MemRef of the same base elemental type, supplied as its second operand. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the write within the memref. The shape of th input vector determines the shape of the slice written to the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector stores will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector store.

mlir
affine.vector_store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector store. Uses symbol keyword for symbols %n and %m.

mlir
affine.vector_store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector store.

mlir
affine.vector_store %v0, %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_write).

source

',12))]),a("details",j,[a("summary",null,[e[39]||(e[39]=a("a",{id:"Reactant.MLIR.Dialects.affine.yield-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.yield-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.yield")],-1)),e[40]||(e[40]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=a("p",null,[a("code",null,"yield")],-1)),e[42]||(e[42]=a("p",null,[s("The "),a("code",null,"affine.yield"),s(" yields zero or more SSA values from an affine op region and terminates the region. The semantics of how the values yielded are used is defined by the parent operation. If "),a("code",null,"affine.yield"),s(" has any operands, the operands must match the parent operation's results. If the parent operation defines no values, then the "),a("code",null,"affine.yield"),s(" may be left out in the custom syntax and the builders will insert one implicitly. Otherwise, it has to be present in the syntax to indicate which values are yielded.")],-1)),e[43]||(e[43]=a("p",null,[a("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Affine.jl#L897-L909",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const C=o(d,[["render",M]]);export{_ as __pageData,C as default}; +}

source

`,10))]),a("details",x,[a("summary",null,[e[27]||(e[27]=a("a",{id:"Reactant.MLIR.Dialects.affine.prefetch-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.prefetch-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.prefetch")],-1)),e[28]||(e[28]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=i('

prefetch

The affine.prefetch op prefetches data from a memref location described with an affine subscript similar to affine.load, and has three attributes: a read/write specifier, a locality hint, and a cache type specifier as shown below:

mlir
affine.prefetch %0[%i, %j + 5], read, locality<3>, data : memref<400x400xi32>

The read/write specifier is either 'read' or 'write', the locality hint specifier ranges from locality<0> (no locality) to locality<3> (extremely local keep in cache). The cache type specifier is either 'data' or 'instr' and specifies whether the prefetch is performed on data cache or on instruction cache.

source

',5))]),a("details",R,[a("summary",null,[e[30]||(e[30]=a("a",{id:"Reactant.MLIR.Dialects.affine.store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.store")],-1)),e[31]||(e[31]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=i('

store

Syntax

operation ::= `affine.store` ssa-use, ssa-use `[` multi-dim-affine-map-of-ssa-ids `]` `:` memref-type

The affine.store op writes an element to a memref, where the index for each memref dimension is an affine expression of loop induction variables and symbols. The affine.store op stores a new value which is the same type as the elements of the memref. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1:

mlir
affine.store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>

Example 2: Uses symbol keyword for symbols %n and %m.

mlir
affine.store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>

source

',9))]),a("details",I,[a("summary",null,[e[33]||(e[33]=a("a",{id:"Reactant.MLIR.Dialects.affine.vector_load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.vector_load-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.vector_load")],-1)),e[34]||(e[34]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=i('

vector_load

The affine.vector_load is the vector counterpart of affine.load. It reads a slice from a MemRef, supplied as its first operand, into a vector of the same base elemental type. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the read within the memref. The shape of the return vector type determines the shape of the slice read from the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector loads will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector load.

mlir
%1 = affine.vector_load %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector load. Uses symbol keyword for symbols %n and %m.

mlir
%1 = affine.vector_load %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector load.

mlir
%1 = affine.vector_load %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_read).

source

',12))]),a("details",k,[a("summary",null,[e[36]||(e[36]=a("a",{id:"Reactant.MLIR.Dialects.affine.vector_store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.vector_store-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.vector_store")],-1)),e[37]||(e[37]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=i('

vector_store

The affine.vector_store is the vector counterpart of affine.store. It writes a vector, supplied as its first operand, into a slice within a MemRef of the same base elemental type, supplied as its second operand. The index for each memref dimension is an affine expression of loop induction variables and symbols. These indices determine the start position of the write within the memref. The shape of th input vector determines the shape of the slice written to the memref. This slice is contiguous along the respective dimensions of the shape. Strided vector stores will be supported in the future. An affine expression of loop IVs and symbols must be specified for each dimension of the memref. The keyword symbol can be used to indicate SSA identifiers which are symbolic.

Example 1: 8-wide f32 vector store.

mlir
affine.vector_store %v0, %0[%i0 + 3, %i1 + 7] : memref<100x100xf32>, vector<8xf32>

Example 2: 4-wide f32 vector store. Uses symbol keyword for symbols %n and %m.

mlir
affine.vector_store %v0, %0[%i0 + symbol(%n), %i1 + symbol(%m)] : memref<100x100xf32>, vector<4xf32>

Example 3: 2-dim f32 vector store.

mlir
affine.vector_store %v0, %0[%i0, %i1] : memref<100x100xf32>, vector<2x8xf32>

TODOs:

(see vector.transfer_write).

source

',12))]),a("details",j,[a("summary",null,[e[39]||(e[39]=a("a",{id:"Reactant.MLIR.Dialects.affine.yield-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.affine.yield-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[a("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.affine.yield")],-1)),e[40]||(e[40]=s()),t(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=a("p",null,[a("code",null,"yield")],-1)),e[42]||(e[42]=a("p",null,[s("The "),a("code",null,"affine.yield"),s(" yields zero or more SSA values from an affine op region and terminates the region. The semantics of how the values yielded are used is defined by the parent operation. If "),a("code",null,"affine.yield"),s(" has any operands, the operands must match the parent operation's results. If the parent operation defines no values, then the "),a("code",null,"affine.yield"),s(" may be left out in the custom syntax and the builders will insert one implicitly. Otherwise, it has to be present in the syntax to indicate which values are yielded.")],-1)),e[43]||(e[43]=a("p",null,[a("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Affine.jl#L897-L909",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const C=o(d,[["render",M]]);export{_ as __pageData,C as default}; diff --git a/previews/PR441/assets/api_api.md.EX21q_Ts.js b/previews/PR441/assets/api_api.md.BAu0cnN7.js similarity index 95% rename from previews/PR441/assets/api_api.md.EX21q_Ts.js rename to previews/PR441/assets/api_api.md.BAu0cnN7.js index 23c13244c..1ee5ec838 100644 --- a/previews/PR441/assets/api_api.md.EX21q_Ts.js +++ b/previews/PR441/assets/api_api.md.BAu0cnN7.js @@ -1,6 +1,6 @@ -import{_ as l,c as p,j as i,a,G as t,a2 as n,B as h,o}from"./chunks/framework.B4pBzPBy.js";const j=JSON.parse('{"title":"Core Reactant API","description":"","frontmatter":{},"headers":[],"relativePath":"api/api.md","filePath":"api/api.md","lastUpdated":null}'),r={name:"api/api.md"},d={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},c={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"};function m(E,s,C,f,F,A){const e=h("Badge");return o(),p("div",null,[s[21]||(s[21]=i("h1",{id:"Core-Reactant-API",tabindex:"-1"},[a("Core Reactant API "),i("a",{class:"header-anchor",href:"#Core-Reactant-API","aria-label":'Permalink to "Core Reactant API {#Core-Reactant-API}"'},"​")],-1)),s[22]||(s[22]=i("h2",{id:"Compile-API",tabindex:"-1"},[a("Compile API "),i("a",{class:"header-anchor",href:"#Compile-API","aria-label":'Permalink to "Compile API {#Compile-API}"'},"​")],-1)),i("details",d,[i("summary",null,[s[0]||(s[0]=i("a",{id:"Reactant.Compiler.@compile",href:"#Reactant.Compiler.@compile"},[i("span",{class:"jlbinding"},"Reactant.Compiler.@compile")],-1)),s[1]||(s[1]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[2]||(s[2]=n('
julia
@compile f(args...)

source

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julia
@jit f(args...)
+import{_ as l,c as p,j as i,a,G as t,a2 as n,B as h,o}from"./chunks/framework.B4pBzPBy.js";const j=JSON.parse('{"title":"Core Reactant API","description":"","frontmatter":{},"headers":[],"relativePath":"api/api.md","filePath":"api/api.md","lastUpdated":null}'),r={name:"api/api.md"},d={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},c={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"};function m(E,s,C,F,f,A){const e=h("Badge");return o(),p("div",null,[s[21]||(s[21]=i("h1",{id:"Core-Reactant-API",tabindex:"-1"},[a("Core Reactant API "),i("a",{class:"header-anchor",href:"#Core-Reactant-API","aria-label":'Permalink to "Core Reactant API {#Core-Reactant-API}"'},"​")],-1)),s[22]||(s[22]=i("h2",{id:"Compile-API",tabindex:"-1"},[a("Compile API "),i("a",{class:"header-anchor",href:"#Compile-API","aria-label":'Permalink to "Compile API {#Compile-API}"'},"​")],-1)),i("details",d,[i("summary",null,[s[0]||(s[0]=i("a",{id:"Reactant.Compiler.@compile",href:"#Reactant.Compiler.@compile"},[i("span",{class:"jlbinding"},"Reactant.Compiler.@compile")],-1)),s[1]||(s[1]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[2]||(s[2]=n('
julia
@compile f(args...)
source
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julia
@jit f(args...)
 
-Run @compile f(args..) then immediately execute it
source
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julia
@trace <expr>
Converts certain expressions like control flow into a Reactant friendly form. Importantly, if no traced value is found inside the expression, then there is no overhead.
Currently Supported
  • if conditions (with elseif and other niceties) (@trace if ...)
  • if statements with a preceeding assignment (@trace a = if ...) (note the positioning of the macro needs to be before the assignment and not before the if)
  • for statements with a single induction variable iterating over a syntactic StepRange of integers.
Special Considerations
  • Apply @trace only at the outermost if. Nested if statements will be automatically expanded into the correct form.
Extended Help
Caveats (Deviations from Core Julia Semantics)
New variables introduced
julia
@trace if x > 0
+Run @compile f(args..) then immediately execute it
source
`,2))]),s[23]||(s[23]=i("h2",{id:"ReactantCore-API",tabindex:"-1"},[a("ReactantCore API "),i("a",{class:"header-anchor",href:"#ReactantCore-API","aria-label":'Permalink to "ReactantCore API {#ReactantCore-API}"'},"​")],-1)),i("details",c,[i("summary",null,[s[6]||(s[6]=i("a",{id:"ReactantCore.@trace",href:"#ReactantCore.@trace"},[i("span",{class:"jlbinding"},"ReactantCore.@trace")],-1)),s[7]||(s[7]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[8]||(s[8]=n(`
julia
@trace <expr>
Converts certain expressions like control flow into a Reactant friendly form. Importantly, if no traced value is found inside the expression, then there is no overhead.
Currently Supported
  • if conditions (with elseif and other niceties) (@trace if ...)
  • if statements with a preceeding assignment (@trace a = if ...) (note the positioning of the macro needs to be before the assignment and not before the if)
  • for statements with a single induction variable iterating over a syntactic StepRange of integers.
Special Considerations
  • Apply @trace only at the outermost if. Nested if statements will be automatically expanded into the correct form.
Extended Help
Caveats (Deviations from Core Julia Semantics)
New variables introduced
julia
@trace if x > 0
     y = x + 1
     p = 1
 else
@@ -24,4 +24,4 @@ import{_ as l,c as p,j as i,a,G as t,a2 as n,B as h,o}from"./chunks/framework.B4
         y = 2.0
     end
     return y, nothing
-end
source
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@code_hlo [optimize = ...] f(args...)
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julia
codegen_unflatten!
Generate Julia code to wrap the XLA buffers back into the output result datatypes. The name is due to its similarity to the unflatten function in jax.tree_util.register_pytree_node.
source
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julia
codegen_flatten!
Generate Julia code to extract the XLA buffers from input arguments. The name is due to its similarity to the flatten function in jax.tree_util.register_pytree_node.
Arguments
  • linear_args: A list of arguments to be flattened.
Returns
  • flatten_names: A list of Symbols representing the names of the flattened arguments.
  • flatten_code: A list of Exprs to extract the XLA buffers from the input arguments.
Note
The linearized arguments do not directly refer to the are the arguments that have been flattened into a single list.
source
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julia
codegen_xla_call
Generate Julia code to call the XLA executable.
Arguments
  • exec: The XLA executable to call.
  • flatten_names: A list of Symbols representing the names of the flattened linear arguments.
  • donated_args_mask: A list of UInt8s representing whether the argument is donated.
  • nresults: The number of results to expect.
source
',5))])])}const D=l(r,[["render",m]]);export{j as __pageData,D as default};
+end

source

`,23))]),s[24]||(s[24]=i("h2",{id:"Inspect-Generated-HLO",tabindex:"-1"},[a("Inspect Generated HLO "),i("a",{class:"header-anchor",href:"#Inspect-Generated-HLO","aria-label":'Permalink to "Inspect Generated HLO {#Inspect-Generated-HLO}"'},"​")],-1)),i("details",g,[i("summary",null,[s[9]||(s[9]=i("a",{id:"Reactant.Compiler.@code_hlo",href:"#Reactant.Compiler.@code_hlo"},[i("span",{class:"jlbinding"},"Reactant.Compiler.@code_hlo")],-1)),s[10]||(s[10]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[11]||(s[11]=n('
julia
@code_hlo [optimize = ...] f(args...)

source

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julia
codegen_unflatten!

Generate Julia code to wrap the XLA buffers back into the output result datatypes. The name is due to its similarity to the unflatten function in jax.tree_util.register_pytree_node.

source

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julia
codegen_flatten!

Generate Julia code to extract the XLA buffers from input arguments. The name is due to its similarity to the flatten function in jax.tree_util.register_pytree_node.

Arguments

Returns

Note

The linearized arguments do not directly refer to the are the arguments that have been flattened into a single list.

source

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julia
codegen_xla_call

Generate Julia code to call the XLA executable.

Arguments

source

',5))])])}const D=l(r,[["render",m]]);export{j as __pageData,D as default}; diff --git a/previews/PR441/assets/api_api.md.EX21q_Ts.lean.js b/previews/PR441/assets/api_api.md.BAu0cnN7.lean.js similarity index 95% rename from previews/PR441/assets/api_api.md.EX21q_Ts.lean.js rename to previews/PR441/assets/api_api.md.BAu0cnN7.lean.js index 23c13244c..1ee5ec838 100644 --- a/previews/PR441/assets/api_api.md.EX21q_Ts.lean.js +++ b/previews/PR441/assets/api_api.md.BAu0cnN7.lean.js @@ -1,6 +1,6 @@ -import{_ as l,c as p,j as i,a,G as t,a2 as n,B as h,o}from"./chunks/framework.B4pBzPBy.js";const j=JSON.parse('{"title":"Core Reactant API","description":"","frontmatter":{},"headers":[],"relativePath":"api/api.md","filePath":"api/api.md","lastUpdated":null}'),r={name:"api/api.md"},d={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},c={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"};function m(E,s,C,f,F,A){const e=h("Badge");return o(),p("div",null,[s[21]||(s[21]=i("h1",{id:"Core-Reactant-API",tabindex:"-1"},[a("Core Reactant API "),i("a",{class:"header-anchor",href:"#Core-Reactant-API","aria-label":'Permalink to "Core Reactant API {#Core-Reactant-API}"'},"​")],-1)),s[22]||(s[22]=i("h2",{id:"Compile-API",tabindex:"-1"},[a("Compile API "),i("a",{class:"header-anchor",href:"#Compile-API","aria-label":'Permalink to "Compile API {#Compile-API}"'},"​")],-1)),i("details",d,[i("summary",null,[s[0]||(s[0]=i("a",{id:"Reactant.Compiler.@compile",href:"#Reactant.Compiler.@compile"},[i("span",{class:"jlbinding"},"Reactant.Compiler.@compile")],-1)),s[1]||(s[1]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[2]||(s[2]=n('
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@compile f(args...)

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julia
@jit f(args...)
+import{_ as l,c as p,j as i,a,G as t,a2 as n,B as h,o}from"./chunks/framework.B4pBzPBy.js";const j=JSON.parse('{"title":"Core Reactant API","description":"","frontmatter":{},"headers":[],"relativePath":"api/api.md","filePath":"api/api.md","lastUpdated":null}'),r={name:"api/api.md"},d={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},c={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"};function m(E,s,C,F,f,A){const e=h("Badge");return o(),p("div",null,[s[21]||(s[21]=i("h1",{id:"Core-Reactant-API",tabindex:"-1"},[a("Core Reactant API "),i("a",{class:"header-anchor",href:"#Core-Reactant-API","aria-label":'Permalink to "Core Reactant API {#Core-Reactant-API}"'},"​")],-1)),s[22]||(s[22]=i("h2",{id:"Compile-API",tabindex:"-1"},[a("Compile API "),i("a",{class:"header-anchor",href:"#Compile-API","aria-label":'Permalink to "Compile API {#Compile-API}"'},"​")],-1)),i("details",d,[i("summary",null,[s[0]||(s[0]=i("a",{id:"Reactant.Compiler.@compile",href:"#Reactant.Compiler.@compile"},[i("span",{class:"jlbinding"},"Reactant.Compiler.@compile")],-1)),s[1]||(s[1]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[2]||(s[2]=n('
julia
@compile f(args...)
source
',2))]),i("details",k,[i("summary",null,[s[3]||(s[3]=i("a",{id:"Reactant.Compiler.@jit",href:"#Reactant.Compiler.@jit"},[i("span",{class:"jlbinding"},"Reactant.Compiler.@jit")],-1)),s[4]||(s[4]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[5]||(s[5]=n(`
julia
@jit f(args...)
 
-Run @compile f(args..) then immediately execute it
source
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julia
@trace <expr>
Converts certain expressions like control flow into a Reactant friendly form. Importantly, if no traced value is found inside the expression, then there is no overhead.
Currently Supported
  • if conditions (with elseif and other niceties) (@trace if ...)
  • if statements with a preceeding assignment (@trace a = if ...) (note the positioning of the macro needs to be before the assignment and not before the if)
  • for statements with a single induction variable iterating over a syntactic StepRange of integers.
Special Considerations
  • Apply @trace only at the outermost if. Nested if statements will be automatically expanded into the correct form.
Extended Help
Caveats (Deviations from Core Julia Semantics)
New variables introduced
julia
@trace if x > 0
+Run @compile f(args..) then immediately execute it
source
`,2))]),s[23]||(s[23]=i("h2",{id:"ReactantCore-API",tabindex:"-1"},[a("ReactantCore API "),i("a",{class:"header-anchor",href:"#ReactantCore-API","aria-label":'Permalink to "ReactantCore API {#ReactantCore-API}"'},"​")],-1)),i("details",c,[i("summary",null,[s[6]||(s[6]=i("a",{id:"ReactantCore.@trace",href:"#ReactantCore.@trace"},[i("span",{class:"jlbinding"},"ReactantCore.@trace")],-1)),s[7]||(s[7]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[8]||(s[8]=n(`
julia
@trace <expr>
Converts certain expressions like control flow into a Reactant friendly form. Importantly, if no traced value is found inside the expression, then there is no overhead.
Currently Supported
  • if conditions (with elseif and other niceties) (@trace if ...)
  • if statements with a preceeding assignment (@trace a = if ...) (note the positioning of the macro needs to be before the assignment and not before the if)
  • for statements with a single induction variable iterating over a syntactic StepRange of integers.
Special Considerations
  • Apply @trace only at the outermost if. Nested if statements will be automatically expanded into the correct form.
Extended Help
Caveats (Deviations from Core Julia Semantics)
New variables introduced
julia
@trace if x > 0
     y = x + 1
     p = 1
 else
@@ -24,4 +24,4 @@ import{_ as l,c as p,j as i,a,G as t,a2 as n,B as h,o}from"./chunks/framework.B4
         y = 2.0
     end
     return y, nothing
-end
source
`,23))]),s[24]||(s[24]=i("h2",{id:"Inspect-Generated-HLO",tabindex:"-1"},[a("Inspect Generated HLO "),i("a",{class:"header-anchor",href:"#Inspect-Generated-HLO","aria-label":'Permalink to "Inspect Generated HLO {#Inspect-Generated-HLO}"'},"​")],-1)),i("details",g,[i("summary",null,[s[9]||(s[9]=i("a",{id:"Reactant.Compiler.@code_hlo",href:"#Reactant.Compiler.@code_hlo"},[i("span",{class:"jlbinding"},"Reactant.Compiler.@code_hlo")],-1)),s[10]||(s[10]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[11]||(s[11]=n('
julia
@code_hlo [optimize = ...] f(args...)
source
',2))]),s[25]||(s[25]=i("br",null,null,-1)),s[26]||(s[26]=i("h1",{id:"Internal-Functionality",tabindex:"-1"},[a("Internal Functionality "),i("a",{class:"header-anchor",href:"#Internal-Functionality","aria-label":'Permalink to "Internal Functionality {#Internal-Functionality}"'},"​")],-1)),s[27]||(s[27]=i("div",{class:"danger custom-block"},[i("p",{class:"custom-block-title"},"Private"),i("p",null,"These functions are not part of the public API and are subject to change at any time.")],-1)),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"Reactant.Compiler.codegen_unflatten!",href:"#Reactant.Compiler.codegen_unflatten!"},[i("span",{class:"jlbinding"},"Reactant.Compiler.codegen_unflatten!")],-1)),s[13]||(s[13]=a()),t(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[14]||(s[14]=n('
julia
codegen_unflatten!
Generate Julia code to wrap the XLA buffers back into the output result datatypes. The name is due to its similarity to the unflatten function in jax.tree_util.register_pytree_node.
source
',3))]),i("details",u,[i("summary",null,[s[15]||(s[15]=i("a",{id:"Reactant.Compiler.codegen_flatten!",href:"#Reactant.Compiler.codegen_flatten!"},[i("span",{class:"jlbinding"},"Reactant.Compiler.codegen_flatten!")],-1)),s[16]||(s[16]=a()),t(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[17]||(s[17]=n('
julia
codegen_flatten!
Generate Julia code to extract the XLA buffers from input arguments. The name is due to its similarity to the flatten function in jax.tree_util.register_pytree_node.
Arguments
  • linear_args: A list of arguments to be flattened.
Returns
  • flatten_names: A list of Symbols representing the names of the flattened arguments.
  • flatten_code: A list of Exprs to extract the XLA buffers from the input arguments.
Note
The linearized arguments do not directly refer to the are the arguments that have been flattened into a single list.
source
',9))]),i("details",b,[i("summary",null,[s[18]||(s[18]=i("a",{id:"Reactant.Compiler.codegen_xla_call",href:"#Reactant.Compiler.codegen_xla_call"},[i("span",{class:"jlbinding"},"Reactant.Compiler.codegen_xla_call")],-1)),s[19]||(s[19]=a()),t(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[20]||(s[20]=n('
julia
codegen_xla_call
Generate Julia code to call the XLA executable.
Arguments
  • exec: The XLA executable to call.
  • flatten_names: A list of Symbols representing the names of the flattened linear arguments.
  • donated_args_mask: A list of UInt8s representing whether the argument is donated.
  • nresults: The number of results to expect.
source
',5))])])}const D=l(r,[["render",m]]);export{j as __pageData,D as default};
+end

source

`,23))]),s[24]||(s[24]=i("h2",{id:"Inspect-Generated-HLO",tabindex:"-1"},[a("Inspect Generated HLO "),i("a",{class:"header-anchor",href:"#Inspect-Generated-HLO","aria-label":'Permalink to "Inspect Generated HLO {#Inspect-Generated-HLO}"'},"​")],-1)),i("details",g,[i("summary",null,[s[9]||(s[9]=i("a",{id:"Reactant.Compiler.@code_hlo",href:"#Reactant.Compiler.@code_hlo"},[i("span",{class:"jlbinding"},"Reactant.Compiler.@code_hlo")],-1)),s[10]||(s[10]=a()),t(e,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),s[11]||(s[11]=n('
julia
@code_hlo [optimize = ...] f(args...)

source

',2))]),s[25]||(s[25]=i("br",null,null,-1)),s[26]||(s[26]=i("h1",{id:"Internal-Functionality",tabindex:"-1"},[a("Internal Functionality "),i("a",{class:"header-anchor",href:"#Internal-Functionality","aria-label":'Permalink to "Internal Functionality {#Internal-Functionality}"'},"​")],-1)),s[27]||(s[27]=i("div",{class:"danger custom-block"},[i("p",{class:"custom-block-title"},"Private"),i("p",null,"These functions are not part of the public API and are subject to change at any time.")],-1)),i("details",y,[i("summary",null,[s[12]||(s[12]=i("a",{id:"Reactant.Compiler.codegen_unflatten!",href:"#Reactant.Compiler.codegen_unflatten!"},[i("span",{class:"jlbinding"},"Reactant.Compiler.codegen_unflatten!")],-1)),s[13]||(s[13]=a()),t(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[14]||(s[14]=n('
julia
codegen_unflatten!

Generate Julia code to wrap the XLA buffers back into the output result datatypes. The name is due to its similarity to the unflatten function in jax.tree_util.register_pytree_node.

source

',3))]),i("details",u,[i("summary",null,[s[15]||(s[15]=i("a",{id:"Reactant.Compiler.codegen_flatten!",href:"#Reactant.Compiler.codegen_flatten!"},[i("span",{class:"jlbinding"},"Reactant.Compiler.codegen_flatten!")],-1)),s[16]||(s[16]=a()),t(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[17]||(s[17]=n('
julia
codegen_flatten!

Generate Julia code to extract the XLA buffers from input arguments. The name is due to its similarity to the flatten function in jax.tree_util.register_pytree_node.

Arguments

Returns

Note

The linearized arguments do not directly refer to the are the arguments that have been flattened into a single list.

source

',9))]),i("details",b,[i("summary",null,[s[18]||(s[18]=i("a",{id:"Reactant.Compiler.codegen_xla_call",href:"#Reactant.Compiler.codegen_xla_call"},[i("span",{class:"jlbinding"},"Reactant.Compiler.codegen_xla_call")],-1)),s[19]||(s[19]=a()),t(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[20]||(s[20]=n('
julia
codegen_xla_call

Generate Julia code to call the XLA executable.

Arguments

source

',5))])])}const D=l(r,[["render",m]]);export{j as __pageData,D as default}; diff --git a/previews/PR441/assets/api_arith.md.BX48b7Bc.js b/previews/PR441/assets/api_arith.md.CMmO_Sp9.js similarity index 92% rename from previews/PR441/assets/api_arith.md.BX48b7Bc.js rename to previews/PR441/assets/api_arith.md.CMmO_Sp9.js index 1eb7af6d2..59327f256 100644 --- a/previews/PR441/assets/api_arith.md.BX48b7Bc.js +++ b/previews/PR441/assets/api_arith.md.CMmO_Sp9.js @@ -1,11 +1,11 @@ -import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const pe=JSON.parse('{"title":"Arithmetic Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/arith.md","filePath":"api/arith.md","lastUpdated":null}'),d={name:"api/arith.md"},c={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},I={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},x={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},w={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},q={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},P={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},H={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Q={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},_={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"};function te(ae,e,se,ne,ie,le){const s=p("Badge");return r(),o("div",null,[e[153]||(e[153]=t("h1",{id:"Arithmetic-Dialect",tabindex:"-1"},[a("Arithmetic Dialect "),t("a",{class:"header-anchor",href:"#Arithmetic-Dialect","aria-label":'Permalink to "Arithmetic Dialect {#Arithmetic-Dialect}"'},"​")],-1)),e[154]||(e[154]=t("p",null,[a("Refer to the "),t("a",{href:"https://mlir.llvm.org/docs/Dialects/ArithOps/",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),t("details",c,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.MLIR.Dialects.arith.addf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addf")],-1)),e[1]||(e[1]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=i(`

addf

The addf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.

Example

mlir
// Scalar addition.
+import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const pe=JSON.parse('{"title":"Arithmetic Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/arith.md","filePath":"api/arith.md","lastUpdated":null}'),c={name:"api/arith.md"},d={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},I={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},x={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},w={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},q={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},P={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},H={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Q={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},_={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"};function te(ae,e,se,ne,ie,le){const s=p("Badge");return r(),o("div",null,[e[153]||(e[153]=t("h1",{id:"Arithmetic-Dialect",tabindex:"-1"},[a("Arithmetic Dialect "),t("a",{class:"header-anchor",href:"#Arithmetic-Dialect","aria-label":'Permalink to "Arithmetic Dialect {#Arithmetic-Dialect}"'},"​")],-1)),e[154]||(e[154]=t("p",null,[a("Refer to the "),t("a",{href:"https://mlir.llvm.org/docs/Dialects/ArithOps/",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),t("details",d,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.MLIR.Dialects.arith.addf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addf")],-1)),e[1]||(e[1]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=i(`
addf
The addf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar addition.
 %a = arith.addf %b, %c : f64
 
 // SIMD vector addition, e.g. for Intel SSE.
 %f = arith.addf %g, %h : vector<4xf32>
 
 // Tensor addition.
-%x = arith.addf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",u,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.MLIR.Dialects.arith.addi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addi")],-1)),e[4]||(e[4]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=i(`
addi
Performs N-bit addition on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the addition modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The addi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar addition.
+%x = arith.addf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",u,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.MLIR.Dialects.arith.addi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addi")],-1)),e[4]||(e[4]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=i(`
addi
Performs N-bit addition on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the addition modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The addi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar addition.
 %a = arith.addi %b, %c : i64
 
 // Scalar addition with overflow flags.
@@ -15,25 +15,25 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 %f = arith.addi %g, %h : vector<4xi32>
 
 // Tensor element-wise addition.
-%x = arith.addi %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",h,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Reactant.MLIR.Dialects.arith.addui_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addui_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addui_extended")],-1)),e[7]||(e[7]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=i(`
addui_extended
Performs (N+1)-bit addition on zero-extended operands. Returns two results: the N-bit sum (same type as both operands), and the overflow bit (boolean-like), where 1 indicates unsigned addition overflow, while 0 indicates no overflow.
Example
mlir
// Scalar addition.
+%x = arith.addi %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",h,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Reactant.MLIR.Dialects.arith.addui_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addui_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addui_extended")],-1)),e[7]||(e[7]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=i(`
addui_extended
Performs (N+1)-bit addition on zero-extended operands. Returns two results: the N-bit sum (same type as both operands), and the overflow bit (boolean-like), where 1 indicates unsigned addition overflow, while 0 indicates no overflow.
Example
mlir
// Scalar addition.
 %sum, %overflow = arith.addui_extended %b, %c : i64, i1
 
 // Vector element-wise addition.
 %d:2 = arith.addui_extended %e, %f : vector<4xi32>, vector<4xi1>
 
 // Tensor element-wise addition.
-%x:2 = arith.addui_extended %y, %z : tensor<4x?xi8>, tensor<4x?xi1>
source
`,5))]),t("details",m,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Reactant.MLIR.Dialects.arith.andi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.andi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.andi")],-1)),e[10]||(e[10]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=i(`
andi
The andi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise and.
+%x:2 = arith.addui_extended %y, %z : tensor<4x?xi8>, tensor<4x?xi1>
source
`,5))]),t("details",m,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Reactant.MLIR.Dialects.arith.andi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.andi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.andi")],-1)),e[10]||(e[10]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=i(`
andi
The andi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise and.
 %a = arith.andi %b, %c : i64
 
 // SIMD vector element-wise bitwise integer and.
 %f = arith.andi %g, %h : vector<4xi32>
 
 // Tensor element-wise bitwise integer and.
-%x = arith.andi %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",g,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Reactant.MLIR.Dialects.arith.bitcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.bitcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.bitcast")],-1)),e[13]||(e[13]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=t("p",null,[t("code",null,"bitcast")],-1)),e[15]||(e[15]=t("p",null,"Bitcast an integer or floating point value to an integer or floating point value of equal bit width. When operating on vectors, casts elementwise.",-1)),e[16]||(e[16]=t("p",null,[a("Note that this implements a logical bitcast independent of target endianness. This allows constant folding without target information and is consitent with the bitcast constant folders in LLVM (see "),t("a",{href:"https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168",target:"_blank",rel:"noreferrer"},"https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168"),a(") For targets where the source and target type have the same endianness (which is the standard), this cast will also change no bits at runtime, but it may still require an operation, for example if the machine has different floating point and integer register files. For targets that have a different endianness for the source and target types (e.g. float is big-endian and integer is little-endian) a proper lowering would add operations to swap the order of words in addition to the bitcast.")],-1)),e[17]||(e[17]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L214-L231",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",b,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Reactant.MLIR.Dialects.arith.ceildivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ceildivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ceildivsi")],-1)),e[19]||(e[19]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=i(`
ceildivsi
Signed integer division. Rounds towards positive infinity, i.e. 7 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
-%a = arith.ceildivsi %b, %c : i64
source
`,6))]),t("details",f,[t("summary",null,[e[21]||(e[21]=t("a",{id:"Reactant.MLIR.Dialects.arith.ceildivui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ceildivui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ceildivui")],-1)),e[22]||(e[22]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=i(`
ceildivui
Unsigned integer division. Rounds towards positive infinity. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 1.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division.
-%a = arith.ceildivui %b, %c : i64
source
`,6))]),t("details",R,[t("summary",null,[e[24]||(e[24]=t("a",{id:"Reactant.MLIR.Dialects.arith.cmpf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.cmpf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.cmpf")],-1)),e[25]||(e[25]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=i(`
cmpf
The cmpf operation compares its two operands according to the float comparison rules and the predicate specified by the respective attribute. The predicate defines the type of comparison: (un)orderedness, (in)equality and signed less/greater than (or equal to) as well as predicates that are always true or false. The operands must have the same type, and this type must be a float type, or a vector or tensor thereof. The result is an i1, or a vector/tensor thereof having the same shape as the inputs. Unlike cmpi, the operands are always treated as signed. The u prefix indicates unordered comparison, not unsigned comparison, so "une" means unordered or not equal. For the sake of readability by humans, custom assembly form for the operation uses a string-typed attribute for the predicate. The value of this attribute corresponds to lower-cased name of the predicate constant, e.g., "one" means "ordered not equal". The string representation of the attribute is merely a syntactic sugar and is converted to an integer attribute by the parser.
Example
mlir
%r1 = arith.cmpf oeq, %0, %1 : f32
+%x = arith.andi %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",b,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Reactant.MLIR.Dialects.arith.bitcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.bitcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.bitcast")],-1)),e[13]||(e[13]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=t("p",null,[t("code",null,"bitcast")],-1)),e[15]||(e[15]=t("p",null,"Bitcast an integer or floating point value to an integer or floating point value of equal bit width. When operating on vectors, casts elementwise.",-1)),e[16]||(e[16]=t("p",null,[a("Note that this implements a logical bitcast independent of target endianness. This allows constant folding without target information and is consitent with the bitcast constant folders in LLVM (see "),t("a",{href:"https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168",target:"_blank",rel:"noreferrer"},"https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168"),a(") For targets where the source and target type have the same endianness (which is the standard), this cast will also change no bits at runtime, but it may still require an operation, for example if the machine has different floating point and integer register files. For targets that have a different endianness for the source and target types (e.g. float is big-endian and integer is little-endian) a proper lowering would add operations to swap the order of words in addition to the bitcast.")],-1)),e[17]||(e[17]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L214-L231",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",g,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Reactant.MLIR.Dialects.arith.ceildivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ceildivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ceildivsi")],-1)),e[19]||(e[19]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=i(`
ceildivsi
Signed integer division. Rounds towards positive infinity, i.e. 7 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
+%a = arith.ceildivsi %b, %c : i64
source
`,6))]),t("details",R,[t("summary",null,[e[21]||(e[21]=t("a",{id:"Reactant.MLIR.Dialects.arith.ceildivui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ceildivui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ceildivui")],-1)),e[22]||(e[22]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=i(`
ceildivui
Unsigned integer division. Rounds towards positive infinity. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 1.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division.
+%a = arith.ceildivui %b, %c : i64
source
`,6))]),t("details",f,[t("summary",null,[e[24]||(e[24]=t("a",{id:"Reactant.MLIR.Dialects.arith.cmpf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.cmpf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.cmpf")],-1)),e[25]||(e[25]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=i(`
cmpf
The cmpf operation compares its two operands according to the float comparison rules and the predicate specified by the respective attribute. The predicate defines the type of comparison: (un)orderedness, (in)equality and signed less/greater than (or equal to) as well as predicates that are always true or false. The operands must have the same type, and this type must be a float type, or a vector or tensor thereof. The result is an i1, or a vector/tensor thereof having the same shape as the inputs. Unlike cmpi, the operands are always treated as signed. The u prefix indicates unordered comparison, not unsigned comparison, so "une" means unordered or not equal. For the sake of readability by humans, custom assembly form for the operation uses a string-typed attribute for the predicate. The value of this attribute corresponds to lower-cased name of the predicate constant, e.g., "one" means "ordered not equal". The string representation of the attribute is merely a syntactic sugar and is converted to an integer attribute by the parser.
Example
mlir
%r1 = arith.cmpf oeq, %0, %1 : f32
 %r2 = arith.cmpf ult, %0, %1 : tensor<42x42xf64>
-%r3 = "arith.cmpf"(%0, %1) {predicate: 0} : (f8, f8) -> i1
source
`,5))]),t("details",v,[t("summary",null,[e[27]||(e[27]=t("a",{id:"Reactant.MLIR.Dialects.arith.cmpi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.cmpi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.cmpi")],-1)),e[28]||(e[28]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=i(`
cmpi
The cmpi operation is a generic comparison for integer-like types. Its two arguments can be integers, vectors or tensors thereof as long as their types match. The operation produces an i1 for the former case, a vector or a tensor of i1 with the same shape as inputs in the other cases.
Its first argument is an attribute that defines which type of comparison is performed. The following comparisons are supported:
  • equal (mnemonic: "eq"; integer value: 0)
  • not equal (mnemonic: "ne"; integer value: 1)
  • signed less than (mnemonic: "slt"; integer value: 2)
  • signed less than or equal (mnemonic: "sle"; integer value: 3)
  • signed greater than (mnemonic: "sgt"; integer value: 4)
  • signed greater than or equal (mnemonic: "sge"; integer value: 5)
  • unsigned less than (mnemonic: "ult"; integer value: 6)
  • unsigned less than or equal (mnemonic: "ule"; integer value: 7)
  • unsigned greater than (mnemonic: "ugt"; integer value: 8)
  • unsigned greater than or equal (mnemonic: "uge"; integer value: 9)
The result is 1 if the comparison is true and 0 otherwise. For vector or tensor operands, the comparison is performed elementwise and the element of the result indicates whether the comparison is true for the operand elements with the same indices as those of the result.
Note: while the custom assembly form uses strings, the actual underlying attribute has integer type (or rather enum class in C++ code) as seen from the generic assembly form. String literals are used to improve readability of the IR by humans.
This operation only applies to integer-like operands, but not floats. The main reason being that comparison operations have diverging sets of attributes: integers require sign specification while floats require various floating point-related particularities, e.g., -ffast-math behavior, IEEE754 compliance, etc (rationale). The type of comparison is specified as attribute to avoid introducing ten similar operations, taking into account that they are often implemented using the same operation downstream (rationale). The separation between signed and unsigned order comparisons is necessary because of integers being signless. The comparison operation must know how to interpret values with the foremost bit being set: negatives in two's complement or large positives (rationale).
Example
mlir
// Custom form of scalar "signed less than" comparison.
+%r3 = "arith.cmpf"(%0, %1) {predicate: 0} : (f8, f8) -> i1
source
`,5))]),t("details",v,[t("summary",null,[e[27]||(e[27]=t("a",{id:"Reactant.MLIR.Dialects.arith.cmpi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.cmpi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.cmpi")],-1)),e[28]||(e[28]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=i(`
cmpi
The cmpi operation is a generic comparison for integer-like types. Its two arguments can be integers, vectors or tensors thereof as long as their types match. The operation produces an i1 for the former case, a vector or a tensor of i1 with the same shape as inputs in the other cases.
Its first argument is an attribute that defines which type of comparison is performed. The following comparisons are supported:
  • equal (mnemonic: "eq"; integer value: 0)
  • not equal (mnemonic: "ne"; integer value: 1)
  • signed less than (mnemonic: "slt"; integer value: 2)
  • signed less than or equal (mnemonic: "sle"; integer value: 3)
  • signed greater than (mnemonic: "sgt"; integer value: 4)
  • signed greater than or equal (mnemonic: "sge"; integer value: 5)
  • unsigned less than (mnemonic: "ult"; integer value: 6)
  • unsigned less than or equal (mnemonic: "ule"; integer value: 7)
  • unsigned greater than (mnemonic: "ugt"; integer value: 8)
  • unsigned greater than or equal (mnemonic: "uge"; integer value: 9)
The result is 1 if the comparison is true and 0 otherwise. For vector or tensor operands, the comparison is performed elementwise and the element of the result indicates whether the comparison is true for the operand elements with the same indices as those of the result.
Note: while the custom assembly form uses strings, the actual underlying attribute has integer type (or rather enum class in C++ code) as seen from the generic assembly form. String literals are used to improve readability of the IR by humans.
This operation only applies to integer-like operands, but not floats. The main reason being that comparison operations have diverging sets of attributes: integers require sign specification while floats require various floating point-related particularities, e.g., -ffast-math behavior, IEEE754 compliance, etc (rationale). The type of comparison is specified as attribute to avoid introducing ten similar operations, taking into account that they are often implemented using the same operation downstream (rationale). The separation between signed and unsigned order comparisons is necessary because of integers being signless. The comparison operation must know how to interpret values with the foremost bit being set: negatives in two's complement or large positives (rationale).
Example
mlir
// Custom form of scalar "signed less than" comparison.
 %x = arith.cmpi slt, %lhs, %rhs : i32
 
 // Generic form of the same operation.
@@ -44,47 +44,47 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 
 // Generic form of the same operation.
 %x = "arith.cmpi"(%lhs, %rhs) {predicate = 0 : i64}
-    : (vector<4xi64>, vector<4xi64>) -> vector<4xi1>
source
`,10))]),t("details",I,[t("summary",null,[e[30]||(e[30]=t("a",{id:"Reactant.MLIR.Dialects.arith.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.arith.constant-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.constant")],-1)),e[31]||(e[31]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=i(`
constant
The constant operation produces an SSA value equal to some integer or floating-point constant specified by an attribute. This is the way MLIR forms simple integer and floating point constants.
Example
// Integer constant
+    : (vector<4xi64>, vector<4xi64>) -> vector<4xi1>
source
`,10))]),t("details",I,[t("summary",null,[e[30]||(e[30]=t("a",{id:"Reactant.MLIR.Dialects.arith.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.arith.constant-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.constant")],-1)),e[31]||(e[31]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=i(`
constant
The constant operation produces an SSA value equal to some integer or floating-point constant specified by an attribute. This is the way MLIR forms simple integer and floating point constants.
Example
// Integer constant
 %1 = arith.constant 42 : i32
 
 // Equivalent generic form
-%1 = "arith.constant"() {value = 42 : i32} : () -> i32
source
`,5))]),t("details",y,[t("summary",null,[e[33]||(e[33]=t("a",{id:"Reactant.MLIR.Dialects.arith.divsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.divsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.divsi")],-1)),e[34]||(e[34]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=i(`
divsi
Signed integer division. Rounds towards zero. Treats the leading bit as sign, i.e. 6 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
+%1 = "arith.constant"() {value = 42 : i32} : () -> i32
source
`,5))]),t("details",y,[t("summary",null,[e[33]||(e[33]=t("a",{id:"Reactant.MLIR.Dialects.arith.divsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.divsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.divsi")],-1)),e[34]||(e[34]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=i(`
divsi
Signed integer division. Rounds towards zero. Treats the leading bit as sign, i.e. 6 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
 %a = arith.divsi %b, %c : i64
 
 // SIMD vector element-wise division.
 %f = arith.divsi %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division.
-%x = arith.divsi %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",M,[t("summary",null,[e[36]||(e[36]=t("a",{id:"Reactant.MLIR.Dialects.arith.divui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.divui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.divui")],-1)),e[37]||(e[37]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=i(`
divui
Unsigned integer division. Rounds towards zero. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 0.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division.
+%x = arith.divsi %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",M,[t("summary",null,[e[36]||(e[36]=t("a",{id:"Reactant.MLIR.Dialects.arith.divui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.divui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.divui")],-1)),e[37]||(e[37]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=i(`
divui
Unsigned integer division. Rounds towards zero. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 0.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division.
 %a = arith.divui %b, %c : i64
 
 // SIMD vector element-wise division.
 %f = arith.divui %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division.
-%x = arith.divui %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",L,[t("summary",null,[e[39]||(e[39]=t("a",{id:"Reactant.MLIR.Dialects.arith.extf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extf")],-1)),e[40]||(e[40]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=t("p",null,[t("code",null,"extf")],-1)),e[42]||(e[42]=t("p",null,"Cast a floating-point value to a larger floating-point-typed value. The destination type must to be strictly wider than the source type. When operating on vectors, casts elementwise.",-1)),e[43]||(e[43]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L632-L638",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",j,[t("summary",null,[e[44]||(e[44]=t("a",{id:"Reactant.MLIR.Dialects.arith.extsi-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extsi-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extsi")],-1)),e[45]||(e[45]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[46]||(e[46]=i(`
extsi
The integer sign extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with copies of the most-significant bit of the input.
Example
mlir
%1 = arith.constant 5 : i3      // %1 is 0b101
+%x = arith.divui %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",L,[t("summary",null,[e[39]||(e[39]=t("a",{id:"Reactant.MLIR.Dialects.arith.extf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extf")],-1)),e[40]||(e[40]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=t("p",null,[t("code",null,"extf")],-1)),e[42]||(e[42]=t("p",null,"Cast a floating-point value to a larger floating-point-typed value. The destination type must to be strictly wider than the source type. When operating on vectors, casts elementwise.",-1)),e[43]||(e[43]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L632-L638",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",j,[t("summary",null,[e[44]||(e[44]=t("a",{id:"Reactant.MLIR.Dialects.arith.extsi-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extsi-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extsi")],-1)),e[45]||(e[45]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[46]||(e[46]=i(`
extsi
The integer sign extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with copies of the most-significant bit of the input.
Example
mlir
%1 = arith.constant 5 : i3      // %1 is 0b101
 %2 = arith.extsi %1 : i3 to i6  // %2 is 0b111101
 %3 = arith.constant 2 : i3      // %3 is 0b010
 %4 = arith.extsi %3 : i3 to i6  // %4 is 0b000010
 
-%5 = arith.extsi %0 : vector<2 x i32> to vector<2 x i64>
source
`,5))]),t("details",x,[t("summary",null,[e[47]||(e[47]=t("a",{id:"Reactant.MLIR.Dialects.arith.extui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extui")],-1)),e[48]||(e[48]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[49]||(e[49]=i(`
extui
The integer zero extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with zeros.
Example
mlir
  %1 = arith.constant 5 : i3      // %1 is 0b101
+%5 = arith.extsi %0 : vector<2 x i32> to vector<2 x i64>
source
`,5))]),t("details",x,[t("summary",null,[e[47]||(e[47]=t("a",{id:"Reactant.MLIR.Dialects.arith.extui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extui")],-1)),e[48]||(e[48]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[49]||(e[49]=i(`
extui
The integer zero extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with zeros.
Example
mlir
  %1 = arith.constant 5 : i3      // %1 is 0b101
   %2 = arith.extui %1 : i3 to i6  // %2 is 0b000101
   %3 = arith.constant 2 : i3      // %3 is 0b010
   %4 = arith.extui %3 : i3 to i6  // %4 is 0b000010
 
-  %5 = arith.extui %0 : vector<2 x i32> to vector<2 x i64>
source
`,5))]),t("details",D,[t("summary",null,[e[50]||(e[50]=t("a",{id:"Reactant.MLIR.Dialects.arith.floordivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.floordivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.floordivsi")],-1)),e[51]||(e[51]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[52]||(e[52]=i(`
floordivsi
Signed integer division. Rounds towards negative infinity, i.e. 5 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
-%a = arith.floordivsi %b, %c : i64
source
`,6))]),t("details",T,[t("summary",null,[e[53]||(e[53]=t("a",{id:"Reactant.MLIR.Dialects.arith.fptosi-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.fptosi-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.fptosi")],-1)),e[54]||(e[54]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[55]||(e[55]=t("p",null,[t("code",null,"fptosi")],-1)),e[56]||(e[56]=t("p",null,"Cast from a value interpreted as floating-point to the nearest (rounding towards zero) signed integer value. When operating on vectors, casts elementwise.",-1)),e[57]||(e[57]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L736-L742",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",w,[t("summary",null,[e[58]||(e[58]=t("a",{id:"Reactant.MLIR.Dialects.arith.fptoui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.fptoui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.fptoui")],-1)),e[59]||(e[59]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[60]||(e[60]=t("p",null,[t("code",null,"fptoui")],-1)),e[61]||(e[61]=t("p",null,"Cast from a value interpreted as floating-point to the nearest (rounding towards zero) unsigned integer value. When operating on vectors, casts elementwise.",-1)),e[62]||(e[62]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L762-L768",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",k,[t("summary",null,[e[63]||(e[63]=t("a",{id:"Reactant.MLIR.Dialects.arith.index_cast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.index_cast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.index_cast")],-1)),e[64]||(e[64]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[65]||(e[65]=t("p",null,[t("code",null,"index_cast")],-1)),e[66]||(e[66]=t("p",null,"Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is sign-extended. If casting to a narrower integer, the value is truncated.",-1)),e[67]||(e[67]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L828-L835",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",V,[t("summary",null,[e[68]||(e[68]=t("a",{id:"Reactant.MLIR.Dialects.arith.index_castui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.index_castui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.index_castui")],-1)),e[69]||(e[69]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=t("p",null,[t("code",null,"index_castui")],-1)),e[71]||(e[71]=t("p",null,"Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is zero-extended. If casting to a narrower integer, the value is truncated.",-1)),e[72]||(e[72]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L855-L862",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",A,[t("summary",null,[e[73]||(e[73]=t("a",{id:"Reactant.MLIR.Dialects.arith.maximumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.maximumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.maximumf")],-1)),e[74]||(e[74]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[75]||(e[75]=i(`
maximumf
Returns the maximum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.
Example
mlir
// Scalar floating-point maximum.
-%a = arith.maximumf %b, %c : f64
source
`,5))]),t("details",E,[t("summary",null,[e[76]||(e[76]=t("a",{id:"Reactant.MLIR.Dialects.arith.maxnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.maxnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.maxnumf")],-1)),e[77]||(e[77]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[78]||(e[78]=i(`
maxnumf
Returns the maximum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.
Example
mlir
// Scalar floating-point maximum.
-%a = arith.maxnumf %b, %c : f64
source
`,5))]),t("details",q,[t("summary",null,[e[79]||(e[79]=t("a",{id:"Reactant.MLIR.Dialects.arith.minimumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.minimumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.minimumf")],-1)),e[80]||(e[80]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[81]||(e[81]=i(`
minimumf
Returns the minimum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.
Example
mlir
// Scalar floating-point minimum.
-%a = arith.minimumf %b, %c : f64
source
`,5))]),t("details",z,[t("summary",null,[e[82]||(e[82]=t("a",{id:"Reactant.MLIR.Dialects.arith.minnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.minnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.minnumf")],-1)),e[83]||(e[83]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[84]||(e[84]=i(`
minnumf
Returns the minimum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.
Example
mlir
// Scalar floating-point minimum.
-%a = arith.minnumf %b, %c : f64
source
`,5))]),t("details",C,[t("summary",null,[e[85]||(e[85]=t("a",{id:"Reactant.MLIR.Dialects.arith.mulf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mulf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mulf")],-1)),e[86]||(e[86]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[87]||(e[87]=i(`
mulf
The mulf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar multiplication.
+  %5 = arith.extui %0 : vector<2 x i32> to vector<2 x i64>
source
`,5))]),t("details",D,[t("summary",null,[e[50]||(e[50]=t("a",{id:"Reactant.MLIR.Dialects.arith.floordivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.floordivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.floordivsi")],-1)),e[51]||(e[51]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[52]||(e[52]=i(`
floordivsi
Signed integer division. Rounds towards negative infinity, i.e. 5 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
+%a = arith.floordivsi %b, %c : i64
source
`,6))]),t("details",T,[t("summary",null,[e[53]||(e[53]=t("a",{id:"Reactant.MLIR.Dialects.arith.fptosi-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.fptosi-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.fptosi")],-1)),e[54]||(e[54]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[55]||(e[55]=t("p",null,[t("code",null,"fptosi")],-1)),e[56]||(e[56]=t("p",null,"Cast from a value interpreted as floating-point to the nearest (rounding towards zero) signed integer value. When operating on vectors, casts elementwise.",-1)),e[57]||(e[57]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L736-L742",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",w,[t("summary",null,[e[58]||(e[58]=t("a",{id:"Reactant.MLIR.Dialects.arith.fptoui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.fptoui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.fptoui")],-1)),e[59]||(e[59]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[60]||(e[60]=t("p",null,[t("code",null,"fptoui")],-1)),e[61]||(e[61]=t("p",null,"Cast from a value interpreted as floating-point to the nearest (rounding towards zero) unsigned integer value. When operating on vectors, casts elementwise.",-1)),e[62]||(e[62]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L762-L768",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",k,[t("summary",null,[e[63]||(e[63]=t("a",{id:"Reactant.MLIR.Dialects.arith.index_cast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.index_cast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.index_cast")],-1)),e[64]||(e[64]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[65]||(e[65]=t("p",null,[t("code",null,"index_cast")],-1)),e[66]||(e[66]=t("p",null,"Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is sign-extended. If casting to a narrower integer, the value is truncated.",-1)),e[67]||(e[67]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L828-L835",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",V,[t("summary",null,[e[68]||(e[68]=t("a",{id:"Reactant.MLIR.Dialects.arith.index_castui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.index_castui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.index_castui")],-1)),e[69]||(e[69]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=t("p",null,[t("code",null,"index_castui")],-1)),e[71]||(e[71]=t("p",null,"Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is zero-extended. If casting to a narrower integer, the value is truncated.",-1)),e[72]||(e[72]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L855-L862",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",A,[t("summary",null,[e[73]||(e[73]=t("a",{id:"Reactant.MLIR.Dialects.arith.maximumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.maximumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.maximumf")],-1)),e[74]||(e[74]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[75]||(e[75]=i(`
maximumf
Returns the maximum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.
Example
mlir
// Scalar floating-point maximum.
+%a = arith.maximumf %b, %c : f64
source
`,5))]),t("details",E,[t("summary",null,[e[76]||(e[76]=t("a",{id:"Reactant.MLIR.Dialects.arith.maxnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.maxnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.maxnumf")],-1)),e[77]||(e[77]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[78]||(e[78]=i(`
maxnumf
Returns the maximum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.
Example
mlir
// Scalar floating-point maximum.
+%a = arith.maxnumf %b, %c : f64
source
`,5))]),t("details",q,[t("summary",null,[e[79]||(e[79]=t("a",{id:"Reactant.MLIR.Dialects.arith.minimumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.minimumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.minimumf")],-1)),e[80]||(e[80]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[81]||(e[81]=i(`
minimumf
Returns the minimum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.
Example
mlir
// Scalar floating-point minimum.
+%a = arith.minimumf %b, %c : f64
source
`,5))]),t("details",z,[t("summary",null,[e[82]||(e[82]=t("a",{id:"Reactant.MLIR.Dialects.arith.minnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.minnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.minnumf")],-1)),e[83]||(e[83]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[84]||(e[84]=i(`
minnumf
Returns the minimum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.
Example
mlir
// Scalar floating-point minimum.
+%a = arith.minnumf %b, %c : f64
source
`,5))]),t("details",C,[t("summary",null,[e[85]||(e[85]=t("a",{id:"Reactant.MLIR.Dialects.arith.mulf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mulf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mulf")],-1)),e[86]||(e[86]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[87]||(e[87]=i(`
mulf
The mulf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar multiplication.
 %a = arith.mulf %b, %c : f64
 
 // SIMD pointwise vector multiplication, e.g. for Intel SSE.
 %f = arith.mulf %g, %h : vector<4xf32>
 
 // Tensor pointwise multiplication.
-%x = arith.mulf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",S,[t("summary",null,[e[88]||(e[88]=t("a",{id:"Reactant.MLIR.Dialects.arith.muli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.muli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.muli")],-1)),e[89]||(e[89]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[90]||(e[90]=i(`
muli
Performs N-bit multiplication on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the multiplication modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The muli operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar multiplication.
+%x = arith.mulf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",S,[t("summary",null,[e[88]||(e[88]=t("a",{id:"Reactant.MLIR.Dialects.arith.muli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.muli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.muli")],-1)),e[89]||(e[89]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[90]||(e[90]=i(`
muli
Performs N-bit multiplication on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the multiplication modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The muli operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar multiplication.
 %a = arith.muli %b, %c : i64
 
 // Scalar multiplication with overflow flags.
@@ -94,49 +94,49 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 %f = arith.muli %g, %h : vector<4xi32>
 
 // Tensor element-wise multiplication.
-%x = arith.muli %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",O,[t("summary",null,[e[91]||(e[91]=t("a",{id:"Reactant.MLIR.Dialects.arith.mulsi_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mulsi_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mulsi_extended")],-1)),e[92]||(e[92]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[93]||(e[93]=i(`
mulsi_extended
Performs (2*N)-bit multiplication on sign-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.
Example
mlir
// Scalar multiplication.
+%x = arith.muli %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",O,[t("summary",null,[e[91]||(e[91]=t("a",{id:"Reactant.MLIR.Dialects.arith.mulsi_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mulsi_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mulsi_extended")],-1)),e[92]||(e[92]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[93]||(e[93]=i(`
mulsi_extended
Performs (2*N)-bit multiplication on sign-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.
Example
mlir
// Scalar multiplication.
 %low, %high = arith.mulsi_extended %a, %b : i32
 
 // Vector element-wise multiplication.
 %c:2 = arith.mulsi_extended %d, %e : vector<4xi32>
 
 // Tensor element-wise multiplication.
-%x:2 = arith.mulsi_extended %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",N,[t("summary",null,[e[94]||(e[94]=t("a",{id:"Reactant.MLIR.Dialects.arith.mului_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mului_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mului_extended")],-1)),e[95]||(e[95]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[96]||(e[96]=i(`
mului_extended
Performs (2*N)-bit multiplication on zero-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.
Example
mlir
// Scalar multiplication.
+%x:2 = arith.mulsi_extended %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",N,[t("summary",null,[e[94]||(e[94]=t("a",{id:"Reactant.MLIR.Dialects.arith.mului_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mului_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mului_extended")],-1)),e[95]||(e[95]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[96]||(e[96]=i(`
mului_extended
Performs (2*N)-bit multiplication on zero-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.
Example
mlir
// Scalar multiplication.
 %low, %high = arith.mului_extended %a, %b : i32
 
 // Vector element-wise multiplication.
 %c:2 = arith.mului_extended %d, %e : vector<4xi32>
 
 // Tensor element-wise multiplication.
-%x:2 = arith.mului_extended %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",W,[t("summary",null,[e[97]||(e[97]=t("a",{id:"Reactant.MLIR.Dialects.arith.negf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.negf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.negf")],-1)),e[98]||(e[98]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[99]||(e[99]=i(`
negf
The negf operation computes the negation of a given value. It takes one operand and returns one result of the same type. This type may be a float scalar type, a vector whose element type is float, or a tensor of floats. It has no standard attributes.
Example
mlir
// Scalar negation value.
+%x:2 = arith.mului_extended %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",W,[t("summary",null,[e[97]||(e[97]=t("a",{id:"Reactant.MLIR.Dialects.arith.negf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.negf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.negf")],-1)),e[98]||(e[98]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[99]||(e[99]=i(`
negf
The negf operation computes the negation of a given value. It takes one operand and returns one result of the same type. This type may be a float scalar type, a vector whose element type is float, or a tensor of floats. It has no standard attributes.
Example
mlir
// Scalar negation value.
 %a = arith.negf %b : f64
 
 // SIMD vector element-wise negation value.
 %f = arith.negf %g : vector<4xf32>
 
 // Tensor element-wise negation value.
-%x = arith.negf %y : tensor<4x?xf8>
source
`,5))]),t("details",B,[t("summary",null,[e[100]||(e[100]=t("a",{id:"Reactant.MLIR.Dialects.arith.ori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ori")],-1)),e[101]||(e[101]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[102]||(e[102]=i(`
ori
The ori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise or.
+%x = arith.negf %y : tensor<4x?xf8>
source
`,5))]),t("details",B,[t("summary",null,[e[100]||(e[100]=t("a",{id:"Reactant.MLIR.Dialects.arith.ori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ori")],-1)),e[101]||(e[101]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[102]||(e[102]=i(`
ori
The ori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise or.
 %a = arith.ori %b, %c : i64
 
 // SIMD vector element-wise bitwise integer or.
 %f = arith.ori %g, %h : vector<4xi32>
 
 // Tensor element-wise bitwise integer or.
-%x = arith.ori %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",P,[t("summary",null,[e[103]||(e[103]=t("a",{id:"Reactant.MLIR.Dialects.arith.remf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remf")],-1)),e[104]||(e[104]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[105]||(e[105]=t("p",null,[t("code",null,"remf")],-1)),e[106]||(e[106]=t("p",null,"Returns the floating point division remainder. The remainder has the same sign as the dividend (lhs operand).",-1)),e[107]||(e[107]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L1431-L1436",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",U,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.Dialects.arith.remsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remsi")],-1)),e[109]||(e[109]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=i(`
remsi
Signed integer division remainder. Treats the leading bit as sign, i.e. 6 % -2 = 0.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar signed integer division remainder.
+%x = arith.ori %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",P,[t("summary",null,[e[103]||(e[103]=t("a",{id:"Reactant.MLIR.Dialects.arith.remf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remf")],-1)),e[104]||(e[104]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[105]||(e[105]=t("p",null,[t("code",null,"remf")],-1)),e[106]||(e[106]=t("p",null,"Returns the floating point division remainder. The remainder has the same sign as the dividend (lhs operand).",-1)),e[107]||(e[107]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L1431-L1436",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",U,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.Dialects.arith.remsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remsi")],-1)),e[109]||(e[109]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=i(`
remsi
Signed integer division remainder. Treats the leading bit as sign, i.e. 6 % -2 = 0.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar signed integer division remainder.
 %a = arith.remsi %b, %c : i64
 
 // SIMD vector element-wise division remainder.
 %f = arith.remsi %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division remainder.
-%x = arith.remsi %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",F,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.Dialects.arith.remui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remui")],-1)),e[112]||(e[112]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=i(`
remui
Unsigned integer division remainder. Treats the leading bit as the most significant, i.e. for i16, 6 % -2 = 6 % (2^16 - 2) = 6.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division remainder.
+%x = arith.remsi %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",F,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.Dialects.arith.remui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remui")],-1)),e[112]||(e[112]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=i(`
remui
Unsigned integer division remainder. Treats the leading bit as the most significant, i.e. for i16, 6 % -2 = 6 % (2^16 - 2) = 6.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division remainder.
 %a = arith.remui %b, %c : i64
 
 // SIMD vector element-wise division remainder.
 %f = arith.remui %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division remainder.
-%x = arith.remui %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",G,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.Dialects.arith.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.select")],-1)),e[115]||(e[115]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=i(`
select
The arith.select operation chooses one value based on a binary condition supplied as its first operand.
If the value of the first operand (the condition) is 1, then the second operand is returned, and the third operand is ignored, even if it was poison.
If the value of the first operand (the condition) is 0, then the third operand is returned, and the second operand is ignored, even if it was poison.
If the value of the first operand (the condition) is poison, then the operation returns poison.
The operation applies to vectors and tensors elementwise given the shape of all operands is identical. The choice is made for each element individually based on the value at the same position as the element in the condition operand. If an i1 is provided as the condition, the entire vector or tensor is chosen.
Example
mlir
// Custom form of scalar selection.
+%x = arith.remui %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",G,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.Dialects.arith.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.select")],-1)),e[115]||(e[115]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=i(`
select
The arith.select operation chooses one value based on a binary condition supplied as its first operand.
If the value of the first operand (the condition) is 1, then the second operand is returned, and the third operand is ignored, even if it was poison.
If the value of the first operand (the condition) is 0, then the third operand is returned, and the second operand is ignored, even if it was poison.
If the value of the first operand (the condition) is poison, then the operation returns poison.
The operation applies to vectors and tensors elementwise given the shape of all operands is identical. The choice is made for each element individually based on the value at the same position as the element in the condition operand. If an i1 is provided as the condition, the entire vector or tensor is chosen.
Example
mlir
// Custom form of scalar selection.
 %x = arith.select %cond, %true, %false : i32
 
 // Generic form of the same operation.
@@ -146,23 +146,23 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 %vx = arith.select %vcond, %vtrue, %vfalse : vector<42xi1>, vector<42xf32>
 
 // Full vector selection.
-%vx = arith.select %cond, %vtrue, %vfalse : vector<42xf32>
source
`,9))]),t("details",$,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.Dialects.arith.shli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shli")],-1)),e[118]||(e[118]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[119]||(e[119]=i(`
shli
The shli operation shifts the integer value of the first operand to the left by the integer value of the second operand. The second operand is interpreted as unsigned. The low order bits are filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
%1 = arith.constant 5 : i8  // %1 is 0b00000101
+%vx = arith.select %cond, %vtrue, %vfalse : vector<42xf32>
source
`,9))]),t("details",$,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.Dialects.arith.shli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shli")],-1)),e[118]||(e[118]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[119]||(e[119]=i(`
shli
The shli operation shifts the integer value of the first operand to the left by the integer value of the second operand. The second operand is interpreted as unsigned. The low order bits are filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
%1 = arith.constant 5 : i8  // %1 is 0b00000101
 %2 = arith.constant 3 : i8
 %3 = arith.shli %1, %2 : i8 // %3 is 0b00101000
-%4 = arith.shli %1, %2 overflow<nsw, nuw> : i8
source
`,6))]),t("details",J,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.Dialects.arith.shrsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shrsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shrsi")],-1)),e[121]||(e[121]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=i(`
shrsi
The shrsi operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as signed, and the second operand is interpreter as unsigned. The high order bits in the output are filled with copies of the most-significant bit of the shifted value (which means that the sign of the value is preserved). If the value of the second operand is greater or equal than bitwidth of the first operand, then the operation returns poison.
Example
mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
+%4 = arith.shli %1, %2 overflow<nsw, nuw> : i8
source
`,6))]),t("details",J,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.Dialects.arith.shrsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shrsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shrsi")],-1)),e[121]||(e[121]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=i(`
shrsi
The shrsi operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as signed, and the second operand is interpreter as unsigned. The high order bits in the output are filled with copies of the most-significant bit of the shifted value (which means that the sign of the value is preserved). If the value of the second operand is greater or equal than bitwidth of the first operand, then the operation returns poison.
Example
mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
 %2 = arith.constant 3 : i8
 %3 = arith.shrsi %1, %2 : (i8, i8) -> i8   // %3 is 0b11110100
 %4 = arith.constant 96 : i8                   // %4 is 0b01100000
-%5 = arith.shrsi %4, %2 : (i8, i8) -> i8   // %5 is 0b00001100
source
`,5))]),t("details",H,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.Dialects.arith.shrui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shrui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shrui")],-1)),e[124]||(e[124]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=i(`
shrui
The shrui operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as unsigned, and the second operand is interpreted as unsigned. The high order bits are always filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.
Example
mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
+%5 = arith.shrsi %4, %2 : (i8, i8) -> i8   // %5 is 0b00001100
source
`,5))]),t("details",H,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.Dialects.arith.shrui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shrui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shrui")],-1)),e[124]||(e[124]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=i(`
shrui
The shrui operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as unsigned, and the second operand is interpreted as unsigned. The high order bits are always filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.
Example
mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
 %2 = arith.constant 3 : i8
-%3 = arith.shrui %1, %2 : (i8, i8) -> i8   // %3 is 0b00010100
source
`,5))]),t("details",K,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.Dialects.arith.sitofp-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.sitofp-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.sitofp")],-1)),e[127]||(e[127]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=t("p",null,[t("code",null,"sitofp")],-1)),e[129]||(e[129]=t("p",null,"Cast from a value interpreted as a signed integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.",-1)),e[130]||(e[130]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L1554-L1561",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Q,[t("summary",null,[e[131]||(e[131]=t("a",{id:"Reactant.MLIR.Dialects.arith.subf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.subf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.subf")],-1)),e[132]||(e[132]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[133]||(e[133]=i(`
subf
The subf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar subtraction.
+%3 = arith.shrui %1, %2 : (i8, i8) -> i8   // %3 is 0b00010100
source
`,5))]),t("details",K,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.Dialects.arith.sitofp-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.sitofp-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.sitofp")],-1)),e[127]||(e[127]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=t("p",null,[t("code",null,"sitofp")],-1)),e[129]||(e[129]=t("p",null,"Cast from a value interpreted as a signed integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.",-1)),e[130]||(e[130]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L1554-L1561",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Q,[t("summary",null,[e[131]||(e[131]=t("a",{id:"Reactant.MLIR.Dialects.arith.subf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.subf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.subf")],-1)),e[132]||(e[132]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[133]||(e[133]=i(`
subf
The subf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar subtraction.
 %a = arith.subf %b, %c : f64
 
 // SIMD vector subtraction, e.g. for Intel SSE.
 %f = arith.subf %g, %h : vector<4xf32>
 
 // Tensor subtraction.
-%x = arith.subf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",X,[t("summary",null,[e[134]||(e[134]=t("a",{id:"Reactant.MLIR.Dialects.arith.subi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.subi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.subi")],-1)),e[135]||(e[135]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[136]||(e[136]=i(`
subi
Performs N-bit subtraction on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the subtraction modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The subi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar subtraction.
+%x = arith.subf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",X,[t("summary",null,[e[134]||(e[134]=t("a",{id:"Reactant.MLIR.Dialects.arith.subi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.subi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.subi")],-1)),e[135]||(e[135]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[136]||(e[136]=i(`
subi
Performs N-bit subtraction on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the subtraction modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The subi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar subtraction.
 %a = arith.subi %b, %c : i64
 
 // Scalar subtraction with overflow flags.
@@ -172,15 +172,15 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 %f = arith.subi %g, %h : vector<4xi32>
 
 // Tensor element-wise subtraction.
-%x = arith.subi %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",Y,[t("summary",null,[e[137]||(e[137]=t("a",{id:"Reactant.MLIR.Dialects.arith.truncf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.truncf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.truncf")],-1)),e[138]||(e[138]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[139]||(e[139]=t("p",null,[t("code",null,"truncf")],-1)),e[140]||(e[140]=t("p",null,"Truncate a floating-point value to a smaller floating-point-typed value. The destination type must be strictly narrower than the source type. If the value cannot be exactly represented, it is rounded using the provided rounding mode or the default one if no rounding mode is provided. When operating on vectors, casts elementwise.",-1)),e[141]||(e[141]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L1827-L1835",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Z,[t("summary",null,[e[142]||(e[142]=t("a",{id:"Reactant.MLIR.Dialects.arith.trunci-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.trunci-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.trunci")],-1)),e[143]||(e[143]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[144]||(e[144]=i(`
trunci
The integer truncation operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be smaller than the input bit-width (N < M). The top-most (N - M) bits of the input are discarded.
Example
mlir
  %1 = arith.constant 21 : i5     // %1 is 0b10101
+%x = arith.subi %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",Y,[t("summary",null,[e[137]||(e[137]=t("a",{id:"Reactant.MLIR.Dialects.arith.truncf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.truncf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.truncf")],-1)),e[138]||(e[138]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[139]||(e[139]=t("p",null,[t("code",null,"truncf")],-1)),e[140]||(e[140]=t("p",null,"Truncate a floating-point value to a smaller floating-point-typed value. The destination type must be strictly narrower than the source type. If the value cannot be exactly represented, it is rounded using the provided rounding mode or the default one if no rounding mode is provided. When operating on vectors, casts elementwise.",-1)),e[141]||(e[141]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L1827-L1835",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Z,[t("summary",null,[e[142]||(e[142]=t("a",{id:"Reactant.MLIR.Dialects.arith.trunci-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.trunci-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.trunci")],-1)),e[143]||(e[143]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[144]||(e[144]=i(`
trunci
The integer truncation operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be smaller than the input bit-width (N < M). The top-most (N - M) bits of the input are discarded.
Example
mlir
  %1 = arith.constant 21 : i5     // %1 is 0b10101
   %2 = arith.trunci %1 : i5 to i4 // %2 is 0b0101
   %3 = arith.trunci %1 : i5 to i3 // %3 is 0b101
 
-  %5 = arith.trunci %0 : vector<2 x i32> to vector<2 x i16>
source
`,5))]),t("details",_,[t("summary",null,[e[145]||(e[145]=t("a",{id:"Reactant.MLIR.Dialects.arith.uitofp-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.uitofp-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.uitofp")],-1)),e[146]||(e[146]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[147]||(e[147]=t("p",null,[t("code",null,"uitofp")],-1)),e[148]||(e[148]=t("p",null,"Cast from a value interpreted as unsigned integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.",-1)),e[149]||(e[149]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L1897-L1904",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",ee,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.Dialects.arith.xori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.xori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.xori")],-1)),e[151]||(e[151]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=i(`
xori
The xori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise xor.
+  %5 = arith.trunci %0 : vector<2 x i32> to vector<2 x i16>
source
`,5))]),t("details",_,[t("summary",null,[e[145]||(e[145]=t("a",{id:"Reactant.MLIR.Dialects.arith.uitofp-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.uitofp-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.uitofp")],-1)),e[146]||(e[146]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[147]||(e[147]=t("p",null,[t("code",null,"uitofp")],-1)),e[148]||(e[148]=t("p",null,"Cast from a value interpreted as unsigned integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.",-1)),e[149]||(e[149]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L1897-L1904",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",ee,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.Dialects.arith.xori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.xori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.xori")],-1)),e[151]||(e[151]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=i(`
xori
The xori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise xor.
 %a = arith.xori %b, %c : i64
 
 // SIMD vector element-wise bitwise integer xor.
 %f = arith.xori %g, %h : vector<4xi32>
 
 // Tensor element-wise bitwise integer xor.
-%x = arith.xori %y, %z : tensor<4x?xi8>
source
`,5))])])}const re=l(d,[["render",te]]);export{pe as __pageData,re as default};
+%x = arith.xori %y, %z : tensor<4x?xi8>

source

`,5))])])}const re=l(c,[["render",te]]);export{pe as __pageData,re as default}; diff --git a/previews/PR441/assets/api_arith.md.BX48b7Bc.lean.js b/previews/PR441/assets/api_arith.md.CMmO_Sp9.lean.js similarity index 92% rename from previews/PR441/assets/api_arith.md.BX48b7Bc.lean.js rename to previews/PR441/assets/api_arith.md.CMmO_Sp9.lean.js index 1eb7af6d2..59327f256 100644 --- a/previews/PR441/assets/api_arith.md.BX48b7Bc.lean.js +++ b/previews/PR441/assets/api_arith.md.CMmO_Sp9.lean.js @@ -1,11 +1,11 @@ -import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const pe=JSON.parse('{"title":"Arithmetic Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/arith.md","filePath":"api/arith.md","lastUpdated":null}'),d={name:"api/arith.md"},c={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},I={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},x={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},w={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},q={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},P={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},H={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Q={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},_={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"};function te(ae,e,se,ne,ie,le){const s=p("Badge");return r(),o("div",null,[e[153]||(e[153]=t("h1",{id:"Arithmetic-Dialect",tabindex:"-1"},[a("Arithmetic Dialect "),t("a",{class:"header-anchor",href:"#Arithmetic-Dialect","aria-label":'Permalink to "Arithmetic Dialect {#Arithmetic-Dialect}"'},"​")],-1)),e[154]||(e[154]=t("p",null,[a("Refer to the "),t("a",{href:"https://mlir.llvm.org/docs/Dialects/ArithOps/",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),t("details",c,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.MLIR.Dialects.arith.addf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addf")],-1)),e[1]||(e[1]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=i(`

addf

The addf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.

Example

mlir
// Scalar addition.
+import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const pe=JSON.parse('{"title":"Arithmetic Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/arith.md","filePath":"api/arith.md","lastUpdated":null}'),c={name:"api/arith.md"},d={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},I={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},x={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},w={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},q={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},P={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},H={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Q={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},_={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"};function te(ae,e,se,ne,ie,le){const s=p("Badge");return r(),o("div",null,[e[153]||(e[153]=t("h1",{id:"Arithmetic-Dialect",tabindex:"-1"},[a("Arithmetic Dialect "),t("a",{class:"header-anchor",href:"#Arithmetic-Dialect","aria-label":'Permalink to "Arithmetic Dialect {#Arithmetic-Dialect}"'},"​")],-1)),e[154]||(e[154]=t("p",null,[a("Refer to the "),t("a",{href:"https://mlir.llvm.org/docs/Dialects/ArithOps/",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),t("details",d,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.MLIR.Dialects.arith.addf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addf")],-1)),e[1]||(e[1]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=i(`
addf
The addf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar addition.
 %a = arith.addf %b, %c : f64
 
 // SIMD vector addition, e.g. for Intel SSE.
 %f = arith.addf %g, %h : vector<4xf32>
 
 // Tensor addition.
-%x = arith.addf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",u,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.MLIR.Dialects.arith.addi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addi")],-1)),e[4]||(e[4]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=i(`
addi
Performs N-bit addition on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the addition modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The addi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar addition.
+%x = arith.addf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",u,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.MLIR.Dialects.arith.addi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addi")],-1)),e[4]||(e[4]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=i(`
addi
Performs N-bit addition on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the addition modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The addi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar addition.
 %a = arith.addi %b, %c : i64
 
 // Scalar addition with overflow flags.
@@ -15,25 +15,25 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 %f = arith.addi %g, %h : vector<4xi32>
 
 // Tensor element-wise addition.
-%x = arith.addi %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",h,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Reactant.MLIR.Dialects.arith.addui_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addui_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addui_extended")],-1)),e[7]||(e[7]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=i(`
addui_extended
Performs (N+1)-bit addition on zero-extended operands. Returns two results: the N-bit sum (same type as both operands), and the overflow bit (boolean-like), where 1 indicates unsigned addition overflow, while 0 indicates no overflow.
Example
mlir
// Scalar addition.
+%x = arith.addi %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",h,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Reactant.MLIR.Dialects.arith.addui_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.addui_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.addui_extended")],-1)),e[7]||(e[7]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=i(`
addui_extended
Performs (N+1)-bit addition on zero-extended operands. Returns two results: the N-bit sum (same type as both operands), and the overflow bit (boolean-like), where 1 indicates unsigned addition overflow, while 0 indicates no overflow.
Example
mlir
// Scalar addition.
 %sum, %overflow = arith.addui_extended %b, %c : i64, i1
 
 // Vector element-wise addition.
 %d:2 = arith.addui_extended %e, %f : vector<4xi32>, vector<4xi1>
 
 // Tensor element-wise addition.
-%x:2 = arith.addui_extended %y, %z : tensor<4x?xi8>, tensor<4x?xi1>
source
`,5))]),t("details",m,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Reactant.MLIR.Dialects.arith.andi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.andi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.andi")],-1)),e[10]||(e[10]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=i(`
andi
The andi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise and.
+%x:2 = arith.addui_extended %y, %z : tensor<4x?xi8>, tensor<4x?xi1>
source
`,5))]),t("details",m,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Reactant.MLIR.Dialects.arith.andi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.andi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.andi")],-1)),e[10]||(e[10]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=i(`
andi
The andi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise and.
 %a = arith.andi %b, %c : i64
 
 // SIMD vector element-wise bitwise integer and.
 %f = arith.andi %g, %h : vector<4xi32>
 
 // Tensor element-wise bitwise integer and.
-%x = arith.andi %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",g,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Reactant.MLIR.Dialects.arith.bitcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.bitcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.bitcast")],-1)),e[13]||(e[13]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=t("p",null,[t("code",null,"bitcast")],-1)),e[15]||(e[15]=t("p",null,"Bitcast an integer or floating point value to an integer or floating point value of equal bit width. When operating on vectors, casts elementwise.",-1)),e[16]||(e[16]=t("p",null,[a("Note that this implements a logical bitcast independent of target endianness. This allows constant folding without target information and is consitent with the bitcast constant folders in LLVM (see "),t("a",{href:"https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168",target:"_blank",rel:"noreferrer"},"https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168"),a(") For targets where the source and target type have the same endianness (which is the standard), this cast will also change no bits at runtime, but it may still require an operation, for example if the machine has different floating point and integer register files. For targets that have a different endianness for the source and target types (e.g. float is big-endian and integer is little-endian) a proper lowering would add operations to swap the order of words in addition to the bitcast.")],-1)),e[17]||(e[17]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L214-L231",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",b,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Reactant.MLIR.Dialects.arith.ceildivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ceildivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ceildivsi")],-1)),e[19]||(e[19]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=i(`
ceildivsi
Signed integer division. Rounds towards positive infinity, i.e. 7 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
-%a = arith.ceildivsi %b, %c : i64
source
`,6))]),t("details",f,[t("summary",null,[e[21]||(e[21]=t("a",{id:"Reactant.MLIR.Dialects.arith.ceildivui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ceildivui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ceildivui")],-1)),e[22]||(e[22]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=i(`
ceildivui
Unsigned integer division. Rounds towards positive infinity. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 1.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division.
-%a = arith.ceildivui %b, %c : i64
source
`,6))]),t("details",R,[t("summary",null,[e[24]||(e[24]=t("a",{id:"Reactant.MLIR.Dialects.arith.cmpf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.cmpf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.cmpf")],-1)),e[25]||(e[25]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=i(`
cmpf
The cmpf operation compares its two operands according to the float comparison rules and the predicate specified by the respective attribute. The predicate defines the type of comparison: (un)orderedness, (in)equality and signed less/greater than (or equal to) as well as predicates that are always true or false. The operands must have the same type, and this type must be a float type, or a vector or tensor thereof. The result is an i1, or a vector/tensor thereof having the same shape as the inputs. Unlike cmpi, the operands are always treated as signed. The u prefix indicates unordered comparison, not unsigned comparison, so "une" means unordered or not equal. For the sake of readability by humans, custom assembly form for the operation uses a string-typed attribute for the predicate. The value of this attribute corresponds to lower-cased name of the predicate constant, e.g., "one" means "ordered not equal". The string representation of the attribute is merely a syntactic sugar and is converted to an integer attribute by the parser.
Example
mlir
%r1 = arith.cmpf oeq, %0, %1 : f32
+%x = arith.andi %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",b,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Reactant.MLIR.Dialects.arith.bitcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.bitcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.bitcast")],-1)),e[13]||(e[13]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=t("p",null,[t("code",null,"bitcast")],-1)),e[15]||(e[15]=t("p",null,"Bitcast an integer or floating point value to an integer or floating point value of equal bit width. When operating on vectors, casts elementwise.",-1)),e[16]||(e[16]=t("p",null,[a("Note that this implements a logical bitcast independent of target endianness. This allows constant folding without target information and is consitent with the bitcast constant folders in LLVM (see "),t("a",{href:"https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168",target:"_blank",rel:"noreferrer"},"https://github.com/llvm/llvm-project/blob/18c19414eb/llvm/lib/IR/ConstantFold.cpp#L168"),a(") For targets where the source and target type have the same endianness (which is the standard), this cast will also change no bits at runtime, but it may still require an operation, for example if the machine has different floating point and integer register files. For targets that have a different endianness for the source and target types (e.g. float is big-endian and integer is little-endian) a proper lowering would add operations to swap the order of words in addition to the bitcast.")],-1)),e[17]||(e[17]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L214-L231",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",g,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Reactant.MLIR.Dialects.arith.ceildivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ceildivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ceildivsi")],-1)),e[19]||(e[19]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=i(`
ceildivsi
Signed integer division. Rounds towards positive infinity, i.e. 7 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
+%a = arith.ceildivsi %b, %c : i64
source
`,6))]),t("details",R,[t("summary",null,[e[21]||(e[21]=t("a",{id:"Reactant.MLIR.Dialects.arith.ceildivui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ceildivui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ceildivui")],-1)),e[22]||(e[22]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=i(`
ceildivui
Unsigned integer division. Rounds towards positive infinity. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 1.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division.
+%a = arith.ceildivui %b, %c : i64
source
`,6))]),t("details",f,[t("summary",null,[e[24]||(e[24]=t("a",{id:"Reactant.MLIR.Dialects.arith.cmpf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.cmpf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.cmpf")],-1)),e[25]||(e[25]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=i(`
cmpf
The cmpf operation compares its two operands according to the float comparison rules and the predicate specified by the respective attribute. The predicate defines the type of comparison: (un)orderedness, (in)equality and signed less/greater than (or equal to) as well as predicates that are always true or false. The operands must have the same type, and this type must be a float type, or a vector or tensor thereof. The result is an i1, or a vector/tensor thereof having the same shape as the inputs. Unlike cmpi, the operands are always treated as signed. The u prefix indicates unordered comparison, not unsigned comparison, so "une" means unordered or not equal. For the sake of readability by humans, custom assembly form for the operation uses a string-typed attribute for the predicate. The value of this attribute corresponds to lower-cased name of the predicate constant, e.g., "one" means "ordered not equal". The string representation of the attribute is merely a syntactic sugar and is converted to an integer attribute by the parser.
Example
mlir
%r1 = arith.cmpf oeq, %0, %1 : f32
 %r2 = arith.cmpf ult, %0, %1 : tensor<42x42xf64>
-%r3 = "arith.cmpf"(%0, %1) {predicate: 0} : (f8, f8) -> i1
source
`,5))]),t("details",v,[t("summary",null,[e[27]||(e[27]=t("a",{id:"Reactant.MLIR.Dialects.arith.cmpi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.cmpi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.cmpi")],-1)),e[28]||(e[28]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=i(`
cmpi
The cmpi operation is a generic comparison for integer-like types. Its two arguments can be integers, vectors or tensors thereof as long as their types match. The operation produces an i1 for the former case, a vector or a tensor of i1 with the same shape as inputs in the other cases.
Its first argument is an attribute that defines which type of comparison is performed. The following comparisons are supported:
  • equal (mnemonic: "eq"; integer value: 0)
  • not equal (mnemonic: "ne"; integer value: 1)
  • signed less than (mnemonic: "slt"; integer value: 2)
  • signed less than or equal (mnemonic: "sle"; integer value: 3)
  • signed greater than (mnemonic: "sgt"; integer value: 4)
  • signed greater than or equal (mnemonic: "sge"; integer value: 5)
  • unsigned less than (mnemonic: "ult"; integer value: 6)
  • unsigned less than or equal (mnemonic: "ule"; integer value: 7)
  • unsigned greater than (mnemonic: "ugt"; integer value: 8)
  • unsigned greater than or equal (mnemonic: "uge"; integer value: 9)
The result is 1 if the comparison is true and 0 otherwise. For vector or tensor operands, the comparison is performed elementwise and the element of the result indicates whether the comparison is true for the operand elements with the same indices as those of the result.
Note: while the custom assembly form uses strings, the actual underlying attribute has integer type (or rather enum class in C++ code) as seen from the generic assembly form. String literals are used to improve readability of the IR by humans.
This operation only applies to integer-like operands, but not floats. The main reason being that comparison operations have diverging sets of attributes: integers require sign specification while floats require various floating point-related particularities, e.g., -ffast-math behavior, IEEE754 compliance, etc (rationale). The type of comparison is specified as attribute to avoid introducing ten similar operations, taking into account that they are often implemented using the same operation downstream (rationale). The separation between signed and unsigned order comparisons is necessary because of integers being signless. The comparison operation must know how to interpret values with the foremost bit being set: negatives in two's complement or large positives (rationale).
Example
mlir
// Custom form of scalar "signed less than" comparison.
+%r3 = "arith.cmpf"(%0, %1) {predicate: 0} : (f8, f8) -> i1
source
`,5))]),t("details",v,[t("summary",null,[e[27]||(e[27]=t("a",{id:"Reactant.MLIR.Dialects.arith.cmpi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.cmpi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.cmpi")],-1)),e[28]||(e[28]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=i(`
cmpi
The cmpi operation is a generic comparison for integer-like types. Its two arguments can be integers, vectors or tensors thereof as long as their types match. The operation produces an i1 for the former case, a vector or a tensor of i1 with the same shape as inputs in the other cases.
Its first argument is an attribute that defines which type of comparison is performed. The following comparisons are supported:
  • equal (mnemonic: "eq"; integer value: 0)
  • not equal (mnemonic: "ne"; integer value: 1)
  • signed less than (mnemonic: "slt"; integer value: 2)
  • signed less than or equal (mnemonic: "sle"; integer value: 3)
  • signed greater than (mnemonic: "sgt"; integer value: 4)
  • signed greater than or equal (mnemonic: "sge"; integer value: 5)
  • unsigned less than (mnemonic: "ult"; integer value: 6)
  • unsigned less than or equal (mnemonic: "ule"; integer value: 7)
  • unsigned greater than (mnemonic: "ugt"; integer value: 8)
  • unsigned greater than or equal (mnemonic: "uge"; integer value: 9)
The result is 1 if the comparison is true and 0 otherwise. For vector or tensor operands, the comparison is performed elementwise and the element of the result indicates whether the comparison is true for the operand elements with the same indices as those of the result.
Note: while the custom assembly form uses strings, the actual underlying attribute has integer type (or rather enum class in C++ code) as seen from the generic assembly form. String literals are used to improve readability of the IR by humans.
This operation only applies to integer-like operands, but not floats. The main reason being that comparison operations have diverging sets of attributes: integers require sign specification while floats require various floating point-related particularities, e.g., -ffast-math behavior, IEEE754 compliance, etc (rationale). The type of comparison is specified as attribute to avoid introducing ten similar operations, taking into account that they are often implemented using the same operation downstream (rationale). The separation between signed and unsigned order comparisons is necessary because of integers being signless. The comparison operation must know how to interpret values with the foremost bit being set: negatives in two's complement or large positives (rationale).
Example
mlir
// Custom form of scalar "signed less than" comparison.
 %x = arith.cmpi slt, %lhs, %rhs : i32
 
 // Generic form of the same operation.
@@ -44,47 +44,47 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 
 // Generic form of the same operation.
 %x = "arith.cmpi"(%lhs, %rhs) {predicate = 0 : i64}
-    : (vector<4xi64>, vector<4xi64>) -> vector<4xi1>
source
`,10))]),t("details",I,[t("summary",null,[e[30]||(e[30]=t("a",{id:"Reactant.MLIR.Dialects.arith.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.arith.constant-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.constant")],-1)),e[31]||(e[31]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=i(`
constant
The constant operation produces an SSA value equal to some integer or floating-point constant specified by an attribute. This is the way MLIR forms simple integer and floating point constants.
Example
// Integer constant
+    : (vector<4xi64>, vector<4xi64>) -> vector<4xi1>
source
`,10))]),t("details",I,[t("summary",null,[e[30]||(e[30]=t("a",{id:"Reactant.MLIR.Dialects.arith.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.arith.constant-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.constant")],-1)),e[31]||(e[31]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=i(`
constant
The constant operation produces an SSA value equal to some integer or floating-point constant specified by an attribute. This is the way MLIR forms simple integer and floating point constants.
Example
// Integer constant
 %1 = arith.constant 42 : i32
 
 // Equivalent generic form
-%1 = "arith.constant"() {value = 42 : i32} : () -> i32
source
`,5))]),t("details",y,[t("summary",null,[e[33]||(e[33]=t("a",{id:"Reactant.MLIR.Dialects.arith.divsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.divsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.divsi")],-1)),e[34]||(e[34]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=i(`
divsi
Signed integer division. Rounds towards zero. Treats the leading bit as sign, i.e. 6 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
+%1 = "arith.constant"() {value = 42 : i32} : () -> i32
source
`,5))]),t("details",y,[t("summary",null,[e[33]||(e[33]=t("a",{id:"Reactant.MLIR.Dialects.arith.divsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.divsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.divsi")],-1)),e[34]||(e[34]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=i(`
divsi
Signed integer division. Rounds towards zero. Treats the leading bit as sign, i.e. 6 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
 %a = arith.divsi %b, %c : i64
 
 // SIMD vector element-wise division.
 %f = arith.divsi %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division.
-%x = arith.divsi %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",M,[t("summary",null,[e[36]||(e[36]=t("a",{id:"Reactant.MLIR.Dialects.arith.divui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.divui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.divui")],-1)),e[37]||(e[37]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=i(`
divui
Unsigned integer division. Rounds towards zero. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 0.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division.
+%x = arith.divsi %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",M,[t("summary",null,[e[36]||(e[36]=t("a",{id:"Reactant.MLIR.Dialects.arith.divui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.divui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.divui")],-1)),e[37]||(e[37]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=i(`
divui
Unsigned integer division. Rounds towards zero. Treats the leading bit as the most significant, i.e. for i16 given two's complement representation, 6 / -2 = 6 / (2^16 - 2) = 0.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division.
 %a = arith.divui %b, %c : i64
 
 // SIMD vector element-wise division.
 %f = arith.divui %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division.
-%x = arith.divui %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",L,[t("summary",null,[e[39]||(e[39]=t("a",{id:"Reactant.MLIR.Dialects.arith.extf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extf")],-1)),e[40]||(e[40]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=t("p",null,[t("code",null,"extf")],-1)),e[42]||(e[42]=t("p",null,"Cast a floating-point value to a larger floating-point-typed value. The destination type must to be strictly wider than the source type. When operating on vectors, casts elementwise.",-1)),e[43]||(e[43]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L632-L638",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",j,[t("summary",null,[e[44]||(e[44]=t("a",{id:"Reactant.MLIR.Dialects.arith.extsi-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extsi-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extsi")],-1)),e[45]||(e[45]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[46]||(e[46]=i(`
extsi
The integer sign extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with copies of the most-significant bit of the input.
Example
mlir
%1 = arith.constant 5 : i3      // %1 is 0b101
+%x = arith.divui %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",L,[t("summary",null,[e[39]||(e[39]=t("a",{id:"Reactant.MLIR.Dialects.arith.extf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extf")],-1)),e[40]||(e[40]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=t("p",null,[t("code",null,"extf")],-1)),e[42]||(e[42]=t("p",null,"Cast a floating-point value to a larger floating-point-typed value. The destination type must to be strictly wider than the source type. When operating on vectors, casts elementwise.",-1)),e[43]||(e[43]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L632-L638",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",j,[t("summary",null,[e[44]||(e[44]=t("a",{id:"Reactant.MLIR.Dialects.arith.extsi-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extsi-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extsi")],-1)),e[45]||(e[45]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[46]||(e[46]=i(`
extsi
The integer sign extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with copies of the most-significant bit of the input.
Example
mlir
%1 = arith.constant 5 : i3      // %1 is 0b101
 %2 = arith.extsi %1 : i3 to i6  // %2 is 0b111101
 %3 = arith.constant 2 : i3      // %3 is 0b010
 %4 = arith.extsi %3 : i3 to i6  // %4 is 0b000010
 
-%5 = arith.extsi %0 : vector<2 x i32> to vector<2 x i64>
source
`,5))]),t("details",x,[t("summary",null,[e[47]||(e[47]=t("a",{id:"Reactant.MLIR.Dialects.arith.extui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extui")],-1)),e[48]||(e[48]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[49]||(e[49]=i(`
extui
The integer zero extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with zeros.
Example
mlir
  %1 = arith.constant 5 : i3      // %1 is 0b101
+%5 = arith.extsi %0 : vector<2 x i32> to vector<2 x i64>
source
`,5))]),t("details",x,[t("summary",null,[e[47]||(e[47]=t("a",{id:"Reactant.MLIR.Dialects.arith.extui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.extui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.extui")],-1)),e[48]||(e[48]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[49]||(e[49]=i(`
extui
The integer zero extension operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be larger than the input bit-width (N > M). The top-most (N - M) bits of the output are filled with zeros.
Example
mlir
  %1 = arith.constant 5 : i3      // %1 is 0b101
   %2 = arith.extui %1 : i3 to i6  // %2 is 0b000101
   %3 = arith.constant 2 : i3      // %3 is 0b010
   %4 = arith.extui %3 : i3 to i6  // %4 is 0b000010
 
-  %5 = arith.extui %0 : vector<2 x i32> to vector<2 x i64>
source
`,5))]),t("details",D,[t("summary",null,[e[50]||(e[50]=t("a",{id:"Reactant.MLIR.Dialects.arith.floordivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.floordivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.floordivsi")],-1)),e[51]||(e[51]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[52]||(e[52]=i(`
floordivsi
Signed integer division. Rounds towards negative infinity, i.e. 5 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
-%a = arith.floordivsi %b, %c : i64
source
`,6))]),t("details",T,[t("summary",null,[e[53]||(e[53]=t("a",{id:"Reactant.MLIR.Dialects.arith.fptosi-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.fptosi-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.fptosi")],-1)),e[54]||(e[54]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[55]||(e[55]=t("p",null,[t("code",null,"fptosi")],-1)),e[56]||(e[56]=t("p",null,"Cast from a value interpreted as floating-point to the nearest (rounding towards zero) signed integer value. When operating on vectors, casts elementwise.",-1)),e[57]||(e[57]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L736-L742",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",w,[t("summary",null,[e[58]||(e[58]=t("a",{id:"Reactant.MLIR.Dialects.arith.fptoui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.fptoui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.fptoui")],-1)),e[59]||(e[59]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[60]||(e[60]=t("p",null,[t("code",null,"fptoui")],-1)),e[61]||(e[61]=t("p",null,"Cast from a value interpreted as floating-point to the nearest (rounding towards zero) unsigned integer value. When operating on vectors, casts elementwise.",-1)),e[62]||(e[62]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L762-L768",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",k,[t("summary",null,[e[63]||(e[63]=t("a",{id:"Reactant.MLIR.Dialects.arith.index_cast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.index_cast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.index_cast")],-1)),e[64]||(e[64]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[65]||(e[65]=t("p",null,[t("code",null,"index_cast")],-1)),e[66]||(e[66]=t("p",null,"Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is sign-extended. If casting to a narrower integer, the value is truncated.",-1)),e[67]||(e[67]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L828-L835",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",V,[t("summary",null,[e[68]||(e[68]=t("a",{id:"Reactant.MLIR.Dialects.arith.index_castui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.index_castui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.index_castui")],-1)),e[69]||(e[69]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=t("p",null,[t("code",null,"index_castui")],-1)),e[71]||(e[71]=t("p",null,"Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is zero-extended. If casting to a narrower integer, the value is truncated.",-1)),e[72]||(e[72]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L855-L862",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",A,[t("summary",null,[e[73]||(e[73]=t("a",{id:"Reactant.MLIR.Dialects.arith.maximumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.maximumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.maximumf")],-1)),e[74]||(e[74]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[75]||(e[75]=i(`
maximumf
Returns the maximum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.
Example
mlir
// Scalar floating-point maximum.
-%a = arith.maximumf %b, %c : f64
source
`,5))]),t("details",E,[t("summary",null,[e[76]||(e[76]=t("a",{id:"Reactant.MLIR.Dialects.arith.maxnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.maxnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.maxnumf")],-1)),e[77]||(e[77]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[78]||(e[78]=i(`
maxnumf
Returns the maximum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.
Example
mlir
// Scalar floating-point maximum.
-%a = arith.maxnumf %b, %c : f64
source
`,5))]),t("details",q,[t("summary",null,[e[79]||(e[79]=t("a",{id:"Reactant.MLIR.Dialects.arith.minimumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.minimumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.minimumf")],-1)),e[80]||(e[80]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[81]||(e[81]=i(`
minimumf
Returns the minimum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.
Example
mlir
// Scalar floating-point minimum.
-%a = arith.minimumf %b, %c : f64
source
`,5))]),t("details",z,[t("summary",null,[e[82]||(e[82]=t("a",{id:"Reactant.MLIR.Dialects.arith.minnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.minnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.minnumf")],-1)),e[83]||(e[83]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[84]||(e[84]=i(`
minnumf
Returns the minimum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.
Example
mlir
// Scalar floating-point minimum.
-%a = arith.minnumf %b, %c : f64
source
`,5))]),t("details",C,[t("summary",null,[e[85]||(e[85]=t("a",{id:"Reactant.MLIR.Dialects.arith.mulf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mulf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mulf")],-1)),e[86]||(e[86]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[87]||(e[87]=i(`
mulf
The mulf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar multiplication.
+  %5 = arith.extui %0 : vector<2 x i32> to vector<2 x i64>
source
`,5))]),t("details",D,[t("summary",null,[e[50]||(e[50]=t("a",{id:"Reactant.MLIR.Dialects.arith.floordivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.floordivsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.floordivsi")],-1)),e[51]||(e[51]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[52]||(e[52]=i(`
floordivsi
Signed integer division. Rounds towards negative infinity, i.e. 5 / -2 = -3.
Divison by zero, or signed division overflow (minimum value divided by -1) is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any of its elements are divided by zero or has a signed division overflow.
Example
mlir
// Scalar signed integer division.
+%a = arith.floordivsi %b, %c : i64
source
`,6))]),t("details",T,[t("summary",null,[e[53]||(e[53]=t("a",{id:"Reactant.MLIR.Dialects.arith.fptosi-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.fptosi-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.fptosi")],-1)),e[54]||(e[54]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[55]||(e[55]=t("p",null,[t("code",null,"fptosi")],-1)),e[56]||(e[56]=t("p",null,"Cast from a value interpreted as floating-point to the nearest (rounding towards zero) signed integer value. When operating on vectors, casts elementwise.",-1)),e[57]||(e[57]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L736-L742",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",w,[t("summary",null,[e[58]||(e[58]=t("a",{id:"Reactant.MLIR.Dialects.arith.fptoui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.fptoui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.fptoui")],-1)),e[59]||(e[59]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[60]||(e[60]=t("p",null,[t("code",null,"fptoui")],-1)),e[61]||(e[61]=t("p",null,"Cast from a value interpreted as floating-point to the nearest (rounding towards zero) unsigned integer value. When operating on vectors, casts elementwise.",-1)),e[62]||(e[62]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L762-L768",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",k,[t("summary",null,[e[63]||(e[63]=t("a",{id:"Reactant.MLIR.Dialects.arith.index_cast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.index_cast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.index_cast")],-1)),e[64]||(e[64]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[65]||(e[65]=t("p",null,[t("code",null,"index_cast")],-1)),e[66]||(e[66]=t("p",null,"Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is sign-extended. If casting to a narrower integer, the value is truncated.",-1)),e[67]||(e[67]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L828-L835",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",V,[t("summary",null,[e[68]||(e[68]=t("a",{id:"Reactant.MLIR.Dialects.arith.index_castui-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.index_castui-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.index_castui")],-1)),e[69]||(e[69]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=t("p",null,[t("code",null,"index_castui")],-1)),e[71]||(e[71]=t("p",null,"Casts between scalar or vector integers and corresponding 'index' scalar or vectors. Index is an integer of platform-specific bit width. If casting to a wider integer, the value is zero-extended. If casting to a narrower integer, the value is truncated.",-1)),e[72]||(e[72]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L855-L862",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",A,[t("summary",null,[e[73]||(e[73]=t("a",{id:"Reactant.MLIR.Dialects.arith.maximumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.maximumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.maximumf")],-1)),e[74]||(e[74]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[75]||(e[75]=i(`
maximumf
Returns the maximum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.
Example
mlir
// Scalar floating-point maximum.
+%a = arith.maximumf %b, %c : f64
source
`,5))]),t("details",E,[t("summary",null,[e[76]||(e[76]=t("a",{id:"Reactant.MLIR.Dialects.arith.maxnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.maxnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.maxnumf")],-1)),e[77]||(e[77]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[78]||(e[78]=i(`
maxnumf
Returns the maximum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.
Example
mlir
// Scalar floating-point maximum.
+%a = arith.maxnumf %b, %c : f64
source
`,5))]),t("details",q,[t("summary",null,[e[79]||(e[79]=t("a",{id:"Reactant.MLIR.Dialects.arith.minimumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.minimumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.minimumf")],-1)),e[80]||(e[80]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[81]||(e[81]=i(`
minimumf
Returns the minimum of the two arguments, treating -0.0 as less than +0.0. If one of the arguments is NaN, then the result is also NaN.
Example
mlir
// Scalar floating-point minimum.
+%a = arith.minimumf %b, %c : f64
source
`,5))]),t("details",z,[t("summary",null,[e[82]||(e[82]=t("a",{id:"Reactant.MLIR.Dialects.arith.minnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.minnumf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.minnumf")],-1)),e[83]||(e[83]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[84]||(e[84]=i(`
minnumf
Returns the minimum of the two arguments. If the arguments are -0.0 and +0.0, then the result is either of them. If one of the arguments is NaN, then the result is the other argument.
Example
mlir
// Scalar floating-point minimum.
+%a = arith.minnumf %b, %c : f64
source
`,5))]),t("details",C,[t("summary",null,[e[85]||(e[85]=t("a",{id:"Reactant.MLIR.Dialects.arith.mulf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mulf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mulf")],-1)),e[86]||(e[86]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[87]||(e[87]=i(`
mulf
The mulf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar multiplication.
 %a = arith.mulf %b, %c : f64
 
 // SIMD pointwise vector multiplication, e.g. for Intel SSE.
 %f = arith.mulf %g, %h : vector<4xf32>
 
 // Tensor pointwise multiplication.
-%x = arith.mulf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",S,[t("summary",null,[e[88]||(e[88]=t("a",{id:"Reactant.MLIR.Dialects.arith.muli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.muli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.muli")],-1)),e[89]||(e[89]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[90]||(e[90]=i(`
muli
Performs N-bit multiplication on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the multiplication modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The muli operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar multiplication.
+%x = arith.mulf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",S,[t("summary",null,[e[88]||(e[88]=t("a",{id:"Reactant.MLIR.Dialects.arith.muli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.muli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.muli")],-1)),e[89]||(e[89]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[90]||(e[90]=i(`
muli
Performs N-bit multiplication on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the multiplication modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The muli operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar multiplication.
 %a = arith.muli %b, %c : i64
 
 // Scalar multiplication with overflow flags.
@@ -94,49 +94,49 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 %f = arith.muli %g, %h : vector<4xi32>
 
 // Tensor element-wise multiplication.
-%x = arith.muli %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",O,[t("summary",null,[e[91]||(e[91]=t("a",{id:"Reactant.MLIR.Dialects.arith.mulsi_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mulsi_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mulsi_extended")],-1)),e[92]||(e[92]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[93]||(e[93]=i(`
mulsi_extended
Performs (2*N)-bit multiplication on sign-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.
Example
mlir
// Scalar multiplication.
+%x = arith.muli %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",O,[t("summary",null,[e[91]||(e[91]=t("a",{id:"Reactant.MLIR.Dialects.arith.mulsi_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mulsi_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mulsi_extended")],-1)),e[92]||(e[92]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[93]||(e[93]=i(`
mulsi_extended
Performs (2*N)-bit multiplication on sign-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.
Example
mlir
// Scalar multiplication.
 %low, %high = arith.mulsi_extended %a, %b : i32
 
 // Vector element-wise multiplication.
 %c:2 = arith.mulsi_extended %d, %e : vector<4xi32>
 
 // Tensor element-wise multiplication.
-%x:2 = arith.mulsi_extended %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",N,[t("summary",null,[e[94]||(e[94]=t("a",{id:"Reactant.MLIR.Dialects.arith.mului_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mului_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mului_extended")],-1)),e[95]||(e[95]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[96]||(e[96]=i(`
mului_extended
Performs (2*N)-bit multiplication on zero-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.
Example
mlir
// Scalar multiplication.
+%x:2 = arith.mulsi_extended %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",N,[t("summary",null,[e[94]||(e[94]=t("a",{id:"Reactant.MLIR.Dialects.arith.mului_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.mului_extended-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.mului_extended")],-1)),e[95]||(e[95]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[96]||(e[96]=i(`
mului_extended
Performs (2*N)-bit multiplication on zero-extended operands. Returns two N-bit results: the low and the high halves of the product. The low half has the same value as the result of regular multiplication arith.muli with the same operands.
Example
mlir
// Scalar multiplication.
 %low, %high = arith.mului_extended %a, %b : i32
 
 // Vector element-wise multiplication.
 %c:2 = arith.mului_extended %d, %e : vector<4xi32>
 
 // Tensor element-wise multiplication.
-%x:2 = arith.mului_extended %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",W,[t("summary",null,[e[97]||(e[97]=t("a",{id:"Reactant.MLIR.Dialects.arith.negf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.negf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.negf")],-1)),e[98]||(e[98]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[99]||(e[99]=i(`
negf
The negf operation computes the negation of a given value. It takes one operand and returns one result of the same type. This type may be a float scalar type, a vector whose element type is float, or a tensor of floats. It has no standard attributes.
Example
mlir
// Scalar negation value.
+%x:2 = arith.mului_extended %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",W,[t("summary",null,[e[97]||(e[97]=t("a",{id:"Reactant.MLIR.Dialects.arith.negf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.negf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.negf")],-1)),e[98]||(e[98]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[99]||(e[99]=i(`
negf
The negf operation computes the negation of a given value. It takes one operand and returns one result of the same type. This type may be a float scalar type, a vector whose element type is float, or a tensor of floats. It has no standard attributes.
Example
mlir
// Scalar negation value.
 %a = arith.negf %b : f64
 
 // SIMD vector element-wise negation value.
 %f = arith.negf %g : vector<4xf32>
 
 // Tensor element-wise negation value.
-%x = arith.negf %y : tensor<4x?xf8>
source
`,5))]),t("details",B,[t("summary",null,[e[100]||(e[100]=t("a",{id:"Reactant.MLIR.Dialects.arith.ori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ori")],-1)),e[101]||(e[101]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[102]||(e[102]=i(`
ori
The ori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise or.
+%x = arith.negf %y : tensor<4x?xf8>
source
`,5))]),t("details",B,[t("summary",null,[e[100]||(e[100]=t("a",{id:"Reactant.MLIR.Dialects.arith.ori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.ori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.ori")],-1)),e[101]||(e[101]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[102]||(e[102]=i(`
ori
The ori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise or.
 %a = arith.ori %b, %c : i64
 
 // SIMD vector element-wise bitwise integer or.
 %f = arith.ori %g, %h : vector<4xi32>
 
 // Tensor element-wise bitwise integer or.
-%x = arith.ori %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",P,[t("summary",null,[e[103]||(e[103]=t("a",{id:"Reactant.MLIR.Dialects.arith.remf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remf")],-1)),e[104]||(e[104]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[105]||(e[105]=t("p",null,[t("code",null,"remf")],-1)),e[106]||(e[106]=t("p",null,"Returns the floating point division remainder. The remainder has the same sign as the dividend (lhs operand).",-1)),e[107]||(e[107]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L1431-L1436",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",U,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.Dialects.arith.remsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remsi")],-1)),e[109]||(e[109]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=i(`
remsi
Signed integer division remainder. Treats the leading bit as sign, i.e. 6 % -2 = 0.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar signed integer division remainder.
+%x = arith.ori %y, %z : tensor<4x?xi8>
source
`,5))]),t("details",P,[t("summary",null,[e[103]||(e[103]=t("a",{id:"Reactant.MLIR.Dialects.arith.remf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remf")],-1)),e[104]||(e[104]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[105]||(e[105]=t("p",null,[t("code",null,"remf")],-1)),e[106]||(e[106]=t("p",null,"Returns the floating point division remainder. The remainder has the same sign as the dividend (lhs operand).",-1)),e[107]||(e[107]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L1431-L1436",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",U,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.Dialects.arith.remsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remsi")],-1)),e[109]||(e[109]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=i(`
remsi
Signed integer division remainder. Treats the leading bit as sign, i.e. 6 % -2 = 0.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar signed integer division remainder.
 %a = arith.remsi %b, %c : i64
 
 // SIMD vector element-wise division remainder.
 %f = arith.remsi %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division remainder.
-%x = arith.remsi %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",F,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.Dialects.arith.remui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remui")],-1)),e[112]||(e[112]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=i(`
remui
Unsigned integer division remainder. Treats the leading bit as the most significant, i.e. for i16, 6 % -2 = 6 % (2^16 - 2) = 6.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division remainder.
+%x = arith.remsi %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",F,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.Dialects.arith.remui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.remui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.remui")],-1)),e[112]||(e[112]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=i(`
remui
Unsigned integer division remainder. Treats the leading bit as the most significant, i.e. for i16, 6 % -2 = 6 % (2^16 - 2) = 6.
Division by zero is undefined behavior. When applied to vector and tensor values, the behavior is undefined if any elements are divided by zero.
Example
mlir
// Scalar unsigned integer division remainder.
 %a = arith.remui %b, %c : i64
 
 // SIMD vector element-wise division remainder.
 %f = arith.remui %g, %h : vector<4xi32>
 
 // Tensor element-wise integer division remainder.
-%x = arith.remui %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",G,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.Dialects.arith.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.select")],-1)),e[115]||(e[115]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=i(`
select
The arith.select operation chooses one value based on a binary condition supplied as its first operand.
If the value of the first operand (the condition) is 1, then the second operand is returned, and the third operand is ignored, even if it was poison.
If the value of the first operand (the condition) is 0, then the third operand is returned, and the second operand is ignored, even if it was poison.
If the value of the first operand (the condition) is poison, then the operation returns poison.
The operation applies to vectors and tensors elementwise given the shape of all operands is identical. The choice is made for each element individually based on the value at the same position as the element in the condition operand. If an i1 is provided as the condition, the entire vector or tensor is chosen.
Example
mlir
// Custom form of scalar selection.
+%x = arith.remui %y, %z : tensor<4x?xi8>
source
`,6))]),t("details",G,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.Dialects.arith.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.select")],-1)),e[115]||(e[115]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=i(`
select
The arith.select operation chooses one value based on a binary condition supplied as its first operand.
If the value of the first operand (the condition) is 1, then the second operand is returned, and the third operand is ignored, even if it was poison.
If the value of the first operand (the condition) is 0, then the third operand is returned, and the second operand is ignored, even if it was poison.
If the value of the first operand (the condition) is poison, then the operation returns poison.
The operation applies to vectors and tensors elementwise given the shape of all operands is identical. The choice is made for each element individually based on the value at the same position as the element in the condition operand. If an i1 is provided as the condition, the entire vector or tensor is chosen.
Example
mlir
// Custom form of scalar selection.
 %x = arith.select %cond, %true, %false : i32
 
 // Generic form of the same operation.
@@ -146,23 +146,23 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 %vx = arith.select %vcond, %vtrue, %vfalse : vector<42xi1>, vector<42xf32>
 
 // Full vector selection.
-%vx = arith.select %cond, %vtrue, %vfalse : vector<42xf32>
source
`,9))]),t("details",$,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.Dialects.arith.shli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shli")],-1)),e[118]||(e[118]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[119]||(e[119]=i(`
shli
The shli operation shifts the integer value of the first operand to the left by the integer value of the second operand. The second operand is interpreted as unsigned. The low order bits are filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
%1 = arith.constant 5 : i8  // %1 is 0b00000101
+%vx = arith.select %cond, %vtrue, %vfalse : vector<42xf32>
source
`,9))]),t("details",$,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.Dialects.arith.shli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shli-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shli")],-1)),e[118]||(e[118]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[119]||(e[119]=i(`
shli
The shli operation shifts the integer value of the first operand to the left by the integer value of the second operand. The second operand is interpreted as unsigned. The low order bits are filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
%1 = arith.constant 5 : i8  // %1 is 0b00000101
 %2 = arith.constant 3 : i8
 %3 = arith.shli %1, %2 : i8 // %3 is 0b00101000
-%4 = arith.shli %1, %2 overflow<nsw, nuw> : i8
source
`,6))]),t("details",J,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.Dialects.arith.shrsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shrsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shrsi")],-1)),e[121]||(e[121]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=i(`
shrsi
The shrsi operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as signed, and the second operand is interpreter as unsigned. The high order bits in the output are filled with copies of the most-significant bit of the shifted value (which means that the sign of the value is preserved). If the value of the second operand is greater or equal than bitwidth of the first operand, then the operation returns poison.
Example
mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
+%4 = arith.shli %1, %2 overflow<nsw, nuw> : i8
source
`,6))]),t("details",J,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.Dialects.arith.shrsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shrsi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shrsi")],-1)),e[121]||(e[121]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=i(`
shrsi
The shrsi operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as signed, and the second operand is interpreter as unsigned. The high order bits in the output are filled with copies of the most-significant bit of the shifted value (which means that the sign of the value is preserved). If the value of the second operand is greater or equal than bitwidth of the first operand, then the operation returns poison.
Example
mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
 %2 = arith.constant 3 : i8
 %3 = arith.shrsi %1, %2 : (i8, i8) -> i8   // %3 is 0b11110100
 %4 = arith.constant 96 : i8                   // %4 is 0b01100000
-%5 = arith.shrsi %4, %2 : (i8, i8) -> i8   // %5 is 0b00001100
source
`,5))]),t("details",H,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.Dialects.arith.shrui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shrui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shrui")],-1)),e[124]||(e[124]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=i(`
shrui
The shrui operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as unsigned, and the second operand is interpreted as unsigned. The high order bits are always filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.
Example
mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
+%5 = arith.shrsi %4, %2 : (i8, i8) -> i8   // %5 is 0b00001100
source
`,5))]),t("details",H,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.Dialects.arith.shrui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.shrui-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.shrui")],-1)),e[124]||(e[124]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=i(`
shrui
The shrui operation shifts an integer value of the first operand to the right by the value of the second operand. The first operand is interpreted as unsigned, and the second operand is interpreted as unsigned. The high order bits are always filled with zeros. If the value of the second operand is greater or equal than the bitwidth of the first operand, then the operation returns poison.
Example
mlir
%1 = arith.constant 160 : i8               // %1 is 0b10100000
 %2 = arith.constant 3 : i8
-%3 = arith.shrui %1, %2 : (i8, i8) -> i8   // %3 is 0b00010100
source
`,5))]),t("details",K,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.Dialects.arith.sitofp-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.sitofp-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.sitofp")],-1)),e[127]||(e[127]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=t("p",null,[t("code",null,"sitofp")],-1)),e[129]||(e[129]=t("p",null,"Cast from a value interpreted as a signed integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.",-1)),e[130]||(e[130]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L1554-L1561",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Q,[t("summary",null,[e[131]||(e[131]=t("a",{id:"Reactant.MLIR.Dialects.arith.subf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.subf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.subf")],-1)),e[132]||(e[132]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[133]||(e[133]=i(`
subf
The subf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar subtraction.
+%3 = arith.shrui %1, %2 : (i8, i8) -> i8   // %3 is 0b00010100
source
`,5))]),t("details",K,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.Dialects.arith.sitofp-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.sitofp-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.sitofp")],-1)),e[127]||(e[127]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=t("p",null,[t("code",null,"sitofp")],-1)),e[129]||(e[129]=t("p",null,"Cast from a value interpreted as a signed integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.",-1)),e[130]||(e[130]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L1554-L1561",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Q,[t("summary",null,[e[131]||(e[131]=t("a",{id:"Reactant.MLIR.Dialects.arith.subf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.subf-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.subf")],-1)),e[132]||(e[132]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[133]||(e[133]=i(`
subf
The subf operation takes two operands and returns one result, each of these is required to be the same type. This type may be a floating point scalar type, a vector whose element type is a floating point type, or a floating point tensor.
Example
mlir
// Scalar subtraction.
 %a = arith.subf %b, %c : f64
 
 // SIMD vector subtraction, e.g. for Intel SSE.
 %f = arith.subf %g, %h : vector<4xf32>
 
 // Tensor subtraction.
-%x = arith.subf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",X,[t("summary",null,[e[134]||(e[134]=t("a",{id:"Reactant.MLIR.Dialects.arith.subi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.subi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.subi")],-1)),e[135]||(e[135]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[136]||(e[136]=i(`
subi
Performs N-bit subtraction on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the subtraction modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The subi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar subtraction.
+%x = arith.subf %y, %z : tensor<4x?xbf16>
TODO: In the distant future, this will accept optional attributes for fast math, contraction, rounding mode, and other controls.
source
`,6))]),t("details",X,[t("summary",null,[e[134]||(e[134]=t("a",{id:"Reactant.MLIR.Dialects.arith.subi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.subi-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.subi")],-1)),e[135]||(e[135]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[136]||(e[136]=i(`
subi
Performs N-bit subtraction on the operands. The operands are interpreted as unsigned bitvectors. The result is represented by a bitvector containing the mathematical value of the subtraction modulo 2^n, where n is the bitwidth. Because arith integers use a two's complement representation, this operation is applicable on both signed and unsigned integer operands.
The subi operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers.
This op supports nuw/nsw overflow flags which stands stand for "No Unsigned Wrap" and "No Signed Wrap", respectively. If the nuw and/or nsw flags are present, and an unsigned/signed overflow occurs (respectively), the result is poison.
Example
mlir
// Scalar subtraction.
 %a = arith.subi %b, %c : i64
 
 // Scalar subtraction with overflow flags.
@@ -172,15 +172,15 @@ import{_ as l,c as o,j as t,a,G as n,a2 as i,B as p,o as r}from"./chunks/framewo
 %f = arith.subi %g, %h : vector<4xi32>
 
 // Tensor element-wise subtraction.
-%x = arith.subi %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",Y,[t("summary",null,[e[137]||(e[137]=t("a",{id:"Reactant.MLIR.Dialects.arith.truncf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.truncf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.truncf")],-1)),e[138]||(e[138]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[139]||(e[139]=t("p",null,[t("code",null,"truncf")],-1)),e[140]||(e[140]=t("p",null,"Truncate a floating-point value to a smaller floating-point-typed value. The destination type must be strictly narrower than the source type. If the value cannot be exactly represented, it is rounded using the provided rounding mode or the default one if no rounding mode is provided. When operating on vectors, casts elementwise.",-1)),e[141]||(e[141]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L1827-L1835",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Z,[t("summary",null,[e[142]||(e[142]=t("a",{id:"Reactant.MLIR.Dialects.arith.trunci-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.trunci-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.trunci")],-1)),e[143]||(e[143]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[144]||(e[144]=i(`
trunci
The integer truncation operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be smaller than the input bit-width (N < M). The top-most (N - M) bits of the input are discarded.
Example
mlir
  %1 = arith.constant 21 : i5     // %1 is 0b10101
+%x = arith.subi %y, %z : tensor<4x?xi8>
source
`,7))]),t("details",Y,[t("summary",null,[e[137]||(e[137]=t("a",{id:"Reactant.MLIR.Dialects.arith.truncf-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.truncf-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.truncf")],-1)),e[138]||(e[138]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[139]||(e[139]=t("p",null,[t("code",null,"truncf")],-1)),e[140]||(e[140]=t("p",null,"Truncate a floating-point value to a smaller floating-point-typed value. The destination type must be strictly narrower than the source type. If the value cannot be exactly represented, it is rounded using the provided rounding mode or the default one if no rounding mode is provided. When operating on vectors, casts elementwise.",-1)),e[141]||(e[141]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L1827-L1835",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Z,[t("summary",null,[e[142]||(e[142]=t("a",{id:"Reactant.MLIR.Dialects.arith.trunci-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.trunci-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.trunci")],-1)),e[143]||(e[143]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[144]||(e[144]=i(`
trunci
The integer truncation operation takes an integer input of width M and an integer destination type of width N. The destination bit-width must be smaller than the input bit-width (N < M). The top-most (N - M) bits of the input are discarded.
Example
mlir
  %1 = arith.constant 21 : i5     // %1 is 0b10101
   %2 = arith.trunci %1 : i5 to i4 // %2 is 0b0101
   %3 = arith.trunci %1 : i5 to i3 // %3 is 0b101
 
-  %5 = arith.trunci %0 : vector<2 x i32> to vector<2 x i16>
source
`,5))]),t("details",_,[t("summary",null,[e[145]||(e[145]=t("a",{id:"Reactant.MLIR.Dialects.arith.uitofp-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.uitofp-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.uitofp")],-1)),e[146]||(e[146]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[147]||(e[147]=t("p",null,[t("code",null,"uitofp")],-1)),e[148]||(e[148]=t("p",null,"Cast from a value interpreted as unsigned integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.",-1)),e[149]||(e[149]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Arith.jl#L1897-L1904",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",ee,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.Dialects.arith.xori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.xori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.xori")],-1)),e[151]||(e[151]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=i(`
xori
The xori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise xor.
+  %5 = arith.trunci %0 : vector<2 x i32> to vector<2 x i16>
source
`,5))]),t("details",_,[t("summary",null,[e[145]||(e[145]=t("a",{id:"Reactant.MLIR.Dialects.arith.uitofp-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.uitofp-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.uitofp")],-1)),e[146]||(e[146]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[147]||(e[147]=t("p",null,[t("code",null,"uitofp")],-1)),e[148]||(e[148]=t("p",null,"Cast from a value interpreted as unsigned integer to the corresponding floating-point value. If the value cannot be exactly represented, it is rounded using the default rounding mode. When operating on vectors, casts elementwise.",-1)),e[149]||(e[149]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Arith.jl#L1897-L1904",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",ee,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.Dialects.arith.xori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.arith.xori-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.arith.xori")],-1)),e[151]||(e[151]=a()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=i(`
xori
The xori operation takes two operands and returns one result, each of these is required to be the same type. This type may be an integer scalar type, a vector whose element type is integer, or a tensor of integers. It has no standard attributes.
Example
mlir
// Scalar integer bitwise xor.
 %a = arith.xori %b, %c : i64
 
 // SIMD vector element-wise bitwise integer xor.
 %f = arith.xori %g, %h : vector<4xi32>
 
 // Tensor element-wise bitwise integer xor.
-%x = arith.xori %y, %z : tensor<4x?xi8>
source
`,5))])])}const re=l(d,[["render",te]]);export{pe as __pageData,re as default};
+%x = arith.xori %y, %z : tensor<4x?xi8>

source

`,5))])])}const re=l(c,[["render",te]]);export{pe as __pageData,re as default}; diff --git a/previews/PR441/assets/api_builtin.md.Dpx_h2Fd.js b/previews/PR441/assets/api_builtin.md.vAmfTwue.js similarity index 92% rename from previews/PR441/assets/api_builtin.md.Dpx_h2Fd.js rename to previews/PR441/assets/api_builtin.md.vAmfTwue.js index 9b0effdc1..e8dd3e9af 100644 --- a/previews/PR441/assets/api_builtin.md.Dpx_h2Fd.js +++ b/previews/PR441/assets/api_builtin.md.vAmfTwue.js @@ -1,6 +1,6 @@ -import{_ as i,c as l,j as n,a,G as t,a2 as o,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const _=JSON.parse('{"title":"Builtin Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/builtin.md","filePath":"api/builtin.md","lastUpdated":null}'),c={name:"api/builtin.md"},d={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"};function m(f,e,b,h,y,g){const s=p("Badge");return r(),l("div",null,[e[6]||(e[6]=n("h1",{id:"Builtin-Dialect",tabindex:"-1"},[a("Builtin Dialect "),n("a",{class:"header-anchor",href:"#Builtin-Dialect","aria-label":'Permalink to "Builtin Dialect {#Builtin-Dialect}"'},"​")],-1)),e[7]||(e[7]=n("p",null,[a("Refer to the "),n("a",{href:"https://mlir.llvm.org/docs/Dialects/Builtin/",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),n("details",d,[n("summary",null,[e[0]||(e[0]=n("a",{id:"Reactant.MLIR.Dialects.builtin.module_-Tuple{}",href:"#Reactant.MLIR.Dialects.builtin.module_-Tuple{}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.builtin.module_")],-1)),e[1]||(e[1]=a()),t(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=o(`

module_

A module represents a top-level container operation. It contains a single graph region containing a single block which can contain any operations and does not have a terminator. Operations within this region cannot implicitly capture values defined outside the module, i.e. Modules are IsolatedFromAbove. Modules have an optional symbol name which can be used to refer to them in operations.

Example

mlir
module {
+import{_ as i,c as l,j as n,a,G as t,a2 as o,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const _=JSON.parse('{"title":"Builtin Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/builtin.md","filePath":"api/builtin.md","lastUpdated":null}'),c={name:"api/builtin.md"},d={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"};function m(b,e,f,h,y,g){const s=p("Badge");return r(),l("div",null,[e[6]||(e[6]=n("h1",{id:"Builtin-Dialect",tabindex:"-1"},[a("Builtin Dialect "),n("a",{class:"header-anchor",href:"#Builtin-Dialect","aria-label":'Permalink to "Builtin Dialect {#Builtin-Dialect}"'},"​")],-1)),e[7]||(e[7]=n("p",null,[a("Refer to the "),n("a",{href:"https://mlir.llvm.org/docs/Dialects/Builtin/",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),n("details",d,[n("summary",null,[e[0]||(e[0]=n("a",{id:"Reactant.MLIR.Dialects.builtin.module_-Tuple{}",href:"#Reactant.MLIR.Dialects.builtin.module_-Tuple{}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.builtin.module_")],-1)),e[1]||(e[1]=a()),t(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=o(`
module_
A module represents a top-level container operation. It contains a single graph region containing a single block which can contain any operations and does not have a terminator. Operations within this region cannot implicitly capture values defined outside the module, i.e. Modules are IsolatedFromAbove. Modules have an optional symbol name which can be used to refer to them in operations.
Example
mlir
module {
   func.func @foo()
-}
source
`,5))]),n("details",u,[n("summary",null,[e[3]||(e[3]=n("a",{id:"Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast")],-1)),e[4]||(e[4]=a()),t(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=o(`
unrealized_conversion_cast
An unrealized_conversion_cast operation represents an unrealized conversion from one set of types to another, that is used to enable the inter-mixing of different type systems. This operation should not be attributed any special representational or execution semantics, and is generally only intended to be used to satisfy the temporary intermixing of type systems during the conversion of one type system to another.
This operation may produce results of arity 1-N, and accept as input operands of arity 0-N.
Example
mlir
// An unrealized 0-1 conversion. These types of conversions are useful in
+}
source
`,5))]),n("details",u,[n("summary",null,[e[3]||(e[3]=n("a",{id:"Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast")],-1)),e[4]||(e[4]=a()),t(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=o(`
unrealized_conversion_cast
An unrealized_conversion_cast operation represents an unrealized conversion from one set of types to another, that is used to enable the inter-mixing of different type systems. This operation should not be attributed any special representational or execution semantics, and is generally only intended to be used to satisfy the temporary intermixing of type systems during the conversion of one type system to another.
This operation may produce results of arity 1-N, and accept as input operands of arity 0-N.
Example
mlir
// An unrealized 0-1 conversion. These types of conversions are useful in
 // cases where a type is removed from the type system, but not all uses have
 // been converted. For example, imagine we have a tuple type that is
 // expanded to its element types. If only some uses of an empty tuple type
@@ -15,4 +15,4 @@ import{_ as i,c as l,j as n,a,G as t,a2 as o,B as p,o as r}from"./chunks/framewo
 %results2:2 = unrealized_conversion_cast %tuple_operand : !foo.tuple_type<!foo.type, !foo.type> to !foo.type, !foo.type
 
 // An unrealized N-1 conversion.
-%result3 = unrealized_conversion_cast %operand, %operand : !foo.type, !foo.type to !bar.tuple_type<!foo.type, !foo.type>
source
`,6))])])}const R=i(c,[["render",m]]);export{_ as __pageData,R as default};
+%result3 = unrealized_conversion_cast %operand, %operand : !foo.type, !foo.type to !bar.tuple_type<!foo.type, !foo.type>

source

`,6))])])}const R=i(c,[["render",m]]);export{_ as __pageData,R as default}; diff --git a/previews/PR441/assets/api_builtin.md.Dpx_h2Fd.lean.js b/previews/PR441/assets/api_builtin.md.vAmfTwue.lean.js similarity index 92% rename from previews/PR441/assets/api_builtin.md.Dpx_h2Fd.lean.js rename to previews/PR441/assets/api_builtin.md.vAmfTwue.lean.js index 9b0effdc1..e8dd3e9af 100644 --- a/previews/PR441/assets/api_builtin.md.Dpx_h2Fd.lean.js +++ b/previews/PR441/assets/api_builtin.md.vAmfTwue.lean.js @@ -1,6 +1,6 @@ -import{_ as i,c as l,j as n,a,G as t,a2 as o,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const _=JSON.parse('{"title":"Builtin Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/builtin.md","filePath":"api/builtin.md","lastUpdated":null}'),c={name:"api/builtin.md"},d={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"};function m(f,e,b,h,y,g){const s=p("Badge");return r(),l("div",null,[e[6]||(e[6]=n("h1",{id:"Builtin-Dialect",tabindex:"-1"},[a("Builtin Dialect "),n("a",{class:"header-anchor",href:"#Builtin-Dialect","aria-label":'Permalink to "Builtin Dialect {#Builtin-Dialect}"'},"​")],-1)),e[7]||(e[7]=n("p",null,[a("Refer to the "),n("a",{href:"https://mlir.llvm.org/docs/Dialects/Builtin/",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),n("details",d,[n("summary",null,[e[0]||(e[0]=n("a",{id:"Reactant.MLIR.Dialects.builtin.module_-Tuple{}",href:"#Reactant.MLIR.Dialects.builtin.module_-Tuple{}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.builtin.module_")],-1)),e[1]||(e[1]=a()),t(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=o(`

module_

A module represents a top-level container operation. It contains a single graph region containing a single block which can contain any operations and does not have a terminator. Operations within this region cannot implicitly capture values defined outside the module, i.e. Modules are IsolatedFromAbove. Modules have an optional symbol name which can be used to refer to them in operations.

Example

mlir
module {
+import{_ as i,c as l,j as n,a,G as t,a2 as o,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const _=JSON.parse('{"title":"Builtin Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/builtin.md","filePath":"api/builtin.md","lastUpdated":null}'),c={name:"api/builtin.md"},d={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"};function m(b,e,f,h,y,g){const s=p("Badge");return r(),l("div",null,[e[6]||(e[6]=n("h1",{id:"Builtin-Dialect",tabindex:"-1"},[a("Builtin Dialect "),n("a",{class:"header-anchor",href:"#Builtin-Dialect","aria-label":'Permalink to "Builtin Dialect {#Builtin-Dialect}"'},"​")],-1)),e[7]||(e[7]=n("p",null,[a("Refer to the "),n("a",{href:"https://mlir.llvm.org/docs/Dialects/Builtin/",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),n("details",d,[n("summary",null,[e[0]||(e[0]=n("a",{id:"Reactant.MLIR.Dialects.builtin.module_-Tuple{}",href:"#Reactant.MLIR.Dialects.builtin.module_-Tuple{}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.builtin.module_")],-1)),e[1]||(e[1]=a()),t(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=o(`
module_
A module represents a top-level container operation. It contains a single graph region containing a single block which can contain any operations and does not have a terminator. Operations within this region cannot implicitly capture values defined outside the module, i.e. Modules are IsolatedFromAbove. Modules have an optional symbol name which can be used to refer to them in operations.
Example
mlir
module {
   func.func @foo()
-}
source
`,5))]),n("details",u,[n("summary",null,[e[3]||(e[3]=n("a",{id:"Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast")],-1)),e[4]||(e[4]=a()),t(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=o(`
unrealized_conversion_cast
An unrealized_conversion_cast operation represents an unrealized conversion from one set of types to another, that is used to enable the inter-mixing of different type systems. This operation should not be attributed any special representational or execution semantics, and is generally only intended to be used to satisfy the temporary intermixing of type systems during the conversion of one type system to another.
This operation may produce results of arity 1-N, and accept as input operands of arity 0-N.
Example
mlir
// An unrealized 0-1 conversion. These types of conversions are useful in
+}
source
`,5))]),n("details",u,[n("summary",null,[e[3]||(e[3]=n("a",{id:"Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.builtin.unrealized_conversion_cast")],-1)),e[4]||(e[4]=a()),t(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=o(`
unrealized_conversion_cast
An unrealized_conversion_cast operation represents an unrealized conversion from one set of types to another, that is used to enable the inter-mixing of different type systems. This operation should not be attributed any special representational or execution semantics, and is generally only intended to be used to satisfy the temporary intermixing of type systems during the conversion of one type system to another.
This operation may produce results of arity 1-N, and accept as input operands of arity 0-N.
Example
mlir
// An unrealized 0-1 conversion. These types of conversions are useful in
 // cases where a type is removed from the type system, but not all uses have
 // been converted. For example, imagine we have a tuple type that is
 // expanded to its element types. If only some uses of an empty tuple type
@@ -15,4 +15,4 @@ import{_ as i,c as l,j as n,a,G as t,a2 as o,B as p,o as r}from"./chunks/framewo
 %results2:2 = unrealized_conversion_cast %tuple_operand : !foo.tuple_type<!foo.type, !foo.type> to !foo.type, !foo.type
 
 // An unrealized N-1 conversion.
-%result3 = unrealized_conversion_cast %operand, %operand : !foo.type, !foo.type to !bar.tuple_type<!foo.type, !foo.type>
source
`,6))])])}const R=i(c,[["render",m]]);export{_ as __pageData,R as default};
+%result3 = unrealized_conversion_cast %operand, %operand : !foo.type, !foo.type to !bar.tuple_type<!foo.type, !foo.type>

source

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ragged_dot

This operation takes three tensor args–-lhs, rhs, and group_sizes–-and a "ragged_dot_dimension_numbers" attribute. Like dot_general, the lhs and rhs are allowed arbitrary batch and contracting dimensions. Additionally, the lhs is required to have one ragged dimension, and the rhs may have at most one group dimension. The op has three modes, depending on the kind of the lhs ragged dimension.

In mode 1, the shape-signature is [b,m,k], [g,b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs non-contracting dimension (m). The dimensions b and k represent batch and contracting dimensions respectively. The rhs is required to have a group dimension (g).

In mode 2, the shape-signature is [b,m,k], [b,k,n], [g] -> [g,b,m,n]. Here the ragged dimension is an lhs/rhs contracting dimension (k).

In mode 3, the shape-signature is [b,m,k], [b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs/rhs batch dimension (b).

source

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top_k

If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j].

For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus,

values.shape = indices.shape = input.shape[:-1] + [k]

If two elements are equal, the lower-index element appears first.

source

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ragged_dot

This operation takes three tensor args–-lhs, rhs, and group_sizes–-and a "ragged_dot_dimension_numbers" attribute. Like dot_general, the lhs and rhs are allowed arbitrary batch and contracting dimensions. Additionally, the lhs is required to have one ragged dimension, and the rhs may have at most one group dimension. The op has three modes, depending on the kind of the lhs ragged dimension.

In mode 1, the shape-signature is [b,m,k], [g,b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs non-contracting dimension (m). The dimensions b and k represent batch and contracting dimensions respectively. The rhs is required to have a group dimension (g).

In mode 2, the shape-signature is [b,m,k], [b,k,n], [g] -> [g,b,m,n]. Here the ragged dimension is an lhs/rhs contracting dimension (k).

In mode 3, the shape-signature is [b,m,k], [b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs/rhs batch dimension (b).

source

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top_k

If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j].

For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus,

values.shape = indices.shape = input.shape[:-1] + [k]

If two elements are equal, the lower-index element appears first.

source

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ragged_dot

This operation takes three tensor args–-lhs, rhs, and group_sizes–-and a "ragged_dot_dimension_numbers" attribute. Like dot_general, the lhs and rhs are allowed arbitrary batch and contracting dimensions. Additionally, the lhs is required to have one ragged dimension, and the rhs may have at most one group dimension. The op has three modes, depending on the kind of the lhs ragged dimension.

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In mode 2, the shape-signature is [b,m,k], [b,k,n], [g] -> [g,b,m,n]. Here the ragged dimension is an lhs/rhs contracting dimension (k).

In mode 3, the shape-signature is [b,m,k], [b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs/rhs batch dimension (b).

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top_k

If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j].

For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus,

values.shape = indices.shape = input.shape[:-1] + [k]

If two elements are equal, the lower-index element appears first.

source

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ragged_dot

This operation takes three tensor args–-lhs, rhs, and group_sizes–-and a "ragged_dot_dimension_numbers" attribute. Like dot_general, the lhs and rhs are allowed arbitrary batch and contracting dimensions. Additionally, the lhs is required to have one ragged dimension, and the rhs may have at most one group dimension. The op has three modes, depending on the kind of the lhs ragged dimension.

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In mode 2, the shape-signature is [b,m,k], [b,k,n], [g] -> [g,b,m,n]. Here the ragged dimension is an lhs/rhs contracting dimension (k).

In mode 3, the shape-signature is [b,m,k], [b,k,n], [g] -> [b,m,n]. Here the ragged dimension is an lhs/rhs batch dimension (b).

source

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top_k

If the input is a vector (rank-1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j].

For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus,

values.shape = indices.shape = input.shape[:-1] + [k]

If two elements are equal, the lower-index element appears first.

source

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For scalar operands, ranked tensor is created.")],-1)),e[9]||(e[9]=t("p",null,[a("NOTE: Only works for scalar and "),t("em",null,"ranked"),a(" tensor operands for now.")],-1)),e[10]||(e[10]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Enzyme.jl#L95-L102",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const j=s(i,[["render",p]]);export{D as __pageData,j as default}; diff --git a/previews/PR441/assets/api_enzyme.md.frKwl5MH.lean.js b/previews/PR441/assets/api_enzyme.md.DEhmqOoH.lean.js similarity index 81% rename from previews/PR441/assets/api_enzyme.md.frKwl5MH.lean.js rename to previews/PR441/assets/api_enzyme.md.DEhmqOoH.lean.js index 3810278df..5fa168e02 100644 --- a/previews/PR441/assets/api_enzyme.md.frKwl5MH.lean.js +++ b/previews/PR441/assets/api_enzyme.md.DEhmqOoH.lean.js @@ -1 +1 @@ -import{_ as s,c as o,j as t,a,G as l,B as r,o as d}from"./chunks/framework.B4pBzPBy.js";const D=JSON.parse('{"title":"Enzyme Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/enzyme.md","filePath":"api/enzyme.md","lastUpdated":null}'),i={name:"api/enzyme.md"},m={class:"jldocstring custom-block"},p={class:"jldocstring custom-block"};function c(u,e,b,y,f,R){const n=r("Badge");return d(),o("div",null,[e[11]||(e[11]=t("h1",{id:"Enzyme-Dialect",tabindex:"-1"},[a("Enzyme Dialect "),t("a",{class:"header-anchor",href:"#Enzyme-Dialect","aria-label":'Permalink to "Enzyme Dialect {#Enzyme-Dialect}"'},"​")],-1)),t("details",m,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.MLIR.Dialects.enzyme.addTo-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.enzyme.addTo-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.enzyme.addTo")],-1)),e[1]||(e[1]=a()),l(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=t("p",null,[t("code",null,"addTo")],-1)),e[3]||(e[3]=t("p",null,"TODO",-1)),e[4]||(e[4]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/Enzyme.jl#L16-L20",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",p,[t("summary",null,[e[5]||(e[5]=t("a",{id:"Reactant.MLIR.Dialects.enzyme.broadcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.enzyme.broadcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.enzyme.broadcast")],-1)),e[6]||(e[6]=a()),l(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[7]||(e[7]=t("p",null,[t("code",null,"broadcast")],-1)),e[8]||(e[8]=t("p",null,[a("Broadcast the operand by adding extra dimensions with sizes provided by the "),t("code",null,"shape"),a(" attribute to the front. 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For scalar operands, ranked tensor is created.")],-1)),e[9]||(e[9]=t("p",null,[a("NOTE: Only works for scalar and "),t("em",null,"ranked"),a(" tensor operands for now.")],-1)),e[10]||(e[10]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/Enzyme.jl#L95-L102",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const j=s(i,[["render",p]]);export{D as __pageData,j as default}; diff --git a/previews/PR441/assets/api_func.md.Wv6kQziA.js b/previews/PR441/assets/api_func.md.CdLx48lL.js similarity index 50% rename from previews/PR441/assets/api_func.md.Wv6kQziA.js rename to previews/PR441/assets/api_func.md.CdLx48lL.js index 8a6939896..0bddab511 100644 --- a/previews/PR441/assets/api_func.md.Wv6kQziA.js +++ b/previews/PR441/assets/api_func.md.CdLx48lL.js @@ -1,9 +1,9 @@ -import{_ as i,c,j as e,a as t,G as s,a2 as l,B as o,o as p}from"./chunks/framework.B4pBzPBy.js";const L=JSON.parse('{"title":"Func Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/func.md","filePath":"api/func.md","lastUpdated":null}'),r={name:"api/func.md"},u={class:"jldocstring custom-block"},d={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"};function g(h,n,R,y,v,x){const a=o("Badge");return p(),c("div",null,[n[15]||(n[15]=e("h1",{id:"Func-Dialect",tabindex:"-1"},[t("Func Dialect "),e("a",{class:"header-anchor",href:"#Func-Dialect","aria-label":'Permalink to "Func Dialect {#Func-Dialect}"'},"​")],-1)),n[16]||(n[16]=e("p",null,[t("Refer to the "),e("a",{href:"https://mlir.llvm.org/docs/Dialects/Func/",target:"_blank",rel:"noreferrer"},"official documentation"),t(" for more details.")],-1)),e("details",u,[e("summary",null,[n[0]||(n[0]=e("a",{id:"Reactant.MLIR.Dialects.func.call-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.call-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.call")],-1)),n[1]||(n[1]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[2]||(n[2]=l('

call

The func.call operation represents a direct call to a function that is within the same symbol scope as the call. The operands and result types of the call must match the specified function type. The callee is encoded as a symbol reference attribute named "callee".

Example

mlir
%2 = func.call @my_add(%0, %1) : (f32, f32) -> f32

source

',5))]),e("details",d,[e("summary",null,[n[3]||(n[3]=e("a",{id:"Reactant.MLIR.Dialects.func.call_indirect-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.call_indirect-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.call_indirect")],-1)),n[4]||(n[4]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[5]||(n[5]=l(`

call_indirect

The func.call_indirect operation represents an indirect call to a value of function type. The operands and result types of the call must match the specified function type.

Function values can be created with the func.constant operation.

Example

mlir
%func = func.constant @my_func : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>
-%result = func.call_indirect %func(%0, %1) : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>

source

`,6))]),e("details",f,[e("summary",null,[n[6]||(n[6]=e("a",{id:"Reactant.MLIR.Dialects.func.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.func.constant-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.constant")],-1)),n[7]||(n[7]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[8]||(n[8]=l(`

constant

The func.constant operation produces an SSA value from a symbol reference to a func.func operation

Example

mlir
// Reference to function @myfn.
+import{_ as c,c as i,j as n,a as t,G as s,a2 as l,B as o,o as p}from"./chunks/framework.B4pBzPBy.js";const L=JSON.parse('{"title":"Func Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/func.md","filePath":"api/func.md","lastUpdated":null}'),r={name:"api/func.md"},u={class:"jldocstring custom-block"},d={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"};function g(h,e,R,y,v,x){const a=o("Badge");return p(),i("div",null,[e[15]||(e[15]=n("h1",{id:"Func-Dialect",tabindex:"-1"},[t("Func Dialect "),n("a",{class:"header-anchor",href:"#Func-Dialect","aria-label":'Permalink to "Func Dialect {#Func-Dialect}"'},"​")],-1)),e[16]||(e[16]=n("p",null,[t("Refer to the "),n("a",{href:"https://mlir.llvm.org/docs/Dialects/Func/",target:"_blank",rel:"noreferrer"},"official documentation"),t(" for more details.")],-1)),n("details",u,[n("summary",null,[e[0]||(e[0]=n("a",{id:"Reactant.MLIR.Dialects.func.call-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.call-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.call")],-1)),e[1]||(e[1]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=l('
call
The func.call operation represents a direct call to a function that is within the same symbol scope as the call. The operands and result types of the call must match the specified function type. The callee is encoded as a symbol reference attribute named "callee".
Example
mlir
%2 = func.call @my_add(%0, %1) : (f32, f32) -> f32
source
',5))]),n("details",d,[n("summary",null,[e[3]||(e[3]=n("a",{id:"Reactant.MLIR.Dialects.func.call_indirect-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.call_indirect-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.call_indirect")],-1)),e[4]||(e[4]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=l(`
call_indirect
The func.call_indirect operation represents an indirect call to a value of function type. The operands and result types of the call must match the specified function type.
Function values can be created with the func.constant operation.
Example
mlir
%func = func.constant @my_func : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>
+%result = func.call_indirect %func(%0, %1) : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>
source
`,6))]),n("details",f,[n("summary",null,[e[6]||(e[6]=n("a",{id:"Reactant.MLIR.Dialects.func.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.func.constant-Tuple{}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.constant")],-1)),e[7]||(e[7]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=l(`
constant
The func.constant operation produces an SSA value from a symbol reference to a func.func operation
Example
mlir
// Reference to function @myfn.
 %2 = func.constant @myfn : (tensor<16xf32>, f32) -> tensor<16xf32>
 
 // Equivalent generic forms
-%2 = "func.constant"() { value = @myfn } : () -> ((tensor<16xf32>, f32) -> tensor<16xf32>)
MLIR does not allow direct references to functions in SSA operands because the compiler is multithreaded, and disallowing SSA values to directly reference a function simplifies this (rationale).
source
`,6))]),e("details",m,[e("summary",null,[n[9]||(n[9]=e("a",{id:"Reactant.MLIR.Dialects.func.func_-Tuple{}",href:"#Reactant.MLIR.Dialects.func.func_-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.func_")],-1)),n[10]||(n[10]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[11]||(n[11]=l(`
func_
Operations within the function cannot implicitly capture values defined outside of the function, i.e. Functions are IsolatedFromAbove. All external references must use function arguments or attributes that establish a symbolic connection (e.g. symbols referenced by name via a string attribute like SymbolRefAttr). An external function declaration (used when referring to a function declared in some other module) has no body. While the MLIR textual form provides a nice inline syntax for function arguments, they are internally represented as “block arguments” to the first block in the region.
Only dialect attribute names may be specified in the attribute dictionaries for function arguments, results, or the function itself.
Example
mlir
// External function definitions.
+%2 = "func.constant"() { value = @myfn } : () -> ((tensor<16xf32>, f32) -> tensor<16xf32>)
MLIR does not allow direct references to functions in SSA operands because the compiler is multithreaded, and disallowing SSA values to directly reference a function simplifies this (rationale).
source
`,6))]),n("details",m,[n("summary",null,[e[9]||(e[9]=n("a",{id:"Reactant.MLIR.Dialects.func.func_-Tuple{}",href:"#Reactant.MLIR.Dialects.func.func_-Tuple{}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.func_")],-1)),e[10]||(e[10]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=l(`
func_
Operations within the function cannot implicitly capture values defined outside of the function, i.e. Functions are IsolatedFromAbove. All external references must use function arguments or attributes that establish a symbolic connection (e.g. symbols referenced by name via a string attribute like SymbolRefAttr). An external function declaration (used when referring to a function declared in some other module) has no body. While the MLIR textual form provides a nice inline syntax for function arguments, they are internally represented as “block arguments” to the first block in the region.
Only dialect attribute names may be specified in the attribute dictionaries for function arguments, results, or the function itself.
Example
mlir
// External function definitions.
 func.func private @abort()
 func.func private @scribble(i32, i64, memref<? x 128 x f32, #layout_map0>) -> f64
 
@@ -20,7 +20,7 @@ import{_ as i,c,j as e,a as t,G as s,a2 as l,B as o,o as p}from"./chunks/framewo
 func.func private @example_fn_result() -> (f64 {dialectName.attrName = 0 : i64})
 
 // A function with an attribute
-func.func private @example_fn_attr() attributes {dialectName.attrName = false}
source
`,6))]),e("details",b,[e("summary",null,[n[12]||(n[12]=e("a",{id:"Reactant.MLIR.Dialects.func.return_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.return_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.return_")],-1)),n[13]||(n[13]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[14]||(n[14]=l(`
return_
The func.return operation represents a return operation within a function. The operation takes variable number of operands and produces no results. The operand number and types must match the signature of the function that contains the operation.
Example
mlir
func.func @foo() : (i32, f8) {
+func.func private @example_fn_attr() attributes {dialectName.attrName = false}
source
`,6))]),n("details",b,[n("summary",null,[e[12]||(e[12]=n("a",{id:"Reactant.MLIR.Dialects.func.return_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.return_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.return_")],-1)),e[13]||(e[13]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=l(`
return_
The func.return operation represents a return operation within a function. The operation takes variable number of operands and produces no results. The operand number and types must match the signature of the function that contains the operation.
Example
mlir
func.func @foo() : (i32, f8) {
   ...
   return %0, %1 : i32, f8
-}
source
`,5))])])}const M=i(r,[["render",g]]);export{L as __pageData,M as default};
+}

source

`,5))])])}const M=c(r,[["render",g]]);export{L as __pageData,M as default}; diff --git a/previews/PR441/assets/api_func.md.Wv6kQziA.lean.js b/previews/PR441/assets/api_func.md.CdLx48lL.lean.js similarity index 50% rename from previews/PR441/assets/api_func.md.Wv6kQziA.lean.js rename to previews/PR441/assets/api_func.md.CdLx48lL.lean.js index 8a6939896..0bddab511 100644 --- a/previews/PR441/assets/api_func.md.Wv6kQziA.lean.js +++ b/previews/PR441/assets/api_func.md.CdLx48lL.lean.js @@ -1,9 +1,9 @@ -import{_ as i,c,j as e,a as t,G as s,a2 as l,B as o,o as p}from"./chunks/framework.B4pBzPBy.js";const L=JSON.parse('{"title":"Func Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/func.md","filePath":"api/func.md","lastUpdated":null}'),r={name:"api/func.md"},u={class:"jldocstring custom-block"},d={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"};function g(h,n,R,y,v,x){const a=o("Badge");return p(),c("div",null,[n[15]||(n[15]=e("h1",{id:"Func-Dialect",tabindex:"-1"},[t("Func Dialect "),e("a",{class:"header-anchor",href:"#Func-Dialect","aria-label":'Permalink to "Func Dialect {#Func-Dialect}"'},"​")],-1)),n[16]||(n[16]=e("p",null,[t("Refer to the "),e("a",{href:"https://mlir.llvm.org/docs/Dialects/Func/",target:"_blank",rel:"noreferrer"},"official documentation"),t(" for more details.")],-1)),e("details",u,[e("summary",null,[n[0]||(n[0]=e("a",{id:"Reactant.MLIR.Dialects.func.call-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.call-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.call")],-1)),n[1]||(n[1]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[2]||(n[2]=l('

call

The func.call operation represents a direct call to a function that is within the same symbol scope as the call. The operands and result types of the call must match the specified function type. The callee is encoded as a symbol reference attribute named "callee".

Example

mlir
%2 = func.call @my_add(%0, %1) : (f32, f32) -> f32

source

',5))]),e("details",d,[e("summary",null,[n[3]||(n[3]=e("a",{id:"Reactant.MLIR.Dialects.func.call_indirect-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.call_indirect-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.call_indirect")],-1)),n[4]||(n[4]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[5]||(n[5]=l(`

call_indirect

The func.call_indirect operation represents an indirect call to a value of function type. The operands and result types of the call must match the specified function type.

Function values can be created with the func.constant operation.

Example

mlir
%func = func.constant @my_func : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>
-%result = func.call_indirect %func(%0, %1) : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>

source

`,6))]),e("details",f,[e("summary",null,[n[6]||(n[6]=e("a",{id:"Reactant.MLIR.Dialects.func.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.func.constant-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.constant")],-1)),n[7]||(n[7]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[8]||(n[8]=l(`

constant

The func.constant operation produces an SSA value from a symbol reference to a func.func operation

Example

mlir
// Reference to function @myfn.
+import{_ as c,c as i,j as n,a as t,G as s,a2 as l,B as o,o as p}from"./chunks/framework.B4pBzPBy.js";const L=JSON.parse('{"title":"Func Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/func.md","filePath":"api/func.md","lastUpdated":null}'),r={name:"api/func.md"},u={class:"jldocstring custom-block"},d={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"};function g(h,e,R,y,v,x){const a=o("Badge");return p(),i("div",null,[e[15]||(e[15]=n("h1",{id:"Func-Dialect",tabindex:"-1"},[t("Func Dialect "),n("a",{class:"header-anchor",href:"#Func-Dialect","aria-label":'Permalink to "Func Dialect {#Func-Dialect}"'},"​")],-1)),e[16]||(e[16]=n("p",null,[t("Refer to the "),n("a",{href:"https://mlir.llvm.org/docs/Dialects/Func/",target:"_blank",rel:"noreferrer"},"official documentation"),t(" for more details.")],-1)),n("details",u,[n("summary",null,[e[0]||(e[0]=n("a",{id:"Reactant.MLIR.Dialects.func.call-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.call-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.call")],-1)),e[1]||(e[1]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=l('
call
The func.call operation represents a direct call to a function that is within the same symbol scope as the call. The operands and result types of the call must match the specified function type. The callee is encoded as a symbol reference attribute named "callee".
Example
mlir
%2 = func.call @my_add(%0, %1) : (f32, f32) -> f32
source
',5))]),n("details",d,[n("summary",null,[e[3]||(e[3]=n("a",{id:"Reactant.MLIR.Dialects.func.call_indirect-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.call_indirect-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.call_indirect")],-1)),e[4]||(e[4]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=l(`
call_indirect
The func.call_indirect operation represents an indirect call to a value of function type. The operands and result types of the call must match the specified function type.
Function values can be created with the func.constant operation.
Example
mlir
%func = func.constant @my_func : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>
+%result = func.call_indirect %func(%0, %1) : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32>
source
`,6))]),n("details",f,[n("summary",null,[e[6]||(e[6]=n("a",{id:"Reactant.MLIR.Dialects.func.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.func.constant-Tuple{}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.constant")],-1)),e[7]||(e[7]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=l(`
constant
The func.constant operation produces an SSA value from a symbol reference to a func.func operation
Example
mlir
// Reference to function @myfn.
 %2 = func.constant @myfn : (tensor<16xf32>, f32) -> tensor<16xf32>
 
 // Equivalent generic forms
-%2 = "func.constant"() { value = @myfn } : () -> ((tensor<16xf32>, f32) -> tensor<16xf32>)
MLIR does not allow direct references to functions in SSA operands because the compiler is multithreaded, and disallowing SSA values to directly reference a function simplifies this (rationale).
source
`,6))]),e("details",m,[e("summary",null,[n[9]||(n[9]=e("a",{id:"Reactant.MLIR.Dialects.func.func_-Tuple{}",href:"#Reactant.MLIR.Dialects.func.func_-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.func_")],-1)),n[10]||(n[10]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[11]||(n[11]=l(`
func_
Operations within the function cannot implicitly capture values defined outside of the function, i.e. Functions are IsolatedFromAbove. All external references must use function arguments or attributes that establish a symbolic connection (e.g. symbols referenced by name via a string attribute like SymbolRefAttr). An external function declaration (used when referring to a function declared in some other module) has no body. While the MLIR textual form provides a nice inline syntax for function arguments, they are internally represented as “block arguments” to the first block in the region.
Only dialect attribute names may be specified in the attribute dictionaries for function arguments, results, or the function itself.
Example
mlir
// External function definitions.
+%2 = "func.constant"() { value = @myfn } : () -> ((tensor<16xf32>, f32) -> tensor<16xf32>)
MLIR does not allow direct references to functions in SSA operands because the compiler is multithreaded, and disallowing SSA values to directly reference a function simplifies this (rationale).
source
`,6))]),n("details",m,[n("summary",null,[e[9]||(e[9]=n("a",{id:"Reactant.MLIR.Dialects.func.func_-Tuple{}",href:"#Reactant.MLIR.Dialects.func.func_-Tuple{}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.func_")],-1)),e[10]||(e[10]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=l(`
func_
Operations within the function cannot implicitly capture values defined outside of the function, i.e. Functions are IsolatedFromAbove. All external references must use function arguments or attributes that establish a symbolic connection (e.g. symbols referenced by name via a string attribute like SymbolRefAttr). An external function declaration (used when referring to a function declared in some other module) has no body. While the MLIR textual form provides a nice inline syntax for function arguments, they are internally represented as “block arguments” to the first block in the region.
Only dialect attribute names may be specified in the attribute dictionaries for function arguments, results, or the function itself.
Example
mlir
// External function definitions.
 func.func private @abort()
 func.func private @scribble(i32, i64, memref<? x 128 x f32, #layout_map0>) -> f64
 
@@ -20,7 +20,7 @@ import{_ as i,c,j as e,a as t,G as s,a2 as l,B as o,o as p}from"./chunks/framewo
 func.func private @example_fn_result() -> (f64 {dialectName.attrName = 0 : i64})
 
 // A function with an attribute
-func.func private @example_fn_attr() attributes {dialectName.attrName = false}
source
`,6))]),e("details",b,[e("summary",null,[n[12]||(n[12]=e("a",{id:"Reactant.MLIR.Dialects.func.return_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.return_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.return_")],-1)),n[13]||(n[13]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),n[14]||(n[14]=l(`
return_
The func.return operation represents a return operation within a function. The operation takes variable number of operands and produces no results. The operand number and types must match the signature of the function that contains the operation.
Example
mlir
func.func @foo() : (i32, f8) {
+func.func private @example_fn_attr() attributes {dialectName.attrName = false}
source
`,6))]),n("details",b,[n("summary",null,[e[12]||(e[12]=n("a",{id:"Reactant.MLIR.Dialects.func.return_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.func.return_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[n("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.func.return_")],-1)),e[13]||(e[13]=t()),s(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=l(`
return_
The func.return operation represents a return operation within a function. The operation takes variable number of operands and produces no results. The operand number and types must match the signature of the function that contains the operation.
Example
mlir
func.func @foo() : (i32, f8) {
   ...
   return %0, %1 : i32, f8
-}
source
`,5))])])}const M=i(r,[["render",g]]);export{L as __pageData,M as default};
+}

source

`,5))])])}const M=c(r,[["render",g]]);export{L as __pageData,M as default}; diff --git a/previews/PR441/assets/api_internal.md.CsBtpNqO.js b/previews/PR441/assets/api_internal.md.tz9hnS-V.js similarity index 92% rename from previews/PR441/assets/api_internal.md.CsBtpNqO.js rename to previews/PR441/assets/api_internal.md.tz9hnS-V.js index 7cfcc65a4..1630113ec 100644 --- a/previews/PR441/assets/api_internal.md.CsBtpNqO.js +++ b/previews/PR441/assets/api_internal.md.tz9hnS-V.js @@ -1 +1 @@ -import{_ as r,c as l,j as t,a,G as s,a2 as i,B as o,o as c}from"./chunks/framework.B4pBzPBy.js";const j=JSON.parse('{"title":"Internal API","description":"","frontmatter":{},"headers":[],"relativePath":"api/internal.md","filePath":"api/internal.md","lastUpdated":null}'),d={name:"api/internal.md"},p={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"};function h(b,e,g,m,f,k){const n=o("Badge");return c(),l("div",null,[e[6]||(e[6]=t("h1",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),t("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e[7]||(e[7]=t("p",null,"These functions are not part of the public API and are subject to change at any time.",-1)),t("details",p,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.REDUB_ARGUMENTS_NAME",href:"#Reactant.REDUB_ARGUMENTS_NAME"},[t("span",{class:"jlbinding"},"Reactant.REDUB_ARGUMENTS_NAME")],-1)),e[1]||(e[1]=a()),s(n,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),e[2]||(e[2]=i('
julia
Reactant.REDUB_ARGUMENTS_NAME

The variable name bound to call_with_reactant's tuple of arguments in its @generated method definition.

This binding can be used to manually reference/destructure call_with_reactants arguments

This is required because user arguments could have a name which clashes with whatever name we choose for our argument. Thus we gensym to create it.

This originates from https://github.com/JuliaLabs/Cassette.jl/blob/c29b237c1ec0deda3a1037ec519eebe216952bfe/src/overdub.jl#L154

source

',6))]),t("details",u,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.within_reactant_interpreter",href:"#Reactant.within_reactant_interpreter"},[t("span",{class:"jlbinding"},"Reactant.within_reactant_interpreter")],-1)),e[4]||(e[4]=a()),s(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[5]||(e[5]=i('
julia
within_reactant_interpreter()

Returns true if we are currently inside the ReactantInterpreter.

source

',3))])])}const _=r(d,[["render",h]]);export{j as __pageData,_ as default}; +import{_ as r,c as l,j as t,a,G as s,a2 as i,B as o,o as c}from"./chunks/framework.B4pBzPBy.js";const j=JSON.parse('{"title":"Internal API","description":"","frontmatter":{},"headers":[],"relativePath":"api/internal.md","filePath":"api/internal.md","lastUpdated":null}'),d={name:"api/internal.md"},p={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"};function h(b,e,g,m,f,k){const n=o("Badge");return c(),l("div",null,[e[6]||(e[6]=t("h1",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),t("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e[7]||(e[7]=t("p",null,"These functions are not part of the public API and are subject to change at any time.",-1)),t("details",p,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.REDUB_ARGUMENTS_NAME",href:"#Reactant.REDUB_ARGUMENTS_NAME"},[t("span",{class:"jlbinding"},"Reactant.REDUB_ARGUMENTS_NAME")],-1)),e[1]||(e[1]=a()),s(n,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),e[2]||(e[2]=i('
julia
Reactant.REDUB_ARGUMENTS_NAME

The variable name bound to call_with_reactant's tuple of arguments in its @generated method definition.

This binding can be used to manually reference/destructure call_with_reactants arguments

This is required because user arguments could have a name which clashes with whatever name we choose for our argument. Thus we gensym to create it.

This originates from https://github.com/JuliaLabs/Cassette.jl/blob/c29b237c1ec0deda3a1037ec519eebe216952bfe/src/overdub.jl#L154

source

',6))]),t("details",u,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.within_reactant_interpreter",href:"#Reactant.within_reactant_interpreter"},[t("span",{class:"jlbinding"},"Reactant.within_reactant_interpreter")],-1)),e[4]||(e[4]=a()),s(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[5]||(e[5]=i('
julia
within_reactant_interpreter()

Returns true if we are currently inside the ReactantInterpreter.

source

',3))])])}const _=r(d,[["render",h]]);export{j as __pageData,_ as default}; diff --git a/previews/PR441/assets/api_internal.md.CsBtpNqO.lean.js b/previews/PR441/assets/api_internal.md.tz9hnS-V.lean.js similarity index 92% rename from previews/PR441/assets/api_internal.md.CsBtpNqO.lean.js rename to previews/PR441/assets/api_internal.md.tz9hnS-V.lean.js index 7cfcc65a4..1630113ec 100644 --- a/previews/PR441/assets/api_internal.md.CsBtpNqO.lean.js +++ b/previews/PR441/assets/api_internal.md.tz9hnS-V.lean.js @@ -1 +1 @@ -import{_ as r,c as l,j as t,a,G as s,a2 as i,B as o,o as c}from"./chunks/framework.B4pBzPBy.js";const j=JSON.parse('{"title":"Internal API","description":"","frontmatter":{},"headers":[],"relativePath":"api/internal.md","filePath":"api/internal.md","lastUpdated":null}'),d={name:"api/internal.md"},p={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"};function h(b,e,g,m,f,k){const n=o("Badge");return c(),l("div",null,[e[6]||(e[6]=t("h1",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),t("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e[7]||(e[7]=t("p",null,"These functions are not part of the public API and are subject to change at any time.",-1)),t("details",p,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.REDUB_ARGUMENTS_NAME",href:"#Reactant.REDUB_ARGUMENTS_NAME"},[t("span",{class:"jlbinding"},"Reactant.REDUB_ARGUMENTS_NAME")],-1)),e[1]||(e[1]=a()),s(n,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),e[2]||(e[2]=i('
julia
Reactant.REDUB_ARGUMENTS_NAME

The variable name bound to call_with_reactant's tuple of arguments in its @generated method definition.

This binding can be used to manually reference/destructure call_with_reactants arguments

This is required because user arguments could have a name which clashes with whatever name we choose for our argument. Thus we gensym to create it.

This originates from https://github.com/JuliaLabs/Cassette.jl/blob/c29b237c1ec0deda3a1037ec519eebe216952bfe/src/overdub.jl#L154

source

',6))]),t("details",u,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.within_reactant_interpreter",href:"#Reactant.within_reactant_interpreter"},[t("span",{class:"jlbinding"},"Reactant.within_reactant_interpreter")],-1)),e[4]||(e[4]=a()),s(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[5]||(e[5]=i('
julia
within_reactant_interpreter()

Returns true if we are currently inside the ReactantInterpreter.

source

',3))])])}const _=r(d,[["render",h]]);export{j as __pageData,_ as default}; +import{_ as r,c as l,j as t,a,G as s,a2 as i,B as o,o as c}from"./chunks/framework.B4pBzPBy.js";const j=JSON.parse('{"title":"Internal API","description":"","frontmatter":{},"headers":[],"relativePath":"api/internal.md","filePath":"api/internal.md","lastUpdated":null}'),d={name:"api/internal.md"},p={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"};function h(b,e,g,m,f,k){const n=o("Badge");return c(),l("div",null,[e[6]||(e[6]=t("h1",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),t("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e[7]||(e[7]=t("p",null,"These functions are not part of the public API and are subject to change at any time.",-1)),t("details",p,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.REDUB_ARGUMENTS_NAME",href:"#Reactant.REDUB_ARGUMENTS_NAME"},[t("span",{class:"jlbinding"},"Reactant.REDUB_ARGUMENTS_NAME")],-1)),e[1]||(e[1]=a()),s(n,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),e[2]||(e[2]=i('
julia
Reactant.REDUB_ARGUMENTS_NAME

The variable name bound to call_with_reactant's tuple of arguments in its @generated method definition.

This binding can be used to manually reference/destructure call_with_reactants arguments

This is required because user arguments could have a name which clashes with whatever name we choose for our argument. Thus we gensym to create it.

This originates from https://github.com/JuliaLabs/Cassette.jl/blob/c29b237c1ec0deda3a1037ec519eebe216952bfe/src/overdub.jl#L154

source

',6))]),t("details",u,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.within_reactant_interpreter",href:"#Reactant.within_reactant_interpreter"},[t("span",{class:"jlbinding"},"Reactant.within_reactant_interpreter")],-1)),e[4]||(e[4]=a()),s(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[5]||(e[5]=i('
julia
within_reactant_interpreter()

Returns true if we are currently inside the ReactantInterpreter.

source

',3))])])}const _=r(d,[["render",h]]);export{j as __pageData,_ as default}; diff --git a/previews/PR441/assets/api_mlirc.md.C4o99U5u.js b/previews/PR441/assets/api_mlirc.md.C2YJ2Laa.js similarity index 62% rename from previews/PR441/assets/api_mlirc.md.C4o99U5u.js rename to previews/PR441/assets/api_mlirc.md.C2YJ2Laa.js index 70d41b903..ad8777f87 100644 --- a/previews/PR441/assets/api_mlirc.md.C4o99U5u.js +++ b/previews/PR441/assets/api_mlirc.md.C2YJ2Laa.js @@ -1,9 +1,9 @@ -import{_ as n,c as p,j as e,a as s,G as i,a2 as l,B as r,o}from"./chunks/framework.B4pBzPBy.js";const zk=JSON.parse('{"title":"Higher level API","description":"","frontmatter":{},"headers":[],"relativePath":"api/mlirc.md","filePath":"api/mlirc.md","lastUpdated":null}'),d={name:"api/mlirc.md"},h={class:"jldocstring custom-block"},c={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring 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Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.

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',3))]),e("details",k,[e("summary",null,[t[21]||(t[21]=e("a",{id:"Reactant.MLIR.IR.AffineMap-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.AffineMap-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.AffineMap")],-1)),t[22]||(t[22]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[23]||(t[23]=l('
julia
AffineMap(ndims, nsymbols; context=context())

Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.

source

',3))]),e("details",R,[e("summary",null,[t[24]||(t[24]=e("a",{id:"Reactant.MLIR.IR.AffineMap-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.AffineMap-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.AffineMap")],-1)),t[25]||(t[25]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[26]||(t[26]=l('
julia
AffineMap(attr)

Returns the affine map wrapped in the given affine map attribute.

source

',3))]),e("details",f,[e("summary",null,[t[27]||(t[27]=e("a",{id:"Reactant.MLIR.IR.AffineMap-Tuple{}",href:"#Reactant.MLIR.IR.AffineMap-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.AffineMap")],-1)),t[28]||(t[28]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[29]||(t[29]=l('
julia
AffineMap(; context=context())

Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.

source

',3))]),e("details",I,[e("summary",null,[t[30]||(t[30]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{AbstractString}",href:"#Reactant.MLIR.IR.Attribute-Tuple{AbstractString}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[31]||(t[31]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[32]||(t[32]=l('
julia
Attribute(str; context=context())

Creates a string attribute in the given context containing the given string.

source

',3))]),e("details",j,[e("summary",null,[t[33]||(t[33]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{Bool}",href:"#Reactant.MLIR.IR.Attribute-Tuple{Bool}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[34]||(t[34]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[35]||(t[35]=l('
julia
Attribute(value; context=context())

Creates a bool attribute in the given context with the given value.

source

',3))]),e("details",M,[e("summary",null,[t[36]||(t[36]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{Dict}",href:"#Reactant.MLIR.IR.Attribute-Tuple{Dict}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[37]||(t[37]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[38]||(t[38]=l('
julia
Attribute(elements; context=context())

Creates a dictionary attribute containing the given list of elements in the provided context.

source

',3))]),e("details",A,[e("summary",null,[t[39]||(t[39]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[40]||(t[40]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[41]||(t[41]=l('
julia
Attribute(affineMap)

Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.

source

',3))]),e("details",L,[e("summary",null,[t[42]||(t[42]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.Type, AbstractString}",href:"#Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.Type, AbstractString}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[43]||(t[43]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[44]||(t[44]=l('
julia
Attribute(type, str)

Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.

source

',3))]),e("details",E,[e("summary",null,[t[45]||(t[45]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[46]||(t[46]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[47]||(t[47]=l('
julia
Attribute(type)

Creates a type attribute wrapping the given type in the same context as the type.

source

',3))]),e("details",v,[e("summary",null,[t[48]||(t[48]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{T} where T<:AbstractFloat",href:"#Reactant.MLIR.IR.Attribute-Tuple{T} where T<:AbstractFloat"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[49]||(t[49]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[50]||(t[50]=l('
julia
Attribute(float; context=context(), location=Location(), check=false)

Creates a floating point attribute in the given context with the given double value and double-precision FP semantics. If check=true, emits appropriate diagnostics on illegal arguments.

source

',3))]),e("details",T,[e("summary",null,[t[51]||(t[51]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{T} where T<:Complex",href:"#Reactant.MLIR.IR.Attribute-Tuple{T} where T<:Complex"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[52]||(t[52]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[53]||(t[53]=l('
julia
Attribute(complex; context=context(), location=Location(), check=false)

Creates a complex attribute in the given context with the given complex value and double-precision FP semantics.

source

',3))]),e("details",C,[e("summary",null,[t[54]||(t[54]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{Vector{Reactant.MLIR.IR.Attribute}}",href:"#Reactant.MLIR.IR.Attribute-Tuple{Vector{Reactant.MLIR.IR.Attribute}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[55]||(t[55]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[56]||(t[56]=l('
julia
Attribute(elements; context=context())

Creates an array element containing the given list of elements in the given context.

source

',3))]),e("details",x,[e("summary",null,[t[57]||(t[57]=e("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{}",href:"#Reactant.MLIR.IR.Attribute-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[58]||(t[58]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[59]||(t[59]=l('
julia
Attribute()

Returns an empty attribute.

source

',3))]),e("details",F,[e("summary",null,[t[60]||(t[60]=e("a",{id:"Reactant.MLIR.IR.Attribute-Union{Tuple{T}, Tuple{T, Any}} where T<:Integer",href:"#Reactant.MLIR.IR.Attribute-Union{Tuple{T}, Tuple{T, Any}} where T<:Integer"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),t[61]||(t[61]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[62]||(t[62]=l('
julia
Attribute(int)

Creates an integer attribute of the given type with the given integer value.

source

',3))]),e("details",P,[e("summary",null,[t[63]||(t[63]=e("a",{id:"Reactant.MLIR.IR.Block-Tuple{Vector{Reactant.MLIR.IR.Type}, Vector{Reactant.MLIR.IR.Location}}",href:"#Reactant.MLIR.IR.Block-Tuple{Vector{Reactant.MLIR.IR.Type}, Vector{Reactant.MLIR.IR.Location}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Block")],-1)),t[64]||(t[64]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[65]||(t[65]=l('
julia
Block(args, locs)

Creates a new empty block with the given argument types and transfers ownership to the caller.

source

',3))]),e("details",D,[e("summary",null,[t[66]||(t[66]=e("a",{id:"Reactant.MLIR.IR.BlockIterator",href:"#Reactant.MLIR.IR.BlockIterator"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.BlockIterator")],-1)),t[67]||(t[67]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[68]||(t[68]=l('
julia
BlockIterator(region::Region)

Iterates over all blocks in the given region.

source

',3))]),e("details",O,[e("summary",null,[t[69]||(t[69]=e("a",{id:"Reactant.MLIR.IR.Context-Tuple{}",href:"#Reactant.MLIR.IR.Context-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Context")],-1)),t[70]||(t[70]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[71]||(t[71]=l('
julia
Context()

Creates an MLIR context and transfers its ownership to the caller.

source

',3))]),e("details",B,[e("summary",null,[t[72]||(t[72]=e("a",{id:"Reactant.MLIR.IR.ExecutionEngine",href:"#Reactant.MLIR.IR.ExecutionEngine"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ExecutionEngine")],-1)),t[73]||(t[73]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[74]||(t[74]=l('
julia
ExecutionEngine(op, optLevel, sharedlibs = [])

Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.

source

',3))]),e("details",G,[e("summary",null,[t[75]||(t[75]=e("a",{id:"Reactant.MLIR.IR.Identifier-Tuple{String}",href:"#Reactant.MLIR.IR.Identifier-Tuple{String}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Identifier")],-1)),t[76]||(t[76]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[77]||(t[77]=l('
julia
Identifier(context, str)

Gets an identifier with the given string value.

source

',3))]),e("details",z,[e("summary",null,[t[78]||(t[78]=e("a",{id:"Reactant.MLIR.IR.IntegerSet-NTuple{4, Any}",href:"#Reactant.MLIR.IR.IntegerSet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.IntegerSet")],-1)),t[79]||(t[79]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[80]||(t[80]=l('
julia
IntegerSet(ndims, nsymbols, constraints, eqflags; context=context())

Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqflags is 1) or inequalities (eqflags is 0). Both constraints and eqflags need to be arrays of the same length.

source

',3))]),e("details",w,[e("summary",null,[t[81]||(t[81]=e("a",{id:"Reactant.MLIR.IR.IntegerSet-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.IntegerSet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.IntegerSet")],-1)),t[82]||(t[82]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[83]||(t[83]=l('
julia
Integerset(ndims, nsymbols; context=context())

Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.

source

',3))]),e("details",S,[e("summary",null,[t[84]||(t[84]=e("a",{id:"Reactant.MLIR.IR.LogicalResult",href:"#Reactant.MLIR.IR.LogicalResult"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.LogicalResult")],-1)),t[85]||(t[85]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[86]||(t[86]=l('
julia
LogicalResult

A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of LogicalResult must only be inspected using the associated functions.

source

',3))]),e("details",N,[e("summary",null,[t[87]||(t[87]=e("a",{id:"Reactant.MLIR.IR.Module",href:"#Reactant.MLIR.IR.Module"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Module")],-1)),t[88]||(t[88]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[89]||(t[89]=l('
julia
Module(location=Location())

Creates a new, empty module and transfers ownership to the caller.

source

',3))]),e("details",V,[e("summary",null,[t[90]||(t[90]=e("a",{id:"Reactant.MLIR.IR.NamedAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.NamedAttribute-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.NamedAttribute")],-1)),t[91]||(t[91]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[92]||(t[92]=l('
julia
NamedAttribute(name, attr)

Associates an attribute with the name. Takes ownership of neither.

source

',3))]),e("details",q,[e("summary",null,[t[93]||(t[93]=e("a",{id:"Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpPassManager")],-1)),t[94]||(t[94]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[95]||(t[95]=l('
julia
OpPassManager(opPassManager, operationName)

Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.

source

',3))]),e("details",U,[e("summary",null,[t[96]||(t[96]=e("a",{id:"Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager, Any}",href:"#Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpPassManager")],-1)),t[97]||(t[97]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[98]||(t[98]=l('
julia
OpPassManager(passManager, operationName)

Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.

source

',3))]),e("details",Q,[e("summary",null,[t[99]||(t[99]=e("a",{id:"Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager}",href:"#Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpPassManager")],-1)),t[100]||(t[100]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[101]||(t[101]=l('
julia
OpPassManager(passManager)

Cast a top-level PassManager to a generic OpPassManager.

source

',3))]),e("details",W,[e("summary",null,[t[102]||(t[102]=e("a",{id:"Reactant.MLIR.IR.Operation-Tuple{Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.Operation-Tuple{Reactant.MLIR.IR.Module}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Operation")],-1)),t[103]||(t[103]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[104]||(t[104]=l('
julia
Operation(module)

Views the module as a generic operation.

source

',3))]),e("details",H,[e("summary",null,[t[105]||(t[105]=e("a",{id:"Reactant.MLIR.IR.OperationIterator",href:"#Reactant.MLIR.IR.OperationIterator"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.OperationIterator")],-1)),t[106]||(t[106]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[107]||(t[107]=l('
julia
OperationIterator(block::Block)

Iterates over all operations for the given block.

source

',3))]),e("details",Z,[e("summary",null,[t[108]||(t[108]=e("a",{id:"Reactant.MLIR.IR.PassManager-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.PassManager-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.PassManager")],-1)),t[109]||(t[109]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[110]||(t[110]=l('
julia
PassManager(anchorOp; context=context())

Create a new top-level PassManager anchored on anchorOp.

source

',3))]),e("details",J,[e("summary",null,[t[111]||(t[111]=e("a",{id:"Reactant.MLIR.IR.PassManager-Tuple{}",href:"#Reactant.MLIR.IR.PassManager-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.PassManager")],-1)),t[112]||(t[112]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[113]||(t[113]=l('
julia
PassManager(; context=context())

Create a new top-level PassManager.

source

',3))]),e("details",K,[e("summary",null,[t[114]||(t[114]=e("a",{id:"Reactant.MLIR.IR.Region-Tuple{}",href:"#Reactant.MLIR.IR.Region-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Region")],-1)),t[115]||(t[115]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[116]||(t[116]=l('
julia
Region()

Creates a new empty region and transfers ownership to the caller.

source

',3))]),e("details",$,[e("summary",null,[t[117]||(t[117]=e("a",{id:"Reactant.MLIR.IR.RegionIterator",href:"#Reactant.MLIR.IR.RegionIterator"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.RegionIterator")],-1)),t[118]||(t[118]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[119]||(t[119]=l('
julia
RegionIterator(::Operation)

Iterates over all sub-regions for the given operation.

source

',3))]),e("details",X,[e("summary",null,[t[120]||(t[120]=e("a",{id:"Reactant.MLIR.IR.SymbolTable-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.SymbolTable-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolTable")],-1)),t[121]||(t[121]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[122]||(t[122]=l('
julia
mlirSymbolTableCreate(operation)

Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.

source

',3))]),e("details",Y,[e("summary",null,[t[123]||(t[123]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.Type-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[124]||(t[124]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[125]||(t[125]=l('
julia
Type(attr)

Returns the type stored in the given type attribute.

source

',3))]),e("details",_,[e("summary",null,[t[126]||(t[126]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Integer}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Integer}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[127]||(t[127]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[128]||(t[128]=l('
julia
Type(T::Core.Type{<:Integer}; context=context()

Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),e("details",tt,[e("summary",null,[t[129]||(t[129]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Signed}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Signed}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[130]||(t[130]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[131]||(t[131]=l('
julia
Type(T::Core.Type{<:Signed}; context=context()

Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),e("details",et,[e("summary",null,[t[132]||(t[132]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Unsigned}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Unsigned}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[133]||(t[133]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[134]||(t[134]=l('
julia
Type(T::Core.Type{<:Unsigned}; context=context()

Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),e("details",st,[e("summary",null,[t[135]||(t[135]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Bool}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Bool}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[136]||(t[136]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[137]||(t[137]=l('
julia
Type(T::Core.Type{Bool}; context=context()

Creates a 1-bit signless integer type in the context. The type is owned by the context.

source

',3))]),e("details",at,[e("summary",null,[t[138]||(t[138]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float16}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float16}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[139]||(t[139]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[140]||(t[140]=l('
julia
Type(::Core.Type{Float16}; context=context())

Creates an f16 type in the given context. The type is owned by the context.

source

',3))]),e("details",it,[e("summary",null,[t[141]||(t[141]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float32}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float32}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[142]||(t[142]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[143]||(t[143]=l('
julia
Type(Core.Type{Float32}; context=context())

Creates an f32 type in the given context. The type is owned by the context.

source

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Type(Core.Type{Float64}; context=context())

Creates a f64 type in the given context. The type is owned by the context.

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Type(::Core.Type{Nothing}; context=context())

Creates a None type in the given context. The type is owned by the context.

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Type(elements; context=context())
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Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.

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Type(Complex{T}) where {T}

Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.

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*(lhs, rhs)

Creates an affine mul expression with 'lhs' and 'rhs'.

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+(lhs, rhs)

Creates an affine add expression with 'lhs' and 'rhs'.

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==(a, b)

Returns true if the two affine expressions are equal.

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==(a, b)

Checks if two affine maps are equal.

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==(a1, a2)

Checks if two attributes are equal.

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==(block, other)

Checks whether two blocks handles point to the same block. This does not perform deep comparison.

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==(ident, other)

Checks whether two identifiers are the same.

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==(s1, s2)

Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.

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==(region, other)

Checks whether two region handles point to the same region. This does not perform deep comparison.

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==(t1, t2)

Checks if two types are equal.

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==(typeID1, typeID2)

Checks if two type ids are equal.

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==(value1, value2)

Returns 1 if two values are equal, 0 otherwise.

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cld(lhs, rhs)

Creates an affine ceildiv expression with 'lhs' and 'rhs'.

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copy(op)

Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.

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div(lhs, rhs)
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Bool(attr)
Returns the value stored in the given bool attribute.
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Float64(attr)
Returns the value stored in the given floating point attribute, interpreting the value as double.
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Int64(attr)
Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.
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String(attr)
Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.
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String(ident)
Gets the string value of the identifier.
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UInt64(attr)
Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.
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AffineMap(ndims, nsymbols, affineExprs; context=context())
Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.
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AffineMap(ndims, nsymbols; context=context())
Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.
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AffineMap(attr)
Returns the affine map wrapped in the given affine map attribute.
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AffineMap(; context=context())
Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.
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Attribute(str; context=context())
Creates a string attribute in the given context containing the given string.
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Attribute(value; context=context())
Creates a bool attribute in the given context with the given value.
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Attribute(elements; context=context())
Creates a dictionary attribute containing the given list of elements in the provided context.
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Attribute(affineMap)
Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.
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Attribute(type, str)
Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.
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Attribute(type)
Creates a type attribute wrapping the given type in the same context as the type.
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Attribute(float; context=context(), location=Location(), check=false)
Creates a floating point attribute in the given context with the given double value and double-precision FP semantics. If check=true, emits appropriate diagnostics on illegal arguments.
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Attribute(complex; context=context(), location=Location(), check=false)
Creates a complex attribute in the given context with the given complex value and double-precision FP semantics.
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Attribute(elements; context=context())
Creates an array element containing the given list of elements in the given context.
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julia
Attribute()
Returns an empty attribute.
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Attribute(int)
Creates an integer attribute of the given type with the given integer value.
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Block(args, locs)
Creates a new empty block with the given argument types and transfers ownership to the caller.
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BlockIterator(region::Region)
Iterates over all blocks in the given region.
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Context()
Creates an MLIR context and transfers its ownership to the caller.
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julia
ExecutionEngine(op, optLevel, sharedlibs = [])
Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.
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julia
Identifier(context, str)
Gets an identifier with the given string value.
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julia
IntegerSet(ndims, nsymbols, constraints, eqflags; context=context())
Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqflags is 1) or inequalities (eqflags is 0). Both constraints and eqflags need to be arrays of the same length.
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julia
Integerset(ndims, nsymbols; context=context())
Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.
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julia
LogicalResult
A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of LogicalResult must only be inspected using the associated functions.
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julia
Module(location=Location())
Creates a new, empty module and transfers ownership to the caller.
source
',3))]),t("details",V,[t("summary",null,[e[90]||(e[90]=t("a",{id:"Reactant.MLIR.IR.NamedAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.NamedAttribute-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.NamedAttribute")],-1)),e[91]||(e[91]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=l('
julia
NamedAttribute(name, attr)
Associates an attribute with the name. Takes ownership of neither.
source
',3))]),t("details",q,[t("summary",null,[e[93]||(e[93]=t("a",{id:"Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpPassManager")],-1)),e[94]||(e[94]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[95]||(e[95]=l('
julia
OpPassManager(opPassManager, operationName)
Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.
source
',3))]),t("details",U,[t("summary",null,[e[96]||(e[96]=t("a",{id:"Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager, Any}",href:"#Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpPassManager")],-1)),e[97]||(e[97]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[98]||(e[98]=l('
julia
OpPassManager(passManager, operationName)
Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.
source
',3))]),t("details",Q,[t("summary",null,[e[99]||(e[99]=t("a",{id:"Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager}",href:"#Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpPassManager")],-1)),e[100]||(e[100]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[101]||(e[101]=l('
julia
OpPassManager(passManager)
Cast a top-level PassManager to a generic OpPassManager.
source
',3))]),t("details",W,[t("summary",null,[e[102]||(e[102]=t("a",{id:"Reactant.MLIR.IR.Operation-Tuple{Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.Operation-Tuple{Reactant.MLIR.IR.Module}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Operation")],-1)),e[103]||(e[103]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[104]||(e[104]=l('
julia
Operation(module)
Views the module as a generic operation.
source
',3))]),t("details",H,[t("summary",null,[e[105]||(e[105]=t("a",{id:"Reactant.MLIR.IR.OperationIterator",href:"#Reactant.MLIR.IR.OperationIterator"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OperationIterator")],-1)),e[106]||(e[106]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[107]||(e[107]=l('
julia
OperationIterator(block::Block)
Iterates over all operations for the given block.
source
',3))]),t("details",Z,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.IR.PassManager-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.PassManager-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.PassManager")],-1)),e[109]||(e[109]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=l('
julia
PassManager(anchorOp; context=context())
Create a new top-level PassManager anchored on anchorOp.
source
',3))]),t("details",J,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.IR.PassManager-Tuple{}",href:"#Reactant.MLIR.IR.PassManager-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.PassManager")],-1)),e[112]||(e[112]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=l('
julia
PassManager(; context=context())
Create a new top-level PassManager.
source
',3))]),t("details",K,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.IR.Region-Tuple{}",href:"#Reactant.MLIR.IR.Region-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Region")],-1)),e[115]||(e[115]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=l('
julia
Region()
Creates a new empty region and transfers ownership to the caller.
source
',3))]),t("details",$,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.IR.RegionIterator",href:"#Reactant.MLIR.IR.RegionIterator"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.RegionIterator")],-1)),e[118]||(e[118]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[119]||(e[119]=l('
julia
RegionIterator(::Operation)
Iterates over all sub-regions for the given operation.
source
',3))]),t("details",X,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.IR.SymbolTable-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.SymbolTable-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolTable")],-1)),e[121]||(e[121]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=l('
julia
mlirSymbolTableCreate(operation)
Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.
source
',3))]),t("details",Y,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.Type-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[124]||(e[124]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=l('
julia
Type(attr)
Returns the type stored in the given type attribute.
source
',3))]),t("details",_,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Integer}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Integer}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[127]||(e[127]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=l('
julia
Type(T::Core.Type{<:Integer}; context=context()
Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),t("details",ee,[t("summary",null,[e[129]||(e[129]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Signed}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Signed}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[130]||(e[130]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[131]||(e[131]=l('
julia
Type(T::Core.Type{<:Signed}; context=context()
Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),t("details",te,[t("summary",null,[e[132]||(e[132]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Unsigned}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Unsigned}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[133]||(e[133]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[134]||(e[134]=l('
julia
Type(T::Core.Type{<:Unsigned}; context=context()
Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),t("details",se,[t("summary",null,[e[135]||(e[135]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Bool}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Bool}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[136]||(e[136]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[137]||(e[137]=l('
julia
Type(T::Core.Type{Bool}; context=context()
Creates a 1-bit signless integer type in the context. The type is owned by the context.
source
',3))]),t("details",ae,[t("summary",null,[e[138]||(e[138]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float16}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float16}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[139]||(e[139]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[140]||(e[140]=l('
julia
Type(::Core.Type{Float16}; context=context())
Creates an f16 type in the given context. The type is owned by the context.
source
',3))]),t("details",ie,[t("summary",null,[e[141]||(e[141]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float32}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float32}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[142]||(e[142]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[143]||(e[143]=l('
julia
Type(Core.Type{Float32}; context=context())
Creates an f32 type in the given context. The type is owned by the context.
source
',3))]),t("details",le,[t("summary",null,[e[144]||(e[144]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float64}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float64}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[145]||(e[145]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[146]||(e[146]=l('
julia
Type(Core.Type{Float64}; context=context())
Creates a f64 type in the given context. The type is owned by the context.
source
',3))]),t("details",ne,[t("summary",null,[e[147]||(e[147]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Nothing}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Nothing}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[148]||(e[148]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[149]||(e[149]=l('
julia
Type(::Core.Type{Nothing}; context=context())
Creates a None type in the given context. The type is owned by the context.
source
',3))]),t("details",pe,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Vector{Reactant.MLIR.IR.Type}}",href:"#Reactant.MLIR.IR.Type-Tuple{Vector{Reactant.MLIR.IR.Type}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[151]||(e[151]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=l(`
julia
Type(elements; context=context())
+Type(::Core.Type{<:Tuple{T...}}; context=context())
Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.
source
`,3))]),t("details",re,[t("summary",null,[e[153]||(e[153]=t("a",{id:"Reactant.MLIR.IR.Type-Union{Tuple{Type{Complex{T}}}, Tuple{T}} where T",href:"#Reactant.MLIR.IR.Type-Union{Tuple{Type{Complex{T}}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[154]||(e[154]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[155]||(e[155]=l('
julia
Type(Complex{T}) where {T}
Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.
source
',3))]),t("details",oe,[t("summary",null,[e[156]||(e[156]=t("a",{id:"Base.:*-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.:*-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.:*")],-1)),e[157]||(e[157]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[158]||(e[158]=l('
julia
*(lhs, rhs)
Creates an affine mul expression with 'lhs' and 'rhs'.
source
',3))]),t("details",de,[t("summary",null,[e[159]||(e[159]=t("a",{id:"Base.:+-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.:+-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.:+")],-1)),e[160]||(e[160]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[161]||(e[161]=l('
julia
+(lhs, rhs)
Creates an affine add expression with 'lhs' and 'rhs'.
source
',3))]),t("details",ce,[t("summary",null,[e[162]||(e[162]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[163]||(e[163]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[164]||(e[164]=l('
julia
==(a, b)
Returns true if the two affine expressions are equal.
source
',3))]),t("details",be,[t("summary",null,[e[165]||(e[165]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.AffineMap, Reactant.MLIR.IR.AffineMap}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.AffineMap, Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[166]||(e[166]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[167]||(e[167]=l('
julia
==(a, b)
Checks if two affine maps are equal.
source
',3))]),t("details",he,[t("summary",null,[e[168]||(e[168]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Attribute}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[169]||(e[169]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[170]||(e[170]=l('
julia
==(a1, a2)
Checks if two attributes are equal.
source
',3))]),t("details",ue,[t("summary",null,[e[171]||(e[171]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[172]||(e[172]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[173]||(e[173]=l('
julia
==(block, other)
Checks whether two blocks handles point to the same block. This does not perform deep comparison.
source
',3))]),t("details",ge,[t("summary",null,[e[174]||(e[174]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Identifier, Reactant.MLIR.IR.Identifier}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Identifier, Reactant.MLIR.IR.Identifier}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[175]||(e[175]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[176]||(e[176]=l('
julia
==(ident, other)
Checks whether two identifiers are the same.
source
',3))]),t("details",ye,[t("summary",null,[e[177]||(e[177]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.IntegerSet, Reactant.MLIR.IR.IntegerSet}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.IntegerSet, Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[178]||(e[178]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[179]||(e[179]=l('
julia
==(s1, s2)
Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.
source
',3))]),t("details",me,[t("summary",null,[e[180]||(e[180]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Region}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Region}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[181]||(e[181]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[182]||(e[182]=l('
julia
==(region, other)
Checks whether two region handles point to the same region. This does not perform deep comparison.
source
',3))]),t("details",ke,[t("summary",null,[e[183]||(e[183]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Type, Reactant.MLIR.IR.Type}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Type, Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[184]||(e[184]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[185]||(e[185]=l('
julia
==(t1, t2)
Checks if two types are equal.
source
',3))]),t("details",Re,[t("summary",null,[e[186]||(e[186]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.TypeID, Reactant.MLIR.IR.TypeID}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.TypeID, Reactant.MLIR.IR.TypeID}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[187]||(e[187]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[188]||(e[188]=l('
julia
==(typeID1, typeID2)
Checks if two type ids are equal.
source
',3))]),t("details",Ie,[t("summary",null,[e[189]||(e[189]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[190]||(e[190]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[191]||(e[191]=l('
julia
==(value1, value2)
Returns 1 if two values are equal, 0 otherwise.
source
',3))]),t("details",je,[t("summary",null,[e[192]||(e[192]=t("a",{id:"Base.cld-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.cld-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.cld")],-1)),e[193]||(e[193]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[194]||(e[194]=l('
julia
cld(lhs, rhs)
Creates an affine ceildiv expression with 'lhs' and 'rhs'.
source
',3))]),t("details",fe,[t("summary",null,[e[195]||(e[195]=t("a",{id:"Base.copy-Tuple{Reactant.MLIR.IR.Operation}",href:"#Base.copy-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Base.copy")],-1)),e[196]||(e[196]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[197]||(e[197]=l('
julia
copy(op)
Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.
source
',3))]),t("details",Me,[t("summary",null,[e[198]||(e[198]=t("a",{id:"Base.div-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.div-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.div")],-1)),e[199]||(e[199]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[200]||(e[200]=l(`
julia
div(lhs, rhs)
 ÷(lhs, rhs)
-fld(lhs, rhs)
Creates an affine floordiv expression with 'lhs' and 'rhs'.
source
`,3))]),e("details",At,[e("summary",null,[t[201]||(t[201]=e("a",{id:"Base.fill-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Type}",href:"#Base.fill-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Base.fill")],-1)),t[202]||(t[202]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[203]||(t[203]=l('
julia
fill(attr, shapedType)
Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).
source
',3))]),e("details",Lt,[e("summary",null,[t[204]||(t[204]=e("a",{id:"Base.gcd-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Base.gcd-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Base.gcd")],-1)),t[205]||(t[205]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[206]||(t[206]=l('
julia
gcd(affineExpr)
Returns the greatest known integral divisor of this affine expression. The result is always positive.
source
',3))]),e("details",Et,[e("summary",null,[t[207]||(t[207]=e("a",{id:"Base.hash-Tuple{Reactant.MLIR.IR.TypeID}",href:"#Base.hash-Tuple{Reactant.MLIR.IR.TypeID}"},[e("span",{class:"jlbinding"},"Base.hash")],-1)),t[208]||(t[208]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[209]||(t[209]=l('
julia
hash(typeID)
Returns the hash value of the type id.
source
',3))]),e("details",vt,[e("summary",null,[t[210]||(t[210]=e("a",{id:"Base.insert!-Tuple{Reactant.MLIR.IR.Block, Any, Reactant.MLIR.IR.Operation}",href:"#Base.insert!-Tuple{Reactant.MLIR.IR.Block, Any, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Base.insert!")],-1)),t[211]||(t[211]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[212]||(t[212]=l('
julia
insert!(block, index, operation)
Takes an operation owned by the caller and inserts it as index to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.
source
',3))]),e("details",Tt,[e("summary",null,[t[213]||(t[213]=e("a",{id:"Base.insert!-Tuple{Reactant.MLIR.IR.Region, Any, Reactant.MLIR.IR.Block}",href:"#Base.insert!-Tuple{Reactant.MLIR.IR.Region, Any, Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Base.insert!")],-1)),t[214]||(t[214]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[215]||(t[215]=l('
julia
insert!(region, index, block)
Takes a block owned by the caller and inserts it at index to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.
source
',3))]),e("details",Ct,[e("summary",null,[t[216]||(t[216]=e("a",{id:"Base.isempty-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.isempty-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Base.isempty")],-1)),t[217]||(t[217]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[218]||(t[218]=l('
julia
isempty(affineMap)
Checks whether the given affine map is an empty affine map.
source
',3))]),e("details",xt,[e("summary",null,[t[219]||(t[219]=e("a",{id:"Base.isperm-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.isperm-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Base.isperm")],-1)),t[220]||(t[220]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[221]||(t[221]=l('
julia
isperm(affineMap)
Checks whether the given affine map represents a symbol-less permutation map.
source
',3))]),e("details",Ft,[e("summary",null,[t[222]||(t[222]=e("a",{id:"Base.mod-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.mod-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Base.mod")],-1)),t[223]||(t[223]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[224]||(t[224]=l('
julia
mod(lhs, rhs)
Creates an affine mod expression with 'lhs' and 'rhs'.
source
',3))]),e("details",Pt,[e("summary",null,[t[225]||(t[225]=e("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Base.ndims")],-1)),t[226]||(t[226]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[227]||(t[227]=l('
julia
ndims(affineMap)
Returns the number of dimensions of the given affine map.
source
',3))]),e("details",Dt,[e("summary",null,[t[228]||(t[228]=e("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Base.ndims")],-1)),t[229]||(t[229]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[230]||(t[230]=l('
julia
ndims(set)
Returns the number of dimensions in the given set.
source
',3))]),e("details",Ot,[e("summary",null,[t[231]||(t[231]=e("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.Type}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Base.ndims")],-1)),t[232]||(t[232]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[233]||(t[233]=l('
julia
ndims(type)
Returns the rank of the given ranked shaped type.
source
',3))]),e("details",Bt,[e("summary",null,[t[234]||(t[234]=e("a",{id:"Base.parse-Tuple{Reactant.MLIR.IR.OpPassManager, String}",href:"#Base.parse-Tuple{Reactant.MLIR.IR.OpPassManager, String}"},[e("span",{class:"jlbinding"},"Base.parse")],-1)),t[235]||(t[235]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[236]||(t[236]=l('
julia
parse(passManager, pipeline)
Parse a textual MLIR pass pipeline and add it to the provided OpPassManager.
source
',3))]),e("details",Gt,[e("summary",null,[t[237]||(t[237]=e("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Attribute}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Attribute}, Any}"},[e("span",{class:"jlbinding"},"Base.parse")],-1)),t[238]||(t[238]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[239]||(t[239]=l('
julia
parse(::Core.Type{Attribute}, str; context=context())
Parses an attribute. The attribute is owned by the context.
source
',3))]),e("details",zt,[e("summary",null,[t[240]||(t[240]=e("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Module}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Module}, Any}"},[e("span",{class:"jlbinding"},"Base.parse")],-1)),t[241]||(t[241]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[242]||(t[242]=l('
julia
parse(::Type{Module}, module; context=context())
Parses a module from the string and transfers ownership to the caller.
source
',3))]),e("details",wt,[e("summary",null,[t[243]||(t[243]=e("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Type}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Type}, Any}"},[e("span",{class:"jlbinding"},"Base.parse")],-1)),t[244]||(t[244]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[245]||(t[245]=l('
julia
parse(type; context=context())
Parses a type. The type is owned by the context.
source
',3))]),e("details",St,[e("summary",null,[t[246]||(t[246]=e("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Base.push!")],-1)),t[247]||(t[247]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[248]||(t[248]=l('
julia
push!(block, operation)
Takes an operation owned by the caller and appends it to the block.
source
',3))]),e("details",Nt,[e("summary",null,[t[249]||(t[249]=e("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Base.push!")],-1)),t[250]||(t[250]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[251]||(t[251]=l('
julia
push!(region, block)
Takes a block owned by the caller and appends it to the given region.
source
',3))]),e("details",Vt,[e("summary",null,[t[252]||(t[252]=e("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Base.push!")],-1)),t[253]||(t[253]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[254]||(t[254]=l('
julia
push!(symboltable, operation)
Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.
source
',3))]),e("details",qt,[e("summary",null,[t[255]||(t[255]=e("a",{id:"Base.replace-Tuple{Reactant.MLIR.IR.AffineMap, Pair{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}, Any, Any}",href:"#Base.replace-Tuple{Reactant.MLIR.IR.AffineMap, Pair{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}, Any, Any}"},[e("span",{class:"jlbinding"},"Base.replace")],-1)),t[256]||(t[256]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[257]||(t[257]=l('
julia
mlirAffineMapReplace(affineMap, expression => replacement, numResultDims, numResultSyms)
Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.
source
',3))]),e("details",Ut,[e("summary",null,[t[258]||(t[258]=e("a",{id:"Base.replace-Tuple{Reactant.MLIR.IR.IntegerSet, Any, Any}",href:"#Base.replace-Tuple{Reactant.MLIR.IR.IntegerSet, Any, Any}"},[e("span",{class:"jlbinding"},"Base.replace")],-1)),t[259]||(t[259]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[260]||(t[260]=l('
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)
Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.
source
',3))]),e("details",Qt,[e("summary",null,[t[261]||(t[261]=e("a",{id:"Base.reshape-Tuple{Reactant.MLIR.IR.Attribute, Any}",href:"#Base.reshape-Tuple{Reactant.MLIR.IR.Attribute, Any}"},[e("span",{class:"jlbinding"},"Base.reshape")],-1)),t[262]||(t[262]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[263]||(t[263]=l('
julia
Base.reshape(attr, shapedType)
Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.
source
',3))]),e("details",Wt,[e("summary",null,[t[264]||(t[264]=e("a",{id:"Base.size-Tuple{Reactant.MLIR.IR.Type, Int64}",href:"#Base.size-Tuple{Reactant.MLIR.IR.Type, Int64}"},[e("span",{class:"jlbinding"},"Base.size")],-1)),t[265]||(t[265]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[266]||(t[266]=l('
julia
size(type, i)
Returns the i-th dimension of the given ranked shaped type.
source
',3))]),e("details",Ht,[e("summary",null,[t[267]||(t[267]=e("a",{id:"Base.write-Tuple{String, Reactant.MLIR.IR.ExecutionEngine}",href:"#Base.write-Tuple{String, Reactant.MLIR.IR.ExecutionEngine}"},[e("span",{class:"jlbinding"},"Base.write")],-1)),t[268]||(t[268]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[269]||(t[269]=l('
julia
write(fileName, jit)
Dump as an object in fileName.
source
',3))]),e("details",Zt,[e("summary",null,[t[270]||(t[270]=e("a",{id:"Reactant.MLIR.IR.AffineDimensionExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.AffineDimensionExpr-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.AffineDimensionExpr")],-1)),t[271]||(t[271]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[272]||(t[272]=l('
julia
AffineDimensionExpr(position; context=context)
Creates an affine dimension expression with 'position' in the context.
source
',3))]),e("details",Jt,[e("summary",null,[t[273]||(t[273]=e("a",{id:"Reactant.MLIR.IR.BFloat16Type-Tuple{}",href:"#Reactant.MLIR.IR.BFloat16Type-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.BFloat16Type")],-1)),t[274]||(t[274]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[275]||(t[275]=e("p",null,"BFloat16Type(; context=context())",-1)),t[276]||(t[276]=e("p",null,"Creates a bf16 type in the given context. The type is owned by the context.",-1)),t[277]||(t[277]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/IR/Type.jl#L157-L161",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Kt,[e("summary",null,[t[278]||(t[278]=e("a",{id:"Reactant.MLIR.IR.ConstantAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.ConstantAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ConstantAffineMap")],-1)),t[279]||(t[279]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[280]||(t[280]=l('
julia
ConstantAffineMap(val; context=context())
Creates a single constant result affine map in the context. The affine map is owned by the context.
source
',3))]),e("details",$t,[e("summary",null,[t[281]||(t[281]=e("a",{id:"Reactant.MLIR.IR.ConstantExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.ConstantExpr-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ConstantExpr")],-1)),t[282]||(t[282]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[283]||(t[283]=l('
julia
ConstantExpr(constant::Int; context=context())
Creates an affine constant expression with 'constant' in the context.
source
',3))]),e("details",Xt,[e("summary",null,[t[284]||(t[284]=e("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{Bool}}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{Bool}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),t[285]||(t[285]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[286]||(t[286]=l('
julia
DenseElementsAttribute(array::AbstractArray)
Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.
source
',3))]),e("details",Yt,[e("summary",null,[t[287]||(t[287]=e("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{String}}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{String}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),t[288]||(t[288]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[289]||(t[289]=l('
julia
DenseElementsAttribute(array::AbstractArray{String})
Creates a dense elements attribute with the given shaped type from string elements.
source
',3))]),e("details",_t,[e("summary",null,[t[290]||(t[290]=e("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{Reactant.MLIR.IR.Type, AbstractArray}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{Reactant.MLIR.IR.Type, AbstractArray}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),t[291]||(t[291]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[292]||(t[292]=l('
julia
DenseElementsAttribute(shapedType, elements)
Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.
source
',3))]),e("details",te,[e("summary",null,[t[293]||(t[293]=e("a",{id:"Reactant.MLIR.IR.FlatSymbolRefAttribute-Tuple{String}",href:"#Reactant.MLIR.IR.FlatSymbolRefAttribute-Tuple{String}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.FlatSymbolRefAttribute")],-1)),t[294]||(t[294]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[295]||(t[295]=l('
julia
FlatSymbolRefAttribute(ctx, symbol)
Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.
source
',3))]),e("details",ee,[e("summary",null,[t[296]||(t[296]=e("a",{id:"Reactant.MLIR.IR.Float8E4M3FN-Tuple{}",href:"#Reactant.MLIR.IR.Float8E4M3FN-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Float8E4M3FN")],-1)),t[297]||(t[297]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[298]||(t[298]=l('
julia
Float8E4M3FN(; context=context())
Creates an f8E4M3FN type in the given context. The type is owned by the context.
source
',3))]),e("details",se,[e("summary",null,[t[299]||(t[299]=e("a",{id:"Reactant.MLIR.IR.Float8E5M2-Tuple{}",href:"#Reactant.MLIR.IR.Float8E5M2-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Float8E5M2")],-1)),t[300]||(t[300]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[301]||(t[301]=l('
julia
Float8E5M2(; context=context())
Creates an f8E5M2 type in the given context. The type is owned by the context.
source
',3))]),e("details",ae,[e("summary",null,[t[302]||(t[302]=e("a",{id:"Reactant.MLIR.IR.FunctionType-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.FunctionType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.FunctionType")],-1)),t[303]||(t[303]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[304]||(t[304]=l('
julia
FunctionType(inputs, results; context=context())
Creates a function type, mapping a list of input types to result types.
source
',3))]),e("details",ie,[e("summary",null,[t[305]||(t[305]=e("a",{id:"Reactant.MLIR.IR.IdentityAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.IdentityAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.IdentityAffineMap")],-1)),t[306]||(t[306]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[307]||(t[307]=l('
julia
IdentityAffineMap(ndims; context=context())
Creates an affine map with 'ndims' identity in the context. The affine map is owned by the context.
source
',3))]),e("details",le,[e("summary",null,[t[308]||(t[308]=e("a",{id:"Reactant.MLIR.IR.IndexType-Tuple{}",href:"#Reactant.MLIR.IR.IndexType-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.IndexType")],-1)),t[309]||(t[309]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[310]||(t[310]=l('
julia
IndexType(; context=context())
Creates an index type in the given context. The type is owned by the context.
source
',3))]),e("details",ne,[e("summary",null,[t[311]||(t[311]=e("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),t[312]||(t[312]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[313]||(t[313]=l('
julia
MemRefType(elementType, rank, shape, layout, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",pe,[e("summary",null,[t[314]||(t[314]=e("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),t[315]||(t[315]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[316]||(t[316]=l('
julia
MemRefType(elementType, rank, shape, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",re,[e("summary",null,[t[317]||(t[317]=e("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),t[318]||(t[318]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[319]||(t[319]=l('
julia
MemRefType(elementType, memorySpace)
Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",oe,[e("summary",null,[t[320]||(t[320]=e("a",{id:"Reactant.MLIR.IR.MinorIdentityAffineMap-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.MinorIdentityAffineMap-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.MinorIdentityAffineMap")],-1)),t[321]||(t[321]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[322]||(t[322]=l('
julia
MinorIdentityAffineMap(ndims, nresults; context=context())
Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),e("details",de,[e("summary",null,[t[323]||(t[323]=e("a",{id:"Reactant.MLIR.IR.OpaqueAttribute-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.IR.OpaqueAttribute-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpaqueAttribute")],-1)),t[324]||(t[324]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[325]||(t[325]=l('
julia
OpaqueAttribute(dialectNamespace, dataLength, data, type; context=context())
Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),e("details",he,[e("summary",null,[t[326]||(t[326]=e("a",{id:"Reactant.MLIR.IR.OpaqueType-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.OpaqueType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpaqueType")],-1)),t[327]||(t[327]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[328]||(t[328]=l('
julia
OpaqueType(dialectNamespace, typeData; context=context())
Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),e("details",ce,[e("summary",null,[t[329]||(t[329]=e("a",{id:"Reactant.MLIR.IR.PermutationAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.PermutationAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.PermutationAffineMap")],-1)),t[330]||(t[330]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[331]||(t[331]=l('
julia
PermutationAffineMap(permutation; context=context())
Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid invalid permutation). The affine map is owned by the context.
source
',3))]),e("details",ue,[e("summary",null,[t[332]||(t[332]=e("a",{id:"Reactant.MLIR.IR.SymbolExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.SymbolExpr-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolExpr")],-1)),t[333]||(t[333]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[334]||(t[334]=l('
julia
SymbolExpr(position; context=context())
Creates an affine symbol expression with 'position' in the context.
source
',3))]),e("details",be,[e("summary",null,[t[335]||(t[335]=e("a",{id:"Reactant.MLIR.IR.SymbolRefAttribute-Tuple{String, Vector{Reactant.MLIR.IR.Attribute}}",href:"#Reactant.MLIR.IR.SymbolRefAttribute-Tuple{String, Vector{Reactant.MLIR.IR.Attribute}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolRefAttribute")],-1)),t[336]||(t[336]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[337]||(t[337]=l('
julia
SymbolRefAttribute(symbol, references; context=context())
Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.
source
',3))]),e("details",ge,[e("summary",null,[t[338]||(t[338]=e("a",{id:"Reactant.MLIR.IR.TensorType",href:"#Reactant.MLIR.IR.TensorType"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.TensorType")],-1)),t[339]||(t[339]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),t[340]||(t[340]=l('
julia
TensorType(shape, elementType, encoding=Attribute(); location=Location(), check=false)
Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull to this parameter. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",ye,[e("summary",null,[t[341]||(t[341]=e("a",{id:"Reactant.MLIR.IR.TensorType-Tuple{Any}",href:"#Reactant.MLIR.IR.TensorType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.TensorType")],-1)),t[342]||(t[342]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[343]||(t[343]=l('
julia
TensorType(elementType)
Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",me,[e("summary",null,[t[344]||(t[344]=e("a",{id:"Reactant.MLIR.IR.UnitAttribute-Tuple{}",href:"#Reactant.MLIR.IR.UnitAttribute-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.UnitAttribute")],-1)),t[345]||(t[345]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[346]||(t[346]=l('
julia
UnitAttribute(; context=context())
Creates a unit attribute in the given context.
source
',3))]),e("details",ke,[e("summary",null,[t[347]||(t[347]=e("a",{id:"Reactant.MLIR.IR.VectorType-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.IR.VectorType-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.VectorType")],-1)),t[348]||(t[348]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[349]||(t[349]=l('
julia
VectorType(rank, shape, elementType; location=Location(), check=false)
Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",Re,[e("summary",null,[t[350]||(t[350]=e("a",{id:"Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_owned_pass!")],-1)),t[351]||(t[351]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[352]||(t[352]=l('
julia
add_owned_pass!(opPassManager, pass)
Add a pass and transfer ownership to the provided OpPassManager. If the pass is not a generic operation pass or matching the type of the provided OpPassManager, a new OpPassManager is implicitly nested under the provided OpPassManager.
source
',3))]),e("details",fe,[e("summary",null,[t[353]||(t[353]=e("a",{id:"Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.PassManager, Any}",href:"#Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.PassManager, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_owned_pass!")],-1)),t[354]||(t[354]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[355]||(t[355]=l('
julia
add_owned_pass!(passManager, pass)
Add a pass and transfer ownership to the provided top-level PassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.
source
',3))]),e("details",Ie,[e("summary",null,[t[356]||(t[356]=e("a",{id:"Reactant.MLIR.IR.add_pipeline!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.add_pipeline!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_pipeline!")],-1)),t[357]||(t[357]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[358]||(t[358]=l('
julia
add_pipeline!(passManager, pipelineElements, callback, userData)
Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
',3))]),e("details",je,[e("summary",null,[t[359]||(t[359]=e("a",{id:"Reactant.MLIR.IR.affinemap-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.affinemap-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.affinemap")],-1)),t[360]||(t[360]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[361]||(t[361]=l('
julia
affinemap(type)
Returns the affine map of the given MemRef type.
source
',3))]),e("details",Me,[e("summary",null,[t[362]||(t[362]=e("a",{id:"Reactant.MLIR.IR.argument-Tuple{Reactant.MLIR.IR.Block, Any}",href:"#Reactant.MLIR.IR.argument-Tuple{Reactant.MLIR.IR.Block, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.argument")],-1)),t[363]||(t[363]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[364]||(t[364]=l('
julia
argument(block, i)
Returns i-th argument of the block.
source
',3))]),e("details",Ae,[e("summary",null,[t[365]||(t[365]=e("a",{id:"Reactant.MLIR.IR.attr!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}",href:"#Reactant.MLIR.IR.attr!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr!")],-1)),t[366]||(t[366]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[367]||(t[367]=l('
julia
attr!(op, name, attr)
Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise.
source
',3))]),e("details",Le,[e("summary",null,[t[368]||(t[368]=e("a",{id:"Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, AbstractString}",href:"#Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, AbstractString}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr")],-1)),t[369]||(t[369]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[370]||(t[370]=l('
julia
attr(op, name)
Returns an attribute attached to the operation given its name.
source
',3))]),e("details",Ee,[e("summary",null,[t[371]||(t[371]=e("a",{id:"Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr")],-1)),t[372]||(t[372]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[373]||(t[373]=l('
julia
attr(op, i)
Return i-th attribute of the operation.
source
',3))]),e("details",ve,[e("summary",null,[t[374]||(t[374]=e("a",{id:"Reactant.MLIR.IR.bitwidth-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.bitwidth-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.bitwidth")],-1)),t[375]||(t[375]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[376]||(t[376]=l('
julia
bitwidth(type)
Returns the bitwidth of an integer type.
source
',3))]),e("details",Te,[e("summary",null,[t[377]||(t[377]=e("a",{id:"Reactant.MLIR.IR.block-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.block-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.block")],-1)),t[378]||(t[378]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[379]||(t[379]=l('
julia
block(op)
Gets the block that owns this operation, returning null if the operation is not owned.
source
',3))]),e("details",Ce,[e("summary",null,[t[380]||(t[380]=e("a",{id:"Reactant.MLIR.IR.block_arg_num-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.block_arg_num-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.block_arg_num")],-1)),t[381]||(t[381]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[382]||(t[382]=l('
julia
block_arg_num(value)
Returns the position of the value in the argument list of its block.
source
',3))]),e("details",xe,[e("summary",null,[t[383]||(t[383]=e("a",{id:"Reactant.MLIR.IR.block_owner-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.block_owner-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.block_owner")],-1)),t[384]||(t[384]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[385]||(t[385]=l('
julia
block_owner(value)
Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.
source
',3))]),e("details",Fe,[e("summary",null,[t[386]||(t[386]=e("a",{id:"Reactant.MLIR.IR.body-Tuple{Any}",href:"#Reactant.MLIR.IR.body-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.body")],-1)),t[387]||(t[387]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[388]||(t[388]=l('
julia
body(module)
Gets the body of the module, i.e. the only block it contains.
source
',3))]),e("details",Pe,[e("summary",null,[t[389]||(t[389]=e("a",{id:"Reactant.MLIR.IR.compose-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.compose-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.compose")],-1)),t[390]||(t[390]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[391]||(t[391]=l('
julia
compose(affineExpr, affineMap)
Composes the given map with the given expression.
source
',3))]),e("details",De,[e("summary",null,[t[392]||(t[392]=e("a",{id:"Reactant.MLIR.IR.constraint-Tuple{Reactant.MLIR.IR.IntegerSet, Any}",href:"#Reactant.MLIR.IR.constraint-Tuple{Reactant.MLIR.IR.IntegerSet, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.constraint")],-1)),t[393]||(t[393]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[394]||(t[394]=l('
julia
mlirIntegerSetGetConstraint(set, i)
Returns i-th constraint of the set.
source
',3))]),e("details",Oe,[e("summary",null,[t[395]||(t[395]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[396]||(t[396]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[397]||(t[397]=l('
julia
context(affineExpr)
Gets the context that owns the affine expression.
source
',3))]),e("details",Be,[e("summary",null,[t[398]||(t[398]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[399]||(t[399]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[400]||(t[400]=l('
julia
context(affineMap)
Gets the context that the given affine map was created with.
source
',3))]),e("details",Ge,[e("summary",null,[t[401]||(t[401]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[402]||(t[402]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[403]||(t[403]=l('
julia
context(attribute)
Gets the context that an attribute was created with.
source
',3))]),e("details",ze,[e("summary",null,[t[404]||(t[404]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Identifier}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Identifier}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[405]||(t[405]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[406]||(t[406]=l('
julia
context(ident)
Returns the context associated with this identifier
source
',3))]),e("details",we,[e("summary",null,[t[407]||(t[407]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[408]||(t[408]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[409]||(t[409]=l('
julia
context(set)
Gets the context in which the given integer set lives.
source
',3))]),e("details",Se,[e("summary",null,[t[410]||(t[410]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Module}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[411]||(t[411]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[412]||(t[412]=l('
julia
context(module)
Gets the context that a module was created with.
source
',3))]),e("details",Ne,[e("summary",null,[t[413]||(t[413]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[414]||(t[414]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[415]||(t[415]=l('
julia
context(op)
Gets the context this operation is associated with.
source
',3))]),e("details",Ve,[e("summary",null,[t[416]||(t[416]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[417]||(t[417]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[418]||(t[418]=l('
julia
context(type)
Gets the context that a type was created with.
source
',3))]),e("details",qe,[e("summary",null,[t[419]||(t[419]=e("a",{id:"Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.data")],-1)),t[420]||(t[420]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[421]||(t[421]=l('
julia
data(attr)
Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.
source
',3))]),e("details",Ue,[e("summary",null,[t[422]||(t[422]=e("a",{id:"Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.data")],-1)),t[423]||(t[423]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[424]||(t[424]=l('
julia
mlirOpaqueTypeGetData(type)
Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.
source
',3))]),e("details",Qe,[e("summary",null,[t[425]||(t[425]=e("a",{id:"Reactant.MLIR.IR.delete!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.delete!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.delete!")],-1)),t[426]||(t[426]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[427]||(t[427]=l('
julia
delete!(symboltable, operation)
Removes the given operation from the symbol table and erases it.
source
',3))]),e("details",We,[e("summary",null,[t[428]||(t[428]=e("a",{id:"Reactant.MLIR.IR.dynsize-Tuple{}",href:"#Reactant.MLIR.IR.dynsize-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.dynsize")],-1)),t[429]||(t[429]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[430]||(t[430]=l('
julia
dynsize()
Returns the value indicating a dynamic size in a shaped type. Prefer isdynsize to direct comparisons with this value.
source
',3))]),e("details",He,[e("summary",null,[t[431]||(t[431]=e("a",{id:"Reactant.MLIR.IR.dynstrideoroffset-Tuple{}",href:"#Reactant.MLIR.IR.dynstrideoroffset-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.dynstrideoroffset")],-1)),t[432]||(t[432]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[433]||(t[433]=l('
julia
mlirShapedTypeGetDynamicStrideOrOffset()
Returns the value indicating a dynamic stride or offset in a shaped type. Prefer isdynstrideoroffset to direct comparisons with this value.
source
',3))]),e("details",Ze,[e("summary",null,[t[434]||(t[434]=e("a",{id:"Reactant.MLIR.IR.enable_ir_printing!-Tuple{Any}",href:"#Reactant.MLIR.IR.enable_ir_printing!-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.enable_ir_printing!")],-1)),t[435]||(t[435]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[436]||(t[436]=l('
julia
enable_ir_printing!(passManager)
Enable mlir-print-ir-after-all.
source
',3))]),e("details",Je,[e("summary",null,[t[437]||(t[437]=e("a",{id:"Reactant.MLIR.IR.enable_verifier!",href:"#Reactant.MLIR.IR.enable_verifier!"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.enable_verifier!")],-1)),t[438]||(t[438]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),t[439]||(t[439]=l('
julia
enable_verifier!(passManager, enable)
Enable / disable verify-each.
source
',3))]),e("details",Ke,[e("summary",null,[t[440]||(t[440]=e("a",{id:"Reactant.MLIR.IR.encoding-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.encoding-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.encoding")],-1)),t[441]||(t[441]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[442]||(t[442]=l('
julia
encoding(type)
Gets the 'encoding' attribute from the ranked tensor type, returning a nothing if none.
source
',3))]),e("details",$e,[e("summary",null,[t[443]||(t[443]=e("a",{id:"Reactant.MLIR.IR.failure-Tuple{}",href:"#Reactant.MLIR.IR.failure-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.failure")],-1)),t[444]||(t[444]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[445]||(t[445]=l('
julia
failure()
Creates a logical result representing a failure.
source
',3))]),e("details",Xe,[e("summary",null,[t[446]||(t[446]=e("a",{id:"Reactant.MLIR.IR.first_block-Tuple{Reactant.MLIR.IR.Region}",href:"#Reactant.MLIR.IR.first_block-Tuple{Reactant.MLIR.IR.Region}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_block")],-1)),t[447]||(t[447]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[448]||(t[448]=l('
julia
first_block(region)
Gets the first block in the region.
source
',3))]),e("details",Ye,[e("summary",null,[t[449]||(t[449]=e("a",{id:"Reactant.MLIR.IR.first_op-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.first_op-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_op")],-1)),t[450]||(t[450]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[451]||(t[451]=l('
julia
first_op(block)
Returns the first operation in the block or nothing if empty.
source
',3))]),e("details",_e,[e("summary",null,[t[452]||(t[452]=e("a",{id:"Reactant.MLIR.IR.first_use-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.first_use-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_use")],-1)),t[453]||(t[453]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[454]||(t[454]=l('
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first_use(value)
Returns an OpOperand representing the first use of the value, or a nothing if there are no uses.
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julia
flatsymbol(attr)
Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.
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julia
hasrank(type)
Checks whether the given shaped type is ranked.
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hasstaticshape(type)
Checks whether the given shaped type has a static shape.
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',3))]),e("details",as,[e("summary",null,[t[464]||(t[464]=e("a",{id:"Reactant.MLIR.IR.input-Tuple{Reactant.MLIR.IR.Type, Any}",href:"#Reactant.MLIR.IR.input-Tuple{Reactant.MLIR.IR.Type, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.input")],-1)),t[465]||(t[465]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[466]||(t[466]=l('
julia
input(type, i)
Returns the i-th input type.
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julia
insert_after!(block, reference, operation)
Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.
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',3))]),e("details",ls,[e("summary",null,[t[470]||(t[470]=e("a",{id:"Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_after!")],-1)),t[471]||(t[471]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[472]||(t[472]=l('
julia
insert_after!(region, reference, block)
Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.
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',3))]),e("details",ns,[e("summary",null,[t[473]||(t[473]=e("a",{id:"Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_before!")],-1)),t[474]||(t[474]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[475]||(t[475]=l('
julia
insert_before!(block, reference, operation)
Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.
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',3))]),e("details",ps,[e("summary",null,[t[476]||(t[476]=e("a",{id:"Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_before!")],-1)),t[477]||(t[477]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[478]||(t[478]=l('
julia
insert_before!(region, reference, block)
Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.
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',3))]),e("details",rs,[e("summary",null,[t[479]||(t[479]=e("a",{id:"Reactant.MLIR.IR.is_block_arg-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.is_block_arg-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_block_arg")],-1)),t[480]||(t[480]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[481]||(t[481]=l('
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is_block_arg(value)
Returns 1 if the value is a block argument, 0 otherwise.
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is_op_res(value)
Returns 1 if the value is an operation result, 0 otherwise.
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julia
is_pure_affine(affineExpr)
Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.
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',3))]),e("details",hs,[e("summary",null,[t[488]||(t[488]=e("a",{id:"Reactant.MLIR.IR.is_registered-Tuple{Any}",href:"#Reactant.MLIR.IR.is_registered-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_registered")],-1)),t[489]||(t[489]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[490]||(t[490]=l('
julia
is_registered(name; context=context())
Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.
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',3))]),e("details",cs,[e("summary",null,[t[491]||(t[491]=e("a",{id:"Reactant.MLIR.IR.is_symbolic_or_constant-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.is_symbolic_or_constant-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_symbolic_or_constant")],-1)),t[492]||(t[492]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[493]||(t[493]=l('
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is_symbolic_or_constant(affineExpr)
Checks whether the given affine expression is made out of only symbols and constants.
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isadd(affineExpr)
Checks whether the given affine expression is an add expression.
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isaffinemap(attr)
Checks whether the given attribute is an affine map attribute.
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isarray(attr)
Checks whether the given attribute is an array attribute.
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isbf16(type)
Checks whether the given type is a bf16 type.
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isbinary(affineExpr)
Checks whether the given affine expression is binary.
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isbool(attr)
Checks whether the given attribute is a bool attribute.
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',3))]),e("details",Rs,[e("summary",null,[t[512]||(t[512]=e("a",{id:"Reactant.MLIR.IR.isceildiv-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isceildiv-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isceildiv")],-1)),t[513]||(t[513]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[514]||(t[514]=l('
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isceildiv(affineExpr)
Checks whether the given affine expression is an ceildiv expression.
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iscomplex(type)
Checks whether the given type is a Complex type.
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',3))]),e("details",Is,[e("summary",null,[t[518]||(t[518]=e("a",{id:"Reactant.MLIR.IR.isconstantexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isconstantexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isconstantexpr")],-1)),t[519]||(t[519]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[520]||(t[520]=l('
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isconstantexpr(affineExpr)
Checks whether the given affine expression is a constant expression.
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',3))]),e("details",js,[e("summary",null,[t[521]||(t[521]=e("a",{id:"Reactant.MLIR.IR.isconstrainteq-Tuple{Reactant.MLIR.IR.IntegerSet, Any}",href:"#Reactant.MLIR.IR.isconstrainteq-Tuple{Reactant.MLIR.IR.IntegerSet, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isconstrainteq")],-1)),t[522]||(t[522]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[523]||(t[523]=l('
julia
mlirIntegerSetIsConstraintEq(set, i)
Returns true of the i-th constraint of the set is an equality constraint, false otherwise.
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',3))]),e("details",Ms,[e("summary",null,[t[524]||(t[524]=e("a",{id:"Reactant.MLIR.IR.isdenseelements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isdenseelements-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdenseelements")],-1)),t[525]||(t[525]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[526]||(t[526]=l('
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isdenseelements(attr)
Checks whether the given attribute is a dense elements attribute.
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',3))]),e("details",As,[e("summary",null,[t[527]||(t[527]=e("a",{id:"Reactant.MLIR.IR.isdict-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isdict-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdict")],-1)),t[528]||(t[528]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[529]||(t[529]=l('
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isdict(attr)
Checks whether the given attribute is a dictionary attribute.
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',3))]),e("details",Ls,[e("summary",null,[t[530]||(t[530]=e("a",{id:"Reactant.MLIR.IR.isdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdimexpr")],-1)),t[531]||(t[531]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[532]||(t[532]=l('
julia
isdimexpr(affineExpr)
Checks whether the given affine expression is a dimension expression.
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julia
isdyndim(type, i)
Checks wither the i-th dimension of the given shaped type is dynamic.
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isdynsize(size)
Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.
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',3))]),e("details",Ts,[e("summary",null,[t[539]||(t[539]=e("a",{id:"Reactant.MLIR.IR.isdynstrideoroffset-Tuple{Any}",href:"#Reactant.MLIR.IR.isdynstrideoroffset-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdynstrideoroffset")],-1)),t[540]||(t[540]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[541]||(t[541]=l('
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)
Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.
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iselements(attr)
Checks whether the given attribute is an elements attribute.
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isempty(set)
Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.
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',3))]),e("details",Fs,[e("summary",null,[t[548]||(t[548]=e("a",{id:"Reactant.MLIR.IR.isf16-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf16-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf16")],-1)),t[549]||(t[549]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[550]||(t[550]=l('
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isf16(type)
Checks whether the given type is an f16 type.
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isf32(type)
Checks whether the given type is an f32 type.
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isf64(type)
Checks whether the given type is an f64 type.
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',3))]),e("details",Os,[e("summary",null,[t[557]||(t[557]=e("a",{id:"Reactant.MLIR.IR.isf8e4m3fn-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf8e4m3fn-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf8e4m3fn")],-1)),t[558]||(t[558]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[559]||(t[559]=l('
julia
isf8e4m3fn(type)
Checks whether the given type is an f8E4M3FN type.
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',3))]),e("details",Bs,[e("summary",null,[t[560]||(t[560]=e("a",{id:"Reactant.MLIR.IR.isf8e5m2-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf8e5m2-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf8e5m2")],-1)),t[561]||(t[561]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[562]||(t[562]=l('
julia
isf8e5m2(type)
Checks whether the given type is an f8E5M2 type.
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isfailure(res)
Checks if the given logical result represents a failure.
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',3))]),e("details",zs,[e("summary",null,[t[566]||(t[566]=e("a",{id:"Reactant.MLIR.IR.isflatsymbolref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isflatsymbolref-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isflatsymbolref")],-1)),t[567]||(t[567]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[568]||(t[568]=l('
julia
isflatsymbolref(attr)
Checks whether the given attribute is a flat symbol reference attribute.
source
',3))]),e("details",ws,[e("summary",null,[t[569]||(t[569]=e("a",{id:"Reactant.MLIR.IR.isfloat-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isfloat-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfloat")],-1)),t[570]||(t[570]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[571]||(t[571]=l('
julia
isfloat(attr)
Checks whether the given attribute is a floating point attribute.
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',3))]),e("details",Ss,[e("summary",null,[t[572]||(t[572]=e("a",{id:"Reactant.MLIR.IR.isfloordiv-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isfloordiv-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfloordiv")],-1)),t[573]||(t[573]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[574]||(t[574]=l('
julia
isfloordiv(affineExpr)
Checks whether the given affine expression is an floordiv expression.
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',3))]),e("details",Ns,[e("summary",null,[t[575]||(t[575]=e("a",{id:"Reactant.MLIR.IR.isfunction-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isfunction-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfunction")],-1)),t[576]||(t[576]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[577]||(t[577]=l('
julia
isfunction(type)
Checks whether the given type is a function type.
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',3))]),e("details",Vs,[e("summary",null,[t[578]||(t[578]=e("a",{id:"Reactant.MLIR.IR.isfunctionofdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr, Any}",href:"#Reactant.MLIR.IR.isfunctionofdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfunctionofdimexpr")],-1)),t[579]||(t[579]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[580]||(t[580]=l('
julia
isfunctionofdimexpr(affineExpr, position)
Checks whether the given affine expression involves AffineDimExpr 'position'.
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',3))]),e("details",qs,[e("summary",null,[t[581]||(t[581]=e("a",{id:"Reactant.MLIR.IR.isidentity-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isidentity-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isidentity")],-1)),t[582]||(t[582]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[583]||(t[583]=l('
julia
isidentity(affineMap)
Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.
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',3))]),e("details",Us,[e("summary",null,[t[584]||(t[584]=e("a",{id:"Reactant.MLIR.IR.isindex-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isindex-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isindex")],-1)),t[585]||(t[585]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[586]||(t[586]=l('
julia
isindex(type)
Checks whether the given type is an index type.
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',3))]),e("details",Qs,[e("summary",null,[t[587]||(t[587]=e("a",{id:"Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isinteger")],-1)),t[588]||(t[588]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[589]||(t[589]=l('
julia
isinteger(attr)
Checks whether the given attribute is an integer attribute.
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',3))]),e("details",Ws,[e("summary",null,[t[590]||(t[590]=e("a",{id:"Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isinteger")],-1)),t[591]||(t[591]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[592]||(t[592]=l('
julia
isinteger(type)
Checks whether the given type is an integer type.
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',3))]),e("details",Hs,[e("summary",null,[t[593]||(t[593]=e("a",{id:"Reactant.MLIR.IR.isintegerset-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isintegerset-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isintegerset")],-1)),t[594]||(t[594]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[595]||(t[595]=l('
julia
isintegerset(attr)
Checks whether the given attribute is an integer set attribute.
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',3))]),e("details",Zs,[e("summary",null,[t[596]||(t[596]=e("a",{id:"Reactant.MLIR.IR.ismemref-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.ismemref-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismemref")],-1)),t[597]||(t[597]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[598]||(t[598]=l('
julia
ismemref(type)
Checks whether the given type is a MemRef type.
source
',3))]),e("details",Js,[e("summary",null,[t[599]||(t[599]=e("a",{id:"Reactant.MLIR.IR.isminoridentity-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isminoridentity-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isminoridentity")],-1)),t[600]||(t[600]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[601]||(t[601]=l('
julia
isminoridentity(affineMap)
Checks whether the given affine map is a minor identity affine map.
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',3))]),e("details",Ks,[e("summary",null,[t[602]||(t[602]=e("a",{id:"Reactant.MLIR.IR.ismod-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.ismod-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismod")],-1)),t[603]||(t[603]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[604]||(t[604]=l('
julia
ismod(affineExpr)
Checks whether the given affine expression is an mod expression.
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',3))]),e("details",$s,[e("summary",null,[t[605]||(t[605]=e("a",{id:"Reactant.MLIR.IR.ismul-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.ismul-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismul")],-1)),t[606]||(t[606]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[607]||(t[607]=l('
julia
ismul(affineExpr)
Checks whether the given affine expression is an mul expression.
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',3))]),e("details",Xs,[e("summary",null,[t[608]||(t[608]=e("a",{id:"Reactant.MLIR.IR.ismultipleof-Tuple{Reactant.MLIR.IR.AffineExpr, Any}",href:"#Reactant.MLIR.IR.ismultipleof-Tuple{Reactant.MLIR.IR.AffineExpr, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismultipleof")],-1)),t[609]||(t[609]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[610]||(t[610]=l('
julia
ismultipleof(affineExpr, factor)
Checks whether the given affine expression is a multiple of 'factor'.
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',3))]),e("details",Ys,[e("summary",null,[t[611]||(t[611]=e("a",{id:"Reactant.MLIR.IR.isnone-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isnone-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isnone")],-1)),t[612]||(t[612]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[613]||(t[613]=l('
julia
mlirTypeIsANone(type)
Checks whether the given type is a None type.
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',3))]),e("details",_s,[e("summary",null,[t[614]||(t[614]=e("a",{id:"Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isopaque")],-1)),t[615]||(t[615]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[616]||(t[616]=l('
julia
isopaque(attr)
Checks whether the given attribute is an opaque attribute.
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',3))]),e("details",ta,[e("summary",null,[t[617]||(t[617]=e("a",{id:"Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isopaque")],-1)),t[618]||(t[618]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[619]||(t[619]=l('
julia
isopaque(type)
Checks whether the given type is an opaque type.
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',3))]),e("details",ea,[e("summary",null,[t[620]||(t[620]=e("a",{id:"Reactant.MLIR.IR.isprojperm-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isprojperm-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isprojperm")],-1)),t[621]||(t[621]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[622]||(t[622]=l('
julia
isprojperm(affineMap)
Checks whether the given affine map represents a subset of a symbol-less permutation map.
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',3))]),e("details",sa,[e("summary",null,[t[623]||(t[623]=e("a",{id:"Reactant.MLIR.IR.isrankedtensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isrankedtensor-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isrankedtensor")],-1)),t[624]||(t[624]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[625]||(t[625]=l('
julia
isrankedtensor(type)
Checks whether the given type is a ranked tensor type.
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',3))]),e("details",aa,[e("summary",null,[t[626]||(t[626]=e("a",{id:"Reactant.MLIR.IR.isshaped-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isshaped-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isshaped")],-1)),t[627]||(t[627]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[628]||(t[628]=l('
julia
isshaped(type)
Checks whether the given type is a Shaped type.
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',3))]),e("details",ia,[e("summary",null,[t[629]||(t[629]=e("a",{id:"Reactant.MLIR.IR.issigned-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.issigned-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issigned")],-1)),t[630]||(t[630]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[631]||(t[631]=l('
julia
issigned(type)
Checks whether the given integer type is signed.
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',3))]),e("details",la,[e("summary",null,[t[632]||(t[632]=e("a",{id:"Reactant.MLIR.IR.issignless-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.issignless-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issignless")],-1)),t[633]||(t[633]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[634]||(t[634]=l('
julia
issignless(type)
Checks whether the given integer type is signless.
source
',3))]),e("details",na,[e("summary",null,[t[635]||(t[635]=e("a",{id:"Reactant.MLIR.IR.issingleconstant-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.issingleconstant-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issingleconstant")],-1)),t[636]||(t[636]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[637]||(t[637]=l('
julia
issingleconstant(affineMap)
Checks whether the given affine map is a single result constant affine map.
source
',3))]),e("details",pa,[e("summary",null,[t[638]||(t[638]=e("a",{id:"Reactant.MLIR.IR.issparseelements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issparseelements-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issparseelements")],-1)),t[639]||(t[639]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[640]||(t[640]=l('
julia
issparseelements(attr)
Checks whether the given attribute is a sparse elements attribute.
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',3))]),e("details",ra,[e("summary",null,[t[641]||(t[641]=e("a",{id:"Reactant.MLIR.IR.issplat-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issplat-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issplat")],-1)),t[642]||(t[642]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[643]||(t[643]=l('
julia
issplat(attr)
Checks whether the given dense elements attribute contains a single replicated value (splat).
source
',3))]),e("details",oa,[e("summary",null,[t[644]||(t[644]=e("a",{id:"Reactant.MLIR.IR.isstring-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isstring-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isstring")],-1)),t[645]||(t[645]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[646]||(t[646]=l('
julia
isstring(attr)
Checks whether the given attribute is a string attribute.
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',3))]),e("details",da,[e("summary",null,[t[647]||(t[647]=e("a",{id:"Reactant.MLIR.IR.issuccess-Tuple{Reactant.MLIR.IR.LogicalResult}",href:"#Reactant.MLIR.IR.issuccess-Tuple{Reactant.MLIR.IR.LogicalResult}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issuccess")],-1)),t[648]||(t[648]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[649]||(t[649]=l('
julia
issuccess(res)
Checks if the given logical result represents a success.
source
',3))]),e("details",ha,[e("summary",null,[t[650]||(t[650]=e("a",{id:"Reactant.MLIR.IR.issymbolexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.issymbolexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issymbolexpr")],-1)),t[651]||(t[651]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[652]||(t[652]=l('
julia
issymbolexpr(affineExpr)
Checks whether the given affine expression is a symbol expression.
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',3))]),e("details",ca,[e("summary",null,[t[653]||(t[653]=e("a",{id:"Reactant.MLIR.IR.issymbolref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issymbolref-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issymbolref")],-1)),t[654]||(t[654]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[655]||(t[655]=l('
julia
issymbolref(attr)
Checks whether the given attribute is a symbol reference attribute.
source
',3))]),e("details",ua,[e("summary",null,[t[656]||(t[656]=e("a",{id:"Reactant.MLIR.IR.istensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.istensor-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.istensor")],-1)),t[657]||(t[657]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[658]||(t[658]=l('
julia
istensor(type)
Checks whether the given type is a Tensor type.
source
',3))]),e("details",ba,[e("summary",null,[t[659]||(t[659]=e("a",{id:"Reactant.MLIR.IR.istuple-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.istuple-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.istuple")],-1)),t[660]||(t[660]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[661]||(t[661]=l('
julia
istuple(type)
Checks whether the given type is a tuple type.
source
',3))]),e("details",ga,[e("summary",null,[t[662]||(t[662]=e("a",{id:"Reactant.MLIR.IR.istype-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.istype-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.istype")],-1)),t[663]||(t[663]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[664]||(t[664]=l('
julia
istype(attr)
Checks whether the given attribute is a type attribute.
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',3))]),e("details",ya,[e("summary",null,[t[665]||(t[665]=e("a",{id:"Reactant.MLIR.IR.isunit-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isunit-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunit")],-1)),t[666]||(t[666]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[667]||(t[667]=l('
julia
isunit(attr)
Checks whether the given attribute is a unit attribute.
source
',3))]),e("details",ma,[e("summary",null,[t[668]||(t[668]=e("a",{id:"Reactant.MLIR.IR.isunrankedmemref-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunrankedmemref-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunrankedmemref")],-1)),t[669]||(t[669]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[670]||(t[670]=l('
julia
mlirTypeIsAUnrankedMemRef(type)
Checks whether the given type is an UnrankedMemRef type.
source
',3))]),e("details",ka,[e("summary",null,[t[671]||(t[671]=e("a",{id:"Reactant.MLIR.IR.isunrankedtensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunrankedtensor-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunrankedtensor")],-1)),t[672]||(t[672]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[673]||(t[673]=l('
julia
isunrankedtensor(type)
Checks whether the given type is an unranked tensor type.
source
',3))]),e("details",Ra,[e("summary",null,[t[674]||(t[674]=e("a",{id:"Reactant.MLIR.IR.isunsigned-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunsigned-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunsigned")],-1)),t[675]||(t[675]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[676]||(t[676]=l('
julia
isunsigned(type)
Checks whether the given integer type is unsigned.
source
',3))]),e("details",fa,[e("summary",null,[t[677]||(t[677]=e("a",{id:"Reactant.MLIR.IR.isvector-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isvector-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isvector")],-1)),t[678]||(t[678]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[679]||(t[679]=l('
julia
isvector(type)
Checks whether the given type is a Vector type.
source
',3))]),e("details",Ia,[e("summary",null,[t[680]||(t[680]=e("a",{id:"Reactant.MLIR.IR.layout-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.layout-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.layout")],-1)),t[681]||(t[681]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[682]||(t[682]=l('
julia
layout(type)
Returns the layout of the given MemRef type.
source
',3))]),e("details",ja,[e("summary",null,[t[683]||(t[683]=e("a",{id:"Reactant.MLIR.IR.leafref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.leafref-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.leafref")],-1)),t[684]||(t[684]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[685]||(t[685]=l('
julia
leafref(attr)
Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.
source
',3))]),e("details",Ma,[e("summary",null,[t[686]||(t[686]=e("a",{id:"Reactant.MLIR.IR.lhs-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.lhs-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.lhs")],-1)),t[687]||(t[687]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[688]||(t[688]=l('
julia
lhs(affineExpr)
Returns the left hand side affine expression of the given affine binary operation expression.
source
',3))]),e("details",Aa,[e("summary",null,[t[689]||(t[689]=e("a",{id:"Reactant.MLIR.IR.location-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.location-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.location")],-1)),t[690]||(t[690]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[691]||(t[691]=l('
julia
location(op)
Gets the location of the operation.
source
',3))]),e("details",La,[e("summary",null,[t[692]||(t[692]=e("a",{id:"Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.ExecutionEngine, String}",href:"#Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.ExecutionEngine, String}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.lookup")],-1)),t[693]||(t[693]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[694]||(t[694]=l('
julia
lookup(jit, name)
Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.
source
',3))]),e("details",Ea,[e("summary",null,[t[695]||(t[695]=e("a",{id:"Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.SymbolTable, AbstractString}",href:"#Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.SymbolTable, AbstractString}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.lookup")],-1)),t[696]||(t[696]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[697]||(t[697]=l('
julia
lookup(symboltable, name)
Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.
source
',3))]),e("details",va,[e("summary",null,[t[698]||(t[698]=e("a",{id:"Reactant.MLIR.IR.majorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.majorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.majorsubmap")],-1)),t[699]||(t[699]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[700]||(t[700]=l('
julia
majorsubmap(affineMap, nresults)
Returns the affine map consisting of the most major nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
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',3))]),e("details",Ta,[e("summary",null,[t[701]||(t[701]=e("a",{id:"Reactant.MLIR.IR.memspace-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.memspace-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.memspace")],-1)),t[702]||(t[702]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[703]||(t[703]=l('
julia
mlirMemRefTypeGetMemorySpace(type)
Returns the memory space of the given MemRef type.
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',3))]),e("details",Ca,[e("summary",null,[t[704]||(t[704]=e("a",{id:"Reactant.MLIR.IR.minorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.minorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.minorsubmap")],-1)),t[705]||(t[705]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[706]||(t[706]=l('
julia
minorsubmap(affineMap, nresults)
Returns the affine map consisting of the most minor nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
source
',3))]),e("details",xa,[e("summary",null,[t[707]||(t[707]=e("a",{id:"Reactant.MLIR.IR.move_after!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.move_after!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.move_after!")],-1)),t[708]||(t[708]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[709]||(t[709]=l('
julia
move_after!(op, other)
Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),e("details",Fa,[e("summary",null,[t[710]||(t[710]=e("a",{id:"Reactant.MLIR.IR.move_before!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.move_before!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.move_before!")],-1)),t[711]||(t[711]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[712]||(t[712]=l('
julia
move_before!(op, other)
Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),e("details",Pa,[e("summary",null,[t[713]||(t[713]=e("a",{id:"Reactant.MLIR.IR.name-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.name-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.name")],-1)),t[714]||(t[714]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[715]||(t[715]=l('
julia
name(op)
Gets the name of the operation as an identifier.
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',3))]),e("details",Da,[e("summary",null,[t[716]||(t[716]=e("a",{id:"Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.namespace")],-1)),t[717]||(t[717]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[718]||(t[718]=l('
julia
mlirOpaqueAttrGetDialectNamespace(attr)
Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.
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',3))]),e("details",Oa,[e("summary",null,[t[719]||(t[719]=e("a",{id:"Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.namespace")],-1)),t[720]||(t[720]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[721]||(t[721]=l('
julia
mlirOpaqueTypeGetDialectNamespace(type)
Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.
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',3))]),e("details",Ba,[e("summary",null,[t[722]||(t[722]=e("a",{id:"Reactant.MLIR.IR.nargs-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.nargs-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nargs")],-1)),t[723]||(t[723]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[724]||(t[724]=l('
julia
nargs(block)
Returns the number of arguments of the block.
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',3))]),e("details",Ga,[e("summary",null,[t[725]||(t[725]=e("a",{id:"Reactant.MLIR.IR.nattrs-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nattrs-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nattrs")],-1)),t[726]||(t[726]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[727]||(t[727]=l('
julia
nattrs(op)
Returns the number of attributes attached to the operation.
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',3))]),e("details",za,[e("summary",null,[t[728]||(t[728]=e("a",{id:"Reactant.MLIR.IR.nconstraints-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nconstraints-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nconstraints")],-1)),t[729]||(t[729]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[730]||(t[730]=l('
julia
nconstraints(set)
Returns the number of constraints (equalities + inequalities) in the given set.
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',3))]),e("details",wa,[e("summary",null,[t[731]||(t[731]=e("a",{id:"Reactant.MLIR.IR.nequalities-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nequalities-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nequalities")],-1)),t[732]||(t[732]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[733]||(t[733]=l('
julia
nequalities(set)
Returns the number of equalities in the given set.
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',3))]),e("details",Sa,[e("summary",null,[t[734]||(t[734]=e("a",{id:"Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.next")],-1)),t[735]||(t[735]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[736]||(t[736]=l('
julia
next(block)
Returns the block immediately following the given block in its parent region or nothing if last.
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',3))]),e("details",Na,[e("summary",null,[t[737]||(t[737]=e("a",{id:"Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.OpOperand}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.next")],-1)),t[738]||(t[738]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[739]||(t[739]=l('
julia
next(opOperand)
Returns an op operand representing the next use of the value, or nothing if there is no next use.
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',3))]),e("details",Va,[e("summary",null,[t[740]||(t[740]=e("a",{id:"Reactant.MLIR.IR.ninequalities-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.ninequalities-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninequalities")],-1)),t[741]||(t[741]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[742]||(t[742]=l('
julia
ninequalities(set)
Returns the number of inequalities in the given set.
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',3))]),e("details",qa,[e("summary",null,[t[743]||(t[743]=e("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),t[744]||(t[744]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[745]||(t[745]=l('
julia
ninputs(affineMap)
Returns the number of inputs (dimensions + symbols) of the given affine map.
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',3))]),e("details",Ua,[e("summary",null,[t[746]||(t[746]=e("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),t[747]||(t[747]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[748]||(t[748]=l('
julia
ninputs(set)
Returns the number of inputs (dimensions + symbols) in the given set.
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',3))]),e("details",Qa,[e("summary",null,[t[749]||(t[749]=e("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),t[750]||(t[750]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[751]||(t[751]=l('
julia
ninputs(type)
Returns the number of input types.
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',3))]),e("details",Wa,[e("summary",null,[t[752]||(t[752]=e("a",{id:"Reactant.MLIR.IR.nnestedrefs-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.nnestedrefs-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nnestedrefs")],-1)),t[753]||(t[753]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[754]||(t[754]=l('
julia
nnestedrefs(attr)
Returns the number of references nested in the given symbol reference attribute.
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',3))]),e("details",Ha,[e("summary",null,[t[755]||(t[755]=e("a",{id:"Reactant.MLIR.IR.noperands-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.noperands-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.noperands")],-1)),t[756]||(t[756]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[757]||(t[757]=l('
julia
noperands(op)
Returns the number of operands of the operation.
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',3))]),e("details",Za,[e("summary",null,[t[758]||(t[758]=e("a",{id:"Reactant.MLIR.IR.nregions-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nregions-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nregions")],-1)),t[759]||(t[759]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[760]||(t[760]=l('
julia
nregions(op)
Returns the number of regions attached to the given operation.
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',3))]),e("details",Ja,[e("summary",null,[t[761]||(t[761]=e("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),t[762]||(t[762]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[763]||(t[763]=l('
julia
nresults(affineMap)
Returns the number of results of the given affine map.
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',3))]),e("details",Ka,[e("summary",null,[t[764]||(t[764]=e("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),t[765]||(t[765]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[766]||(t[766]=l('
julia
nresults(op)
Returns the number of results of the operation.
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',3))]),e("details",$a,[e("summary",null,[t[767]||(t[767]=e("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),t[768]||(t[768]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[769]||(t[769]=l('
julia
nresults(type)
Returns the number of result types.
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',3))]),e("details",Xa,[e("summary",null,[t[770]||(t[770]=e("a",{id:"Reactant.MLIR.IR.nsuccessors-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nsuccessors-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsuccessors")],-1)),t[771]||(t[771]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[772]||(t[772]=l('
julia
nsuccessors(op)
Returns the number of successor blocks of the operation.
source
',3))]),e("details",Ya,[e("summary",null,[t[773]||(t[773]=e("a",{id:"Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsymbols")],-1)),t[774]||(t[774]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[775]||(t[775]=l('
julia
nsymbols(affineMap)
Returns the number of symbols of the given affine map.
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',3))]),e("details",_a,[e("summary",null,[t[776]||(t[776]=e("a",{id:"Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsymbols")],-1)),t[777]||(t[777]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[778]||(t[778]=l('
julia
nsymbols(set)
Returns the number of symbols in the given set.
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',3))]),e("details",ti,[e("summary",null,[t[779]||(t[779]=e("a",{id:"Reactant.MLIR.IR.op_owner-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.op_owner-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.op_owner")],-1)),t[780]||(t[780]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[781]||(t[781]=l('
julia
op_owner(value)
Returns an operation that produced this value as its result. Asserts if the value is not an op result.
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',3))]),e("details",ei,[e("summary",null,[t[782]||(t[782]=e("a",{id:"Reactant.MLIR.IR.op_res_num-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.op_res_num-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.op_res_num")],-1)),t[783]||(t[783]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[784]||(t[784]=l('
julia
op_res_num(value)
Returns the position of the value in the list of results of the operation that produced it.
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',3))]),e("details",si,[e("summary",null,[t[785]||(t[785]=e("a",{id:"Reactant.MLIR.IR.operand",href:"#Reactant.MLIR.IR.operand"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.operand")],-1)),t[786]||(t[786]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),t[787]||(t[787]=l('
julia
operand(op, i)
Returns i-th operand of the operation.
source
',3))]),e("details",ai,[e("summary",null,[t[788]||(t[788]=e("a",{id:"Reactant.MLIR.IR.operand!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}",href:"#Reactant.MLIR.IR.operand!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.operand!")],-1)),t[789]||(t[789]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[790]||(t[790]=l('
julia
operand!(op, i, value)
Sets the i-th operand of the operation.
source
',3))]),e("details",ii,[e("summary",null,[t[791]||(t[791]=e("a",{id:"Reactant.MLIR.IR.operandindex-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.operandindex-Tuple{Reactant.MLIR.IR.OpOperand}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.operandindex")],-1)),t[792]||(t[792]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[793]||(t[793]=l('
julia
operandindex(opOperand)
Returns the operand number of an op operand.
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',3))]),e("details",li,[e("summary",null,[t[794]||(t[794]=e("a",{id:"Reactant.MLIR.IR.owner-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.owner-Tuple{Reactant.MLIR.IR.OpOperand}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.owner")],-1)),t[795]||(t[795]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[796]||(t[796]=l('
julia
owner(opOperand)
Returns the owner operation of an op operand.
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',3))]),e("details",ni,[e("summary",null,[t[797]||(t[797]=e("a",{id:"Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_op")],-1)),t[798]||(t[798]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[799]||(t[799]=l('
julia
parent_op(block)
Returns the closest surrounding operation that contains this block.
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',3))]),e("details",pi,[e("summary",null,[t[800]||(t[800]=e("a",{id:"Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_op")],-1)),t[801]||(t[801]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[802]||(t[802]=l('
julia
parent_op(op)
Gets the operation that owns this operation, returning null if the operation is not owned.
source
',3))]),e("details",ri,[e("summary",null,[t[803]||(t[803]=e("a",{id:"Reactant.MLIR.IR.parent_region-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.parent_region-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_region")],-1)),t[804]||(t[804]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[805]||(t[805]=l('
julia
parent_region(block)
Returns the region that contains this block.
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',3))]),e("details",oi,[e("summary",null,[t[806]||(t[806]=e("a",{id:"Reactant.MLIR.IR.position-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.position-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.position")],-1)),t[807]||(t[807]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[808]||(t[808]=l('
julia
position(affineExpr)
Returns the position of the given affine dimension expression, affine symbol expression or ...
source
',3))]),e("details",di,[e("summary",null,[t[809]||(t[809]=e("a",{id:"Reactant.MLIR.IR.push_argument!-Tuple{Reactant.MLIR.IR.Block, Any}",href:"#Reactant.MLIR.IR.push_argument!-Tuple{Reactant.MLIR.IR.Block, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.push_argument!")],-1)),t[810]||(t[810]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[811]||(t[811]=l('
julia
push_argument!(block, type; location=Location())
Appends an argument of the specified type to the block. Returns the newly added argument.
source
',3))]),e("details",hi,[e("summary",null,[t[812]||(t[812]=e("a",{id:"Reactant.MLIR.IR.region-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.region-Tuple{Reactant.MLIR.IR.Operation, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.region")],-1)),t[813]||(t[813]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[814]||(t[814]=l('
julia
region(op, i)
Returns i-th region attached to the operation.
source
',3))]),e("details",ci,[e("summary",null,[t[815]||(t[815]=e("a",{id:"Reactant.MLIR.IR.result",href:"#Reactant.MLIR.IR.result"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),t[816]||(t[816]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),t[817]||(t[817]=l('
julia
result(op, i)
Returns i-th result of the operation.
source
',3))]),e("details",ui,[e("summary",null,[t[818]||(t[818]=e("a",{id:"Reactant.MLIR.IR.result-2",href:"#Reactant.MLIR.IR.result-2"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),t[819]||(t[819]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),t[820]||(t[820]=l('
julia
result(type, i)
Returns the i-th result type.
source
',3))]),e("details",bi,[e("summary",null,[t[821]||(t[821]=e("a",{id:"Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),t[822]||(t[822]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[823]||(t[823]=l('
julia
result(affineMap, pos)
Returns the result at the given position.
source
',3))]),e("details",gi,[e("summary",null,[t[824]||(t[824]=e("a",{id:"Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),t[825]||(t[825]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[826]||(t[826]=l('
julia
result(affineMap)
Returns the constant result of the given affine map. The function asserts that the map has a single constant result.
source
',3))]),e("details",yi,[e("summary",null,[t[827]||(t[827]=e("a",{id:"Reactant.MLIR.IR.rhs-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.rhs-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.rhs")],-1)),t[828]||(t[828]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[829]||(t[829]=l('
julia
rhs(affineExpr)
Returns the right hand side affine expression of the given affine binary operation expression.
source
',3))]),e("details",mi,[e("summary",null,[t[830]||(t[830]=e("a",{id:"Reactant.MLIR.IR.rmattr!-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.rmattr!-Tuple{Reactant.MLIR.IR.Operation, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.rmattr!")],-1)),t[831]||(t[831]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[832]||(t[832]=l('
julia
rmattr!(op, name)
Removes an attribute by name. Returns false if the attribute was not found and true if removed.
source
',3))]),e("details",ki,[e("summary",null,[t[833]||(t[833]=e("a",{id:"Reactant.MLIR.IR.rmfromparent!-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.rmfromparent!-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.rmfromparent!")],-1)),t[834]||(t[834]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[835]||(t[835]=l('
julia
rmfromparent!(op)
Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.
source
',3))]),e("details",Ri,[e("summary",null,[t[836]||(t[836]=e("a",{id:"Reactant.MLIR.IR.rootref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.rootref-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.rootref")],-1)),t[837]||(t[837]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[838]||(t[838]=l('
julia
rootref(attr)
Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.
source
',3))]),e("details",fi,[e("summary",null,[t[839]||(t[839]=e("a",{id:"Reactant.MLIR.IR.run!-Tuple{Reactant.MLIR.IR.PassManager, Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.run!-Tuple{Reactant.MLIR.IR.PassManager, Reactant.MLIR.IR.Module}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.run!")],-1)),t[840]||(t[840]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[841]||(t[841]=l('
julia
run!(passManager, module)
Run the provided passManager on the given module.
source
',3))]),e("details",Ii,[e("summary",null,[t[842]||(t[842]=e("a",{id:"Reactant.MLIR.IR.submap-Tuple{Reactant.MLIR.IR.AffineMap, Vector{Int64}}",href:"#Reactant.MLIR.IR.submap-Tuple{Reactant.MLIR.IR.AffineMap, Vector{Int64}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.submap")],-1)),t[843]||(t[843]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[844]||(t[844]=l('
julia
submap(affineMap, positions)
Returns the affine map consisting of the positions subset.
source
',3))]),e("details",ji,[e("summary",null,[t[845]||(t[845]=e("a",{id:"Reactant.MLIR.IR.success-Tuple{}",href:"#Reactant.MLIR.IR.success-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.success")],-1)),t[846]||(t[846]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[847]||(t[847]=l('
julia
success()
Creates a logical result representing a success.
source
',3))]),e("details",Mi,[e("summary",null,[t[848]||(t[848]=e("a",{id:"Reactant.MLIR.IR.successor-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.successor-Tuple{Reactant.MLIR.IR.Operation, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.successor")],-1)),t[849]||(t[849]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[850]||(t[850]=l('
julia
successor(op, i)
Returns i-th successor of the operation.
source
',3))]),e("details",Ai,[e("summary",null,[t[851]||(t[851]=e("a",{id:"Reactant.MLIR.IR.terminator-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.terminator-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.terminator")],-1)),t[852]||(t[852]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[853]||(t[853]=l('
julia
terminator(block)
Returns the terminator operation in the block or nothing if no terminator.
source
',3))]),e("details",Li,[e("summary",null,[t[854]||(t[854]=e("a",{id:"Reactant.MLIR.IR.type!-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.type!-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.type!")],-1)),t[855]||(t[855]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[856]||(t[856]=l('
julia
set_type!(value, type)
Sets the type of the block argument to the given type.
source
',3))]),e("details",Ei,[e("summary",null,[t[857]||(t[857]=e("a",{id:"Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.type")],-1)),t[858]||(t[858]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[859]||(t[859]=l('
julia
type(attribute)
Gets the type of this attribute.
source
',3))]),e("details",vi,[e("summary",null,[t[860]||(t[860]=e("a",{id:"Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.type")],-1)),t[861]||(t[861]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[862]||(t[862]=l('
julia
type(value)
Returns the type of the value.
source
',3))]),e("details",Ti,[e("summary",null,[t[863]||(t[863]=e("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),t[864]||(t[864]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[865]||(t[865]=l('
julia
typeid(attribute)
Gets the type id of the attribute.
source
',3))]),e("details",Ci,[e("summary",null,[t[866]||(t[866]=e("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),t[867]||(t[867]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[868]||(t[868]=l('
julia
typeid(op)
Gets the type id of the operation. Returns null if the operation does not have a registered operation description.
source
',3))]),e("details",xi,[e("summary",null,[t[869]||(t[869]=e("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),t[870]||(t[870]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[871]||(t[871]=l('
julia
typeid(type)
Gets the type ID of the type.
source
',3))]),e("details",Fi,[e("summary",null,[t[872]||(t[872]=e("a",{id:"Reactant.MLIR.IR.value-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.value-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.value")],-1)),t[873]||(t[873]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[874]||(t[874]=l('
julia
value(affineExpr)
Returns the value of the given affine constant expression.
source
',3))]),e("details",Pi,[e("summary",null,[t[875]||(t[875]=e("a",{id:"Reactant.MLIR.IR.verify-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.verify-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.verify")],-1)),t[876]||(t[876]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[877]||(t[877]=l('
julia
verify(op)
Verify the operation and return true if it passes, false if it fails.
source
',3))]),e("details",Di,[e("summary",null,[t[878]||(t[878]=e("a",{id:"Reactant.MLIR.IR.verifyall-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.verifyall-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.verifyall")],-1)),t[879]||(t[879]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[880]||(t[880]=l('
julia
verifyall(operation; debug=false)
Prints the operations which could not be verified.
source
',3))]),e("details",Oi,[e("summary",null,[t[881]||(t[881]=e("a",{id:"Reactant.MLIR.IR.@affinemap-Tuple{Any}",href:"#Reactant.MLIR.IR.@affinemap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.@affinemap")],-1)),t[882]||(t[882]=s()),i(a,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),t[883]||(t[883]=l(`
julia
@affinemap (d1, d2, d3, ...)[s1, s2, ...] -> (d0 + d1, ...)
Returns an affine map from the provided Julia expression. On the right hand side are allowed the following function calls:
  • +, *, ÷, %, fld, cld
The rhs can only contains dimensions and symbols present on the left hand side or integer literals.
julia
julia> using Reactant.MLIR: IR
+fld(lhs, rhs)
Creates an affine floordiv expression with 'lhs' and 'rhs'.
source
`,3))]),t("details",Ae,[t("summary",null,[e[201]||(e[201]=t("a",{id:"Base.fill-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Type}",href:"#Base.fill-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Base.fill")],-1)),e[202]||(e[202]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[203]||(e[203]=l('
julia
fill(attr, shapedType)
Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).
source
',3))]),t("details",Le,[t("summary",null,[e[204]||(e[204]=t("a",{id:"Base.gcd-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Base.gcd-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.gcd")],-1)),e[205]||(e[205]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[206]||(e[206]=l('
julia
gcd(affineExpr)
Returns the greatest known integral divisor of this affine expression. The result is always positive.
source
',3))]),t("details",Ee,[t("summary",null,[e[207]||(e[207]=t("a",{id:"Base.hash-Tuple{Reactant.MLIR.IR.TypeID}",href:"#Base.hash-Tuple{Reactant.MLIR.IR.TypeID}"},[t("span",{class:"jlbinding"},"Base.hash")],-1)),e[208]||(e[208]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[209]||(e[209]=l('
julia
hash(typeID)
Returns the hash value of the type id.
source
',3))]),t("details",ve,[t("summary",null,[e[210]||(e[210]=t("a",{id:"Base.insert!-Tuple{Reactant.MLIR.IR.Block, Any, Reactant.MLIR.IR.Operation}",href:"#Base.insert!-Tuple{Reactant.MLIR.IR.Block, Any, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Base.insert!")],-1)),e[211]||(e[211]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[212]||(e[212]=l('
julia
insert!(block, index, operation)
Takes an operation owned by the caller and inserts it as index to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.
source
',3))]),t("details",Te,[t("summary",null,[e[213]||(e[213]=t("a",{id:"Base.insert!-Tuple{Reactant.MLIR.IR.Region, Any, Reactant.MLIR.IR.Block}",href:"#Base.insert!-Tuple{Reactant.MLIR.IR.Region, Any, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Base.insert!")],-1)),e[214]||(e[214]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[215]||(e[215]=l('
julia
insert!(region, index, block)
Takes a block owned by the caller and inserts it at index to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.
source
',3))]),t("details",Ce,[t("summary",null,[e[216]||(e[216]=t("a",{id:"Base.isempty-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.isempty-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Base.isempty")],-1)),e[217]||(e[217]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[218]||(e[218]=l('
julia
isempty(affineMap)
Checks whether the given affine map is an empty affine map.
source
',3))]),t("details",xe,[t("summary",null,[e[219]||(e[219]=t("a",{id:"Base.isperm-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.isperm-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Base.isperm")],-1)),e[220]||(e[220]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[221]||(e[221]=l('
julia
isperm(affineMap)
Checks whether the given affine map represents a symbol-less permutation map.
source
',3))]),t("details",Fe,[t("summary",null,[e[222]||(e[222]=t("a",{id:"Base.mod-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.mod-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.mod")],-1)),e[223]||(e[223]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[224]||(e[224]=l('
julia
mod(lhs, rhs)
Creates an affine mod expression with 'lhs' and 'rhs'.
source
',3))]),t("details",Pe,[t("summary",null,[e[225]||(e[225]=t("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Base.ndims")],-1)),e[226]||(e[226]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[227]||(e[227]=l('
julia
ndims(affineMap)
Returns the number of dimensions of the given affine map.
source
',3))]),t("details",De,[t("summary",null,[e[228]||(e[228]=t("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Base.ndims")],-1)),e[229]||(e[229]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[230]||(e[230]=l('
julia
ndims(set)
Returns the number of dimensions in the given set.
source
',3))]),t("details",Oe,[t("summary",null,[e[231]||(e[231]=t("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.Type}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Base.ndims")],-1)),e[232]||(e[232]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[233]||(e[233]=l('
julia
ndims(type)
Returns the rank of the given ranked shaped type.
source
',3))]),t("details",Be,[t("summary",null,[e[234]||(e[234]=t("a",{id:"Base.parse-Tuple{Reactant.MLIR.IR.OpPassManager, String}",href:"#Base.parse-Tuple{Reactant.MLIR.IR.OpPassManager, String}"},[t("span",{class:"jlbinding"},"Base.parse")],-1)),e[235]||(e[235]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[236]||(e[236]=l('
julia
parse(passManager, pipeline)
Parse a textual MLIR pass pipeline and add it to the provided OpPassManager.
source
',3))]),t("details",Ge,[t("summary",null,[e[237]||(e[237]=t("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Attribute}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Attribute}, Any}"},[t("span",{class:"jlbinding"},"Base.parse")],-1)),e[238]||(e[238]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[239]||(e[239]=l('
julia
parse(::Core.Type{Attribute}, str; context=context())
Parses an attribute. The attribute is owned by the context.
source
',3))]),t("details",ze,[t("summary",null,[e[240]||(e[240]=t("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Module}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Module}, Any}"},[t("span",{class:"jlbinding"},"Base.parse")],-1)),e[241]||(e[241]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=l('
julia
parse(::Type{Module}, module; context=context())
Parses a module from the string and transfers ownership to the caller.
source
',3))]),t("details",we,[t("summary",null,[e[243]||(e[243]=t("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Type}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Type}, Any}"},[t("span",{class:"jlbinding"},"Base.parse")],-1)),e[244]||(e[244]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[245]||(e[245]=l('
julia
parse(type; context=context())
Parses a type. The type is owned by the context.
source
',3))]),t("details",Se,[t("summary",null,[e[246]||(e[246]=t("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Base.push!")],-1)),e[247]||(e[247]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[248]||(e[248]=l('
julia
push!(block, operation)
Takes an operation owned by the caller and appends it to the block.
source
',3))]),t("details",Ne,[t("summary",null,[e[249]||(e[249]=t("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Base.push!")],-1)),e[250]||(e[250]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[251]||(e[251]=l('
julia
push!(region, block)
Takes a block owned by the caller and appends it to the given region.
source
',3))]),t("details",Ve,[t("summary",null,[e[252]||(e[252]=t("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Base.push!")],-1)),e[253]||(e[253]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=l('
julia
push!(symboltable, operation)
Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.
source
',3))]),t("details",qe,[t("summary",null,[e[255]||(e[255]=t("a",{id:"Base.replace-Tuple{Reactant.MLIR.IR.AffineMap, Pair{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}, Any, Any}",href:"#Base.replace-Tuple{Reactant.MLIR.IR.AffineMap, Pair{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}, Any, Any}"},[t("span",{class:"jlbinding"},"Base.replace")],-1)),e[256]||(e[256]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[257]||(e[257]=l('
julia
mlirAffineMapReplace(affineMap, expression => replacement, numResultDims, numResultSyms)
Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.
source
',3))]),t("details",Ue,[t("summary",null,[e[258]||(e[258]=t("a",{id:"Base.replace-Tuple{Reactant.MLIR.IR.IntegerSet, Any, Any}",href:"#Base.replace-Tuple{Reactant.MLIR.IR.IntegerSet, Any, Any}"},[t("span",{class:"jlbinding"},"Base.replace")],-1)),e[259]||(e[259]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=l('
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)
Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.
source
',3))]),t("details",Qe,[t("summary",null,[e[261]||(e[261]=t("a",{id:"Base.reshape-Tuple{Reactant.MLIR.IR.Attribute, Any}",href:"#Base.reshape-Tuple{Reactant.MLIR.IR.Attribute, Any}"},[t("span",{class:"jlbinding"},"Base.reshape")],-1)),e[262]||(e[262]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[263]||(e[263]=l('
julia
Base.reshape(attr, shapedType)
Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.
source
',3))]),t("details",We,[t("summary",null,[e[264]||(e[264]=t("a",{id:"Base.size-Tuple{Reactant.MLIR.IR.Type, Int64}",href:"#Base.size-Tuple{Reactant.MLIR.IR.Type, Int64}"},[t("span",{class:"jlbinding"},"Base.size")],-1)),e[265]||(e[265]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[266]||(e[266]=l('
julia
size(type, i)
Returns the i-th dimension of the given ranked shaped type.
source
',3))]),t("details",He,[t("summary",null,[e[267]||(e[267]=t("a",{id:"Base.write-Tuple{String, Reactant.MLIR.IR.ExecutionEngine}",href:"#Base.write-Tuple{String, Reactant.MLIR.IR.ExecutionEngine}"},[t("span",{class:"jlbinding"},"Base.write")],-1)),e[268]||(e[268]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[269]||(e[269]=l('
julia
write(fileName, jit)
Dump as an object in fileName.
source
',3))]),t("details",Ze,[t("summary",null,[e[270]||(e[270]=t("a",{id:"Reactant.MLIR.IR.AffineDimensionExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.AffineDimensionExpr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.AffineDimensionExpr")],-1)),e[271]||(e[271]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[272]||(e[272]=l('
julia
AffineDimensionExpr(position; context=context)
Creates an affine dimension expression with 'position' in the context.
source
',3))]),t("details",Je,[t("summary",null,[e[273]||(e[273]=t("a",{id:"Reactant.MLIR.IR.BFloat16Type-Tuple{}",href:"#Reactant.MLIR.IR.BFloat16Type-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.BFloat16Type")],-1)),e[274]||(e[274]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[275]||(e[275]=t("p",null,"BFloat16Type(; context=context())",-1)),e[276]||(e[276]=t("p",null,"Creates a bf16 type in the given context. The type is owned by the context.",-1)),e[277]||(e[277]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/IR/Type.jl#L157-L161",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Ke,[t("summary",null,[e[278]||(e[278]=t("a",{id:"Reactant.MLIR.IR.ConstantAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.ConstantAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ConstantAffineMap")],-1)),e[279]||(e[279]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[280]||(e[280]=l('
julia
ConstantAffineMap(val; context=context())
Creates a single constant result affine map in the context. The affine map is owned by the context.
source
',3))]),t("details",$e,[t("summary",null,[e[281]||(e[281]=t("a",{id:"Reactant.MLIR.IR.ConstantExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.ConstantExpr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ConstantExpr")],-1)),e[282]||(e[282]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[283]||(e[283]=l('
julia
ConstantExpr(constant::Int; context=context())
Creates an affine constant expression with 'constant' in the context.
source
',3))]),t("details",Xe,[t("summary",null,[e[284]||(e[284]=t("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{Bool}}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{Bool}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),e[285]||(e[285]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[286]||(e[286]=l('
julia
DenseElementsAttribute(array::AbstractArray)
Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.
source
',3))]),t("details",Ye,[t("summary",null,[e[287]||(e[287]=t("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{String}}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{String}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),e[288]||(e[288]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[289]||(e[289]=l('
julia
DenseElementsAttribute(array::AbstractArray{String})
Creates a dense elements attribute with the given shaped type from string elements.
source
',3))]),t("details",_e,[t("summary",null,[e[290]||(e[290]=t("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{Reactant.MLIR.IR.Type, AbstractArray}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{Reactant.MLIR.IR.Type, AbstractArray}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),e[291]||(e[291]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[292]||(e[292]=l('
julia
DenseElementsAttribute(shapedType, elements)
Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.
source
',3))]),t("details",et,[t("summary",null,[e[293]||(e[293]=t("a",{id:"Reactant.MLIR.IR.FlatSymbolRefAttribute-Tuple{String}",href:"#Reactant.MLIR.IR.FlatSymbolRefAttribute-Tuple{String}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.FlatSymbolRefAttribute")],-1)),e[294]||(e[294]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[295]||(e[295]=l('
julia
FlatSymbolRefAttribute(ctx, symbol)
Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.
source
',3))]),t("details",tt,[t("summary",null,[e[296]||(e[296]=t("a",{id:"Reactant.MLIR.IR.Float8E4M3FN-Tuple{}",href:"#Reactant.MLIR.IR.Float8E4M3FN-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Float8E4M3FN")],-1)),e[297]||(e[297]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[298]||(e[298]=l('
julia
Float8E4M3FN(; context=context())
Creates an f8E4M3FN type in the given context. The type is owned by the context.
source
',3))]),t("details",st,[t("summary",null,[e[299]||(e[299]=t("a",{id:"Reactant.MLIR.IR.Float8E5M2-Tuple{}",href:"#Reactant.MLIR.IR.Float8E5M2-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Float8E5M2")],-1)),e[300]||(e[300]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[301]||(e[301]=l('
julia
Float8E5M2(; context=context())
Creates an f8E5M2 type in the given context. The type is owned by the context.
source
',3))]),t("details",at,[t("summary",null,[e[302]||(e[302]=t("a",{id:"Reactant.MLIR.IR.FunctionType-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.FunctionType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.FunctionType")],-1)),e[303]||(e[303]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[304]||(e[304]=l('
julia
FunctionType(inputs, results; context=context())
Creates a function type, mapping a list of input types to result types.
source
',3))]),t("details",it,[t("summary",null,[e[305]||(e[305]=t("a",{id:"Reactant.MLIR.IR.IdentityAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.IdentityAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.IdentityAffineMap")],-1)),e[306]||(e[306]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[307]||(e[307]=l('
julia
IdentityAffineMap(ndims; context=context())
Creates an affine map with 'ndims' identity in the context. The affine map is owned by the context.
source
',3))]),t("details",lt,[t("summary",null,[e[308]||(e[308]=t("a",{id:"Reactant.MLIR.IR.IndexType-Tuple{}",href:"#Reactant.MLIR.IR.IndexType-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.IndexType")],-1)),e[309]||(e[309]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[310]||(e[310]=l('
julia
IndexType(; context=context())
Creates an index type in the given context. The type is owned by the context.
source
',3))]),t("details",nt,[t("summary",null,[e[311]||(e[311]=t("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),e[312]||(e[312]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[313]||(e[313]=l('
julia
MemRefType(elementType, rank, shape, layout, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",pt,[t("summary",null,[e[314]||(e[314]=t("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),e[315]||(e[315]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[316]||(e[316]=l('
julia
MemRefType(elementType, rank, shape, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",rt,[t("summary",null,[e[317]||(e[317]=t("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),e[318]||(e[318]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[319]||(e[319]=l('
julia
MemRefType(elementType, memorySpace)
Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",ot,[t("summary",null,[e[320]||(e[320]=t("a",{id:"Reactant.MLIR.IR.MinorIdentityAffineMap-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.MinorIdentityAffineMap-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.MinorIdentityAffineMap")],-1)),e[321]||(e[321]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[322]||(e[322]=l('
julia
MinorIdentityAffineMap(ndims, nresults; context=context())
Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),t("details",dt,[t("summary",null,[e[323]||(e[323]=t("a",{id:"Reactant.MLIR.IR.OpaqueAttribute-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.IR.OpaqueAttribute-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpaqueAttribute")],-1)),e[324]||(e[324]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[325]||(e[325]=l('
julia
OpaqueAttribute(dialectNamespace, dataLength, data, type; context=context())
Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),t("details",ct,[t("summary",null,[e[326]||(e[326]=t("a",{id:"Reactant.MLIR.IR.OpaqueType-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.OpaqueType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpaqueType")],-1)),e[327]||(e[327]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[328]||(e[328]=l('
julia
OpaqueType(dialectNamespace, typeData; context=context())
Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),t("details",bt,[t("summary",null,[e[329]||(e[329]=t("a",{id:"Reactant.MLIR.IR.PermutationAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.PermutationAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.PermutationAffineMap")],-1)),e[330]||(e[330]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[331]||(e[331]=l('
julia
PermutationAffineMap(permutation; context=context())
Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid invalid permutation). The affine map is owned by the context.
source
',3))]),t("details",ht,[t("summary",null,[e[332]||(e[332]=t("a",{id:"Reactant.MLIR.IR.SymbolExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.SymbolExpr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolExpr")],-1)),e[333]||(e[333]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[334]||(e[334]=l('
julia
SymbolExpr(position; context=context())
Creates an affine symbol expression with 'position' in the context.
source
',3))]),t("details",ut,[t("summary",null,[e[335]||(e[335]=t("a",{id:"Reactant.MLIR.IR.SymbolRefAttribute-Tuple{String, Vector{Reactant.MLIR.IR.Attribute}}",href:"#Reactant.MLIR.IR.SymbolRefAttribute-Tuple{String, Vector{Reactant.MLIR.IR.Attribute}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolRefAttribute")],-1)),e[336]||(e[336]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[337]||(e[337]=l('
julia
SymbolRefAttribute(symbol, references; context=context())
Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.
source
',3))]),t("details",gt,[t("summary",null,[e[338]||(e[338]=t("a",{id:"Reactant.MLIR.IR.TensorType",href:"#Reactant.MLIR.IR.TensorType"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.TensorType")],-1)),e[339]||(e[339]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[340]||(e[340]=l('
julia
TensorType(shape, elementType, encoding=Attribute(); location=Location(), check=false)
Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull to this parameter. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",yt,[t("summary",null,[e[341]||(e[341]=t("a",{id:"Reactant.MLIR.IR.TensorType-Tuple{Any}",href:"#Reactant.MLIR.IR.TensorType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.TensorType")],-1)),e[342]||(e[342]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[343]||(e[343]=l('
julia
TensorType(elementType)
Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",mt,[t("summary",null,[e[344]||(e[344]=t("a",{id:"Reactant.MLIR.IR.UnitAttribute-Tuple{}",href:"#Reactant.MLIR.IR.UnitAttribute-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.UnitAttribute")],-1)),e[345]||(e[345]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[346]||(e[346]=l('
julia
UnitAttribute(; context=context())
Creates a unit attribute in the given context.
source
',3))]),t("details",kt,[t("summary",null,[e[347]||(e[347]=t("a",{id:"Reactant.MLIR.IR.VectorType-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.IR.VectorType-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.VectorType")],-1)),e[348]||(e[348]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[349]||(e[349]=l('
julia
VectorType(rank, shape, elementType; location=Location(), check=false)
Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",Rt,[t("summary",null,[e[350]||(e[350]=t("a",{id:"Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_owned_pass!")],-1)),e[351]||(e[351]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[352]||(e[352]=l('
julia
add_owned_pass!(opPassManager, pass)
Add a pass and transfer ownership to the provided OpPassManager. If the pass is not a generic operation pass or matching the type of the provided OpPassManager, a new OpPassManager is implicitly nested under the provided OpPassManager.
source
',3))]),t("details",It,[t("summary",null,[e[353]||(e[353]=t("a",{id:"Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.PassManager, Any}",href:"#Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.PassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_owned_pass!")],-1)),e[354]||(e[354]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[355]||(e[355]=l('
julia
add_owned_pass!(passManager, pass)
Add a pass and transfer ownership to the provided top-level PassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.
source
',3))]),t("details",jt,[t("summary",null,[e[356]||(e[356]=t("a",{id:"Reactant.MLIR.IR.add_pipeline!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.add_pipeline!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_pipeline!")],-1)),e[357]||(e[357]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[358]||(e[358]=l('
julia
add_pipeline!(passManager, pipelineElements, callback, userData)
Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
',3))]),t("details",ft,[t("summary",null,[e[359]||(e[359]=t("a",{id:"Reactant.MLIR.IR.affinemap-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.affinemap-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.affinemap")],-1)),e[360]||(e[360]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[361]||(e[361]=l('
julia
affinemap(type)
Returns the affine map of the given MemRef type.
source
',3))]),t("details",Mt,[t("summary",null,[e[362]||(e[362]=t("a",{id:"Reactant.MLIR.IR.argument-Tuple{Reactant.MLIR.IR.Block, Any}",href:"#Reactant.MLIR.IR.argument-Tuple{Reactant.MLIR.IR.Block, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.argument")],-1)),e[363]||(e[363]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[364]||(e[364]=l('
julia
argument(block, i)
Returns i-th argument of the block.
source
',3))]),t("details",At,[t("summary",null,[e[365]||(e[365]=t("a",{id:"Reactant.MLIR.IR.attr!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}",href:"#Reactant.MLIR.IR.attr!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr!")],-1)),e[366]||(e[366]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[367]||(e[367]=l('
julia
attr!(op, name, attr)
Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise.
source
',3))]),t("details",Lt,[t("summary",null,[e[368]||(e[368]=t("a",{id:"Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, AbstractString}",href:"#Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, AbstractString}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr")],-1)),e[369]||(e[369]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[370]||(e[370]=l('
julia
attr(op, name)
Returns an attribute attached to the operation given its name.
source
',3))]),t("details",Et,[t("summary",null,[e[371]||(e[371]=t("a",{id:"Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr")],-1)),e[372]||(e[372]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[373]||(e[373]=l('
julia
attr(op, i)
Return i-th attribute of the operation.
source
',3))]),t("details",vt,[t("summary",null,[e[374]||(e[374]=t("a",{id:"Reactant.MLIR.IR.bitwidth-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.bitwidth-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.bitwidth")],-1)),e[375]||(e[375]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[376]||(e[376]=l('
julia
bitwidth(type)
Returns the bitwidth of an integer type.
source
',3))]),t("details",Tt,[t("summary",null,[e[377]||(e[377]=t("a",{id:"Reactant.MLIR.IR.block-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.block-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.block")],-1)),e[378]||(e[378]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[379]||(e[379]=l('
julia
block(op)
Gets the block that owns this operation, returning null if the operation is not owned.
source
',3))]),t("details",Ct,[t("summary",null,[e[380]||(e[380]=t("a",{id:"Reactant.MLIR.IR.block_arg_num-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.block_arg_num-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.block_arg_num")],-1)),e[381]||(e[381]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[382]||(e[382]=l('
julia
block_arg_num(value)
Returns the position of the value in the argument list of its block.
source
',3))]),t("details",xt,[t("summary",null,[e[383]||(e[383]=t("a",{id:"Reactant.MLIR.IR.block_owner-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.block_owner-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.block_owner")],-1)),e[384]||(e[384]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[385]||(e[385]=l('
julia
block_owner(value)
Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.
source
',3))]),t("details",Ft,[t("summary",null,[e[386]||(e[386]=t("a",{id:"Reactant.MLIR.IR.body-Tuple{Any}",href:"#Reactant.MLIR.IR.body-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.body")],-1)),e[387]||(e[387]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[388]||(e[388]=l('
julia
body(module)
Gets the body of the module, i.e. the only block it contains.
source
',3))]),t("details",Pt,[t("summary",null,[e[389]||(e[389]=t("a",{id:"Reactant.MLIR.IR.compose-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.compose-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.compose")],-1)),e[390]||(e[390]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[391]||(e[391]=l('
julia
compose(affineExpr, affineMap)
Composes the given map with the given expression.
source
',3))]),t("details",Dt,[t("summary",null,[e[392]||(e[392]=t("a",{id:"Reactant.MLIR.IR.constraint-Tuple{Reactant.MLIR.IR.IntegerSet, Any}",href:"#Reactant.MLIR.IR.constraint-Tuple{Reactant.MLIR.IR.IntegerSet, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.constraint")],-1)),e[393]||(e[393]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[394]||(e[394]=l('
julia
mlirIntegerSetGetConstraint(set, i)
Returns i-th constraint of the set.
source
',3))]),t("details",Ot,[t("summary",null,[e[395]||(e[395]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[396]||(e[396]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[397]||(e[397]=l('
julia
context(affineExpr)
Gets the context that owns the affine expression.
source
',3))]),t("details",Bt,[t("summary",null,[e[398]||(e[398]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[399]||(e[399]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[400]||(e[400]=l('
julia
context(affineMap)
Gets the context that the given affine map was created with.
source
',3))]),t("details",Gt,[t("summary",null,[e[401]||(e[401]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[402]||(e[402]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[403]||(e[403]=l('
julia
context(attribute)
Gets the context that an attribute was created with.
source
',3))]),t("details",zt,[t("summary",null,[e[404]||(e[404]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Identifier}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Identifier}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[405]||(e[405]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[406]||(e[406]=l('
julia
context(ident)
Returns the context associated with this identifier
source
',3))]),t("details",wt,[t("summary",null,[e[407]||(e[407]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[408]||(e[408]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[409]||(e[409]=l('
julia
context(set)
Gets the context in which the given integer set lives.
source
',3))]),t("details",St,[t("summary",null,[e[410]||(e[410]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Module}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[411]||(e[411]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[412]||(e[412]=l('
julia
context(module)
Gets the context that a module was created with.
source
',3))]),t("details",Nt,[t("summary",null,[e[413]||(e[413]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[414]||(e[414]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[415]||(e[415]=l('
julia
context(op)
Gets the context this operation is associated with.
source
',3))]),t("details",Vt,[t("summary",null,[e[416]||(e[416]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[417]||(e[417]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[418]||(e[418]=l('
julia
context(type)
Gets the context that a type was created with.
source
',3))]),t("details",qt,[t("summary",null,[e[419]||(e[419]=t("a",{id:"Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.data")],-1)),e[420]||(e[420]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[421]||(e[421]=l('
julia
data(attr)
Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.
source
',3))]),t("details",Ut,[t("summary",null,[e[422]||(e[422]=t("a",{id:"Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.data")],-1)),e[423]||(e[423]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[424]||(e[424]=l('
julia
mlirOpaqueTypeGetData(type)
Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.
source
',3))]),t("details",Qt,[t("summary",null,[e[425]||(e[425]=t("a",{id:"Reactant.MLIR.IR.delete!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.delete!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.delete!")],-1)),e[426]||(e[426]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[427]||(e[427]=l('
julia
delete!(symboltable, operation)
Removes the given operation from the symbol table and erases it.
source
',3))]),t("details",Wt,[t("summary",null,[e[428]||(e[428]=t("a",{id:"Reactant.MLIR.IR.dynsize-Tuple{}",href:"#Reactant.MLIR.IR.dynsize-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.dynsize")],-1)),e[429]||(e[429]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[430]||(e[430]=l('
julia
dynsize()
Returns the value indicating a dynamic size in a shaped type. Prefer isdynsize to direct comparisons with this value.
source
',3))]),t("details",Ht,[t("summary",null,[e[431]||(e[431]=t("a",{id:"Reactant.MLIR.IR.dynstrideoroffset-Tuple{}",href:"#Reactant.MLIR.IR.dynstrideoroffset-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.dynstrideoroffset")],-1)),e[432]||(e[432]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[433]||(e[433]=l('
julia
mlirShapedTypeGetDynamicStrideOrOffset()
Returns the value indicating a dynamic stride or offset in a shaped type. Prefer isdynstrideoroffset to direct comparisons with this value.
source
',3))]),t("details",Zt,[t("summary",null,[e[434]||(e[434]=t("a",{id:"Reactant.MLIR.IR.enable_ir_printing!-Tuple{Any}",href:"#Reactant.MLIR.IR.enable_ir_printing!-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.enable_ir_printing!")],-1)),e[435]||(e[435]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[436]||(e[436]=l('
julia
enable_ir_printing!(passManager)
Enable mlir-print-ir-after-all.
source
',3))]),t("details",Jt,[t("summary",null,[e[437]||(e[437]=t("a",{id:"Reactant.MLIR.IR.enable_verifier!",href:"#Reactant.MLIR.IR.enable_verifier!"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.enable_verifier!")],-1)),e[438]||(e[438]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[439]||(e[439]=l('
julia
enable_verifier!(passManager, enable)
Enable / disable verify-each.
source
',3))]),t("details",Kt,[t("summary",null,[e[440]||(e[440]=t("a",{id:"Reactant.MLIR.IR.encoding-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.encoding-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.encoding")],-1)),e[441]||(e[441]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[442]||(e[442]=l('
julia
encoding(type)
Gets the 'encoding' attribute from the ranked tensor type, returning a nothing if none.
source
',3))]),t("details",$t,[t("summary",null,[e[443]||(e[443]=t("a",{id:"Reactant.MLIR.IR.failure-Tuple{}",href:"#Reactant.MLIR.IR.failure-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.failure")],-1)),e[444]||(e[444]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[445]||(e[445]=l('
julia
failure()
Creates a logical result representing a failure.
source
',3))]),t("details",Xt,[t("summary",null,[e[446]||(e[446]=t("a",{id:"Reactant.MLIR.IR.first_block-Tuple{Reactant.MLIR.IR.Region}",href:"#Reactant.MLIR.IR.first_block-Tuple{Reactant.MLIR.IR.Region}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_block")],-1)),e[447]||(e[447]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[448]||(e[448]=l('
julia
first_block(region)
Gets the first block in the region.
source
',3))]),t("details",Yt,[t("summary",null,[e[449]||(e[449]=t("a",{id:"Reactant.MLIR.IR.first_op-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.first_op-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_op")],-1)),e[450]||(e[450]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[451]||(e[451]=l('
julia
first_op(block)
Returns the first operation in the block or nothing if empty.
source
',3))]),t("details",_t,[t("summary",null,[e[452]||(e[452]=t("a",{id:"Reactant.MLIR.IR.first_use-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.first_use-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_use")],-1)),e[453]||(e[453]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[454]||(e[454]=l('
julia
first_use(value)
Returns an OpOperand representing the first use of the value, or a nothing if there are no uses.
source
',3))]),t("details",es,[t("summary",null,[e[455]||(e[455]=t("a",{id:"Reactant.MLIR.IR.flatsymbol-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.flatsymbol-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.flatsymbol")],-1)),e[456]||(e[456]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[457]||(e[457]=l('
julia
flatsymbol(attr)
Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.
source
',3))]),t("details",ts,[t("summary",null,[e[458]||(e[458]=t("a",{id:"Reactant.MLIR.IR.hasrank-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.hasrank-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.hasrank")],-1)),e[459]||(e[459]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[460]||(e[460]=l('
julia
hasrank(type)
Checks whether the given shaped type is ranked.
source
',3))]),t("details",ss,[t("summary",null,[e[461]||(e[461]=t("a",{id:"Reactant.MLIR.IR.hasstaticshape-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.hasstaticshape-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.hasstaticshape")],-1)),e[462]||(e[462]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[463]||(e[463]=l('
julia
hasstaticshape(type)
Checks whether the given shaped type has a static shape.
source
',3))]),t("details",as,[t("summary",null,[e[464]||(e[464]=t("a",{id:"Reactant.MLIR.IR.input-Tuple{Reactant.MLIR.IR.Type, Any}",href:"#Reactant.MLIR.IR.input-Tuple{Reactant.MLIR.IR.Type, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.input")],-1)),e[465]||(e[465]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[466]||(e[466]=l('
julia
input(type, i)
Returns the i-th input type.
source
',3))]),t("details",is,[t("summary",null,[e[467]||(e[467]=t("a",{id:"Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_after!")],-1)),e[468]||(e[468]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[469]||(e[469]=l('
julia
insert_after!(block, reference, operation)
Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.
source
',3))]),t("details",ls,[t("summary",null,[e[470]||(e[470]=t("a",{id:"Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_after!")],-1)),e[471]||(e[471]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[472]||(e[472]=l('
julia
insert_after!(region, reference, block)
Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.
source
',3))]),t("details",ns,[t("summary",null,[e[473]||(e[473]=t("a",{id:"Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_before!")],-1)),e[474]||(e[474]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[475]||(e[475]=l('
julia
insert_before!(block, reference, operation)
Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.
source
',3))]),t("details",ps,[t("summary",null,[e[476]||(e[476]=t("a",{id:"Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_before!")],-1)),e[477]||(e[477]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[478]||(e[478]=l('
julia
insert_before!(region, reference, block)
Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.
source
',3))]),t("details",rs,[t("summary",null,[e[479]||(e[479]=t("a",{id:"Reactant.MLIR.IR.is_block_arg-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.is_block_arg-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_block_arg")],-1)),e[480]||(e[480]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[481]||(e[481]=l('
julia
is_block_arg(value)
Returns 1 if the value is a block argument, 0 otherwise.
source
',3))]),t("details",os,[t("summary",null,[e[482]||(e[482]=t("a",{id:"Reactant.MLIR.IR.is_op_res-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.is_op_res-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_op_res")],-1)),e[483]||(e[483]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[484]||(e[484]=l('
julia
is_op_res(value)
Returns 1 if the value is an operation result, 0 otherwise.
source
',3))]),t("details",ds,[t("summary",null,[e[485]||(e[485]=t("a",{id:"Reactant.MLIR.IR.is_pure_affine-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.is_pure_affine-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_pure_affine")],-1)),e[486]||(e[486]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[487]||(e[487]=l('
julia
is_pure_affine(affineExpr)
Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.
source
',3))]),t("details",cs,[t("summary",null,[e[488]||(e[488]=t("a",{id:"Reactant.MLIR.IR.is_registered-Tuple{Any}",href:"#Reactant.MLIR.IR.is_registered-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_registered")],-1)),e[489]||(e[489]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[490]||(e[490]=l('
julia
is_registered(name; context=context())
Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.
source
',3))]),t("details",bs,[t("summary",null,[e[491]||(e[491]=t("a",{id:"Reactant.MLIR.IR.is_symbolic_or_constant-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.is_symbolic_or_constant-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_symbolic_or_constant")],-1)),e[492]||(e[492]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[493]||(e[493]=l('
julia
is_symbolic_or_constant(affineExpr)
Checks whether the given affine expression is made out of only symbols and constants.
source
',3))]),t("details",hs,[t("summary",null,[e[494]||(e[494]=t("a",{id:"Reactant.MLIR.IR.isadd-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isadd-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isadd")],-1)),e[495]||(e[495]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[496]||(e[496]=l('
julia
isadd(affineExpr)
Checks whether the given affine expression is an add expression.
source
',3))]),t("details",us,[t("summary",null,[e[497]||(e[497]=t("a",{id:"Reactant.MLIR.IR.isaffinemap-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isaffinemap-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isaffinemap")],-1)),e[498]||(e[498]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[499]||(e[499]=l('
julia
isaffinemap(attr)
Checks whether the given attribute is an affine map attribute.
source
',3))]),t("details",gs,[t("summary",null,[e[500]||(e[500]=t("a",{id:"Reactant.MLIR.IR.isarray-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isarray-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isarray")],-1)),e[501]||(e[501]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[502]||(e[502]=l('
julia
isarray(attr)
Checks whether the given attribute is an array attribute.
source
',3))]),t("details",ys,[t("summary",null,[e[503]||(e[503]=t("a",{id:"Reactant.MLIR.IR.isbf16-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isbf16-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isbf16")],-1)),e[504]||(e[504]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[505]||(e[505]=l('
julia
isbf16(type)
Checks whether the given type is a bf16 type.
source
',3))]),t("details",ms,[t("summary",null,[e[506]||(e[506]=t("a",{id:"Reactant.MLIR.IR.isbinary-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isbinary-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isbinary")],-1)),e[507]||(e[507]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[508]||(e[508]=l('
julia
isbinary(affineExpr)
Checks whether the given affine expression is binary.
source
',3))]),t("details",ks,[t("summary",null,[e[509]||(e[509]=t("a",{id:"Reactant.MLIR.IR.isbool-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isbool-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isbool")],-1)),e[510]||(e[510]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[511]||(e[511]=l('
julia
isbool(attr)
Checks whether the given attribute is a bool attribute.
source
',3))]),t("details",Rs,[t("summary",null,[e[512]||(e[512]=t("a",{id:"Reactant.MLIR.IR.isceildiv-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isceildiv-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isceildiv")],-1)),e[513]||(e[513]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[514]||(e[514]=l('
julia
isceildiv(affineExpr)
Checks whether the given affine expression is an ceildiv expression.
source
',3))]),t("details",Is,[t("summary",null,[e[515]||(e[515]=t("a",{id:"Reactant.MLIR.IR.iscomplex-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.iscomplex-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.iscomplex")],-1)),e[516]||(e[516]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[517]||(e[517]=l('
julia
iscomplex(type)
Checks whether the given type is a Complex type.
source
',3))]),t("details",js,[t("summary",null,[e[518]||(e[518]=t("a",{id:"Reactant.MLIR.IR.isconstantexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isconstantexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isconstantexpr")],-1)),e[519]||(e[519]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[520]||(e[520]=l('
julia
isconstantexpr(affineExpr)
Checks whether the given affine expression is a constant expression.
source
',3))]),t("details",fs,[t("summary",null,[e[521]||(e[521]=t("a",{id:"Reactant.MLIR.IR.isconstrainteq-Tuple{Reactant.MLIR.IR.IntegerSet, Any}",href:"#Reactant.MLIR.IR.isconstrainteq-Tuple{Reactant.MLIR.IR.IntegerSet, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isconstrainteq")],-1)),e[522]||(e[522]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[523]||(e[523]=l('
julia
mlirIntegerSetIsConstraintEq(set, i)
Returns true of the i-th constraint of the set is an equality constraint, false otherwise.
source
',3))]),t("details",Ms,[t("summary",null,[e[524]||(e[524]=t("a",{id:"Reactant.MLIR.IR.isdenseelements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isdenseelements-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdenseelements")],-1)),e[525]||(e[525]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[526]||(e[526]=l('
julia
isdenseelements(attr)
Checks whether the given attribute is a dense elements attribute.
source
',3))]),t("details",As,[t("summary",null,[e[527]||(e[527]=t("a",{id:"Reactant.MLIR.IR.isdict-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isdict-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdict")],-1)),e[528]||(e[528]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[529]||(e[529]=l('
julia
isdict(attr)
Checks whether the given attribute is a dictionary attribute.
source
',3))]),t("details",Ls,[t("summary",null,[e[530]||(e[530]=t("a",{id:"Reactant.MLIR.IR.isdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdimexpr")],-1)),e[531]||(e[531]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[532]||(e[532]=l('
julia
isdimexpr(affineExpr)
Checks whether the given affine expression is a dimension expression.
source
',3))]),t("details",Es,[t("summary",null,[e[533]||(e[533]=t("a",{id:"Reactant.MLIR.IR.isdyndim-Tuple{Reactant.MLIR.IR.Type, Int64}",href:"#Reactant.MLIR.IR.isdyndim-Tuple{Reactant.MLIR.IR.Type, Int64}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdyndim")],-1)),e[534]||(e[534]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[535]||(e[535]=l('
julia
isdyndim(type, i)
Checks wither the i-th dimension of the given shaped type is dynamic.
source
',3))]),t("details",vs,[t("summary",null,[e[536]||(e[536]=t("a",{id:"Reactant.MLIR.IR.isdynsize-Tuple{Any}",href:"#Reactant.MLIR.IR.isdynsize-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdynsize")],-1)),e[537]||(e[537]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[538]||(e[538]=l('
julia
isdynsize(size)
Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.
source
',3))]),t("details",Ts,[t("summary",null,[e[539]||(e[539]=t("a",{id:"Reactant.MLIR.IR.isdynstrideoroffset-Tuple{Any}",href:"#Reactant.MLIR.IR.isdynstrideoroffset-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdynstrideoroffset")],-1)),e[540]||(e[540]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[541]||(e[541]=l('
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)
Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.
source
',3))]),t("details",Cs,[t("summary",null,[e[542]||(e[542]=t("a",{id:"Reactant.MLIR.IR.iselements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.iselements-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.iselements")],-1)),e[543]||(e[543]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[544]||(e[544]=l('
julia
iselements(attr)
Checks whether the given attribute is an elements attribute.
source
',3))]),t("details",xs,[t("summary",null,[e[545]||(e[545]=t("a",{id:"Reactant.MLIR.IR.isempty-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.isempty-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isempty")],-1)),e[546]||(e[546]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[547]||(e[547]=l('
julia
isempty(set)
Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.
source
',3))]),t("details",Fs,[t("summary",null,[e[548]||(e[548]=t("a",{id:"Reactant.MLIR.IR.isf16-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf16-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf16")],-1)),e[549]||(e[549]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[550]||(e[550]=l('
julia
isf16(type)
Checks whether the given type is an f16 type.
source
',3))]),t("details",Ps,[t("summary",null,[e[551]||(e[551]=t("a",{id:"Reactant.MLIR.IR.isf32-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf32-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf32")],-1)),e[552]||(e[552]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[553]||(e[553]=l('
julia
isf32(type)
Checks whether the given type is an f32 type.
source
',3))]),t("details",Ds,[t("summary",null,[e[554]||(e[554]=t("a",{id:"Reactant.MLIR.IR.isf64-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf64-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf64")],-1)),e[555]||(e[555]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[556]||(e[556]=l('
julia
isf64(type)
Checks whether the given type is an f64 type.
source
',3))]),t("details",Os,[t("summary",null,[e[557]||(e[557]=t("a",{id:"Reactant.MLIR.IR.isf8e4m3fn-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf8e4m3fn-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf8e4m3fn")],-1)),e[558]||(e[558]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[559]||(e[559]=l('
julia
isf8e4m3fn(type)
Checks whether the given type is an f8E4M3FN type.
source
',3))]),t("details",Bs,[t("summary",null,[e[560]||(e[560]=t("a",{id:"Reactant.MLIR.IR.isf8e5m2-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf8e5m2-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf8e5m2")],-1)),e[561]||(e[561]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[562]||(e[562]=l('
julia
isf8e5m2(type)
Checks whether the given type is an f8E5M2 type.
source
',3))]),t("details",Gs,[t("summary",null,[e[563]||(e[563]=t("a",{id:"Reactant.MLIR.IR.isfailure-Tuple{Reactant.MLIR.IR.LogicalResult}",href:"#Reactant.MLIR.IR.isfailure-Tuple{Reactant.MLIR.IR.LogicalResult}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfailure")],-1)),e[564]||(e[564]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[565]||(e[565]=l('
julia
isfailure(res)
Checks if the given logical result represents a failure.
source
',3))]),t("details",zs,[t("summary",null,[e[566]||(e[566]=t("a",{id:"Reactant.MLIR.IR.isflatsymbolref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isflatsymbolref-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isflatsymbolref")],-1)),e[567]||(e[567]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[568]||(e[568]=l('
julia
isflatsymbolref(attr)
Checks whether the given attribute is a flat symbol reference attribute.
source
',3))]),t("details",ws,[t("summary",null,[e[569]||(e[569]=t("a",{id:"Reactant.MLIR.IR.isfloat-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isfloat-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfloat")],-1)),e[570]||(e[570]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[571]||(e[571]=l('
julia
isfloat(attr)
Checks whether the given attribute is a floating point attribute.
source
',3))]),t("details",Ss,[t("summary",null,[e[572]||(e[572]=t("a",{id:"Reactant.MLIR.IR.isfloordiv-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isfloordiv-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfloordiv")],-1)),e[573]||(e[573]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[574]||(e[574]=l('
julia
isfloordiv(affineExpr)
Checks whether the given affine expression is an floordiv expression.
source
',3))]),t("details",Ns,[t("summary",null,[e[575]||(e[575]=t("a",{id:"Reactant.MLIR.IR.isfunction-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isfunction-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfunction")],-1)),e[576]||(e[576]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[577]||(e[577]=l('
julia
isfunction(type)
Checks whether the given type is a function type.
source
',3))]),t("details",Vs,[t("summary",null,[e[578]||(e[578]=t("a",{id:"Reactant.MLIR.IR.isfunctionofdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr, Any}",href:"#Reactant.MLIR.IR.isfunctionofdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfunctionofdimexpr")],-1)),e[579]||(e[579]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[580]||(e[580]=l('
julia
isfunctionofdimexpr(affineExpr, position)
Checks whether the given affine expression involves AffineDimExpr 'position'.
source
',3))]),t("details",qs,[t("summary",null,[e[581]||(e[581]=t("a",{id:"Reactant.MLIR.IR.isidentity-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isidentity-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isidentity")],-1)),e[582]||(e[582]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[583]||(e[583]=l('
julia
isidentity(affineMap)
Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),t("details",Us,[t("summary",null,[e[584]||(e[584]=t("a",{id:"Reactant.MLIR.IR.isindex-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isindex-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isindex")],-1)),e[585]||(e[585]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[586]||(e[586]=l('
julia
isindex(type)
Checks whether the given type is an index type.
source
',3))]),t("details",Qs,[t("summary",null,[e[587]||(e[587]=t("a",{id:"Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isinteger")],-1)),e[588]||(e[588]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[589]||(e[589]=l('
julia
isinteger(attr)
Checks whether the given attribute is an integer attribute.
source
',3))]),t("details",Ws,[t("summary",null,[e[590]||(e[590]=t("a",{id:"Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isinteger")],-1)),e[591]||(e[591]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[592]||(e[592]=l('
julia
isinteger(type)
Checks whether the given type is an integer type.
source
',3))]),t("details",Hs,[t("summary",null,[e[593]||(e[593]=t("a",{id:"Reactant.MLIR.IR.isintegerset-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isintegerset-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isintegerset")],-1)),e[594]||(e[594]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[595]||(e[595]=l('
julia
isintegerset(attr)
Checks whether the given attribute is an integer set attribute.
source
',3))]),t("details",Zs,[t("summary",null,[e[596]||(e[596]=t("a",{id:"Reactant.MLIR.IR.ismemref-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.ismemref-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismemref")],-1)),e[597]||(e[597]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[598]||(e[598]=l('
julia
ismemref(type)
Checks whether the given type is a MemRef type.
source
',3))]),t("details",Js,[t("summary",null,[e[599]||(e[599]=t("a",{id:"Reactant.MLIR.IR.isminoridentity-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isminoridentity-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isminoridentity")],-1)),e[600]||(e[600]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[601]||(e[601]=l('
julia
isminoridentity(affineMap)
Checks whether the given affine map is a minor identity affine map.
source
',3))]),t("details",Ks,[t("summary",null,[e[602]||(e[602]=t("a",{id:"Reactant.MLIR.IR.ismod-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.ismod-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismod")],-1)),e[603]||(e[603]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[604]||(e[604]=l('
julia
ismod(affineExpr)
Checks whether the given affine expression is an mod expression.
source
',3))]),t("details",$s,[t("summary",null,[e[605]||(e[605]=t("a",{id:"Reactant.MLIR.IR.ismul-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.ismul-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismul")],-1)),e[606]||(e[606]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[607]||(e[607]=l('
julia
ismul(affineExpr)
Checks whether the given affine expression is an mul expression.
source
',3))]),t("details",Xs,[t("summary",null,[e[608]||(e[608]=t("a",{id:"Reactant.MLIR.IR.ismultipleof-Tuple{Reactant.MLIR.IR.AffineExpr, Any}",href:"#Reactant.MLIR.IR.ismultipleof-Tuple{Reactant.MLIR.IR.AffineExpr, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismultipleof")],-1)),e[609]||(e[609]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[610]||(e[610]=l('
julia
ismultipleof(affineExpr, factor)
Checks whether the given affine expression is a multiple of 'factor'.
source
',3))]),t("details",Ys,[t("summary",null,[e[611]||(e[611]=t("a",{id:"Reactant.MLIR.IR.isnone-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isnone-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isnone")],-1)),e[612]||(e[612]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[613]||(e[613]=l('
julia
mlirTypeIsANone(type)
Checks whether the given type is a None type.
source
',3))]),t("details",_s,[t("summary",null,[e[614]||(e[614]=t("a",{id:"Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isopaque")],-1)),e[615]||(e[615]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[616]||(e[616]=l('
julia
isopaque(attr)
Checks whether the given attribute is an opaque attribute.
source
',3))]),t("details",ea,[t("summary",null,[e[617]||(e[617]=t("a",{id:"Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isopaque")],-1)),e[618]||(e[618]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[619]||(e[619]=l('
julia
isopaque(type)
Checks whether the given type is an opaque type.
source
',3))]),t("details",ta,[t("summary",null,[e[620]||(e[620]=t("a",{id:"Reactant.MLIR.IR.isprojperm-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isprojperm-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isprojperm")],-1)),e[621]||(e[621]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[622]||(e[622]=l('
julia
isprojperm(affineMap)
Checks whether the given affine map represents a subset of a symbol-less permutation map.
source
',3))]),t("details",sa,[t("summary",null,[e[623]||(e[623]=t("a",{id:"Reactant.MLIR.IR.isrankedtensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isrankedtensor-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isrankedtensor")],-1)),e[624]||(e[624]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[625]||(e[625]=l('
julia
isrankedtensor(type)
Checks whether the given type is a ranked tensor type.
source
',3))]),t("details",aa,[t("summary",null,[e[626]||(e[626]=t("a",{id:"Reactant.MLIR.IR.isshaped-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isshaped-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isshaped")],-1)),e[627]||(e[627]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[628]||(e[628]=l('
julia
isshaped(type)
Checks whether the given type is a Shaped type.
source
',3))]),t("details",ia,[t("summary",null,[e[629]||(e[629]=t("a",{id:"Reactant.MLIR.IR.issigned-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.issigned-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issigned")],-1)),e[630]||(e[630]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[631]||(e[631]=l('
julia
issigned(type)
Checks whether the given integer type is signed.
source
',3))]),t("details",la,[t("summary",null,[e[632]||(e[632]=t("a",{id:"Reactant.MLIR.IR.issignless-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.issignless-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issignless")],-1)),e[633]||(e[633]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[634]||(e[634]=l('
julia
issignless(type)
Checks whether the given integer type is signless.
source
',3))]),t("details",na,[t("summary",null,[e[635]||(e[635]=t("a",{id:"Reactant.MLIR.IR.issingleconstant-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.issingleconstant-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issingleconstant")],-1)),e[636]||(e[636]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[637]||(e[637]=l('
julia
issingleconstant(affineMap)
Checks whether the given affine map is a single result constant affine map.
source
',3))]),t("details",pa,[t("summary",null,[e[638]||(e[638]=t("a",{id:"Reactant.MLIR.IR.issparseelements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issparseelements-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issparseelements")],-1)),e[639]||(e[639]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[640]||(e[640]=l('
julia
issparseelements(attr)
Checks whether the given attribute is a sparse elements attribute.
source
',3))]),t("details",ra,[t("summary",null,[e[641]||(e[641]=t("a",{id:"Reactant.MLIR.IR.issplat-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issplat-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issplat")],-1)),e[642]||(e[642]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[643]||(e[643]=l('
julia
issplat(attr)
Checks whether the given dense elements attribute contains a single replicated value (splat).
source
',3))]),t("details",oa,[t("summary",null,[e[644]||(e[644]=t("a",{id:"Reactant.MLIR.IR.isstring-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isstring-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isstring")],-1)),e[645]||(e[645]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[646]||(e[646]=l('
julia
isstring(attr)
Checks whether the given attribute is a string attribute.
source
',3))]),t("details",da,[t("summary",null,[e[647]||(e[647]=t("a",{id:"Reactant.MLIR.IR.issuccess-Tuple{Reactant.MLIR.IR.LogicalResult}",href:"#Reactant.MLIR.IR.issuccess-Tuple{Reactant.MLIR.IR.LogicalResult}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issuccess")],-1)),e[648]||(e[648]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[649]||(e[649]=l('
julia
issuccess(res)
Checks if the given logical result represents a success.
source
',3))]),t("details",ca,[t("summary",null,[e[650]||(e[650]=t("a",{id:"Reactant.MLIR.IR.issymbolexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.issymbolexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issymbolexpr")],-1)),e[651]||(e[651]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[652]||(e[652]=l('
julia
issymbolexpr(affineExpr)
Checks whether the given affine expression is a symbol expression.
source
',3))]),t("details",ba,[t("summary",null,[e[653]||(e[653]=t("a",{id:"Reactant.MLIR.IR.issymbolref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issymbolref-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issymbolref")],-1)),e[654]||(e[654]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[655]||(e[655]=l('
julia
issymbolref(attr)
Checks whether the given attribute is a symbol reference attribute.
source
',3))]),t("details",ha,[t("summary",null,[e[656]||(e[656]=t("a",{id:"Reactant.MLIR.IR.istensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.istensor-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.istensor")],-1)),e[657]||(e[657]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[658]||(e[658]=l('
julia
istensor(type)
Checks whether the given type is a Tensor type.
source
',3))]),t("details",ua,[t("summary",null,[e[659]||(e[659]=t("a",{id:"Reactant.MLIR.IR.istuple-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.istuple-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.istuple")],-1)),e[660]||(e[660]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[661]||(e[661]=l('
julia
istuple(type)
Checks whether the given type is a tuple type.
source
',3))]),t("details",ga,[t("summary",null,[e[662]||(e[662]=t("a",{id:"Reactant.MLIR.IR.istype-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.istype-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.istype")],-1)),e[663]||(e[663]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[664]||(e[664]=l('
julia
istype(attr)
Checks whether the given attribute is a type attribute.
source
',3))]),t("details",ya,[t("summary",null,[e[665]||(e[665]=t("a",{id:"Reactant.MLIR.IR.isunit-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isunit-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunit")],-1)),e[666]||(e[666]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[667]||(e[667]=l('
julia
isunit(attr)
Checks whether the given attribute is a unit attribute.
source
',3))]),t("details",ma,[t("summary",null,[e[668]||(e[668]=t("a",{id:"Reactant.MLIR.IR.isunrankedmemref-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunrankedmemref-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunrankedmemref")],-1)),e[669]||(e[669]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[670]||(e[670]=l('
julia
mlirTypeIsAUnrankedMemRef(type)
Checks whether the given type is an UnrankedMemRef type.
source
',3))]),t("details",ka,[t("summary",null,[e[671]||(e[671]=t("a",{id:"Reactant.MLIR.IR.isunrankedtensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunrankedtensor-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunrankedtensor")],-1)),e[672]||(e[672]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[673]||(e[673]=l('
julia
isunrankedtensor(type)
Checks whether the given type is an unranked tensor type.
source
',3))]),t("details",Ra,[t("summary",null,[e[674]||(e[674]=t("a",{id:"Reactant.MLIR.IR.isunsigned-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunsigned-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunsigned")],-1)),e[675]||(e[675]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[676]||(e[676]=l('
julia
isunsigned(type)
Checks whether the given integer type is unsigned.
source
',3))]),t("details",Ia,[t("summary",null,[e[677]||(e[677]=t("a",{id:"Reactant.MLIR.IR.isvector-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isvector-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isvector")],-1)),e[678]||(e[678]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[679]||(e[679]=l('
julia
isvector(type)
Checks whether the given type is a Vector type.
source
',3))]),t("details",ja,[t("summary",null,[e[680]||(e[680]=t("a",{id:"Reactant.MLIR.IR.layout-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.layout-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.layout")],-1)),e[681]||(e[681]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[682]||(e[682]=l('
julia
layout(type)
Returns the layout of the given MemRef type.
source
',3))]),t("details",fa,[t("summary",null,[e[683]||(e[683]=t("a",{id:"Reactant.MLIR.IR.leafref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.leafref-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.leafref")],-1)),e[684]||(e[684]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[685]||(e[685]=l('
julia
leafref(attr)
Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.
source
',3))]),t("details",Ma,[t("summary",null,[e[686]||(e[686]=t("a",{id:"Reactant.MLIR.IR.lhs-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.lhs-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.lhs")],-1)),e[687]||(e[687]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[688]||(e[688]=l('
julia
lhs(affineExpr)
Returns the left hand side affine expression of the given affine binary operation expression.
source
',3))]),t("details",Aa,[t("summary",null,[e[689]||(e[689]=t("a",{id:"Reactant.MLIR.IR.location-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.location-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.location")],-1)),e[690]||(e[690]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[691]||(e[691]=l('
julia
location(op)
Gets the location of the operation.
source
',3))]),t("details",La,[t("summary",null,[e[692]||(e[692]=t("a",{id:"Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.ExecutionEngine, String}",href:"#Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.ExecutionEngine, String}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.lookup")],-1)),e[693]||(e[693]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[694]||(e[694]=l('
julia
lookup(jit, name)
Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.
source
',3))]),t("details",Ea,[t("summary",null,[e[695]||(e[695]=t("a",{id:"Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.SymbolTable, AbstractString}",href:"#Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.SymbolTable, AbstractString}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.lookup")],-1)),e[696]||(e[696]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[697]||(e[697]=l('
julia
lookup(symboltable, name)
Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.
source
',3))]),t("details",va,[t("summary",null,[e[698]||(e[698]=t("a",{id:"Reactant.MLIR.IR.majorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.majorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.majorsubmap")],-1)),e[699]||(e[699]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[700]||(e[700]=l('
julia
majorsubmap(affineMap, nresults)
Returns the affine map consisting of the most major nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
source
',3))]),t("details",Ta,[t("summary",null,[e[701]||(e[701]=t("a",{id:"Reactant.MLIR.IR.memspace-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.memspace-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.memspace")],-1)),e[702]||(e[702]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[703]||(e[703]=l('
julia
mlirMemRefTypeGetMemorySpace(type)
Returns the memory space of the given MemRef type.
source
',3))]),t("details",Ca,[t("summary",null,[e[704]||(e[704]=t("a",{id:"Reactant.MLIR.IR.minorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.minorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.minorsubmap")],-1)),e[705]||(e[705]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[706]||(e[706]=l('
julia
minorsubmap(affineMap, nresults)
Returns the affine map consisting of the most minor nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
source
',3))]),t("details",xa,[t("summary",null,[e[707]||(e[707]=t("a",{id:"Reactant.MLIR.IR.move_after!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.move_after!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.move_after!")],-1)),e[708]||(e[708]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[709]||(e[709]=l('
julia
move_after!(op, other)
Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),t("details",Fa,[t("summary",null,[e[710]||(e[710]=t("a",{id:"Reactant.MLIR.IR.move_before!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.move_before!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.move_before!")],-1)),e[711]||(e[711]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[712]||(e[712]=l('
julia
move_before!(op, other)
Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),t("details",Pa,[t("summary",null,[e[713]||(e[713]=t("a",{id:"Reactant.MLIR.IR.name-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.name-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.name")],-1)),e[714]||(e[714]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[715]||(e[715]=l('
julia
name(op)
Gets the name of the operation as an identifier.
source
',3))]),t("details",Da,[t("summary",null,[e[716]||(e[716]=t("a",{id:"Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.namespace")],-1)),e[717]||(e[717]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[718]||(e[718]=l('
julia
mlirOpaqueAttrGetDialectNamespace(attr)
Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.
source
',3))]),t("details",Oa,[t("summary",null,[e[719]||(e[719]=t("a",{id:"Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.namespace")],-1)),e[720]||(e[720]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[721]||(e[721]=l('
julia
mlirOpaqueTypeGetDialectNamespace(type)
Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.
source
',3))]),t("details",Ba,[t("summary",null,[e[722]||(e[722]=t("a",{id:"Reactant.MLIR.IR.nargs-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.nargs-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nargs")],-1)),e[723]||(e[723]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[724]||(e[724]=l('
julia
nargs(block)
Returns the number of arguments of the block.
source
',3))]),t("details",Ga,[t("summary",null,[e[725]||(e[725]=t("a",{id:"Reactant.MLIR.IR.nattrs-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nattrs-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nattrs")],-1)),e[726]||(e[726]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[727]||(e[727]=l('
julia
nattrs(op)
Returns the number of attributes attached to the operation.
source
',3))]),t("details",za,[t("summary",null,[e[728]||(e[728]=t("a",{id:"Reactant.MLIR.IR.nconstraints-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nconstraints-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nconstraints")],-1)),e[729]||(e[729]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[730]||(e[730]=l('
julia
nconstraints(set)
Returns the number of constraints (equalities + inequalities) in the given set.
source
',3))]),t("details",wa,[t("summary",null,[e[731]||(e[731]=t("a",{id:"Reactant.MLIR.IR.nequalities-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nequalities-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nequalities")],-1)),e[732]||(e[732]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[733]||(e[733]=l('
julia
nequalities(set)
Returns the number of equalities in the given set.
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',3))]),t("details",Sa,[t("summary",null,[e[734]||(e[734]=t("a",{id:"Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.next")],-1)),e[735]||(e[735]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[736]||(e[736]=l('
julia
next(block)
Returns the block immediately following the given block in its parent region or nothing if last.
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',3))]),t("details",Na,[t("summary",null,[e[737]||(e[737]=t("a",{id:"Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.OpOperand}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.next")],-1)),e[738]||(e[738]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[739]||(e[739]=l('
julia
next(opOperand)
Returns an op operand representing the next use of the value, or nothing if there is no next use.
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',3))]),t("details",Va,[t("summary",null,[e[740]||(e[740]=t("a",{id:"Reactant.MLIR.IR.ninequalities-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.ninequalities-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninequalities")],-1)),e[741]||(e[741]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[742]||(e[742]=l('
julia
ninequalities(set)
Returns the number of inequalities in the given set.
source
',3))]),t("details",qa,[t("summary",null,[e[743]||(e[743]=t("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),e[744]||(e[744]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[745]||(e[745]=l('
julia
ninputs(affineMap)
Returns the number of inputs (dimensions + symbols) of the given affine map.
source
',3))]),t("details",Ua,[t("summary",null,[e[746]||(e[746]=t("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),e[747]||(e[747]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[748]||(e[748]=l('
julia
ninputs(set)
Returns the number of inputs (dimensions + symbols) in the given set.
source
',3))]),t("details",Qa,[t("summary",null,[e[749]||(e[749]=t("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),e[750]||(e[750]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[751]||(e[751]=l('
julia
ninputs(type)
Returns the number of input types.
source
',3))]),t("details",Wa,[t("summary",null,[e[752]||(e[752]=t("a",{id:"Reactant.MLIR.IR.nnestedrefs-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.nnestedrefs-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nnestedrefs")],-1)),e[753]||(e[753]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[754]||(e[754]=l('
julia
nnestedrefs(attr)
Returns the number of references nested in the given symbol reference attribute.
source
',3))]),t("details",Ha,[t("summary",null,[e[755]||(e[755]=t("a",{id:"Reactant.MLIR.IR.noperands-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.noperands-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.noperands")],-1)),e[756]||(e[756]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[757]||(e[757]=l('
julia
noperands(op)
Returns the number of operands of the operation.
source
',3))]),t("details",Za,[t("summary",null,[e[758]||(e[758]=t("a",{id:"Reactant.MLIR.IR.nregions-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nregions-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nregions")],-1)),e[759]||(e[759]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[760]||(e[760]=l('
julia
nregions(op)
Returns the number of regions attached to the given operation.
source
',3))]),t("details",Ja,[t("summary",null,[e[761]||(e[761]=t("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),e[762]||(e[762]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[763]||(e[763]=l('
julia
nresults(affineMap)
Returns the number of results of the given affine map.
source
',3))]),t("details",Ka,[t("summary",null,[e[764]||(e[764]=t("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),e[765]||(e[765]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[766]||(e[766]=l('
julia
nresults(op)
Returns the number of results of the operation.
source
',3))]),t("details",$a,[t("summary",null,[e[767]||(e[767]=t("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),e[768]||(e[768]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[769]||(e[769]=l('
julia
nresults(type)
Returns the number of result types.
source
',3))]),t("details",Xa,[t("summary",null,[e[770]||(e[770]=t("a",{id:"Reactant.MLIR.IR.nsuccessors-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nsuccessors-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsuccessors")],-1)),e[771]||(e[771]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[772]||(e[772]=l('
julia
nsuccessors(op)
Returns the number of successor blocks of the operation.
source
',3))]),t("details",Ya,[t("summary",null,[e[773]||(e[773]=t("a",{id:"Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsymbols")],-1)),e[774]||(e[774]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[775]||(e[775]=l('
julia
nsymbols(affineMap)
Returns the number of symbols of the given affine map.
source
',3))]),t("details",_a,[t("summary",null,[e[776]||(e[776]=t("a",{id:"Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsymbols")],-1)),e[777]||(e[777]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[778]||(e[778]=l('
julia
nsymbols(set)
Returns the number of symbols in the given set.
source
',3))]),t("details",ei,[t("summary",null,[e[779]||(e[779]=t("a",{id:"Reactant.MLIR.IR.op_owner-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.op_owner-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.op_owner")],-1)),e[780]||(e[780]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[781]||(e[781]=l('
julia
op_owner(value)
Returns an operation that produced this value as its result. Asserts if the value is not an op result.
source
',3))]),t("details",ti,[t("summary",null,[e[782]||(e[782]=t("a",{id:"Reactant.MLIR.IR.op_res_num-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.op_res_num-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.op_res_num")],-1)),e[783]||(e[783]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[784]||(e[784]=l('
julia
op_res_num(value)
Returns the position of the value in the list of results of the operation that produced it.
source
',3))]),t("details",si,[t("summary",null,[e[785]||(e[785]=t("a",{id:"Reactant.MLIR.IR.operand",href:"#Reactant.MLIR.IR.operand"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.operand")],-1)),e[786]||(e[786]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[787]||(e[787]=l('
julia
operand(op, i)
Returns i-th operand of the operation.
source
',3))]),t("details",ai,[t("summary",null,[e[788]||(e[788]=t("a",{id:"Reactant.MLIR.IR.operand!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}",href:"#Reactant.MLIR.IR.operand!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.operand!")],-1)),e[789]||(e[789]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[790]||(e[790]=l('
julia
operand!(op, i, value)
Sets the i-th operand of the operation.
source
',3))]),t("details",ii,[t("summary",null,[e[791]||(e[791]=t("a",{id:"Reactant.MLIR.IR.operandindex-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.operandindex-Tuple{Reactant.MLIR.IR.OpOperand}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.operandindex")],-1)),e[792]||(e[792]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[793]||(e[793]=l('
julia
operandindex(opOperand)
Returns the operand number of an op operand.
source
',3))]),t("details",li,[t("summary",null,[e[794]||(e[794]=t("a",{id:"Reactant.MLIR.IR.owner-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.owner-Tuple{Reactant.MLIR.IR.OpOperand}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.owner")],-1)),e[795]||(e[795]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[796]||(e[796]=l('
julia
owner(opOperand)
Returns the owner operation of an op operand.
source
',3))]),t("details",ni,[t("summary",null,[e[797]||(e[797]=t("a",{id:"Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_op")],-1)),e[798]||(e[798]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[799]||(e[799]=l('
julia
parent_op(block)
Returns the closest surrounding operation that contains this block.
source
',3))]),t("details",pi,[t("summary",null,[e[800]||(e[800]=t("a",{id:"Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_op")],-1)),e[801]||(e[801]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[802]||(e[802]=l('
julia
parent_op(op)
Gets the operation that owns this operation, returning null if the operation is not owned.
source
',3))]),t("details",ri,[t("summary",null,[e[803]||(e[803]=t("a",{id:"Reactant.MLIR.IR.parent_region-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.parent_region-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_region")],-1)),e[804]||(e[804]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[805]||(e[805]=l('
julia
parent_region(block)
Returns the region that contains this block.
source
',3))]),t("details",oi,[t("summary",null,[e[806]||(e[806]=t("a",{id:"Reactant.MLIR.IR.position-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.position-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.position")],-1)),e[807]||(e[807]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[808]||(e[808]=l('
julia
position(affineExpr)
Returns the position of the given affine dimension expression, affine symbol expression or ...
source
',3))]),t("details",di,[t("summary",null,[e[809]||(e[809]=t("a",{id:"Reactant.MLIR.IR.push_argument!-Tuple{Reactant.MLIR.IR.Block, Any}",href:"#Reactant.MLIR.IR.push_argument!-Tuple{Reactant.MLIR.IR.Block, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.push_argument!")],-1)),e[810]||(e[810]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[811]||(e[811]=l('
julia
push_argument!(block, type; location=Location())
Appends an argument of the specified type to the block. Returns the newly added argument.
source
',3))]),t("details",ci,[t("summary",null,[e[812]||(e[812]=t("a",{id:"Reactant.MLIR.IR.region-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.region-Tuple{Reactant.MLIR.IR.Operation, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.region")],-1)),e[813]||(e[813]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[814]||(e[814]=l('
julia
region(op, i)
Returns i-th region attached to the operation.
source
',3))]),t("details",bi,[t("summary",null,[e[815]||(e[815]=t("a",{id:"Reactant.MLIR.IR.result",href:"#Reactant.MLIR.IR.result"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),e[816]||(e[816]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[817]||(e[817]=l('
julia
result(type, i)
Returns the i-th result type.
source
',3))]),t("details",hi,[t("summary",null,[e[818]||(e[818]=t("a",{id:"Reactant.MLIR.IR.result-2",href:"#Reactant.MLIR.IR.result-2"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),e[819]||(e[819]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[820]||(e[820]=l('
julia
result(op, i)
Returns i-th result of the operation.
source
',3))]),t("details",ui,[t("summary",null,[e[821]||(e[821]=t("a",{id:"Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),e[822]||(e[822]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[823]||(e[823]=l('
julia
result(affineMap, pos)
Returns the result at the given position.
source
',3))]),t("details",gi,[t("summary",null,[e[824]||(e[824]=t("a",{id:"Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),e[825]||(e[825]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[826]||(e[826]=l('
julia
result(affineMap)
Returns the constant result of the given affine map. The function asserts that the map has a single constant result.
source
',3))]),t("details",yi,[t("summary",null,[e[827]||(e[827]=t("a",{id:"Reactant.MLIR.IR.rhs-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.rhs-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.rhs")],-1)),e[828]||(e[828]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[829]||(e[829]=l('
julia
rhs(affineExpr)
Returns the right hand side affine expression of the given affine binary operation expression.
source
',3))]),t("details",mi,[t("summary",null,[e[830]||(e[830]=t("a",{id:"Reactant.MLIR.IR.rmattr!-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.rmattr!-Tuple{Reactant.MLIR.IR.Operation, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.rmattr!")],-1)),e[831]||(e[831]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[832]||(e[832]=l('
julia
rmattr!(op, name)
Removes an attribute by name. Returns false if the attribute was not found and true if removed.
source
',3))]),t("details",ki,[t("summary",null,[e[833]||(e[833]=t("a",{id:"Reactant.MLIR.IR.rmfromparent!-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.rmfromparent!-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.rmfromparent!")],-1)),e[834]||(e[834]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[835]||(e[835]=l('
julia
rmfromparent!(op)
Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.
source
',3))]),t("details",Ri,[t("summary",null,[e[836]||(e[836]=t("a",{id:"Reactant.MLIR.IR.rootref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.rootref-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.rootref")],-1)),e[837]||(e[837]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[838]||(e[838]=l('
julia
rootref(attr)
Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.
source
',3))]),t("details",Ii,[t("summary",null,[e[839]||(e[839]=t("a",{id:"Reactant.MLIR.IR.run!-Tuple{Reactant.MLIR.IR.PassManager, Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.run!-Tuple{Reactant.MLIR.IR.PassManager, Reactant.MLIR.IR.Module}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.run!")],-1)),e[840]||(e[840]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[841]||(e[841]=l('
julia
run!(passManager, module)
Run the provided passManager on the given module.
source
',3))]),t("details",ji,[t("summary",null,[e[842]||(e[842]=t("a",{id:"Reactant.MLIR.IR.submap-Tuple{Reactant.MLIR.IR.AffineMap, Vector{Int64}}",href:"#Reactant.MLIR.IR.submap-Tuple{Reactant.MLIR.IR.AffineMap, Vector{Int64}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.submap")],-1)),e[843]||(e[843]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[844]||(e[844]=l('
julia
submap(affineMap, positions)
Returns the affine map consisting of the positions subset.
source
',3))]),t("details",fi,[t("summary",null,[e[845]||(e[845]=t("a",{id:"Reactant.MLIR.IR.success-Tuple{}",href:"#Reactant.MLIR.IR.success-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.success")],-1)),e[846]||(e[846]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[847]||(e[847]=l('
julia
success()
Creates a logical result representing a success.
source
',3))]),t("details",Mi,[t("summary",null,[e[848]||(e[848]=t("a",{id:"Reactant.MLIR.IR.successor-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.successor-Tuple{Reactant.MLIR.IR.Operation, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.successor")],-1)),e[849]||(e[849]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[850]||(e[850]=l('
julia
successor(op, i)
Returns i-th successor of the operation.
source
',3))]),t("details",Ai,[t("summary",null,[e[851]||(e[851]=t("a",{id:"Reactant.MLIR.IR.terminator-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.terminator-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.terminator")],-1)),e[852]||(e[852]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[853]||(e[853]=l('
julia
terminator(block)
Returns the terminator operation in the block or nothing if no terminator.
source
',3))]),t("details",Li,[t("summary",null,[e[854]||(e[854]=t("a",{id:"Reactant.MLIR.IR.type!-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.type!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.type!")],-1)),e[855]||(e[855]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[856]||(e[856]=l('
julia
set_type!(value, type)
Sets the type of the block argument to the given type.
source
',3))]),t("details",Ei,[t("summary",null,[e[857]||(e[857]=t("a",{id:"Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.type")],-1)),e[858]||(e[858]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[859]||(e[859]=l('
julia
type(attribute)
Gets the type of this attribute.
source
',3))]),t("details",vi,[t("summary",null,[e[860]||(e[860]=t("a",{id:"Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.type")],-1)),e[861]||(e[861]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[862]||(e[862]=l('
julia
type(value)
Returns the type of the value.
source
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julia
typeid(attribute)
Gets the type id of the attribute.
source
',3))]),t("details",Ci,[t("summary",null,[e[866]||(e[866]=t("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),e[867]||(e[867]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[868]||(e[868]=l('
julia
typeid(op)
Gets the type id of the operation. Returns null if the operation does not have a registered operation description.
source
',3))]),t("details",xi,[t("summary",null,[e[869]||(e[869]=t("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),e[870]||(e[870]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[871]||(e[871]=l('
julia
typeid(type)
Gets the type ID of the type.
source
',3))]),t("details",Fi,[t("summary",null,[e[872]||(e[872]=t("a",{id:"Reactant.MLIR.IR.value-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.value-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.value")],-1)),e[873]||(e[873]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[874]||(e[874]=l('
julia
value(affineExpr)
Returns the value of the given affine constant expression.
source
',3))]),t("details",Pi,[t("summary",null,[e[875]||(e[875]=t("a",{id:"Reactant.MLIR.IR.verify-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.verify-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.verify")],-1)),e[876]||(e[876]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[877]||(e[877]=l('
julia
verify(op)
Verify the operation and return true if it passes, false if it fails.
source
',3))]),t("details",Di,[t("summary",null,[e[878]||(e[878]=t("a",{id:"Reactant.MLIR.IR.verifyall-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.verifyall-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.verifyall")],-1)),e[879]||(e[879]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[880]||(e[880]=l('
julia
verifyall(operation; debug=false)
Prints the operations which could not be verified.
source
',3))]),t("details",Oi,[t("summary",null,[e[881]||(e[881]=t("a",{id:"Reactant.MLIR.IR.@affinemap-Tuple{Any}",href:"#Reactant.MLIR.IR.@affinemap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.@affinemap")],-1)),e[882]||(e[882]=s()),i(a,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),e[883]||(e[883]=l(`
julia
@affinemap (d1, d2, d3, ...)[s1, s2, ...] -> (d0 + d1, ...)
Returns an affine map from the provided Julia expression. On the right hand side are allowed the following function calls:
  • +, *, ÷, %, fld, cld
The rhs can only contains dimensions and symbols present on the left hand side or integer literals.
julia
julia> using Reactant.MLIR: IR
 
 julia> IR.context!(IR.Context()) do
            IR.@affinemap (d1, d2)[s0] -> (d1 + s0, d2 % 10)
        end
-MLIR.IR.AffineMap(#= (d0, d1)[s0] -> (d0 + s0, d1 mod 10) =#)
source
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std::pair",-1)),t[996]||(t[996]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L9740-L9744",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",al,[e("summary",null,[t[997]||(t[997]=e("a",{id:"Reactant.MLIR.API.LLVMValueRef",href:"#Reactant.MLIR.API.LLVMValueRef"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMValueRef")],-1)),t[998]||(t[998]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[999]||(t[999]=e("p",null,"Represents an individual value in LLVM IR.",-1)),t[1e3]||(t[1e3]=e("p",null,"This models llvm::Value.",-1)),t[1001]||(t[1001]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L9704-L9708",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",il,[e("summary",null,[t[1002]||(t[1002]=e("a",{id:"Reactant.MLIR.API.MlirDiagnostic",href:"#Reactant.MLIR.API.MlirDiagnostic"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirDiagnostic")],-1)),t[1003]||(t[1003]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1004]||(t[1004]=l('
julia
MlirDiagnostic
An opaque reference to a diagnostic, always owned by the diagnostics engine (context). Must not be stored outside of the diagnostic handler.
source
',3))]),e("details",ll,[e("summary",null,[t[1005]||(t[1005]=e("a",{id:"Reactant.MLIR.API.MlirDiagnosticHandler",href:"#Reactant.MLIR.API.MlirDiagnosticHandler"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirDiagnosticHandler")],-1)),t[1006]||(t[1006]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1007]||(t[1007]=e("p",null,[s("Diagnostic handler type. Accepts a reference to a diagnostic, which is only guaranteed to be live during the call. The handler is passed the "),e("code",null,"userData"),s(" that was provided when the handler was attached to a context. If the handler processed the diagnostic completely, it is expected to return success. Otherwise, it is expected to return failure to indicate that other handlers should attempt to process the diagnostic.")],-1)),t[1008]||(t[1008]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L6744-L6746",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",nl,[e("summary",null,[t[1009]||(t[1009]=e("a",{id:"Reactant.MLIR.API.MlirDiagnosticHandlerID",href:"#Reactant.MLIR.API.MlirDiagnosticHandlerID"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirDiagnosticHandlerID")],-1)),t[1010]||(t[1010]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1011]||(t[1011]=e("p",null,"Opaque identifier of a diagnostic handler, useful to detach a handler.",-1)),t[1012]||(t[1012]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L6738-L6740",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",pl,[e("summary",null,[t[1013]||(t[1013]=e("a",{id:"Reactant.MLIR.API.MlirDiagnosticSeverity",href:"#Reactant.MLIR.API.MlirDiagnosticSeverity"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirDiagnosticSeverity")],-1)),t[1014]||(t[1014]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1015]||(t[1015]=l('
julia
MlirDiagnosticSeverity
Severity of a diagnostic.
source
',3))]),e("details",rl,[e("summary",null,[t[1016]||(t[1016]=e("a",{id:"Reactant.MLIR.API.MlirExternalPassCallbacks",href:"#Reactant.MLIR.API.MlirExternalPassCallbacks"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirExternalPassCallbacks")],-1)),t[1017]||(t[1017]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1018]||(t[1018]=l('
julia
MlirExternalPassCallbacks
Structure of external MlirPass callbacks. All callbacks are required to be set unless otherwise specified.
FieldNote
constructThis callback is called from the pass is created. This is analogous to a C++ pass constructor.
destructThis callback is called when the pass is destroyed This is analogous to a C++ pass destructor.
initializeThis callback is optional. The callback is called before the pass is run, allowing a chance to initialize any complex state necessary for running the pass. See Pass::initialize(MLIRContext *).
cloneThis callback is called when the pass is cloned. See Pass::clonePass().
runThis callback is called when the pass is run. See Pass::runOnOperation().
source
',4))]),e("details",ol,[e("summary",null,[t[1019]||(t[1019]=e("a",{id:"Reactant.MLIR.API.MlirLlvmThreadPool",href:"#Reactant.MLIR.API.MlirLlvmThreadPool"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirLlvmThreadPool")],-1)),t[1020]||(t[1020]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1021]||(t[1021]=l('
julia
MlirLlvmThreadPool
Re-export llvm::ThreadPool so as to avoid including the LLVM C API directly.
source
',3))]),e("details",dl,[e("summary",null,[t[1022]||(t[1022]=e("a",{id:"Reactant.MLIR.API.MlirLogicalResult",href:"#Reactant.MLIR.API.MlirLogicalResult"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirLogicalResult")],-1)),t[1023]||(t[1023]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1024]||(t[1024]=l('
julia
MlirLogicalResult
A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of MlirLogicalResult must only be inspected using the associated functions.
source
',3))]),e("details",hl,[e("summary",null,[t[1025]||(t[1025]=e("a",{id:"Reactant.MLIR.API.MlirNamedAttribute",href:"#Reactant.MLIR.API.MlirNamedAttribute"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirNamedAttribute")],-1)),t[1026]||(t[1026]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1027]||(t[1027]=l('
julia
MlirNamedAttribute
Named MLIR attribute.
A named attribute is essentially a (name, attribute) pair where the name is a string.
source
',4))]),e("details",cl,[e("summary",null,[t[1028]||(t[1028]=e("a",{id:"Reactant.MLIR.API.MlirOperationState",href:"#Reactant.MLIR.API.MlirOperationState"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirOperationState")],-1)),t[1029]||(t[1029]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1030]||(t[1030]=l('
julia
MlirOperationState
An auxiliary class for constructing operations.
This class contains all the information necessary to construct the operation. It owns the MlirRegions it has pointers to and does not own anything else. By default, the state can be constructed from a name and location, the latter being also used to access the context, and has no other components. These components can be added progressively until the operation is constructed. Users are not expected to rely on the internals of this class and should use mlirOperationState* functions instead.
source
',4))]),e("details",ul,[e("summary",null,[t[1031]||(t[1031]=e("a",{id:"Reactant.MLIR.API.MlirOperationWalkCallback",href:"#Reactant.MLIR.API.MlirOperationWalkCallback"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirOperationWalkCallback")],-1)),t[1032]||(t[1032]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1033]||(t[1033]=e("p",null,[s("Operation walker type. The handler is passed an (opaque) reference to an operation and a pointer to a "),e("code",null,"userData"),s(".")],-1)),t[1034]||(t[1034]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L1495-L1497",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",bl,[e("summary",null,[t[1035]||(t[1035]=e("a",{id:"Reactant.MLIR.API.MlirShapedTypeComponentsCallback",href:"#Reactant.MLIR.API.MlirShapedTypeComponentsCallback"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirShapedTypeComponentsCallback")],-1)),t[1036]||(t[1036]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1037]||(t[1037]=e("p",null,"These callbacks are used to return multiple shaped type components from functions while transferring ownership to the caller. The first argument is the has rank boolean followed by the the rank and a pointer to the shape (if applicable). The next argument is the element type, then the attribute. The last argument is an opaque pointer forwarded to the callback by the caller. This callback will be called potentially multiple times for each shaped type components.",-1)),t[1038]||(t[1038]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L9139-L9141",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",gl,[e("summary",null,[t[1039]||(t[1039]=e("a",{id:"Reactant.MLIR.API.MlirSparseTensorLevelType",href:"#Reactant.MLIR.API.MlirSparseTensorLevelType"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirSparseTensorLevelType")],-1)),t[1040]||(t[1040]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1041]||(t[1041]=e("p",null,"Dimension level types (and properties) that define sparse tensors. See the documentation in SparseTensorAttrDefs.td for their meaning.",-1)),t[1042]||(t[1042]=e("p",null,"These correspond to SparseTensorEncodingAttr::LevelType in the C++ API. If updating, keep them in sync and update the static_assert in the impl file.",-1)),t[1043]||(t[1043]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L8491-L8495",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",yl,[e("summary",null,[t[1044]||(t[1044]=e("a",{id:"Reactant.MLIR.API.MlirStringCallback",href:"#Reactant.MLIR.API.MlirStringCallback"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirStringCallback")],-1)),t[1045]||(t[1045]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1046]||(t[1046]=e("p",null,"A callback for returning string references.",-1)),t[1047]||(t[1047]=e("p",null,[s("This function is called back by the functions that need to return a reference to the portion of the string with the following arguments: - an "),e("a",{href:"/Reactant.jl/previews/PR441/api/mlirc#Reactant.MLIR.API.MlirStringRef"},[e("code",null,"MlirStringRef")]),s(" representing the current portion of the string - a pointer to user data forwarded from the printing call.")],-1)),t[1048]||(t[1048]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L108-L112",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ml,[e("summary",null,[t[1049]||(t[1049]=e("a",{id:"Reactant.MLIR.API.MlirStringRef",href:"#Reactant.MLIR.API.MlirStringRef"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirStringRef")],-1)),t[1050]||(t[1050]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1051]||(t[1051]=l('
julia
MlirStringRef
A pointer to a sized fragment of a string, not necessarily null-terminated. Does not own the underlying string. This is equivalent to llvm::StringRef.
FieldNote
dataPointer to the first symbol.
lengthLength of the fragment.
source
',4))]),e("details",kl,[e("summary",null,[t[1052]||(t[1052]=e("a",{id:"Reactant.MLIR.API.MlirTypesCallback",href:"#Reactant.MLIR.API.MlirTypesCallback"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirTypesCallback")],-1)),t[1053]||(t[1053]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1054]||(t[1054]=e("p",null,"These callbacks are used to return multiple types from functions while transferring ownership to the caller. The first argument is the number of consecutive elements pointed to by the second argument. The third argument is an opaque pointer forwarded to the callback by the caller.",-1)),t[1055]||(t[1055]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L9091-L9093",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Rl,[e("summary",null,[t[1056]||(t[1056]=e("a",{id:"Reactant.MLIR.API.MlirWalkOrder",href:"#Reactant.MLIR.API.MlirWalkOrder"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirWalkOrder")],-1)),t[1057]||(t[1057]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1058]||(t[1058]=l('
julia
MlirWalkOrder
Traversal order for operation walk.
source
',3))]),e("details",fl,[e("summary",null,[t[1059]||(t[1059]=e("a",{id:"Reactant.MLIR.API.MlirWalkResult",href:"#Reactant.MLIR.API.MlirWalkResult"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirWalkResult")],-1)),t[1060]||(t[1060]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1061]||(t[1061]=l('
julia
MlirWalkResult
Operation walk result.
source
',3))]),e("details",Il,[e("summary",null,[t[1062]||(t[1062]=e("a",{id:"Reactant.MLIR.API.LLVMAddSymbol-Tuple{Any, Any}",href:"#Reactant.MLIR.API.LLVMAddSymbol-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMAddSymbol")],-1)),t[1063]||(t[1063]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1064]||(t[1064]=l('
julia
LLVMAddSymbol(symbolName, symbolValue)
This functions permanently adds the symbol symbolName with the value symbolValue. These symbols are searched before any libraries.
See also
sys::DynamicLibrary::AddSymbol()
source
',5))]),e("details",jl,[e("summary",null,[t[1065]||(t[1065]=e("a",{id:"Reactant.MLIR.API.LLVMLoadLibraryPermanently-Tuple{Any}",href:"#Reactant.MLIR.API.LLVMLoadLibraryPermanently-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMLoadLibraryPermanently")],-1)),t[1066]||(t[1066]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1067]||(t[1067]=l('
julia
LLVMLoadLibraryPermanently(Filename)
This function permanently loads the dynamic library at the given path. It is safe to call this function multiple times for the same library.
See also
sys::DynamicLibrary::LoadLibraryPermanently()
source
',5))]),e("details",Ml,[e("summary",null,[t[1068]||(t[1068]=e("a",{id:"Reactant.MLIR.API.LLVMParseCommandLineOptions-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.LLVMParseCommandLineOptions-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMParseCommandLineOptions")],-1)),t[1069]||(t[1069]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1070]||(t[1070]=l('
julia
LLVMParseCommandLineOptions(argc, argv, Overview)
This function parses the given arguments using the LLVM command line parser. Note that the only stable thing about this function is its signature; you cannot rely on any particular set of command line arguments being interpreted the same way across LLVM versions.
See also
llvm:🆑:ParseCommandLineOptions()
source
',5))]),e("details",Al,[e("summary",null,[t[1071]||(t[1071]=e("a",{id:"Reactant.MLIR.API.LLVMSearchForAddressOfSymbol-Tuple{Any}",href:"#Reactant.MLIR.API.LLVMSearchForAddressOfSymbol-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMSearchForAddressOfSymbol")],-1)),t[1072]||(t[1072]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1073]||(t[1073]=l('
julia
LLVMSearchForAddressOfSymbol(symbolName)
This function will search through all previously loaded dynamic libraries for the symbol symbolName. If it is found, the address of that symbol is returned. If not, null is returned.
See also
sys::DynamicLibrary::SearchForAddressOfSymbol()
source
',5))]),e("details",Ll,[e("summary",null,[t[1074]||(t[1074]=e("a",{id:"Reactant.MLIR.API.mlirAffineAddExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineAddExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineAddExprGet")],-1)),t[1075]||(t[1075]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1076]||(t[1076]=l('
julia
mlirAffineAddExprGet(lhs, rhs)
Creates an affine add expression with 'lhs' and 'rhs'.
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',3))]),e("details",El,[e("summary",null,[t[1077]||(t[1077]=e("a",{id:"Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS")],-1)),t[1078]||(t[1078]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1079]||(t[1079]=l('
julia
mlirAffineBinaryOpExprGetLHS(affineExpr)
Returns the left hand side affine expression of the given affine binary operation expression.
source
',3))]),e("details",vl,[e("summary",null,[t[1080]||(t[1080]=e("a",{id:"Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS")],-1)),t[1081]||(t[1081]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1082]||(t[1082]=l('
julia
mlirAffineBinaryOpExprGetRHS(affineExpr)
Returns the right hand side affine expression of the given affine binary operation expression.
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',3))]),e("details",Tl,[e("summary",null,[t[1083]||(t[1083]=e("a",{id:"Reactant.MLIR.API.mlirAffineCeilDivExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineCeilDivExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineCeilDivExprGet")],-1)),t[1084]||(t[1084]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1085]||(t[1085]=l('
julia
mlirAffineCeilDivExprGet(lhs, rhs)
Creates an affine ceildiv expression with 'lhs' and 'rhs'.
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',3))]),e("details",Cl,[e("summary",null,[t[1086]||(t[1086]=e("a",{id:"Reactant.MLIR.API.mlirAffineConstantExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineConstantExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineConstantExprGet")],-1)),t[1087]||(t[1087]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1088]||(t[1088]=l('
julia
mlirAffineConstantExprGet(ctx, constant)
Creates an affine constant expression with 'constant' in the context.
source
',3))]),e("details",xl,[e("summary",null,[t[1089]||(t[1089]=e("a",{id:"Reactant.MLIR.API.mlirAffineConstantExprGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineConstantExprGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineConstantExprGetValue")],-1)),t[1090]||(t[1090]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1091]||(t[1091]=l('
julia
mlirAffineConstantExprGetValue(affineExpr)
Returns the value of the given affine constant expression.
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',3))]),e("details",Fl,[e("summary",null,[t[1092]||(t[1092]=e("a",{id:"Reactant.MLIR.API.mlirAffineDimExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineDimExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineDimExprGet")],-1)),t[1093]||(t[1093]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1094]||(t[1094]=l('
julia
mlirAffineDimExprGet(ctx, position)
Creates an affine dimension expression with 'position' in the context.
source
',3))]),e("details",Pl,[e("summary",null,[t[1095]||(t[1095]=e("a",{id:"Reactant.MLIR.API.mlirAffineDimExprGetPosition-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineDimExprGetPosition-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineDimExprGetPosition")],-1)),t[1096]||(t[1096]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1097]||(t[1097]=l('
julia
mlirAffineDimExprGetPosition(affineExpr)
Returns the position of the given affine dimension expression.
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',3))]),e("details",Dl,[e("summary",null,[t[1098]||(t[1098]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprCompose-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprCompose-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprCompose")],-1)),t[1099]||(t[1099]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1100]||(t[1100]=l('
julia
mlirAffineExprCompose(affineExpr, affineMap)
Composes the given map with the given expression.
source
',3))]),e("details",Ol,[e("summary",null,[t[1101]||(t[1101]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprDump")],-1)),t[1102]||(t[1102]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1103]||(t[1103]=l('
julia
mlirAffineExprDump(affineExpr)
Prints the affine expression to the standard error stream.
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',3))]),e("details",Bl,[e("summary",null,[t[1104]||(t[1104]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprEqual")],-1)),t[1105]||(t[1105]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1106]||(t[1106]=l('
julia
mlirAffineExprEqual(lhs, rhs)
Returns true if the two affine expressions are equal.
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',3))]),e("details",Gl,[e("summary",null,[t[1107]||(t[1107]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprGetContext")],-1)),t[1108]||(t[1108]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1109]||(t[1109]=l('
julia
mlirAffineExprGetContext(affineExpr)
Gets the context that owns the affine expression.
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',3))]),e("details",zl,[e("summary",null,[t[1110]||(t[1110]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor")],-1)),t[1111]||(t[1111]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1112]||(t[1112]=l('
julia
mlirAffineExprGetLargestKnownDivisor(affineExpr)
Returns the greatest known integral divisor of this affine expression. The result is always positive.
source
',3))]),e("details",wl,[e("summary",null,[t[1113]||(t[1113]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAAdd-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAAdd-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAAdd")],-1)),t[1114]||(t[1114]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1115]||(t[1115]=l('
julia
mlirAffineExprIsAAdd(affineExpr)
Checks whether the given affine expression is an add expression.
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',3))]),e("details",Sl,[e("summary",null,[t[1116]||(t[1116]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsABinary-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsABinary-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsABinary")],-1)),t[1117]||(t[1117]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1118]||(t[1118]=l('
julia
mlirAffineExprIsABinary(affineExpr)
Checks whether the given affine expression is binary.
source
',3))]),e("details",Nl,[e("summary",null,[t[1119]||(t[1119]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsACeilDiv-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsACeilDiv-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsACeilDiv")],-1)),t[1120]||(t[1120]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1121]||(t[1121]=l('
julia
mlirAffineExprIsACeilDiv(affineExpr)
Checks whether the given affine expression is an ceildiv expression.
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',3))]),e("details",Vl,[e("summary",null,[t[1122]||(t[1122]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAConstant-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAConstant")],-1)),t[1123]||(t[1123]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1124]||(t[1124]=l('
julia
mlirAffineExprIsAConstant(affineExpr)
Checks whether the given affine expression is a constant expression.
source
',3))]),e("details",ql,[e("summary",null,[t[1125]||(t[1125]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsADim-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsADim-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsADim")],-1)),t[1126]||(t[1126]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1127]||(t[1127]=l('
julia
mlirAffineExprIsADim(affineExpr)
Checks whether the given affine expression is a dimension expression.
source
',3))]),e("details",Ul,[e("summary",null,[t[1128]||(t[1128]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAFloorDiv-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAFloorDiv-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAFloorDiv")],-1)),t[1129]||(t[1129]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1130]||(t[1130]=l('
julia
mlirAffineExprIsAFloorDiv(affineExpr)
Checks whether the given affine expression is an floordiv expression.
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',3))]),e("details",Ql,[e("summary",null,[t[1131]||(t[1131]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAMod-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAMod-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAMod")],-1)),t[1132]||(t[1132]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1133]||(t[1133]=l('
julia
mlirAffineExprIsAMod(affineExpr)
Checks whether the given affine expression is an mod expression.
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',3))]),e("details",Wl,[e("summary",null,[t[1134]||(t[1134]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAMul-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAMul-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAMul")],-1)),t[1135]||(t[1135]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1136]||(t[1136]=l('
julia
mlirAffineExprIsAMul(affineExpr)
Checks whether the given affine expression is an mul expression.
source
',3))]),e("details",Hl,[e("summary",null,[t[1137]||(t[1137]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsASymbol-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsASymbol-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsASymbol")],-1)),t[1138]||(t[1138]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1139]||(t[1139]=l('
julia
mlirAffineExprIsASymbol(affineExpr)
Checks whether the given affine expression is a symbol expression.
source
',3))]),e("details",Zl,[e("summary",null,[t[1140]||(t[1140]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim")],-1)),t[1141]||(t[1141]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1142]||(t[1142]=l('
julia
mlirAffineExprIsFunctionOfDim(affineExpr, position)
Checks whether the given affine expression involves AffineDimExpr 'position'.
source
',3))]),e("details",Jl,[e("summary",null,[t[1143]||(t[1143]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsMultipleOf-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsMultipleOf-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsMultipleOf")],-1)),t[1144]||(t[1144]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1145]||(t[1145]=l('
julia
mlirAffineExprIsMultipleOf(affineExpr, factor)
Checks whether the given affine expression is a multiple of 'factor'.
source
',3))]),e("details",Kl,[e("summary",null,[t[1146]||(t[1146]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsNull")],-1)),t[1147]||(t[1147]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1148]||(t[1148]=l('
julia
mlirAffineExprIsNull(affineExpr)
Returns true if the given affine expression is a null expression. Note constant zero is not a null expression.
source
',3))]),e("details",$l,[e("summary",null,[t[1149]||(t[1149]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsPureAffine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsPureAffine-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsPureAffine")],-1)),t[1150]||(t[1150]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1151]||(t[1151]=l('
julia
mlirAffineExprIsPureAffine(affineExpr)
Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.
source
',3))]),e("details",Xl,[e("summary",null,[t[1152]||(t[1152]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant")],-1)),t[1153]||(t[1153]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1154]||(t[1154]=l('
julia
mlirAffineExprIsSymbolicOrConstant(affineExpr)
Checks whether the given affine expression is made out of only symbols and constants.
source
',3))]),e("details",Yl,[e("summary",null,[t[1155]||(t[1155]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprPrint")],-1)),t[1156]||(t[1156]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1157]||(t[1157]=l('
julia
mlirAffineExprPrint(affineExpr, callback, userData)
Prints an affine expression by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",_l,[e("summary",null,[t[1158]||(t[1158]=e("a",{id:"Reactant.MLIR.API.mlirAffineFloorDivExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineFloorDivExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineFloorDivExprGet")],-1)),t[1159]||(t[1159]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1160]||(t[1160]=l('
julia
mlirAffineFloorDivExprGet(lhs, rhs)
Creates an affine floordiv expression with 'lhs' and 'rhs'.
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',3))]),e("details",tn,[e("summary",null,[t[1161]||(t[1161]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGet")],-1)),t[1162]||(t[1162]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1163]||(t[1163]=l('
julia
mlirAffineMapAttrGet(map)
Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.
source
',3))]),e("details",en,[e("summary",null,[t[1164]||(t[1164]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGetTypeID")],-1)),t[1165]||(t[1165]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1166]||(t[1166]=l('
julia
mlirAffineMapAttrGetTypeID()
Returns the typeID of an AffineMap attribute.
source
',3))]),e("details",sn,[e("summary",null,[t[1167]||(t[1167]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGetValue")],-1)),t[1168]||(t[1168]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1169]||(t[1169]=l('
julia
mlirAffineMapAttrGetValue(attr)
Returns the affine map wrapped in the given affine map attribute.
source
',3))]),e("details",an,[e("summary",null,[t[1170]||(t[1170]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols")],-1)),t[1171]||(t[1171]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1172]||(t[1172]=l('
julia
mlirAffineMapCompressUnusedSymbols(affineMaps, size, result, populateResult)
Returns the simplified affine map resulting from dropping the symbols that do not appear in any of the individual maps in affineMaps. Asserts that all maps in affineMaps are normalized to the same number of dims and symbols. Takes a callback populateResult to fill the res container with value m at entry idx. This allows returning without worrying about ownership considerations.
source
',3))]),e("details",ln,[e("summary",null,[t[1173]||(t[1173]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapConstantGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapConstantGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapConstantGet")],-1)),t[1174]||(t[1174]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1175]||(t[1175]=l('
julia
mlirAffineMapConstantGet(ctx, val)
Creates a single constant result affine map in the context. The affine map is owned by the context.
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',3))]),e("details",nn,[e("summary",null,[t[1176]||(t[1176]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapDump")],-1)),t[1177]||(t[1177]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1178]||(t[1178]=l('
julia
mlirAffineMapDump(affineMap)
Prints the affine map to the standard error stream.
source
',3))]),e("details",pn,[e("summary",null,[t[1179]||(t[1179]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapEmptyGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapEmptyGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapEmptyGet")],-1)),t[1180]||(t[1180]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1181]||(t[1181]=l('
julia
mlirAffineMapEmptyGet(ctx)
Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.
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',3))]),e("details",rn,[e("summary",null,[t[1182]||(t[1182]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapEqual")],-1)),t[1183]||(t[1183]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1184]||(t[1184]=l('
julia
mlirAffineMapEqual(a1, a2)
Checks if two affine maps are equal.
source
',3))]),e("details",on,[e("summary",null,[t[1185]||(t[1185]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGet")],-1)),t[1186]||(t[1186]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1187]||(t[1187]=l('
julia
mlirAffineMapGet(ctx, dimCount, symbolCount, nAffineExprs, affineExprs)
Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.
source
',3))]),e("details",dn,[e("summary",null,[t[1188]||(t[1188]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetContext")],-1)),t[1189]||(t[1189]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1190]||(t[1190]=l('
julia
mlirAffineMapGetContext(affineMap)
Gets the context that the given affine map was created with
source
',3))]),e("details",hn,[e("summary",null,[t[1191]||(t[1191]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetMajorSubMap-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetMajorSubMap-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetMajorSubMap")],-1)),t[1192]||(t[1192]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1193]||(t[1193]=l('
julia
mlirAffineMapGetMajorSubMap(affineMap, numResults)
Returns the affine map consisting of the most major numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.
source
',3))]),e("details",cn,[e("summary",null,[t[1194]||(t[1194]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetMinorSubMap-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetMinorSubMap-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetMinorSubMap")],-1)),t[1195]||(t[1195]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1196]||(t[1196]=l('
julia
mlirAffineMapGetMinorSubMap(affineMap, numResults)
Returns the affine map consisting of the most minor numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.
source
',3))]),e("details",un,[e("summary",null,[t[1197]||(t[1197]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumDims-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumDims")],-1)),t[1198]||(t[1198]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1199]||(t[1199]=l('
julia
mlirAffineMapGetNumDims(affineMap)
Returns the number of dimensions of the given affine map.
source
',3))]),e("details",bn,[e("summary",null,[t[1200]||(t[1200]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumInputs-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumInputs")],-1)),t[1201]||(t[1201]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1202]||(t[1202]=l('
julia
mlirAffineMapGetNumInputs(affineMap)
Returns the number of inputs (dimensions + symbols) of the given affine map.
source
',3))]),e("details",gn,[e("summary",null,[t[1203]||(t[1203]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumResults-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumResults")],-1)),t[1204]||(t[1204]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1205]||(t[1205]=l('
julia
mlirAffineMapGetNumResults(affineMap)
Returns the number of results of the given affine map.
source
',3))]),e("details",yn,[e("summary",null,[t[1206]||(t[1206]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumSymbols-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumSymbols-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumSymbols")],-1)),t[1207]||(t[1207]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1208]||(t[1208]=l('
julia
mlirAffineMapGetNumSymbols(affineMap)
Returns the number of symbols of the given affine map.
source
',3))]),e("details",mn,[e("summary",null,[t[1209]||(t[1209]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetResult-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetResult")],-1)),t[1210]||(t[1210]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1211]||(t[1211]=l('
julia
mlirAffineMapGetResult(affineMap, pos)
Returns the result at the given position.
source
',3))]),e("details",kn,[e("summary",null,[t[1212]||(t[1212]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult")],-1)),t[1213]||(t[1213]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1214]||(t[1214]=l('
julia
mlirAffineMapGetSingleConstantResult(affineMap)
Returns the constant result of the given affine map. The function asserts that the map has a single constant result.
source
',3))]),e("details",Rn,[e("summary",null,[t[1215]||(t[1215]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetSubMap-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetSubMap-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetSubMap")],-1)),t[1216]||(t[1216]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1217]||(t[1217]=l('
julia
mlirAffineMapGetSubMap(affineMap, size, resultPos)
Returns the affine map consisting of the resultPos subset.
source
',3))]),e("details",fn,[e("summary",null,[t[1218]||(t[1218]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsEmpty-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsEmpty")],-1)),t[1219]||(t[1219]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1220]||(t[1220]=l('
julia
mlirAffineMapIsEmpty(affineMap)
Checks whether the given affine map is an empty affine map.
source
',3))]),e("details",In,[e("summary",null,[t[1221]||(t[1221]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsIdentity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsIdentity-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsIdentity")],-1)),t[1222]||(t[1222]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1223]||(t[1223]=l('
julia
mlirAffineMapIsIdentity(affineMap)
Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),e("details",jn,[e("summary",null,[t[1224]||(t[1224]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsMinorIdentity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsMinorIdentity-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsMinorIdentity")],-1)),t[1225]||(t[1225]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1226]||(t[1226]=l('
julia
mlirAffineMapIsMinorIdentity(affineMap)
Checks whether the given affine map is a minor identity affine map.
source
',3))]),e("details",Mn,[e("summary",null,[t[1227]||(t[1227]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsNull")],-1)),t[1228]||(t[1228]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1229]||(t[1229]=l('
julia
mlirAffineMapIsNull(affineMap)
Checks whether an affine map is null.
source
',3))]),e("details",An,[e("summary",null,[t[1230]||(t[1230]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsPermutation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsPermutation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsPermutation")],-1)),t[1231]||(t[1231]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1232]||(t[1232]=l('
julia
mlirAffineMapIsPermutation(affineMap)
Checks whether the given affine map represents a symbol-less permutation map.
source
',3))]),e("details",Ln,[e("summary",null,[t[1233]||(t[1233]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation")],-1)),t[1234]||(t[1234]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1235]||(t[1235]=l('
julia
mlirAffineMapIsProjectedPermutation(affineMap)
Checks whether the given affine map represents a subset of a symbol-less permutation map.
source
',3))]),e("details",En,[e("summary",null,[t[1236]||(t[1236]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsSingleConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsSingleConstant-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsSingleConstant")],-1)),t[1237]||(t[1237]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1238]||(t[1238]=l('
julia
mlirAffineMapIsSingleConstant(affineMap)
Checks whether the given affine map is a single result constant affine map.
source
',3))]),e("details",vn,[e("summary",null,[t[1239]||(t[1239]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapMinorIdentityGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapMinorIdentityGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapMinorIdentityGet")],-1)),t[1240]||(t[1240]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1241]||(t[1241]=l('
julia
mlirAffineMapMinorIdentityGet(ctx, dims, results)
Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),e("details",Tn,[e("summary",null,[t[1242]||(t[1242]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet")],-1)),t[1243]||(t[1243]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1244]||(t[1244]=l('
julia
mlirAffineMapMultiDimIdentityGet(ctx, numDims)
Creates an affine map with 'numDims' identity in the context. The affine map is owned by the context.
source
',3))]),e("details",Cn,[e("summary",null,[t[1245]||(t[1245]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapPermutationGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapPermutationGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapPermutationGet")],-1)),t[1246]||(t[1246]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1247]||(t[1247]=l('
julia
mlirAffineMapPermutationGet(ctx, size, permutation)
Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid permutation.) The affine map is owned by the context.
source
',3))]),e("details",xn,[e("summary",null,[t[1248]||(t[1248]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapPrint")],-1)),t[1249]||(t[1249]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1250]||(t[1250]=l('
julia
mlirAffineMapPrint(affineMap, callback, userData)
Prints an affine map by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",Fn,[e("summary",null,[t[1251]||(t[1251]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapReplace-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAffineMapReplace-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapReplace")],-1)),t[1252]||(t[1252]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1253]||(t[1253]=l('
julia
mlirAffineMapReplace(affineMap, expression, replacement, numResultDims, numResultSyms)
Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.
source
',3))]),e("details",Pn,[e("summary",null,[t[1254]||(t[1254]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapZeroResultGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapZeroResultGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapZeroResultGet")],-1)),t[1255]||(t[1255]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1256]||(t[1256]=l('
julia
mlirAffineMapZeroResultGet(ctx, dimCount, symbolCount)
Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.
source
',3))]),e("details",Dn,[e("summary",null,[t[1257]||(t[1257]=e("a",{id:"Reactant.MLIR.API.mlirAffineModExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineModExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineModExprGet")],-1)),t[1258]||(t[1258]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1259]||(t[1259]=l('
julia
mlirAffineModExprGet(lhs, rhs)
Creates an affine mod expression with 'lhs' and 'rhs'.
source
',3))]),e("details",On,[e("summary",null,[t[1260]||(t[1260]=e("a",{id:"Reactant.MLIR.API.mlirAffineMulExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMulExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMulExprGet")],-1)),t[1261]||(t[1261]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1262]||(t[1262]=l('
julia
mlirAffineMulExprGet(lhs, rhs)
Creates an affine mul expression with 'lhs' and 'rhs'.
source
',3))]),e("details",Bn,[e("summary",null,[t[1263]||(t[1263]=e("a",{id:"Reactant.MLIR.API.mlirAffineSymbolExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineSymbolExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineSymbolExprGet")],-1)),t[1264]||(t[1264]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1265]||(t[1265]=l('
julia
mlirAffineSymbolExprGet(ctx, position)
Creates an affine symbol expression with 'position' in the context.
source
',3))]),e("details",Gn,[e("summary",null,[t[1266]||(t[1266]=e("a",{id:"Reactant.MLIR.API.mlirAffineSymbolExprGetPosition-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineSymbolExprGetPosition-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineSymbolExprGetPosition")],-1)),t[1267]||(t[1267]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1268]||(t[1268]=l('
julia
mlirAffineSymbolExprGetPosition(affineExpr)
Returns the position of the given affine symbol expression.
source
',3))]),e("details",zn,[e("summary",null,[t[1269]||(t[1269]=e("a",{id:"Reactant.MLIR.API.mlirAnyQuantizedTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAnyQuantizedTypeGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAnyQuantizedTypeGet")],-1)),t[1270]||(t[1270]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1271]||(t[1271]=l('
julia
mlirAnyQuantizedTypeGet(flags, storageType, expressedType, storageTypeMin, storageTypeMax)
Creates an instance of AnyQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.
source
',3))]),e("details",wn,[e("summary",null,[t[1272]||(t[1272]=e("a",{id:"Reactant.MLIR.API.mlirArrayAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGet")],-1)),t[1273]||(t[1273]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1274]||(t[1274]=l('
julia
mlirArrayAttrGet(ctx, numElements, elements)
Creates an array element containing the given list of elements in the given context.
source
',3))]),e("details",Sn,[e("summary",null,[t[1275]||(t[1275]=e("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGetElement-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetElement")],-1)),t[1276]||(t[1276]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1277]||(t[1277]=l('
julia
mlirArrayAttrGetElement(attr, pos)
Returns pos-th element stored in the given array attribute.
source
',3))]),e("details",Nn,[e("summary",null,[t[1278]||(t[1278]=e("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGetNumElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetNumElements")],-1)),t[1279]||(t[1279]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1280]||(t[1280]=l('
julia
mlirArrayAttrGetNumElements(attr)
Returns the number of elements stored in the given array attribute.
source
',3))]),e("details",Vn,[e("summary",null,[t[1281]||(t[1281]=e("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirArrayAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetTypeID")],-1)),t[1282]||(t[1282]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1283]||(t[1283]=l('
julia
mlirArrayAttrGetTypeID()
Returns the typeID of an Array attribute.
source
',3))]),e("details",qn,[e("summary",null,[t[1284]||(t[1284]=e("a",{id:"Reactant.MLIR.API.mlirAsmStateCreateForOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAsmStateCreateForOperation-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateCreateForOperation")],-1)),t[1285]||(t[1285]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1286]||(t[1286]=l('
julia
mlirAsmStateCreateForOperation(op, flags)
Creates new AsmState, as with AsmState the IR should not be mutated in-between using this state. Must be freed with a call to mlirAsmStateDestroy().
source
',3))]),e("details",Un,[e("summary",null,[t[1287]||(t[1287]=e("a",{id:"Reactant.MLIR.API.mlirAsmStateCreateForValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAsmStateCreateForValue-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateCreateForValue")],-1)),t[1288]||(t[1288]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1289]||(t[1289]=l('
julia
mlirAsmStateCreateForValue(value, flags)
Creates new AsmState from value. Must be freed with a call to mlirAsmStateDestroy().
source
',3))]),e("details",Qn,[e("summary",null,[t[1290]||(t[1290]=e("a",{id:"Reactant.MLIR.API.mlirAsmStateDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAsmStateDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateDestroy")],-1)),t[1291]||(t[1291]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1292]||(t[1292]=l('
julia
mlirAsmStateDestroy(state)
Destroys printing flags created with mlirAsmStateCreate.
source
',3))]),e("details",Wn,[e("summary",null,[t[1293]||(t[1293]=e("a",{id:"Reactant.MLIR.API.mlirAttributeDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeDump")],-1)),t[1294]||(t[1294]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1295]||(t[1295]=l('
julia
mlirAttributeDump(attr)
Prints the attribute to the standard error stream.
source
',3))]),e("details",Hn,[e("summary",null,[t[1296]||(t[1296]=e("a",{id:"Reactant.MLIR.API.mlirAttributeEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAttributeEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeEqual")],-1)),t[1297]||(t[1297]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1298]||(t[1298]=l('
julia
mlirAttributeEqual(a1, a2)
Checks if two attributes are equal.
source
',3))]),e("details",Zn,[e("summary",null,[t[1299]||(t[1299]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetContext")],-1)),t[1300]||(t[1300]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1301]||(t[1301]=l('
julia
mlirAttributeGetContext(attribute)
Gets the context that an attribute was created with.
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',3))]),e("details",Jn,[e("summary",null,[t[1302]||(t[1302]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetDialect-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetDialect-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetDialect")],-1)),t[1303]||(t[1303]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1304]||(t[1304]=l('
julia
mlirAttributeGetDialect(attribute)
Gets the dialect of the attribute.
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',3))]),e("details",Kn,[e("summary",null,[t[1305]||(t[1305]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetNull-Tuple{}",href:"#Reactant.MLIR.API.mlirAttributeGetNull-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetNull")],-1)),t[1306]||(t[1306]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1307]||(t[1307]=l('
julia
mlirAttributeGetNull()
Returns an empty attribute.
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',3))]),e("details",$n,[e("summary",null,[t[1308]||(t[1308]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetType")],-1)),t[1309]||(t[1309]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1310]||(t[1310]=l('
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mlirAttributeGetType(attribute)
Gets the type of this attribute.
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',3))]),e("details",Xn,[e("summary",null,[t[1311]||(t[1311]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetTypeID-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetTypeID")],-1)),t[1312]||(t[1312]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1313]||(t[1313]=l('
julia
mlirAttributeGetTypeID(attribute)
Gets the type id of the attribute.
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',3))]),e("details",Yn,[e("summary",null,[t[1314]||(t[1314]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAAffineMap-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAAffineMap")],-1)),t[1315]||(t[1315]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1316]||(t[1316]=l('
julia
mlirAttributeIsAAffineMap(attr)
Checks whether the given attribute is an affine map attribute.
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',3))]),e("details",_n,[e("summary",null,[t[1317]||(t[1317]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAArray-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAArray-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAArray")],-1)),t[1318]||(t[1318]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1319]||(t[1319]=l('
julia
mlirAttributeIsAArray(attr)
Checks whether the given attribute is an array attribute.
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',3))]),e("details",tp,[e("summary",null,[t[1320]||(t[1320]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsABool-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsABool-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsABool")],-1)),t[1321]||(t[1321]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1322]||(t[1322]=l('
julia
mlirAttributeIsABool(attr)
Checks whether the given attribute is a bool attribute.
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',3))]),e("details",ep,[e("summary",null,[t[1323]||(t[1323]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsADenseBoolArray-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADenseBoolArray-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADenseBoolArray")],-1)),t[1324]||(t[1324]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1325]||(t[1325]=l('
julia
mlirAttributeIsADenseBoolArray(attr)
Checks whether the given attribute is a dense array attribute.
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',3))]),e("details",sp,[e("summary",null,[t[1326]||(t[1326]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsADenseElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADenseElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADenseElements")],-1)),t[1327]||(t[1327]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1328]||(t[1328]=l('
julia
mlirAttributeIsADenseElements(attr)
Checks whether the given attribute is a dense elements attribute.
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',3))]),e("details",ap,[e("summary",null,[t[1329]||(t[1329]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsADictionary-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADictionary-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADictionary")],-1)),t[1330]||(t[1330]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1331]||(t[1331]=l('
julia
mlirAttributeIsADictionary(attr)
Checks whether the given attribute is a dictionary attribute.
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',3))]),e("details",ip,[e("summary",null,[t[1332]||(t[1332]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAElements")],-1)),t[1333]||(t[1333]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1334]||(t[1334]=l('
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mlirAttributeIsAElements(attr)
Checks whether the given attribute is an elements attribute.
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mlirAttributeIsAFlatSymbolRef(attr)
Checks whether the given attribute is a flat symbol reference attribute.
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',3))]),e("details",np,[e("summary",null,[t[1338]||(t[1338]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAFloat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAFloat-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAFloat")],-1)),t[1339]||(t[1339]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1340]||(t[1340]=l('
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mlirAttributeIsAFloat(attr)
Checks whether the given attribute is a floating point attribute.
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',3))]),e("details",pp,[e("summary",null,[t[1341]||(t[1341]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAInteger-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAInteger-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAInteger")],-1)),t[1342]||(t[1342]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1343]||(t[1343]=l('
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mlirAttributeIsAInteger(attr)
Checks whether the given attribute is an integer attribute.
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',3))]),e("details",rp,[e("summary",null,[t[1344]||(t[1344]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAIntegerSet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAIntegerSet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAIntegerSet")],-1)),t[1345]||(t[1345]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1346]||(t[1346]=l('
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mlirAttributeIsAIntegerSet(attr)
Checks whether the given attribute is an integer set attribute.
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',3))]),e("details",op,[e("summary",null,[t[1347]||(t[1347]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAOpaque-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAOpaque")],-1)),t[1348]||(t[1348]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1349]||(t[1349]=l('
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mlirAttributeIsAOpaque(attr)
Checks whether the given attribute is an opaque attribute.
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',3))]),e("details",dp,[e("summary",null,[t[1350]||(t[1350]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsASparseElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASparseElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASparseElements")],-1)),t[1351]||(t[1351]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1352]||(t[1352]=l('
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mlirAttributeIsASparseElements(attr)
Checks whether the given attribute is a sparse elements attribute.
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mlirAttributeIsASparseTensorEncodingAttr(attr)
Checks whether the given attribute is a sparse\\_tensor.encoding attribute.
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',3))]),e("details",cp,[e("summary",null,[t[1356]||(t[1356]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAString-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAString-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAString")],-1)),t[1357]||(t[1357]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1358]||(t[1358]=l('
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mlirAttributeIsAString(attr)
Checks whether the given attribute is a string attribute.
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',3))]),e("details",up,[e("summary",null,[t[1359]||(t[1359]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsASymbolRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASymbolRef-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASymbolRef")],-1)),t[1360]||(t[1360]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1361]||(t[1361]=l('
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mlirAttributeIsASymbolRef(attr)
Checks whether the given attribute is a symbol reference attribute.
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',3))]),e("details",bp,[e("summary",null,[t[1362]||(t[1362]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAType")],-1)),t[1363]||(t[1363]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1364]||(t[1364]=l('
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mlirAttributeIsAType(attr)
Checks whether the given attribute is a type attribute.
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',3))]),e("details",gp,[e("summary",null,[t[1365]||(t[1365]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAUnit-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAUnit-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAUnit")],-1)),t[1366]||(t[1366]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1367]||(t[1367]=l('
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mlirAttributeIsAUnit(attr)
Checks whether the given attribute is a unit attribute.
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',3))]),e("details",yp,[e("summary",null,[t[1368]||(t[1368]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsNull")],-1)),t[1369]||(t[1369]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1370]||(t[1370]=l('
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mlirAttributeIsNull(attr)
Checks whether an attribute is null.
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',3))]),e("details",mp,[e("summary",null,[t[1371]||(t[1371]=e("a",{id:"Reactant.MLIR.API.mlirAttributeParseGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAttributeParseGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeParseGet")],-1)),t[1372]||(t[1372]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1373]||(t[1373]=l('
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mlirAttributeParseGet(context, attr)
Parses an attribute. The attribute is owned by the context.
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',3))]),e("details",kp,[e("summary",null,[t[1374]||(t[1374]=e("a",{id:"Reactant.MLIR.API.mlirAttributePrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAttributePrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributePrint")],-1)),t[1375]||(t[1375]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1376]||(t[1376]=l('
julia
mlirAttributePrint(attr, callback, userData)
Prints an attribute by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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',3))]),e("details",Rp,[e("summary",null,[t[1377]||(t[1377]=e("a",{id:"Reactant.MLIR.API.mlirBF16TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBF16TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBF16TypeGet")],-1)),t[1378]||(t[1378]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1379]||(t[1379]=l('
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mlirBF16TypeGet(ctx)
Creates a bf16 type in the given context. The type is owned by the context.
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',3))]),e("details",fp,[e("summary",null,[t[1380]||(t[1380]=e("a",{id:"Reactant.MLIR.API.mlirBFloat16TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirBFloat16TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBFloat16TypeGetTypeID")],-1)),t[1381]||(t[1381]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1382]||(t[1382]=l('
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mlirBFloat16TypeGetTypeID()
Returns the typeID of an BFloat16 type.
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',3))]),e("details",Ip,[e("summary",null,[t[1383]||(t[1383]=e("a",{id:"Reactant.MLIR.API.mlirBlockAddArgument-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockAddArgument-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockAddArgument")],-1)),t[1384]||(t[1384]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1385]||(t[1385]=l('
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mlirBlockAddArgument(block, type, loc)
Appends an argument of the specified type to the block. Returns the newly added argument.
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',3))]),e("details",jp,[e("summary",null,[t[1386]||(t[1386]=e("a",{id:"Reactant.MLIR.API.mlirBlockAppendOwnedOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockAppendOwnedOperation-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockAppendOwnedOperation")],-1)),t[1387]||(t[1387]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1388]||(t[1388]=l('
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mlirBlockAppendOwnedOperation(block, operation)
Takes an operation owned by the caller and appends it to the block.
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',3))]),e("details",Mp,[e("summary",null,[t[1389]||(t[1389]=e("a",{id:"Reactant.MLIR.API.mlirBlockArgumentGetArgNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentGetArgNumber-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentGetArgNumber")],-1)),t[1390]||(t[1390]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1391]||(t[1391]=l('
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mlirBlockArgumentGetArgNumber(value)
Returns the position of the value in the argument list of its block.
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',3))]),e("details",Ap,[e("summary",null,[t[1392]||(t[1392]=e("a",{id:"Reactant.MLIR.API.mlirBlockArgumentGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentGetOwner-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentGetOwner")],-1)),t[1393]||(t[1393]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1394]||(t[1394]=l('
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mlirBlockArgumentGetOwner(value)
Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.
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',3))]),e("details",Lp,[e("summary",null,[t[1395]||(t[1395]=e("a",{id:"Reactant.MLIR.API.mlirBlockArgumentSetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentSetType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentSetType")],-1)),t[1396]||(t[1396]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1397]||(t[1397]=l('
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mlirBlockArgumentSetType(value, type)
Sets the type of the block argument to the given type.
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',3))]),e("details",Ep,[e("summary",null,[t[1398]||(t[1398]=e("a",{id:"Reactant.MLIR.API.mlirBlockCreate-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockCreate-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockCreate")],-1)),t[1399]||(t[1399]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1400]||(t[1400]=l('
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mlirBlockCreate(nArgs, args, locs)
Creates a new empty block with the given argument types and transfers ownership to the caller.
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',3))]),e("details",vp,[e("summary",null,[t[1401]||(t[1401]=e("a",{id:"Reactant.MLIR.API.mlirBlockDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockDestroy")],-1)),t[1402]||(t[1402]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1403]||(t[1403]=l('
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mlirBlockDestroy(block)
Takes a block owned by the caller and destroys it.
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',3))]),e("details",Tp,[e("summary",null,[t[1404]||(t[1404]=e("a",{id:"Reactant.MLIR.API.mlirBlockDetach-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockDetach-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockDetach")],-1)),t[1405]||(t[1405]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1406]||(t[1406]=l('
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mlirBlockDetach(block)
Detach a block from the owning region and assume ownership.
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',3))]),e("details",Cp,[e("summary",null,[t[1407]||(t[1407]=e("a",{id:"Reactant.MLIR.API.mlirBlockEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockEqual")],-1)),t[1408]||(t[1408]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1409]||(t[1409]=l('
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mlirBlockEqual(block, other)
Checks whether two blocks handles point to the same block. This does not perform deep comparison.
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',3))]),e("details",xp,[e("summary",null,[t[1410]||(t[1410]=e("a",{id:"Reactant.MLIR.API.mlirBlockEraseArgument-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockEraseArgument-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockEraseArgument")],-1)),t[1411]||(t[1411]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1412]||(t[1412]=l('
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mlirBlockEraseArgument(block, index)
Erase the argument at 'index' and remove it from the argument list.
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',3))]),e("details",Fp,[e("summary",null,[t[1413]||(t[1413]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetArgument-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockGetArgument-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetArgument")],-1)),t[1414]||(t[1414]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1415]||(t[1415]=l('
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mlirBlockGetArgument(block, pos)
Returns pos-th argument of the block.
source
',3))]),e("details",Pp,[e("summary",null,[t[1416]||(t[1416]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetFirstOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetFirstOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetFirstOperation")],-1)),t[1417]||(t[1417]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1418]||(t[1418]=l('
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mlirBlockGetFirstOperation(block)
Returns the first operation in the block.
source
',3))]),e("details",Dp,[e("summary",null,[t[1419]||(t[1419]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetNextInRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetNextInRegion-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetNextInRegion")],-1)),t[1420]||(t[1420]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1421]||(t[1421]=l('
julia
mlirBlockGetNextInRegion(block)
Returns the block immediately following the given block in its parent region.
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',3))]),e("details",Op,[e("summary",null,[t[1422]||(t[1422]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetNumArguments-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetNumArguments-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetNumArguments")],-1)),t[1423]||(t[1423]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1424]||(t[1424]=l('
julia
mlirBlockGetNumArguments(block)
Returns the number of arguments of the block.
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',3))]),e("details",Bp,[e("summary",null,[t[1425]||(t[1425]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetParentOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetParentOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetParentOperation")],-1)),t[1426]||(t[1426]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1427]||(t[1427]=l('
julia
mlirBlockGetParentOperation(arg1)
Returns the closest surrounding operation that contains this block.
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',3))]),e("details",Gp,[e("summary",null,[t[1428]||(t[1428]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetParentRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetParentRegion-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetParentRegion")],-1)),t[1429]||(t[1429]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1430]||(t[1430]=l('
julia
mlirBlockGetParentRegion(block)
Returns the region that contains this block.
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',3))]),e("details",zp,[e("summary",null,[t[1431]||(t[1431]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetTerminator-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetTerminator-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetTerminator")],-1)),t[1432]||(t[1432]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1433]||(t[1433]=l('
julia
mlirBlockGetTerminator(block)
Returns the terminator operation in the block or null if no terminator.
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',3))]),e("details",wp,[e("summary",null,[t[1434]||(t[1434]=e("a",{id:"Reactant.MLIR.API.mlirBlockInsertArgument-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertArgument-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertArgument")],-1)),t[1435]||(t[1435]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1436]||(t[1436]=l('
julia
mlirBlockInsertArgument(block, pos, type, loc)
Inserts an argument of the specified type at a specified index to the block. Returns the newly added argument.
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',3))]),e("details",Sp,[e("summary",null,[t[1437]||(t[1437]=e("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperation-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperation")],-1)),t[1438]||(t[1438]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1439]||(t[1439]=l('
julia
mlirBlockInsertOwnedOperation(block, pos, operation)
Takes an operation owned by the caller and inserts it as pos to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.
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',3))]),e("details",Np,[e("summary",null,[t[1440]||(t[1440]=e("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter")],-1)),t[1441]||(t[1441]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1442]||(t[1442]=l('
julia
mlirBlockInsertOwnedOperationAfter(block, reference, operation)
Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.
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',3))]),e("details",Vp,[e("summary",null,[t[1443]||(t[1443]=e("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore")],-1)),t[1444]||(t[1444]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1445]||(t[1445]=l('
julia
mlirBlockInsertOwnedOperationBefore(block, reference, operation)
Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.
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',3))]),e("details",qp,[e("summary",null,[t[1446]||(t[1446]=e("a",{id:"Reactant.MLIR.API.mlirBlockIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockIsNull")],-1)),t[1447]||(t[1447]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1448]||(t[1448]=l('
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mlirBlockIsNull(block)
Checks whether a block is null.
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',3))]),e("details",Up,[e("summary",null,[t[1449]||(t[1449]=e("a",{id:"Reactant.MLIR.API.mlirBlockPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockPrint")],-1)),t[1450]||(t[1450]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1451]||(t[1451]=l('
julia
mlirBlockPrint(block, callback, userData)
Prints a block by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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',3))]),e("details",Qp,[e("summary",null,[t[1452]||(t[1452]=e("a",{id:"Reactant.MLIR.API.mlirBoolAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBoolAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBoolAttrGet")],-1)),t[1453]||(t[1453]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1454]||(t[1454]=l('
julia
mlirBoolAttrGet(ctx, value)
Creates a bool attribute in the given context with the given value.
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',3))]),e("details",Wp,[e("summary",null,[t[1455]||(t[1455]=e("a",{id:"Reactant.MLIR.API.mlirBoolAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBoolAttrGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBoolAttrGetValue")],-1)),t[1456]||(t[1456]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1457]||(t[1457]=l('
julia
mlirBoolAttrGetValue(attr)
Returns the value stored in the given bool attribute.
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',3))]),e("details",Hp,[e("summary",null,[t[1458]||(t[1458]=e("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigCreate")],-1)),t[1459]||(t[1459]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1460]||(t[1460]=l('
julia
mlirBytecodeWriterConfigCreate()
Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirBytecodeWriterConfigDestroy().
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',3))]),e("details",Zp,[e("summary",null,[t[1461]||(t[1461]=e("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion")],-1)),t[1462]||(t[1462]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1463]||(t[1463]=l('
julia
mlirBytecodeWriterConfigDesiredEmitVersion(flags, version)
Sets the version to emit in the writer config.
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',3))]),e("details",Jp,[e("summary",null,[t[1464]||(t[1464]=e("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy")],-1)),t[1465]||(t[1465]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1466]||(t[1466]=l('
julia
mlirBytecodeWriterConfigDestroy(config)
Destroys printing flags created with mlirBytecodeWriterConfigCreate.
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',3))]),e("details",Kp,[e("summary",null,[t[1467]||(t[1467]=e("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet")],-1)),t[1468]||(t[1468]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1469]||(t[1469]=l('
julia
mlirCalibratedQuantizedTypeGet(expressedType, min, max)
Creates an instance of CalibratedQuantizedType with the given parameters in the same context as expressedType and returns it. The instance is owned by the context.
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',3))]),e("details",$p,[e("summary",null,[t[1470]||(t[1470]=e("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax-Tuple{Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax")],-1)),t[1471]||(t[1471]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1472]||(t[1472]=l('
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mlirCalibratedQuantizedTypeGetMax(type)
Returns the max value of the given calibrated quantized type.
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',3))]),e("details",Xp,[e("summary",null,[t[1473]||(t[1473]=e("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin-Tuple{Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin")],-1)),t[1474]||(t[1474]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1475]||(t[1475]=l('
julia
mlirCalibratedQuantizedTypeGetMin(type)
Returns the min value of the given calibrated quantized type.
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',3))]),e("details",Yp,[e("summary",null,[t[1476]||(t[1476]=e("a",{id:"Reactant.MLIR.API.mlirComplexTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirComplexTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGet")],-1)),t[1477]||(t[1477]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1478]||(t[1478]=l('
julia
mlirComplexTypeGet(elementType)
Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.
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',3))]),e("details",_p,[e("summary",null,[t[1479]||(t[1479]=e("a",{id:"Reactant.MLIR.API.mlirComplexTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirComplexTypeGetElementType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGetElementType")],-1)),t[1480]||(t[1480]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1481]||(t[1481]=l('
julia
mlirComplexTypeGetElementType(type)
Returns the element type of the given complex type.
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',3))]),e("details",tr,[e("summary",null,[t[1482]||(t[1482]=e("a",{id:"Reactant.MLIR.API.mlirComplexTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirComplexTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGetTypeID")],-1)),t[1483]||(t[1483]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1484]||(t[1484]=l('
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mlirComplexTypeGetTypeID()
Returns the typeID of an Complex type.
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',3))]),e("details",er,[e("summary",null,[t[1485]||(t[1485]=e("a",{id:"Reactant.MLIR.API.mlirContextAppendDialectRegistry-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextAppendDialectRegistry-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextAppendDialectRegistry")],-1)),t[1486]||(t[1486]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1487]||(t[1487]=l('
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mlirContextAppendDialectRegistry(ctx, registry)
Append the contents of the given dialect registry to the registry associated with the context.
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',3))]),e("details",sr,[e("summary",null,[t[1488]||(t[1488]=e("a",{id:"Reactant.MLIR.API.mlirContextAttachDiagnosticHandler-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirContextAttachDiagnosticHandler-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextAttachDiagnosticHandler")],-1)),t[1489]||(t[1489]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1490]||(t[1490]=l('
julia
mlirContextAttachDiagnosticHandler(context, handler, userData, deleteUserData)
Attaches the diagnostic handler to the context. Handlers are invoked in the reverse order of attachment until one of them processes the diagnostic completely. When a handler is invoked it is passed the userData that was provided when it was attached. If non-NULL, deleteUserData is called once the system no longer needs to call the handler (for instance after the handler is detached or the context is destroyed). Returns an identifier that can be used to detach the handler.
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',3))]),e("details",ar,[e("summary",null,[t[1491]||(t[1491]=e("a",{id:"Reactant.MLIR.API.mlirContextCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirContextCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreate")],-1)),t[1492]||(t[1492]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1493]||(t[1493]=l('
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mlirContextCreate()
Creates an MLIR context and transfers its ownership to the caller. This sets the default multithreading option (enabled).
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',3))]),e("details",ir,[e("summary",null,[t[1494]||(t[1494]=e("a",{id:"Reactant.MLIR.API.mlirContextCreateWithRegistry-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextCreateWithRegistry-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreateWithRegistry")],-1)),t[1495]||(t[1495]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1496]||(t[1496]=l('
julia
mlirContextCreateWithRegistry(registry, threadingEnabled)
Creates an MLIR context, setting the multithreading setting explicitly and pre-loading the dialects from the provided DialectRegistry.
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',3))]),e("details",lr,[e("summary",null,[t[1497]||(t[1497]=e("a",{id:"Reactant.MLIR.API.mlirContextCreateWithThreading-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextCreateWithThreading-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreateWithThreading")],-1)),t[1498]||(t[1498]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1499]||(t[1499]=l('
julia
mlirContextCreateWithThreading(threadingEnabled)
Creates an MLIR context with an explicit setting of the multithreading setting and transfers its ownership to the caller.
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',3))]),e("details",nr,[e("summary",null,[t[1500]||(t[1500]=e("a",{id:"Reactant.MLIR.API.mlirContextDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextDestroy")],-1)),t[1501]||(t[1501]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1502]||(t[1502]=l('
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mlirContextDestroy(context)
Takes an MLIR context owned by the caller and destroys it.
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',3))]),e("details",pr,[e("summary",null,[t[1503]||(t[1503]=e("a",{id:"Reactant.MLIR.API.mlirContextDetachDiagnosticHandler-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextDetachDiagnosticHandler-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextDetachDiagnosticHandler")],-1)),t[1504]||(t[1504]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1505]||(t[1505]=l('
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mlirContextDetachDiagnosticHandler(context, id)
Detaches an attached diagnostic handler from the context given its identifier.
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',3))]),e("details",rr,[e("summary",null,[t[1506]||(t[1506]=e("a",{id:"Reactant.MLIR.API.mlirContextEnableMultithreading-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextEnableMultithreading-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextEnableMultithreading")],-1)),t[1507]||(t[1507]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1508]||(t[1508]=l('
julia
mlirContextEnableMultithreading(context, enable)
Set threading mode (must be set to false to mlir-print-ir-after-all).
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',3))]),e("details",or,[e("summary",null,[t[1509]||(t[1509]=e("a",{id:"Reactant.MLIR.API.mlirContextEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextEqual")],-1)),t[1510]||(t[1510]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1511]||(t[1511]=l('
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mlirContextEqual(ctx1, ctx2)
Checks if two contexts are equal.
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',3))]),e("details",dr,[e("summary",null,[t[1512]||(t[1512]=e("a",{id:"Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects")],-1)),t[1513]||(t[1513]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1514]||(t[1514]=l('
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mlirContextGetAllowUnregisteredDialects(context)
Returns whether the context allows unregistered dialects.
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mlirContextGetNumLoadedDialects(context)
Returns the number of dialects loaded by the context.
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',3))]),e("details",cr,[e("summary",null,[t[1518]||(t[1518]=e("a",{id:"Reactant.MLIR.API.mlirContextGetNumRegisteredDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetNumRegisteredDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetNumRegisteredDialects")],-1)),t[1519]||(t[1519]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1520]||(t[1520]=l('
julia
mlirContextGetNumRegisteredDialects(context)
Returns the number of dialects registered with the given context. A registered dialect will be loaded if needed by the parser.
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julia
mlirContextGetOrLoadDialect(context, name)
Gets the dialect instance owned by the given context using the dialect namespace to identify it, loads (i.e., constructs the instance of) the dialect if necessary. If the dialect is not registered with the context, returns null. Use mlirContextLoad<Name>Dialect to load an unregistered dialect.
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',3))]),e("details",br,[e("summary",null,[t[1524]||(t[1524]=e("a",{id:"Reactant.MLIR.API.mlirContextIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextIsNull")],-1)),t[1525]||(t[1525]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1526]||(t[1526]=l('
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mlirContextIsNull(context)
Checks whether a context is null.
source
',3))]),e("details",gr,[e("summary",null,[t[1527]||(t[1527]=e("a",{id:"Reactant.MLIR.API.mlirContextIsRegisteredOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextIsRegisteredOperation-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextIsRegisteredOperation")],-1)),t[1528]||(t[1528]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1529]||(t[1529]=l('
julia
mlirContextIsRegisteredOperation(context, name)
Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.
source
',3))]),e("details",yr,[e("summary",null,[t[1530]||(t[1530]=e("a",{id:"Reactant.MLIR.API.mlirContextLoadAllAvailableDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextLoadAllAvailableDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextLoadAllAvailableDialects")],-1)),t[1531]||(t[1531]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1532]||(t[1532]=l('
julia
mlirContextLoadAllAvailableDialects(context)
Eagerly loads all available dialects registered with a context, making them available for use for IR construction.
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',3))]),e("details",mr,[e("summary",null,[t[1533]||(t[1533]=e("a",{id:"Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects")],-1)),t[1534]||(t[1534]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1535]||(t[1535]=l('
julia
mlirContextSetAllowUnregisteredDialects(context, allow)
Sets whether unregistered dialects are allowed in this context.
source
',3))]),e("details",kr,[e("summary",null,[t[1536]||(t[1536]=e("a",{id:"Reactant.MLIR.API.mlirContextSetThreadPool-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextSetThreadPool-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextSetThreadPool")],-1)),t[1537]||(t[1537]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1538]||(t[1538]=l('
julia
mlirContextSetThreadPool(context, threadPool)
Sets the thread pool of the context explicitly, enabling multithreading in the process. This API should be used to avoid re-creating thread pools in long-running applications that perform multiple compilations, see the C++ documentation for MLIRContext for details.
source
',3))]),e("details",Rr,[e("summary",null,[t[1539]||(t[1539]=e("a",{id:"Reactant.MLIR.API.mlirCreateExternalPass-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirCreateExternalPass-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCreateExternalPass")],-1)),t[1540]||(t[1540]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1541]||(t[1541]=l('
julia
mlirCreateExternalPass(passID, name, argument, description, opName, nDependentDialects, dependentDialects, callbacks, userData)
Creates an external MlirPass that calls the supplied callbacks using the supplied userData. If opName is empty, the pass is a generic operation pass. Otherwise it is an operation pass specific to the specified pass name.
source
',3))]),e("details",fr,[e("summary",null,[t[1542]||(t[1542]=e("a",{id:"Reactant.MLIR.API.mlirDenseArrayGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseArrayGetNumElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseArrayGetNumElements")],-1)),t[1543]||(t[1543]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1544]||(t[1544]=l('
julia
mlirDenseArrayGetNumElements(attr)
Get the size of a dense array.
source
',3))]),e("details",Ir,[e("summary",null,[t[1545]||(t[1545]=e("a",{id:"Reactant.MLIR.API.mlirDenseBoolArrayGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolArrayGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolArrayGet")],-1)),t[1546]||(t[1546]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1547]||(t[1547]=l('
julia
mlirDenseBoolArrayGet(ctx, size, values)
Create a dense array attribute with the given elements.
source
',3))]),e("details",jr,[e("summary",null,[t[1548]||(t[1548]=e("a",{id:"Reactant.MLIR.API.mlirDenseBoolArrayGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolArrayGetElement-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolArrayGetElement")],-1)),t[1549]||(t[1549]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1550]||(t[1550]=l('
julia
mlirDenseBoolArrayGetElement(attr, pos)
Get an element of a dense array.
source
',3))]),e("details",Mr,[e("summary",null,[t[1551]||(t[1551]=e("a",{id:"Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue")],-1)),t[1552]||(t[1552]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1553]||(t[1553]=l('
julia
mlirDenseBoolResourceElementsAttrGetValue(attr, pos)
Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense resource elements attribute.
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',3))]),e("details",Ar,[e("summary",null,[t[1554]||(t[1554]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrBoolGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrBoolGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrBoolGet")],-1)),t[1555]||(t[1555]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1556]||(t[1556]=l('
julia
mlirDenseElementsAttrBoolGet(shapedType, numElements, elements)
Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.
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',3))]),e("details",Lr,[e("summary",null,[t[1557]||(t[1557]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGet")],-1)),t[1558]||(t[1558]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1559]||(t[1559]=l('
julia
mlirDenseElementsAttrGet(shapedType, numElements, elements)
Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.
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',3))]),e("details",Er,[e("summary",null,[t[1560]||(t[1560]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue")],-1)),t[1561]||(t[1561]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1562]||(t[1562]=l('
julia
mlirDenseElementsAttrGetBoolValue(attr, pos)
Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense elements attribute.
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',3))]),e("details",vr,[e("summary",null,[t[1563]||(t[1563]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetRawData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetRawData-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetRawData")],-1)),t[1564]||(t[1564]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1565]||(t[1565]=l('
julia
mlirDenseElementsAttrGetRawData(attr)
Returns the raw data of the given dense elements attribute.
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',3))]),e("details",Tr,[e("summary",null,[t[1566]||(t[1566]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue")],-1)),t[1567]||(t[1567]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1568]||(t[1568]=l('
julia
mlirDenseElementsAttrGetSplatValue(attr)
Returns the single replicated value (splat) of a specific type contained by the given dense elements attribute.
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',3))]),e("details",Cr,[e("summary",null,[t[1569]||(t[1569]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrIsSplat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrIsSplat-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrIsSplat")],-1)),t[1570]||(t[1570]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1571]||(t[1571]=l('
julia
mlirDenseElementsAttrIsSplat(attr)
Checks whether the given dense elements attribute contains a single replicated value (splat).
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',3))]),e("details",xr,[e("summary",null,[t[1572]||(t[1572]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet")],-1)),t[1573]||(t[1573]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1574]||(t[1574]=l('
julia
mlirDenseElementsAttrRawBufferGet(shapedType, rawBufferSize, rawBuffer)
Creates a dense elements attribute with the given Shaped type and elements populated from a packed, row-major opaque buffer of contents.
The format of the raw buffer is a densely packed array of values that can be bitcast to the storage format of the element type specified. Types that are not byte aligned will be: - For bitwidth > 1: Rounded up to the next byte. - For bitwidth = 1: Packed into 8bit bytes with bits corresponding to the linear order of the shape type from MSB to LSB, padded to on the right.
A raw buffer of a single element (or for 1-bit, a byte of value 0 or 255) will be interpreted as a splat. User code should be prepared for additional, conformant patterns to be identified as splats in the future.
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',5))]),e("details",Fr,[e("summary",null,[t[1575]||(t[1575]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet")],-1)),t[1576]||(t[1576]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1577]||(t[1577]=l('
julia
mlirDenseElementsAttrReshapeGet(attr, shapedType)
Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.
source
',3))]),e("details",Pr,[e("summary",null,[t[1578]||(t[1578]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrSplatGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrSplatGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrSplatGet")],-1)),t[1579]||(t[1579]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1580]||(t[1580]=l('
julia
mlirDenseElementsAttrSplatGet(shapedType, element)
Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).
source
',3))]),e("details",Dr,[e("summary",null,[t[1581]||(t[1581]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrStringGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrStringGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrStringGet")],-1)),t[1582]||(t[1582]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1583]||(t[1583]=l('
julia
mlirDenseElementsAttrStringGet(shapedType, numElements, strs)
Creates a dense elements attribute with the given shaped type from string elements.
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',3))]),e("details",Or,[e("summary",null,[t[1584]||(t[1584]=e("a",{id:"Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID")],-1)),t[1585]||(t[1585]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1586]||(t[1586]=l('
julia
mlirDenseIntOrFPElementsAttrGetTypeID()
Returns the typeID of an DenseIntOrFPElements attribute.
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',3))]),e("details",Br,[e("summary",null,[t[1587]||(t[1587]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetLocation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetLocation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetLocation")],-1)),t[1588]||(t[1588]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1589]||(t[1589]=l('
julia
mlirDiagnosticGetLocation(diagnostic)
Returns the location at which the diagnostic is reported.
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',3))]),e("details",Gr,[e("summary",null,[t[1590]||(t[1590]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetNote-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetNote-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetNote")],-1)),t[1591]||(t[1591]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1592]||(t[1592]=l('
julia
mlirDiagnosticGetNote(diagnostic, pos)
Returns pos-th note attached to the diagnostic. Expects pos to be a valid zero-based index into the list of notes.
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',3))]),e("details",zr,[e("summary",null,[t[1593]||(t[1593]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetNumNotes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetNumNotes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetNumNotes")],-1)),t[1594]||(t[1594]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1595]||(t[1595]=l('
julia
mlirDiagnosticGetNumNotes(diagnostic)
Returns the number of notes attached to the diagnostic.
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',3))]),e("details",wr,[e("summary",null,[t[1596]||(t[1596]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetSeverity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetSeverity-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetSeverity")],-1)),t[1597]||(t[1597]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1598]||(t[1598]=l('
julia
mlirDiagnosticGetSeverity(diagnostic)
Returns the severity of the diagnostic.
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',3))]),e("details",Sr,[e("summary",null,[t[1599]||(t[1599]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDiagnosticPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticPrint")],-1)),t[1600]||(t[1600]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1601]||(t[1601]=l('
julia
mlirDiagnosticPrint(diagnostic, callback, userData)
Prints a diagnostic using the provided callback.
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',3))]),e("details",Nr,[e("summary",null,[t[1602]||(t[1602]=e("a",{id:"Reactant.MLIR.API.mlirDialectEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectEqual")],-1)),t[1603]||(t[1603]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1604]||(t[1604]=l('
julia
mlirDialectEqual(dialect1, dialect2)
Checks if two dialects that belong to the same context are equal. Dialects from different contexts will not compare equal.
source
',3))]),e("details",Vr,[e("summary",null,[t[1605]||(t[1605]=e("a",{id:"Reactant.MLIR.API.mlirDialectGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectGetContext")],-1)),t[1606]||(t[1606]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1607]||(t[1607]=l('
julia
mlirDialectGetContext(dialect)
Returns the context that owns the dialect.
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',3))]),e("details",qr,[e("summary",null,[t[1608]||(t[1608]=e("a",{id:"Reactant.MLIR.API.mlirDialectGetNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectGetNamespace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectGetNamespace")],-1)),t[1609]||(t[1609]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1610]||(t[1610]=l('
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mlirDialectGetNamespace(dialect)
Returns the namespace of the given dialect.
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',3))]),e("details",Ur,[e("summary",null,[t[1611]||(t[1611]=e("a",{id:"Reactant.MLIR.API.mlirDialectHandleGetNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectHandleGetNamespace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleGetNamespace")],-1)),t[1612]||(t[1612]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1613]||(t[1613]=l('
julia
mlirDialectHandleGetNamespace(arg1)
Returns the namespace associated with the provided dialect handle.
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',3))]),e("details",Qr,[e("summary",null,[t[1614]||(t[1614]=e("a",{id:"Reactant.MLIR.API.mlirDialectHandleInsertDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleInsertDialect-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleInsertDialect")],-1)),t[1615]||(t[1615]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1616]||(t[1616]=l('
julia
mlirDialectHandleInsertDialect(arg1, arg2)
Inserts the dialect associated with the provided dialect handle into the provided dialect registry
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',3))]),e("details",Wr,[e("summary",null,[t[1617]||(t[1617]=e("a",{id:"Reactant.MLIR.API.mlirDialectHandleLoadDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleLoadDialect-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleLoadDialect")],-1)),t[1618]||(t[1618]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1619]||(t[1619]=l('
julia
mlirDialectHandleLoadDialect(arg1, arg2)
Loads the dialect associated with the provided dialect handle.
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',3))]),e("details",Hr,[e("summary",null,[t[1620]||(t[1620]=e("a",{id:"Reactant.MLIR.API.mlirDialectHandleRegisterDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleRegisterDialect-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleRegisterDialect")],-1)),t[1621]||(t[1621]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1622]||(t[1622]=l('
julia
mlirDialectHandleRegisterDialect(arg1, arg2)
Registers the dialect associated with the provided dialect handle.
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',3))]),e("details",Zr,[e("summary",null,[t[1623]||(t[1623]=e("a",{id:"Reactant.MLIR.API.mlirDialectIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectIsNull")],-1)),t[1624]||(t[1624]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1625]||(t[1625]=l('
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mlirDialectIsNull(dialect)
Checks if the dialect is null.
source
',3))]),e("details",Jr,[e("summary",null,[t[1626]||(t[1626]=e("a",{id:"Reactant.MLIR.API.mlirDialectRegistryCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirDialectRegistryCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryCreate")],-1)),t[1627]||(t[1627]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1628]||(t[1628]=l('
julia
mlirDialectRegistryCreate()
Creates a dialect registry and transfers its ownership to the caller.
source
',3))]),e("details",Kr,[e("summary",null,[t[1629]||(t[1629]=e("a",{id:"Reactant.MLIR.API.mlirDialectRegistryDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectRegistryDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryDestroy")],-1)),t[1630]||(t[1630]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1631]||(t[1631]=l('
julia
mlirDialectRegistryDestroy(registry)
Takes a dialect registry owned by the caller and destroys it.
source
',3))]),e("details",$r,[e("summary",null,[t[1632]||(t[1632]=e("a",{id:"Reactant.MLIR.API.mlirDialectRegistryIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectRegistryIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryIsNull")],-1)),t[1633]||(t[1633]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1634]||(t[1634]=l('
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mlirDialectRegistryIsNull(registry)
Checks if the dialect registry is null.
source
',3))]),e("details",Xr,[e("summary",null,[t[1635]||(t[1635]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGet")],-1)),t[1636]||(t[1636]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1637]||(t[1637]=l('
julia
mlirDictionaryAttrGet(ctx, numElements, elements)
Creates a dictionary attribute containing the given list of elements in the provided context.
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',3))]),e("details",Yr,[e("summary",null,[t[1638]||(t[1638]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetElement-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetElement")],-1)),t[1639]||(t[1639]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1640]||(t[1640]=l('
julia
mlirDictionaryAttrGetElement(attr, pos)
Returns pos-th element of the given dictionary attribute.
source
',3))]),e("details",_r,[e("summary",null,[t[1641]||(t[1641]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetElementByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetElementByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetElementByName")],-1)),t[1642]||(t[1642]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1643]||(t[1643]=l('
julia
mlirDictionaryAttrGetElementByName(attr, name)
Returns the dictionary attribute element with the given name or NULL if the given name does not exist in the dictionary.
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',3))]),e("details",to,[e("summary",null,[t[1644]||(t[1644]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetNumElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetNumElements")],-1)),t[1645]||(t[1645]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1646]||(t[1646]=l('
julia
mlirDictionaryAttrGetNumElements(attr)
Returns the number of attributes contained in a dictionary attribute.
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',3))]),e("details",eo,[e("summary",null,[t[1647]||(t[1647]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetTypeID")],-1)),t[1648]||(t[1648]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1649]||(t[1649]=l('
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mlirDictionaryAttrGetTypeID()
Returns the typeID of a Dictionary attribute.
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',3))]),e("details",so,[e("summary",null,[t[1650]||(t[1650]=e("a",{id:"Reactant.MLIR.API.mlirDisctinctAttrCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDisctinctAttrCreate-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDisctinctAttrCreate")],-1)),t[1651]||(t[1651]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1652]||(t[1652]=l('
julia
mlirDisctinctAttrCreate(referencedAttr)
Creates a DisctinctAttr with the referenced attribute.
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',3))]),e("details",ao,[e("summary",null,[t[1653]||(t[1653]=e("a",{id:"Reactant.MLIR.API.mlirElementsAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirElementsAttrGetNumElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrGetNumElements")],-1)),t[1654]||(t[1654]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1655]||(t[1655]=l('
julia
mlirElementsAttrGetNumElements(attr)
Gets the total number of elements in the given elements attribute. In order to iterate over the attribute, obtain its type, which must be a statically shaped type and use its sizes to build a multi-dimensional index.
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',3))]),e("details",io,[e("summary",null,[t[1656]||(t[1656]=e("a",{id:"Reactant.MLIR.API.mlirElementsAttrGetValue-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirElementsAttrGetValue-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrGetValue")],-1)),t[1657]||(t[1657]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1658]||(t[1658]=l('
julia
mlirElementsAttrGetValue(attr, rank, idxs)
Returns the element at the given rank-dimensional index.
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julia
mlirElementsAttrIsValidIndex(attr, rank, idxs)
Checks whether the given rank-dimensional index is valid in the given elements attribute.
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',3))]),e("details",no,[e("summary",null,[t[1662]||(t[1662]=e("a",{id:"Reactant.MLIR.API.mlirEmitError-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirEmitError-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirEmitError")],-1)),t[1663]||(t[1663]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1664]||(t[1664]=l('
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mlirEmitError(location, message)
Emits an error at the given location through the diagnostics engine. Used for testing purposes.
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',3))]),e("details",po,[e("summary",null,[t[1665]||(t[1665]=e("a",{id:"Reactant.MLIR.API.mlirEnableGlobalDebug-Tuple{Any}",href:"#Reactant.MLIR.API.mlirEnableGlobalDebug-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirEnableGlobalDebug")],-1)),t[1666]||(t[1666]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1667]||(t[1667]=l('
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mlirEnableGlobalDebug(enable)
Sets the global debugging flag.
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',3))]),e("details",ro,[e("summary",null,[t[1668]||(t[1668]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineCreate-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineCreate-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineCreate")],-1)),t[1669]||(t[1669]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1670]||(t[1670]=l('
julia
mlirExecutionEngineCreate(op, optLevel, numPaths, sharedLibPaths, enableObjectDump)
Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.
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',3))]),e("details",oo,[e("summary",null,[t[1671]||(t[1671]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineDestroy")],-1)),t[1672]||(t[1672]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1673]||(t[1673]=l('
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mlirExecutionEngineDestroy(jit)
Destroy an ExecutionEngine instance.
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julia
mlirExecutionEngineDumpToObjectFile(jit, fileName)
Dump as an object in fileName.
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mlirExecutionEngineInvokePacked(jit, name, arguments)
Invoke a native function in the execution engine by name with the arguments and result of the invoked function passed as an array of pointers. The function must have been tagged with the llvm.emit\\_c\\_interface attribute. Returns a failure if the execution fails for any reason (the function name can't be resolved for instance).
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',3))]),e("details",uo,[e("summary",null,[t[1680]||(t[1680]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineIsNull")],-1)),t[1681]||(t[1681]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1682]||(t[1682]=l('
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mlirExecutionEngineIsNull(jit)
Checks whether an execution engine is null.
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mlirExecutionEngineLookup(jit, name)
Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.
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mlirExecutionEngineLookupPacked(jit, name)
Lookup the wrapper of the native function in the execution engine with the given name, returns nullptr if the function can't be looked-up.
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mlirExecutionEngineRegisterSymbol(jit, name, sym)
Register a symbol with the jit: this symbol will be accessible to the jitted code.
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mlirExternalPassSignalFailure(pass)
This signals that the pass has failed. This is only valid to call during the run callback of MlirExternalPassCallbacks. See Pass::signalPassFailure().
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mlirF16TypeGet(ctx)
Creates an f16 type in the given context. The type is owned by the context.
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mlirF32TypeGet(ctx)
Creates an f32 type in the given context. The type is owned by the context.
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',3))]),e("details",fo,[e("summary",null,[t[1701]||(t[1701]=e("a",{id:"Reactant.MLIR.API.mlirF64TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF64TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF64TypeGet")],-1)),t[1702]||(t[1702]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1703]||(t[1703]=l('
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mlirF64TypeGet(ctx)
Creates a f64 type in the given context. The type is owned by the context.
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',3))]),e("details",Io,[e("summary",null,[t[1704]||(t[1704]=e("a",{id:"Reactant.MLIR.API.mlirFlatSymbolRefAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirFlatSymbolRefAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFlatSymbolRefAttrGet")],-1)),t[1705]||(t[1705]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1706]||(t[1706]=l('
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mlirFlatSymbolRefAttrGet(ctx, symbol)
Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.
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mlirFlatSymbolRefAttrGetValue(attr)
Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.
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mlirFloat16TypeGetTypeID()
Returns the typeID of an Float16 type.
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',3))]),e("details",Ao,[e("summary",null,[t[1713]||(t[1713]=e("a",{id:"Reactant.MLIR.API.mlirFloat32TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat32TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat32TypeGetTypeID")],-1)),t[1714]||(t[1714]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1715]||(t[1715]=l('
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mlirFloat32TypeGetTypeID()
Returns the typeID of an Float32 type.
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julia
mlirFloat4E2M1FNTypeGet(ctx)
Creates an f4E2M1FN type in the given context. The type is owned by the context.
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',3))]),e("details",Eo,[e("summary",null,[t[1719]||(t[1719]=e("a",{id:"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID")],-1)),t[1720]||(t[1720]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1721]||(t[1721]=l('
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mlirFloat4E2M1FNTypeGetTypeID()
Returns the typeID of an Float4E2M1FN type.
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',3))]),e("details",vo,[e("summary",null,[t[1722]||(t[1722]=e("a",{id:"Reactant.MLIR.API.mlirFloat64TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat64TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat64TypeGetTypeID")],-1)),t[1723]||(t[1723]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1724]||(t[1724]=l('
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mlirFloat64TypeGetTypeID()
Returns the typeID of an Float64 type.
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',3))]),e("details",To,[e("summary",null,[t[1725]||(t[1725]=e("a",{id:"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet")],-1)),t[1726]||(t[1726]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1727]||(t[1727]=l('
julia
mlirFloat6E2M3FNTypeGet(ctx)
Creates an f6E2M3FN type in the given context. The type is owned by the context.
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',3))]),e("details",Co,[e("summary",null,[t[1728]||(t[1728]=e("a",{id:"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID")],-1)),t[1729]||(t[1729]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1730]||(t[1730]=l('
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mlirFloat6E2M3FNTypeGetTypeID()
Returns the typeID of an Float6E2M3FN type.
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',3))]),e("details",xo,[e("summary",null,[t[1731]||(t[1731]=e("a",{id:"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet")],-1)),t[1732]||(t[1732]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1733]||(t[1733]=l('
julia
mlirFloat6E3M2FNTypeGet(ctx)
Creates an f6E3M2FN type in the given context. The type is owned by the context.
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',3))]),e("details",Fo,[e("summary",null,[t[1734]||(t[1734]=e("a",{id:"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID")],-1)),t[1735]||(t[1735]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1736]||(t[1736]=l('
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mlirFloat6E3M2FNTypeGetTypeID()
Returns the typeID of an Float6E3M2FN type.
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julia
mlirFloat8E3M4TypeGet(ctx)
Creates an f8E3M4 type in the given context. The type is owned by the context.
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',3))]),e("details",Do,[e("summary",null,[t[1740]||(t[1740]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID")],-1)),t[1741]||(t[1741]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1742]||(t[1742]=l('
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mlirFloat8E3M4TypeGetTypeID()
Returns the typeID of an Float8E3M4 type.
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mlirFloat8E4M3B11FNUZTypeGet(ctx)
Creates an f8E4M3B11FNUZ type in the given context. The type is owned by the context.
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mlirFloat8E4M3B11FNUZTypeGetTypeID()
Returns the typeID of an Float8E4M3B11FNUZ type.
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julia
mlirFloat8E4M3FNTypeGet(ctx)
Creates an f8E4M3FN type in the given context. The type is owned by the context.
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',3))]),e("details",zo,[e("summary",null,[t[1752]||(t[1752]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID")],-1)),t[1753]||(t[1753]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1754]||(t[1754]=l('
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mlirFloat8E4M3FNTypeGetTypeID()
Returns the typeID of an Float8E4M3FN type.
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',3))]),e("details",wo,[e("summary",null,[t[1755]||(t[1755]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet")],-1)),t[1756]||(t[1756]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1757]||(t[1757]=l('
julia
mlirFloat8E4M3FNUZTypeGet(ctx)
Creates an f8E4M3FNUZ type in the given context. The type is owned by the context.
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',3))]),e("details",So,[e("summary",null,[t[1758]||(t[1758]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID")],-1)),t[1759]||(t[1759]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1760]||(t[1760]=l('
julia
mlirFloat8E4M3FNUZTypeGetTypeID()
Returns the typeID of an Float8E4M3FNUZ type.
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julia
mlirFloat8E4M3TypeGet(ctx)
Creates an f8E4M3 type in the given context. The type is owned by the context.
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',3))]),e("details",Vo,[e("summary",null,[t[1764]||(t[1764]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID")],-1)),t[1765]||(t[1765]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1766]||(t[1766]=l('
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mlirFloat8E4M3TypeGetTypeID()
Returns the typeID of an Float8E4M3 type.
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julia
mlirFloat8E5M2FNUZTypeGet(ctx)
Creates an f8E5M2FNUZ type in the given context. The type is owned by the context.
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',3))]),e("details",Uo,[e("summary",null,[t[1770]||(t[1770]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID")],-1)),t[1771]||(t[1771]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1772]||(t[1772]=l('
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mlirFloat8E5M2FNUZTypeGetTypeID()
Returns the typeID of an Float8E5M2FNUZ type.
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julia
mlirFloat8E5M2TypeGet(ctx)
Creates an f8E5M2 type in the given context. The type is owned by the context.
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',3))]),e("details",Wo,[e("summary",null,[t[1776]||(t[1776]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID")],-1)),t[1777]||(t[1777]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1778]||(t[1778]=l('
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mlirFloat8E5M2TypeGetTypeID()
Returns the typeID of an Float8E5M2 type.
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',3))]),e("details",Ho,[e("summary",null,[t[1779]||(t[1779]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet")],-1)),t[1780]||(t[1780]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1781]||(t[1781]=l('
julia
mlirFloat8E8M0FNUTypeGet(ctx)
Creates an f8E8M0FNU type in the given context. The type is owned by the context.
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mlirFloat8E8M0FNUTypeGetTypeID()
Returns the typeID of an Float8E8M0FNU type.
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mlirFloatAttrDoubleGet(ctx, type, value)
Creates a floating point attribute in the given context with the given double value and double-precision FP semantics.
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julia
mlirFloatAttrDoubleGetChecked(loc, type, value)
Same as "mlirFloatAttrDoubleGet", but if the type is not valid for a construction of a FloatAttr, returns a null MlirAttribute.
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mlirFloatAttrGetTypeID()
Returns the typeID of a Float attribute.
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',3))]),e("details",Xo,[e("summary",null,[t[1794]||(t[1794]=e("a",{id:"Reactant.MLIR.API.mlirFloatAttrGetValueDouble-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloatAttrGetValueDouble-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrGetValueDouble")],-1)),t[1795]||(t[1795]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1796]||(t[1796]=l('
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mlirFloatAttrGetValueDouble(attr)
Returns the value stored in the given floating point attribute, interpreting the value as double.
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mlirFloatTF32TypeGetTypeID()
Returns the typeID of a TF32 type.
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mlirFloatTypeGetWidth(type)
Returns the bitwidth of a floating-point type.
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mlirFreezeRewritePattern(op)
FrozenRewritePatternSet API
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',3))]),e("details",ed,[e("summary",null,[t[1806]||(t[1806]=e("a",{id:"Reactant.MLIR.API.mlirFuncSetArgAttr-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirFuncSetArgAttr-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFuncSetArgAttr")],-1)),t[1807]||(t[1807]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1808]||(t[1808]=l('
julia
mlirFuncSetArgAttr(op, pos, name, attr)
Sets the argument attribute 'name' of an argument at index 'pos'. Asserts that the operation is a FuncOp.
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',3))]),e("details",sd,[e("summary",null,[t[1809]||(t[1809]=e("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGet")],-1)),t[1810]||(t[1810]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1811]||(t[1811]=l('
julia
mlirFunctionTypeGet(ctx, numInputs, inputs, numResults, results)
Creates a function type, mapping a list of input types to result types.
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mlirFunctionTypeGetInput(type, pos)
Returns the pos-th input type.
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julia
mlirFunctionTypeGetNumInputs(type)
Returns the number of input types.
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',3))]),e("details",ld,[e("summary",null,[t[1818]||(t[1818]=e("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetNumResults-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetNumResults")],-1)),t[1819]||(t[1819]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1820]||(t[1820]=l('
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mlirFunctionTypeGetNumResults(type)
Returns the number of result types.
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mlirFunctionTypeGetResult(type, pos)
Returns the pos-th result type.
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mlirFunctionTypeGetTypeID()
Returns the typeID of an Function type.
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mlirIRRewriterCreate(context)
Create an IRRewriter and transfer ownership to the caller.
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',3))]),e("details",od,[e("summary",null,[t[1830]||(t[1830]=e("a",{id:"Reactant.MLIR.API.mlirIRRewriterCreateFromOp-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIRRewriterCreateFromOp-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIRRewriterCreateFromOp")],-1)),t[1831]||(t[1831]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1832]||(t[1832]=l('
julia
mlirIRRewriterCreateFromOp(op)
Create an IRRewriter and transfer ownership to the caller. Additionally set the insertion point before the operation.
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',3))]),e("details",dd,[e("summary",null,[t[1833]||(t[1833]=e("a",{id:"Reactant.MLIR.API.mlirIRRewriterDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIRRewriterDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIRRewriterDestroy")],-1)),t[1834]||(t[1834]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1835]||(t[1835]=l('
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mlirIRRewriterDestroy(rewriter)
Takes an IRRewriter owned by the caller and destroys it. It is the responsibility of the user to only pass an IRRewriter class.
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mlirIdentifierEqual(ident, other)
Checks whether two identifiers are the same.
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',3))]),e("details",cd,[e("summary",null,[t[1839]||(t[1839]=e("a",{id:"Reactant.MLIR.API.mlirIdentifierGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIdentifierGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierGet")],-1)),t[1840]||(t[1840]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1841]||(t[1841]=l('
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mlirIdentifierGet(context, str)
Gets an identifier with the given string value.
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',3))]),e("details",ud,[e("summary",null,[t[1842]||(t[1842]=e("a",{id:"Reactant.MLIR.API.mlirIdentifierGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIdentifierGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierGetContext")],-1)),t[1843]||(t[1843]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1844]||(t[1844]=l('
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mlirIdentifierGetContext(arg1)
Returns the context associated with this identifier
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',3))]),e("details",bd,[e("summary",null,[t[1845]||(t[1845]=e("a",{id:"Reactant.MLIR.API.mlirIdentifierStr-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIdentifierStr-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierStr")],-1)),t[1846]||(t[1846]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1847]||(t[1847]=l('
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mlirIdentifierStr(ident)
Gets the string value of the identifier.
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',3))]),e("details",gd,[e("summary",null,[t[1848]||(t[1848]=e("a",{id:"Reactant.MLIR.API.mlirIndexTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIndexTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIndexTypeGet")],-1)),t[1849]||(t[1849]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1850]||(t[1850]=l('
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mlirIndexTypeGet(ctx)
Creates an index type in the given context. The type is owned by the context.
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',3))]),e("details",yd,[e("summary",null,[t[1851]||(t[1851]=e("a",{id:"Reactant.MLIR.API.mlirIndexTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIndexTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIndexTypeGetTypeID")],-1)),t[1852]||(t[1852]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1853]||(t[1853]=l('
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mlirIndexTypeGetTypeID()
Returns the typeID of an Index type.
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',3))]),e("details",md,[e("summary",null,[t[1854]||(t[1854]=e("a",{id:"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes-NTuple{11, Any}",href:"#Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes-NTuple{11, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes")],-1)),t[1855]||(t[1855]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1856]||(t[1856]=l('
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mlirInferShapedTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)
Infers the return shaped type components of the operation. Calls callback with the types of inferred arguments on success. Returns failure otherwise.
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',3))]),e("details",kd,[e("summary",null,[t[1857]||(t[1857]=e("a",{id:"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID")],-1)),t[1858]||(t[1858]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1859]||(t[1859]=l('
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mlirInferShapedTypeOpInterfaceTypeID()
Returns the interface TypeID of the InferShapedTypeOpInterface.
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',3))]),e("details",Rd,[e("summary",null,[t[1860]||(t[1860]=e("a",{id:"Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes-NTuple{11, Any}",href:"#Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes-NTuple{11, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes")],-1)),t[1861]||(t[1861]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1862]||(t[1862]=l('
julia
mlirInferTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)
Infers the return types of the operation identified by its canonical given the arguments that will be supplied to its generic builder. Calls callback with the types of inferred arguments, potentially several times, on success. Returns failure otherwise.
source
',3))]),e("details",fd,[e("summary",null,[t[1863]||(t[1863]=e("a",{id:"Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID")],-1)),t[1864]||(t[1864]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1865]||(t[1865]=l('
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mlirInferTypeOpInterfaceTypeID()
Returns the interface TypeID of the InferTypeOpInterface.
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',3))]),e("details",Id,[e("summary",null,[t[1866]||(t[1866]=e("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGet")],-1)),t[1867]||(t[1867]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1868]||(t[1868]=l('
julia
mlirIntegerAttrGet(type, value)
Creates an integer attribute of the given type with the given integer value.
source
',3))]),e("details",jd,[e("summary",null,[t[1869]||(t[1869]=e("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetTypeID")],-1)),t[1870]||(t[1870]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1871]||(t[1871]=l('
julia
mlirIntegerAttrGetTypeID()
Returns the typeID of an Integer attribute.
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',3))]),e("details",Md,[e("summary",null,[t[1872]||(t[1872]=e("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueInt-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueInt")],-1)),t[1873]||(t[1873]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1874]||(t[1874]=l('
julia
mlirIntegerAttrGetValueInt(attr)
Returns the value stored in the given integer attribute, assuming the value is of signless type and fits into a signed 64-bit integer.
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',3))]),e("details",Ad,[e("summary",null,[t[1875]||(t[1875]=e("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueSInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueSInt-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueSInt")],-1)),t[1876]||(t[1876]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1877]||(t[1877]=l('
julia
mlirIntegerAttrGetValueSInt(attr)
Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.
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',3))]),e("details",Ld,[e("summary",null,[t[1878]||(t[1878]=e("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueUInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueUInt-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueUInt")],-1)),t[1879]||(t[1879]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1880]||(t[1880]=l('
julia
mlirIntegerAttrGetValueUInt(attr)
Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.
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',3))]),e("details",Ed,[e("summary",null,[t[1881]||(t[1881]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetAttrGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetAttrGet")],-1)),t[1882]||(t[1882]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1883]||(t[1883]=l('
julia
mlirIntegerSetAttrGet(set)
Creates an integer set attribute wrapping the given set. The attribute belongs to the same context as the integer set.
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',3))]),e("details",vd,[e("summary",null,[t[1884]||(t[1884]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID")],-1)),t[1885]||(t[1885]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1886]||(t[1886]=l('
julia
mlirIntegerSetAttrGetTypeID()
Returns the typeID of an IntegerSet attribute.
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',3))]),e("details",Td,[e("summary",null,[t[1887]||(t[1887]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetAttrGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetAttrGetValue")],-1)),t[1888]||(t[1888]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1889]||(t[1889]=l('
julia
mlirIntegerSetAttrGetValue(attr)
Returns the integer set wrapped in the given integer set attribute.
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',3))]),e("details",Cd,[e("summary",null,[t[1890]||(t[1890]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetDump")],-1)),t[1891]||(t[1891]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1892]||(t[1892]=l('
julia
mlirIntegerSetDump(set)
Prints an integer set to the standard error stream.
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',3))]),e("details",xd,[e("summary",null,[t[1893]||(t[1893]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetEmptyGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetEmptyGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetEmptyGet")],-1)),t[1894]||(t[1894]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1895]||(t[1895]=l('
julia
mlirIntegerSetEmptyGet(context, numDims, numSymbols)
Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.
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',3))]),e("details",Fd,[e("summary",null,[t[1896]||(t[1896]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetEqual")],-1)),t[1897]||(t[1897]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1898]||(t[1898]=l('
julia
mlirIntegerSetEqual(s1, s2)
Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.
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',3))]),e("details",Pd,[e("summary",null,[t[1899]||(t[1899]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGet-NTuple{6, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGet-NTuple{6, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGet")],-1)),t[1900]||(t[1900]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1901]||(t[1901]=l('
julia
mlirIntegerSetGet(context, numDims, numSymbols, numConstraints, constraints, eqFlags)
Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqFlags is 1) or inequalities (eqFlags is 0). Both constraints and eqFlags are expected to point to at least numConstraint consecutive values.
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',3))]),e("details",Dd,[e("summary",null,[t[1902]||(t[1902]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetConstraint-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetConstraint-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetConstraint")],-1)),t[1903]||(t[1903]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1904]||(t[1904]=l('
julia
mlirIntegerSetGetConstraint(set, pos)
Returns pos-th constraint of the set.
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',3))]),e("details",Od,[e("summary",null,[t[1905]||(t[1905]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetContext")],-1)),t[1906]||(t[1906]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1907]||(t[1907]=l('
julia
mlirIntegerSetGetContext(set)
Gets the context in which the given integer set lives.
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',3))]),e("details",Bd,[e("summary",null,[t[1908]||(t[1908]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumConstraints-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumConstraints-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumConstraints")],-1)),t[1909]||(t[1909]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1910]||(t[1910]=l('
julia
mlirIntegerSetGetNumConstraints(set)
Returns the number of constraints (equalities + inequalities) in the given set.
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',3))]),e("details",Gd,[e("summary",null,[t[1911]||(t[1911]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumDims-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumDims")],-1)),t[1912]||(t[1912]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1913]||(t[1913]=l('
julia
mlirIntegerSetGetNumDims(set)
Returns the number of dimensions in the given set.
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',3))]),e("details",zd,[e("summary",null,[t[1914]||(t[1914]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumEqualities-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumEqualities-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumEqualities")],-1)),t[1915]||(t[1915]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1916]||(t[1916]=l('
julia
mlirIntegerSetGetNumEqualities(set)
Returns the number of equalities in the given set.
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',3))]),e("details",wd,[e("summary",null,[t[1917]||(t[1917]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumInequalities-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumInequalities-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumInequalities")],-1)),t[1918]||(t[1918]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1919]||(t[1919]=l('
julia
mlirIntegerSetGetNumInequalities(set)
Returns the number of inequalities in the given set.
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',3))]),e("details",Sd,[e("summary",null,[t[1920]||(t[1920]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumInputs-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumInputs")],-1)),t[1921]||(t[1921]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1922]||(t[1922]=l('
julia
mlirIntegerSetGetNumInputs(set)
Returns the number of inputs (dimensions + symbols) in the given set.
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',3))]),e("details",Nd,[e("summary",null,[t[1923]||(t[1923]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumSymbols-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumSymbols-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumSymbols")],-1)),t[1924]||(t[1924]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1925]||(t[1925]=l('
julia
mlirIntegerSetGetNumSymbols(set)
Returns the number of symbols in the given set.
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',3))]),e("details",Vd,[e("summary",null,[t[1926]||(t[1926]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty")],-1)),t[1927]||(t[1927]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1928]||(t[1928]=l('
julia
mlirIntegerSetIsCanonicalEmpty(set)
Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.
source
',3))]),e("details",qd,[e("summary",null,[t[1929]||(t[1929]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsConstraintEq-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsConstraintEq-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsConstraintEq")],-1)),t[1930]||(t[1930]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1931]||(t[1931]=l('
julia
mlirIntegerSetIsConstraintEq(set, pos)
Returns true of the pos-th constraint of the set is an equality constraint, false otherwise.
source
',3))]),e("details",Ud,[e("summary",null,[t[1932]||(t[1932]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsNull")],-1)),t[1933]||(t[1933]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1934]||(t[1934]=l('
julia
mlirIntegerSetIsNull(set)
Checks whether an integer set is a null object.
source
',3))]),e("details",Qd,[e("summary",null,[t[1935]||(t[1935]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetPrint")],-1)),t[1936]||(t[1936]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1937]||(t[1937]=l('
julia
mlirIntegerSetPrint(set, callback, userData)
Prints an integer set by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",Wd,[e("summary",null,[t[1938]||(t[1938]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetReplaceGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetReplaceGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetReplaceGet")],-1)),t[1939]||(t[1939]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1940]||(t[1940]=l('
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)
Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.
source
',3))]),e("details",Hd,[e("summary",null,[t[1941]||(t[1941]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGet")],-1)),t[1942]||(t[1942]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1943]||(t[1943]=l('
julia
mlirIntegerTypeGet(ctx, bitwidth)
Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.
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',3))]),e("details",Zd,[e("summary",null,[t[1944]||(t[1944]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGetTypeID")],-1)),t[1945]||(t[1945]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1946]||(t[1946]=l('
julia
mlirIntegerTypeGetTypeID()
Returns the typeID of an Integer type.
source
',3))]),e("details",Jd,[e("summary",null,[t[1947]||(t[1947]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGetWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeGetWidth-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGetWidth")],-1)),t[1948]||(t[1948]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1949]||(t[1949]=l('
julia
mlirIntegerTypeGetWidth(type)
Returns the bitwidth of an integer type.
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',3))]),e("details",Kd,[e("summary",null,[t[1950]||(t[1950]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsSigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsSigned-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsSigned")],-1)),t[1951]||(t[1951]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1952]||(t[1952]=l('
julia
mlirIntegerTypeIsSigned(type)
Checks whether the given integer type is signed.
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',3))]),e("details",$d,[e("summary",null,[t[1953]||(t[1953]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsSignless-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsSignless-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsSignless")],-1)),t[1954]||(t[1954]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1955]||(t[1955]=l('
julia
mlirIntegerTypeIsSignless(type)
Checks whether the given integer type is signless.
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',3))]),e("details",Xd,[e("summary",null,[t[1956]||(t[1956]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsUnsigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsUnsigned-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsUnsigned")],-1)),t[1957]||(t[1957]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1958]||(t[1958]=l('
julia
mlirIntegerTypeIsUnsigned(type)
Checks whether the given integer type is unsigned.
source
',3))]),e("details",Yd,[e("summary",null,[t[1959]||(t[1959]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeSignedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeSignedGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeSignedGet")],-1)),t[1960]||(t[1960]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1961]||(t[1961]=l('
julia
mlirIntegerTypeSignedGet(ctx, bitwidth)
Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.
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',3))]),e("details",_d,[e("summary",null,[t[1962]||(t[1962]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeUnsignedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeUnsignedGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeUnsignedGet")],-1)),t[1963]||(t[1963]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1964]||(t[1964]=l('
julia
mlirIntegerTypeUnsignedGet(ctx, bitwidth)
Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),e("details",th,[e("summary",null,[t[1965]||(t[1965]=e("a",{id:"Reactant.MLIR.API.mlirIsCurrentDebugType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIsCurrentDebugType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIsCurrentDebugType")],-1)),t[1966]||(t[1966]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1967]||(t[1967]=l('
julia
mlirIsCurrentDebugType(type)
Checks if type is set as the current debug type.
source
',3))]),e("details",eh,[e("summary",null,[t[1968]||(t[1968]=e("a",{id:"Reactant.MLIR.API.mlirIsGlobalDebugEnabled-Tuple{}",href:"#Reactant.MLIR.API.mlirIsGlobalDebugEnabled-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIsGlobalDebugEnabled")],-1)),t[1969]||(t[1969]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1970]||(t[1970]=l('
julia
mlirIsGlobalDebugEnabled()
Retuns true if the global debugging flag is set, false otherwise.
source
',3))]),e("details",sh,[e("summary",null,[t[1971]||(t[1971]=e("a",{id:"Reactant.MLIR.API.mlirLLVMArrayTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMArrayTypeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMArrayTypeGet")],-1)),t[1972]||(t[1972]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1973]||(t[1973]=l('
julia
mlirLLVMArrayTypeGet(elementType, numElements)
Creates an llvm.array type.
source
',3))]),e("details",ah,[e("summary",null,[t[1974]||(t[1974]=e("a",{id:"Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType")],-1)),t[1975]||(t[1975]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1976]||(t[1976]=l('
julia
mlirLLVMArrayTypeGetElementType(type)
Returns the element type of the llvm.array type.
source
',3))]),e("details",ih,[e("summary",null,[t[1977]||(t[1977]=e("a",{id:"Reactant.MLIR.API.mlirLLVMCConvAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMCConvAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMCConvAttrGet")],-1)),t[1978]||(t[1978]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1979]||(t[1979]=l('
julia
mlirLLVMCConvAttrGet(ctx, cconv)
Creates a LLVM CConv attribute.
source
',3))]),e("details",lh,[e("summary",null,[t[1980]||(t[1980]=e("a",{id:"Reactant.MLIR.API.mlirLLVMComdatAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMComdatAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMComdatAttrGet")],-1)),t[1981]||(t[1981]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1982]||(t[1982]=l('
julia
mlirLLVMComdatAttrGet(ctx, comdat)
Creates a LLVM Comdat attribute.
source
',3))]),e("details",nh,[e("summary",null,[t[1983]||(t[1983]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet")],-1)),t[1984]||(t[1984]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1985]||(t[1985]=l('
julia
mlirLLVMDIAnnotationAttrGet(ctx, name, value)
Creates a LLVM DIAnnotation attribute.
source
',3))]),e("details",ph,[e("summary",null,[t[1986]||(t[1986]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet")],-1)),t[1987]||(t[1987]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1988]||(t[1988]=l('
julia
mlirLLVMDIBasicTypeAttrGet(ctx, tag, name, sizeInBits, encoding)
Creates a LLVM DIBasicType attribute.
source
',3))]),e("details",rh,[e("summary",null,[t[1989]||(t[1989]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet-NTuple{8, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet")],-1)),t[1990]||(t[1990]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1991]||(t[1991]=l('
julia
mlirLLVMDICompileUnitAttrGet(ctx, id, sourceLanguage, file, producer, isOptimized, emissionKind, nameTableKind)
Creates a LLVM DICompileUnit attribute.
source
',3))]),e("details",oh,[e("summary",null,[t[1992]||(t[1992]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet-NTuple{18, Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet-NTuple{18, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet")],-1)),t[1993]||(t[1993]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1994]||(t[1994]=l('
julia
mlirLLVMDICompositeTypeAttrGet(ctx, recId, isRecSelf, tag, name, file, line, scope, baseType, flags, sizeInBits, alignInBits, nElements, elements, dataLocation, rank, allocated, associated)
Creates a LLVM DICompositeType attribute.
source
',3))]),e("details",dh,[e("summary",null,[t[1995]||(t[1995]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf")],-1)),t[1996]||(t[1996]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1997]||(t[1997]=l('
julia
mlirLLVMDICompositeTypeAttrGetRecSelf(recId)
Creates a self-referencing LLVM DICompositeType attribute.
source
',3))]),e("details",hh,[e("summary",null,[t[1998]||(t[1998]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet")],-1)),t[1999]||(t[1999]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2e3]||(t[2e3]=l('
julia
mlirLLVMDIDerivedTypeAttrGet(ctx, tag, name, baseType, sizeInBits, alignInBits, offsetInBits, dwarfAddressSpace, extraData)
Creates a LLVM DIDerivedType attribute. Note that dwarfAddressSpace is an optional field, where MLIR_CAPI_DWARF_ADDRESS_SPACE_NULL indicates null and non-negative values indicate a value present.
source
',3))]),e("details",ch,[e("summary",null,[t[2001]||(t[2001]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType")],-1)),t[2002]||(t[2002]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2003]||(t[2003]=l('
julia
mlirLLVMDIDerivedTypeAttrGetBaseType(diDerivedType)
Gets the base type from a LLVM DIDerivedType attribute.
source
',3))]),e("details",uh,[e("summary",null,[t[2004]||(t[2004]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet")],-1)),t[2005]||(t[2005]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2006]||(t[2006]=l('
julia
mlirLLVMDIExpressionAttrGet(ctx, nOperations, operations)
Creates a LLVM DIExpression attribute.
source
',3))]),e("details",bh,[e("summary",null,[t[2007]||(t[2007]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet")],-1)),t[2008]||(t[2008]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2009]||(t[2009]=l('
julia
mlirLLVMDIExpressionElemAttrGet(ctx, opcode, nArguments, arguments)
Creates a LLVM DIExpressionElem attribute.
source
',3))]),e("details",gh,[e("summary",null,[t[2010]||(t[2010]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIFileAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIFileAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIFileAttrGet")],-1)),t[2011]||(t[2011]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2012]||(t[2012]=l('
julia
mlirLLVMDIFileAttrGet(ctx, name, directory)
Creates a LLVM DIFileAttr attribute.
source
',3))]),e("details",yh,[e("summary",null,[t[2013]||(t[2013]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet")],-1)),t[2014]||(t[2014]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2015]||(t[2015]=l('
julia
mlirLLVMDIFlagsAttrGet(ctx, value)
Creates a LLVM DIFlags attribute.
source
',3))]),e("details",mh,[e("summary",null,[t[2016]||(t[2016]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet")],-1)),t[2017]||(t[2017]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2018]||(t[2018]=l('
julia
mlirLLVMDIImportedEntityAttrGet(ctx, tag, scope, entity, file, line, name, nElements, elements)
Creates a LLVM DIImportedEntityAttr attribute.
source
',3))]),e("details",kh,[e("summary",null,[t[2019]||(t[2019]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet")],-1)),t[2020]||(t[2020]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2021]||(t[2021]=l('
julia
mlirLLVMDILexicalBlockAttrGet(ctx, scope, file, line, column)
Creates a LLVM DILexicalBlock attribute.
source
',3))]),e("details",Rh,[e("summary",null,[t[2022]||(t[2022]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet")],-1)),t[2023]||(t[2023]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2024]||(t[2024]=l('
julia
mlirLLVMDILexicalBlockFileAttrGet(ctx, scope, file, discriminator)
Creates a LLVM DILexicalBlockFile attribute.
source
',3))]),e("details",fh,[e("summary",null,[t[2025]||(t[2025]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet")],-1)),t[2026]||(t[2026]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2027]||(t[2027]=l('
julia
mlirLLVMDILocalVariableAttrGet(ctx, scope, name, diFile, line, arg, alignInBits, diType, flags)
Creates a LLVM DILocalVariableAttr attribute.
source
',3))]),e("details",Ih,[e("summary",null,[t[2028]||(t[2028]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIModuleAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIModuleAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIModuleAttrGet")],-1)),t[2029]||(t[2029]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2030]||(t[2030]=l('
julia
mlirLLVMDIModuleAttrGet(ctx, file, scope, name, configMacros, includePath, apinotes, line, isDecl)
Creates a LLVM DIModuleAttr attribute.
source
',3))]),e("details",jh,[e("summary",null,[t[2031]||(t[2031]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope")],-1)),t[2032]||(t[2032]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2033]||(t[2033]=l('
julia
mlirLLVMDIModuleAttrGetScope(diModule)
Gets the scope of this DIModuleAttr.
source
',3))]),e("details",Mh,[e("summary",null,[t[2034]||(t[2034]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet")],-1)),t[2035]||(t[2035]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2036]||(t[2036]=l('
julia
mlirLLVMDINullTypeAttrGet(ctx)
Creates a LLVM DINullType attribute.
source
',3))]),e("details",Ah,[e("summary",null,[t[2037]||(t[2037]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet-NTuple{17, Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet-NTuple{17, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet")],-1)),t[2038]||(t[2038]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2039]||(t[2039]=l('
julia
mlirLLVMDISubprogramAttrGet(ctx, recId, isRecSelf, id, compileUnit, scope, name, linkageName, file, line, scopeLine, subprogramFlags, type, nRetainedNodes, retainedNodes, nAnnotations, annotations)
Creates a LLVM DISubprogramAttr attribute.
source
',3))]),e("details",Lh,[e("summary",null,[t[2040]||(t[2040]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit")],-1)),t[2041]||(t[2041]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2042]||(t[2042]=l('
julia
mlirLLVMDISubprogramAttrGetCompileUnit(diSubprogram)
Gets the compile unit from this DISubprogram.
source
',3))]),e("details",Eh,[e("summary",null,[t[2043]||(t[2043]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile")],-1)),t[2044]||(t[2044]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2045]||(t[2045]=l('
julia
mlirLLVMDISubprogramAttrGetFile(diSubprogram)
Gets the file from this DISubprogramAttr.
source
',3))]),e("details",vh,[e("summary",null,[t[2046]||(t[2046]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine")],-1)),t[2047]||(t[2047]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2048]||(t[2048]=l('
julia
mlirLLVMDISubprogramAttrGetLine(diSubprogram)
Gets the line from this DISubprogramAttr.
source
',3))]),e("details",Th,[e("summary",null,[t[2049]||(t[2049]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf")],-1)),t[2050]||(t[2050]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2051]||(t[2051]=l('
julia
mlirLLVMDISubprogramAttrGetRecSelf(recId)
Creates a self-referencing LLVM DISubprogramAttr attribute.
source
',3))]),e("details",Ch,[e("summary",null,[t[2052]||(t[2052]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope")],-1)),t[2053]||(t[2053]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2054]||(t[2054]=l('
julia
mlirLLVMDISubprogramAttrGetScope(diSubprogram)
Gets the scope from this DISubprogramAttr.
source
',3))]),e("details",xh,[e("summary",null,[t[2055]||(t[2055]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine")],-1)),t[2056]||(t[2056]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2057]||(t[2057]=l('
julia
mlirLLVMDISubprogramAttrGetScopeLine(diSubprogram)
Gets the scope line from this DISubprogram.
source
',3))]),e("details",Fh,[e("summary",null,[t[2058]||(t[2058]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType")],-1)),t[2059]||(t[2059]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2060]||(t[2060]=l('
julia
mlirLLVMDISubprogramAttrGetType(diSubprogram)
Gets the type from this DISubprogramAttr.
source
',3))]),e("details",Ph,[e("summary",null,[t[2061]||(t[2061]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet")],-1)),t[2062]||(t[2062]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2063]||(t[2063]=l('
julia
mlirLLVMDISubroutineTypeAttrGet(ctx, callingConvention, nTypes, types)
Creates a LLVM DISubroutineTypeAttr attribute.
source
',3))]),e("details",Dh,[e("summary",null,[t[2064]||(t[2064]=e("a",{id:"Reactant.MLIR.API.mlirLLVMFunctionTypeGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMFunctionTypeGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMFunctionTypeGet")],-1)),t[2065]||(t[2065]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2066]||(t[2066]=l('
julia
mlirLLVMFunctionTypeGet(resultType, nArgumentTypes, argumentTypes, isVarArg)
Creates an llvm.func type.
source
',3))]),e("details",Oh,[e("summary",null,[t[2067]||(t[2067]=e("a",{id:"Reactant.MLIR.API.mlirLLVMLinkageAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMLinkageAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMLinkageAttrGet")],-1)),t[2068]||(t[2068]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2069]||(t[2069]=l('
julia
mlirLLVMLinkageAttrGet(ctx, linkage)
Creates a LLVM Linkage attribute.
source
',3))]),e("details",Bh,[e("summary",null,[t[2070]||(t[2070]=e("a",{id:"Reactant.MLIR.API.mlirLLVMPointerTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMPointerTypeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMPointerTypeGet")],-1)),t[2071]||(t[2071]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2072]||(t[2072]=l('
julia
mlirLLVMPointerTypeGet(ctx, addressSpace)
Creates an llvm.ptr type.
source
',3))]),e("details",Gh,[e("summary",null,[t[2073]||(t[2073]=e("a",{id:"Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace")],-1)),t[2074]||(t[2074]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2075]||(t[2075]=l('
julia
mlirLLVMPointerTypeGetAddressSpace(pointerType)
Returns address space of llvm.ptr
source
',3))]),e("details",zh,[e("summary",null,[t[2076]||(t[2076]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetElementType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetElementType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetElementType")],-1)),t[2077]||(t[2077]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2078]||(t[2078]=l('
julia
mlirLLVMStructTypeGetElementType(type, position)
Returns the positions-th field of the struct. Asserts if the struct is opaque, not yet initialized or if the position is out of range.
source
',3))]),e("details",wh,[e("summary",null,[t[2079]||(t[2079]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier")],-1)),t[2080]||(t[2080]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2081]||(t[2081]=l('
julia
mlirLLVMStructTypeGetIdentifier(type)
Returns the identifier of the identified struct. Asserts that the struct is identified, i.e., not literal.
source
',3))]),e("details",Sh,[e("summary",null,[t[2082]||(t[2082]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes")],-1)),t[2083]||(t[2083]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2084]||(t[2084]=l('
julia
mlirLLVMStructTypeGetNumElementTypes(type)
Returns the number of fields in the struct. Asserts if the struct is opaque or not yet initialized.
source
',3))]),e("details",Nh,[e("summary",null,[t[2085]||(t[2085]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet")],-1)),t[2086]||(t[2086]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2087]||(t[2087]=l('
julia
mlirLLVMStructTypeIdentifiedGet(ctx, name)
Creates an LLVM identified struct type with no body. If a struct type with this name already exists in the context, returns that type. Use mlirLLVMStructTypeIdentifiedNewGet to create a fresh struct type, potentially renaming it. The body should be set separatelty by calling mlirLLVMStructTypeSetBody, if it isn't set already.
source
',3))]),e("details",Vh,[e("summary",null,[t[2088]||(t[2088]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet")],-1)),t[2089]||(t[2089]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2090]||(t[2090]=l('
julia
mlirLLVMStructTypeIdentifiedNewGet(ctx, name, nFieldTypes, fieldTypes, isPacked)
Creates an LLVM identified struct type with no body and a name starting with the given prefix. If a struct with the exact name as the given prefix already exists, appends an unspecified suffix to the name so that the name is unique in context.
source
',3))]),e("details",qh,[e("summary",null,[t[2091]||(t[2091]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral")],-1)),t[2092]||(t[2092]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2093]||(t[2093]=l('
julia
mlirLLVMStructTypeIsLiteral(type)
Returns true if the type is a literal (unnamed) LLVM struct type.
source
',3))]),e("details",Uh,[e("summary",null,[t[2094]||(t[2094]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque")],-1)),t[2095]||(t[2095]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2096]||(t[2096]=l('
julia
mlirLLVMStructTypeIsOpaque(type)
Returns true is the struct is explicitly opaque (will not have a body) or uninitialized (will eventually have a body).
source
',3))]),e("details",Qh,[e("summary",null,[t[2097]||(t[2097]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsPacked-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsPacked-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsPacked")],-1)),t[2098]||(t[2098]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2099]||(t[2099]=l('
julia
mlirLLVMStructTypeIsPacked(type)
Returns true if the struct is packed.
source
',3))]),e("details",Wh,[e("summary",null,[t[2100]||(t[2100]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet")],-1)),t[2101]||(t[2101]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2102]||(t[2102]=l('
julia
mlirLLVMStructTypeLiteralGet(ctx, nFieldTypes, fieldTypes, isPacked)
Creates an LLVM literal (unnamed) struct type. This may assert if the fields have types not compatible with the LLVM dialect. For a graceful failure, use the checked version.
source
',3))]),e("details",Hh,[e("summary",null,[t[2103]||(t[2103]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked")],-1)),t[2104]||(t[2104]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2105]||(t[2105]=l('
julia
mlirLLVMStructTypeLiteralGetChecked(loc, nFieldTypes, fieldTypes, isPacked)
Creates an LLVM literal (unnamed) struct type if possible. Emits a diagnostic at the given location and returns null otherwise.
source
',3))]),e("details",Zh,[e("summary",null,[t[2106]||(t[2106]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeSetBody-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeSetBody-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeSetBody")],-1)),t[2107]||(t[2107]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2108]||(t[2108]=l('
julia
mlirLLVMStructTypeSetBody(structType, nFieldTypes, fieldTypes, isPacked)
Sets the body of the identified struct if it hasn't been set yet. Returns whether the operation was successful.
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',3))]),e("details",Jh,[e("summary",null,[t[2109]||(t[2109]=e("a",{id:"Reactant.MLIR.API.mlirLLVMVoidTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMVoidTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMVoidTypeGet")],-1)),t[2110]||(t[2110]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2111]||(t[2111]=l('
julia
mlirLLVMVoidTypeGet(ctx)
Creates an llmv.void type.
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',3))]),e("details",Kh,[e("summary",null,[t[2112]||(t[2112]=e("a",{id:"Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion")],-1)),t[2113]||(t[2113]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2114]||(t[2114]=l('
julia
mlirLinalgFillBuiltinNamedOpRegion(mlirOp)
Apply the special region builder for the builtin named Linalg op. Assert that mlirOp is a builtin named Linalg op.
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',3))]),e("details",$h,[e("summary",null,[t[2115]||(t[2115]=e("a",{id:"Reactant.MLIR.API.mlirLlvmThreadPoolCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirLlvmThreadPoolCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLlvmThreadPoolCreate")],-1)),t[2116]||(t[2116]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2117]||(t[2117]=l('
julia
mlirLlvmThreadPoolCreate()
Create an LLVM thread pool. This is reexported here to avoid directly pulling in the LLVM headers directly.
source
',3))]),e("details",Xh,[e("summary",null,[t[2118]||(t[2118]=e("a",{id:"Reactant.MLIR.API.mlirLlvmThreadPoolDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLlvmThreadPoolDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLlvmThreadPoolDestroy")],-1)),t[2119]||(t[2119]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2120]||(t[2120]=l('
julia
mlirLlvmThreadPoolDestroy(pool)
Destroy an LLVM thread pool.
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',3))]),e("details",Yh,[e("summary",null,[t[2121]||(t[2121]=e("a",{id:"Reactant.MLIR.API.mlirLoadIRDLDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLoadIRDLDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLoadIRDLDialects")],-1)),t[2122]||(t[2122]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2123]||(t[2123]=l('
julia
mlirLoadIRDLDialects(_module)
Loads all IRDL dialects in the provided module, registering the dialects in the module's associated context.
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',3))]),e("details",_h,[e("summary",null,[t[2124]||(t[2124]=e("a",{id:"Reactant.MLIR.API.mlirLocationCallSiteGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLocationCallSiteGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationCallSiteGet")],-1)),t[2125]||(t[2125]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2126]||(t[2126]=l('
julia
mlirLocationCallSiteGet(callee, caller)
Creates a call site location with a callee and a caller.
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',3))]),e("details",tc,[e("summary",null,[t[2127]||(t[2127]=e("a",{id:"Reactant.MLIR.API.mlirLocationEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLocationEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationEqual")],-1)),t[2128]||(t[2128]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2129]||(t[2129]=l('
julia
mlirLocationEqual(l1, l2)
Checks if two locations are equal.
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',3))]),e("details",ec,[e("summary",null,[t[2130]||(t[2130]=e("a",{id:"Reactant.MLIR.API.mlirLocationFileLineColGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLocationFileLineColGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFileLineColGet")],-1)),t[2131]||(t[2131]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2132]||(t[2132]=l('
julia
mlirLocationFileLineColGet(context, filename, line, col)
Creates an File/Line/Column location owned by the given context.
source
',3))]),e("details",sc,[e("summary",null,[t[2133]||(t[2133]=e("a",{id:"Reactant.MLIR.API.mlirLocationFromAttribute-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationFromAttribute-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFromAttribute")],-1)),t[2134]||(t[2134]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2135]||(t[2135]=l('
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mlirLocationFromAttribute(attribute)
Creates a location from a location attribute.
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',3))]),e("details",ac,[e("summary",null,[t[2136]||(t[2136]=e("a",{id:"Reactant.MLIR.API.mlirLocationFusedGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLocationFusedGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFusedGet")],-1)),t[2137]||(t[2137]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2138]||(t[2138]=l('
julia
mlirLocationFusedGet(ctx, nLocations, locations, metadata)
Creates a fused location with an array of locations and metadata.
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',3))]),e("details",ic,[e("summary",null,[t[2139]||(t[2139]=e("a",{id:"Reactant.MLIR.API.mlirLocationGetAttribute-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationGetAttribute-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationGetAttribute")],-1)),t[2140]||(t[2140]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2141]||(t[2141]=l('
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mlirLocationGetAttribute(location)
Returns the underlying location attribute of this location.
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',3))]),e("details",lc,[e("summary",null,[t[2142]||(t[2142]=e("a",{id:"Reactant.MLIR.API.mlirLocationGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationGetContext")],-1)),t[2143]||(t[2143]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2144]||(t[2144]=l('
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mlirLocationGetContext(location)
Gets the context that a location was created with.
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',3))]),e("details",nc,[e("summary",null,[t[2145]||(t[2145]=e("a",{id:"Reactant.MLIR.API.mlirLocationIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationIsNull")],-1)),t[2146]||(t[2146]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2147]||(t[2147]=l('
julia
mlirLocationIsNull(location)
Checks if the location is null.
source
',3))]),e("details",pc,[e("summary",null,[t[2148]||(t[2148]=e("a",{id:"Reactant.MLIR.API.mlirLocationNameGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLocationNameGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationNameGet")],-1)),t[2149]||(t[2149]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2150]||(t[2150]=l('
julia
mlirLocationNameGet(context, name, childLoc)
Creates a name location owned by the given context. Providing null location for childLoc is allowed and if childLoc is null location, then the behavior is the same as having unknown child location.
source
',3))]),e("details",rc,[e("summary",null,[t[2151]||(t[2151]=e("a",{id:"Reactant.MLIR.API.mlirLocationPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLocationPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationPrint")],-1)),t[2152]||(t[2152]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2153]||(t[2153]=l('
julia
mlirLocationPrint(location, callback, userData)
Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",oc,[e("summary",null,[t[2154]||(t[2154]=e("a",{id:"Reactant.MLIR.API.mlirLocationUnknownGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationUnknownGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationUnknownGet")],-1)),t[2155]||(t[2155]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2156]||(t[2156]=l('
julia
mlirLocationUnknownGet(context)
Creates a location with unknown position owned by the given context.
source
',3))]),e("details",dc,[e("summary",null,[t[2157]||(t[2157]=e("a",{id:"Reactant.MLIR.API.mlirLogicalResultFailure-Tuple{}",href:"#Reactant.MLIR.API.mlirLogicalResultFailure-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultFailure")],-1)),t[2158]||(t[2158]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2159]||(t[2159]=l('
julia
mlirLogicalResultFailure()
Creates a logical result representing a failure.
source
',3))]),e("details",hc,[e("summary",null,[t[2160]||(t[2160]=e("a",{id:"Reactant.MLIR.API.mlirLogicalResultIsFailure-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLogicalResultIsFailure-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultIsFailure")],-1)),t[2161]||(t[2161]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2162]||(t[2162]=l('
julia
mlirLogicalResultIsFailure(res)
Checks if the given logical result represents a failure.
source
',3))]),e("details",cc,[e("summary",null,[t[2163]||(t[2163]=e("a",{id:"Reactant.MLIR.API.mlirLogicalResultIsSuccess-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLogicalResultIsSuccess-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultIsSuccess")],-1)),t[2164]||(t[2164]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2165]||(t[2165]=l('
julia
mlirLogicalResultIsSuccess(res)
Checks if the given logical result represents a success.
source
',3))]),e("details",uc,[e("summary",null,[t[2166]||(t[2166]=e("a",{id:"Reactant.MLIR.API.mlirLogicalResultSuccess-Tuple{}",href:"#Reactant.MLIR.API.mlirLogicalResultSuccess-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultSuccess")],-1)),t[2167]||(t[2167]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2168]||(t[2168]=l('
julia
mlirLogicalResultSuccess()
Creates a logical result representing a success.
source
',3))]),e("details",bc,[e("summary",null,[t[2169]||(t[2169]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeContiguousGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeContiguousGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeContiguousGet")],-1)),t[2170]||(t[2170]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2171]||(t[2171]=l('
julia
mlirMemRefTypeContiguousGet(elementType, rank, shape, memorySpace)
Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context.
source
',3))]),e("details",gc,[e("summary",null,[t[2172]||(t[2172]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked")],-1)),t[2173]||(t[2173]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2174]||(t[2174]=l('
julia
mlirMemRefTypeContiguousGetChecked(loc, elementType, rank, shape, memorySpace)
Same as "mlirMemRefTypeContiguousGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
',3))]),e("details",yc,[e("summary",null,[t[2175]||(t[2175]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGet")],-1)),t[2176]||(t[2176]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2177]||(t[2177]=l('
julia
mlirMemRefTypeGet(elementType, rank, shape, layout, memorySpace)
Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context.
source
',3))]),e("details",mc,[e("summary",null,[t[2178]||(t[2178]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetAffineMap-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetAffineMap")],-1)),t[2179]||(t[2179]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2180]||(t[2180]=l('
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mlirMemRefTypeGetAffineMap(type)
Returns the affine map of the given MemRef type.
source
',3))]),e("details",kc,[e("summary",null,[t[2181]||(t[2181]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetChecked-NTuple{6, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetChecked-NTuple{6, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetChecked")],-1)),t[2182]||(t[2182]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2183]||(t[2183]=l('
julia
mlirMemRefTypeGetChecked(loc, elementType, rank, shape, layout, memorySpace)
Same as "mlirMemRefTypeGet" but returns a nullptr-wrapping MlirType o illegal arguments, emitting appropriate diagnostics.
source
',3))]),e("details",Rc,[e("summary",null,[t[2184]||(t[2184]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetLayout-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetLayout-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetLayout")],-1)),t[2185]||(t[2185]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2186]||(t[2186]=l('
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mlirMemRefTypeGetLayout(type)
Returns the layout of the given MemRef type.
source
',3))]),e("details",fc,[e("summary",null,[t[2187]||(t[2187]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace")],-1)),t[2188]||(t[2188]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2189]||(t[2189]=l('
julia
mlirMemRefTypeGetMemorySpace(type)
Returns the memory space of the given MemRef type.
source
',3))]),e("details",Ic,[e("summary",null,[t[2190]||(t[2190]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset")],-1)),t[2191]||(t[2191]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2192]||(t[2192]=l('
julia
mlirMemRefTypeGetStridesAndOffset(type, strides, offset)
Returns the strides of the MemRef if the layout map is in strided form. Both strides and offset are out params. strides must point to pre-allocated memory of length equal to the rank of the memref.
source
',3))]),e("details",jc,[e("summary",null,[t[2193]||(t[2193]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetTypeID")],-1)),t[2194]||(t[2194]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2195]||(t[2195]=l('
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mlirMemRefTypeGetTypeID()
Returns the typeID of an MemRef type.
source
',3))]),e("details",Mc,[e("summary",null,[t[2196]||(t[2196]=e("a",{id:"Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone")],-1)),t[2197]||(t[2197]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2198]||(t[2198]=l('
julia
mlirMergeSymbolsIntoFromClone(target, other)
Merge the symbols from other into target, potentially renaming them to avoid conflicts. Private symbols may be renamed during the merge, public symbols must have at most one declaration. A name conflict in public symbols is reported as an error before returning a failure.
Note that this clones the other operation unlike the C++ counterpart that takes ownership.
source
',4))]),e("details",Ac,[e("summary",null,[t[2199]||(t[2199]=e("a",{id:"Reactant.MLIR.API.mlirModuleCreateEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleCreateEmpty-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleCreateEmpty")],-1)),t[2200]||(t[2200]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2201]||(t[2201]=l('
julia
mlirModuleCreateEmpty(location)
Creates a new, empty module and transfers ownership to the caller.
source
',3))]),e("details",Lc,[e("summary",null,[t[2202]||(t[2202]=e("a",{id:"Reactant.MLIR.API.mlirModuleCreateParse-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirModuleCreateParse-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleCreateParse")],-1)),t[2203]||(t[2203]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2204]||(t[2204]=l('
julia
mlirModuleCreateParse(context, _module)
Parses a module from the string and transfers ownership to the caller.
source
',3))]),e("details",Ec,[e("summary",null,[t[2205]||(t[2205]=e("a",{id:"Reactant.MLIR.API.mlirModuleDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleDestroy")],-1)),t[2206]||(t[2206]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2207]||(t[2207]=l('
julia
mlirModuleDestroy(_module)
Takes a module owned by the caller and deletes it.
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',3))]),e("details",vc,[e("summary",null,[t[2208]||(t[2208]=e("a",{id:"Reactant.MLIR.API.mlirModuleFromOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleFromOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleFromOperation")],-1)),t[2209]||(t[2209]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2210]||(t[2210]=l('
julia
mlirModuleFromOperation(op)
Views the generic operation as a module. The returned module is null when the input operation was not a ModuleOp.
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',3))]),e("details",Tc,[e("summary",null,[t[2211]||(t[2211]=e("a",{id:"Reactant.MLIR.API.mlirModuleGetBody-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetBody-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetBody")],-1)),t[2212]||(t[2212]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2213]||(t[2213]=l('
julia
mlirModuleGetBody(_module)
Gets the body of the module, i.e. the only block it contains.
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',3))]),e("details",Cc,[e("summary",null,[t[2214]||(t[2214]=e("a",{id:"Reactant.MLIR.API.mlirModuleGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetContext")],-1)),t[2215]||(t[2215]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2216]||(t[2216]=l('
julia
mlirModuleGetContext(_module)
Gets the context that a module was created with.
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',3))]),e("details",xc,[e("summary",null,[t[2217]||(t[2217]=e("a",{id:"Reactant.MLIR.API.mlirModuleGetOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetOperation")],-1)),t[2218]||(t[2218]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2219]||(t[2219]=l('
julia
mlirModuleGetOperation(_module)
Views the module as a generic operation.
source
',3))]),e("details",Fc,[e("summary",null,[t[2220]||(t[2220]=e("a",{id:"Reactant.MLIR.API.mlirModuleIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleIsNull")],-1)),t[2221]||(t[2221]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2222]||(t[2222]=l('
julia
mlirModuleIsNull(_module)
Checks whether a module is null.
source
',3))]),e("details",Pc,[e("summary",null,[t[2223]||(t[2223]=e("a",{id:"Reactant.MLIR.API.mlirNamedAttributeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirNamedAttributeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNamedAttributeGet")],-1)),t[2224]||(t[2224]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2225]||(t[2225]=l('
julia
mlirNamedAttributeGet(name, attr)
Associates an attribute with the name. Takes ownership of neither.
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',3))]),e("details",Dc,[e("summary",null,[t[2226]||(t[2226]=e("a",{id:"Reactant.MLIR.API.mlirNoneTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirNoneTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNoneTypeGet")],-1)),t[2227]||(t[2227]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2228]||(t[2228]=l('
julia
mlirNoneTypeGet(ctx)
Creates a None type in the given context. The type is owned by the context.
source
',3))]),e("details",Oc,[e("summary",null,[t[2229]||(t[2229]=e("a",{id:"Reactant.MLIR.API.mlirNoneTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirNoneTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNoneTypeGetTypeID")],-1)),t[2230]||(t[2230]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2231]||(t[2231]=l('
julia
mlirNoneTypeGetTypeID()
Returns the typeID of an None type.
source
',3))]),e("details",Bc,[e("summary",null,[t[2232]||(t[2232]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandGetNextUse-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetNextUse-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetNextUse")],-1)),t[2233]||(t[2233]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2234]||(t[2234]=l('
julia
mlirOpOperandGetNextUse(opOperand)
Returns an op operand representing the next use of the value, or a null op operand if there is no next use.
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',3))]),e("details",Gc,[e("summary",null,[t[2235]||(t[2235]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandGetOperandNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetOperandNumber-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetOperandNumber")],-1)),t[2236]||(t[2236]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2237]||(t[2237]=l('
julia
mlirOpOperandGetOperandNumber(opOperand)
Returns the operand number of an op operand.
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',3))]),e("details",zc,[e("summary",null,[t[2238]||(t[2238]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetOwner-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetOwner")],-1)),t[2239]||(t[2239]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2240]||(t[2240]=l('
julia
mlirOpOperandGetOwner(opOperand)
Returns the owner operation of an op operand.
source
',3))]),e("details",wc,[e("summary",null,[t[2241]||(t[2241]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetValue")],-1)),t[2242]||(t[2242]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2243]||(t[2243]=l('
julia
mlirOpOperandGetValue(opOperand)
Returns the value of an op operand.
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',3))]),e("details",Sc,[e("summary",null,[t[2244]||(t[2244]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandIsNull")],-1)),t[2245]||(t[2245]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2246]||(t[2246]=l('
julia
mlirOpOperandIsNull(opOperand)
Returns whether the op operand is null.
source
',3))]),e("details",Nc,[e("summary",null,[t[2247]||(t[2247]=e("a",{id:"Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass")],-1)),t[2248]||(t[2248]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2249]||(t[2249]=l('
julia
mlirOpPassManagerAddOwnedPass(passManager, pass)
Add a pass and transfer ownership to the provided mlirOpPassManager. If the pass is not a generic operation pass or matching the type of the provided PassManager, a new OpPassManager is implicitly nested under the provided PassManager.
source
',3))]),e("details",Vc,[e("summary",null,[t[2250]||(t[2250]=e("a",{id:"Reactant.MLIR.API.mlirOpPassManagerAddPipeline-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerAddPipeline-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerAddPipeline")],-1)),t[2251]||(t[2251]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2252]||(t[2252]=l('
julia
mlirOpPassManagerAddPipeline(passManager, pipelineElements, callback, userData)
Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
',3))]),e("details",qc,[e("summary",null,[t[2253]||(t[2253]=e("a",{id:"Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder")],-1)),t[2254]||(t[2254]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2255]||(t[2255]=l('
julia
mlirOpPassManagerGetNestedUnder(passManager, operationName)
Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.
source
',3))]),e("details",Uc,[e("summary",null,[t[2256]||(t[2256]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified")],-1)),t[2257]||(t[2257]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2258]||(t[2258]=l('
julia
mlirOpPrintingFlagsAssumeVerified(flags)
Do not verify the operation when using custom operation printers.
source
',3))]),e("details",Qc,[e("summary",null,[t[2259]||(t[2259]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsCreate")],-1)),t[2260]||(t[2260]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2261]||(t[2261]=l('
julia
mlirOpPrintingFlagsCreate()
Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirOpPrintingFlagsDestroy().
source
',3))]),e("details",Wc,[e("summary",null,[t[2262]||(t[2262]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsDestroy")],-1)),t[2263]||(t[2263]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2264]||(t[2264]=l('
julia
mlirOpPrintingFlagsDestroy(flags)
Destroys printing flags created with mlirOpPrintingFlagsCreate.
source
',3))]),e("details",Hc,[e("summary",null,[t[2265]||(t[2265]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs")],-1)),t[2266]||(t[2266]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2267]||(t[2267]=l('
julia
mlirOpPrintingFlagsElideLargeElementsAttrs(flags, largeElementLimit)
Enables the elision of large elements attributes by printing a lexically valid but otherwise meaningless form instead of the element data. The largeElementLimit is used to configure what is considered to be a "large" ElementsAttr by providing an upper limit to the number of elements.
source
',3))]),e("details",Zc,[e("summary",null,[t[2268]||(t[2268]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString")],-1)),t[2269]||(t[2269]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2270]||(t[2270]=l('
julia
mlirOpPrintingFlagsElideLargeResourceString(flags, largeResourceLimit)
Enables the elision of large resources strings by omitting them from the dialect_resources section. The largeResourceLimit is used to configure what is considered to be a "large" resource by providing an upper limit to the string size.
source
',3))]),e("details",Jc,[e("summary",null,[t[2271]||(t[2271]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo")],-1)),t[2272]||(t[2272]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2273]||(t[2273]=l('
julia
mlirOpPrintingFlagsEnableDebugInfo(flags, enable, prettyForm)
Enable or disable printing of debug information (based on enable). If 'prettyForm' is set to true, debug information is printed in a more readable 'pretty' form. Note: The IR generated with 'prettyForm' is not parsable.
source
',3))]),e("details",Kc,[e("summary",null,[t[2274]||(t[2274]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm")],-1)),t[2275]||(t[2275]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2276]||(t[2276]=l('
julia
mlirOpPrintingFlagsPrintGenericOpForm(flags)
Always print operations in the generic form.
source
',3))]),e("details",$c,[e("summary",null,[t[2277]||(t[2277]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions")],-1)),t[2278]||(t[2278]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2279]||(t[2279]=l('
julia
mlirOpPrintingFlagsSkipRegions(flags)
Skip printing regions.
source
',3))]),e("details",Xc,[e("summary",null,[t[2280]||(t[2280]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope")],-1)),t[2281]||(t[2281]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2282]||(t[2282]=l('
julia
mlirOpPrintingFlagsUseLocalScope(flags)
Use local scope when printing the operation. This allows for using the printer in a more localized and thread-safe setting, but may not necessarily be identical to what the IR will look like when dumping the full module.
source
',3))]),e("details",Yc,[e("summary",null,[t[2283]||(t[2283]=e("a",{id:"Reactant.MLIR.API.mlirOpResultGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpResultGetOwner-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpResultGetOwner")],-1)),t[2284]||(t[2284]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2285]||(t[2285]=l('
julia
mlirOpResultGetOwner(value)
Returns an operation that produced this value as its result. Asserts if the value is not an op result.
source
',3))]),e("details",_c,[e("summary",null,[t[2286]||(t[2286]=e("a",{id:"Reactant.MLIR.API.mlirOpResultGetResultNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpResultGetResultNumber-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpResultGetResultNumber")],-1)),t[2287]||(t[2287]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2288]||(t[2288]=l('
julia
mlirOpResultGetResultNumber(value)
Returns the position of the value in the list of results of the operation that produced it.
source
',3))]),e("details",tu,[e("summary",null,[t[2289]||(t[2289]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGet")],-1)),t[2290]||(t[2290]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2291]||(t[2291]=l('
julia
mlirOpaqueAttrGet(ctx, dialectNamespace, dataLength, data, type)
Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),e("details",eu,[e("summary",null,[t[2292]||(t[2292]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetData-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetData")],-1)),t[2293]||(t[2293]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2294]||(t[2294]=l('
julia
mlirOpaqueAttrGetData(attr)
Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.
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',3))]),e("details",su,[e("summary",null,[t[2295]||(t[2295]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace")],-1)),t[2296]||(t[2296]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2297]||(t[2297]=l('
julia
mlirOpaqueAttrGetDialectNamespace(attr)
Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.
source
',3))]),e("details",au,[e("summary",null,[t[2298]||(t[2298]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetTypeID")],-1)),t[2299]||(t[2299]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2300]||(t[2300]=l('
julia
mlirOpaqueAttrGetTypeID()
Returns the typeID of an Opaque attribute.
source
',3))]),e("details",iu,[e("summary",null,[t[2301]||(t[2301]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGet")],-1)),t[2302]||(t[2302]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2303]||(t[2303]=l('
julia
mlirOpaqueTypeGet(ctx, dialectNamespace, typeData)
Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),e("details",lu,[e("summary",null,[t[2304]||(t[2304]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetData-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetData")],-1)),t[2305]||(t[2305]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2306]||(t[2306]=l('
julia
mlirOpaqueTypeGetData(type)
Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.
source
',3))]),e("details",nu,[e("summary",null,[t[2307]||(t[2307]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace")],-1)),t[2308]||(t[2308]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2309]||(t[2309]=l('
julia
mlirOpaqueTypeGetDialectNamespace(type)
Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.
source
',3))]),e("details",pu,[e("summary",null,[t[2310]||(t[2310]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetTypeID")],-1)),t[2311]||(t[2311]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2312]||(t[2312]=l('
julia
mlirOpaqueTypeGetTypeID()
Returns the typeID of an Opaque type.
source
',3))]),e("details",ru,[e("summary",null,[t[2313]||(t[2313]=e("a",{id:"Reactant.MLIR.API.mlirOperationClone-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationClone-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationClone")],-1)),t[2314]||(t[2314]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2315]||(t[2315]=l('
julia
mlirOperationClone(op)
Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.
source
',3))]),e("details",ou,[e("summary",null,[t[2316]||(t[2316]=e("a",{id:"Reactant.MLIR.API.mlirOperationCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationCreate-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationCreate")],-1)),t[2317]||(t[2317]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2318]||(t[2318]=l('
julia
mlirOperationCreate(state)
Creates an operation and transfers ownership to the caller. Note that caller owned child objects are transferred in this call and must not be further used. Particularly, this applies to any regions added to the state (the implementation may invalidate any such pointers).
This call can fail under the following conditions, in which case, it will return a null operation and emit diagnostics: - Result type inference is enabled and cannot be performed.
source
',4))]),e("details",du,[e("summary",null,[t[2319]||(t[2319]=e("a",{id:"Reactant.MLIR.API.mlirOperationCreateParse-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationCreateParse-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationCreateParse")],-1)),t[2320]||(t[2320]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2321]||(t[2321]=l('
julia
mlirOperationCreateParse(context, sourceStr, sourceName)
Parses an operation, giving ownership to the caller. If parsing fails a null operation will be returned, and an error diagnostic emitted.
sourceStr may be either the text assembly format, or binary bytecode format. sourceName is used as the file name of the source; any IR without locations will get a FileLineColLoc location with sourceName as the file name.
source
',4))]),e("details",hu,[e("summary",null,[t[2322]||(t[2322]=e("a",{id:"Reactant.MLIR.API.mlirOperationDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationDestroy")],-1)),t[2323]||(t[2323]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2324]||(t[2324]=l('
julia
mlirOperationDestroy(op)
Takes an operation owned by the caller and destroys it.
source
',3))]),e("details",cu,[e("summary",null,[t[2325]||(t[2325]=e("a",{id:"Reactant.MLIR.API.mlirOperationDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationDump")],-1)),t[2326]||(t[2326]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2327]||(t[2327]=l('
julia
mlirOperationDump(op)
Prints an operation to stderr.
source
',3))]),e("details",uu,[e("summary",null,[t[2328]||(t[2328]=e("a",{id:"Reactant.MLIR.API.mlirOperationEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationEqual")],-1)),t[2329]||(t[2329]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2330]||(t[2330]=l('
julia
mlirOperationEqual(op, other)
Checks whether two operation handles point to the same operation. This does not perform deep comparison.
source
',3))]),e("details",bu,[e("summary",null,[t[2331]||(t[2331]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetAttribute-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetAttribute")],-1)),t[2332]||(t[2332]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2333]||(t[2333]=l('
julia
mlirOperationGetAttribute(op, pos)
Return pos-th attribute of the operation. Deprecated, please use mlirOperationGetInherentAttribute or mlirOperationGetDiscardableAttribute.
source
',3))]),e("details",gu,[e("summary",null,[t[2334]||(t[2334]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetAttributeByName")],-1)),t[2335]||(t[2335]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2336]||(t[2336]=l('
julia
mlirOperationGetAttributeByName(op, name)
Returns an attribute attached to the operation given its name. Deprecated, please use mlirOperationGetInherentAttributeByName or mlirOperationGetDiscardableAttributeByName.
source
',3))]),e("details",yu,[e("summary",null,[t[2337]||(t[2337]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetBlock")],-1)),t[2338]||(t[2338]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2339]||(t[2339]=l('
julia
mlirOperationGetBlock(op)
Gets the block that owns this operation, returning null if the operation is not owned.
source
',3))]),e("details",mu,[e("summary",null,[t[2340]||(t[2340]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetContext")],-1)),t[2341]||(t[2341]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2342]||(t[2342]=l('
julia
mlirOperationGetContext(op)
Gets the context this operation is associated with
source
',3))]),e("details",ku,[e("summary",null,[t[2343]||(t[2343]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetDiscardableAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetDiscardableAttribute-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetDiscardableAttribute")],-1)),t[2344]||(t[2344]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2345]||(t[2345]=l('
julia
mlirOperationGetDiscardableAttribute(op, pos)
Return pos-th discardable attribute of the operation.
source
',3))]),e("details",Ru,[e("summary",null,[t[2346]||(t[2346]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName")],-1)),t[2347]||(t[2347]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2348]||(t[2348]=l('
julia
mlirOperationGetDiscardableAttributeByName(op, name)
Returns a discardable attribute attached to the operation given its name.
source
',3))]),e("details",fu,[e("summary",null,[t[2349]||(t[2349]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetFirstRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetFirstRegion-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetFirstRegion")],-1)),t[2350]||(t[2350]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2351]||(t[2351]=l('
julia
mlirOperationGetFirstRegion(op)
Returns first region attached to the operation.
source
',3))]),e("details",Iu,[e("summary",null,[t[2352]||(t[2352]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetInherentAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetInherentAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetInherentAttributeByName")],-1)),t[2353]||(t[2353]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2354]||(t[2354]=l('
julia
mlirOperationGetInherentAttributeByName(op, name)
Returns an inherent attribute attached to the operation given its name.
source
',3))]),e("details",ju,[e("summary",null,[t[2355]||(t[2355]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetLocation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetLocation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetLocation")],-1)),t[2356]||(t[2356]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2357]||(t[2357]=l('
julia
mlirOperationGetLocation(op)
Gets the location of the operation.
source
',3))]),e("details",Mu,[e("summary",null,[t[2358]||(t[2358]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetName-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetName-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetName")],-1)),t[2359]||(t[2359]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2360]||(t[2360]=l('
julia
mlirOperationGetName(op)
Gets the name of the operation as an identifier.
source
',3))]),e("details",Au,[e("summary",null,[t[2361]||(t[2361]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNextInBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNextInBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNextInBlock")],-1)),t[2362]||(t[2362]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2363]||(t[2363]=l('
julia
mlirOperationGetNextInBlock(op)
Returns an operation immediately following the given operation it its enclosing block.
source
',3))]),e("details",Lu,[e("summary",null,[t[2364]||(t[2364]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumAttributes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumAttributes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumAttributes")],-1)),t[2365]||(t[2365]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2366]||(t[2366]=l('
julia
mlirOperationGetNumAttributes(op)
Returns the number of attributes attached to the operation. Deprecated, please use mlirOperationGetNumInherentAttributes or mlirOperationGetNumDiscardableAttributes.
source
',3))]),e("details",Eu,[e("summary",null,[t[2367]||(t[2367]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes")],-1)),t[2368]||(t[2368]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2369]||(t[2369]=l('
julia
mlirOperationGetNumDiscardableAttributes(op)
Returns the number of discardable attributes attached to the operation.
source
',3))]),e("details",vu,[e("summary",null,[t[2370]||(t[2370]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumOperands-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumOperands-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumOperands")],-1)),t[2371]||(t[2371]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2372]||(t[2372]=l('
julia
mlirOperationGetNumOperands(op)
Returns the number of operands of the operation.
source
',3))]),e("details",Tu,[e("summary",null,[t[2373]||(t[2373]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumRegions-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumRegions-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumRegions")],-1)),t[2374]||(t[2374]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2375]||(t[2375]=l('
julia
mlirOperationGetNumRegions(op)
Returns the number of regions attached to the given operation.
source
',3))]),e("details",Cu,[e("summary",null,[t[2376]||(t[2376]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumResults-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumResults")],-1)),t[2377]||(t[2377]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2378]||(t[2378]=l('
julia
mlirOperationGetNumResults(op)
Returns the number of results of the operation.
source
',3))]),e("details",xu,[e("summary",null,[t[2379]||(t[2379]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumSuccessors-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumSuccessors-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumSuccessors")],-1)),t[2380]||(t[2380]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2381]||(t[2381]=l('
julia
mlirOperationGetNumSuccessors(op)
Returns the number of successor blocks of the operation.
source
',3))]),e("details",Fu,[e("summary",null,[t[2382]||(t[2382]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetOperand-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetOperand-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetOperand")],-1)),t[2383]||(t[2383]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2384]||(t[2384]=l('
julia
mlirOperationGetOperand(op, pos)
Returns pos-th operand of the operation.
source
',3))]),e("details",Pu,[e("summary",null,[t[2385]||(t[2385]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetParentOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetParentOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetParentOperation")],-1)),t[2386]||(t[2386]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2387]||(t[2387]=l('
julia
mlirOperationGetParentOperation(op)
Gets the operation that owns this operation, returning null if the operation is not owned.
source
',3))]),e("details",Du,[e("summary",null,[t[2388]||(t[2388]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetRegion-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetRegion-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetRegion")],-1)),t[2389]||(t[2389]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2390]||(t[2390]=l('
julia
mlirOperationGetRegion(op, pos)
Returns pos-th region attached to the operation.
source
',3))]),e("details",Ou,[e("summary",null,[t[2391]||(t[2391]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetResult-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetResult")],-1)),t[2392]||(t[2392]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2393]||(t[2393]=l('
julia
mlirOperationGetResult(op, pos)
Returns pos-th result of the operation.
source
',3))]),e("details",Bu,[e("summary",null,[t[2394]||(t[2394]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetSuccessor-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetSuccessor-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetSuccessor")],-1)),t[2395]||(t[2395]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2396]||(t[2396]=l('
julia
mlirOperationGetSuccessor(op, pos)
Returns pos-th successor of the operation.
source
',3))]),e("details",Gu,[e("summary",null,[t[2397]||(t[2397]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetTypeID-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetTypeID")],-1)),t[2398]||(t[2398]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2399]||(t[2399]=l('
julia
mlirOperationGetTypeID(op)
Gets the type id of the operation. Returns null if the operation does not have a registered operation description.
source
',3))]),e("details",zu,[e("summary",null,[t[2400]||(t[2400]=e("a",{id:"Reactant.MLIR.API.mlirOperationHasInherentAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationHasInherentAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationHasInherentAttributeByName")],-1)),t[2401]||(t[2401]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2402]||(t[2402]=l('
julia
mlirOperationHasInherentAttributeByName(op, name)
Returns true if this operation defines an inherent attribute with this name. Note: the attribute can be optional, so mlirOperationGetInherentAttributeByName can still return a null attribute.
source
',3))]),e("details",wu,[e("summary",null,[t[2403]||(t[2403]=e("a",{id:"Reactant.MLIR.API.mlirOperationImplementsInterface-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationImplementsInterface-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationImplementsInterface")],-1)),t[2404]||(t[2404]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2405]||(t[2405]=l('
julia
mlirOperationImplementsInterface(operation, interfaceTypeID)
Returns true if the given operation implements an interface identified by its TypeID.
source
',3))]),e("details",Su,[e("summary",null,[t[2406]||(t[2406]=e("a",{id:"Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic")],-1)),t[2407]||(t[2407]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2408]||(t[2408]=l('
julia
mlirOperationImplementsInterfaceStatic(operationName, context, interfaceTypeID)
Returns true if the operation identified by its canonical string name implements the interface identified by its TypeID in the given context. Note that interfaces may be attached to operations in some contexts and not others.
source
',3))]),e("details",Nu,[e("summary",null,[t[2409]||(t[2409]=e("a",{id:"Reactant.MLIR.API.mlirOperationIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationIsNull")],-1)),t[2410]||(t[2410]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2411]||(t[2411]=l('
julia
mlirOperationIsNull(op)
Checks whether the underlying operation is null.
source
',3))]),e("details",Vu,[e("summary",null,[t[2412]||(t[2412]=e("a",{id:"Reactant.MLIR.API.mlirOperationMoveAfter-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationMoveAfter-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationMoveAfter")],-1)),t[2413]||(t[2413]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2414]||(t[2414]=l('
julia
mlirOperationMoveAfter(op, other)
Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),e("details",qu,[e("summary",null,[t[2415]||(t[2415]=e("a",{id:"Reactant.MLIR.API.mlirOperationMoveBefore-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationMoveBefore-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationMoveBefore")],-1)),t[2416]||(t[2416]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2417]||(t[2417]=l('
julia
mlirOperationMoveBefore(op, other)
Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),e("details",Uu,[e("summary",null,[t[2418]||(t[2418]=e("a",{id:"Reactant.MLIR.API.mlirOperationPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrint")],-1)),t[2419]||(t[2419]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2420]||(t[2420]=l('
julia
mlirOperationPrint(op, callback, userData)
Prints an operation by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",Qu,[e("summary",null,[t[2421]||(t[2421]=e("a",{id:"Reactant.MLIR.API.mlirOperationPrintWithFlags-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationPrintWithFlags-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrintWithFlags")],-1)),t[2422]||(t[2422]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2423]||(t[2423]=l('
julia
mlirOperationPrintWithFlags(op, flags, callback, userData)
Same as mlirOperationPrint but accepts flags controlling the printing behavior.
source
',3))]),e("details",Wu,[e("summary",null,[t[2424]||(t[2424]=e("a",{id:"Reactant.MLIR.API.mlirOperationPrintWithState-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationPrintWithState-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrintWithState")],-1)),t[2425]||(t[2425]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2426]||(t[2426]=l('
julia
mlirOperationPrintWithState(op, state, callback, userData)
Same as mlirOperationPrint but accepts AsmState controlling the printing behavior as well as caching computed names.
source
',3))]),e("details",Hu,[e("summary",null,[t[2427]||(t[2427]=e("a",{id:"Reactant.MLIR.API.mlirOperationRemoveAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveAttributeByName")],-1)),t[2428]||(t[2428]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2429]||(t[2429]=l('
julia
mlirOperationRemoveAttributeByName(op, name)
Removes an attribute by name. Returns false if the attribute was not found and true if removed. Deprecated, please use mlirOperationRemoveInherentAttributeByName or mlirOperationRemoveDiscardableAttributeByName.
source
',3))]),e("details",Zu,[e("summary",null,[t[2430]||(t[2430]=e("a",{id:"Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName")],-1)),t[2431]||(t[2431]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2432]||(t[2432]=l('
julia
mlirOperationRemoveDiscardableAttributeByName(op, name)
Removes a discardable attribute by name. Returns false if the attribute was not found and true if removed.
source
',3))]),e("details",Ju,[e("summary",null,[t[2433]||(t[2433]=e("a",{id:"Reactant.MLIR.API.mlirOperationRemoveFromParent-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveFromParent-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveFromParent")],-1)),t[2434]||(t[2434]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2435]||(t[2435]=l('
julia
mlirOperationRemoveFromParent(op)
Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.
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',3))]),e("details",Ku,[e("summary",null,[t[2436]||(t[2436]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetAttributeByName-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetAttributeByName")],-1)),t[2437]||(t[2437]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2438]||(t[2438]=l('
julia
mlirOperationSetAttributeByName(op, name, attr)
Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise. Deprecated, please use mlirOperationSetInherentAttributeByName or mlirOperationSetDiscardableAttributeByName.
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',3))]),e("details",$u,[e("summary",null,[t[2439]||(t[2439]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName")],-1)),t[2440]||(t[2440]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2441]||(t[2441]=l('
julia
mlirOperationSetDiscardableAttributeByName(op, name, attr)
Sets a discardable attribute by name, replacing the existing if it exists or adding a new one otherwise. The new attr Attribute is not allowed to be null, use mlirOperationRemoveDiscardableAttributeByName to remove an Attribute instead.
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',3))]),e("details",Xu,[e("summary",null,[t[2442]||(t[2442]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetInherentAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetInherentAttributeByName-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetInherentAttributeByName")],-1)),t[2443]||(t[2443]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2444]||(t[2444]=l('
julia
mlirOperationSetInherentAttributeByName(op, name, attr)
Sets an inherent attribute by name, replacing the existing if it exists. This has no effect if "name" does not match an inherent attribute.
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',3))]),e("details",Yu,[e("summary",null,[t[2445]||(t[2445]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetOperand-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetOperand-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetOperand")],-1)),t[2446]||(t[2446]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2447]||(t[2447]=l('
julia
mlirOperationSetOperand(op, pos, newValue)
Sets the pos-th operand of the operation.
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',3))]),e("details",_u,[e("summary",null,[t[2448]||(t[2448]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetOperands-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetOperands-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetOperands")],-1)),t[2449]||(t[2449]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2450]||(t[2450]=l('
julia
mlirOperationSetOperands(op, nOperands, operands)
Replaces the operands of the operation.
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',3))]),e("details",tb,[e("summary",null,[t[2451]||(t[2451]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetSuccessor-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetSuccessor-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetSuccessor")],-1)),t[2452]||(t[2452]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2453]||(t[2453]=l('
julia
mlirOperationSetSuccessor(op, pos, block)
Set pos-th successor of the operation.
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',3))]),e("details",eb,[e("summary",null,[t[2454]||(t[2454]=e("a",{id:"Reactant.MLIR.API.mlirOperationStateAddResults-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationStateAddResults-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateAddResults")],-1)),t[2455]||(t[2455]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2456]||(t[2456]=l('
julia
mlirOperationStateAddResults(state, n, results)
Adds a list of components to the operation state.
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',3))]),e("details",sb,[e("summary",null,[t[2457]||(t[2457]=e("a",{id:"Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference")],-1)),t[2458]||(t[2458]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2459]||(t[2459]=l('
julia
mlirOperationStateEnableResultTypeInference(state)
Enables result type inference for the operation under construction. If enabled, then the caller must not have called mlirOperationStateAddResults(). Note that if enabled, the mlirOperationCreate() call is failable: it will return a null operation on inference failure and will emit diagnostics.
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',3))]),e("details",ab,[e("summary",null,[t[2460]||(t[2460]=e("a",{id:"Reactant.MLIR.API.mlirOperationStateGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationStateGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateGet")],-1)),t[2461]||(t[2461]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2462]||(t[2462]=l('
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mlirOperationStateGet(name, loc)
Constructs an operation state from a name and a location.
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',3))]),e("details",ib,[e("summary",null,[t[2463]||(t[2463]=e("a",{id:"Reactant.MLIR.API.mlirOperationVerify-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationVerify-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationVerify")],-1)),t[2464]||(t[2464]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2465]||(t[2465]=l('
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mlirOperationVerify(op)
Verify the operation and return true if it passes, false if it fails.
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julia
mlirOperationWalk(op, callback, userData, walkOrder)
Walks operation op in walkOrder and calls callback on that operation. *userData is passed to the callback as well and can be used to tunnel some context or other data into the callback.
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',3))]),e("details",nb,[e("summary",null,[t[2469]||(t[2469]=e("a",{id:"Reactant.MLIR.API.mlirOperationWriteBytecode-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationWriteBytecode-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWriteBytecode")],-1)),t[2470]||(t[2470]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2471]||(t[2471]=l('
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mlirOperationWriteBytecode(op, callback, userData)
Same as mlirOperationPrint but writing the bytecode format.
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julia
mlirOperationWriteBytecodeWithConfig(op, config, callback, userData)
Same as mlirOperationWriteBytecode but with writer config and returns failure only if desired bytecode could not be honored.
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',3))]),e("details",rb,[e("summary",null,[t[2475]||(t[2475]=e("a",{id:"Reactant.MLIR.API.mlirParsePassPipeline-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirParsePassPipeline-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirParsePassPipeline")],-1)),t[2476]||(t[2476]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2477]||(t[2477]=l('
julia
mlirParsePassPipeline(passManager, pipeline, callback, userData)
Parse a textual MLIR pass pipeline and assign it to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
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',3))]),e("details",ob,[e("summary",null,[t[2478]||(t[2478]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerAddOwnedPass-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerAddOwnedPass-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerAddOwnedPass")],-1)),t[2479]||(t[2479]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2480]||(t[2480]=l('
julia
mlirPassManagerAddOwnedPass(passManager, pass)
Add a pass and transfer ownership to the provided top-level mlirPassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.
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mlirPassManagerCreate(ctx)
Create a new top-level PassManager with the default anchor.
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',3))]),e("details",hb,[e("summary",null,[t[2484]||(t[2484]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerCreateOnOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerCreateOnOperation-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerCreateOnOperation")],-1)),t[2485]||(t[2485]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2486]||(t[2486]=l('
julia
mlirPassManagerCreateOnOperation(ctx, anchorOp)
Create a new top-level PassManager anchored on anchorOp.
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mlirPassManagerDestroy(passManager)
Destroy the provided PassManager.
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julia
mlirPassManagerEnableIRPrinting(passManager, printBeforeAll, printAfterAll, printModuleScope, printAfterOnlyOnChange, printAfterOnlyOnFailure, flags, treePrintingPath)
Enable IR printing. The treePrintingPath argument is an optional path to a directory where the dumps will be produced. If it isn't provided then dumps are produced to stderr.
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',3))]),e("details",bb,[e("summary",null,[t[2493]||(t[2493]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerEnableVerifier-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerEnableVerifier-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerEnableVerifier")],-1)),t[2494]||(t[2494]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2495]||(t[2495]=l('
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mlirPassManagerEnableVerifier(passManager, enable)
Enable / disable verify-each.
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',3))]),e("details",gb,[e("summary",null,[t[2496]||(t[2496]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager")],-1)),t[2497]||(t[2497]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2498]||(t[2498]=l('
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mlirPassManagerGetAsOpPassManager(passManager)
Cast a top-level PassManager to a generic OpPassManager.
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',3))]),e("details",yb,[e("summary",null,[t[2499]||(t[2499]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerGetNestedUnder-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerGetNestedUnder-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerGetNestedUnder")],-1)),t[2500]||(t[2500]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2501]||(t[2501]=l('
julia
mlirPassManagerGetNestedUnder(passManager, operationName)
Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.
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',3))]),e("details",mb,[e("summary",null,[t[2502]||(t[2502]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerIsNull")],-1)),t[2503]||(t[2503]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2504]||(t[2504]=l('
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mlirPassManagerIsNull(passManager)
Checks if a PassManager is null.
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',3))]),e("details",kb,[e("summary",null,[t[2505]||(t[2505]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerRunOnOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerRunOnOp-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerRunOnOp")],-1)),t[2506]||(t[2506]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2507]||(t[2507]=l('
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mlirPassManagerRunOnOp(passManager, op)
Run the provided passManager on the given op.
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',3))]),e("details",Rb,[e("summary",null,[t[2508]||(t[2508]=e("a",{id:"Reactant.MLIR.API.mlirPrintPassPipeline-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirPrintPassPipeline-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPrintPassPipeline")],-1)),t[2509]||(t[2509]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2510]||(t[2510]=l('
julia
mlirPrintPassPipeline(passManager, callback, userData)
Print a textual MLIR pass pipeline by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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',3))]),e("details",fb,[e("summary",null,[t[2511]||(t[2511]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType")],-1)),t[2512]||(t[2512]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2513]||(t[2513]=l('
julia
mlirQuantizedTypeCastExpressedToStorageType(type, candidate)
Casts from a type based on the expressed type of the given quantized type to equivalent type based on storage type of the same quantized type.
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',3))]),e("details",Ib,[e("summary",null,[t[2514]||(t[2514]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType")],-1)),t[2515]||(t[2515]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2516]||(t[2516]=l('
julia
mlirQuantizedTypeCastFromExpressedType(type, candidate)
Casts from a type based on the expressed type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.
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',3))]),e("details",jb,[e("summary",null,[t[2517]||(t[2517]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType")],-1)),t[2518]||(t[2518]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2519]||(t[2519]=l('
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mlirQuantizedTypeCastFromStorageType(type, candidate)
Casts from a type based on the storage type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.
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',3))]),e("details",Mb,[e("summary",null,[t[2520]||(t[2520]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType")],-1)),t[2521]||(t[2521]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2522]||(t[2522]=l('
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mlirQuantizedTypeCastToExpressedType(type)
Casts from a type based on a quantized type to a corresponding typed based on the expressed type. Returns a null type if the cast is not valid.
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',3))]),e("details",Ab,[e("summary",null,[t[2523]||(t[2523]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType")],-1)),t[2524]||(t[2524]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2525]||(t[2525]=l('
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mlirQuantizedTypeCastToStorageType(type)
Casts from a type based on a quantized type to a corresponding typed based on the storage type. Returns a null type if the cast is not valid.
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',3))]),e("details",Lb,[e("summary",null,[t[2526]||(t[2526]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger")],-1)),t[2527]||(t[2527]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2528]||(t[2528]=l('
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mlirQuantizedTypeGetDefaultMaximumForInteger(isSigned, integralWidth)
Returns the maximum possible value stored by a quantized type.
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',3))]),e("details",Eb,[e("summary",null,[t[2529]||(t[2529]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger")],-1)),t[2530]||(t[2530]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2531]||(t[2531]=l('
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mlirQuantizedTypeGetDefaultMinimumForInteger(isSigned, integralWidth)
Returns the minimum possible value stored by a quantized type.
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',3))]),e("details",vb,[e("summary",null,[t[2532]||(t[2532]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType")],-1)),t[2533]||(t[2533]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2534]||(t[2534]=l('
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mlirQuantizedTypeGetExpressedType(type)
Gets the original type approximated by the given quantized type.
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',3))]),e("details",Tb,[e("summary",null,[t[2535]||(t[2535]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetFlags-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetFlags-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetFlags")],-1)),t[2536]||(t[2536]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2537]||(t[2537]=l('
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mlirQuantizedTypeGetFlags(type)
Gets the flags associated with the given quantized type.
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',3))]),e("details",Cb,[e("summary",null,[t[2538]||(t[2538]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType")],-1)),t[2539]||(t[2539]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2540]||(t[2540]=l('
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mlirQuantizedTypeGetQuantizedElementType(type)
Returns the element type of the given quantized type as another quantized type.
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',3))]),e("details",xb,[e("summary",null,[t[2541]||(t[2541]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag-Tuple{}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag")],-1)),t[2542]||(t[2542]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2543]||(t[2543]=l('
julia
mlirQuantizedTypeGetSignedFlag()
Returns the bit flag used to indicate signedness of a quantized type.
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',3))]),e("details",Fb,[e("summary",null,[t[2544]||(t[2544]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageType")],-1)),t[2545]||(t[2545]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2546]||(t[2546]=l('
julia
mlirQuantizedTypeGetStorageType(type)
Returns the underlying type used to store the values.
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',3))]),e("details",Pb,[e("summary",null,[t[2547]||(t[2547]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth")],-1)),t[2548]||(t[2548]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2549]||(t[2549]=l('
julia
mlirQuantizedTypeGetStorageTypeIntegralWidth(type)
Returns the integral bitwidth that the storage type of the given quantized type can represent exactly.
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',3))]),e("details",Db,[e("summary",null,[t[2550]||(t[2550]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax")],-1)),t[2551]||(t[2551]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2552]||(t[2552]=l('
julia
mlirQuantizedTypeGetStorageTypeMax(type)
Returns the maximum value that the storage type of the given quantized type can take.
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',3))]),e("details",Ob,[e("summary",null,[t[2553]||(t[2553]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin")],-1)),t[2554]||(t[2554]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2555]||(t[2555]=l('
julia
mlirQuantizedTypeGetStorageTypeMin(type)
Returns the minimum value that the storage type of the given quantized type can take.
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',3))]),e("details",Bb,[e("summary",null,[t[2556]||(t[2556]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType")],-1)),t[2557]||(t[2557]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2558]||(t[2558]=l('
julia
mlirQuantizedTypeIsCompatibleExpressedType(type, candidate)
Returns true if the candidate type is compatible with the given quantized type.
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',3))]),e("details",Gb,[e("summary",null,[t[2559]||(t[2559]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeIsSigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeIsSigned-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeIsSigned")],-1)),t[2560]||(t[2560]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2561]||(t[2561]=l('
julia
mlirQuantizedTypeIsSigned(type)
Returns true if the given type is signed, false otherwise.
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',3))]),e("details",zb,[e("summary",null,[t[2562]||(t[2562]=e("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGet")],-1)),t[2563]||(t[2563]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2564]||(t[2564]=l('
julia
mlirRankedTensorTypeGet(rank, shape, elementType, encoding)
Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull() to this parameter.
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',3))]),e("details",wb,[e("summary",null,[t[2565]||(t[2565]=e("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetChecked-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetChecked")],-1)),t[2566]||(t[2566]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2567]||(t[2567]=l('
julia
mlirRankedTensorTypeGetChecked(loc, rank, shape, elementType, encoding)
Same as "mlirRankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
',3))]),e("details",Sb,[e("summary",null,[t[2568]||(t[2568]=e("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding")],-1)),t[2569]||(t[2569]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2570]||(t[2570]=l('
julia
mlirRankedTensorTypeGetEncoding(type)
Gets the 'encoding' attribute from the ranked tensor type, returning a null attribute if none.
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',3))]),e("details",Nb,[e("summary",null,[t[2571]||(t[2571]=e("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID")],-1)),t[2572]||(t[2572]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2573]||(t[2573]=l('
julia
mlirRankedTensorTypeGetTypeID()
Returns the typeID of an RankedTensor type.
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',3))]),e("details",Vb,[e("summary",null,[t[2574]||(t[2574]=e("a",{id:"Reactant.MLIR.API.mlirRegionAppendOwnedBlock-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionAppendOwnedBlock-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionAppendOwnedBlock")],-1)),t[2575]||(t[2575]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2576]||(t[2576]=l('
julia
mlirRegionAppendOwnedBlock(region, block)
Takes a block owned by the caller and appends it to the given region.
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',3))]),e("details",qb,[e("summary",null,[t[2577]||(t[2577]=e("a",{id:"Reactant.MLIR.API.mlirRegionCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirRegionCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionCreate")],-1)),t[2578]||(t[2578]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2579]||(t[2579]=l('
julia
mlirRegionCreate()
Creates a new empty region and transfers ownership to the caller.
source
',3))]),e("details",Ub,[e("summary",null,[t[2580]||(t[2580]=e("a",{id:"Reactant.MLIR.API.mlirRegionDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionDestroy")],-1)),t[2581]||(t[2581]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2582]||(t[2582]=l('
julia
mlirRegionDestroy(region)
Takes a region owned by the caller and destroys it.
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',3))]),e("details",Qb,[e("summary",null,[t[2583]||(t[2583]=e("a",{id:"Reactant.MLIR.API.mlirRegionEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionEqual")],-1)),t[2584]||(t[2584]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2585]||(t[2585]=l('
julia
mlirRegionEqual(region, other)
Checks whether two region handles point to the same region. This does not perform deep comparison.
source
',3))]),e("details",Wb,[e("summary",null,[t[2586]||(t[2586]=e("a",{id:"Reactant.MLIR.API.mlirRegionGetFirstBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionGetFirstBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionGetFirstBlock")],-1)),t[2587]||(t[2587]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2588]||(t[2588]=l('
julia
mlirRegionGetFirstBlock(region)
Gets the first block in the region.
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',3))]),e("details",Hb,[e("summary",null,[t[2589]||(t[2589]=e("a",{id:"Reactant.MLIR.API.mlirRegionGetNextInOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionGetNextInOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionGetNextInOperation")],-1)),t[2590]||(t[2590]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2591]||(t[2591]=l('
julia
mlirRegionGetNextInOperation(region)
Returns the region immediately following the given region in its parent operation.
source
',3))]),e("details",Zb,[e("summary",null,[t[2592]||(t[2592]=e("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlock-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlock-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlock")],-1)),t[2593]||(t[2593]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2594]||(t[2594]=l('
julia
mlirRegionInsertOwnedBlock(region, pos, block)
Takes a block owned by the caller and inserts it at pos to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.
source
',3))]),e("details",Jb,[e("summary",null,[t[2595]||(t[2595]=e("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter")],-1)),t[2596]||(t[2596]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2597]||(t[2597]=l('
julia
mlirRegionInsertOwnedBlockAfter(region, reference, block)
Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.
source
',3))]),e("details",Kb,[e("summary",null,[t[2598]||(t[2598]=e("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore")],-1)),t[2599]||(t[2599]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2600]||(t[2600]=l('
julia
mlirRegionInsertOwnedBlockBefore(region, reference, block)
Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.
source
',3))]),e("details",$b,[e("summary",null,[t[2601]||(t[2601]=e("a",{id:"Reactant.MLIR.API.mlirRegionIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionIsNull")],-1)),t[2602]||(t[2602]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2603]||(t[2603]=l('
julia
mlirRegionIsNull(region)
Checks whether a region is null.
source
',3))]),e("details",Xb,[e("summary",null,[t[2604]||(t[2604]=e("a",{id:"Reactant.MLIR.API.mlirRegionTakeBody-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionTakeBody-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionTakeBody")],-1)),t[2605]||(t[2605]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2606]||(t[2606]=l('
julia
mlirRegionTakeBody(target, source)
Moves the entire content of the source region to the target region.
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',3))]),e("details",Yb,[e("summary",null,[t[2607]||(t[2607]=e("a",{id:"Reactant.MLIR.API.mlirRegisterAllDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegisterAllDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllDialects")],-1)),t[2608]||(t[2608]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2609]||(t[2609]=l('
julia
mlirRegisterAllDialects(registry)
Appends all upstream dialects and extensions to the dialect registry.
source
',3))]),e("details",_b,[e("summary",null,[t[2610]||(t[2610]=e("a",{id:"Reactant.MLIR.API.mlirRegisterAllLLVMTranslations-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegisterAllLLVMTranslations-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllLLVMTranslations")],-1)),t[2611]||(t[2611]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2612]||(t[2612]=l('
julia
mlirRegisterAllLLVMTranslations(context)
Register all translations to LLVM IR for dialects that can support it.
source
',3))]),e("details",tg,[e("summary",null,[t[2613]||(t[2613]=e("a",{id:"Reactant.MLIR.API.mlirRegisterAllPasses-Tuple{}",href:"#Reactant.MLIR.API.mlirRegisterAllPasses-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllPasses")],-1)),t[2614]||(t[2614]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2615]||(t[2615]=l('
julia
mlirRegisterAllPasses()
Register all compiler passes of MLIR.
source
',3))]),e("details",eg,[e("summary",null,[t[2616]||(t[2616]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCancelOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCancelOpModification-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCancelOpModification")],-1)),t[2617]||(t[2617]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2618]||(t[2618]=l('
julia
mlirRewriterBaseCancelOpModification(rewriter, op)
This method cancels a pending in-place modification. This can only be called on operations that were provided to a call to startOpModification.
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',3))]),e("details",sg,[e("summary",null,[t[2619]||(t[2619]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint")],-1)),t[2620]||(t[2620]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2621]||(t[2621]=l('
julia
mlirRewriterBaseClearInsertionPoint(rewriter)
Reset the insertion point to no location. Creating an operation without a set insertion point is an error, but this can still be useful when the current insertion point a builder refers to is being removed.
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',3))]),e("details",ag,[e("summary",null,[t[2622]||(t[2622]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseClone-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseClone-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseClone")],-1)),t[2623]||(t[2623]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2624]||(t[2624]=l('
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mlirRewriterBaseClone(rewriter, op)
Creates a deep copy of the specified operation.
source
',3))]),e("details",ig,[e("summary",null,[t[2625]||(t[2625]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore")],-1)),t[2626]||(t[2626]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2627]||(t[2627]=l('
julia
mlirRewriterBaseCloneRegionBefore(rewriter, region, before)
Clone the blocks that belong to "region" before the given position in another region "parent".
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',3))]),e("details",lg,[e("summary",null,[t[2628]||(t[2628]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions")],-1)),t[2629]||(t[2629]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2630]||(t[2630]=l('
julia
mlirRewriterBaseCloneWithoutRegions(rewriter, op)
Creates a deep copy of this operation but keep the operation regions empty.
source
',3))]),e("details",ng,[e("summary",null,[t[2631]||(t[2631]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore")],-1)),t[2632]||(t[2632]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2633]||(t[2633]=l('
julia
mlirRewriterBaseCreateBlockBefore(rewriter, insertBefore, nArgTypes, argTypes, locations)
Add new block with 'argTypes' arguments and set the insertion point to the end of it. The block is placed before 'insertBefore'. locs contains the locations of the inserted arguments, and should match the size of argTypes.
source
',3))]),e("details",pg,[e("summary",null,[t[2634]||(t[2634]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseEraseBlock-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseEraseBlock-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseEraseBlock")],-1)),t[2635]||(t[2635]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2636]||(t[2636]=l('
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mlirRewriterBaseEraseBlock(rewriter, block)
Erases a block along with all operations inside it.
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',3))]),e("details",rg,[e("summary",null,[t[2637]||(t[2637]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseEraseOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseEraseOp-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseEraseOp")],-1)),t[2638]||(t[2638]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2639]||(t[2639]=l('
julia
mlirRewriterBaseEraseOp(rewriter, op)
Erases an operation that is known to have no uses.
source
',3))]),e("details",og,[e("summary",null,[t[2640]||(t[2640]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification")],-1)),t[2641]||(t[2641]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2642]||(t[2642]=l('
julia
mlirRewriterBaseFinalizeOpModification(rewriter, op)
This method is used to signal the end of an in-place modification of the given operation. This can only be called on operations that were provided to a call to startOpModification.
source
',3))]),e("details",dg,[e("summary",null,[t[2643]||(t[2643]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetBlock")],-1)),t[2644]||(t[2644]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2645]||(t[2645]=l('
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mlirRewriterBaseGetBlock(rewriter)
Returns the current block of the rewriter.
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',3))]),e("details",hg,[e("summary",null,[t[2646]||(t[2646]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetContext")],-1)),t[2647]||(t[2647]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2648]||(t[2648]=l('
julia
mlirRewriterBaseGetContext(rewriter)
Get the MLIR context referenced by the rewriter.
source
',3))]),e("details",cg,[e("summary",null,[t[2649]||(t[2649]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock")],-1)),t[2650]||(t[2650]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2651]||(t[2651]=l('
julia
mlirRewriterBaseGetInsertionBlock(rewriter)
Return the block the current insertion point belongs to. Note that the insertion point is not necessarily the end of the block.
source
',3))]),e("details",ug,[e("summary",null,[t[2652]||(t[2652]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore")],-1)),t[2653]||(t[2653]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2654]||(t[2654]=l('
julia
mlirRewriterBaseInlineBlockBefore(rewriter, source, op, nArgValues, argValues)
Inline the operations of block 'source' before the operation 'op'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'
The source block must have no successors. Otherwise, the resulting IR would have unreachable operations.
source
',4))]),e("details",bg,[e("summary",null,[t[2655]||(t[2655]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore")],-1)),t[2656]||(t[2656]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2657]||(t[2657]=l('
julia
mlirRewriterBaseInlineRegionBefore(rewriter, region, before)
Move the blocks that belong to "region" before the given position in another region "parent". The two regions must be different. The caller is responsible for creating or updating the operation transferring flow of control to the region and passing it the correct block arguments.
source
',3))]),e("details",gg,[e("summary",null,[t[2658]||(t[2658]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInsert-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInsert-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInsert")],-1)),t[2659]||(t[2659]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2660]||(t[2660]=l('
julia
mlirRewriterBaseInsert(rewriter, op)
Insert the given operation at the current insertion point and return it.
source
',3))]),e("details",yg,[e("summary",null,[t[2661]||(t[2661]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMergeBlocks-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMergeBlocks-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMergeBlocks")],-1)),t[2662]||(t[2662]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2663]||(t[2663]=l('
julia
mlirRewriterBaseMergeBlocks(rewriter, source, dest, nArgValues, argValues)
Inline the operations of block 'source' into the end of block 'dest'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'
The dest block must have no successors. Otherwise, the resulting IR would have unreachable operation.
source
',4))]),e("details",mg,[e("summary",null,[t[2664]||(t[2664]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore")],-1)),t[2665]||(t[2665]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2666]||(t[2666]=l('
julia
mlirRewriterBaseMoveBlockBefore(rewriter, block, existingBlock)
Unlink this block and insert it right before existingBlock.
source
',3))]),e("details",kg,[e("summary",null,[t[2667]||(t[2667]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter")],-1)),t[2668]||(t[2668]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2669]||(t[2669]=l('
julia
mlirRewriterBaseMoveOpAfter(rewriter, op, existingOp)
Unlink this operation from its current block and insert it right after existingOp which may be in the same or another block in the same function.
source
',3))]),e("details",Rg,[e("summary",null,[t[2670]||(t[2670]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore")],-1)),t[2671]||(t[2671]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2672]||(t[2672]=l('
julia
mlirRewriterBaseMoveOpBefore(rewriter, op, existingOp)
Unlink this operation from its current block and insert it right before existingOp which may be in the same or another block in the same function.
source
',3))]),e("details",fg,[e("summary",null,[t[2673]||(t[2673]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation")],-1)),t[2674]||(t[2674]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2675]||(t[2675]=l('
julia
mlirRewriterBaseReplaceAllOpUsesWithOperation(rewriter, from, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.
source
',3))]),e("details",Ig,[e("summary",null,[t[2676]||(t[2676]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange")],-1)),t[2677]||(t[2677]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2678]||(t[2678]=l('
julia
mlirRewriterBaseReplaceAllOpUsesWithValueRange(rewriter, from, nTo, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.
source
',3))]),e("details",jg,[e("summary",null,[t[2679]||(t[2679]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept")],-1)),t[2680]||(t[2680]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2681]||(t[2681]=l('
julia
mlirRewriterBaseReplaceAllUsesExcept(rewriter, from, to, exceptedUser)
Find uses of from and replace them with to except if the user is exceptedUser. Also notify the listener about every in-place op modification (for every use that was replaced).
source
',3))]),e("details",Mg,[e("summary",null,[t[2682]||(t[2682]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith")],-1)),t[2683]||(t[2683]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2684]||(t[2684]=l('
julia
mlirRewriterBaseReplaceAllUsesWith(rewriter, from, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).
source
',3))]),e("details",Ag,[e("summary",null,[t[2685]||(t[2685]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith")],-1)),t[2686]||(t[2686]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2687]||(t[2687]=l('
julia
mlirRewriterBaseReplaceAllValueRangeUsesWith(rewriter, nValues, from, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).
source
',3))]),e("details",Lg,[e("summary",null,[t[2688]||(t[2688]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock")],-1)),t[2689]||(t[2689]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2690]||(t[2690]=l('
julia
mlirRewriterBaseReplaceOpUsesWithinBlock(rewriter, op, nNewValues, newValues, block)
Find uses of from within block and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced). The optional allUsesReplaced flag is set to "true" if all uses were replaced.
source
',3))]),e("details",Eg,[e("summary",null,[t[2691]||(t[2691]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation")],-1)),t[2692]||(t[2692]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2693]||(t[2693]=l('
julia
mlirRewriterBaseReplaceOpWithOperation(rewriter, op, newOp)
Replace the results of the given (original) operation with the specified new op (replacement). The result types of the two ops must match. The original op is erased.
source
',3))]),e("details",vg,[e("summary",null,[t[2694]||(t[2694]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues")],-1)),t[2695]||(t[2695]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2696]||(t[2696]=l('
julia
mlirRewriterBaseReplaceOpWithValues(rewriter, op, nValues, values)
Replace the results of the given (original) operation with the specified list of values (replacements). The result types of the given op and the replacements must match. The original op is erased.
source
',3))]),e("details",Tg,[e("summary",null,[t[2697]||(t[2697]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter")],-1)),t[2698]||(t[2698]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2699]||(t[2699]=l('
julia
mlirRewriterBaseSetInsertionPointAfter(rewriter, op)
Sets the insertion point to the node after the specified operation, which will cause subsequent insertions to go right after it.
source
',3))]),e("details",Cg,[e("summary",null,[t[2700]||(t[2700]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue")],-1)),t[2701]||(t[2701]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2702]||(t[2702]=l('
julia
mlirRewriterBaseSetInsertionPointAfterValue(rewriter, value)
Sets the insertion point to the node after the specified value. If value has a defining operation, sets the insertion point to the node after such defining operation. This will cause subsequent insertions to go right after it. Otherwise, value is a BlockArgument. Sets the insertion point to the start of its block.
source
',3))]),e("details",xg,[e("summary",null,[t[2703]||(t[2703]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore")],-1)),t[2704]||(t[2704]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2705]||(t[2705]=l('
julia
mlirRewriterBaseSetInsertionPointBefore(rewriter, op)
Sets the insertion point to the specified operation, which will cause subsequent insertions to go right before it.
source
',3))]),e("details",Fg,[e("summary",null,[t[2706]||(t[2706]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd")],-1)),t[2707]||(t[2707]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2708]||(t[2708]=l('
julia
mlirRewriterBaseSetInsertionPointToEnd(rewriter, block)
Sets the insertion point to the end of the specified block.
source
',3))]),e("details",Pg,[e("summary",null,[t[2709]||(t[2709]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart")],-1)),t[2710]||(t[2710]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2711]||(t[2711]=l('
julia
mlirRewriterBaseSetInsertionPointToStart(rewriter, block)
Sets the insertion point to the start of the specified block.
source
',3))]),e("details",Dg,[e("summary",null,[t[2712]||(t[2712]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseStartOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseStartOpModification-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseStartOpModification")],-1)),t[2713]||(t[2713]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2714]||(t[2714]=l('
julia
mlirRewriterBaseStartOpModification(rewriter, op)
This method is used to notify the rewriter that an in-place operation modification is about to happen. A call to this function must be followed by a call to either finalizeOpModification or cancelOpModification. This is a minor efficiency win (it avoids creating a new operation and removing the old one) but also often allows simpler code in the client.
source
',3))]),e("details",Og,[e("summary",null,[t[2715]||(t[2715]=e("a",{id:"Reactant.MLIR.API.mlirSetGlobalDebugType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSetGlobalDebugType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSetGlobalDebugType")],-1)),t[2716]||(t[2716]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2717]||(t[2717]=l('
julia
mlirSetGlobalDebugType(type)
Sets the current debug type, similarly to -debug-only=type in the command-line tools. Note that global debug should be enabled for any output to be produced.
source
',3))]),e("details",Bg,[e("summary",null,[t[2718]||(t[2718]=e("a",{id:"Reactant.MLIR.API.mlirSetGlobalDebugTypes-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSetGlobalDebugTypes-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSetGlobalDebugTypes")],-1)),t[2719]||(t[2719]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2720]||(t[2720]=l('
julia
mlirSetGlobalDebugTypes(types, n)
Sets multiple current debug types, similarly to `-debug-only=type1,type2" in the command-line tools. Note that global debug should be enabled for any output to be produced.
source
',3))]),e("details",Gg,[e("summary",null,[t[2721]||(t[2721]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDimSize-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDimSize-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDimSize")],-1)),t[2722]||(t[2722]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2723]||(t[2723]=l('
julia
mlirShapedTypeGetDimSize(type, dim)
Returns the dim-th dimension of the given ranked shaped type.
source
',3))]),e("details",zg,[e("summary",null,[t[2724]||(t[2724]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDynamicSize-Tuple{}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDynamicSize-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDynamicSize")],-1)),t[2725]||(t[2725]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2726]||(t[2726]=l('
julia
mlirShapedTypeGetDynamicSize()
Returns the value indicating a dynamic size in a shaped type. Prefer mlirShapedTypeIsDynamicSize to direct comparisons with this value.
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',3))]),e("details",wg,[e("summary",null,[t[2727]||(t[2727]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset-Tuple{}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset")],-1)),t[2728]||(t[2728]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2729]||(t[2729]=l('
julia
mlirShapedTypeGetDynamicStrideOrOffset()
Returns the value indicating a dynamic stride or offset in a shaped type. Prefer mlirShapedTypeGetDynamicStrideOrOffset to direct comparisons with this value.
source
',3))]),e("details",Sg,[e("summary",null,[t[2730]||(t[2730]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetElementType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetElementType")],-1)),t[2731]||(t[2731]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2732]||(t[2732]=l('
julia
mlirShapedTypeGetElementType(type)
Returns the element type of the shaped type.
source
',3))]),e("details",Ng,[e("summary",null,[t[2733]||(t[2733]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetRank-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetRank")],-1)),t[2734]||(t[2734]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2735]||(t[2735]=l('
julia
mlirShapedTypeGetRank(type)
Returns the rank of the given ranked shaped type.
source
',3))]),e("details",Vg,[e("summary",null,[t[2736]||(t[2736]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeHasRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeHasRank-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeHasRank")],-1)),t[2737]||(t[2737]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2738]||(t[2738]=l('
julia
mlirShapedTypeHasRank(type)
Checks whether the given shaped type is ranked.
source
',3))]),e("details",qg,[e("summary",null,[t[2739]||(t[2739]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeHasStaticShape-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeHasStaticShape-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeHasStaticShape")],-1)),t[2740]||(t[2740]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2741]||(t[2741]=l('
julia
mlirShapedTypeHasStaticShape(type)
Checks whether the given shaped type has a static shape.
source
',3))]),e("details",Ug,[e("summary",null,[t[2742]||(t[2742]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicDim-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicDim-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicDim")],-1)),t[2743]||(t[2743]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2744]||(t[2744]=l('
julia
mlirShapedTypeIsDynamicDim(type, dim)
Checks wither the dim-th dimension of the given shaped type is dynamic.
source
',3))]),e("details",Qg,[e("summary",null,[t[2745]||(t[2745]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicSize-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicSize-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicSize")],-1)),t[2746]||(t[2746]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2747]||(t[2747]=l('
julia
mlirShapedTypeIsDynamicSize(size)
Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.
source
',3))]),e("details",Wg,[e("summary",null,[t[2748]||(t[2748]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset")],-1)),t[2749]||(t[2749]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2750]||(t[2750]=l('
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)
Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.
source
',3))]),e("details",Hg,[e("summary",null,[t[2751]||(t[2751]=e("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetIndices-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetIndices-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetIndices")],-1)),t[2752]||(t[2752]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2753]||(t[2753]=l('
julia
mlirSparseElementsAttrGetIndices(attr)
Returns the dense elements attribute containing 64-bit integer indices of non-null elements in the given sparse elements attribute.
source
',3))]),e("details",Zg,[e("summary",null,[t[2754]||(t[2754]=e("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID")],-1)),t[2755]||(t[2755]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2756]||(t[2756]=l('
julia
mlirSparseElementsAttrGetTypeID()
Returns the typeID of a SparseElements attribute.
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julia
mlirSparseElementsAttrGetValues(attr)
Returns the dense elements attribute containing the non-null elements in the given sparse elements attribute.
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',3))]),e("details",Kg,[e("summary",null,[t[2760]||(t[2760]=e("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttribute-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttribute-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttribute")],-1)),t[2761]||(t[2761]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2762]||(t[2762]=l('
julia
mlirSparseElementsAttribute(shapedType, denseIndices, denseValues)
Creates a sparse elements attribute of the given shape from a list of indices and a list of associated values. Both lists are expected to be dense elements attributes with the same number of elements. The list of indices is expected to contain 64-bit integers. The attribute is created in the same context as the type.
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',3))]),e("details",$g,[e("summary",null,[t[2763]||(t[2763]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet")],-1)),t[2764]||(t[2764]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2765]||(t[2765]=l('
julia
mlirSparseTensorEncodingAttrGet(ctx, lvlRank, lvlTypes, dimToLvl, lvlTodim, posWidth, crdWidth, explicitVal, implicitVal)
Creates a sparse\\_tensor.encoding attribute with the given parameters.
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',3))]),e("details",Xg,[e("summary",null,[t[2766]||(t[2766]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth")],-1)),t[2767]||(t[2767]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2768]||(t[2768]=l('
julia
mlirSparseTensorEncodingAttrGetCrdWidth(attr)
Returns the coordinate bitwidth of the sparse\\_tensor.encoding attribute.
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julia
mlirSparseTensorEncodingAttrGetDimToLvl(attr)
Returns the dimension-to-level mapping of the sparse\\_tensor.encoding attribute.
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julia
mlirSparseTensorEncodingAttrGetExplicitVal(attr)
Returns the explicit value of the sparse\\_tensor.encoding attribute.
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julia
mlirSparseTensorEncodingAttrGetImplicitVal(attr)
Returns the implicit value of the sparse\\_tensor.encoding attribute.
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julia
mlirSparseTensorEncodingAttrGetLvlFmt(attr, lvl)
Returns a specified level-format of the sparse\\_tensor.encoding attribute.
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julia
mlirSparseTensorEncodingAttrGetLvlToDim(attr)
Returns the level-to-dimension mapping of the sparse\\_tensor.encoding attribute.
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julia
mlirSparseTensorEncodingAttrGetLvlType(attr, lvl)
Returns a specified level-type of the sparse\\_tensor.encoding attribute.
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julia
mlirSparseTensorEncodingAttrGetPosWidth(attr)
Returns the position bitwidth of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",ly,[e("summary",null,[t[2790]||(t[2790]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank")],-1)),t[2791]||(t[2791]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2792]||(t[2792]=l('
julia
mlirSparseTensorEncodingGetLvlRank(attr)
Returns the level-rank of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",ny,[e("summary",null,[t[2793]||(t[2793]=e("a",{id:"Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID")],-1)),t[2794]||(t[2794]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2795]||(t[2795]=l('
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mlirStridedLayoutAttrGetTypeID()
Returns the typeID of a StridedLayout attribute.
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',3))]),e("details",py,[e("summary",null,[t[2796]||(t[2796]=e("a",{id:"Reactant.MLIR.API.mlirStringAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGet")],-1)),t[2797]||(t[2797]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2798]||(t[2798]=l('
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mlirStringAttrGet(ctx, str)
Creates a string attribute in the given context containing the given string.
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julia
mlirStringAttrGetTypeID()
Returns the typeID of a String attribute.
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julia
mlirStringAttrGetValue(attr)
Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.
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julia
mlirStringAttrTypedGet(type, str)
Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.
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',3))]),e("details",hy,[e("summary",null,[t[2808]||(t[2808]=e("a",{id:"Reactant.MLIR.API.mlirStringRefCreate-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringRefCreate-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefCreate")],-1)),t[2809]||(t[2809]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2810]||(t[2810]=l('
julia
mlirStringRefCreate(str, length)
Constructs a string reference from the pointer and length. The pointer need not reference to a null-terminated string.
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',3))]),e("details",cy,[e("summary",null,[t[2811]||(t[2811]=e("a",{id:"Reactant.MLIR.API.mlirStringRefCreateFromCString-Tuple{Any}",href:"#Reactant.MLIR.API.mlirStringRefCreateFromCString-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefCreateFromCString")],-1)),t[2812]||(t[2812]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2813]||(t[2813]=l('
julia
mlirStringRefCreateFromCString(str)
Constructs a string reference from a null-terminated C string. Prefer mlirStringRefCreate if the length of the string is known.
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',3))]),e("details",uy,[e("summary",null,[t[2814]||(t[2814]=e("a",{id:"Reactant.MLIR.API.mlirStringRefEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringRefEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefEqual")],-1)),t[2815]||(t[2815]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2816]||(t[2816]=l('
julia
mlirStringRefEqual(string, other)
Returns true if two string references are equal, false otherwise.
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',3))]),e("details",by,[e("summary",null,[t[2817]||(t[2817]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGet")],-1)),t[2818]||(t[2818]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2819]||(t[2819]=l('
julia
mlirSymbolRefAttrGet(ctx, symbol, numReferences, references)
Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.
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',3))]),e("details",gy,[e("summary",null,[t[2820]||(t[2820]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference")],-1)),t[2821]||(t[2821]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2822]||(t[2822]=l('
julia
mlirSymbolRefAttrGetLeafReference(attr)
Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.
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',3))]),e("details",yy,[e("summary",null,[t[2823]||(t[2823]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference")],-1)),t[2824]||(t[2824]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2825]||(t[2825]=l('
julia
mlirSymbolRefAttrGetNestedReference(attr, pos)
Returns pos-th reference nested in the given symbol reference attribute.
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julia
mlirSymbolRefAttrGetNumNestedReferences(attr)
Returns the number of references nested in the given symbol reference attribute.
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',3))]),e("details",ky,[e("summary",null,[t[2829]||(t[2829]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference")],-1)),t[2830]||(t[2830]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2831]||(t[2831]=l('
julia
mlirSymbolRefAttrGetRootReference(attr)
Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.
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',3))]),e("details",Ry,[e("summary",null,[t[2832]||(t[2832]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID")],-1)),t[2833]||(t[2833]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2834]||(t[2834]=l('
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mlirSymbolRefAttrGetTypeID()
Returns the typeID of an SymbolRef attribute.
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',3))]),e("details",fy,[e("summary",null,[t[2835]||(t[2835]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableCreate-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableCreate")],-1)),t[2836]||(t[2836]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2837]||(t[2837]=l('
julia
mlirSymbolTableCreate(operation)
Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.
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',3))]),e("details",Iy,[e("summary",null,[t[2838]||(t[2838]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableDestroy")],-1)),t[2839]||(t[2839]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2840]||(t[2840]=l('
julia
mlirSymbolTableDestroy(symbolTable)
Destroys the symbol table created with mlirSymbolTableCreate. This does not affect the operations in the table.
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',3))]),e("details",jy,[e("summary",null,[t[2841]||(t[2841]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableErase-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableErase-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableErase")],-1)),t[2842]||(t[2842]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2843]||(t[2843]=l('
julia
mlirSymbolTableErase(symbolTable, operation)
Removes the given operation from the symbol table and erases it.
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',3))]),e("details",My,[e("summary",null,[t[2844]||(t[2844]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName")],-1)),t[2845]||(t[2845]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2846]||(t[2846]=l('
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mlirSymbolTableGetSymbolAttributeName()
Returns the name of the attribute used to store symbol names compatible with symbol tables.
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',3))]),e("details",Ay,[e("summary",null,[t[2847]||(t[2847]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName")],-1)),t[2848]||(t[2848]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2849]||(t[2849]=l('
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mlirSymbolTableGetVisibilityAttributeName()
Returns the name of the attribute used to store symbol visibility.
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',3))]),e("details",Ly,[e("summary",null,[t[2850]||(t[2850]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableInsert-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableInsert-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableInsert")],-1)),t[2851]||(t[2851]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2852]||(t[2852]=l('
julia
mlirSymbolTableInsert(symbolTable, operation)
Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.
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',3))]),e("details",Ey,[e("summary",null,[t[2853]||(t[2853]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableIsNull")],-1)),t[2854]||(t[2854]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2855]||(t[2855]=l('
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mlirSymbolTableIsNull(symbolTable)
Returns true if the symbol table is null.
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julia
mlirSymbolTableLookup(symbolTable, name)
Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.
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',3))]),e("details",Ty,[e("summary",null,[t[2859]||(t[2859]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses")],-1)),t[2860]||(t[2860]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2861]||(t[2861]=l('
julia
mlirSymbolTableReplaceAllSymbolUses(oldSymbol, newSymbol, from)
Attempt to replace all uses that are nested within the given operation of the given symbol 'oldSymbol' with the provided 'newSymbol'. This does not traverse into nested symbol tables. Will fail atomically if there are any unknown operations that may be potential symbol tables.
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',3))]),e("details",Cy,[e("summary",null,[t[2862]||(t[2862]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables")],-1)),t[2863]||(t[2863]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2864]||(t[2864]=l('
julia
mlirSymbolTableWalkSymbolTables(from, allSymUsesVisible, callback, userData)
Walks all symbol table operations nested within, and including, op. For each symbol table operation, the provided callback is invoked with the op and a boolean signifying if the symbols within that symbol table can be treated as if all uses within the IR are visible to the caller. allSymUsesVisible identifies whether all of the symbol uses of symbols within op are visible.
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',3))]),e("details",xy,[e("summary",null,[t[2865]||(t[2865]=e("a",{id:"Reactant.MLIR.API.mlirTF32TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTF32TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTF32TypeGet")],-1)),t[2866]||(t[2866]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2867]||(t[2867]=l('
julia
mlirTF32TypeGet(ctx)
Creates a TF32 type in the given context. The type is owned by the context.
source
',3))]),e("details",Fy,[e("summary",null,[t[2868]||(t[2868]=e("a",{id:"Reactant.MLIR.API.mlirTransformApplyNamedSequence-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirTransformApplyNamedSequence-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformApplyNamedSequence")],-1)),t[2869]||(t[2869]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2870]||(t[2870]=l('
julia
mlirTransformApplyNamedSequence(payload, transformRoot, transformModule, transformOptions)
Applies the transformation script starting at the given transform root operation to the given payload operation. The module containing the transform root as well as the transform options should be provided. The transform operation must implement TransformOpInterface and the module must be a ModuleOp. Returns the status of the application.
source
',3))]),e("details",Py,[e("summary",null,[t[2871]||(t[2871]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirTransformOptionsCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsCreate")],-1)),t[2872]||(t[2872]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2873]||(t[2873]=l('
julia
mlirTransformOptionsCreate()
Creates a default-initialized transform options object.
source
',3))]),e("details",Dy,[e("summary",null,[t[2874]||(t[2874]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsDestroy")],-1)),t[2875]||(t[2875]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2876]||(t[2876]=l('
julia
mlirTransformOptionsDestroy(transformOptions)
Destroys a transform options object previously created by mlirTransformOptionsCreate.
source
',3))]),e("details",Oy,[e("summary",null,[t[2877]||(t[2877]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks")],-1)),t[2878]||(t[2878]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2879]||(t[2879]=l('
julia
mlirTransformOptionsEnableExpensiveChecks(transformOptions, enable)
Enables or disables expensive checks in transform options.
source
',3))]),e("details",By,[e("summary",null,[t[2880]||(t[2880]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp")],-1)),t[2881]||(t[2881]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2882]||(t[2882]=l('
julia
mlirTransformOptionsEnforceSingleTopLevelTransformOp(transformOptions, enable)
Enables or disables the enforcement of the top-level transform op being single in transform options.
source
',3))]),e("details",Gy,[e("summary",null,[t[2883]||(t[2883]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp")],-1)),t[2884]||(t[2884]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2885]||(t[2885]=l('
julia
mlirTransformOptionsGetEnforceSingleTopLevelTransformOp(transformOptions)
Returns true if the enforcement of the top-level transform op being single is enabled in transform options.
source
',3))]),e("details",zy,[e("summary",null,[t[2886]||(t[2886]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled")],-1)),t[2887]||(t[2887]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2888]||(t[2888]=l('
julia
mlirTransformOptionsGetExpensiveChecksEnabled(transformOptions)
Returns true if expensive checks are enabled in transform options.
source
',3))]),e("details",wy,[e("summary",null,[t[2889]||(t[2889]=e("a",{id:"Reactant.MLIR.API.mlirTranslateModuleToLLVMIR-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTranslateModuleToLLVMIR-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTranslateModuleToLLVMIR")],-1)),t[2890]||(t[2890]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2891]||(t[2891]=l('
julia
mlirTranslateModuleToLLVMIR(_module, context)
Translate operation that satisfies LLVM dialect module requirements into an LLVM IR module living in the given context. This translates operations from any dilalect that has a registered implementation of LLVMTranslationDialectInterface.
Returns
the generated LLVM IR Module from the translated MLIR module, it is owned by the caller.
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',5))]),e("details",Sy,[e("summary",null,[t[2892]||(t[2892]=e("a",{id:"Reactant.MLIR.API.mlirTupleTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGet")],-1)),t[2893]||(t[2893]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2894]||(t[2894]=l('
julia
mlirTupleTypeGet(ctx, numElements, elements)
Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.
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',3))]),e("details",Ny,[e("summary",null,[t[2895]||(t[2895]=e("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetNumTypes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGetNumTypes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetNumTypes")],-1)),t[2896]||(t[2896]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2897]||(t[2897]=l('
julia
mlirTupleTypeGetNumTypes(type)
Returns the number of types contained in a tuple.
source
',3))]),e("details",Vy,[e("summary",null,[t[2898]||(t[2898]=e("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGetType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetType")],-1)),t[2899]||(t[2899]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2900]||(t[2900]=l('
julia
mlirTupleTypeGetType(type, pos)
Returns the pos-th type in the tuple type.
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julia
mlirTupleTypeGetTypeID()
Returns the typeID of an Tuple type.
source
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julia
mlirTypeAttrGet(type)
Creates a type attribute wrapping the given type in the same context as the type.
source
',3))]),e("details",Qy,[e("summary",null,[t[2907]||(t[2907]=e("a",{id:"Reactant.MLIR.API.mlirTypeAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirTypeAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGetTypeID")],-1)),t[2908]||(t[2908]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2909]||(t[2909]=l('
julia
mlirTypeAttrGetTypeID()
Returns the typeID of a Type attribute.
source
',3))]),e("details",Wy,[e("summary",null,[t[2910]||(t[2910]=e("a",{id:"Reactant.MLIR.API.mlirTypeAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeAttrGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGetValue")],-1)),t[2911]||(t[2911]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2912]||(t[2912]=l('
julia
mlirTypeAttrGetValue(attr)
Returns the type stored in the given type attribute.
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',3))]),e("details",Hy,[e("summary",null,[t[2913]||(t[2913]=e("a",{id:"Reactant.MLIR.API.mlirTypeDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeDump")],-1)),t[2914]||(t[2914]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2915]||(t[2915]=l('
julia
mlirTypeDump(type)
Prints the type to the standard error stream.
source
',3))]),e("details",Zy,[e("summary",null,[t[2916]||(t[2916]=e("a",{id:"Reactant.MLIR.API.mlirTypeEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeEqual")],-1)),t[2917]||(t[2917]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2918]||(t[2918]=l('
julia
mlirTypeEqual(t1, t2)
Checks if two types are equal.
source
',3))]),e("details",Jy,[e("summary",null,[t[2919]||(t[2919]=e("a",{id:"Reactant.MLIR.API.mlirTypeGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetContext")],-1)),t[2920]||(t[2920]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2921]||(t[2921]=l('
julia
mlirTypeGetContext(type)
Gets the context that a type was created with.
source
',3))]),e("details",Ky,[e("summary",null,[t[2922]||(t[2922]=e("a",{id:"Reactant.MLIR.API.mlirTypeGetDialect-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetDialect-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetDialect")],-1)),t[2923]||(t[2923]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2924]||(t[2924]=l('
julia
mlirTypeGetDialect(type)
Gets the dialect a type belongs to.
source
',3))]),e("details",$y,[e("summary",null,[t[2925]||(t[2925]=e("a",{id:"Reactant.MLIR.API.mlirTypeGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetTypeID-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetTypeID")],-1)),t[2926]||(t[2926]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2927]||(t[2927]=l('
julia
mlirTypeGetTypeID(type)
Gets the type ID of the type.
source
',3))]),e("details",Xy,[e("summary",null,[t[2928]||(t[2928]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID")],-1)),t[2929]||(t[2929]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2930]||(t[2930]=l('
julia
mlirTypeIDAllocatorAllocateTypeID(allocator)
Allocates a type id that is valid for the lifetime of the allocator
source
',3))]),e("details",Yy,[e("summary",null,[t[2931]||(t[2931]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorCreate")],-1)),t[2932]||(t[2932]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2933]||(t[2933]=l('
julia
mlirTypeIDAllocatorCreate()
Creates a type id allocator for dynamic type id creation
source
',3))]),e("details",_y,[e("summary",null,[t[2934]||(t[2934]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorDestroy")],-1)),t[2935]||(t[2935]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2936]||(t[2936]=l('
julia
mlirTypeIDAllocatorDestroy(allocator)
Deallocates the allocator and all allocated type ids
source
',3))]),e("details",tm,[e("summary",null,[t[2937]||(t[2937]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDCreate-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDCreate")],-1)),t[2938]||(t[2938]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2939]||(t[2939]=l('
julia
mlirTypeIDCreate(ptr)
ptr must be 8 byte aligned and unique to a type valid for the duration of the returned type id's usage
source
',3))]),e("details",em,[e("summary",null,[t[2940]||(t[2940]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeIDEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDEqual")],-1)),t[2941]||(t[2941]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2942]||(t[2942]=l('
julia
mlirTypeIDEqual(typeID1, typeID2)
Checks if two type ids are equal.
source
',3))]),e("details",sm,[e("summary",null,[t[2943]||(t[2943]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDHashValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDHashValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDHashValue")],-1)),t[2944]||(t[2944]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2945]||(t[2945]=l('
julia
mlirTypeIDHashValue(typeID)
Returns the hash value of the type id.
source
',3))]),e("details",am,[e("summary",null,[t[2946]||(t[2946]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDIsNull")],-1)),t[2947]||(t[2947]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2948]||(t[2948]=l('
julia
mlirTypeIDIsNull(typeID)
Checks whether a type id is null.
source
',3))]),e("details",im,[e("summary",null,[t[2949]||(t[2949]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType")],-1)),t[2950]||(t[2950]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2951]||(t[2951]=l('
julia
mlirTypeIsAAnyQuantizedType(type)
Returns true if the given type is an AnyQuantizedType.
source
',3))]),e("details",lm,[e("summary",null,[t[2952]||(t[2952]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsABF16-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsABF16-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsABF16")],-1)),t[2953]||(t[2953]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2954]||(t[2954]=l('
julia
mlirTypeIsABF16(type)
Checks whether the given type is a bf16 type.
source
',3))]),e("details",nm,[e("summary",null,[t[2955]||(t[2955]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType")],-1)),t[2956]||(t[2956]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2957]||(t[2957]=l('
julia
mlirTypeIsACalibratedQuantizedType(type)
Returns true if the given type is a CalibratedQuantizedType.
source
',3))]),e("details",pm,[e("summary",null,[t[2958]||(t[2958]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAComplex-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAComplex-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAComplex")],-1)),t[2959]||(t[2959]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2960]||(t[2960]=l('
julia
mlirTypeIsAComplex(type)
Checks whether the given type is a Complex type.
source
',3))]),e("details",rm,[e("summary",null,[t[2961]||(t[2961]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAF16-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF16-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF16")],-1)),t[2962]||(t[2962]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2963]||(t[2963]=l('
julia
mlirTypeIsAF16(type)
Checks whether the given type is an f16 type.
source
',3))]),e("details",om,[e("summary",null,[t[2964]||(t[2964]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAF32-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF32-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF32")],-1)),t[2965]||(t[2965]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2966]||(t[2966]=l('
julia
mlirTypeIsAF32(type)
Checks whether the given type is an f32 type.
source
',3))]),e("details",dm,[e("summary",null,[t[2967]||(t[2967]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAF64-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF64-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF64")],-1)),t[2968]||(t[2968]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2969]||(t[2969]=l('
julia
mlirTypeIsAF64(type)
Checks whether the given type is an f64 type.
source
',3))]),e("details",hm,[e("summary",null,[t[2970]||(t[2970]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat")],-1)),t[2971]||(t[2971]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2972]||(t[2972]=l('
julia
mlirTypeIsAFloat(type)
Checks whether the given type is a floating-point type.
source
',3))]),e("details",cm,[e("summary",null,[t[2973]||(t[2973]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN")],-1)),t[2974]||(t[2974]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2975]||(t[2975]=l('
julia
mlirTypeIsAFloat4E2M1FN(type)
Checks whether the given type is an f4E2M1FN type.
source
',3))]),e("details",um,[e("summary",null,[t[2976]||(t[2976]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN")],-1)),t[2977]||(t[2977]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2978]||(t[2978]=l('
julia
mlirTypeIsAFloat6E2M3FN(type)
Checks whether the given type is an f6E2M3FN type.
source
',3))]),e("details",bm,[e("summary",null,[t[2979]||(t[2979]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN")],-1)),t[2980]||(t[2980]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2981]||(t[2981]=l('
julia
mlirTypeIsAFloat6E3M2FN(type)
Checks whether the given type is an f6E3M2FN type.
source
',3))]),e("details",gm,[e("summary",null,[t[2982]||(t[2982]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E3M4-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E3M4-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E3M4")],-1)),t[2983]||(t[2983]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2984]||(t[2984]=l('
julia
mlirTypeIsAFloat8E3M4(type)
Checks whether the given type is an f8E3M4 type.
source
',3))]),e("details",ym,[e("summary",null,[t[2985]||(t[2985]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3")],-1)),t[2986]||(t[2986]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2987]||(t[2987]=l('
julia
mlirTypeIsAFloat8E4M3(type)
Checks whether the given type is an f8E4M3 type.
source
',3))]),e("details",mm,[e("summary",null,[t[2988]||(t[2988]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ")],-1)),t[2989]||(t[2989]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2990]||(t[2990]=l('
julia
mlirTypeIsAFloat8E4M3B11FNUZ(type)
Checks whether the given type is an f8E4M3B11FNUZ type.
source
',3))]),e("details",km,[e("summary",null,[t[2991]||(t[2991]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN")],-1)),t[2992]||(t[2992]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2993]||(t[2993]=l('
julia
mlirTypeIsAFloat8E4M3FN(type)
Checks whether the given type is an f8E4M3FN type.
source
',3))]),e("details",Rm,[e("summary",null,[t[2994]||(t[2994]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ")],-1)),t[2995]||(t[2995]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2996]||(t[2996]=l('
julia
mlirTypeIsAFloat8E4M3FNUZ(type)
Checks whether the given type is an f8E4M3FNUZ type.
source
',3))]),e("details",fm,[e("summary",null,[t[2997]||(t[2997]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E5M2-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2")],-1)),t[2998]||(t[2998]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2999]||(t[2999]=l('
julia
mlirTypeIsAFloat8E5M2(type)
Checks whether the given type is an f8E5M2 type.
source
',3))]),e("details",Im,[e("summary",null,[t[3e3]||(t[3e3]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ")],-1)),t[3001]||(t[3001]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3002]||(t[3002]=l('
julia
mlirTypeIsAFloat8E5M2FNUZ(type)
Checks whether the given type is an f8E5M2FNUZ type.
source
',3))]),e("details",jm,[e("summary",null,[t[3003]||(t[3003]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU")],-1)),t[3004]||(t[3004]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3005]||(t[3005]=l('
julia
mlirTypeIsAFloat8E8M0FNU(type)
Checks whether the given type is an f8E8M0FNU type.
source
',3))]),e("details",Mm,[e("summary",null,[t[3006]||(t[3006]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFunction-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFunction-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFunction")],-1)),t[3007]||(t[3007]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3008]||(t[3008]=l('
julia
mlirTypeIsAFunction(type)
Checks whether the given type is a function type.
source
',3))]),e("details",Am,[e("summary",null,[t[3009]||(t[3009]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAIndex-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAIndex-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAIndex")],-1)),t[3010]||(t[3010]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3011]||(t[3011]=l('
julia
mlirTypeIsAIndex(type)
Checks whether the given type is an index type.
source
',3))]),e("details",Lm,[e("summary",null,[t[3012]||(t[3012]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAInteger-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAInteger-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAInteger")],-1)),t[3013]||(t[3013]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3014]||(t[3014]=l('
julia
mlirTypeIsAInteger(type)
Checks whether the given type is an integer type.
source
',3))]),e("details",Em,[e("summary",null,[t[3015]||(t[3015]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsALLVMPointerType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsALLVMPointerType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsALLVMPointerType")],-1)),t[3016]||(t[3016]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3017]||(t[3017]=l('
julia
mlirTypeIsALLVMPointerType(type)
Returns true if the type is an LLVM dialect pointer type.
source
',3))]),e("details",vm,[e("summary",null,[t[3018]||(t[3018]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsALLVMStructType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsALLVMStructType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsALLVMStructType")],-1)),t[3019]||(t[3019]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3020]||(t[3020]=l('
julia
mlirTypeIsALLVMStructType(type)
Returns true if the type is an LLVM dialect struct type.
source
',3))]),e("details",Tm,[e("summary",null,[t[3021]||(t[3021]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAMemRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAMemRef-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAMemRef")],-1)),t[3022]||(t[3022]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3023]||(t[3023]=l('
julia
mlirTypeIsAMemRef(type)
Checks whether the given type is a MemRef type.
source
',3))]),e("details",Cm,[e("summary",null,[t[3024]||(t[3024]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsANone-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsANone-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsANone")],-1)),t[3025]||(t[3025]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3026]||(t[3026]=l('
julia
mlirTypeIsANone(type)
Checks whether the given type is a None type.
source
',3))]),e("details",xm,[e("summary",null,[t[3027]||(t[3027]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAOpaque-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAOpaque")],-1)),t[3028]||(t[3028]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3029]||(t[3029]=l('
julia
mlirTypeIsAOpaque(type)
Checks whether the given type is an opaque type.
source
',3))]),e("details",Fm,[e("summary",null,[t[3030]||(t[3030]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAQuantizedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAQuantizedType")],-1)),t[3031]||(t[3031]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3032]||(t[3032]=l('
julia
mlirTypeIsAQuantizedType(type)
Returns true if the given type is a quantization dialect type.
source
',3))]),e("details",Pm,[e("summary",null,[t[3033]||(t[3033]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsARankedTensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsARankedTensor-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsARankedTensor")],-1)),t[3034]||(t[3034]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3035]||(t[3035]=l('
julia
mlirTypeIsARankedTensor(type)
Checks whether the given type is a ranked tensor type.
source
',3))]),e("details",Dm,[e("summary",null,[t[3036]||(t[3036]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAShaped-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAShaped-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAShaped")],-1)),t[3037]||(t[3037]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3038]||(t[3038]=l('
julia
mlirTypeIsAShaped(type)
Checks whether the given type is a Shaped type.
source
',3))]),e("details",Om,[e("summary",null,[t[3039]||(t[3039]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsATF32-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATF32-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATF32")],-1)),t[3040]||(t[3040]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3041]||(t[3041]=l('
julia
mlirTypeIsATF32(type)
Checks whether the given type is an TF32 type.
source
',3))]),e("details",Bm,[e("summary",null,[t[3042]||(t[3042]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsATensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATensor-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATensor")],-1)),t[3043]||(t[3043]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3044]||(t[3044]=l('
julia
mlirTypeIsATensor(type)
Checks whether the given type is a Tensor type.
source
',3))]),e("details",Gm,[e("summary",null,[t[3045]||(t[3045]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsATuple-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATuple-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATuple")],-1)),t[3046]||(t[3046]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3047]||(t[3047]=l('
julia
mlirTypeIsATuple(type)
Checks whether the given type is a tuple type.
source
',3))]),e("details",zm,[e("summary",null,[t[3048]||(t[3048]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType")],-1)),t[3049]||(t[3049]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3050]||(t[3050]=l('
julia
mlirTypeIsAUniformQuantizedPerAxisType(type)
Returns true if the given type is a UniformQuantizedPerAxisType.
source
',3))]),e("details",wm,[e("summary",null,[t[3051]||(t[3051]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType")],-1)),t[3052]||(t[3052]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3053]||(t[3053]=l('
julia
mlirTypeIsAUniformQuantizedType(type)
Returns true if the given type is a UniformQuantizedType.
source
',3))]),e("details",Sm,[e("summary",null,[t[3054]||(t[3054]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef")],-1)),t[3055]||(t[3055]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3056]||(t[3056]=l('
julia
mlirTypeIsAUnrankedMemRef(type)
Checks whether the given type is an UnrankedMemRef type.
source
',3))]),e("details",Nm,[e("summary",null,[t[3057]||(t[3057]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAUnrankedTensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUnrankedTensor-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUnrankedTensor")],-1)),t[3058]||(t[3058]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3059]||(t[3059]=l('
julia
mlirTypeIsAUnrankedTensor(type)
Checks whether the given type is an unranked tensor type.
source
',3))]),e("details",Vm,[e("summary",null,[t[3060]||(t[3060]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAVector-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAVector-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAVector")],-1)),t[3061]||(t[3061]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3062]||(t[3062]=l('
julia
mlirTypeIsAVector(type)
Checks whether the given type is a Vector type.
source
',3))]),e("details",qm,[e("summary",null,[t[3063]||(t[3063]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsNull")],-1)),t[3064]||(t[3064]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3065]||(t[3065]=l('
julia
mlirTypeIsNull(type)
Checks whether a type is null.
source
',3))]),e("details",Um,[e("summary",null,[t[3066]||(t[3066]=e("a",{id:"Reactant.MLIR.API.mlirTypeParseGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeParseGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeParseGet")],-1)),t[3067]||(t[3067]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3068]||(t[3068]=l('
julia
mlirTypeParseGet(context, type)
Parses a type. The type is owned by the context.
source
',3))]),e("details",Qm,[e("summary",null,[t[3069]||(t[3069]=e("a",{id:"Reactant.MLIR.API.mlirTypePrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirTypePrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypePrint")],-1)),t[3070]||(t[3070]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3071]||(t[3071]=l('
julia
mlirTypePrint(type, callback, userData)
Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",Wm,[e("summary",null,[t[3072]||(t[3072]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet")],-1)),t[3073]||(t[3073]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3074]||(t[3074]=l('
julia
mlirUniformQuantizedPerAxisTypeGet(flags, storageType, expressedType, nDims, scales, zeroPoints, quantizedDimension, storageTypeMin, storageTypeMax)
Creates an instance of UniformQuantizedPerAxisType with the given parameters in the same context as storageType and returns it. scales and zeroPoints point to nDims number of elements. The instance is owned by the context.
source
',3))]),e("details",Hm,[e("summary",null,[t[3075]||(t[3075]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims")],-1)),t[3076]||(t[3076]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3077]||(t[3077]=l('
julia
mlirUniformQuantizedPerAxisTypeGetNumDims(type)
Returns the number of axes in the given quantized per-axis type.
source
',3))]),e("details",Zm,[e("summary",null,[t[3078]||(t[3078]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension")],-1)),t[3079]||(t[3079]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3080]||(t[3080]=l('
julia
mlirUniformQuantizedPerAxisTypeGetQuantizedDimension(type)
Returns the index of the quantized dimension in the given quantized per-axis type.
source
',3))]),e("details",Jm,[e("summary",null,[t[3081]||(t[3081]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale")],-1)),t[3082]||(t[3082]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3083]||(t[3083]=l('
julia
mlirUniformQuantizedPerAxisTypeGetScale(type, pos)
Returns pos-th scale of the given quantized per-axis type.
source
',3))]),e("details",Km,[e("summary",null,[t[3084]||(t[3084]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint")],-1)),t[3085]||(t[3085]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3086]||(t[3086]=l('
julia
mlirUniformQuantizedPerAxisTypeGetZeroPoint(type, pos)
Returns pos-th zero point of the given quantized per-axis type.
source
',3))]),e("details",$m,[e("summary",null,[t[3087]||(t[3087]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint")],-1)),t[3088]||(t[3088]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3089]||(t[3089]=l('
julia
mlirUniformQuantizedPerAxisTypeIsFixedPoint(type)
Returns true if the given uniform quantized per-axis type is fixed-point.
source
',3))]),e("details",Xm,[e("summary",null,[t[3090]||(t[3090]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGet-NTuple{7, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGet-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGet")],-1)),t[3091]||(t[3091]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3092]||(t[3092]=l('
julia
mlirUniformQuantizedTypeGet(flags, storageType, expressedType, scale, zeroPoint, storageTypeMin, storageTypeMax)
Creates an instance of UniformQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.
source
',3))]),e("details",Ym,[e("summary",null,[t[3093]||(t[3093]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale")],-1)),t[3094]||(t[3094]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3095]||(t[3095]=l('
julia
mlirUniformQuantizedTypeGetScale(type)
Returns the scale of the given uniform quantized type.
source
',3))]),e("details",_m,[e("summary",null,[t[3096]||(t[3096]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint")],-1)),t[3097]||(t[3097]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3098]||(t[3098]=l('
julia
mlirUniformQuantizedTypeGetZeroPoint(type)
Returns the zero point of the given uniform quantized type.
source
',3))]),e("details",tk,[e("summary",null,[t[3099]||(t[3099]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint")],-1)),t[3100]||(t[3100]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3101]||(t[3101]=l('
julia
mlirUniformQuantizedTypeIsFixedPoint(type)
Returns true if the given uniform quantized type is fixed-point.
source
',3))]),e("details",ek,[e("summary",null,[t[3102]||(t[3102]=e("a",{id:"Reactant.MLIR.API.mlirUnitAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnitAttrGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnitAttrGet")],-1)),t[3103]||(t[3103]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3104]||(t[3104]=l('
julia
mlirUnitAttrGet(ctx)
Creates a unit attribute in the given context.
source
',3))]),e("details",sk,[e("summary",null,[t[3105]||(t[3105]=e("a",{id:"Reactant.MLIR.API.mlirUnitAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnitAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnitAttrGetTypeID")],-1)),t[3106]||(t[3106]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3107]||(t[3107]=l('
julia
mlirUnitAttrGetTypeID()
Returns the typeID of a Unit attribute.
source
',3))]),e("details",ak,[e("summary",null,[t[3108]||(t[3108]=e("a",{id:"Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet-NTuple{8, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet")],-1)),t[3109]||(t[3109]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3110]||(t[3110]=l('
julia
mlirUnmanagedDenseResourceElementsAttrGet(shapedType, name, data, dataLength, dataAlignment, dataIsMutable, deleter, userData)
Unlike the typed accessors below, constructs the attribute with a raw data buffer and no type/alignment checking. Use a more strongly typed accessor if possible. If dataIsMutable is false, then an immutable AsmResourceBlob will be created and that passed data contents will be treated as const. If the deleter is non NULL, then it will be called when the data buffer can no longer be accessed (passing userData to it).
source
',3))]),e("details",ik,[e("summary",null,[t[3111]||(t[3111]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGet")],-1)),t[3112]||(t[3112]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3113]||(t[3113]=l('
julia
mlirUnrankedMemRefTypeGet(elementType, memorySpace)
Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type.
source
',3))]),e("details",lk,[e("summary",null,[t[3114]||(t[3114]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked")],-1)),t[3115]||(t[3115]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3116]||(t[3116]=l('
julia
mlirUnrankedMemRefTypeGetChecked(loc, elementType, memorySpace)
Same as "mlirUnrankedMemRefTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
',3))]),e("details",nk,[e("summary",null,[t[3117]||(t[3117]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID")],-1)),t[3118]||(t[3118]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3119]||(t[3119]=l('
julia
mlirUnrankedMemRefTypeGetTypeID()
Returns the typeID of an UnrankedMemRef type.
source
',3))]),e("details",pk,[e("summary",null,[t[3120]||(t[3120]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace")],-1)),t[3121]||(t[3121]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3122]||(t[3122]=l('
julia
mlirUnrankedMemrefGetMemorySpace(type)
Returns the memory spcae of the given Unranked MemRef type.
source
',3))]),e("details",rk,[e("summary",null,[t[3123]||(t[3123]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGet")],-1)),t[3124]||(t[3124]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3125]||(t[3125]=l('
julia
mlirUnrankedTensorTypeGet(elementType)
Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context.
source
',3))]),e("details",ok,[e("summary",null,[t[3126]||(t[3126]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked")],-1)),t[3127]||(t[3127]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3128]||(t[3128]=l('
julia
mlirUnrankedTensorTypeGetChecked(loc, elementType)
Same as "mlirUnrankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
',3))]),e("details",dk,[e("summary",null,[t[3129]||(t[3129]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID")],-1)),t[3130]||(t[3130]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3131]||(t[3131]=l('
julia
mlirUnrankedTensorTypeGetTypeID()
Returns the typeID of an UnrankedTensor type.
source
',3))]),e("details",hk,[e("summary",null,[t[3132]||(t[3132]=e("a",{id:"Reactant.MLIR.API.mlirValueDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueDump")],-1)),t[3133]||(t[3133]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3134]||(t[3134]=l('
julia
mlirValueDump(value)
Prints the value to the standard error stream.
source
',3))]),e("details",ck,[e("summary",null,[t[3135]||(t[3135]=e("a",{id:"Reactant.MLIR.API.mlirValueEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirValueEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueEqual")],-1)),t[3136]||(t[3136]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3137]||(t[3137]=l('
julia
mlirValueEqual(value1, value2)
Returns 1 if two values are equal, 0 otherwise.
source
',3))]),e("details",uk,[e("summary",null,[t[3138]||(t[3138]=e("a",{id:"Reactant.MLIR.API.mlirValueGetFirstUse-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueGetFirstUse-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueGetFirstUse")],-1)),t[3139]||(t[3139]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3140]||(t[3140]=l('
julia
mlirValueGetFirstUse(value)
Returns an op operand representing the first use of the value, or a null op operand if there are no uses.
source
',3))]),e("details",bk,[e("summary",null,[t[3141]||(t[3141]=e("a",{id:"Reactant.MLIR.API.mlirValueGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueGetType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueGetType")],-1)),t[3142]||(t[3142]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3143]||(t[3143]=l('
julia
mlirValueGetType(value)
Returns the type of the value.
source
',3))]),e("details",gk,[e("summary",null,[t[3144]||(t[3144]=e("a",{id:"Reactant.MLIR.API.mlirValueIsABlockArgument-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueIsABlockArgument-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueIsABlockArgument")],-1)),t[3145]||(t[3145]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3146]||(t[3146]=l('
julia
mlirValueIsABlockArgument(value)
Returns 1 if the value is a block argument, 0 otherwise.
source
',3))]),e("details",yk,[e("summary",null,[t[3147]||(t[3147]=e("a",{id:"Reactant.MLIR.API.mlirValueIsAOpResult-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueIsAOpResult-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueIsAOpResult")],-1)),t[3148]||(t[3148]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3149]||(t[3149]=l('
julia
mlirValueIsAOpResult(value)
Returns 1 if the value is an operation result, 0 otherwise.
source
',3))]),e("details",mk,[e("summary",null,[t[3150]||(t[3150]=e("a",{id:"Reactant.MLIR.API.mlirValueIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueIsNull")],-1)),t[3151]||(t[3151]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3152]||(t[3152]=l('
julia
mlirValueIsNull(value)
Returns whether the value is null.
source
',3))]),e("details",kk,[e("summary",null,[t[3153]||(t[3153]=e("a",{id:"Reactant.MLIR.API.mlirValuePrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirValuePrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValuePrint")],-1)),t[3154]||(t[3154]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3155]||(t[3155]=l('
julia
mlirValuePrint(value, callback, userData)
Prints a value by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",Rk,[e("summary",null,[t[3156]||(t[3156]=e("a",{id:"Reactant.MLIR.API.mlirValuePrintAsOperand-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirValuePrintAsOperand-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValuePrintAsOperand")],-1)),t[3157]||(t[3157]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3158]||(t[3158]=l('
julia
mlirValuePrintAsOperand(value, state, callback, userData)
Prints a value as an operand (i.e., the ValueID).
source
',3))]),e("details",fk,[e("summary",null,[t[3159]||(t[3159]=e("a",{id:"Reactant.MLIR.API.mlirValueReplaceAllUsesExcept-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirValueReplaceAllUsesExcept-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueReplaceAllUsesExcept")],-1)),t[3160]||(t[3160]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3161]||(t[3161]=l('
julia
mlirValueReplaceAllUsesExcept(of, with, numExceptions, exceptions)
Replace all uses of 'of' value with 'with' value, updating anything in the IR that uses 'of' to use 'with' instead, except if the user is listed in 'exceptions'. The 'exceptions' parameter is an array of MlirOperation pointers with a length of 'numExceptions'.
source
',3))]),e("details",Ik,[e("summary",null,[t[3162]||(t[3162]=e("a",{id:"Reactant.MLIR.API.mlirValueReplaceAllUsesOfWith-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirValueReplaceAllUsesOfWith-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueReplaceAllUsesOfWith")],-1)),t[3163]||(t[3163]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3164]||(t[3164]=l('
julia
mlirValueReplaceAllUsesOfWith(of, with)
Replace all uses of 'of' value with the 'with' value, updating anything in the IR that uses 'of' to use the other value instead. When this returns there are zero uses of 'of'.
source
',3))]),e("details",jk,[e("summary",null,[t[3165]||(t[3165]=e("a",{id:"Reactant.MLIR.API.mlirValueSetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirValueSetType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueSetType")],-1)),t[3166]||(t[3166]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3167]||(t[3167]=l('
julia
mlirValueSetType(value, type)
Set the type of the value.
source
',3))]),e("details",Mk,[e("summary",null,[t[3168]||(t[3168]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGet")],-1)),t[3169]||(t[3169]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3170]||(t[3170]=l('
julia
mlirVectorTypeGet(rank, shape, elementType)
Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context.
source
',3))]),e("details",Ak,[e("summary",null,[t[3171]||(t[3171]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetChecked-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeGetChecked-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetChecked")],-1)),t[3172]||(t[3172]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3173]||(t[3173]=l('
julia
mlirVectorTypeGetChecked(loc, rank, shape, elementType)
Same as "mlirVectorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
',3))]),e("details",Lk,[e("summary",null,[t[3174]||(t[3174]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetScalable-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeGetScalable-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetScalable")],-1)),t[3175]||(t[3175]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3176]||(t[3176]=l('
julia
mlirVectorTypeGetScalable(rank, shape, scalable, elementType)
Creates a scalable vector type with the shape identified by its rank and dimensions. A subset of dimensions may be marked as scalable via the corresponding flag list, which is expected to have as many entries as the rank of the vector. The vector is created in the same context as the element type.
source
',3))]),e("details",Ek,[e("summary",null,[t[3177]||(t[3177]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetScalableChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeGetScalableChecked-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetScalableChecked")],-1)),t[3178]||(t[3178]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3179]||(t[3179]=l('
julia
mlirVectorTypeGetScalableChecked(loc, rank, shape, scalable, elementType)
Same as "mlirVectorTypeGetScalable" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
',3))]),e("details",vk,[e("summary",null,[t[3180]||(t[3180]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirVectorTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetTypeID")],-1)),t[3181]||(t[3181]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3182]||(t[3182]=l('
julia
mlirVectorTypeGetTypeID()
Returns the typeID of an Vector type.
source
',3))]),e("details",Tk,[e("summary",null,[t[3183]||(t[3183]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeIsDimScalable-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeIsDimScalable-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeIsDimScalable")],-1)),t[3184]||(t[3184]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3185]||(t[3185]=l('
julia
mlirVectorTypeIsDimScalable(type, dim)
Checks whether the "dim"-th dimension of the given vector is scalable.
source
',3))]),e("details",Ck,[e("summary",null,[t[3186]||(t[3186]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeIsScalable-Tuple{Any}",href:"#Reactant.MLIR.API.mlirVectorTypeIsScalable-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeIsScalable")],-1)),t[3187]||(t[3187]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3188]||(t[3188]=l('
julia
mlirVectorTypeIsScalable(type)
Checks whether the given vector type is scalable, i.e., has at least one scalable dimension.
source
',3))]),t[3191]||(t[3191]=e("h1",{id:"Other-Functions",tabindex:"-1"},[s("Other Functions "),e("a",{class:"header-anchor",href:"#Other-Functions","aria-label":'Permalink to "Other Functions {#Other-Functions}"'},"​")],-1))])}const wk=n(d,[["render",xk]]);export{zk as __pageData,wk as default};
+MLIR.IR.AffineMap(#= (d0, d1)[s0] -> (d0 + s0, d1 mod 10) =#)

source

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julia
MlirDiagnostic

An opaque reference to a diagnostic, always owned by the diagnostics engine (context). Must not be stored outside of the diagnostic handler.

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julia
MlirDiagnosticSeverity

Severity of a diagnostic.

source

',3))]),t("details",rl,[t("summary",null,[e[1016]||(e[1016]=t("a",{id:"Reactant.MLIR.API.MlirExternalPassCallbacks",href:"#Reactant.MLIR.API.MlirExternalPassCallbacks"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirExternalPassCallbacks")],-1)),e[1017]||(e[1017]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1018]||(e[1018]=l('
julia
MlirExternalPassCallbacks

Structure of external MlirPass callbacks. All callbacks are required to be set unless otherwise specified.

FieldNote
constructThis callback is called from the pass is created. This is analogous to a C++ pass constructor.
destructThis callback is called when the pass is destroyed This is analogous to a C++ pass destructor.
initializeThis callback is optional. The callback is called before the pass is run, allowing a chance to initialize any complex state necessary for running the pass. See Pass::initialize(MLIRContext *).
cloneThis callback is called when the pass is cloned. See Pass::clonePass().
runThis callback is called when the pass is run. See Pass::runOnOperation().

source

',4))]),t("details",ol,[t("summary",null,[e[1019]||(e[1019]=t("a",{id:"Reactant.MLIR.API.MlirLlvmThreadPool",href:"#Reactant.MLIR.API.MlirLlvmThreadPool"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirLlvmThreadPool")],-1)),e[1020]||(e[1020]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1021]||(e[1021]=l('
julia
MlirLlvmThreadPool

Re-export llvm::ThreadPool so as to avoid including the LLVM C API directly.

source

',3))]),t("details",dl,[t("summary",null,[e[1022]||(e[1022]=t("a",{id:"Reactant.MLIR.API.MlirLogicalResult",href:"#Reactant.MLIR.API.MlirLogicalResult"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirLogicalResult")],-1)),e[1023]||(e[1023]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1024]||(e[1024]=l('
julia
MlirLogicalResult

A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of MlirLogicalResult must only be inspected using the associated functions.

source

',3))]),t("details",cl,[t("summary",null,[e[1025]||(e[1025]=t("a",{id:"Reactant.MLIR.API.MlirNamedAttribute",href:"#Reactant.MLIR.API.MlirNamedAttribute"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirNamedAttribute")],-1)),e[1026]||(e[1026]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1027]||(e[1027]=l('
julia
MlirNamedAttribute

Named MLIR attribute.

A named attribute is essentially a (name, attribute) pair where the name is a string.

source

',4))]),t("details",bl,[t("summary",null,[e[1028]||(e[1028]=t("a",{id:"Reactant.MLIR.API.MlirOperationState",href:"#Reactant.MLIR.API.MlirOperationState"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirOperationState")],-1)),e[1029]||(e[1029]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1030]||(e[1030]=l('
julia
MlirOperationState

An auxiliary class for constructing operations.

This class contains all the information necessary to construct the operation. It owns the MlirRegions it has pointers to and does not own anything else. By default, the state can be constructed from a name and location, the latter being also used to access the context, and has no other components. These components can be added progressively until the operation is constructed. Users are not expected to rely on the internals of this class and should use mlirOperationState* functions instead.

source

',4))]),t("details",hl,[t("summary",null,[e[1031]||(e[1031]=t("a",{id:"Reactant.MLIR.API.MlirOperationWalkCallback",href:"#Reactant.MLIR.API.MlirOperationWalkCallback"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirOperationWalkCallback")],-1)),e[1032]||(e[1032]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1033]||(e[1033]=t("p",null,[s("Operation walker type. The handler is passed an (opaque) reference to an operation and a pointer to a "),t("code",null,"userData"),s(".")],-1)),e[1034]||(e[1034]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L1495-L1497",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",ul,[t("summary",null,[e[1035]||(e[1035]=t("a",{id:"Reactant.MLIR.API.MlirShapedTypeComponentsCallback",href:"#Reactant.MLIR.API.MlirShapedTypeComponentsCallback"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirShapedTypeComponentsCallback")],-1)),e[1036]||(e[1036]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1037]||(e[1037]=t("p",null,"These callbacks are used to return multiple shaped type components from functions while transferring ownership to the caller. The first argument is the has rank boolean followed by the the rank and a pointer to the shape (if applicable). The next argument is the element type, then the attribute. The last argument is an opaque pointer forwarded to the callback by the caller. This callback will be called potentially multiple times for each shaped type components.",-1)),e[1038]||(e[1038]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L9139-L9141",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",gl,[t("summary",null,[e[1039]||(e[1039]=t("a",{id:"Reactant.MLIR.API.MlirSparseTensorLevelType",href:"#Reactant.MLIR.API.MlirSparseTensorLevelType"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirSparseTensorLevelType")],-1)),e[1040]||(e[1040]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1041]||(e[1041]=t("p",null,"Dimension level types (and properties) that define sparse tensors. See the documentation in SparseTensorAttrDefs.td for their meaning.",-1)),e[1042]||(e[1042]=t("p",null,"These correspond to SparseTensorEncodingAttr::LevelType in the C++ API. If updating, keep them in sync and update the static_assert in the impl file.",-1)),e[1043]||(e[1043]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L8491-L8495",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",yl,[t("summary",null,[e[1044]||(e[1044]=t("a",{id:"Reactant.MLIR.API.MlirStringCallback",href:"#Reactant.MLIR.API.MlirStringCallback"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirStringCallback")],-1)),e[1045]||(e[1045]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1046]||(e[1046]=t("p",null,"A callback for returning string references.",-1)),e[1047]||(e[1047]=t("p",null,[s("This function is called back by the functions that need to return a reference to the portion of the string with the following arguments: - an "),t("a",{href:"/Reactant.jl/previews/PR441/api/mlirc#Reactant.MLIR.API.MlirStringRef"},[t("code",null,"MlirStringRef")]),s(" representing the current portion of the string - a pointer to user data forwarded from the printing call.")],-1)),e[1048]||(e[1048]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L108-L112",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",ml,[t("summary",null,[e[1049]||(e[1049]=t("a",{id:"Reactant.MLIR.API.MlirStringRef",href:"#Reactant.MLIR.API.MlirStringRef"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirStringRef")],-1)),e[1050]||(e[1050]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1051]||(e[1051]=l('
julia
MlirStringRef

A pointer to a sized fragment of a string, not necessarily null-terminated. Does not own the underlying string. This is equivalent to llvm::StringRef.

FieldNote
dataPointer to the first symbol.
lengthLength of the fragment.

source

',4))]),t("details",kl,[t("summary",null,[e[1052]||(e[1052]=t("a",{id:"Reactant.MLIR.API.MlirTypesCallback",href:"#Reactant.MLIR.API.MlirTypesCallback"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirTypesCallback")],-1)),e[1053]||(e[1053]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1054]||(e[1054]=t("p",null,"These callbacks are used to return multiple types from functions while transferring ownership to the caller. The first argument is the number of consecutive elements pointed to by the second argument. The third argument is an opaque pointer forwarded to the callback by the caller.",-1)),e[1055]||(e[1055]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L9091-L9093",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Rl,[t("summary",null,[e[1056]||(e[1056]=t("a",{id:"Reactant.MLIR.API.MlirWalkOrder",href:"#Reactant.MLIR.API.MlirWalkOrder"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirWalkOrder")],-1)),e[1057]||(e[1057]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1058]||(e[1058]=l('
julia
MlirWalkOrder

Traversal order for operation walk.

source

',3))]),t("details",Il,[t("summary",null,[e[1059]||(e[1059]=t("a",{id:"Reactant.MLIR.API.MlirWalkResult",href:"#Reactant.MLIR.API.MlirWalkResult"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirWalkResult")],-1)),e[1060]||(e[1060]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1061]||(e[1061]=l('
julia
MlirWalkResult

Operation walk result.

source

',3))]),t("details",jl,[t("summary",null,[e[1062]||(e[1062]=t("a",{id:"Reactant.MLIR.API.LLVMAddSymbol-Tuple{Any, Any}",href:"#Reactant.MLIR.API.LLVMAddSymbol-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMAddSymbol")],-1)),e[1063]||(e[1063]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1064]||(e[1064]=l('
julia
LLVMAddSymbol(symbolName, symbolValue)

This functions permanently adds the symbol symbolName with the value symbolValue. These symbols are searched before any libraries.

See also

sys::DynamicLibrary::AddSymbol()

source

',5))]),t("details",fl,[t("summary",null,[e[1065]||(e[1065]=t("a",{id:"Reactant.MLIR.API.LLVMLoadLibraryPermanently-Tuple{Any}",href:"#Reactant.MLIR.API.LLVMLoadLibraryPermanently-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMLoadLibraryPermanently")],-1)),e[1066]||(e[1066]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1067]||(e[1067]=l('
julia
LLVMLoadLibraryPermanently(Filename)

This function permanently loads the dynamic library at the given path. It is safe to call this function multiple times for the same library.

See also

sys::DynamicLibrary::LoadLibraryPermanently()

source

',5))]),t("details",Ml,[t("summary",null,[e[1068]||(e[1068]=t("a",{id:"Reactant.MLIR.API.LLVMParseCommandLineOptions-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.LLVMParseCommandLineOptions-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMParseCommandLineOptions")],-1)),e[1069]||(e[1069]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1070]||(e[1070]=l('
julia
LLVMParseCommandLineOptions(argc, argv, Overview)

This function parses the given arguments using the LLVM command line parser. Note that the only stable thing about this function is its signature; you cannot rely on any particular set of command line arguments being interpreted the same way across LLVM versions.

See also

llvm:🆑:ParseCommandLineOptions()

source

',5))]),t("details",Al,[t("summary",null,[e[1071]||(e[1071]=t("a",{id:"Reactant.MLIR.API.LLVMSearchForAddressOfSymbol-Tuple{Any}",href:"#Reactant.MLIR.API.LLVMSearchForAddressOfSymbol-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMSearchForAddressOfSymbol")],-1)),e[1072]||(e[1072]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1073]||(e[1073]=l('
julia
LLVMSearchForAddressOfSymbol(symbolName)

This function will search through all previously loaded dynamic libraries for the symbol symbolName. If it is found, the address of that symbol is returned. If not, null is returned.

See also

sys::DynamicLibrary::SearchForAddressOfSymbol()

source

',5))]),t("details",Ll,[t("summary",null,[e[1074]||(e[1074]=t("a",{id:"Reactant.MLIR.API.mlirAffineAddExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineAddExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineAddExprGet")],-1)),e[1075]||(e[1075]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1076]||(e[1076]=l('
julia
mlirAffineAddExprGet(lhs, rhs)

Creates an affine add expression with 'lhs' and 'rhs'.

source

',3))]),t("details",El,[t("summary",null,[e[1077]||(e[1077]=t("a",{id:"Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS")],-1)),e[1078]||(e[1078]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1079]||(e[1079]=l('
julia
mlirAffineBinaryOpExprGetLHS(affineExpr)

Returns the left hand side affine expression of the given affine binary operation expression.

source

',3))]),t("details",vl,[t("summary",null,[e[1080]||(e[1080]=t("a",{id:"Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS")],-1)),e[1081]||(e[1081]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1082]||(e[1082]=l('
julia
mlirAffineBinaryOpExprGetRHS(affineExpr)

Returns the right hand side affine expression of the given affine binary operation expression.

source

',3))]),t("details",Tl,[t("summary",null,[e[1083]||(e[1083]=t("a",{id:"Reactant.MLIR.API.mlirAffineCeilDivExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineCeilDivExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineCeilDivExprGet")],-1)),e[1084]||(e[1084]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1085]||(e[1085]=l('
julia
mlirAffineCeilDivExprGet(lhs, rhs)

Creates an affine ceildiv expression with 'lhs' and 'rhs'.

source

',3))]),t("details",Cl,[t("summary",null,[e[1086]||(e[1086]=t("a",{id:"Reactant.MLIR.API.mlirAffineConstantExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineConstantExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineConstantExprGet")],-1)),e[1087]||(e[1087]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1088]||(e[1088]=l('
julia
mlirAffineConstantExprGet(ctx, constant)

Creates an affine constant expression with 'constant' in the context.

source

',3))]),t("details",xl,[t("summary",null,[e[1089]||(e[1089]=t("a",{id:"Reactant.MLIR.API.mlirAffineConstantExprGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineConstantExprGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineConstantExprGetValue")],-1)),e[1090]||(e[1090]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1091]||(e[1091]=l('
julia
mlirAffineConstantExprGetValue(affineExpr)

Returns the value of the given affine constant expression.

source

',3))]),t("details",Fl,[t("summary",null,[e[1092]||(e[1092]=t("a",{id:"Reactant.MLIR.API.mlirAffineDimExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineDimExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineDimExprGet")],-1)),e[1093]||(e[1093]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1094]||(e[1094]=l('
julia
mlirAffineDimExprGet(ctx, position)

Creates an affine dimension expression with 'position' in the context.

source

',3))]),t("details",Pl,[t("summary",null,[e[1095]||(e[1095]=t("a",{id:"Reactant.MLIR.API.mlirAffineDimExprGetPosition-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineDimExprGetPosition-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineDimExprGetPosition")],-1)),e[1096]||(e[1096]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1097]||(e[1097]=l('
julia
mlirAffineDimExprGetPosition(affineExpr)

Returns the position of the given affine dimension expression.

source

',3))]),t("details",Dl,[t("summary",null,[e[1098]||(e[1098]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprCompose-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprCompose-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprCompose")],-1)),e[1099]||(e[1099]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1100]||(e[1100]=l('
julia
mlirAffineExprCompose(affineExpr, affineMap)

Composes the given map with the given expression.

source

',3))]),t("details",Ol,[t("summary",null,[e[1101]||(e[1101]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprDump")],-1)),e[1102]||(e[1102]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1103]||(e[1103]=l('
julia
mlirAffineExprDump(affineExpr)

Prints the affine expression to the standard error stream.

source

',3))]),t("details",Bl,[t("summary",null,[e[1104]||(e[1104]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprEqual")],-1)),e[1105]||(e[1105]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1106]||(e[1106]=l('
julia
mlirAffineExprEqual(lhs, rhs)

Returns true if the two affine expressions are equal.

source

',3))]),t("details",Gl,[t("summary",null,[e[1107]||(e[1107]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprGetContext")],-1)),e[1108]||(e[1108]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1109]||(e[1109]=l('
julia
mlirAffineExprGetContext(affineExpr)

Gets the context that owns the affine expression.

source

',3))]),t("details",zl,[t("summary",null,[e[1110]||(e[1110]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor")],-1)),e[1111]||(e[1111]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1112]||(e[1112]=l('
julia
mlirAffineExprGetLargestKnownDivisor(affineExpr)

Returns the greatest known integral divisor of this affine expression. The result is always positive.

source

',3))]),t("details",wl,[t("summary",null,[e[1113]||(e[1113]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAAdd-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAAdd-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAAdd")],-1)),e[1114]||(e[1114]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1115]||(e[1115]=l('
julia
mlirAffineExprIsAAdd(affineExpr)

Checks whether the given affine expression is an add expression.

source

',3))]),t("details",Sl,[t("summary",null,[e[1116]||(e[1116]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsABinary-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsABinary-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsABinary")],-1)),e[1117]||(e[1117]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1118]||(e[1118]=l('
julia
mlirAffineExprIsABinary(affineExpr)

Checks whether the given affine expression is binary.

source

',3))]),t("details",Nl,[t("summary",null,[e[1119]||(e[1119]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsACeilDiv-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsACeilDiv-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsACeilDiv")],-1)),e[1120]||(e[1120]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1121]||(e[1121]=l('
julia
mlirAffineExprIsACeilDiv(affineExpr)

Checks whether the given affine expression is an ceildiv expression.

source

',3))]),t("details",Vl,[t("summary",null,[e[1122]||(e[1122]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAConstant-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAConstant")],-1)),e[1123]||(e[1123]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1124]||(e[1124]=l('
julia
mlirAffineExprIsAConstant(affineExpr)

Checks whether the given affine expression is a constant expression.

source

',3))]),t("details",ql,[t("summary",null,[e[1125]||(e[1125]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsADim-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsADim-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsADim")],-1)),e[1126]||(e[1126]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1127]||(e[1127]=l('
julia
mlirAffineExprIsADim(affineExpr)

Checks whether the given affine expression is a dimension expression.

source

',3))]),t("details",Ul,[t("summary",null,[e[1128]||(e[1128]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAFloorDiv-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAFloorDiv-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAFloorDiv")],-1)),e[1129]||(e[1129]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1130]||(e[1130]=l('
julia
mlirAffineExprIsAFloorDiv(affineExpr)

Checks whether the given affine expression is an floordiv expression.

source

',3))]),t("details",Ql,[t("summary",null,[e[1131]||(e[1131]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAMod-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAMod-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAMod")],-1)),e[1132]||(e[1132]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1133]||(e[1133]=l('
julia
mlirAffineExprIsAMod(affineExpr)

Checks whether the given affine expression is an mod expression.

source

',3))]),t("details",Wl,[t("summary",null,[e[1134]||(e[1134]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAMul-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAMul-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAMul")],-1)),e[1135]||(e[1135]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1136]||(e[1136]=l('
julia
mlirAffineExprIsAMul(affineExpr)

Checks whether the given affine expression is an mul expression.

source

',3))]),t("details",Hl,[t("summary",null,[e[1137]||(e[1137]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsASymbol-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsASymbol-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsASymbol")],-1)),e[1138]||(e[1138]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1139]||(e[1139]=l('
julia
mlirAffineExprIsASymbol(affineExpr)

Checks whether the given affine expression is a symbol expression.

source

',3))]),t("details",Zl,[t("summary",null,[e[1140]||(e[1140]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim")],-1)),e[1141]||(e[1141]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1142]||(e[1142]=l('
julia
mlirAffineExprIsFunctionOfDim(affineExpr, position)

Checks whether the given affine expression involves AffineDimExpr 'position'.

source

',3))]),t("details",Jl,[t("summary",null,[e[1143]||(e[1143]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsMultipleOf-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsMultipleOf-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsMultipleOf")],-1)),e[1144]||(e[1144]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1145]||(e[1145]=l('
julia
mlirAffineExprIsMultipleOf(affineExpr, factor)

Checks whether the given affine expression is a multiple of 'factor'.

source

',3))]),t("details",Kl,[t("summary",null,[e[1146]||(e[1146]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsNull")],-1)),e[1147]||(e[1147]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1148]||(e[1148]=l('
julia
mlirAffineExprIsNull(affineExpr)

Returns true if the given affine expression is a null expression. Note constant zero is not a null expression.

source

',3))]),t("details",$l,[t("summary",null,[e[1149]||(e[1149]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsPureAffine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsPureAffine-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsPureAffine")],-1)),e[1150]||(e[1150]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1151]||(e[1151]=l('
julia
mlirAffineExprIsPureAffine(affineExpr)

Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.

source

',3))]),t("details",Xl,[t("summary",null,[e[1152]||(e[1152]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant")],-1)),e[1153]||(e[1153]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1154]||(e[1154]=l('
julia
mlirAffineExprIsSymbolicOrConstant(affineExpr)

Checks whether the given affine expression is made out of only symbols and constants.

source

',3))]),t("details",Yl,[t("summary",null,[e[1155]||(e[1155]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprPrint")],-1)),e[1156]||(e[1156]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1157]||(e[1157]=l('
julia
mlirAffineExprPrint(affineExpr, callback, userData)

Prints an affine expression by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",_l,[t("summary",null,[e[1158]||(e[1158]=t("a",{id:"Reactant.MLIR.API.mlirAffineFloorDivExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineFloorDivExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineFloorDivExprGet")],-1)),e[1159]||(e[1159]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1160]||(e[1160]=l('
julia
mlirAffineFloorDivExprGet(lhs, rhs)

Creates an affine floordiv expression with 'lhs' and 'rhs'.

source

',3))]),t("details",en,[t("summary",null,[e[1161]||(e[1161]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGet")],-1)),e[1162]||(e[1162]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1163]||(e[1163]=l('
julia
mlirAffineMapAttrGet(map)

Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.

source

',3))]),t("details",tn,[t("summary",null,[e[1164]||(e[1164]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGetTypeID")],-1)),e[1165]||(e[1165]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1166]||(e[1166]=l('
julia
mlirAffineMapAttrGetTypeID()

Returns the typeID of an AffineMap attribute.

source

',3))]),t("details",sn,[t("summary",null,[e[1167]||(e[1167]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGetValue")],-1)),e[1168]||(e[1168]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1169]||(e[1169]=l('
julia
mlirAffineMapAttrGetValue(attr)

Returns the affine map wrapped in the given affine map attribute.

source

',3))]),t("details",an,[t("summary",null,[e[1170]||(e[1170]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols")],-1)),e[1171]||(e[1171]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1172]||(e[1172]=l('
julia
mlirAffineMapCompressUnusedSymbols(affineMaps, size, result, populateResult)

Returns the simplified affine map resulting from dropping the symbols that do not appear in any of the individual maps in affineMaps. Asserts that all maps in affineMaps are normalized to the same number of dims and symbols. Takes a callback populateResult to fill the res container with value m at entry idx. This allows returning without worrying about ownership considerations.

source

',3))]),t("details",ln,[t("summary",null,[e[1173]||(e[1173]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapConstantGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapConstantGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapConstantGet")],-1)),e[1174]||(e[1174]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1175]||(e[1175]=l('
julia
mlirAffineMapConstantGet(ctx, val)

Creates a single constant result affine map in the context. The affine map is owned by the context.

source

',3))]),t("details",nn,[t("summary",null,[e[1176]||(e[1176]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapDump")],-1)),e[1177]||(e[1177]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1178]||(e[1178]=l('
julia
mlirAffineMapDump(affineMap)

Prints the affine map to the standard error stream.

source

',3))]),t("details",pn,[t("summary",null,[e[1179]||(e[1179]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapEmptyGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapEmptyGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapEmptyGet")],-1)),e[1180]||(e[1180]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1181]||(e[1181]=l('
julia
mlirAffineMapEmptyGet(ctx)

Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.

source

',3))]),t("details",rn,[t("summary",null,[e[1182]||(e[1182]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapEqual")],-1)),e[1183]||(e[1183]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1184]||(e[1184]=l('
julia
mlirAffineMapEqual(a1, a2)

Checks if two affine maps are equal.

source

',3))]),t("details",on,[t("summary",null,[e[1185]||(e[1185]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGet")],-1)),e[1186]||(e[1186]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1187]||(e[1187]=l('
julia
mlirAffineMapGet(ctx, dimCount, symbolCount, nAffineExprs, affineExprs)

Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.

source

',3))]),t("details",dn,[t("summary",null,[e[1188]||(e[1188]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetContext")],-1)),e[1189]||(e[1189]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1190]||(e[1190]=l('
julia
mlirAffineMapGetContext(affineMap)

Gets the context that the given affine map was created with

source

',3))]),t("details",cn,[t("summary",null,[e[1191]||(e[1191]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetMajorSubMap-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetMajorSubMap-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetMajorSubMap")],-1)),e[1192]||(e[1192]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1193]||(e[1193]=l('
julia
mlirAffineMapGetMajorSubMap(affineMap, numResults)

Returns the affine map consisting of the most major numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.

source

',3))]),t("details",bn,[t("summary",null,[e[1194]||(e[1194]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetMinorSubMap-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetMinorSubMap-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetMinorSubMap")],-1)),e[1195]||(e[1195]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1196]||(e[1196]=l('
julia
mlirAffineMapGetMinorSubMap(affineMap, numResults)

Returns the affine map consisting of the most minor numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.

source

',3))]),t("details",hn,[t("summary",null,[e[1197]||(e[1197]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumDims-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumDims")],-1)),e[1198]||(e[1198]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1199]||(e[1199]=l('
julia
mlirAffineMapGetNumDims(affineMap)

Returns the number of dimensions of the given affine map.

source

',3))]),t("details",un,[t("summary",null,[e[1200]||(e[1200]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumInputs-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumInputs")],-1)),e[1201]||(e[1201]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1202]||(e[1202]=l('
julia
mlirAffineMapGetNumInputs(affineMap)

Returns the number of inputs (dimensions + symbols) of the given affine map.

source

',3))]),t("details",gn,[t("summary",null,[e[1203]||(e[1203]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumResults-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumResults")],-1)),e[1204]||(e[1204]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1205]||(e[1205]=l('
julia
mlirAffineMapGetNumResults(affineMap)

Returns the number of results of the given affine map.

source

',3))]),t("details",yn,[t("summary",null,[e[1206]||(e[1206]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumSymbols-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumSymbols-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumSymbols")],-1)),e[1207]||(e[1207]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1208]||(e[1208]=l('
julia
mlirAffineMapGetNumSymbols(affineMap)

Returns the number of symbols of the given affine map.

source

',3))]),t("details",mn,[t("summary",null,[e[1209]||(e[1209]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetResult-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetResult")],-1)),e[1210]||(e[1210]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1211]||(e[1211]=l('
julia
mlirAffineMapGetResult(affineMap, pos)

Returns the result at the given position.

source

',3))]),t("details",kn,[t("summary",null,[e[1212]||(e[1212]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult")],-1)),e[1213]||(e[1213]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1214]||(e[1214]=l('
julia
mlirAffineMapGetSingleConstantResult(affineMap)

Returns the constant result of the given affine map. The function asserts that the map has a single constant result.

source

',3))]),t("details",Rn,[t("summary",null,[e[1215]||(e[1215]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetSubMap-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetSubMap-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetSubMap")],-1)),e[1216]||(e[1216]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1217]||(e[1217]=l('
julia
mlirAffineMapGetSubMap(affineMap, size, resultPos)

Returns the affine map consisting of the resultPos subset.

source

',3))]),t("details",In,[t("summary",null,[e[1218]||(e[1218]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsEmpty-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsEmpty")],-1)),e[1219]||(e[1219]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1220]||(e[1220]=l('
julia
mlirAffineMapIsEmpty(affineMap)

Checks whether the given affine map is an empty affine map.

source

',3))]),t("details",jn,[t("summary",null,[e[1221]||(e[1221]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsIdentity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsIdentity-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsIdentity")],-1)),e[1222]||(e[1222]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1223]||(e[1223]=l('
julia
mlirAffineMapIsIdentity(affineMap)

Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.

source

',3))]),t("details",fn,[t("summary",null,[e[1224]||(e[1224]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsMinorIdentity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsMinorIdentity-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsMinorIdentity")],-1)),e[1225]||(e[1225]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1226]||(e[1226]=l('
julia
mlirAffineMapIsMinorIdentity(affineMap)

Checks whether the given affine map is a minor identity affine map.

source

',3))]),t("details",Mn,[t("summary",null,[e[1227]||(e[1227]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsNull")],-1)),e[1228]||(e[1228]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1229]||(e[1229]=l('
julia
mlirAffineMapIsNull(affineMap)

Checks whether an affine map is null.

source

',3))]),t("details",An,[t("summary",null,[e[1230]||(e[1230]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsPermutation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsPermutation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsPermutation")],-1)),e[1231]||(e[1231]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1232]||(e[1232]=l('
julia
mlirAffineMapIsPermutation(affineMap)

Checks whether the given affine map represents a symbol-less permutation map.

source

',3))]),t("details",Ln,[t("summary",null,[e[1233]||(e[1233]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation")],-1)),e[1234]||(e[1234]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1235]||(e[1235]=l('
julia
mlirAffineMapIsProjectedPermutation(affineMap)

Checks whether the given affine map represents a subset of a symbol-less permutation map.

source

',3))]),t("details",En,[t("summary",null,[e[1236]||(e[1236]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsSingleConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsSingleConstant-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsSingleConstant")],-1)),e[1237]||(e[1237]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1238]||(e[1238]=l('
julia
mlirAffineMapIsSingleConstant(affineMap)

Checks whether the given affine map is a single result constant affine map.

source

',3))]),t("details",vn,[t("summary",null,[e[1239]||(e[1239]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapMinorIdentityGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapMinorIdentityGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapMinorIdentityGet")],-1)),e[1240]||(e[1240]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1241]||(e[1241]=l('
julia
mlirAffineMapMinorIdentityGet(ctx, dims, results)

Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.

source

',3))]),t("details",Tn,[t("summary",null,[e[1242]||(e[1242]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet")],-1)),e[1243]||(e[1243]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1244]||(e[1244]=l('
julia
mlirAffineMapMultiDimIdentityGet(ctx, numDims)

Creates an affine map with 'numDims' identity in the context. The affine map is owned by the context.

source

',3))]),t("details",Cn,[t("summary",null,[e[1245]||(e[1245]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapPermutationGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapPermutationGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapPermutationGet")],-1)),e[1246]||(e[1246]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1247]||(e[1247]=l('
julia
mlirAffineMapPermutationGet(ctx, size, permutation)

Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid permutation.) The affine map is owned by the context.

source

',3))]),t("details",xn,[t("summary",null,[e[1248]||(e[1248]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapPrint")],-1)),e[1249]||(e[1249]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1250]||(e[1250]=l('
julia
mlirAffineMapPrint(affineMap, callback, userData)

Prints an affine map by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Fn,[t("summary",null,[e[1251]||(e[1251]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapReplace-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAffineMapReplace-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapReplace")],-1)),e[1252]||(e[1252]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1253]||(e[1253]=l('
julia
mlirAffineMapReplace(affineMap, expression, replacement, numResultDims, numResultSyms)

Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.

source

',3))]),t("details",Pn,[t("summary",null,[e[1254]||(e[1254]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapZeroResultGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapZeroResultGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapZeroResultGet")],-1)),e[1255]||(e[1255]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1256]||(e[1256]=l('
julia
mlirAffineMapZeroResultGet(ctx, dimCount, symbolCount)

Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.

source

',3))]),t("details",Dn,[t("summary",null,[e[1257]||(e[1257]=t("a",{id:"Reactant.MLIR.API.mlirAffineModExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineModExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineModExprGet")],-1)),e[1258]||(e[1258]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1259]||(e[1259]=l('
julia
mlirAffineModExprGet(lhs, rhs)

Creates an affine mod expression with 'lhs' and 'rhs'.

source

',3))]),t("details",On,[t("summary",null,[e[1260]||(e[1260]=t("a",{id:"Reactant.MLIR.API.mlirAffineMulExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMulExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMulExprGet")],-1)),e[1261]||(e[1261]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1262]||(e[1262]=l('
julia
mlirAffineMulExprGet(lhs, rhs)

Creates an affine mul expression with 'lhs' and 'rhs'.

source

',3))]),t("details",Bn,[t("summary",null,[e[1263]||(e[1263]=t("a",{id:"Reactant.MLIR.API.mlirAffineSymbolExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineSymbolExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineSymbolExprGet")],-1)),e[1264]||(e[1264]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1265]||(e[1265]=l('
julia
mlirAffineSymbolExprGet(ctx, position)

Creates an affine symbol expression with 'position' in the context.

source

',3))]),t("details",Gn,[t("summary",null,[e[1266]||(e[1266]=t("a",{id:"Reactant.MLIR.API.mlirAffineSymbolExprGetPosition-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineSymbolExprGetPosition-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineSymbolExprGetPosition")],-1)),e[1267]||(e[1267]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1268]||(e[1268]=l('
julia
mlirAffineSymbolExprGetPosition(affineExpr)

Returns the position of the given affine symbol expression.

source

',3))]),t("details",zn,[t("summary",null,[e[1269]||(e[1269]=t("a",{id:"Reactant.MLIR.API.mlirAnyQuantizedTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAnyQuantizedTypeGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAnyQuantizedTypeGet")],-1)),e[1270]||(e[1270]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1271]||(e[1271]=l('
julia
mlirAnyQuantizedTypeGet(flags, storageType, expressedType, storageTypeMin, storageTypeMax)

Creates an instance of AnyQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.

source

',3))]),t("details",wn,[t("summary",null,[e[1272]||(e[1272]=t("a",{id:"Reactant.MLIR.API.mlirArrayAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGet")],-1)),e[1273]||(e[1273]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1274]||(e[1274]=l('
julia
mlirArrayAttrGet(ctx, numElements, elements)

Creates an array element containing the given list of elements in the given context.

source

',3))]),t("details",Sn,[t("summary",null,[e[1275]||(e[1275]=t("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGetElement-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetElement")],-1)),e[1276]||(e[1276]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1277]||(e[1277]=l('
julia
mlirArrayAttrGetElement(attr, pos)

Returns pos-th element stored in the given array attribute.

source

',3))]),t("details",Nn,[t("summary",null,[e[1278]||(e[1278]=t("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGetNumElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetNumElements")],-1)),e[1279]||(e[1279]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1280]||(e[1280]=l('
julia
mlirArrayAttrGetNumElements(attr)

Returns the number of elements stored in the given array attribute.

source

',3))]),t("details",Vn,[t("summary",null,[e[1281]||(e[1281]=t("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirArrayAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetTypeID")],-1)),e[1282]||(e[1282]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1283]||(e[1283]=l('
julia
mlirArrayAttrGetTypeID()

Returns the typeID of an Array attribute.

source

',3))]),t("details",qn,[t("summary",null,[e[1284]||(e[1284]=t("a",{id:"Reactant.MLIR.API.mlirAsmStateCreateForOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAsmStateCreateForOperation-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateCreateForOperation")],-1)),e[1285]||(e[1285]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1286]||(e[1286]=l('
julia
mlirAsmStateCreateForOperation(op, flags)

Creates new AsmState, as with AsmState the IR should not be mutated in-between using this state. Must be freed with a call to mlirAsmStateDestroy().

source

',3))]),t("details",Un,[t("summary",null,[e[1287]||(e[1287]=t("a",{id:"Reactant.MLIR.API.mlirAsmStateCreateForValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAsmStateCreateForValue-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateCreateForValue")],-1)),e[1288]||(e[1288]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1289]||(e[1289]=l('
julia
mlirAsmStateCreateForValue(value, flags)

Creates new AsmState from value. Must be freed with a call to mlirAsmStateDestroy().

source

',3))]),t("details",Qn,[t("summary",null,[e[1290]||(e[1290]=t("a",{id:"Reactant.MLIR.API.mlirAsmStateDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAsmStateDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateDestroy")],-1)),e[1291]||(e[1291]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1292]||(e[1292]=l('
julia
mlirAsmStateDestroy(state)

Destroys printing flags created with mlirAsmStateCreate.

source

',3))]),t("details",Wn,[t("summary",null,[e[1293]||(e[1293]=t("a",{id:"Reactant.MLIR.API.mlirAttributeDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeDump")],-1)),e[1294]||(e[1294]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1295]||(e[1295]=l('
julia
mlirAttributeDump(attr)

Prints the attribute to the standard error stream.

source

',3))]),t("details",Hn,[t("summary",null,[e[1296]||(e[1296]=t("a",{id:"Reactant.MLIR.API.mlirAttributeEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAttributeEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeEqual")],-1)),e[1297]||(e[1297]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1298]||(e[1298]=l('
julia
mlirAttributeEqual(a1, a2)

Checks if two attributes are equal.

source

',3))]),t("details",Zn,[t("summary",null,[e[1299]||(e[1299]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetContext")],-1)),e[1300]||(e[1300]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1301]||(e[1301]=l('
julia
mlirAttributeGetContext(attribute)

Gets the context that an attribute was created with.

source

',3))]),t("details",Jn,[t("summary",null,[e[1302]||(e[1302]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetDialect-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetDialect-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetDialect")],-1)),e[1303]||(e[1303]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1304]||(e[1304]=l('
julia
mlirAttributeGetDialect(attribute)

Gets the dialect of the attribute.

source

',3))]),t("details",Kn,[t("summary",null,[e[1305]||(e[1305]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetNull-Tuple{}",href:"#Reactant.MLIR.API.mlirAttributeGetNull-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetNull")],-1)),e[1306]||(e[1306]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1307]||(e[1307]=l('
julia
mlirAttributeGetNull()

Returns an empty attribute.

source

',3))]),t("details",$n,[t("summary",null,[e[1308]||(e[1308]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetType")],-1)),e[1309]||(e[1309]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1310]||(e[1310]=l('
julia
mlirAttributeGetType(attribute)

Gets the type of this attribute.

source

',3))]),t("details",Xn,[t("summary",null,[e[1311]||(e[1311]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetTypeID-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetTypeID")],-1)),e[1312]||(e[1312]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1313]||(e[1313]=l('
julia
mlirAttributeGetTypeID(attribute)

Gets the type id of the attribute.

source

',3))]),t("details",Yn,[t("summary",null,[e[1314]||(e[1314]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAAffineMap-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAAffineMap")],-1)),e[1315]||(e[1315]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1316]||(e[1316]=l('
julia
mlirAttributeIsAAffineMap(attr)

Checks whether the given attribute is an affine map attribute.

source

',3))]),t("details",_n,[t("summary",null,[e[1317]||(e[1317]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAArray-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAArray-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAArray")],-1)),e[1318]||(e[1318]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1319]||(e[1319]=l('
julia
mlirAttributeIsAArray(attr)

Checks whether the given attribute is an array attribute.

source

',3))]),t("details",ep,[t("summary",null,[e[1320]||(e[1320]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsABool-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsABool-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsABool")],-1)),e[1321]||(e[1321]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1322]||(e[1322]=l('
julia
mlirAttributeIsABool(attr)

Checks whether the given attribute is a bool attribute.

source

',3))]),t("details",tp,[t("summary",null,[e[1323]||(e[1323]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsADenseBoolArray-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADenseBoolArray-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADenseBoolArray")],-1)),e[1324]||(e[1324]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1325]||(e[1325]=l('
julia
mlirAttributeIsADenseBoolArray(attr)

Checks whether the given attribute is a dense array attribute.

source

',3))]),t("details",sp,[t("summary",null,[e[1326]||(e[1326]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsADenseElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADenseElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADenseElements")],-1)),e[1327]||(e[1327]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1328]||(e[1328]=l('
julia
mlirAttributeIsADenseElements(attr)

Checks whether the given attribute is a dense elements attribute.

source

',3))]),t("details",ap,[t("summary",null,[e[1329]||(e[1329]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsADictionary-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADictionary-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADictionary")],-1)),e[1330]||(e[1330]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1331]||(e[1331]=l('
julia
mlirAttributeIsADictionary(attr)

Checks whether the given attribute is a dictionary attribute.

source

',3))]),t("details",ip,[t("summary",null,[e[1332]||(e[1332]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAElements")],-1)),e[1333]||(e[1333]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1334]||(e[1334]=l('
julia
mlirAttributeIsAElements(attr)

Checks whether the given attribute is an elements attribute.

source

',3))]),t("details",lp,[t("summary",null,[e[1335]||(e[1335]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef")],-1)),e[1336]||(e[1336]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1337]||(e[1337]=l('
julia
mlirAttributeIsAFlatSymbolRef(attr)

Checks whether the given attribute is a flat symbol reference attribute.

source

',3))]),t("details",np,[t("summary",null,[e[1338]||(e[1338]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAFloat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAFloat-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAFloat")],-1)),e[1339]||(e[1339]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1340]||(e[1340]=l('
julia
mlirAttributeIsAFloat(attr)

Checks whether the given attribute is a floating point attribute.

source

',3))]),t("details",pp,[t("summary",null,[e[1341]||(e[1341]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAInteger-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAInteger-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAInteger")],-1)),e[1342]||(e[1342]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1343]||(e[1343]=l('
julia
mlirAttributeIsAInteger(attr)

Checks whether the given attribute is an integer attribute.

source

',3))]),t("details",rp,[t("summary",null,[e[1344]||(e[1344]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAIntegerSet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAIntegerSet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAIntegerSet")],-1)),e[1345]||(e[1345]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1346]||(e[1346]=l('
julia
mlirAttributeIsAIntegerSet(attr)

Checks whether the given attribute is an integer set attribute.

source

',3))]),t("details",op,[t("summary",null,[e[1347]||(e[1347]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAOpaque-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAOpaque")],-1)),e[1348]||(e[1348]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1349]||(e[1349]=l('
julia
mlirAttributeIsAOpaque(attr)

Checks whether the given attribute is an opaque attribute.

source

',3))]),t("details",dp,[t("summary",null,[e[1350]||(e[1350]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsASparseElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASparseElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASparseElements")],-1)),e[1351]||(e[1351]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1352]||(e[1352]=l('
julia
mlirAttributeIsASparseElements(attr)

Checks whether the given attribute is a sparse elements attribute.

source

',3))]),t("details",cp,[t("summary",null,[e[1353]||(e[1353]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr")],-1)),e[1354]||(e[1354]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1355]||(e[1355]=l('
julia
mlirAttributeIsASparseTensorEncodingAttr(attr)

Checks whether the given attribute is a sparse\\_tensor.encoding attribute.

source

',3))]),t("details",bp,[t("summary",null,[e[1356]||(e[1356]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAString-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAString-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAString")],-1)),e[1357]||(e[1357]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1358]||(e[1358]=l('
julia
mlirAttributeIsAString(attr)

Checks whether the given attribute is a string attribute.

source

',3))]),t("details",hp,[t("summary",null,[e[1359]||(e[1359]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsASymbolRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASymbolRef-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASymbolRef")],-1)),e[1360]||(e[1360]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1361]||(e[1361]=l('
julia
mlirAttributeIsASymbolRef(attr)

Checks whether the given attribute is a symbol reference attribute.

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',3))]),t("details",up,[t("summary",null,[e[1362]||(e[1362]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAType")],-1)),e[1363]||(e[1363]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1364]||(e[1364]=l('
julia
mlirAttributeIsAType(attr)

Checks whether the given attribute is a type attribute.

source

',3))]),t("details",gp,[t("summary",null,[e[1365]||(e[1365]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAUnit-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAUnit-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAUnit")],-1)),e[1366]||(e[1366]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1367]||(e[1367]=l('
julia
mlirAttributeIsAUnit(attr)

Checks whether the given attribute is a unit attribute.

source

',3))]),t("details",yp,[t("summary",null,[e[1368]||(e[1368]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsNull")],-1)),e[1369]||(e[1369]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1370]||(e[1370]=l('
julia
mlirAttributeIsNull(attr)

Checks whether an attribute is null.

source

',3))]),t("details",mp,[t("summary",null,[e[1371]||(e[1371]=t("a",{id:"Reactant.MLIR.API.mlirAttributeParseGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAttributeParseGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeParseGet")],-1)),e[1372]||(e[1372]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1373]||(e[1373]=l('
julia
mlirAttributeParseGet(context, attr)

Parses an attribute. The attribute is owned by the context.

source

',3))]),t("details",kp,[t("summary",null,[e[1374]||(e[1374]=t("a",{id:"Reactant.MLIR.API.mlirAttributePrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAttributePrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributePrint")],-1)),e[1375]||(e[1375]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1376]||(e[1376]=l('
julia
mlirAttributePrint(attr, callback, userData)

Prints an attribute by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Rp,[t("summary",null,[e[1377]||(e[1377]=t("a",{id:"Reactant.MLIR.API.mlirBF16TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBF16TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBF16TypeGet")],-1)),e[1378]||(e[1378]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1379]||(e[1379]=l('
julia
mlirBF16TypeGet(ctx)

Creates a bf16 type in the given context. The type is owned by the context.

source

',3))]),t("details",Ip,[t("summary",null,[e[1380]||(e[1380]=t("a",{id:"Reactant.MLIR.API.mlirBFloat16TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirBFloat16TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBFloat16TypeGetTypeID")],-1)),e[1381]||(e[1381]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1382]||(e[1382]=l('
julia
mlirBFloat16TypeGetTypeID()

Returns the typeID of an BFloat16 type.

source

',3))]),t("details",jp,[t("summary",null,[e[1383]||(e[1383]=t("a",{id:"Reactant.MLIR.API.mlirBlockAddArgument-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockAddArgument-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockAddArgument")],-1)),e[1384]||(e[1384]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1385]||(e[1385]=l('
julia
mlirBlockAddArgument(block, type, loc)

Appends an argument of the specified type to the block. Returns the newly added argument.

source

',3))]),t("details",fp,[t("summary",null,[e[1386]||(e[1386]=t("a",{id:"Reactant.MLIR.API.mlirBlockAppendOwnedOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockAppendOwnedOperation-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockAppendOwnedOperation")],-1)),e[1387]||(e[1387]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1388]||(e[1388]=l('
julia
mlirBlockAppendOwnedOperation(block, operation)

Takes an operation owned by the caller and appends it to the block.

source

',3))]),t("details",Mp,[t("summary",null,[e[1389]||(e[1389]=t("a",{id:"Reactant.MLIR.API.mlirBlockArgumentGetArgNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentGetArgNumber-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentGetArgNumber")],-1)),e[1390]||(e[1390]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1391]||(e[1391]=l('
julia
mlirBlockArgumentGetArgNumber(value)

Returns the position of the value in the argument list of its block.

source

',3))]),t("details",Ap,[t("summary",null,[e[1392]||(e[1392]=t("a",{id:"Reactant.MLIR.API.mlirBlockArgumentGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentGetOwner-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentGetOwner")],-1)),e[1393]||(e[1393]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1394]||(e[1394]=l('
julia
mlirBlockArgumentGetOwner(value)

Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.

source

',3))]),t("details",Lp,[t("summary",null,[e[1395]||(e[1395]=t("a",{id:"Reactant.MLIR.API.mlirBlockArgumentSetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentSetType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentSetType")],-1)),e[1396]||(e[1396]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1397]||(e[1397]=l('
julia
mlirBlockArgumentSetType(value, type)

Sets the type of the block argument to the given type.

source

',3))]),t("details",Ep,[t("summary",null,[e[1398]||(e[1398]=t("a",{id:"Reactant.MLIR.API.mlirBlockCreate-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockCreate-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockCreate")],-1)),e[1399]||(e[1399]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1400]||(e[1400]=l('
julia
mlirBlockCreate(nArgs, args, locs)

Creates a new empty block with the given argument types and transfers ownership to the caller.

source

',3))]),t("details",vp,[t("summary",null,[e[1401]||(e[1401]=t("a",{id:"Reactant.MLIR.API.mlirBlockDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockDestroy")],-1)),e[1402]||(e[1402]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1403]||(e[1403]=l('
julia
mlirBlockDestroy(block)

Takes a block owned by the caller and destroys it.

source

',3))]),t("details",Tp,[t("summary",null,[e[1404]||(e[1404]=t("a",{id:"Reactant.MLIR.API.mlirBlockDetach-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockDetach-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockDetach")],-1)),e[1405]||(e[1405]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1406]||(e[1406]=l('
julia
mlirBlockDetach(block)

Detach a block from the owning region and assume ownership.

source

',3))]),t("details",Cp,[t("summary",null,[e[1407]||(e[1407]=t("a",{id:"Reactant.MLIR.API.mlirBlockEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockEqual")],-1)),e[1408]||(e[1408]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1409]||(e[1409]=l('
julia
mlirBlockEqual(block, other)

Checks whether two blocks handles point to the same block. This does not perform deep comparison.

source

',3))]),t("details",xp,[t("summary",null,[e[1410]||(e[1410]=t("a",{id:"Reactant.MLIR.API.mlirBlockEraseArgument-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockEraseArgument-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockEraseArgument")],-1)),e[1411]||(e[1411]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1412]||(e[1412]=l('
julia
mlirBlockEraseArgument(block, index)

Erase the argument at 'index' and remove it from the argument list.

source

',3))]),t("details",Fp,[t("summary",null,[e[1413]||(e[1413]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetArgument-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockGetArgument-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetArgument")],-1)),e[1414]||(e[1414]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1415]||(e[1415]=l('
julia
mlirBlockGetArgument(block, pos)

Returns pos-th argument of the block.

source

',3))]),t("details",Pp,[t("summary",null,[e[1416]||(e[1416]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetFirstOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetFirstOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetFirstOperation")],-1)),e[1417]||(e[1417]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1418]||(e[1418]=l('
julia
mlirBlockGetFirstOperation(block)

Returns the first operation in the block.

source

',3))]),t("details",Dp,[t("summary",null,[e[1419]||(e[1419]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetNextInRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetNextInRegion-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetNextInRegion")],-1)),e[1420]||(e[1420]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1421]||(e[1421]=l('
julia
mlirBlockGetNextInRegion(block)

Returns the block immediately following the given block in its parent region.

source

',3))]),t("details",Op,[t("summary",null,[e[1422]||(e[1422]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetNumArguments-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetNumArguments-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetNumArguments")],-1)),e[1423]||(e[1423]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1424]||(e[1424]=l('
julia
mlirBlockGetNumArguments(block)

Returns the number of arguments of the block.

source

',3))]),t("details",Bp,[t("summary",null,[e[1425]||(e[1425]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetParentOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetParentOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetParentOperation")],-1)),e[1426]||(e[1426]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1427]||(e[1427]=l('
julia
mlirBlockGetParentOperation(arg1)

Returns the closest surrounding operation that contains this block.

source

',3))]),t("details",Gp,[t("summary",null,[e[1428]||(e[1428]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetParentRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetParentRegion-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetParentRegion")],-1)),e[1429]||(e[1429]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1430]||(e[1430]=l('
julia
mlirBlockGetParentRegion(block)

Returns the region that contains this block.

source

',3))]),t("details",zp,[t("summary",null,[e[1431]||(e[1431]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetTerminator-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetTerminator-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetTerminator")],-1)),e[1432]||(e[1432]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1433]||(e[1433]=l('
julia
mlirBlockGetTerminator(block)

Returns the terminator operation in the block or null if no terminator.

source

',3))]),t("details",wp,[t("summary",null,[e[1434]||(e[1434]=t("a",{id:"Reactant.MLIR.API.mlirBlockInsertArgument-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertArgument-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertArgument")],-1)),e[1435]||(e[1435]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1436]||(e[1436]=l('
julia
mlirBlockInsertArgument(block, pos, type, loc)

Inserts an argument of the specified type at a specified index to the block. Returns the newly added argument.

source

',3))]),t("details",Sp,[t("summary",null,[e[1437]||(e[1437]=t("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperation-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperation")],-1)),e[1438]||(e[1438]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1439]||(e[1439]=l('
julia
mlirBlockInsertOwnedOperation(block, pos, operation)

Takes an operation owned by the caller and inserts it as pos to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.

source

',3))]),t("details",Np,[t("summary",null,[e[1440]||(e[1440]=t("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter")],-1)),e[1441]||(e[1441]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1442]||(e[1442]=l('
julia
mlirBlockInsertOwnedOperationAfter(block, reference, operation)

Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.

source

',3))]),t("details",Vp,[t("summary",null,[e[1443]||(e[1443]=t("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore")],-1)),e[1444]||(e[1444]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1445]||(e[1445]=l('
julia
mlirBlockInsertOwnedOperationBefore(block, reference, operation)

Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.

source

',3))]),t("details",qp,[t("summary",null,[e[1446]||(e[1446]=t("a",{id:"Reactant.MLIR.API.mlirBlockIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockIsNull")],-1)),e[1447]||(e[1447]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1448]||(e[1448]=l('
julia
mlirBlockIsNull(block)

Checks whether a block is null.

source

',3))]),t("details",Up,[t("summary",null,[e[1449]||(e[1449]=t("a",{id:"Reactant.MLIR.API.mlirBlockPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockPrint")],-1)),e[1450]||(e[1450]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1451]||(e[1451]=l('
julia
mlirBlockPrint(block, callback, userData)

Prints a block by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Qp,[t("summary",null,[e[1452]||(e[1452]=t("a",{id:"Reactant.MLIR.API.mlirBoolAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBoolAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBoolAttrGet")],-1)),e[1453]||(e[1453]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1454]||(e[1454]=l('
julia
mlirBoolAttrGet(ctx, value)

Creates a bool attribute in the given context with the given value.

source

',3))]),t("details",Wp,[t("summary",null,[e[1455]||(e[1455]=t("a",{id:"Reactant.MLIR.API.mlirBoolAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBoolAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBoolAttrGetValue")],-1)),e[1456]||(e[1456]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1457]||(e[1457]=l('
julia
mlirBoolAttrGetValue(attr)

Returns the value stored in the given bool attribute.

source

',3))]),t("details",Hp,[t("summary",null,[e[1458]||(e[1458]=t("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigCreate")],-1)),e[1459]||(e[1459]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1460]||(e[1460]=l('
julia
mlirBytecodeWriterConfigCreate()

Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirBytecodeWriterConfigDestroy().

source

',3))]),t("details",Zp,[t("summary",null,[e[1461]||(e[1461]=t("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion")],-1)),e[1462]||(e[1462]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1463]||(e[1463]=l('
julia
mlirBytecodeWriterConfigDesiredEmitVersion(flags, version)

Sets the version to emit in the writer config.

source

',3))]),t("details",Jp,[t("summary",null,[e[1464]||(e[1464]=t("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy")],-1)),e[1465]||(e[1465]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1466]||(e[1466]=l('
julia
mlirBytecodeWriterConfigDestroy(config)

Destroys printing flags created with mlirBytecodeWriterConfigCreate.

source

',3))]),t("details",Kp,[t("summary",null,[e[1467]||(e[1467]=t("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet")],-1)),e[1468]||(e[1468]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1469]||(e[1469]=l('
julia
mlirCalibratedQuantizedTypeGet(expressedType, min, max)

Creates an instance of CalibratedQuantizedType with the given parameters in the same context as expressedType and returns it. The instance is owned by the context.

source

',3))]),t("details",$p,[t("summary",null,[e[1470]||(e[1470]=t("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax-Tuple{Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax")],-1)),e[1471]||(e[1471]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1472]||(e[1472]=l('
julia
mlirCalibratedQuantizedTypeGetMax(type)

Returns the max value of the given calibrated quantized type.

source

',3))]),t("details",Xp,[t("summary",null,[e[1473]||(e[1473]=t("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin-Tuple{Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin")],-1)),e[1474]||(e[1474]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1475]||(e[1475]=l('
julia
mlirCalibratedQuantizedTypeGetMin(type)

Returns the min value of the given calibrated quantized type.

source

',3))]),t("details",Yp,[t("summary",null,[e[1476]||(e[1476]=t("a",{id:"Reactant.MLIR.API.mlirComplexTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirComplexTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGet")],-1)),e[1477]||(e[1477]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1478]||(e[1478]=l('
julia
mlirComplexTypeGet(elementType)

Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.

source

',3))]),t("details",_p,[t("summary",null,[e[1479]||(e[1479]=t("a",{id:"Reactant.MLIR.API.mlirComplexTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirComplexTypeGetElementType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGetElementType")],-1)),e[1480]||(e[1480]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1481]||(e[1481]=l('
julia
mlirComplexTypeGetElementType(type)

Returns the element type of the given complex type.

source

',3))]),t("details",er,[t("summary",null,[e[1482]||(e[1482]=t("a",{id:"Reactant.MLIR.API.mlirComplexTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirComplexTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGetTypeID")],-1)),e[1483]||(e[1483]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1484]||(e[1484]=l('
julia
mlirComplexTypeGetTypeID()

Returns the typeID of an Complex type.

source

',3))]),t("details",tr,[t("summary",null,[e[1485]||(e[1485]=t("a",{id:"Reactant.MLIR.API.mlirContextAppendDialectRegistry-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextAppendDialectRegistry-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextAppendDialectRegistry")],-1)),e[1486]||(e[1486]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1487]||(e[1487]=l('
julia
mlirContextAppendDialectRegistry(ctx, registry)

Append the contents of the given dialect registry to the registry associated with the context.

source

',3))]),t("details",sr,[t("summary",null,[e[1488]||(e[1488]=t("a",{id:"Reactant.MLIR.API.mlirContextAttachDiagnosticHandler-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirContextAttachDiagnosticHandler-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextAttachDiagnosticHandler")],-1)),e[1489]||(e[1489]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1490]||(e[1490]=l('
julia
mlirContextAttachDiagnosticHandler(context, handler, userData, deleteUserData)

Attaches the diagnostic handler to the context. Handlers are invoked in the reverse order of attachment until one of them processes the diagnostic completely. When a handler is invoked it is passed the userData that was provided when it was attached. If non-NULL, deleteUserData is called once the system no longer needs to call the handler (for instance after the handler is detached or the context is destroyed). Returns an identifier that can be used to detach the handler.

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',3))]),t("details",ar,[t("summary",null,[e[1491]||(e[1491]=t("a",{id:"Reactant.MLIR.API.mlirContextCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirContextCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreate")],-1)),e[1492]||(e[1492]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1493]||(e[1493]=l('
julia
mlirContextCreate()

Creates an MLIR context and transfers its ownership to the caller. This sets the default multithreading option (enabled).

source

',3))]),t("details",ir,[t("summary",null,[e[1494]||(e[1494]=t("a",{id:"Reactant.MLIR.API.mlirContextCreateWithRegistry-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextCreateWithRegistry-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreateWithRegistry")],-1)),e[1495]||(e[1495]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1496]||(e[1496]=l('
julia
mlirContextCreateWithRegistry(registry, threadingEnabled)

Creates an MLIR context, setting the multithreading setting explicitly and pre-loading the dialects from the provided DialectRegistry.

source

',3))]),t("details",lr,[t("summary",null,[e[1497]||(e[1497]=t("a",{id:"Reactant.MLIR.API.mlirContextCreateWithThreading-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextCreateWithThreading-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreateWithThreading")],-1)),e[1498]||(e[1498]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1499]||(e[1499]=l('
julia
mlirContextCreateWithThreading(threadingEnabled)

Creates an MLIR context with an explicit setting of the multithreading setting and transfers its ownership to the caller.

source

',3))]),t("details",nr,[t("summary",null,[e[1500]||(e[1500]=t("a",{id:"Reactant.MLIR.API.mlirContextDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextDestroy")],-1)),e[1501]||(e[1501]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1502]||(e[1502]=l('
julia
mlirContextDestroy(context)

Takes an MLIR context owned by the caller and destroys it.

source

',3))]),t("details",pr,[t("summary",null,[e[1503]||(e[1503]=t("a",{id:"Reactant.MLIR.API.mlirContextDetachDiagnosticHandler-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextDetachDiagnosticHandler-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextDetachDiagnosticHandler")],-1)),e[1504]||(e[1504]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1505]||(e[1505]=l('
julia
mlirContextDetachDiagnosticHandler(context, id)

Detaches an attached diagnostic handler from the context given its identifier.

source

',3))]),t("details",rr,[t("summary",null,[e[1506]||(e[1506]=t("a",{id:"Reactant.MLIR.API.mlirContextEnableMultithreading-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextEnableMultithreading-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextEnableMultithreading")],-1)),e[1507]||(e[1507]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1508]||(e[1508]=l('
julia
mlirContextEnableMultithreading(context, enable)

Set threading mode (must be set to false to mlir-print-ir-after-all).

source

',3))]),t("details",or,[t("summary",null,[e[1509]||(e[1509]=t("a",{id:"Reactant.MLIR.API.mlirContextEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextEqual")],-1)),e[1510]||(e[1510]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1511]||(e[1511]=l('
julia
mlirContextEqual(ctx1, ctx2)

Checks if two contexts are equal.

source

',3))]),t("details",dr,[t("summary",null,[e[1512]||(e[1512]=t("a",{id:"Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects")],-1)),e[1513]||(e[1513]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1514]||(e[1514]=l('
julia
mlirContextGetAllowUnregisteredDialects(context)

Returns whether the context allows unregistered dialects.

source

',3))]),t("details",cr,[t("summary",null,[e[1515]||(e[1515]=t("a",{id:"Reactant.MLIR.API.mlirContextGetNumLoadedDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetNumLoadedDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetNumLoadedDialects")],-1)),e[1516]||(e[1516]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1517]||(e[1517]=l('
julia
mlirContextGetNumLoadedDialects(context)

Returns the number of dialects loaded by the context.

source

',3))]),t("details",br,[t("summary",null,[e[1518]||(e[1518]=t("a",{id:"Reactant.MLIR.API.mlirContextGetNumRegisteredDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetNumRegisteredDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetNumRegisteredDialects")],-1)),e[1519]||(e[1519]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1520]||(e[1520]=l('
julia
mlirContextGetNumRegisteredDialects(context)

Returns the number of dialects registered with the given context. A registered dialect will be loaded if needed by the parser.

source

',3))]),t("details",hr,[t("summary",null,[e[1521]||(e[1521]=t("a",{id:"Reactant.MLIR.API.mlirContextGetOrLoadDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextGetOrLoadDialect-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetOrLoadDialect")],-1)),e[1522]||(e[1522]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1523]||(e[1523]=l('
julia
mlirContextGetOrLoadDialect(context, name)

Gets the dialect instance owned by the given context using the dialect namespace to identify it, loads (i.e., constructs the instance of) the dialect if necessary. If the dialect is not registered with the context, returns null. Use mlirContextLoad<Name>Dialect to load an unregistered dialect.

source

',3))]),t("details",ur,[t("summary",null,[e[1524]||(e[1524]=t("a",{id:"Reactant.MLIR.API.mlirContextIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextIsNull")],-1)),e[1525]||(e[1525]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1526]||(e[1526]=l('
julia
mlirContextIsNull(context)

Checks whether a context is null.

source

',3))]),t("details",gr,[t("summary",null,[e[1527]||(e[1527]=t("a",{id:"Reactant.MLIR.API.mlirContextIsRegisteredOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextIsRegisteredOperation-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextIsRegisteredOperation")],-1)),e[1528]||(e[1528]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1529]||(e[1529]=l('
julia
mlirContextIsRegisteredOperation(context, name)

Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.

source

',3))]),t("details",yr,[t("summary",null,[e[1530]||(e[1530]=t("a",{id:"Reactant.MLIR.API.mlirContextLoadAllAvailableDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextLoadAllAvailableDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextLoadAllAvailableDialects")],-1)),e[1531]||(e[1531]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1532]||(e[1532]=l('
julia
mlirContextLoadAllAvailableDialects(context)

Eagerly loads all available dialects registered with a context, making them available for use for IR construction.

source

',3))]),t("details",mr,[t("summary",null,[e[1533]||(e[1533]=t("a",{id:"Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects")],-1)),e[1534]||(e[1534]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1535]||(e[1535]=l('
julia
mlirContextSetAllowUnregisteredDialects(context, allow)

Sets whether unregistered dialects are allowed in this context.

source

',3))]),t("details",kr,[t("summary",null,[e[1536]||(e[1536]=t("a",{id:"Reactant.MLIR.API.mlirContextSetThreadPool-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextSetThreadPool-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextSetThreadPool")],-1)),e[1537]||(e[1537]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1538]||(e[1538]=l('
julia
mlirContextSetThreadPool(context, threadPool)

Sets the thread pool of the context explicitly, enabling multithreading in the process. This API should be used to avoid re-creating thread pools in long-running applications that perform multiple compilations, see the C++ documentation for MLIRContext for details.

source

',3))]),t("details",Rr,[t("summary",null,[e[1539]||(e[1539]=t("a",{id:"Reactant.MLIR.API.mlirCreateExternalPass-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirCreateExternalPass-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCreateExternalPass")],-1)),e[1540]||(e[1540]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1541]||(e[1541]=l('
julia
mlirCreateExternalPass(passID, name, argument, description, opName, nDependentDialects, dependentDialects, callbacks, userData)

Creates an external MlirPass that calls the supplied callbacks using the supplied userData. If opName is empty, the pass is a generic operation pass. Otherwise it is an operation pass specific to the specified pass name.

source

',3))]),t("details",Ir,[t("summary",null,[e[1542]||(e[1542]=t("a",{id:"Reactant.MLIR.API.mlirDenseArrayGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseArrayGetNumElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseArrayGetNumElements")],-1)),e[1543]||(e[1543]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1544]||(e[1544]=l('
julia
mlirDenseArrayGetNumElements(attr)

Get the size of a dense array.

source

',3))]),t("details",jr,[t("summary",null,[e[1545]||(e[1545]=t("a",{id:"Reactant.MLIR.API.mlirDenseBoolArrayGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolArrayGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolArrayGet")],-1)),e[1546]||(e[1546]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1547]||(e[1547]=l('
julia
mlirDenseBoolArrayGet(ctx, size, values)

Create a dense array attribute with the given elements.

source

',3))]),t("details",fr,[t("summary",null,[e[1548]||(e[1548]=t("a",{id:"Reactant.MLIR.API.mlirDenseBoolArrayGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolArrayGetElement-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolArrayGetElement")],-1)),e[1549]||(e[1549]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1550]||(e[1550]=l('
julia
mlirDenseBoolArrayGetElement(attr, pos)

Get an element of a dense array.

source

',3))]),t("details",Mr,[t("summary",null,[e[1551]||(e[1551]=t("a",{id:"Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue")],-1)),e[1552]||(e[1552]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1553]||(e[1553]=l('
julia
mlirDenseBoolResourceElementsAttrGetValue(attr, pos)

Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense resource elements attribute.

source

',3))]),t("details",Ar,[t("summary",null,[e[1554]||(e[1554]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrBoolGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrBoolGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrBoolGet")],-1)),e[1555]||(e[1555]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1556]||(e[1556]=l('
julia
mlirDenseElementsAttrBoolGet(shapedType, numElements, elements)

Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.

source

',3))]),t("details",Lr,[t("summary",null,[e[1557]||(e[1557]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGet")],-1)),e[1558]||(e[1558]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1559]||(e[1559]=l('
julia
mlirDenseElementsAttrGet(shapedType, numElements, elements)

Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.

source

',3))]),t("details",Er,[t("summary",null,[e[1560]||(e[1560]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue")],-1)),e[1561]||(e[1561]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1562]||(e[1562]=l('
julia
mlirDenseElementsAttrGetBoolValue(attr, pos)

Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense elements attribute.

source

',3))]),t("details",vr,[t("summary",null,[e[1563]||(e[1563]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetRawData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetRawData-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetRawData")],-1)),e[1564]||(e[1564]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1565]||(e[1565]=l('
julia
mlirDenseElementsAttrGetRawData(attr)

Returns the raw data of the given dense elements attribute.

source

',3))]),t("details",Tr,[t("summary",null,[e[1566]||(e[1566]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue")],-1)),e[1567]||(e[1567]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1568]||(e[1568]=l('
julia
mlirDenseElementsAttrGetSplatValue(attr)

Returns the single replicated value (splat) of a specific type contained by the given dense elements attribute.

source

',3))]),t("details",Cr,[t("summary",null,[e[1569]||(e[1569]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrIsSplat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrIsSplat-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrIsSplat")],-1)),e[1570]||(e[1570]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1571]||(e[1571]=l('
julia
mlirDenseElementsAttrIsSplat(attr)

Checks whether the given dense elements attribute contains a single replicated value (splat).

source

',3))]),t("details",xr,[t("summary",null,[e[1572]||(e[1572]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet")],-1)),e[1573]||(e[1573]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1574]||(e[1574]=l('
julia
mlirDenseElementsAttrRawBufferGet(shapedType, rawBufferSize, rawBuffer)

Creates a dense elements attribute with the given Shaped type and elements populated from a packed, row-major opaque buffer of contents.

The format of the raw buffer is a densely packed array of values that can be bitcast to the storage format of the element type specified. Types that are not byte aligned will be: - For bitwidth > 1: Rounded up to the next byte. - For bitwidth = 1: Packed into 8bit bytes with bits corresponding to the linear order of the shape type from MSB to LSB, padded to on the right.

A raw buffer of a single element (or for 1-bit, a byte of value 0 or 255) will be interpreted as a splat. User code should be prepared for additional, conformant patterns to be identified as splats in the future.

source

',5))]),t("details",Fr,[t("summary",null,[e[1575]||(e[1575]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet")],-1)),e[1576]||(e[1576]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1577]||(e[1577]=l('
julia
mlirDenseElementsAttrReshapeGet(attr, shapedType)

Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.

source

',3))]),t("details",Pr,[t("summary",null,[e[1578]||(e[1578]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrSplatGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrSplatGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrSplatGet")],-1)),e[1579]||(e[1579]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1580]||(e[1580]=l('
julia
mlirDenseElementsAttrSplatGet(shapedType, element)

Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).

source

',3))]),t("details",Dr,[t("summary",null,[e[1581]||(e[1581]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrStringGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrStringGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrStringGet")],-1)),e[1582]||(e[1582]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1583]||(e[1583]=l('
julia
mlirDenseElementsAttrStringGet(shapedType, numElements, strs)

Creates a dense elements attribute with the given shaped type from string elements.

source

',3))]),t("details",Or,[t("summary",null,[e[1584]||(e[1584]=t("a",{id:"Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID")],-1)),e[1585]||(e[1585]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1586]||(e[1586]=l('
julia
mlirDenseIntOrFPElementsAttrGetTypeID()

Returns the typeID of an DenseIntOrFPElements attribute.

source

',3))]),t("details",Br,[t("summary",null,[e[1587]||(e[1587]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetLocation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetLocation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetLocation")],-1)),e[1588]||(e[1588]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1589]||(e[1589]=l('
julia
mlirDiagnosticGetLocation(diagnostic)

Returns the location at which the diagnostic is reported.

source

',3))]),t("details",Gr,[t("summary",null,[e[1590]||(e[1590]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetNote-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetNote-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetNote")],-1)),e[1591]||(e[1591]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1592]||(e[1592]=l('
julia
mlirDiagnosticGetNote(diagnostic, pos)

Returns pos-th note attached to the diagnostic. Expects pos to be a valid zero-based index into the list of notes.

source

',3))]),t("details",zr,[t("summary",null,[e[1593]||(e[1593]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetNumNotes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetNumNotes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetNumNotes")],-1)),e[1594]||(e[1594]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1595]||(e[1595]=l('
julia
mlirDiagnosticGetNumNotes(diagnostic)

Returns the number of notes attached to the diagnostic.

source

',3))]),t("details",wr,[t("summary",null,[e[1596]||(e[1596]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetSeverity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetSeverity-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetSeverity")],-1)),e[1597]||(e[1597]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1598]||(e[1598]=l('
julia
mlirDiagnosticGetSeverity(diagnostic)

Returns the severity of the diagnostic.

source

',3))]),t("details",Sr,[t("summary",null,[e[1599]||(e[1599]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDiagnosticPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticPrint")],-1)),e[1600]||(e[1600]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1601]||(e[1601]=l('
julia
mlirDiagnosticPrint(diagnostic, callback, userData)

Prints a diagnostic using the provided callback.

source

',3))]),t("details",Nr,[t("summary",null,[e[1602]||(e[1602]=t("a",{id:"Reactant.MLIR.API.mlirDialectEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectEqual")],-1)),e[1603]||(e[1603]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1604]||(e[1604]=l('
julia
mlirDialectEqual(dialect1, dialect2)

Checks if two dialects that belong to the same context are equal. Dialects from different contexts will not compare equal.

source

',3))]),t("details",Vr,[t("summary",null,[e[1605]||(e[1605]=t("a",{id:"Reactant.MLIR.API.mlirDialectGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectGetContext")],-1)),e[1606]||(e[1606]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1607]||(e[1607]=l('
julia
mlirDialectGetContext(dialect)

Returns the context that owns the dialect.

source

',3))]),t("details",qr,[t("summary",null,[e[1608]||(e[1608]=t("a",{id:"Reactant.MLIR.API.mlirDialectGetNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectGetNamespace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectGetNamespace")],-1)),e[1609]||(e[1609]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1610]||(e[1610]=l('
julia
mlirDialectGetNamespace(dialect)

Returns the namespace of the given dialect.

source

',3))]),t("details",Ur,[t("summary",null,[e[1611]||(e[1611]=t("a",{id:"Reactant.MLIR.API.mlirDialectHandleGetNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectHandleGetNamespace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleGetNamespace")],-1)),e[1612]||(e[1612]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1613]||(e[1613]=l('
julia
mlirDialectHandleGetNamespace(arg1)

Returns the namespace associated with the provided dialect handle.

source

',3))]),t("details",Qr,[t("summary",null,[e[1614]||(e[1614]=t("a",{id:"Reactant.MLIR.API.mlirDialectHandleInsertDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleInsertDialect-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleInsertDialect")],-1)),e[1615]||(e[1615]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1616]||(e[1616]=l('
julia
mlirDialectHandleInsertDialect(arg1, arg2)

Inserts the dialect associated with the provided dialect handle into the provided dialect registry

source

',3))]),t("details",Wr,[t("summary",null,[e[1617]||(e[1617]=t("a",{id:"Reactant.MLIR.API.mlirDialectHandleLoadDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleLoadDialect-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleLoadDialect")],-1)),e[1618]||(e[1618]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1619]||(e[1619]=l('
julia
mlirDialectHandleLoadDialect(arg1, arg2)

Loads the dialect associated with the provided dialect handle.

source

',3))]),t("details",Hr,[t("summary",null,[e[1620]||(e[1620]=t("a",{id:"Reactant.MLIR.API.mlirDialectHandleRegisterDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleRegisterDialect-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleRegisterDialect")],-1)),e[1621]||(e[1621]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1622]||(e[1622]=l('
julia
mlirDialectHandleRegisterDialect(arg1, arg2)

Registers the dialect associated with the provided dialect handle.

source

',3))]),t("details",Zr,[t("summary",null,[e[1623]||(e[1623]=t("a",{id:"Reactant.MLIR.API.mlirDialectIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectIsNull")],-1)),e[1624]||(e[1624]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1625]||(e[1625]=l('
julia
mlirDialectIsNull(dialect)

Checks if the dialect is null.

source

',3))]),t("details",Jr,[t("summary",null,[e[1626]||(e[1626]=t("a",{id:"Reactant.MLIR.API.mlirDialectRegistryCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirDialectRegistryCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryCreate")],-1)),e[1627]||(e[1627]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1628]||(e[1628]=l('
julia
mlirDialectRegistryCreate()

Creates a dialect registry and transfers its ownership to the caller.

source

',3))]),t("details",Kr,[t("summary",null,[e[1629]||(e[1629]=t("a",{id:"Reactant.MLIR.API.mlirDialectRegistryDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectRegistryDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryDestroy")],-1)),e[1630]||(e[1630]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1631]||(e[1631]=l('
julia
mlirDialectRegistryDestroy(registry)

Takes a dialect registry owned by the caller and destroys it.

source

',3))]),t("details",$r,[t("summary",null,[e[1632]||(e[1632]=t("a",{id:"Reactant.MLIR.API.mlirDialectRegistryIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectRegistryIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryIsNull")],-1)),e[1633]||(e[1633]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1634]||(e[1634]=l('
julia
mlirDialectRegistryIsNull(registry)

Checks if the dialect registry is null.

source

',3))]),t("details",Xr,[t("summary",null,[e[1635]||(e[1635]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGet")],-1)),e[1636]||(e[1636]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1637]||(e[1637]=l('
julia
mlirDictionaryAttrGet(ctx, numElements, elements)

Creates a dictionary attribute containing the given list of elements in the provided context.

source

',3))]),t("details",Yr,[t("summary",null,[e[1638]||(e[1638]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetElement-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetElement")],-1)),e[1639]||(e[1639]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1640]||(e[1640]=l('
julia
mlirDictionaryAttrGetElement(attr, pos)

Returns pos-th element of the given dictionary attribute.

source

',3))]),t("details",_r,[t("summary",null,[e[1641]||(e[1641]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetElementByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetElementByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetElementByName")],-1)),e[1642]||(e[1642]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1643]||(e[1643]=l('
julia
mlirDictionaryAttrGetElementByName(attr, name)

Returns the dictionary attribute element with the given name or NULL if the given name does not exist in the dictionary.

source

',3))]),t("details",eo,[t("summary",null,[e[1644]||(e[1644]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetNumElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetNumElements")],-1)),e[1645]||(e[1645]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1646]||(e[1646]=l('
julia
mlirDictionaryAttrGetNumElements(attr)

Returns the number of attributes contained in a dictionary attribute.

source

',3))]),t("details",to,[t("summary",null,[e[1647]||(e[1647]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetTypeID")],-1)),e[1648]||(e[1648]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1649]||(e[1649]=l('
julia
mlirDictionaryAttrGetTypeID()

Returns the typeID of a Dictionary attribute.

source

',3))]),t("details",so,[t("summary",null,[e[1650]||(e[1650]=t("a",{id:"Reactant.MLIR.API.mlirDisctinctAttrCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDisctinctAttrCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDisctinctAttrCreate")],-1)),e[1651]||(e[1651]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1652]||(e[1652]=l('
julia
mlirDisctinctAttrCreate(referencedAttr)

Creates a DisctinctAttr with the referenced attribute.

source

',3))]),t("details",ao,[t("summary",null,[e[1653]||(e[1653]=t("a",{id:"Reactant.MLIR.API.mlirElementsAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirElementsAttrGetNumElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrGetNumElements")],-1)),e[1654]||(e[1654]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1655]||(e[1655]=l('
julia
mlirElementsAttrGetNumElements(attr)

Gets the total number of elements in the given elements attribute. In order to iterate over the attribute, obtain its type, which must be a statically shaped type and use its sizes to build a multi-dimensional index.

source

',3))]),t("details",io,[t("summary",null,[e[1656]||(e[1656]=t("a",{id:"Reactant.MLIR.API.mlirElementsAttrGetValue-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirElementsAttrGetValue-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrGetValue")],-1)),e[1657]||(e[1657]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1658]||(e[1658]=l('
julia
mlirElementsAttrGetValue(attr, rank, idxs)

Returns the element at the given rank-dimensional index.

source

',3))]),t("details",lo,[t("summary",null,[e[1659]||(e[1659]=t("a",{id:"Reactant.MLIR.API.mlirElementsAttrIsValidIndex-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirElementsAttrIsValidIndex-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrIsValidIndex")],-1)),e[1660]||(e[1660]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1661]||(e[1661]=l('
julia
mlirElementsAttrIsValidIndex(attr, rank, idxs)

Checks whether the given rank-dimensional index is valid in the given elements attribute.

source

',3))]),t("details",no,[t("summary",null,[e[1662]||(e[1662]=t("a",{id:"Reactant.MLIR.API.mlirEmitError-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirEmitError-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirEmitError")],-1)),e[1663]||(e[1663]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1664]||(e[1664]=l('
julia
mlirEmitError(location, message)

Emits an error at the given location through the diagnostics engine. Used for testing purposes.

source

',3))]),t("details",po,[t("summary",null,[e[1665]||(e[1665]=t("a",{id:"Reactant.MLIR.API.mlirEnableGlobalDebug-Tuple{Any}",href:"#Reactant.MLIR.API.mlirEnableGlobalDebug-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirEnableGlobalDebug")],-1)),e[1666]||(e[1666]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1667]||(e[1667]=l('
julia
mlirEnableGlobalDebug(enable)

Sets the global debugging flag.

source

',3))]),t("details",ro,[t("summary",null,[e[1668]||(e[1668]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineCreate-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineCreate-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineCreate")],-1)),e[1669]||(e[1669]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1670]||(e[1670]=l('
julia
mlirExecutionEngineCreate(op, optLevel, numPaths, sharedLibPaths, enableObjectDump)

Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.

source

',3))]),t("details",oo,[t("summary",null,[e[1671]||(e[1671]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineDestroy")],-1)),e[1672]||(e[1672]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1673]||(e[1673]=l('
julia
mlirExecutionEngineDestroy(jit)

Destroy an ExecutionEngine instance.

source

',3))]),t("details",co,[t("summary",null,[e[1674]||(e[1674]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile")],-1)),e[1675]||(e[1675]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1676]||(e[1676]=l('
julia
mlirExecutionEngineDumpToObjectFile(jit, fileName)

Dump as an object in fileName.

source

',3))]),t("details",bo,[t("summary",null,[e[1677]||(e[1677]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineInvokePacked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineInvokePacked-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineInvokePacked")],-1)),e[1678]||(e[1678]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1679]||(e[1679]=l('
julia
mlirExecutionEngineInvokePacked(jit, name, arguments)

Invoke a native function in the execution engine by name with the arguments and result of the invoked function passed as an array of pointers. The function must have been tagged with the llvm.emit\\_c\\_interface attribute. Returns a failure if the execution fails for any reason (the function name can't be resolved for instance).

source

',3))]),t("details",ho,[t("summary",null,[e[1680]||(e[1680]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineIsNull")],-1)),e[1681]||(e[1681]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1682]||(e[1682]=l('
julia
mlirExecutionEngineIsNull(jit)

Checks whether an execution engine is null.

source

',3))]),t("details",uo,[t("summary",null,[e[1683]||(e[1683]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineLookup-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineLookup-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineLookup")],-1)),e[1684]||(e[1684]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1685]||(e[1685]=l('
julia
mlirExecutionEngineLookup(jit, name)

Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.

source

',3))]),t("details",go,[t("summary",null,[e[1686]||(e[1686]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineLookupPacked-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineLookupPacked-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineLookupPacked")],-1)),e[1687]||(e[1687]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1688]||(e[1688]=l('
julia
mlirExecutionEngineLookupPacked(jit, name)

Lookup the wrapper of the native function in the execution engine with the given name, returns nullptr if the function can't be looked-up.

source

',3))]),t("details",yo,[t("summary",null,[e[1689]||(e[1689]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol")],-1)),e[1690]||(e[1690]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1691]||(e[1691]=l('
julia
mlirExecutionEngineRegisterSymbol(jit, name, sym)

Register a symbol with the jit: this symbol will be accessible to the jitted code.

source

',3))]),t("details",mo,[t("summary",null,[e[1692]||(e[1692]=t("a",{id:"Reactant.MLIR.API.mlirExternalPassSignalFailure-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExternalPassSignalFailure-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExternalPassSignalFailure")],-1)),e[1693]||(e[1693]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1694]||(e[1694]=l('
julia
mlirExternalPassSignalFailure(pass)

This signals that the pass has failed. This is only valid to call during the run callback of MlirExternalPassCallbacks. See Pass::signalPassFailure().

source

',3))]),t("details",ko,[t("summary",null,[e[1695]||(e[1695]=t("a",{id:"Reactant.MLIR.API.mlirF16TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF16TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF16TypeGet")],-1)),e[1696]||(e[1696]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1697]||(e[1697]=l('
julia
mlirF16TypeGet(ctx)

Creates an f16 type in the given context. The type is owned by the context.

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',3))]),t("details",Ro,[t("summary",null,[e[1698]||(e[1698]=t("a",{id:"Reactant.MLIR.API.mlirF32TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF32TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF32TypeGet")],-1)),e[1699]||(e[1699]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1700]||(e[1700]=l('
julia
mlirF32TypeGet(ctx)

Creates an f32 type in the given context. The type is owned by the context.

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',3))]),t("details",Io,[t("summary",null,[e[1701]||(e[1701]=t("a",{id:"Reactant.MLIR.API.mlirF64TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF64TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF64TypeGet")],-1)),e[1702]||(e[1702]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1703]||(e[1703]=l('
julia
mlirF64TypeGet(ctx)

Creates a f64 type in the given context. The type is owned by the context.

source

',3))]),t("details",jo,[t("summary",null,[e[1704]||(e[1704]=t("a",{id:"Reactant.MLIR.API.mlirFlatSymbolRefAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirFlatSymbolRefAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFlatSymbolRefAttrGet")],-1)),e[1705]||(e[1705]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1706]||(e[1706]=l('
julia
mlirFlatSymbolRefAttrGet(ctx, symbol)

Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.

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',3))]),t("details",fo,[t("summary",null,[e[1707]||(e[1707]=t("a",{id:"Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue")],-1)),e[1708]||(e[1708]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1709]||(e[1709]=l('
julia
mlirFlatSymbolRefAttrGetValue(attr)

Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",Mo,[t("summary",null,[e[1710]||(e[1710]=t("a",{id:"Reactant.MLIR.API.mlirFloat16TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat16TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat16TypeGetTypeID")],-1)),e[1711]||(e[1711]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1712]||(e[1712]=l('
julia
mlirFloat16TypeGetTypeID()

Returns the typeID of an Float16 type.

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',3))]),t("details",Ao,[t("summary",null,[e[1713]||(e[1713]=t("a",{id:"Reactant.MLIR.API.mlirFloat32TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat32TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat32TypeGetTypeID")],-1)),e[1714]||(e[1714]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1715]||(e[1715]=l('
julia
mlirFloat32TypeGetTypeID()

Returns the typeID of an Float32 type.

source

',3))]),t("details",Lo,[t("summary",null,[e[1716]||(e[1716]=t("a",{id:"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet")],-1)),e[1717]||(e[1717]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1718]||(e[1718]=l('
julia
mlirFloat4E2M1FNTypeGet(ctx)

Creates an f4E2M1FN type in the given context. The type is owned by the context.

source

',3))]),t("details",Eo,[t("summary",null,[e[1719]||(e[1719]=t("a",{id:"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID")],-1)),e[1720]||(e[1720]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1721]||(e[1721]=l('
julia
mlirFloat4E2M1FNTypeGetTypeID()

Returns the typeID of an Float4E2M1FN type.

source

',3))]),t("details",vo,[t("summary",null,[e[1722]||(e[1722]=t("a",{id:"Reactant.MLIR.API.mlirFloat64TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat64TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat64TypeGetTypeID")],-1)),e[1723]||(e[1723]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1724]||(e[1724]=l('
julia
mlirFloat64TypeGetTypeID()

Returns the typeID of an Float64 type.

source

',3))]),t("details",To,[t("summary",null,[e[1725]||(e[1725]=t("a",{id:"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet")],-1)),e[1726]||(e[1726]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1727]||(e[1727]=l('
julia
mlirFloat6E2M3FNTypeGet(ctx)

Creates an f6E2M3FN type in the given context. The type is owned by the context.

source

',3))]),t("details",Co,[t("summary",null,[e[1728]||(e[1728]=t("a",{id:"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID")],-1)),e[1729]||(e[1729]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1730]||(e[1730]=l('
julia
mlirFloat6E2M3FNTypeGetTypeID()

Returns the typeID of an Float6E2M3FN type.

source

',3))]),t("details",xo,[t("summary",null,[e[1731]||(e[1731]=t("a",{id:"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet")],-1)),e[1732]||(e[1732]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1733]||(e[1733]=l('
julia
mlirFloat6E3M2FNTypeGet(ctx)

Creates an f6E3M2FN type in the given context. The type is owned by the context.

source

',3))]),t("details",Fo,[t("summary",null,[e[1734]||(e[1734]=t("a",{id:"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID")],-1)),e[1735]||(e[1735]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1736]||(e[1736]=l('
julia
mlirFloat6E3M2FNTypeGetTypeID()

Returns the typeID of an Float6E3M2FN type.

source

',3))]),t("details",Po,[t("summary",null,[e[1737]||(e[1737]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E3M4TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E3M4TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E3M4TypeGet")],-1)),e[1738]||(e[1738]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1739]||(e[1739]=l('
julia
mlirFloat8E3M4TypeGet(ctx)

Creates an f8E3M4 type in the given context. The type is owned by the context.

source

',3))]),t("details",Do,[t("summary",null,[e[1740]||(e[1740]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID")],-1)),e[1741]||(e[1741]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1742]||(e[1742]=l('
julia
mlirFloat8E3M4TypeGetTypeID()

Returns the typeID of an Float8E3M4 type.

source

',3))]),t("details",Oo,[t("summary",null,[e[1743]||(e[1743]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGet")],-1)),e[1744]||(e[1744]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1745]||(e[1745]=l('
julia
mlirFloat8E4M3B11FNUZTypeGet(ctx)

Creates an f8E4M3B11FNUZ type in the given context. The type is owned by the context.

source

',3))]),t("details",Bo,[t("summary",null,[e[1746]||(e[1746]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGetTypeID")],-1)),e[1747]||(e[1747]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1748]||(e[1748]=l('
julia
mlirFloat8E4M3B11FNUZTypeGetTypeID()

Returns the typeID of an Float8E4M3B11FNUZ type.

source

',3))]),t("details",Go,[t("summary",null,[e[1749]||(e[1749]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGet")],-1)),e[1750]||(e[1750]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1751]||(e[1751]=l('
julia
mlirFloat8E4M3FNTypeGet(ctx)

Creates an f8E4M3FN type in the given context. The type is owned by the context.

source

',3))]),t("details",zo,[t("summary",null,[e[1752]||(e[1752]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID")],-1)),e[1753]||(e[1753]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1754]||(e[1754]=l('
julia
mlirFloat8E4M3FNTypeGetTypeID()

Returns the typeID of an Float8E4M3FN type.

source

',3))]),t("details",wo,[t("summary",null,[e[1755]||(e[1755]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet")],-1)),e[1756]||(e[1756]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1757]||(e[1757]=l('
julia
mlirFloat8E4M3FNUZTypeGet(ctx)

Creates an f8E4M3FNUZ type in the given context. The type is owned by the context.

source

',3))]),t("details",So,[t("summary",null,[e[1758]||(e[1758]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID")],-1)),e[1759]||(e[1759]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1760]||(e[1760]=l('
julia
mlirFloat8E4M3FNUZTypeGetTypeID()

Returns the typeID of an Float8E4M3FNUZ type.

source

',3))]),t("details",No,[t("summary",null,[e[1761]||(e[1761]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3TypeGet")],-1)),e[1762]||(e[1762]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1763]||(e[1763]=l('
julia
mlirFloat8E4M3TypeGet(ctx)

Creates an f8E4M3 type in the given context. The type is owned by the context.

source

',3))]),t("details",Vo,[t("summary",null,[e[1764]||(e[1764]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID")],-1)),e[1765]||(e[1765]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1766]||(e[1766]=l('
julia
mlirFloat8E4M3TypeGetTypeID()

Returns the typeID of an Float8E4M3 type.

source

',3))]),t("details",qo,[t("summary",null,[e[1767]||(e[1767]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGet")],-1)),e[1768]||(e[1768]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1769]||(e[1769]=l('
julia
mlirFloat8E5M2FNUZTypeGet(ctx)

Creates an f8E5M2FNUZ type in the given context. The type is owned by the context.

source

',3))]),t("details",Uo,[t("summary",null,[e[1770]||(e[1770]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID")],-1)),e[1771]||(e[1771]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1772]||(e[1772]=l('
julia
mlirFloat8E5M2FNUZTypeGetTypeID()

Returns the typeID of an Float8E5M2FNUZ type.

source

',3))]),t("details",Qo,[t("summary",null,[e[1773]||(e[1773]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E5M2TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2TypeGet")],-1)),e[1774]||(e[1774]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1775]||(e[1775]=l('
julia
mlirFloat8E5M2TypeGet(ctx)

Creates an f8E5M2 type in the given context. The type is owned by the context.

source

',3))]),t("details",Wo,[t("summary",null,[e[1776]||(e[1776]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID")],-1)),e[1777]||(e[1777]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1778]||(e[1778]=l('
julia
mlirFloat8E5M2TypeGetTypeID()

Returns the typeID of an Float8E5M2 type.

source

',3))]),t("details",Ho,[t("summary",null,[e[1779]||(e[1779]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet")],-1)),e[1780]||(e[1780]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1781]||(e[1781]=l('
julia
mlirFloat8E8M0FNUTypeGet(ctx)

Creates an f8E8M0FNU type in the given context. The type is owned by the context.

source

',3))]),t("details",Zo,[t("summary",null,[e[1782]||(e[1782]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID")],-1)),e[1783]||(e[1783]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1784]||(e[1784]=l('
julia
mlirFloat8E8M0FNUTypeGetTypeID()

Returns the typeID of an Float8E8M0FNU type.

source

',3))]),t("details",Jo,[t("summary",null,[e[1785]||(e[1785]=t("a",{id:"Reactant.MLIR.API.mlirFloatAttrDoubleGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirFloatAttrDoubleGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrDoubleGet")],-1)),e[1786]||(e[1786]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1787]||(e[1787]=l('
julia
mlirFloatAttrDoubleGet(ctx, type, value)

Creates a floating point attribute in the given context with the given double value and double-precision FP semantics.

source

',3))]),t("details",Ko,[t("summary",null,[e[1788]||(e[1788]=t("a",{id:"Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked")],-1)),e[1789]||(e[1789]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1790]||(e[1790]=l('
julia
mlirFloatAttrDoubleGetChecked(loc, type, value)

Same as "mlirFloatAttrDoubleGet", but if the type is not valid for a construction of a FloatAttr, returns a null MlirAttribute.

source

',3))]),t("details",$o,[t("summary",null,[e[1791]||(e[1791]=t("a",{id:"Reactant.MLIR.API.mlirFloatAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloatAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrGetTypeID")],-1)),e[1792]||(e[1792]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1793]||(e[1793]=l('
julia
mlirFloatAttrGetTypeID()

Returns the typeID of a Float attribute.

source

',3))]),t("details",Xo,[t("summary",null,[e[1794]||(e[1794]=t("a",{id:"Reactant.MLIR.API.mlirFloatAttrGetValueDouble-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloatAttrGetValueDouble-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrGetValueDouble")],-1)),e[1795]||(e[1795]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1796]||(e[1796]=l('
julia
mlirFloatAttrGetValueDouble(attr)

Returns the value stored in the given floating point attribute, interpreting the value as double.

source

',3))]),t("details",Yo,[t("summary",null,[e[1797]||(e[1797]=t("a",{id:"Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID")],-1)),e[1798]||(e[1798]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1799]||(e[1799]=l('
julia
mlirFloatTF32TypeGetTypeID()

Returns the typeID of a TF32 type.

source

',3))]),t("details",_o,[t("summary",null,[e[1800]||(e[1800]=t("a",{id:"Reactant.MLIR.API.mlirFloatTypeGetWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloatTypeGetWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatTypeGetWidth")],-1)),e[1801]||(e[1801]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1802]||(e[1802]=l('
julia
mlirFloatTypeGetWidth(type)

Returns the bitwidth of a floating-point type.

source

',3))]),t("details",ed,[t("summary",null,[e[1803]||(e[1803]=t("a",{id:"Reactant.MLIR.API.mlirFreezeRewritePattern-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFreezeRewritePattern-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFreezeRewritePattern")],-1)),e[1804]||(e[1804]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1805]||(e[1805]=l('
julia
mlirFreezeRewritePattern(op)

FrozenRewritePatternSet API

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',3))]),t("details",td,[t("summary",null,[e[1806]||(e[1806]=t("a",{id:"Reactant.MLIR.API.mlirFuncSetArgAttr-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirFuncSetArgAttr-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFuncSetArgAttr")],-1)),e[1807]||(e[1807]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1808]||(e[1808]=l('
julia
mlirFuncSetArgAttr(op, pos, name, attr)

Sets the argument attribute 'name' of an argument at index 'pos'. Asserts that the operation is a FuncOp.

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',3))]),t("details",sd,[t("summary",null,[e[1809]||(e[1809]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGet")],-1)),e[1810]||(e[1810]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1811]||(e[1811]=l('
julia
mlirFunctionTypeGet(ctx, numInputs, inputs, numResults, results)

Creates a function type, mapping a list of input types to result types.

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',3))]),t("details",ad,[t("summary",null,[e[1812]||(e[1812]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetInput-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetInput-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetInput")],-1)),e[1813]||(e[1813]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1814]||(e[1814]=l('
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mlirFunctionTypeGetInput(type, pos)

Returns the pos-th input type.

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',3))]),t("details",id,[t("summary",null,[e[1815]||(e[1815]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetNumInputs-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetNumInputs")],-1)),e[1816]||(e[1816]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1817]||(e[1817]=l('
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mlirFunctionTypeGetNumInputs(type)

Returns the number of input types.

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',3))]),t("details",ld,[t("summary",null,[e[1818]||(e[1818]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetNumResults-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetNumResults")],-1)),e[1819]||(e[1819]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1820]||(e[1820]=l('
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mlirFunctionTypeGetNumResults(type)

Returns the number of result types.

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',3))]),t("details",nd,[t("summary",null,[e[1821]||(e[1821]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetResult-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetResult")],-1)),e[1822]||(e[1822]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1823]||(e[1823]=l('
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mlirFunctionTypeGetResult(type, pos)

Returns the pos-th result type.

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',3))]),t("details",pd,[t("summary",null,[e[1824]||(e[1824]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetTypeID")],-1)),e[1825]||(e[1825]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1826]||(e[1826]=l('
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mlirFunctionTypeGetTypeID()

Returns the typeID of an Function type.

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',3))]),t("details",rd,[t("summary",null,[e[1827]||(e[1827]=t("a",{id:"Reactant.MLIR.API.mlirIRRewriterCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIRRewriterCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIRRewriterCreate")],-1)),e[1828]||(e[1828]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1829]||(e[1829]=l('
julia
mlirIRRewriterCreate(context)

Create an IRRewriter and transfer ownership to the caller.

source

',3))]),t("details",od,[t("summary",null,[e[1830]||(e[1830]=t("a",{id:"Reactant.MLIR.API.mlirIRRewriterCreateFromOp-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIRRewriterCreateFromOp-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIRRewriterCreateFromOp")],-1)),e[1831]||(e[1831]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1832]||(e[1832]=l('
julia
mlirIRRewriterCreateFromOp(op)

Create an IRRewriter and transfer ownership to the caller. Additionally set the insertion point before the operation.

source

',3))]),t("details",dd,[t("summary",null,[e[1833]||(e[1833]=t("a",{id:"Reactant.MLIR.API.mlirIRRewriterDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIRRewriterDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIRRewriterDestroy")],-1)),e[1834]||(e[1834]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1835]||(e[1835]=l('
julia
mlirIRRewriterDestroy(rewriter)

Takes an IRRewriter owned by the caller and destroys it. It is the responsibility of the user to only pass an IRRewriter class.

source

',3))]),t("details",cd,[t("summary",null,[e[1836]||(e[1836]=t("a",{id:"Reactant.MLIR.API.mlirIdentifierEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIdentifierEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierEqual")],-1)),e[1837]||(e[1837]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1838]||(e[1838]=l('
julia
mlirIdentifierEqual(ident, other)

Checks whether two identifiers are the same.

source

',3))]),t("details",bd,[t("summary",null,[e[1839]||(e[1839]=t("a",{id:"Reactant.MLIR.API.mlirIdentifierGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIdentifierGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierGet")],-1)),e[1840]||(e[1840]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1841]||(e[1841]=l('
julia
mlirIdentifierGet(context, str)

Gets an identifier with the given string value.

source

',3))]),t("details",hd,[t("summary",null,[e[1842]||(e[1842]=t("a",{id:"Reactant.MLIR.API.mlirIdentifierGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIdentifierGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierGetContext")],-1)),e[1843]||(e[1843]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1844]||(e[1844]=l('
julia
mlirIdentifierGetContext(arg1)

Returns the context associated with this identifier

source

',3))]),t("details",ud,[t("summary",null,[e[1845]||(e[1845]=t("a",{id:"Reactant.MLIR.API.mlirIdentifierStr-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIdentifierStr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierStr")],-1)),e[1846]||(e[1846]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1847]||(e[1847]=l('
julia
mlirIdentifierStr(ident)

Gets the string value of the identifier.

source

',3))]),t("details",gd,[t("summary",null,[e[1848]||(e[1848]=t("a",{id:"Reactant.MLIR.API.mlirIndexTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIndexTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIndexTypeGet")],-1)),e[1849]||(e[1849]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1850]||(e[1850]=l('
julia
mlirIndexTypeGet(ctx)

Creates an index type in the given context. The type is owned by the context.

source

',3))]),t("details",yd,[t("summary",null,[e[1851]||(e[1851]=t("a",{id:"Reactant.MLIR.API.mlirIndexTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIndexTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIndexTypeGetTypeID")],-1)),e[1852]||(e[1852]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1853]||(e[1853]=l('
julia
mlirIndexTypeGetTypeID()

Returns the typeID of an Index type.

source

',3))]),t("details",md,[t("summary",null,[e[1854]||(e[1854]=t("a",{id:"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes-NTuple{11, Any}",href:"#Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes-NTuple{11, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes")],-1)),e[1855]||(e[1855]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1856]||(e[1856]=l('
julia
mlirInferShapedTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)

Infers the return shaped type components of the operation. Calls callback with the types of inferred arguments on success. Returns failure otherwise.

source

',3))]),t("details",kd,[t("summary",null,[e[1857]||(e[1857]=t("a",{id:"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID")],-1)),e[1858]||(e[1858]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1859]||(e[1859]=l('
julia
mlirInferShapedTypeOpInterfaceTypeID()

Returns the interface TypeID of the InferShapedTypeOpInterface.

source

',3))]),t("details",Rd,[t("summary",null,[e[1860]||(e[1860]=t("a",{id:"Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes-NTuple{11, Any}",href:"#Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes-NTuple{11, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes")],-1)),e[1861]||(e[1861]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1862]||(e[1862]=l('
julia
mlirInferTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)

Infers the return types of the operation identified by its canonical given the arguments that will be supplied to its generic builder. Calls callback with the types of inferred arguments, potentially several times, on success. Returns failure otherwise.

source

',3))]),t("details",Id,[t("summary",null,[e[1863]||(e[1863]=t("a",{id:"Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID")],-1)),e[1864]||(e[1864]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1865]||(e[1865]=l('
julia
mlirInferTypeOpInterfaceTypeID()

Returns the interface TypeID of the InferTypeOpInterface.

source

',3))]),t("details",jd,[t("summary",null,[e[1866]||(e[1866]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGet")],-1)),e[1867]||(e[1867]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1868]||(e[1868]=l('
julia
mlirIntegerAttrGet(type, value)

Creates an integer attribute of the given type with the given integer value.

source

',3))]),t("details",fd,[t("summary",null,[e[1869]||(e[1869]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetTypeID")],-1)),e[1870]||(e[1870]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1871]||(e[1871]=l('
julia
mlirIntegerAttrGetTypeID()

Returns the typeID of an Integer attribute.

source

',3))]),t("details",Md,[t("summary",null,[e[1872]||(e[1872]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueInt-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueInt")],-1)),e[1873]||(e[1873]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1874]||(e[1874]=l('
julia
mlirIntegerAttrGetValueInt(attr)

Returns the value stored in the given integer attribute, assuming the value is of signless type and fits into a signed 64-bit integer.

source

',3))]),t("details",Ad,[t("summary",null,[e[1875]||(e[1875]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueSInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueSInt-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueSInt")],-1)),e[1876]||(e[1876]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1877]||(e[1877]=l('
julia
mlirIntegerAttrGetValueSInt(attr)

Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.

source

',3))]),t("details",Ld,[t("summary",null,[e[1878]||(e[1878]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueUInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueUInt-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueUInt")],-1)),e[1879]||(e[1879]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1880]||(e[1880]=l('
julia
mlirIntegerAttrGetValueUInt(attr)

Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.

source

',3))]),t("details",Ed,[t("summary",null,[e[1881]||(e[1881]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetAttrGet")],-1)),e[1882]||(e[1882]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1883]||(e[1883]=l('
julia
mlirIntegerSetAttrGet(set)

Creates an integer set attribute wrapping the given set. The attribute belongs to the same context as the integer set.

source

',3))]),t("details",vd,[t("summary",null,[e[1884]||(e[1884]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID")],-1)),e[1885]||(e[1885]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1886]||(e[1886]=l('
julia
mlirIntegerSetAttrGetTypeID()

Returns the typeID of an IntegerSet attribute.

source

',3))]),t("details",Td,[t("summary",null,[e[1887]||(e[1887]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetAttrGetValue")],-1)),e[1888]||(e[1888]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1889]||(e[1889]=l('
julia
mlirIntegerSetAttrGetValue(attr)

Returns the integer set wrapped in the given integer set attribute.

source

',3))]),t("details",Cd,[t("summary",null,[e[1890]||(e[1890]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetDump")],-1)),e[1891]||(e[1891]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1892]||(e[1892]=l('
julia
mlirIntegerSetDump(set)

Prints an integer set to the standard error stream.

source

',3))]),t("details",xd,[t("summary",null,[e[1893]||(e[1893]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetEmptyGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetEmptyGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetEmptyGet")],-1)),e[1894]||(e[1894]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1895]||(e[1895]=l('
julia
mlirIntegerSetEmptyGet(context, numDims, numSymbols)

Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.

source

',3))]),t("details",Fd,[t("summary",null,[e[1896]||(e[1896]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetEqual")],-1)),e[1897]||(e[1897]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1898]||(e[1898]=l('
julia
mlirIntegerSetEqual(s1, s2)

Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.

source

',3))]),t("details",Pd,[t("summary",null,[e[1899]||(e[1899]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGet-NTuple{6, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGet-NTuple{6, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGet")],-1)),e[1900]||(e[1900]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1901]||(e[1901]=l('
julia
mlirIntegerSetGet(context, numDims, numSymbols, numConstraints, constraints, eqFlags)

Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqFlags is 1) or inequalities (eqFlags is 0). Both constraints and eqFlags are expected to point to at least numConstraint consecutive values.

source

',3))]),t("details",Dd,[t("summary",null,[e[1902]||(e[1902]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetConstraint-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetConstraint-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetConstraint")],-1)),e[1903]||(e[1903]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1904]||(e[1904]=l('
julia
mlirIntegerSetGetConstraint(set, pos)

Returns pos-th constraint of the set.

source

',3))]),t("details",Od,[t("summary",null,[e[1905]||(e[1905]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetContext")],-1)),e[1906]||(e[1906]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1907]||(e[1907]=l('
julia
mlirIntegerSetGetContext(set)

Gets the context in which the given integer set lives.

source

',3))]),t("details",Bd,[t("summary",null,[e[1908]||(e[1908]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumConstraints-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumConstraints-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumConstraints")],-1)),e[1909]||(e[1909]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1910]||(e[1910]=l('
julia
mlirIntegerSetGetNumConstraints(set)

Returns the number of constraints (equalities + inequalities) in the given set.

source

',3))]),t("details",Gd,[t("summary",null,[e[1911]||(e[1911]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumDims-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumDims")],-1)),e[1912]||(e[1912]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1913]||(e[1913]=l('
julia
mlirIntegerSetGetNumDims(set)

Returns the number of dimensions in the given set.

source

',3))]),t("details",zd,[t("summary",null,[e[1914]||(e[1914]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumEqualities-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumEqualities-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumEqualities")],-1)),e[1915]||(e[1915]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1916]||(e[1916]=l('
julia
mlirIntegerSetGetNumEqualities(set)

Returns the number of equalities in the given set.

source

',3))]),t("details",wd,[t("summary",null,[e[1917]||(e[1917]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumInequalities-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumInequalities-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumInequalities")],-1)),e[1918]||(e[1918]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1919]||(e[1919]=l('
julia
mlirIntegerSetGetNumInequalities(set)

Returns the number of inequalities in the given set.

source

',3))]),t("details",Sd,[t("summary",null,[e[1920]||(e[1920]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumInputs-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumInputs")],-1)),e[1921]||(e[1921]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1922]||(e[1922]=l('
julia
mlirIntegerSetGetNumInputs(set)

Returns the number of inputs (dimensions + symbols) in the given set.

source

',3))]),t("details",Nd,[t("summary",null,[e[1923]||(e[1923]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumSymbols-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumSymbols-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumSymbols")],-1)),e[1924]||(e[1924]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1925]||(e[1925]=l('
julia
mlirIntegerSetGetNumSymbols(set)

Returns the number of symbols in the given set.

source

',3))]),t("details",Vd,[t("summary",null,[e[1926]||(e[1926]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty")],-1)),e[1927]||(e[1927]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1928]||(e[1928]=l('
julia
mlirIntegerSetIsCanonicalEmpty(set)

Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.

source

',3))]),t("details",qd,[t("summary",null,[e[1929]||(e[1929]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsConstraintEq-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsConstraintEq-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsConstraintEq")],-1)),e[1930]||(e[1930]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1931]||(e[1931]=l('
julia
mlirIntegerSetIsConstraintEq(set, pos)

Returns true of the pos-th constraint of the set is an equality constraint, false otherwise.

source

',3))]),t("details",Ud,[t("summary",null,[e[1932]||(e[1932]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsNull")],-1)),e[1933]||(e[1933]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1934]||(e[1934]=l('
julia
mlirIntegerSetIsNull(set)

Checks whether an integer set is a null object.

source

',3))]),t("details",Qd,[t("summary",null,[e[1935]||(e[1935]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetPrint")],-1)),e[1936]||(e[1936]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1937]||(e[1937]=l('
julia
mlirIntegerSetPrint(set, callback, userData)

Prints an integer set by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Wd,[t("summary",null,[e[1938]||(e[1938]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetReplaceGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetReplaceGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetReplaceGet")],-1)),e[1939]||(e[1939]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1940]||(e[1940]=l('
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)

Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.

source

',3))]),t("details",Hd,[t("summary",null,[e[1941]||(e[1941]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGet")],-1)),e[1942]||(e[1942]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1943]||(e[1943]=l('
julia
mlirIntegerTypeGet(ctx, bitwidth)

Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),t("details",Zd,[t("summary",null,[e[1944]||(e[1944]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGetTypeID")],-1)),e[1945]||(e[1945]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1946]||(e[1946]=l('
julia
mlirIntegerTypeGetTypeID()

Returns the typeID of an Integer type.

source

',3))]),t("details",Jd,[t("summary",null,[e[1947]||(e[1947]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGetWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeGetWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGetWidth")],-1)),e[1948]||(e[1948]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1949]||(e[1949]=l('
julia
mlirIntegerTypeGetWidth(type)

Returns the bitwidth of an integer type.

source

',3))]),t("details",Kd,[t("summary",null,[e[1950]||(e[1950]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsSigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsSigned-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsSigned")],-1)),e[1951]||(e[1951]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1952]||(e[1952]=l('
julia
mlirIntegerTypeIsSigned(type)

Checks whether the given integer type is signed.

source

',3))]),t("details",$d,[t("summary",null,[e[1953]||(e[1953]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsSignless-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsSignless-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsSignless")],-1)),e[1954]||(e[1954]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1955]||(e[1955]=l('
julia
mlirIntegerTypeIsSignless(type)

Checks whether the given integer type is signless.

source

',3))]),t("details",Xd,[t("summary",null,[e[1956]||(e[1956]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsUnsigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsUnsigned-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsUnsigned")],-1)),e[1957]||(e[1957]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1958]||(e[1958]=l('
julia
mlirIntegerTypeIsUnsigned(type)

Checks whether the given integer type is unsigned.

source

',3))]),t("details",Yd,[t("summary",null,[e[1959]||(e[1959]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeSignedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeSignedGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeSignedGet")],-1)),e[1960]||(e[1960]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1961]||(e[1961]=l('
julia
mlirIntegerTypeSignedGet(ctx, bitwidth)

Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),t("details",_d,[t("summary",null,[e[1962]||(e[1962]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeUnsignedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeUnsignedGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeUnsignedGet")],-1)),e[1963]||(e[1963]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1964]||(e[1964]=l('
julia
mlirIntegerTypeUnsignedGet(ctx, bitwidth)

Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),t("details",ec,[t("summary",null,[e[1965]||(e[1965]=t("a",{id:"Reactant.MLIR.API.mlirIsCurrentDebugType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIsCurrentDebugType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIsCurrentDebugType")],-1)),e[1966]||(e[1966]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1967]||(e[1967]=l('
julia
mlirIsCurrentDebugType(type)

Checks if type is set as the current debug type.

source

',3))]),t("details",tc,[t("summary",null,[e[1968]||(e[1968]=t("a",{id:"Reactant.MLIR.API.mlirIsGlobalDebugEnabled-Tuple{}",href:"#Reactant.MLIR.API.mlirIsGlobalDebugEnabled-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIsGlobalDebugEnabled")],-1)),e[1969]||(e[1969]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1970]||(e[1970]=l('
julia
mlirIsGlobalDebugEnabled()

Retuns true if the global debugging flag is set, false otherwise.

source

',3))]),t("details",sc,[t("summary",null,[e[1971]||(e[1971]=t("a",{id:"Reactant.MLIR.API.mlirLLVMArrayTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMArrayTypeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMArrayTypeGet")],-1)),e[1972]||(e[1972]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1973]||(e[1973]=l('
julia
mlirLLVMArrayTypeGet(elementType, numElements)

Creates an llvm.array type.

source

',3))]),t("details",ac,[t("summary",null,[e[1974]||(e[1974]=t("a",{id:"Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType")],-1)),e[1975]||(e[1975]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1976]||(e[1976]=l('
julia
mlirLLVMArrayTypeGetElementType(type)

Returns the element type of the llvm.array type.

source

',3))]),t("details",ic,[t("summary",null,[e[1977]||(e[1977]=t("a",{id:"Reactant.MLIR.API.mlirLLVMCConvAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMCConvAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMCConvAttrGet")],-1)),e[1978]||(e[1978]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1979]||(e[1979]=l('
julia
mlirLLVMCConvAttrGet(ctx, cconv)

Creates a LLVM CConv attribute.

source

',3))]),t("details",lc,[t("summary",null,[e[1980]||(e[1980]=t("a",{id:"Reactant.MLIR.API.mlirLLVMComdatAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMComdatAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMComdatAttrGet")],-1)),e[1981]||(e[1981]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1982]||(e[1982]=l('
julia
mlirLLVMComdatAttrGet(ctx, comdat)

Creates a LLVM Comdat attribute.

source

',3))]),t("details",nc,[t("summary",null,[e[1983]||(e[1983]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet")],-1)),e[1984]||(e[1984]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1985]||(e[1985]=l('
julia
mlirLLVMDIAnnotationAttrGet(ctx, name, value)

Creates a LLVM DIAnnotation attribute.

source

',3))]),t("details",pc,[t("summary",null,[e[1986]||(e[1986]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet")],-1)),e[1987]||(e[1987]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1988]||(e[1988]=l('
julia
mlirLLVMDIBasicTypeAttrGet(ctx, tag, name, sizeInBits, encoding)

Creates a LLVM DIBasicType attribute.

source

',3))]),t("details",rc,[t("summary",null,[e[1989]||(e[1989]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet-NTuple{8, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet")],-1)),e[1990]||(e[1990]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1991]||(e[1991]=l('
julia
mlirLLVMDICompileUnitAttrGet(ctx, id, sourceLanguage, file, producer, isOptimized, emissionKind, nameTableKind)

Creates a LLVM DICompileUnit attribute.

source

',3))]),t("details",oc,[t("summary",null,[e[1992]||(e[1992]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet-NTuple{18, Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet-NTuple{18, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet")],-1)),e[1993]||(e[1993]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1994]||(e[1994]=l('
julia
mlirLLVMDICompositeTypeAttrGet(ctx, recId, isRecSelf, tag, name, file, line, scope, baseType, flags, sizeInBits, alignInBits, nElements, elements, dataLocation, rank, allocated, associated)

Creates a LLVM DICompositeType attribute.

source

',3))]),t("details",dc,[t("summary",null,[e[1995]||(e[1995]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf")],-1)),e[1996]||(e[1996]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1997]||(e[1997]=l('
julia
mlirLLVMDICompositeTypeAttrGetRecSelf(recId)

Creates a self-referencing LLVM DICompositeType attribute.

source

',3))]),t("details",cc,[t("summary",null,[e[1998]||(e[1998]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet")],-1)),e[1999]||(e[1999]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2e3]||(e[2e3]=l('
julia
mlirLLVMDIDerivedTypeAttrGet(ctx, tag, name, baseType, sizeInBits, alignInBits, offsetInBits, dwarfAddressSpace, extraData)

Creates a LLVM DIDerivedType attribute. Note that dwarfAddressSpace is an optional field, where MLIR_CAPI_DWARF_ADDRESS_SPACE_NULL indicates null and non-negative values indicate a value present.

source

',3))]),t("details",bc,[t("summary",null,[e[2001]||(e[2001]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType")],-1)),e[2002]||(e[2002]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2003]||(e[2003]=l('
julia
mlirLLVMDIDerivedTypeAttrGetBaseType(diDerivedType)

Gets the base type from a LLVM DIDerivedType attribute.

source

',3))]),t("details",hc,[t("summary",null,[e[2004]||(e[2004]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet")],-1)),e[2005]||(e[2005]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2006]||(e[2006]=l('
julia
mlirLLVMDIExpressionAttrGet(ctx, nOperations, operations)

Creates a LLVM DIExpression attribute.

source

',3))]),t("details",uc,[t("summary",null,[e[2007]||(e[2007]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet")],-1)),e[2008]||(e[2008]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2009]||(e[2009]=l('
julia
mlirLLVMDIExpressionElemAttrGet(ctx, opcode, nArguments, arguments)

Creates a LLVM DIExpressionElem attribute.

source

',3))]),t("details",gc,[t("summary",null,[e[2010]||(e[2010]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIFileAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIFileAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIFileAttrGet")],-1)),e[2011]||(e[2011]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2012]||(e[2012]=l('
julia
mlirLLVMDIFileAttrGet(ctx, name, directory)

Creates a LLVM DIFileAttr attribute.

source

',3))]),t("details",yc,[t("summary",null,[e[2013]||(e[2013]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet")],-1)),e[2014]||(e[2014]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2015]||(e[2015]=l('
julia
mlirLLVMDIFlagsAttrGet(ctx, value)

Creates a LLVM DIFlags attribute.

source

',3))]),t("details",mc,[t("summary",null,[e[2016]||(e[2016]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet")],-1)),e[2017]||(e[2017]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2018]||(e[2018]=l('
julia
mlirLLVMDIImportedEntityAttrGet(ctx, tag, scope, entity, file, line, name, nElements, elements)

Creates a LLVM DIImportedEntityAttr attribute.

source

',3))]),t("details",kc,[t("summary",null,[e[2019]||(e[2019]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet")],-1)),e[2020]||(e[2020]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2021]||(e[2021]=l('
julia
mlirLLVMDILexicalBlockAttrGet(ctx, scope, file, line, column)

Creates a LLVM DILexicalBlock attribute.

source

',3))]),t("details",Rc,[t("summary",null,[e[2022]||(e[2022]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet")],-1)),e[2023]||(e[2023]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2024]||(e[2024]=l('
julia
mlirLLVMDILexicalBlockFileAttrGet(ctx, scope, file, discriminator)

Creates a LLVM DILexicalBlockFile attribute.

source

',3))]),t("details",Ic,[t("summary",null,[e[2025]||(e[2025]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet")],-1)),e[2026]||(e[2026]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2027]||(e[2027]=l('
julia
mlirLLVMDILocalVariableAttrGet(ctx, scope, name, diFile, line, arg, alignInBits, diType, flags)

Creates a LLVM DILocalVariableAttr attribute.

source

',3))]),t("details",jc,[t("summary",null,[e[2028]||(e[2028]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIModuleAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIModuleAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIModuleAttrGet")],-1)),e[2029]||(e[2029]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2030]||(e[2030]=l('
julia
mlirLLVMDIModuleAttrGet(ctx, file, scope, name, configMacros, includePath, apinotes, line, isDecl)

Creates a LLVM DIModuleAttr attribute.

source

',3))]),t("details",fc,[t("summary",null,[e[2031]||(e[2031]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope")],-1)),e[2032]||(e[2032]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2033]||(e[2033]=l('
julia
mlirLLVMDIModuleAttrGetScope(diModule)

Gets the scope of this DIModuleAttr.

source

',3))]),t("details",Mc,[t("summary",null,[e[2034]||(e[2034]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet")],-1)),e[2035]||(e[2035]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2036]||(e[2036]=l('
julia
mlirLLVMDINullTypeAttrGet(ctx)

Creates a LLVM DINullType attribute.

source

',3))]),t("details",Ac,[t("summary",null,[e[2037]||(e[2037]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet-NTuple{17, Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet-NTuple{17, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet")],-1)),e[2038]||(e[2038]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2039]||(e[2039]=l('
julia
mlirLLVMDISubprogramAttrGet(ctx, recId, isRecSelf, id, compileUnit, scope, name, linkageName, file, line, scopeLine, subprogramFlags, type, nRetainedNodes, retainedNodes, nAnnotations, annotations)

Creates a LLVM DISubprogramAttr attribute.

source

',3))]),t("details",Lc,[t("summary",null,[e[2040]||(e[2040]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit")],-1)),e[2041]||(e[2041]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2042]||(e[2042]=l('
julia
mlirLLVMDISubprogramAttrGetCompileUnit(diSubprogram)

Gets the compile unit from this DISubprogram.

source

',3))]),t("details",Ec,[t("summary",null,[e[2043]||(e[2043]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile")],-1)),e[2044]||(e[2044]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2045]||(e[2045]=l('
julia
mlirLLVMDISubprogramAttrGetFile(diSubprogram)

Gets the file from this DISubprogramAttr.

source

',3))]),t("details",vc,[t("summary",null,[e[2046]||(e[2046]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine")],-1)),e[2047]||(e[2047]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2048]||(e[2048]=l('
julia
mlirLLVMDISubprogramAttrGetLine(diSubprogram)

Gets the line from this DISubprogramAttr.

source

',3))]),t("details",Tc,[t("summary",null,[e[2049]||(e[2049]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf")],-1)),e[2050]||(e[2050]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2051]||(e[2051]=l('
julia
mlirLLVMDISubprogramAttrGetRecSelf(recId)

Creates a self-referencing LLVM DISubprogramAttr attribute.

source

',3))]),t("details",Cc,[t("summary",null,[e[2052]||(e[2052]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope")],-1)),e[2053]||(e[2053]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2054]||(e[2054]=l('
julia
mlirLLVMDISubprogramAttrGetScope(diSubprogram)

Gets the scope from this DISubprogramAttr.

source

',3))]),t("details",xc,[t("summary",null,[e[2055]||(e[2055]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine")],-1)),e[2056]||(e[2056]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2057]||(e[2057]=l('
julia
mlirLLVMDISubprogramAttrGetScopeLine(diSubprogram)

Gets the scope line from this DISubprogram.

source

',3))]),t("details",Fc,[t("summary",null,[e[2058]||(e[2058]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType")],-1)),e[2059]||(e[2059]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2060]||(e[2060]=l('
julia
mlirLLVMDISubprogramAttrGetType(diSubprogram)

Gets the type from this DISubprogramAttr.

source

',3))]),t("details",Pc,[t("summary",null,[e[2061]||(e[2061]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet")],-1)),e[2062]||(e[2062]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2063]||(e[2063]=l('
julia
mlirLLVMDISubroutineTypeAttrGet(ctx, callingConvention, nTypes, types)

Creates a LLVM DISubroutineTypeAttr attribute.

source

',3))]),t("details",Dc,[t("summary",null,[e[2064]||(e[2064]=t("a",{id:"Reactant.MLIR.API.mlirLLVMFunctionTypeGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMFunctionTypeGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMFunctionTypeGet")],-1)),e[2065]||(e[2065]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2066]||(e[2066]=l('
julia
mlirLLVMFunctionTypeGet(resultType, nArgumentTypes, argumentTypes, isVarArg)

Creates an llvm.func type.

source

',3))]),t("details",Oc,[t("summary",null,[e[2067]||(e[2067]=t("a",{id:"Reactant.MLIR.API.mlirLLVMLinkageAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMLinkageAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMLinkageAttrGet")],-1)),e[2068]||(e[2068]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2069]||(e[2069]=l('
julia
mlirLLVMLinkageAttrGet(ctx, linkage)

Creates a LLVM Linkage attribute.

source

',3))]),t("details",Bc,[t("summary",null,[e[2070]||(e[2070]=t("a",{id:"Reactant.MLIR.API.mlirLLVMPointerTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMPointerTypeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMPointerTypeGet")],-1)),e[2071]||(e[2071]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2072]||(e[2072]=l('
julia
mlirLLVMPointerTypeGet(ctx, addressSpace)

Creates an llvm.ptr type.

source

',3))]),t("details",Gc,[t("summary",null,[e[2073]||(e[2073]=t("a",{id:"Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace")],-1)),e[2074]||(e[2074]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2075]||(e[2075]=l('
julia
mlirLLVMPointerTypeGetAddressSpace(pointerType)

Returns address space of llvm.ptr

source

',3))]),t("details",zc,[t("summary",null,[e[2076]||(e[2076]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetElementType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetElementType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetElementType")],-1)),e[2077]||(e[2077]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2078]||(e[2078]=l('
julia
mlirLLVMStructTypeGetElementType(type, position)

Returns the positions-th field of the struct. Asserts if the struct is opaque, not yet initialized or if the position is out of range.

source

',3))]),t("details",wc,[t("summary",null,[e[2079]||(e[2079]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier")],-1)),e[2080]||(e[2080]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2081]||(e[2081]=l('
julia
mlirLLVMStructTypeGetIdentifier(type)

Returns the identifier of the identified struct. Asserts that the struct is identified, i.e., not literal.

source

',3))]),t("details",Sc,[t("summary",null,[e[2082]||(e[2082]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes")],-1)),e[2083]||(e[2083]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2084]||(e[2084]=l('
julia
mlirLLVMStructTypeGetNumElementTypes(type)

Returns the number of fields in the struct. Asserts if the struct is opaque or not yet initialized.

source

',3))]),t("details",Nc,[t("summary",null,[e[2085]||(e[2085]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet")],-1)),e[2086]||(e[2086]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2087]||(e[2087]=l('
julia
mlirLLVMStructTypeIdentifiedGet(ctx, name)

Creates an LLVM identified struct type with no body. If a struct type with this name already exists in the context, returns that type. Use mlirLLVMStructTypeIdentifiedNewGet to create a fresh struct type, potentially renaming it. The body should be set separatelty by calling mlirLLVMStructTypeSetBody, if it isn't set already.

source

',3))]),t("details",Vc,[t("summary",null,[e[2088]||(e[2088]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet")],-1)),e[2089]||(e[2089]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2090]||(e[2090]=l('
julia
mlirLLVMStructTypeIdentifiedNewGet(ctx, name, nFieldTypes, fieldTypes, isPacked)

Creates an LLVM identified struct type with no body and a name starting with the given prefix. If a struct with the exact name as the given prefix already exists, appends an unspecified suffix to the name so that the name is unique in context.

source

',3))]),t("details",qc,[t("summary",null,[e[2091]||(e[2091]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral")],-1)),e[2092]||(e[2092]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2093]||(e[2093]=l('
julia
mlirLLVMStructTypeIsLiteral(type)

Returns true if the type is a literal (unnamed) LLVM struct type.

source

',3))]),t("details",Uc,[t("summary",null,[e[2094]||(e[2094]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque")],-1)),e[2095]||(e[2095]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2096]||(e[2096]=l('
julia
mlirLLVMStructTypeIsOpaque(type)

Returns true is the struct is explicitly opaque (will not have a body) or uninitialized (will eventually have a body).

source

',3))]),t("details",Qc,[t("summary",null,[e[2097]||(e[2097]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsPacked-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsPacked-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsPacked")],-1)),e[2098]||(e[2098]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2099]||(e[2099]=l('
julia
mlirLLVMStructTypeIsPacked(type)

Returns true if the struct is packed.

source

',3))]),t("details",Wc,[t("summary",null,[e[2100]||(e[2100]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet")],-1)),e[2101]||(e[2101]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2102]||(e[2102]=l('
julia
mlirLLVMStructTypeLiteralGet(ctx, nFieldTypes, fieldTypes, isPacked)

Creates an LLVM literal (unnamed) struct type. This may assert if the fields have types not compatible with the LLVM dialect. For a graceful failure, use the checked version.

source

',3))]),t("details",Hc,[t("summary",null,[e[2103]||(e[2103]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked")],-1)),e[2104]||(e[2104]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2105]||(e[2105]=l('
julia
mlirLLVMStructTypeLiteralGetChecked(loc, nFieldTypes, fieldTypes, isPacked)

Creates an LLVM literal (unnamed) struct type if possible. Emits a diagnostic at the given location and returns null otherwise.

source

',3))]),t("details",Zc,[t("summary",null,[e[2106]||(e[2106]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeSetBody-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeSetBody-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeSetBody")],-1)),e[2107]||(e[2107]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2108]||(e[2108]=l('
julia
mlirLLVMStructTypeSetBody(structType, nFieldTypes, fieldTypes, isPacked)

Sets the body of the identified struct if it hasn't been set yet. Returns whether the operation was successful.

source

',3))]),t("details",Jc,[t("summary",null,[e[2109]||(e[2109]=t("a",{id:"Reactant.MLIR.API.mlirLLVMVoidTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMVoidTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMVoidTypeGet")],-1)),e[2110]||(e[2110]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2111]||(e[2111]=l('
julia
mlirLLVMVoidTypeGet(ctx)

Creates an llmv.void type.

source

',3))]),t("details",Kc,[t("summary",null,[e[2112]||(e[2112]=t("a",{id:"Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion")],-1)),e[2113]||(e[2113]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2114]||(e[2114]=l('
julia
mlirLinalgFillBuiltinNamedOpRegion(mlirOp)

Apply the special region builder for the builtin named Linalg op. Assert that mlirOp is a builtin named Linalg op.

source

',3))]),t("details",$c,[t("summary",null,[e[2115]||(e[2115]=t("a",{id:"Reactant.MLIR.API.mlirLlvmThreadPoolCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirLlvmThreadPoolCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLlvmThreadPoolCreate")],-1)),e[2116]||(e[2116]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2117]||(e[2117]=l('
julia
mlirLlvmThreadPoolCreate()

Create an LLVM thread pool. This is reexported here to avoid directly pulling in the LLVM headers directly.

source

',3))]),t("details",Xc,[t("summary",null,[e[2118]||(e[2118]=t("a",{id:"Reactant.MLIR.API.mlirLlvmThreadPoolDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLlvmThreadPoolDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLlvmThreadPoolDestroy")],-1)),e[2119]||(e[2119]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2120]||(e[2120]=l('
julia
mlirLlvmThreadPoolDestroy(pool)

Destroy an LLVM thread pool.

source

',3))]),t("details",Yc,[t("summary",null,[e[2121]||(e[2121]=t("a",{id:"Reactant.MLIR.API.mlirLoadIRDLDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLoadIRDLDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLoadIRDLDialects")],-1)),e[2122]||(e[2122]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2123]||(e[2123]=l('
julia
mlirLoadIRDLDialects(_module)

Loads all IRDL dialects in the provided module, registering the dialects in the module's associated context.

source

',3))]),t("details",_c,[t("summary",null,[e[2124]||(e[2124]=t("a",{id:"Reactant.MLIR.API.mlirLocationCallSiteGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLocationCallSiteGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationCallSiteGet")],-1)),e[2125]||(e[2125]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2126]||(e[2126]=l('
julia
mlirLocationCallSiteGet(callee, caller)

Creates a call site location with a callee and a caller.

source

',3))]),t("details",eb,[t("summary",null,[e[2127]||(e[2127]=t("a",{id:"Reactant.MLIR.API.mlirLocationEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLocationEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationEqual")],-1)),e[2128]||(e[2128]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2129]||(e[2129]=l('
julia
mlirLocationEqual(l1, l2)

Checks if two locations are equal.

source

',3))]),t("details",tb,[t("summary",null,[e[2130]||(e[2130]=t("a",{id:"Reactant.MLIR.API.mlirLocationFileLineColGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLocationFileLineColGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFileLineColGet")],-1)),e[2131]||(e[2131]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2132]||(e[2132]=l('
julia
mlirLocationFileLineColGet(context, filename, line, col)

Creates an File/Line/Column location owned by the given context.

source

',3))]),t("details",sb,[t("summary",null,[e[2133]||(e[2133]=t("a",{id:"Reactant.MLIR.API.mlirLocationFromAttribute-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationFromAttribute-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFromAttribute")],-1)),e[2134]||(e[2134]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2135]||(e[2135]=l('
julia
mlirLocationFromAttribute(attribute)

Creates a location from a location attribute.

source

',3))]),t("details",ab,[t("summary",null,[e[2136]||(e[2136]=t("a",{id:"Reactant.MLIR.API.mlirLocationFusedGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLocationFusedGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFusedGet")],-1)),e[2137]||(e[2137]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2138]||(e[2138]=l('
julia
mlirLocationFusedGet(ctx, nLocations, locations, metadata)

Creates a fused location with an array of locations and metadata.

source

',3))]),t("details",ib,[t("summary",null,[e[2139]||(e[2139]=t("a",{id:"Reactant.MLIR.API.mlirLocationGetAttribute-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationGetAttribute-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationGetAttribute")],-1)),e[2140]||(e[2140]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2141]||(e[2141]=l('
julia
mlirLocationGetAttribute(location)

Returns the underlying location attribute of this location.

source

',3))]),t("details",lb,[t("summary",null,[e[2142]||(e[2142]=t("a",{id:"Reactant.MLIR.API.mlirLocationGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationGetContext")],-1)),e[2143]||(e[2143]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2144]||(e[2144]=l('
julia
mlirLocationGetContext(location)

Gets the context that a location was created with.

source

',3))]),t("details",nb,[t("summary",null,[e[2145]||(e[2145]=t("a",{id:"Reactant.MLIR.API.mlirLocationIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationIsNull")],-1)),e[2146]||(e[2146]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2147]||(e[2147]=l('
julia
mlirLocationIsNull(location)

Checks if the location is null.

source

',3))]),t("details",pb,[t("summary",null,[e[2148]||(e[2148]=t("a",{id:"Reactant.MLIR.API.mlirLocationNameGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLocationNameGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationNameGet")],-1)),e[2149]||(e[2149]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2150]||(e[2150]=l('
julia
mlirLocationNameGet(context, name, childLoc)

Creates a name location owned by the given context. Providing null location for childLoc is allowed and if childLoc is null location, then the behavior is the same as having unknown child location.

source

',3))]),t("details",rb,[t("summary",null,[e[2151]||(e[2151]=t("a",{id:"Reactant.MLIR.API.mlirLocationPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLocationPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationPrint")],-1)),e[2152]||(e[2152]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2153]||(e[2153]=l('
julia
mlirLocationPrint(location, callback, userData)

Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",ob,[t("summary",null,[e[2154]||(e[2154]=t("a",{id:"Reactant.MLIR.API.mlirLocationUnknownGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationUnknownGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationUnknownGet")],-1)),e[2155]||(e[2155]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2156]||(e[2156]=l('
julia
mlirLocationUnknownGet(context)

Creates a location with unknown position owned by the given context.

source

',3))]),t("details",db,[t("summary",null,[e[2157]||(e[2157]=t("a",{id:"Reactant.MLIR.API.mlirLogicalResultFailure-Tuple{}",href:"#Reactant.MLIR.API.mlirLogicalResultFailure-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultFailure")],-1)),e[2158]||(e[2158]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2159]||(e[2159]=l('
julia
mlirLogicalResultFailure()

Creates a logical result representing a failure.

source

',3))]),t("details",cb,[t("summary",null,[e[2160]||(e[2160]=t("a",{id:"Reactant.MLIR.API.mlirLogicalResultIsFailure-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLogicalResultIsFailure-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultIsFailure")],-1)),e[2161]||(e[2161]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2162]||(e[2162]=l('
julia
mlirLogicalResultIsFailure(res)

Checks if the given logical result represents a failure.

source

',3))]),t("details",bb,[t("summary",null,[e[2163]||(e[2163]=t("a",{id:"Reactant.MLIR.API.mlirLogicalResultIsSuccess-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLogicalResultIsSuccess-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultIsSuccess")],-1)),e[2164]||(e[2164]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2165]||(e[2165]=l('
julia
mlirLogicalResultIsSuccess(res)

Checks if the given logical result represents a success.

source

',3))]),t("details",hb,[t("summary",null,[e[2166]||(e[2166]=t("a",{id:"Reactant.MLIR.API.mlirLogicalResultSuccess-Tuple{}",href:"#Reactant.MLIR.API.mlirLogicalResultSuccess-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultSuccess")],-1)),e[2167]||(e[2167]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2168]||(e[2168]=l('
julia
mlirLogicalResultSuccess()

Creates a logical result representing a success.

source

',3))]),t("details",ub,[t("summary",null,[e[2169]||(e[2169]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeContiguousGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeContiguousGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeContiguousGet")],-1)),e[2170]||(e[2170]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2171]||(e[2171]=l('
julia
mlirMemRefTypeContiguousGet(elementType, rank, shape, memorySpace)

Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context.

source

',3))]),t("details",gb,[t("summary",null,[e[2172]||(e[2172]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked")],-1)),e[2173]||(e[2173]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2174]||(e[2174]=l('
julia
mlirMemRefTypeContiguousGetChecked(loc, elementType, rank, shape, memorySpace)

Same as "mlirMemRefTypeContiguousGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",yb,[t("summary",null,[e[2175]||(e[2175]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGet")],-1)),e[2176]||(e[2176]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2177]||(e[2177]=l('
julia
mlirMemRefTypeGet(elementType, rank, shape, layout, memorySpace)

Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context.

source

',3))]),t("details",mb,[t("summary",null,[e[2178]||(e[2178]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetAffineMap-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetAffineMap")],-1)),e[2179]||(e[2179]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2180]||(e[2180]=l('
julia
mlirMemRefTypeGetAffineMap(type)

Returns the affine map of the given MemRef type.

source

',3))]),t("details",kb,[t("summary",null,[e[2181]||(e[2181]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetChecked-NTuple{6, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetChecked-NTuple{6, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetChecked")],-1)),e[2182]||(e[2182]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2183]||(e[2183]=l('
julia
mlirMemRefTypeGetChecked(loc, elementType, rank, shape, layout, memorySpace)

Same as "mlirMemRefTypeGet" but returns a nullptr-wrapping MlirType o illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",Rb,[t("summary",null,[e[2184]||(e[2184]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetLayout-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetLayout-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetLayout")],-1)),e[2185]||(e[2185]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2186]||(e[2186]=l('
julia
mlirMemRefTypeGetLayout(type)

Returns the layout of the given MemRef type.

source

',3))]),t("details",Ib,[t("summary",null,[e[2187]||(e[2187]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace")],-1)),e[2188]||(e[2188]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2189]||(e[2189]=l('
julia
mlirMemRefTypeGetMemorySpace(type)

Returns the memory space of the given MemRef type.

source

',3))]),t("details",jb,[t("summary",null,[e[2190]||(e[2190]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset")],-1)),e[2191]||(e[2191]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2192]||(e[2192]=l('
julia
mlirMemRefTypeGetStridesAndOffset(type, strides, offset)

Returns the strides of the MemRef if the layout map is in strided form. Both strides and offset are out params. strides must point to pre-allocated memory of length equal to the rank of the memref.

source

',3))]),t("details",fb,[t("summary",null,[e[2193]||(e[2193]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetTypeID")],-1)),e[2194]||(e[2194]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2195]||(e[2195]=l('
julia
mlirMemRefTypeGetTypeID()

Returns the typeID of an MemRef type.

source

',3))]),t("details",Mb,[t("summary",null,[e[2196]||(e[2196]=t("a",{id:"Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone")],-1)),e[2197]||(e[2197]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2198]||(e[2198]=l('
julia
mlirMergeSymbolsIntoFromClone(target, other)

Merge the symbols from other into target, potentially renaming them to avoid conflicts. Private symbols may be renamed during the merge, public symbols must have at most one declaration. A name conflict in public symbols is reported as an error before returning a failure.

Note that this clones the other operation unlike the C++ counterpart that takes ownership.

source

',4))]),t("details",Ab,[t("summary",null,[e[2199]||(e[2199]=t("a",{id:"Reactant.MLIR.API.mlirModuleCreateEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleCreateEmpty-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleCreateEmpty")],-1)),e[2200]||(e[2200]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2201]||(e[2201]=l('
julia
mlirModuleCreateEmpty(location)

Creates a new, empty module and transfers ownership to the caller.

source

',3))]),t("details",Lb,[t("summary",null,[e[2202]||(e[2202]=t("a",{id:"Reactant.MLIR.API.mlirModuleCreateParse-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirModuleCreateParse-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleCreateParse")],-1)),e[2203]||(e[2203]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2204]||(e[2204]=l('
julia
mlirModuleCreateParse(context, _module)

Parses a module from the string and transfers ownership to the caller.

source

',3))]),t("details",Eb,[t("summary",null,[e[2205]||(e[2205]=t("a",{id:"Reactant.MLIR.API.mlirModuleDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleDestroy")],-1)),e[2206]||(e[2206]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2207]||(e[2207]=l('
julia
mlirModuleDestroy(_module)

Takes a module owned by the caller and deletes it.

source

',3))]),t("details",vb,[t("summary",null,[e[2208]||(e[2208]=t("a",{id:"Reactant.MLIR.API.mlirModuleFromOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleFromOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleFromOperation")],-1)),e[2209]||(e[2209]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2210]||(e[2210]=l('
julia
mlirModuleFromOperation(op)

Views the generic operation as a module. The returned module is null when the input operation was not a ModuleOp.

source

',3))]),t("details",Tb,[t("summary",null,[e[2211]||(e[2211]=t("a",{id:"Reactant.MLIR.API.mlirModuleGetBody-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetBody-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetBody")],-1)),e[2212]||(e[2212]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2213]||(e[2213]=l('
julia
mlirModuleGetBody(_module)

Gets the body of the module, i.e. the only block it contains.

source

',3))]),t("details",Cb,[t("summary",null,[e[2214]||(e[2214]=t("a",{id:"Reactant.MLIR.API.mlirModuleGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetContext")],-1)),e[2215]||(e[2215]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2216]||(e[2216]=l('
julia
mlirModuleGetContext(_module)

Gets the context that a module was created with.

source

',3))]),t("details",xb,[t("summary",null,[e[2217]||(e[2217]=t("a",{id:"Reactant.MLIR.API.mlirModuleGetOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetOperation")],-1)),e[2218]||(e[2218]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2219]||(e[2219]=l('
julia
mlirModuleGetOperation(_module)

Views the module as a generic operation.

source

',3))]),t("details",Fb,[t("summary",null,[e[2220]||(e[2220]=t("a",{id:"Reactant.MLIR.API.mlirModuleIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleIsNull")],-1)),e[2221]||(e[2221]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2222]||(e[2222]=l('
julia
mlirModuleIsNull(_module)

Checks whether a module is null.

source

',3))]),t("details",Pb,[t("summary",null,[e[2223]||(e[2223]=t("a",{id:"Reactant.MLIR.API.mlirNamedAttributeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirNamedAttributeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNamedAttributeGet")],-1)),e[2224]||(e[2224]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2225]||(e[2225]=l('
julia
mlirNamedAttributeGet(name, attr)

Associates an attribute with the name. Takes ownership of neither.

source

',3))]),t("details",Db,[t("summary",null,[e[2226]||(e[2226]=t("a",{id:"Reactant.MLIR.API.mlirNoneTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirNoneTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNoneTypeGet")],-1)),e[2227]||(e[2227]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2228]||(e[2228]=l('
julia
mlirNoneTypeGet(ctx)

Creates a None type in the given context. The type is owned by the context.

source

',3))]),t("details",Ob,[t("summary",null,[e[2229]||(e[2229]=t("a",{id:"Reactant.MLIR.API.mlirNoneTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirNoneTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNoneTypeGetTypeID")],-1)),e[2230]||(e[2230]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2231]||(e[2231]=l('
julia
mlirNoneTypeGetTypeID()

Returns the typeID of an None type.

source

',3))]),t("details",Bb,[t("summary",null,[e[2232]||(e[2232]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandGetNextUse-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetNextUse-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetNextUse")],-1)),e[2233]||(e[2233]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2234]||(e[2234]=l('
julia
mlirOpOperandGetNextUse(opOperand)

Returns an op operand representing the next use of the value, or a null op operand if there is no next use.

source

',3))]),t("details",Gb,[t("summary",null,[e[2235]||(e[2235]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandGetOperandNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetOperandNumber-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetOperandNumber")],-1)),e[2236]||(e[2236]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2237]||(e[2237]=l('
julia
mlirOpOperandGetOperandNumber(opOperand)

Returns the operand number of an op operand.

source

',3))]),t("details",zb,[t("summary",null,[e[2238]||(e[2238]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetOwner-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetOwner")],-1)),e[2239]||(e[2239]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2240]||(e[2240]=l('
julia
mlirOpOperandGetOwner(opOperand)

Returns the owner operation of an op operand.

source

',3))]),t("details",wb,[t("summary",null,[e[2241]||(e[2241]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetValue")],-1)),e[2242]||(e[2242]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2243]||(e[2243]=l('
julia
mlirOpOperandGetValue(opOperand)

Returns the value of an op operand.

source

',3))]),t("details",Sb,[t("summary",null,[e[2244]||(e[2244]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandIsNull")],-1)),e[2245]||(e[2245]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2246]||(e[2246]=l('
julia
mlirOpOperandIsNull(opOperand)

Returns whether the op operand is null.

source

',3))]),t("details",Nb,[t("summary",null,[e[2247]||(e[2247]=t("a",{id:"Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass")],-1)),e[2248]||(e[2248]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2249]||(e[2249]=l('
julia
mlirOpPassManagerAddOwnedPass(passManager, pass)

Add a pass and transfer ownership to the provided mlirOpPassManager. If the pass is not a generic operation pass or matching the type of the provided PassManager, a new OpPassManager is implicitly nested under the provided PassManager.

source

',3))]),t("details",Vb,[t("summary",null,[e[2250]||(e[2250]=t("a",{id:"Reactant.MLIR.API.mlirOpPassManagerAddPipeline-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerAddPipeline-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerAddPipeline")],-1)),e[2251]||(e[2251]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2252]||(e[2252]=l('
julia
mlirOpPassManagerAddPipeline(passManager, pipelineElements, callback, userData)

Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.

source

',3))]),t("details",qb,[t("summary",null,[e[2253]||(e[2253]=t("a",{id:"Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder")],-1)),e[2254]||(e[2254]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2255]||(e[2255]=l('
julia
mlirOpPassManagerGetNestedUnder(passManager, operationName)

Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.

source

',3))]),t("details",Ub,[t("summary",null,[e[2256]||(e[2256]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified")],-1)),e[2257]||(e[2257]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2258]||(e[2258]=l('
julia
mlirOpPrintingFlagsAssumeVerified(flags)

Do not verify the operation when using custom operation printers.

source

',3))]),t("details",Qb,[t("summary",null,[e[2259]||(e[2259]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsCreate")],-1)),e[2260]||(e[2260]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2261]||(e[2261]=l('
julia
mlirOpPrintingFlagsCreate()

Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirOpPrintingFlagsDestroy().

source

',3))]),t("details",Wb,[t("summary",null,[e[2262]||(e[2262]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsDestroy")],-1)),e[2263]||(e[2263]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2264]||(e[2264]=l('
julia
mlirOpPrintingFlagsDestroy(flags)

Destroys printing flags created with mlirOpPrintingFlagsCreate.

source

',3))]),t("details",Hb,[t("summary",null,[e[2265]||(e[2265]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs")],-1)),e[2266]||(e[2266]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2267]||(e[2267]=l('
julia
mlirOpPrintingFlagsElideLargeElementsAttrs(flags, largeElementLimit)

Enables the elision of large elements attributes by printing a lexically valid but otherwise meaningless form instead of the element data. The largeElementLimit is used to configure what is considered to be a "large" ElementsAttr by providing an upper limit to the number of elements.

source

',3))]),t("details",Zb,[t("summary",null,[e[2268]||(e[2268]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString")],-1)),e[2269]||(e[2269]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2270]||(e[2270]=l('
julia
mlirOpPrintingFlagsElideLargeResourceString(flags, largeResourceLimit)

Enables the elision of large resources strings by omitting them from the dialect_resources section. The largeResourceLimit is used to configure what is considered to be a "large" resource by providing an upper limit to the string size.

source

',3))]),t("details",Jb,[t("summary",null,[e[2271]||(e[2271]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo")],-1)),e[2272]||(e[2272]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2273]||(e[2273]=l('
julia
mlirOpPrintingFlagsEnableDebugInfo(flags, enable, prettyForm)

Enable or disable printing of debug information (based on enable). If 'prettyForm' is set to true, debug information is printed in a more readable 'pretty' form. Note: The IR generated with 'prettyForm' is not parsable.

source

',3))]),t("details",Kb,[t("summary",null,[e[2274]||(e[2274]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm")],-1)),e[2275]||(e[2275]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2276]||(e[2276]=l('
julia
mlirOpPrintingFlagsPrintGenericOpForm(flags)

Always print operations in the generic form.

source

',3))]),t("details",$b,[t("summary",null,[e[2277]||(e[2277]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions")],-1)),e[2278]||(e[2278]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2279]||(e[2279]=l('
julia
mlirOpPrintingFlagsSkipRegions(flags)

Skip printing regions.

source

',3))]),t("details",Xb,[t("summary",null,[e[2280]||(e[2280]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope")],-1)),e[2281]||(e[2281]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2282]||(e[2282]=l('
julia
mlirOpPrintingFlagsUseLocalScope(flags)

Use local scope when printing the operation. This allows for using the printer in a more localized and thread-safe setting, but may not necessarily be identical to what the IR will look like when dumping the full module.

source

',3))]),t("details",Yb,[t("summary",null,[e[2283]||(e[2283]=t("a",{id:"Reactant.MLIR.API.mlirOpResultGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpResultGetOwner-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpResultGetOwner")],-1)),e[2284]||(e[2284]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2285]||(e[2285]=l('
julia
mlirOpResultGetOwner(value)

Returns an operation that produced this value as its result. Asserts if the value is not an op result.

source

',3))]),t("details",_b,[t("summary",null,[e[2286]||(e[2286]=t("a",{id:"Reactant.MLIR.API.mlirOpResultGetResultNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpResultGetResultNumber-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpResultGetResultNumber")],-1)),e[2287]||(e[2287]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2288]||(e[2288]=l('
julia
mlirOpResultGetResultNumber(value)

Returns the position of the value in the list of results of the operation that produced it.

source

',3))]),t("details",eh,[t("summary",null,[e[2289]||(e[2289]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGet")],-1)),e[2290]||(e[2290]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2291]||(e[2291]=l('
julia
mlirOpaqueAttrGet(ctx, dialectNamespace, dataLength, data, type)

Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).

source

',3))]),t("details",th,[t("summary",null,[e[2292]||(e[2292]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetData-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetData")],-1)),e[2293]||(e[2293]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2294]||(e[2294]=l('
julia
mlirOpaqueAttrGetData(attr)

Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",sh,[t("summary",null,[e[2295]||(e[2295]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace")],-1)),e[2296]||(e[2296]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2297]||(e[2297]=l('
julia
mlirOpaqueAttrGetDialectNamespace(attr)

Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.

source

',3))]),t("details",ah,[t("summary",null,[e[2298]||(e[2298]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetTypeID")],-1)),e[2299]||(e[2299]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2300]||(e[2300]=l('
julia
mlirOpaqueAttrGetTypeID()

Returns the typeID of an Opaque attribute.

source

',3))]),t("details",ih,[t("summary",null,[e[2301]||(e[2301]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGet")],-1)),e[2302]||(e[2302]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2303]||(e[2303]=l('
julia
mlirOpaqueTypeGet(ctx, dialectNamespace, typeData)

Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).

source

',3))]),t("details",lh,[t("summary",null,[e[2304]||(e[2304]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetData-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetData")],-1)),e[2305]||(e[2305]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2306]||(e[2306]=l('
julia
mlirOpaqueTypeGetData(type)

Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.

source

',3))]),t("details",nh,[t("summary",null,[e[2307]||(e[2307]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace")],-1)),e[2308]||(e[2308]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2309]||(e[2309]=l('
julia
mlirOpaqueTypeGetDialectNamespace(type)

Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.

source

',3))]),t("details",ph,[t("summary",null,[e[2310]||(e[2310]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetTypeID")],-1)),e[2311]||(e[2311]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2312]||(e[2312]=l('
julia
mlirOpaqueTypeGetTypeID()

Returns the typeID of an Opaque type.

source

',3))]),t("details",rh,[t("summary",null,[e[2313]||(e[2313]=t("a",{id:"Reactant.MLIR.API.mlirOperationClone-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationClone-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationClone")],-1)),e[2314]||(e[2314]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2315]||(e[2315]=l('
julia
mlirOperationClone(op)

Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.

source

',3))]),t("details",oh,[t("summary",null,[e[2316]||(e[2316]=t("a",{id:"Reactant.MLIR.API.mlirOperationCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationCreate")],-1)),e[2317]||(e[2317]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2318]||(e[2318]=l('
julia
mlirOperationCreate(state)

Creates an operation and transfers ownership to the caller. Note that caller owned child objects are transferred in this call and must not be further used. Particularly, this applies to any regions added to the state (the implementation may invalidate any such pointers).

This call can fail under the following conditions, in which case, it will return a null operation and emit diagnostics: - Result type inference is enabled and cannot be performed.

source

',4))]),t("details",dh,[t("summary",null,[e[2319]||(e[2319]=t("a",{id:"Reactant.MLIR.API.mlirOperationCreateParse-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationCreateParse-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationCreateParse")],-1)),e[2320]||(e[2320]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2321]||(e[2321]=l('
julia
mlirOperationCreateParse(context, sourceStr, sourceName)

Parses an operation, giving ownership to the caller. If parsing fails a null operation will be returned, and an error diagnostic emitted.

sourceStr may be either the text assembly format, or binary bytecode format. sourceName is used as the file name of the source; any IR without locations will get a FileLineColLoc location with sourceName as the file name.

source

',4))]),t("details",ch,[t("summary",null,[e[2322]||(e[2322]=t("a",{id:"Reactant.MLIR.API.mlirOperationDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationDestroy")],-1)),e[2323]||(e[2323]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2324]||(e[2324]=l('
julia
mlirOperationDestroy(op)

Takes an operation owned by the caller and destroys it.

source

',3))]),t("details",bh,[t("summary",null,[e[2325]||(e[2325]=t("a",{id:"Reactant.MLIR.API.mlirOperationDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationDump")],-1)),e[2326]||(e[2326]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2327]||(e[2327]=l('
julia
mlirOperationDump(op)

Prints an operation to stderr.

source

',3))]),t("details",hh,[t("summary",null,[e[2328]||(e[2328]=t("a",{id:"Reactant.MLIR.API.mlirOperationEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationEqual")],-1)),e[2329]||(e[2329]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2330]||(e[2330]=l('
julia
mlirOperationEqual(op, other)

Checks whether two operation handles point to the same operation. This does not perform deep comparison.

source

',3))]),t("details",uh,[t("summary",null,[e[2331]||(e[2331]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetAttribute-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetAttribute")],-1)),e[2332]||(e[2332]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2333]||(e[2333]=l('
julia
mlirOperationGetAttribute(op, pos)

Return pos-th attribute of the operation. Deprecated, please use mlirOperationGetInherentAttribute or mlirOperationGetDiscardableAttribute.

source

',3))]),t("details",gh,[t("summary",null,[e[2334]||(e[2334]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetAttributeByName")],-1)),e[2335]||(e[2335]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2336]||(e[2336]=l('
julia
mlirOperationGetAttributeByName(op, name)

Returns an attribute attached to the operation given its name. Deprecated, please use mlirOperationGetInherentAttributeByName or mlirOperationGetDiscardableAttributeByName.

source

',3))]),t("details",yh,[t("summary",null,[e[2337]||(e[2337]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetBlock")],-1)),e[2338]||(e[2338]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2339]||(e[2339]=l('
julia
mlirOperationGetBlock(op)

Gets the block that owns this operation, returning null if the operation is not owned.

source

',3))]),t("details",mh,[t("summary",null,[e[2340]||(e[2340]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetContext")],-1)),e[2341]||(e[2341]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2342]||(e[2342]=l('
julia
mlirOperationGetContext(op)

Gets the context this operation is associated with

source

',3))]),t("details",kh,[t("summary",null,[e[2343]||(e[2343]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetDiscardableAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetDiscardableAttribute-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetDiscardableAttribute")],-1)),e[2344]||(e[2344]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2345]||(e[2345]=l('
julia
mlirOperationGetDiscardableAttribute(op, pos)

Return pos-th discardable attribute of the operation.

source

',3))]),t("details",Rh,[t("summary",null,[e[2346]||(e[2346]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName")],-1)),e[2347]||(e[2347]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2348]||(e[2348]=l('
julia
mlirOperationGetDiscardableAttributeByName(op, name)

Returns a discardable attribute attached to the operation given its name.

source

',3))]),t("details",Ih,[t("summary",null,[e[2349]||(e[2349]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetFirstRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetFirstRegion-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetFirstRegion")],-1)),e[2350]||(e[2350]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2351]||(e[2351]=l('
julia
mlirOperationGetFirstRegion(op)

Returns first region attached to the operation.

source

',3))]),t("details",jh,[t("summary",null,[e[2352]||(e[2352]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetInherentAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetInherentAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetInherentAttributeByName")],-1)),e[2353]||(e[2353]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2354]||(e[2354]=l('
julia
mlirOperationGetInherentAttributeByName(op, name)

Returns an inherent attribute attached to the operation given its name.

source

',3))]),t("details",fh,[t("summary",null,[e[2355]||(e[2355]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetLocation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetLocation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetLocation")],-1)),e[2356]||(e[2356]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2357]||(e[2357]=l('
julia
mlirOperationGetLocation(op)

Gets the location of the operation.

source

',3))]),t("details",Mh,[t("summary",null,[e[2358]||(e[2358]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetName-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetName-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetName")],-1)),e[2359]||(e[2359]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2360]||(e[2360]=l('
julia
mlirOperationGetName(op)

Gets the name of the operation as an identifier.

source

',3))]),t("details",Ah,[t("summary",null,[e[2361]||(e[2361]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNextInBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNextInBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNextInBlock")],-1)),e[2362]||(e[2362]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2363]||(e[2363]=l('
julia
mlirOperationGetNextInBlock(op)

Returns an operation immediately following the given operation it its enclosing block.

source

',3))]),t("details",Lh,[t("summary",null,[e[2364]||(e[2364]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumAttributes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumAttributes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumAttributes")],-1)),e[2365]||(e[2365]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2366]||(e[2366]=l('
julia
mlirOperationGetNumAttributes(op)

Returns the number of attributes attached to the operation. Deprecated, please use mlirOperationGetNumInherentAttributes or mlirOperationGetNumDiscardableAttributes.

source

',3))]),t("details",Eh,[t("summary",null,[e[2367]||(e[2367]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes")],-1)),e[2368]||(e[2368]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2369]||(e[2369]=l('
julia
mlirOperationGetNumDiscardableAttributes(op)

Returns the number of discardable attributes attached to the operation.

source

',3))]),t("details",vh,[t("summary",null,[e[2370]||(e[2370]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumOperands-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumOperands-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumOperands")],-1)),e[2371]||(e[2371]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2372]||(e[2372]=l('
julia
mlirOperationGetNumOperands(op)

Returns the number of operands of the operation.

source

',3))]),t("details",Th,[t("summary",null,[e[2373]||(e[2373]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumRegions-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumRegions-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumRegions")],-1)),e[2374]||(e[2374]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2375]||(e[2375]=l('
julia
mlirOperationGetNumRegions(op)

Returns the number of regions attached to the given operation.

source

',3))]),t("details",Ch,[t("summary",null,[e[2376]||(e[2376]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumResults-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumResults")],-1)),e[2377]||(e[2377]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2378]||(e[2378]=l('
julia
mlirOperationGetNumResults(op)

Returns the number of results of the operation.

source

',3))]),t("details",xh,[t("summary",null,[e[2379]||(e[2379]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumSuccessors-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumSuccessors-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumSuccessors")],-1)),e[2380]||(e[2380]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2381]||(e[2381]=l('
julia
mlirOperationGetNumSuccessors(op)

Returns the number of successor blocks of the operation.

source

',3))]),t("details",Fh,[t("summary",null,[e[2382]||(e[2382]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetOperand-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetOperand-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetOperand")],-1)),e[2383]||(e[2383]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2384]||(e[2384]=l('
julia
mlirOperationGetOperand(op, pos)

Returns pos-th operand of the operation.

source

',3))]),t("details",Ph,[t("summary",null,[e[2385]||(e[2385]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetParentOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetParentOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetParentOperation")],-1)),e[2386]||(e[2386]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2387]||(e[2387]=l('
julia
mlirOperationGetParentOperation(op)

Gets the operation that owns this operation, returning null if the operation is not owned.

source

',3))]),t("details",Dh,[t("summary",null,[e[2388]||(e[2388]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetRegion-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetRegion-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetRegion")],-1)),e[2389]||(e[2389]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2390]||(e[2390]=l('
julia
mlirOperationGetRegion(op, pos)

Returns pos-th region attached to the operation.

source

',3))]),t("details",Oh,[t("summary",null,[e[2391]||(e[2391]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetResult-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetResult")],-1)),e[2392]||(e[2392]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2393]||(e[2393]=l('
julia
mlirOperationGetResult(op, pos)

Returns pos-th result of the operation.

source

',3))]),t("details",Bh,[t("summary",null,[e[2394]||(e[2394]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetSuccessor-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetSuccessor-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetSuccessor")],-1)),e[2395]||(e[2395]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2396]||(e[2396]=l('
julia
mlirOperationGetSuccessor(op, pos)

Returns pos-th successor of the operation.

source

',3))]),t("details",Gh,[t("summary",null,[e[2397]||(e[2397]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetTypeID-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetTypeID")],-1)),e[2398]||(e[2398]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2399]||(e[2399]=l('
julia
mlirOperationGetTypeID(op)

Gets the type id of the operation. Returns null if the operation does not have a registered operation description.

source

',3))]),t("details",zh,[t("summary",null,[e[2400]||(e[2400]=t("a",{id:"Reactant.MLIR.API.mlirOperationHasInherentAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationHasInherentAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationHasInherentAttributeByName")],-1)),e[2401]||(e[2401]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2402]||(e[2402]=l('
julia
mlirOperationHasInherentAttributeByName(op, name)

Returns true if this operation defines an inherent attribute with this name. Note: the attribute can be optional, so mlirOperationGetInherentAttributeByName can still return a null attribute.

source

',3))]),t("details",wh,[t("summary",null,[e[2403]||(e[2403]=t("a",{id:"Reactant.MLIR.API.mlirOperationImplementsInterface-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationImplementsInterface-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationImplementsInterface")],-1)),e[2404]||(e[2404]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2405]||(e[2405]=l('
julia
mlirOperationImplementsInterface(operation, interfaceTypeID)

Returns true if the given operation implements an interface identified by its TypeID.

source

',3))]),t("details",Sh,[t("summary",null,[e[2406]||(e[2406]=t("a",{id:"Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic")],-1)),e[2407]||(e[2407]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2408]||(e[2408]=l('
julia
mlirOperationImplementsInterfaceStatic(operationName, context, interfaceTypeID)

Returns true if the operation identified by its canonical string name implements the interface identified by its TypeID in the given context. Note that interfaces may be attached to operations in some contexts and not others.

source

',3))]),t("details",Nh,[t("summary",null,[e[2409]||(e[2409]=t("a",{id:"Reactant.MLIR.API.mlirOperationIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationIsNull")],-1)),e[2410]||(e[2410]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2411]||(e[2411]=l('
julia
mlirOperationIsNull(op)

Checks whether the underlying operation is null.

source

',3))]),t("details",Vh,[t("summary",null,[e[2412]||(e[2412]=t("a",{id:"Reactant.MLIR.API.mlirOperationMoveAfter-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationMoveAfter-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationMoveAfter")],-1)),e[2413]||(e[2413]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2414]||(e[2414]=l('
julia
mlirOperationMoveAfter(op, other)

Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.

source

',3))]),t("details",qh,[t("summary",null,[e[2415]||(e[2415]=t("a",{id:"Reactant.MLIR.API.mlirOperationMoveBefore-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationMoveBefore-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationMoveBefore")],-1)),e[2416]||(e[2416]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2417]||(e[2417]=l('
julia
mlirOperationMoveBefore(op, other)

Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.

source

',3))]),t("details",Uh,[t("summary",null,[e[2418]||(e[2418]=t("a",{id:"Reactant.MLIR.API.mlirOperationPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrint")],-1)),e[2419]||(e[2419]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2420]||(e[2420]=l('
julia
mlirOperationPrint(op, callback, userData)

Prints an operation by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Qh,[t("summary",null,[e[2421]||(e[2421]=t("a",{id:"Reactant.MLIR.API.mlirOperationPrintWithFlags-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationPrintWithFlags-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrintWithFlags")],-1)),e[2422]||(e[2422]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2423]||(e[2423]=l('
julia
mlirOperationPrintWithFlags(op, flags, callback, userData)

Same as mlirOperationPrint but accepts flags controlling the printing behavior.

source

',3))]),t("details",Wh,[t("summary",null,[e[2424]||(e[2424]=t("a",{id:"Reactant.MLIR.API.mlirOperationPrintWithState-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationPrintWithState-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrintWithState")],-1)),e[2425]||(e[2425]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2426]||(e[2426]=l('
julia
mlirOperationPrintWithState(op, state, callback, userData)

Same as mlirOperationPrint but accepts AsmState controlling the printing behavior as well as caching computed names.

source

',3))]),t("details",Hh,[t("summary",null,[e[2427]||(e[2427]=t("a",{id:"Reactant.MLIR.API.mlirOperationRemoveAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveAttributeByName")],-1)),e[2428]||(e[2428]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2429]||(e[2429]=l('
julia
mlirOperationRemoveAttributeByName(op, name)

Removes an attribute by name. Returns false if the attribute was not found and true if removed. Deprecated, please use mlirOperationRemoveInherentAttributeByName or mlirOperationRemoveDiscardableAttributeByName.

source

',3))]),t("details",Zh,[t("summary",null,[e[2430]||(e[2430]=t("a",{id:"Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName")],-1)),e[2431]||(e[2431]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2432]||(e[2432]=l('
julia
mlirOperationRemoveDiscardableAttributeByName(op, name)

Removes a discardable attribute by name. Returns false if the attribute was not found and true if removed.

source

',3))]),t("details",Jh,[t("summary",null,[e[2433]||(e[2433]=t("a",{id:"Reactant.MLIR.API.mlirOperationRemoveFromParent-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveFromParent-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveFromParent")],-1)),e[2434]||(e[2434]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2435]||(e[2435]=l('
julia
mlirOperationRemoveFromParent(op)

Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.

source

',3))]),t("details",Kh,[t("summary",null,[e[2436]||(e[2436]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetAttributeByName-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetAttributeByName")],-1)),e[2437]||(e[2437]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2438]||(e[2438]=l('
julia
mlirOperationSetAttributeByName(op, name, attr)

Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise. Deprecated, please use mlirOperationSetInherentAttributeByName or mlirOperationSetDiscardableAttributeByName.

source

',3))]),t("details",$h,[t("summary",null,[e[2439]||(e[2439]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName")],-1)),e[2440]||(e[2440]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2441]||(e[2441]=l('
julia
mlirOperationSetDiscardableAttributeByName(op, name, attr)

Sets a discardable attribute by name, replacing the existing if it exists or adding a new one otherwise. The new attr Attribute is not allowed to be null, use mlirOperationRemoveDiscardableAttributeByName to remove an Attribute instead.

source

',3))]),t("details",Xh,[t("summary",null,[e[2442]||(e[2442]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetInherentAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetInherentAttributeByName-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetInherentAttributeByName")],-1)),e[2443]||(e[2443]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2444]||(e[2444]=l('
julia
mlirOperationSetInherentAttributeByName(op, name, attr)

Sets an inherent attribute by name, replacing the existing if it exists. This has no effect if "name" does not match an inherent attribute.

source

',3))]),t("details",Yh,[t("summary",null,[e[2445]||(e[2445]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetOperand-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetOperand-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetOperand")],-1)),e[2446]||(e[2446]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2447]||(e[2447]=l('
julia
mlirOperationSetOperand(op, pos, newValue)

Sets the pos-th operand of the operation.

source

',3))]),t("details",_h,[t("summary",null,[e[2448]||(e[2448]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetOperands-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetOperands-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetOperands")],-1)),e[2449]||(e[2449]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2450]||(e[2450]=l('
julia
mlirOperationSetOperands(op, nOperands, operands)

Replaces the operands of the operation.

source

',3))]),t("details",eu,[t("summary",null,[e[2451]||(e[2451]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetSuccessor-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetSuccessor-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetSuccessor")],-1)),e[2452]||(e[2452]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2453]||(e[2453]=l('
julia
mlirOperationSetSuccessor(op, pos, block)

Set pos-th successor of the operation.

source

',3))]),t("details",tu,[t("summary",null,[e[2454]||(e[2454]=t("a",{id:"Reactant.MLIR.API.mlirOperationStateAddResults-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationStateAddResults-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateAddResults")],-1)),e[2455]||(e[2455]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2456]||(e[2456]=l('
julia
mlirOperationStateAddResults(state, n, results)

Adds a list of components to the operation state.

source

',3))]),t("details",su,[t("summary",null,[e[2457]||(e[2457]=t("a",{id:"Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference")],-1)),e[2458]||(e[2458]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2459]||(e[2459]=l('
julia
mlirOperationStateEnableResultTypeInference(state)

Enables result type inference for the operation under construction. If enabled, then the caller must not have called mlirOperationStateAddResults(). Note that if enabled, the mlirOperationCreate() call is failable: it will return a null operation on inference failure and will emit diagnostics.

source

',3))]),t("details",au,[t("summary",null,[e[2460]||(e[2460]=t("a",{id:"Reactant.MLIR.API.mlirOperationStateGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationStateGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateGet")],-1)),e[2461]||(e[2461]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2462]||(e[2462]=l('
julia
mlirOperationStateGet(name, loc)

Constructs an operation state from a name and a location.

source

',3))]),t("details",iu,[t("summary",null,[e[2463]||(e[2463]=t("a",{id:"Reactant.MLIR.API.mlirOperationVerify-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationVerify-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationVerify")],-1)),e[2464]||(e[2464]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2465]||(e[2465]=l('
julia
mlirOperationVerify(op)

Verify the operation and return true if it passes, false if it fails.

source

',3))]),t("details",lu,[t("summary",null,[e[2466]||(e[2466]=t("a",{id:"Reactant.MLIR.API.mlirOperationWalk-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationWalk-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWalk")],-1)),e[2467]||(e[2467]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2468]||(e[2468]=l('
julia
mlirOperationWalk(op, callback, userData, walkOrder)

Walks operation op in walkOrder and calls callback on that operation. *userData is passed to the callback as well and can be used to tunnel some context or other data into the callback.

source

',3))]),t("details",nu,[t("summary",null,[e[2469]||(e[2469]=t("a",{id:"Reactant.MLIR.API.mlirOperationWriteBytecode-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationWriteBytecode-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWriteBytecode")],-1)),e[2470]||(e[2470]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2471]||(e[2471]=l('
julia
mlirOperationWriteBytecode(op, callback, userData)

Same as mlirOperationPrint but writing the bytecode format.

source

',3))]),t("details",pu,[t("summary",null,[e[2472]||(e[2472]=t("a",{id:"Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig")],-1)),e[2473]||(e[2473]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2474]||(e[2474]=l('
julia
mlirOperationWriteBytecodeWithConfig(op, config, callback, userData)

Same as mlirOperationWriteBytecode but with writer config and returns failure only if desired bytecode could not be honored.

source

',3))]),t("details",ru,[t("summary",null,[e[2475]||(e[2475]=t("a",{id:"Reactant.MLIR.API.mlirParsePassPipeline-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirParsePassPipeline-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirParsePassPipeline")],-1)),e[2476]||(e[2476]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2477]||(e[2477]=l('
julia
mlirParsePassPipeline(passManager, pipeline, callback, userData)

Parse a textual MLIR pass pipeline and assign it to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.

source

',3))]),t("details",ou,[t("summary",null,[e[2478]||(e[2478]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerAddOwnedPass-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerAddOwnedPass-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerAddOwnedPass")],-1)),e[2479]||(e[2479]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2480]||(e[2480]=l('
julia
mlirPassManagerAddOwnedPass(passManager, pass)

Add a pass and transfer ownership to the provided top-level mlirPassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.

source

',3))]),t("details",du,[t("summary",null,[e[2481]||(e[2481]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerCreate")],-1)),e[2482]||(e[2482]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2483]||(e[2483]=l('
julia
mlirPassManagerCreate(ctx)

Create a new top-level PassManager with the default anchor.

source

',3))]),t("details",cu,[t("summary",null,[e[2484]||(e[2484]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerCreateOnOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerCreateOnOperation-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerCreateOnOperation")],-1)),e[2485]||(e[2485]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2486]||(e[2486]=l('
julia
mlirPassManagerCreateOnOperation(ctx, anchorOp)

Create a new top-level PassManager anchored on anchorOp.

source

',3))]),t("details",bu,[t("summary",null,[e[2487]||(e[2487]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerDestroy")],-1)),e[2488]||(e[2488]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2489]||(e[2489]=l('
julia
mlirPassManagerDestroy(passManager)

Destroy the provided PassManager.

source

',3))]),t("details",hu,[t("summary",null,[e[2490]||(e[2490]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerEnableIRPrinting-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirPassManagerEnableIRPrinting-NTuple{8, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerEnableIRPrinting")],-1)),e[2491]||(e[2491]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2492]||(e[2492]=l('
julia
mlirPassManagerEnableIRPrinting(passManager, printBeforeAll, printAfterAll, printModuleScope, printAfterOnlyOnChange, printAfterOnlyOnFailure, flags, treePrintingPath)

Enable IR printing. The treePrintingPath argument is an optional path to a directory where the dumps will be produced. If it isn't provided then dumps are produced to stderr.

source

',3))]),t("details",uu,[t("summary",null,[e[2493]||(e[2493]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerEnableVerifier-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerEnableVerifier-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerEnableVerifier")],-1)),e[2494]||(e[2494]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2495]||(e[2495]=l('
julia
mlirPassManagerEnableVerifier(passManager, enable)

Enable / disable verify-each.

source

',3))]),t("details",gu,[t("summary",null,[e[2496]||(e[2496]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager")],-1)),e[2497]||(e[2497]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2498]||(e[2498]=l('
julia
mlirPassManagerGetAsOpPassManager(passManager)

Cast a top-level PassManager to a generic OpPassManager.

source

',3))]),t("details",yu,[t("summary",null,[e[2499]||(e[2499]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerGetNestedUnder-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerGetNestedUnder-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerGetNestedUnder")],-1)),e[2500]||(e[2500]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2501]||(e[2501]=l('
julia
mlirPassManagerGetNestedUnder(passManager, operationName)

Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.

source

',3))]),t("details",mu,[t("summary",null,[e[2502]||(e[2502]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerIsNull")],-1)),e[2503]||(e[2503]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2504]||(e[2504]=l('
julia
mlirPassManagerIsNull(passManager)

Checks if a PassManager is null.

source

',3))]),t("details",ku,[t("summary",null,[e[2505]||(e[2505]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerRunOnOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerRunOnOp-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerRunOnOp")],-1)),e[2506]||(e[2506]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2507]||(e[2507]=l('
julia
mlirPassManagerRunOnOp(passManager, op)

Run the provided passManager on the given op.

source

',3))]),t("details",Ru,[t("summary",null,[e[2508]||(e[2508]=t("a",{id:"Reactant.MLIR.API.mlirPrintPassPipeline-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirPrintPassPipeline-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPrintPassPipeline")],-1)),e[2509]||(e[2509]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2510]||(e[2510]=l('
julia
mlirPrintPassPipeline(passManager, callback, userData)

Print a textual MLIR pass pipeline by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Iu,[t("summary",null,[e[2511]||(e[2511]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType")],-1)),e[2512]||(e[2512]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2513]||(e[2513]=l('
julia
mlirQuantizedTypeCastExpressedToStorageType(type, candidate)

Casts from a type based on the expressed type of the given quantized type to equivalent type based on storage type of the same quantized type.

source

',3))]),t("details",ju,[t("summary",null,[e[2514]||(e[2514]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType")],-1)),e[2515]||(e[2515]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2516]||(e[2516]=l('
julia
mlirQuantizedTypeCastFromExpressedType(type, candidate)

Casts from a type based on the expressed type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.

source

',3))]),t("details",fu,[t("summary",null,[e[2517]||(e[2517]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType")],-1)),e[2518]||(e[2518]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2519]||(e[2519]=l('
julia
mlirQuantizedTypeCastFromStorageType(type, candidate)

Casts from a type based on the storage type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.

source

',3))]),t("details",Mu,[t("summary",null,[e[2520]||(e[2520]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType")],-1)),e[2521]||(e[2521]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2522]||(e[2522]=l('
julia
mlirQuantizedTypeCastToExpressedType(type)

Casts from a type based on a quantized type to a corresponding typed based on the expressed type. Returns a null type if the cast is not valid.

source

',3))]),t("details",Au,[t("summary",null,[e[2523]||(e[2523]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType")],-1)),e[2524]||(e[2524]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2525]||(e[2525]=l('
julia
mlirQuantizedTypeCastToStorageType(type)

Casts from a type based on a quantized type to a corresponding typed based on the storage type. Returns a null type if the cast is not valid.

source

',3))]),t("details",Lu,[t("summary",null,[e[2526]||(e[2526]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger")],-1)),e[2527]||(e[2527]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2528]||(e[2528]=l('
julia
mlirQuantizedTypeGetDefaultMaximumForInteger(isSigned, integralWidth)

Returns the maximum possible value stored by a quantized type.

source

',3))]),t("details",Eu,[t("summary",null,[e[2529]||(e[2529]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger")],-1)),e[2530]||(e[2530]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2531]||(e[2531]=l('
julia
mlirQuantizedTypeGetDefaultMinimumForInteger(isSigned, integralWidth)

Returns the minimum possible value stored by a quantized type.

source

',3))]),t("details",vu,[t("summary",null,[e[2532]||(e[2532]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType")],-1)),e[2533]||(e[2533]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2534]||(e[2534]=l('
julia
mlirQuantizedTypeGetExpressedType(type)

Gets the original type approximated by the given quantized type.

source

',3))]),t("details",Tu,[t("summary",null,[e[2535]||(e[2535]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetFlags-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetFlags-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetFlags")],-1)),e[2536]||(e[2536]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2537]||(e[2537]=l('
julia
mlirQuantizedTypeGetFlags(type)

Gets the flags associated with the given quantized type.

source

',3))]),t("details",Cu,[t("summary",null,[e[2538]||(e[2538]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType")],-1)),e[2539]||(e[2539]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2540]||(e[2540]=l('
julia
mlirQuantizedTypeGetQuantizedElementType(type)

Returns the element type of the given quantized type as another quantized type.

source

',3))]),t("details",xu,[t("summary",null,[e[2541]||(e[2541]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag-Tuple{}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag")],-1)),e[2542]||(e[2542]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2543]||(e[2543]=l('
julia
mlirQuantizedTypeGetSignedFlag()

Returns the bit flag used to indicate signedness of a quantized type.

source

',3))]),t("details",Fu,[t("summary",null,[e[2544]||(e[2544]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageType")],-1)),e[2545]||(e[2545]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2546]||(e[2546]=l('
julia
mlirQuantizedTypeGetStorageType(type)

Returns the underlying type used to store the values.

source

',3))]),t("details",Pu,[t("summary",null,[e[2547]||(e[2547]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth")],-1)),e[2548]||(e[2548]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2549]||(e[2549]=l('
julia
mlirQuantizedTypeGetStorageTypeIntegralWidth(type)

Returns the integral bitwidth that the storage type of the given quantized type can represent exactly.

source

',3))]),t("details",Du,[t("summary",null,[e[2550]||(e[2550]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax")],-1)),e[2551]||(e[2551]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2552]||(e[2552]=l('
julia
mlirQuantizedTypeGetStorageTypeMax(type)

Returns the maximum value that the storage type of the given quantized type can take.

source

',3))]),t("details",Ou,[t("summary",null,[e[2553]||(e[2553]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin")],-1)),e[2554]||(e[2554]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2555]||(e[2555]=l('
julia
mlirQuantizedTypeGetStorageTypeMin(type)

Returns the minimum value that the storage type of the given quantized type can take.

source

',3))]),t("details",Bu,[t("summary",null,[e[2556]||(e[2556]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType")],-1)),e[2557]||(e[2557]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2558]||(e[2558]=l('
julia
mlirQuantizedTypeIsCompatibleExpressedType(type, candidate)

Returns true if the candidate type is compatible with the given quantized type.

source

',3))]),t("details",Gu,[t("summary",null,[e[2559]||(e[2559]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeIsSigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeIsSigned-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeIsSigned")],-1)),e[2560]||(e[2560]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2561]||(e[2561]=l('
julia
mlirQuantizedTypeIsSigned(type)

Returns true if the given type is signed, false otherwise.

source

',3))]),t("details",zu,[t("summary",null,[e[2562]||(e[2562]=t("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGet")],-1)),e[2563]||(e[2563]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2564]||(e[2564]=l('
julia
mlirRankedTensorTypeGet(rank, shape, elementType, encoding)

Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull() to this parameter.

source

',3))]),t("details",wu,[t("summary",null,[e[2565]||(e[2565]=t("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetChecked-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetChecked")],-1)),e[2566]||(e[2566]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2567]||(e[2567]=l('
julia
mlirRankedTensorTypeGetChecked(loc, rank, shape, elementType, encoding)

Same as "mlirRankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",Su,[t("summary",null,[e[2568]||(e[2568]=t("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding")],-1)),e[2569]||(e[2569]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2570]||(e[2570]=l('
julia
mlirRankedTensorTypeGetEncoding(type)

Gets the 'encoding' attribute from the ranked tensor type, returning a null attribute if none.

source

',3))]),t("details",Nu,[t("summary",null,[e[2571]||(e[2571]=t("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID")],-1)),e[2572]||(e[2572]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2573]||(e[2573]=l('
julia
mlirRankedTensorTypeGetTypeID()

Returns the typeID of an RankedTensor type.

source

',3))]),t("details",Vu,[t("summary",null,[e[2574]||(e[2574]=t("a",{id:"Reactant.MLIR.API.mlirRegionAppendOwnedBlock-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionAppendOwnedBlock-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionAppendOwnedBlock")],-1)),e[2575]||(e[2575]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2576]||(e[2576]=l('
julia
mlirRegionAppendOwnedBlock(region, block)

Takes a block owned by the caller and appends it to the given region.

source

',3))]),t("details",qu,[t("summary",null,[e[2577]||(e[2577]=t("a",{id:"Reactant.MLIR.API.mlirRegionCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirRegionCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionCreate")],-1)),e[2578]||(e[2578]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2579]||(e[2579]=l('
julia
mlirRegionCreate()

Creates a new empty region and transfers ownership to the caller.

source

',3))]),t("details",Uu,[t("summary",null,[e[2580]||(e[2580]=t("a",{id:"Reactant.MLIR.API.mlirRegionDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionDestroy")],-1)),e[2581]||(e[2581]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2582]||(e[2582]=l('
julia
mlirRegionDestroy(region)

Takes a region owned by the caller and destroys it.

source

',3))]),t("details",Qu,[t("summary",null,[e[2583]||(e[2583]=t("a",{id:"Reactant.MLIR.API.mlirRegionEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionEqual")],-1)),e[2584]||(e[2584]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2585]||(e[2585]=l('
julia
mlirRegionEqual(region, other)

Checks whether two region handles point to the same region. This does not perform deep comparison.

source

',3))]),t("details",Wu,[t("summary",null,[e[2586]||(e[2586]=t("a",{id:"Reactant.MLIR.API.mlirRegionGetFirstBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionGetFirstBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionGetFirstBlock")],-1)),e[2587]||(e[2587]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2588]||(e[2588]=l('
julia
mlirRegionGetFirstBlock(region)

Gets the first block in the region.

source

',3))]),t("details",Hu,[t("summary",null,[e[2589]||(e[2589]=t("a",{id:"Reactant.MLIR.API.mlirRegionGetNextInOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionGetNextInOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionGetNextInOperation")],-1)),e[2590]||(e[2590]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2591]||(e[2591]=l('
julia
mlirRegionGetNextInOperation(region)

Returns the region immediately following the given region in its parent operation.

source

',3))]),t("details",Zu,[t("summary",null,[e[2592]||(e[2592]=t("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlock-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlock-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlock")],-1)),e[2593]||(e[2593]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2594]||(e[2594]=l('
julia
mlirRegionInsertOwnedBlock(region, pos, block)

Takes a block owned by the caller and inserts it at pos to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.

source

',3))]),t("details",Ju,[t("summary",null,[e[2595]||(e[2595]=t("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter")],-1)),e[2596]||(e[2596]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2597]||(e[2597]=l('
julia
mlirRegionInsertOwnedBlockAfter(region, reference, block)

Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.

source

',3))]),t("details",Ku,[t("summary",null,[e[2598]||(e[2598]=t("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore")],-1)),e[2599]||(e[2599]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2600]||(e[2600]=l('
julia
mlirRegionInsertOwnedBlockBefore(region, reference, block)

Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.

source

',3))]),t("details",$u,[t("summary",null,[e[2601]||(e[2601]=t("a",{id:"Reactant.MLIR.API.mlirRegionIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionIsNull")],-1)),e[2602]||(e[2602]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2603]||(e[2603]=l('
julia
mlirRegionIsNull(region)

Checks whether a region is null.

source

',3))]),t("details",Xu,[t("summary",null,[e[2604]||(e[2604]=t("a",{id:"Reactant.MLIR.API.mlirRegionTakeBody-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionTakeBody-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionTakeBody")],-1)),e[2605]||(e[2605]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2606]||(e[2606]=l('
julia
mlirRegionTakeBody(target, source)

Moves the entire content of the source region to the target region.

source

',3))]),t("details",Yu,[t("summary",null,[e[2607]||(e[2607]=t("a",{id:"Reactant.MLIR.API.mlirRegisterAllDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegisterAllDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllDialects")],-1)),e[2608]||(e[2608]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2609]||(e[2609]=l('
julia
mlirRegisterAllDialects(registry)

Appends all upstream dialects and extensions to the dialect registry.

source

',3))]),t("details",_u,[t("summary",null,[e[2610]||(e[2610]=t("a",{id:"Reactant.MLIR.API.mlirRegisterAllLLVMTranslations-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegisterAllLLVMTranslations-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllLLVMTranslations")],-1)),e[2611]||(e[2611]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2612]||(e[2612]=l('
julia
mlirRegisterAllLLVMTranslations(context)

Register all translations to LLVM IR for dialects that can support it.

source

',3))]),t("details",eg,[t("summary",null,[e[2613]||(e[2613]=t("a",{id:"Reactant.MLIR.API.mlirRegisterAllPasses-Tuple{}",href:"#Reactant.MLIR.API.mlirRegisterAllPasses-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllPasses")],-1)),e[2614]||(e[2614]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2615]||(e[2615]=l('
julia
mlirRegisterAllPasses()

Register all compiler passes of MLIR.

source

',3))]),t("details",tg,[t("summary",null,[e[2616]||(e[2616]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCancelOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCancelOpModification-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCancelOpModification")],-1)),e[2617]||(e[2617]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2618]||(e[2618]=l('
julia
mlirRewriterBaseCancelOpModification(rewriter, op)

This method cancels a pending in-place modification. This can only be called on operations that were provided to a call to startOpModification.

source

',3))]),t("details",sg,[t("summary",null,[e[2619]||(e[2619]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint")],-1)),e[2620]||(e[2620]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2621]||(e[2621]=l('
julia
mlirRewriterBaseClearInsertionPoint(rewriter)

Reset the insertion point to no location. Creating an operation without a set insertion point is an error, but this can still be useful when the current insertion point a builder refers to is being removed.

source

',3))]),t("details",ag,[t("summary",null,[e[2622]||(e[2622]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseClone-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseClone-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseClone")],-1)),e[2623]||(e[2623]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2624]||(e[2624]=l('
julia
mlirRewriterBaseClone(rewriter, op)

Creates a deep copy of the specified operation.

source

',3))]),t("details",ig,[t("summary",null,[e[2625]||(e[2625]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore")],-1)),e[2626]||(e[2626]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2627]||(e[2627]=l('
julia
mlirRewriterBaseCloneRegionBefore(rewriter, region, before)

Clone the blocks that belong to "region" before the given position in another region "parent".

source

',3))]),t("details",lg,[t("summary",null,[e[2628]||(e[2628]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions")],-1)),e[2629]||(e[2629]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2630]||(e[2630]=l('
julia
mlirRewriterBaseCloneWithoutRegions(rewriter, op)

Creates a deep copy of this operation but keep the operation regions empty.

source

',3))]),t("details",ng,[t("summary",null,[e[2631]||(e[2631]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore")],-1)),e[2632]||(e[2632]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2633]||(e[2633]=l('
julia
mlirRewriterBaseCreateBlockBefore(rewriter, insertBefore, nArgTypes, argTypes, locations)

Add new block with 'argTypes' arguments and set the insertion point to the end of it. The block is placed before 'insertBefore'. locs contains the locations of the inserted arguments, and should match the size of argTypes.

source

',3))]),t("details",pg,[t("summary",null,[e[2634]||(e[2634]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseEraseBlock-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseEraseBlock-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseEraseBlock")],-1)),e[2635]||(e[2635]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2636]||(e[2636]=l('
julia
mlirRewriterBaseEraseBlock(rewriter, block)

Erases a block along with all operations inside it.

source

',3))]),t("details",rg,[t("summary",null,[e[2637]||(e[2637]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseEraseOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseEraseOp-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseEraseOp")],-1)),e[2638]||(e[2638]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2639]||(e[2639]=l('
julia
mlirRewriterBaseEraseOp(rewriter, op)

Erases an operation that is known to have no uses.

source

',3))]),t("details",og,[t("summary",null,[e[2640]||(e[2640]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification")],-1)),e[2641]||(e[2641]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2642]||(e[2642]=l('
julia
mlirRewriterBaseFinalizeOpModification(rewriter, op)

This method is used to signal the end of an in-place modification of the given operation. This can only be called on operations that were provided to a call to startOpModification.

source

',3))]),t("details",dg,[t("summary",null,[e[2643]||(e[2643]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetBlock")],-1)),e[2644]||(e[2644]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2645]||(e[2645]=l('
julia
mlirRewriterBaseGetBlock(rewriter)

Returns the current block of the rewriter.

source

',3))]),t("details",cg,[t("summary",null,[e[2646]||(e[2646]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetContext")],-1)),e[2647]||(e[2647]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2648]||(e[2648]=l('
julia
mlirRewriterBaseGetContext(rewriter)

Get the MLIR context referenced by the rewriter.

source

',3))]),t("details",bg,[t("summary",null,[e[2649]||(e[2649]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock")],-1)),e[2650]||(e[2650]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2651]||(e[2651]=l('
julia
mlirRewriterBaseGetInsertionBlock(rewriter)

Return the block the current insertion point belongs to. Note that the insertion point is not necessarily the end of the block.

source

',3))]),t("details",hg,[t("summary",null,[e[2652]||(e[2652]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore")],-1)),e[2653]||(e[2653]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2654]||(e[2654]=l('
julia
mlirRewriterBaseInlineBlockBefore(rewriter, source, op, nArgValues, argValues)

Inline the operations of block 'source' before the operation 'op'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'

The source block must have no successors. Otherwise, the resulting IR would have unreachable operations.

source

',4))]),t("details",ug,[t("summary",null,[e[2655]||(e[2655]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore")],-1)),e[2656]||(e[2656]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2657]||(e[2657]=l('
julia
mlirRewriterBaseInlineRegionBefore(rewriter, region, before)

Move the blocks that belong to "region" before the given position in another region "parent". The two regions must be different. The caller is responsible for creating or updating the operation transferring flow of control to the region and passing it the correct block arguments.

source

',3))]),t("details",gg,[t("summary",null,[e[2658]||(e[2658]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInsert-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInsert-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInsert")],-1)),e[2659]||(e[2659]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2660]||(e[2660]=l('
julia
mlirRewriterBaseInsert(rewriter, op)

Insert the given operation at the current insertion point and return it.

source

',3))]),t("details",yg,[t("summary",null,[e[2661]||(e[2661]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMergeBlocks-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMergeBlocks-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMergeBlocks")],-1)),e[2662]||(e[2662]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2663]||(e[2663]=l('
julia
mlirRewriterBaseMergeBlocks(rewriter, source, dest, nArgValues, argValues)

Inline the operations of block 'source' into the end of block 'dest'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'

The dest block must have no successors. Otherwise, the resulting IR would have unreachable operation.

source

',4))]),t("details",mg,[t("summary",null,[e[2664]||(e[2664]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore")],-1)),e[2665]||(e[2665]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2666]||(e[2666]=l('
julia
mlirRewriterBaseMoveBlockBefore(rewriter, block, existingBlock)

Unlink this block and insert it right before existingBlock.

source

',3))]),t("details",kg,[t("summary",null,[e[2667]||(e[2667]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter")],-1)),e[2668]||(e[2668]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2669]||(e[2669]=l('
julia
mlirRewriterBaseMoveOpAfter(rewriter, op, existingOp)

Unlink this operation from its current block and insert it right after existingOp which may be in the same or another block in the same function.

source

',3))]),t("details",Rg,[t("summary",null,[e[2670]||(e[2670]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore")],-1)),e[2671]||(e[2671]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2672]||(e[2672]=l('
julia
mlirRewriterBaseMoveOpBefore(rewriter, op, existingOp)

Unlink this operation from its current block and insert it right before existingOp which may be in the same or another block in the same function.

source

',3))]),t("details",Ig,[t("summary",null,[e[2673]||(e[2673]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation")],-1)),e[2674]||(e[2674]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2675]||(e[2675]=l('
julia
mlirRewriterBaseReplaceAllOpUsesWithOperation(rewriter, from, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.

source

',3))]),t("details",jg,[t("summary",null,[e[2676]||(e[2676]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange")],-1)),e[2677]||(e[2677]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2678]||(e[2678]=l('
julia
mlirRewriterBaseReplaceAllOpUsesWithValueRange(rewriter, from, nTo, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.

source

',3))]),t("details",fg,[t("summary",null,[e[2679]||(e[2679]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept")],-1)),e[2680]||(e[2680]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2681]||(e[2681]=l('
julia
mlirRewriterBaseReplaceAllUsesExcept(rewriter, from, to, exceptedUser)

Find uses of from and replace them with to except if the user is exceptedUser. Also notify the listener about every in-place op modification (for every use that was replaced).

source

',3))]),t("details",Mg,[t("summary",null,[e[2682]||(e[2682]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith")],-1)),e[2683]||(e[2683]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2684]||(e[2684]=l('
julia
mlirRewriterBaseReplaceAllUsesWith(rewriter, from, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).

source

',3))]),t("details",Ag,[t("summary",null,[e[2685]||(e[2685]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith")],-1)),e[2686]||(e[2686]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2687]||(e[2687]=l('
julia
mlirRewriterBaseReplaceAllValueRangeUsesWith(rewriter, nValues, from, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).

source

',3))]),t("details",Lg,[t("summary",null,[e[2688]||(e[2688]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock")],-1)),e[2689]||(e[2689]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2690]||(e[2690]=l('
julia
mlirRewriterBaseReplaceOpUsesWithinBlock(rewriter, op, nNewValues, newValues, block)

Find uses of from within block and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced). The optional allUsesReplaced flag is set to "true" if all uses were replaced.

source

',3))]),t("details",Eg,[t("summary",null,[e[2691]||(e[2691]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation")],-1)),e[2692]||(e[2692]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2693]||(e[2693]=l('
julia
mlirRewriterBaseReplaceOpWithOperation(rewriter, op, newOp)

Replace the results of the given (original) operation with the specified new op (replacement). The result types of the two ops must match. The original op is erased.

source

',3))]),t("details",vg,[t("summary",null,[e[2694]||(e[2694]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues")],-1)),e[2695]||(e[2695]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2696]||(e[2696]=l('
julia
mlirRewriterBaseReplaceOpWithValues(rewriter, op, nValues, values)

Replace the results of the given (original) operation with the specified list of values (replacements). The result types of the given op and the replacements must match. The original op is erased.

source

',3))]),t("details",Tg,[t("summary",null,[e[2697]||(e[2697]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter")],-1)),e[2698]||(e[2698]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2699]||(e[2699]=l('
julia
mlirRewriterBaseSetInsertionPointAfter(rewriter, op)

Sets the insertion point to the node after the specified operation, which will cause subsequent insertions to go right after it.

source

',3))]),t("details",Cg,[t("summary",null,[e[2700]||(e[2700]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue")],-1)),e[2701]||(e[2701]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2702]||(e[2702]=l('
julia
mlirRewriterBaseSetInsertionPointAfterValue(rewriter, value)

Sets the insertion point to the node after the specified value. If value has a defining operation, sets the insertion point to the node after such defining operation. This will cause subsequent insertions to go right after it. Otherwise, value is a BlockArgument. Sets the insertion point to the start of its block.

source

',3))]),t("details",xg,[t("summary",null,[e[2703]||(e[2703]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore")],-1)),e[2704]||(e[2704]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2705]||(e[2705]=l('
julia
mlirRewriterBaseSetInsertionPointBefore(rewriter, op)

Sets the insertion point to the specified operation, which will cause subsequent insertions to go right before it.

source

',3))]),t("details",Fg,[t("summary",null,[e[2706]||(e[2706]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd")],-1)),e[2707]||(e[2707]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2708]||(e[2708]=l('
julia
mlirRewriterBaseSetInsertionPointToEnd(rewriter, block)

Sets the insertion point to the end of the specified block.

source

',3))]),t("details",Pg,[t("summary",null,[e[2709]||(e[2709]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart")],-1)),e[2710]||(e[2710]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2711]||(e[2711]=l('
julia
mlirRewriterBaseSetInsertionPointToStart(rewriter, block)

Sets the insertion point to the start of the specified block.

source

',3))]),t("details",Dg,[t("summary",null,[e[2712]||(e[2712]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseStartOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseStartOpModification-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseStartOpModification")],-1)),e[2713]||(e[2713]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2714]||(e[2714]=l('
julia
mlirRewriterBaseStartOpModification(rewriter, op)

This method is used to notify the rewriter that an in-place operation modification is about to happen. A call to this function must be followed by a call to either finalizeOpModification or cancelOpModification. This is a minor efficiency win (it avoids creating a new operation and removing the old one) but also often allows simpler code in the client.

source

',3))]),t("details",Og,[t("summary",null,[e[2715]||(e[2715]=t("a",{id:"Reactant.MLIR.API.mlirSetGlobalDebugType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSetGlobalDebugType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSetGlobalDebugType")],-1)),e[2716]||(e[2716]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2717]||(e[2717]=l('
julia
mlirSetGlobalDebugType(type)

Sets the current debug type, similarly to -debug-only=type in the command-line tools. Note that global debug should be enabled for any output to be produced.

source

',3))]),t("details",Bg,[t("summary",null,[e[2718]||(e[2718]=t("a",{id:"Reactant.MLIR.API.mlirSetGlobalDebugTypes-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSetGlobalDebugTypes-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSetGlobalDebugTypes")],-1)),e[2719]||(e[2719]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2720]||(e[2720]=l('
julia
mlirSetGlobalDebugTypes(types, n)

Sets multiple current debug types, similarly to `-debug-only=type1,type2" in the command-line tools. Note that global debug should be enabled for any output to be produced.

source

',3))]),t("details",Gg,[t("summary",null,[e[2721]||(e[2721]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDimSize-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDimSize-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDimSize")],-1)),e[2722]||(e[2722]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2723]||(e[2723]=l('
julia
mlirShapedTypeGetDimSize(type, dim)

Returns the dim-th dimension of the given ranked shaped type.

source

',3))]),t("details",zg,[t("summary",null,[e[2724]||(e[2724]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDynamicSize-Tuple{}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDynamicSize-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDynamicSize")],-1)),e[2725]||(e[2725]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2726]||(e[2726]=l('
julia
mlirShapedTypeGetDynamicSize()

Returns the value indicating a dynamic size in a shaped type. Prefer mlirShapedTypeIsDynamicSize to direct comparisons with this value.

source

',3))]),t("details",wg,[t("summary",null,[e[2727]||(e[2727]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset-Tuple{}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset")],-1)),e[2728]||(e[2728]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2729]||(e[2729]=l('
julia
mlirShapedTypeGetDynamicStrideOrOffset()

Returns the value indicating a dynamic stride or offset in a shaped type. Prefer mlirShapedTypeGetDynamicStrideOrOffset to direct comparisons with this value.

source

',3))]),t("details",Sg,[t("summary",null,[e[2730]||(e[2730]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetElementType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetElementType")],-1)),e[2731]||(e[2731]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2732]||(e[2732]=l('
julia
mlirShapedTypeGetElementType(type)

Returns the element type of the shaped type.

source

',3))]),t("details",Ng,[t("summary",null,[e[2733]||(e[2733]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetRank-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetRank")],-1)),e[2734]||(e[2734]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2735]||(e[2735]=l('
julia
mlirShapedTypeGetRank(type)

Returns the rank of the given ranked shaped type.

source

',3))]),t("details",Vg,[t("summary",null,[e[2736]||(e[2736]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeHasRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeHasRank-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeHasRank")],-1)),e[2737]||(e[2737]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2738]||(e[2738]=l('
julia
mlirShapedTypeHasRank(type)

Checks whether the given shaped type is ranked.

source

',3))]),t("details",qg,[t("summary",null,[e[2739]||(e[2739]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeHasStaticShape-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeHasStaticShape-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeHasStaticShape")],-1)),e[2740]||(e[2740]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2741]||(e[2741]=l('
julia
mlirShapedTypeHasStaticShape(type)

Checks whether the given shaped type has a static shape.

source

',3))]),t("details",Ug,[t("summary",null,[e[2742]||(e[2742]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicDim-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicDim-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicDim")],-1)),e[2743]||(e[2743]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2744]||(e[2744]=l('
julia
mlirShapedTypeIsDynamicDim(type, dim)

Checks wither the dim-th dimension of the given shaped type is dynamic.

source

',3))]),t("details",Qg,[t("summary",null,[e[2745]||(e[2745]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicSize-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicSize-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicSize")],-1)),e[2746]||(e[2746]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2747]||(e[2747]=l('
julia
mlirShapedTypeIsDynamicSize(size)

Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.

source

',3))]),t("details",Wg,[t("summary",null,[e[2748]||(e[2748]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset")],-1)),e[2749]||(e[2749]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2750]||(e[2750]=l('
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)

Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.

source

',3))]),t("details",Hg,[t("summary",null,[e[2751]||(e[2751]=t("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetIndices-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetIndices-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetIndices")],-1)),e[2752]||(e[2752]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2753]||(e[2753]=l('
julia
mlirSparseElementsAttrGetIndices(attr)

Returns the dense elements attribute containing 64-bit integer indices of non-null elements in the given sparse elements attribute.

source

',3))]),t("details",Zg,[t("summary",null,[e[2754]||(e[2754]=t("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID")],-1)),e[2755]||(e[2755]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2756]||(e[2756]=l('
julia
mlirSparseElementsAttrGetTypeID()

Returns the typeID of a SparseElements attribute.

source

',3))]),t("details",Jg,[t("summary",null,[e[2757]||(e[2757]=t("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetValues-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetValues-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetValues")],-1)),e[2758]||(e[2758]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2759]||(e[2759]=l('
julia
mlirSparseElementsAttrGetValues(attr)

Returns the dense elements attribute containing the non-null elements in the given sparse elements attribute.

source

',3))]),t("details",Kg,[t("summary",null,[e[2760]||(e[2760]=t("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttribute-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttribute-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttribute")],-1)),e[2761]||(e[2761]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2762]||(e[2762]=l('
julia
mlirSparseElementsAttribute(shapedType, denseIndices, denseValues)

Creates a sparse elements attribute of the given shape from a list of indices and a list of associated values. Both lists are expected to be dense elements attributes with the same number of elements. The list of indices is expected to contain 64-bit integers. The attribute is created in the same context as the type.

source

',3))]),t("details",$g,[t("summary",null,[e[2763]||(e[2763]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet")],-1)),e[2764]||(e[2764]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2765]||(e[2765]=l('
julia
mlirSparseTensorEncodingAttrGet(ctx, lvlRank, lvlTypes, dimToLvl, lvlTodim, posWidth, crdWidth, explicitVal, implicitVal)

Creates a sparse\\_tensor.encoding attribute with the given parameters.

source

',3))]),t("details",Xg,[t("summary",null,[e[2766]||(e[2766]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth")],-1)),e[2767]||(e[2767]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2768]||(e[2768]=l('
julia
mlirSparseTensorEncodingAttrGetCrdWidth(attr)

Returns the coordinate bitwidth of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",Yg,[t("summary",null,[e[2769]||(e[2769]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl")],-1)),e[2770]||(e[2770]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2771]||(e[2771]=l('
julia
mlirSparseTensorEncodingAttrGetDimToLvl(attr)

Returns the dimension-to-level mapping of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",_g,[t("summary",null,[e[2772]||(e[2772]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal")],-1)),e[2773]||(e[2773]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2774]||(e[2774]=l('
julia
mlirSparseTensorEncodingAttrGetExplicitVal(attr)

Returns the explicit value of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ey,[t("summary",null,[e[2775]||(e[2775]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal")],-1)),e[2776]||(e[2776]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2777]||(e[2777]=l('
julia
mlirSparseTensorEncodingAttrGetImplicitVal(attr)

Returns the implicit value of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ty,[t("summary",null,[e[2778]||(e[2778]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt")],-1)),e[2779]||(e[2779]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2780]||(e[2780]=l('
julia
mlirSparseTensorEncodingAttrGetLvlFmt(attr, lvl)

Returns a specified level-format of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",sy,[t("summary",null,[e[2781]||(e[2781]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim")],-1)),e[2782]||(e[2782]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2783]||(e[2783]=l('
julia
mlirSparseTensorEncodingAttrGetLvlToDim(attr)

Returns the level-to-dimension mapping of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ay,[t("summary",null,[e[2784]||(e[2784]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType")],-1)),e[2785]||(e[2785]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2786]||(e[2786]=l('
julia
mlirSparseTensorEncodingAttrGetLvlType(attr, lvl)

Returns a specified level-type of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",iy,[t("summary",null,[e[2787]||(e[2787]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth")],-1)),e[2788]||(e[2788]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2789]||(e[2789]=l('
julia
mlirSparseTensorEncodingAttrGetPosWidth(attr)

Returns the position bitwidth of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ly,[t("summary",null,[e[2790]||(e[2790]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank")],-1)),e[2791]||(e[2791]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2792]||(e[2792]=l('
julia
mlirSparseTensorEncodingGetLvlRank(attr)

Returns the level-rank of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ny,[t("summary",null,[e[2793]||(e[2793]=t("a",{id:"Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID")],-1)),e[2794]||(e[2794]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2795]||(e[2795]=l('
julia
mlirStridedLayoutAttrGetTypeID()

Returns the typeID of a StridedLayout attribute.

source

',3))]),t("details",py,[t("summary",null,[e[2796]||(e[2796]=t("a",{id:"Reactant.MLIR.API.mlirStringAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGet")],-1)),e[2797]||(e[2797]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2798]||(e[2798]=l('
julia
mlirStringAttrGet(ctx, str)

Creates a string attribute in the given context containing the given string.

source

',3))]),t("details",ry,[t("summary",null,[e[2799]||(e[2799]=t("a",{id:"Reactant.MLIR.API.mlirStringAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirStringAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGetTypeID")],-1)),e[2800]||(e[2800]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2801]||(e[2801]=l('
julia
mlirStringAttrGetTypeID()

Returns the typeID of a String attribute.

source

',3))]),t("details",oy,[t("summary",null,[e[2802]||(e[2802]=t("a",{id:"Reactant.MLIR.API.mlirStringAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirStringAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGetValue")],-1)),e[2803]||(e[2803]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2804]||(e[2804]=l('
julia
mlirStringAttrGetValue(attr)

Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",dy,[t("summary",null,[e[2805]||(e[2805]=t("a",{id:"Reactant.MLIR.API.mlirStringAttrTypedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringAttrTypedGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrTypedGet")],-1)),e[2806]||(e[2806]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2807]||(e[2807]=l('
julia
mlirStringAttrTypedGet(type, str)

Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.

source

',3))]),t("details",cy,[t("summary",null,[e[2808]||(e[2808]=t("a",{id:"Reactant.MLIR.API.mlirStringRefCreate-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringRefCreate-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefCreate")],-1)),e[2809]||(e[2809]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2810]||(e[2810]=l('
julia
mlirStringRefCreate(str, length)

Constructs a string reference from the pointer and length. The pointer need not reference to a null-terminated string.

source

',3))]),t("details",by,[t("summary",null,[e[2811]||(e[2811]=t("a",{id:"Reactant.MLIR.API.mlirStringRefCreateFromCString-Tuple{Any}",href:"#Reactant.MLIR.API.mlirStringRefCreateFromCString-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefCreateFromCString")],-1)),e[2812]||(e[2812]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2813]||(e[2813]=l('
julia
mlirStringRefCreateFromCString(str)

Constructs a string reference from a null-terminated C string. Prefer mlirStringRefCreate if the length of the string is known.

source

',3))]),t("details",hy,[t("summary",null,[e[2814]||(e[2814]=t("a",{id:"Reactant.MLIR.API.mlirStringRefEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringRefEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefEqual")],-1)),e[2815]||(e[2815]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2816]||(e[2816]=l('
julia
mlirStringRefEqual(string, other)

Returns true if two string references are equal, false otherwise.

source

',3))]),t("details",uy,[t("summary",null,[e[2817]||(e[2817]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGet")],-1)),e[2818]||(e[2818]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2819]||(e[2819]=l('
julia
mlirSymbolRefAttrGet(ctx, symbol, numReferences, references)

Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.

source

',3))]),t("details",gy,[t("summary",null,[e[2820]||(e[2820]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference")],-1)),e[2821]||(e[2821]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2822]||(e[2822]=l('
julia
mlirSymbolRefAttrGetLeafReference(attr)

Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",yy,[t("summary",null,[e[2823]||(e[2823]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference")],-1)),e[2824]||(e[2824]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2825]||(e[2825]=l('
julia
mlirSymbolRefAttrGetNestedReference(attr, pos)

Returns pos-th reference nested in the given symbol reference attribute.

source

',3))]),t("details",my,[t("summary",null,[e[2826]||(e[2826]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences")],-1)),e[2827]||(e[2827]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2828]||(e[2828]=l('
julia
mlirSymbolRefAttrGetNumNestedReferences(attr)

Returns the number of references nested in the given symbol reference attribute.

source

',3))]),t("details",ky,[t("summary",null,[e[2829]||(e[2829]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference")],-1)),e[2830]||(e[2830]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2831]||(e[2831]=l('
julia
mlirSymbolRefAttrGetRootReference(attr)

Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",Ry,[t("summary",null,[e[2832]||(e[2832]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID")],-1)),e[2833]||(e[2833]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2834]||(e[2834]=l('
julia
mlirSymbolRefAttrGetTypeID()

Returns the typeID of an SymbolRef attribute.

source

',3))]),t("details",Iy,[t("summary",null,[e[2835]||(e[2835]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableCreate")],-1)),e[2836]||(e[2836]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2837]||(e[2837]=l('
julia
mlirSymbolTableCreate(operation)

Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.

source

',3))]),t("details",jy,[t("summary",null,[e[2838]||(e[2838]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableDestroy")],-1)),e[2839]||(e[2839]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2840]||(e[2840]=l('
julia
mlirSymbolTableDestroy(symbolTable)

Destroys the symbol table created with mlirSymbolTableCreate. This does not affect the operations in the table.

source

',3))]),t("details",fy,[t("summary",null,[e[2841]||(e[2841]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableErase-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableErase-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableErase")],-1)),e[2842]||(e[2842]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2843]||(e[2843]=l('
julia
mlirSymbolTableErase(symbolTable, operation)

Removes the given operation from the symbol table and erases it.

source

',3))]),t("details",My,[t("summary",null,[e[2844]||(e[2844]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName")],-1)),e[2845]||(e[2845]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2846]||(e[2846]=l('
julia
mlirSymbolTableGetSymbolAttributeName()

Returns the name of the attribute used to store symbol names compatible with symbol tables.

source

',3))]),t("details",Ay,[t("summary",null,[e[2847]||(e[2847]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName")],-1)),e[2848]||(e[2848]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2849]||(e[2849]=l('
julia
mlirSymbolTableGetVisibilityAttributeName()

Returns the name of the attribute used to store symbol visibility.

source

',3))]),t("details",Ly,[t("summary",null,[e[2850]||(e[2850]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableInsert-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableInsert-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableInsert")],-1)),e[2851]||(e[2851]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2852]||(e[2852]=l('
julia
mlirSymbolTableInsert(symbolTable, operation)

Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.

source

',3))]),t("details",Ey,[t("summary",null,[e[2853]||(e[2853]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableIsNull")],-1)),e[2854]||(e[2854]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2855]||(e[2855]=l('
julia
mlirSymbolTableIsNull(symbolTable)

Returns true if the symbol table is null.

source

',3))]),t("details",vy,[t("summary",null,[e[2856]||(e[2856]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableLookup-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableLookup-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableLookup")],-1)),e[2857]||(e[2857]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2858]||(e[2858]=l('
julia
mlirSymbolTableLookup(symbolTable, name)

Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.

source

',3))]),t("details",Ty,[t("summary",null,[e[2859]||(e[2859]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses")],-1)),e[2860]||(e[2860]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2861]||(e[2861]=l('
julia
mlirSymbolTableReplaceAllSymbolUses(oldSymbol, newSymbol, from)

Attempt to replace all uses that are nested within the given operation of the given symbol 'oldSymbol' with the provided 'newSymbol'. This does not traverse into nested symbol tables. Will fail atomically if there are any unknown operations that may be potential symbol tables.

source

',3))]),t("details",Cy,[t("summary",null,[e[2862]||(e[2862]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables")],-1)),e[2863]||(e[2863]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2864]||(e[2864]=l('
julia
mlirSymbolTableWalkSymbolTables(from, allSymUsesVisible, callback, userData)

Walks all symbol table operations nested within, and including, op. For each symbol table operation, the provided callback is invoked with the op and a boolean signifying if the symbols within that symbol table can be treated as if all uses within the IR are visible to the caller. allSymUsesVisible identifies whether all of the symbol uses of symbols within op are visible.

source

',3))]),t("details",xy,[t("summary",null,[e[2865]||(e[2865]=t("a",{id:"Reactant.MLIR.API.mlirTF32TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTF32TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTF32TypeGet")],-1)),e[2866]||(e[2866]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2867]||(e[2867]=l('
julia
mlirTF32TypeGet(ctx)

Creates a TF32 type in the given context. The type is owned by the context.

source

',3))]),t("details",Fy,[t("summary",null,[e[2868]||(e[2868]=t("a",{id:"Reactant.MLIR.API.mlirTransformApplyNamedSequence-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirTransformApplyNamedSequence-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformApplyNamedSequence")],-1)),e[2869]||(e[2869]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2870]||(e[2870]=l('
julia
mlirTransformApplyNamedSequence(payload, transformRoot, transformModule, transformOptions)

Applies the transformation script starting at the given transform root operation to the given payload operation. The module containing the transform root as well as the transform options should be provided. The transform operation must implement TransformOpInterface and the module must be a ModuleOp. Returns the status of the application.

source

',3))]),t("details",Py,[t("summary",null,[e[2871]||(e[2871]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirTransformOptionsCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsCreate")],-1)),e[2872]||(e[2872]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2873]||(e[2873]=l('
julia
mlirTransformOptionsCreate()

Creates a default-initialized transform options object.

source

',3))]),t("details",Dy,[t("summary",null,[e[2874]||(e[2874]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsDestroy")],-1)),e[2875]||(e[2875]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2876]||(e[2876]=l('
julia
mlirTransformOptionsDestroy(transformOptions)

Destroys a transform options object previously created by mlirTransformOptionsCreate.

source

',3))]),t("details",Oy,[t("summary",null,[e[2877]||(e[2877]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks")],-1)),e[2878]||(e[2878]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2879]||(e[2879]=l('
julia
mlirTransformOptionsEnableExpensiveChecks(transformOptions, enable)

Enables or disables expensive checks in transform options.

source

',3))]),t("details",By,[t("summary",null,[e[2880]||(e[2880]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp")],-1)),e[2881]||(e[2881]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2882]||(e[2882]=l('
julia
mlirTransformOptionsEnforceSingleTopLevelTransformOp(transformOptions, enable)

Enables or disables the enforcement of the top-level transform op being single in transform options.

source

',3))]),t("details",Gy,[t("summary",null,[e[2883]||(e[2883]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp")],-1)),e[2884]||(e[2884]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2885]||(e[2885]=l('
julia
mlirTransformOptionsGetEnforceSingleTopLevelTransformOp(transformOptions)

Returns true if the enforcement of the top-level transform op being single is enabled in transform options.

source

',3))]),t("details",zy,[t("summary",null,[e[2886]||(e[2886]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled")],-1)),e[2887]||(e[2887]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2888]||(e[2888]=l('
julia
mlirTransformOptionsGetExpensiveChecksEnabled(transformOptions)

Returns true if expensive checks are enabled in transform options.

source

',3))]),t("details",wy,[t("summary",null,[e[2889]||(e[2889]=t("a",{id:"Reactant.MLIR.API.mlirTranslateModuleToLLVMIR-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTranslateModuleToLLVMIR-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTranslateModuleToLLVMIR")],-1)),e[2890]||(e[2890]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2891]||(e[2891]=l('
julia
mlirTranslateModuleToLLVMIR(_module, context)

Translate operation that satisfies LLVM dialect module requirements into an LLVM IR module living in the given context. This translates operations from any dilalect that has a registered implementation of LLVMTranslationDialectInterface.

Returns

the generated LLVM IR Module from the translated MLIR module, it is owned by the caller.

source

',5))]),t("details",Sy,[t("summary",null,[e[2892]||(e[2892]=t("a",{id:"Reactant.MLIR.API.mlirTupleTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGet")],-1)),e[2893]||(e[2893]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2894]||(e[2894]=l('
julia
mlirTupleTypeGet(ctx, numElements, elements)

Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.

source

',3))]),t("details",Ny,[t("summary",null,[e[2895]||(e[2895]=t("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetNumTypes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGetNumTypes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetNumTypes")],-1)),e[2896]||(e[2896]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2897]||(e[2897]=l('
julia
mlirTupleTypeGetNumTypes(type)

Returns the number of types contained in a tuple.

source

',3))]),t("details",Vy,[t("summary",null,[e[2898]||(e[2898]=t("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGetType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetType")],-1)),e[2899]||(e[2899]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2900]||(e[2900]=l('
julia
mlirTupleTypeGetType(type, pos)

Returns the pos-th type in the tuple type.

source

',3))]),t("details",qy,[t("summary",null,[e[2901]||(e[2901]=t("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirTupleTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetTypeID")],-1)),e[2902]||(e[2902]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2903]||(e[2903]=l('
julia
mlirTupleTypeGetTypeID()

Returns the typeID of an Tuple type.

source

',3))]),t("details",Uy,[t("summary",null,[e[2904]||(e[2904]=t("a",{id:"Reactant.MLIR.API.mlirTypeAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGet")],-1)),e[2905]||(e[2905]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2906]||(e[2906]=l('
julia
mlirTypeAttrGet(type)

Creates a type attribute wrapping the given type in the same context as the type.

source

',3))]),t("details",Qy,[t("summary",null,[e[2907]||(e[2907]=t("a",{id:"Reactant.MLIR.API.mlirTypeAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirTypeAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGetTypeID")],-1)),e[2908]||(e[2908]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2909]||(e[2909]=l('
julia
mlirTypeAttrGetTypeID()

Returns the typeID of a Type attribute.

source

',3))]),t("details",Wy,[t("summary",null,[e[2910]||(e[2910]=t("a",{id:"Reactant.MLIR.API.mlirTypeAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGetValue")],-1)),e[2911]||(e[2911]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2912]||(e[2912]=l('
julia
mlirTypeAttrGetValue(attr)

Returns the type stored in the given type attribute.

source

',3))]),t("details",Hy,[t("summary",null,[e[2913]||(e[2913]=t("a",{id:"Reactant.MLIR.API.mlirTypeDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeDump")],-1)),e[2914]||(e[2914]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2915]||(e[2915]=l('
julia
mlirTypeDump(type)

Prints the type to the standard error stream.

source

',3))]),t("details",Zy,[t("summary",null,[e[2916]||(e[2916]=t("a",{id:"Reactant.MLIR.API.mlirTypeEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeEqual")],-1)),e[2917]||(e[2917]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2918]||(e[2918]=l('
julia
mlirTypeEqual(t1, t2)

Checks if two types are equal.

source

',3))]),t("details",Jy,[t("summary",null,[e[2919]||(e[2919]=t("a",{id:"Reactant.MLIR.API.mlirTypeGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetContext")],-1)),e[2920]||(e[2920]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2921]||(e[2921]=l('
julia
mlirTypeGetContext(type)

Gets the context that a type was created with.

source

',3))]),t("details",Ky,[t("summary",null,[e[2922]||(e[2922]=t("a",{id:"Reactant.MLIR.API.mlirTypeGetDialect-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetDialect-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetDialect")],-1)),e[2923]||(e[2923]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2924]||(e[2924]=l('
julia
mlirTypeGetDialect(type)

Gets the dialect a type belongs to.

source

',3))]),t("details",$y,[t("summary",null,[e[2925]||(e[2925]=t("a",{id:"Reactant.MLIR.API.mlirTypeGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetTypeID-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetTypeID")],-1)),e[2926]||(e[2926]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2927]||(e[2927]=l('
julia
mlirTypeGetTypeID(type)

Gets the type ID of the type.

source

',3))]),t("details",Xy,[t("summary",null,[e[2928]||(e[2928]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID")],-1)),e[2929]||(e[2929]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2930]||(e[2930]=l('
julia
mlirTypeIDAllocatorAllocateTypeID(allocator)

Allocates a type id that is valid for the lifetime of the allocator

source

',3))]),t("details",Yy,[t("summary",null,[e[2931]||(e[2931]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorCreate")],-1)),e[2932]||(e[2932]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2933]||(e[2933]=l('
julia
mlirTypeIDAllocatorCreate()

Creates a type id allocator for dynamic type id creation

source

',3))]),t("details",_y,[t("summary",null,[e[2934]||(e[2934]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorDestroy")],-1)),e[2935]||(e[2935]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2936]||(e[2936]=l('
julia
mlirTypeIDAllocatorDestroy(allocator)

Deallocates the allocator and all allocated type ids

source

',3))]),t("details",em,[t("summary",null,[e[2937]||(e[2937]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDCreate")],-1)),e[2938]||(e[2938]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2939]||(e[2939]=l('
julia
mlirTypeIDCreate(ptr)

ptr must be 8 byte aligned and unique to a type valid for the duration of the returned type id's usage

source

',3))]),t("details",tm,[t("summary",null,[e[2940]||(e[2940]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeIDEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDEqual")],-1)),e[2941]||(e[2941]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2942]||(e[2942]=l('
julia
mlirTypeIDEqual(typeID1, typeID2)

Checks if two type ids are equal.

source

',3))]),t("details",sm,[t("summary",null,[e[2943]||(e[2943]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDHashValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDHashValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDHashValue")],-1)),e[2944]||(e[2944]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2945]||(e[2945]=l('
julia
mlirTypeIDHashValue(typeID)

Returns the hash value of the type id.

source

',3))]),t("details",am,[t("summary",null,[e[2946]||(e[2946]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDIsNull")],-1)),e[2947]||(e[2947]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2948]||(e[2948]=l('
julia
mlirTypeIDIsNull(typeID)

Checks whether a type id is null.

source

',3))]),t("details",im,[t("summary",null,[e[2949]||(e[2949]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType")],-1)),e[2950]||(e[2950]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2951]||(e[2951]=l('
julia
mlirTypeIsAAnyQuantizedType(type)

Returns true if the given type is an AnyQuantizedType.

source

',3))]),t("details",lm,[t("summary",null,[e[2952]||(e[2952]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsABF16-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsABF16-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsABF16")],-1)),e[2953]||(e[2953]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2954]||(e[2954]=l('
julia
mlirTypeIsABF16(type)

Checks whether the given type is a bf16 type.

source

',3))]),t("details",nm,[t("summary",null,[e[2955]||(e[2955]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType")],-1)),e[2956]||(e[2956]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2957]||(e[2957]=l('
julia
mlirTypeIsACalibratedQuantizedType(type)

Returns true if the given type is a CalibratedQuantizedType.

source

',3))]),t("details",pm,[t("summary",null,[e[2958]||(e[2958]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAComplex-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAComplex-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAComplex")],-1)),e[2959]||(e[2959]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2960]||(e[2960]=l('
julia
mlirTypeIsAComplex(type)

Checks whether the given type is a Complex type.

source

',3))]),t("details",rm,[t("summary",null,[e[2961]||(e[2961]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAF16-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF16-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF16")],-1)),e[2962]||(e[2962]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2963]||(e[2963]=l('
julia
mlirTypeIsAF16(type)

Checks whether the given type is an f16 type.

source

',3))]),t("details",om,[t("summary",null,[e[2964]||(e[2964]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAF32-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF32-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF32")],-1)),e[2965]||(e[2965]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2966]||(e[2966]=l('
julia
mlirTypeIsAF32(type)

Checks whether the given type is an f32 type.

source

',3))]),t("details",dm,[t("summary",null,[e[2967]||(e[2967]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAF64-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF64-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF64")],-1)),e[2968]||(e[2968]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2969]||(e[2969]=l('
julia
mlirTypeIsAF64(type)

Checks whether the given type is an f64 type.

source

',3))]),t("details",cm,[t("summary",null,[e[2970]||(e[2970]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat")],-1)),e[2971]||(e[2971]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2972]||(e[2972]=l('
julia
mlirTypeIsAFloat(type)

Checks whether the given type is a floating-point type.

source

',3))]),t("details",bm,[t("summary",null,[e[2973]||(e[2973]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN")],-1)),e[2974]||(e[2974]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2975]||(e[2975]=l('
julia
mlirTypeIsAFloat4E2M1FN(type)

Checks whether the given type is an f4E2M1FN type.

source

',3))]),t("details",hm,[t("summary",null,[e[2976]||(e[2976]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN")],-1)),e[2977]||(e[2977]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2978]||(e[2978]=l('
julia
mlirTypeIsAFloat6E2M3FN(type)

Checks whether the given type is an f6E2M3FN type.

source

',3))]),t("details",um,[t("summary",null,[e[2979]||(e[2979]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN")],-1)),e[2980]||(e[2980]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2981]||(e[2981]=l('
julia
mlirTypeIsAFloat6E3M2FN(type)

Checks whether the given type is an f6E3M2FN type.

source

',3))]),t("details",gm,[t("summary",null,[e[2982]||(e[2982]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E3M4-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E3M4-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E3M4")],-1)),e[2983]||(e[2983]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2984]||(e[2984]=l('
julia
mlirTypeIsAFloat8E3M4(type)

Checks whether the given type is an f8E3M4 type.

source

',3))]),t("details",ym,[t("summary",null,[e[2985]||(e[2985]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3")],-1)),e[2986]||(e[2986]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2987]||(e[2987]=l('
julia
mlirTypeIsAFloat8E4M3(type)

Checks whether the given type is an f8E4M3 type.

source

',3))]),t("details",mm,[t("summary",null,[e[2988]||(e[2988]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ")],-1)),e[2989]||(e[2989]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2990]||(e[2990]=l('
julia
mlirTypeIsAFloat8E4M3B11FNUZ(type)

Checks whether the given type is an f8E4M3B11FNUZ type.

source

',3))]),t("details",km,[t("summary",null,[e[2991]||(e[2991]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN")],-1)),e[2992]||(e[2992]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2993]||(e[2993]=l('
julia
mlirTypeIsAFloat8E4M3FN(type)

Checks whether the given type is an f8E4M3FN type.

source

',3))]),t("details",Rm,[t("summary",null,[e[2994]||(e[2994]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ")],-1)),e[2995]||(e[2995]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2996]||(e[2996]=l('
julia
mlirTypeIsAFloat8E4M3FNUZ(type)

Checks whether the given type is an f8E4M3FNUZ type.

source

',3))]),t("details",Im,[t("summary",null,[e[2997]||(e[2997]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E5M2-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2")],-1)),e[2998]||(e[2998]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2999]||(e[2999]=l('
julia
mlirTypeIsAFloat8E5M2(type)

Checks whether the given type is an f8E5M2 type.

source

',3))]),t("details",jm,[t("summary",null,[e[3e3]||(e[3e3]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ")],-1)),e[3001]||(e[3001]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3002]||(e[3002]=l('
julia
mlirTypeIsAFloat8E5M2FNUZ(type)

Checks whether the given type is an f8E5M2FNUZ type.

source

',3))]),t("details",fm,[t("summary",null,[e[3003]||(e[3003]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU")],-1)),e[3004]||(e[3004]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3005]||(e[3005]=l('
julia
mlirTypeIsAFloat8E8M0FNU(type)

Checks whether the given type is an f8E8M0FNU type.

source

',3))]),t("details",Mm,[t("summary",null,[e[3006]||(e[3006]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFunction-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFunction-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFunction")],-1)),e[3007]||(e[3007]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3008]||(e[3008]=l('
julia
mlirTypeIsAFunction(type)

Checks whether the given type is a function type.

source

',3))]),t("details",Am,[t("summary",null,[e[3009]||(e[3009]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAIndex-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAIndex-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAIndex")],-1)),e[3010]||(e[3010]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3011]||(e[3011]=l('
julia
mlirTypeIsAIndex(type)

Checks whether the given type is an index type.

source

',3))]),t("details",Lm,[t("summary",null,[e[3012]||(e[3012]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAInteger-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAInteger-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAInteger")],-1)),e[3013]||(e[3013]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3014]||(e[3014]=l('
julia
mlirTypeIsAInteger(type)

Checks whether the given type is an integer type.

source

',3))]),t("details",Em,[t("summary",null,[e[3015]||(e[3015]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsALLVMPointerType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsALLVMPointerType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsALLVMPointerType")],-1)),e[3016]||(e[3016]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3017]||(e[3017]=l('
julia
mlirTypeIsALLVMPointerType(type)

Returns true if the type is an LLVM dialect pointer type.

source

',3))]),t("details",vm,[t("summary",null,[e[3018]||(e[3018]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsALLVMStructType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsALLVMStructType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsALLVMStructType")],-1)),e[3019]||(e[3019]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3020]||(e[3020]=l('
julia
mlirTypeIsALLVMStructType(type)

Returns true if the type is an LLVM dialect struct type.

source

',3))]),t("details",Tm,[t("summary",null,[e[3021]||(e[3021]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAMemRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAMemRef-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAMemRef")],-1)),e[3022]||(e[3022]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3023]||(e[3023]=l('
julia
mlirTypeIsAMemRef(type)

Checks whether the given type is a MemRef type.

source

',3))]),t("details",Cm,[t("summary",null,[e[3024]||(e[3024]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsANone-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsANone-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsANone")],-1)),e[3025]||(e[3025]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3026]||(e[3026]=l('
julia
mlirTypeIsANone(type)

Checks whether the given type is a None type.

source

',3))]),t("details",xm,[t("summary",null,[e[3027]||(e[3027]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAOpaque-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAOpaque")],-1)),e[3028]||(e[3028]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3029]||(e[3029]=l('
julia
mlirTypeIsAOpaque(type)

Checks whether the given type is an opaque type.

source

',3))]),t("details",Fm,[t("summary",null,[e[3030]||(e[3030]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAQuantizedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAQuantizedType")],-1)),e[3031]||(e[3031]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3032]||(e[3032]=l('
julia
mlirTypeIsAQuantizedType(type)

Returns true if the given type is a quantization dialect type.

source

',3))]),t("details",Pm,[t("summary",null,[e[3033]||(e[3033]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsARankedTensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsARankedTensor-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsARankedTensor")],-1)),e[3034]||(e[3034]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3035]||(e[3035]=l('
julia
mlirTypeIsARankedTensor(type)

Checks whether the given type is a ranked tensor type.

source

',3))]),t("details",Dm,[t("summary",null,[e[3036]||(e[3036]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAShaped-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAShaped-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAShaped")],-1)),e[3037]||(e[3037]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3038]||(e[3038]=l('
julia
mlirTypeIsAShaped(type)

Checks whether the given type is a Shaped type.

source

',3))]),t("details",Om,[t("summary",null,[e[3039]||(e[3039]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsATF32-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATF32-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATF32")],-1)),e[3040]||(e[3040]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3041]||(e[3041]=l('
julia
mlirTypeIsATF32(type)

Checks whether the given type is an TF32 type.

source

',3))]),t("details",Bm,[t("summary",null,[e[3042]||(e[3042]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsATensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATensor-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATensor")],-1)),e[3043]||(e[3043]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3044]||(e[3044]=l('
julia
mlirTypeIsATensor(type)

Checks whether the given type is a Tensor type.

source

',3))]),t("details",Gm,[t("summary",null,[e[3045]||(e[3045]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsATuple-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATuple-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATuple")],-1)),e[3046]||(e[3046]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3047]||(e[3047]=l('
julia
mlirTypeIsATuple(type)

Checks whether the given type is a tuple type.

source

',3))]),t("details",zm,[t("summary",null,[e[3048]||(e[3048]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType")],-1)),e[3049]||(e[3049]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3050]||(e[3050]=l('
julia
mlirTypeIsAUniformQuantizedPerAxisType(type)

Returns true if the given type is a UniformQuantizedPerAxisType.

source

',3))]),t("details",wm,[t("summary",null,[e[3051]||(e[3051]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType")],-1)),e[3052]||(e[3052]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3053]||(e[3053]=l('
julia
mlirTypeIsAUniformQuantizedType(type)

Returns true if the given type is a UniformQuantizedType.

source

',3))]),t("details",Sm,[t("summary",null,[e[3054]||(e[3054]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef")],-1)),e[3055]||(e[3055]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3056]||(e[3056]=l('
julia
mlirTypeIsAUnrankedMemRef(type)

Checks whether the given type is an UnrankedMemRef type.

source

',3))]),t("details",Nm,[t("summary",null,[e[3057]||(e[3057]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAUnrankedTensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUnrankedTensor-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUnrankedTensor")],-1)),e[3058]||(e[3058]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3059]||(e[3059]=l('
julia
mlirTypeIsAUnrankedTensor(type)

Checks whether the given type is an unranked tensor type.

source

',3))]),t("details",Vm,[t("summary",null,[e[3060]||(e[3060]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAVector-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAVector-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAVector")],-1)),e[3061]||(e[3061]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3062]||(e[3062]=l('
julia
mlirTypeIsAVector(type)

Checks whether the given type is a Vector type.

source

',3))]),t("details",qm,[t("summary",null,[e[3063]||(e[3063]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsNull")],-1)),e[3064]||(e[3064]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3065]||(e[3065]=l('
julia
mlirTypeIsNull(type)

Checks whether a type is null.

source

',3))]),t("details",Um,[t("summary",null,[e[3066]||(e[3066]=t("a",{id:"Reactant.MLIR.API.mlirTypeParseGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeParseGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeParseGet")],-1)),e[3067]||(e[3067]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3068]||(e[3068]=l('
julia
mlirTypeParseGet(context, type)

Parses a type. The type is owned by the context.

source

',3))]),t("details",Qm,[t("summary",null,[e[3069]||(e[3069]=t("a",{id:"Reactant.MLIR.API.mlirTypePrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirTypePrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypePrint")],-1)),e[3070]||(e[3070]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3071]||(e[3071]=l('
julia
mlirTypePrint(type, callback, userData)

Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Wm,[t("summary",null,[e[3072]||(e[3072]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet")],-1)),e[3073]||(e[3073]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3074]||(e[3074]=l('
julia
mlirUniformQuantizedPerAxisTypeGet(flags, storageType, expressedType, nDims, scales, zeroPoints, quantizedDimension, storageTypeMin, storageTypeMax)

Creates an instance of UniformQuantizedPerAxisType with the given parameters in the same context as storageType and returns it. scales and zeroPoints point to nDims number of elements. The instance is owned by the context.

source

',3))]),t("details",Hm,[t("summary",null,[e[3075]||(e[3075]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims")],-1)),e[3076]||(e[3076]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3077]||(e[3077]=l('
julia
mlirUniformQuantizedPerAxisTypeGetNumDims(type)

Returns the number of axes in the given quantized per-axis type.

source

',3))]),t("details",Zm,[t("summary",null,[e[3078]||(e[3078]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension")],-1)),e[3079]||(e[3079]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3080]||(e[3080]=l('
julia
mlirUniformQuantizedPerAxisTypeGetQuantizedDimension(type)

Returns the index of the quantized dimension in the given quantized per-axis type.

source

',3))]),t("details",Jm,[t("summary",null,[e[3081]||(e[3081]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale")],-1)),e[3082]||(e[3082]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3083]||(e[3083]=l('
julia
mlirUniformQuantizedPerAxisTypeGetScale(type, pos)

Returns pos-th scale of the given quantized per-axis type.

source

',3))]),t("details",Km,[t("summary",null,[e[3084]||(e[3084]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint")],-1)),e[3085]||(e[3085]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3086]||(e[3086]=l('
julia
mlirUniformQuantizedPerAxisTypeGetZeroPoint(type, pos)

Returns pos-th zero point of the given quantized per-axis type.

source

',3))]),t("details",$m,[t("summary",null,[e[3087]||(e[3087]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint")],-1)),e[3088]||(e[3088]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3089]||(e[3089]=l('
julia
mlirUniformQuantizedPerAxisTypeIsFixedPoint(type)

Returns true if the given uniform quantized per-axis type is fixed-point.

source

',3))]),t("details",Xm,[t("summary",null,[e[3090]||(e[3090]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGet-NTuple{7, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGet-NTuple{7, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGet")],-1)),e[3091]||(e[3091]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3092]||(e[3092]=l('
julia
mlirUniformQuantizedTypeGet(flags, storageType, expressedType, scale, zeroPoint, storageTypeMin, storageTypeMax)

Creates an instance of UniformQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.

source

',3))]),t("details",Ym,[t("summary",null,[e[3093]||(e[3093]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale")],-1)),e[3094]||(e[3094]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3095]||(e[3095]=l('
julia
mlirUniformQuantizedTypeGetScale(type)

Returns the scale of the given uniform quantized type.

source

',3))]),t("details",_m,[t("summary",null,[e[3096]||(e[3096]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint")],-1)),e[3097]||(e[3097]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3098]||(e[3098]=l('
julia
mlirUniformQuantizedTypeGetZeroPoint(type)

Returns the zero point of the given uniform quantized type.

source

',3))]),t("details",ek,[t("summary",null,[e[3099]||(e[3099]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint")],-1)),e[3100]||(e[3100]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3101]||(e[3101]=l('
julia
mlirUniformQuantizedTypeIsFixedPoint(type)

Returns true if the given uniform quantized type is fixed-point.

source

',3))]),t("details",tk,[t("summary",null,[e[3102]||(e[3102]=t("a",{id:"Reactant.MLIR.API.mlirUnitAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnitAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnitAttrGet")],-1)),e[3103]||(e[3103]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3104]||(e[3104]=l('
julia
mlirUnitAttrGet(ctx)

Creates a unit attribute in the given context.

source

',3))]),t("details",sk,[t("summary",null,[e[3105]||(e[3105]=t("a",{id:"Reactant.MLIR.API.mlirUnitAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnitAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnitAttrGetTypeID")],-1)),e[3106]||(e[3106]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3107]||(e[3107]=l('
julia
mlirUnitAttrGetTypeID()

Returns the typeID of a Unit attribute.

source

',3))]),t("details",ak,[t("summary",null,[e[3108]||(e[3108]=t("a",{id:"Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet-NTuple{8, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet")],-1)),e[3109]||(e[3109]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3110]||(e[3110]=l('
julia
mlirUnmanagedDenseResourceElementsAttrGet(shapedType, name, data, dataLength, dataAlignment, dataIsMutable, deleter, userData)

Unlike the typed accessors below, constructs the attribute with a raw data buffer and no type/alignment checking. Use a more strongly typed accessor if possible. If dataIsMutable is false, then an immutable AsmResourceBlob will be created and that passed data contents will be treated as const. If the deleter is non NULL, then it will be called when the data buffer can no longer be accessed (passing userData to it).

source

',3))]),t("details",ik,[t("summary",null,[e[3111]||(e[3111]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGet")],-1)),e[3112]||(e[3112]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3113]||(e[3113]=l('
julia
mlirUnrankedMemRefTypeGet(elementType, memorySpace)

Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type.

source

',3))]),t("details",lk,[t("summary",null,[e[3114]||(e[3114]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked")],-1)),e[3115]||(e[3115]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3116]||(e[3116]=l('
julia
mlirUnrankedMemRefTypeGetChecked(loc, elementType, memorySpace)

Same as "mlirUnrankedMemRefTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",nk,[t("summary",null,[e[3117]||(e[3117]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID")],-1)),e[3118]||(e[3118]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3119]||(e[3119]=l('
julia
mlirUnrankedMemRefTypeGetTypeID()

Returns the typeID of an UnrankedMemRef type.

source

',3))]),t("details",pk,[t("summary",null,[e[3120]||(e[3120]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace")],-1)),e[3121]||(e[3121]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3122]||(e[3122]=l('
julia
mlirUnrankedMemrefGetMemorySpace(type)

Returns the memory spcae of the given Unranked MemRef type.

source

',3))]),t("details",rk,[t("summary",null,[e[3123]||(e[3123]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGet")],-1)),e[3124]||(e[3124]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3125]||(e[3125]=l('
julia
mlirUnrankedTensorTypeGet(elementType)

Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context.

source

',3))]),t("details",ok,[t("summary",null,[e[3126]||(e[3126]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked")],-1)),e[3127]||(e[3127]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3128]||(e[3128]=l('
julia
mlirUnrankedTensorTypeGetChecked(loc, elementType)

Same as "mlirUnrankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",dk,[t("summary",null,[e[3129]||(e[3129]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID")],-1)),e[3130]||(e[3130]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3131]||(e[3131]=l('
julia
mlirUnrankedTensorTypeGetTypeID()

Returns the typeID of an UnrankedTensor type.

source

',3))]),t("details",ck,[t("summary",null,[e[3132]||(e[3132]=t("a",{id:"Reactant.MLIR.API.mlirValueDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueDump")],-1)),e[3133]||(e[3133]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3134]||(e[3134]=l('
julia
mlirValueDump(value)

Prints the value to the standard error stream.

source

',3))]),t("details",bk,[t("summary",null,[e[3135]||(e[3135]=t("a",{id:"Reactant.MLIR.API.mlirValueEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirValueEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueEqual")],-1)),e[3136]||(e[3136]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3137]||(e[3137]=l('
julia
mlirValueEqual(value1, value2)

Returns 1 if two values are equal, 0 otherwise.

source

',3))]),t("details",hk,[t("summary",null,[e[3138]||(e[3138]=t("a",{id:"Reactant.MLIR.API.mlirValueGetFirstUse-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueGetFirstUse-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueGetFirstUse")],-1)),e[3139]||(e[3139]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3140]||(e[3140]=l('
julia
mlirValueGetFirstUse(value)

Returns an op operand representing the first use of the value, or a null op operand if there are no uses.

source

',3))]),t("details",uk,[t("summary",null,[e[3141]||(e[3141]=t("a",{id:"Reactant.MLIR.API.mlirValueGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueGetType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueGetType")],-1)),e[3142]||(e[3142]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3143]||(e[3143]=l('
julia
mlirValueGetType(value)

Returns the type of the value.

source

',3))]),t("details",gk,[t("summary",null,[e[3144]||(e[3144]=t("a",{id:"Reactant.MLIR.API.mlirValueIsABlockArgument-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueIsABlockArgument-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueIsABlockArgument")],-1)),e[3145]||(e[3145]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3146]||(e[3146]=l('
julia
mlirValueIsABlockArgument(value)

Returns 1 if the value is a block argument, 0 otherwise.

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mlirValueIsAOpResult(value)

Returns 1 if the value is an operation result, 0 otherwise.

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mlirValueIsNull(value)

Returns whether the value is null.

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mlirValuePrint(value, callback, userData)

Prints a value by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

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mlirValuePrintAsOperand(value, state, callback, userData)

Prints a value as an operand (i.e., the ValueID).

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mlirValueReplaceAllUsesExcept(of, with, numExceptions, exceptions)

Replace all uses of 'of' value with 'with' value, updating anything in the IR that uses 'of' to use 'with' instead, except if the user is listed in 'exceptions'. The 'exceptions' parameter is an array of MlirOperation pointers with a length of 'numExceptions'.

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mlirValueReplaceAllUsesOfWith(of, with)

Replace all uses of 'of' value with the 'with' value, updating anything in the IR that uses 'of' to use the other value instead. When this returns there are zero uses of 'of'.

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mlirValueSetType(value, type)

Set the type of the value.

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mlirVectorTypeGet(rank, shape, elementType)

Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context.

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mlirVectorTypeGetChecked(loc, rank, shape, elementType)

Same as "mlirVectorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

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mlirVectorTypeGetScalable(rank, shape, scalable, elementType)

Creates a scalable vector type with the shape identified by its rank and dimensions. A subset of dimensions may be marked as scalable via the corresponding flag list, which is expected to have as many entries as the rank of the vector. The vector is created in the same context as the element type.

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mlirVectorTypeGetScalableChecked(loc, rank, shape, scalable, elementType)

Same as "mlirVectorTypeGetScalable" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

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mlirVectorTypeGetTypeID()

Returns the typeID of an Vector type.

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mlirVectorTypeIsDimScalable(type, dim)

Checks whether the "dim"-th dimension of the given vector is scalable.

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mlirVectorTypeIsScalable(type)

Checks whether the given vector type is scalable, i.e., has at least one scalable dimension.

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Returns the value stored in the given bool attribute.

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Float64(attr)

Returns the value stored in the given floating point attribute, interpreting the value as double.

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Int64(attr)

Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.

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String(attr)

Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.

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String(ident)

Gets the string value of the identifier.

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UInt64(attr)

Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.

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AffineMap(ndims, nsymbols, affineExprs; context=context())

Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.

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AffineMap(ndims, nsymbols; context=context())

Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.

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AffineMap(attr)

Returns the affine map wrapped in the given affine map attribute.

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julia
AffineMap(; context=context())

Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.

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Attribute(str; context=context())

Creates a string attribute in the given context containing the given string.

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Attribute(value; context=context())

Creates a bool attribute in the given context with the given value.

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Attribute(elements; context=context())

Creates a dictionary attribute containing the given list of elements in the provided context.

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Attribute(affineMap)

Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.

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Attribute(type, str)

Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.

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Attribute(type)

Creates a type attribute wrapping the given type in the same context as the type.

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Attribute(float; context=context(), location=Location(), check=false)

Creates a floating point attribute in the given context with the given double value and double-precision FP semantics. If check=true, emits appropriate diagnostics on illegal arguments.

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Attribute(complex; context=context(), location=Location(), check=false)

Creates a complex attribute in the given context with the given complex value and double-precision FP semantics.

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Attribute(elements; context=context())

Creates an array element containing the given list of elements in the given context.

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Attribute()

Returns an empty attribute.

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Attribute(int)

Creates an integer attribute of the given type with the given integer value.

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Block(args, locs)

Creates a new empty block with the given argument types and transfers ownership to the caller.

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BlockIterator(region::Region)

Iterates over all blocks in the given region.

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Context()

Creates an MLIR context and transfers its ownership to the caller.

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julia
ExecutionEngine(op, optLevel, sharedlibs = [])

Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.

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Identifier(context, str)

Gets an identifier with the given string value.

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julia
IntegerSet(ndims, nsymbols, constraints, eqflags; context=context())

Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqflags is 1) or inequalities (eqflags is 0). Both constraints and eqflags need to be arrays of the same length.

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Integerset(ndims, nsymbols; context=context())

Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.

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julia
LogicalResult

A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of LogicalResult must only be inspected using the associated functions.

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Module(location=Location())

Creates a new, empty module and transfers ownership to the caller.

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julia
NamedAttribute(name, attr)

Associates an attribute with the name. Takes ownership of neither.

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julia
OpPassManager(opPassManager, operationName)

Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.

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julia
OpPassManager(passManager, operationName)

Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.

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julia
OpPassManager(passManager)

Cast a top-level PassManager to a generic OpPassManager.

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',3))]),e("details",W,[e("summary",null,[t[102]||(t[102]=e("a",{id:"Reactant.MLIR.IR.Operation-Tuple{Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.Operation-Tuple{Reactant.MLIR.IR.Module}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Operation")],-1)),t[103]||(t[103]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[104]||(t[104]=l('
julia
Operation(module)

Views the module as a generic operation.

source

',3))]),e("details",H,[e("summary",null,[t[105]||(t[105]=e("a",{id:"Reactant.MLIR.IR.OperationIterator",href:"#Reactant.MLIR.IR.OperationIterator"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.OperationIterator")],-1)),t[106]||(t[106]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[107]||(t[107]=l('
julia
OperationIterator(block::Block)

Iterates over all operations for the given block.

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',3))]),e("details",Z,[e("summary",null,[t[108]||(t[108]=e("a",{id:"Reactant.MLIR.IR.PassManager-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.PassManager-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.PassManager")],-1)),t[109]||(t[109]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[110]||(t[110]=l('
julia
PassManager(anchorOp; context=context())

Create a new top-level PassManager anchored on anchorOp.

source

',3))]),e("details",J,[e("summary",null,[t[111]||(t[111]=e("a",{id:"Reactant.MLIR.IR.PassManager-Tuple{}",href:"#Reactant.MLIR.IR.PassManager-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.PassManager")],-1)),t[112]||(t[112]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[113]||(t[113]=l('
julia
PassManager(; context=context())

Create a new top-level PassManager.

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',3))]),e("details",K,[e("summary",null,[t[114]||(t[114]=e("a",{id:"Reactant.MLIR.IR.Region-Tuple{}",href:"#Reactant.MLIR.IR.Region-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Region")],-1)),t[115]||(t[115]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[116]||(t[116]=l('
julia
Region()

Creates a new empty region and transfers ownership to the caller.

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',3))]),e("details",$,[e("summary",null,[t[117]||(t[117]=e("a",{id:"Reactant.MLIR.IR.RegionIterator",href:"#Reactant.MLIR.IR.RegionIterator"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.RegionIterator")],-1)),t[118]||(t[118]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[119]||(t[119]=l('
julia
RegionIterator(::Operation)

Iterates over all sub-regions for the given operation.

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',3))]),e("details",X,[e("summary",null,[t[120]||(t[120]=e("a",{id:"Reactant.MLIR.IR.SymbolTable-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.SymbolTable-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolTable")],-1)),t[121]||(t[121]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[122]||(t[122]=l('
julia
mlirSymbolTableCreate(operation)

Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.

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',3))]),e("details",Y,[e("summary",null,[t[123]||(t[123]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.Type-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[124]||(t[124]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[125]||(t[125]=l('
julia
Type(attr)

Returns the type stored in the given type attribute.

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',3))]),e("details",_,[e("summary",null,[t[126]||(t[126]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Integer}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Integer}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[127]||(t[127]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[128]||(t[128]=l('
julia
Type(T::Core.Type{<:Integer}; context=context()

Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),e("details",tt,[e("summary",null,[t[129]||(t[129]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Signed}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Signed}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[130]||(t[130]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[131]||(t[131]=l('
julia
Type(T::Core.Type{<:Signed}; context=context()

Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),e("details",et,[e("summary",null,[t[132]||(t[132]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Unsigned}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Unsigned}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[133]||(t[133]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[134]||(t[134]=l('
julia
Type(T::Core.Type{<:Unsigned}; context=context()

Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),e("details",st,[e("summary",null,[t[135]||(t[135]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Bool}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Bool}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[136]||(t[136]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[137]||(t[137]=l('
julia
Type(T::Core.Type{Bool}; context=context()

Creates a 1-bit signless integer type in the context. The type is owned by the context.

source

',3))]),e("details",at,[e("summary",null,[t[138]||(t[138]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float16}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float16}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[139]||(t[139]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[140]||(t[140]=l('
julia
Type(::Core.Type{Float16}; context=context())

Creates an f16 type in the given context. The type is owned by the context.

source

',3))]),e("details",it,[e("summary",null,[t[141]||(t[141]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float32}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float32}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[142]||(t[142]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[143]||(t[143]=l('
julia
Type(Core.Type{Float32}; context=context())

Creates an f32 type in the given context. The type is owned by the context.

source

',3))]),e("details",lt,[e("summary",null,[t[144]||(t[144]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float64}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float64}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[145]||(t[145]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[146]||(t[146]=l('
julia
Type(Core.Type{Float64}; context=context())

Creates a f64 type in the given context. The type is owned by the context.

source

',3))]),e("details",nt,[e("summary",null,[t[147]||(t[147]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Nothing}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Nothing}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[148]||(t[148]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[149]||(t[149]=l('
julia
Type(::Core.Type{Nothing}; context=context())

Creates a None type in the given context. The type is owned by the context.

source

',3))]),e("details",pt,[e("summary",null,[t[150]||(t[150]=e("a",{id:"Reactant.MLIR.IR.Type-Tuple{Vector{Reactant.MLIR.IR.Type}}",href:"#Reactant.MLIR.IR.Type-Tuple{Vector{Reactant.MLIR.IR.Type}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[151]||(t[151]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[152]||(t[152]=l(`
julia
Type(elements; context=context())
-Type(::Core.Type{<:Tuple{T...}}; context=context())

Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.

source

`,3))]),e("details",rt,[e("summary",null,[t[153]||(t[153]=e("a",{id:"Reactant.MLIR.IR.Type-Union{Tuple{Type{Complex{T}}}, Tuple{T}} where T",href:"#Reactant.MLIR.IR.Type-Union{Tuple{Type{Complex{T}}}, Tuple{T}} where T"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),t[154]||(t[154]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[155]||(t[155]=l('
julia
Type(Complex{T}) where {T}

Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.

source

',3))]),e("details",ot,[e("summary",null,[t[156]||(t[156]=e("a",{id:"Base.:*-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.:*-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Base.:*")],-1)),t[157]||(t[157]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[158]||(t[158]=l('
julia
*(lhs, rhs)

Creates an affine mul expression with 'lhs' and 'rhs'.

source

',3))]),e("details",dt,[e("summary",null,[t[159]||(t[159]=e("a",{id:"Base.:+-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.:+-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Base.:+")],-1)),t[160]||(t[160]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[161]||(t[161]=l('
julia
+(lhs, rhs)

Creates an affine add expression with 'lhs' and 'rhs'.

source

',3))]),e("details",ht,[e("summary",null,[t[162]||(t[162]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[163]||(t[163]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[164]||(t[164]=l('
julia
==(a, b)

Returns true if the two affine expressions are equal.

source

',3))]),e("details",ct,[e("summary",null,[t[165]||(t[165]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.AffineMap, Reactant.MLIR.IR.AffineMap}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.AffineMap, Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[166]||(t[166]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[167]||(t[167]=l('
julia
==(a, b)

Checks if two affine maps are equal.

source

',3))]),e("details",ut,[e("summary",null,[t[168]||(t[168]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Attribute}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[169]||(t[169]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[170]||(t[170]=l('
julia
==(a1, a2)

Checks if two attributes are equal.

source

',3))]),e("details",bt,[e("summary",null,[t[171]||(t[171]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[172]||(t[172]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[173]||(t[173]=l('
julia
==(block, other)

Checks whether two blocks handles point to the same block. This does not perform deep comparison.

source

',3))]),e("details",gt,[e("summary",null,[t[174]||(t[174]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Identifier, Reactant.MLIR.IR.Identifier}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Identifier, Reactant.MLIR.IR.Identifier}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[175]||(t[175]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[176]||(t[176]=l('
julia
==(ident, other)

Checks whether two identifiers are the same.

source

',3))]),e("details",yt,[e("summary",null,[t[177]||(t[177]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.IntegerSet, Reactant.MLIR.IR.IntegerSet}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.IntegerSet, Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[178]||(t[178]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[179]||(t[179]=l('
julia
==(s1, s2)

Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.

source

',3))]),e("details",mt,[e("summary",null,[t[180]||(t[180]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Region}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Region}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[181]||(t[181]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[182]||(t[182]=l('
julia
==(region, other)

Checks whether two region handles point to the same region. This does not perform deep comparison.

source

',3))]),e("details",kt,[e("summary",null,[t[183]||(t[183]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Type, Reactant.MLIR.IR.Type}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Type, Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[184]||(t[184]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[185]||(t[185]=l('
julia
==(t1, t2)

Checks if two types are equal.

source

',3))]),e("details",Rt,[e("summary",null,[t[186]||(t[186]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.TypeID, Reactant.MLIR.IR.TypeID}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.TypeID, Reactant.MLIR.IR.TypeID}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[187]||(t[187]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[188]||(t[188]=l('
julia
==(typeID1, typeID2)

Checks if two type ids are equal.

source

',3))]),e("details",ft,[e("summary",null,[t[189]||(t[189]=e("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Base.:==")],-1)),t[190]||(t[190]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[191]||(t[191]=l('
julia
==(value1, value2)

Returns 1 if two values are equal, 0 otherwise.

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',3))]),e("details",It,[e("summary",null,[t[192]||(t[192]=e("a",{id:"Base.cld-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.cld-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Base.cld")],-1)),t[193]||(t[193]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[194]||(t[194]=l('
julia
cld(lhs, rhs)

Creates an affine ceildiv expression with 'lhs' and 'rhs'.

source

',3))]),e("details",jt,[e("summary",null,[t[195]||(t[195]=e("a",{id:"Base.copy-Tuple{Reactant.MLIR.IR.Operation}",href:"#Base.copy-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Base.copy")],-1)),t[196]||(t[196]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[197]||(t[197]=l('
julia
copy(op)

Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.

source

',3))]),e("details",Mt,[e("summary",null,[t[198]||(t[198]=e("a",{id:"Base.div-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.div-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Base.div")],-1)),t[199]||(t[199]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[200]||(t[200]=l(`
julia
div(lhs, rhs)
+import{_ as n,c as p,j as t,a as s,G as i,a2 as l,B as r,o}from"./chunks/framework.B4pBzPBy.js";const zk=JSON.parse('{"title":"Higher level API","description":"","frontmatter":{},"headers":[],"relativePath":"api/mlirc.md","filePath":"api/mlirc.md","lastUpdated":null}'),d={name:"api/mlirc.md"},c={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},I={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},C={class:"jldocstring 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Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.
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',3))]),t("details",L,[t("summary",null,[e[42]||(e[42]=t("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.Type, AbstractString}",href:"#Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.Type, AbstractString}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),e[43]||(e[43]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[44]||(e[44]=l('
julia
Attribute(type, str)
Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.
source
',3))]),t("details",E,[t("summary",null,[e[45]||(e[45]=t("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.Attribute-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),e[46]||(e[46]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[47]||(e[47]=l('
julia
Attribute(type)
Creates a type attribute wrapping the given type in the same context as the type.
source
',3))]),t("details",v,[t("summary",null,[e[48]||(e[48]=t("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{T} where T<:AbstractFloat",href:"#Reactant.MLIR.IR.Attribute-Tuple{T} where T<:AbstractFloat"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),e[49]||(e[49]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[50]||(e[50]=l('
julia
Attribute(float; context=context(), location=Location(), check=false)
Creates a floating point attribute in the given context with the given double value and double-precision FP semantics. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",T,[t("summary",null,[e[51]||(e[51]=t("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{T} where T<:Complex",href:"#Reactant.MLIR.IR.Attribute-Tuple{T} where T<:Complex"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),e[52]||(e[52]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[53]||(e[53]=l('
julia
Attribute(complex; context=context(), location=Location(), check=false)
Creates a complex attribute in the given context with the given complex value and double-precision FP semantics.
source
',3))]),t("details",C,[t("summary",null,[e[54]||(e[54]=t("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{Vector{Reactant.MLIR.IR.Attribute}}",href:"#Reactant.MLIR.IR.Attribute-Tuple{Vector{Reactant.MLIR.IR.Attribute}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),e[55]||(e[55]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[56]||(e[56]=l('
julia
Attribute(elements; context=context())
Creates an array element containing the given list of elements in the given context.
source
',3))]),t("details",x,[t("summary",null,[e[57]||(e[57]=t("a",{id:"Reactant.MLIR.IR.Attribute-Tuple{}",href:"#Reactant.MLIR.IR.Attribute-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),e[58]||(e[58]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[59]||(e[59]=l('
julia
Attribute()
Returns an empty attribute.
source
',3))]),t("details",F,[t("summary",null,[e[60]||(e[60]=t("a",{id:"Reactant.MLIR.IR.Attribute-Union{Tuple{T}, Tuple{T, Any}} where T<:Integer",href:"#Reactant.MLIR.IR.Attribute-Union{Tuple{T}, Tuple{T, Any}} where T<:Integer"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Attribute")],-1)),e[61]||(e[61]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[62]||(e[62]=l('
julia
Attribute(int)
Creates an integer attribute of the given type with the given integer value.
source
',3))]),t("details",P,[t("summary",null,[e[63]||(e[63]=t("a",{id:"Reactant.MLIR.IR.Block-Tuple{Vector{Reactant.MLIR.IR.Type}, Vector{Reactant.MLIR.IR.Location}}",href:"#Reactant.MLIR.IR.Block-Tuple{Vector{Reactant.MLIR.IR.Type}, Vector{Reactant.MLIR.IR.Location}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Block")],-1)),e[64]||(e[64]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[65]||(e[65]=l('
julia
Block(args, locs)
Creates a new empty block with the given argument types and transfers ownership to the caller.
source
',3))]),t("details",D,[t("summary",null,[e[66]||(e[66]=t("a",{id:"Reactant.MLIR.IR.BlockIterator",href:"#Reactant.MLIR.IR.BlockIterator"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.BlockIterator")],-1)),e[67]||(e[67]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[68]||(e[68]=l('
julia
BlockIterator(region::Region)
Iterates over all blocks in the given region.
source
',3))]),t("details",O,[t("summary",null,[e[69]||(e[69]=t("a",{id:"Reactant.MLIR.IR.Context-Tuple{}",href:"#Reactant.MLIR.IR.Context-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Context")],-1)),e[70]||(e[70]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[71]||(e[71]=l('
julia
Context()
Creates an MLIR context and transfers its ownership to the caller.
source
',3))]),t("details",B,[t("summary",null,[e[72]||(e[72]=t("a",{id:"Reactant.MLIR.IR.ExecutionEngine",href:"#Reactant.MLIR.IR.ExecutionEngine"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ExecutionEngine")],-1)),e[73]||(e[73]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[74]||(e[74]=l('
julia
ExecutionEngine(op, optLevel, sharedlibs = [])
Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.
source
',3))]),t("details",G,[t("summary",null,[e[75]||(e[75]=t("a",{id:"Reactant.MLIR.IR.Identifier-Tuple{String}",href:"#Reactant.MLIR.IR.Identifier-Tuple{String}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Identifier")],-1)),e[76]||(e[76]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[77]||(e[77]=l('
julia
Identifier(context, str)
Gets an identifier with the given string value.
source
',3))]),t("details",z,[t("summary",null,[e[78]||(e[78]=t("a",{id:"Reactant.MLIR.IR.IntegerSet-NTuple{4, Any}",href:"#Reactant.MLIR.IR.IntegerSet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.IntegerSet")],-1)),e[79]||(e[79]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[80]||(e[80]=l('
julia
IntegerSet(ndims, nsymbols, constraints, eqflags; context=context())
Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqflags is 1) or inequalities (eqflags is 0). Both constraints and eqflags need to be arrays of the same length.
source
',3))]),t("details",w,[t("summary",null,[e[81]||(e[81]=t("a",{id:"Reactant.MLIR.IR.IntegerSet-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.IntegerSet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.IntegerSet")],-1)),e[82]||(e[82]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[83]||(e[83]=l('
julia
Integerset(ndims, nsymbols; context=context())
Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.
source
',3))]),t("details",S,[t("summary",null,[e[84]||(e[84]=t("a",{id:"Reactant.MLIR.IR.LogicalResult",href:"#Reactant.MLIR.IR.LogicalResult"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.LogicalResult")],-1)),e[85]||(e[85]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[86]||(e[86]=l('
julia
LogicalResult
A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of LogicalResult must only be inspected using the associated functions.
source
',3))]),t("details",N,[t("summary",null,[e[87]||(e[87]=t("a",{id:"Reactant.MLIR.IR.Module",href:"#Reactant.MLIR.IR.Module"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Module")],-1)),e[88]||(e[88]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[89]||(e[89]=l('
julia
Module(location=Location())
Creates a new, empty module and transfers ownership to the caller.
source
',3))]),t("details",V,[t("summary",null,[e[90]||(e[90]=t("a",{id:"Reactant.MLIR.IR.NamedAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.NamedAttribute-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.NamedAttribute")],-1)),e[91]||(e[91]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=l('
julia
NamedAttribute(name, attr)
Associates an attribute with the name. Takes ownership of neither.
source
',3))]),t("details",q,[t("summary",null,[e[93]||(e[93]=t("a",{id:"Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpPassManager")],-1)),e[94]||(e[94]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[95]||(e[95]=l('
julia
OpPassManager(opPassManager, operationName)
Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.
source
',3))]),t("details",U,[t("summary",null,[e[96]||(e[96]=t("a",{id:"Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager, Any}",href:"#Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpPassManager")],-1)),e[97]||(e[97]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[98]||(e[98]=l('
julia
OpPassManager(passManager, operationName)
Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.
source
',3))]),t("details",Q,[t("summary",null,[e[99]||(e[99]=t("a",{id:"Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager}",href:"#Reactant.MLIR.IR.OpPassManager-Tuple{Reactant.MLIR.IR.PassManager}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpPassManager")],-1)),e[100]||(e[100]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[101]||(e[101]=l('
julia
OpPassManager(passManager)
Cast a top-level PassManager to a generic OpPassManager.
source
',3))]),t("details",W,[t("summary",null,[e[102]||(e[102]=t("a",{id:"Reactant.MLIR.IR.Operation-Tuple{Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.Operation-Tuple{Reactant.MLIR.IR.Module}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Operation")],-1)),e[103]||(e[103]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[104]||(e[104]=l('
julia
Operation(module)
Views the module as a generic operation.
source
',3))]),t("details",H,[t("summary",null,[e[105]||(e[105]=t("a",{id:"Reactant.MLIR.IR.OperationIterator",href:"#Reactant.MLIR.IR.OperationIterator"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OperationIterator")],-1)),e[106]||(e[106]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[107]||(e[107]=l('
julia
OperationIterator(block::Block)
Iterates over all operations for the given block.
source
',3))]),t("details",Z,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.IR.PassManager-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.PassManager-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.PassManager")],-1)),e[109]||(e[109]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=l('
julia
PassManager(anchorOp; context=context())
Create a new top-level PassManager anchored on anchorOp.
source
',3))]),t("details",J,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.IR.PassManager-Tuple{}",href:"#Reactant.MLIR.IR.PassManager-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.PassManager")],-1)),e[112]||(e[112]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=l('
julia
PassManager(; context=context())
Create a new top-level PassManager.
source
',3))]),t("details",K,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.IR.Region-Tuple{}",href:"#Reactant.MLIR.IR.Region-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Region")],-1)),e[115]||(e[115]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=l('
julia
Region()
Creates a new empty region and transfers ownership to the caller.
source
',3))]),t("details",$,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.IR.RegionIterator",href:"#Reactant.MLIR.IR.RegionIterator"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.RegionIterator")],-1)),e[118]||(e[118]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[119]||(e[119]=l('
julia
RegionIterator(::Operation)
Iterates over all sub-regions for the given operation.
source
',3))]),t("details",X,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.IR.SymbolTable-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.SymbolTable-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolTable")],-1)),e[121]||(e[121]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=l('
julia
mlirSymbolTableCreate(operation)
Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.
source
',3))]),t("details",Y,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.Type-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[124]||(e[124]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=l('
julia
Type(attr)
Returns the type stored in the given type attribute.
source
',3))]),t("details",_,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Integer}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Integer}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[127]||(e[127]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=l('
julia
Type(T::Core.Type{<:Integer}; context=context()
Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),t("details",ee,[t("summary",null,[e[129]||(e[129]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Signed}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Signed}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[130]||(e[130]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[131]||(e[131]=l('
julia
Type(T::Core.Type{<:Signed}; context=context()
Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),t("details",te,[t("summary",null,[e[132]||(e[132]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{<:Unsigned}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{<:Unsigned}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[133]||(e[133]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[134]||(e[134]=l('
julia
Type(T::Core.Type{<:Unsigned}; context=context()
Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),t("details",se,[t("summary",null,[e[135]||(e[135]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Bool}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Bool}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[136]||(e[136]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[137]||(e[137]=l('
julia
Type(T::Core.Type{Bool}; context=context()
Creates a 1-bit signless integer type in the context. The type is owned by the context.
source
',3))]),t("details",ae,[t("summary",null,[e[138]||(e[138]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float16}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float16}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[139]||(e[139]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[140]||(e[140]=l('
julia
Type(::Core.Type{Float16}; context=context())
Creates an f16 type in the given context. The type is owned by the context.
source
',3))]),t("details",ie,[t("summary",null,[e[141]||(e[141]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float32}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float32}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[142]||(e[142]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[143]||(e[143]=l('
julia
Type(Core.Type{Float32}; context=context())
Creates an f32 type in the given context. The type is owned by the context.
source
',3))]),t("details",le,[t("summary",null,[e[144]||(e[144]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Float64}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Float64}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[145]||(e[145]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[146]||(e[146]=l('
julia
Type(Core.Type{Float64}; context=context())
Creates a f64 type in the given context. The type is owned by the context.
source
',3))]),t("details",ne,[t("summary",null,[e[147]||(e[147]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Type{Nothing}}",href:"#Reactant.MLIR.IR.Type-Tuple{Type{Nothing}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[148]||(e[148]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[149]||(e[149]=l('
julia
Type(::Core.Type{Nothing}; context=context())
Creates a None type in the given context. The type is owned by the context.
source
',3))]),t("details",pe,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.IR.Type-Tuple{Vector{Reactant.MLIR.IR.Type}}",href:"#Reactant.MLIR.IR.Type-Tuple{Vector{Reactant.MLIR.IR.Type}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[151]||(e[151]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=l(`
julia
Type(elements; context=context())
+Type(::Core.Type{<:Tuple{T...}}; context=context())
Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.
source
`,3))]),t("details",re,[t("summary",null,[e[153]||(e[153]=t("a",{id:"Reactant.MLIR.IR.Type-Union{Tuple{Type{Complex{T}}}, Tuple{T}} where T",href:"#Reactant.MLIR.IR.Type-Union{Tuple{Type{Complex{T}}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Type")],-1)),e[154]||(e[154]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[155]||(e[155]=l('
julia
Type(Complex{T}) where {T}
Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.
source
',3))]),t("details",oe,[t("summary",null,[e[156]||(e[156]=t("a",{id:"Base.:*-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.:*-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.:*")],-1)),e[157]||(e[157]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[158]||(e[158]=l('
julia
*(lhs, rhs)
Creates an affine mul expression with 'lhs' and 'rhs'.
source
',3))]),t("details",de,[t("summary",null,[e[159]||(e[159]=t("a",{id:"Base.:+-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.:+-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.:+")],-1)),e[160]||(e[160]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[161]||(e[161]=l('
julia
+(lhs, rhs)
Creates an affine add expression with 'lhs' and 'rhs'.
source
',3))]),t("details",ce,[t("summary",null,[e[162]||(e[162]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[163]||(e[163]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[164]||(e[164]=l('
julia
==(a, b)
Returns true if the two affine expressions are equal.
source
',3))]),t("details",be,[t("summary",null,[e[165]||(e[165]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.AffineMap, Reactant.MLIR.IR.AffineMap}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.AffineMap, Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[166]||(e[166]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[167]||(e[167]=l('
julia
==(a, b)
Checks if two affine maps are equal.
source
',3))]),t("details",he,[t("summary",null,[e[168]||(e[168]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Attribute}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[169]||(e[169]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[170]||(e[170]=l('
julia
==(a1, a2)
Checks if two attributes are equal.
source
',3))]),t("details",ue,[t("summary",null,[e[171]||(e[171]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[172]||(e[172]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[173]||(e[173]=l('
julia
==(block, other)
Checks whether two blocks handles point to the same block. This does not perform deep comparison.
source
',3))]),t("details",ge,[t("summary",null,[e[174]||(e[174]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Identifier, Reactant.MLIR.IR.Identifier}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Identifier, Reactant.MLIR.IR.Identifier}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[175]||(e[175]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[176]||(e[176]=l('
julia
==(ident, other)
Checks whether two identifiers are the same.
source
',3))]),t("details",ye,[t("summary",null,[e[177]||(e[177]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.IntegerSet, Reactant.MLIR.IR.IntegerSet}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.IntegerSet, Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[178]||(e[178]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[179]||(e[179]=l('
julia
==(s1, s2)
Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.
source
',3))]),t("details",me,[t("summary",null,[e[180]||(e[180]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Region}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Region}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[181]||(e[181]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[182]||(e[182]=l('
julia
==(region, other)
Checks whether two region handles point to the same region. This does not perform deep comparison.
source
',3))]),t("details",ke,[t("summary",null,[e[183]||(e[183]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Type, Reactant.MLIR.IR.Type}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Type, Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[184]||(e[184]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[185]||(e[185]=l('
julia
==(t1, t2)
Checks if two types are equal.
source
',3))]),t("details",Re,[t("summary",null,[e[186]||(e[186]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.TypeID, Reactant.MLIR.IR.TypeID}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.TypeID, Reactant.MLIR.IR.TypeID}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[187]||(e[187]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[188]||(e[188]=l('
julia
==(typeID1, typeID2)
Checks if two type ids are equal.
source
',3))]),t("details",Ie,[t("summary",null,[e[189]||(e[189]=t("a",{id:"Base.:==-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Base.:==-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Base.:==")],-1)),e[190]||(e[190]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[191]||(e[191]=l('
julia
==(value1, value2)
Returns 1 if two values are equal, 0 otherwise.
source
',3))]),t("details",je,[t("summary",null,[e[192]||(e[192]=t("a",{id:"Base.cld-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.cld-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.cld")],-1)),e[193]||(e[193]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[194]||(e[194]=l('
julia
cld(lhs, rhs)
Creates an affine ceildiv expression with 'lhs' and 'rhs'.
source
',3))]),t("details",fe,[t("summary",null,[e[195]||(e[195]=t("a",{id:"Base.copy-Tuple{Reactant.MLIR.IR.Operation}",href:"#Base.copy-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Base.copy")],-1)),e[196]||(e[196]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[197]||(e[197]=l('
julia
copy(op)
Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.
source
',3))]),t("details",Me,[t("summary",null,[e[198]||(e[198]=t("a",{id:"Base.div-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.div-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.div")],-1)),e[199]||(e[199]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[200]||(e[200]=l(`
julia
div(lhs, rhs)
 ÷(lhs, rhs)
-fld(lhs, rhs)
Creates an affine floordiv expression with 'lhs' and 'rhs'.
source
`,3))]),e("details",At,[e("summary",null,[t[201]||(t[201]=e("a",{id:"Base.fill-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Type}",href:"#Base.fill-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Base.fill")],-1)),t[202]||(t[202]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[203]||(t[203]=l('
julia
fill(attr, shapedType)
Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).
source
',3))]),e("details",Lt,[e("summary",null,[t[204]||(t[204]=e("a",{id:"Base.gcd-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Base.gcd-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Base.gcd")],-1)),t[205]||(t[205]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[206]||(t[206]=l('
julia
gcd(affineExpr)
Returns the greatest known integral divisor of this affine expression. The result is always positive.
source
',3))]),e("details",Et,[e("summary",null,[t[207]||(t[207]=e("a",{id:"Base.hash-Tuple{Reactant.MLIR.IR.TypeID}",href:"#Base.hash-Tuple{Reactant.MLIR.IR.TypeID}"},[e("span",{class:"jlbinding"},"Base.hash")],-1)),t[208]||(t[208]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[209]||(t[209]=l('
julia
hash(typeID)
Returns the hash value of the type id.
source
',3))]),e("details",vt,[e("summary",null,[t[210]||(t[210]=e("a",{id:"Base.insert!-Tuple{Reactant.MLIR.IR.Block, Any, Reactant.MLIR.IR.Operation}",href:"#Base.insert!-Tuple{Reactant.MLIR.IR.Block, Any, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Base.insert!")],-1)),t[211]||(t[211]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[212]||(t[212]=l('
julia
insert!(block, index, operation)
Takes an operation owned by the caller and inserts it as index to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.
source
',3))]),e("details",Tt,[e("summary",null,[t[213]||(t[213]=e("a",{id:"Base.insert!-Tuple{Reactant.MLIR.IR.Region, Any, Reactant.MLIR.IR.Block}",href:"#Base.insert!-Tuple{Reactant.MLIR.IR.Region, Any, Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Base.insert!")],-1)),t[214]||(t[214]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[215]||(t[215]=l('
julia
insert!(region, index, block)
Takes a block owned by the caller and inserts it at index to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.
source
',3))]),e("details",Ct,[e("summary",null,[t[216]||(t[216]=e("a",{id:"Base.isempty-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.isempty-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Base.isempty")],-1)),t[217]||(t[217]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[218]||(t[218]=l('
julia
isempty(affineMap)
Checks whether the given affine map is an empty affine map.
source
',3))]),e("details",xt,[e("summary",null,[t[219]||(t[219]=e("a",{id:"Base.isperm-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.isperm-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Base.isperm")],-1)),t[220]||(t[220]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[221]||(t[221]=l('
julia
isperm(affineMap)
Checks whether the given affine map represents a symbol-less permutation map.
source
',3))]),e("details",Ft,[e("summary",null,[t[222]||(t[222]=e("a",{id:"Base.mod-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.mod-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Base.mod")],-1)),t[223]||(t[223]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[224]||(t[224]=l('
julia
mod(lhs, rhs)
Creates an affine mod expression with 'lhs' and 'rhs'.
source
',3))]),e("details",Pt,[e("summary",null,[t[225]||(t[225]=e("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Base.ndims")],-1)),t[226]||(t[226]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[227]||(t[227]=l('
julia
ndims(affineMap)
Returns the number of dimensions of the given affine map.
source
',3))]),e("details",Dt,[e("summary",null,[t[228]||(t[228]=e("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Base.ndims")],-1)),t[229]||(t[229]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[230]||(t[230]=l('
julia
ndims(set)
Returns the number of dimensions in the given set.
source
',3))]),e("details",Ot,[e("summary",null,[t[231]||(t[231]=e("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.Type}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Base.ndims")],-1)),t[232]||(t[232]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[233]||(t[233]=l('
julia
ndims(type)
Returns the rank of the given ranked shaped type.
source
',3))]),e("details",Bt,[e("summary",null,[t[234]||(t[234]=e("a",{id:"Base.parse-Tuple{Reactant.MLIR.IR.OpPassManager, String}",href:"#Base.parse-Tuple{Reactant.MLIR.IR.OpPassManager, String}"},[e("span",{class:"jlbinding"},"Base.parse")],-1)),t[235]||(t[235]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[236]||(t[236]=l('
julia
parse(passManager, pipeline)
Parse a textual MLIR pass pipeline and add it to the provided OpPassManager.
source
',3))]),e("details",Gt,[e("summary",null,[t[237]||(t[237]=e("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Attribute}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Attribute}, Any}"},[e("span",{class:"jlbinding"},"Base.parse")],-1)),t[238]||(t[238]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[239]||(t[239]=l('
julia
parse(::Core.Type{Attribute}, str; context=context())
Parses an attribute. The attribute is owned by the context.
source
',3))]),e("details",zt,[e("summary",null,[t[240]||(t[240]=e("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Module}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Module}, Any}"},[e("span",{class:"jlbinding"},"Base.parse")],-1)),t[241]||(t[241]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[242]||(t[242]=l('
julia
parse(::Type{Module}, module; context=context())
Parses a module from the string and transfers ownership to the caller.
source
',3))]),e("details",wt,[e("summary",null,[t[243]||(t[243]=e("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Type}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Type}, Any}"},[e("span",{class:"jlbinding"},"Base.parse")],-1)),t[244]||(t[244]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[245]||(t[245]=l('
julia
parse(type; context=context())
Parses a type. The type is owned by the context.
source
',3))]),e("details",St,[e("summary",null,[t[246]||(t[246]=e("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Base.push!")],-1)),t[247]||(t[247]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[248]||(t[248]=l('
julia
push!(block, operation)
Takes an operation owned by the caller and appends it to the block.
source
',3))]),e("details",Nt,[e("summary",null,[t[249]||(t[249]=e("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Base.push!")],-1)),t[250]||(t[250]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[251]||(t[251]=l('
julia
push!(region, block)
Takes a block owned by the caller and appends it to the given region.
source
',3))]),e("details",Vt,[e("summary",null,[t[252]||(t[252]=e("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Base.push!")],-1)),t[253]||(t[253]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[254]||(t[254]=l('
julia
push!(symboltable, operation)
Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.
source
',3))]),e("details",qt,[e("summary",null,[t[255]||(t[255]=e("a",{id:"Base.replace-Tuple{Reactant.MLIR.IR.AffineMap, Pair{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}, Any, Any}",href:"#Base.replace-Tuple{Reactant.MLIR.IR.AffineMap, Pair{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}, Any, Any}"},[e("span",{class:"jlbinding"},"Base.replace")],-1)),t[256]||(t[256]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[257]||(t[257]=l('
julia
mlirAffineMapReplace(affineMap, expression => replacement, numResultDims, numResultSyms)
Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.
source
',3))]),e("details",Ut,[e("summary",null,[t[258]||(t[258]=e("a",{id:"Base.replace-Tuple{Reactant.MLIR.IR.IntegerSet, Any, Any}",href:"#Base.replace-Tuple{Reactant.MLIR.IR.IntegerSet, Any, Any}"},[e("span",{class:"jlbinding"},"Base.replace")],-1)),t[259]||(t[259]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[260]||(t[260]=l('
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)
Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.
source
',3))]),e("details",Qt,[e("summary",null,[t[261]||(t[261]=e("a",{id:"Base.reshape-Tuple{Reactant.MLIR.IR.Attribute, Any}",href:"#Base.reshape-Tuple{Reactant.MLIR.IR.Attribute, Any}"},[e("span",{class:"jlbinding"},"Base.reshape")],-1)),t[262]||(t[262]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[263]||(t[263]=l('
julia
Base.reshape(attr, shapedType)
Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.
source
',3))]),e("details",Wt,[e("summary",null,[t[264]||(t[264]=e("a",{id:"Base.size-Tuple{Reactant.MLIR.IR.Type, Int64}",href:"#Base.size-Tuple{Reactant.MLIR.IR.Type, Int64}"},[e("span",{class:"jlbinding"},"Base.size")],-1)),t[265]||(t[265]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[266]||(t[266]=l('
julia
size(type, i)
Returns the i-th dimension of the given ranked shaped type.
source
',3))]),e("details",Ht,[e("summary",null,[t[267]||(t[267]=e("a",{id:"Base.write-Tuple{String, Reactant.MLIR.IR.ExecutionEngine}",href:"#Base.write-Tuple{String, Reactant.MLIR.IR.ExecutionEngine}"},[e("span",{class:"jlbinding"},"Base.write")],-1)),t[268]||(t[268]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[269]||(t[269]=l('
julia
write(fileName, jit)
Dump as an object in fileName.
source
',3))]),e("details",Zt,[e("summary",null,[t[270]||(t[270]=e("a",{id:"Reactant.MLIR.IR.AffineDimensionExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.AffineDimensionExpr-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.AffineDimensionExpr")],-1)),t[271]||(t[271]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[272]||(t[272]=l('
julia
AffineDimensionExpr(position; context=context)
Creates an affine dimension expression with 'position' in the context.
source
',3))]),e("details",Jt,[e("summary",null,[t[273]||(t[273]=e("a",{id:"Reactant.MLIR.IR.BFloat16Type-Tuple{}",href:"#Reactant.MLIR.IR.BFloat16Type-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.BFloat16Type")],-1)),t[274]||(t[274]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[275]||(t[275]=e("p",null,"BFloat16Type(; context=context())",-1)),t[276]||(t[276]=e("p",null,"Creates a bf16 type in the given context. The type is owned by the context.",-1)),t[277]||(t[277]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/IR/Type.jl#L157-L161",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Kt,[e("summary",null,[t[278]||(t[278]=e("a",{id:"Reactant.MLIR.IR.ConstantAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.ConstantAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ConstantAffineMap")],-1)),t[279]||(t[279]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[280]||(t[280]=l('
julia
ConstantAffineMap(val; context=context())
Creates a single constant result affine map in the context. The affine map is owned by the context.
source
',3))]),e("details",$t,[e("summary",null,[t[281]||(t[281]=e("a",{id:"Reactant.MLIR.IR.ConstantExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.ConstantExpr-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ConstantExpr")],-1)),t[282]||(t[282]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[283]||(t[283]=l('
julia
ConstantExpr(constant::Int; context=context())
Creates an affine constant expression with 'constant' in the context.
source
',3))]),e("details",Xt,[e("summary",null,[t[284]||(t[284]=e("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{Bool}}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{Bool}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),t[285]||(t[285]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[286]||(t[286]=l('
julia
DenseElementsAttribute(array::AbstractArray)
Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.
source
',3))]),e("details",Yt,[e("summary",null,[t[287]||(t[287]=e("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{String}}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{String}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),t[288]||(t[288]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[289]||(t[289]=l('
julia
DenseElementsAttribute(array::AbstractArray{String})
Creates a dense elements attribute with the given shaped type from string elements.
source
',3))]),e("details",_t,[e("summary",null,[t[290]||(t[290]=e("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{Reactant.MLIR.IR.Type, AbstractArray}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{Reactant.MLIR.IR.Type, AbstractArray}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),t[291]||(t[291]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[292]||(t[292]=l('
julia
DenseElementsAttribute(shapedType, elements)
Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.
source
',3))]),e("details",te,[e("summary",null,[t[293]||(t[293]=e("a",{id:"Reactant.MLIR.IR.FlatSymbolRefAttribute-Tuple{String}",href:"#Reactant.MLIR.IR.FlatSymbolRefAttribute-Tuple{String}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.FlatSymbolRefAttribute")],-1)),t[294]||(t[294]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[295]||(t[295]=l('
julia
FlatSymbolRefAttribute(ctx, symbol)
Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.
source
',3))]),e("details",ee,[e("summary",null,[t[296]||(t[296]=e("a",{id:"Reactant.MLIR.IR.Float8E4M3FN-Tuple{}",href:"#Reactant.MLIR.IR.Float8E4M3FN-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Float8E4M3FN")],-1)),t[297]||(t[297]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[298]||(t[298]=l('
julia
Float8E4M3FN(; context=context())
Creates an f8E4M3FN type in the given context. The type is owned by the context.
source
',3))]),e("details",se,[e("summary",null,[t[299]||(t[299]=e("a",{id:"Reactant.MLIR.IR.Float8E5M2-Tuple{}",href:"#Reactant.MLIR.IR.Float8E5M2-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.Float8E5M2")],-1)),t[300]||(t[300]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[301]||(t[301]=l('
julia
Float8E5M2(; context=context())
Creates an f8E5M2 type in the given context. The type is owned by the context.
source
',3))]),e("details",ae,[e("summary",null,[t[302]||(t[302]=e("a",{id:"Reactant.MLIR.IR.FunctionType-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.FunctionType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.FunctionType")],-1)),t[303]||(t[303]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[304]||(t[304]=l('
julia
FunctionType(inputs, results; context=context())
Creates a function type, mapping a list of input types to result types.
source
',3))]),e("details",ie,[e("summary",null,[t[305]||(t[305]=e("a",{id:"Reactant.MLIR.IR.IdentityAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.IdentityAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.IdentityAffineMap")],-1)),t[306]||(t[306]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[307]||(t[307]=l('
julia
IdentityAffineMap(ndims; context=context())
Creates an affine map with 'ndims' identity in the context. The affine map is owned by the context.
source
',3))]),e("details",le,[e("summary",null,[t[308]||(t[308]=e("a",{id:"Reactant.MLIR.IR.IndexType-Tuple{}",href:"#Reactant.MLIR.IR.IndexType-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.IndexType")],-1)),t[309]||(t[309]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[310]||(t[310]=l('
julia
IndexType(; context=context())
Creates an index type in the given context. The type is owned by the context.
source
',3))]),e("details",ne,[e("summary",null,[t[311]||(t[311]=e("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),t[312]||(t[312]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[313]||(t[313]=l('
julia
MemRefType(elementType, rank, shape, layout, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",pe,[e("summary",null,[t[314]||(t[314]=e("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),t[315]||(t[315]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[316]||(t[316]=l('
julia
MemRefType(elementType, rank, shape, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",re,[e("summary",null,[t[317]||(t[317]=e("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),t[318]||(t[318]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[319]||(t[319]=l('
julia
MemRefType(elementType, memorySpace)
Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",oe,[e("summary",null,[t[320]||(t[320]=e("a",{id:"Reactant.MLIR.IR.MinorIdentityAffineMap-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.MinorIdentityAffineMap-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.MinorIdentityAffineMap")],-1)),t[321]||(t[321]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[322]||(t[322]=l('
julia
MinorIdentityAffineMap(ndims, nresults; context=context())
Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),e("details",de,[e("summary",null,[t[323]||(t[323]=e("a",{id:"Reactant.MLIR.IR.OpaqueAttribute-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.IR.OpaqueAttribute-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpaqueAttribute")],-1)),t[324]||(t[324]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[325]||(t[325]=l('
julia
OpaqueAttribute(dialectNamespace, dataLength, data, type; context=context())
Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),e("details",he,[e("summary",null,[t[326]||(t[326]=e("a",{id:"Reactant.MLIR.IR.OpaqueType-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.OpaqueType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpaqueType")],-1)),t[327]||(t[327]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[328]||(t[328]=l('
julia
OpaqueType(dialectNamespace, typeData; context=context())
Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),e("details",ce,[e("summary",null,[t[329]||(t[329]=e("a",{id:"Reactant.MLIR.IR.PermutationAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.PermutationAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.PermutationAffineMap")],-1)),t[330]||(t[330]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[331]||(t[331]=l('
julia
PermutationAffineMap(permutation; context=context())
Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid invalid permutation). The affine map is owned by the context.
source
',3))]),e("details",ue,[e("summary",null,[t[332]||(t[332]=e("a",{id:"Reactant.MLIR.IR.SymbolExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.SymbolExpr-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolExpr")],-1)),t[333]||(t[333]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[334]||(t[334]=l('
julia
SymbolExpr(position; context=context())
Creates an affine symbol expression with 'position' in the context.
source
',3))]),e("details",be,[e("summary",null,[t[335]||(t[335]=e("a",{id:"Reactant.MLIR.IR.SymbolRefAttribute-Tuple{String, Vector{Reactant.MLIR.IR.Attribute}}",href:"#Reactant.MLIR.IR.SymbolRefAttribute-Tuple{String, Vector{Reactant.MLIR.IR.Attribute}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolRefAttribute")],-1)),t[336]||(t[336]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[337]||(t[337]=l('
julia
SymbolRefAttribute(symbol, references; context=context())
Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.
source
',3))]),e("details",ge,[e("summary",null,[t[338]||(t[338]=e("a",{id:"Reactant.MLIR.IR.TensorType",href:"#Reactant.MLIR.IR.TensorType"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.TensorType")],-1)),t[339]||(t[339]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),t[340]||(t[340]=l('
julia
TensorType(shape, elementType, encoding=Attribute(); location=Location(), check=false)
Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull to this parameter. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",ye,[e("summary",null,[t[341]||(t[341]=e("a",{id:"Reactant.MLIR.IR.TensorType-Tuple{Any}",href:"#Reactant.MLIR.IR.TensorType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.TensorType")],-1)),t[342]||(t[342]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[343]||(t[343]=l('
julia
TensorType(elementType)
Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",me,[e("summary",null,[t[344]||(t[344]=e("a",{id:"Reactant.MLIR.IR.UnitAttribute-Tuple{}",href:"#Reactant.MLIR.IR.UnitAttribute-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.UnitAttribute")],-1)),t[345]||(t[345]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[346]||(t[346]=l('
julia
UnitAttribute(; context=context())
Creates a unit attribute in the given context.
source
',3))]),e("details",ke,[e("summary",null,[t[347]||(t[347]=e("a",{id:"Reactant.MLIR.IR.VectorType-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.IR.VectorType-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.VectorType")],-1)),t[348]||(t[348]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[349]||(t[349]=l('
julia
VectorType(rank, shape, elementType; location=Location(), check=false)
Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),e("details",Re,[e("summary",null,[t[350]||(t[350]=e("a",{id:"Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_owned_pass!")],-1)),t[351]||(t[351]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[352]||(t[352]=l('
julia
add_owned_pass!(opPassManager, pass)
Add a pass and transfer ownership to the provided OpPassManager. If the pass is not a generic operation pass or matching the type of the provided OpPassManager, a new OpPassManager is implicitly nested under the provided OpPassManager.
source
',3))]),e("details",fe,[e("summary",null,[t[353]||(t[353]=e("a",{id:"Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.PassManager, Any}",href:"#Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.PassManager, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_owned_pass!")],-1)),t[354]||(t[354]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[355]||(t[355]=l('
julia
add_owned_pass!(passManager, pass)
Add a pass and transfer ownership to the provided top-level PassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.
source
',3))]),e("details",Ie,[e("summary",null,[t[356]||(t[356]=e("a",{id:"Reactant.MLIR.IR.add_pipeline!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.add_pipeline!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_pipeline!")],-1)),t[357]||(t[357]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[358]||(t[358]=l('
julia
add_pipeline!(passManager, pipelineElements, callback, userData)
Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
',3))]),e("details",je,[e("summary",null,[t[359]||(t[359]=e("a",{id:"Reactant.MLIR.IR.affinemap-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.affinemap-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.affinemap")],-1)),t[360]||(t[360]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[361]||(t[361]=l('
julia
affinemap(type)
Returns the affine map of the given MemRef type.
source
',3))]),e("details",Me,[e("summary",null,[t[362]||(t[362]=e("a",{id:"Reactant.MLIR.IR.argument-Tuple{Reactant.MLIR.IR.Block, Any}",href:"#Reactant.MLIR.IR.argument-Tuple{Reactant.MLIR.IR.Block, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.argument")],-1)),t[363]||(t[363]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[364]||(t[364]=l('
julia
argument(block, i)
Returns i-th argument of the block.
source
',3))]),e("details",Ae,[e("summary",null,[t[365]||(t[365]=e("a",{id:"Reactant.MLIR.IR.attr!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}",href:"#Reactant.MLIR.IR.attr!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr!")],-1)),t[366]||(t[366]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[367]||(t[367]=l('
julia
attr!(op, name, attr)
Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise.
source
',3))]),e("details",Le,[e("summary",null,[t[368]||(t[368]=e("a",{id:"Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, AbstractString}",href:"#Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, AbstractString}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr")],-1)),t[369]||(t[369]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[370]||(t[370]=l('
julia
attr(op, name)
Returns an attribute attached to the operation given its name.
source
',3))]),e("details",Ee,[e("summary",null,[t[371]||(t[371]=e("a",{id:"Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr")],-1)),t[372]||(t[372]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[373]||(t[373]=l('
julia
attr(op, i)
Return i-th attribute of the operation.
source
',3))]),e("details",ve,[e("summary",null,[t[374]||(t[374]=e("a",{id:"Reactant.MLIR.IR.bitwidth-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.bitwidth-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.bitwidth")],-1)),t[375]||(t[375]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[376]||(t[376]=l('
julia
bitwidth(type)
Returns the bitwidth of an integer type.
source
',3))]),e("details",Te,[e("summary",null,[t[377]||(t[377]=e("a",{id:"Reactant.MLIR.IR.block-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.block-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.block")],-1)),t[378]||(t[378]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[379]||(t[379]=l('
julia
block(op)
Gets the block that owns this operation, returning null if the operation is not owned.
source
',3))]),e("details",Ce,[e("summary",null,[t[380]||(t[380]=e("a",{id:"Reactant.MLIR.IR.block_arg_num-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.block_arg_num-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.block_arg_num")],-1)),t[381]||(t[381]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[382]||(t[382]=l('
julia
block_arg_num(value)
Returns the position of the value in the argument list of its block.
source
',3))]),e("details",xe,[e("summary",null,[t[383]||(t[383]=e("a",{id:"Reactant.MLIR.IR.block_owner-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.block_owner-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.block_owner")],-1)),t[384]||(t[384]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[385]||(t[385]=l('
julia
block_owner(value)
Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.
source
',3))]),e("details",Fe,[e("summary",null,[t[386]||(t[386]=e("a",{id:"Reactant.MLIR.IR.body-Tuple{Any}",href:"#Reactant.MLIR.IR.body-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.body")],-1)),t[387]||(t[387]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[388]||(t[388]=l('
julia
body(module)
Gets the body of the module, i.e. the only block it contains.
source
',3))]),e("details",Pe,[e("summary",null,[t[389]||(t[389]=e("a",{id:"Reactant.MLIR.IR.compose-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.compose-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.compose")],-1)),t[390]||(t[390]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[391]||(t[391]=l('
julia
compose(affineExpr, affineMap)
Composes the given map with the given expression.
source
',3))]),e("details",De,[e("summary",null,[t[392]||(t[392]=e("a",{id:"Reactant.MLIR.IR.constraint-Tuple{Reactant.MLIR.IR.IntegerSet, Any}",href:"#Reactant.MLIR.IR.constraint-Tuple{Reactant.MLIR.IR.IntegerSet, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.constraint")],-1)),t[393]||(t[393]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[394]||(t[394]=l('
julia
mlirIntegerSetGetConstraint(set, i)
Returns i-th constraint of the set.
source
',3))]),e("details",Oe,[e("summary",null,[t[395]||(t[395]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[396]||(t[396]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[397]||(t[397]=l('
julia
context(affineExpr)
Gets the context that owns the affine expression.
source
',3))]),e("details",Be,[e("summary",null,[t[398]||(t[398]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[399]||(t[399]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[400]||(t[400]=l('
julia
context(affineMap)
Gets the context that the given affine map was created with.
source
',3))]),e("details",Ge,[e("summary",null,[t[401]||(t[401]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[402]||(t[402]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[403]||(t[403]=l('
julia
context(attribute)
Gets the context that an attribute was created with.
source
',3))]),e("details",ze,[e("summary",null,[t[404]||(t[404]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Identifier}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Identifier}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[405]||(t[405]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[406]||(t[406]=l('
julia
context(ident)
Returns the context associated with this identifier
source
',3))]),e("details",we,[e("summary",null,[t[407]||(t[407]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[408]||(t[408]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[409]||(t[409]=l('
julia
context(set)
Gets the context in which the given integer set lives.
source
',3))]),e("details",Se,[e("summary",null,[t[410]||(t[410]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Module}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[411]||(t[411]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[412]||(t[412]=l('
julia
context(module)
Gets the context that a module was created with.
source
',3))]),e("details",Ne,[e("summary",null,[t[413]||(t[413]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[414]||(t[414]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[415]||(t[415]=l('
julia
context(op)
Gets the context this operation is associated with.
source
',3))]),e("details",Ve,[e("summary",null,[t[416]||(t[416]=e("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),t[417]||(t[417]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[418]||(t[418]=l('
julia
context(type)
Gets the context that a type was created with.
source
',3))]),e("details",qe,[e("summary",null,[t[419]||(t[419]=e("a",{id:"Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.data")],-1)),t[420]||(t[420]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[421]||(t[421]=l('
julia
data(attr)
Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.
source
',3))]),e("details",Ue,[e("summary",null,[t[422]||(t[422]=e("a",{id:"Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.data")],-1)),t[423]||(t[423]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[424]||(t[424]=l('
julia
mlirOpaqueTypeGetData(type)
Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.
source
',3))]),e("details",Qe,[e("summary",null,[t[425]||(t[425]=e("a",{id:"Reactant.MLIR.IR.delete!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.delete!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.delete!")],-1)),t[426]||(t[426]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[427]||(t[427]=l('
julia
delete!(symboltable, operation)
Removes the given operation from the symbol table and erases it.
source
',3))]),e("details",We,[e("summary",null,[t[428]||(t[428]=e("a",{id:"Reactant.MLIR.IR.dynsize-Tuple{}",href:"#Reactant.MLIR.IR.dynsize-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.dynsize")],-1)),t[429]||(t[429]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[430]||(t[430]=l('
julia
dynsize()
Returns the value indicating a dynamic size in a shaped type. Prefer isdynsize to direct comparisons with this value.
source
',3))]),e("details",He,[e("summary",null,[t[431]||(t[431]=e("a",{id:"Reactant.MLIR.IR.dynstrideoroffset-Tuple{}",href:"#Reactant.MLIR.IR.dynstrideoroffset-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.dynstrideoroffset")],-1)),t[432]||(t[432]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[433]||(t[433]=l('
julia
mlirShapedTypeGetDynamicStrideOrOffset()
Returns the value indicating a dynamic stride or offset in a shaped type. Prefer isdynstrideoroffset to direct comparisons with this value.
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',3))]),e("details",Ze,[e("summary",null,[t[434]||(t[434]=e("a",{id:"Reactant.MLIR.IR.enable_ir_printing!-Tuple{Any}",href:"#Reactant.MLIR.IR.enable_ir_printing!-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.enable_ir_printing!")],-1)),t[435]||(t[435]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[436]||(t[436]=l('
julia
enable_ir_printing!(passManager)
Enable mlir-print-ir-after-all.
source
',3))]),e("details",Je,[e("summary",null,[t[437]||(t[437]=e("a",{id:"Reactant.MLIR.IR.enable_verifier!",href:"#Reactant.MLIR.IR.enable_verifier!"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.enable_verifier!")],-1)),t[438]||(t[438]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),t[439]||(t[439]=l('
julia
enable_verifier!(passManager, enable)
Enable / disable verify-each.
source
',3))]),e("details",Ke,[e("summary",null,[t[440]||(t[440]=e("a",{id:"Reactant.MLIR.IR.encoding-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.encoding-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.encoding")],-1)),t[441]||(t[441]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[442]||(t[442]=l('
julia
encoding(type)
Gets the 'encoding' attribute from the ranked tensor type, returning a nothing if none.
source
',3))]),e("details",$e,[e("summary",null,[t[443]||(t[443]=e("a",{id:"Reactant.MLIR.IR.failure-Tuple{}",href:"#Reactant.MLIR.IR.failure-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.failure")],-1)),t[444]||(t[444]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[445]||(t[445]=l('
julia
failure()
Creates a logical result representing a failure.
source
',3))]),e("details",Xe,[e("summary",null,[t[446]||(t[446]=e("a",{id:"Reactant.MLIR.IR.first_block-Tuple{Reactant.MLIR.IR.Region}",href:"#Reactant.MLIR.IR.first_block-Tuple{Reactant.MLIR.IR.Region}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_block")],-1)),t[447]||(t[447]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[448]||(t[448]=l('
julia
first_block(region)
Gets the first block in the region.
source
',3))]),e("details",Ye,[e("summary",null,[t[449]||(t[449]=e("a",{id:"Reactant.MLIR.IR.first_op-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.first_op-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_op")],-1)),t[450]||(t[450]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[451]||(t[451]=l('
julia
first_op(block)
Returns the first operation in the block or nothing if empty.
source
',3))]),e("details",_e,[e("summary",null,[t[452]||(t[452]=e("a",{id:"Reactant.MLIR.IR.first_use-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.first_use-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_use")],-1)),t[453]||(t[453]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[454]||(t[454]=l('
julia
first_use(value)
Returns an OpOperand representing the first use of the value, or a nothing if there are no uses.
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',3))]),e("details",ts,[e("summary",null,[t[455]||(t[455]=e("a",{id:"Reactant.MLIR.IR.flatsymbol-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.flatsymbol-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.flatsymbol")],-1)),t[456]||(t[456]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[457]||(t[457]=l('
julia
flatsymbol(attr)
Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.
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',3))]),e("details",es,[e("summary",null,[t[458]||(t[458]=e("a",{id:"Reactant.MLIR.IR.hasrank-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.hasrank-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.hasrank")],-1)),t[459]||(t[459]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[460]||(t[460]=l('
julia
hasrank(type)
Checks whether the given shaped type is ranked.
source
',3))]),e("details",ss,[e("summary",null,[t[461]||(t[461]=e("a",{id:"Reactant.MLIR.IR.hasstaticshape-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.hasstaticshape-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.hasstaticshape")],-1)),t[462]||(t[462]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[463]||(t[463]=l('
julia
hasstaticshape(type)
Checks whether the given shaped type has a static shape.
source
',3))]),e("details",as,[e("summary",null,[t[464]||(t[464]=e("a",{id:"Reactant.MLIR.IR.input-Tuple{Reactant.MLIR.IR.Type, Any}",href:"#Reactant.MLIR.IR.input-Tuple{Reactant.MLIR.IR.Type, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.input")],-1)),t[465]||(t[465]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[466]||(t[466]=l('
julia
input(type, i)
Returns the i-th input type.
source
',3))]),e("details",is,[e("summary",null,[t[467]||(t[467]=e("a",{id:"Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_after!")],-1)),t[468]||(t[468]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[469]||(t[469]=l('
julia
insert_after!(block, reference, operation)
Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.
source
',3))]),e("details",ls,[e("summary",null,[t[470]||(t[470]=e("a",{id:"Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_after!")],-1)),t[471]||(t[471]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[472]||(t[472]=l('
julia
insert_after!(region, reference, block)
Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.
source
',3))]),e("details",ns,[e("summary",null,[t[473]||(t[473]=e("a",{id:"Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_before!")],-1)),t[474]||(t[474]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[475]||(t[475]=l('
julia
insert_before!(block, reference, operation)
Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.
source
',3))]),e("details",ps,[e("summary",null,[t[476]||(t[476]=e("a",{id:"Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_before!")],-1)),t[477]||(t[477]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[478]||(t[478]=l('
julia
insert_before!(region, reference, block)
Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.
source
',3))]),e("details",rs,[e("summary",null,[t[479]||(t[479]=e("a",{id:"Reactant.MLIR.IR.is_block_arg-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.is_block_arg-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_block_arg")],-1)),t[480]||(t[480]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[481]||(t[481]=l('
julia
is_block_arg(value)
Returns 1 if the value is a block argument, 0 otherwise.
source
',3))]),e("details",os,[e("summary",null,[t[482]||(t[482]=e("a",{id:"Reactant.MLIR.IR.is_op_res-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.is_op_res-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_op_res")],-1)),t[483]||(t[483]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[484]||(t[484]=l('
julia
is_op_res(value)
Returns 1 if the value is an operation result, 0 otherwise.
source
',3))]),e("details",ds,[e("summary",null,[t[485]||(t[485]=e("a",{id:"Reactant.MLIR.IR.is_pure_affine-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.is_pure_affine-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_pure_affine")],-1)),t[486]||(t[486]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[487]||(t[487]=l('
julia
is_pure_affine(affineExpr)
Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.
source
',3))]),e("details",hs,[e("summary",null,[t[488]||(t[488]=e("a",{id:"Reactant.MLIR.IR.is_registered-Tuple{Any}",href:"#Reactant.MLIR.IR.is_registered-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_registered")],-1)),t[489]||(t[489]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[490]||(t[490]=l('
julia
is_registered(name; context=context())
Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.
source
',3))]),e("details",cs,[e("summary",null,[t[491]||(t[491]=e("a",{id:"Reactant.MLIR.IR.is_symbolic_or_constant-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.is_symbolic_or_constant-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_symbolic_or_constant")],-1)),t[492]||(t[492]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[493]||(t[493]=l('
julia
is_symbolic_or_constant(affineExpr)
Checks whether the given affine expression is made out of only symbols and constants.
source
',3))]),e("details",us,[e("summary",null,[t[494]||(t[494]=e("a",{id:"Reactant.MLIR.IR.isadd-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isadd-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isadd")],-1)),t[495]||(t[495]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[496]||(t[496]=l('
julia
isadd(affineExpr)
Checks whether the given affine expression is an add expression.
source
',3))]),e("details",bs,[e("summary",null,[t[497]||(t[497]=e("a",{id:"Reactant.MLIR.IR.isaffinemap-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isaffinemap-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isaffinemap")],-1)),t[498]||(t[498]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[499]||(t[499]=l('
julia
isaffinemap(attr)
Checks whether the given attribute is an affine map attribute.
source
',3))]),e("details",gs,[e("summary",null,[t[500]||(t[500]=e("a",{id:"Reactant.MLIR.IR.isarray-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isarray-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isarray")],-1)),t[501]||(t[501]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[502]||(t[502]=l('
julia
isarray(attr)
Checks whether the given attribute is an array attribute.
source
',3))]),e("details",ys,[e("summary",null,[t[503]||(t[503]=e("a",{id:"Reactant.MLIR.IR.isbf16-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isbf16-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isbf16")],-1)),t[504]||(t[504]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[505]||(t[505]=l('
julia
isbf16(type)
Checks whether the given type is a bf16 type.
source
',3))]),e("details",ms,[e("summary",null,[t[506]||(t[506]=e("a",{id:"Reactant.MLIR.IR.isbinary-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isbinary-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isbinary")],-1)),t[507]||(t[507]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[508]||(t[508]=l('
julia
isbinary(affineExpr)
Checks whether the given affine expression is binary.
source
',3))]),e("details",ks,[e("summary",null,[t[509]||(t[509]=e("a",{id:"Reactant.MLIR.IR.isbool-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isbool-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isbool")],-1)),t[510]||(t[510]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[511]||(t[511]=l('
julia
isbool(attr)
Checks whether the given attribute is a bool attribute.
source
',3))]),e("details",Rs,[e("summary",null,[t[512]||(t[512]=e("a",{id:"Reactant.MLIR.IR.isceildiv-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isceildiv-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isceildiv")],-1)),t[513]||(t[513]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[514]||(t[514]=l('
julia
isceildiv(affineExpr)
Checks whether the given affine expression is an ceildiv expression.
source
',3))]),e("details",fs,[e("summary",null,[t[515]||(t[515]=e("a",{id:"Reactant.MLIR.IR.iscomplex-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.iscomplex-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.iscomplex")],-1)),t[516]||(t[516]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[517]||(t[517]=l('
julia
iscomplex(type)
Checks whether the given type is a Complex type.
source
',3))]),e("details",Is,[e("summary",null,[t[518]||(t[518]=e("a",{id:"Reactant.MLIR.IR.isconstantexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isconstantexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isconstantexpr")],-1)),t[519]||(t[519]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[520]||(t[520]=l('
julia
isconstantexpr(affineExpr)
Checks whether the given affine expression is a constant expression.
source
',3))]),e("details",js,[e("summary",null,[t[521]||(t[521]=e("a",{id:"Reactant.MLIR.IR.isconstrainteq-Tuple{Reactant.MLIR.IR.IntegerSet, Any}",href:"#Reactant.MLIR.IR.isconstrainteq-Tuple{Reactant.MLIR.IR.IntegerSet, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isconstrainteq")],-1)),t[522]||(t[522]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[523]||(t[523]=l('
julia
mlirIntegerSetIsConstraintEq(set, i)
Returns true of the i-th constraint of the set is an equality constraint, false otherwise.
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',3))]),e("details",Ms,[e("summary",null,[t[524]||(t[524]=e("a",{id:"Reactant.MLIR.IR.isdenseelements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isdenseelements-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdenseelements")],-1)),t[525]||(t[525]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[526]||(t[526]=l('
julia
isdenseelements(attr)
Checks whether the given attribute is a dense elements attribute.
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',3))]),e("details",As,[e("summary",null,[t[527]||(t[527]=e("a",{id:"Reactant.MLIR.IR.isdict-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isdict-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdict")],-1)),t[528]||(t[528]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[529]||(t[529]=l('
julia
isdict(attr)
Checks whether the given attribute is a dictionary attribute.
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',3))]),e("details",Ls,[e("summary",null,[t[530]||(t[530]=e("a",{id:"Reactant.MLIR.IR.isdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdimexpr")],-1)),t[531]||(t[531]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[532]||(t[532]=l('
julia
isdimexpr(affineExpr)
Checks whether the given affine expression is a dimension expression.
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',3))]),e("details",Es,[e("summary",null,[t[533]||(t[533]=e("a",{id:"Reactant.MLIR.IR.isdyndim-Tuple{Reactant.MLIR.IR.Type, Int64}",href:"#Reactant.MLIR.IR.isdyndim-Tuple{Reactant.MLIR.IR.Type, Int64}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdyndim")],-1)),t[534]||(t[534]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[535]||(t[535]=l('
julia
isdyndim(type, i)
Checks wither the i-th dimension of the given shaped type is dynamic.
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',3))]),e("details",vs,[e("summary",null,[t[536]||(t[536]=e("a",{id:"Reactant.MLIR.IR.isdynsize-Tuple{Any}",href:"#Reactant.MLIR.IR.isdynsize-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdynsize")],-1)),t[537]||(t[537]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[538]||(t[538]=l('
julia
isdynsize(size)
Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.
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',3))]),e("details",Ts,[e("summary",null,[t[539]||(t[539]=e("a",{id:"Reactant.MLIR.IR.isdynstrideoroffset-Tuple{Any}",href:"#Reactant.MLIR.IR.isdynstrideoroffset-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdynstrideoroffset")],-1)),t[540]||(t[540]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[541]||(t[541]=l('
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)
Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.
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',3))]),e("details",Cs,[e("summary",null,[t[542]||(t[542]=e("a",{id:"Reactant.MLIR.IR.iselements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.iselements-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.iselements")],-1)),t[543]||(t[543]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[544]||(t[544]=l('
julia
iselements(attr)
Checks whether the given attribute is an elements attribute.
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',3))]),e("details",xs,[e("summary",null,[t[545]||(t[545]=e("a",{id:"Reactant.MLIR.IR.isempty-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.isempty-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isempty")],-1)),t[546]||(t[546]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[547]||(t[547]=l('
julia
isempty(set)
Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.
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',3))]),e("details",Fs,[e("summary",null,[t[548]||(t[548]=e("a",{id:"Reactant.MLIR.IR.isf16-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf16-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf16")],-1)),t[549]||(t[549]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[550]||(t[550]=l('
julia
isf16(type)
Checks whether the given type is an f16 type.
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',3))]),e("details",Ps,[e("summary",null,[t[551]||(t[551]=e("a",{id:"Reactant.MLIR.IR.isf32-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf32-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf32")],-1)),t[552]||(t[552]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[553]||(t[553]=l('
julia
isf32(type)
Checks whether the given type is an f32 type.
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',3))]),e("details",Ds,[e("summary",null,[t[554]||(t[554]=e("a",{id:"Reactant.MLIR.IR.isf64-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf64-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf64")],-1)),t[555]||(t[555]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[556]||(t[556]=l('
julia
isf64(type)
Checks whether the given type is an f64 type.
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',3))]),e("details",Os,[e("summary",null,[t[557]||(t[557]=e("a",{id:"Reactant.MLIR.IR.isf8e4m3fn-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf8e4m3fn-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf8e4m3fn")],-1)),t[558]||(t[558]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[559]||(t[559]=l('
julia
isf8e4m3fn(type)
Checks whether the given type is an f8E4M3FN type.
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',3))]),e("details",Bs,[e("summary",null,[t[560]||(t[560]=e("a",{id:"Reactant.MLIR.IR.isf8e5m2-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf8e5m2-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf8e5m2")],-1)),t[561]||(t[561]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[562]||(t[562]=l('
julia
isf8e5m2(type)
Checks whether the given type is an f8E5M2 type.
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',3))]),e("details",Gs,[e("summary",null,[t[563]||(t[563]=e("a",{id:"Reactant.MLIR.IR.isfailure-Tuple{Reactant.MLIR.IR.LogicalResult}",href:"#Reactant.MLIR.IR.isfailure-Tuple{Reactant.MLIR.IR.LogicalResult}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfailure")],-1)),t[564]||(t[564]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[565]||(t[565]=l('
julia
isfailure(res)
Checks if the given logical result represents a failure.
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',3))]),e("details",zs,[e("summary",null,[t[566]||(t[566]=e("a",{id:"Reactant.MLIR.IR.isflatsymbolref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isflatsymbolref-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isflatsymbolref")],-1)),t[567]||(t[567]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[568]||(t[568]=l('
julia
isflatsymbolref(attr)
Checks whether the given attribute is a flat symbol reference attribute.
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',3))]),e("details",ws,[e("summary",null,[t[569]||(t[569]=e("a",{id:"Reactant.MLIR.IR.isfloat-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isfloat-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfloat")],-1)),t[570]||(t[570]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[571]||(t[571]=l('
julia
isfloat(attr)
Checks whether the given attribute is a floating point attribute.
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',3))]),e("details",Ss,[e("summary",null,[t[572]||(t[572]=e("a",{id:"Reactant.MLIR.IR.isfloordiv-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isfloordiv-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfloordiv")],-1)),t[573]||(t[573]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[574]||(t[574]=l('
julia
isfloordiv(affineExpr)
Checks whether the given affine expression is an floordiv expression.
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',3))]),e("details",Ns,[e("summary",null,[t[575]||(t[575]=e("a",{id:"Reactant.MLIR.IR.isfunction-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isfunction-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfunction")],-1)),t[576]||(t[576]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[577]||(t[577]=l('
julia
isfunction(type)
Checks whether the given type is a function type.
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',3))]),e("details",Vs,[e("summary",null,[t[578]||(t[578]=e("a",{id:"Reactant.MLIR.IR.isfunctionofdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr, Any}",href:"#Reactant.MLIR.IR.isfunctionofdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfunctionofdimexpr")],-1)),t[579]||(t[579]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[580]||(t[580]=l('
julia
isfunctionofdimexpr(affineExpr, position)
Checks whether the given affine expression involves AffineDimExpr 'position'.
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',3))]),e("details",qs,[e("summary",null,[t[581]||(t[581]=e("a",{id:"Reactant.MLIR.IR.isidentity-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isidentity-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isidentity")],-1)),t[582]||(t[582]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[583]||(t[583]=l('
julia
isidentity(affineMap)
Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.
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',3))]),e("details",Us,[e("summary",null,[t[584]||(t[584]=e("a",{id:"Reactant.MLIR.IR.isindex-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isindex-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isindex")],-1)),t[585]||(t[585]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[586]||(t[586]=l('
julia
isindex(type)
Checks whether the given type is an index type.
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',3))]),e("details",Qs,[e("summary",null,[t[587]||(t[587]=e("a",{id:"Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isinteger")],-1)),t[588]||(t[588]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[589]||(t[589]=l('
julia
isinteger(attr)
Checks whether the given attribute is an integer attribute.
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',3))]),e("details",Ws,[e("summary",null,[t[590]||(t[590]=e("a",{id:"Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isinteger")],-1)),t[591]||(t[591]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[592]||(t[592]=l('
julia
isinteger(type)
Checks whether the given type is an integer type.
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',3))]),e("details",Hs,[e("summary",null,[t[593]||(t[593]=e("a",{id:"Reactant.MLIR.IR.isintegerset-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isintegerset-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isintegerset")],-1)),t[594]||(t[594]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[595]||(t[595]=l('
julia
isintegerset(attr)
Checks whether the given attribute is an integer set attribute.
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',3))]),e("details",Zs,[e("summary",null,[t[596]||(t[596]=e("a",{id:"Reactant.MLIR.IR.ismemref-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.ismemref-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismemref")],-1)),t[597]||(t[597]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[598]||(t[598]=l('
julia
ismemref(type)
Checks whether the given type is a MemRef type.
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',3))]),e("details",Js,[e("summary",null,[t[599]||(t[599]=e("a",{id:"Reactant.MLIR.IR.isminoridentity-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isminoridentity-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isminoridentity")],-1)),t[600]||(t[600]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[601]||(t[601]=l('
julia
isminoridentity(affineMap)
Checks whether the given affine map is a minor identity affine map.
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',3))]),e("details",Ks,[e("summary",null,[t[602]||(t[602]=e("a",{id:"Reactant.MLIR.IR.ismod-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.ismod-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismod")],-1)),t[603]||(t[603]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[604]||(t[604]=l('
julia
ismod(affineExpr)
Checks whether the given affine expression is an mod expression.
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',3))]),e("details",$s,[e("summary",null,[t[605]||(t[605]=e("a",{id:"Reactant.MLIR.IR.ismul-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.ismul-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismul")],-1)),t[606]||(t[606]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[607]||(t[607]=l('
julia
ismul(affineExpr)
Checks whether the given affine expression is an mul expression.
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',3))]),e("details",Xs,[e("summary",null,[t[608]||(t[608]=e("a",{id:"Reactant.MLIR.IR.ismultipleof-Tuple{Reactant.MLIR.IR.AffineExpr, Any}",href:"#Reactant.MLIR.IR.ismultipleof-Tuple{Reactant.MLIR.IR.AffineExpr, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismultipleof")],-1)),t[609]||(t[609]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[610]||(t[610]=l('
julia
ismultipleof(affineExpr, factor)
Checks whether the given affine expression is a multiple of 'factor'.
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',3))]),e("details",Ys,[e("summary",null,[t[611]||(t[611]=e("a",{id:"Reactant.MLIR.IR.isnone-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isnone-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isnone")],-1)),t[612]||(t[612]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[613]||(t[613]=l('
julia
mlirTypeIsANone(type)
Checks whether the given type is a None type.
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',3))]),e("details",_s,[e("summary",null,[t[614]||(t[614]=e("a",{id:"Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isopaque")],-1)),t[615]||(t[615]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[616]||(t[616]=l('
julia
isopaque(attr)
Checks whether the given attribute is an opaque attribute.
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',3))]),e("details",ta,[e("summary",null,[t[617]||(t[617]=e("a",{id:"Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isopaque")],-1)),t[618]||(t[618]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[619]||(t[619]=l('
julia
isopaque(type)
Checks whether the given type is an opaque type.
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',3))]),e("details",ea,[e("summary",null,[t[620]||(t[620]=e("a",{id:"Reactant.MLIR.IR.isprojperm-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isprojperm-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isprojperm")],-1)),t[621]||(t[621]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[622]||(t[622]=l('
julia
isprojperm(affineMap)
Checks whether the given affine map represents a subset of a symbol-less permutation map.
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',3))]),e("details",sa,[e("summary",null,[t[623]||(t[623]=e("a",{id:"Reactant.MLIR.IR.isrankedtensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isrankedtensor-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isrankedtensor")],-1)),t[624]||(t[624]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[625]||(t[625]=l('
julia
isrankedtensor(type)
Checks whether the given type is a ranked tensor type.
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',3))]),e("details",aa,[e("summary",null,[t[626]||(t[626]=e("a",{id:"Reactant.MLIR.IR.isshaped-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isshaped-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isshaped")],-1)),t[627]||(t[627]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[628]||(t[628]=l('
julia
isshaped(type)
Checks whether the given type is a Shaped type.
source
',3))]),e("details",ia,[e("summary",null,[t[629]||(t[629]=e("a",{id:"Reactant.MLIR.IR.issigned-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.issigned-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issigned")],-1)),t[630]||(t[630]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[631]||(t[631]=l('
julia
issigned(type)
Checks whether the given integer type is signed.
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',3))]),e("details",la,[e("summary",null,[t[632]||(t[632]=e("a",{id:"Reactant.MLIR.IR.issignless-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.issignless-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issignless")],-1)),t[633]||(t[633]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[634]||(t[634]=l('
julia
issignless(type)
Checks whether the given integer type is signless.
source
',3))]),e("details",na,[e("summary",null,[t[635]||(t[635]=e("a",{id:"Reactant.MLIR.IR.issingleconstant-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.issingleconstant-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issingleconstant")],-1)),t[636]||(t[636]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[637]||(t[637]=l('
julia
issingleconstant(affineMap)
Checks whether the given affine map is a single result constant affine map.
source
',3))]),e("details",pa,[e("summary",null,[t[638]||(t[638]=e("a",{id:"Reactant.MLIR.IR.issparseelements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issparseelements-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issparseelements")],-1)),t[639]||(t[639]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[640]||(t[640]=l('
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issparseelements(attr)
Checks whether the given attribute is a sparse elements attribute.
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julia
issplat(attr)
Checks whether the given dense elements attribute contains a single replicated value (splat).
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',3))]),e("details",oa,[e("summary",null,[t[644]||(t[644]=e("a",{id:"Reactant.MLIR.IR.isstring-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isstring-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isstring")],-1)),t[645]||(t[645]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[646]||(t[646]=l('
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isstring(attr)
Checks whether the given attribute is a string attribute.
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',3))]),e("details",da,[e("summary",null,[t[647]||(t[647]=e("a",{id:"Reactant.MLIR.IR.issuccess-Tuple{Reactant.MLIR.IR.LogicalResult}",href:"#Reactant.MLIR.IR.issuccess-Tuple{Reactant.MLIR.IR.LogicalResult}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issuccess")],-1)),t[648]||(t[648]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[649]||(t[649]=l('
julia
issuccess(res)
Checks if the given logical result represents a success.
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',3))]),e("details",ha,[e("summary",null,[t[650]||(t[650]=e("a",{id:"Reactant.MLIR.IR.issymbolexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.issymbolexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issymbolexpr")],-1)),t[651]||(t[651]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[652]||(t[652]=l('
julia
issymbolexpr(affineExpr)
Checks whether the given affine expression is a symbol expression.
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',3))]),e("details",ca,[e("summary",null,[t[653]||(t[653]=e("a",{id:"Reactant.MLIR.IR.issymbolref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issymbolref-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.issymbolref")],-1)),t[654]||(t[654]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[655]||(t[655]=l('
julia
issymbolref(attr)
Checks whether the given attribute is a symbol reference attribute.
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',3))]),e("details",ua,[e("summary",null,[t[656]||(t[656]=e("a",{id:"Reactant.MLIR.IR.istensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.istensor-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.istensor")],-1)),t[657]||(t[657]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[658]||(t[658]=l('
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istensor(type)
Checks whether the given type is a Tensor type.
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julia
istuple(type)
Checks whether the given type is a tuple type.
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',3))]),e("details",ga,[e("summary",null,[t[662]||(t[662]=e("a",{id:"Reactant.MLIR.IR.istype-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.istype-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.istype")],-1)),t[663]||(t[663]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[664]||(t[664]=l('
julia
istype(attr)
Checks whether the given attribute is a type attribute.
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',3))]),e("details",ya,[e("summary",null,[t[665]||(t[665]=e("a",{id:"Reactant.MLIR.IR.isunit-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isunit-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunit")],-1)),t[666]||(t[666]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[667]||(t[667]=l('
julia
isunit(attr)
Checks whether the given attribute is a unit attribute.
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',3))]),e("details",ma,[e("summary",null,[t[668]||(t[668]=e("a",{id:"Reactant.MLIR.IR.isunrankedmemref-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunrankedmemref-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunrankedmemref")],-1)),t[669]||(t[669]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[670]||(t[670]=l('
julia
mlirTypeIsAUnrankedMemRef(type)
Checks whether the given type is an UnrankedMemRef type.
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julia
isunrankedtensor(type)
Checks whether the given type is an unranked tensor type.
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',3))]),e("details",Ra,[e("summary",null,[t[674]||(t[674]=e("a",{id:"Reactant.MLIR.IR.isunsigned-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunsigned-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunsigned")],-1)),t[675]||(t[675]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[676]||(t[676]=l('
julia
isunsigned(type)
Checks whether the given integer type is unsigned.
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',3))]),e("details",fa,[e("summary",null,[t[677]||(t[677]=e("a",{id:"Reactant.MLIR.IR.isvector-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isvector-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.isvector")],-1)),t[678]||(t[678]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[679]||(t[679]=l('
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isvector(type)
Checks whether the given type is a Vector type.
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',3))]),e("details",Ia,[e("summary",null,[t[680]||(t[680]=e("a",{id:"Reactant.MLIR.IR.layout-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.layout-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.layout")],-1)),t[681]||(t[681]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[682]||(t[682]=l('
julia
layout(type)
Returns the layout of the given MemRef type.
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julia
leafref(attr)
Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.
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',3))]),e("details",Ma,[e("summary",null,[t[686]||(t[686]=e("a",{id:"Reactant.MLIR.IR.lhs-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.lhs-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.lhs")],-1)),t[687]||(t[687]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[688]||(t[688]=l('
julia
lhs(affineExpr)
Returns the left hand side affine expression of the given affine binary operation expression.
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location(op)
Gets the location of the operation.
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',3))]),e("details",La,[e("summary",null,[t[692]||(t[692]=e("a",{id:"Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.ExecutionEngine, String}",href:"#Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.ExecutionEngine, String}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.lookup")],-1)),t[693]||(t[693]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[694]||(t[694]=l('
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lookup(jit, name)
Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.
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',3))]),e("details",Ea,[e("summary",null,[t[695]||(t[695]=e("a",{id:"Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.SymbolTable, AbstractString}",href:"#Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.SymbolTable, AbstractString}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.lookup")],-1)),t[696]||(t[696]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[697]||(t[697]=l('
julia
lookup(symboltable, name)
Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.
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',3))]),e("details",va,[e("summary",null,[t[698]||(t[698]=e("a",{id:"Reactant.MLIR.IR.majorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.majorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.majorsubmap")],-1)),t[699]||(t[699]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[700]||(t[700]=l('
julia
majorsubmap(affineMap, nresults)
Returns the affine map consisting of the most major nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
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',3))]),e("details",Ta,[e("summary",null,[t[701]||(t[701]=e("a",{id:"Reactant.MLIR.IR.memspace-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.memspace-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.memspace")],-1)),t[702]||(t[702]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[703]||(t[703]=l('
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mlirMemRefTypeGetMemorySpace(type)
Returns the memory space of the given MemRef type.
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',3))]),e("details",Ca,[e("summary",null,[t[704]||(t[704]=e("a",{id:"Reactant.MLIR.IR.minorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.minorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.minorsubmap")],-1)),t[705]||(t[705]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[706]||(t[706]=l('
julia
minorsubmap(affineMap, nresults)
Returns the affine map consisting of the most minor nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
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',3))]),e("details",xa,[e("summary",null,[t[707]||(t[707]=e("a",{id:"Reactant.MLIR.IR.move_after!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.move_after!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.move_after!")],-1)),t[708]||(t[708]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[709]||(t[709]=l('
julia
move_after!(op, other)
Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
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',3))]),e("details",Fa,[e("summary",null,[t[710]||(t[710]=e("a",{id:"Reactant.MLIR.IR.move_before!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.move_before!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.move_before!")],-1)),t[711]||(t[711]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[712]||(t[712]=l('
julia
move_before!(op, other)
Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),e("details",Pa,[e("summary",null,[t[713]||(t[713]=e("a",{id:"Reactant.MLIR.IR.name-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.name-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.name")],-1)),t[714]||(t[714]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[715]||(t[715]=l('
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name(op)
Gets the name of the operation as an identifier.
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',3))]),e("details",Da,[e("summary",null,[t[716]||(t[716]=e("a",{id:"Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.namespace")],-1)),t[717]||(t[717]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[718]||(t[718]=l('
julia
mlirOpaqueAttrGetDialectNamespace(attr)
Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.
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',3))]),e("details",Oa,[e("summary",null,[t[719]||(t[719]=e("a",{id:"Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.namespace")],-1)),t[720]||(t[720]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[721]||(t[721]=l('
julia
mlirOpaqueTypeGetDialectNamespace(type)
Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.
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',3))]),e("details",Ba,[e("summary",null,[t[722]||(t[722]=e("a",{id:"Reactant.MLIR.IR.nargs-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.nargs-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nargs")],-1)),t[723]||(t[723]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[724]||(t[724]=l('
julia
nargs(block)
Returns the number of arguments of the block.
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',3))]),e("details",Ga,[e("summary",null,[t[725]||(t[725]=e("a",{id:"Reactant.MLIR.IR.nattrs-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nattrs-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nattrs")],-1)),t[726]||(t[726]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[727]||(t[727]=l('
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nattrs(op)
Returns the number of attributes attached to the operation.
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',3))]),e("details",za,[e("summary",null,[t[728]||(t[728]=e("a",{id:"Reactant.MLIR.IR.nconstraints-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nconstraints-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nconstraints")],-1)),t[729]||(t[729]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[730]||(t[730]=l('
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nconstraints(set)
Returns the number of constraints (equalities + inequalities) in the given set.
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',3))]),e("details",wa,[e("summary",null,[t[731]||(t[731]=e("a",{id:"Reactant.MLIR.IR.nequalities-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nequalities-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nequalities")],-1)),t[732]||(t[732]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[733]||(t[733]=l('
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nequalities(set)
Returns the number of equalities in the given set.
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',3))]),e("details",Sa,[e("summary",null,[t[734]||(t[734]=e("a",{id:"Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.next")],-1)),t[735]||(t[735]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[736]||(t[736]=l('
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next(block)
Returns the block immediately following the given block in its parent region or nothing if last.
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',3))]),e("details",Na,[e("summary",null,[t[737]||(t[737]=e("a",{id:"Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.OpOperand}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.next")],-1)),t[738]||(t[738]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[739]||(t[739]=l('
julia
next(opOperand)
Returns an op operand representing the next use of the value, or nothing if there is no next use.
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',3))]),e("details",Va,[e("summary",null,[t[740]||(t[740]=e("a",{id:"Reactant.MLIR.IR.ninequalities-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.ninequalities-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninequalities")],-1)),t[741]||(t[741]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[742]||(t[742]=l('
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ninequalities(set)
Returns the number of inequalities in the given set.
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',3))]),e("details",qa,[e("summary",null,[t[743]||(t[743]=e("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),t[744]||(t[744]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[745]||(t[745]=l('
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ninputs(affineMap)
Returns the number of inputs (dimensions + symbols) of the given affine map.
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',3))]),e("details",Ua,[e("summary",null,[t[746]||(t[746]=e("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),t[747]||(t[747]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[748]||(t[748]=l('
julia
ninputs(set)
Returns the number of inputs (dimensions + symbols) in the given set.
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',3))]),e("details",Qa,[e("summary",null,[t[749]||(t[749]=e("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),t[750]||(t[750]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[751]||(t[751]=l('
julia
ninputs(type)
Returns the number of input types.
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',3))]),e("details",Wa,[e("summary",null,[t[752]||(t[752]=e("a",{id:"Reactant.MLIR.IR.nnestedrefs-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.nnestedrefs-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nnestedrefs")],-1)),t[753]||(t[753]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[754]||(t[754]=l('
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nnestedrefs(attr)
Returns the number of references nested in the given symbol reference attribute.
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',3))]),e("details",Ha,[e("summary",null,[t[755]||(t[755]=e("a",{id:"Reactant.MLIR.IR.noperands-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.noperands-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.noperands")],-1)),t[756]||(t[756]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[757]||(t[757]=l('
julia
noperands(op)
Returns the number of operands of the operation.
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',3))]),e("details",Za,[e("summary",null,[t[758]||(t[758]=e("a",{id:"Reactant.MLIR.IR.nregions-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nregions-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nregions")],-1)),t[759]||(t[759]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[760]||(t[760]=l('
julia
nregions(op)
Returns the number of regions attached to the given operation.
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',3))]),e("details",Ja,[e("summary",null,[t[761]||(t[761]=e("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),t[762]||(t[762]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[763]||(t[763]=l('
julia
nresults(affineMap)
Returns the number of results of the given affine map.
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',3))]),e("details",Ka,[e("summary",null,[t[764]||(t[764]=e("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),t[765]||(t[765]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[766]||(t[766]=l('
julia
nresults(op)
Returns the number of results of the operation.
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',3))]),e("details",$a,[e("summary",null,[t[767]||(t[767]=e("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),t[768]||(t[768]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[769]||(t[769]=l('
julia
nresults(type)
Returns the number of result types.
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julia
nsuccessors(op)
Returns the number of successor blocks of the operation.
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',3))]),e("details",Ya,[e("summary",null,[t[773]||(t[773]=e("a",{id:"Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsymbols")],-1)),t[774]||(t[774]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[775]||(t[775]=l('
julia
nsymbols(affineMap)
Returns the number of symbols of the given affine map.
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',3))]),e("details",_a,[e("summary",null,[t[776]||(t[776]=e("a",{id:"Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.IntegerSet}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsymbols")],-1)),t[777]||(t[777]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[778]||(t[778]=l('
julia
nsymbols(set)
Returns the number of symbols in the given set.
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',3))]),e("details",ti,[e("summary",null,[t[779]||(t[779]=e("a",{id:"Reactant.MLIR.IR.op_owner-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.op_owner-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.op_owner")],-1)),t[780]||(t[780]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[781]||(t[781]=l('
julia
op_owner(value)
Returns an operation that produced this value as its result. Asserts if the value is not an op result.
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',3))]),e("details",ei,[e("summary",null,[t[782]||(t[782]=e("a",{id:"Reactant.MLIR.IR.op_res_num-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.op_res_num-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.op_res_num")],-1)),t[783]||(t[783]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[784]||(t[784]=l('
julia
op_res_num(value)
Returns the position of the value in the list of results of the operation that produced it.
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julia
operand(op, i)
Returns i-th operand of the operation.
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julia
operand!(op, i, value)
Sets the i-th operand of the operation.
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',3))]),e("details",ii,[e("summary",null,[t[791]||(t[791]=e("a",{id:"Reactant.MLIR.IR.operandindex-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.operandindex-Tuple{Reactant.MLIR.IR.OpOperand}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.operandindex")],-1)),t[792]||(t[792]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[793]||(t[793]=l('
julia
operandindex(opOperand)
Returns the operand number of an op operand.
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',3))]),e("details",li,[e("summary",null,[t[794]||(t[794]=e("a",{id:"Reactant.MLIR.IR.owner-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.owner-Tuple{Reactant.MLIR.IR.OpOperand}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.owner")],-1)),t[795]||(t[795]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[796]||(t[796]=l('
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owner(opOperand)
Returns the owner operation of an op operand.
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julia
parent_op(block)
Returns the closest surrounding operation that contains this block.
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',3))]),e("details",pi,[e("summary",null,[t[800]||(t[800]=e("a",{id:"Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_op")],-1)),t[801]||(t[801]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[802]||(t[802]=l('
julia
parent_op(op)
Gets the operation that owns this operation, returning null if the operation is not owned.
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',3))]),e("details",ri,[e("summary",null,[t[803]||(t[803]=e("a",{id:"Reactant.MLIR.IR.parent_region-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.parent_region-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_region")],-1)),t[804]||(t[804]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[805]||(t[805]=l('
julia
parent_region(block)
Returns the region that contains this block.
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position(affineExpr)
Returns the position of the given affine dimension expression, affine symbol expression or ...
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',3))]),e("details",di,[e("summary",null,[t[809]||(t[809]=e("a",{id:"Reactant.MLIR.IR.push_argument!-Tuple{Reactant.MLIR.IR.Block, Any}",href:"#Reactant.MLIR.IR.push_argument!-Tuple{Reactant.MLIR.IR.Block, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.push_argument!")],-1)),t[810]||(t[810]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[811]||(t[811]=l('
julia
push_argument!(block, type; location=Location())
Appends an argument of the specified type to the block. Returns the newly added argument.
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',3))]),e("details",hi,[e("summary",null,[t[812]||(t[812]=e("a",{id:"Reactant.MLIR.IR.region-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.region-Tuple{Reactant.MLIR.IR.Operation, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.region")],-1)),t[813]||(t[813]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[814]||(t[814]=l('
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region(op, i)
Returns i-th region attached to the operation.
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',3))]),e("details",ci,[e("summary",null,[t[815]||(t[815]=e("a",{id:"Reactant.MLIR.IR.result",href:"#Reactant.MLIR.IR.result"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),t[816]||(t[816]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),t[817]||(t[817]=l('
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result(op, i)
Returns i-th result of the operation.
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',3))]),e("details",ui,[e("summary",null,[t[818]||(t[818]=e("a",{id:"Reactant.MLIR.IR.result-2",href:"#Reactant.MLIR.IR.result-2"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),t[819]||(t[819]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),t[820]||(t[820]=l('
julia
result(type, i)
Returns the i-th result type.
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',3))]),e("details",bi,[e("summary",null,[t[821]||(t[821]=e("a",{id:"Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),t[822]||(t[822]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[823]||(t[823]=l('
julia
result(affineMap, pos)
Returns the result at the given position.
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',3))]),e("details",gi,[e("summary",null,[t[824]||(t[824]=e("a",{id:"Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),t[825]||(t[825]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[826]||(t[826]=l('
julia
result(affineMap)
Returns the constant result of the given affine map. The function asserts that the map has a single constant result.
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',3))]),e("details",yi,[e("summary",null,[t[827]||(t[827]=e("a",{id:"Reactant.MLIR.IR.rhs-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.rhs-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.rhs")],-1)),t[828]||(t[828]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[829]||(t[829]=l('
julia
rhs(affineExpr)
Returns the right hand side affine expression of the given affine binary operation expression.
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',3))]),e("details",mi,[e("summary",null,[t[830]||(t[830]=e("a",{id:"Reactant.MLIR.IR.rmattr!-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.rmattr!-Tuple{Reactant.MLIR.IR.Operation, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.rmattr!")],-1)),t[831]||(t[831]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[832]||(t[832]=l('
julia
rmattr!(op, name)
Removes an attribute by name. Returns false if the attribute was not found and true if removed.
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',3))]),e("details",ki,[e("summary",null,[t[833]||(t[833]=e("a",{id:"Reactant.MLIR.IR.rmfromparent!-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.rmfromparent!-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.rmfromparent!")],-1)),t[834]||(t[834]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[835]||(t[835]=l('
julia
rmfromparent!(op)
Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.
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',3))]),e("details",Ri,[e("summary",null,[t[836]||(t[836]=e("a",{id:"Reactant.MLIR.IR.rootref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.rootref-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.rootref")],-1)),t[837]||(t[837]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[838]||(t[838]=l('
julia
rootref(attr)
Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.
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',3))]),e("details",fi,[e("summary",null,[t[839]||(t[839]=e("a",{id:"Reactant.MLIR.IR.run!-Tuple{Reactant.MLIR.IR.PassManager, Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.run!-Tuple{Reactant.MLIR.IR.PassManager, Reactant.MLIR.IR.Module}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.run!")],-1)),t[840]||(t[840]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[841]||(t[841]=l('
julia
run!(passManager, module)
Run the provided passManager on the given module.
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',3))]),e("details",Ii,[e("summary",null,[t[842]||(t[842]=e("a",{id:"Reactant.MLIR.IR.submap-Tuple{Reactant.MLIR.IR.AffineMap, Vector{Int64}}",href:"#Reactant.MLIR.IR.submap-Tuple{Reactant.MLIR.IR.AffineMap, Vector{Int64}}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.submap")],-1)),t[843]||(t[843]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[844]||(t[844]=l('
julia
submap(affineMap, positions)
Returns the affine map consisting of the positions subset.
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',3))]),e("details",ji,[e("summary",null,[t[845]||(t[845]=e("a",{id:"Reactant.MLIR.IR.success-Tuple{}",href:"#Reactant.MLIR.IR.success-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.success")],-1)),t[846]||(t[846]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[847]||(t[847]=l('
julia
success()
Creates a logical result representing a success.
source
',3))]),e("details",Mi,[e("summary",null,[t[848]||(t[848]=e("a",{id:"Reactant.MLIR.IR.successor-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.successor-Tuple{Reactant.MLIR.IR.Operation, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.successor")],-1)),t[849]||(t[849]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[850]||(t[850]=l('
julia
successor(op, i)
Returns i-th successor of the operation.
source
',3))]),e("details",Ai,[e("summary",null,[t[851]||(t[851]=e("a",{id:"Reactant.MLIR.IR.terminator-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.terminator-Tuple{Reactant.MLIR.IR.Block}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.terminator")],-1)),t[852]||(t[852]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[853]||(t[853]=l('
julia
terminator(block)
Returns the terminator operation in the block or nothing if no terminator.
source
',3))]),e("details",Li,[e("summary",null,[t[854]||(t[854]=e("a",{id:"Reactant.MLIR.IR.type!-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.type!-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.type!")],-1)),t[855]||(t[855]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[856]||(t[856]=l('
julia
set_type!(value, type)
Sets the type of the block argument to the given type.
source
',3))]),e("details",Ei,[e("summary",null,[t[857]||(t[857]=e("a",{id:"Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.type")],-1)),t[858]||(t[858]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[859]||(t[859]=l('
julia
type(attribute)
Gets the type of this attribute.
source
',3))]),e("details",vi,[e("summary",null,[t[860]||(t[860]=e("a",{id:"Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Value}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.type")],-1)),t[861]||(t[861]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[862]||(t[862]=l('
julia
type(value)
Returns the type of the value.
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',3))]),e("details",Ti,[e("summary",null,[t[863]||(t[863]=e("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Attribute}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),t[864]||(t[864]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[865]||(t[865]=l('
julia
typeid(attribute)
Gets the type id of the attribute.
source
',3))]),e("details",Ci,[e("summary",null,[t[866]||(t[866]=e("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),t[867]||(t[867]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[868]||(t[868]=l('
julia
typeid(op)
Gets the type id of the operation. Returns null if the operation does not have a registered operation description.
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',3))]),e("details",xi,[e("summary",null,[t[869]||(t[869]=e("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Type}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),t[870]||(t[870]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[871]||(t[871]=l('
julia
typeid(type)
Gets the type ID of the type.
source
',3))]),e("details",Fi,[e("summary",null,[t[872]||(t[872]=e("a",{id:"Reactant.MLIR.IR.value-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.value-Tuple{Reactant.MLIR.IR.AffineExpr}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.value")],-1)),t[873]||(t[873]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[874]||(t[874]=l('
julia
value(affineExpr)
Returns the value of the given affine constant expression.
source
',3))]),e("details",Pi,[e("summary",null,[t[875]||(t[875]=e("a",{id:"Reactant.MLIR.IR.verify-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.verify-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.verify")],-1)),t[876]||(t[876]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[877]||(t[877]=l('
julia
verify(op)
Verify the operation and return true if it passes, false if it fails.
source
',3))]),e("details",Di,[e("summary",null,[t[878]||(t[878]=e("a",{id:"Reactant.MLIR.IR.verifyall-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.verifyall-Tuple{Reactant.MLIR.IR.Operation}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.verifyall")],-1)),t[879]||(t[879]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[880]||(t[880]=l('
julia
verifyall(operation; debug=false)
Prints the operations which could not be verified.
source
',3))]),e("details",Oi,[e("summary",null,[t[881]||(t[881]=e("a",{id:"Reactant.MLIR.IR.@affinemap-Tuple{Any}",href:"#Reactant.MLIR.IR.@affinemap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.IR.@affinemap")],-1)),t[882]||(t[882]=s()),i(a,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),t[883]||(t[883]=l(`
julia
@affinemap (d1, d2, d3, ...)[s1, s2, ...] -> (d0 + d1, ...)
Returns an affine map from the provided Julia expression. On the right hand side are allowed the following function calls:
  • +, *, ÷, %, fld, cld
The rhs can only contains dimensions and symbols present on the left hand side or integer literals.
julia
julia> using Reactant.MLIR: IR
+fld(lhs, rhs)
Creates an affine floordiv expression with 'lhs' and 'rhs'.
source
`,3))]),t("details",Ae,[t("summary",null,[e[201]||(e[201]=t("a",{id:"Base.fill-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Type}",href:"#Base.fill-Tuple{Reactant.MLIR.IR.Attribute, Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Base.fill")],-1)),e[202]||(e[202]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[203]||(e[203]=l('
julia
fill(attr, shapedType)
Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).
source
',3))]),t("details",Le,[t("summary",null,[e[204]||(e[204]=t("a",{id:"Base.gcd-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Base.gcd-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.gcd")],-1)),e[205]||(e[205]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[206]||(e[206]=l('
julia
gcd(affineExpr)
Returns the greatest known integral divisor of this affine expression. The result is always positive.
source
',3))]),t("details",Ee,[t("summary",null,[e[207]||(e[207]=t("a",{id:"Base.hash-Tuple{Reactant.MLIR.IR.TypeID}",href:"#Base.hash-Tuple{Reactant.MLIR.IR.TypeID}"},[t("span",{class:"jlbinding"},"Base.hash")],-1)),e[208]||(e[208]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[209]||(e[209]=l('
julia
hash(typeID)
Returns the hash value of the type id.
source
',3))]),t("details",ve,[t("summary",null,[e[210]||(e[210]=t("a",{id:"Base.insert!-Tuple{Reactant.MLIR.IR.Block, Any, Reactant.MLIR.IR.Operation}",href:"#Base.insert!-Tuple{Reactant.MLIR.IR.Block, Any, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Base.insert!")],-1)),e[211]||(e[211]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[212]||(e[212]=l('
julia
insert!(block, index, operation)
Takes an operation owned by the caller and inserts it as index to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.
source
',3))]),t("details",Te,[t("summary",null,[e[213]||(e[213]=t("a",{id:"Base.insert!-Tuple{Reactant.MLIR.IR.Region, Any, Reactant.MLIR.IR.Block}",href:"#Base.insert!-Tuple{Reactant.MLIR.IR.Region, Any, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Base.insert!")],-1)),e[214]||(e[214]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[215]||(e[215]=l('
julia
insert!(region, index, block)
Takes a block owned by the caller and inserts it at index to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.
source
',3))]),t("details",Ce,[t("summary",null,[e[216]||(e[216]=t("a",{id:"Base.isempty-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.isempty-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Base.isempty")],-1)),e[217]||(e[217]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[218]||(e[218]=l('
julia
isempty(affineMap)
Checks whether the given affine map is an empty affine map.
source
',3))]),t("details",xe,[t("summary",null,[e[219]||(e[219]=t("a",{id:"Base.isperm-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.isperm-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Base.isperm")],-1)),e[220]||(e[220]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[221]||(e[221]=l('
julia
isperm(affineMap)
Checks whether the given affine map represents a symbol-less permutation map.
source
',3))]),t("details",Fe,[t("summary",null,[e[222]||(e[222]=t("a",{id:"Base.mod-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}",href:"#Base.mod-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Base.mod")],-1)),e[223]||(e[223]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[224]||(e[224]=l('
julia
mod(lhs, rhs)
Creates an affine mod expression with 'lhs' and 'rhs'.
source
',3))]),t("details",Pe,[t("summary",null,[e[225]||(e[225]=t("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Base.ndims")],-1)),e[226]||(e[226]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[227]||(e[227]=l('
julia
ndims(affineMap)
Returns the number of dimensions of the given affine map.
source
',3))]),t("details",De,[t("summary",null,[e[228]||(e[228]=t("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Base.ndims")],-1)),e[229]||(e[229]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[230]||(e[230]=l('
julia
ndims(set)
Returns the number of dimensions in the given set.
source
',3))]),t("details",Oe,[t("summary",null,[e[231]||(e[231]=t("a",{id:"Base.ndims-Tuple{Reactant.MLIR.IR.Type}",href:"#Base.ndims-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Base.ndims")],-1)),e[232]||(e[232]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[233]||(e[233]=l('
julia
ndims(type)
Returns the rank of the given ranked shaped type.
source
',3))]),t("details",Be,[t("summary",null,[e[234]||(e[234]=t("a",{id:"Base.parse-Tuple{Reactant.MLIR.IR.OpPassManager, String}",href:"#Base.parse-Tuple{Reactant.MLIR.IR.OpPassManager, String}"},[t("span",{class:"jlbinding"},"Base.parse")],-1)),e[235]||(e[235]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[236]||(e[236]=l('
julia
parse(passManager, pipeline)
Parse a textual MLIR pass pipeline and add it to the provided OpPassManager.
source
',3))]),t("details",Ge,[t("summary",null,[e[237]||(e[237]=t("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Attribute}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Attribute}, Any}"},[t("span",{class:"jlbinding"},"Base.parse")],-1)),e[238]||(e[238]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[239]||(e[239]=l('
julia
parse(::Core.Type{Attribute}, str; context=context())
Parses an attribute. The attribute is owned by the context.
source
',3))]),t("details",ze,[t("summary",null,[e[240]||(e[240]=t("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Module}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Module}, Any}"},[t("span",{class:"jlbinding"},"Base.parse")],-1)),e[241]||(e[241]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=l('
julia
parse(::Type{Module}, module; context=context())
Parses a module from the string and transfers ownership to the caller.
source
',3))]),t("details",we,[t("summary",null,[e[243]||(e[243]=t("a",{id:"Base.parse-Tuple{Type{Reactant.MLIR.IR.Type}, Any}",href:"#Base.parse-Tuple{Type{Reactant.MLIR.IR.Type}, Any}"},[t("span",{class:"jlbinding"},"Base.parse")],-1)),e[244]||(e[244]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[245]||(e[245]=l('
julia
parse(type; context=context())
Parses a type. The type is owned by the context.
source
',3))]),t("details",Se,[t("summary",null,[e[246]||(e[246]=t("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Base.push!")],-1)),e[247]||(e[247]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[248]||(e[248]=l('
julia
push!(block, operation)
Takes an operation owned by the caller and appends it to the block.
source
',3))]),t("details",Ne,[t("summary",null,[e[249]||(e[249]=t("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Base.push!")],-1)),e[250]||(e[250]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[251]||(e[251]=l('
julia
push!(region, block)
Takes a block owned by the caller and appends it to the given region.
source
',3))]),t("details",Ve,[t("summary",null,[e[252]||(e[252]=t("a",{id:"Base.push!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}",href:"#Base.push!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Base.push!")],-1)),e[253]||(e[253]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=l('
julia
push!(symboltable, operation)
Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.
source
',3))]),t("details",qe,[t("summary",null,[e[255]||(e[255]=t("a",{id:"Base.replace-Tuple{Reactant.MLIR.IR.AffineMap, Pair{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}, Any, Any}",href:"#Base.replace-Tuple{Reactant.MLIR.IR.AffineMap, Pair{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineExpr}, Any, Any}"},[t("span",{class:"jlbinding"},"Base.replace")],-1)),e[256]||(e[256]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[257]||(e[257]=l('
julia
mlirAffineMapReplace(affineMap, expression => replacement, numResultDims, numResultSyms)
Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.
source
',3))]),t("details",Ue,[t("summary",null,[e[258]||(e[258]=t("a",{id:"Base.replace-Tuple{Reactant.MLIR.IR.IntegerSet, Any, Any}",href:"#Base.replace-Tuple{Reactant.MLIR.IR.IntegerSet, Any, Any}"},[t("span",{class:"jlbinding"},"Base.replace")],-1)),e[259]||(e[259]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=l('
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)
Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.
source
',3))]),t("details",Qe,[t("summary",null,[e[261]||(e[261]=t("a",{id:"Base.reshape-Tuple{Reactant.MLIR.IR.Attribute, Any}",href:"#Base.reshape-Tuple{Reactant.MLIR.IR.Attribute, Any}"},[t("span",{class:"jlbinding"},"Base.reshape")],-1)),e[262]||(e[262]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[263]||(e[263]=l('
julia
Base.reshape(attr, shapedType)
Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.
source
',3))]),t("details",We,[t("summary",null,[e[264]||(e[264]=t("a",{id:"Base.size-Tuple{Reactant.MLIR.IR.Type, Int64}",href:"#Base.size-Tuple{Reactant.MLIR.IR.Type, Int64}"},[t("span",{class:"jlbinding"},"Base.size")],-1)),e[265]||(e[265]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[266]||(e[266]=l('
julia
size(type, i)
Returns the i-th dimension of the given ranked shaped type.
source
',3))]),t("details",He,[t("summary",null,[e[267]||(e[267]=t("a",{id:"Base.write-Tuple{String, Reactant.MLIR.IR.ExecutionEngine}",href:"#Base.write-Tuple{String, Reactant.MLIR.IR.ExecutionEngine}"},[t("span",{class:"jlbinding"},"Base.write")],-1)),e[268]||(e[268]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[269]||(e[269]=l('
julia
write(fileName, jit)
Dump as an object in fileName.
source
',3))]),t("details",Ze,[t("summary",null,[e[270]||(e[270]=t("a",{id:"Reactant.MLIR.IR.AffineDimensionExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.AffineDimensionExpr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.AffineDimensionExpr")],-1)),e[271]||(e[271]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[272]||(e[272]=l('
julia
AffineDimensionExpr(position; context=context)
Creates an affine dimension expression with 'position' in the context.
source
',3))]),t("details",Je,[t("summary",null,[e[273]||(e[273]=t("a",{id:"Reactant.MLIR.IR.BFloat16Type-Tuple{}",href:"#Reactant.MLIR.IR.BFloat16Type-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.BFloat16Type")],-1)),e[274]||(e[274]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[275]||(e[275]=t("p",null,"BFloat16Type(; context=context())",-1)),e[276]||(e[276]=t("p",null,"Creates a bf16 type in the given context. The type is owned by the context.",-1)),e[277]||(e[277]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/IR/Type.jl#L157-L161",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Ke,[t("summary",null,[e[278]||(e[278]=t("a",{id:"Reactant.MLIR.IR.ConstantAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.ConstantAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ConstantAffineMap")],-1)),e[279]||(e[279]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[280]||(e[280]=l('
julia
ConstantAffineMap(val; context=context())
Creates a single constant result affine map in the context. The affine map is owned by the context.
source
',3))]),t("details",$e,[t("summary",null,[e[281]||(e[281]=t("a",{id:"Reactant.MLIR.IR.ConstantExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.ConstantExpr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ConstantExpr")],-1)),e[282]||(e[282]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[283]||(e[283]=l('
julia
ConstantExpr(constant::Int; context=context())
Creates an affine constant expression with 'constant' in the context.
source
',3))]),t("details",Xe,[t("summary",null,[e[284]||(e[284]=t("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{Bool}}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{Bool}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),e[285]||(e[285]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[286]||(e[286]=l('
julia
DenseElementsAttribute(array::AbstractArray)
Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.
source
',3))]),t("details",Ye,[t("summary",null,[e[287]||(e[287]=t("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{String}}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{AbstractArray{String}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),e[288]||(e[288]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[289]||(e[289]=l('
julia
DenseElementsAttribute(array::AbstractArray{String})
Creates a dense elements attribute with the given shaped type from string elements.
source
',3))]),t("details",_e,[t("summary",null,[e[290]||(e[290]=t("a",{id:"Reactant.MLIR.IR.DenseElementsAttribute-Tuple{Reactant.MLIR.IR.Type, AbstractArray}",href:"#Reactant.MLIR.IR.DenseElementsAttribute-Tuple{Reactant.MLIR.IR.Type, AbstractArray}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.DenseElementsAttribute")],-1)),e[291]||(e[291]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[292]||(e[292]=l('
julia
DenseElementsAttribute(shapedType, elements)
Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.
source
',3))]),t("details",et,[t("summary",null,[e[293]||(e[293]=t("a",{id:"Reactant.MLIR.IR.FlatSymbolRefAttribute-Tuple{String}",href:"#Reactant.MLIR.IR.FlatSymbolRefAttribute-Tuple{String}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.FlatSymbolRefAttribute")],-1)),e[294]||(e[294]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[295]||(e[295]=l('
julia
FlatSymbolRefAttribute(ctx, symbol)
Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.
source
',3))]),t("details",tt,[t("summary",null,[e[296]||(e[296]=t("a",{id:"Reactant.MLIR.IR.Float8E4M3FN-Tuple{}",href:"#Reactant.MLIR.IR.Float8E4M3FN-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Float8E4M3FN")],-1)),e[297]||(e[297]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[298]||(e[298]=l('
julia
Float8E4M3FN(; context=context())
Creates an f8E4M3FN type in the given context. The type is owned by the context.
source
',3))]),t("details",st,[t("summary",null,[e[299]||(e[299]=t("a",{id:"Reactant.MLIR.IR.Float8E5M2-Tuple{}",href:"#Reactant.MLIR.IR.Float8E5M2-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.Float8E5M2")],-1)),e[300]||(e[300]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[301]||(e[301]=l('
julia
Float8E5M2(; context=context())
Creates an f8E5M2 type in the given context. The type is owned by the context.
source
',3))]),t("details",at,[t("summary",null,[e[302]||(e[302]=t("a",{id:"Reactant.MLIR.IR.FunctionType-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.FunctionType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.FunctionType")],-1)),e[303]||(e[303]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[304]||(e[304]=l('
julia
FunctionType(inputs, results; context=context())
Creates a function type, mapping a list of input types to result types.
source
',3))]),t("details",it,[t("summary",null,[e[305]||(e[305]=t("a",{id:"Reactant.MLIR.IR.IdentityAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.IdentityAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.IdentityAffineMap")],-1)),e[306]||(e[306]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[307]||(e[307]=l('
julia
IdentityAffineMap(ndims; context=context())
Creates an affine map with 'ndims' identity in the context. The affine map is owned by the context.
source
',3))]),t("details",lt,[t("summary",null,[e[308]||(e[308]=t("a",{id:"Reactant.MLIR.IR.IndexType-Tuple{}",href:"#Reactant.MLIR.IR.IndexType-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.IndexType")],-1)),e[309]||(e[309]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[310]||(e[310]=l('
julia
IndexType(; context=context())
Creates an index type in the given context. The type is owned by the context.
source
',3))]),t("details",nt,[t("summary",null,[e[311]||(e[311]=t("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),e[312]||(e[312]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[313]||(e[313]=l('
julia
MemRefType(elementType, rank, shape, layout, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",pt,[t("summary",null,[e[314]||(e[314]=t("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),e[315]||(e[315]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[316]||(e[316]=l('
julia
MemRefType(elementType, rank, shape, memorySpace; location=Location(), check=false)
Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",rt,[t("summary",null,[e[317]||(e[317]=t("a",{id:"Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any}",href:"#Reactant.MLIR.IR.MemRefType-Tuple{Reactant.MLIR.IR.Type, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.MemRefType")],-1)),e[318]||(e[318]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[319]||(e[319]=l('
julia
MemRefType(elementType, memorySpace)
Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",ot,[t("summary",null,[e[320]||(e[320]=t("a",{id:"Reactant.MLIR.IR.MinorIdentityAffineMap-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.MinorIdentityAffineMap-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.MinorIdentityAffineMap")],-1)),e[321]||(e[321]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[322]||(e[322]=l('
julia
MinorIdentityAffineMap(ndims, nresults; context=context())
Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),t("details",dt,[t("summary",null,[e[323]||(e[323]=t("a",{id:"Reactant.MLIR.IR.OpaqueAttribute-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.IR.OpaqueAttribute-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpaqueAttribute")],-1)),e[324]||(e[324]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[325]||(e[325]=l('
julia
OpaqueAttribute(dialectNamespace, dataLength, data, type; context=context())
Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),t("details",ct,[t("summary",null,[e[326]||(e[326]=t("a",{id:"Reactant.MLIR.IR.OpaqueType-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.OpaqueType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.OpaqueType")],-1)),e[327]||(e[327]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[328]||(e[328]=l('
julia
OpaqueType(dialectNamespace, typeData; context=context())
Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),t("details",bt,[t("summary",null,[e[329]||(e[329]=t("a",{id:"Reactant.MLIR.IR.PermutationAffineMap-Tuple{Any}",href:"#Reactant.MLIR.IR.PermutationAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.PermutationAffineMap")],-1)),e[330]||(e[330]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[331]||(e[331]=l('
julia
PermutationAffineMap(permutation; context=context())
Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid invalid permutation). The affine map is owned by the context.
source
',3))]),t("details",ht,[t("summary",null,[e[332]||(e[332]=t("a",{id:"Reactant.MLIR.IR.SymbolExpr-Tuple{Any}",href:"#Reactant.MLIR.IR.SymbolExpr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolExpr")],-1)),e[333]||(e[333]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[334]||(e[334]=l('
julia
SymbolExpr(position; context=context())
Creates an affine symbol expression with 'position' in the context.
source
',3))]),t("details",ut,[t("summary",null,[e[335]||(e[335]=t("a",{id:"Reactant.MLIR.IR.SymbolRefAttribute-Tuple{String, Vector{Reactant.MLIR.IR.Attribute}}",href:"#Reactant.MLIR.IR.SymbolRefAttribute-Tuple{String, Vector{Reactant.MLIR.IR.Attribute}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.SymbolRefAttribute")],-1)),e[336]||(e[336]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[337]||(e[337]=l('
julia
SymbolRefAttribute(symbol, references; context=context())
Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.
source
',3))]),t("details",gt,[t("summary",null,[e[338]||(e[338]=t("a",{id:"Reactant.MLIR.IR.TensorType",href:"#Reactant.MLIR.IR.TensorType"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.TensorType")],-1)),e[339]||(e[339]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[340]||(e[340]=l('
julia
TensorType(shape, elementType, encoding=Attribute(); location=Location(), check=false)
Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull to this parameter. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",yt,[t("summary",null,[e[341]||(e[341]=t("a",{id:"Reactant.MLIR.IR.TensorType-Tuple{Any}",href:"#Reactant.MLIR.IR.TensorType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.TensorType")],-1)),e[342]||(e[342]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[343]||(e[343]=l('
julia
TensorType(elementType)
Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",mt,[t("summary",null,[e[344]||(e[344]=t("a",{id:"Reactant.MLIR.IR.UnitAttribute-Tuple{}",href:"#Reactant.MLIR.IR.UnitAttribute-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.UnitAttribute")],-1)),e[345]||(e[345]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[346]||(e[346]=l('
julia
UnitAttribute(; context=context())
Creates a unit attribute in the given context.
source
',3))]),t("details",kt,[t("summary",null,[e[347]||(e[347]=t("a",{id:"Reactant.MLIR.IR.VectorType-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.IR.VectorType-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.VectorType")],-1)),e[348]||(e[348]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[349]||(e[349]=l('
julia
VectorType(rank, shape, elementType; location=Location(), check=false)
Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context. If check=true, emits appropriate diagnostics on illegal arguments.
source
',3))]),t("details",Rt,[t("summary",null,[e[350]||(e[350]=t("a",{id:"Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_owned_pass!")],-1)),e[351]||(e[351]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[352]||(e[352]=l('
julia
add_owned_pass!(opPassManager, pass)
Add a pass and transfer ownership to the provided OpPassManager. If the pass is not a generic operation pass or matching the type of the provided OpPassManager, a new OpPassManager is implicitly nested under the provided OpPassManager.
source
',3))]),t("details",It,[t("summary",null,[e[353]||(e[353]=t("a",{id:"Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.PassManager, Any}",href:"#Reactant.MLIR.IR.add_owned_pass!-Tuple{Reactant.MLIR.IR.PassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_owned_pass!")],-1)),e[354]||(e[354]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[355]||(e[355]=l('
julia
add_owned_pass!(passManager, pass)
Add a pass and transfer ownership to the provided top-level PassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.
source
',3))]),t("details",jt,[t("summary",null,[e[356]||(e[356]=t("a",{id:"Reactant.MLIR.IR.add_pipeline!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}",href:"#Reactant.MLIR.IR.add_pipeline!-Tuple{Reactant.MLIR.IR.OpPassManager, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.add_pipeline!")],-1)),e[357]||(e[357]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[358]||(e[358]=l('
julia
add_pipeline!(passManager, pipelineElements, callback, userData)
Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
',3))]),t("details",ft,[t("summary",null,[e[359]||(e[359]=t("a",{id:"Reactant.MLIR.IR.affinemap-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.affinemap-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.affinemap")],-1)),e[360]||(e[360]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[361]||(e[361]=l('
julia
affinemap(type)
Returns the affine map of the given MemRef type.
source
',3))]),t("details",Mt,[t("summary",null,[e[362]||(e[362]=t("a",{id:"Reactant.MLIR.IR.argument-Tuple{Reactant.MLIR.IR.Block, Any}",href:"#Reactant.MLIR.IR.argument-Tuple{Reactant.MLIR.IR.Block, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.argument")],-1)),e[363]||(e[363]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[364]||(e[364]=l('
julia
argument(block, i)
Returns i-th argument of the block.
source
',3))]),t("details",At,[t("summary",null,[e[365]||(e[365]=t("a",{id:"Reactant.MLIR.IR.attr!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}",href:"#Reactant.MLIR.IR.attr!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr!")],-1)),e[366]||(e[366]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[367]||(e[367]=l('
julia
attr!(op, name, attr)
Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise.
source
',3))]),t("details",Lt,[t("summary",null,[e[368]||(e[368]=t("a",{id:"Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, AbstractString}",href:"#Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, AbstractString}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr")],-1)),e[369]||(e[369]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[370]||(e[370]=l('
julia
attr(op, name)
Returns an attribute attached to the operation given its name.
source
',3))]),t("details",Et,[t("summary",null,[e[371]||(e[371]=t("a",{id:"Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.attr-Tuple{Reactant.MLIR.IR.Operation, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.attr")],-1)),e[372]||(e[372]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[373]||(e[373]=l('
julia
attr(op, i)
Return i-th attribute of the operation.
source
',3))]),t("details",vt,[t("summary",null,[e[374]||(e[374]=t("a",{id:"Reactant.MLIR.IR.bitwidth-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.bitwidth-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.bitwidth")],-1)),e[375]||(e[375]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[376]||(e[376]=l('
julia
bitwidth(type)
Returns the bitwidth of an integer type.
source
',3))]),t("details",Tt,[t("summary",null,[e[377]||(e[377]=t("a",{id:"Reactant.MLIR.IR.block-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.block-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.block")],-1)),e[378]||(e[378]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[379]||(e[379]=l('
julia
block(op)
Gets the block that owns this operation, returning null if the operation is not owned.
source
',3))]),t("details",Ct,[t("summary",null,[e[380]||(e[380]=t("a",{id:"Reactant.MLIR.IR.block_arg_num-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.block_arg_num-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.block_arg_num")],-1)),e[381]||(e[381]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[382]||(e[382]=l('
julia
block_arg_num(value)
Returns the position of the value in the argument list of its block.
source
',3))]),t("details",xt,[t("summary",null,[e[383]||(e[383]=t("a",{id:"Reactant.MLIR.IR.block_owner-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.block_owner-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.block_owner")],-1)),e[384]||(e[384]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[385]||(e[385]=l('
julia
block_owner(value)
Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.
source
',3))]),t("details",Ft,[t("summary",null,[e[386]||(e[386]=t("a",{id:"Reactant.MLIR.IR.body-Tuple{Any}",href:"#Reactant.MLIR.IR.body-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.body")],-1)),e[387]||(e[387]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[388]||(e[388]=l('
julia
body(module)
Gets the body of the module, i.e. the only block it contains.
source
',3))]),t("details",Pt,[t("summary",null,[e[389]||(e[389]=t("a",{id:"Reactant.MLIR.IR.compose-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.compose-Tuple{Reactant.MLIR.IR.AffineExpr, Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.compose")],-1)),e[390]||(e[390]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[391]||(e[391]=l('
julia
compose(affineExpr, affineMap)
Composes the given map with the given expression.
source
',3))]),t("details",Dt,[t("summary",null,[e[392]||(e[392]=t("a",{id:"Reactant.MLIR.IR.constraint-Tuple{Reactant.MLIR.IR.IntegerSet, Any}",href:"#Reactant.MLIR.IR.constraint-Tuple{Reactant.MLIR.IR.IntegerSet, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.constraint")],-1)),e[393]||(e[393]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[394]||(e[394]=l('
julia
mlirIntegerSetGetConstraint(set, i)
Returns i-th constraint of the set.
source
',3))]),t("details",Ot,[t("summary",null,[e[395]||(e[395]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[396]||(e[396]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[397]||(e[397]=l('
julia
context(affineExpr)
Gets the context that owns the affine expression.
source
',3))]),t("details",Bt,[t("summary",null,[e[398]||(e[398]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[399]||(e[399]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[400]||(e[400]=l('
julia
context(affineMap)
Gets the context that the given affine map was created with.
source
',3))]),t("details",Gt,[t("summary",null,[e[401]||(e[401]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[402]||(e[402]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[403]||(e[403]=l('
julia
context(attribute)
Gets the context that an attribute was created with.
source
',3))]),t("details",zt,[t("summary",null,[e[404]||(e[404]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Identifier}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Identifier}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[405]||(e[405]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[406]||(e[406]=l('
julia
context(ident)
Returns the context associated with this identifier
source
',3))]),t("details",wt,[t("summary",null,[e[407]||(e[407]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[408]||(e[408]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[409]||(e[409]=l('
julia
context(set)
Gets the context in which the given integer set lives.
source
',3))]),t("details",St,[t("summary",null,[e[410]||(e[410]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Module}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[411]||(e[411]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[412]||(e[412]=l('
julia
context(module)
Gets the context that a module was created with.
source
',3))]),t("details",Nt,[t("summary",null,[e[413]||(e[413]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[414]||(e[414]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[415]||(e[415]=l('
julia
context(op)
Gets the context this operation is associated with.
source
',3))]),t("details",Vt,[t("summary",null,[e[416]||(e[416]=t("a",{id:"Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.context-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.context")],-1)),e[417]||(e[417]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[418]||(e[418]=l('
julia
context(type)
Gets the context that a type was created with.
source
',3))]),t("details",qt,[t("summary",null,[e[419]||(e[419]=t("a",{id:"Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.data")],-1)),e[420]||(e[420]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[421]||(e[421]=l('
julia
data(attr)
Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.
source
',3))]),t("details",Ut,[t("summary",null,[e[422]||(e[422]=t("a",{id:"Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.data-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.data")],-1)),e[423]||(e[423]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[424]||(e[424]=l('
julia
mlirOpaqueTypeGetData(type)
Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.
source
',3))]),t("details",Qt,[t("summary",null,[e[425]||(e[425]=t("a",{id:"Reactant.MLIR.IR.delete!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.delete!-Tuple{Reactant.MLIR.IR.SymbolTable, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.delete!")],-1)),e[426]||(e[426]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[427]||(e[427]=l('
julia
delete!(symboltable, operation)
Removes the given operation from the symbol table and erases it.
source
',3))]),t("details",Wt,[t("summary",null,[e[428]||(e[428]=t("a",{id:"Reactant.MLIR.IR.dynsize-Tuple{}",href:"#Reactant.MLIR.IR.dynsize-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.dynsize")],-1)),e[429]||(e[429]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[430]||(e[430]=l('
julia
dynsize()
Returns the value indicating a dynamic size in a shaped type. Prefer isdynsize to direct comparisons with this value.
source
',3))]),t("details",Ht,[t("summary",null,[e[431]||(e[431]=t("a",{id:"Reactant.MLIR.IR.dynstrideoroffset-Tuple{}",href:"#Reactant.MLIR.IR.dynstrideoroffset-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.dynstrideoroffset")],-1)),e[432]||(e[432]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[433]||(e[433]=l('
julia
mlirShapedTypeGetDynamicStrideOrOffset()
Returns the value indicating a dynamic stride or offset in a shaped type. Prefer isdynstrideoroffset to direct comparisons with this value.
source
',3))]),t("details",Zt,[t("summary",null,[e[434]||(e[434]=t("a",{id:"Reactant.MLIR.IR.enable_ir_printing!-Tuple{Any}",href:"#Reactant.MLIR.IR.enable_ir_printing!-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.enable_ir_printing!")],-1)),e[435]||(e[435]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[436]||(e[436]=l('
julia
enable_ir_printing!(passManager)
Enable mlir-print-ir-after-all.
source
',3))]),t("details",Jt,[t("summary",null,[e[437]||(e[437]=t("a",{id:"Reactant.MLIR.IR.enable_verifier!",href:"#Reactant.MLIR.IR.enable_verifier!"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.enable_verifier!")],-1)),e[438]||(e[438]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[439]||(e[439]=l('
julia
enable_verifier!(passManager, enable)
Enable / disable verify-each.
source
',3))]),t("details",Kt,[t("summary",null,[e[440]||(e[440]=t("a",{id:"Reactant.MLIR.IR.encoding-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.encoding-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.encoding")],-1)),e[441]||(e[441]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[442]||(e[442]=l('
julia
encoding(type)
Gets the 'encoding' attribute from the ranked tensor type, returning a nothing if none.
source
',3))]),t("details",$t,[t("summary",null,[e[443]||(e[443]=t("a",{id:"Reactant.MLIR.IR.failure-Tuple{}",href:"#Reactant.MLIR.IR.failure-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.failure")],-1)),e[444]||(e[444]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[445]||(e[445]=l('
julia
failure()
Creates a logical result representing a failure.
source
',3))]),t("details",Xt,[t("summary",null,[e[446]||(e[446]=t("a",{id:"Reactant.MLIR.IR.first_block-Tuple{Reactant.MLIR.IR.Region}",href:"#Reactant.MLIR.IR.first_block-Tuple{Reactant.MLIR.IR.Region}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_block")],-1)),e[447]||(e[447]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[448]||(e[448]=l('
julia
first_block(region)
Gets the first block in the region.
source
',3))]),t("details",Yt,[t("summary",null,[e[449]||(e[449]=t("a",{id:"Reactant.MLIR.IR.first_op-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.first_op-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_op")],-1)),e[450]||(e[450]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[451]||(e[451]=l('
julia
first_op(block)
Returns the first operation in the block or nothing if empty.
source
',3))]),t("details",_t,[t("summary",null,[e[452]||(e[452]=t("a",{id:"Reactant.MLIR.IR.first_use-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.first_use-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.first_use")],-1)),e[453]||(e[453]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[454]||(e[454]=l('
julia
first_use(value)
Returns an OpOperand representing the first use of the value, or a nothing if there are no uses.
source
',3))]),t("details",es,[t("summary",null,[e[455]||(e[455]=t("a",{id:"Reactant.MLIR.IR.flatsymbol-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.flatsymbol-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.flatsymbol")],-1)),e[456]||(e[456]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[457]||(e[457]=l('
julia
flatsymbol(attr)
Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.
source
',3))]),t("details",ts,[t("summary",null,[e[458]||(e[458]=t("a",{id:"Reactant.MLIR.IR.hasrank-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.hasrank-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.hasrank")],-1)),e[459]||(e[459]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[460]||(e[460]=l('
julia
hasrank(type)
Checks whether the given shaped type is ranked.
source
',3))]),t("details",ss,[t("summary",null,[e[461]||(e[461]=t("a",{id:"Reactant.MLIR.IR.hasstaticshape-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.hasstaticshape-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.hasstaticshape")],-1)),e[462]||(e[462]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[463]||(e[463]=l('
julia
hasstaticshape(type)
Checks whether the given shaped type has a static shape.
source
',3))]),t("details",as,[t("summary",null,[e[464]||(e[464]=t("a",{id:"Reactant.MLIR.IR.input-Tuple{Reactant.MLIR.IR.Type, Any}",href:"#Reactant.MLIR.IR.input-Tuple{Reactant.MLIR.IR.Type, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.input")],-1)),e[465]||(e[465]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[466]||(e[466]=l('
julia
input(type, i)
Returns the i-th input type.
source
',3))]),t("details",is,[t("summary",null,[e[467]||(e[467]=t("a",{id:"Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_after!")],-1)),e[468]||(e[468]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[469]||(e[469]=l('
julia
insert_after!(block, reference, operation)
Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.
source
',3))]),t("details",ls,[t("summary",null,[e[470]||(e[470]=t("a",{id:"Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.insert_after!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_after!")],-1)),e[471]||(e[471]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[472]||(e[472]=l('
julia
insert_after!(region, reference, block)
Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.
source
',3))]),t("details",ns,[t("summary",null,[e[473]||(e[473]=t("a",{id:"Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_before!")],-1)),e[474]||(e[474]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[475]||(e[475]=l('
julia
insert_before!(block, reference, operation)
Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.
source
',3))]),t("details",ps,[t("summary",null,[e[476]||(e[476]=t("a",{id:"Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.insert_before!-Tuple{Reactant.MLIR.IR.Region, Reactant.MLIR.IR.Block, Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.insert_before!")],-1)),e[477]||(e[477]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[478]||(e[478]=l('
julia
insert_before!(region, reference, block)
Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.
source
',3))]),t("details",rs,[t("summary",null,[e[479]||(e[479]=t("a",{id:"Reactant.MLIR.IR.is_block_arg-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.is_block_arg-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_block_arg")],-1)),e[480]||(e[480]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[481]||(e[481]=l('
julia
is_block_arg(value)
Returns 1 if the value is a block argument, 0 otherwise.
source
',3))]),t("details",os,[t("summary",null,[e[482]||(e[482]=t("a",{id:"Reactant.MLIR.IR.is_op_res-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.is_op_res-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_op_res")],-1)),e[483]||(e[483]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[484]||(e[484]=l('
julia
is_op_res(value)
Returns 1 if the value is an operation result, 0 otherwise.
source
',3))]),t("details",ds,[t("summary",null,[e[485]||(e[485]=t("a",{id:"Reactant.MLIR.IR.is_pure_affine-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.is_pure_affine-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_pure_affine")],-1)),e[486]||(e[486]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[487]||(e[487]=l('
julia
is_pure_affine(affineExpr)
Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.
source
',3))]),t("details",cs,[t("summary",null,[e[488]||(e[488]=t("a",{id:"Reactant.MLIR.IR.is_registered-Tuple{Any}",href:"#Reactant.MLIR.IR.is_registered-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_registered")],-1)),e[489]||(e[489]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[490]||(e[490]=l('
julia
is_registered(name; context=context())
Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.
source
',3))]),t("details",bs,[t("summary",null,[e[491]||(e[491]=t("a",{id:"Reactant.MLIR.IR.is_symbolic_or_constant-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.is_symbolic_or_constant-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.is_symbolic_or_constant")],-1)),e[492]||(e[492]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[493]||(e[493]=l('
julia
is_symbolic_or_constant(affineExpr)
Checks whether the given affine expression is made out of only symbols and constants.
source
',3))]),t("details",hs,[t("summary",null,[e[494]||(e[494]=t("a",{id:"Reactant.MLIR.IR.isadd-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isadd-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isadd")],-1)),e[495]||(e[495]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[496]||(e[496]=l('
julia
isadd(affineExpr)
Checks whether the given affine expression is an add expression.
source
',3))]),t("details",us,[t("summary",null,[e[497]||(e[497]=t("a",{id:"Reactant.MLIR.IR.isaffinemap-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isaffinemap-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isaffinemap")],-1)),e[498]||(e[498]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[499]||(e[499]=l('
julia
isaffinemap(attr)
Checks whether the given attribute is an affine map attribute.
source
',3))]),t("details",gs,[t("summary",null,[e[500]||(e[500]=t("a",{id:"Reactant.MLIR.IR.isarray-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isarray-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isarray")],-1)),e[501]||(e[501]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[502]||(e[502]=l('
julia
isarray(attr)
Checks whether the given attribute is an array attribute.
source
',3))]),t("details",ys,[t("summary",null,[e[503]||(e[503]=t("a",{id:"Reactant.MLIR.IR.isbf16-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isbf16-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isbf16")],-1)),e[504]||(e[504]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[505]||(e[505]=l('
julia
isbf16(type)
Checks whether the given type is a bf16 type.
source
',3))]),t("details",ms,[t("summary",null,[e[506]||(e[506]=t("a",{id:"Reactant.MLIR.IR.isbinary-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isbinary-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isbinary")],-1)),e[507]||(e[507]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[508]||(e[508]=l('
julia
isbinary(affineExpr)
Checks whether the given affine expression is binary.
source
',3))]),t("details",ks,[t("summary",null,[e[509]||(e[509]=t("a",{id:"Reactant.MLIR.IR.isbool-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isbool-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isbool")],-1)),e[510]||(e[510]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[511]||(e[511]=l('
julia
isbool(attr)
Checks whether the given attribute is a bool attribute.
source
',3))]),t("details",Rs,[t("summary",null,[e[512]||(e[512]=t("a",{id:"Reactant.MLIR.IR.isceildiv-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isceildiv-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isceildiv")],-1)),e[513]||(e[513]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[514]||(e[514]=l('
julia
isceildiv(affineExpr)
Checks whether the given affine expression is an ceildiv expression.
source
',3))]),t("details",Is,[t("summary",null,[e[515]||(e[515]=t("a",{id:"Reactant.MLIR.IR.iscomplex-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.iscomplex-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.iscomplex")],-1)),e[516]||(e[516]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[517]||(e[517]=l('
julia
iscomplex(type)
Checks whether the given type is a Complex type.
source
',3))]),t("details",js,[t("summary",null,[e[518]||(e[518]=t("a",{id:"Reactant.MLIR.IR.isconstantexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isconstantexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isconstantexpr")],-1)),e[519]||(e[519]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[520]||(e[520]=l('
julia
isconstantexpr(affineExpr)
Checks whether the given affine expression is a constant expression.
source
',3))]),t("details",fs,[t("summary",null,[e[521]||(e[521]=t("a",{id:"Reactant.MLIR.IR.isconstrainteq-Tuple{Reactant.MLIR.IR.IntegerSet, Any}",href:"#Reactant.MLIR.IR.isconstrainteq-Tuple{Reactant.MLIR.IR.IntegerSet, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isconstrainteq")],-1)),e[522]||(e[522]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[523]||(e[523]=l('
julia
mlirIntegerSetIsConstraintEq(set, i)
Returns true of the i-th constraint of the set is an equality constraint, false otherwise.
source
',3))]),t("details",Ms,[t("summary",null,[e[524]||(e[524]=t("a",{id:"Reactant.MLIR.IR.isdenseelements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isdenseelements-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdenseelements")],-1)),e[525]||(e[525]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[526]||(e[526]=l('
julia
isdenseelements(attr)
Checks whether the given attribute is a dense elements attribute.
source
',3))]),t("details",As,[t("summary",null,[e[527]||(e[527]=t("a",{id:"Reactant.MLIR.IR.isdict-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isdict-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdict")],-1)),e[528]||(e[528]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[529]||(e[529]=l('
julia
isdict(attr)
Checks whether the given attribute is a dictionary attribute.
source
',3))]),t("details",Ls,[t("summary",null,[e[530]||(e[530]=t("a",{id:"Reactant.MLIR.IR.isdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdimexpr")],-1)),e[531]||(e[531]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[532]||(e[532]=l('
julia
isdimexpr(affineExpr)
Checks whether the given affine expression is a dimension expression.
source
',3))]),t("details",Es,[t("summary",null,[e[533]||(e[533]=t("a",{id:"Reactant.MLIR.IR.isdyndim-Tuple{Reactant.MLIR.IR.Type, Int64}",href:"#Reactant.MLIR.IR.isdyndim-Tuple{Reactant.MLIR.IR.Type, Int64}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdyndim")],-1)),e[534]||(e[534]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[535]||(e[535]=l('
julia
isdyndim(type, i)
Checks wither the i-th dimension of the given shaped type is dynamic.
source
',3))]),t("details",vs,[t("summary",null,[e[536]||(e[536]=t("a",{id:"Reactant.MLIR.IR.isdynsize-Tuple{Any}",href:"#Reactant.MLIR.IR.isdynsize-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdynsize")],-1)),e[537]||(e[537]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[538]||(e[538]=l('
julia
isdynsize(size)
Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.
source
',3))]),t("details",Ts,[t("summary",null,[e[539]||(e[539]=t("a",{id:"Reactant.MLIR.IR.isdynstrideoroffset-Tuple{Any}",href:"#Reactant.MLIR.IR.isdynstrideoroffset-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isdynstrideoroffset")],-1)),e[540]||(e[540]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[541]||(e[541]=l('
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)
Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.
source
',3))]),t("details",Cs,[t("summary",null,[e[542]||(e[542]=t("a",{id:"Reactant.MLIR.IR.iselements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.iselements-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.iselements")],-1)),e[543]||(e[543]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[544]||(e[544]=l('
julia
iselements(attr)
Checks whether the given attribute is an elements attribute.
source
',3))]),t("details",xs,[t("summary",null,[e[545]||(e[545]=t("a",{id:"Reactant.MLIR.IR.isempty-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.isempty-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isempty")],-1)),e[546]||(e[546]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[547]||(e[547]=l('
julia
isempty(set)
Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.
source
',3))]),t("details",Fs,[t("summary",null,[e[548]||(e[548]=t("a",{id:"Reactant.MLIR.IR.isf16-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf16-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf16")],-1)),e[549]||(e[549]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[550]||(e[550]=l('
julia
isf16(type)
Checks whether the given type is an f16 type.
source
',3))]),t("details",Ps,[t("summary",null,[e[551]||(e[551]=t("a",{id:"Reactant.MLIR.IR.isf32-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf32-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf32")],-1)),e[552]||(e[552]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[553]||(e[553]=l('
julia
isf32(type)
Checks whether the given type is an f32 type.
source
',3))]),t("details",Ds,[t("summary",null,[e[554]||(e[554]=t("a",{id:"Reactant.MLIR.IR.isf64-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf64-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf64")],-1)),e[555]||(e[555]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[556]||(e[556]=l('
julia
isf64(type)
Checks whether the given type is an f64 type.
source
',3))]),t("details",Os,[t("summary",null,[e[557]||(e[557]=t("a",{id:"Reactant.MLIR.IR.isf8e4m3fn-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf8e4m3fn-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf8e4m3fn")],-1)),e[558]||(e[558]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[559]||(e[559]=l('
julia
isf8e4m3fn(type)
Checks whether the given type is an f8E4M3FN type.
source
',3))]),t("details",Bs,[t("summary",null,[e[560]||(e[560]=t("a",{id:"Reactant.MLIR.IR.isf8e5m2-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isf8e5m2-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isf8e5m2")],-1)),e[561]||(e[561]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[562]||(e[562]=l('
julia
isf8e5m2(type)
Checks whether the given type is an f8E5M2 type.
source
',3))]),t("details",Gs,[t("summary",null,[e[563]||(e[563]=t("a",{id:"Reactant.MLIR.IR.isfailure-Tuple{Reactant.MLIR.IR.LogicalResult}",href:"#Reactant.MLIR.IR.isfailure-Tuple{Reactant.MLIR.IR.LogicalResult}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfailure")],-1)),e[564]||(e[564]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[565]||(e[565]=l('
julia
isfailure(res)
Checks if the given logical result represents a failure.
source
',3))]),t("details",zs,[t("summary",null,[e[566]||(e[566]=t("a",{id:"Reactant.MLIR.IR.isflatsymbolref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isflatsymbolref-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isflatsymbolref")],-1)),e[567]||(e[567]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[568]||(e[568]=l('
julia
isflatsymbolref(attr)
Checks whether the given attribute is a flat symbol reference attribute.
source
',3))]),t("details",ws,[t("summary",null,[e[569]||(e[569]=t("a",{id:"Reactant.MLIR.IR.isfloat-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isfloat-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfloat")],-1)),e[570]||(e[570]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[571]||(e[571]=l('
julia
isfloat(attr)
Checks whether the given attribute is a floating point attribute.
source
',3))]),t("details",Ss,[t("summary",null,[e[572]||(e[572]=t("a",{id:"Reactant.MLIR.IR.isfloordiv-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.isfloordiv-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfloordiv")],-1)),e[573]||(e[573]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[574]||(e[574]=l('
julia
isfloordiv(affineExpr)
Checks whether the given affine expression is an floordiv expression.
source
',3))]),t("details",Ns,[t("summary",null,[e[575]||(e[575]=t("a",{id:"Reactant.MLIR.IR.isfunction-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isfunction-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfunction")],-1)),e[576]||(e[576]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[577]||(e[577]=l('
julia
isfunction(type)
Checks whether the given type is a function type.
source
',3))]),t("details",Vs,[t("summary",null,[e[578]||(e[578]=t("a",{id:"Reactant.MLIR.IR.isfunctionofdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr, Any}",href:"#Reactant.MLIR.IR.isfunctionofdimexpr-Tuple{Reactant.MLIR.IR.AffineExpr, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isfunctionofdimexpr")],-1)),e[579]||(e[579]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[580]||(e[580]=l('
julia
isfunctionofdimexpr(affineExpr, position)
Checks whether the given affine expression involves AffineDimExpr 'position'.
source
',3))]),t("details",qs,[t("summary",null,[e[581]||(e[581]=t("a",{id:"Reactant.MLIR.IR.isidentity-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isidentity-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isidentity")],-1)),e[582]||(e[582]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[583]||(e[583]=l('
julia
isidentity(affineMap)
Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),t("details",Us,[t("summary",null,[e[584]||(e[584]=t("a",{id:"Reactant.MLIR.IR.isindex-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isindex-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isindex")],-1)),e[585]||(e[585]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[586]||(e[586]=l('
julia
isindex(type)
Checks whether the given type is an index type.
source
',3))]),t("details",Qs,[t("summary",null,[e[587]||(e[587]=t("a",{id:"Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isinteger")],-1)),e[588]||(e[588]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[589]||(e[589]=l('
julia
isinteger(attr)
Checks whether the given attribute is an integer attribute.
source
',3))]),t("details",Ws,[t("summary",null,[e[590]||(e[590]=t("a",{id:"Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isinteger-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isinteger")],-1)),e[591]||(e[591]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[592]||(e[592]=l('
julia
isinteger(type)
Checks whether the given type is an integer type.
source
',3))]),t("details",Hs,[t("summary",null,[e[593]||(e[593]=t("a",{id:"Reactant.MLIR.IR.isintegerset-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isintegerset-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isintegerset")],-1)),e[594]||(e[594]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[595]||(e[595]=l('
julia
isintegerset(attr)
Checks whether the given attribute is an integer set attribute.
source
',3))]),t("details",Zs,[t("summary",null,[e[596]||(e[596]=t("a",{id:"Reactant.MLIR.IR.ismemref-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.ismemref-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismemref")],-1)),e[597]||(e[597]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[598]||(e[598]=l('
julia
ismemref(type)
Checks whether the given type is a MemRef type.
source
',3))]),t("details",Js,[t("summary",null,[e[599]||(e[599]=t("a",{id:"Reactant.MLIR.IR.isminoridentity-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isminoridentity-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isminoridentity")],-1)),e[600]||(e[600]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[601]||(e[601]=l('
julia
isminoridentity(affineMap)
Checks whether the given affine map is a minor identity affine map.
source
',3))]),t("details",Ks,[t("summary",null,[e[602]||(e[602]=t("a",{id:"Reactant.MLIR.IR.ismod-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.ismod-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismod")],-1)),e[603]||(e[603]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[604]||(e[604]=l('
julia
ismod(affineExpr)
Checks whether the given affine expression is an mod expression.
source
',3))]),t("details",$s,[t("summary",null,[e[605]||(e[605]=t("a",{id:"Reactant.MLIR.IR.ismul-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.ismul-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismul")],-1)),e[606]||(e[606]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[607]||(e[607]=l('
julia
ismul(affineExpr)
Checks whether the given affine expression is an mul expression.
source
',3))]),t("details",Xs,[t("summary",null,[e[608]||(e[608]=t("a",{id:"Reactant.MLIR.IR.ismultipleof-Tuple{Reactant.MLIR.IR.AffineExpr, Any}",href:"#Reactant.MLIR.IR.ismultipleof-Tuple{Reactant.MLIR.IR.AffineExpr, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ismultipleof")],-1)),e[609]||(e[609]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[610]||(e[610]=l('
julia
ismultipleof(affineExpr, factor)
Checks whether the given affine expression is a multiple of 'factor'.
source
',3))]),t("details",Ys,[t("summary",null,[e[611]||(e[611]=t("a",{id:"Reactant.MLIR.IR.isnone-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isnone-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isnone")],-1)),e[612]||(e[612]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[613]||(e[613]=l('
julia
mlirTypeIsANone(type)
Checks whether the given type is a None type.
source
',3))]),t("details",_s,[t("summary",null,[e[614]||(e[614]=t("a",{id:"Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isopaque")],-1)),e[615]||(e[615]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[616]||(e[616]=l('
julia
isopaque(attr)
Checks whether the given attribute is an opaque attribute.
source
',3))]),t("details",ea,[t("summary",null,[e[617]||(e[617]=t("a",{id:"Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isopaque-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isopaque")],-1)),e[618]||(e[618]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[619]||(e[619]=l('
julia
isopaque(type)
Checks whether the given type is an opaque type.
source
',3))]),t("details",ta,[t("summary",null,[e[620]||(e[620]=t("a",{id:"Reactant.MLIR.IR.isprojperm-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.isprojperm-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isprojperm")],-1)),e[621]||(e[621]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[622]||(e[622]=l('
julia
isprojperm(affineMap)
Checks whether the given affine map represents a subset of a symbol-less permutation map.
source
',3))]),t("details",sa,[t("summary",null,[e[623]||(e[623]=t("a",{id:"Reactant.MLIR.IR.isrankedtensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isrankedtensor-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isrankedtensor")],-1)),e[624]||(e[624]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[625]||(e[625]=l('
julia
isrankedtensor(type)
Checks whether the given type is a ranked tensor type.
source
',3))]),t("details",aa,[t("summary",null,[e[626]||(e[626]=t("a",{id:"Reactant.MLIR.IR.isshaped-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isshaped-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isshaped")],-1)),e[627]||(e[627]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[628]||(e[628]=l('
julia
isshaped(type)
Checks whether the given type is a Shaped type.
source
',3))]),t("details",ia,[t("summary",null,[e[629]||(e[629]=t("a",{id:"Reactant.MLIR.IR.issigned-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.issigned-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issigned")],-1)),e[630]||(e[630]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[631]||(e[631]=l('
julia
issigned(type)
Checks whether the given integer type is signed.
source
',3))]),t("details",la,[t("summary",null,[e[632]||(e[632]=t("a",{id:"Reactant.MLIR.IR.issignless-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.issignless-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issignless")],-1)),e[633]||(e[633]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[634]||(e[634]=l('
julia
issignless(type)
Checks whether the given integer type is signless.
source
',3))]),t("details",na,[t("summary",null,[e[635]||(e[635]=t("a",{id:"Reactant.MLIR.IR.issingleconstant-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.issingleconstant-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issingleconstant")],-1)),e[636]||(e[636]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[637]||(e[637]=l('
julia
issingleconstant(affineMap)
Checks whether the given affine map is a single result constant affine map.
source
',3))]),t("details",pa,[t("summary",null,[e[638]||(e[638]=t("a",{id:"Reactant.MLIR.IR.issparseelements-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issparseelements-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issparseelements")],-1)),e[639]||(e[639]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[640]||(e[640]=l('
julia
issparseelements(attr)
Checks whether the given attribute is a sparse elements attribute.
source
',3))]),t("details",ra,[t("summary",null,[e[641]||(e[641]=t("a",{id:"Reactant.MLIR.IR.issplat-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issplat-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issplat")],-1)),e[642]||(e[642]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[643]||(e[643]=l('
julia
issplat(attr)
Checks whether the given dense elements attribute contains a single replicated value (splat).
source
',3))]),t("details",oa,[t("summary",null,[e[644]||(e[644]=t("a",{id:"Reactant.MLIR.IR.isstring-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isstring-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isstring")],-1)),e[645]||(e[645]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[646]||(e[646]=l('
julia
isstring(attr)
Checks whether the given attribute is a string attribute.
source
',3))]),t("details",da,[t("summary",null,[e[647]||(e[647]=t("a",{id:"Reactant.MLIR.IR.issuccess-Tuple{Reactant.MLIR.IR.LogicalResult}",href:"#Reactant.MLIR.IR.issuccess-Tuple{Reactant.MLIR.IR.LogicalResult}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issuccess")],-1)),e[648]||(e[648]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[649]||(e[649]=l('
julia
issuccess(res)
Checks if the given logical result represents a success.
source
',3))]),t("details",ca,[t("summary",null,[e[650]||(e[650]=t("a",{id:"Reactant.MLIR.IR.issymbolexpr-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.issymbolexpr-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issymbolexpr")],-1)),e[651]||(e[651]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[652]||(e[652]=l('
julia
issymbolexpr(affineExpr)
Checks whether the given affine expression is a symbol expression.
source
',3))]),t("details",ba,[t("summary",null,[e[653]||(e[653]=t("a",{id:"Reactant.MLIR.IR.issymbolref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.issymbolref-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.issymbolref")],-1)),e[654]||(e[654]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[655]||(e[655]=l('
julia
issymbolref(attr)
Checks whether the given attribute is a symbol reference attribute.
source
',3))]),t("details",ha,[t("summary",null,[e[656]||(e[656]=t("a",{id:"Reactant.MLIR.IR.istensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.istensor-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.istensor")],-1)),e[657]||(e[657]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[658]||(e[658]=l('
julia
istensor(type)
Checks whether the given type is a Tensor type.
source
',3))]),t("details",ua,[t("summary",null,[e[659]||(e[659]=t("a",{id:"Reactant.MLIR.IR.istuple-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.istuple-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.istuple")],-1)),e[660]||(e[660]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[661]||(e[661]=l('
julia
istuple(type)
Checks whether the given type is a tuple type.
source
',3))]),t("details",ga,[t("summary",null,[e[662]||(e[662]=t("a",{id:"Reactant.MLIR.IR.istype-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.istype-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.istype")],-1)),e[663]||(e[663]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[664]||(e[664]=l('
julia
istype(attr)
Checks whether the given attribute is a type attribute.
source
',3))]),t("details",ya,[t("summary",null,[e[665]||(e[665]=t("a",{id:"Reactant.MLIR.IR.isunit-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.isunit-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunit")],-1)),e[666]||(e[666]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[667]||(e[667]=l('
julia
isunit(attr)
Checks whether the given attribute is a unit attribute.
source
',3))]),t("details",ma,[t("summary",null,[e[668]||(e[668]=t("a",{id:"Reactant.MLIR.IR.isunrankedmemref-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunrankedmemref-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunrankedmemref")],-1)),e[669]||(e[669]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[670]||(e[670]=l('
julia
mlirTypeIsAUnrankedMemRef(type)
Checks whether the given type is an UnrankedMemRef type.
source
',3))]),t("details",ka,[t("summary",null,[e[671]||(e[671]=t("a",{id:"Reactant.MLIR.IR.isunrankedtensor-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunrankedtensor-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunrankedtensor")],-1)),e[672]||(e[672]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[673]||(e[673]=l('
julia
isunrankedtensor(type)
Checks whether the given type is an unranked tensor type.
source
',3))]),t("details",Ra,[t("summary",null,[e[674]||(e[674]=t("a",{id:"Reactant.MLIR.IR.isunsigned-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isunsigned-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isunsigned")],-1)),e[675]||(e[675]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[676]||(e[676]=l('
julia
isunsigned(type)
Checks whether the given integer type is unsigned.
source
',3))]),t("details",Ia,[t("summary",null,[e[677]||(e[677]=t("a",{id:"Reactant.MLIR.IR.isvector-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.isvector-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.isvector")],-1)),e[678]||(e[678]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[679]||(e[679]=l('
julia
isvector(type)
Checks whether the given type is a Vector type.
source
',3))]),t("details",ja,[t("summary",null,[e[680]||(e[680]=t("a",{id:"Reactant.MLIR.IR.layout-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.layout-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.layout")],-1)),e[681]||(e[681]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[682]||(e[682]=l('
julia
layout(type)
Returns the layout of the given MemRef type.
source
',3))]),t("details",fa,[t("summary",null,[e[683]||(e[683]=t("a",{id:"Reactant.MLIR.IR.leafref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.leafref-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.leafref")],-1)),e[684]||(e[684]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[685]||(e[685]=l('
julia
leafref(attr)
Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.
source
',3))]),t("details",Ma,[t("summary",null,[e[686]||(e[686]=t("a",{id:"Reactant.MLIR.IR.lhs-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.lhs-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.lhs")],-1)),e[687]||(e[687]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[688]||(e[688]=l('
julia
lhs(affineExpr)
Returns the left hand side affine expression of the given affine binary operation expression.
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',3))]),t("details",Aa,[t("summary",null,[e[689]||(e[689]=t("a",{id:"Reactant.MLIR.IR.location-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.location-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.location")],-1)),e[690]||(e[690]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[691]||(e[691]=l('
julia
location(op)
Gets the location of the operation.
source
',3))]),t("details",La,[t("summary",null,[e[692]||(e[692]=t("a",{id:"Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.ExecutionEngine, String}",href:"#Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.ExecutionEngine, String}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.lookup")],-1)),e[693]||(e[693]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[694]||(e[694]=l('
julia
lookup(jit, name)
Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.
source
',3))]),t("details",Ea,[t("summary",null,[e[695]||(e[695]=t("a",{id:"Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.SymbolTable, AbstractString}",href:"#Reactant.MLIR.IR.lookup-Tuple{Reactant.MLIR.IR.SymbolTable, AbstractString}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.lookup")],-1)),e[696]||(e[696]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[697]||(e[697]=l('
julia
lookup(symboltable, name)
Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.
source
',3))]),t("details",va,[t("summary",null,[e[698]||(e[698]=t("a",{id:"Reactant.MLIR.IR.majorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.majorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.majorsubmap")],-1)),e[699]||(e[699]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[700]||(e[700]=l('
julia
majorsubmap(affineMap, nresults)
Returns the affine map consisting of the most major nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
source
',3))]),t("details",Ta,[t("summary",null,[e[701]||(e[701]=t("a",{id:"Reactant.MLIR.IR.memspace-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.memspace-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.memspace")],-1)),e[702]||(e[702]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[703]||(e[703]=l('
julia
mlirMemRefTypeGetMemorySpace(type)
Returns the memory space of the given MemRef type.
source
',3))]),t("details",Ca,[t("summary",null,[e[704]||(e[704]=t("a",{id:"Reactant.MLIR.IR.minorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.minorsubmap-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.minorsubmap")],-1)),e[705]||(e[705]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[706]||(e[706]=l('
julia
minorsubmap(affineMap, nresults)
Returns the affine map consisting of the most minor nresults results. Returns the null AffineMap if the nresults is equal to zero. Returns the affineMap if nresults is greater or equals to number of results of the given affine map.
source
',3))]),t("details",xa,[t("summary",null,[e[707]||(e[707]=t("a",{id:"Reactant.MLIR.IR.move_after!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.move_after!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.move_after!")],-1)),e[708]||(e[708]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[709]||(e[709]=l('
julia
move_after!(op, other)
Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),t("details",Fa,[t("summary",null,[e[710]||(e[710]=t("a",{id:"Reactant.MLIR.IR.move_before!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.move_before!-Tuple{Reactant.MLIR.IR.Operation, Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.move_before!")],-1)),e[711]||(e[711]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[712]||(e[712]=l('
julia
move_before!(op, other)
Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),t("details",Pa,[t("summary",null,[e[713]||(e[713]=t("a",{id:"Reactant.MLIR.IR.name-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.name-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.name")],-1)),e[714]||(e[714]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[715]||(e[715]=l('
julia
name(op)
Gets the name of the operation as an identifier.
source
',3))]),t("details",Da,[t("summary",null,[e[716]||(e[716]=t("a",{id:"Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.namespace")],-1)),e[717]||(e[717]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[718]||(e[718]=l('
julia
mlirOpaqueAttrGetDialectNamespace(attr)
Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.
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',3))]),t("details",Oa,[t("summary",null,[e[719]||(e[719]=t("a",{id:"Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.namespace-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.namespace")],-1)),e[720]||(e[720]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[721]||(e[721]=l('
julia
mlirOpaqueTypeGetDialectNamespace(type)
Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.
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',3))]),t("details",Ba,[t("summary",null,[e[722]||(e[722]=t("a",{id:"Reactant.MLIR.IR.nargs-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.nargs-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nargs")],-1)),e[723]||(e[723]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[724]||(e[724]=l('
julia
nargs(block)
Returns the number of arguments of the block.
source
',3))]),t("details",Ga,[t("summary",null,[e[725]||(e[725]=t("a",{id:"Reactant.MLIR.IR.nattrs-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nattrs-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nattrs")],-1)),e[726]||(e[726]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[727]||(e[727]=l('
julia
nattrs(op)
Returns the number of attributes attached to the operation.
source
',3))]),t("details",za,[t("summary",null,[e[728]||(e[728]=t("a",{id:"Reactant.MLIR.IR.nconstraints-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nconstraints-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nconstraints")],-1)),e[729]||(e[729]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[730]||(e[730]=l('
julia
nconstraints(set)
Returns the number of constraints (equalities + inequalities) in the given set.
source
',3))]),t("details",wa,[t("summary",null,[e[731]||(e[731]=t("a",{id:"Reactant.MLIR.IR.nequalities-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nequalities-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nequalities")],-1)),e[732]||(e[732]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[733]||(e[733]=l('
julia
nequalities(set)
Returns the number of equalities in the given set.
source
',3))]),t("details",Sa,[t("summary",null,[e[734]||(e[734]=t("a",{id:"Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.next")],-1)),e[735]||(e[735]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[736]||(e[736]=l('
julia
next(block)
Returns the block immediately following the given block in its parent region or nothing if last.
source
',3))]),t("details",Na,[t("summary",null,[e[737]||(e[737]=t("a",{id:"Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.next-Tuple{Reactant.MLIR.IR.OpOperand}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.next")],-1)),e[738]||(e[738]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[739]||(e[739]=l('
julia
next(opOperand)
Returns an op operand representing the next use of the value, or nothing if there is no next use.
source
',3))]),t("details",Va,[t("summary",null,[e[740]||(e[740]=t("a",{id:"Reactant.MLIR.IR.ninequalities-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.ninequalities-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninequalities")],-1)),e[741]||(e[741]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[742]||(e[742]=l('
julia
ninequalities(set)
Returns the number of inequalities in the given set.
source
',3))]),t("details",qa,[t("summary",null,[e[743]||(e[743]=t("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),e[744]||(e[744]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[745]||(e[745]=l('
julia
ninputs(affineMap)
Returns the number of inputs (dimensions + symbols) of the given affine map.
source
',3))]),t("details",Ua,[t("summary",null,[e[746]||(e[746]=t("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),e[747]||(e[747]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[748]||(e[748]=l('
julia
ninputs(set)
Returns the number of inputs (dimensions + symbols) in the given set.
source
',3))]),t("details",Qa,[t("summary",null,[e[749]||(e[749]=t("a",{id:"Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.ninputs-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.ninputs")],-1)),e[750]||(e[750]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[751]||(e[751]=l('
julia
ninputs(type)
Returns the number of input types.
source
',3))]),t("details",Wa,[t("summary",null,[e[752]||(e[752]=t("a",{id:"Reactant.MLIR.IR.nnestedrefs-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.nnestedrefs-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nnestedrefs")],-1)),e[753]||(e[753]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[754]||(e[754]=l('
julia
nnestedrefs(attr)
Returns the number of references nested in the given symbol reference attribute.
source
',3))]),t("details",Ha,[t("summary",null,[e[755]||(e[755]=t("a",{id:"Reactant.MLIR.IR.noperands-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.noperands-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.noperands")],-1)),e[756]||(e[756]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[757]||(e[757]=l('
julia
noperands(op)
Returns the number of operands of the operation.
source
',3))]),t("details",Za,[t("summary",null,[e[758]||(e[758]=t("a",{id:"Reactant.MLIR.IR.nregions-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nregions-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nregions")],-1)),e[759]||(e[759]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[760]||(e[760]=l('
julia
nregions(op)
Returns the number of regions attached to the given operation.
source
',3))]),t("details",Ja,[t("summary",null,[e[761]||(e[761]=t("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),e[762]||(e[762]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[763]||(e[763]=l('
julia
nresults(affineMap)
Returns the number of results of the given affine map.
source
',3))]),t("details",Ka,[t("summary",null,[e[764]||(e[764]=t("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),e[765]||(e[765]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[766]||(e[766]=l('
julia
nresults(op)
Returns the number of results of the operation.
source
',3))]),t("details",$a,[t("summary",null,[e[767]||(e[767]=t("a",{id:"Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.nresults-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nresults")],-1)),e[768]||(e[768]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[769]||(e[769]=l('
julia
nresults(type)
Returns the number of result types.
source
',3))]),t("details",Xa,[t("summary",null,[e[770]||(e[770]=t("a",{id:"Reactant.MLIR.IR.nsuccessors-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.nsuccessors-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsuccessors")],-1)),e[771]||(e[771]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[772]||(e[772]=l('
julia
nsuccessors(op)
Returns the number of successor blocks of the operation.
source
',3))]),t("details",Ya,[t("summary",null,[e[773]||(e[773]=t("a",{id:"Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsymbols")],-1)),e[774]||(e[774]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[775]||(e[775]=l('
julia
nsymbols(affineMap)
Returns the number of symbols of the given affine map.
source
',3))]),t("details",_a,[t("summary",null,[e[776]||(e[776]=t("a",{id:"Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.IntegerSet}",href:"#Reactant.MLIR.IR.nsymbols-Tuple{Reactant.MLIR.IR.IntegerSet}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.nsymbols")],-1)),e[777]||(e[777]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[778]||(e[778]=l('
julia
nsymbols(set)
Returns the number of symbols in the given set.
source
',3))]),t("details",ei,[t("summary",null,[e[779]||(e[779]=t("a",{id:"Reactant.MLIR.IR.op_owner-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.op_owner-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.op_owner")],-1)),e[780]||(e[780]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[781]||(e[781]=l('
julia
op_owner(value)
Returns an operation that produced this value as its result. Asserts if the value is not an op result.
source
',3))]),t("details",ti,[t("summary",null,[e[782]||(e[782]=t("a",{id:"Reactant.MLIR.IR.op_res_num-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.op_res_num-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.op_res_num")],-1)),e[783]||(e[783]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[784]||(e[784]=l('
julia
op_res_num(value)
Returns the position of the value in the list of results of the operation that produced it.
source
',3))]),t("details",si,[t("summary",null,[e[785]||(e[785]=t("a",{id:"Reactant.MLIR.IR.operand",href:"#Reactant.MLIR.IR.operand"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.operand")],-1)),e[786]||(e[786]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[787]||(e[787]=l('
julia
operand(op, i)
Returns i-th operand of the operation.
source
',3))]),t("details",ai,[t("summary",null,[e[788]||(e[788]=t("a",{id:"Reactant.MLIR.IR.operand!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}",href:"#Reactant.MLIR.IR.operand!-Tuple{Reactant.MLIR.IR.Operation, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.operand!")],-1)),e[789]||(e[789]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[790]||(e[790]=l('
julia
operand!(op, i, value)
Sets the i-th operand of the operation.
source
',3))]),t("details",ii,[t("summary",null,[e[791]||(e[791]=t("a",{id:"Reactant.MLIR.IR.operandindex-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.operandindex-Tuple{Reactant.MLIR.IR.OpOperand}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.operandindex")],-1)),e[792]||(e[792]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[793]||(e[793]=l('
julia
operandindex(opOperand)
Returns the operand number of an op operand.
source
',3))]),t("details",li,[t("summary",null,[e[794]||(e[794]=t("a",{id:"Reactant.MLIR.IR.owner-Tuple{Reactant.MLIR.IR.OpOperand}",href:"#Reactant.MLIR.IR.owner-Tuple{Reactant.MLIR.IR.OpOperand}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.owner")],-1)),e[795]||(e[795]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[796]||(e[796]=l('
julia
owner(opOperand)
Returns the owner operation of an op operand.
source
',3))]),t("details",ni,[t("summary",null,[e[797]||(e[797]=t("a",{id:"Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_op")],-1)),e[798]||(e[798]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[799]||(e[799]=l('
julia
parent_op(block)
Returns the closest surrounding operation that contains this block.
source
',3))]),t("details",pi,[t("summary",null,[e[800]||(e[800]=t("a",{id:"Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.parent_op-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_op")],-1)),e[801]||(e[801]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[802]||(e[802]=l('
julia
parent_op(op)
Gets the operation that owns this operation, returning null if the operation is not owned.
source
',3))]),t("details",ri,[t("summary",null,[e[803]||(e[803]=t("a",{id:"Reactant.MLIR.IR.parent_region-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.parent_region-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.parent_region")],-1)),e[804]||(e[804]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[805]||(e[805]=l('
julia
parent_region(block)
Returns the region that contains this block.
source
',3))]),t("details",oi,[t("summary",null,[e[806]||(e[806]=t("a",{id:"Reactant.MLIR.IR.position-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.position-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.position")],-1)),e[807]||(e[807]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[808]||(e[808]=l('
julia
position(affineExpr)
Returns the position of the given affine dimension expression, affine symbol expression or ...
source
',3))]),t("details",di,[t("summary",null,[e[809]||(e[809]=t("a",{id:"Reactant.MLIR.IR.push_argument!-Tuple{Reactant.MLIR.IR.Block, Any}",href:"#Reactant.MLIR.IR.push_argument!-Tuple{Reactant.MLIR.IR.Block, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.push_argument!")],-1)),e[810]||(e[810]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[811]||(e[811]=l('
julia
push_argument!(block, type; location=Location())
Appends an argument of the specified type to the block. Returns the newly added argument.
source
',3))]),t("details",ci,[t("summary",null,[e[812]||(e[812]=t("a",{id:"Reactant.MLIR.IR.region-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.region-Tuple{Reactant.MLIR.IR.Operation, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.region")],-1)),e[813]||(e[813]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[814]||(e[814]=l('
julia
region(op, i)
Returns i-th region attached to the operation.
source
',3))]),t("details",bi,[t("summary",null,[e[815]||(e[815]=t("a",{id:"Reactant.MLIR.IR.result",href:"#Reactant.MLIR.IR.result"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),e[816]||(e[816]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[817]||(e[817]=l('
julia
result(type, i)
Returns the i-th result type.
source
',3))]),t("details",hi,[t("summary",null,[e[818]||(e[818]=t("a",{id:"Reactant.MLIR.IR.result-2",href:"#Reactant.MLIR.IR.result-2"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),e[819]||(e[819]=s()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[820]||(e[820]=l('
julia
result(op, i)
Returns i-th result of the operation.
source
',3))]),t("details",ui,[t("summary",null,[e[821]||(e[821]=t("a",{id:"Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap, Any}",href:"#Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),e[822]||(e[822]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[823]||(e[823]=l('
julia
result(affineMap, pos)
Returns the result at the given position.
source
',3))]),t("details",gi,[t("summary",null,[e[824]||(e[824]=t("a",{id:"Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap}",href:"#Reactant.MLIR.IR.result-Tuple{Reactant.MLIR.IR.AffineMap}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.result")],-1)),e[825]||(e[825]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[826]||(e[826]=l('
julia
result(affineMap)
Returns the constant result of the given affine map. The function asserts that the map has a single constant result.
source
',3))]),t("details",yi,[t("summary",null,[e[827]||(e[827]=t("a",{id:"Reactant.MLIR.IR.rhs-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.rhs-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.rhs")],-1)),e[828]||(e[828]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[829]||(e[829]=l('
julia
rhs(affineExpr)
Returns the right hand side affine expression of the given affine binary operation expression.
source
',3))]),t("details",mi,[t("summary",null,[e[830]||(e[830]=t("a",{id:"Reactant.MLIR.IR.rmattr!-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.rmattr!-Tuple{Reactant.MLIR.IR.Operation, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.rmattr!")],-1)),e[831]||(e[831]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[832]||(e[832]=l('
julia
rmattr!(op, name)
Removes an attribute by name. Returns false if the attribute was not found and true if removed.
source
',3))]),t("details",ki,[t("summary",null,[e[833]||(e[833]=t("a",{id:"Reactant.MLIR.IR.rmfromparent!-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.rmfromparent!-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.rmfromparent!")],-1)),e[834]||(e[834]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[835]||(e[835]=l('
julia
rmfromparent!(op)
Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.
source
',3))]),t("details",Ri,[t("summary",null,[e[836]||(e[836]=t("a",{id:"Reactant.MLIR.IR.rootref-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.rootref-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.rootref")],-1)),e[837]||(e[837]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[838]||(e[838]=l('
julia
rootref(attr)
Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.
source
',3))]),t("details",Ii,[t("summary",null,[e[839]||(e[839]=t("a",{id:"Reactant.MLIR.IR.run!-Tuple{Reactant.MLIR.IR.PassManager, Reactant.MLIR.IR.Module}",href:"#Reactant.MLIR.IR.run!-Tuple{Reactant.MLIR.IR.PassManager, Reactant.MLIR.IR.Module}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.run!")],-1)),e[840]||(e[840]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[841]||(e[841]=l('
julia
run!(passManager, module)
Run the provided passManager on the given module.
source
',3))]),t("details",ji,[t("summary",null,[e[842]||(e[842]=t("a",{id:"Reactant.MLIR.IR.submap-Tuple{Reactant.MLIR.IR.AffineMap, Vector{Int64}}",href:"#Reactant.MLIR.IR.submap-Tuple{Reactant.MLIR.IR.AffineMap, Vector{Int64}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.submap")],-1)),e[843]||(e[843]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[844]||(e[844]=l('
julia
submap(affineMap, positions)
Returns the affine map consisting of the positions subset.
source
',3))]),t("details",fi,[t("summary",null,[e[845]||(e[845]=t("a",{id:"Reactant.MLIR.IR.success-Tuple{}",href:"#Reactant.MLIR.IR.success-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.success")],-1)),e[846]||(e[846]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[847]||(e[847]=l('
julia
success()
Creates a logical result representing a success.
source
',3))]),t("details",Mi,[t("summary",null,[e[848]||(e[848]=t("a",{id:"Reactant.MLIR.IR.successor-Tuple{Reactant.MLIR.IR.Operation, Any}",href:"#Reactant.MLIR.IR.successor-Tuple{Reactant.MLIR.IR.Operation, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.successor")],-1)),e[849]||(e[849]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[850]||(e[850]=l('
julia
successor(op, i)
Returns i-th successor of the operation.
source
',3))]),t("details",Ai,[t("summary",null,[e[851]||(e[851]=t("a",{id:"Reactant.MLIR.IR.terminator-Tuple{Reactant.MLIR.IR.Block}",href:"#Reactant.MLIR.IR.terminator-Tuple{Reactant.MLIR.IR.Block}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.terminator")],-1)),e[852]||(e[852]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[853]||(e[853]=l('
julia
terminator(block)
Returns the terminator operation in the block or nothing if no terminator.
source
',3))]),t("details",Li,[t("summary",null,[e[854]||(e[854]=t("a",{id:"Reactant.MLIR.IR.type!-Tuple{Any, Any}",href:"#Reactant.MLIR.IR.type!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.type!")],-1)),e[855]||(e[855]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[856]||(e[856]=l('
julia
set_type!(value, type)
Sets the type of the block argument to the given type.
source
',3))]),t("details",Ei,[t("summary",null,[e[857]||(e[857]=t("a",{id:"Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.type")],-1)),e[858]||(e[858]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[859]||(e[859]=l('
julia
type(attribute)
Gets the type of this attribute.
source
',3))]),t("details",vi,[t("summary",null,[e[860]||(e[860]=t("a",{id:"Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.IR.type-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.type")],-1)),e[861]||(e[861]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[862]||(e[862]=l('
julia
type(value)
Returns the type of the value.
source
',3))]),t("details",Ti,[t("summary",null,[e[863]||(e[863]=t("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Attribute}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Attribute}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),e[864]||(e[864]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[865]||(e[865]=l('
julia
typeid(attribute)
Gets the type id of the attribute.
source
',3))]),t("details",Ci,[t("summary",null,[e[866]||(e[866]=t("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),e[867]||(e[867]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[868]||(e[868]=l('
julia
typeid(op)
Gets the type id of the operation. Returns null if the operation does not have a registered operation description.
source
',3))]),t("details",xi,[t("summary",null,[e[869]||(e[869]=t("a",{id:"Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Type}",href:"#Reactant.MLIR.IR.typeid-Tuple{Reactant.MLIR.IR.Type}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.typeid")],-1)),e[870]||(e[870]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[871]||(e[871]=l('
julia
typeid(type)
Gets the type ID of the type.
source
',3))]),t("details",Fi,[t("summary",null,[e[872]||(e[872]=t("a",{id:"Reactant.MLIR.IR.value-Tuple{Reactant.MLIR.IR.AffineExpr}",href:"#Reactant.MLIR.IR.value-Tuple{Reactant.MLIR.IR.AffineExpr}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.value")],-1)),e[873]||(e[873]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[874]||(e[874]=l('
julia
value(affineExpr)
Returns the value of the given affine constant expression.
source
',3))]),t("details",Pi,[t("summary",null,[e[875]||(e[875]=t("a",{id:"Reactant.MLIR.IR.verify-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.verify-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.verify")],-1)),e[876]||(e[876]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[877]||(e[877]=l('
julia
verify(op)
Verify the operation and return true if it passes, false if it fails.
source
',3))]),t("details",Di,[t("summary",null,[e[878]||(e[878]=t("a",{id:"Reactant.MLIR.IR.verifyall-Tuple{Reactant.MLIR.IR.Operation}",href:"#Reactant.MLIR.IR.verifyall-Tuple{Reactant.MLIR.IR.Operation}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.verifyall")],-1)),e[879]||(e[879]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[880]||(e[880]=l('
julia
verifyall(operation; debug=false)
Prints the operations which could not be verified.
source
',3))]),t("details",Oi,[t("summary",null,[e[881]||(e[881]=t("a",{id:"Reactant.MLIR.IR.@affinemap-Tuple{Any}",href:"#Reactant.MLIR.IR.@affinemap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.IR.@affinemap")],-1)),e[882]||(e[882]=s()),i(a,{type:"info",class:"jlObjectType jlMacro",text:"Macro"})]),e[883]||(e[883]=l(`
julia
@affinemap (d1, d2, d3, ...)[s1, s2, ...] -> (d0 + d1, ...)
Returns an affine map from the provided Julia expression. On the right hand side are allowed the following function calls:
  • +, *, ÷, %, fld, cld
The rhs can only contains dimensions and symbols present on the left hand side or integer literals.
julia
julia> using Reactant.MLIR: IR
 
 julia> IR.context!(IR.Context()) do
            IR.@affinemap (d1, d2)[s0] -> (d1 + s0, d2 % 10)
        end
-MLIR.IR.AffineMap(#= (d0, d1)[s0] -> (d0 + s0, d1 mod 10) =#)
source
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An opaque reference to a diagnostic, always owned by the diagnostics engine (context). Must not be stored outside of the diagnostic handler.
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julia
MlirDiagnosticSeverity
Severity of a diagnostic.
source
',3))]),e("details",rl,[e("summary",null,[t[1016]||(t[1016]=e("a",{id:"Reactant.MLIR.API.MlirExternalPassCallbacks",href:"#Reactant.MLIR.API.MlirExternalPassCallbacks"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirExternalPassCallbacks")],-1)),t[1017]||(t[1017]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1018]||(t[1018]=l('
julia
MlirExternalPassCallbacks
Structure of external MlirPass callbacks. All callbacks are required to be set unless otherwise specified.
FieldNote
constructThis callback is called from the pass is created. This is analogous to a C++ pass constructor.
destructThis callback is called when the pass is destroyed This is analogous to a C++ pass destructor.
initializeThis callback is optional. The callback is called before the pass is run, allowing a chance to initialize any complex state necessary for running the pass. See Pass::initialize(MLIRContext *).
cloneThis callback is called when the pass is cloned. See Pass::clonePass().
runThis callback is called when the pass is run. See Pass::runOnOperation().
source
',4))]),e("details",ol,[e("summary",null,[t[1019]||(t[1019]=e("a",{id:"Reactant.MLIR.API.MlirLlvmThreadPool",href:"#Reactant.MLIR.API.MlirLlvmThreadPool"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirLlvmThreadPool")],-1)),t[1020]||(t[1020]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1021]||(t[1021]=l('
julia
MlirLlvmThreadPool
Re-export llvm::ThreadPool so as to avoid including the LLVM C API directly.
source
',3))]),e("details",dl,[e("summary",null,[t[1022]||(t[1022]=e("a",{id:"Reactant.MLIR.API.MlirLogicalResult",href:"#Reactant.MLIR.API.MlirLogicalResult"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirLogicalResult")],-1)),t[1023]||(t[1023]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1024]||(t[1024]=l('
julia
MlirLogicalResult
A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of MlirLogicalResult must only be inspected using the associated functions.
source
',3))]),e("details",hl,[e("summary",null,[t[1025]||(t[1025]=e("a",{id:"Reactant.MLIR.API.MlirNamedAttribute",href:"#Reactant.MLIR.API.MlirNamedAttribute"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirNamedAttribute")],-1)),t[1026]||(t[1026]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1027]||(t[1027]=l('
julia
MlirNamedAttribute
Named MLIR attribute.
A named attribute is essentially a (name, attribute) pair where the name is a string.
source
',4))]),e("details",cl,[e("summary",null,[t[1028]||(t[1028]=e("a",{id:"Reactant.MLIR.API.MlirOperationState",href:"#Reactant.MLIR.API.MlirOperationState"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirOperationState")],-1)),t[1029]||(t[1029]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1030]||(t[1030]=l('
julia
MlirOperationState
An auxiliary class for constructing operations.
This class contains all the information necessary to construct the operation. It owns the MlirRegions it has pointers to and does not own anything else. By default, the state can be constructed from a name and location, the latter being also used to access the context, and has no other components. These components can be added progressively until the operation is constructed. Users are not expected to rely on the internals of this class and should use mlirOperationState* functions instead.
source
',4))]),e("details",ul,[e("summary",null,[t[1031]||(t[1031]=e("a",{id:"Reactant.MLIR.API.MlirOperationWalkCallback",href:"#Reactant.MLIR.API.MlirOperationWalkCallback"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirOperationWalkCallback")],-1)),t[1032]||(t[1032]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1033]||(t[1033]=e("p",null,[s("Operation walker type. The handler is passed an (opaque) reference to an operation and a pointer to a "),e("code",null,"userData"),s(".")],-1)),t[1034]||(t[1034]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L1495-L1497",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",bl,[e("summary",null,[t[1035]||(t[1035]=e("a",{id:"Reactant.MLIR.API.MlirShapedTypeComponentsCallback",href:"#Reactant.MLIR.API.MlirShapedTypeComponentsCallback"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirShapedTypeComponentsCallback")],-1)),t[1036]||(t[1036]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1037]||(t[1037]=e("p",null,"These callbacks are used to return multiple shaped type components from functions while transferring ownership to the caller. The first argument is the has rank boolean followed by the the rank and a pointer to the shape (if applicable). The next argument is the element type, then the attribute. The last argument is an opaque pointer forwarded to the callback by the caller. This callback will be called potentially multiple times for each shaped type components.",-1)),t[1038]||(t[1038]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L9139-L9141",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",gl,[e("summary",null,[t[1039]||(t[1039]=e("a",{id:"Reactant.MLIR.API.MlirSparseTensorLevelType",href:"#Reactant.MLIR.API.MlirSparseTensorLevelType"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirSparseTensorLevelType")],-1)),t[1040]||(t[1040]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1041]||(t[1041]=e("p",null,"Dimension level types (and properties) that define sparse tensors. See the documentation in SparseTensorAttrDefs.td for their meaning.",-1)),t[1042]||(t[1042]=e("p",null,"These correspond to SparseTensorEncodingAttr::LevelType in the C++ API. If updating, keep them in sync and update the static_assert in the impl file.",-1)),t[1043]||(t[1043]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L8491-L8495",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",yl,[e("summary",null,[t[1044]||(t[1044]=e("a",{id:"Reactant.MLIR.API.MlirStringCallback",href:"#Reactant.MLIR.API.MlirStringCallback"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirStringCallback")],-1)),t[1045]||(t[1045]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1046]||(t[1046]=e("p",null,"A callback for returning string references.",-1)),t[1047]||(t[1047]=e("p",null,[s("This function is called back by the functions that need to return a reference to the portion of the string with the following arguments: - an "),e("a",{href:"/Reactant.jl/previews/PR441/api/mlirc#Reactant.MLIR.API.MlirStringRef"},[e("code",null,"MlirStringRef")]),s(" representing the current portion of the string - a pointer to user data forwarded from the printing call.")],-1)),t[1048]||(t[1048]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L108-L112",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ml,[e("summary",null,[t[1049]||(t[1049]=e("a",{id:"Reactant.MLIR.API.MlirStringRef",href:"#Reactant.MLIR.API.MlirStringRef"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirStringRef")],-1)),t[1050]||(t[1050]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1051]||(t[1051]=l('
julia
MlirStringRef
A pointer to a sized fragment of a string, not necessarily null-terminated. Does not own the underlying string. This is equivalent to llvm::StringRef.
FieldNote
dataPointer to the first symbol.
lengthLength of the fragment.
source
',4))]),e("details",kl,[e("summary",null,[t[1052]||(t[1052]=e("a",{id:"Reactant.MLIR.API.MlirTypesCallback",href:"#Reactant.MLIR.API.MlirTypesCallback"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirTypesCallback")],-1)),t[1053]||(t[1053]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1054]||(t[1054]=e("p",null,"These callbacks are used to return multiple types from functions while transferring ownership to the caller. The first argument is the number of consecutive elements pointed to by the second argument. The third argument is an opaque pointer forwarded to the callback by the caller.",-1)),t[1055]||(t[1055]=e("p",null,[e("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/libMLIR_h.jl#L9091-L9093",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Rl,[e("summary",null,[t[1056]||(t[1056]=e("a",{id:"Reactant.MLIR.API.MlirWalkOrder",href:"#Reactant.MLIR.API.MlirWalkOrder"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirWalkOrder")],-1)),t[1057]||(t[1057]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1058]||(t[1058]=l('
julia
MlirWalkOrder
Traversal order for operation walk.
source
',3))]),e("details",fl,[e("summary",null,[t[1059]||(t[1059]=e("a",{id:"Reactant.MLIR.API.MlirWalkResult",href:"#Reactant.MLIR.API.MlirWalkResult"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirWalkResult")],-1)),t[1060]||(t[1060]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),t[1061]||(t[1061]=l('
julia
MlirWalkResult
Operation walk result.
source
',3))]),e("details",Il,[e("summary",null,[t[1062]||(t[1062]=e("a",{id:"Reactant.MLIR.API.LLVMAddSymbol-Tuple{Any, Any}",href:"#Reactant.MLIR.API.LLVMAddSymbol-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMAddSymbol")],-1)),t[1063]||(t[1063]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1064]||(t[1064]=l('
julia
LLVMAddSymbol(symbolName, symbolValue)
This functions permanently adds the symbol symbolName with the value symbolValue. These symbols are searched before any libraries.
See also
sys::DynamicLibrary::AddSymbol()
source
',5))]),e("details",jl,[e("summary",null,[t[1065]||(t[1065]=e("a",{id:"Reactant.MLIR.API.LLVMLoadLibraryPermanently-Tuple{Any}",href:"#Reactant.MLIR.API.LLVMLoadLibraryPermanently-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMLoadLibraryPermanently")],-1)),t[1066]||(t[1066]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1067]||(t[1067]=l('
julia
LLVMLoadLibraryPermanently(Filename)
This function permanently loads the dynamic library at the given path. It is safe to call this function multiple times for the same library.
See also
sys::DynamicLibrary::LoadLibraryPermanently()
source
',5))]),e("details",Ml,[e("summary",null,[t[1068]||(t[1068]=e("a",{id:"Reactant.MLIR.API.LLVMParseCommandLineOptions-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.LLVMParseCommandLineOptions-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMParseCommandLineOptions")],-1)),t[1069]||(t[1069]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1070]||(t[1070]=l('
julia
LLVMParseCommandLineOptions(argc, argv, Overview)
This function parses the given arguments using the LLVM command line parser. Note that the only stable thing about this function is its signature; you cannot rely on any particular set of command line arguments being interpreted the same way across LLVM versions.
See also
llvm:🆑:ParseCommandLineOptions()
source
',5))]),e("details",Al,[e("summary",null,[t[1071]||(t[1071]=e("a",{id:"Reactant.MLIR.API.LLVMSearchForAddressOfSymbol-Tuple{Any}",href:"#Reactant.MLIR.API.LLVMSearchForAddressOfSymbol-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMSearchForAddressOfSymbol")],-1)),t[1072]||(t[1072]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1073]||(t[1073]=l('
julia
LLVMSearchForAddressOfSymbol(symbolName)
This function will search through all previously loaded dynamic libraries for the symbol symbolName. If it is found, the address of that symbol is returned. If not, null is returned.
See also
sys::DynamicLibrary::SearchForAddressOfSymbol()
source
',5))]),e("details",Ll,[e("summary",null,[t[1074]||(t[1074]=e("a",{id:"Reactant.MLIR.API.mlirAffineAddExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineAddExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineAddExprGet")],-1)),t[1075]||(t[1075]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1076]||(t[1076]=l('
julia
mlirAffineAddExprGet(lhs, rhs)
Creates an affine add expression with 'lhs' and 'rhs'.
source
',3))]),e("details",El,[e("summary",null,[t[1077]||(t[1077]=e("a",{id:"Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS")],-1)),t[1078]||(t[1078]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1079]||(t[1079]=l('
julia
mlirAffineBinaryOpExprGetLHS(affineExpr)
Returns the left hand side affine expression of the given affine binary operation expression.
source
',3))]),e("details",vl,[e("summary",null,[t[1080]||(t[1080]=e("a",{id:"Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS")],-1)),t[1081]||(t[1081]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1082]||(t[1082]=l('
julia
mlirAffineBinaryOpExprGetRHS(affineExpr)
Returns the right hand side affine expression of the given affine binary operation expression.
source
',3))]),e("details",Tl,[e("summary",null,[t[1083]||(t[1083]=e("a",{id:"Reactant.MLIR.API.mlirAffineCeilDivExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineCeilDivExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineCeilDivExprGet")],-1)),t[1084]||(t[1084]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1085]||(t[1085]=l('
julia
mlirAffineCeilDivExprGet(lhs, rhs)
Creates an affine ceildiv expression with 'lhs' and 'rhs'.
source
',3))]),e("details",Cl,[e("summary",null,[t[1086]||(t[1086]=e("a",{id:"Reactant.MLIR.API.mlirAffineConstantExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineConstantExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineConstantExprGet")],-1)),t[1087]||(t[1087]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1088]||(t[1088]=l('
julia
mlirAffineConstantExprGet(ctx, constant)
Creates an affine constant expression with 'constant' in the context.
source
',3))]),e("details",xl,[e("summary",null,[t[1089]||(t[1089]=e("a",{id:"Reactant.MLIR.API.mlirAffineConstantExprGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineConstantExprGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineConstantExprGetValue")],-1)),t[1090]||(t[1090]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1091]||(t[1091]=l('
julia
mlirAffineConstantExprGetValue(affineExpr)
Returns the value of the given affine constant expression.
source
',3))]),e("details",Fl,[e("summary",null,[t[1092]||(t[1092]=e("a",{id:"Reactant.MLIR.API.mlirAffineDimExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineDimExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineDimExprGet")],-1)),t[1093]||(t[1093]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1094]||(t[1094]=l('
julia
mlirAffineDimExprGet(ctx, position)
Creates an affine dimension expression with 'position' in the context.
source
',3))]),e("details",Pl,[e("summary",null,[t[1095]||(t[1095]=e("a",{id:"Reactant.MLIR.API.mlirAffineDimExprGetPosition-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineDimExprGetPosition-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineDimExprGetPosition")],-1)),t[1096]||(t[1096]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1097]||(t[1097]=l('
julia
mlirAffineDimExprGetPosition(affineExpr)
Returns the position of the given affine dimension expression.
source
',3))]),e("details",Dl,[e("summary",null,[t[1098]||(t[1098]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprCompose-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprCompose-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprCompose")],-1)),t[1099]||(t[1099]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1100]||(t[1100]=l('
julia
mlirAffineExprCompose(affineExpr, affineMap)
Composes the given map with the given expression.
source
',3))]),e("details",Ol,[e("summary",null,[t[1101]||(t[1101]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprDump")],-1)),t[1102]||(t[1102]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1103]||(t[1103]=l('
julia
mlirAffineExprDump(affineExpr)
Prints the affine expression to the standard error stream.
source
',3))]),e("details",Bl,[e("summary",null,[t[1104]||(t[1104]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprEqual")],-1)),t[1105]||(t[1105]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1106]||(t[1106]=l('
julia
mlirAffineExprEqual(lhs, rhs)
Returns true if the two affine expressions are equal.
source
',3))]),e("details",Gl,[e("summary",null,[t[1107]||(t[1107]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprGetContext")],-1)),t[1108]||(t[1108]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1109]||(t[1109]=l('
julia
mlirAffineExprGetContext(affineExpr)
Gets the context that owns the affine expression.
source
',3))]),e("details",zl,[e("summary",null,[t[1110]||(t[1110]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor")],-1)),t[1111]||(t[1111]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1112]||(t[1112]=l('
julia
mlirAffineExprGetLargestKnownDivisor(affineExpr)
Returns the greatest known integral divisor of this affine expression. The result is always positive.
source
',3))]),e("details",wl,[e("summary",null,[t[1113]||(t[1113]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAAdd-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAAdd-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAAdd")],-1)),t[1114]||(t[1114]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1115]||(t[1115]=l('
julia
mlirAffineExprIsAAdd(affineExpr)
Checks whether the given affine expression is an add expression.
source
',3))]),e("details",Sl,[e("summary",null,[t[1116]||(t[1116]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsABinary-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsABinary-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsABinary")],-1)),t[1117]||(t[1117]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1118]||(t[1118]=l('
julia
mlirAffineExprIsABinary(affineExpr)
Checks whether the given affine expression is binary.
source
',3))]),e("details",Nl,[e("summary",null,[t[1119]||(t[1119]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsACeilDiv-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsACeilDiv-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsACeilDiv")],-1)),t[1120]||(t[1120]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1121]||(t[1121]=l('
julia
mlirAffineExprIsACeilDiv(affineExpr)
Checks whether the given affine expression is an ceildiv expression.
source
',3))]),e("details",Vl,[e("summary",null,[t[1122]||(t[1122]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAConstant-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAConstant")],-1)),t[1123]||(t[1123]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1124]||(t[1124]=l('
julia
mlirAffineExprIsAConstant(affineExpr)
Checks whether the given affine expression is a constant expression.
source
',3))]),e("details",ql,[e("summary",null,[t[1125]||(t[1125]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsADim-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsADim-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsADim")],-1)),t[1126]||(t[1126]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1127]||(t[1127]=l('
julia
mlirAffineExprIsADim(affineExpr)
Checks whether the given affine expression is a dimension expression.
source
',3))]),e("details",Ul,[e("summary",null,[t[1128]||(t[1128]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAFloorDiv-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAFloorDiv-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAFloorDiv")],-1)),t[1129]||(t[1129]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1130]||(t[1130]=l('
julia
mlirAffineExprIsAFloorDiv(affineExpr)
Checks whether the given affine expression is an floordiv expression.
source
',3))]),e("details",Ql,[e("summary",null,[t[1131]||(t[1131]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAMod-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAMod-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAMod")],-1)),t[1132]||(t[1132]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1133]||(t[1133]=l('
julia
mlirAffineExprIsAMod(affineExpr)
Checks whether the given affine expression is an mod expression.
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',3))]),e("details",Wl,[e("summary",null,[t[1134]||(t[1134]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAMul-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAMul-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAMul")],-1)),t[1135]||(t[1135]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1136]||(t[1136]=l('
julia
mlirAffineExprIsAMul(affineExpr)
Checks whether the given affine expression is an mul expression.
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',3))]),e("details",Hl,[e("summary",null,[t[1137]||(t[1137]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsASymbol-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsASymbol-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsASymbol")],-1)),t[1138]||(t[1138]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1139]||(t[1139]=l('
julia
mlirAffineExprIsASymbol(affineExpr)
Checks whether the given affine expression is a symbol expression.
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',3))]),e("details",Zl,[e("summary",null,[t[1140]||(t[1140]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim")],-1)),t[1141]||(t[1141]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1142]||(t[1142]=l('
julia
mlirAffineExprIsFunctionOfDim(affineExpr, position)
Checks whether the given affine expression involves AffineDimExpr 'position'.
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',3))]),e("details",Jl,[e("summary",null,[t[1143]||(t[1143]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsMultipleOf-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsMultipleOf-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsMultipleOf")],-1)),t[1144]||(t[1144]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1145]||(t[1145]=l('
julia
mlirAffineExprIsMultipleOf(affineExpr, factor)
Checks whether the given affine expression is a multiple of 'factor'.
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',3))]),e("details",Kl,[e("summary",null,[t[1146]||(t[1146]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsNull")],-1)),t[1147]||(t[1147]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1148]||(t[1148]=l('
julia
mlirAffineExprIsNull(affineExpr)
Returns true if the given affine expression is a null expression. Note constant zero is not a null expression.
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',3))]),e("details",$l,[e("summary",null,[t[1149]||(t[1149]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsPureAffine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsPureAffine-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsPureAffine")],-1)),t[1150]||(t[1150]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1151]||(t[1151]=l('
julia
mlirAffineExprIsPureAffine(affineExpr)
Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.
source
',3))]),e("details",Xl,[e("summary",null,[t[1152]||(t[1152]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant")],-1)),t[1153]||(t[1153]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1154]||(t[1154]=l('
julia
mlirAffineExprIsSymbolicOrConstant(affineExpr)
Checks whether the given affine expression is made out of only symbols and constants.
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',3))]),e("details",Yl,[e("summary",null,[t[1155]||(t[1155]=e("a",{id:"Reactant.MLIR.API.mlirAffineExprPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprPrint")],-1)),t[1156]||(t[1156]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1157]||(t[1157]=l('
julia
mlirAffineExprPrint(affineExpr, callback, userData)
Prints an affine expression by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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',3))]),e("details",_l,[e("summary",null,[t[1158]||(t[1158]=e("a",{id:"Reactant.MLIR.API.mlirAffineFloorDivExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineFloorDivExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineFloorDivExprGet")],-1)),t[1159]||(t[1159]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1160]||(t[1160]=l('
julia
mlirAffineFloorDivExprGet(lhs, rhs)
Creates an affine floordiv expression with 'lhs' and 'rhs'.
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',3))]),e("details",tn,[e("summary",null,[t[1161]||(t[1161]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGet")],-1)),t[1162]||(t[1162]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1163]||(t[1163]=l('
julia
mlirAffineMapAttrGet(map)
Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.
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',3))]),e("details",en,[e("summary",null,[t[1164]||(t[1164]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGetTypeID")],-1)),t[1165]||(t[1165]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1166]||(t[1166]=l('
julia
mlirAffineMapAttrGetTypeID()
Returns the typeID of an AffineMap attribute.
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',3))]),e("details",sn,[e("summary",null,[t[1167]||(t[1167]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGetValue")],-1)),t[1168]||(t[1168]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1169]||(t[1169]=l('
julia
mlirAffineMapAttrGetValue(attr)
Returns the affine map wrapped in the given affine map attribute.
source
',3))]),e("details",an,[e("summary",null,[t[1170]||(t[1170]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols")],-1)),t[1171]||(t[1171]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1172]||(t[1172]=l('
julia
mlirAffineMapCompressUnusedSymbols(affineMaps, size, result, populateResult)
Returns the simplified affine map resulting from dropping the symbols that do not appear in any of the individual maps in affineMaps. Asserts that all maps in affineMaps are normalized to the same number of dims and symbols. Takes a callback populateResult to fill the res container with value m at entry idx. This allows returning without worrying about ownership considerations.
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',3))]),e("details",ln,[e("summary",null,[t[1173]||(t[1173]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapConstantGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapConstantGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapConstantGet")],-1)),t[1174]||(t[1174]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1175]||(t[1175]=l('
julia
mlirAffineMapConstantGet(ctx, val)
Creates a single constant result affine map in the context. The affine map is owned by the context.
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',3))]),e("details",nn,[e("summary",null,[t[1176]||(t[1176]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapDump")],-1)),t[1177]||(t[1177]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1178]||(t[1178]=l('
julia
mlirAffineMapDump(affineMap)
Prints the affine map to the standard error stream.
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',3))]),e("details",pn,[e("summary",null,[t[1179]||(t[1179]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapEmptyGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapEmptyGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapEmptyGet")],-1)),t[1180]||(t[1180]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1181]||(t[1181]=l('
julia
mlirAffineMapEmptyGet(ctx)
Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.
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',3))]),e("details",rn,[e("summary",null,[t[1182]||(t[1182]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapEqual")],-1)),t[1183]||(t[1183]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1184]||(t[1184]=l('
julia
mlirAffineMapEqual(a1, a2)
Checks if two affine maps are equal.
source
',3))]),e("details",on,[e("summary",null,[t[1185]||(t[1185]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGet")],-1)),t[1186]||(t[1186]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1187]||(t[1187]=l('
julia
mlirAffineMapGet(ctx, dimCount, symbolCount, nAffineExprs, affineExprs)
Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.
source
',3))]),e("details",dn,[e("summary",null,[t[1188]||(t[1188]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetContext")],-1)),t[1189]||(t[1189]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1190]||(t[1190]=l('
julia
mlirAffineMapGetContext(affineMap)
Gets the context that the given affine map was created with
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',3))]),e("details",hn,[e("summary",null,[t[1191]||(t[1191]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetMajorSubMap-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetMajorSubMap-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetMajorSubMap")],-1)),t[1192]||(t[1192]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1193]||(t[1193]=l('
julia
mlirAffineMapGetMajorSubMap(affineMap, numResults)
Returns the affine map consisting of the most major numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.
source
',3))]),e("details",cn,[e("summary",null,[t[1194]||(t[1194]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetMinorSubMap-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetMinorSubMap-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetMinorSubMap")],-1)),t[1195]||(t[1195]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1196]||(t[1196]=l('
julia
mlirAffineMapGetMinorSubMap(affineMap, numResults)
Returns the affine map consisting of the most minor numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.
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',3))]),e("details",un,[e("summary",null,[t[1197]||(t[1197]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumDims-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumDims")],-1)),t[1198]||(t[1198]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1199]||(t[1199]=l('
julia
mlirAffineMapGetNumDims(affineMap)
Returns the number of dimensions of the given affine map.
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',3))]),e("details",bn,[e("summary",null,[t[1200]||(t[1200]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumInputs-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumInputs")],-1)),t[1201]||(t[1201]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1202]||(t[1202]=l('
julia
mlirAffineMapGetNumInputs(affineMap)
Returns the number of inputs (dimensions + symbols) of the given affine map.
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',3))]),e("details",gn,[e("summary",null,[t[1203]||(t[1203]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumResults-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumResults")],-1)),t[1204]||(t[1204]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1205]||(t[1205]=l('
julia
mlirAffineMapGetNumResults(affineMap)
Returns the number of results of the given affine map.
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',3))]),e("details",yn,[e("summary",null,[t[1206]||(t[1206]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumSymbols-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumSymbols-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumSymbols")],-1)),t[1207]||(t[1207]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1208]||(t[1208]=l('
julia
mlirAffineMapGetNumSymbols(affineMap)
Returns the number of symbols of the given affine map.
source
',3))]),e("details",mn,[e("summary",null,[t[1209]||(t[1209]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetResult-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetResult")],-1)),t[1210]||(t[1210]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1211]||(t[1211]=l('
julia
mlirAffineMapGetResult(affineMap, pos)
Returns the result at the given position.
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',3))]),e("details",kn,[e("summary",null,[t[1212]||(t[1212]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult")],-1)),t[1213]||(t[1213]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1214]||(t[1214]=l('
julia
mlirAffineMapGetSingleConstantResult(affineMap)
Returns the constant result of the given affine map. The function asserts that the map has a single constant result.
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',3))]),e("details",Rn,[e("summary",null,[t[1215]||(t[1215]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapGetSubMap-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetSubMap-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetSubMap")],-1)),t[1216]||(t[1216]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1217]||(t[1217]=l('
julia
mlirAffineMapGetSubMap(affineMap, size, resultPos)
Returns the affine map consisting of the resultPos subset.
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',3))]),e("details",fn,[e("summary",null,[t[1218]||(t[1218]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsEmpty-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsEmpty")],-1)),t[1219]||(t[1219]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1220]||(t[1220]=l('
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mlirAffineMapIsEmpty(affineMap)
Checks whether the given affine map is an empty affine map.
source
',3))]),e("details",In,[e("summary",null,[t[1221]||(t[1221]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsIdentity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsIdentity-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsIdentity")],-1)),t[1222]||(t[1222]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1223]||(t[1223]=l('
julia
mlirAffineMapIsIdentity(affineMap)
Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),e("details",jn,[e("summary",null,[t[1224]||(t[1224]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsMinorIdentity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsMinorIdentity-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsMinorIdentity")],-1)),t[1225]||(t[1225]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1226]||(t[1226]=l('
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mlirAffineMapIsMinorIdentity(affineMap)
Checks whether the given affine map is a minor identity affine map.
source
',3))]),e("details",Mn,[e("summary",null,[t[1227]||(t[1227]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsNull")],-1)),t[1228]||(t[1228]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1229]||(t[1229]=l('
julia
mlirAffineMapIsNull(affineMap)
Checks whether an affine map is null.
source
',3))]),e("details",An,[e("summary",null,[t[1230]||(t[1230]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsPermutation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsPermutation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsPermutation")],-1)),t[1231]||(t[1231]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1232]||(t[1232]=l('
julia
mlirAffineMapIsPermutation(affineMap)
Checks whether the given affine map represents a symbol-less permutation map.
source
',3))]),e("details",Ln,[e("summary",null,[t[1233]||(t[1233]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation")],-1)),t[1234]||(t[1234]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1235]||(t[1235]=l('
julia
mlirAffineMapIsProjectedPermutation(affineMap)
Checks whether the given affine map represents a subset of a symbol-less permutation map.
source
',3))]),e("details",En,[e("summary",null,[t[1236]||(t[1236]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapIsSingleConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsSingleConstant-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsSingleConstant")],-1)),t[1237]||(t[1237]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1238]||(t[1238]=l('
julia
mlirAffineMapIsSingleConstant(affineMap)
Checks whether the given affine map is a single result constant affine map.
source
',3))]),e("details",vn,[e("summary",null,[t[1239]||(t[1239]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapMinorIdentityGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapMinorIdentityGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapMinorIdentityGet")],-1)),t[1240]||(t[1240]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1241]||(t[1241]=l('
julia
mlirAffineMapMinorIdentityGet(ctx, dims, results)
Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.
source
',3))]),e("details",Tn,[e("summary",null,[t[1242]||(t[1242]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet")],-1)),t[1243]||(t[1243]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1244]||(t[1244]=l('
julia
mlirAffineMapMultiDimIdentityGet(ctx, numDims)
Creates an affine map with 'numDims' identity in the context. The affine map is owned by the context.
source
',3))]),e("details",Cn,[e("summary",null,[t[1245]||(t[1245]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapPermutationGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapPermutationGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapPermutationGet")],-1)),t[1246]||(t[1246]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1247]||(t[1247]=l('
julia
mlirAffineMapPermutationGet(ctx, size, permutation)
Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid permutation.) The affine map is owned by the context.
source
',3))]),e("details",xn,[e("summary",null,[t[1248]||(t[1248]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapPrint")],-1)),t[1249]||(t[1249]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1250]||(t[1250]=l('
julia
mlirAffineMapPrint(affineMap, callback, userData)
Prints an affine map by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",Fn,[e("summary",null,[t[1251]||(t[1251]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapReplace-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAffineMapReplace-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapReplace")],-1)),t[1252]||(t[1252]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1253]||(t[1253]=l('
julia
mlirAffineMapReplace(affineMap, expression, replacement, numResultDims, numResultSyms)
Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.
source
',3))]),e("details",Pn,[e("summary",null,[t[1254]||(t[1254]=e("a",{id:"Reactant.MLIR.API.mlirAffineMapZeroResultGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapZeroResultGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapZeroResultGet")],-1)),t[1255]||(t[1255]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1256]||(t[1256]=l('
julia
mlirAffineMapZeroResultGet(ctx, dimCount, symbolCount)
Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.
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',3))]),e("details",Dn,[e("summary",null,[t[1257]||(t[1257]=e("a",{id:"Reactant.MLIR.API.mlirAffineModExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineModExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineModExprGet")],-1)),t[1258]||(t[1258]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1259]||(t[1259]=l('
julia
mlirAffineModExprGet(lhs, rhs)
Creates an affine mod expression with 'lhs' and 'rhs'.
source
',3))]),e("details",On,[e("summary",null,[t[1260]||(t[1260]=e("a",{id:"Reactant.MLIR.API.mlirAffineMulExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMulExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMulExprGet")],-1)),t[1261]||(t[1261]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1262]||(t[1262]=l('
julia
mlirAffineMulExprGet(lhs, rhs)
Creates an affine mul expression with 'lhs' and 'rhs'.
source
',3))]),e("details",Bn,[e("summary",null,[t[1263]||(t[1263]=e("a",{id:"Reactant.MLIR.API.mlirAffineSymbolExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineSymbolExprGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineSymbolExprGet")],-1)),t[1264]||(t[1264]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1265]||(t[1265]=l('
julia
mlirAffineSymbolExprGet(ctx, position)
Creates an affine symbol expression with 'position' in the context.
source
',3))]),e("details",Gn,[e("summary",null,[t[1266]||(t[1266]=e("a",{id:"Reactant.MLIR.API.mlirAffineSymbolExprGetPosition-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineSymbolExprGetPosition-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineSymbolExprGetPosition")],-1)),t[1267]||(t[1267]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1268]||(t[1268]=l('
julia
mlirAffineSymbolExprGetPosition(affineExpr)
Returns the position of the given affine symbol expression.
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',3))]),e("details",zn,[e("summary",null,[t[1269]||(t[1269]=e("a",{id:"Reactant.MLIR.API.mlirAnyQuantizedTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAnyQuantizedTypeGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAnyQuantizedTypeGet")],-1)),t[1270]||(t[1270]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1271]||(t[1271]=l('
julia
mlirAnyQuantizedTypeGet(flags, storageType, expressedType, storageTypeMin, storageTypeMax)
Creates an instance of AnyQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.
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',3))]),e("details",wn,[e("summary",null,[t[1272]||(t[1272]=e("a",{id:"Reactant.MLIR.API.mlirArrayAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGet")],-1)),t[1273]||(t[1273]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1274]||(t[1274]=l('
julia
mlirArrayAttrGet(ctx, numElements, elements)
Creates an array element containing the given list of elements in the given context.
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',3))]),e("details",Sn,[e("summary",null,[t[1275]||(t[1275]=e("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGetElement-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetElement")],-1)),t[1276]||(t[1276]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1277]||(t[1277]=l('
julia
mlirArrayAttrGetElement(attr, pos)
Returns pos-th element stored in the given array attribute.
source
',3))]),e("details",Nn,[e("summary",null,[t[1278]||(t[1278]=e("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGetNumElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetNumElements")],-1)),t[1279]||(t[1279]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1280]||(t[1280]=l('
julia
mlirArrayAttrGetNumElements(attr)
Returns the number of elements stored in the given array attribute.
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',3))]),e("details",Vn,[e("summary",null,[t[1281]||(t[1281]=e("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirArrayAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetTypeID")],-1)),t[1282]||(t[1282]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1283]||(t[1283]=l('
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mlirArrayAttrGetTypeID()
Returns the typeID of an Array attribute.
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',3))]),e("details",qn,[e("summary",null,[t[1284]||(t[1284]=e("a",{id:"Reactant.MLIR.API.mlirAsmStateCreateForOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAsmStateCreateForOperation-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateCreateForOperation")],-1)),t[1285]||(t[1285]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1286]||(t[1286]=l('
julia
mlirAsmStateCreateForOperation(op, flags)
Creates new AsmState, as with AsmState the IR should not be mutated in-between using this state. Must be freed with a call to mlirAsmStateDestroy().
source
',3))]),e("details",Un,[e("summary",null,[t[1287]||(t[1287]=e("a",{id:"Reactant.MLIR.API.mlirAsmStateCreateForValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAsmStateCreateForValue-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateCreateForValue")],-1)),t[1288]||(t[1288]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1289]||(t[1289]=l('
julia
mlirAsmStateCreateForValue(value, flags)
Creates new AsmState from value. Must be freed with a call to mlirAsmStateDestroy().
source
',3))]),e("details",Qn,[e("summary",null,[t[1290]||(t[1290]=e("a",{id:"Reactant.MLIR.API.mlirAsmStateDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAsmStateDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateDestroy")],-1)),t[1291]||(t[1291]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1292]||(t[1292]=l('
julia
mlirAsmStateDestroy(state)
Destroys printing flags created with mlirAsmStateCreate.
source
',3))]),e("details",Wn,[e("summary",null,[t[1293]||(t[1293]=e("a",{id:"Reactant.MLIR.API.mlirAttributeDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeDump")],-1)),t[1294]||(t[1294]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1295]||(t[1295]=l('
julia
mlirAttributeDump(attr)
Prints the attribute to the standard error stream.
source
',3))]),e("details",Hn,[e("summary",null,[t[1296]||(t[1296]=e("a",{id:"Reactant.MLIR.API.mlirAttributeEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAttributeEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeEqual")],-1)),t[1297]||(t[1297]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1298]||(t[1298]=l('
julia
mlirAttributeEqual(a1, a2)
Checks if two attributes are equal.
source
',3))]),e("details",Zn,[e("summary",null,[t[1299]||(t[1299]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetContext")],-1)),t[1300]||(t[1300]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1301]||(t[1301]=l('
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mlirAttributeGetContext(attribute)
Gets the context that an attribute was created with.
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',3))]),e("details",Jn,[e("summary",null,[t[1302]||(t[1302]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetDialect-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetDialect-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetDialect")],-1)),t[1303]||(t[1303]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1304]||(t[1304]=l('
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mlirAttributeGetDialect(attribute)
Gets the dialect of the attribute.
source
',3))]),e("details",Kn,[e("summary",null,[t[1305]||(t[1305]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetNull-Tuple{}",href:"#Reactant.MLIR.API.mlirAttributeGetNull-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetNull")],-1)),t[1306]||(t[1306]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1307]||(t[1307]=l('
julia
mlirAttributeGetNull()
Returns an empty attribute.
source
',3))]),e("details",$n,[e("summary",null,[t[1308]||(t[1308]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetType")],-1)),t[1309]||(t[1309]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1310]||(t[1310]=l('
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mlirAttributeGetType(attribute)
Gets the type of this attribute.
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',3))]),e("details",Xn,[e("summary",null,[t[1311]||(t[1311]=e("a",{id:"Reactant.MLIR.API.mlirAttributeGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetTypeID-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetTypeID")],-1)),t[1312]||(t[1312]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1313]||(t[1313]=l('
julia
mlirAttributeGetTypeID(attribute)
Gets the type id of the attribute.
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',3))]),e("details",Yn,[e("summary",null,[t[1314]||(t[1314]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAAffineMap-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAAffineMap")],-1)),t[1315]||(t[1315]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1316]||(t[1316]=l('
julia
mlirAttributeIsAAffineMap(attr)
Checks whether the given attribute is an affine map attribute.
source
',3))]),e("details",_n,[e("summary",null,[t[1317]||(t[1317]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAArray-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAArray-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAArray")],-1)),t[1318]||(t[1318]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1319]||(t[1319]=l('
julia
mlirAttributeIsAArray(attr)
Checks whether the given attribute is an array attribute.
source
',3))]),e("details",tp,[e("summary",null,[t[1320]||(t[1320]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsABool-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsABool-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsABool")],-1)),t[1321]||(t[1321]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1322]||(t[1322]=l('
julia
mlirAttributeIsABool(attr)
Checks whether the given attribute is a bool attribute.
source
',3))]),e("details",ep,[e("summary",null,[t[1323]||(t[1323]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsADenseBoolArray-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADenseBoolArray-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADenseBoolArray")],-1)),t[1324]||(t[1324]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1325]||(t[1325]=l('
julia
mlirAttributeIsADenseBoolArray(attr)
Checks whether the given attribute is a dense array attribute.
source
',3))]),e("details",sp,[e("summary",null,[t[1326]||(t[1326]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsADenseElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADenseElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADenseElements")],-1)),t[1327]||(t[1327]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1328]||(t[1328]=l('
julia
mlirAttributeIsADenseElements(attr)
Checks whether the given attribute is a dense elements attribute.
source
',3))]),e("details",ap,[e("summary",null,[t[1329]||(t[1329]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsADictionary-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADictionary-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADictionary")],-1)),t[1330]||(t[1330]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1331]||(t[1331]=l('
julia
mlirAttributeIsADictionary(attr)
Checks whether the given attribute is a dictionary attribute.
source
',3))]),e("details",ip,[e("summary",null,[t[1332]||(t[1332]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAElements")],-1)),t[1333]||(t[1333]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1334]||(t[1334]=l('
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mlirAttributeIsAElements(attr)
Checks whether the given attribute is an elements attribute.
source
',3))]),e("details",lp,[e("summary",null,[t[1335]||(t[1335]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef")],-1)),t[1336]||(t[1336]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1337]||(t[1337]=l('
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mlirAttributeIsAFlatSymbolRef(attr)
Checks whether the given attribute is a flat symbol reference attribute.
source
',3))]),e("details",np,[e("summary",null,[t[1338]||(t[1338]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAFloat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAFloat-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAFloat")],-1)),t[1339]||(t[1339]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1340]||(t[1340]=l('
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mlirAttributeIsAFloat(attr)
Checks whether the given attribute is a floating point attribute.
source
',3))]),e("details",pp,[e("summary",null,[t[1341]||(t[1341]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAInteger-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAInteger-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAInteger")],-1)),t[1342]||(t[1342]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1343]||(t[1343]=l('
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mlirAttributeIsAInteger(attr)
Checks whether the given attribute is an integer attribute.
source
',3))]),e("details",rp,[e("summary",null,[t[1344]||(t[1344]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAIntegerSet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAIntegerSet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAIntegerSet")],-1)),t[1345]||(t[1345]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1346]||(t[1346]=l('
julia
mlirAttributeIsAIntegerSet(attr)
Checks whether the given attribute is an integer set attribute.
source
',3))]),e("details",op,[e("summary",null,[t[1347]||(t[1347]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAOpaque-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAOpaque")],-1)),t[1348]||(t[1348]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1349]||(t[1349]=l('
julia
mlirAttributeIsAOpaque(attr)
Checks whether the given attribute is an opaque attribute.
source
',3))]),e("details",dp,[e("summary",null,[t[1350]||(t[1350]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsASparseElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASparseElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASparseElements")],-1)),t[1351]||(t[1351]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1352]||(t[1352]=l('
julia
mlirAttributeIsASparseElements(attr)
Checks whether the given attribute is a sparse elements attribute.
source
',3))]),e("details",hp,[e("summary",null,[t[1353]||(t[1353]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr")],-1)),t[1354]||(t[1354]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1355]||(t[1355]=l('
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mlirAttributeIsASparseTensorEncodingAttr(attr)
Checks whether the given attribute is a sparse\\_tensor.encoding attribute.
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',3))]),e("details",cp,[e("summary",null,[t[1356]||(t[1356]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAString-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAString-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAString")],-1)),t[1357]||(t[1357]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1358]||(t[1358]=l('
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mlirAttributeIsAString(attr)
Checks whether the given attribute is a string attribute.
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',3))]),e("details",up,[e("summary",null,[t[1359]||(t[1359]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsASymbolRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASymbolRef-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASymbolRef")],-1)),t[1360]||(t[1360]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1361]||(t[1361]=l('
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mlirAttributeIsASymbolRef(attr)
Checks whether the given attribute is a symbol reference attribute.
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',3))]),e("details",bp,[e("summary",null,[t[1362]||(t[1362]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAType")],-1)),t[1363]||(t[1363]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1364]||(t[1364]=l('
julia
mlirAttributeIsAType(attr)
Checks whether the given attribute is a type attribute.
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',3))]),e("details",gp,[e("summary",null,[t[1365]||(t[1365]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsAUnit-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAUnit-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAUnit")],-1)),t[1366]||(t[1366]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1367]||(t[1367]=l('
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mlirAttributeIsAUnit(attr)
Checks whether the given attribute is a unit attribute.
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',3))]),e("details",yp,[e("summary",null,[t[1368]||(t[1368]=e("a",{id:"Reactant.MLIR.API.mlirAttributeIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsNull")],-1)),t[1369]||(t[1369]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1370]||(t[1370]=l('
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mlirAttributeIsNull(attr)
Checks whether an attribute is null.
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',3))]),e("details",mp,[e("summary",null,[t[1371]||(t[1371]=e("a",{id:"Reactant.MLIR.API.mlirAttributeParseGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAttributeParseGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeParseGet")],-1)),t[1372]||(t[1372]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1373]||(t[1373]=l('
julia
mlirAttributeParseGet(context, attr)
Parses an attribute. The attribute is owned by the context.
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julia
mlirAttributePrint(attr, callback, userData)
Prints an attribute by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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',3))]),e("details",Rp,[e("summary",null,[t[1377]||(t[1377]=e("a",{id:"Reactant.MLIR.API.mlirBF16TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBF16TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBF16TypeGet")],-1)),t[1378]||(t[1378]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1379]||(t[1379]=l('
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mlirBF16TypeGet(ctx)
Creates a bf16 type in the given context. The type is owned by the context.
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',3))]),e("details",fp,[e("summary",null,[t[1380]||(t[1380]=e("a",{id:"Reactant.MLIR.API.mlirBFloat16TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirBFloat16TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBFloat16TypeGetTypeID")],-1)),t[1381]||(t[1381]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1382]||(t[1382]=l('
julia
mlirBFloat16TypeGetTypeID()
Returns the typeID of an BFloat16 type.
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',3))]),e("details",Ip,[e("summary",null,[t[1383]||(t[1383]=e("a",{id:"Reactant.MLIR.API.mlirBlockAddArgument-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockAddArgument-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockAddArgument")],-1)),t[1384]||(t[1384]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1385]||(t[1385]=l('
julia
mlirBlockAddArgument(block, type, loc)
Appends an argument of the specified type to the block. Returns the newly added argument.
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',3))]),e("details",jp,[e("summary",null,[t[1386]||(t[1386]=e("a",{id:"Reactant.MLIR.API.mlirBlockAppendOwnedOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockAppendOwnedOperation-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockAppendOwnedOperation")],-1)),t[1387]||(t[1387]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1388]||(t[1388]=l('
julia
mlirBlockAppendOwnedOperation(block, operation)
Takes an operation owned by the caller and appends it to the block.
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',3))]),e("details",Mp,[e("summary",null,[t[1389]||(t[1389]=e("a",{id:"Reactant.MLIR.API.mlirBlockArgumentGetArgNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentGetArgNumber-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentGetArgNumber")],-1)),t[1390]||(t[1390]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1391]||(t[1391]=l('
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mlirBlockArgumentGetArgNumber(value)
Returns the position of the value in the argument list of its block.
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julia
mlirBlockArgumentGetOwner(value)
Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.
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',3))]),e("details",Lp,[e("summary",null,[t[1395]||(t[1395]=e("a",{id:"Reactant.MLIR.API.mlirBlockArgumentSetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentSetType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentSetType")],-1)),t[1396]||(t[1396]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1397]||(t[1397]=l('
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mlirBlockArgumentSetType(value, type)
Sets the type of the block argument to the given type.
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mlirBlockCreate(nArgs, args, locs)
Creates a new empty block with the given argument types and transfers ownership to the caller.
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mlirBlockDestroy(block)
Takes a block owned by the caller and destroys it.
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mlirBlockDetach(block)
Detach a block from the owning region and assume ownership.
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mlirBlockEqual(block, other)
Checks whether two blocks handles point to the same block. This does not perform deep comparison.
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',3))]),e("details",xp,[e("summary",null,[t[1410]||(t[1410]=e("a",{id:"Reactant.MLIR.API.mlirBlockEraseArgument-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockEraseArgument-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockEraseArgument")],-1)),t[1411]||(t[1411]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1412]||(t[1412]=l('
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mlirBlockEraseArgument(block, index)
Erase the argument at 'index' and remove it from the argument list.
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',3))]),e("details",Fp,[e("summary",null,[t[1413]||(t[1413]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetArgument-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockGetArgument-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetArgument")],-1)),t[1414]||(t[1414]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1415]||(t[1415]=l('
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mlirBlockGetArgument(block, pos)
Returns pos-th argument of the block.
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',3))]),e("details",Pp,[e("summary",null,[t[1416]||(t[1416]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetFirstOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetFirstOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetFirstOperation")],-1)),t[1417]||(t[1417]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1418]||(t[1418]=l('
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mlirBlockGetFirstOperation(block)
Returns the first operation in the block.
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',3))]),e("details",Dp,[e("summary",null,[t[1419]||(t[1419]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetNextInRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetNextInRegion-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetNextInRegion")],-1)),t[1420]||(t[1420]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1421]||(t[1421]=l('
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mlirBlockGetNextInRegion(block)
Returns the block immediately following the given block in its parent region.
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',3))]),e("details",Op,[e("summary",null,[t[1422]||(t[1422]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetNumArguments-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetNumArguments-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetNumArguments")],-1)),t[1423]||(t[1423]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1424]||(t[1424]=l('
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mlirBlockGetNumArguments(block)
Returns the number of arguments of the block.
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',3))]),e("details",Bp,[e("summary",null,[t[1425]||(t[1425]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetParentOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetParentOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetParentOperation")],-1)),t[1426]||(t[1426]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1427]||(t[1427]=l('
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mlirBlockGetParentOperation(arg1)
Returns the closest surrounding operation that contains this block.
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',3))]),e("details",Gp,[e("summary",null,[t[1428]||(t[1428]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetParentRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetParentRegion-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetParentRegion")],-1)),t[1429]||(t[1429]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1430]||(t[1430]=l('
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mlirBlockGetParentRegion(block)
Returns the region that contains this block.
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',3))]),e("details",zp,[e("summary",null,[t[1431]||(t[1431]=e("a",{id:"Reactant.MLIR.API.mlirBlockGetTerminator-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetTerminator-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetTerminator")],-1)),t[1432]||(t[1432]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1433]||(t[1433]=l('
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mlirBlockGetTerminator(block)
Returns the terminator operation in the block or null if no terminator.
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',3))]),e("details",wp,[e("summary",null,[t[1434]||(t[1434]=e("a",{id:"Reactant.MLIR.API.mlirBlockInsertArgument-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertArgument-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertArgument")],-1)),t[1435]||(t[1435]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1436]||(t[1436]=l('
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mlirBlockInsertArgument(block, pos, type, loc)
Inserts an argument of the specified type at a specified index to the block. Returns the newly added argument.
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',3))]),e("details",Sp,[e("summary",null,[t[1437]||(t[1437]=e("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperation-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperation")],-1)),t[1438]||(t[1438]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1439]||(t[1439]=l('
julia
mlirBlockInsertOwnedOperation(block, pos, operation)
Takes an operation owned by the caller and inserts it as pos to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.
source
',3))]),e("details",Np,[e("summary",null,[t[1440]||(t[1440]=e("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter")],-1)),t[1441]||(t[1441]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1442]||(t[1442]=l('
julia
mlirBlockInsertOwnedOperationAfter(block, reference, operation)
Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.
source
',3))]),e("details",Vp,[e("summary",null,[t[1443]||(t[1443]=e("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore")],-1)),t[1444]||(t[1444]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1445]||(t[1445]=l('
julia
mlirBlockInsertOwnedOperationBefore(block, reference, operation)
Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.
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',3))]),e("details",qp,[e("summary",null,[t[1446]||(t[1446]=e("a",{id:"Reactant.MLIR.API.mlirBlockIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockIsNull")],-1)),t[1447]||(t[1447]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1448]||(t[1448]=l('
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mlirBlockIsNull(block)
Checks whether a block is null.
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',3))]),e("details",Up,[e("summary",null,[t[1449]||(t[1449]=e("a",{id:"Reactant.MLIR.API.mlirBlockPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockPrint")],-1)),t[1450]||(t[1450]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1451]||(t[1451]=l('
julia
mlirBlockPrint(block, callback, userData)
Prints a block by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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',3))]),e("details",Qp,[e("summary",null,[t[1452]||(t[1452]=e("a",{id:"Reactant.MLIR.API.mlirBoolAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBoolAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBoolAttrGet")],-1)),t[1453]||(t[1453]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1454]||(t[1454]=l('
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mlirBoolAttrGet(ctx, value)
Creates a bool attribute in the given context with the given value.
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',3))]),e("details",Wp,[e("summary",null,[t[1455]||(t[1455]=e("a",{id:"Reactant.MLIR.API.mlirBoolAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBoolAttrGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBoolAttrGetValue")],-1)),t[1456]||(t[1456]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1457]||(t[1457]=l('
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mlirBoolAttrGetValue(attr)
Returns the value stored in the given bool attribute.
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',3))]),e("details",Hp,[e("summary",null,[t[1458]||(t[1458]=e("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigCreate")],-1)),t[1459]||(t[1459]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1460]||(t[1460]=l('
julia
mlirBytecodeWriterConfigCreate()
Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirBytecodeWriterConfigDestroy().
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',3))]),e("details",Zp,[e("summary",null,[t[1461]||(t[1461]=e("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion")],-1)),t[1462]||(t[1462]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1463]||(t[1463]=l('
julia
mlirBytecodeWriterConfigDesiredEmitVersion(flags, version)
Sets the version to emit in the writer config.
source
',3))]),e("details",Jp,[e("summary",null,[t[1464]||(t[1464]=e("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy")],-1)),t[1465]||(t[1465]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1466]||(t[1466]=l('
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mlirBytecodeWriterConfigDestroy(config)
Destroys printing flags created with mlirBytecodeWriterConfigCreate.
source
',3))]),e("details",Kp,[e("summary",null,[t[1467]||(t[1467]=e("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet")],-1)),t[1468]||(t[1468]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1469]||(t[1469]=l('
julia
mlirCalibratedQuantizedTypeGet(expressedType, min, max)
Creates an instance of CalibratedQuantizedType with the given parameters in the same context as expressedType and returns it. The instance is owned by the context.
source
',3))]),e("details",$p,[e("summary",null,[t[1470]||(t[1470]=e("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax-Tuple{Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax")],-1)),t[1471]||(t[1471]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1472]||(t[1472]=l('
julia
mlirCalibratedQuantizedTypeGetMax(type)
Returns the max value of the given calibrated quantized type.
source
',3))]),e("details",Xp,[e("summary",null,[t[1473]||(t[1473]=e("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin-Tuple{Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin")],-1)),t[1474]||(t[1474]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1475]||(t[1475]=l('
julia
mlirCalibratedQuantizedTypeGetMin(type)
Returns the min value of the given calibrated quantized type.
source
',3))]),e("details",Yp,[e("summary",null,[t[1476]||(t[1476]=e("a",{id:"Reactant.MLIR.API.mlirComplexTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirComplexTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGet")],-1)),t[1477]||(t[1477]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1478]||(t[1478]=l('
julia
mlirComplexTypeGet(elementType)
Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.
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',3))]),e("details",_p,[e("summary",null,[t[1479]||(t[1479]=e("a",{id:"Reactant.MLIR.API.mlirComplexTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirComplexTypeGetElementType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGetElementType")],-1)),t[1480]||(t[1480]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1481]||(t[1481]=l('
julia
mlirComplexTypeGetElementType(type)
Returns the element type of the given complex type.
source
',3))]),e("details",tr,[e("summary",null,[t[1482]||(t[1482]=e("a",{id:"Reactant.MLIR.API.mlirComplexTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirComplexTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGetTypeID")],-1)),t[1483]||(t[1483]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1484]||(t[1484]=l('
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mlirComplexTypeGetTypeID()
Returns the typeID of an Complex type.
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',3))]),e("details",er,[e("summary",null,[t[1485]||(t[1485]=e("a",{id:"Reactant.MLIR.API.mlirContextAppendDialectRegistry-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextAppendDialectRegistry-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextAppendDialectRegistry")],-1)),t[1486]||(t[1486]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1487]||(t[1487]=l('
julia
mlirContextAppendDialectRegistry(ctx, registry)
Append the contents of the given dialect registry to the registry associated with the context.
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',3))]),e("details",sr,[e("summary",null,[t[1488]||(t[1488]=e("a",{id:"Reactant.MLIR.API.mlirContextAttachDiagnosticHandler-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirContextAttachDiagnosticHandler-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextAttachDiagnosticHandler")],-1)),t[1489]||(t[1489]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1490]||(t[1490]=l('
julia
mlirContextAttachDiagnosticHandler(context, handler, userData, deleteUserData)
Attaches the diagnostic handler to the context. Handlers are invoked in the reverse order of attachment until one of them processes the diagnostic completely. When a handler is invoked it is passed the userData that was provided when it was attached. If non-NULL, deleteUserData is called once the system no longer needs to call the handler (for instance after the handler is detached or the context is destroyed). Returns an identifier that can be used to detach the handler.
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',3))]),e("details",ar,[e("summary",null,[t[1491]||(t[1491]=e("a",{id:"Reactant.MLIR.API.mlirContextCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirContextCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreate")],-1)),t[1492]||(t[1492]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1493]||(t[1493]=l('
julia
mlirContextCreate()
Creates an MLIR context and transfers its ownership to the caller. This sets the default multithreading option (enabled).
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',3))]),e("details",ir,[e("summary",null,[t[1494]||(t[1494]=e("a",{id:"Reactant.MLIR.API.mlirContextCreateWithRegistry-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextCreateWithRegistry-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreateWithRegistry")],-1)),t[1495]||(t[1495]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1496]||(t[1496]=l('
julia
mlirContextCreateWithRegistry(registry, threadingEnabled)
Creates an MLIR context, setting the multithreading setting explicitly and pre-loading the dialects from the provided DialectRegistry.
source
',3))]),e("details",lr,[e("summary",null,[t[1497]||(t[1497]=e("a",{id:"Reactant.MLIR.API.mlirContextCreateWithThreading-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextCreateWithThreading-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreateWithThreading")],-1)),t[1498]||(t[1498]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1499]||(t[1499]=l('
julia
mlirContextCreateWithThreading(threadingEnabled)
Creates an MLIR context with an explicit setting of the multithreading setting and transfers its ownership to the caller.
source
',3))]),e("details",nr,[e("summary",null,[t[1500]||(t[1500]=e("a",{id:"Reactant.MLIR.API.mlirContextDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextDestroy")],-1)),t[1501]||(t[1501]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1502]||(t[1502]=l('
julia
mlirContextDestroy(context)
Takes an MLIR context owned by the caller and destroys it.
source
',3))]),e("details",pr,[e("summary",null,[t[1503]||(t[1503]=e("a",{id:"Reactant.MLIR.API.mlirContextDetachDiagnosticHandler-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextDetachDiagnosticHandler-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextDetachDiagnosticHandler")],-1)),t[1504]||(t[1504]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1505]||(t[1505]=l('
julia
mlirContextDetachDiagnosticHandler(context, id)
Detaches an attached diagnostic handler from the context given its identifier.
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',3))]),e("details",rr,[e("summary",null,[t[1506]||(t[1506]=e("a",{id:"Reactant.MLIR.API.mlirContextEnableMultithreading-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextEnableMultithreading-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextEnableMultithreading")],-1)),t[1507]||(t[1507]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1508]||(t[1508]=l('
julia
mlirContextEnableMultithreading(context, enable)
Set threading mode (must be set to false to mlir-print-ir-after-all).
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',3))]),e("details",or,[e("summary",null,[t[1509]||(t[1509]=e("a",{id:"Reactant.MLIR.API.mlirContextEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextEqual")],-1)),t[1510]||(t[1510]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1511]||(t[1511]=l('
julia
mlirContextEqual(ctx1, ctx2)
Checks if two contexts are equal.
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',3))]),e("details",dr,[e("summary",null,[t[1512]||(t[1512]=e("a",{id:"Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects")],-1)),t[1513]||(t[1513]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1514]||(t[1514]=l('
julia
mlirContextGetAllowUnregisteredDialects(context)
Returns whether the context allows unregistered dialects.
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julia
mlirContextGetNumLoadedDialects(context)
Returns the number of dialects loaded by the context.
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',3))]),e("details",cr,[e("summary",null,[t[1518]||(t[1518]=e("a",{id:"Reactant.MLIR.API.mlirContextGetNumRegisteredDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetNumRegisteredDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetNumRegisteredDialects")],-1)),t[1519]||(t[1519]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1520]||(t[1520]=l('
julia
mlirContextGetNumRegisteredDialects(context)
Returns the number of dialects registered with the given context. A registered dialect will be loaded if needed by the parser.
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',3))]),e("details",ur,[e("summary",null,[t[1521]||(t[1521]=e("a",{id:"Reactant.MLIR.API.mlirContextGetOrLoadDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextGetOrLoadDialect-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetOrLoadDialect")],-1)),t[1522]||(t[1522]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1523]||(t[1523]=l('
julia
mlirContextGetOrLoadDialect(context, name)
Gets the dialect instance owned by the given context using the dialect namespace to identify it, loads (i.e., constructs the instance of) the dialect if necessary. If the dialect is not registered with the context, returns null. Use mlirContextLoad<Name>Dialect to load an unregistered dialect.
source
',3))]),e("details",br,[e("summary",null,[t[1524]||(t[1524]=e("a",{id:"Reactant.MLIR.API.mlirContextIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextIsNull")],-1)),t[1525]||(t[1525]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1526]||(t[1526]=l('
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mlirContextIsNull(context)
Checks whether a context is null.
source
',3))]),e("details",gr,[e("summary",null,[t[1527]||(t[1527]=e("a",{id:"Reactant.MLIR.API.mlirContextIsRegisteredOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextIsRegisteredOperation-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextIsRegisteredOperation")],-1)),t[1528]||(t[1528]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1529]||(t[1529]=l('
julia
mlirContextIsRegisteredOperation(context, name)
Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.
source
',3))]),e("details",yr,[e("summary",null,[t[1530]||(t[1530]=e("a",{id:"Reactant.MLIR.API.mlirContextLoadAllAvailableDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextLoadAllAvailableDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextLoadAllAvailableDialects")],-1)),t[1531]||(t[1531]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1532]||(t[1532]=l('
julia
mlirContextLoadAllAvailableDialects(context)
Eagerly loads all available dialects registered with a context, making them available for use for IR construction.
source
',3))]),e("details",mr,[e("summary",null,[t[1533]||(t[1533]=e("a",{id:"Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects")],-1)),t[1534]||(t[1534]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1535]||(t[1535]=l('
julia
mlirContextSetAllowUnregisteredDialects(context, allow)
Sets whether unregistered dialects are allowed in this context.
source
',3))]),e("details",kr,[e("summary",null,[t[1536]||(t[1536]=e("a",{id:"Reactant.MLIR.API.mlirContextSetThreadPool-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextSetThreadPool-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextSetThreadPool")],-1)),t[1537]||(t[1537]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1538]||(t[1538]=l('
julia
mlirContextSetThreadPool(context, threadPool)
Sets the thread pool of the context explicitly, enabling multithreading in the process. This API should be used to avoid re-creating thread pools in long-running applications that perform multiple compilations, see the C++ documentation for MLIRContext for details.
source
',3))]),e("details",Rr,[e("summary",null,[t[1539]||(t[1539]=e("a",{id:"Reactant.MLIR.API.mlirCreateExternalPass-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirCreateExternalPass-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCreateExternalPass")],-1)),t[1540]||(t[1540]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1541]||(t[1541]=l('
julia
mlirCreateExternalPass(passID, name, argument, description, opName, nDependentDialects, dependentDialects, callbacks, userData)
Creates an external MlirPass that calls the supplied callbacks using the supplied userData. If opName is empty, the pass is a generic operation pass. Otherwise it is an operation pass specific to the specified pass name.
source
',3))]),e("details",fr,[e("summary",null,[t[1542]||(t[1542]=e("a",{id:"Reactant.MLIR.API.mlirDenseArrayGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseArrayGetNumElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseArrayGetNumElements")],-1)),t[1543]||(t[1543]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1544]||(t[1544]=l('
julia
mlirDenseArrayGetNumElements(attr)
Get the size of a dense array.
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',3))]),e("details",Ir,[e("summary",null,[t[1545]||(t[1545]=e("a",{id:"Reactant.MLIR.API.mlirDenseBoolArrayGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolArrayGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolArrayGet")],-1)),t[1546]||(t[1546]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1547]||(t[1547]=l('
julia
mlirDenseBoolArrayGet(ctx, size, values)
Create a dense array attribute with the given elements.
source
',3))]),e("details",jr,[e("summary",null,[t[1548]||(t[1548]=e("a",{id:"Reactant.MLIR.API.mlirDenseBoolArrayGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolArrayGetElement-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolArrayGetElement")],-1)),t[1549]||(t[1549]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1550]||(t[1550]=l('
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mlirDenseBoolArrayGetElement(attr, pos)
Get an element of a dense array.
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',3))]),e("details",Mr,[e("summary",null,[t[1551]||(t[1551]=e("a",{id:"Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue")],-1)),t[1552]||(t[1552]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1553]||(t[1553]=l('
julia
mlirDenseBoolResourceElementsAttrGetValue(attr, pos)
Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense resource elements attribute.
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',3))]),e("details",Ar,[e("summary",null,[t[1554]||(t[1554]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrBoolGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrBoolGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrBoolGet")],-1)),t[1555]||(t[1555]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1556]||(t[1556]=l('
julia
mlirDenseElementsAttrBoolGet(shapedType, numElements, elements)
Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.
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',3))]),e("details",Lr,[e("summary",null,[t[1557]||(t[1557]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGet")],-1)),t[1558]||(t[1558]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1559]||(t[1559]=l('
julia
mlirDenseElementsAttrGet(shapedType, numElements, elements)
Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.
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',3))]),e("details",Er,[e("summary",null,[t[1560]||(t[1560]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue")],-1)),t[1561]||(t[1561]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1562]||(t[1562]=l('
julia
mlirDenseElementsAttrGetBoolValue(attr, pos)
Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense elements attribute.
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',3))]),e("details",vr,[e("summary",null,[t[1563]||(t[1563]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetRawData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetRawData-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetRawData")],-1)),t[1564]||(t[1564]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1565]||(t[1565]=l('
julia
mlirDenseElementsAttrGetRawData(attr)
Returns the raw data of the given dense elements attribute.
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',3))]),e("details",Tr,[e("summary",null,[t[1566]||(t[1566]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue")],-1)),t[1567]||(t[1567]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1568]||(t[1568]=l('
julia
mlirDenseElementsAttrGetSplatValue(attr)
Returns the single replicated value (splat) of a specific type contained by the given dense elements attribute.
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',3))]),e("details",Cr,[e("summary",null,[t[1569]||(t[1569]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrIsSplat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrIsSplat-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrIsSplat")],-1)),t[1570]||(t[1570]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1571]||(t[1571]=l('
julia
mlirDenseElementsAttrIsSplat(attr)
Checks whether the given dense elements attribute contains a single replicated value (splat).
source
',3))]),e("details",xr,[e("summary",null,[t[1572]||(t[1572]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet")],-1)),t[1573]||(t[1573]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1574]||(t[1574]=l('
julia
mlirDenseElementsAttrRawBufferGet(shapedType, rawBufferSize, rawBuffer)
Creates a dense elements attribute with the given Shaped type and elements populated from a packed, row-major opaque buffer of contents.
The format of the raw buffer is a densely packed array of values that can be bitcast to the storage format of the element type specified. Types that are not byte aligned will be: - For bitwidth > 1: Rounded up to the next byte. - For bitwidth = 1: Packed into 8bit bytes with bits corresponding to the linear order of the shape type from MSB to LSB, padded to on the right.
A raw buffer of a single element (or for 1-bit, a byte of value 0 or 255) will be interpreted as a splat. User code should be prepared for additional, conformant patterns to be identified as splats in the future.
source
',5))]),e("details",Fr,[e("summary",null,[t[1575]||(t[1575]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet")],-1)),t[1576]||(t[1576]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1577]||(t[1577]=l('
julia
mlirDenseElementsAttrReshapeGet(attr, shapedType)
Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.
source
',3))]),e("details",Pr,[e("summary",null,[t[1578]||(t[1578]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrSplatGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrSplatGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrSplatGet")],-1)),t[1579]||(t[1579]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1580]||(t[1580]=l('
julia
mlirDenseElementsAttrSplatGet(shapedType, element)
Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).
source
',3))]),e("details",Dr,[e("summary",null,[t[1581]||(t[1581]=e("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrStringGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrStringGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrStringGet")],-1)),t[1582]||(t[1582]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1583]||(t[1583]=l('
julia
mlirDenseElementsAttrStringGet(shapedType, numElements, strs)
Creates a dense elements attribute with the given shaped type from string elements.
source
',3))]),e("details",Or,[e("summary",null,[t[1584]||(t[1584]=e("a",{id:"Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID")],-1)),t[1585]||(t[1585]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1586]||(t[1586]=l('
julia
mlirDenseIntOrFPElementsAttrGetTypeID()
Returns the typeID of an DenseIntOrFPElements attribute.
source
',3))]),e("details",Br,[e("summary",null,[t[1587]||(t[1587]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetLocation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetLocation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetLocation")],-1)),t[1588]||(t[1588]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1589]||(t[1589]=l('
julia
mlirDiagnosticGetLocation(diagnostic)
Returns the location at which the diagnostic is reported.
source
',3))]),e("details",Gr,[e("summary",null,[t[1590]||(t[1590]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetNote-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetNote-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetNote")],-1)),t[1591]||(t[1591]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1592]||(t[1592]=l('
julia
mlirDiagnosticGetNote(diagnostic, pos)
Returns pos-th note attached to the diagnostic. Expects pos to be a valid zero-based index into the list of notes.
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',3))]),e("details",zr,[e("summary",null,[t[1593]||(t[1593]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetNumNotes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetNumNotes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetNumNotes")],-1)),t[1594]||(t[1594]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1595]||(t[1595]=l('
julia
mlirDiagnosticGetNumNotes(diagnostic)
Returns the number of notes attached to the diagnostic.
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',3))]),e("details",wr,[e("summary",null,[t[1596]||(t[1596]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetSeverity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetSeverity-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetSeverity")],-1)),t[1597]||(t[1597]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1598]||(t[1598]=l('
julia
mlirDiagnosticGetSeverity(diagnostic)
Returns the severity of the diagnostic.
source
',3))]),e("details",Sr,[e("summary",null,[t[1599]||(t[1599]=e("a",{id:"Reactant.MLIR.API.mlirDiagnosticPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDiagnosticPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticPrint")],-1)),t[1600]||(t[1600]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1601]||(t[1601]=l('
julia
mlirDiagnosticPrint(diagnostic, callback, userData)
Prints a diagnostic using the provided callback.
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',3))]),e("details",Nr,[e("summary",null,[t[1602]||(t[1602]=e("a",{id:"Reactant.MLIR.API.mlirDialectEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectEqual")],-1)),t[1603]||(t[1603]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1604]||(t[1604]=l('
julia
mlirDialectEqual(dialect1, dialect2)
Checks if two dialects that belong to the same context are equal. Dialects from different contexts will not compare equal.
source
',3))]),e("details",Vr,[e("summary",null,[t[1605]||(t[1605]=e("a",{id:"Reactant.MLIR.API.mlirDialectGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectGetContext")],-1)),t[1606]||(t[1606]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1607]||(t[1607]=l('
julia
mlirDialectGetContext(dialect)
Returns the context that owns the dialect.
source
',3))]),e("details",qr,[e("summary",null,[t[1608]||(t[1608]=e("a",{id:"Reactant.MLIR.API.mlirDialectGetNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectGetNamespace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectGetNamespace")],-1)),t[1609]||(t[1609]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1610]||(t[1610]=l('
julia
mlirDialectGetNamespace(dialect)
Returns the namespace of the given dialect.
source
',3))]),e("details",Ur,[e("summary",null,[t[1611]||(t[1611]=e("a",{id:"Reactant.MLIR.API.mlirDialectHandleGetNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectHandleGetNamespace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleGetNamespace")],-1)),t[1612]||(t[1612]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1613]||(t[1613]=l('
julia
mlirDialectHandleGetNamespace(arg1)
Returns the namespace associated with the provided dialect handle.
source
',3))]),e("details",Qr,[e("summary",null,[t[1614]||(t[1614]=e("a",{id:"Reactant.MLIR.API.mlirDialectHandleInsertDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleInsertDialect-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleInsertDialect")],-1)),t[1615]||(t[1615]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1616]||(t[1616]=l('
julia
mlirDialectHandleInsertDialect(arg1, arg2)
Inserts the dialect associated with the provided dialect handle into the provided dialect registry
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',3))]),e("details",Wr,[e("summary",null,[t[1617]||(t[1617]=e("a",{id:"Reactant.MLIR.API.mlirDialectHandleLoadDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleLoadDialect-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleLoadDialect")],-1)),t[1618]||(t[1618]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1619]||(t[1619]=l('
julia
mlirDialectHandleLoadDialect(arg1, arg2)
Loads the dialect associated with the provided dialect handle.
source
',3))]),e("details",Hr,[e("summary",null,[t[1620]||(t[1620]=e("a",{id:"Reactant.MLIR.API.mlirDialectHandleRegisterDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleRegisterDialect-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleRegisterDialect")],-1)),t[1621]||(t[1621]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1622]||(t[1622]=l('
julia
mlirDialectHandleRegisterDialect(arg1, arg2)
Registers the dialect associated with the provided dialect handle.
source
',3))]),e("details",Zr,[e("summary",null,[t[1623]||(t[1623]=e("a",{id:"Reactant.MLIR.API.mlirDialectIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectIsNull")],-1)),t[1624]||(t[1624]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1625]||(t[1625]=l('
julia
mlirDialectIsNull(dialect)
Checks if the dialect is null.
source
',3))]),e("details",Jr,[e("summary",null,[t[1626]||(t[1626]=e("a",{id:"Reactant.MLIR.API.mlirDialectRegistryCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirDialectRegistryCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryCreate")],-1)),t[1627]||(t[1627]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1628]||(t[1628]=l('
julia
mlirDialectRegistryCreate()
Creates a dialect registry and transfers its ownership to the caller.
source
',3))]),e("details",Kr,[e("summary",null,[t[1629]||(t[1629]=e("a",{id:"Reactant.MLIR.API.mlirDialectRegistryDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectRegistryDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryDestroy")],-1)),t[1630]||(t[1630]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1631]||(t[1631]=l('
julia
mlirDialectRegistryDestroy(registry)
Takes a dialect registry owned by the caller and destroys it.
source
',3))]),e("details",$r,[e("summary",null,[t[1632]||(t[1632]=e("a",{id:"Reactant.MLIR.API.mlirDialectRegistryIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectRegistryIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryIsNull")],-1)),t[1633]||(t[1633]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1634]||(t[1634]=l('
julia
mlirDialectRegistryIsNull(registry)
Checks if the dialect registry is null.
source
',3))]),e("details",Xr,[e("summary",null,[t[1635]||(t[1635]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGet")],-1)),t[1636]||(t[1636]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1637]||(t[1637]=l('
julia
mlirDictionaryAttrGet(ctx, numElements, elements)
Creates a dictionary attribute containing the given list of elements in the provided context.
source
',3))]),e("details",Yr,[e("summary",null,[t[1638]||(t[1638]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetElement-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetElement")],-1)),t[1639]||(t[1639]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1640]||(t[1640]=l('
julia
mlirDictionaryAttrGetElement(attr, pos)
Returns pos-th element of the given dictionary attribute.
source
',3))]),e("details",_r,[e("summary",null,[t[1641]||(t[1641]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetElementByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetElementByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetElementByName")],-1)),t[1642]||(t[1642]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1643]||(t[1643]=l('
julia
mlirDictionaryAttrGetElementByName(attr, name)
Returns the dictionary attribute element with the given name or NULL if the given name does not exist in the dictionary.
source
',3))]),e("details",to,[e("summary",null,[t[1644]||(t[1644]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetNumElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetNumElements")],-1)),t[1645]||(t[1645]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1646]||(t[1646]=l('
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mlirDictionaryAttrGetNumElements(attr)
Returns the number of attributes contained in a dictionary attribute.
source
',3))]),e("details",eo,[e("summary",null,[t[1647]||(t[1647]=e("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetTypeID")],-1)),t[1648]||(t[1648]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1649]||(t[1649]=l('
julia
mlirDictionaryAttrGetTypeID()
Returns the typeID of a Dictionary attribute.
source
',3))]),e("details",so,[e("summary",null,[t[1650]||(t[1650]=e("a",{id:"Reactant.MLIR.API.mlirDisctinctAttrCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDisctinctAttrCreate-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDisctinctAttrCreate")],-1)),t[1651]||(t[1651]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1652]||(t[1652]=l('
julia
mlirDisctinctAttrCreate(referencedAttr)
Creates a DisctinctAttr with the referenced attribute.
source
',3))]),e("details",ao,[e("summary",null,[t[1653]||(t[1653]=e("a",{id:"Reactant.MLIR.API.mlirElementsAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirElementsAttrGetNumElements-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrGetNumElements")],-1)),t[1654]||(t[1654]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1655]||(t[1655]=l('
julia
mlirElementsAttrGetNumElements(attr)
Gets the total number of elements in the given elements attribute. In order to iterate over the attribute, obtain its type, which must be a statically shaped type and use its sizes to build a multi-dimensional index.
source
',3))]),e("details",io,[e("summary",null,[t[1656]||(t[1656]=e("a",{id:"Reactant.MLIR.API.mlirElementsAttrGetValue-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirElementsAttrGetValue-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrGetValue")],-1)),t[1657]||(t[1657]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1658]||(t[1658]=l('
julia
mlirElementsAttrGetValue(attr, rank, idxs)
Returns the element at the given rank-dimensional index.
source
',3))]),e("details",lo,[e("summary",null,[t[1659]||(t[1659]=e("a",{id:"Reactant.MLIR.API.mlirElementsAttrIsValidIndex-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirElementsAttrIsValidIndex-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrIsValidIndex")],-1)),t[1660]||(t[1660]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1661]||(t[1661]=l('
julia
mlirElementsAttrIsValidIndex(attr, rank, idxs)
Checks whether the given rank-dimensional index is valid in the given elements attribute.
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',3))]),e("details",no,[e("summary",null,[t[1662]||(t[1662]=e("a",{id:"Reactant.MLIR.API.mlirEmitError-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirEmitError-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirEmitError")],-1)),t[1663]||(t[1663]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1664]||(t[1664]=l('
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mlirEmitError(location, message)
Emits an error at the given location through the diagnostics engine. Used for testing purposes.
source
',3))]),e("details",po,[e("summary",null,[t[1665]||(t[1665]=e("a",{id:"Reactant.MLIR.API.mlirEnableGlobalDebug-Tuple{Any}",href:"#Reactant.MLIR.API.mlirEnableGlobalDebug-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirEnableGlobalDebug")],-1)),t[1666]||(t[1666]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1667]||(t[1667]=l('
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mlirEnableGlobalDebug(enable)
Sets the global debugging flag.
source
',3))]),e("details",ro,[e("summary",null,[t[1668]||(t[1668]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineCreate-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineCreate-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineCreate")],-1)),t[1669]||(t[1669]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1670]||(t[1670]=l('
julia
mlirExecutionEngineCreate(op, optLevel, numPaths, sharedLibPaths, enableObjectDump)
Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.
source
',3))]),e("details",oo,[e("summary",null,[t[1671]||(t[1671]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineDestroy")],-1)),t[1672]||(t[1672]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1673]||(t[1673]=l('
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mlirExecutionEngineDestroy(jit)
Destroy an ExecutionEngine instance.
source
',3))]),e("details",ho,[e("summary",null,[t[1674]||(t[1674]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile")],-1)),t[1675]||(t[1675]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1676]||(t[1676]=l('
julia
mlirExecutionEngineDumpToObjectFile(jit, fileName)
Dump as an object in fileName.
source
',3))]),e("details",co,[e("summary",null,[t[1677]||(t[1677]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineInvokePacked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineInvokePacked-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineInvokePacked")],-1)),t[1678]||(t[1678]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1679]||(t[1679]=l('
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mlirExecutionEngineInvokePacked(jit, name, arguments)
Invoke a native function in the execution engine by name with the arguments and result of the invoked function passed as an array of pointers. The function must have been tagged with the llvm.emit\\_c\\_interface attribute. Returns a failure if the execution fails for any reason (the function name can't be resolved for instance).
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',3))]),e("details",uo,[e("summary",null,[t[1680]||(t[1680]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineIsNull")],-1)),t[1681]||(t[1681]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1682]||(t[1682]=l('
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mlirExecutionEngineIsNull(jit)
Checks whether an execution engine is null.
source
',3))]),e("details",bo,[e("summary",null,[t[1683]||(t[1683]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineLookup-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineLookup-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineLookup")],-1)),t[1684]||(t[1684]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1685]||(t[1685]=l('
julia
mlirExecutionEngineLookup(jit, name)
Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.
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',3))]),e("details",go,[e("summary",null,[t[1686]||(t[1686]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineLookupPacked-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineLookupPacked-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineLookupPacked")],-1)),t[1687]||(t[1687]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1688]||(t[1688]=l('
julia
mlirExecutionEngineLookupPacked(jit, name)
Lookup the wrapper of the native function in the execution engine with the given name, returns nullptr if the function can't be looked-up.
source
',3))]),e("details",yo,[e("summary",null,[t[1689]||(t[1689]=e("a",{id:"Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol")],-1)),t[1690]||(t[1690]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1691]||(t[1691]=l('
julia
mlirExecutionEngineRegisterSymbol(jit, name, sym)
Register a symbol with the jit: this symbol will be accessible to the jitted code.
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',3))]),e("details",mo,[e("summary",null,[t[1692]||(t[1692]=e("a",{id:"Reactant.MLIR.API.mlirExternalPassSignalFailure-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExternalPassSignalFailure-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExternalPassSignalFailure")],-1)),t[1693]||(t[1693]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1694]||(t[1694]=l('
julia
mlirExternalPassSignalFailure(pass)
This signals that the pass has failed. This is only valid to call during the run callback of MlirExternalPassCallbacks. See Pass::signalPassFailure().
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',3))]),e("details",ko,[e("summary",null,[t[1695]||(t[1695]=e("a",{id:"Reactant.MLIR.API.mlirF16TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF16TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF16TypeGet")],-1)),t[1696]||(t[1696]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1697]||(t[1697]=l('
julia
mlirF16TypeGet(ctx)
Creates an f16 type in the given context. The type is owned by the context.
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',3))]),e("details",Ro,[e("summary",null,[t[1698]||(t[1698]=e("a",{id:"Reactant.MLIR.API.mlirF32TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF32TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF32TypeGet")],-1)),t[1699]||(t[1699]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1700]||(t[1700]=l('
julia
mlirF32TypeGet(ctx)
Creates an f32 type in the given context. The type is owned by the context.
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',3))]),e("details",fo,[e("summary",null,[t[1701]||(t[1701]=e("a",{id:"Reactant.MLIR.API.mlirF64TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF64TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF64TypeGet")],-1)),t[1702]||(t[1702]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1703]||(t[1703]=l('
julia
mlirF64TypeGet(ctx)
Creates a f64 type in the given context. The type is owned by the context.
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',3))]),e("details",Io,[e("summary",null,[t[1704]||(t[1704]=e("a",{id:"Reactant.MLIR.API.mlirFlatSymbolRefAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirFlatSymbolRefAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFlatSymbolRefAttrGet")],-1)),t[1705]||(t[1705]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1706]||(t[1706]=l('
julia
mlirFlatSymbolRefAttrGet(ctx, symbol)
Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.
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',3))]),e("details",jo,[e("summary",null,[t[1707]||(t[1707]=e("a",{id:"Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue")],-1)),t[1708]||(t[1708]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1709]||(t[1709]=l('
julia
mlirFlatSymbolRefAttrGetValue(attr)
Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.
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',3))]),e("details",Mo,[e("summary",null,[t[1710]||(t[1710]=e("a",{id:"Reactant.MLIR.API.mlirFloat16TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat16TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat16TypeGetTypeID")],-1)),t[1711]||(t[1711]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1712]||(t[1712]=l('
julia
mlirFloat16TypeGetTypeID()
Returns the typeID of an Float16 type.
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',3))]),e("details",Ao,[e("summary",null,[t[1713]||(t[1713]=e("a",{id:"Reactant.MLIR.API.mlirFloat32TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat32TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat32TypeGetTypeID")],-1)),t[1714]||(t[1714]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1715]||(t[1715]=l('
julia
mlirFloat32TypeGetTypeID()
Returns the typeID of an Float32 type.
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',3))]),e("details",Lo,[e("summary",null,[t[1716]||(t[1716]=e("a",{id:"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet")],-1)),t[1717]||(t[1717]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1718]||(t[1718]=l('
julia
mlirFloat4E2M1FNTypeGet(ctx)
Creates an f4E2M1FN type in the given context. The type is owned by the context.
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',3))]),e("details",Eo,[e("summary",null,[t[1719]||(t[1719]=e("a",{id:"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID")],-1)),t[1720]||(t[1720]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1721]||(t[1721]=l('
julia
mlirFloat4E2M1FNTypeGetTypeID()
Returns the typeID of an Float4E2M1FN type.
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',3))]),e("details",vo,[e("summary",null,[t[1722]||(t[1722]=e("a",{id:"Reactant.MLIR.API.mlirFloat64TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat64TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat64TypeGetTypeID")],-1)),t[1723]||(t[1723]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1724]||(t[1724]=l('
julia
mlirFloat64TypeGetTypeID()
Returns the typeID of an Float64 type.
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',3))]),e("details",To,[e("summary",null,[t[1725]||(t[1725]=e("a",{id:"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet")],-1)),t[1726]||(t[1726]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1727]||(t[1727]=l('
julia
mlirFloat6E2M3FNTypeGet(ctx)
Creates an f6E2M3FN type in the given context. The type is owned by the context.
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julia
mlirFloat6E2M3FNTypeGetTypeID()
Returns the typeID of an Float6E2M3FN type.
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',3))]),e("details",xo,[e("summary",null,[t[1731]||(t[1731]=e("a",{id:"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet")],-1)),t[1732]||(t[1732]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1733]||(t[1733]=l('
julia
mlirFloat6E3M2FNTypeGet(ctx)
Creates an f6E3M2FN type in the given context. The type is owned by the context.
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',3))]),e("details",Fo,[e("summary",null,[t[1734]||(t[1734]=e("a",{id:"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID")],-1)),t[1735]||(t[1735]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1736]||(t[1736]=l('
julia
mlirFloat6E3M2FNTypeGetTypeID()
Returns the typeID of an Float6E3M2FN type.
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',3))]),e("details",Po,[e("summary",null,[t[1737]||(t[1737]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E3M4TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E3M4TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E3M4TypeGet")],-1)),t[1738]||(t[1738]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1739]||(t[1739]=l('
julia
mlirFloat8E3M4TypeGet(ctx)
Creates an f8E3M4 type in the given context. The type is owned by the context.
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',3))]),e("details",Do,[e("summary",null,[t[1740]||(t[1740]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID")],-1)),t[1741]||(t[1741]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1742]||(t[1742]=l('
julia
mlirFloat8E3M4TypeGetTypeID()
Returns the typeID of an Float8E3M4 type.
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julia
mlirFloat8E4M3B11FNUZTypeGet(ctx)
Creates an f8E4M3B11FNUZ type in the given context. The type is owned by the context.
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julia
mlirFloat8E4M3B11FNUZTypeGetTypeID()
Returns the typeID of an Float8E4M3B11FNUZ type.
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julia
mlirFloat8E4M3FNTypeGet(ctx)
Creates an f8E4M3FN type in the given context. The type is owned by the context.
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',3))]),e("details",zo,[e("summary",null,[t[1752]||(t[1752]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID")],-1)),t[1753]||(t[1753]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1754]||(t[1754]=l('
julia
mlirFloat8E4M3FNTypeGetTypeID()
Returns the typeID of an Float8E4M3FN type.
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',3))]),e("details",wo,[e("summary",null,[t[1755]||(t[1755]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet")],-1)),t[1756]||(t[1756]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1757]||(t[1757]=l('
julia
mlirFloat8E4M3FNUZTypeGet(ctx)
Creates an f8E4M3FNUZ type in the given context. The type is owned by the context.
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',3))]),e("details",So,[e("summary",null,[t[1758]||(t[1758]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID")],-1)),t[1759]||(t[1759]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1760]||(t[1760]=l('
julia
mlirFloat8E4M3FNUZTypeGetTypeID()
Returns the typeID of an Float8E4M3FNUZ type.
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',3))]),e("details",No,[e("summary",null,[t[1761]||(t[1761]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3TypeGet")],-1)),t[1762]||(t[1762]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1763]||(t[1763]=l('
julia
mlirFloat8E4M3TypeGet(ctx)
Creates an f8E4M3 type in the given context. The type is owned by the context.
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',3))]),e("details",Vo,[e("summary",null,[t[1764]||(t[1764]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID")],-1)),t[1765]||(t[1765]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1766]||(t[1766]=l('
julia
mlirFloat8E4M3TypeGetTypeID()
Returns the typeID of an Float8E4M3 type.
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julia
mlirFloat8E5M2FNUZTypeGet(ctx)
Creates an f8E5M2FNUZ type in the given context. The type is owned by the context.
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',3))]),e("details",Uo,[e("summary",null,[t[1770]||(t[1770]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID")],-1)),t[1771]||(t[1771]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1772]||(t[1772]=l('
julia
mlirFloat8E5M2FNUZTypeGetTypeID()
Returns the typeID of an Float8E5M2FNUZ type.
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',3))]),e("details",Qo,[e("summary",null,[t[1773]||(t[1773]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E5M2TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2TypeGet")],-1)),t[1774]||(t[1774]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1775]||(t[1775]=l('
julia
mlirFloat8E5M2TypeGet(ctx)
Creates an f8E5M2 type in the given context. The type is owned by the context.
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',3))]),e("details",Wo,[e("summary",null,[t[1776]||(t[1776]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID")],-1)),t[1777]||(t[1777]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1778]||(t[1778]=l('
julia
mlirFloat8E5M2TypeGetTypeID()
Returns the typeID of an Float8E5M2 type.
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',3))]),e("details",Ho,[e("summary",null,[t[1779]||(t[1779]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet")],-1)),t[1780]||(t[1780]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1781]||(t[1781]=l('
julia
mlirFloat8E8M0FNUTypeGet(ctx)
Creates an f8E8M0FNU type in the given context. The type is owned by the context.
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',3))]),e("details",Zo,[e("summary",null,[t[1782]||(t[1782]=e("a",{id:"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID")],-1)),t[1783]||(t[1783]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1784]||(t[1784]=l('
julia
mlirFloat8E8M0FNUTypeGetTypeID()
Returns the typeID of an Float8E8M0FNU type.
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',3))]),e("details",Jo,[e("summary",null,[t[1785]||(t[1785]=e("a",{id:"Reactant.MLIR.API.mlirFloatAttrDoubleGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirFloatAttrDoubleGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrDoubleGet")],-1)),t[1786]||(t[1786]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1787]||(t[1787]=l('
julia
mlirFloatAttrDoubleGet(ctx, type, value)
Creates a floating point attribute in the given context with the given double value and double-precision FP semantics.
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',3))]),e("details",Ko,[e("summary",null,[t[1788]||(t[1788]=e("a",{id:"Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked")],-1)),t[1789]||(t[1789]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1790]||(t[1790]=l('
julia
mlirFloatAttrDoubleGetChecked(loc, type, value)
Same as "mlirFloatAttrDoubleGet", but if the type is not valid for a construction of a FloatAttr, returns a null MlirAttribute.
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',3))]),e("details",$o,[e("summary",null,[t[1791]||(t[1791]=e("a",{id:"Reactant.MLIR.API.mlirFloatAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloatAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrGetTypeID")],-1)),t[1792]||(t[1792]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1793]||(t[1793]=l('
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mlirFloatAttrGetTypeID()
Returns the typeID of a Float attribute.
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',3))]),e("details",Xo,[e("summary",null,[t[1794]||(t[1794]=e("a",{id:"Reactant.MLIR.API.mlirFloatAttrGetValueDouble-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloatAttrGetValueDouble-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrGetValueDouble")],-1)),t[1795]||(t[1795]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1796]||(t[1796]=l('
julia
mlirFloatAttrGetValueDouble(attr)
Returns the value stored in the given floating point attribute, interpreting the value as double.
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',3))]),e("details",Yo,[e("summary",null,[t[1797]||(t[1797]=e("a",{id:"Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID")],-1)),t[1798]||(t[1798]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1799]||(t[1799]=l('
julia
mlirFloatTF32TypeGetTypeID()
Returns the typeID of a TF32 type.
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',3))]),e("details",_o,[e("summary",null,[t[1800]||(t[1800]=e("a",{id:"Reactant.MLIR.API.mlirFloatTypeGetWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloatTypeGetWidth-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatTypeGetWidth")],-1)),t[1801]||(t[1801]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1802]||(t[1802]=l('
julia
mlirFloatTypeGetWidth(type)
Returns the bitwidth of a floating-point type.
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',3))]),e("details",td,[e("summary",null,[t[1803]||(t[1803]=e("a",{id:"Reactant.MLIR.API.mlirFreezeRewritePattern-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFreezeRewritePattern-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFreezeRewritePattern")],-1)),t[1804]||(t[1804]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1805]||(t[1805]=l('
julia
mlirFreezeRewritePattern(op)
FrozenRewritePatternSet API
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mlirFuncSetArgAttr(op, pos, name, attr)
Sets the argument attribute 'name' of an argument at index 'pos'. Asserts that the operation is a FuncOp.
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',3))]),e("details",sd,[e("summary",null,[t[1809]||(t[1809]=e("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGet")],-1)),t[1810]||(t[1810]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1811]||(t[1811]=l('
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mlirFunctionTypeGet(ctx, numInputs, inputs, numResults, results)
Creates a function type, mapping a list of input types to result types.
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mlirFunctionTypeGetInput(type, pos)
Returns the pos-th input type.
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mlirFunctionTypeGetNumInputs(type)
Returns the number of input types.
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mlirFunctionTypeGetNumResults(type)
Returns the number of result types.
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mlirFunctionTypeGetResult(type, pos)
Returns the pos-th result type.
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mlirFunctionTypeGetTypeID()
Returns the typeID of an Function type.
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mlirIRRewriterCreate(context)
Create an IRRewriter and transfer ownership to the caller.
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julia
mlirIRRewriterCreateFromOp(op)
Create an IRRewriter and transfer ownership to the caller. Additionally set the insertion point before the operation.
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mlirIRRewriterDestroy(rewriter)
Takes an IRRewriter owned by the caller and destroys it. It is the responsibility of the user to only pass an IRRewriter class.
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mlirIdentifierEqual(ident, other)
Checks whether two identifiers are the same.
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',3))]),e("details",cd,[e("summary",null,[t[1839]||(t[1839]=e("a",{id:"Reactant.MLIR.API.mlirIdentifierGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIdentifierGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierGet")],-1)),t[1840]||(t[1840]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1841]||(t[1841]=l('
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mlirIdentifierGet(context, str)
Gets an identifier with the given string value.
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mlirIdentifierGetContext(arg1)
Returns the context associated with this identifier
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mlirIdentifierStr(ident)
Gets the string value of the identifier.
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',3))]),e("details",gd,[e("summary",null,[t[1848]||(t[1848]=e("a",{id:"Reactant.MLIR.API.mlirIndexTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIndexTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIndexTypeGet")],-1)),t[1849]||(t[1849]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1850]||(t[1850]=l('
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mlirIndexTypeGet(ctx)
Creates an index type in the given context. The type is owned by the context.
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mlirIndexTypeGetTypeID()
Returns the typeID of an Index type.
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mlirInferShapedTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)
Infers the return shaped type components of the operation. Calls callback with the types of inferred arguments on success. Returns failure otherwise.
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mlirInferShapedTypeOpInterfaceTypeID()
Returns the interface TypeID of the InferShapedTypeOpInterface.
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julia
mlirInferTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)
Infers the return types of the operation identified by its canonical given the arguments that will be supplied to its generic builder. Calls callback with the types of inferred arguments, potentially several times, on success. Returns failure otherwise.
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',3))]),e("details",fd,[e("summary",null,[t[1863]||(t[1863]=e("a",{id:"Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID")],-1)),t[1864]||(t[1864]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1865]||(t[1865]=l('
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mlirInferTypeOpInterfaceTypeID()
Returns the interface TypeID of the InferTypeOpInterface.
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mlirIntegerAttrGet(type, value)
Creates an integer attribute of the given type with the given integer value.
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',3))]),e("details",jd,[e("summary",null,[t[1869]||(t[1869]=e("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetTypeID")],-1)),t[1870]||(t[1870]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1871]||(t[1871]=l('
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mlirIntegerAttrGetTypeID()
Returns the typeID of an Integer attribute.
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mlirIntegerAttrGetValueInt(attr)
Returns the value stored in the given integer attribute, assuming the value is of signless type and fits into a signed 64-bit integer.
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mlirIntegerAttrGetValueSInt(attr)
Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.
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',3))]),e("details",Ld,[e("summary",null,[t[1878]||(t[1878]=e("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueUInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueUInt-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueUInt")],-1)),t[1879]||(t[1879]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1880]||(t[1880]=l('
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mlirIntegerAttrGetValueUInt(attr)
Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.
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mlirIntegerSetAttrGet(set)
Creates an integer set attribute wrapping the given set. The attribute belongs to the same context as the integer set.
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mlirIntegerSetAttrGetTypeID()
Returns the typeID of an IntegerSet attribute.
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mlirIntegerSetAttrGetValue(attr)
Returns the integer set wrapped in the given integer set attribute.
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',3))]),e("details",Cd,[e("summary",null,[t[1890]||(t[1890]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetDump")],-1)),t[1891]||(t[1891]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1892]||(t[1892]=l('
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mlirIntegerSetDump(set)
Prints an integer set to the standard error stream.
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mlirIntegerSetEmptyGet(context, numDims, numSymbols)
Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.
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mlirIntegerSetEqual(s1, s2)
Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.
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',3))]),e("details",Pd,[e("summary",null,[t[1899]||(t[1899]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGet-NTuple{6, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGet-NTuple{6, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGet")],-1)),t[1900]||(t[1900]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1901]||(t[1901]=l('
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mlirIntegerSetGet(context, numDims, numSymbols, numConstraints, constraints, eqFlags)
Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqFlags is 1) or inequalities (eqFlags is 0). Both constraints and eqFlags are expected to point to at least numConstraint consecutive values.
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mlirIntegerSetGetConstraint(set, pos)
Returns pos-th constraint of the set.
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mlirIntegerSetGetContext(set)
Gets the context in which the given integer set lives.
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mlirIntegerSetGetNumConstraints(set)
Returns the number of constraints (equalities + inequalities) in the given set.
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',3))]),e("details",Gd,[e("summary",null,[t[1911]||(t[1911]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumDims-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumDims")],-1)),t[1912]||(t[1912]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1913]||(t[1913]=l('
julia
mlirIntegerSetGetNumDims(set)
Returns the number of dimensions in the given set.
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',3))]),e("details",zd,[e("summary",null,[t[1914]||(t[1914]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumEqualities-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumEqualities-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumEqualities")],-1)),t[1915]||(t[1915]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1916]||(t[1916]=l('
julia
mlirIntegerSetGetNumEqualities(set)
Returns the number of equalities in the given set.
source
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julia
mlirIntegerSetGetNumInequalities(set)
Returns the number of inequalities in the given set.
source
',3))]),e("details",Sd,[e("summary",null,[t[1920]||(t[1920]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumInputs-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumInputs")],-1)),t[1921]||(t[1921]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1922]||(t[1922]=l('
julia
mlirIntegerSetGetNumInputs(set)
Returns the number of inputs (dimensions + symbols) in the given set.
source
',3))]),e("details",Nd,[e("summary",null,[t[1923]||(t[1923]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumSymbols-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumSymbols-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumSymbols")],-1)),t[1924]||(t[1924]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1925]||(t[1925]=l('
julia
mlirIntegerSetGetNumSymbols(set)
Returns the number of symbols in the given set.
source
',3))]),e("details",Vd,[e("summary",null,[t[1926]||(t[1926]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty")],-1)),t[1927]||(t[1927]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1928]||(t[1928]=l('
julia
mlirIntegerSetIsCanonicalEmpty(set)
Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.
source
',3))]),e("details",qd,[e("summary",null,[t[1929]||(t[1929]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsConstraintEq-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsConstraintEq-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsConstraintEq")],-1)),t[1930]||(t[1930]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1931]||(t[1931]=l('
julia
mlirIntegerSetIsConstraintEq(set, pos)
Returns true of the pos-th constraint of the set is an equality constraint, false otherwise.
source
',3))]),e("details",Ud,[e("summary",null,[t[1932]||(t[1932]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsNull")],-1)),t[1933]||(t[1933]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1934]||(t[1934]=l('
julia
mlirIntegerSetIsNull(set)
Checks whether an integer set is a null object.
source
',3))]),e("details",Qd,[e("summary",null,[t[1935]||(t[1935]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetPrint")],-1)),t[1936]||(t[1936]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1937]||(t[1937]=l('
julia
mlirIntegerSetPrint(set, callback, userData)
Prints an integer set by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",Wd,[e("summary",null,[t[1938]||(t[1938]=e("a",{id:"Reactant.MLIR.API.mlirIntegerSetReplaceGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetReplaceGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetReplaceGet")],-1)),t[1939]||(t[1939]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1940]||(t[1940]=l('
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)
Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.
source
',3))]),e("details",Hd,[e("summary",null,[t[1941]||(t[1941]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGet")],-1)),t[1942]||(t[1942]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1943]||(t[1943]=l('
julia
mlirIntegerTypeGet(ctx, bitwidth)
Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),e("details",Zd,[e("summary",null,[t[1944]||(t[1944]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGetTypeID")],-1)),t[1945]||(t[1945]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1946]||(t[1946]=l('
julia
mlirIntegerTypeGetTypeID()
Returns the typeID of an Integer type.
source
',3))]),e("details",Jd,[e("summary",null,[t[1947]||(t[1947]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGetWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeGetWidth-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGetWidth")],-1)),t[1948]||(t[1948]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1949]||(t[1949]=l('
julia
mlirIntegerTypeGetWidth(type)
Returns the bitwidth of an integer type.
source
',3))]),e("details",Kd,[e("summary",null,[t[1950]||(t[1950]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsSigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsSigned-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsSigned")],-1)),t[1951]||(t[1951]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1952]||(t[1952]=l('
julia
mlirIntegerTypeIsSigned(type)
Checks whether the given integer type is signed.
source
',3))]),e("details",$d,[e("summary",null,[t[1953]||(t[1953]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsSignless-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsSignless-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsSignless")],-1)),t[1954]||(t[1954]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1955]||(t[1955]=l('
julia
mlirIntegerTypeIsSignless(type)
Checks whether the given integer type is signless.
source
',3))]),e("details",Xd,[e("summary",null,[t[1956]||(t[1956]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsUnsigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsUnsigned-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsUnsigned")],-1)),t[1957]||(t[1957]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1958]||(t[1958]=l('
julia
mlirIntegerTypeIsUnsigned(type)
Checks whether the given integer type is unsigned.
source
',3))]),e("details",Yd,[e("summary",null,[t[1959]||(t[1959]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeSignedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeSignedGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeSignedGet")],-1)),t[1960]||(t[1960]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1961]||(t[1961]=l('
julia
mlirIntegerTypeSignedGet(ctx, bitwidth)
Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),e("details",_d,[e("summary",null,[t[1962]||(t[1962]=e("a",{id:"Reactant.MLIR.API.mlirIntegerTypeUnsignedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeUnsignedGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeUnsignedGet")],-1)),t[1963]||(t[1963]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1964]||(t[1964]=l('
julia
mlirIntegerTypeUnsignedGet(ctx, bitwidth)
Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.
source
',3))]),e("details",th,[e("summary",null,[t[1965]||(t[1965]=e("a",{id:"Reactant.MLIR.API.mlirIsCurrentDebugType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIsCurrentDebugType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIsCurrentDebugType")],-1)),t[1966]||(t[1966]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1967]||(t[1967]=l('
julia
mlirIsCurrentDebugType(type)
Checks if type is set as the current debug type.
source
',3))]),e("details",eh,[e("summary",null,[t[1968]||(t[1968]=e("a",{id:"Reactant.MLIR.API.mlirIsGlobalDebugEnabled-Tuple{}",href:"#Reactant.MLIR.API.mlirIsGlobalDebugEnabled-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIsGlobalDebugEnabled")],-1)),t[1969]||(t[1969]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1970]||(t[1970]=l('
julia
mlirIsGlobalDebugEnabled()
Retuns true if the global debugging flag is set, false otherwise.
source
',3))]),e("details",sh,[e("summary",null,[t[1971]||(t[1971]=e("a",{id:"Reactant.MLIR.API.mlirLLVMArrayTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMArrayTypeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMArrayTypeGet")],-1)),t[1972]||(t[1972]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1973]||(t[1973]=l('
julia
mlirLLVMArrayTypeGet(elementType, numElements)
Creates an llvm.array type.
source
',3))]),e("details",ah,[e("summary",null,[t[1974]||(t[1974]=e("a",{id:"Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType")],-1)),t[1975]||(t[1975]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1976]||(t[1976]=l('
julia
mlirLLVMArrayTypeGetElementType(type)
Returns the element type of the llvm.array type.
source
',3))]),e("details",ih,[e("summary",null,[t[1977]||(t[1977]=e("a",{id:"Reactant.MLIR.API.mlirLLVMCConvAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMCConvAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMCConvAttrGet")],-1)),t[1978]||(t[1978]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1979]||(t[1979]=l('
julia
mlirLLVMCConvAttrGet(ctx, cconv)
Creates a LLVM CConv attribute.
source
',3))]),e("details",lh,[e("summary",null,[t[1980]||(t[1980]=e("a",{id:"Reactant.MLIR.API.mlirLLVMComdatAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMComdatAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMComdatAttrGet")],-1)),t[1981]||(t[1981]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1982]||(t[1982]=l('
julia
mlirLLVMComdatAttrGet(ctx, comdat)
Creates a LLVM Comdat attribute.
source
',3))]),e("details",nh,[e("summary",null,[t[1983]||(t[1983]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet")],-1)),t[1984]||(t[1984]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1985]||(t[1985]=l('
julia
mlirLLVMDIAnnotationAttrGet(ctx, name, value)
Creates a LLVM DIAnnotation attribute.
source
',3))]),e("details",ph,[e("summary",null,[t[1986]||(t[1986]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet")],-1)),t[1987]||(t[1987]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1988]||(t[1988]=l('
julia
mlirLLVMDIBasicTypeAttrGet(ctx, tag, name, sizeInBits, encoding)
Creates a LLVM DIBasicType attribute.
source
',3))]),e("details",rh,[e("summary",null,[t[1989]||(t[1989]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet-NTuple{8, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet")],-1)),t[1990]||(t[1990]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1991]||(t[1991]=l('
julia
mlirLLVMDICompileUnitAttrGet(ctx, id, sourceLanguage, file, producer, isOptimized, emissionKind, nameTableKind)
Creates a LLVM DICompileUnit attribute.
source
',3))]),e("details",oh,[e("summary",null,[t[1992]||(t[1992]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet-NTuple{18, Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet-NTuple{18, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet")],-1)),t[1993]||(t[1993]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1994]||(t[1994]=l('
julia
mlirLLVMDICompositeTypeAttrGet(ctx, recId, isRecSelf, tag, name, file, line, scope, baseType, flags, sizeInBits, alignInBits, nElements, elements, dataLocation, rank, allocated, associated)
Creates a LLVM DICompositeType attribute.
source
',3))]),e("details",dh,[e("summary",null,[t[1995]||(t[1995]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf")],-1)),t[1996]||(t[1996]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[1997]||(t[1997]=l('
julia
mlirLLVMDICompositeTypeAttrGetRecSelf(recId)
Creates a self-referencing LLVM DICompositeType attribute.
source
',3))]),e("details",hh,[e("summary",null,[t[1998]||(t[1998]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet")],-1)),t[1999]||(t[1999]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2e3]||(t[2e3]=l('
julia
mlirLLVMDIDerivedTypeAttrGet(ctx, tag, name, baseType, sizeInBits, alignInBits, offsetInBits, dwarfAddressSpace, extraData)
Creates a LLVM DIDerivedType attribute. Note that dwarfAddressSpace is an optional field, where MLIR_CAPI_DWARF_ADDRESS_SPACE_NULL indicates null and non-negative values indicate a value present.
source
',3))]),e("details",ch,[e("summary",null,[t[2001]||(t[2001]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType")],-1)),t[2002]||(t[2002]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2003]||(t[2003]=l('
julia
mlirLLVMDIDerivedTypeAttrGetBaseType(diDerivedType)
Gets the base type from a LLVM DIDerivedType attribute.
source
',3))]),e("details",uh,[e("summary",null,[t[2004]||(t[2004]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet")],-1)),t[2005]||(t[2005]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2006]||(t[2006]=l('
julia
mlirLLVMDIExpressionAttrGet(ctx, nOperations, operations)
Creates a LLVM DIExpression attribute.
source
',3))]),e("details",bh,[e("summary",null,[t[2007]||(t[2007]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet")],-1)),t[2008]||(t[2008]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2009]||(t[2009]=l('
julia
mlirLLVMDIExpressionElemAttrGet(ctx, opcode, nArguments, arguments)
Creates a LLVM DIExpressionElem attribute.
source
',3))]),e("details",gh,[e("summary",null,[t[2010]||(t[2010]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIFileAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIFileAttrGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIFileAttrGet")],-1)),t[2011]||(t[2011]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2012]||(t[2012]=l('
julia
mlirLLVMDIFileAttrGet(ctx, name, directory)
Creates a LLVM DIFileAttr attribute.
source
',3))]),e("details",yh,[e("summary",null,[t[2013]||(t[2013]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet")],-1)),t[2014]||(t[2014]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2015]||(t[2015]=l('
julia
mlirLLVMDIFlagsAttrGet(ctx, value)
Creates a LLVM DIFlags attribute.
source
',3))]),e("details",mh,[e("summary",null,[t[2016]||(t[2016]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet")],-1)),t[2017]||(t[2017]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2018]||(t[2018]=l('
julia
mlirLLVMDIImportedEntityAttrGet(ctx, tag, scope, entity, file, line, name, nElements, elements)
Creates a LLVM DIImportedEntityAttr attribute.
source
',3))]),e("details",kh,[e("summary",null,[t[2019]||(t[2019]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet")],-1)),t[2020]||(t[2020]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2021]||(t[2021]=l('
julia
mlirLLVMDILexicalBlockAttrGet(ctx, scope, file, line, column)
Creates a LLVM DILexicalBlock attribute.
source
',3))]),e("details",Rh,[e("summary",null,[t[2022]||(t[2022]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet")],-1)),t[2023]||(t[2023]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2024]||(t[2024]=l('
julia
mlirLLVMDILexicalBlockFileAttrGet(ctx, scope, file, discriminator)
Creates a LLVM DILexicalBlockFile attribute.
source
',3))]),e("details",fh,[e("summary",null,[t[2025]||(t[2025]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet")],-1)),t[2026]||(t[2026]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2027]||(t[2027]=l('
julia
mlirLLVMDILocalVariableAttrGet(ctx, scope, name, diFile, line, arg, alignInBits, diType, flags)
Creates a LLVM DILocalVariableAttr attribute.
source
',3))]),e("details",Ih,[e("summary",null,[t[2028]||(t[2028]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIModuleAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIModuleAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIModuleAttrGet")],-1)),t[2029]||(t[2029]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2030]||(t[2030]=l('
julia
mlirLLVMDIModuleAttrGet(ctx, file, scope, name, configMacros, includePath, apinotes, line, isDecl)
Creates a LLVM DIModuleAttr attribute.
source
',3))]),e("details",jh,[e("summary",null,[t[2031]||(t[2031]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope")],-1)),t[2032]||(t[2032]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2033]||(t[2033]=l('
julia
mlirLLVMDIModuleAttrGetScope(diModule)
Gets the scope of this DIModuleAttr.
source
',3))]),e("details",Mh,[e("summary",null,[t[2034]||(t[2034]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet")],-1)),t[2035]||(t[2035]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2036]||(t[2036]=l('
julia
mlirLLVMDINullTypeAttrGet(ctx)
Creates a LLVM DINullType attribute.
source
',3))]),e("details",Ah,[e("summary",null,[t[2037]||(t[2037]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet-NTuple{17, Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet-NTuple{17, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet")],-1)),t[2038]||(t[2038]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2039]||(t[2039]=l('
julia
mlirLLVMDISubprogramAttrGet(ctx, recId, isRecSelf, id, compileUnit, scope, name, linkageName, file, line, scopeLine, subprogramFlags, type, nRetainedNodes, retainedNodes, nAnnotations, annotations)
Creates a LLVM DISubprogramAttr attribute.
source
',3))]),e("details",Lh,[e("summary",null,[t[2040]||(t[2040]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit")],-1)),t[2041]||(t[2041]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2042]||(t[2042]=l('
julia
mlirLLVMDISubprogramAttrGetCompileUnit(diSubprogram)
Gets the compile unit from this DISubprogram.
source
',3))]),e("details",Eh,[e("summary",null,[t[2043]||(t[2043]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile")],-1)),t[2044]||(t[2044]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2045]||(t[2045]=l('
julia
mlirLLVMDISubprogramAttrGetFile(diSubprogram)
Gets the file from this DISubprogramAttr.
source
',3))]),e("details",vh,[e("summary",null,[t[2046]||(t[2046]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine")],-1)),t[2047]||(t[2047]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2048]||(t[2048]=l('
julia
mlirLLVMDISubprogramAttrGetLine(diSubprogram)
Gets the line from this DISubprogramAttr.
source
',3))]),e("details",Th,[e("summary",null,[t[2049]||(t[2049]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf")],-1)),t[2050]||(t[2050]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2051]||(t[2051]=l('
julia
mlirLLVMDISubprogramAttrGetRecSelf(recId)
Creates a self-referencing LLVM DISubprogramAttr attribute.
source
',3))]),e("details",Ch,[e("summary",null,[t[2052]||(t[2052]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope")],-1)),t[2053]||(t[2053]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2054]||(t[2054]=l('
julia
mlirLLVMDISubprogramAttrGetScope(diSubprogram)
Gets the scope from this DISubprogramAttr.
source
',3))]),e("details",xh,[e("summary",null,[t[2055]||(t[2055]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine")],-1)),t[2056]||(t[2056]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2057]||(t[2057]=l('
julia
mlirLLVMDISubprogramAttrGetScopeLine(diSubprogram)
Gets the scope line from this DISubprogram.
source
',3))]),e("details",Fh,[e("summary",null,[t[2058]||(t[2058]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType")],-1)),t[2059]||(t[2059]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2060]||(t[2060]=l('
julia
mlirLLVMDISubprogramAttrGetType(diSubprogram)
Gets the type from this DISubprogramAttr.
source
',3))]),e("details",Ph,[e("summary",null,[t[2061]||(t[2061]=e("a",{id:"Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet")],-1)),t[2062]||(t[2062]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2063]||(t[2063]=l('
julia
mlirLLVMDISubroutineTypeAttrGet(ctx, callingConvention, nTypes, types)
Creates a LLVM DISubroutineTypeAttr attribute.
source
',3))]),e("details",Dh,[e("summary",null,[t[2064]||(t[2064]=e("a",{id:"Reactant.MLIR.API.mlirLLVMFunctionTypeGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMFunctionTypeGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMFunctionTypeGet")],-1)),t[2065]||(t[2065]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2066]||(t[2066]=l('
julia
mlirLLVMFunctionTypeGet(resultType, nArgumentTypes, argumentTypes, isVarArg)
Creates an llvm.func type.
source
',3))]),e("details",Oh,[e("summary",null,[t[2067]||(t[2067]=e("a",{id:"Reactant.MLIR.API.mlirLLVMLinkageAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMLinkageAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMLinkageAttrGet")],-1)),t[2068]||(t[2068]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2069]||(t[2069]=l('
julia
mlirLLVMLinkageAttrGet(ctx, linkage)
Creates a LLVM Linkage attribute.
source
',3))]),e("details",Bh,[e("summary",null,[t[2070]||(t[2070]=e("a",{id:"Reactant.MLIR.API.mlirLLVMPointerTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMPointerTypeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMPointerTypeGet")],-1)),t[2071]||(t[2071]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2072]||(t[2072]=l('
julia
mlirLLVMPointerTypeGet(ctx, addressSpace)
Creates an llvm.ptr type.
source
',3))]),e("details",Gh,[e("summary",null,[t[2073]||(t[2073]=e("a",{id:"Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace")],-1)),t[2074]||(t[2074]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2075]||(t[2075]=l('
julia
mlirLLVMPointerTypeGetAddressSpace(pointerType)
Returns address space of llvm.ptr
source
',3))]),e("details",zh,[e("summary",null,[t[2076]||(t[2076]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetElementType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetElementType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetElementType")],-1)),t[2077]||(t[2077]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2078]||(t[2078]=l('
julia
mlirLLVMStructTypeGetElementType(type, position)
Returns the positions-th field of the struct. Asserts if the struct is opaque, not yet initialized or if the position is out of range.
source
',3))]),e("details",wh,[e("summary",null,[t[2079]||(t[2079]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier")],-1)),t[2080]||(t[2080]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2081]||(t[2081]=l('
julia
mlirLLVMStructTypeGetIdentifier(type)
Returns the identifier of the identified struct. Asserts that the struct is identified, i.e., not literal.
source
',3))]),e("details",Sh,[e("summary",null,[t[2082]||(t[2082]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes")],-1)),t[2083]||(t[2083]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2084]||(t[2084]=l('
julia
mlirLLVMStructTypeGetNumElementTypes(type)
Returns the number of fields in the struct. Asserts if the struct is opaque or not yet initialized.
source
',3))]),e("details",Nh,[e("summary",null,[t[2085]||(t[2085]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet")],-1)),t[2086]||(t[2086]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2087]||(t[2087]=l('
julia
mlirLLVMStructTypeIdentifiedGet(ctx, name)
Creates an LLVM identified struct type with no body. If a struct type with this name already exists in the context, returns that type. Use mlirLLVMStructTypeIdentifiedNewGet to create a fresh struct type, potentially renaming it. The body should be set separatelty by calling mlirLLVMStructTypeSetBody, if it isn't set already.
source
',3))]),e("details",Vh,[e("summary",null,[t[2088]||(t[2088]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet")],-1)),t[2089]||(t[2089]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2090]||(t[2090]=l('
julia
mlirLLVMStructTypeIdentifiedNewGet(ctx, name, nFieldTypes, fieldTypes, isPacked)
Creates an LLVM identified struct type with no body and a name starting with the given prefix. If a struct with the exact name as the given prefix already exists, appends an unspecified suffix to the name so that the name is unique in context.
source
',3))]),e("details",qh,[e("summary",null,[t[2091]||(t[2091]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral")],-1)),t[2092]||(t[2092]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2093]||(t[2093]=l('
julia
mlirLLVMStructTypeIsLiteral(type)
Returns true if the type is a literal (unnamed) LLVM struct type.
source
',3))]),e("details",Uh,[e("summary",null,[t[2094]||(t[2094]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque")],-1)),t[2095]||(t[2095]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2096]||(t[2096]=l('
julia
mlirLLVMStructTypeIsOpaque(type)
Returns true is the struct is explicitly opaque (will not have a body) or uninitialized (will eventually have a body).
source
',3))]),e("details",Qh,[e("summary",null,[t[2097]||(t[2097]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsPacked-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsPacked-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsPacked")],-1)),t[2098]||(t[2098]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2099]||(t[2099]=l('
julia
mlirLLVMStructTypeIsPacked(type)
Returns true if the struct is packed.
source
',3))]),e("details",Wh,[e("summary",null,[t[2100]||(t[2100]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet")],-1)),t[2101]||(t[2101]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2102]||(t[2102]=l('
julia
mlirLLVMStructTypeLiteralGet(ctx, nFieldTypes, fieldTypes, isPacked)
Creates an LLVM literal (unnamed) struct type. This may assert if the fields have types not compatible with the LLVM dialect. For a graceful failure, use the checked version.
source
',3))]),e("details",Hh,[e("summary",null,[t[2103]||(t[2103]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked")],-1)),t[2104]||(t[2104]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2105]||(t[2105]=l('
julia
mlirLLVMStructTypeLiteralGetChecked(loc, nFieldTypes, fieldTypes, isPacked)
Creates an LLVM literal (unnamed) struct type if possible. Emits a diagnostic at the given location and returns null otherwise.
source
',3))]),e("details",Zh,[e("summary",null,[t[2106]||(t[2106]=e("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeSetBody-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeSetBody-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeSetBody")],-1)),t[2107]||(t[2107]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2108]||(t[2108]=l('
julia
mlirLLVMStructTypeSetBody(structType, nFieldTypes, fieldTypes, isPacked)
Sets the body of the identified struct if it hasn't been set yet. Returns whether the operation was successful.
source
',3))]),e("details",Jh,[e("summary",null,[t[2109]||(t[2109]=e("a",{id:"Reactant.MLIR.API.mlirLLVMVoidTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMVoidTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMVoidTypeGet")],-1)),t[2110]||(t[2110]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2111]||(t[2111]=l('
julia
mlirLLVMVoidTypeGet(ctx)
Creates an llmv.void type.
source
',3))]),e("details",Kh,[e("summary",null,[t[2112]||(t[2112]=e("a",{id:"Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion")],-1)),t[2113]||(t[2113]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2114]||(t[2114]=l('
julia
mlirLinalgFillBuiltinNamedOpRegion(mlirOp)
Apply the special region builder for the builtin named Linalg op. Assert that mlirOp is a builtin named Linalg op.
source
',3))]),e("details",$h,[e("summary",null,[t[2115]||(t[2115]=e("a",{id:"Reactant.MLIR.API.mlirLlvmThreadPoolCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirLlvmThreadPoolCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLlvmThreadPoolCreate")],-1)),t[2116]||(t[2116]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2117]||(t[2117]=l('
julia
mlirLlvmThreadPoolCreate()
Create an LLVM thread pool. This is reexported here to avoid directly pulling in the LLVM headers directly.
source
',3))]),e("details",Xh,[e("summary",null,[t[2118]||(t[2118]=e("a",{id:"Reactant.MLIR.API.mlirLlvmThreadPoolDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLlvmThreadPoolDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLlvmThreadPoolDestroy")],-1)),t[2119]||(t[2119]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2120]||(t[2120]=l('
julia
mlirLlvmThreadPoolDestroy(pool)
Destroy an LLVM thread pool.
source
',3))]),e("details",Yh,[e("summary",null,[t[2121]||(t[2121]=e("a",{id:"Reactant.MLIR.API.mlirLoadIRDLDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLoadIRDLDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLoadIRDLDialects")],-1)),t[2122]||(t[2122]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2123]||(t[2123]=l('
julia
mlirLoadIRDLDialects(_module)
Loads all IRDL dialects in the provided module, registering the dialects in the module's associated context.
source
',3))]),e("details",_h,[e("summary",null,[t[2124]||(t[2124]=e("a",{id:"Reactant.MLIR.API.mlirLocationCallSiteGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLocationCallSiteGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationCallSiteGet")],-1)),t[2125]||(t[2125]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2126]||(t[2126]=l('
julia
mlirLocationCallSiteGet(callee, caller)
Creates a call site location with a callee and a caller.
source
',3))]),e("details",tc,[e("summary",null,[t[2127]||(t[2127]=e("a",{id:"Reactant.MLIR.API.mlirLocationEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLocationEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationEqual")],-1)),t[2128]||(t[2128]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2129]||(t[2129]=l('
julia
mlirLocationEqual(l1, l2)
Checks if two locations are equal.
source
',3))]),e("details",ec,[e("summary",null,[t[2130]||(t[2130]=e("a",{id:"Reactant.MLIR.API.mlirLocationFileLineColGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLocationFileLineColGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFileLineColGet")],-1)),t[2131]||(t[2131]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2132]||(t[2132]=l('
julia
mlirLocationFileLineColGet(context, filename, line, col)
Creates an File/Line/Column location owned by the given context.
source
',3))]),e("details",sc,[e("summary",null,[t[2133]||(t[2133]=e("a",{id:"Reactant.MLIR.API.mlirLocationFromAttribute-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationFromAttribute-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFromAttribute")],-1)),t[2134]||(t[2134]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2135]||(t[2135]=l('
julia
mlirLocationFromAttribute(attribute)
Creates a location from a location attribute.
source
',3))]),e("details",ac,[e("summary",null,[t[2136]||(t[2136]=e("a",{id:"Reactant.MLIR.API.mlirLocationFusedGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLocationFusedGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFusedGet")],-1)),t[2137]||(t[2137]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2138]||(t[2138]=l('
julia
mlirLocationFusedGet(ctx, nLocations, locations, metadata)
Creates a fused location with an array of locations and metadata.
source
',3))]),e("details",ic,[e("summary",null,[t[2139]||(t[2139]=e("a",{id:"Reactant.MLIR.API.mlirLocationGetAttribute-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationGetAttribute-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationGetAttribute")],-1)),t[2140]||(t[2140]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2141]||(t[2141]=l('
julia
mlirLocationGetAttribute(location)
Returns the underlying location attribute of this location.
source
',3))]),e("details",lc,[e("summary",null,[t[2142]||(t[2142]=e("a",{id:"Reactant.MLIR.API.mlirLocationGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationGetContext")],-1)),t[2143]||(t[2143]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2144]||(t[2144]=l('
julia
mlirLocationGetContext(location)
Gets the context that a location was created with.
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',3))]),e("details",nc,[e("summary",null,[t[2145]||(t[2145]=e("a",{id:"Reactant.MLIR.API.mlirLocationIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationIsNull")],-1)),t[2146]||(t[2146]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2147]||(t[2147]=l('
julia
mlirLocationIsNull(location)
Checks if the location is null.
source
',3))]),e("details",pc,[e("summary",null,[t[2148]||(t[2148]=e("a",{id:"Reactant.MLIR.API.mlirLocationNameGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLocationNameGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationNameGet")],-1)),t[2149]||(t[2149]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2150]||(t[2150]=l('
julia
mlirLocationNameGet(context, name, childLoc)
Creates a name location owned by the given context. Providing null location for childLoc is allowed and if childLoc is null location, then the behavior is the same as having unknown child location.
source
',3))]),e("details",rc,[e("summary",null,[t[2151]||(t[2151]=e("a",{id:"Reactant.MLIR.API.mlirLocationPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLocationPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationPrint")],-1)),t[2152]||(t[2152]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2153]||(t[2153]=l('
julia
mlirLocationPrint(location, callback, userData)
Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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',3))]),e("details",oc,[e("summary",null,[t[2154]||(t[2154]=e("a",{id:"Reactant.MLIR.API.mlirLocationUnknownGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationUnknownGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationUnknownGet")],-1)),t[2155]||(t[2155]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2156]||(t[2156]=l('
julia
mlirLocationUnknownGet(context)
Creates a location with unknown position owned by the given context.
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',3))]),e("details",dc,[e("summary",null,[t[2157]||(t[2157]=e("a",{id:"Reactant.MLIR.API.mlirLogicalResultFailure-Tuple{}",href:"#Reactant.MLIR.API.mlirLogicalResultFailure-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultFailure")],-1)),t[2158]||(t[2158]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2159]||(t[2159]=l('
julia
mlirLogicalResultFailure()
Creates a logical result representing a failure.
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',3))]),e("details",hc,[e("summary",null,[t[2160]||(t[2160]=e("a",{id:"Reactant.MLIR.API.mlirLogicalResultIsFailure-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLogicalResultIsFailure-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultIsFailure")],-1)),t[2161]||(t[2161]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2162]||(t[2162]=l('
julia
mlirLogicalResultIsFailure(res)
Checks if the given logical result represents a failure.
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',3))]),e("details",cc,[e("summary",null,[t[2163]||(t[2163]=e("a",{id:"Reactant.MLIR.API.mlirLogicalResultIsSuccess-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLogicalResultIsSuccess-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultIsSuccess")],-1)),t[2164]||(t[2164]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2165]||(t[2165]=l('
julia
mlirLogicalResultIsSuccess(res)
Checks if the given logical result represents a success.
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',3))]),e("details",uc,[e("summary",null,[t[2166]||(t[2166]=e("a",{id:"Reactant.MLIR.API.mlirLogicalResultSuccess-Tuple{}",href:"#Reactant.MLIR.API.mlirLogicalResultSuccess-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultSuccess")],-1)),t[2167]||(t[2167]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2168]||(t[2168]=l('
julia
mlirLogicalResultSuccess()
Creates a logical result representing a success.
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',3))]),e("details",bc,[e("summary",null,[t[2169]||(t[2169]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeContiguousGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeContiguousGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeContiguousGet")],-1)),t[2170]||(t[2170]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2171]||(t[2171]=l('
julia
mlirMemRefTypeContiguousGet(elementType, rank, shape, memorySpace)
Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context.
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',3))]),e("details",gc,[e("summary",null,[t[2172]||(t[2172]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked")],-1)),t[2173]||(t[2173]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2174]||(t[2174]=l('
julia
mlirMemRefTypeContiguousGetChecked(loc, elementType, rank, shape, memorySpace)
Same as "mlirMemRefTypeContiguousGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
source
',3))]),e("details",yc,[e("summary",null,[t[2175]||(t[2175]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGet")],-1)),t[2176]||(t[2176]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2177]||(t[2177]=l('
julia
mlirMemRefTypeGet(elementType, rank, shape, layout, memorySpace)
Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context.
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',3))]),e("details",mc,[e("summary",null,[t[2178]||(t[2178]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetAffineMap-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetAffineMap-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetAffineMap")],-1)),t[2179]||(t[2179]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2180]||(t[2180]=l('
julia
mlirMemRefTypeGetAffineMap(type)
Returns the affine map of the given MemRef type.
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',3))]),e("details",kc,[e("summary",null,[t[2181]||(t[2181]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetChecked-NTuple{6, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetChecked-NTuple{6, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetChecked")],-1)),t[2182]||(t[2182]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2183]||(t[2183]=l('
julia
mlirMemRefTypeGetChecked(loc, elementType, rank, shape, layout, memorySpace)
Same as "mlirMemRefTypeGet" but returns a nullptr-wrapping MlirType o illegal arguments, emitting appropriate diagnostics.
source
',3))]),e("details",Rc,[e("summary",null,[t[2184]||(t[2184]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetLayout-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetLayout-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetLayout")],-1)),t[2185]||(t[2185]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2186]||(t[2186]=l('
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mlirMemRefTypeGetLayout(type)
Returns the layout of the given MemRef type.
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',3))]),e("details",fc,[e("summary",null,[t[2187]||(t[2187]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace")],-1)),t[2188]||(t[2188]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2189]||(t[2189]=l('
julia
mlirMemRefTypeGetMemorySpace(type)
Returns the memory space of the given MemRef type.
source
',3))]),e("details",Ic,[e("summary",null,[t[2190]||(t[2190]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset")],-1)),t[2191]||(t[2191]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2192]||(t[2192]=l('
julia
mlirMemRefTypeGetStridesAndOffset(type, strides, offset)
Returns the strides of the MemRef if the layout map is in strided form. Both strides and offset are out params. strides must point to pre-allocated memory of length equal to the rank of the memref.
source
',3))]),e("details",jc,[e("summary",null,[t[2193]||(t[2193]=e("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetTypeID")],-1)),t[2194]||(t[2194]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2195]||(t[2195]=l('
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mlirMemRefTypeGetTypeID()
Returns the typeID of an MemRef type.
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',3))]),e("details",Mc,[e("summary",null,[t[2196]||(t[2196]=e("a",{id:"Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone")],-1)),t[2197]||(t[2197]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2198]||(t[2198]=l('
julia
mlirMergeSymbolsIntoFromClone(target, other)
Merge the symbols from other into target, potentially renaming them to avoid conflicts. Private symbols may be renamed during the merge, public symbols must have at most one declaration. A name conflict in public symbols is reported as an error before returning a failure.
Note that this clones the other operation unlike the C++ counterpart that takes ownership.
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',4))]),e("details",Ac,[e("summary",null,[t[2199]||(t[2199]=e("a",{id:"Reactant.MLIR.API.mlirModuleCreateEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleCreateEmpty-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleCreateEmpty")],-1)),t[2200]||(t[2200]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2201]||(t[2201]=l('
julia
mlirModuleCreateEmpty(location)
Creates a new, empty module and transfers ownership to the caller.
source
',3))]),e("details",Lc,[e("summary",null,[t[2202]||(t[2202]=e("a",{id:"Reactant.MLIR.API.mlirModuleCreateParse-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirModuleCreateParse-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleCreateParse")],-1)),t[2203]||(t[2203]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2204]||(t[2204]=l('
julia
mlirModuleCreateParse(context, _module)
Parses a module from the string and transfers ownership to the caller.
source
',3))]),e("details",Ec,[e("summary",null,[t[2205]||(t[2205]=e("a",{id:"Reactant.MLIR.API.mlirModuleDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleDestroy")],-1)),t[2206]||(t[2206]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2207]||(t[2207]=l('
julia
mlirModuleDestroy(_module)
Takes a module owned by the caller and deletes it.
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',3))]),e("details",vc,[e("summary",null,[t[2208]||(t[2208]=e("a",{id:"Reactant.MLIR.API.mlirModuleFromOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleFromOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleFromOperation")],-1)),t[2209]||(t[2209]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2210]||(t[2210]=l('
julia
mlirModuleFromOperation(op)
Views the generic operation as a module. The returned module is null when the input operation was not a ModuleOp.
source
',3))]),e("details",Tc,[e("summary",null,[t[2211]||(t[2211]=e("a",{id:"Reactant.MLIR.API.mlirModuleGetBody-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetBody-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetBody")],-1)),t[2212]||(t[2212]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2213]||(t[2213]=l('
julia
mlirModuleGetBody(_module)
Gets the body of the module, i.e. the only block it contains.
source
',3))]),e("details",Cc,[e("summary",null,[t[2214]||(t[2214]=e("a",{id:"Reactant.MLIR.API.mlirModuleGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetContext")],-1)),t[2215]||(t[2215]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2216]||(t[2216]=l('
julia
mlirModuleGetContext(_module)
Gets the context that a module was created with.
source
',3))]),e("details",xc,[e("summary",null,[t[2217]||(t[2217]=e("a",{id:"Reactant.MLIR.API.mlirModuleGetOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetOperation")],-1)),t[2218]||(t[2218]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2219]||(t[2219]=l('
julia
mlirModuleGetOperation(_module)
Views the module as a generic operation.
source
',3))]),e("details",Fc,[e("summary",null,[t[2220]||(t[2220]=e("a",{id:"Reactant.MLIR.API.mlirModuleIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleIsNull")],-1)),t[2221]||(t[2221]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2222]||(t[2222]=l('
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mlirModuleIsNull(_module)
Checks whether a module is null.
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',3))]),e("details",Pc,[e("summary",null,[t[2223]||(t[2223]=e("a",{id:"Reactant.MLIR.API.mlirNamedAttributeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirNamedAttributeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNamedAttributeGet")],-1)),t[2224]||(t[2224]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2225]||(t[2225]=l('
julia
mlirNamedAttributeGet(name, attr)
Associates an attribute with the name. Takes ownership of neither.
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',3))]),e("details",Dc,[e("summary",null,[t[2226]||(t[2226]=e("a",{id:"Reactant.MLIR.API.mlirNoneTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirNoneTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNoneTypeGet")],-1)),t[2227]||(t[2227]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2228]||(t[2228]=l('
julia
mlirNoneTypeGet(ctx)
Creates a None type in the given context. The type is owned by the context.
source
',3))]),e("details",Oc,[e("summary",null,[t[2229]||(t[2229]=e("a",{id:"Reactant.MLIR.API.mlirNoneTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirNoneTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNoneTypeGetTypeID")],-1)),t[2230]||(t[2230]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2231]||(t[2231]=l('
julia
mlirNoneTypeGetTypeID()
Returns the typeID of an None type.
source
',3))]),e("details",Bc,[e("summary",null,[t[2232]||(t[2232]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandGetNextUse-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetNextUse-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetNextUse")],-1)),t[2233]||(t[2233]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2234]||(t[2234]=l('
julia
mlirOpOperandGetNextUse(opOperand)
Returns an op operand representing the next use of the value, or a null op operand if there is no next use.
source
',3))]),e("details",Gc,[e("summary",null,[t[2235]||(t[2235]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandGetOperandNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetOperandNumber-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetOperandNumber")],-1)),t[2236]||(t[2236]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2237]||(t[2237]=l('
julia
mlirOpOperandGetOperandNumber(opOperand)
Returns the operand number of an op operand.
source
',3))]),e("details",zc,[e("summary",null,[t[2238]||(t[2238]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetOwner-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetOwner")],-1)),t[2239]||(t[2239]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2240]||(t[2240]=l('
julia
mlirOpOperandGetOwner(opOperand)
Returns the owner operation of an op operand.
source
',3))]),e("details",wc,[e("summary",null,[t[2241]||(t[2241]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetValue")],-1)),t[2242]||(t[2242]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2243]||(t[2243]=l('
julia
mlirOpOperandGetValue(opOperand)
Returns the value of an op operand.
source
',3))]),e("details",Sc,[e("summary",null,[t[2244]||(t[2244]=e("a",{id:"Reactant.MLIR.API.mlirOpOperandIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandIsNull")],-1)),t[2245]||(t[2245]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2246]||(t[2246]=l('
julia
mlirOpOperandIsNull(opOperand)
Returns whether the op operand is null.
source
',3))]),e("details",Nc,[e("summary",null,[t[2247]||(t[2247]=e("a",{id:"Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass")],-1)),t[2248]||(t[2248]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2249]||(t[2249]=l('
julia
mlirOpPassManagerAddOwnedPass(passManager, pass)
Add a pass and transfer ownership to the provided mlirOpPassManager. If the pass is not a generic operation pass or matching the type of the provided PassManager, a new OpPassManager is implicitly nested under the provided PassManager.
source
',3))]),e("details",Vc,[e("summary",null,[t[2250]||(t[2250]=e("a",{id:"Reactant.MLIR.API.mlirOpPassManagerAddPipeline-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerAddPipeline-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerAddPipeline")],-1)),t[2251]||(t[2251]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2252]||(t[2252]=l('
julia
mlirOpPassManagerAddPipeline(passManager, pipelineElements, callback, userData)
Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
',3))]),e("details",qc,[e("summary",null,[t[2253]||(t[2253]=e("a",{id:"Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder")],-1)),t[2254]||(t[2254]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2255]||(t[2255]=l('
julia
mlirOpPassManagerGetNestedUnder(passManager, operationName)
Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.
source
',3))]),e("details",Uc,[e("summary",null,[t[2256]||(t[2256]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified")],-1)),t[2257]||(t[2257]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2258]||(t[2258]=l('
julia
mlirOpPrintingFlagsAssumeVerified(flags)
Do not verify the operation when using custom operation printers.
source
',3))]),e("details",Qc,[e("summary",null,[t[2259]||(t[2259]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsCreate")],-1)),t[2260]||(t[2260]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2261]||(t[2261]=l('
julia
mlirOpPrintingFlagsCreate()
Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirOpPrintingFlagsDestroy().
source
',3))]),e("details",Wc,[e("summary",null,[t[2262]||(t[2262]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsDestroy")],-1)),t[2263]||(t[2263]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2264]||(t[2264]=l('
julia
mlirOpPrintingFlagsDestroy(flags)
Destroys printing flags created with mlirOpPrintingFlagsCreate.
source
',3))]),e("details",Hc,[e("summary",null,[t[2265]||(t[2265]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs")],-1)),t[2266]||(t[2266]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2267]||(t[2267]=l('
julia
mlirOpPrintingFlagsElideLargeElementsAttrs(flags, largeElementLimit)
Enables the elision of large elements attributes by printing a lexically valid but otherwise meaningless form instead of the element data. The largeElementLimit is used to configure what is considered to be a "large" ElementsAttr by providing an upper limit to the number of elements.
source
',3))]),e("details",Zc,[e("summary",null,[t[2268]||(t[2268]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString")],-1)),t[2269]||(t[2269]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2270]||(t[2270]=l('
julia
mlirOpPrintingFlagsElideLargeResourceString(flags, largeResourceLimit)
Enables the elision of large resources strings by omitting them from the dialect_resources section. The largeResourceLimit is used to configure what is considered to be a "large" resource by providing an upper limit to the string size.
source
',3))]),e("details",Jc,[e("summary",null,[t[2271]||(t[2271]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo")],-1)),t[2272]||(t[2272]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2273]||(t[2273]=l('
julia
mlirOpPrintingFlagsEnableDebugInfo(flags, enable, prettyForm)
Enable or disable printing of debug information (based on enable). If 'prettyForm' is set to true, debug information is printed in a more readable 'pretty' form. Note: The IR generated with 'prettyForm' is not parsable.
source
',3))]),e("details",Kc,[e("summary",null,[t[2274]||(t[2274]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm")],-1)),t[2275]||(t[2275]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2276]||(t[2276]=l('
julia
mlirOpPrintingFlagsPrintGenericOpForm(flags)
Always print operations in the generic form.
source
',3))]),e("details",$c,[e("summary",null,[t[2277]||(t[2277]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions")],-1)),t[2278]||(t[2278]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2279]||(t[2279]=l('
julia
mlirOpPrintingFlagsSkipRegions(flags)
Skip printing regions.
source
',3))]),e("details",Xc,[e("summary",null,[t[2280]||(t[2280]=e("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope")],-1)),t[2281]||(t[2281]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2282]||(t[2282]=l('
julia
mlirOpPrintingFlagsUseLocalScope(flags)
Use local scope when printing the operation. This allows for using the printer in a more localized and thread-safe setting, but may not necessarily be identical to what the IR will look like when dumping the full module.
source
',3))]),e("details",Yc,[e("summary",null,[t[2283]||(t[2283]=e("a",{id:"Reactant.MLIR.API.mlirOpResultGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpResultGetOwner-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpResultGetOwner")],-1)),t[2284]||(t[2284]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2285]||(t[2285]=l('
julia
mlirOpResultGetOwner(value)
Returns an operation that produced this value as its result. Asserts if the value is not an op result.
source
',3))]),e("details",_c,[e("summary",null,[t[2286]||(t[2286]=e("a",{id:"Reactant.MLIR.API.mlirOpResultGetResultNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpResultGetResultNumber-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpResultGetResultNumber")],-1)),t[2287]||(t[2287]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2288]||(t[2288]=l('
julia
mlirOpResultGetResultNumber(value)
Returns the position of the value in the list of results of the operation that produced it.
source
',3))]),e("details",tu,[e("summary",null,[t[2289]||(t[2289]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGet-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGet")],-1)),t[2290]||(t[2290]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2291]||(t[2291]=l('
julia
mlirOpaqueAttrGet(ctx, dialectNamespace, dataLength, data, type)
Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),e("details",eu,[e("summary",null,[t[2292]||(t[2292]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetData-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetData")],-1)),t[2293]||(t[2293]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2294]||(t[2294]=l('
julia
mlirOpaqueAttrGetData(attr)
Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.
source
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julia
mlirOpaqueAttrGetDialectNamespace(attr)
Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.
source
',3))]),e("details",au,[e("summary",null,[t[2298]||(t[2298]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetTypeID")],-1)),t[2299]||(t[2299]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2300]||(t[2300]=l('
julia
mlirOpaqueAttrGetTypeID()
Returns the typeID of an Opaque attribute.
source
',3))]),e("details",iu,[e("summary",null,[t[2301]||(t[2301]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGet")],-1)),t[2302]||(t[2302]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2303]||(t[2303]=l('
julia
mlirOpaqueTypeGet(ctx, dialectNamespace, typeData)
Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).
source
',3))]),e("details",lu,[e("summary",null,[t[2304]||(t[2304]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetData-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetData")],-1)),t[2305]||(t[2305]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2306]||(t[2306]=l('
julia
mlirOpaqueTypeGetData(type)
Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.
source
',3))]),e("details",nu,[e("summary",null,[t[2307]||(t[2307]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace")],-1)),t[2308]||(t[2308]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2309]||(t[2309]=l('
julia
mlirOpaqueTypeGetDialectNamespace(type)
Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.
source
',3))]),e("details",pu,[e("summary",null,[t[2310]||(t[2310]=e("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetTypeID")],-1)),t[2311]||(t[2311]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2312]||(t[2312]=l('
julia
mlirOpaqueTypeGetTypeID()
Returns the typeID of an Opaque type.
source
',3))]),e("details",ru,[e("summary",null,[t[2313]||(t[2313]=e("a",{id:"Reactant.MLIR.API.mlirOperationClone-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationClone-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationClone")],-1)),t[2314]||(t[2314]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2315]||(t[2315]=l('
julia
mlirOperationClone(op)
Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.
source
',3))]),e("details",ou,[e("summary",null,[t[2316]||(t[2316]=e("a",{id:"Reactant.MLIR.API.mlirOperationCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationCreate-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationCreate")],-1)),t[2317]||(t[2317]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2318]||(t[2318]=l('
julia
mlirOperationCreate(state)
Creates an operation and transfers ownership to the caller. Note that caller owned child objects are transferred in this call and must not be further used. Particularly, this applies to any regions added to the state (the implementation may invalidate any such pointers).
This call can fail under the following conditions, in which case, it will return a null operation and emit diagnostics: - Result type inference is enabled and cannot be performed.
source
',4))]),e("details",du,[e("summary",null,[t[2319]||(t[2319]=e("a",{id:"Reactant.MLIR.API.mlirOperationCreateParse-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationCreateParse-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationCreateParse")],-1)),t[2320]||(t[2320]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2321]||(t[2321]=l('
julia
mlirOperationCreateParse(context, sourceStr, sourceName)
Parses an operation, giving ownership to the caller. If parsing fails a null operation will be returned, and an error diagnostic emitted.
sourceStr may be either the text assembly format, or binary bytecode format. sourceName is used as the file name of the source; any IR without locations will get a FileLineColLoc location with sourceName as the file name.
source
',4))]),e("details",hu,[e("summary",null,[t[2322]||(t[2322]=e("a",{id:"Reactant.MLIR.API.mlirOperationDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationDestroy")],-1)),t[2323]||(t[2323]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2324]||(t[2324]=l('
julia
mlirOperationDestroy(op)
Takes an operation owned by the caller and destroys it.
source
',3))]),e("details",cu,[e("summary",null,[t[2325]||(t[2325]=e("a",{id:"Reactant.MLIR.API.mlirOperationDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationDump")],-1)),t[2326]||(t[2326]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2327]||(t[2327]=l('
julia
mlirOperationDump(op)
Prints an operation to stderr.
source
',3))]),e("details",uu,[e("summary",null,[t[2328]||(t[2328]=e("a",{id:"Reactant.MLIR.API.mlirOperationEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationEqual")],-1)),t[2329]||(t[2329]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2330]||(t[2330]=l('
julia
mlirOperationEqual(op, other)
Checks whether two operation handles point to the same operation. This does not perform deep comparison.
source
',3))]),e("details",bu,[e("summary",null,[t[2331]||(t[2331]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetAttribute-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetAttribute")],-1)),t[2332]||(t[2332]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2333]||(t[2333]=l('
julia
mlirOperationGetAttribute(op, pos)
Return pos-th attribute of the operation. Deprecated, please use mlirOperationGetInherentAttribute or mlirOperationGetDiscardableAttribute.
source
',3))]),e("details",gu,[e("summary",null,[t[2334]||(t[2334]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetAttributeByName")],-1)),t[2335]||(t[2335]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2336]||(t[2336]=l('
julia
mlirOperationGetAttributeByName(op, name)
Returns an attribute attached to the operation given its name. Deprecated, please use mlirOperationGetInherentAttributeByName or mlirOperationGetDiscardableAttributeByName.
source
',3))]),e("details",yu,[e("summary",null,[t[2337]||(t[2337]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetBlock")],-1)),t[2338]||(t[2338]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2339]||(t[2339]=l('
julia
mlirOperationGetBlock(op)
Gets the block that owns this operation, returning null if the operation is not owned.
source
',3))]),e("details",mu,[e("summary",null,[t[2340]||(t[2340]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetContext")],-1)),t[2341]||(t[2341]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2342]||(t[2342]=l('
julia
mlirOperationGetContext(op)
Gets the context this operation is associated with
source
',3))]),e("details",ku,[e("summary",null,[t[2343]||(t[2343]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetDiscardableAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetDiscardableAttribute-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetDiscardableAttribute")],-1)),t[2344]||(t[2344]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2345]||(t[2345]=l('
julia
mlirOperationGetDiscardableAttribute(op, pos)
Return pos-th discardable attribute of the operation.
source
',3))]),e("details",Ru,[e("summary",null,[t[2346]||(t[2346]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName")],-1)),t[2347]||(t[2347]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2348]||(t[2348]=l('
julia
mlirOperationGetDiscardableAttributeByName(op, name)
Returns a discardable attribute attached to the operation given its name.
source
',3))]),e("details",fu,[e("summary",null,[t[2349]||(t[2349]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetFirstRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetFirstRegion-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetFirstRegion")],-1)),t[2350]||(t[2350]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2351]||(t[2351]=l('
julia
mlirOperationGetFirstRegion(op)
Returns first region attached to the operation.
source
',3))]),e("details",Iu,[e("summary",null,[t[2352]||(t[2352]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetInherentAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetInherentAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetInherentAttributeByName")],-1)),t[2353]||(t[2353]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2354]||(t[2354]=l('
julia
mlirOperationGetInherentAttributeByName(op, name)
Returns an inherent attribute attached to the operation given its name.
source
',3))]),e("details",ju,[e("summary",null,[t[2355]||(t[2355]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetLocation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetLocation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetLocation")],-1)),t[2356]||(t[2356]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2357]||(t[2357]=l('
julia
mlirOperationGetLocation(op)
Gets the location of the operation.
source
',3))]),e("details",Mu,[e("summary",null,[t[2358]||(t[2358]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetName-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetName-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetName")],-1)),t[2359]||(t[2359]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2360]||(t[2360]=l('
julia
mlirOperationGetName(op)
Gets the name of the operation as an identifier.
source
',3))]),e("details",Au,[e("summary",null,[t[2361]||(t[2361]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNextInBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNextInBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNextInBlock")],-1)),t[2362]||(t[2362]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2363]||(t[2363]=l('
julia
mlirOperationGetNextInBlock(op)
Returns an operation immediately following the given operation it its enclosing block.
source
',3))]),e("details",Lu,[e("summary",null,[t[2364]||(t[2364]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumAttributes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumAttributes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumAttributes")],-1)),t[2365]||(t[2365]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2366]||(t[2366]=l('
julia
mlirOperationGetNumAttributes(op)
Returns the number of attributes attached to the operation. Deprecated, please use mlirOperationGetNumInherentAttributes or mlirOperationGetNumDiscardableAttributes.
source
',3))]),e("details",Eu,[e("summary",null,[t[2367]||(t[2367]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes")],-1)),t[2368]||(t[2368]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2369]||(t[2369]=l('
julia
mlirOperationGetNumDiscardableAttributes(op)
Returns the number of discardable attributes attached to the operation.
source
',3))]),e("details",vu,[e("summary",null,[t[2370]||(t[2370]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumOperands-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumOperands-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumOperands")],-1)),t[2371]||(t[2371]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2372]||(t[2372]=l('
julia
mlirOperationGetNumOperands(op)
Returns the number of operands of the operation.
source
',3))]),e("details",Tu,[e("summary",null,[t[2373]||(t[2373]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumRegions-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumRegions-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumRegions")],-1)),t[2374]||(t[2374]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2375]||(t[2375]=l('
julia
mlirOperationGetNumRegions(op)
Returns the number of regions attached to the given operation.
source
',3))]),e("details",Cu,[e("summary",null,[t[2376]||(t[2376]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumResults-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumResults")],-1)),t[2377]||(t[2377]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2378]||(t[2378]=l('
julia
mlirOperationGetNumResults(op)
Returns the number of results of the operation.
source
',3))]),e("details",xu,[e("summary",null,[t[2379]||(t[2379]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetNumSuccessors-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumSuccessors-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumSuccessors")],-1)),t[2380]||(t[2380]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2381]||(t[2381]=l('
julia
mlirOperationGetNumSuccessors(op)
Returns the number of successor blocks of the operation.
source
',3))]),e("details",Fu,[e("summary",null,[t[2382]||(t[2382]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetOperand-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetOperand-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetOperand")],-1)),t[2383]||(t[2383]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2384]||(t[2384]=l('
julia
mlirOperationGetOperand(op, pos)
Returns pos-th operand of the operation.
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',3))]),e("details",Pu,[e("summary",null,[t[2385]||(t[2385]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetParentOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetParentOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetParentOperation")],-1)),t[2386]||(t[2386]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2387]||(t[2387]=l('
julia
mlirOperationGetParentOperation(op)
Gets the operation that owns this operation, returning null if the operation is not owned.
source
',3))]),e("details",Du,[e("summary",null,[t[2388]||(t[2388]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetRegion-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetRegion-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetRegion")],-1)),t[2389]||(t[2389]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2390]||(t[2390]=l('
julia
mlirOperationGetRegion(op, pos)
Returns pos-th region attached to the operation.
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',3))]),e("details",Ou,[e("summary",null,[t[2391]||(t[2391]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetResult-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetResult")],-1)),t[2392]||(t[2392]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2393]||(t[2393]=l('
julia
mlirOperationGetResult(op, pos)
Returns pos-th result of the operation.
source
',3))]),e("details",Bu,[e("summary",null,[t[2394]||(t[2394]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetSuccessor-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetSuccessor-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetSuccessor")],-1)),t[2395]||(t[2395]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2396]||(t[2396]=l('
julia
mlirOperationGetSuccessor(op, pos)
Returns pos-th successor of the operation.
source
',3))]),e("details",Gu,[e("summary",null,[t[2397]||(t[2397]=e("a",{id:"Reactant.MLIR.API.mlirOperationGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetTypeID-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetTypeID")],-1)),t[2398]||(t[2398]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2399]||(t[2399]=l('
julia
mlirOperationGetTypeID(op)
Gets the type id of the operation. Returns null if the operation does not have a registered operation description.
source
',3))]),e("details",zu,[e("summary",null,[t[2400]||(t[2400]=e("a",{id:"Reactant.MLIR.API.mlirOperationHasInherentAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationHasInherentAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationHasInherentAttributeByName")],-1)),t[2401]||(t[2401]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2402]||(t[2402]=l('
julia
mlirOperationHasInherentAttributeByName(op, name)
Returns true if this operation defines an inherent attribute with this name. Note: the attribute can be optional, so mlirOperationGetInherentAttributeByName can still return a null attribute.
source
',3))]),e("details",wu,[e("summary",null,[t[2403]||(t[2403]=e("a",{id:"Reactant.MLIR.API.mlirOperationImplementsInterface-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationImplementsInterface-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationImplementsInterface")],-1)),t[2404]||(t[2404]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2405]||(t[2405]=l('
julia
mlirOperationImplementsInterface(operation, interfaceTypeID)
Returns true if the given operation implements an interface identified by its TypeID.
source
',3))]),e("details",Su,[e("summary",null,[t[2406]||(t[2406]=e("a",{id:"Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic")],-1)),t[2407]||(t[2407]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2408]||(t[2408]=l('
julia
mlirOperationImplementsInterfaceStatic(operationName, context, interfaceTypeID)
Returns true if the operation identified by its canonical string name implements the interface identified by its TypeID in the given context. Note that interfaces may be attached to operations in some contexts and not others.
source
',3))]),e("details",Nu,[e("summary",null,[t[2409]||(t[2409]=e("a",{id:"Reactant.MLIR.API.mlirOperationIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationIsNull")],-1)),t[2410]||(t[2410]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2411]||(t[2411]=l('
julia
mlirOperationIsNull(op)
Checks whether the underlying operation is null.
source
',3))]),e("details",Vu,[e("summary",null,[t[2412]||(t[2412]=e("a",{id:"Reactant.MLIR.API.mlirOperationMoveAfter-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationMoveAfter-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationMoveAfter")],-1)),t[2413]||(t[2413]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2414]||(t[2414]=l('
julia
mlirOperationMoveAfter(op, other)
Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),e("details",qu,[e("summary",null,[t[2415]||(t[2415]=e("a",{id:"Reactant.MLIR.API.mlirOperationMoveBefore-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationMoveBefore-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationMoveBefore")],-1)),t[2416]||(t[2416]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2417]||(t[2417]=l('
julia
mlirOperationMoveBefore(op, other)
Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.
source
',3))]),e("details",Uu,[e("summary",null,[t[2418]||(t[2418]=e("a",{id:"Reactant.MLIR.API.mlirOperationPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationPrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrint")],-1)),t[2419]||(t[2419]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2420]||(t[2420]=l('
julia
mlirOperationPrint(op, callback, userData)
Prints an operation by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
source
',3))]),e("details",Qu,[e("summary",null,[t[2421]||(t[2421]=e("a",{id:"Reactant.MLIR.API.mlirOperationPrintWithFlags-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationPrintWithFlags-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrintWithFlags")],-1)),t[2422]||(t[2422]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2423]||(t[2423]=l('
julia
mlirOperationPrintWithFlags(op, flags, callback, userData)
Same as mlirOperationPrint but accepts flags controlling the printing behavior.
source
',3))]),e("details",Wu,[e("summary",null,[t[2424]||(t[2424]=e("a",{id:"Reactant.MLIR.API.mlirOperationPrintWithState-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationPrintWithState-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrintWithState")],-1)),t[2425]||(t[2425]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2426]||(t[2426]=l('
julia
mlirOperationPrintWithState(op, state, callback, userData)
Same as mlirOperationPrint but accepts AsmState controlling the printing behavior as well as caching computed names.
source
',3))]),e("details",Hu,[e("summary",null,[t[2427]||(t[2427]=e("a",{id:"Reactant.MLIR.API.mlirOperationRemoveAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveAttributeByName")],-1)),t[2428]||(t[2428]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2429]||(t[2429]=l('
julia
mlirOperationRemoveAttributeByName(op, name)
Removes an attribute by name. Returns false if the attribute was not found and true if removed. Deprecated, please use mlirOperationRemoveInherentAttributeByName or mlirOperationRemoveDiscardableAttributeByName.
source
',3))]),e("details",Zu,[e("summary",null,[t[2430]||(t[2430]=e("a",{id:"Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName")],-1)),t[2431]||(t[2431]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2432]||(t[2432]=l('
julia
mlirOperationRemoveDiscardableAttributeByName(op, name)
Removes a discardable attribute by name. Returns false if the attribute was not found and true if removed.
source
',3))]),e("details",Ju,[e("summary",null,[t[2433]||(t[2433]=e("a",{id:"Reactant.MLIR.API.mlirOperationRemoveFromParent-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveFromParent-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveFromParent")],-1)),t[2434]||(t[2434]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2435]||(t[2435]=l('
julia
mlirOperationRemoveFromParent(op)
Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.
source
',3))]),e("details",Ku,[e("summary",null,[t[2436]||(t[2436]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetAttributeByName-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetAttributeByName")],-1)),t[2437]||(t[2437]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2438]||(t[2438]=l('
julia
mlirOperationSetAttributeByName(op, name, attr)
Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise. Deprecated, please use mlirOperationSetInherentAttributeByName or mlirOperationSetDiscardableAttributeByName.
source
',3))]),e("details",$u,[e("summary",null,[t[2439]||(t[2439]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName")],-1)),t[2440]||(t[2440]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2441]||(t[2441]=l('
julia
mlirOperationSetDiscardableAttributeByName(op, name, attr)
Sets a discardable attribute by name, replacing the existing if it exists or adding a new one otherwise. The new attr Attribute is not allowed to be null, use mlirOperationRemoveDiscardableAttributeByName to remove an Attribute instead.
source
',3))]),e("details",Xu,[e("summary",null,[t[2442]||(t[2442]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetInherentAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetInherentAttributeByName-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetInherentAttributeByName")],-1)),t[2443]||(t[2443]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2444]||(t[2444]=l('
julia
mlirOperationSetInherentAttributeByName(op, name, attr)
Sets an inherent attribute by name, replacing the existing if it exists. This has no effect if "name" does not match an inherent attribute.
source
',3))]),e("details",Yu,[e("summary",null,[t[2445]||(t[2445]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetOperand-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetOperand-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetOperand")],-1)),t[2446]||(t[2446]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2447]||(t[2447]=l('
julia
mlirOperationSetOperand(op, pos, newValue)
Sets the pos-th operand of the operation.
source
',3))]),e("details",_u,[e("summary",null,[t[2448]||(t[2448]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetOperands-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetOperands-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetOperands")],-1)),t[2449]||(t[2449]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2450]||(t[2450]=l('
julia
mlirOperationSetOperands(op, nOperands, operands)
Replaces the operands of the operation.
source
',3))]),e("details",tb,[e("summary",null,[t[2451]||(t[2451]=e("a",{id:"Reactant.MLIR.API.mlirOperationSetSuccessor-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetSuccessor-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetSuccessor")],-1)),t[2452]||(t[2452]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2453]||(t[2453]=l('
julia
mlirOperationSetSuccessor(op, pos, block)
Set pos-th successor of the operation.
source
',3))]),e("details",eb,[e("summary",null,[t[2454]||(t[2454]=e("a",{id:"Reactant.MLIR.API.mlirOperationStateAddResults-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationStateAddResults-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateAddResults")],-1)),t[2455]||(t[2455]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2456]||(t[2456]=l('
julia
mlirOperationStateAddResults(state, n, results)
Adds a list of components to the operation state.
source
',3))]),e("details",sb,[e("summary",null,[t[2457]||(t[2457]=e("a",{id:"Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference")],-1)),t[2458]||(t[2458]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2459]||(t[2459]=l('
julia
mlirOperationStateEnableResultTypeInference(state)
Enables result type inference for the operation under construction. If enabled, then the caller must not have called mlirOperationStateAddResults(). Note that if enabled, the mlirOperationCreate() call is failable: it will return a null operation on inference failure and will emit diagnostics.
source
',3))]),e("details",ab,[e("summary",null,[t[2460]||(t[2460]=e("a",{id:"Reactant.MLIR.API.mlirOperationStateGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationStateGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateGet")],-1)),t[2461]||(t[2461]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2462]||(t[2462]=l('
julia
mlirOperationStateGet(name, loc)
Constructs an operation state from a name and a location.
source
',3))]),e("details",ib,[e("summary",null,[t[2463]||(t[2463]=e("a",{id:"Reactant.MLIR.API.mlirOperationVerify-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationVerify-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationVerify")],-1)),t[2464]||(t[2464]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2465]||(t[2465]=l('
julia
mlirOperationVerify(op)
Verify the operation and return true if it passes, false if it fails.
source
',3))]),e("details",lb,[e("summary",null,[t[2466]||(t[2466]=e("a",{id:"Reactant.MLIR.API.mlirOperationWalk-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationWalk-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWalk")],-1)),t[2467]||(t[2467]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2468]||(t[2468]=l('
julia
mlirOperationWalk(op, callback, userData, walkOrder)
Walks operation op in walkOrder and calls callback on that operation. *userData is passed to the callback as well and can be used to tunnel some context or other data into the callback.
source
',3))]),e("details",nb,[e("summary",null,[t[2469]||(t[2469]=e("a",{id:"Reactant.MLIR.API.mlirOperationWriteBytecode-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationWriteBytecode-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWriteBytecode")],-1)),t[2470]||(t[2470]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2471]||(t[2471]=l('
julia
mlirOperationWriteBytecode(op, callback, userData)
Same as mlirOperationPrint but writing the bytecode format.
source
',3))]),e("details",pb,[e("summary",null,[t[2472]||(t[2472]=e("a",{id:"Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig")],-1)),t[2473]||(t[2473]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2474]||(t[2474]=l('
julia
mlirOperationWriteBytecodeWithConfig(op, config, callback, userData)
Same as mlirOperationWriteBytecode but with writer config and returns failure only if desired bytecode could not be honored.
source
',3))]),e("details",rb,[e("summary",null,[t[2475]||(t[2475]=e("a",{id:"Reactant.MLIR.API.mlirParsePassPipeline-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirParsePassPipeline-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirParsePassPipeline")],-1)),t[2476]||(t[2476]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2477]||(t[2477]=l('
julia
mlirParsePassPipeline(passManager, pipeline, callback, userData)
Parse a textual MLIR pass pipeline and assign it to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.
source
',3))]),e("details",ob,[e("summary",null,[t[2478]||(t[2478]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerAddOwnedPass-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerAddOwnedPass-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerAddOwnedPass")],-1)),t[2479]||(t[2479]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2480]||(t[2480]=l('
julia
mlirPassManagerAddOwnedPass(passManager, pass)
Add a pass and transfer ownership to the provided top-level mlirPassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.
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',3))]),e("details",db,[e("summary",null,[t[2481]||(t[2481]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerCreate-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerCreate")],-1)),t[2482]||(t[2482]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2483]||(t[2483]=l('
julia
mlirPassManagerCreate(ctx)
Create a new top-level PassManager with the default anchor.
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',3))]),e("details",hb,[e("summary",null,[t[2484]||(t[2484]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerCreateOnOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerCreateOnOperation-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerCreateOnOperation")],-1)),t[2485]||(t[2485]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2486]||(t[2486]=l('
julia
mlirPassManagerCreateOnOperation(ctx, anchorOp)
Create a new top-level PassManager anchored on anchorOp.
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',3))]),e("details",cb,[e("summary",null,[t[2487]||(t[2487]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerDestroy")],-1)),t[2488]||(t[2488]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2489]||(t[2489]=l('
julia
mlirPassManagerDestroy(passManager)
Destroy the provided PassManager.
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',3))]),e("details",ub,[e("summary",null,[t[2490]||(t[2490]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerEnableIRPrinting-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirPassManagerEnableIRPrinting-NTuple{8, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerEnableIRPrinting")],-1)),t[2491]||(t[2491]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2492]||(t[2492]=l('
julia
mlirPassManagerEnableIRPrinting(passManager, printBeforeAll, printAfterAll, printModuleScope, printAfterOnlyOnChange, printAfterOnlyOnFailure, flags, treePrintingPath)
Enable IR printing. The treePrintingPath argument is an optional path to a directory where the dumps will be produced. If it isn't provided then dumps are produced to stderr.
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',3))]),e("details",bb,[e("summary",null,[t[2493]||(t[2493]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerEnableVerifier-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerEnableVerifier-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerEnableVerifier")],-1)),t[2494]||(t[2494]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2495]||(t[2495]=l('
julia
mlirPassManagerEnableVerifier(passManager, enable)
Enable / disable verify-each.
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',3))]),e("details",gb,[e("summary",null,[t[2496]||(t[2496]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager")],-1)),t[2497]||(t[2497]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2498]||(t[2498]=l('
julia
mlirPassManagerGetAsOpPassManager(passManager)
Cast a top-level PassManager to a generic OpPassManager.
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',3))]),e("details",yb,[e("summary",null,[t[2499]||(t[2499]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerGetNestedUnder-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerGetNestedUnder-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerGetNestedUnder")],-1)),t[2500]||(t[2500]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2501]||(t[2501]=l('
julia
mlirPassManagerGetNestedUnder(passManager, operationName)
Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.
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',3))]),e("details",mb,[e("summary",null,[t[2502]||(t[2502]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerIsNull")],-1)),t[2503]||(t[2503]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2504]||(t[2504]=l('
julia
mlirPassManagerIsNull(passManager)
Checks if a PassManager is null.
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',3))]),e("details",kb,[e("summary",null,[t[2505]||(t[2505]=e("a",{id:"Reactant.MLIR.API.mlirPassManagerRunOnOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerRunOnOp-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerRunOnOp")],-1)),t[2506]||(t[2506]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2507]||(t[2507]=l('
julia
mlirPassManagerRunOnOp(passManager, op)
Run the provided passManager on the given op.
source
',3))]),e("details",Rb,[e("summary",null,[t[2508]||(t[2508]=e("a",{id:"Reactant.MLIR.API.mlirPrintPassPipeline-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirPrintPassPipeline-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPrintPassPipeline")],-1)),t[2509]||(t[2509]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2510]||(t[2510]=l('
julia
mlirPrintPassPipeline(passManager, callback, userData)
Print a textual MLIR pass pipeline by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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',3))]),e("details",fb,[e("summary",null,[t[2511]||(t[2511]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType")],-1)),t[2512]||(t[2512]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2513]||(t[2513]=l('
julia
mlirQuantizedTypeCastExpressedToStorageType(type, candidate)
Casts from a type based on the expressed type of the given quantized type to equivalent type based on storage type of the same quantized type.
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',3))]),e("details",Ib,[e("summary",null,[t[2514]||(t[2514]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType")],-1)),t[2515]||(t[2515]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2516]||(t[2516]=l('
julia
mlirQuantizedTypeCastFromExpressedType(type, candidate)
Casts from a type based on the expressed type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.
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',3))]),e("details",jb,[e("summary",null,[t[2517]||(t[2517]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType")],-1)),t[2518]||(t[2518]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2519]||(t[2519]=l('
julia
mlirQuantizedTypeCastFromStorageType(type, candidate)
Casts from a type based on the storage type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.
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',3))]),e("details",Mb,[e("summary",null,[t[2520]||(t[2520]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType")],-1)),t[2521]||(t[2521]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2522]||(t[2522]=l('
julia
mlirQuantizedTypeCastToExpressedType(type)
Casts from a type based on a quantized type to a corresponding typed based on the expressed type. Returns a null type if the cast is not valid.
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',3))]),e("details",Ab,[e("summary",null,[t[2523]||(t[2523]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType")],-1)),t[2524]||(t[2524]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2525]||(t[2525]=l('
julia
mlirQuantizedTypeCastToStorageType(type)
Casts from a type based on a quantized type to a corresponding typed based on the storage type. Returns a null type if the cast is not valid.
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',3))]),e("details",Lb,[e("summary",null,[t[2526]||(t[2526]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger")],-1)),t[2527]||(t[2527]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2528]||(t[2528]=l('
julia
mlirQuantizedTypeGetDefaultMaximumForInteger(isSigned, integralWidth)
Returns the maximum possible value stored by a quantized type.
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',3))]),e("details",Eb,[e("summary",null,[t[2529]||(t[2529]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger")],-1)),t[2530]||(t[2530]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2531]||(t[2531]=l('
julia
mlirQuantizedTypeGetDefaultMinimumForInteger(isSigned, integralWidth)
Returns the minimum possible value stored by a quantized type.
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',3))]),e("details",vb,[e("summary",null,[t[2532]||(t[2532]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType")],-1)),t[2533]||(t[2533]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2534]||(t[2534]=l('
julia
mlirQuantizedTypeGetExpressedType(type)
Gets the original type approximated by the given quantized type.
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',3))]),e("details",Tb,[e("summary",null,[t[2535]||(t[2535]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetFlags-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetFlags-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetFlags")],-1)),t[2536]||(t[2536]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2537]||(t[2537]=l('
julia
mlirQuantizedTypeGetFlags(type)
Gets the flags associated with the given quantized type.
source
',3))]),e("details",Cb,[e("summary",null,[t[2538]||(t[2538]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType")],-1)),t[2539]||(t[2539]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2540]||(t[2540]=l('
julia
mlirQuantizedTypeGetQuantizedElementType(type)
Returns the element type of the given quantized type as another quantized type.
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',3))]),e("details",xb,[e("summary",null,[t[2541]||(t[2541]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag-Tuple{}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag")],-1)),t[2542]||(t[2542]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2543]||(t[2543]=l('
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mlirQuantizedTypeGetSignedFlag()
Returns the bit flag used to indicate signedness of a quantized type.
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',3))]),e("details",Fb,[e("summary",null,[t[2544]||(t[2544]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageType")],-1)),t[2545]||(t[2545]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2546]||(t[2546]=l('
julia
mlirQuantizedTypeGetStorageType(type)
Returns the underlying type used to store the values.
source
',3))]),e("details",Pb,[e("summary",null,[t[2547]||(t[2547]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth")],-1)),t[2548]||(t[2548]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2549]||(t[2549]=l('
julia
mlirQuantizedTypeGetStorageTypeIntegralWidth(type)
Returns the integral bitwidth that the storage type of the given quantized type can represent exactly.
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',3))]),e("details",Db,[e("summary",null,[t[2550]||(t[2550]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax")],-1)),t[2551]||(t[2551]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2552]||(t[2552]=l('
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mlirQuantizedTypeGetStorageTypeMax(type)
Returns the maximum value that the storage type of the given quantized type can take.
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',3))]),e("details",Ob,[e("summary",null,[t[2553]||(t[2553]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin")],-1)),t[2554]||(t[2554]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2555]||(t[2555]=l('
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mlirQuantizedTypeGetStorageTypeMin(type)
Returns the minimum value that the storage type of the given quantized type can take.
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',3))]),e("details",Bb,[e("summary",null,[t[2556]||(t[2556]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType")],-1)),t[2557]||(t[2557]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2558]||(t[2558]=l('
julia
mlirQuantizedTypeIsCompatibleExpressedType(type, candidate)
Returns true if the candidate type is compatible with the given quantized type.
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',3))]),e("details",Gb,[e("summary",null,[t[2559]||(t[2559]=e("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeIsSigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeIsSigned-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeIsSigned")],-1)),t[2560]||(t[2560]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2561]||(t[2561]=l('
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mlirQuantizedTypeIsSigned(type)
Returns true if the given type is signed, false otherwise.
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',3))]),e("details",zb,[e("summary",null,[t[2562]||(t[2562]=e("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGet")],-1)),t[2563]||(t[2563]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2564]||(t[2564]=l('
julia
mlirRankedTensorTypeGet(rank, shape, elementType, encoding)
Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull() to this parameter.
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',3))]),e("details",wb,[e("summary",null,[t[2565]||(t[2565]=e("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetChecked-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetChecked")],-1)),t[2566]||(t[2566]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2567]||(t[2567]=l('
julia
mlirRankedTensorTypeGetChecked(loc, rank, shape, elementType, encoding)
Same as "mlirRankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
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',3))]),e("details",Sb,[e("summary",null,[t[2568]||(t[2568]=e("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding")],-1)),t[2569]||(t[2569]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2570]||(t[2570]=l('
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mlirRankedTensorTypeGetEncoding(type)
Gets the 'encoding' attribute from the ranked tensor type, returning a null attribute if none.
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',3))]),e("details",Nb,[e("summary",null,[t[2571]||(t[2571]=e("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID")],-1)),t[2572]||(t[2572]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2573]||(t[2573]=l('
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mlirRankedTensorTypeGetTypeID()
Returns the typeID of an RankedTensor type.
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',3))]),e("details",Vb,[e("summary",null,[t[2574]||(t[2574]=e("a",{id:"Reactant.MLIR.API.mlirRegionAppendOwnedBlock-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionAppendOwnedBlock-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionAppendOwnedBlock")],-1)),t[2575]||(t[2575]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2576]||(t[2576]=l('
julia
mlirRegionAppendOwnedBlock(region, block)
Takes a block owned by the caller and appends it to the given region.
source
',3))]),e("details",qb,[e("summary",null,[t[2577]||(t[2577]=e("a",{id:"Reactant.MLIR.API.mlirRegionCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirRegionCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionCreate")],-1)),t[2578]||(t[2578]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2579]||(t[2579]=l('
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mlirRegionCreate()
Creates a new empty region and transfers ownership to the caller.
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',3))]),e("details",Ub,[e("summary",null,[t[2580]||(t[2580]=e("a",{id:"Reactant.MLIR.API.mlirRegionDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionDestroy")],-1)),t[2581]||(t[2581]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2582]||(t[2582]=l('
julia
mlirRegionDestroy(region)
Takes a region owned by the caller and destroys it.
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',3))]),e("details",Qb,[e("summary",null,[t[2583]||(t[2583]=e("a",{id:"Reactant.MLIR.API.mlirRegionEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionEqual")],-1)),t[2584]||(t[2584]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2585]||(t[2585]=l('
julia
mlirRegionEqual(region, other)
Checks whether two region handles point to the same region. This does not perform deep comparison.
source
',3))]),e("details",Wb,[e("summary",null,[t[2586]||(t[2586]=e("a",{id:"Reactant.MLIR.API.mlirRegionGetFirstBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionGetFirstBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionGetFirstBlock")],-1)),t[2587]||(t[2587]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2588]||(t[2588]=l('
julia
mlirRegionGetFirstBlock(region)
Gets the first block in the region.
source
',3))]),e("details",Hb,[e("summary",null,[t[2589]||(t[2589]=e("a",{id:"Reactant.MLIR.API.mlirRegionGetNextInOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionGetNextInOperation-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionGetNextInOperation")],-1)),t[2590]||(t[2590]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2591]||(t[2591]=l('
julia
mlirRegionGetNextInOperation(region)
Returns the region immediately following the given region in its parent operation.
source
',3))]),e("details",Zb,[e("summary",null,[t[2592]||(t[2592]=e("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlock-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlock-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlock")],-1)),t[2593]||(t[2593]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2594]||(t[2594]=l('
julia
mlirRegionInsertOwnedBlock(region, pos, block)
Takes a block owned by the caller and inserts it at pos to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.
source
',3))]),e("details",Jb,[e("summary",null,[t[2595]||(t[2595]=e("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter")],-1)),t[2596]||(t[2596]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2597]||(t[2597]=l('
julia
mlirRegionInsertOwnedBlockAfter(region, reference, block)
Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.
source
',3))]),e("details",Kb,[e("summary",null,[t[2598]||(t[2598]=e("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore")],-1)),t[2599]||(t[2599]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2600]||(t[2600]=l('
julia
mlirRegionInsertOwnedBlockBefore(region, reference, block)
Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.
source
',3))]),e("details",$b,[e("summary",null,[t[2601]||(t[2601]=e("a",{id:"Reactant.MLIR.API.mlirRegionIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionIsNull")],-1)),t[2602]||(t[2602]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2603]||(t[2603]=l('
julia
mlirRegionIsNull(region)
Checks whether a region is null.
source
',3))]),e("details",Xb,[e("summary",null,[t[2604]||(t[2604]=e("a",{id:"Reactant.MLIR.API.mlirRegionTakeBody-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionTakeBody-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionTakeBody")],-1)),t[2605]||(t[2605]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2606]||(t[2606]=l('
julia
mlirRegionTakeBody(target, source)
Moves the entire content of the source region to the target region.
source
',3))]),e("details",Yb,[e("summary",null,[t[2607]||(t[2607]=e("a",{id:"Reactant.MLIR.API.mlirRegisterAllDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegisterAllDialects-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllDialects")],-1)),t[2608]||(t[2608]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2609]||(t[2609]=l('
julia
mlirRegisterAllDialects(registry)
Appends all upstream dialects and extensions to the dialect registry.
source
',3))]),e("details",_b,[e("summary",null,[t[2610]||(t[2610]=e("a",{id:"Reactant.MLIR.API.mlirRegisterAllLLVMTranslations-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegisterAllLLVMTranslations-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllLLVMTranslations")],-1)),t[2611]||(t[2611]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2612]||(t[2612]=l('
julia
mlirRegisterAllLLVMTranslations(context)
Register all translations to LLVM IR for dialects that can support it.
source
',3))]),e("details",tg,[e("summary",null,[t[2613]||(t[2613]=e("a",{id:"Reactant.MLIR.API.mlirRegisterAllPasses-Tuple{}",href:"#Reactant.MLIR.API.mlirRegisterAllPasses-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllPasses")],-1)),t[2614]||(t[2614]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2615]||(t[2615]=l('
julia
mlirRegisterAllPasses()
Register all compiler passes of MLIR.
source
',3))]),e("details",eg,[e("summary",null,[t[2616]||(t[2616]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCancelOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCancelOpModification-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCancelOpModification")],-1)),t[2617]||(t[2617]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2618]||(t[2618]=l('
julia
mlirRewriterBaseCancelOpModification(rewriter, op)
This method cancels a pending in-place modification. This can only be called on operations that were provided to a call to startOpModification.
source
',3))]),e("details",sg,[e("summary",null,[t[2619]||(t[2619]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint")],-1)),t[2620]||(t[2620]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2621]||(t[2621]=l('
julia
mlirRewriterBaseClearInsertionPoint(rewriter)
Reset the insertion point to no location. Creating an operation without a set insertion point is an error, but this can still be useful when the current insertion point a builder refers to is being removed.
source
',3))]),e("details",ag,[e("summary",null,[t[2622]||(t[2622]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseClone-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseClone-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseClone")],-1)),t[2623]||(t[2623]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2624]||(t[2624]=l('
julia
mlirRewriterBaseClone(rewriter, op)
Creates a deep copy of the specified operation.
source
',3))]),e("details",ig,[e("summary",null,[t[2625]||(t[2625]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore")],-1)),t[2626]||(t[2626]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2627]||(t[2627]=l('
julia
mlirRewriterBaseCloneRegionBefore(rewriter, region, before)
Clone the blocks that belong to "region" before the given position in another region "parent".
source
',3))]),e("details",lg,[e("summary",null,[t[2628]||(t[2628]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions")],-1)),t[2629]||(t[2629]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2630]||(t[2630]=l('
julia
mlirRewriterBaseCloneWithoutRegions(rewriter, op)
Creates a deep copy of this operation but keep the operation regions empty.
source
',3))]),e("details",ng,[e("summary",null,[t[2631]||(t[2631]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore")],-1)),t[2632]||(t[2632]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2633]||(t[2633]=l('
julia
mlirRewriterBaseCreateBlockBefore(rewriter, insertBefore, nArgTypes, argTypes, locations)
Add new block with 'argTypes' arguments and set the insertion point to the end of it. The block is placed before 'insertBefore'. locs contains the locations of the inserted arguments, and should match the size of argTypes.
source
',3))]),e("details",pg,[e("summary",null,[t[2634]||(t[2634]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseEraseBlock-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseEraseBlock-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseEraseBlock")],-1)),t[2635]||(t[2635]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2636]||(t[2636]=l('
julia
mlirRewriterBaseEraseBlock(rewriter, block)
Erases a block along with all operations inside it.
source
',3))]),e("details",rg,[e("summary",null,[t[2637]||(t[2637]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseEraseOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseEraseOp-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseEraseOp")],-1)),t[2638]||(t[2638]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2639]||(t[2639]=l('
julia
mlirRewriterBaseEraseOp(rewriter, op)
Erases an operation that is known to have no uses.
source
',3))]),e("details",og,[e("summary",null,[t[2640]||(t[2640]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification")],-1)),t[2641]||(t[2641]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2642]||(t[2642]=l('
julia
mlirRewriterBaseFinalizeOpModification(rewriter, op)
This method is used to signal the end of an in-place modification of the given operation. This can only be called on operations that were provided to a call to startOpModification.
source
',3))]),e("details",dg,[e("summary",null,[t[2643]||(t[2643]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetBlock")],-1)),t[2644]||(t[2644]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2645]||(t[2645]=l('
julia
mlirRewriterBaseGetBlock(rewriter)
Returns the current block of the rewriter.
source
',3))]),e("details",hg,[e("summary",null,[t[2646]||(t[2646]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetContext")],-1)),t[2647]||(t[2647]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2648]||(t[2648]=l('
julia
mlirRewriterBaseGetContext(rewriter)
Get the MLIR context referenced by the rewriter.
source
',3))]),e("details",cg,[e("summary",null,[t[2649]||(t[2649]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock")],-1)),t[2650]||(t[2650]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2651]||(t[2651]=l('
julia
mlirRewriterBaseGetInsertionBlock(rewriter)
Return the block the current insertion point belongs to. Note that the insertion point is not necessarily the end of the block.
source
',3))]),e("details",ug,[e("summary",null,[t[2652]||(t[2652]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore")],-1)),t[2653]||(t[2653]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2654]||(t[2654]=l('
julia
mlirRewriterBaseInlineBlockBefore(rewriter, source, op, nArgValues, argValues)
Inline the operations of block 'source' before the operation 'op'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'
The source block must have no successors. Otherwise, the resulting IR would have unreachable operations.
source
',4))]),e("details",bg,[e("summary",null,[t[2655]||(t[2655]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore")],-1)),t[2656]||(t[2656]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2657]||(t[2657]=l('
julia
mlirRewriterBaseInlineRegionBefore(rewriter, region, before)
Move the blocks that belong to "region" before the given position in another region "parent". The two regions must be different. The caller is responsible for creating or updating the operation transferring flow of control to the region and passing it the correct block arguments.
source
',3))]),e("details",gg,[e("summary",null,[t[2658]||(t[2658]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInsert-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInsert-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInsert")],-1)),t[2659]||(t[2659]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2660]||(t[2660]=l('
julia
mlirRewriterBaseInsert(rewriter, op)
Insert the given operation at the current insertion point and return it.
source
',3))]),e("details",yg,[e("summary",null,[t[2661]||(t[2661]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMergeBlocks-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMergeBlocks-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMergeBlocks")],-1)),t[2662]||(t[2662]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2663]||(t[2663]=l('
julia
mlirRewriterBaseMergeBlocks(rewriter, source, dest, nArgValues, argValues)
Inline the operations of block 'source' into the end of block 'dest'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'
The dest block must have no successors. Otherwise, the resulting IR would have unreachable operation.
source
',4))]),e("details",mg,[e("summary",null,[t[2664]||(t[2664]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore")],-1)),t[2665]||(t[2665]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2666]||(t[2666]=l('
julia
mlirRewriterBaseMoveBlockBefore(rewriter, block, existingBlock)
Unlink this block and insert it right before existingBlock.
source
',3))]),e("details",kg,[e("summary",null,[t[2667]||(t[2667]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter")],-1)),t[2668]||(t[2668]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2669]||(t[2669]=l('
julia
mlirRewriterBaseMoveOpAfter(rewriter, op, existingOp)
Unlink this operation from its current block and insert it right after existingOp which may be in the same or another block in the same function.
source
',3))]),e("details",Rg,[e("summary",null,[t[2670]||(t[2670]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore")],-1)),t[2671]||(t[2671]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2672]||(t[2672]=l('
julia
mlirRewriterBaseMoveOpBefore(rewriter, op, existingOp)
Unlink this operation from its current block and insert it right before existingOp which may be in the same or another block in the same function.
source
',3))]),e("details",fg,[e("summary",null,[t[2673]||(t[2673]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation")],-1)),t[2674]||(t[2674]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2675]||(t[2675]=l('
julia
mlirRewriterBaseReplaceAllOpUsesWithOperation(rewriter, from, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.
source
',3))]),e("details",Ig,[e("summary",null,[t[2676]||(t[2676]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange")],-1)),t[2677]||(t[2677]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2678]||(t[2678]=l('
julia
mlirRewriterBaseReplaceAllOpUsesWithValueRange(rewriter, from, nTo, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.
source
',3))]),e("details",jg,[e("summary",null,[t[2679]||(t[2679]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept")],-1)),t[2680]||(t[2680]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2681]||(t[2681]=l('
julia
mlirRewriterBaseReplaceAllUsesExcept(rewriter, from, to, exceptedUser)
Find uses of from and replace them with to except if the user is exceptedUser. Also notify the listener about every in-place op modification (for every use that was replaced).
source
',3))]),e("details",Mg,[e("summary",null,[t[2682]||(t[2682]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith")],-1)),t[2683]||(t[2683]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2684]||(t[2684]=l('
julia
mlirRewriterBaseReplaceAllUsesWith(rewriter, from, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).
source
',3))]),e("details",Ag,[e("summary",null,[t[2685]||(t[2685]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith")],-1)),t[2686]||(t[2686]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2687]||(t[2687]=l('
julia
mlirRewriterBaseReplaceAllValueRangeUsesWith(rewriter, nValues, from, to)
Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).
source
',3))]),e("details",Lg,[e("summary",null,[t[2688]||(t[2688]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock")],-1)),t[2689]||(t[2689]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2690]||(t[2690]=l('
julia
mlirRewriterBaseReplaceOpUsesWithinBlock(rewriter, op, nNewValues, newValues, block)
Find uses of from within block and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced). The optional allUsesReplaced flag is set to "true" if all uses were replaced.
source
',3))]),e("details",Eg,[e("summary",null,[t[2691]||(t[2691]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation")],-1)),t[2692]||(t[2692]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2693]||(t[2693]=l('
julia
mlirRewriterBaseReplaceOpWithOperation(rewriter, op, newOp)
Replace the results of the given (original) operation with the specified new op (replacement). The result types of the two ops must match. The original op is erased.
source
',3))]),e("details",vg,[e("summary",null,[t[2694]||(t[2694]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues")],-1)),t[2695]||(t[2695]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2696]||(t[2696]=l('
julia
mlirRewriterBaseReplaceOpWithValues(rewriter, op, nValues, values)
Replace the results of the given (original) operation with the specified list of values (replacements). The result types of the given op and the replacements must match. The original op is erased.
source
',3))]),e("details",Tg,[e("summary",null,[t[2697]||(t[2697]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter")],-1)),t[2698]||(t[2698]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2699]||(t[2699]=l('
julia
mlirRewriterBaseSetInsertionPointAfter(rewriter, op)
Sets the insertion point to the node after the specified operation, which will cause subsequent insertions to go right after it.
source
',3))]),e("details",Cg,[e("summary",null,[t[2700]||(t[2700]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue")],-1)),t[2701]||(t[2701]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2702]||(t[2702]=l('
julia
mlirRewriterBaseSetInsertionPointAfterValue(rewriter, value)
Sets the insertion point to the node after the specified value. If value has a defining operation, sets the insertion point to the node after such defining operation. This will cause subsequent insertions to go right after it. Otherwise, value is a BlockArgument. Sets the insertion point to the start of its block.
source
',3))]),e("details",xg,[e("summary",null,[t[2703]||(t[2703]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore")],-1)),t[2704]||(t[2704]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2705]||(t[2705]=l('
julia
mlirRewriterBaseSetInsertionPointBefore(rewriter, op)
Sets the insertion point to the specified operation, which will cause subsequent insertions to go right before it.
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',3))]),e("details",Fg,[e("summary",null,[t[2706]||(t[2706]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd")],-1)),t[2707]||(t[2707]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2708]||(t[2708]=l('
julia
mlirRewriterBaseSetInsertionPointToEnd(rewriter, block)
Sets the insertion point to the end of the specified block.
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',3))]),e("details",Pg,[e("summary",null,[t[2709]||(t[2709]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart")],-1)),t[2710]||(t[2710]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2711]||(t[2711]=l('
julia
mlirRewriterBaseSetInsertionPointToStart(rewriter, block)
Sets the insertion point to the start of the specified block.
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',3))]),e("details",Dg,[e("summary",null,[t[2712]||(t[2712]=e("a",{id:"Reactant.MLIR.API.mlirRewriterBaseStartOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseStartOpModification-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseStartOpModification")],-1)),t[2713]||(t[2713]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2714]||(t[2714]=l('
julia
mlirRewriterBaseStartOpModification(rewriter, op)
This method is used to notify the rewriter that an in-place operation modification is about to happen. A call to this function must be followed by a call to either finalizeOpModification or cancelOpModification. This is a minor efficiency win (it avoids creating a new operation and removing the old one) but also often allows simpler code in the client.
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',3))]),e("details",Og,[e("summary",null,[t[2715]||(t[2715]=e("a",{id:"Reactant.MLIR.API.mlirSetGlobalDebugType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSetGlobalDebugType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSetGlobalDebugType")],-1)),t[2716]||(t[2716]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2717]||(t[2717]=l('
julia
mlirSetGlobalDebugType(type)
Sets the current debug type, similarly to -debug-only=type in the command-line tools. Note that global debug should be enabled for any output to be produced.
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',3))]),e("details",Bg,[e("summary",null,[t[2718]||(t[2718]=e("a",{id:"Reactant.MLIR.API.mlirSetGlobalDebugTypes-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSetGlobalDebugTypes-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSetGlobalDebugTypes")],-1)),t[2719]||(t[2719]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2720]||(t[2720]=l('
julia
mlirSetGlobalDebugTypes(types, n)
Sets multiple current debug types, similarly to `-debug-only=type1,type2" in the command-line tools. Note that global debug should be enabled for any output to be produced.
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',3))]),e("details",Gg,[e("summary",null,[t[2721]||(t[2721]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDimSize-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDimSize-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDimSize")],-1)),t[2722]||(t[2722]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2723]||(t[2723]=l('
julia
mlirShapedTypeGetDimSize(type, dim)
Returns the dim-th dimension of the given ranked shaped type.
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julia
mlirShapedTypeGetDynamicSize()
Returns the value indicating a dynamic size in a shaped type. Prefer mlirShapedTypeIsDynamicSize to direct comparisons with this value.
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',3))]),e("details",wg,[e("summary",null,[t[2727]||(t[2727]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset-Tuple{}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset")],-1)),t[2728]||(t[2728]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2729]||(t[2729]=l('
julia
mlirShapedTypeGetDynamicStrideOrOffset()
Returns the value indicating a dynamic stride or offset in a shaped type. Prefer mlirShapedTypeGetDynamicStrideOrOffset to direct comparisons with this value.
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',3))]),e("details",Sg,[e("summary",null,[t[2730]||(t[2730]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetElementType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetElementType")],-1)),t[2731]||(t[2731]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2732]||(t[2732]=l('
julia
mlirShapedTypeGetElementType(type)
Returns the element type of the shaped type.
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',3))]),e("details",Ng,[e("summary",null,[t[2733]||(t[2733]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetRank-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetRank")],-1)),t[2734]||(t[2734]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2735]||(t[2735]=l('
julia
mlirShapedTypeGetRank(type)
Returns the rank of the given ranked shaped type.
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',3))]),e("details",Vg,[e("summary",null,[t[2736]||(t[2736]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeHasRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeHasRank-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeHasRank")],-1)),t[2737]||(t[2737]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2738]||(t[2738]=l('
julia
mlirShapedTypeHasRank(type)
Checks whether the given shaped type is ranked.
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',3))]),e("details",qg,[e("summary",null,[t[2739]||(t[2739]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeHasStaticShape-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeHasStaticShape-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeHasStaticShape")],-1)),t[2740]||(t[2740]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2741]||(t[2741]=l('
julia
mlirShapedTypeHasStaticShape(type)
Checks whether the given shaped type has a static shape.
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',3))]),e("details",Ug,[e("summary",null,[t[2742]||(t[2742]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicDim-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicDim-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicDim")],-1)),t[2743]||(t[2743]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2744]||(t[2744]=l('
julia
mlirShapedTypeIsDynamicDim(type, dim)
Checks wither the dim-th dimension of the given shaped type is dynamic.
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',3))]),e("details",Qg,[e("summary",null,[t[2745]||(t[2745]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicSize-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicSize-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicSize")],-1)),t[2746]||(t[2746]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2747]||(t[2747]=l('
julia
mlirShapedTypeIsDynamicSize(size)
Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.
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',3))]),e("details",Wg,[e("summary",null,[t[2748]||(t[2748]=e("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset")],-1)),t[2749]||(t[2749]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2750]||(t[2750]=l('
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)
Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.
source
',3))]),e("details",Hg,[e("summary",null,[t[2751]||(t[2751]=e("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetIndices-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetIndices-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetIndices")],-1)),t[2752]||(t[2752]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2753]||(t[2753]=l('
julia
mlirSparseElementsAttrGetIndices(attr)
Returns the dense elements attribute containing 64-bit integer indices of non-null elements in the given sparse elements attribute.
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',3))]),e("details",Zg,[e("summary",null,[t[2754]||(t[2754]=e("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID")],-1)),t[2755]||(t[2755]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2756]||(t[2756]=l('
julia
mlirSparseElementsAttrGetTypeID()
Returns the typeID of a SparseElements attribute.
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',3))]),e("details",Jg,[e("summary",null,[t[2757]||(t[2757]=e("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetValues-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetValues-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetValues")],-1)),t[2758]||(t[2758]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2759]||(t[2759]=l('
julia
mlirSparseElementsAttrGetValues(attr)
Returns the dense elements attribute containing the non-null elements in the given sparse elements attribute.
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',3))]),e("details",Kg,[e("summary",null,[t[2760]||(t[2760]=e("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttribute-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttribute-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttribute")],-1)),t[2761]||(t[2761]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2762]||(t[2762]=l('
julia
mlirSparseElementsAttribute(shapedType, denseIndices, denseValues)
Creates a sparse elements attribute of the given shape from a list of indices and a list of associated values. Both lists are expected to be dense elements attributes with the same number of elements. The list of indices is expected to contain 64-bit integers. The attribute is created in the same context as the type.
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',3))]),e("details",$g,[e("summary",null,[t[2763]||(t[2763]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet")],-1)),t[2764]||(t[2764]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2765]||(t[2765]=l('
julia
mlirSparseTensorEncodingAttrGet(ctx, lvlRank, lvlTypes, dimToLvl, lvlTodim, posWidth, crdWidth, explicitVal, implicitVal)
Creates a sparse\\_tensor.encoding attribute with the given parameters.
source
',3))]),e("details",Xg,[e("summary",null,[t[2766]||(t[2766]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth")],-1)),t[2767]||(t[2767]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2768]||(t[2768]=l('
julia
mlirSparseTensorEncodingAttrGetCrdWidth(attr)
Returns the coordinate bitwidth of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",Yg,[e("summary",null,[t[2769]||(t[2769]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl")],-1)),t[2770]||(t[2770]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2771]||(t[2771]=l('
julia
mlirSparseTensorEncodingAttrGetDimToLvl(attr)
Returns the dimension-to-level mapping of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",_g,[e("summary",null,[t[2772]||(t[2772]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal")],-1)),t[2773]||(t[2773]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2774]||(t[2774]=l('
julia
mlirSparseTensorEncodingAttrGetExplicitVal(attr)
Returns the explicit value of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",ty,[e("summary",null,[t[2775]||(t[2775]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal")],-1)),t[2776]||(t[2776]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2777]||(t[2777]=l('
julia
mlirSparseTensorEncodingAttrGetImplicitVal(attr)
Returns the implicit value of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",ey,[e("summary",null,[t[2778]||(t[2778]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt")],-1)),t[2779]||(t[2779]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2780]||(t[2780]=l('
julia
mlirSparseTensorEncodingAttrGetLvlFmt(attr, lvl)
Returns a specified level-format of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",sy,[e("summary",null,[t[2781]||(t[2781]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim")],-1)),t[2782]||(t[2782]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2783]||(t[2783]=l('
julia
mlirSparseTensorEncodingAttrGetLvlToDim(attr)
Returns the level-to-dimension mapping of the sparse\\_tensor.encoding attribute.
source
',3))]),e("details",ay,[e("summary",null,[t[2784]||(t[2784]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType")],-1)),t[2785]||(t[2785]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2786]||(t[2786]=l('
julia
mlirSparseTensorEncodingAttrGetLvlType(attr, lvl)
Returns a specified level-type of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",iy,[e("summary",null,[t[2787]||(t[2787]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth")],-1)),t[2788]||(t[2788]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2789]||(t[2789]=l('
julia
mlirSparseTensorEncodingAttrGetPosWidth(attr)
Returns the position bitwidth of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",ly,[e("summary",null,[t[2790]||(t[2790]=e("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank")],-1)),t[2791]||(t[2791]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2792]||(t[2792]=l('
julia
mlirSparseTensorEncodingGetLvlRank(attr)
Returns the level-rank of the sparse\\_tensor.encoding attribute.
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',3))]),e("details",ny,[e("summary",null,[t[2793]||(t[2793]=e("a",{id:"Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID")],-1)),t[2794]||(t[2794]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2795]||(t[2795]=l('
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mlirStridedLayoutAttrGetTypeID()
Returns the typeID of a StridedLayout attribute.
source
',3))]),e("details",py,[e("summary",null,[t[2796]||(t[2796]=e("a",{id:"Reactant.MLIR.API.mlirStringAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringAttrGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGet")],-1)),t[2797]||(t[2797]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2798]||(t[2798]=l('
julia
mlirStringAttrGet(ctx, str)
Creates a string attribute in the given context containing the given string.
source
',3))]),e("details",ry,[e("summary",null,[t[2799]||(t[2799]=e("a",{id:"Reactant.MLIR.API.mlirStringAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirStringAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGetTypeID")],-1)),t[2800]||(t[2800]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2801]||(t[2801]=l('
julia
mlirStringAttrGetTypeID()
Returns the typeID of a String attribute.
source
',3))]),e("details",oy,[e("summary",null,[t[2802]||(t[2802]=e("a",{id:"Reactant.MLIR.API.mlirStringAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirStringAttrGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGetValue")],-1)),t[2803]||(t[2803]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2804]||(t[2804]=l('
julia
mlirStringAttrGetValue(attr)
Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.
source
',3))]),e("details",dy,[e("summary",null,[t[2805]||(t[2805]=e("a",{id:"Reactant.MLIR.API.mlirStringAttrTypedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringAttrTypedGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrTypedGet")],-1)),t[2806]||(t[2806]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2807]||(t[2807]=l('
julia
mlirStringAttrTypedGet(type, str)
Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.
source
',3))]),e("details",hy,[e("summary",null,[t[2808]||(t[2808]=e("a",{id:"Reactant.MLIR.API.mlirStringRefCreate-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringRefCreate-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefCreate")],-1)),t[2809]||(t[2809]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2810]||(t[2810]=l('
julia
mlirStringRefCreate(str, length)
Constructs a string reference from the pointer and length. The pointer need not reference to a null-terminated string.
source
',3))]),e("details",cy,[e("summary",null,[t[2811]||(t[2811]=e("a",{id:"Reactant.MLIR.API.mlirStringRefCreateFromCString-Tuple{Any}",href:"#Reactant.MLIR.API.mlirStringRefCreateFromCString-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefCreateFromCString")],-1)),t[2812]||(t[2812]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2813]||(t[2813]=l('
julia
mlirStringRefCreateFromCString(str)
Constructs a string reference from a null-terminated C string. Prefer mlirStringRefCreate if the length of the string is known.
source
',3))]),e("details",uy,[e("summary",null,[t[2814]||(t[2814]=e("a",{id:"Reactant.MLIR.API.mlirStringRefEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringRefEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefEqual")],-1)),t[2815]||(t[2815]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2816]||(t[2816]=l('
julia
mlirStringRefEqual(string, other)
Returns true if two string references are equal, false otherwise.
source
',3))]),e("details",by,[e("summary",null,[t[2817]||(t[2817]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGet-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGet")],-1)),t[2818]||(t[2818]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2819]||(t[2819]=l('
julia
mlirSymbolRefAttrGet(ctx, symbol, numReferences, references)
Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.
source
',3))]),e("details",gy,[e("summary",null,[t[2820]||(t[2820]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference")],-1)),t[2821]||(t[2821]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2822]||(t[2822]=l('
julia
mlirSymbolRefAttrGetLeafReference(attr)
Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.
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',3))]),e("details",yy,[e("summary",null,[t[2823]||(t[2823]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference")],-1)),t[2824]||(t[2824]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2825]||(t[2825]=l('
julia
mlirSymbolRefAttrGetNestedReference(attr, pos)
Returns pos-th reference nested in the given symbol reference attribute.
source
',3))]),e("details",my,[e("summary",null,[t[2826]||(t[2826]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences")],-1)),t[2827]||(t[2827]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2828]||(t[2828]=l('
julia
mlirSymbolRefAttrGetNumNestedReferences(attr)
Returns the number of references nested in the given symbol reference attribute.
source
',3))]),e("details",ky,[e("summary",null,[t[2829]||(t[2829]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference")],-1)),t[2830]||(t[2830]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2831]||(t[2831]=l('
julia
mlirSymbolRefAttrGetRootReference(attr)
Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.
source
',3))]),e("details",Ry,[e("summary",null,[t[2832]||(t[2832]=e("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID")],-1)),t[2833]||(t[2833]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2834]||(t[2834]=l('
julia
mlirSymbolRefAttrGetTypeID()
Returns the typeID of an SymbolRef attribute.
source
',3))]),e("details",fy,[e("summary",null,[t[2835]||(t[2835]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableCreate-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableCreate")],-1)),t[2836]||(t[2836]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2837]||(t[2837]=l('
julia
mlirSymbolTableCreate(operation)
Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.
source
',3))]),e("details",Iy,[e("summary",null,[t[2838]||(t[2838]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableDestroy")],-1)),t[2839]||(t[2839]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2840]||(t[2840]=l('
julia
mlirSymbolTableDestroy(symbolTable)
Destroys the symbol table created with mlirSymbolTableCreate. This does not affect the operations in the table.
source
',3))]),e("details",jy,[e("summary",null,[t[2841]||(t[2841]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableErase-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableErase-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableErase")],-1)),t[2842]||(t[2842]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2843]||(t[2843]=l('
julia
mlirSymbolTableErase(symbolTable, operation)
Removes the given operation from the symbol table and erases it.
source
',3))]),e("details",My,[e("summary",null,[t[2844]||(t[2844]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName")],-1)),t[2845]||(t[2845]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2846]||(t[2846]=l('
julia
mlirSymbolTableGetSymbolAttributeName()
Returns the name of the attribute used to store symbol names compatible with symbol tables.
source
',3))]),e("details",Ay,[e("summary",null,[t[2847]||(t[2847]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName")],-1)),t[2848]||(t[2848]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2849]||(t[2849]=l('
julia
mlirSymbolTableGetVisibilityAttributeName()
Returns the name of the attribute used to store symbol visibility.
source
',3))]),e("details",Ly,[e("summary",null,[t[2850]||(t[2850]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableInsert-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableInsert-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableInsert")],-1)),t[2851]||(t[2851]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2852]||(t[2852]=l('
julia
mlirSymbolTableInsert(symbolTable, operation)
Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.
source
',3))]),e("details",Ey,[e("summary",null,[t[2853]||(t[2853]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableIsNull")],-1)),t[2854]||(t[2854]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2855]||(t[2855]=l('
julia
mlirSymbolTableIsNull(symbolTable)
Returns true if the symbol table is null.
source
',3))]),e("details",vy,[e("summary",null,[t[2856]||(t[2856]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableLookup-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableLookup-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableLookup")],-1)),t[2857]||(t[2857]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2858]||(t[2858]=l('
julia
mlirSymbolTableLookup(symbolTable, name)
Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.
source
',3))]),e("details",Ty,[e("summary",null,[t[2859]||(t[2859]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses")],-1)),t[2860]||(t[2860]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2861]||(t[2861]=l('
julia
mlirSymbolTableReplaceAllSymbolUses(oldSymbol, newSymbol, from)
Attempt to replace all uses that are nested within the given operation of the given symbol 'oldSymbol' with the provided 'newSymbol'. This does not traverse into nested symbol tables. Will fail atomically if there are any unknown operations that may be potential symbol tables.
source
',3))]),e("details",Cy,[e("summary",null,[t[2862]||(t[2862]=e("a",{id:"Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables")],-1)),t[2863]||(t[2863]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2864]||(t[2864]=l('
julia
mlirSymbolTableWalkSymbolTables(from, allSymUsesVisible, callback, userData)
Walks all symbol table operations nested within, and including, op. For each symbol table operation, the provided callback is invoked with the op and a boolean signifying if the symbols within that symbol table can be treated as if all uses within the IR are visible to the caller. allSymUsesVisible identifies whether all of the symbol uses of symbols within op are visible.
source
',3))]),e("details",xy,[e("summary",null,[t[2865]||(t[2865]=e("a",{id:"Reactant.MLIR.API.mlirTF32TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTF32TypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTF32TypeGet")],-1)),t[2866]||(t[2866]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2867]||(t[2867]=l('
julia
mlirTF32TypeGet(ctx)
Creates a TF32 type in the given context. The type is owned by the context.
source
',3))]),e("details",Fy,[e("summary",null,[t[2868]||(t[2868]=e("a",{id:"Reactant.MLIR.API.mlirTransformApplyNamedSequence-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirTransformApplyNamedSequence-NTuple{4, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformApplyNamedSequence")],-1)),t[2869]||(t[2869]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2870]||(t[2870]=l('
julia
mlirTransformApplyNamedSequence(payload, transformRoot, transformModule, transformOptions)
Applies the transformation script starting at the given transform root operation to the given payload operation. The module containing the transform root as well as the transform options should be provided. The transform operation must implement TransformOpInterface and the module must be a ModuleOp. Returns the status of the application.
source
',3))]),e("details",Py,[e("summary",null,[t[2871]||(t[2871]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirTransformOptionsCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsCreate")],-1)),t[2872]||(t[2872]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2873]||(t[2873]=l('
julia
mlirTransformOptionsCreate()
Creates a default-initialized transform options object.
source
',3))]),e("details",Dy,[e("summary",null,[t[2874]||(t[2874]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsDestroy")],-1)),t[2875]||(t[2875]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2876]||(t[2876]=l('
julia
mlirTransformOptionsDestroy(transformOptions)
Destroys a transform options object previously created by mlirTransformOptionsCreate.
source
',3))]),e("details",Oy,[e("summary",null,[t[2877]||(t[2877]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks")],-1)),t[2878]||(t[2878]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2879]||(t[2879]=l('
julia
mlirTransformOptionsEnableExpensiveChecks(transformOptions, enable)
Enables or disables expensive checks in transform options.
source
',3))]),e("details",By,[e("summary",null,[t[2880]||(t[2880]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp")],-1)),t[2881]||(t[2881]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2882]||(t[2882]=l('
julia
mlirTransformOptionsEnforceSingleTopLevelTransformOp(transformOptions, enable)
Enables or disables the enforcement of the top-level transform op being single in transform options.
source
',3))]),e("details",Gy,[e("summary",null,[t[2883]||(t[2883]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp")],-1)),t[2884]||(t[2884]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2885]||(t[2885]=l('
julia
mlirTransformOptionsGetEnforceSingleTopLevelTransformOp(transformOptions)
Returns true if the enforcement of the top-level transform op being single is enabled in transform options.
source
',3))]),e("details",zy,[e("summary",null,[t[2886]||(t[2886]=e("a",{id:"Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled")],-1)),t[2887]||(t[2887]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2888]||(t[2888]=l('
julia
mlirTransformOptionsGetExpensiveChecksEnabled(transformOptions)
Returns true if expensive checks are enabled in transform options.
source
',3))]),e("details",wy,[e("summary",null,[t[2889]||(t[2889]=e("a",{id:"Reactant.MLIR.API.mlirTranslateModuleToLLVMIR-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTranslateModuleToLLVMIR-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTranslateModuleToLLVMIR")],-1)),t[2890]||(t[2890]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2891]||(t[2891]=l('
julia
mlirTranslateModuleToLLVMIR(_module, context)
Translate operation that satisfies LLVM dialect module requirements into an LLVM IR module living in the given context. This translates operations from any dilalect that has a registered implementation of LLVMTranslationDialectInterface.
Returns
the generated LLVM IR Module from the translated MLIR module, it is owned by the caller.
source
',5))]),e("details",Sy,[e("summary",null,[t[2892]||(t[2892]=e("a",{id:"Reactant.MLIR.API.mlirTupleTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGet-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGet")],-1)),t[2893]||(t[2893]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2894]||(t[2894]=l('
julia
mlirTupleTypeGet(ctx, numElements, elements)
Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.
source
',3))]),e("details",Ny,[e("summary",null,[t[2895]||(t[2895]=e("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetNumTypes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGetNumTypes-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetNumTypes")],-1)),t[2896]||(t[2896]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2897]||(t[2897]=l('
julia
mlirTupleTypeGetNumTypes(type)
Returns the number of types contained in a tuple.
source
',3))]),e("details",Vy,[e("summary",null,[t[2898]||(t[2898]=e("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGetType-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetType")],-1)),t[2899]||(t[2899]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2900]||(t[2900]=l('
julia
mlirTupleTypeGetType(type, pos)
Returns the pos-th type in the tuple type.
source
',3))]),e("details",qy,[e("summary",null,[t[2901]||(t[2901]=e("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirTupleTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetTypeID")],-1)),t[2902]||(t[2902]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2903]||(t[2903]=l('
julia
mlirTupleTypeGetTypeID()
Returns the typeID of an Tuple type.
source
',3))]),e("details",Uy,[e("summary",null,[t[2904]||(t[2904]=e("a",{id:"Reactant.MLIR.API.mlirTypeAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeAttrGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGet")],-1)),t[2905]||(t[2905]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2906]||(t[2906]=l('
julia
mlirTypeAttrGet(type)
Creates a type attribute wrapping the given type in the same context as the type.
source
',3))]),e("details",Qy,[e("summary",null,[t[2907]||(t[2907]=e("a",{id:"Reactant.MLIR.API.mlirTypeAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirTypeAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGetTypeID")],-1)),t[2908]||(t[2908]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2909]||(t[2909]=l('
julia
mlirTypeAttrGetTypeID()
Returns the typeID of a Type attribute.
source
',3))]),e("details",Wy,[e("summary",null,[t[2910]||(t[2910]=e("a",{id:"Reactant.MLIR.API.mlirTypeAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeAttrGetValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGetValue")],-1)),t[2911]||(t[2911]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2912]||(t[2912]=l('
julia
mlirTypeAttrGetValue(attr)
Returns the type stored in the given type attribute.
source
',3))]),e("details",Hy,[e("summary",null,[t[2913]||(t[2913]=e("a",{id:"Reactant.MLIR.API.mlirTypeDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeDump-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeDump")],-1)),t[2914]||(t[2914]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2915]||(t[2915]=l('
julia
mlirTypeDump(type)
Prints the type to the standard error stream.
source
',3))]),e("details",Zy,[e("summary",null,[t[2916]||(t[2916]=e("a",{id:"Reactant.MLIR.API.mlirTypeEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeEqual")],-1)),t[2917]||(t[2917]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2918]||(t[2918]=l('
julia
mlirTypeEqual(t1, t2)
Checks if two types are equal.
source
',3))]),e("details",Jy,[e("summary",null,[t[2919]||(t[2919]=e("a",{id:"Reactant.MLIR.API.mlirTypeGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetContext-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetContext")],-1)),t[2920]||(t[2920]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2921]||(t[2921]=l('
julia
mlirTypeGetContext(type)
Gets the context that a type was created with.
source
',3))]),e("details",Ky,[e("summary",null,[t[2922]||(t[2922]=e("a",{id:"Reactant.MLIR.API.mlirTypeGetDialect-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetDialect-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetDialect")],-1)),t[2923]||(t[2923]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2924]||(t[2924]=l('
julia
mlirTypeGetDialect(type)
Gets the dialect a type belongs to.
source
',3))]),e("details",$y,[e("summary",null,[t[2925]||(t[2925]=e("a",{id:"Reactant.MLIR.API.mlirTypeGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetTypeID-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetTypeID")],-1)),t[2926]||(t[2926]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2927]||(t[2927]=l('
julia
mlirTypeGetTypeID(type)
Gets the type ID of the type.
source
',3))]),e("details",Xy,[e("summary",null,[t[2928]||(t[2928]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID")],-1)),t[2929]||(t[2929]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2930]||(t[2930]=l('
julia
mlirTypeIDAllocatorAllocateTypeID(allocator)
Allocates a type id that is valid for the lifetime of the allocator
source
',3))]),e("details",Yy,[e("summary",null,[t[2931]||(t[2931]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorCreate-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorCreate")],-1)),t[2932]||(t[2932]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2933]||(t[2933]=l('
julia
mlirTypeIDAllocatorCreate()
Creates a type id allocator for dynamic type id creation
source
',3))]),e("details",_y,[e("summary",null,[t[2934]||(t[2934]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorDestroy-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorDestroy")],-1)),t[2935]||(t[2935]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2936]||(t[2936]=l('
julia
mlirTypeIDAllocatorDestroy(allocator)
Deallocates the allocator and all allocated type ids
source
',3))]),e("details",tm,[e("summary",null,[t[2937]||(t[2937]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDCreate-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDCreate")],-1)),t[2938]||(t[2938]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2939]||(t[2939]=l('
julia
mlirTypeIDCreate(ptr)
ptr must be 8 byte aligned and unique to a type valid for the duration of the returned type id's usage
source
',3))]),e("details",em,[e("summary",null,[t[2940]||(t[2940]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeIDEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDEqual")],-1)),t[2941]||(t[2941]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2942]||(t[2942]=l('
julia
mlirTypeIDEqual(typeID1, typeID2)
Checks if two type ids are equal.
source
',3))]),e("details",sm,[e("summary",null,[t[2943]||(t[2943]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDHashValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDHashValue-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDHashValue")],-1)),t[2944]||(t[2944]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2945]||(t[2945]=l('
julia
mlirTypeIDHashValue(typeID)
Returns the hash value of the type id.
source
',3))]),e("details",am,[e("summary",null,[t[2946]||(t[2946]=e("a",{id:"Reactant.MLIR.API.mlirTypeIDIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDIsNull")],-1)),t[2947]||(t[2947]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2948]||(t[2948]=l('
julia
mlirTypeIDIsNull(typeID)
Checks whether a type id is null.
source
',3))]),e("details",im,[e("summary",null,[t[2949]||(t[2949]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType")],-1)),t[2950]||(t[2950]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2951]||(t[2951]=l('
julia
mlirTypeIsAAnyQuantizedType(type)
Returns true if the given type is an AnyQuantizedType.
source
',3))]),e("details",lm,[e("summary",null,[t[2952]||(t[2952]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsABF16-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsABF16-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsABF16")],-1)),t[2953]||(t[2953]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2954]||(t[2954]=l('
julia
mlirTypeIsABF16(type)
Checks whether the given type is a bf16 type.
source
',3))]),e("details",nm,[e("summary",null,[t[2955]||(t[2955]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType")],-1)),t[2956]||(t[2956]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2957]||(t[2957]=l('
julia
mlirTypeIsACalibratedQuantizedType(type)
Returns true if the given type is a CalibratedQuantizedType.
source
',3))]),e("details",pm,[e("summary",null,[t[2958]||(t[2958]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAComplex-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAComplex-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAComplex")],-1)),t[2959]||(t[2959]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2960]||(t[2960]=l('
julia
mlirTypeIsAComplex(type)
Checks whether the given type is a Complex type.
source
',3))]),e("details",rm,[e("summary",null,[t[2961]||(t[2961]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAF16-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF16-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF16")],-1)),t[2962]||(t[2962]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2963]||(t[2963]=l('
julia
mlirTypeIsAF16(type)
Checks whether the given type is an f16 type.
source
',3))]),e("details",om,[e("summary",null,[t[2964]||(t[2964]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAF32-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF32-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF32")],-1)),t[2965]||(t[2965]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2966]||(t[2966]=l('
julia
mlirTypeIsAF32(type)
Checks whether the given type is an f32 type.
source
',3))]),e("details",dm,[e("summary",null,[t[2967]||(t[2967]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAF64-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF64-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF64")],-1)),t[2968]||(t[2968]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2969]||(t[2969]=l('
julia
mlirTypeIsAF64(type)
Checks whether the given type is an f64 type.
source
',3))]),e("details",hm,[e("summary",null,[t[2970]||(t[2970]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat")],-1)),t[2971]||(t[2971]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2972]||(t[2972]=l('
julia
mlirTypeIsAFloat(type)
Checks whether the given type is a floating-point type.
source
',3))]),e("details",cm,[e("summary",null,[t[2973]||(t[2973]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN")],-1)),t[2974]||(t[2974]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2975]||(t[2975]=l('
julia
mlirTypeIsAFloat4E2M1FN(type)
Checks whether the given type is an f4E2M1FN type.
source
',3))]),e("details",um,[e("summary",null,[t[2976]||(t[2976]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN")],-1)),t[2977]||(t[2977]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2978]||(t[2978]=l('
julia
mlirTypeIsAFloat6E2M3FN(type)
Checks whether the given type is an f6E2M3FN type.
source
',3))]),e("details",bm,[e("summary",null,[t[2979]||(t[2979]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN")],-1)),t[2980]||(t[2980]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2981]||(t[2981]=l('
julia
mlirTypeIsAFloat6E3M2FN(type)
Checks whether the given type is an f6E3M2FN type.
source
',3))]),e("details",gm,[e("summary",null,[t[2982]||(t[2982]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E3M4-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E3M4-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E3M4")],-1)),t[2983]||(t[2983]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2984]||(t[2984]=l('
julia
mlirTypeIsAFloat8E3M4(type)
Checks whether the given type is an f8E3M4 type.
source
',3))]),e("details",ym,[e("summary",null,[t[2985]||(t[2985]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3")],-1)),t[2986]||(t[2986]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2987]||(t[2987]=l('
julia
mlirTypeIsAFloat8E4M3(type)
Checks whether the given type is an f8E4M3 type.
source
',3))]),e("details",mm,[e("summary",null,[t[2988]||(t[2988]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ")],-1)),t[2989]||(t[2989]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2990]||(t[2990]=l('
julia
mlirTypeIsAFloat8E4M3B11FNUZ(type)
Checks whether the given type is an f8E4M3B11FNUZ type.
source
',3))]),e("details",km,[e("summary",null,[t[2991]||(t[2991]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN")],-1)),t[2992]||(t[2992]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2993]||(t[2993]=l('
julia
mlirTypeIsAFloat8E4M3FN(type)
Checks whether the given type is an f8E4M3FN type.
source
',3))]),e("details",Rm,[e("summary",null,[t[2994]||(t[2994]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ")],-1)),t[2995]||(t[2995]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2996]||(t[2996]=l('
julia
mlirTypeIsAFloat8E4M3FNUZ(type)
Checks whether the given type is an f8E4M3FNUZ type.
source
',3))]),e("details",fm,[e("summary",null,[t[2997]||(t[2997]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E5M2-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2")],-1)),t[2998]||(t[2998]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[2999]||(t[2999]=l('
julia
mlirTypeIsAFloat8E5M2(type)
Checks whether the given type is an f8E5M2 type.
source
',3))]),e("details",Im,[e("summary",null,[t[3e3]||(t[3e3]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ")],-1)),t[3001]||(t[3001]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3002]||(t[3002]=l('
julia
mlirTypeIsAFloat8E5M2FNUZ(type)
Checks whether the given type is an f8E5M2FNUZ type.
source
',3))]),e("details",jm,[e("summary",null,[t[3003]||(t[3003]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU")],-1)),t[3004]||(t[3004]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3005]||(t[3005]=l('
julia
mlirTypeIsAFloat8E8M0FNU(type)
Checks whether the given type is an f8E8M0FNU type.
source
',3))]),e("details",Mm,[e("summary",null,[t[3006]||(t[3006]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAFunction-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFunction-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFunction")],-1)),t[3007]||(t[3007]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3008]||(t[3008]=l('
julia
mlirTypeIsAFunction(type)
Checks whether the given type is a function type.
source
',3))]),e("details",Am,[e("summary",null,[t[3009]||(t[3009]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAIndex-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAIndex-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAIndex")],-1)),t[3010]||(t[3010]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3011]||(t[3011]=l('
julia
mlirTypeIsAIndex(type)
Checks whether the given type is an index type.
source
',3))]),e("details",Lm,[e("summary",null,[t[3012]||(t[3012]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAInteger-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAInteger-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAInteger")],-1)),t[3013]||(t[3013]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3014]||(t[3014]=l('
julia
mlirTypeIsAInteger(type)
Checks whether the given type is an integer type.
source
',3))]),e("details",Em,[e("summary",null,[t[3015]||(t[3015]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsALLVMPointerType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsALLVMPointerType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsALLVMPointerType")],-1)),t[3016]||(t[3016]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3017]||(t[3017]=l('
julia
mlirTypeIsALLVMPointerType(type)
Returns true if the type is an LLVM dialect pointer type.
source
',3))]),e("details",vm,[e("summary",null,[t[3018]||(t[3018]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsALLVMStructType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsALLVMStructType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsALLVMStructType")],-1)),t[3019]||(t[3019]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3020]||(t[3020]=l('
julia
mlirTypeIsALLVMStructType(type)
Returns true if the type is an LLVM dialect struct type.
source
',3))]),e("details",Tm,[e("summary",null,[t[3021]||(t[3021]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAMemRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAMemRef-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAMemRef")],-1)),t[3022]||(t[3022]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3023]||(t[3023]=l('
julia
mlirTypeIsAMemRef(type)
Checks whether the given type is a MemRef type.
source
',3))]),e("details",Cm,[e("summary",null,[t[3024]||(t[3024]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsANone-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsANone-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsANone")],-1)),t[3025]||(t[3025]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3026]||(t[3026]=l('
julia
mlirTypeIsANone(type)
Checks whether the given type is a None type.
source
',3))]),e("details",xm,[e("summary",null,[t[3027]||(t[3027]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAOpaque-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAOpaque")],-1)),t[3028]||(t[3028]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3029]||(t[3029]=l('
julia
mlirTypeIsAOpaque(type)
Checks whether the given type is an opaque type.
source
',3))]),e("details",Fm,[e("summary",null,[t[3030]||(t[3030]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAQuantizedType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAQuantizedType")],-1)),t[3031]||(t[3031]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3032]||(t[3032]=l('
julia
mlirTypeIsAQuantizedType(type)
Returns true if the given type is a quantization dialect type.
source
',3))]),e("details",Pm,[e("summary",null,[t[3033]||(t[3033]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsARankedTensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsARankedTensor-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsARankedTensor")],-1)),t[3034]||(t[3034]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3035]||(t[3035]=l('
julia
mlirTypeIsARankedTensor(type)
Checks whether the given type is a ranked tensor type.
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',3))]),e("details",Dm,[e("summary",null,[t[3036]||(t[3036]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAShaped-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAShaped-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAShaped")],-1)),t[3037]||(t[3037]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3038]||(t[3038]=l('
julia
mlirTypeIsAShaped(type)
Checks whether the given type is a Shaped type.
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',3))]),e("details",Om,[e("summary",null,[t[3039]||(t[3039]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsATF32-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATF32-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATF32")],-1)),t[3040]||(t[3040]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3041]||(t[3041]=l('
julia
mlirTypeIsATF32(type)
Checks whether the given type is an TF32 type.
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',3))]),e("details",Bm,[e("summary",null,[t[3042]||(t[3042]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsATensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATensor-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATensor")],-1)),t[3043]||(t[3043]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3044]||(t[3044]=l('
julia
mlirTypeIsATensor(type)
Checks whether the given type is a Tensor type.
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',3))]),e("details",Gm,[e("summary",null,[t[3045]||(t[3045]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsATuple-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATuple-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATuple")],-1)),t[3046]||(t[3046]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3047]||(t[3047]=l('
julia
mlirTypeIsATuple(type)
Checks whether the given type is a tuple type.
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',3))]),e("details",zm,[e("summary",null,[t[3048]||(t[3048]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType")],-1)),t[3049]||(t[3049]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3050]||(t[3050]=l('
julia
mlirTypeIsAUniformQuantizedPerAxisType(type)
Returns true if the given type is a UniformQuantizedPerAxisType.
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julia
mlirTypeIsAUniformQuantizedType(type)
Returns true if the given type is a UniformQuantizedType.
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',3))]),e("details",Sm,[e("summary",null,[t[3054]||(t[3054]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef")],-1)),t[3055]||(t[3055]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3056]||(t[3056]=l('
julia
mlirTypeIsAUnrankedMemRef(type)
Checks whether the given type is an UnrankedMemRef type.
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',3))]),e("details",Nm,[e("summary",null,[t[3057]||(t[3057]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAUnrankedTensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUnrankedTensor-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUnrankedTensor")],-1)),t[3058]||(t[3058]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3059]||(t[3059]=l('
julia
mlirTypeIsAUnrankedTensor(type)
Checks whether the given type is an unranked tensor type.
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',3))]),e("details",Vm,[e("summary",null,[t[3060]||(t[3060]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsAVector-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAVector-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAVector")],-1)),t[3061]||(t[3061]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3062]||(t[3062]=l('
julia
mlirTypeIsAVector(type)
Checks whether the given type is a Vector type.
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',3))]),e("details",qm,[e("summary",null,[t[3063]||(t[3063]=e("a",{id:"Reactant.MLIR.API.mlirTypeIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsNull-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsNull")],-1)),t[3064]||(t[3064]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3065]||(t[3065]=l('
julia
mlirTypeIsNull(type)
Checks whether a type is null.
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',3))]),e("details",Um,[e("summary",null,[t[3066]||(t[3066]=e("a",{id:"Reactant.MLIR.API.mlirTypeParseGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeParseGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeParseGet")],-1)),t[3067]||(t[3067]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3068]||(t[3068]=l('
julia
mlirTypeParseGet(context, type)
Parses a type. The type is owned by the context.
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',3))]),e("details",Qm,[e("summary",null,[t[3069]||(t[3069]=e("a",{id:"Reactant.MLIR.API.mlirTypePrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirTypePrint-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypePrint")],-1)),t[3070]||(t[3070]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3071]||(t[3071]=l('
julia
mlirTypePrint(type, callback, userData)
Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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',3))]),e("details",Wm,[e("summary",null,[t[3072]||(t[3072]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet-NTuple{9, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet")],-1)),t[3073]||(t[3073]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3074]||(t[3074]=l('
julia
mlirUniformQuantizedPerAxisTypeGet(flags, storageType, expressedType, nDims, scales, zeroPoints, quantizedDimension, storageTypeMin, storageTypeMax)
Creates an instance of UniformQuantizedPerAxisType with the given parameters in the same context as storageType and returns it. scales and zeroPoints point to nDims number of elements. The instance is owned by the context.
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',3))]),e("details",Hm,[e("summary",null,[t[3075]||(t[3075]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims")],-1)),t[3076]||(t[3076]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3077]||(t[3077]=l('
julia
mlirUniformQuantizedPerAxisTypeGetNumDims(type)
Returns the number of axes in the given quantized per-axis type.
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',3))]),e("details",Zm,[e("summary",null,[t[3078]||(t[3078]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension")],-1)),t[3079]||(t[3079]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3080]||(t[3080]=l('
julia
mlirUniformQuantizedPerAxisTypeGetQuantizedDimension(type)
Returns the index of the quantized dimension in the given quantized per-axis type.
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julia
mlirUniformQuantizedPerAxisTypeGetScale(type, pos)
Returns pos-th scale of the given quantized per-axis type.
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julia
mlirUniformQuantizedPerAxisTypeGetZeroPoint(type, pos)
Returns pos-th zero point of the given quantized per-axis type.
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',3))]),e("details",$m,[e("summary",null,[t[3087]||(t[3087]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint")],-1)),t[3088]||(t[3088]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3089]||(t[3089]=l('
julia
mlirUniformQuantizedPerAxisTypeIsFixedPoint(type)
Returns true if the given uniform quantized per-axis type is fixed-point.
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',3))]),e("details",Xm,[e("summary",null,[t[3090]||(t[3090]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGet-NTuple{7, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGet-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGet")],-1)),t[3091]||(t[3091]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3092]||(t[3092]=l('
julia
mlirUniformQuantizedTypeGet(flags, storageType, expressedType, scale, zeroPoint, storageTypeMin, storageTypeMax)
Creates an instance of UniformQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.
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',3))]),e("details",Ym,[e("summary",null,[t[3093]||(t[3093]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale")],-1)),t[3094]||(t[3094]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3095]||(t[3095]=l('
julia
mlirUniformQuantizedTypeGetScale(type)
Returns the scale of the given uniform quantized type.
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',3))]),e("details",_m,[e("summary",null,[t[3096]||(t[3096]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint")],-1)),t[3097]||(t[3097]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3098]||(t[3098]=l('
julia
mlirUniformQuantizedTypeGetZeroPoint(type)
Returns the zero point of the given uniform quantized type.
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',3))]),e("details",tk,[e("summary",null,[t[3099]||(t[3099]=e("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint")],-1)),t[3100]||(t[3100]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3101]||(t[3101]=l('
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mlirUniformQuantizedTypeIsFixedPoint(type)
Returns true if the given uniform quantized type is fixed-point.
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',3))]),e("details",ek,[e("summary",null,[t[3102]||(t[3102]=e("a",{id:"Reactant.MLIR.API.mlirUnitAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnitAttrGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnitAttrGet")],-1)),t[3103]||(t[3103]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3104]||(t[3104]=l('
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mlirUnitAttrGet(ctx)
Creates a unit attribute in the given context.
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',3))]),e("details",sk,[e("summary",null,[t[3105]||(t[3105]=e("a",{id:"Reactant.MLIR.API.mlirUnitAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnitAttrGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnitAttrGetTypeID")],-1)),t[3106]||(t[3106]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3107]||(t[3107]=l('
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mlirUnitAttrGetTypeID()
Returns the typeID of a Unit attribute.
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',3))]),e("details",ak,[e("summary",null,[t[3108]||(t[3108]=e("a",{id:"Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet-NTuple{8, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet")],-1)),t[3109]||(t[3109]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3110]||(t[3110]=l('
julia
mlirUnmanagedDenseResourceElementsAttrGet(shapedType, name, data, dataLength, dataAlignment, dataIsMutable, deleter, userData)
Unlike the typed accessors below, constructs the attribute with a raw data buffer and no type/alignment checking. Use a more strongly typed accessor if possible. If dataIsMutable is false, then an immutable AsmResourceBlob will be created and that passed data contents will be treated as const. If the deleter is non NULL, then it will be called when the data buffer can no longer be accessed (passing userData to it).
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',3))]),e("details",ik,[e("summary",null,[t[3111]||(t[3111]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGet-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGet")],-1)),t[3112]||(t[3112]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3113]||(t[3113]=l('
julia
mlirUnrankedMemRefTypeGet(elementType, memorySpace)
Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type.
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',3))]),e("details",lk,[e("summary",null,[t[3114]||(t[3114]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked")],-1)),t[3115]||(t[3115]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3116]||(t[3116]=l('
julia
mlirUnrankedMemRefTypeGetChecked(loc, elementType, memorySpace)
Same as "mlirUnrankedMemRefTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
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',3))]),e("details",nk,[e("summary",null,[t[3117]||(t[3117]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID")],-1)),t[3118]||(t[3118]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3119]||(t[3119]=l('
julia
mlirUnrankedMemRefTypeGetTypeID()
Returns the typeID of an UnrankedMemRef type.
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',3))]),e("details",pk,[e("summary",null,[t[3120]||(t[3120]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace")],-1)),t[3121]||(t[3121]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3122]||(t[3122]=l('
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mlirUnrankedMemrefGetMemorySpace(type)
Returns the memory spcae of the given Unranked MemRef type.
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',3))]),e("details",rk,[e("summary",null,[t[3123]||(t[3123]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGet-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGet")],-1)),t[3124]||(t[3124]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3125]||(t[3125]=l('
julia
mlirUnrankedTensorTypeGet(elementType)
Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context.
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',3))]),e("details",ok,[e("summary",null,[t[3126]||(t[3126]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked")],-1)),t[3127]||(t[3127]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3128]||(t[3128]=l('
julia
mlirUnrankedTensorTypeGetChecked(loc, elementType)
Same as "mlirUnrankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
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',3))]),e("details",dk,[e("summary",null,[t[3129]||(t[3129]=e("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID")],-1)),t[3130]||(t[3130]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3131]||(t[3131]=l('
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mlirUnrankedTensorTypeGetTypeID()
Returns the typeID of an UnrankedTensor type.
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mlirValueDump(value)
Prints the value to the standard error stream.
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',3))]),e("details",ck,[e("summary",null,[t[3135]||(t[3135]=e("a",{id:"Reactant.MLIR.API.mlirValueEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirValueEqual-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueEqual")],-1)),t[3136]||(t[3136]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3137]||(t[3137]=l('
julia
mlirValueEqual(value1, value2)
Returns 1 if two values are equal, 0 otherwise.
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julia
mlirValueGetFirstUse(value)
Returns an op operand representing the first use of the value, or a null op operand if there are no uses.
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',3))]),e("details",bk,[e("summary",null,[t[3141]||(t[3141]=e("a",{id:"Reactant.MLIR.API.mlirValueGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueGetType-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueGetType")],-1)),t[3142]||(t[3142]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3143]||(t[3143]=l('
julia
mlirValueGetType(value)
Returns the type of the value.
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',3))]),e("details",gk,[e("summary",null,[t[3144]||(t[3144]=e("a",{id:"Reactant.MLIR.API.mlirValueIsABlockArgument-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueIsABlockArgument-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueIsABlockArgument")],-1)),t[3145]||(t[3145]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3146]||(t[3146]=l('
julia
mlirValueIsABlockArgument(value)
Returns 1 if the value is a block argument, 0 otherwise.
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julia
mlirValueIsAOpResult(value)
Returns 1 if the value is an operation result, 0 otherwise.
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mlirValueIsNull(value)
Returns whether the value is null.
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mlirValuePrint(value, callback, userData)
Prints a value by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.
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mlirValuePrintAsOperand(value, state, callback, userData)
Prints a value as an operand (i.e., the ValueID).
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mlirValueReplaceAllUsesExcept(of, with, numExceptions, exceptions)
Replace all uses of 'of' value with 'with' value, updating anything in the IR that uses 'of' to use 'with' instead, except if the user is listed in 'exceptions'. The 'exceptions' parameter is an array of MlirOperation pointers with a length of 'numExceptions'.
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mlirValueReplaceAllUsesOfWith(of, with)
Replace all uses of 'of' value with the 'with' value, updating anything in the IR that uses 'of' to use the other value instead. When this returns there are zero uses of 'of'.
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mlirValueSetType(value, type)
Set the type of the value.
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julia
mlirVectorTypeGet(rank, shape, elementType)
Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context.
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julia
mlirVectorTypeGetChecked(loc, rank, shape, elementType)
Same as "mlirVectorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
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mlirVectorTypeGetScalable(rank, shape, scalable, elementType)
Creates a scalable vector type with the shape identified by its rank and dimensions. A subset of dimensions may be marked as scalable via the corresponding flag list, which is expected to have as many entries as the rank of the vector. The vector is created in the same context as the element type.
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',3))]),e("details",Ek,[e("summary",null,[t[3177]||(t[3177]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetScalableChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeGetScalableChecked-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetScalableChecked")],-1)),t[3178]||(t[3178]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3179]||(t[3179]=l('
julia
mlirVectorTypeGetScalableChecked(loc, rank, shape, scalable, elementType)
Same as "mlirVectorTypeGetScalable" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.
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',3))]),e("details",vk,[e("summary",null,[t[3180]||(t[3180]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirVectorTypeGetTypeID-Tuple{}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetTypeID")],-1)),t[3181]||(t[3181]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3182]||(t[3182]=l('
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mlirVectorTypeGetTypeID()
Returns the typeID of an Vector type.
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mlirVectorTypeIsDimScalable(type, dim)
Checks whether the "dim"-th dimension of the given vector is scalable.
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',3))]),e("details",Ck,[e("summary",null,[t[3186]||(t[3186]=e("a",{id:"Reactant.MLIR.API.mlirVectorTypeIsScalable-Tuple{Any}",href:"#Reactant.MLIR.API.mlirVectorTypeIsScalable-Tuple{Any}"},[e("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeIsScalable")],-1)),t[3187]||(t[3187]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),t[3188]||(t[3188]=l('
julia
mlirVectorTypeIsScalable(type)
Checks whether the given vector type is scalable, i.e., has at least one scalable dimension.
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',3))]),t[3191]||(t[3191]=e("h1",{id:"Other-Functions",tabindex:"-1"},[s("Other Functions "),e("a",{class:"header-anchor",href:"#Other-Functions","aria-label":'Permalink to "Other Functions {#Other-Functions}"'},"​")],-1))])}const wk=n(d,[["render",xk]]);export{zk as __pageData,wk as default};
+MLIR.IR.AffineMap(#= (d0, d1)[s0] -> (d0 + s0, d1 mod 10) =#)

source

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julia
MlirDiagnostic

An opaque reference to a diagnostic, always owned by the diagnostics engine (context). Must not be stored outside of the diagnostic handler.

source

',3))]),t("details",ll,[t("summary",null,[e[1005]||(e[1005]=t("a",{id:"Reactant.MLIR.API.MlirDiagnosticHandler",href:"#Reactant.MLIR.API.MlirDiagnosticHandler"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirDiagnosticHandler")],-1)),e[1006]||(e[1006]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1007]||(e[1007]=t("p",null,[s("Diagnostic handler type. Accepts a reference to a diagnostic, which is only guaranteed to be live during the call. The handler is passed the "),t("code",null,"userData"),s(" that was provided when the handler was attached to a context. If the handler processed the diagnostic completely, it is expected to return success. Otherwise, it is expected to return failure to indicate that other handlers should attempt to process the diagnostic.")],-1)),e[1008]||(e[1008]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L6744-L6746",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",nl,[t("summary",null,[e[1009]||(e[1009]=t("a",{id:"Reactant.MLIR.API.MlirDiagnosticHandlerID",href:"#Reactant.MLIR.API.MlirDiagnosticHandlerID"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirDiagnosticHandlerID")],-1)),e[1010]||(e[1010]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1011]||(e[1011]=t("p",null,"Opaque identifier of a diagnostic handler, useful to detach a handler.",-1)),e[1012]||(e[1012]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L6738-L6740",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",pl,[t("summary",null,[e[1013]||(e[1013]=t("a",{id:"Reactant.MLIR.API.MlirDiagnosticSeverity",href:"#Reactant.MLIR.API.MlirDiagnosticSeverity"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirDiagnosticSeverity")],-1)),e[1014]||(e[1014]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1015]||(e[1015]=l('
julia
MlirDiagnosticSeverity

Severity of a diagnostic.

source

',3))]),t("details",rl,[t("summary",null,[e[1016]||(e[1016]=t("a",{id:"Reactant.MLIR.API.MlirExternalPassCallbacks",href:"#Reactant.MLIR.API.MlirExternalPassCallbacks"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirExternalPassCallbacks")],-1)),e[1017]||(e[1017]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1018]||(e[1018]=l('
julia
MlirExternalPassCallbacks

Structure of external MlirPass callbacks. All callbacks are required to be set unless otherwise specified.

FieldNote
constructThis callback is called from the pass is created. This is analogous to a C++ pass constructor.
destructThis callback is called when the pass is destroyed This is analogous to a C++ pass destructor.
initializeThis callback is optional. The callback is called before the pass is run, allowing a chance to initialize any complex state necessary for running the pass. See Pass::initialize(MLIRContext *).
cloneThis callback is called when the pass is cloned. See Pass::clonePass().
runThis callback is called when the pass is run. See Pass::runOnOperation().

source

',4))]),t("details",ol,[t("summary",null,[e[1019]||(e[1019]=t("a",{id:"Reactant.MLIR.API.MlirLlvmThreadPool",href:"#Reactant.MLIR.API.MlirLlvmThreadPool"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirLlvmThreadPool")],-1)),e[1020]||(e[1020]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1021]||(e[1021]=l('
julia
MlirLlvmThreadPool

Re-export llvm::ThreadPool so as to avoid including the LLVM C API directly.

source

',3))]),t("details",dl,[t("summary",null,[e[1022]||(e[1022]=t("a",{id:"Reactant.MLIR.API.MlirLogicalResult",href:"#Reactant.MLIR.API.MlirLogicalResult"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirLogicalResult")],-1)),e[1023]||(e[1023]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1024]||(e[1024]=l('
julia
MlirLogicalResult

A logical result value, essentially a boolean with named states. LLVM convention for using boolean values to designate success or failure of an operation is a moving target, so MLIR opted for an explicit class. Instances of MlirLogicalResult must only be inspected using the associated functions.

source

',3))]),t("details",cl,[t("summary",null,[e[1025]||(e[1025]=t("a",{id:"Reactant.MLIR.API.MlirNamedAttribute",href:"#Reactant.MLIR.API.MlirNamedAttribute"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirNamedAttribute")],-1)),e[1026]||(e[1026]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1027]||(e[1027]=l('
julia
MlirNamedAttribute

Named MLIR attribute.

A named attribute is essentially a (name, attribute) pair where the name is a string.

source

',4))]),t("details",bl,[t("summary",null,[e[1028]||(e[1028]=t("a",{id:"Reactant.MLIR.API.MlirOperationState",href:"#Reactant.MLIR.API.MlirOperationState"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirOperationState")],-1)),e[1029]||(e[1029]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1030]||(e[1030]=l('
julia
MlirOperationState

An auxiliary class for constructing operations.

This class contains all the information necessary to construct the operation. It owns the MlirRegions it has pointers to and does not own anything else. By default, the state can be constructed from a name and location, the latter being also used to access the context, and has no other components. These components can be added progressively until the operation is constructed. Users are not expected to rely on the internals of this class and should use mlirOperationState* functions instead.

source

',4))]),t("details",hl,[t("summary",null,[e[1031]||(e[1031]=t("a",{id:"Reactant.MLIR.API.MlirOperationWalkCallback",href:"#Reactant.MLIR.API.MlirOperationWalkCallback"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirOperationWalkCallback")],-1)),e[1032]||(e[1032]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1033]||(e[1033]=t("p",null,[s("Operation walker type. The handler is passed an (opaque) reference to an operation and a pointer to a "),t("code",null,"userData"),s(".")],-1)),e[1034]||(e[1034]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L1495-L1497",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",ul,[t("summary",null,[e[1035]||(e[1035]=t("a",{id:"Reactant.MLIR.API.MlirShapedTypeComponentsCallback",href:"#Reactant.MLIR.API.MlirShapedTypeComponentsCallback"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirShapedTypeComponentsCallback")],-1)),e[1036]||(e[1036]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1037]||(e[1037]=t("p",null,"These callbacks are used to return multiple shaped type components from functions while transferring ownership to the caller. The first argument is the has rank boolean followed by the the rank and a pointer to the shape (if applicable). The next argument is the element type, then the attribute. The last argument is an opaque pointer forwarded to the callback by the caller. This callback will be called potentially multiple times for each shaped type components.",-1)),e[1038]||(e[1038]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L9139-L9141",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",gl,[t("summary",null,[e[1039]||(e[1039]=t("a",{id:"Reactant.MLIR.API.MlirSparseTensorLevelType",href:"#Reactant.MLIR.API.MlirSparseTensorLevelType"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirSparseTensorLevelType")],-1)),e[1040]||(e[1040]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1041]||(e[1041]=t("p",null,"Dimension level types (and properties) that define sparse tensors. See the documentation in SparseTensorAttrDefs.td for their meaning.",-1)),e[1042]||(e[1042]=t("p",null,"These correspond to SparseTensorEncodingAttr::LevelType in the C++ API. If updating, keep them in sync and update the static_assert in the impl file.",-1)),e[1043]||(e[1043]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L8491-L8495",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",yl,[t("summary",null,[e[1044]||(e[1044]=t("a",{id:"Reactant.MLIR.API.MlirStringCallback",href:"#Reactant.MLIR.API.MlirStringCallback"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirStringCallback")],-1)),e[1045]||(e[1045]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1046]||(e[1046]=t("p",null,"A callback for returning string references.",-1)),e[1047]||(e[1047]=t("p",null,[s("This function is called back by the functions that need to return a reference to the portion of the string with the following arguments: - an "),t("a",{href:"/Reactant.jl/previews/PR441/api/mlirc#Reactant.MLIR.API.MlirStringRef"},[t("code",null,"MlirStringRef")]),s(" representing the current portion of the string - a pointer to user data forwarded from the printing call.")],-1)),e[1048]||(e[1048]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L108-L112",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",ml,[t("summary",null,[e[1049]||(e[1049]=t("a",{id:"Reactant.MLIR.API.MlirStringRef",href:"#Reactant.MLIR.API.MlirStringRef"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirStringRef")],-1)),e[1050]||(e[1050]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1051]||(e[1051]=l('
julia
MlirStringRef

A pointer to a sized fragment of a string, not necessarily null-terminated. Does not own the underlying string. This is equivalent to llvm::StringRef.

FieldNote
dataPointer to the first symbol.
lengthLength of the fragment.

source

',4))]),t("details",kl,[t("summary",null,[e[1052]||(e[1052]=t("a",{id:"Reactant.MLIR.API.MlirTypesCallback",href:"#Reactant.MLIR.API.MlirTypesCallback"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirTypesCallback")],-1)),e[1053]||(e[1053]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1054]||(e[1054]=t("p",null,"These callbacks are used to return multiple types from functions while transferring ownership to the caller. The first argument is the number of consecutive elements pointed to by the second argument. The third argument is an opaque pointer forwarded to the callback by the caller.",-1)),e[1055]||(e[1055]=t("p",null,[t("a",{href:"https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/libMLIR_h.jl#L9091-L9093",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",Rl,[t("summary",null,[e[1056]||(e[1056]=t("a",{id:"Reactant.MLIR.API.MlirWalkOrder",href:"#Reactant.MLIR.API.MlirWalkOrder"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirWalkOrder")],-1)),e[1057]||(e[1057]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1058]||(e[1058]=l('
julia
MlirWalkOrder

Traversal order for operation walk.

source

',3))]),t("details",Il,[t("summary",null,[e[1059]||(e[1059]=t("a",{id:"Reactant.MLIR.API.MlirWalkResult",href:"#Reactant.MLIR.API.MlirWalkResult"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.MlirWalkResult")],-1)),e[1060]||(e[1060]=s()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[1061]||(e[1061]=l('
julia
MlirWalkResult

Operation walk result.

source

',3))]),t("details",jl,[t("summary",null,[e[1062]||(e[1062]=t("a",{id:"Reactant.MLIR.API.LLVMAddSymbol-Tuple{Any, Any}",href:"#Reactant.MLIR.API.LLVMAddSymbol-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMAddSymbol")],-1)),e[1063]||(e[1063]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1064]||(e[1064]=l('
julia
LLVMAddSymbol(symbolName, symbolValue)

This functions permanently adds the symbol symbolName with the value symbolValue. These symbols are searched before any libraries.

See also

sys::DynamicLibrary::AddSymbol()

source

',5))]),t("details",fl,[t("summary",null,[e[1065]||(e[1065]=t("a",{id:"Reactant.MLIR.API.LLVMLoadLibraryPermanently-Tuple{Any}",href:"#Reactant.MLIR.API.LLVMLoadLibraryPermanently-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMLoadLibraryPermanently")],-1)),e[1066]||(e[1066]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1067]||(e[1067]=l('
julia
LLVMLoadLibraryPermanently(Filename)

This function permanently loads the dynamic library at the given path. It is safe to call this function multiple times for the same library.

See also

sys::DynamicLibrary::LoadLibraryPermanently()

source

',5))]),t("details",Ml,[t("summary",null,[e[1068]||(e[1068]=t("a",{id:"Reactant.MLIR.API.LLVMParseCommandLineOptions-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.LLVMParseCommandLineOptions-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMParseCommandLineOptions")],-1)),e[1069]||(e[1069]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1070]||(e[1070]=l('
julia
LLVMParseCommandLineOptions(argc, argv, Overview)

This function parses the given arguments using the LLVM command line parser. Note that the only stable thing about this function is its signature; you cannot rely on any particular set of command line arguments being interpreted the same way across LLVM versions.

See also

llvm:🆑:ParseCommandLineOptions()

source

',5))]),t("details",Al,[t("summary",null,[e[1071]||(e[1071]=t("a",{id:"Reactant.MLIR.API.LLVMSearchForAddressOfSymbol-Tuple{Any}",href:"#Reactant.MLIR.API.LLVMSearchForAddressOfSymbol-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.LLVMSearchForAddressOfSymbol")],-1)),e[1072]||(e[1072]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1073]||(e[1073]=l('
julia
LLVMSearchForAddressOfSymbol(symbolName)

This function will search through all previously loaded dynamic libraries for the symbol symbolName. If it is found, the address of that symbol is returned. If not, null is returned.

See also

sys::DynamicLibrary::SearchForAddressOfSymbol()

source

',5))]),t("details",Ll,[t("summary",null,[e[1074]||(e[1074]=t("a",{id:"Reactant.MLIR.API.mlirAffineAddExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineAddExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineAddExprGet")],-1)),e[1075]||(e[1075]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1076]||(e[1076]=l('
julia
mlirAffineAddExprGet(lhs, rhs)

Creates an affine add expression with 'lhs' and 'rhs'.

source

',3))]),t("details",El,[t("summary",null,[e[1077]||(e[1077]=t("a",{id:"Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineBinaryOpExprGetLHS")],-1)),e[1078]||(e[1078]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1079]||(e[1079]=l('
julia
mlirAffineBinaryOpExprGetLHS(affineExpr)

Returns the left hand side affine expression of the given affine binary operation expression.

source

',3))]),t("details",vl,[t("summary",null,[e[1080]||(e[1080]=t("a",{id:"Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineBinaryOpExprGetRHS")],-1)),e[1081]||(e[1081]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1082]||(e[1082]=l('
julia
mlirAffineBinaryOpExprGetRHS(affineExpr)

Returns the right hand side affine expression of the given affine binary operation expression.

source

',3))]),t("details",Tl,[t("summary",null,[e[1083]||(e[1083]=t("a",{id:"Reactant.MLIR.API.mlirAffineCeilDivExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineCeilDivExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineCeilDivExprGet")],-1)),e[1084]||(e[1084]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1085]||(e[1085]=l('
julia
mlirAffineCeilDivExprGet(lhs, rhs)

Creates an affine ceildiv expression with 'lhs' and 'rhs'.

source

',3))]),t("details",Cl,[t("summary",null,[e[1086]||(e[1086]=t("a",{id:"Reactant.MLIR.API.mlirAffineConstantExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineConstantExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineConstantExprGet")],-1)),e[1087]||(e[1087]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1088]||(e[1088]=l('
julia
mlirAffineConstantExprGet(ctx, constant)

Creates an affine constant expression with 'constant' in the context.

source

',3))]),t("details",xl,[t("summary",null,[e[1089]||(e[1089]=t("a",{id:"Reactant.MLIR.API.mlirAffineConstantExprGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineConstantExprGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineConstantExprGetValue")],-1)),e[1090]||(e[1090]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1091]||(e[1091]=l('
julia
mlirAffineConstantExprGetValue(affineExpr)

Returns the value of the given affine constant expression.

source

',3))]),t("details",Fl,[t("summary",null,[e[1092]||(e[1092]=t("a",{id:"Reactant.MLIR.API.mlirAffineDimExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineDimExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineDimExprGet")],-1)),e[1093]||(e[1093]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1094]||(e[1094]=l('
julia
mlirAffineDimExprGet(ctx, position)

Creates an affine dimension expression with 'position' in the context.

source

',3))]),t("details",Pl,[t("summary",null,[e[1095]||(e[1095]=t("a",{id:"Reactant.MLIR.API.mlirAffineDimExprGetPosition-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineDimExprGetPosition-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineDimExprGetPosition")],-1)),e[1096]||(e[1096]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1097]||(e[1097]=l('
julia
mlirAffineDimExprGetPosition(affineExpr)

Returns the position of the given affine dimension expression.

source

',3))]),t("details",Dl,[t("summary",null,[e[1098]||(e[1098]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprCompose-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprCompose-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprCompose")],-1)),e[1099]||(e[1099]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1100]||(e[1100]=l('
julia
mlirAffineExprCompose(affineExpr, affineMap)

Composes the given map with the given expression.

source

',3))]),t("details",Ol,[t("summary",null,[e[1101]||(e[1101]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprDump")],-1)),e[1102]||(e[1102]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1103]||(e[1103]=l('
julia
mlirAffineExprDump(affineExpr)

Prints the affine expression to the standard error stream.

source

',3))]),t("details",Bl,[t("summary",null,[e[1104]||(e[1104]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprEqual")],-1)),e[1105]||(e[1105]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1106]||(e[1106]=l('
julia
mlirAffineExprEqual(lhs, rhs)

Returns true if the two affine expressions are equal.

source

',3))]),t("details",Gl,[t("summary",null,[e[1107]||(e[1107]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprGetContext")],-1)),e[1108]||(e[1108]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1109]||(e[1109]=l('
julia
mlirAffineExprGetContext(affineExpr)

Gets the context that owns the affine expression.

source

',3))]),t("details",zl,[t("summary",null,[e[1110]||(e[1110]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprGetLargestKnownDivisor")],-1)),e[1111]||(e[1111]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1112]||(e[1112]=l('
julia
mlirAffineExprGetLargestKnownDivisor(affineExpr)

Returns the greatest known integral divisor of this affine expression. The result is always positive.

source

',3))]),t("details",wl,[t("summary",null,[e[1113]||(e[1113]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAAdd-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAAdd-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAAdd")],-1)),e[1114]||(e[1114]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1115]||(e[1115]=l('
julia
mlirAffineExprIsAAdd(affineExpr)

Checks whether the given affine expression is an add expression.

source

',3))]),t("details",Sl,[t("summary",null,[e[1116]||(e[1116]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsABinary-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsABinary-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsABinary")],-1)),e[1117]||(e[1117]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1118]||(e[1118]=l('
julia
mlirAffineExprIsABinary(affineExpr)

Checks whether the given affine expression is binary.

source

',3))]),t("details",Nl,[t("summary",null,[e[1119]||(e[1119]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsACeilDiv-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsACeilDiv-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsACeilDiv")],-1)),e[1120]||(e[1120]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1121]||(e[1121]=l('
julia
mlirAffineExprIsACeilDiv(affineExpr)

Checks whether the given affine expression is an ceildiv expression.

source

',3))]),t("details",Vl,[t("summary",null,[e[1122]||(e[1122]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAConstant-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAConstant")],-1)),e[1123]||(e[1123]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1124]||(e[1124]=l('
julia
mlirAffineExprIsAConstant(affineExpr)

Checks whether the given affine expression is a constant expression.

source

',3))]),t("details",ql,[t("summary",null,[e[1125]||(e[1125]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsADim-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsADim-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsADim")],-1)),e[1126]||(e[1126]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1127]||(e[1127]=l('
julia
mlirAffineExprIsADim(affineExpr)

Checks whether the given affine expression is a dimension expression.

source

',3))]),t("details",Ul,[t("summary",null,[e[1128]||(e[1128]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAFloorDiv-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAFloorDiv-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAFloorDiv")],-1)),e[1129]||(e[1129]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1130]||(e[1130]=l('
julia
mlirAffineExprIsAFloorDiv(affineExpr)

Checks whether the given affine expression is an floordiv expression.

source

',3))]),t("details",Ql,[t("summary",null,[e[1131]||(e[1131]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAMod-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAMod-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAMod")],-1)),e[1132]||(e[1132]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1133]||(e[1133]=l('
julia
mlirAffineExprIsAMod(affineExpr)

Checks whether the given affine expression is an mod expression.

source

',3))]),t("details",Wl,[t("summary",null,[e[1134]||(e[1134]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsAMul-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsAMul-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsAMul")],-1)),e[1135]||(e[1135]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1136]||(e[1136]=l('
julia
mlirAffineExprIsAMul(affineExpr)

Checks whether the given affine expression is an mul expression.

source

',3))]),t("details",Hl,[t("summary",null,[e[1137]||(e[1137]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsASymbol-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsASymbol-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsASymbol")],-1)),e[1138]||(e[1138]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1139]||(e[1139]=l('
julia
mlirAffineExprIsASymbol(affineExpr)

Checks whether the given affine expression is a symbol expression.

source

',3))]),t("details",Zl,[t("summary",null,[e[1140]||(e[1140]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsFunctionOfDim")],-1)),e[1141]||(e[1141]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1142]||(e[1142]=l('
julia
mlirAffineExprIsFunctionOfDim(affineExpr, position)

Checks whether the given affine expression involves AffineDimExpr 'position'.

source

',3))]),t("details",Jl,[t("summary",null,[e[1143]||(e[1143]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsMultipleOf-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsMultipleOf-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsMultipleOf")],-1)),e[1144]||(e[1144]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1145]||(e[1145]=l('
julia
mlirAffineExprIsMultipleOf(affineExpr, factor)

Checks whether the given affine expression is a multiple of 'factor'.

source

',3))]),t("details",Kl,[t("summary",null,[e[1146]||(e[1146]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsNull")],-1)),e[1147]||(e[1147]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1148]||(e[1148]=l('
julia
mlirAffineExprIsNull(affineExpr)

Returns true if the given affine expression is a null expression. Note constant zero is not a null expression.

source

',3))]),t("details",$l,[t("summary",null,[e[1149]||(e[1149]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsPureAffine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsPureAffine-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsPureAffine")],-1)),e[1150]||(e[1150]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1151]||(e[1151]=l('
julia
mlirAffineExprIsPureAffine(affineExpr)

Checks whether the given affine expression is a pure affine expression, i.e. mul, floordiv, ceildic, and mod is only allowed w.r.t constants.

source

',3))]),t("details",Xl,[t("summary",null,[e[1152]||(e[1152]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprIsSymbolicOrConstant")],-1)),e[1153]||(e[1153]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1154]||(e[1154]=l('
julia
mlirAffineExprIsSymbolicOrConstant(affineExpr)

Checks whether the given affine expression is made out of only symbols and constants.

source

',3))]),t("details",Yl,[t("summary",null,[e[1155]||(e[1155]=t("a",{id:"Reactant.MLIR.API.mlirAffineExprPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineExprPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineExprPrint")],-1)),e[1156]||(e[1156]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1157]||(e[1157]=l('
julia
mlirAffineExprPrint(affineExpr, callback, userData)

Prints an affine expression by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",_l,[t("summary",null,[e[1158]||(e[1158]=t("a",{id:"Reactant.MLIR.API.mlirAffineFloorDivExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineFloorDivExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineFloorDivExprGet")],-1)),e[1159]||(e[1159]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1160]||(e[1160]=l('
julia
mlirAffineFloorDivExprGet(lhs, rhs)

Creates an affine floordiv expression with 'lhs' and 'rhs'.

source

',3))]),t("details",en,[t("summary",null,[e[1161]||(e[1161]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGet")],-1)),e[1162]||(e[1162]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1163]||(e[1163]=l('
julia
mlirAffineMapAttrGet(map)

Creates an affine map attribute wrapping the given map. The attribute belongs to the same context as the affine map.

source

',3))]),t("details",tn,[t("summary",null,[e[1164]||(e[1164]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGetTypeID")],-1)),e[1165]||(e[1165]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1166]||(e[1166]=l('
julia
mlirAffineMapAttrGetTypeID()

Returns the typeID of an AffineMap attribute.

source

',3))]),t("details",sn,[t("summary",null,[e[1167]||(e[1167]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapAttrGetValue")],-1)),e[1168]||(e[1168]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1169]||(e[1169]=l('
julia
mlirAffineMapAttrGetValue(attr)

Returns the affine map wrapped in the given affine map attribute.

source

',3))]),t("details",an,[t("summary",null,[e[1170]||(e[1170]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapCompressUnusedSymbols")],-1)),e[1171]||(e[1171]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1172]||(e[1172]=l('
julia
mlirAffineMapCompressUnusedSymbols(affineMaps, size, result, populateResult)

Returns the simplified affine map resulting from dropping the symbols that do not appear in any of the individual maps in affineMaps. Asserts that all maps in affineMaps are normalized to the same number of dims and symbols. Takes a callback populateResult to fill the res container with value m at entry idx. This allows returning without worrying about ownership considerations.

source

',3))]),t("details",ln,[t("summary",null,[e[1173]||(e[1173]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapConstantGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapConstantGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapConstantGet")],-1)),e[1174]||(e[1174]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1175]||(e[1175]=l('
julia
mlirAffineMapConstantGet(ctx, val)

Creates a single constant result affine map in the context. The affine map is owned by the context.

source

',3))]),t("details",nn,[t("summary",null,[e[1176]||(e[1176]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapDump")],-1)),e[1177]||(e[1177]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1178]||(e[1178]=l('
julia
mlirAffineMapDump(affineMap)

Prints the affine map to the standard error stream.

source

',3))]),t("details",pn,[t("summary",null,[e[1179]||(e[1179]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapEmptyGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapEmptyGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapEmptyGet")],-1)),e[1180]||(e[1180]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1181]||(e[1181]=l('
julia
mlirAffineMapEmptyGet(ctx)

Creates a zero result affine map with no dimensions or symbols in the context. The affine map is owned by the context.

source

',3))]),t("details",rn,[t("summary",null,[e[1182]||(e[1182]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapEqual")],-1)),e[1183]||(e[1183]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1184]||(e[1184]=l('
julia
mlirAffineMapEqual(a1, a2)

Checks if two affine maps are equal.

source

',3))]),t("details",on,[t("summary",null,[e[1185]||(e[1185]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGet")],-1)),e[1186]||(e[1186]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1187]||(e[1187]=l('
julia
mlirAffineMapGet(ctx, dimCount, symbolCount, nAffineExprs, affineExprs)

Creates an affine map with results defined by the given list of affine expressions. The map resulting map also has the requested number of input dimensions and symbols, regardless of them being used in the results.

source

',3))]),t("details",dn,[t("summary",null,[e[1188]||(e[1188]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetContext")],-1)),e[1189]||(e[1189]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1190]||(e[1190]=l('
julia
mlirAffineMapGetContext(affineMap)

Gets the context that the given affine map was created with

source

',3))]),t("details",cn,[t("summary",null,[e[1191]||(e[1191]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetMajorSubMap-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetMajorSubMap-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetMajorSubMap")],-1)),e[1192]||(e[1192]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1193]||(e[1193]=l('
julia
mlirAffineMapGetMajorSubMap(affineMap, numResults)

Returns the affine map consisting of the most major numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.

source

',3))]),t("details",bn,[t("summary",null,[e[1194]||(e[1194]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetMinorSubMap-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetMinorSubMap-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetMinorSubMap")],-1)),e[1195]||(e[1195]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1196]||(e[1196]=l('
julia
mlirAffineMapGetMinorSubMap(affineMap, numResults)

Returns the affine map consisting of the most minor numResults results. Returns the null AffineMap if the numResults is equal to zero. Returns the affineMap if numResults is greater or equals to number of results of the given affine map.

source

',3))]),t("details",hn,[t("summary",null,[e[1197]||(e[1197]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumDims-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumDims")],-1)),e[1198]||(e[1198]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1199]||(e[1199]=l('
julia
mlirAffineMapGetNumDims(affineMap)

Returns the number of dimensions of the given affine map.

source

',3))]),t("details",un,[t("summary",null,[e[1200]||(e[1200]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumInputs-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumInputs")],-1)),e[1201]||(e[1201]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1202]||(e[1202]=l('
julia
mlirAffineMapGetNumInputs(affineMap)

Returns the number of inputs (dimensions + symbols) of the given affine map.

source

',3))]),t("details",gn,[t("summary",null,[e[1203]||(e[1203]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumResults-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumResults")],-1)),e[1204]||(e[1204]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1205]||(e[1205]=l('
julia
mlirAffineMapGetNumResults(affineMap)

Returns the number of results of the given affine map.

source

',3))]),t("details",yn,[t("summary",null,[e[1206]||(e[1206]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetNumSymbols-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetNumSymbols-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetNumSymbols")],-1)),e[1207]||(e[1207]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1208]||(e[1208]=l('
julia
mlirAffineMapGetNumSymbols(affineMap)

Returns the number of symbols of the given affine map.

source

',3))]),t("details",mn,[t("summary",null,[e[1209]||(e[1209]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetResult-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetResult")],-1)),e[1210]||(e[1210]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1211]||(e[1211]=l('
julia
mlirAffineMapGetResult(affineMap, pos)

Returns the result at the given position.

source

',3))]),t("details",kn,[t("summary",null,[e[1212]||(e[1212]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetSingleConstantResult")],-1)),e[1213]||(e[1213]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1214]||(e[1214]=l('
julia
mlirAffineMapGetSingleConstantResult(affineMap)

Returns the constant result of the given affine map. The function asserts that the map has a single constant result.

source

',3))]),t("details",Rn,[t("summary",null,[e[1215]||(e[1215]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapGetSubMap-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapGetSubMap-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapGetSubMap")],-1)),e[1216]||(e[1216]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1217]||(e[1217]=l('
julia
mlirAffineMapGetSubMap(affineMap, size, resultPos)

Returns the affine map consisting of the resultPos subset.

source

',3))]),t("details",In,[t("summary",null,[e[1218]||(e[1218]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsEmpty-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsEmpty")],-1)),e[1219]||(e[1219]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1220]||(e[1220]=l('
julia
mlirAffineMapIsEmpty(affineMap)

Checks whether the given affine map is an empty affine map.

source

',3))]),t("details",jn,[t("summary",null,[e[1221]||(e[1221]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsIdentity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsIdentity-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsIdentity")],-1)),e[1222]||(e[1222]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1223]||(e[1223]=l('
julia
mlirAffineMapIsIdentity(affineMap)

Checks whether the given affine map is an identity affine map. The function asserts that the number of dimensions is greater or equal to the number of results.

source

',3))]),t("details",fn,[t("summary",null,[e[1224]||(e[1224]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsMinorIdentity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsMinorIdentity-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsMinorIdentity")],-1)),e[1225]||(e[1225]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1226]||(e[1226]=l('
julia
mlirAffineMapIsMinorIdentity(affineMap)

Checks whether the given affine map is a minor identity affine map.

source

',3))]),t("details",Mn,[t("summary",null,[e[1227]||(e[1227]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsNull")],-1)),e[1228]||(e[1228]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1229]||(e[1229]=l('
julia
mlirAffineMapIsNull(affineMap)

Checks whether an affine map is null.

source

',3))]),t("details",An,[t("summary",null,[e[1230]||(e[1230]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsPermutation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsPermutation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsPermutation")],-1)),e[1231]||(e[1231]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1232]||(e[1232]=l('
julia
mlirAffineMapIsPermutation(affineMap)

Checks whether the given affine map represents a symbol-less permutation map.

source

',3))]),t("details",Ln,[t("summary",null,[e[1233]||(e[1233]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsProjectedPermutation")],-1)),e[1234]||(e[1234]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1235]||(e[1235]=l('
julia
mlirAffineMapIsProjectedPermutation(affineMap)

Checks whether the given affine map represents a subset of a symbol-less permutation map.

source

',3))]),t("details",En,[t("summary",null,[e[1236]||(e[1236]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapIsSingleConstant-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineMapIsSingleConstant-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapIsSingleConstant")],-1)),e[1237]||(e[1237]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1238]||(e[1238]=l('
julia
mlirAffineMapIsSingleConstant(affineMap)

Checks whether the given affine map is a single result constant affine map.

source

',3))]),t("details",vn,[t("summary",null,[e[1239]||(e[1239]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapMinorIdentityGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapMinorIdentityGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapMinorIdentityGet")],-1)),e[1240]||(e[1240]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1241]||(e[1241]=l('
julia
mlirAffineMapMinorIdentityGet(ctx, dims, results)

Creates an identity affine map on the most minor dimensions in the context. The affine map is owned by the context. The function asserts that the number of dimensions is greater or equal to the number of results.

source

',3))]),t("details",Tn,[t("summary",null,[e[1242]||(e[1242]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapMultiDimIdentityGet")],-1)),e[1243]||(e[1243]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1244]||(e[1244]=l('
julia
mlirAffineMapMultiDimIdentityGet(ctx, numDims)

Creates an affine map with 'numDims' identity in the context. The affine map is owned by the context.

source

',3))]),t("details",Cn,[t("summary",null,[e[1245]||(e[1245]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapPermutationGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapPermutationGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapPermutationGet")],-1)),e[1246]||(e[1246]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1247]||(e[1247]=l('
julia
mlirAffineMapPermutationGet(ctx, size, permutation)

Creates an affine map with a permutation expression and its size in the context. The permutation expression is a non-empty vector of integers. The elements of the permutation vector must be continuous from 0 and cannot be repeated (i.e. [1,2,0] is a valid permutation. [2,0] or [1,1,2] is an invalid permutation.) The affine map is owned by the context.

source

',3))]),t("details",xn,[t("summary",null,[e[1248]||(e[1248]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapPrint")],-1)),e[1249]||(e[1249]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1250]||(e[1250]=l('
julia
mlirAffineMapPrint(affineMap, callback, userData)

Prints an affine map by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Fn,[t("summary",null,[e[1251]||(e[1251]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapReplace-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAffineMapReplace-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapReplace")],-1)),e[1252]||(e[1252]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1253]||(e[1253]=l('
julia
mlirAffineMapReplace(affineMap, expression, replacement, numResultDims, numResultSyms)

Apply AffineExpr::replace(map) to each of the results and return a new new AffineMap with the new results and the specified number of dims and symbols.

source

',3))]),t("details",Pn,[t("summary",null,[e[1254]||(e[1254]=t("a",{id:"Reactant.MLIR.API.mlirAffineMapZeroResultGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMapZeroResultGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMapZeroResultGet")],-1)),e[1255]||(e[1255]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1256]||(e[1256]=l('
julia
mlirAffineMapZeroResultGet(ctx, dimCount, symbolCount)

Creates a zero result affine map of the given dimensions and symbols in the context. The affine map is owned by the context.

source

',3))]),t("details",Dn,[t("summary",null,[e[1257]||(e[1257]=t("a",{id:"Reactant.MLIR.API.mlirAffineModExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineModExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineModExprGet")],-1)),e[1258]||(e[1258]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1259]||(e[1259]=l('
julia
mlirAffineModExprGet(lhs, rhs)

Creates an affine mod expression with 'lhs' and 'rhs'.

source

',3))]),t("details",On,[t("summary",null,[e[1260]||(e[1260]=t("a",{id:"Reactant.MLIR.API.mlirAffineMulExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineMulExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineMulExprGet")],-1)),e[1261]||(e[1261]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1262]||(e[1262]=l('
julia
mlirAffineMulExprGet(lhs, rhs)

Creates an affine mul expression with 'lhs' and 'rhs'.

source

',3))]),t("details",Bn,[t("summary",null,[e[1263]||(e[1263]=t("a",{id:"Reactant.MLIR.API.mlirAffineSymbolExprGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAffineSymbolExprGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineSymbolExprGet")],-1)),e[1264]||(e[1264]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1265]||(e[1265]=l('
julia
mlirAffineSymbolExprGet(ctx, position)

Creates an affine symbol expression with 'position' in the context.

source

',3))]),t("details",Gn,[t("summary",null,[e[1266]||(e[1266]=t("a",{id:"Reactant.MLIR.API.mlirAffineSymbolExprGetPosition-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAffineSymbolExprGetPosition-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAffineSymbolExprGetPosition")],-1)),e[1267]||(e[1267]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1268]||(e[1268]=l('
julia
mlirAffineSymbolExprGetPosition(affineExpr)

Returns the position of the given affine symbol expression.

source

',3))]),t("details",zn,[t("summary",null,[e[1269]||(e[1269]=t("a",{id:"Reactant.MLIR.API.mlirAnyQuantizedTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirAnyQuantizedTypeGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAnyQuantizedTypeGet")],-1)),e[1270]||(e[1270]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1271]||(e[1271]=l('
julia
mlirAnyQuantizedTypeGet(flags, storageType, expressedType, storageTypeMin, storageTypeMax)

Creates an instance of AnyQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.

source

',3))]),t("details",wn,[t("summary",null,[e[1272]||(e[1272]=t("a",{id:"Reactant.MLIR.API.mlirArrayAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGet")],-1)),e[1273]||(e[1273]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1274]||(e[1274]=l('
julia
mlirArrayAttrGet(ctx, numElements, elements)

Creates an array element containing the given list of elements in the given context.

source

',3))]),t("details",Sn,[t("summary",null,[e[1275]||(e[1275]=t("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGetElement-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetElement")],-1)),e[1276]||(e[1276]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1277]||(e[1277]=l('
julia
mlirArrayAttrGetElement(attr, pos)

Returns pos-th element stored in the given array attribute.

source

',3))]),t("details",Nn,[t("summary",null,[e[1278]||(e[1278]=t("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirArrayAttrGetNumElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetNumElements")],-1)),e[1279]||(e[1279]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1280]||(e[1280]=l('
julia
mlirArrayAttrGetNumElements(attr)

Returns the number of elements stored in the given array attribute.

source

',3))]),t("details",Vn,[t("summary",null,[e[1281]||(e[1281]=t("a",{id:"Reactant.MLIR.API.mlirArrayAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirArrayAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirArrayAttrGetTypeID")],-1)),e[1282]||(e[1282]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1283]||(e[1283]=l('
julia
mlirArrayAttrGetTypeID()

Returns the typeID of an Array attribute.

source

',3))]),t("details",qn,[t("summary",null,[e[1284]||(e[1284]=t("a",{id:"Reactant.MLIR.API.mlirAsmStateCreateForOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAsmStateCreateForOperation-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateCreateForOperation")],-1)),e[1285]||(e[1285]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1286]||(e[1286]=l('
julia
mlirAsmStateCreateForOperation(op, flags)

Creates new AsmState, as with AsmState the IR should not be mutated in-between using this state. Must be freed with a call to mlirAsmStateDestroy().

source

',3))]),t("details",Un,[t("summary",null,[e[1287]||(e[1287]=t("a",{id:"Reactant.MLIR.API.mlirAsmStateCreateForValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAsmStateCreateForValue-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateCreateForValue")],-1)),e[1288]||(e[1288]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1289]||(e[1289]=l('
julia
mlirAsmStateCreateForValue(value, flags)

Creates new AsmState from value. Must be freed with a call to mlirAsmStateDestroy().

source

',3))]),t("details",Qn,[t("summary",null,[e[1290]||(e[1290]=t("a",{id:"Reactant.MLIR.API.mlirAsmStateDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAsmStateDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAsmStateDestroy")],-1)),e[1291]||(e[1291]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1292]||(e[1292]=l('
julia
mlirAsmStateDestroy(state)

Destroys printing flags created with mlirAsmStateCreate.

source

',3))]),t("details",Wn,[t("summary",null,[e[1293]||(e[1293]=t("a",{id:"Reactant.MLIR.API.mlirAttributeDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeDump")],-1)),e[1294]||(e[1294]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1295]||(e[1295]=l('
julia
mlirAttributeDump(attr)

Prints the attribute to the standard error stream.

source

',3))]),t("details",Hn,[t("summary",null,[e[1296]||(e[1296]=t("a",{id:"Reactant.MLIR.API.mlirAttributeEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAttributeEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeEqual")],-1)),e[1297]||(e[1297]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1298]||(e[1298]=l('
julia
mlirAttributeEqual(a1, a2)

Checks if two attributes are equal.

source

',3))]),t("details",Zn,[t("summary",null,[e[1299]||(e[1299]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetContext")],-1)),e[1300]||(e[1300]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1301]||(e[1301]=l('
julia
mlirAttributeGetContext(attribute)

Gets the context that an attribute was created with.

source

',3))]),t("details",Jn,[t("summary",null,[e[1302]||(e[1302]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetDialect-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetDialect-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetDialect")],-1)),e[1303]||(e[1303]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1304]||(e[1304]=l('
julia
mlirAttributeGetDialect(attribute)

Gets the dialect of the attribute.

source

',3))]),t("details",Kn,[t("summary",null,[e[1305]||(e[1305]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetNull-Tuple{}",href:"#Reactant.MLIR.API.mlirAttributeGetNull-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetNull")],-1)),e[1306]||(e[1306]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1307]||(e[1307]=l('
julia
mlirAttributeGetNull()

Returns an empty attribute.

source

',3))]),t("details",$n,[t("summary",null,[e[1308]||(e[1308]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetType")],-1)),e[1309]||(e[1309]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1310]||(e[1310]=l('
julia
mlirAttributeGetType(attribute)

Gets the type of this attribute.

source

',3))]),t("details",Xn,[t("summary",null,[e[1311]||(e[1311]=t("a",{id:"Reactant.MLIR.API.mlirAttributeGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeGetTypeID-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeGetTypeID")],-1)),e[1312]||(e[1312]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1313]||(e[1313]=l('
julia
mlirAttributeGetTypeID(attribute)

Gets the type id of the attribute.

source

',3))]),t("details",Yn,[t("summary",null,[e[1314]||(e[1314]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAAffineMap-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAAffineMap")],-1)),e[1315]||(e[1315]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1316]||(e[1316]=l('
julia
mlirAttributeIsAAffineMap(attr)

Checks whether the given attribute is an affine map attribute.

source

',3))]),t("details",_n,[t("summary",null,[e[1317]||(e[1317]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAArray-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAArray-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAArray")],-1)),e[1318]||(e[1318]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1319]||(e[1319]=l('
julia
mlirAttributeIsAArray(attr)

Checks whether the given attribute is an array attribute.

source

',3))]),t("details",ep,[t("summary",null,[e[1320]||(e[1320]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsABool-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsABool-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsABool")],-1)),e[1321]||(e[1321]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1322]||(e[1322]=l('
julia
mlirAttributeIsABool(attr)

Checks whether the given attribute is a bool attribute.

source

',3))]),t("details",tp,[t("summary",null,[e[1323]||(e[1323]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsADenseBoolArray-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADenseBoolArray-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADenseBoolArray")],-1)),e[1324]||(e[1324]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1325]||(e[1325]=l('
julia
mlirAttributeIsADenseBoolArray(attr)

Checks whether the given attribute is a dense array attribute.

source

',3))]),t("details",sp,[t("summary",null,[e[1326]||(e[1326]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsADenseElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADenseElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADenseElements")],-1)),e[1327]||(e[1327]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1328]||(e[1328]=l('
julia
mlirAttributeIsADenseElements(attr)

Checks whether the given attribute is a dense elements attribute.

source

',3))]),t("details",ap,[t("summary",null,[e[1329]||(e[1329]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsADictionary-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsADictionary-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsADictionary")],-1)),e[1330]||(e[1330]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1331]||(e[1331]=l('
julia
mlirAttributeIsADictionary(attr)

Checks whether the given attribute is a dictionary attribute.

source

',3))]),t("details",ip,[t("summary",null,[e[1332]||(e[1332]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAElements")],-1)),e[1333]||(e[1333]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1334]||(e[1334]=l('
julia
mlirAttributeIsAElements(attr)

Checks whether the given attribute is an elements attribute.

source

',3))]),t("details",lp,[t("summary",null,[e[1335]||(e[1335]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAFlatSymbolRef")],-1)),e[1336]||(e[1336]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1337]||(e[1337]=l('
julia
mlirAttributeIsAFlatSymbolRef(attr)

Checks whether the given attribute is a flat symbol reference attribute.

source

',3))]),t("details",np,[t("summary",null,[e[1338]||(e[1338]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAFloat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAFloat-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAFloat")],-1)),e[1339]||(e[1339]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1340]||(e[1340]=l('
julia
mlirAttributeIsAFloat(attr)

Checks whether the given attribute is a floating point attribute.

source

',3))]),t("details",pp,[t("summary",null,[e[1341]||(e[1341]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAInteger-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAInteger-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAInteger")],-1)),e[1342]||(e[1342]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1343]||(e[1343]=l('
julia
mlirAttributeIsAInteger(attr)

Checks whether the given attribute is an integer attribute.

source

',3))]),t("details",rp,[t("summary",null,[e[1344]||(e[1344]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAIntegerSet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAIntegerSet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAIntegerSet")],-1)),e[1345]||(e[1345]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1346]||(e[1346]=l('
julia
mlirAttributeIsAIntegerSet(attr)

Checks whether the given attribute is an integer set attribute.

source

',3))]),t("details",op,[t("summary",null,[e[1347]||(e[1347]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAOpaque-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAOpaque")],-1)),e[1348]||(e[1348]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1349]||(e[1349]=l('
julia
mlirAttributeIsAOpaque(attr)

Checks whether the given attribute is an opaque attribute.

source

',3))]),t("details",dp,[t("summary",null,[e[1350]||(e[1350]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsASparseElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASparseElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASparseElements")],-1)),e[1351]||(e[1351]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1352]||(e[1352]=l('
julia
mlirAttributeIsASparseElements(attr)

Checks whether the given attribute is a sparse elements attribute.

source

',3))]),t("details",cp,[t("summary",null,[e[1353]||(e[1353]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASparseTensorEncodingAttr")],-1)),e[1354]||(e[1354]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1355]||(e[1355]=l('
julia
mlirAttributeIsASparseTensorEncodingAttr(attr)

Checks whether the given attribute is a sparse\\_tensor.encoding attribute.

source

',3))]),t("details",bp,[t("summary",null,[e[1356]||(e[1356]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAString-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAString-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAString")],-1)),e[1357]||(e[1357]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1358]||(e[1358]=l('
julia
mlirAttributeIsAString(attr)

Checks whether the given attribute is a string attribute.

source

',3))]),t("details",hp,[t("summary",null,[e[1359]||(e[1359]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsASymbolRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsASymbolRef-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsASymbolRef")],-1)),e[1360]||(e[1360]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1361]||(e[1361]=l('
julia
mlirAttributeIsASymbolRef(attr)

Checks whether the given attribute is a symbol reference attribute.

source

',3))]),t("details",up,[t("summary",null,[e[1362]||(e[1362]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAType")],-1)),e[1363]||(e[1363]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1364]||(e[1364]=l('
julia
mlirAttributeIsAType(attr)

Checks whether the given attribute is a type attribute.

source

',3))]),t("details",gp,[t("summary",null,[e[1365]||(e[1365]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsAUnit-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsAUnit-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsAUnit")],-1)),e[1366]||(e[1366]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1367]||(e[1367]=l('
julia
mlirAttributeIsAUnit(attr)

Checks whether the given attribute is a unit attribute.

source

',3))]),t("details",yp,[t("summary",null,[e[1368]||(e[1368]=t("a",{id:"Reactant.MLIR.API.mlirAttributeIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirAttributeIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeIsNull")],-1)),e[1369]||(e[1369]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1370]||(e[1370]=l('
julia
mlirAttributeIsNull(attr)

Checks whether an attribute is null.

source

',3))]),t("details",mp,[t("summary",null,[e[1371]||(e[1371]=t("a",{id:"Reactant.MLIR.API.mlirAttributeParseGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirAttributeParseGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributeParseGet")],-1)),e[1372]||(e[1372]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1373]||(e[1373]=l('
julia
mlirAttributeParseGet(context, attr)

Parses an attribute. The attribute is owned by the context.

source

',3))]),t("details",kp,[t("summary",null,[e[1374]||(e[1374]=t("a",{id:"Reactant.MLIR.API.mlirAttributePrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirAttributePrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirAttributePrint")],-1)),e[1375]||(e[1375]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1376]||(e[1376]=l('
julia
mlirAttributePrint(attr, callback, userData)

Prints an attribute by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Rp,[t("summary",null,[e[1377]||(e[1377]=t("a",{id:"Reactant.MLIR.API.mlirBF16TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBF16TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBF16TypeGet")],-1)),e[1378]||(e[1378]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1379]||(e[1379]=l('
julia
mlirBF16TypeGet(ctx)

Creates a bf16 type in the given context. The type is owned by the context.

source

',3))]),t("details",Ip,[t("summary",null,[e[1380]||(e[1380]=t("a",{id:"Reactant.MLIR.API.mlirBFloat16TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirBFloat16TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBFloat16TypeGetTypeID")],-1)),e[1381]||(e[1381]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1382]||(e[1382]=l('
julia
mlirBFloat16TypeGetTypeID()

Returns the typeID of an BFloat16 type.

source

',3))]),t("details",jp,[t("summary",null,[e[1383]||(e[1383]=t("a",{id:"Reactant.MLIR.API.mlirBlockAddArgument-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockAddArgument-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockAddArgument")],-1)),e[1384]||(e[1384]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1385]||(e[1385]=l('
julia
mlirBlockAddArgument(block, type, loc)

Appends an argument of the specified type to the block. Returns the newly added argument.

source

',3))]),t("details",fp,[t("summary",null,[e[1386]||(e[1386]=t("a",{id:"Reactant.MLIR.API.mlirBlockAppendOwnedOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockAppendOwnedOperation-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockAppendOwnedOperation")],-1)),e[1387]||(e[1387]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1388]||(e[1388]=l('
julia
mlirBlockAppendOwnedOperation(block, operation)

Takes an operation owned by the caller and appends it to the block.

source

',3))]),t("details",Mp,[t("summary",null,[e[1389]||(e[1389]=t("a",{id:"Reactant.MLIR.API.mlirBlockArgumentGetArgNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentGetArgNumber-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentGetArgNumber")],-1)),e[1390]||(e[1390]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1391]||(e[1391]=l('
julia
mlirBlockArgumentGetArgNumber(value)

Returns the position of the value in the argument list of its block.

source

',3))]),t("details",Ap,[t("summary",null,[e[1392]||(e[1392]=t("a",{id:"Reactant.MLIR.API.mlirBlockArgumentGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentGetOwner-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentGetOwner")],-1)),e[1393]||(e[1393]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1394]||(e[1394]=l('
julia
mlirBlockArgumentGetOwner(value)

Returns the block in which this value is defined as an argument. Asserts if the value is not a block argument.

source

',3))]),t("details",Lp,[t("summary",null,[e[1395]||(e[1395]=t("a",{id:"Reactant.MLIR.API.mlirBlockArgumentSetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockArgumentSetType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockArgumentSetType")],-1)),e[1396]||(e[1396]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1397]||(e[1397]=l('
julia
mlirBlockArgumentSetType(value, type)

Sets the type of the block argument to the given type.

source

',3))]),t("details",Ep,[t("summary",null,[e[1398]||(e[1398]=t("a",{id:"Reactant.MLIR.API.mlirBlockCreate-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockCreate-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockCreate")],-1)),e[1399]||(e[1399]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1400]||(e[1400]=l('
julia
mlirBlockCreate(nArgs, args, locs)

Creates a new empty block with the given argument types and transfers ownership to the caller.

source

',3))]),t("details",vp,[t("summary",null,[e[1401]||(e[1401]=t("a",{id:"Reactant.MLIR.API.mlirBlockDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockDestroy")],-1)),e[1402]||(e[1402]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1403]||(e[1403]=l('
julia
mlirBlockDestroy(block)

Takes a block owned by the caller and destroys it.

source

',3))]),t("details",Tp,[t("summary",null,[e[1404]||(e[1404]=t("a",{id:"Reactant.MLIR.API.mlirBlockDetach-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockDetach-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockDetach")],-1)),e[1405]||(e[1405]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1406]||(e[1406]=l('
julia
mlirBlockDetach(block)

Detach a block from the owning region and assume ownership.

source

',3))]),t("details",Cp,[t("summary",null,[e[1407]||(e[1407]=t("a",{id:"Reactant.MLIR.API.mlirBlockEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockEqual")],-1)),e[1408]||(e[1408]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1409]||(e[1409]=l('
julia
mlirBlockEqual(block, other)

Checks whether two blocks handles point to the same block. This does not perform deep comparison.

source

',3))]),t("details",xp,[t("summary",null,[e[1410]||(e[1410]=t("a",{id:"Reactant.MLIR.API.mlirBlockEraseArgument-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockEraseArgument-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockEraseArgument")],-1)),e[1411]||(e[1411]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1412]||(e[1412]=l('
julia
mlirBlockEraseArgument(block, index)

Erase the argument at 'index' and remove it from the argument list.

source

',3))]),t("details",Fp,[t("summary",null,[e[1413]||(e[1413]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetArgument-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBlockGetArgument-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetArgument")],-1)),e[1414]||(e[1414]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1415]||(e[1415]=l('
julia
mlirBlockGetArgument(block, pos)

Returns pos-th argument of the block.

source

',3))]),t("details",Pp,[t("summary",null,[e[1416]||(e[1416]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetFirstOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetFirstOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetFirstOperation")],-1)),e[1417]||(e[1417]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1418]||(e[1418]=l('
julia
mlirBlockGetFirstOperation(block)

Returns the first operation in the block.

source

',3))]),t("details",Dp,[t("summary",null,[e[1419]||(e[1419]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetNextInRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetNextInRegion-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetNextInRegion")],-1)),e[1420]||(e[1420]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1421]||(e[1421]=l('
julia
mlirBlockGetNextInRegion(block)

Returns the block immediately following the given block in its parent region.

source

',3))]),t("details",Op,[t("summary",null,[e[1422]||(e[1422]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetNumArguments-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetNumArguments-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetNumArguments")],-1)),e[1423]||(e[1423]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1424]||(e[1424]=l('
julia
mlirBlockGetNumArguments(block)

Returns the number of arguments of the block.

source

',3))]),t("details",Bp,[t("summary",null,[e[1425]||(e[1425]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetParentOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetParentOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetParentOperation")],-1)),e[1426]||(e[1426]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1427]||(e[1427]=l('
julia
mlirBlockGetParentOperation(arg1)

Returns the closest surrounding operation that contains this block.

source

',3))]),t("details",Gp,[t("summary",null,[e[1428]||(e[1428]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetParentRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetParentRegion-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetParentRegion")],-1)),e[1429]||(e[1429]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1430]||(e[1430]=l('
julia
mlirBlockGetParentRegion(block)

Returns the region that contains this block.

source

',3))]),t("details",zp,[t("summary",null,[e[1431]||(e[1431]=t("a",{id:"Reactant.MLIR.API.mlirBlockGetTerminator-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockGetTerminator-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockGetTerminator")],-1)),e[1432]||(e[1432]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1433]||(e[1433]=l('
julia
mlirBlockGetTerminator(block)

Returns the terminator operation in the block or null if no terminator.

source

',3))]),t("details",wp,[t("summary",null,[e[1434]||(e[1434]=t("a",{id:"Reactant.MLIR.API.mlirBlockInsertArgument-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertArgument-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertArgument")],-1)),e[1435]||(e[1435]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1436]||(e[1436]=l('
julia
mlirBlockInsertArgument(block, pos, type, loc)

Inserts an argument of the specified type at a specified index to the block. Returns the newly added argument.

source

',3))]),t("details",Sp,[t("summary",null,[e[1437]||(e[1437]=t("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperation-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperation")],-1)),e[1438]||(e[1438]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1439]||(e[1439]=l('
julia
mlirBlockInsertOwnedOperation(block, pos, operation)

Takes an operation owned by the caller and inserts it as pos to the block. This is an expensive operation that scans the block linearly, prefer insertBefore/After instead.

source

',3))]),t("details",Np,[t("summary",null,[e[1440]||(e[1440]=t("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperationAfter")],-1)),e[1441]||(e[1441]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1442]||(e[1442]=l('
julia
mlirBlockInsertOwnedOperationAfter(block, reference, operation)

Takes an operation owned by the caller and inserts it after the (non-owned) reference operation in the given block. If the reference is null, prepends the operation. Otherwise, the reference must belong to the block.

source

',3))]),t("details",Vp,[t("summary",null,[e[1443]||(e[1443]=t("a",{id:"Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockInsertOwnedOperationBefore")],-1)),e[1444]||(e[1444]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1445]||(e[1445]=l('
julia
mlirBlockInsertOwnedOperationBefore(block, reference, operation)

Takes an operation owned by the caller and inserts it before the (non-owned) reference operation in the given block. If the reference is null, appends the operation. Otherwise, the reference must belong to the block.

source

',3))]),t("details",qp,[t("summary",null,[e[1446]||(e[1446]=t("a",{id:"Reactant.MLIR.API.mlirBlockIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBlockIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockIsNull")],-1)),e[1447]||(e[1447]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1448]||(e[1448]=l('
julia
mlirBlockIsNull(block)

Checks whether a block is null.

source

',3))]),t("details",Up,[t("summary",null,[e[1449]||(e[1449]=t("a",{id:"Reactant.MLIR.API.mlirBlockPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirBlockPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBlockPrint")],-1)),e[1450]||(e[1450]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1451]||(e[1451]=l('
julia
mlirBlockPrint(block, callback, userData)

Prints a block by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Qp,[t("summary",null,[e[1452]||(e[1452]=t("a",{id:"Reactant.MLIR.API.mlirBoolAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBoolAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBoolAttrGet")],-1)),e[1453]||(e[1453]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1454]||(e[1454]=l('
julia
mlirBoolAttrGet(ctx, value)

Creates a bool attribute in the given context with the given value.

source

',3))]),t("details",Wp,[t("summary",null,[e[1455]||(e[1455]=t("a",{id:"Reactant.MLIR.API.mlirBoolAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBoolAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBoolAttrGetValue")],-1)),e[1456]||(e[1456]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1457]||(e[1457]=l('
julia
mlirBoolAttrGetValue(attr)

Returns the value stored in the given bool attribute.

source

',3))]),t("details",Hp,[t("summary",null,[e[1458]||(e[1458]=t("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigCreate")],-1)),e[1459]||(e[1459]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1460]||(e[1460]=l('
julia
mlirBytecodeWriterConfigCreate()

Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirBytecodeWriterConfigDestroy().

source

',3))]),t("details",Zp,[t("summary",null,[e[1461]||(e[1461]=t("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigDesiredEmitVersion")],-1)),e[1462]||(e[1462]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1463]||(e[1463]=l('
julia
mlirBytecodeWriterConfigDesiredEmitVersion(flags, version)

Sets the version to emit in the writer config.

source

',3))]),t("details",Jp,[t("summary",null,[e[1464]||(e[1464]=t("a",{id:"Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirBytecodeWriterConfigDestroy")],-1)),e[1465]||(e[1465]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1466]||(e[1466]=l('
julia
mlirBytecodeWriterConfigDestroy(config)

Destroys printing flags created with mlirBytecodeWriterConfigCreate.

source

',3))]),t("details",Kp,[t("summary",null,[e[1467]||(e[1467]=t("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGet")],-1)),e[1468]||(e[1468]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1469]||(e[1469]=l('
julia
mlirCalibratedQuantizedTypeGet(expressedType, min, max)

Creates an instance of CalibratedQuantizedType with the given parameters in the same context as expressedType and returns it. The instance is owned by the context.

source

',3))]),t("details",$p,[t("summary",null,[e[1470]||(e[1470]=t("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax-Tuple{Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMax")],-1)),e[1471]||(e[1471]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1472]||(e[1472]=l('
julia
mlirCalibratedQuantizedTypeGetMax(type)

Returns the max value of the given calibrated quantized type.

source

',3))]),t("details",Xp,[t("summary",null,[e[1473]||(e[1473]=t("a",{id:"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin-Tuple{Any}",href:"#Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCalibratedQuantizedTypeGetMin")],-1)),e[1474]||(e[1474]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1475]||(e[1475]=l('
julia
mlirCalibratedQuantizedTypeGetMin(type)

Returns the min value of the given calibrated quantized type.

source

',3))]),t("details",Yp,[t("summary",null,[e[1476]||(e[1476]=t("a",{id:"Reactant.MLIR.API.mlirComplexTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirComplexTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGet")],-1)),e[1477]||(e[1477]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1478]||(e[1478]=l('
julia
mlirComplexTypeGet(elementType)

Creates a complex type with the given element type in the same context as the element type. The type is owned by the context.

source

',3))]),t("details",_p,[t("summary",null,[e[1479]||(e[1479]=t("a",{id:"Reactant.MLIR.API.mlirComplexTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirComplexTypeGetElementType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGetElementType")],-1)),e[1480]||(e[1480]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1481]||(e[1481]=l('
julia
mlirComplexTypeGetElementType(type)

Returns the element type of the given complex type.

source

',3))]),t("details",er,[t("summary",null,[e[1482]||(e[1482]=t("a",{id:"Reactant.MLIR.API.mlirComplexTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirComplexTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirComplexTypeGetTypeID")],-1)),e[1483]||(e[1483]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1484]||(e[1484]=l('
julia
mlirComplexTypeGetTypeID()

Returns the typeID of an Complex type.

source

',3))]),t("details",tr,[t("summary",null,[e[1485]||(e[1485]=t("a",{id:"Reactant.MLIR.API.mlirContextAppendDialectRegistry-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextAppendDialectRegistry-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextAppendDialectRegistry")],-1)),e[1486]||(e[1486]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1487]||(e[1487]=l('
julia
mlirContextAppendDialectRegistry(ctx, registry)

Append the contents of the given dialect registry to the registry associated with the context.

source

',3))]),t("details",sr,[t("summary",null,[e[1488]||(e[1488]=t("a",{id:"Reactant.MLIR.API.mlirContextAttachDiagnosticHandler-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirContextAttachDiagnosticHandler-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextAttachDiagnosticHandler")],-1)),e[1489]||(e[1489]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1490]||(e[1490]=l('
julia
mlirContextAttachDiagnosticHandler(context, handler, userData, deleteUserData)

Attaches the diagnostic handler to the context. Handlers are invoked in the reverse order of attachment until one of them processes the diagnostic completely. When a handler is invoked it is passed the userData that was provided when it was attached. If non-NULL, deleteUserData is called once the system no longer needs to call the handler (for instance after the handler is detached or the context is destroyed). Returns an identifier that can be used to detach the handler.

source

',3))]),t("details",ar,[t("summary",null,[e[1491]||(e[1491]=t("a",{id:"Reactant.MLIR.API.mlirContextCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirContextCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreate")],-1)),e[1492]||(e[1492]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1493]||(e[1493]=l('
julia
mlirContextCreate()

Creates an MLIR context and transfers its ownership to the caller. This sets the default multithreading option (enabled).

source

',3))]),t("details",ir,[t("summary",null,[e[1494]||(e[1494]=t("a",{id:"Reactant.MLIR.API.mlirContextCreateWithRegistry-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextCreateWithRegistry-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreateWithRegistry")],-1)),e[1495]||(e[1495]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1496]||(e[1496]=l('
julia
mlirContextCreateWithRegistry(registry, threadingEnabled)

Creates an MLIR context, setting the multithreading setting explicitly and pre-loading the dialects from the provided DialectRegistry.

source

',3))]),t("details",lr,[t("summary",null,[e[1497]||(e[1497]=t("a",{id:"Reactant.MLIR.API.mlirContextCreateWithThreading-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextCreateWithThreading-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextCreateWithThreading")],-1)),e[1498]||(e[1498]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1499]||(e[1499]=l('
julia
mlirContextCreateWithThreading(threadingEnabled)

Creates an MLIR context with an explicit setting of the multithreading setting and transfers its ownership to the caller.

source

',3))]),t("details",nr,[t("summary",null,[e[1500]||(e[1500]=t("a",{id:"Reactant.MLIR.API.mlirContextDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextDestroy")],-1)),e[1501]||(e[1501]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1502]||(e[1502]=l('
julia
mlirContextDestroy(context)

Takes an MLIR context owned by the caller and destroys it.

source

',3))]),t("details",pr,[t("summary",null,[e[1503]||(e[1503]=t("a",{id:"Reactant.MLIR.API.mlirContextDetachDiagnosticHandler-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextDetachDiagnosticHandler-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextDetachDiagnosticHandler")],-1)),e[1504]||(e[1504]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1505]||(e[1505]=l('
julia
mlirContextDetachDiagnosticHandler(context, id)

Detaches an attached diagnostic handler from the context given its identifier.

source

',3))]),t("details",rr,[t("summary",null,[e[1506]||(e[1506]=t("a",{id:"Reactant.MLIR.API.mlirContextEnableMultithreading-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextEnableMultithreading-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextEnableMultithreading")],-1)),e[1507]||(e[1507]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1508]||(e[1508]=l('
julia
mlirContextEnableMultithreading(context, enable)

Set threading mode (must be set to false to mlir-print-ir-after-all).

source

',3))]),t("details",or,[t("summary",null,[e[1509]||(e[1509]=t("a",{id:"Reactant.MLIR.API.mlirContextEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextEqual")],-1)),e[1510]||(e[1510]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1511]||(e[1511]=l('
julia
mlirContextEqual(ctx1, ctx2)

Checks if two contexts are equal.

source

',3))]),t("details",dr,[t("summary",null,[e[1512]||(e[1512]=t("a",{id:"Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetAllowUnregisteredDialects")],-1)),e[1513]||(e[1513]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1514]||(e[1514]=l('
julia
mlirContextGetAllowUnregisteredDialects(context)

Returns whether the context allows unregistered dialects.

source

',3))]),t("details",cr,[t("summary",null,[e[1515]||(e[1515]=t("a",{id:"Reactant.MLIR.API.mlirContextGetNumLoadedDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetNumLoadedDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetNumLoadedDialects")],-1)),e[1516]||(e[1516]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1517]||(e[1517]=l('
julia
mlirContextGetNumLoadedDialects(context)

Returns the number of dialects loaded by the context.

source

',3))]),t("details",br,[t("summary",null,[e[1518]||(e[1518]=t("a",{id:"Reactant.MLIR.API.mlirContextGetNumRegisteredDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextGetNumRegisteredDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetNumRegisteredDialects")],-1)),e[1519]||(e[1519]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1520]||(e[1520]=l('
julia
mlirContextGetNumRegisteredDialects(context)

Returns the number of dialects registered with the given context. A registered dialect will be loaded if needed by the parser.

source

',3))]),t("details",hr,[t("summary",null,[e[1521]||(e[1521]=t("a",{id:"Reactant.MLIR.API.mlirContextGetOrLoadDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextGetOrLoadDialect-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextGetOrLoadDialect")],-1)),e[1522]||(e[1522]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1523]||(e[1523]=l('
julia
mlirContextGetOrLoadDialect(context, name)

Gets the dialect instance owned by the given context using the dialect namespace to identify it, loads (i.e., constructs the instance of) the dialect if necessary. If the dialect is not registered with the context, returns null. Use mlirContextLoad<Name>Dialect to load an unregistered dialect.

source

',3))]),t("details",ur,[t("summary",null,[e[1524]||(e[1524]=t("a",{id:"Reactant.MLIR.API.mlirContextIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextIsNull")],-1)),e[1525]||(e[1525]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1526]||(e[1526]=l('
julia
mlirContextIsNull(context)

Checks whether a context is null.

source

',3))]),t("details",gr,[t("summary",null,[e[1527]||(e[1527]=t("a",{id:"Reactant.MLIR.API.mlirContextIsRegisteredOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextIsRegisteredOperation-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextIsRegisteredOperation")],-1)),e[1528]||(e[1528]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1529]||(e[1529]=l('
julia
mlirContextIsRegisteredOperation(context, name)

Returns whether the given fully-qualified operation (i.e. 'dialect.operation') is registered with the context. This will return true if the dialect is loaded and the operation is registered within the dialect.

source

',3))]),t("details",yr,[t("summary",null,[e[1530]||(e[1530]=t("a",{id:"Reactant.MLIR.API.mlirContextLoadAllAvailableDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirContextLoadAllAvailableDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextLoadAllAvailableDialects")],-1)),e[1531]||(e[1531]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1532]||(e[1532]=l('
julia
mlirContextLoadAllAvailableDialects(context)

Eagerly loads all available dialects registered with a context, making them available for use for IR construction.

source

',3))]),t("details",mr,[t("summary",null,[e[1533]||(e[1533]=t("a",{id:"Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextSetAllowUnregisteredDialects")],-1)),e[1534]||(e[1534]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1535]||(e[1535]=l('
julia
mlirContextSetAllowUnregisteredDialects(context, allow)

Sets whether unregistered dialects are allowed in this context.

source

',3))]),t("details",kr,[t("summary",null,[e[1536]||(e[1536]=t("a",{id:"Reactant.MLIR.API.mlirContextSetThreadPool-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirContextSetThreadPool-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirContextSetThreadPool")],-1)),e[1537]||(e[1537]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1538]||(e[1538]=l('
julia
mlirContextSetThreadPool(context, threadPool)

Sets the thread pool of the context explicitly, enabling multithreading in the process. This API should be used to avoid re-creating thread pools in long-running applications that perform multiple compilations, see the C++ documentation for MLIRContext for details.

source

',3))]),t("details",Rr,[t("summary",null,[e[1539]||(e[1539]=t("a",{id:"Reactant.MLIR.API.mlirCreateExternalPass-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirCreateExternalPass-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirCreateExternalPass")],-1)),e[1540]||(e[1540]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1541]||(e[1541]=l('
julia
mlirCreateExternalPass(passID, name, argument, description, opName, nDependentDialects, dependentDialects, callbacks, userData)

Creates an external MlirPass that calls the supplied callbacks using the supplied userData. If opName is empty, the pass is a generic operation pass. Otherwise it is an operation pass specific to the specified pass name.

source

',3))]),t("details",Ir,[t("summary",null,[e[1542]||(e[1542]=t("a",{id:"Reactant.MLIR.API.mlirDenseArrayGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseArrayGetNumElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseArrayGetNumElements")],-1)),e[1543]||(e[1543]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1544]||(e[1544]=l('
julia
mlirDenseArrayGetNumElements(attr)

Get the size of a dense array.

source

',3))]),t("details",jr,[t("summary",null,[e[1545]||(e[1545]=t("a",{id:"Reactant.MLIR.API.mlirDenseBoolArrayGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolArrayGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolArrayGet")],-1)),e[1546]||(e[1546]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1547]||(e[1547]=l('
julia
mlirDenseBoolArrayGet(ctx, size, values)

Create a dense array attribute with the given elements.

source

',3))]),t("details",fr,[t("summary",null,[e[1548]||(e[1548]=t("a",{id:"Reactant.MLIR.API.mlirDenseBoolArrayGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolArrayGetElement-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolArrayGetElement")],-1)),e[1549]||(e[1549]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1550]||(e[1550]=l('
julia
mlirDenseBoolArrayGetElement(attr, pos)

Get an element of a dense array.

source

',3))]),t("details",Mr,[t("summary",null,[e[1551]||(e[1551]=t("a",{id:"Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseBoolResourceElementsAttrGetValue")],-1)),e[1552]||(e[1552]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1553]||(e[1553]=l('
julia
mlirDenseBoolResourceElementsAttrGetValue(attr, pos)

Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense resource elements attribute.

source

',3))]),t("details",Ar,[t("summary",null,[e[1554]||(e[1554]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrBoolGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrBoolGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrBoolGet")],-1)),e[1555]||(e[1555]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1556]||(e[1556]=l('
julia
mlirDenseElementsAttrBoolGet(shapedType, numElements, elements)

Creates a dense elements attribute with the given shaped type from elements of a specific type. Expects the element type of the shaped type to match the data element type.

source

',3))]),t("details",Lr,[t("summary",null,[e[1557]||(e[1557]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGet")],-1)),e[1558]||(e[1558]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1559]||(e[1559]=l('
julia
mlirDenseElementsAttrGet(shapedType, numElements, elements)

Creates a dense elements attribute with the given Shaped type and elements in the same context as the type.

source

',3))]),t("details",Er,[t("summary",null,[e[1560]||(e[1560]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetBoolValue")],-1)),e[1561]||(e[1561]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1562]||(e[1562]=l('
julia
mlirDenseElementsAttrGetBoolValue(attr, pos)

Returns the pos-th value (flat contiguous indexing) of a specific type contained by the given dense elements attribute.

source

',3))]),t("details",vr,[t("summary",null,[e[1563]||(e[1563]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetRawData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetRawData-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetRawData")],-1)),e[1564]||(e[1564]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1565]||(e[1565]=l('
julia
mlirDenseElementsAttrGetRawData(attr)

Returns the raw data of the given dense elements attribute.

source

',3))]),t("details",Tr,[t("summary",null,[e[1566]||(e[1566]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrGetSplatValue")],-1)),e[1567]||(e[1567]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1568]||(e[1568]=l('
julia
mlirDenseElementsAttrGetSplatValue(attr)

Returns the single replicated value (splat) of a specific type contained by the given dense elements attribute.

source

',3))]),t("details",Cr,[t("summary",null,[e[1569]||(e[1569]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrIsSplat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrIsSplat-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrIsSplat")],-1)),e[1570]||(e[1570]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1571]||(e[1571]=l('
julia
mlirDenseElementsAttrIsSplat(attr)

Checks whether the given dense elements attribute contains a single replicated value (splat).

source

',3))]),t("details",xr,[t("summary",null,[e[1572]||(e[1572]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrRawBufferGet")],-1)),e[1573]||(e[1573]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1574]||(e[1574]=l('
julia
mlirDenseElementsAttrRawBufferGet(shapedType, rawBufferSize, rawBuffer)

Creates a dense elements attribute with the given Shaped type and elements populated from a packed, row-major opaque buffer of contents.

The format of the raw buffer is a densely packed array of values that can be bitcast to the storage format of the element type specified. Types that are not byte aligned will be: - For bitwidth > 1: Rounded up to the next byte. - For bitwidth = 1: Packed into 8bit bytes with bits corresponding to the linear order of the shape type from MSB to LSB, padded to on the right.

A raw buffer of a single element (or for 1-bit, a byte of value 0 or 255) will be interpreted as a splat. User code should be prepared for additional, conformant patterns to be identified as splats in the future.

source

',5))]),t("details",Fr,[t("summary",null,[e[1575]||(e[1575]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrReshapeGet")],-1)),e[1576]||(e[1576]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1577]||(e[1577]=l('
julia
mlirDenseElementsAttrReshapeGet(attr, shapedType)

Creates a dense elements attribute that has the same data as the given dense elements attribute and a different shaped type. The new type must have the same total number of elements.

source

',3))]),t("details",Pr,[t("summary",null,[e[1578]||(e[1578]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrSplatGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrSplatGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrSplatGet")],-1)),e[1579]||(e[1579]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1580]||(e[1580]=l('
julia
mlirDenseElementsAttrSplatGet(shapedType, element)

Creates a dense elements attribute with the given Shaped type containing a single replicated element (splat).

source

',3))]),t("details",Dr,[t("summary",null,[e[1581]||(e[1581]=t("a",{id:"Reactant.MLIR.API.mlirDenseElementsAttrStringGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDenseElementsAttrStringGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseElementsAttrStringGet")],-1)),e[1582]||(e[1582]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1583]||(e[1583]=l('
julia
mlirDenseElementsAttrStringGet(shapedType, numElements, strs)

Creates a dense elements attribute with the given shaped type from string elements.

source

',3))]),t("details",Or,[t("summary",null,[e[1584]||(e[1584]=t("a",{id:"Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDenseIntOrFPElementsAttrGetTypeID")],-1)),e[1585]||(e[1585]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1586]||(e[1586]=l('
julia
mlirDenseIntOrFPElementsAttrGetTypeID()

Returns the typeID of an DenseIntOrFPElements attribute.

source

',3))]),t("details",Br,[t("summary",null,[e[1587]||(e[1587]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetLocation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetLocation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetLocation")],-1)),e[1588]||(e[1588]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1589]||(e[1589]=l('
julia
mlirDiagnosticGetLocation(diagnostic)

Returns the location at which the diagnostic is reported.

source

',3))]),t("details",Gr,[t("summary",null,[e[1590]||(e[1590]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetNote-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetNote-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetNote")],-1)),e[1591]||(e[1591]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1592]||(e[1592]=l('
julia
mlirDiagnosticGetNote(diagnostic, pos)

Returns pos-th note attached to the diagnostic. Expects pos to be a valid zero-based index into the list of notes.

source

',3))]),t("details",zr,[t("summary",null,[e[1593]||(e[1593]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetNumNotes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetNumNotes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetNumNotes")],-1)),e[1594]||(e[1594]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1595]||(e[1595]=l('
julia
mlirDiagnosticGetNumNotes(diagnostic)

Returns the number of notes attached to the diagnostic.

source

',3))]),t("details",wr,[t("summary",null,[e[1596]||(e[1596]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticGetSeverity-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDiagnosticGetSeverity-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticGetSeverity")],-1)),e[1597]||(e[1597]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1598]||(e[1598]=l('
julia
mlirDiagnosticGetSeverity(diagnostic)

Returns the severity of the diagnostic.

source

',3))]),t("details",Sr,[t("summary",null,[e[1599]||(e[1599]=t("a",{id:"Reactant.MLIR.API.mlirDiagnosticPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDiagnosticPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDiagnosticPrint")],-1)),e[1600]||(e[1600]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1601]||(e[1601]=l('
julia
mlirDiagnosticPrint(diagnostic, callback, userData)

Prints a diagnostic using the provided callback.

source

',3))]),t("details",Nr,[t("summary",null,[e[1602]||(e[1602]=t("a",{id:"Reactant.MLIR.API.mlirDialectEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectEqual")],-1)),e[1603]||(e[1603]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1604]||(e[1604]=l('
julia
mlirDialectEqual(dialect1, dialect2)

Checks if two dialects that belong to the same context are equal. Dialects from different contexts will not compare equal.

source

',3))]),t("details",Vr,[t("summary",null,[e[1605]||(e[1605]=t("a",{id:"Reactant.MLIR.API.mlirDialectGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectGetContext")],-1)),e[1606]||(e[1606]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1607]||(e[1607]=l('
julia
mlirDialectGetContext(dialect)

Returns the context that owns the dialect.

source

',3))]),t("details",qr,[t("summary",null,[e[1608]||(e[1608]=t("a",{id:"Reactant.MLIR.API.mlirDialectGetNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectGetNamespace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectGetNamespace")],-1)),e[1609]||(e[1609]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1610]||(e[1610]=l('
julia
mlirDialectGetNamespace(dialect)

Returns the namespace of the given dialect.

source

',3))]),t("details",Ur,[t("summary",null,[e[1611]||(e[1611]=t("a",{id:"Reactant.MLIR.API.mlirDialectHandleGetNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectHandleGetNamespace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleGetNamespace")],-1)),e[1612]||(e[1612]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1613]||(e[1613]=l('
julia
mlirDialectHandleGetNamespace(arg1)

Returns the namespace associated with the provided dialect handle.

source

',3))]),t("details",Qr,[t("summary",null,[e[1614]||(e[1614]=t("a",{id:"Reactant.MLIR.API.mlirDialectHandleInsertDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleInsertDialect-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleInsertDialect")],-1)),e[1615]||(e[1615]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1616]||(e[1616]=l('
julia
mlirDialectHandleInsertDialect(arg1, arg2)

Inserts the dialect associated with the provided dialect handle into the provided dialect registry

source

',3))]),t("details",Wr,[t("summary",null,[e[1617]||(e[1617]=t("a",{id:"Reactant.MLIR.API.mlirDialectHandleLoadDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleLoadDialect-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleLoadDialect")],-1)),e[1618]||(e[1618]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1619]||(e[1619]=l('
julia
mlirDialectHandleLoadDialect(arg1, arg2)

Loads the dialect associated with the provided dialect handle.

source

',3))]),t("details",Hr,[t("summary",null,[e[1620]||(e[1620]=t("a",{id:"Reactant.MLIR.API.mlirDialectHandleRegisterDialect-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDialectHandleRegisterDialect-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectHandleRegisterDialect")],-1)),e[1621]||(e[1621]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1622]||(e[1622]=l('
julia
mlirDialectHandleRegisterDialect(arg1, arg2)

Registers the dialect associated with the provided dialect handle.

source

',3))]),t("details",Zr,[t("summary",null,[e[1623]||(e[1623]=t("a",{id:"Reactant.MLIR.API.mlirDialectIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectIsNull")],-1)),e[1624]||(e[1624]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1625]||(e[1625]=l('
julia
mlirDialectIsNull(dialect)

Checks if the dialect is null.

source

',3))]),t("details",Jr,[t("summary",null,[e[1626]||(e[1626]=t("a",{id:"Reactant.MLIR.API.mlirDialectRegistryCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirDialectRegistryCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryCreate")],-1)),e[1627]||(e[1627]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1628]||(e[1628]=l('
julia
mlirDialectRegistryCreate()

Creates a dialect registry and transfers its ownership to the caller.

source

',3))]),t("details",Kr,[t("summary",null,[e[1629]||(e[1629]=t("a",{id:"Reactant.MLIR.API.mlirDialectRegistryDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectRegistryDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryDestroy")],-1)),e[1630]||(e[1630]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1631]||(e[1631]=l('
julia
mlirDialectRegistryDestroy(registry)

Takes a dialect registry owned by the caller and destroys it.

source

',3))]),t("details",$r,[t("summary",null,[e[1632]||(e[1632]=t("a",{id:"Reactant.MLIR.API.mlirDialectRegistryIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDialectRegistryIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDialectRegistryIsNull")],-1)),e[1633]||(e[1633]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1634]||(e[1634]=l('
julia
mlirDialectRegistryIsNull(registry)

Checks if the dialect registry is null.

source

',3))]),t("details",Xr,[t("summary",null,[e[1635]||(e[1635]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGet")],-1)),e[1636]||(e[1636]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1637]||(e[1637]=l('
julia
mlirDictionaryAttrGet(ctx, numElements, elements)

Creates a dictionary attribute containing the given list of elements in the provided context.

source

',3))]),t("details",Yr,[t("summary",null,[e[1638]||(e[1638]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetElement-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetElement-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetElement")],-1)),e[1639]||(e[1639]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1640]||(e[1640]=l('
julia
mlirDictionaryAttrGetElement(attr, pos)

Returns pos-th element of the given dictionary attribute.

source

',3))]),t("details",_r,[t("summary",null,[e[1641]||(e[1641]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetElementByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetElementByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetElementByName")],-1)),e[1642]||(e[1642]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1643]||(e[1643]=l('
julia
mlirDictionaryAttrGetElementByName(attr, name)

Returns the dictionary attribute element with the given name or NULL if the given name does not exist in the dictionary.

source

',3))]),t("details",eo,[t("summary",null,[e[1644]||(e[1644]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetNumElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetNumElements")],-1)),e[1645]||(e[1645]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1646]||(e[1646]=l('
julia
mlirDictionaryAttrGetNumElements(attr)

Returns the number of attributes contained in a dictionary attribute.

source

',3))]),t("details",to,[t("summary",null,[e[1647]||(e[1647]=t("a",{id:"Reactant.MLIR.API.mlirDictionaryAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirDictionaryAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDictionaryAttrGetTypeID")],-1)),e[1648]||(e[1648]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1649]||(e[1649]=l('
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mlirDictionaryAttrGetTypeID()

Returns the typeID of a Dictionary attribute.

source

',3))]),t("details",so,[t("summary",null,[e[1650]||(e[1650]=t("a",{id:"Reactant.MLIR.API.mlirDisctinctAttrCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirDisctinctAttrCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirDisctinctAttrCreate")],-1)),e[1651]||(e[1651]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1652]||(e[1652]=l('
julia
mlirDisctinctAttrCreate(referencedAttr)

Creates a DisctinctAttr with the referenced attribute.

source

',3))]),t("details",ao,[t("summary",null,[e[1653]||(e[1653]=t("a",{id:"Reactant.MLIR.API.mlirElementsAttrGetNumElements-Tuple{Any}",href:"#Reactant.MLIR.API.mlirElementsAttrGetNumElements-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrGetNumElements")],-1)),e[1654]||(e[1654]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1655]||(e[1655]=l('
julia
mlirElementsAttrGetNumElements(attr)

Gets the total number of elements in the given elements attribute. In order to iterate over the attribute, obtain its type, which must be a statically shaped type and use its sizes to build a multi-dimensional index.

source

',3))]),t("details",io,[t("summary",null,[e[1656]||(e[1656]=t("a",{id:"Reactant.MLIR.API.mlirElementsAttrGetValue-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirElementsAttrGetValue-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrGetValue")],-1)),e[1657]||(e[1657]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1658]||(e[1658]=l('
julia
mlirElementsAttrGetValue(attr, rank, idxs)

Returns the element at the given rank-dimensional index.

source

',3))]),t("details",lo,[t("summary",null,[e[1659]||(e[1659]=t("a",{id:"Reactant.MLIR.API.mlirElementsAttrIsValidIndex-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirElementsAttrIsValidIndex-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirElementsAttrIsValidIndex")],-1)),e[1660]||(e[1660]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1661]||(e[1661]=l('
julia
mlirElementsAttrIsValidIndex(attr, rank, idxs)

Checks whether the given rank-dimensional index is valid in the given elements attribute.

source

',3))]),t("details",no,[t("summary",null,[e[1662]||(e[1662]=t("a",{id:"Reactant.MLIR.API.mlirEmitError-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirEmitError-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirEmitError")],-1)),e[1663]||(e[1663]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1664]||(e[1664]=l('
julia
mlirEmitError(location, message)

Emits an error at the given location through the diagnostics engine. Used for testing purposes.

source

',3))]),t("details",po,[t("summary",null,[e[1665]||(e[1665]=t("a",{id:"Reactant.MLIR.API.mlirEnableGlobalDebug-Tuple{Any}",href:"#Reactant.MLIR.API.mlirEnableGlobalDebug-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirEnableGlobalDebug")],-1)),e[1666]||(e[1666]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1667]||(e[1667]=l('
julia
mlirEnableGlobalDebug(enable)

Sets the global debugging flag.

source

',3))]),t("details",ro,[t("summary",null,[e[1668]||(e[1668]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineCreate-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineCreate-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineCreate")],-1)),e[1669]||(e[1669]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1670]||(e[1670]=l('
julia
mlirExecutionEngineCreate(op, optLevel, numPaths, sharedLibPaths, enableObjectDump)

Creates an ExecutionEngine for the provided ModuleOp. The ModuleOp is expected to be "translatable" to LLVM IR (only contains operations in dialects that implement the LLVMTranslationDialectInterface). The module ownership stays with the client and can be destroyed as soon as the call returns. optLevel is the optimization level to be used for transformation and code generation. LLVM passes at optLevel are run before code generation. The number and array of paths corresponding to shared libraries that will be loaded are specified via numPaths and sharedLibPaths respectively. TODO: figure out other options.

source

',3))]),t("details",oo,[t("summary",null,[e[1671]||(e[1671]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineDestroy")],-1)),e[1672]||(e[1672]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1673]||(e[1673]=l('
julia
mlirExecutionEngineDestroy(jit)

Destroy an ExecutionEngine instance.

source

',3))]),t("details",co,[t("summary",null,[e[1674]||(e[1674]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineDumpToObjectFile")],-1)),e[1675]||(e[1675]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1676]||(e[1676]=l('
julia
mlirExecutionEngineDumpToObjectFile(jit, fileName)

Dump as an object in fileName.

source

',3))]),t("details",bo,[t("summary",null,[e[1677]||(e[1677]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineInvokePacked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineInvokePacked-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineInvokePacked")],-1)),e[1678]||(e[1678]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1679]||(e[1679]=l('
julia
mlirExecutionEngineInvokePacked(jit, name, arguments)

Invoke a native function in the execution engine by name with the arguments and result of the invoked function passed as an array of pointers. The function must have been tagged with the llvm.emit\\_c\\_interface attribute. Returns a failure if the execution fails for any reason (the function name can't be resolved for instance).

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',3))]),t("details",ho,[t("summary",null,[e[1680]||(e[1680]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineIsNull")],-1)),e[1681]||(e[1681]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1682]||(e[1682]=l('
julia
mlirExecutionEngineIsNull(jit)

Checks whether an execution engine is null.

source

',3))]),t("details",uo,[t("summary",null,[e[1683]||(e[1683]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineLookup-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineLookup-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineLookup")],-1)),e[1684]||(e[1684]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1685]||(e[1685]=l('
julia
mlirExecutionEngineLookup(jit, name)

Lookup a native function in the execution engine by name, returns nullptr if the name can't be looked-up.

source

',3))]),t("details",go,[t("summary",null,[e[1686]||(e[1686]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineLookupPacked-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineLookupPacked-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineLookupPacked")],-1)),e[1687]||(e[1687]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1688]||(e[1688]=l('
julia
mlirExecutionEngineLookupPacked(jit, name)

Lookup the wrapper of the native function in the execution engine with the given name, returns nullptr if the function can't be looked-up.

source

',3))]),t("details",yo,[t("summary",null,[e[1689]||(e[1689]=t("a",{id:"Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExecutionEngineRegisterSymbol")],-1)),e[1690]||(e[1690]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1691]||(e[1691]=l('
julia
mlirExecutionEngineRegisterSymbol(jit, name, sym)

Register a symbol with the jit: this symbol will be accessible to the jitted code.

source

',3))]),t("details",mo,[t("summary",null,[e[1692]||(e[1692]=t("a",{id:"Reactant.MLIR.API.mlirExternalPassSignalFailure-Tuple{Any}",href:"#Reactant.MLIR.API.mlirExternalPassSignalFailure-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirExternalPassSignalFailure")],-1)),e[1693]||(e[1693]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1694]||(e[1694]=l('
julia
mlirExternalPassSignalFailure(pass)

This signals that the pass has failed. This is only valid to call during the run callback of MlirExternalPassCallbacks. See Pass::signalPassFailure().

source

',3))]),t("details",ko,[t("summary",null,[e[1695]||(e[1695]=t("a",{id:"Reactant.MLIR.API.mlirF16TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF16TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF16TypeGet")],-1)),e[1696]||(e[1696]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1697]||(e[1697]=l('
julia
mlirF16TypeGet(ctx)

Creates an f16 type in the given context. The type is owned by the context.

source

',3))]),t("details",Ro,[t("summary",null,[e[1698]||(e[1698]=t("a",{id:"Reactant.MLIR.API.mlirF32TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF32TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF32TypeGet")],-1)),e[1699]||(e[1699]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1700]||(e[1700]=l('
julia
mlirF32TypeGet(ctx)

Creates an f32 type in the given context. The type is owned by the context.

source

',3))]),t("details",Io,[t("summary",null,[e[1701]||(e[1701]=t("a",{id:"Reactant.MLIR.API.mlirF64TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirF64TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirF64TypeGet")],-1)),e[1702]||(e[1702]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1703]||(e[1703]=l('
julia
mlirF64TypeGet(ctx)

Creates a f64 type in the given context. The type is owned by the context.

source

',3))]),t("details",jo,[t("summary",null,[e[1704]||(e[1704]=t("a",{id:"Reactant.MLIR.API.mlirFlatSymbolRefAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirFlatSymbolRefAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFlatSymbolRefAttrGet")],-1)),e[1705]||(e[1705]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1706]||(e[1706]=l('
julia
mlirFlatSymbolRefAttrGet(ctx, symbol)

Creates a flat symbol reference attribute in the given context referencing a symbol identified by the given string.

source

',3))]),t("details",fo,[t("summary",null,[e[1707]||(e[1707]=t("a",{id:"Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFlatSymbolRefAttrGetValue")],-1)),e[1708]||(e[1708]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1709]||(e[1709]=l('
julia
mlirFlatSymbolRefAttrGetValue(attr)

Returns the referenced symbol as a string reference. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",Mo,[t("summary",null,[e[1710]||(e[1710]=t("a",{id:"Reactant.MLIR.API.mlirFloat16TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat16TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat16TypeGetTypeID")],-1)),e[1711]||(e[1711]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1712]||(e[1712]=l('
julia
mlirFloat16TypeGetTypeID()

Returns the typeID of an Float16 type.

source

',3))]),t("details",Ao,[t("summary",null,[e[1713]||(e[1713]=t("a",{id:"Reactant.MLIR.API.mlirFloat32TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat32TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat32TypeGetTypeID")],-1)),e[1714]||(e[1714]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1715]||(e[1715]=l('
julia
mlirFloat32TypeGetTypeID()

Returns the typeID of an Float32 type.

source

',3))]),t("details",Lo,[t("summary",null,[e[1716]||(e[1716]=t("a",{id:"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGet")],-1)),e[1717]||(e[1717]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1718]||(e[1718]=l('
julia
mlirFloat4E2M1FNTypeGet(ctx)

Creates an f4E2M1FN type in the given context. The type is owned by the context.

source

',3))]),t("details",Eo,[t("summary",null,[e[1719]||(e[1719]=t("a",{id:"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat4E2M1FNTypeGetTypeID")],-1)),e[1720]||(e[1720]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1721]||(e[1721]=l('
julia
mlirFloat4E2M1FNTypeGetTypeID()

Returns the typeID of an Float4E2M1FN type.

source

',3))]),t("details",vo,[t("summary",null,[e[1722]||(e[1722]=t("a",{id:"Reactant.MLIR.API.mlirFloat64TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat64TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat64TypeGetTypeID")],-1)),e[1723]||(e[1723]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1724]||(e[1724]=l('
julia
mlirFloat64TypeGetTypeID()

Returns the typeID of an Float64 type.

source

',3))]),t("details",To,[t("summary",null,[e[1725]||(e[1725]=t("a",{id:"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGet")],-1)),e[1726]||(e[1726]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1727]||(e[1727]=l('
julia
mlirFloat6E2M3FNTypeGet(ctx)

Creates an f6E2M3FN type in the given context. The type is owned by the context.

source

',3))]),t("details",Co,[t("summary",null,[e[1728]||(e[1728]=t("a",{id:"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E2M3FNTypeGetTypeID")],-1)),e[1729]||(e[1729]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1730]||(e[1730]=l('
julia
mlirFloat6E2M3FNTypeGetTypeID()

Returns the typeID of an Float6E2M3FN type.

source

',3))]),t("details",xo,[t("summary",null,[e[1731]||(e[1731]=t("a",{id:"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGet")],-1)),e[1732]||(e[1732]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1733]||(e[1733]=l('
julia
mlirFloat6E3M2FNTypeGet(ctx)

Creates an f6E3M2FN type in the given context. The type is owned by the context.

source

',3))]),t("details",Fo,[t("summary",null,[e[1734]||(e[1734]=t("a",{id:"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat6E3M2FNTypeGetTypeID")],-1)),e[1735]||(e[1735]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1736]||(e[1736]=l('
julia
mlirFloat6E3M2FNTypeGetTypeID()

Returns the typeID of an Float6E3M2FN type.

source

',3))]),t("details",Po,[t("summary",null,[e[1737]||(e[1737]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E3M4TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E3M4TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E3M4TypeGet")],-1)),e[1738]||(e[1738]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1739]||(e[1739]=l('
julia
mlirFloat8E3M4TypeGet(ctx)

Creates an f8E3M4 type in the given context. The type is owned by the context.

source

',3))]),t("details",Do,[t("summary",null,[e[1740]||(e[1740]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E3M4TypeGetTypeID")],-1)),e[1741]||(e[1741]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1742]||(e[1742]=l('
julia
mlirFloat8E3M4TypeGetTypeID()

Returns the typeID of an Float8E3M4 type.

source

',3))]),t("details",Oo,[t("summary",null,[e[1743]||(e[1743]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGet")],-1)),e[1744]||(e[1744]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1745]||(e[1745]=l('
julia
mlirFloat8E4M3B11FNUZTypeGet(ctx)

Creates an f8E4M3B11FNUZ type in the given context. The type is owned by the context.

source

',3))]),t("details",Bo,[t("summary",null,[e[1746]||(e[1746]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3B11FNUZTypeGetTypeID")],-1)),e[1747]||(e[1747]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1748]||(e[1748]=l('
julia
mlirFloat8E4M3B11FNUZTypeGetTypeID()

Returns the typeID of an Float8E4M3B11FNUZ type.

source

',3))]),t("details",Go,[t("summary",null,[e[1749]||(e[1749]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGet")],-1)),e[1750]||(e[1750]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1751]||(e[1751]=l('
julia
mlirFloat8E4M3FNTypeGet(ctx)

Creates an f8E4M3FN type in the given context. The type is owned by the context.

source

',3))]),t("details",zo,[t("summary",null,[e[1752]||(e[1752]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNTypeGetTypeID")],-1)),e[1753]||(e[1753]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1754]||(e[1754]=l('
julia
mlirFloat8E4M3FNTypeGetTypeID()

Returns the typeID of an Float8E4M3FN type.

source

',3))]),t("details",wo,[t("summary",null,[e[1755]||(e[1755]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGet")],-1)),e[1756]||(e[1756]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1757]||(e[1757]=l('
julia
mlirFloat8E4M3FNUZTypeGet(ctx)

Creates an f8E4M3FNUZ type in the given context. The type is owned by the context.

source

',3))]),t("details",So,[t("summary",null,[e[1758]||(e[1758]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3FNUZTypeGetTypeID")],-1)),e[1759]||(e[1759]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1760]||(e[1760]=l('
julia
mlirFloat8E4M3FNUZTypeGetTypeID()

Returns the typeID of an Float8E4M3FNUZ type.

source

',3))]),t("details",No,[t("summary",null,[e[1761]||(e[1761]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E4M3TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3TypeGet")],-1)),e[1762]||(e[1762]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1763]||(e[1763]=l('
julia
mlirFloat8E4M3TypeGet(ctx)

Creates an f8E4M3 type in the given context. The type is owned by the context.

source

',3))]),t("details",Vo,[t("summary",null,[e[1764]||(e[1764]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E4M3TypeGetTypeID")],-1)),e[1765]||(e[1765]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1766]||(e[1766]=l('
julia
mlirFloat8E4M3TypeGetTypeID()

Returns the typeID of an Float8E4M3 type.

source

',3))]),t("details",qo,[t("summary",null,[e[1767]||(e[1767]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGet")],-1)),e[1768]||(e[1768]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1769]||(e[1769]=l('
julia
mlirFloat8E5M2FNUZTypeGet(ctx)

Creates an f8E5M2FNUZ type in the given context. The type is owned by the context.

source

',3))]),t("details",Uo,[t("summary",null,[e[1770]||(e[1770]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2FNUZTypeGetTypeID")],-1)),e[1771]||(e[1771]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1772]||(e[1772]=l('
julia
mlirFloat8E5M2FNUZTypeGetTypeID()

Returns the typeID of an Float8E5M2FNUZ type.

source

',3))]),t("details",Qo,[t("summary",null,[e[1773]||(e[1773]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E5M2TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2TypeGet")],-1)),e[1774]||(e[1774]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1775]||(e[1775]=l('
julia
mlirFloat8E5M2TypeGet(ctx)

Creates an f8E5M2 type in the given context. The type is owned by the context.

source

',3))]),t("details",Wo,[t("summary",null,[e[1776]||(e[1776]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E5M2TypeGetTypeID")],-1)),e[1777]||(e[1777]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1778]||(e[1778]=l('
julia
mlirFloat8E5M2TypeGetTypeID()

Returns the typeID of an Float8E5M2 type.

source

',3))]),t("details",Ho,[t("summary",null,[e[1779]||(e[1779]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGet")],-1)),e[1780]||(e[1780]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1781]||(e[1781]=l('
julia
mlirFloat8E8M0FNUTypeGet(ctx)

Creates an f8E8M0FNU type in the given context. The type is owned by the context.

source

',3))]),t("details",Zo,[t("summary",null,[e[1782]||(e[1782]=t("a",{id:"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloat8E8M0FNUTypeGetTypeID")],-1)),e[1783]||(e[1783]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1784]||(e[1784]=l('
julia
mlirFloat8E8M0FNUTypeGetTypeID()

Returns the typeID of an Float8E8M0FNU type.

source

',3))]),t("details",Jo,[t("summary",null,[e[1785]||(e[1785]=t("a",{id:"Reactant.MLIR.API.mlirFloatAttrDoubleGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirFloatAttrDoubleGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrDoubleGet")],-1)),e[1786]||(e[1786]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1787]||(e[1787]=l('
julia
mlirFloatAttrDoubleGet(ctx, type, value)

Creates a floating point attribute in the given context with the given double value and double-precision FP semantics.

source

',3))]),t("details",Ko,[t("summary",null,[e[1788]||(e[1788]=t("a",{id:"Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrDoubleGetChecked")],-1)),e[1789]||(e[1789]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1790]||(e[1790]=l('
julia
mlirFloatAttrDoubleGetChecked(loc, type, value)

Same as "mlirFloatAttrDoubleGet", but if the type is not valid for a construction of a FloatAttr, returns a null MlirAttribute.

source

',3))]),t("details",$o,[t("summary",null,[e[1791]||(e[1791]=t("a",{id:"Reactant.MLIR.API.mlirFloatAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloatAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrGetTypeID")],-1)),e[1792]||(e[1792]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1793]||(e[1793]=l('
julia
mlirFloatAttrGetTypeID()

Returns the typeID of a Float attribute.

source

',3))]),t("details",Xo,[t("summary",null,[e[1794]||(e[1794]=t("a",{id:"Reactant.MLIR.API.mlirFloatAttrGetValueDouble-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloatAttrGetValueDouble-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatAttrGetValueDouble")],-1)),e[1795]||(e[1795]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1796]||(e[1796]=l('
julia
mlirFloatAttrGetValueDouble(attr)

Returns the value stored in the given floating point attribute, interpreting the value as double.

source

',3))]),t("details",Yo,[t("summary",null,[e[1797]||(e[1797]=t("a",{id:"Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatTF32TypeGetTypeID")],-1)),e[1798]||(e[1798]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1799]||(e[1799]=l('
julia
mlirFloatTF32TypeGetTypeID()

Returns the typeID of a TF32 type.

source

',3))]),t("details",_o,[t("summary",null,[e[1800]||(e[1800]=t("a",{id:"Reactant.MLIR.API.mlirFloatTypeGetWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFloatTypeGetWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFloatTypeGetWidth")],-1)),e[1801]||(e[1801]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1802]||(e[1802]=l('
julia
mlirFloatTypeGetWidth(type)

Returns the bitwidth of a floating-point type.

source

',3))]),t("details",ed,[t("summary",null,[e[1803]||(e[1803]=t("a",{id:"Reactant.MLIR.API.mlirFreezeRewritePattern-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFreezeRewritePattern-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFreezeRewritePattern")],-1)),e[1804]||(e[1804]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1805]||(e[1805]=l('
julia
mlirFreezeRewritePattern(op)

FrozenRewritePatternSet API

source

',3))]),t("details",td,[t("summary",null,[e[1806]||(e[1806]=t("a",{id:"Reactant.MLIR.API.mlirFuncSetArgAttr-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirFuncSetArgAttr-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFuncSetArgAttr")],-1)),e[1807]||(e[1807]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1808]||(e[1808]=l('
julia
mlirFuncSetArgAttr(op, pos, name, attr)

Sets the argument attribute 'name' of an argument at index 'pos'. Asserts that the operation is a FuncOp.

source

',3))]),t("details",sd,[t("summary",null,[e[1809]||(e[1809]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGet")],-1)),e[1810]||(e[1810]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1811]||(e[1811]=l('
julia
mlirFunctionTypeGet(ctx, numInputs, inputs, numResults, results)

Creates a function type, mapping a list of input types to result types.

source

',3))]),t("details",ad,[t("summary",null,[e[1812]||(e[1812]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetInput-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetInput-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetInput")],-1)),e[1813]||(e[1813]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1814]||(e[1814]=l('
julia
mlirFunctionTypeGetInput(type, pos)

Returns the pos-th input type.

source

',3))]),t("details",id,[t("summary",null,[e[1815]||(e[1815]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetNumInputs-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetNumInputs")],-1)),e[1816]||(e[1816]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1817]||(e[1817]=l('
julia
mlirFunctionTypeGetNumInputs(type)

Returns the number of input types.

source

',3))]),t("details",ld,[t("summary",null,[e[1818]||(e[1818]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetNumResults-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetNumResults")],-1)),e[1819]||(e[1819]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1820]||(e[1820]=l('
julia
mlirFunctionTypeGetNumResults(type)

Returns the number of result types.

source

',3))]),t("details",nd,[t("summary",null,[e[1821]||(e[1821]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetResult-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetResult")],-1)),e[1822]||(e[1822]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1823]||(e[1823]=l('
julia
mlirFunctionTypeGetResult(type, pos)

Returns the pos-th result type.

source

',3))]),t("details",pd,[t("summary",null,[e[1824]||(e[1824]=t("a",{id:"Reactant.MLIR.API.mlirFunctionTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirFunctionTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirFunctionTypeGetTypeID")],-1)),e[1825]||(e[1825]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1826]||(e[1826]=l('
julia
mlirFunctionTypeGetTypeID()

Returns the typeID of an Function type.

source

',3))]),t("details",rd,[t("summary",null,[e[1827]||(e[1827]=t("a",{id:"Reactant.MLIR.API.mlirIRRewriterCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIRRewriterCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIRRewriterCreate")],-1)),e[1828]||(e[1828]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1829]||(e[1829]=l('
julia
mlirIRRewriterCreate(context)

Create an IRRewriter and transfer ownership to the caller.

source

',3))]),t("details",od,[t("summary",null,[e[1830]||(e[1830]=t("a",{id:"Reactant.MLIR.API.mlirIRRewriterCreateFromOp-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIRRewriterCreateFromOp-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIRRewriterCreateFromOp")],-1)),e[1831]||(e[1831]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1832]||(e[1832]=l('
julia
mlirIRRewriterCreateFromOp(op)

Create an IRRewriter and transfer ownership to the caller. Additionally set the insertion point before the operation.

source

',3))]),t("details",dd,[t("summary",null,[e[1833]||(e[1833]=t("a",{id:"Reactant.MLIR.API.mlirIRRewriterDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIRRewriterDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIRRewriterDestroy")],-1)),e[1834]||(e[1834]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1835]||(e[1835]=l('
julia
mlirIRRewriterDestroy(rewriter)

Takes an IRRewriter owned by the caller and destroys it. It is the responsibility of the user to only pass an IRRewriter class.

source

',3))]),t("details",cd,[t("summary",null,[e[1836]||(e[1836]=t("a",{id:"Reactant.MLIR.API.mlirIdentifierEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIdentifierEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierEqual")],-1)),e[1837]||(e[1837]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1838]||(e[1838]=l('
julia
mlirIdentifierEqual(ident, other)

Checks whether two identifiers are the same.

source

',3))]),t("details",bd,[t("summary",null,[e[1839]||(e[1839]=t("a",{id:"Reactant.MLIR.API.mlirIdentifierGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIdentifierGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierGet")],-1)),e[1840]||(e[1840]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1841]||(e[1841]=l('
julia
mlirIdentifierGet(context, str)

Gets an identifier with the given string value.

source

',3))]),t("details",hd,[t("summary",null,[e[1842]||(e[1842]=t("a",{id:"Reactant.MLIR.API.mlirIdentifierGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIdentifierGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierGetContext")],-1)),e[1843]||(e[1843]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1844]||(e[1844]=l('
julia
mlirIdentifierGetContext(arg1)

Returns the context associated with this identifier

source

',3))]),t("details",ud,[t("summary",null,[e[1845]||(e[1845]=t("a",{id:"Reactant.MLIR.API.mlirIdentifierStr-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIdentifierStr-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIdentifierStr")],-1)),e[1846]||(e[1846]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1847]||(e[1847]=l('
julia
mlirIdentifierStr(ident)

Gets the string value of the identifier.

source

',3))]),t("details",gd,[t("summary",null,[e[1848]||(e[1848]=t("a",{id:"Reactant.MLIR.API.mlirIndexTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIndexTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIndexTypeGet")],-1)),e[1849]||(e[1849]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1850]||(e[1850]=l('
julia
mlirIndexTypeGet(ctx)

Creates an index type in the given context. The type is owned by the context.

source

',3))]),t("details",yd,[t("summary",null,[e[1851]||(e[1851]=t("a",{id:"Reactant.MLIR.API.mlirIndexTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIndexTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIndexTypeGetTypeID")],-1)),e[1852]||(e[1852]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1853]||(e[1853]=l('
julia
mlirIndexTypeGetTypeID()

Returns the typeID of an Index type.

source

',3))]),t("details",md,[t("summary",null,[e[1854]||(e[1854]=t("a",{id:"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes-NTuple{11, Any}",href:"#Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes-NTuple{11, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceInferReturnTypes")],-1)),e[1855]||(e[1855]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1856]||(e[1856]=l('
julia
mlirInferShapedTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)

Infers the return shaped type components of the operation. Calls callback with the types of inferred arguments on success. Returns failure otherwise.

source

',3))]),t("details",kd,[t("summary",null,[e[1857]||(e[1857]=t("a",{id:"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferShapedTypeOpInterfaceTypeID")],-1)),e[1858]||(e[1858]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1859]||(e[1859]=l('
julia
mlirInferShapedTypeOpInterfaceTypeID()

Returns the interface TypeID of the InferShapedTypeOpInterface.

source

',3))]),t("details",Rd,[t("summary",null,[e[1860]||(e[1860]=t("a",{id:"Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes-NTuple{11, Any}",href:"#Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes-NTuple{11, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferTypeOpInterfaceInferReturnTypes")],-1)),e[1861]||(e[1861]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1862]||(e[1862]=l('
julia
mlirInferTypeOpInterfaceInferReturnTypes(opName, context, location, nOperands, operands, attributes, properties, nRegions, regions, callback, userData)

Infers the return types of the operation identified by its canonical given the arguments that will be supplied to its generic builder. Calls callback with the types of inferred arguments, potentially several times, on success. Returns failure otherwise.

source

',3))]),t("details",Id,[t("summary",null,[e[1863]||(e[1863]=t("a",{id:"Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirInferTypeOpInterfaceTypeID")],-1)),e[1864]||(e[1864]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1865]||(e[1865]=l('
julia
mlirInferTypeOpInterfaceTypeID()

Returns the interface TypeID of the InferTypeOpInterface.

source

',3))]),t("details",jd,[t("summary",null,[e[1866]||(e[1866]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGet")],-1)),e[1867]||(e[1867]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1868]||(e[1868]=l('
julia
mlirIntegerAttrGet(type, value)

Creates an integer attribute of the given type with the given integer value.

source

',3))]),t("details",fd,[t("summary",null,[e[1869]||(e[1869]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetTypeID")],-1)),e[1870]||(e[1870]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1871]||(e[1871]=l('
julia
mlirIntegerAttrGetTypeID()

Returns the typeID of an Integer attribute.

source

',3))]),t("details",Md,[t("summary",null,[e[1872]||(e[1872]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueInt-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueInt")],-1)),e[1873]||(e[1873]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1874]||(e[1874]=l('
julia
mlirIntegerAttrGetValueInt(attr)

Returns the value stored in the given integer attribute, assuming the value is of signless type and fits into a signed 64-bit integer.

source

',3))]),t("details",Ad,[t("summary",null,[e[1875]||(e[1875]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueSInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueSInt-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueSInt")],-1)),e[1876]||(e[1876]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1877]||(e[1877]=l('
julia
mlirIntegerAttrGetValueSInt(attr)

Returns the value stored in the given integer attribute, assuming the value is of signed type and fits into a signed 64-bit integer.

source

',3))]),t("details",Ld,[t("summary",null,[e[1878]||(e[1878]=t("a",{id:"Reactant.MLIR.API.mlirIntegerAttrGetValueUInt-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerAttrGetValueUInt-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerAttrGetValueUInt")],-1)),e[1879]||(e[1879]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1880]||(e[1880]=l('
julia
mlirIntegerAttrGetValueUInt(attr)

Returns the value stored in the given integer attribute, assuming the value is of unsigned type and fits into an unsigned 64-bit integer.

source

',3))]),t("details",Ed,[t("summary",null,[e[1881]||(e[1881]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetAttrGet")],-1)),e[1882]||(e[1882]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1883]||(e[1883]=l('
julia
mlirIntegerSetAttrGet(set)

Creates an integer set attribute wrapping the given set. The attribute belongs to the same context as the integer set.

source

',3))]),t("details",vd,[t("summary",null,[e[1884]||(e[1884]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetAttrGetTypeID")],-1)),e[1885]||(e[1885]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1886]||(e[1886]=l('
julia
mlirIntegerSetAttrGetTypeID()

Returns the typeID of an IntegerSet attribute.

source

',3))]),t("details",Td,[t("summary",null,[e[1887]||(e[1887]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetAttrGetValue")],-1)),e[1888]||(e[1888]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1889]||(e[1889]=l('
julia
mlirIntegerSetAttrGetValue(attr)

Returns the integer set wrapped in the given integer set attribute.

source

',3))]),t("details",Cd,[t("summary",null,[e[1890]||(e[1890]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetDump")],-1)),e[1891]||(e[1891]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1892]||(e[1892]=l('
julia
mlirIntegerSetDump(set)

Prints an integer set to the standard error stream.

source

',3))]),t("details",xd,[t("summary",null,[e[1893]||(e[1893]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetEmptyGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetEmptyGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetEmptyGet")],-1)),e[1894]||(e[1894]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1895]||(e[1895]=l('
julia
mlirIntegerSetEmptyGet(context, numDims, numSymbols)

Gets or creates a new canonically empty integer set with the give number of dimensions and symbols in the given context.

source

',3))]),t("details",Fd,[t("summary",null,[e[1896]||(e[1896]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetEqual")],-1)),e[1897]||(e[1897]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1898]||(e[1898]=l('
julia
mlirIntegerSetEqual(s1, s2)

Checks if two integer set objects are equal. This is a "shallow" comparison of two objects. Only the sets with some small number of constraints are uniqued and compare equal here. Set objects that represent the same integer set with different constraints may be considered non-equal by this check. Set difference followed by an (expensive) emptiness check should be used to check equivalence of the underlying integer sets.

source

',3))]),t("details",Pd,[t("summary",null,[e[1899]||(e[1899]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGet-NTuple{6, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGet-NTuple{6, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGet")],-1)),e[1900]||(e[1900]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1901]||(e[1901]=l('
julia
mlirIntegerSetGet(context, numDims, numSymbols, numConstraints, constraints, eqFlags)

Gets or creates a new integer set in the given context. The set is defined by a list of affine constraints, with the given number of input dimensions and symbols, which are treated as either equalities (eqFlags is 1) or inequalities (eqFlags is 0). Both constraints and eqFlags are expected to point to at least numConstraint consecutive values.

source

',3))]),t("details",Dd,[t("summary",null,[e[1902]||(e[1902]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetConstraint-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetConstraint-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetConstraint")],-1)),e[1903]||(e[1903]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1904]||(e[1904]=l('
julia
mlirIntegerSetGetConstraint(set, pos)

Returns pos-th constraint of the set.

source

',3))]),t("details",Od,[t("summary",null,[e[1905]||(e[1905]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetContext")],-1)),e[1906]||(e[1906]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1907]||(e[1907]=l('
julia
mlirIntegerSetGetContext(set)

Gets the context in which the given integer set lives.

source

',3))]),t("details",Bd,[t("summary",null,[e[1908]||(e[1908]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumConstraints-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumConstraints-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumConstraints")],-1)),e[1909]||(e[1909]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1910]||(e[1910]=l('
julia
mlirIntegerSetGetNumConstraints(set)

Returns the number of constraints (equalities + inequalities) in the given set.

source

',3))]),t("details",Gd,[t("summary",null,[e[1911]||(e[1911]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumDims-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumDims")],-1)),e[1912]||(e[1912]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1913]||(e[1913]=l('
julia
mlirIntegerSetGetNumDims(set)

Returns the number of dimensions in the given set.

source

',3))]),t("details",zd,[t("summary",null,[e[1914]||(e[1914]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumEqualities-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumEqualities-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumEqualities")],-1)),e[1915]||(e[1915]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1916]||(e[1916]=l('
julia
mlirIntegerSetGetNumEqualities(set)

Returns the number of equalities in the given set.

source

',3))]),t("details",wd,[t("summary",null,[e[1917]||(e[1917]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumInequalities-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumInequalities-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumInequalities")],-1)),e[1918]||(e[1918]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1919]||(e[1919]=l('
julia
mlirIntegerSetGetNumInequalities(set)

Returns the number of inequalities in the given set.

source

',3))]),t("details",Sd,[t("summary",null,[e[1920]||(e[1920]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumInputs-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumInputs-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumInputs")],-1)),e[1921]||(e[1921]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1922]||(e[1922]=l('
julia
mlirIntegerSetGetNumInputs(set)

Returns the number of inputs (dimensions + symbols) in the given set.

source

',3))]),t("details",Nd,[t("summary",null,[e[1923]||(e[1923]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetGetNumSymbols-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetGetNumSymbols-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetGetNumSymbols")],-1)),e[1924]||(e[1924]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1925]||(e[1925]=l('
julia
mlirIntegerSetGetNumSymbols(set)

Returns the number of symbols in the given set.

source

',3))]),t("details",Vd,[t("summary",null,[e[1926]||(e[1926]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsCanonicalEmpty")],-1)),e[1927]||(e[1927]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1928]||(e[1928]=l('
julia
mlirIntegerSetIsCanonicalEmpty(set)

Checks whether the given set is a canonical empty set, e.g., the set returned by mlirIntegerSetEmptyGet.

source

',3))]),t("details",qd,[t("summary",null,[e[1929]||(e[1929]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsConstraintEq-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsConstraintEq-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsConstraintEq")],-1)),e[1930]||(e[1930]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1931]||(e[1931]=l('
julia
mlirIntegerSetIsConstraintEq(set, pos)

Returns true of the pos-th constraint of the set is an equality constraint, false otherwise.

source

',3))]),t("details",Ud,[t("summary",null,[e[1932]||(e[1932]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerSetIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetIsNull")],-1)),e[1933]||(e[1933]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1934]||(e[1934]=l('
julia
mlirIntegerSetIsNull(set)

Checks whether an integer set is a null object.

source

',3))]),t("details",Qd,[t("summary",null,[e[1935]||(e[1935]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetPrint")],-1)),e[1936]||(e[1936]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1937]||(e[1937]=l('
julia
mlirIntegerSetPrint(set, callback, userData)

Prints an integer set by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Wd,[t("summary",null,[e[1938]||(e[1938]=t("a",{id:"Reactant.MLIR.API.mlirIntegerSetReplaceGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirIntegerSetReplaceGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerSetReplaceGet")],-1)),e[1939]||(e[1939]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1940]||(e[1940]=l('
julia
mlirIntegerSetReplaceGet(set, dimReplacements, symbolReplacements, numResultDims, numResultSymbols)

Gets or creates a new integer set in which the values and dimensions of the given set are replaced with the given affine expressions. dimReplacements and symbolReplacements are expected to point to at least as many consecutive expressions as the given set has dimensions and symbols, respectively. The new set will have numResultDims and numResultSymbols dimensions and symbols, respectively.

source

',3))]),t("details",Hd,[t("summary",null,[e[1941]||(e[1941]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGet")],-1)),e[1942]||(e[1942]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1943]||(e[1943]=l('
julia
mlirIntegerTypeGet(ctx, bitwidth)

Creates a signless integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),t("details",Zd,[t("summary",null,[e[1944]||(e[1944]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirIntegerTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGetTypeID")],-1)),e[1945]||(e[1945]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1946]||(e[1946]=l('
julia
mlirIntegerTypeGetTypeID()

Returns the typeID of an Integer type.

source

',3))]),t("details",Jd,[t("summary",null,[e[1947]||(e[1947]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeGetWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeGetWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeGetWidth")],-1)),e[1948]||(e[1948]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1949]||(e[1949]=l('
julia
mlirIntegerTypeGetWidth(type)

Returns the bitwidth of an integer type.

source

',3))]),t("details",Kd,[t("summary",null,[e[1950]||(e[1950]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsSigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsSigned-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsSigned")],-1)),e[1951]||(e[1951]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1952]||(e[1952]=l('
julia
mlirIntegerTypeIsSigned(type)

Checks whether the given integer type is signed.

source

',3))]),t("details",$d,[t("summary",null,[e[1953]||(e[1953]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsSignless-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsSignless-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsSignless")],-1)),e[1954]||(e[1954]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1955]||(e[1955]=l('
julia
mlirIntegerTypeIsSignless(type)

Checks whether the given integer type is signless.

source

',3))]),t("details",Xd,[t("summary",null,[e[1956]||(e[1956]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeIsUnsigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeIsUnsigned-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeIsUnsigned")],-1)),e[1957]||(e[1957]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1958]||(e[1958]=l('
julia
mlirIntegerTypeIsUnsigned(type)

Checks whether the given integer type is unsigned.

source

',3))]),t("details",Yd,[t("summary",null,[e[1959]||(e[1959]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeSignedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeSignedGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeSignedGet")],-1)),e[1960]||(e[1960]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1961]||(e[1961]=l('
julia
mlirIntegerTypeSignedGet(ctx, bitwidth)

Creates a signed integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),t("details",_d,[t("summary",null,[e[1962]||(e[1962]=t("a",{id:"Reactant.MLIR.API.mlirIntegerTypeUnsignedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirIntegerTypeUnsignedGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIntegerTypeUnsignedGet")],-1)),e[1963]||(e[1963]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1964]||(e[1964]=l('
julia
mlirIntegerTypeUnsignedGet(ctx, bitwidth)

Creates an unsigned integer type of the given bitwidth in the context. The type is owned by the context.

source

',3))]),t("details",ec,[t("summary",null,[e[1965]||(e[1965]=t("a",{id:"Reactant.MLIR.API.mlirIsCurrentDebugType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirIsCurrentDebugType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIsCurrentDebugType")],-1)),e[1966]||(e[1966]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1967]||(e[1967]=l('
julia
mlirIsCurrentDebugType(type)

Checks if type is set as the current debug type.

source

',3))]),t("details",tc,[t("summary",null,[e[1968]||(e[1968]=t("a",{id:"Reactant.MLIR.API.mlirIsGlobalDebugEnabled-Tuple{}",href:"#Reactant.MLIR.API.mlirIsGlobalDebugEnabled-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirIsGlobalDebugEnabled")],-1)),e[1969]||(e[1969]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1970]||(e[1970]=l('
julia
mlirIsGlobalDebugEnabled()

Retuns true if the global debugging flag is set, false otherwise.

source

',3))]),t("details",sc,[t("summary",null,[e[1971]||(e[1971]=t("a",{id:"Reactant.MLIR.API.mlirLLVMArrayTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMArrayTypeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMArrayTypeGet")],-1)),e[1972]||(e[1972]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1973]||(e[1973]=l('
julia
mlirLLVMArrayTypeGet(elementType, numElements)

Creates an llvm.array type.

source

',3))]),t("details",ac,[t("summary",null,[e[1974]||(e[1974]=t("a",{id:"Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMArrayTypeGetElementType")],-1)),e[1975]||(e[1975]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1976]||(e[1976]=l('
julia
mlirLLVMArrayTypeGetElementType(type)

Returns the element type of the llvm.array type.

source

',3))]),t("details",ic,[t("summary",null,[e[1977]||(e[1977]=t("a",{id:"Reactant.MLIR.API.mlirLLVMCConvAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMCConvAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMCConvAttrGet")],-1)),e[1978]||(e[1978]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1979]||(e[1979]=l('
julia
mlirLLVMCConvAttrGet(ctx, cconv)

Creates a LLVM CConv attribute.

source

',3))]),t("details",lc,[t("summary",null,[e[1980]||(e[1980]=t("a",{id:"Reactant.MLIR.API.mlirLLVMComdatAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMComdatAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMComdatAttrGet")],-1)),e[1981]||(e[1981]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1982]||(e[1982]=l('
julia
mlirLLVMComdatAttrGet(ctx, comdat)

Creates a LLVM Comdat attribute.

source

',3))]),t("details",nc,[t("summary",null,[e[1983]||(e[1983]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIAnnotationAttrGet")],-1)),e[1984]||(e[1984]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1985]||(e[1985]=l('
julia
mlirLLVMDIAnnotationAttrGet(ctx, name, value)

Creates a LLVM DIAnnotation attribute.

source

',3))]),t("details",pc,[t("summary",null,[e[1986]||(e[1986]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIBasicTypeAttrGet")],-1)),e[1987]||(e[1987]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1988]||(e[1988]=l('
julia
mlirLLVMDIBasicTypeAttrGet(ctx, tag, name, sizeInBits, encoding)

Creates a LLVM DIBasicType attribute.

source

',3))]),t("details",rc,[t("summary",null,[e[1989]||(e[1989]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet-NTuple{8, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompileUnitAttrGet")],-1)),e[1990]||(e[1990]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1991]||(e[1991]=l('
julia
mlirLLVMDICompileUnitAttrGet(ctx, id, sourceLanguage, file, producer, isOptimized, emissionKind, nameTableKind)

Creates a LLVM DICompileUnit attribute.

source

',3))]),t("details",oc,[t("summary",null,[e[1992]||(e[1992]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet-NTuple{18, Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet-NTuple{18, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGet")],-1)),e[1993]||(e[1993]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1994]||(e[1994]=l('
julia
mlirLLVMDICompositeTypeAttrGet(ctx, recId, isRecSelf, tag, name, file, line, scope, baseType, flags, sizeInBits, alignInBits, nElements, elements, dataLocation, rank, allocated, associated)

Creates a LLVM DICompositeType attribute.

source

',3))]),t("details",dc,[t("summary",null,[e[1995]||(e[1995]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDICompositeTypeAttrGetRecSelf")],-1)),e[1996]||(e[1996]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[1997]||(e[1997]=l('
julia
mlirLLVMDICompositeTypeAttrGetRecSelf(recId)

Creates a self-referencing LLVM DICompositeType attribute.

source

',3))]),t("details",cc,[t("summary",null,[e[1998]||(e[1998]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGet")],-1)),e[1999]||(e[1999]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2e3]||(e[2e3]=l('
julia
mlirLLVMDIDerivedTypeAttrGet(ctx, tag, name, baseType, sizeInBits, alignInBits, offsetInBits, dwarfAddressSpace, extraData)

Creates a LLVM DIDerivedType attribute. Note that dwarfAddressSpace is an optional field, where MLIR_CAPI_DWARF_ADDRESS_SPACE_NULL indicates null and non-negative values indicate a value present.

source

',3))]),t("details",bc,[t("summary",null,[e[2001]||(e[2001]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIDerivedTypeAttrGetBaseType")],-1)),e[2002]||(e[2002]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2003]||(e[2003]=l('
julia
mlirLLVMDIDerivedTypeAttrGetBaseType(diDerivedType)

Gets the base type from a LLVM DIDerivedType attribute.

source

',3))]),t("details",hc,[t("summary",null,[e[2004]||(e[2004]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIExpressionAttrGet")],-1)),e[2005]||(e[2005]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2006]||(e[2006]=l('
julia
mlirLLVMDIExpressionAttrGet(ctx, nOperations, operations)

Creates a LLVM DIExpression attribute.

source

',3))]),t("details",uc,[t("summary",null,[e[2007]||(e[2007]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIExpressionElemAttrGet")],-1)),e[2008]||(e[2008]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2009]||(e[2009]=l('
julia
mlirLLVMDIExpressionElemAttrGet(ctx, opcode, nArguments, arguments)

Creates a LLVM DIExpressionElem attribute.

source

',3))]),t("details",gc,[t("summary",null,[e[2010]||(e[2010]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIFileAttrGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIFileAttrGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIFileAttrGet")],-1)),e[2011]||(e[2011]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2012]||(e[2012]=l('
julia
mlirLLVMDIFileAttrGet(ctx, name, directory)

Creates a LLVM DIFileAttr attribute.

source

',3))]),t("details",yc,[t("summary",null,[e[2013]||(e[2013]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIFlagsAttrGet")],-1)),e[2014]||(e[2014]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2015]||(e[2015]=l('
julia
mlirLLVMDIFlagsAttrGet(ctx, value)

Creates a LLVM DIFlags attribute.

source

',3))]),t("details",mc,[t("summary",null,[e[2016]||(e[2016]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIImportedEntityAttrGet")],-1)),e[2017]||(e[2017]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2018]||(e[2018]=l('
julia
mlirLLVMDIImportedEntityAttrGet(ctx, tag, scope, entity, file, line, name, nElements, elements)

Creates a LLVM DIImportedEntityAttr attribute.

source

',3))]),t("details",kc,[t("summary",null,[e[2019]||(e[2019]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILexicalBlockAttrGet")],-1)),e[2020]||(e[2020]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2021]||(e[2021]=l('
julia
mlirLLVMDILexicalBlockAttrGet(ctx, scope, file, line, column)

Creates a LLVM DILexicalBlock attribute.

source

',3))]),t("details",Rc,[t("summary",null,[e[2022]||(e[2022]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILexicalBlockFileAttrGet")],-1)),e[2023]||(e[2023]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2024]||(e[2024]=l('
julia
mlirLLVMDILexicalBlockFileAttrGet(ctx, scope, file, discriminator)

Creates a LLVM DILexicalBlockFile attribute.

source

',3))]),t("details",Ic,[t("summary",null,[e[2025]||(e[2025]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDILocalVariableAttrGet")],-1)),e[2026]||(e[2026]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2027]||(e[2027]=l('
julia
mlirLLVMDILocalVariableAttrGet(ctx, scope, name, diFile, line, arg, alignInBits, diType, flags)

Creates a LLVM DILocalVariableAttr attribute.

source

',3))]),t("details",jc,[t("summary",null,[e[2028]||(e[2028]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIModuleAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirLLVMDIModuleAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIModuleAttrGet")],-1)),e[2029]||(e[2029]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2030]||(e[2030]=l('
julia
mlirLLVMDIModuleAttrGet(ctx, file, scope, name, configMacros, includePath, apinotes, line, isDecl)

Creates a LLVM DIModuleAttr attribute.

source

',3))]),t("details",fc,[t("summary",null,[e[2031]||(e[2031]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDIModuleAttrGetScope")],-1)),e[2032]||(e[2032]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2033]||(e[2033]=l('
julia
mlirLLVMDIModuleAttrGetScope(diModule)

Gets the scope of this DIModuleAttr.

source

',3))]),t("details",Mc,[t("summary",null,[e[2034]||(e[2034]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDINullTypeAttrGet")],-1)),e[2035]||(e[2035]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2036]||(e[2036]=l('
julia
mlirLLVMDINullTypeAttrGet(ctx)

Creates a LLVM DINullType attribute.

source

',3))]),t("details",Ac,[t("summary",null,[e[2037]||(e[2037]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet-NTuple{17, Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet-NTuple{17, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGet")],-1)),e[2038]||(e[2038]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2039]||(e[2039]=l('
julia
mlirLLVMDISubprogramAttrGet(ctx, recId, isRecSelf, id, compileUnit, scope, name, linkageName, file, line, scopeLine, subprogramFlags, type, nRetainedNodes, retainedNodes, nAnnotations, annotations)

Creates a LLVM DISubprogramAttr attribute.

source

',3))]),t("details",Lc,[t("summary",null,[e[2040]||(e[2040]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetCompileUnit")],-1)),e[2041]||(e[2041]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2042]||(e[2042]=l('
julia
mlirLLVMDISubprogramAttrGetCompileUnit(diSubprogram)

Gets the compile unit from this DISubprogram.

source

',3))]),t("details",Ec,[t("summary",null,[e[2043]||(e[2043]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetFile")],-1)),e[2044]||(e[2044]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2045]||(e[2045]=l('
julia
mlirLLVMDISubprogramAttrGetFile(diSubprogram)

Gets the file from this DISubprogramAttr.

source

',3))]),t("details",vc,[t("summary",null,[e[2046]||(e[2046]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetLine")],-1)),e[2047]||(e[2047]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2048]||(e[2048]=l('
julia
mlirLLVMDISubprogramAttrGetLine(diSubprogram)

Gets the line from this DISubprogramAttr.

source

',3))]),t("details",Tc,[t("summary",null,[e[2049]||(e[2049]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetRecSelf")],-1)),e[2050]||(e[2050]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2051]||(e[2051]=l('
julia
mlirLLVMDISubprogramAttrGetRecSelf(recId)

Creates a self-referencing LLVM DISubprogramAttr attribute.

source

',3))]),t("details",Cc,[t("summary",null,[e[2052]||(e[2052]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScope")],-1)),e[2053]||(e[2053]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2054]||(e[2054]=l('
julia
mlirLLVMDISubprogramAttrGetScope(diSubprogram)

Gets the scope from this DISubprogramAttr.

source

',3))]),t("details",xc,[t("summary",null,[e[2055]||(e[2055]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetScopeLine")],-1)),e[2056]||(e[2056]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2057]||(e[2057]=l('
julia
mlirLLVMDISubprogramAttrGetScopeLine(diSubprogram)

Gets the scope line from this DISubprogram.

source

',3))]),t("details",Fc,[t("summary",null,[e[2058]||(e[2058]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubprogramAttrGetType")],-1)),e[2059]||(e[2059]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2060]||(e[2060]=l('
julia
mlirLLVMDISubprogramAttrGetType(diSubprogram)

Gets the type from this DISubprogramAttr.

source

',3))]),t("details",Pc,[t("summary",null,[e[2061]||(e[2061]=t("a",{id:"Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMDISubroutineTypeAttrGet")],-1)),e[2062]||(e[2062]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2063]||(e[2063]=l('
julia
mlirLLVMDISubroutineTypeAttrGet(ctx, callingConvention, nTypes, types)

Creates a LLVM DISubroutineTypeAttr attribute.

source

',3))]),t("details",Dc,[t("summary",null,[e[2064]||(e[2064]=t("a",{id:"Reactant.MLIR.API.mlirLLVMFunctionTypeGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMFunctionTypeGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMFunctionTypeGet")],-1)),e[2065]||(e[2065]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2066]||(e[2066]=l('
julia
mlirLLVMFunctionTypeGet(resultType, nArgumentTypes, argumentTypes, isVarArg)

Creates an llvm.func type.

source

',3))]),t("details",Oc,[t("summary",null,[e[2067]||(e[2067]=t("a",{id:"Reactant.MLIR.API.mlirLLVMLinkageAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMLinkageAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMLinkageAttrGet")],-1)),e[2068]||(e[2068]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2069]||(e[2069]=l('
julia
mlirLLVMLinkageAttrGet(ctx, linkage)

Creates a LLVM Linkage attribute.

source

',3))]),t("details",Bc,[t("summary",null,[e[2070]||(e[2070]=t("a",{id:"Reactant.MLIR.API.mlirLLVMPointerTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMPointerTypeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMPointerTypeGet")],-1)),e[2071]||(e[2071]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2072]||(e[2072]=l('
julia
mlirLLVMPointerTypeGet(ctx, addressSpace)

Creates an llvm.ptr type.

source

',3))]),t("details",Gc,[t("summary",null,[e[2073]||(e[2073]=t("a",{id:"Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMPointerTypeGetAddressSpace")],-1)),e[2074]||(e[2074]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2075]||(e[2075]=l('
julia
mlirLLVMPointerTypeGetAddressSpace(pointerType)

Returns address space of llvm.ptr

source

',3))]),t("details",zc,[t("summary",null,[e[2076]||(e[2076]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetElementType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetElementType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetElementType")],-1)),e[2077]||(e[2077]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2078]||(e[2078]=l('
julia
mlirLLVMStructTypeGetElementType(type, position)

Returns the positions-th field of the struct. Asserts if the struct is opaque, not yet initialized or if the position is out of range.

source

',3))]),t("details",wc,[t("summary",null,[e[2079]||(e[2079]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetIdentifier")],-1)),e[2080]||(e[2080]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2081]||(e[2081]=l('
julia
mlirLLVMStructTypeGetIdentifier(type)

Returns the identifier of the identified struct. Asserts that the struct is identified, i.e., not literal.

source

',3))]),t("details",Sc,[t("summary",null,[e[2082]||(e[2082]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeGetNumElementTypes")],-1)),e[2083]||(e[2083]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2084]||(e[2084]=l('
julia
mlirLLVMStructTypeGetNumElementTypes(type)

Returns the number of fields in the struct. Asserts if the struct is opaque or not yet initialized.

source

',3))]),t("details",Nc,[t("summary",null,[e[2085]||(e[2085]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedGet")],-1)),e[2086]||(e[2086]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2087]||(e[2087]=l('
julia
mlirLLVMStructTypeIdentifiedGet(ctx, name)

Creates an LLVM identified struct type with no body. If a struct type with this name already exists in the context, returns that type. Use mlirLLVMStructTypeIdentifiedNewGet to create a fresh struct type, potentially renaming it. The body should be set separatelty by calling mlirLLVMStructTypeSetBody, if it isn't set already.

source

',3))]),t("details",Vc,[t("summary",null,[e[2088]||(e[2088]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIdentifiedNewGet")],-1)),e[2089]||(e[2089]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2090]||(e[2090]=l('
julia
mlirLLVMStructTypeIdentifiedNewGet(ctx, name, nFieldTypes, fieldTypes, isPacked)

Creates an LLVM identified struct type with no body and a name starting with the given prefix. If a struct with the exact name as the given prefix already exists, appends an unspecified suffix to the name so that the name is unique in context.

source

',3))]),t("details",qc,[t("summary",null,[e[2091]||(e[2091]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsLiteral")],-1)),e[2092]||(e[2092]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2093]||(e[2093]=l('
julia
mlirLLVMStructTypeIsLiteral(type)

Returns true if the type is a literal (unnamed) LLVM struct type.

source

',3))]),t("details",Uc,[t("summary",null,[e[2094]||(e[2094]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsOpaque")],-1)),e[2095]||(e[2095]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2096]||(e[2096]=l('
julia
mlirLLVMStructTypeIsOpaque(type)

Returns true is the struct is explicitly opaque (will not have a body) or uninitialized (will eventually have a body).

source

',3))]),t("details",Qc,[t("summary",null,[e[2097]||(e[2097]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeIsPacked-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeIsPacked-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeIsPacked")],-1)),e[2098]||(e[2098]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2099]||(e[2099]=l('
julia
mlirLLVMStructTypeIsPacked(type)

Returns true if the struct is packed.

source

',3))]),t("details",Wc,[t("summary",null,[e[2100]||(e[2100]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGet")],-1)),e[2101]||(e[2101]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2102]||(e[2102]=l('
julia
mlirLLVMStructTypeLiteralGet(ctx, nFieldTypes, fieldTypes, isPacked)

Creates an LLVM literal (unnamed) struct type. This may assert if the fields have types not compatible with the LLVM dialect. For a graceful failure, use the checked version.

source

',3))]),t("details",Hc,[t("summary",null,[e[2103]||(e[2103]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeLiteralGetChecked")],-1)),e[2104]||(e[2104]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2105]||(e[2105]=l('
julia
mlirLLVMStructTypeLiteralGetChecked(loc, nFieldTypes, fieldTypes, isPacked)

Creates an LLVM literal (unnamed) struct type if possible. Emits a diagnostic at the given location and returns null otherwise.

source

',3))]),t("details",Zc,[t("summary",null,[e[2106]||(e[2106]=t("a",{id:"Reactant.MLIR.API.mlirLLVMStructTypeSetBody-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLLVMStructTypeSetBody-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMStructTypeSetBody")],-1)),e[2107]||(e[2107]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2108]||(e[2108]=l('
julia
mlirLLVMStructTypeSetBody(structType, nFieldTypes, fieldTypes, isPacked)

Sets the body of the identified struct if it hasn't been set yet. Returns whether the operation was successful.

source

',3))]),t("details",Jc,[t("summary",null,[e[2109]||(e[2109]=t("a",{id:"Reactant.MLIR.API.mlirLLVMVoidTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLLVMVoidTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLLVMVoidTypeGet")],-1)),e[2110]||(e[2110]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2111]||(e[2111]=l('
julia
mlirLLVMVoidTypeGet(ctx)

Creates an llmv.void type.

source

',3))]),t("details",Kc,[t("summary",null,[e[2112]||(e[2112]=t("a",{id:"Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLinalgFillBuiltinNamedOpRegion")],-1)),e[2113]||(e[2113]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2114]||(e[2114]=l('
julia
mlirLinalgFillBuiltinNamedOpRegion(mlirOp)

Apply the special region builder for the builtin named Linalg op. Assert that mlirOp is a builtin named Linalg op.

source

',3))]),t("details",$c,[t("summary",null,[e[2115]||(e[2115]=t("a",{id:"Reactant.MLIR.API.mlirLlvmThreadPoolCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirLlvmThreadPoolCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLlvmThreadPoolCreate")],-1)),e[2116]||(e[2116]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2117]||(e[2117]=l('
julia
mlirLlvmThreadPoolCreate()

Create an LLVM thread pool. This is reexported here to avoid directly pulling in the LLVM headers directly.

source

',3))]),t("details",Xc,[t("summary",null,[e[2118]||(e[2118]=t("a",{id:"Reactant.MLIR.API.mlirLlvmThreadPoolDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLlvmThreadPoolDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLlvmThreadPoolDestroy")],-1)),e[2119]||(e[2119]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2120]||(e[2120]=l('
julia
mlirLlvmThreadPoolDestroy(pool)

Destroy an LLVM thread pool.

source

',3))]),t("details",Yc,[t("summary",null,[e[2121]||(e[2121]=t("a",{id:"Reactant.MLIR.API.mlirLoadIRDLDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLoadIRDLDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLoadIRDLDialects")],-1)),e[2122]||(e[2122]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2123]||(e[2123]=l('
julia
mlirLoadIRDLDialects(_module)

Loads all IRDL dialects in the provided module, registering the dialects in the module's associated context.

source

',3))]),t("details",_c,[t("summary",null,[e[2124]||(e[2124]=t("a",{id:"Reactant.MLIR.API.mlirLocationCallSiteGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLocationCallSiteGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationCallSiteGet")],-1)),e[2125]||(e[2125]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2126]||(e[2126]=l('
julia
mlirLocationCallSiteGet(callee, caller)

Creates a call site location with a callee and a caller.

source

',3))]),t("details",eb,[t("summary",null,[e[2127]||(e[2127]=t("a",{id:"Reactant.MLIR.API.mlirLocationEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirLocationEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationEqual")],-1)),e[2128]||(e[2128]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2129]||(e[2129]=l('
julia
mlirLocationEqual(l1, l2)

Checks if two locations are equal.

source

',3))]),t("details",tb,[t("summary",null,[e[2130]||(e[2130]=t("a",{id:"Reactant.MLIR.API.mlirLocationFileLineColGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLocationFileLineColGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFileLineColGet")],-1)),e[2131]||(e[2131]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2132]||(e[2132]=l('
julia
mlirLocationFileLineColGet(context, filename, line, col)

Creates an File/Line/Column location owned by the given context.

source

',3))]),t("details",sb,[t("summary",null,[e[2133]||(e[2133]=t("a",{id:"Reactant.MLIR.API.mlirLocationFromAttribute-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationFromAttribute-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFromAttribute")],-1)),e[2134]||(e[2134]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2135]||(e[2135]=l('
julia
mlirLocationFromAttribute(attribute)

Creates a location from a location attribute.

source

',3))]),t("details",ab,[t("summary",null,[e[2136]||(e[2136]=t("a",{id:"Reactant.MLIR.API.mlirLocationFusedGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirLocationFusedGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationFusedGet")],-1)),e[2137]||(e[2137]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2138]||(e[2138]=l('
julia
mlirLocationFusedGet(ctx, nLocations, locations, metadata)

Creates a fused location with an array of locations and metadata.

source

',3))]),t("details",ib,[t("summary",null,[e[2139]||(e[2139]=t("a",{id:"Reactant.MLIR.API.mlirLocationGetAttribute-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationGetAttribute-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationGetAttribute")],-1)),e[2140]||(e[2140]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2141]||(e[2141]=l('
julia
mlirLocationGetAttribute(location)

Returns the underlying location attribute of this location.

source

',3))]),t("details",lb,[t("summary",null,[e[2142]||(e[2142]=t("a",{id:"Reactant.MLIR.API.mlirLocationGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationGetContext")],-1)),e[2143]||(e[2143]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2144]||(e[2144]=l('
julia
mlirLocationGetContext(location)

Gets the context that a location was created with.

source

',3))]),t("details",nb,[t("summary",null,[e[2145]||(e[2145]=t("a",{id:"Reactant.MLIR.API.mlirLocationIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationIsNull")],-1)),e[2146]||(e[2146]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2147]||(e[2147]=l('
julia
mlirLocationIsNull(location)

Checks if the location is null.

source

',3))]),t("details",pb,[t("summary",null,[e[2148]||(e[2148]=t("a",{id:"Reactant.MLIR.API.mlirLocationNameGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLocationNameGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationNameGet")],-1)),e[2149]||(e[2149]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2150]||(e[2150]=l('
julia
mlirLocationNameGet(context, name, childLoc)

Creates a name location owned by the given context. Providing null location for childLoc is allowed and if childLoc is null location, then the behavior is the same as having unknown child location.

source

',3))]),t("details",rb,[t("summary",null,[e[2151]||(e[2151]=t("a",{id:"Reactant.MLIR.API.mlirLocationPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirLocationPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationPrint")],-1)),e[2152]||(e[2152]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2153]||(e[2153]=l('
julia
mlirLocationPrint(location, callback, userData)

Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",ob,[t("summary",null,[e[2154]||(e[2154]=t("a",{id:"Reactant.MLIR.API.mlirLocationUnknownGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLocationUnknownGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLocationUnknownGet")],-1)),e[2155]||(e[2155]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2156]||(e[2156]=l('
julia
mlirLocationUnknownGet(context)

Creates a location with unknown position owned by the given context.

source

',3))]),t("details",db,[t("summary",null,[e[2157]||(e[2157]=t("a",{id:"Reactant.MLIR.API.mlirLogicalResultFailure-Tuple{}",href:"#Reactant.MLIR.API.mlirLogicalResultFailure-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultFailure")],-1)),e[2158]||(e[2158]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2159]||(e[2159]=l('
julia
mlirLogicalResultFailure()

Creates a logical result representing a failure.

source

',3))]),t("details",cb,[t("summary",null,[e[2160]||(e[2160]=t("a",{id:"Reactant.MLIR.API.mlirLogicalResultIsFailure-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLogicalResultIsFailure-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultIsFailure")],-1)),e[2161]||(e[2161]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2162]||(e[2162]=l('
julia
mlirLogicalResultIsFailure(res)

Checks if the given logical result represents a failure.

source

',3))]),t("details",bb,[t("summary",null,[e[2163]||(e[2163]=t("a",{id:"Reactant.MLIR.API.mlirLogicalResultIsSuccess-Tuple{Any}",href:"#Reactant.MLIR.API.mlirLogicalResultIsSuccess-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultIsSuccess")],-1)),e[2164]||(e[2164]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2165]||(e[2165]=l('
julia
mlirLogicalResultIsSuccess(res)

Checks if the given logical result represents a success.

source

',3))]),t("details",hb,[t("summary",null,[e[2166]||(e[2166]=t("a",{id:"Reactant.MLIR.API.mlirLogicalResultSuccess-Tuple{}",href:"#Reactant.MLIR.API.mlirLogicalResultSuccess-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirLogicalResultSuccess")],-1)),e[2167]||(e[2167]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2168]||(e[2168]=l('
julia
mlirLogicalResultSuccess()

Creates a logical result representing a success.

source

',3))]),t("details",ub,[t("summary",null,[e[2169]||(e[2169]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeContiguousGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeContiguousGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeContiguousGet")],-1)),e[2170]||(e[2170]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2171]||(e[2171]=l('
julia
mlirMemRefTypeContiguousGet(elementType, rank, shape, memorySpace)

Creates a MemRef type with the given rank, shape, memory space and element type in the same context as the element type. The type has no affine maps, i.e. represents a default row-major contiguous memref. The type is owned by the context.

source

',3))]),t("details",gb,[t("summary",null,[e[2172]||(e[2172]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeContiguousGetChecked")],-1)),e[2173]||(e[2173]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2174]||(e[2174]=l('
julia
mlirMemRefTypeContiguousGetChecked(loc, elementType, rank, shape, memorySpace)

Same as "mlirMemRefTypeContiguousGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",yb,[t("summary",null,[e[2175]||(e[2175]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGet")],-1)),e[2176]||(e[2176]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2177]||(e[2177]=l('
julia
mlirMemRefTypeGet(elementType, rank, shape, layout, memorySpace)

Creates a MemRef type with the given rank and shape, a potentially empty list of affine layout maps, the given memory space and element type, in the same context as element type. The type is owned by the context.

source

',3))]),t("details",mb,[t("summary",null,[e[2178]||(e[2178]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetAffineMap-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetAffineMap-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetAffineMap")],-1)),e[2179]||(e[2179]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2180]||(e[2180]=l('
julia
mlirMemRefTypeGetAffineMap(type)

Returns the affine map of the given MemRef type.

source

',3))]),t("details",kb,[t("summary",null,[e[2181]||(e[2181]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetChecked-NTuple{6, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetChecked-NTuple{6, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetChecked")],-1)),e[2182]||(e[2182]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2183]||(e[2183]=l('
julia
mlirMemRefTypeGetChecked(loc, elementType, rank, shape, layout, memorySpace)

Same as "mlirMemRefTypeGet" but returns a nullptr-wrapping MlirType o illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",Rb,[t("summary",null,[e[2184]||(e[2184]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetLayout-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetLayout-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetLayout")],-1)),e[2185]||(e[2185]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2186]||(e[2186]=l('
julia
mlirMemRefTypeGetLayout(type)

Returns the layout of the given MemRef type.

source

',3))]),t("details",Ib,[t("summary",null,[e[2187]||(e[2187]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetMemorySpace")],-1)),e[2188]||(e[2188]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2189]||(e[2189]=l('
julia
mlirMemRefTypeGetMemorySpace(type)

Returns the memory space of the given MemRef type.

source

',3))]),t("details",jb,[t("summary",null,[e[2190]||(e[2190]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetStridesAndOffset")],-1)),e[2191]||(e[2191]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2192]||(e[2192]=l('
julia
mlirMemRefTypeGetStridesAndOffset(type, strides, offset)

Returns the strides of the MemRef if the layout map is in strided form. Both strides and offset are out params. strides must point to pre-allocated memory of length equal to the rank of the memref.

source

',3))]),t("details",fb,[t("summary",null,[e[2193]||(e[2193]=t("a",{id:"Reactant.MLIR.API.mlirMemRefTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirMemRefTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMemRefTypeGetTypeID")],-1)),e[2194]||(e[2194]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2195]||(e[2195]=l('
julia
mlirMemRefTypeGetTypeID()

Returns the typeID of an MemRef type.

source

',3))]),t("details",Mb,[t("summary",null,[e[2196]||(e[2196]=t("a",{id:"Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirMergeSymbolsIntoFromClone")],-1)),e[2197]||(e[2197]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2198]||(e[2198]=l('
julia
mlirMergeSymbolsIntoFromClone(target, other)

Merge the symbols from other into target, potentially renaming them to avoid conflicts. Private symbols may be renamed during the merge, public symbols must have at most one declaration. A name conflict in public symbols is reported as an error before returning a failure.

Note that this clones the other operation unlike the C++ counterpart that takes ownership.

source

',4))]),t("details",Ab,[t("summary",null,[e[2199]||(e[2199]=t("a",{id:"Reactant.MLIR.API.mlirModuleCreateEmpty-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleCreateEmpty-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleCreateEmpty")],-1)),e[2200]||(e[2200]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2201]||(e[2201]=l('
julia
mlirModuleCreateEmpty(location)

Creates a new, empty module and transfers ownership to the caller.

source

',3))]),t("details",Lb,[t("summary",null,[e[2202]||(e[2202]=t("a",{id:"Reactant.MLIR.API.mlirModuleCreateParse-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirModuleCreateParse-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleCreateParse")],-1)),e[2203]||(e[2203]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2204]||(e[2204]=l('
julia
mlirModuleCreateParse(context, _module)

Parses a module from the string and transfers ownership to the caller.

source

',3))]),t("details",Eb,[t("summary",null,[e[2205]||(e[2205]=t("a",{id:"Reactant.MLIR.API.mlirModuleDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleDestroy")],-1)),e[2206]||(e[2206]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2207]||(e[2207]=l('
julia
mlirModuleDestroy(_module)

Takes a module owned by the caller and deletes it.

source

',3))]),t("details",vb,[t("summary",null,[e[2208]||(e[2208]=t("a",{id:"Reactant.MLIR.API.mlirModuleFromOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleFromOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleFromOperation")],-1)),e[2209]||(e[2209]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2210]||(e[2210]=l('
julia
mlirModuleFromOperation(op)

Views the generic operation as a module. The returned module is null when the input operation was not a ModuleOp.

source

',3))]),t("details",Tb,[t("summary",null,[e[2211]||(e[2211]=t("a",{id:"Reactant.MLIR.API.mlirModuleGetBody-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetBody-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetBody")],-1)),e[2212]||(e[2212]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2213]||(e[2213]=l('
julia
mlirModuleGetBody(_module)

Gets the body of the module, i.e. the only block it contains.

source

',3))]),t("details",Cb,[t("summary",null,[e[2214]||(e[2214]=t("a",{id:"Reactant.MLIR.API.mlirModuleGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetContext")],-1)),e[2215]||(e[2215]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2216]||(e[2216]=l('
julia
mlirModuleGetContext(_module)

Gets the context that a module was created with.

source

',3))]),t("details",xb,[t("summary",null,[e[2217]||(e[2217]=t("a",{id:"Reactant.MLIR.API.mlirModuleGetOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleGetOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleGetOperation")],-1)),e[2218]||(e[2218]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2219]||(e[2219]=l('
julia
mlirModuleGetOperation(_module)

Views the module as a generic operation.

source

',3))]),t("details",Fb,[t("summary",null,[e[2220]||(e[2220]=t("a",{id:"Reactant.MLIR.API.mlirModuleIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirModuleIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirModuleIsNull")],-1)),e[2221]||(e[2221]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2222]||(e[2222]=l('
julia
mlirModuleIsNull(_module)

Checks whether a module is null.

source

',3))]),t("details",Pb,[t("summary",null,[e[2223]||(e[2223]=t("a",{id:"Reactant.MLIR.API.mlirNamedAttributeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirNamedAttributeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNamedAttributeGet")],-1)),e[2224]||(e[2224]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2225]||(e[2225]=l('
julia
mlirNamedAttributeGet(name, attr)

Associates an attribute with the name. Takes ownership of neither.

source

',3))]),t("details",Db,[t("summary",null,[e[2226]||(e[2226]=t("a",{id:"Reactant.MLIR.API.mlirNoneTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirNoneTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNoneTypeGet")],-1)),e[2227]||(e[2227]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2228]||(e[2228]=l('
julia
mlirNoneTypeGet(ctx)

Creates a None type in the given context. The type is owned by the context.

source

',3))]),t("details",Ob,[t("summary",null,[e[2229]||(e[2229]=t("a",{id:"Reactant.MLIR.API.mlirNoneTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirNoneTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirNoneTypeGetTypeID")],-1)),e[2230]||(e[2230]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2231]||(e[2231]=l('
julia
mlirNoneTypeGetTypeID()

Returns the typeID of an None type.

source

',3))]),t("details",Bb,[t("summary",null,[e[2232]||(e[2232]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandGetNextUse-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetNextUse-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetNextUse")],-1)),e[2233]||(e[2233]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2234]||(e[2234]=l('
julia
mlirOpOperandGetNextUse(opOperand)

Returns an op operand representing the next use of the value, or a null op operand if there is no next use.

source

',3))]),t("details",Gb,[t("summary",null,[e[2235]||(e[2235]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandGetOperandNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetOperandNumber-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetOperandNumber")],-1)),e[2236]||(e[2236]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2237]||(e[2237]=l('
julia
mlirOpOperandGetOperandNumber(opOperand)

Returns the operand number of an op operand.

source

',3))]),t("details",zb,[t("summary",null,[e[2238]||(e[2238]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetOwner-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetOwner")],-1)),e[2239]||(e[2239]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2240]||(e[2240]=l('
julia
mlirOpOperandGetOwner(opOperand)

Returns the owner operation of an op operand.

source

',3))]),t("details",wb,[t("summary",null,[e[2241]||(e[2241]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandGetValue")],-1)),e[2242]||(e[2242]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2243]||(e[2243]=l('
julia
mlirOpOperandGetValue(opOperand)

Returns the value of an op operand.

source

',3))]),t("details",Sb,[t("summary",null,[e[2244]||(e[2244]=t("a",{id:"Reactant.MLIR.API.mlirOpOperandIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpOperandIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpOperandIsNull")],-1)),e[2245]||(e[2245]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2246]||(e[2246]=l('
julia
mlirOpOperandIsNull(opOperand)

Returns whether the op operand is null.

source

',3))]),t("details",Nb,[t("summary",null,[e[2247]||(e[2247]=t("a",{id:"Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerAddOwnedPass")],-1)),e[2248]||(e[2248]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2249]||(e[2249]=l('
julia
mlirOpPassManagerAddOwnedPass(passManager, pass)

Add a pass and transfer ownership to the provided mlirOpPassManager. If the pass is not a generic operation pass or matching the type of the provided PassManager, a new OpPassManager is implicitly nested under the provided PassManager.

source

',3))]),t("details",Vb,[t("summary",null,[e[2250]||(e[2250]=t("a",{id:"Reactant.MLIR.API.mlirOpPassManagerAddPipeline-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerAddPipeline-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerAddPipeline")],-1)),e[2251]||(e[2251]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2252]||(e[2252]=l('
julia
mlirOpPassManagerAddPipeline(passManager, pipelineElements, callback, userData)

Parse a sequence of textual MLIR pass pipeline elements and add them to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.

source

',3))]),t("details",qb,[t("summary",null,[e[2253]||(e[2253]=t("a",{id:"Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPassManagerGetNestedUnder")],-1)),e[2254]||(e[2254]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2255]||(e[2255]=l('
julia
mlirOpPassManagerGetNestedUnder(passManager, operationName)

Nest an OpPassManager under the provided OpPassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed.

source

',3))]),t("details",Ub,[t("summary",null,[e[2256]||(e[2256]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsAssumeVerified")],-1)),e[2257]||(e[2257]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2258]||(e[2258]=l('
julia
mlirOpPrintingFlagsAssumeVerified(flags)

Do not verify the operation when using custom operation printers.

source

',3))]),t("details",Qb,[t("summary",null,[e[2259]||(e[2259]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsCreate")],-1)),e[2260]||(e[2260]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2261]||(e[2261]=l('
julia
mlirOpPrintingFlagsCreate()

Creates new printing flags with defaults, intended for customization. Must be freed with a call to mlirOpPrintingFlagsDestroy().

source

',3))]),t("details",Wb,[t("summary",null,[e[2262]||(e[2262]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsDestroy")],-1)),e[2263]||(e[2263]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2264]||(e[2264]=l('
julia
mlirOpPrintingFlagsDestroy(flags)

Destroys printing flags created with mlirOpPrintingFlagsCreate.

source

',3))]),t("details",Hb,[t("summary",null,[e[2265]||(e[2265]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeElementsAttrs")],-1)),e[2266]||(e[2266]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2267]||(e[2267]=l('
julia
mlirOpPrintingFlagsElideLargeElementsAttrs(flags, largeElementLimit)

Enables the elision of large elements attributes by printing a lexically valid but otherwise meaningless form instead of the element data. The largeElementLimit is used to configure what is considered to be a "large" ElementsAttr by providing an upper limit to the number of elements.

source

',3))]),t("details",Zb,[t("summary",null,[e[2268]||(e[2268]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsElideLargeResourceString")],-1)),e[2269]||(e[2269]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2270]||(e[2270]=l('
julia
mlirOpPrintingFlagsElideLargeResourceString(flags, largeResourceLimit)

Enables the elision of large resources strings by omitting them from the dialect_resources section. The largeResourceLimit is used to configure what is considered to be a "large" resource by providing an upper limit to the string size.

source

',3))]),t("details",Jb,[t("summary",null,[e[2271]||(e[2271]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsEnableDebugInfo")],-1)),e[2272]||(e[2272]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2273]||(e[2273]=l('
julia
mlirOpPrintingFlagsEnableDebugInfo(flags, enable, prettyForm)

Enable or disable printing of debug information (based on enable). If 'prettyForm' is set to true, debug information is printed in a more readable 'pretty' form. Note: The IR generated with 'prettyForm' is not parsable.

source

',3))]),t("details",Kb,[t("summary",null,[e[2274]||(e[2274]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsPrintGenericOpForm")],-1)),e[2275]||(e[2275]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2276]||(e[2276]=l('
julia
mlirOpPrintingFlagsPrintGenericOpForm(flags)

Always print operations in the generic form.

source

',3))]),t("details",$b,[t("summary",null,[e[2277]||(e[2277]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsSkipRegions")],-1)),e[2278]||(e[2278]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2279]||(e[2279]=l('
julia
mlirOpPrintingFlagsSkipRegions(flags)

Skip printing regions.

source

',3))]),t("details",Xb,[t("summary",null,[e[2280]||(e[2280]=t("a",{id:"Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpPrintingFlagsUseLocalScope")],-1)),e[2281]||(e[2281]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2282]||(e[2282]=l('
julia
mlirOpPrintingFlagsUseLocalScope(flags)

Use local scope when printing the operation. This allows for using the printer in a more localized and thread-safe setting, but may not necessarily be identical to what the IR will look like when dumping the full module.

source

',3))]),t("details",Yb,[t("summary",null,[e[2283]||(e[2283]=t("a",{id:"Reactant.MLIR.API.mlirOpResultGetOwner-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpResultGetOwner-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpResultGetOwner")],-1)),e[2284]||(e[2284]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2285]||(e[2285]=l('
julia
mlirOpResultGetOwner(value)

Returns an operation that produced this value as its result. Asserts if the value is not an op result.

source

',3))]),t("details",_b,[t("summary",null,[e[2286]||(e[2286]=t("a",{id:"Reactant.MLIR.API.mlirOpResultGetResultNumber-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpResultGetResultNumber-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpResultGetResultNumber")],-1)),e[2287]||(e[2287]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2288]||(e[2288]=l('
julia
mlirOpResultGetResultNumber(value)

Returns the position of the value in the list of results of the operation that produced it.

source

',3))]),t("details",eh,[t("summary",null,[e[2289]||(e[2289]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGet-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGet-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGet")],-1)),e[2290]||(e[2290]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2291]||(e[2291]=l('
julia
mlirOpaqueAttrGet(ctx, dialectNamespace, dataLength, data, type)

Creates an opaque attribute in the given context associated with the dialect identified by its namespace. The attribute contains opaque byte data of the specified length (data need not be null-terminated).

source

',3))]),t("details",th,[t("summary",null,[e[2292]||(e[2292]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetData-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetData")],-1)),e[2293]||(e[2293]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2294]||(e[2294]=l('
julia
mlirOpaqueAttrGetData(attr)

Returns the raw data as a string reference. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",sh,[t("summary",null,[e[2295]||(e[2295]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetDialectNamespace")],-1)),e[2296]||(e[2296]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2297]||(e[2297]=l('
julia
mlirOpaqueAttrGetDialectNamespace(attr)

Returns the namespace of the dialect with which the given opaque attribute is associated. The namespace string is owned by the context.

source

',3))]),t("details",ah,[t("summary",null,[e[2298]||(e[2298]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirOpaqueAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueAttrGetTypeID")],-1)),e[2299]||(e[2299]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2300]||(e[2300]=l('
julia
mlirOpaqueAttrGetTypeID()

Returns the typeID of an Opaque attribute.

source

',3))]),t("details",ih,[t("summary",null,[e[2301]||(e[2301]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGet")],-1)),e[2302]||(e[2302]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2303]||(e[2303]=l('
julia
mlirOpaqueTypeGet(ctx, dialectNamespace, typeData)

Creates an opaque type in the given context associated with the dialect identified by its namespace. The type contains opaque byte data of the specified length (data need not be null-terminated).

source

',3))]),t("details",lh,[t("summary",null,[e[2304]||(e[2304]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetData-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetData-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetData")],-1)),e[2305]||(e[2305]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2306]||(e[2306]=l('
julia
mlirOpaqueTypeGetData(type)

Returns the raw data as a string reference. The data remains live as long as the context in which the type lives.

source

',3))]),t("details",nh,[t("summary",null,[e[2307]||(e[2307]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetDialectNamespace")],-1)),e[2308]||(e[2308]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2309]||(e[2309]=l('
julia
mlirOpaqueTypeGetDialectNamespace(type)

Returns the namespace of the dialect with which the given opaque type is associated. The namespace string is owned by the context.

source

',3))]),t("details",ph,[t("summary",null,[e[2310]||(e[2310]=t("a",{id:"Reactant.MLIR.API.mlirOpaqueTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirOpaqueTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOpaqueTypeGetTypeID")],-1)),e[2311]||(e[2311]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2312]||(e[2312]=l('
julia
mlirOpaqueTypeGetTypeID()

Returns the typeID of an Opaque type.

source

',3))]),t("details",rh,[t("summary",null,[e[2313]||(e[2313]=t("a",{id:"Reactant.MLIR.API.mlirOperationClone-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationClone-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationClone")],-1)),e[2314]||(e[2314]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2315]||(e[2315]=l('
julia
mlirOperationClone(op)

Creates a deep copy of an operation. The operation is not inserted and ownership is transferred to the caller.

source

',3))]),t("details",oh,[t("summary",null,[e[2316]||(e[2316]=t("a",{id:"Reactant.MLIR.API.mlirOperationCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationCreate")],-1)),e[2317]||(e[2317]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2318]||(e[2318]=l('
julia
mlirOperationCreate(state)

Creates an operation and transfers ownership to the caller. Note that caller owned child objects are transferred in this call and must not be further used. Particularly, this applies to any regions added to the state (the implementation may invalidate any such pointers).

This call can fail under the following conditions, in which case, it will return a null operation and emit diagnostics: - Result type inference is enabled and cannot be performed.

source

',4))]),t("details",dh,[t("summary",null,[e[2319]||(e[2319]=t("a",{id:"Reactant.MLIR.API.mlirOperationCreateParse-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationCreateParse-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationCreateParse")],-1)),e[2320]||(e[2320]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2321]||(e[2321]=l('
julia
mlirOperationCreateParse(context, sourceStr, sourceName)

Parses an operation, giving ownership to the caller. If parsing fails a null operation will be returned, and an error diagnostic emitted.

sourceStr may be either the text assembly format, or binary bytecode format. sourceName is used as the file name of the source; any IR without locations will get a FileLineColLoc location with sourceName as the file name.

source

',4))]),t("details",ch,[t("summary",null,[e[2322]||(e[2322]=t("a",{id:"Reactant.MLIR.API.mlirOperationDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationDestroy")],-1)),e[2323]||(e[2323]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2324]||(e[2324]=l('
julia
mlirOperationDestroy(op)

Takes an operation owned by the caller and destroys it.

source

',3))]),t("details",bh,[t("summary",null,[e[2325]||(e[2325]=t("a",{id:"Reactant.MLIR.API.mlirOperationDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationDump")],-1)),e[2326]||(e[2326]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2327]||(e[2327]=l('
julia
mlirOperationDump(op)

Prints an operation to stderr.

source

',3))]),t("details",hh,[t("summary",null,[e[2328]||(e[2328]=t("a",{id:"Reactant.MLIR.API.mlirOperationEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationEqual")],-1)),e[2329]||(e[2329]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2330]||(e[2330]=l('
julia
mlirOperationEqual(op, other)

Checks whether two operation handles point to the same operation. This does not perform deep comparison.

source

',3))]),t("details",uh,[t("summary",null,[e[2331]||(e[2331]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetAttribute-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetAttribute")],-1)),e[2332]||(e[2332]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2333]||(e[2333]=l('
julia
mlirOperationGetAttribute(op, pos)

Return pos-th attribute of the operation. Deprecated, please use mlirOperationGetInherentAttribute or mlirOperationGetDiscardableAttribute.

source

',3))]),t("details",gh,[t("summary",null,[e[2334]||(e[2334]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetAttributeByName")],-1)),e[2335]||(e[2335]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2336]||(e[2336]=l('
julia
mlirOperationGetAttributeByName(op, name)

Returns an attribute attached to the operation given its name. Deprecated, please use mlirOperationGetInherentAttributeByName or mlirOperationGetDiscardableAttributeByName.

source

',3))]),t("details",yh,[t("summary",null,[e[2337]||(e[2337]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetBlock")],-1)),e[2338]||(e[2338]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2339]||(e[2339]=l('
julia
mlirOperationGetBlock(op)

Gets the block that owns this operation, returning null if the operation is not owned.

source

',3))]),t("details",mh,[t("summary",null,[e[2340]||(e[2340]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetContext")],-1)),e[2341]||(e[2341]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2342]||(e[2342]=l('
julia
mlirOperationGetContext(op)

Gets the context this operation is associated with

source

',3))]),t("details",kh,[t("summary",null,[e[2343]||(e[2343]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetDiscardableAttribute-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetDiscardableAttribute-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetDiscardableAttribute")],-1)),e[2344]||(e[2344]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2345]||(e[2345]=l('
julia
mlirOperationGetDiscardableAttribute(op, pos)

Return pos-th discardable attribute of the operation.

source

',3))]),t("details",Rh,[t("summary",null,[e[2346]||(e[2346]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetDiscardableAttributeByName")],-1)),e[2347]||(e[2347]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2348]||(e[2348]=l('
julia
mlirOperationGetDiscardableAttributeByName(op, name)

Returns a discardable attribute attached to the operation given its name.

source

',3))]),t("details",Ih,[t("summary",null,[e[2349]||(e[2349]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetFirstRegion-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetFirstRegion-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetFirstRegion")],-1)),e[2350]||(e[2350]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2351]||(e[2351]=l('
julia
mlirOperationGetFirstRegion(op)

Returns first region attached to the operation.

source

',3))]),t("details",jh,[t("summary",null,[e[2352]||(e[2352]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetInherentAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetInherentAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetInherentAttributeByName")],-1)),e[2353]||(e[2353]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2354]||(e[2354]=l('
julia
mlirOperationGetInherentAttributeByName(op, name)

Returns an inherent attribute attached to the operation given its name.

source

',3))]),t("details",fh,[t("summary",null,[e[2355]||(e[2355]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetLocation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetLocation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetLocation")],-1)),e[2356]||(e[2356]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2357]||(e[2357]=l('
julia
mlirOperationGetLocation(op)

Gets the location of the operation.

source

',3))]),t("details",Mh,[t("summary",null,[e[2358]||(e[2358]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetName-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetName-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetName")],-1)),e[2359]||(e[2359]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2360]||(e[2360]=l('
julia
mlirOperationGetName(op)

Gets the name of the operation as an identifier.

source

',3))]),t("details",Ah,[t("summary",null,[e[2361]||(e[2361]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNextInBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNextInBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNextInBlock")],-1)),e[2362]||(e[2362]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2363]||(e[2363]=l('
julia
mlirOperationGetNextInBlock(op)

Returns an operation immediately following the given operation it its enclosing block.

source

',3))]),t("details",Lh,[t("summary",null,[e[2364]||(e[2364]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumAttributes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumAttributes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumAttributes")],-1)),e[2365]||(e[2365]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2366]||(e[2366]=l('
julia
mlirOperationGetNumAttributes(op)

Returns the number of attributes attached to the operation. Deprecated, please use mlirOperationGetNumInherentAttributes or mlirOperationGetNumDiscardableAttributes.

source

',3))]),t("details",Eh,[t("summary",null,[e[2367]||(e[2367]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumDiscardableAttributes")],-1)),e[2368]||(e[2368]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2369]||(e[2369]=l('
julia
mlirOperationGetNumDiscardableAttributes(op)

Returns the number of discardable attributes attached to the operation.

source

',3))]),t("details",vh,[t("summary",null,[e[2370]||(e[2370]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumOperands-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumOperands-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumOperands")],-1)),e[2371]||(e[2371]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2372]||(e[2372]=l('
julia
mlirOperationGetNumOperands(op)

Returns the number of operands of the operation.

source

',3))]),t("details",Th,[t("summary",null,[e[2373]||(e[2373]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumRegions-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumRegions-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumRegions")],-1)),e[2374]||(e[2374]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2375]||(e[2375]=l('
julia
mlirOperationGetNumRegions(op)

Returns the number of regions attached to the given operation.

source

',3))]),t("details",Ch,[t("summary",null,[e[2376]||(e[2376]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumResults-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumResults-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumResults")],-1)),e[2377]||(e[2377]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2378]||(e[2378]=l('
julia
mlirOperationGetNumResults(op)

Returns the number of results of the operation.

source

',3))]),t("details",xh,[t("summary",null,[e[2379]||(e[2379]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetNumSuccessors-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetNumSuccessors-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetNumSuccessors")],-1)),e[2380]||(e[2380]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2381]||(e[2381]=l('
julia
mlirOperationGetNumSuccessors(op)

Returns the number of successor blocks of the operation.

source

',3))]),t("details",Fh,[t("summary",null,[e[2382]||(e[2382]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetOperand-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetOperand-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetOperand")],-1)),e[2383]||(e[2383]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2384]||(e[2384]=l('
julia
mlirOperationGetOperand(op, pos)

Returns pos-th operand of the operation.

source

',3))]),t("details",Ph,[t("summary",null,[e[2385]||(e[2385]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetParentOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetParentOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetParentOperation")],-1)),e[2386]||(e[2386]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2387]||(e[2387]=l('
julia
mlirOperationGetParentOperation(op)

Gets the operation that owns this operation, returning null if the operation is not owned.

source

',3))]),t("details",Dh,[t("summary",null,[e[2388]||(e[2388]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetRegion-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetRegion-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetRegion")],-1)),e[2389]||(e[2389]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2390]||(e[2390]=l('
julia
mlirOperationGetRegion(op, pos)

Returns pos-th region attached to the operation.

source

',3))]),t("details",Oh,[t("summary",null,[e[2391]||(e[2391]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetResult-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetResult-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetResult")],-1)),e[2392]||(e[2392]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2393]||(e[2393]=l('
julia
mlirOperationGetResult(op, pos)

Returns pos-th result of the operation.

source

',3))]),t("details",Bh,[t("summary",null,[e[2394]||(e[2394]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetSuccessor-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationGetSuccessor-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetSuccessor")],-1)),e[2395]||(e[2395]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2396]||(e[2396]=l('
julia
mlirOperationGetSuccessor(op, pos)

Returns pos-th successor of the operation.

source

',3))]),t("details",Gh,[t("summary",null,[e[2397]||(e[2397]=t("a",{id:"Reactant.MLIR.API.mlirOperationGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationGetTypeID-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationGetTypeID")],-1)),e[2398]||(e[2398]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2399]||(e[2399]=l('
julia
mlirOperationGetTypeID(op)

Gets the type id of the operation. Returns null if the operation does not have a registered operation description.

source

',3))]),t("details",zh,[t("summary",null,[e[2400]||(e[2400]=t("a",{id:"Reactant.MLIR.API.mlirOperationHasInherentAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationHasInherentAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationHasInherentAttributeByName")],-1)),e[2401]||(e[2401]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2402]||(e[2402]=l('
julia
mlirOperationHasInherentAttributeByName(op, name)

Returns true if this operation defines an inherent attribute with this name. Note: the attribute can be optional, so mlirOperationGetInherentAttributeByName can still return a null attribute.

source

',3))]),t("details",wh,[t("summary",null,[e[2403]||(e[2403]=t("a",{id:"Reactant.MLIR.API.mlirOperationImplementsInterface-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationImplementsInterface-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationImplementsInterface")],-1)),e[2404]||(e[2404]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2405]||(e[2405]=l('
julia
mlirOperationImplementsInterface(operation, interfaceTypeID)

Returns true if the given operation implements an interface identified by its TypeID.

source

',3))]),t("details",Sh,[t("summary",null,[e[2406]||(e[2406]=t("a",{id:"Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationImplementsInterfaceStatic")],-1)),e[2407]||(e[2407]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2408]||(e[2408]=l('
julia
mlirOperationImplementsInterfaceStatic(operationName, context, interfaceTypeID)

Returns true if the operation identified by its canonical string name implements the interface identified by its TypeID in the given context. Note that interfaces may be attached to operations in some contexts and not others.

source

',3))]),t("details",Nh,[t("summary",null,[e[2409]||(e[2409]=t("a",{id:"Reactant.MLIR.API.mlirOperationIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationIsNull")],-1)),e[2410]||(e[2410]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2411]||(e[2411]=l('
julia
mlirOperationIsNull(op)

Checks whether the underlying operation is null.

source

',3))]),t("details",Vh,[t("summary",null,[e[2412]||(e[2412]=t("a",{id:"Reactant.MLIR.API.mlirOperationMoveAfter-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationMoveAfter-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationMoveAfter")],-1)),e[2413]||(e[2413]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2414]||(e[2414]=l('
julia
mlirOperationMoveAfter(op, other)

Moves the given operation immediately after the other operation in its parent block. The given operation may be owned by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.

source

',3))]),t("details",qh,[t("summary",null,[e[2415]||(e[2415]=t("a",{id:"Reactant.MLIR.API.mlirOperationMoveBefore-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationMoveBefore-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationMoveBefore")],-1)),e[2416]||(e[2416]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2417]||(e[2417]=l('
julia
mlirOperationMoveBefore(op, other)

Moves the given operation immediately before the other operation in its parent block. The given operation may be owner by the caller or by its current block. The other operation must belong to a block. In any case, the ownership is transferred to the block of the other operation.

source

',3))]),t("details",Uh,[t("summary",null,[e[2418]||(e[2418]=t("a",{id:"Reactant.MLIR.API.mlirOperationPrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationPrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrint")],-1)),e[2419]||(e[2419]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2420]||(e[2420]=l('
julia
mlirOperationPrint(op, callback, userData)

Prints an operation by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Qh,[t("summary",null,[e[2421]||(e[2421]=t("a",{id:"Reactant.MLIR.API.mlirOperationPrintWithFlags-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationPrintWithFlags-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrintWithFlags")],-1)),e[2422]||(e[2422]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2423]||(e[2423]=l('
julia
mlirOperationPrintWithFlags(op, flags, callback, userData)

Same as mlirOperationPrint but accepts flags controlling the printing behavior.

source

',3))]),t("details",Wh,[t("summary",null,[e[2424]||(e[2424]=t("a",{id:"Reactant.MLIR.API.mlirOperationPrintWithState-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationPrintWithState-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationPrintWithState")],-1)),e[2425]||(e[2425]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2426]||(e[2426]=l('
julia
mlirOperationPrintWithState(op, state, callback, userData)

Same as mlirOperationPrint but accepts AsmState controlling the printing behavior as well as caching computed names.

source

',3))]),t("details",Hh,[t("summary",null,[e[2427]||(e[2427]=t("a",{id:"Reactant.MLIR.API.mlirOperationRemoveAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveAttributeByName")],-1)),e[2428]||(e[2428]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2429]||(e[2429]=l('
julia
mlirOperationRemoveAttributeByName(op, name)

Removes an attribute by name. Returns false if the attribute was not found and true if removed. Deprecated, please use mlirOperationRemoveInherentAttributeByName or mlirOperationRemoveDiscardableAttributeByName.

source

',3))]),t("details",Zh,[t("summary",null,[e[2430]||(e[2430]=t("a",{id:"Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveDiscardableAttributeByName")],-1)),e[2431]||(e[2431]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2432]||(e[2432]=l('
julia
mlirOperationRemoveDiscardableAttributeByName(op, name)

Removes a discardable attribute by name. Returns false if the attribute was not found and true if removed.

source

',3))]),t("details",Jh,[t("summary",null,[e[2433]||(e[2433]=t("a",{id:"Reactant.MLIR.API.mlirOperationRemoveFromParent-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationRemoveFromParent-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationRemoveFromParent")],-1)),e[2434]||(e[2434]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2435]||(e[2435]=l('
julia
mlirOperationRemoveFromParent(op)

Removes the given operation from its parent block. The operation is not destroyed. The ownership of the operation is transferred to the caller.

source

',3))]),t("details",Kh,[t("summary",null,[e[2436]||(e[2436]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetAttributeByName-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetAttributeByName")],-1)),e[2437]||(e[2437]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2438]||(e[2438]=l('
julia
mlirOperationSetAttributeByName(op, name, attr)

Sets an attribute by name, replacing the existing if it exists or adding a new one otherwise. Deprecated, please use mlirOperationSetInherentAttributeByName or mlirOperationSetDiscardableAttributeByName.

source

',3))]),t("details",$h,[t("summary",null,[e[2439]||(e[2439]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetDiscardableAttributeByName")],-1)),e[2440]||(e[2440]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2441]||(e[2441]=l('
julia
mlirOperationSetDiscardableAttributeByName(op, name, attr)

Sets a discardable attribute by name, replacing the existing if it exists or adding a new one otherwise. The new attr Attribute is not allowed to be null, use mlirOperationRemoveDiscardableAttributeByName to remove an Attribute instead.

source

',3))]),t("details",Xh,[t("summary",null,[e[2442]||(e[2442]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetInherentAttributeByName-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetInherentAttributeByName-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetInherentAttributeByName")],-1)),e[2443]||(e[2443]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2444]||(e[2444]=l('
julia
mlirOperationSetInherentAttributeByName(op, name, attr)

Sets an inherent attribute by name, replacing the existing if it exists. This has no effect if "name" does not match an inherent attribute.

source

',3))]),t("details",Yh,[t("summary",null,[e[2445]||(e[2445]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetOperand-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetOperand-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetOperand")],-1)),e[2446]||(e[2446]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2447]||(e[2447]=l('
julia
mlirOperationSetOperand(op, pos, newValue)

Sets the pos-th operand of the operation.

source

',3))]),t("details",_h,[t("summary",null,[e[2448]||(e[2448]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetOperands-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetOperands-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetOperands")],-1)),e[2449]||(e[2449]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2450]||(e[2450]=l('
julia
mlirOperationSetOperands(op, nOperands, operands)

Replaces the operands of the operation.

source

',3))]),t("details",eu,[t("summary",null,[e[2451]||(e[2451]=t("a",{id:"Reactant.MLIR.API.mlirOperationSetSuccessor-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationSetSuccessor-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationSetSuccessor")],-1)),e[2452]||(e[2452]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2453]||(e[2453]=l('
julia
mlirOperationSetSuccessor(op, pos, block)

Set pos-th successor of the operation.

source

',3))]),t("details",tu,[t("summary",null,[e[2454]||(e[2454]=t("a",{id:"Reactant.MLIR.API.mlirOperationStateAddResults-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationStateAddResults-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateAddResults")],-1)),e[2455]||(e[2455]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2456]||(e[2456]=l('
julia
mlirOperationStateAddResults(state, n, results)

Adds a list of components to the operation state.

source

',3))]),t("details",su,[t("summary",null,[e[2457]||(e[2457]=t("a",{id:"Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateEnableResultTypeInference")],-1)),e[2458]||(e[2458]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2459]||(e[2459]=l('
julia
mlirOperationStateEnableResultTypeInference(state)

Enables result type inference for the operation under construction. If enabled, then the caller must not have called mlirOperationStateAddResults(). Note that if enabled, the mlirOperationCreate() call is failable: it will return a null operation on inference failure and will emit diagnostics.

source

',3))]),t("details",au,[t("summary",null,[e[2460]||(e[2460]=t("a",{id:"Reactant.MLIR.API.mlirOperationStateGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirOperationStateGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationStateGet")],-1)),e[2461]||(e[2461]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2462]||(e[2462]=l('
julia
mlirOperationStateGet(name, loc)

Constructs an operation state from a name and a location.

source

',3))]),t("details",iu,[t("summary",null,[e[2463]||(e[2463]=t("a",{id:"Reactant.MLIR.API.mlirOperationVerify-Tuple{Any}",href:"#Reactant.MLIR.API.mlirOperationVerify-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationVerify")],-1)),e[2464]||(e[2464]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2465]||(e[2465]=l('
julia
mlirOperationVerify(op)

Verify the operation and return true if it passes, false if it fails.

source

',3))]),t("details",lu,[t("summary",null,[e[2466]||(e[2466]=t("a",{id:"Reactant.MLIR.API.mlirOperationWalk-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationWalk-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWalk")],-1)),e[2467]||(e[2467]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2468]||(e[2468]=l('
julia
mlirOperationWalk(op, callback, userData, walkOrder)

Walks operation op in walkOrder and calls callback on that operation. *userData is passed to the callback as well and can be used to tunnel some context or other data into the callback.

source

',3))]),t("details",nu,[t("summary",null,[e[2469]||(e[2469]=t("a",{id:"Reactant.MLIR.API.mlirOperationWriteBytecode-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirOperationWriteBytecode-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWriteBytecode")],-1)),e[2470]||(e[2470]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2471]||(e[2471]=l('
julia
mlirOperationWriteBytecode(op, callback, userData)

Same as mlirOperationPrint but writing the bytecode format.

source

',3))]),t("details",pu,[t("summary",null,[e[2472]||(e[2472]=t("a",{id:"Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirOperationWriteBytecodeWithConfig")],-1)),e[2473]||(e[2473]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2474]||(e[2474]=l('
julia
mlirOperationWriteBytecodeWithConfig(op, config, callback, userData)

Same as mlirOperationWriteBytecode but with writer config and returns failure only if desired bytecode could not be honored.

source

',3))]),t("details",ru,[t("summary",null,[e[2475]||(e[2475]=t("a",{id:"Reactant.MLIR.API.mlirParsePassPipeline-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirParsePassPipeline-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirParsePassPipeline")],-1)),e[2476]||(e[2476]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2477]||(e[2477]=l('
julia
mlirParsePassPipeline(passManager, pipeline, callback, userData)

Parse a textual MLIR pass pipeline and assign it to the provided OpPassManager. If parsing fails an error message is reported using the provided callback.

source

',3))]),t("details",ou,[t("summary",null,[e[2478]||(e[2478]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerAddOwnedPass-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerAddOwnedPass-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerAddOwnedPass")],-1)),e[2479]||(e[2479]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2480]||(e[2480]=l('
julia
mlirPassManagerAddOwnedPass(passManager, pass)

Add a pass and transfer ownership to the provided top-level mlirPassManager. If the pass is not a generic operation pass or a ModulePass, a new OpPassManager is implicitly nested under the provided PassManager.

source

',3))]),t("details",du,[t("summary",null,[e[2481]||(e[2481]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerCreate")],-1)),e[2482]||(e[2482]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2483]||(e[2483]=l('
julia
mlirPassManagerCreate(ctx)

Create a new top-level PassManager with the default anchor.

source

',3))]),t("details",cu,[t("summary",null,[e[2484]||(e[2484]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerCreateOnOperation-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerCreateOnOperation-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerCreateOnOperation")],-1)),e[2485]||(e[2485]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2486]||(e[2486]=l('
julia
mlirPassManagerCreateOnOperation(ctx, anchorOp)

Create a new top-level PassManager anchored on anchorOp.

source

',3))]),t("details",bu,[t("summary",null,[e[2487]||(e[2487]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerDestroy")],-1)),e[2488]||(e[2488]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2489]||(e[2489]=l('
julia
mlirPassManagerDestroy(passManager)

Destroy the provided PassManager.

source

',3))]),t("details",hu,[t("summary",null,[e[2490]||(e[2490]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerEnableIRPrinting-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirPassManagerEnableIRPrinting-NTuple{8, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerEnableIRPrinting")],-1)),e[2491]||(e[2491]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2492]||(e[2492]=l('
julia
mlirPassManagerEnableIRPrinting(passManager, printBeforeAll, printAfterAll, printModuleScope, printAfterOnlyOnChange, printAfterOnlyOnFailure, flags, treePrintingPath)

Enable IR printing. The treePrintingPath argument is an optional path to a directory where the dumps will be produced. If it isn't provided then dumps are produced to stderr.

source

',3))]),t("details",uu,[t("summary",null,[e[2493]||(e[2493]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerEnableVerifier-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerEnableVerifier-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerEnableVerifier")],-1)),e[2494]||(e[2494]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2495]||(e[2495]=l('
julia
mlirPassManagerEnableVerifier(passManager, enable)

Enable / disable verify-each.

source

',3))]),t("details",gu,[t("summary",null,[e[2496]||(e[2496]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerGetAsOpPassManager")],-1)),e[2497]||(e[2497]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2498]||(e[2498]=l('
julia
mlirPassManagerGetAsOpPassManager(passManager)

Cast a top-level PassManager to a generic OpPassManager.

source

',3))]),t("details",yu,[t("summary",null,[e[2499]||(e[2499]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerGetNestedUnder-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerGetNestedUnder-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerGetNestedUnder")],-1)),e[2500]||(e[2500]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2501]||(e[2501]=l('
julia
mlirPassManagerGetNestedUnder(passManager, operationName)

Nest an OpPassManager under the top-level PassManager, the nested passmanager will only run on operations matching the provided name. The returned OpPassManager will be destroyed when the parent is destroyed. To further nest more OpPassManager under the newly returned one, see mlirOpPassManagerNest below.

source

',3))]),t("details",mu,[t("summary",null,[e[2502]||(e[2502]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirPassManagerIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerIsNull")],-1)),e[2503]||(e[2503]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2504]||(e[2504]=l('
julia
mlirPassManagerIsNull(passManager)

Checks if a PassManager is null.

source

',3))]),t("details",ku,[t("summary",null,[e[2505]||(e[2505]=t("a",{id:"Reactant.MLIR.API.mlirPassManagerRunOnOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirPassManagerRunOnOp-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPassManagerRunOnOp")],-1)),e[2506]||(e[2506]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2507]||(e[2507]=l('
julia
mlirPassManagerRunOnOp(passManager, op)

Run the provided passManager on the given op.

source

',3))]),t("details",Ru,[t("summary",null,[e[2508]||(e[2508]=t("a",{id:"Reactant.MLIR.API.mlirPrintPassPipeline-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirPrintPassPipeline-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirPrintPassPipeline")],-1)),e[2509]||(e[2509]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2510]||(e[2510]=l('
julia
mlirPrintPassPipeline(passManager, callback, userData)

Print a textual MLIR pass pipeline by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Iu,[t("summary",null,[e[2511]||(e[2511]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastExpressedToStorageType")],-1)),e[2512]||(e[2512]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2513]||(e[2513]=l('
julia
mlirQuantizedTypeCastExpressedToStorageType(type, candidate)

Casts from a type based on the expressed type of the given quantized type to equivalent type based on storage type of the same quantized type.

source

',3))]),t("details",ju,[t("summary",null,[e[2514]||(e[2514]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastFromExpressedType")],-1)),e[2515]||(e[2515]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2516]||(e[2516]=l('
julia
mlirQuantizedTypeCastFromExpressedType(type, candidate)

Casts from a type based on the expressed type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.

source

',3))]),t("details",fu,[t("summary",null,[e[2517]||(e[2517]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastFromStorageType")],-1)),e[2518]||(e[2518]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2519]||(e[2519]=l('
julia
mlirQuantizedTypeCastFromStorageType(type, candidate)

Casts from a type based on the storage type of the given type to a corresponding type based on the given type. Returns a null type if the cast is not valid.

source

',3))]),t("details",Mu,[t("summary",null,[e[2520]||(e[2520]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastToExpressedType")],-1)),e[2521]||(e[2521]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2522]||(e[2522]=l('
julia
mlirQuantizedTypeCastToExpressedType(type)

Casts from a type based on a quantized type to a corresponding typed based on the expressed type. Returns a null type if the cast is not valid.

source

',3))]),t("details",Au,[t("summary",null,[e[2523]||(e[2523]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeCastToStorageType")],-1)),e[2524]||(e[2524]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2525]||(e[2525]=l('
julia
mlirQuantizedTypeCastToStorageType(type)

Casts from a type based on a quantized type to a corresponding typed based on the storage type. Returns a null type if the cast is not valid.

source

',3))]),t("details",Lu,[t("summary",null,[e[2526]||(e[2526]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMaximumForInteger")],-1)),e[2527]||(e[2527]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2528]||(e[2528]=l('
julia
mlirQuantizedTypeGetDefaultMaximumForInteger(isSigned, integralWidth)

Returns the maximum possible value stored by a quantized type.

source

',3))]),t("details",Eu,[t("summary",null,[e[2529]||(e[2529]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetDefaultMinimumForInteger")],-1)),e[2530]||(e[2530]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2531]||(e[2531]=l('
julia
mlirQuantizedTypeGetDefaultMinimumForInteger(isSigned, integralWidth)

Returns the minimum possible value stored by a quantized type.

source

',3))]),t("details",vu,[t("summary",null,[e[2532]||(e[2532]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetExpressedType")],-1)),e[2533]||(e[2533]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2534]||(e[2534]=l('
julia
mlirQuantizedTypeGetExpressedType(type)

Gets the original type approximated by the given quantized type.

source

',3))]),t("details",Tu,[t("summary",null,[e[2535]||(e[2535]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetFlags-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetFlags-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetFlags")],-1)),e[2536]||(e[2536]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2537]||(e[2537]=l('
julia
mlirQuantizedTypeGetFlags(type)

Gets the flags associated with the given quantized type.

source

',3))]),t("details",Cu,[t("summary",null,[e[2538]||(e[2538]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetQuantizedElementType")],-1)),e[2539]||(e[2539]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2540]||(e[2540]=l('
julia
mlirQuantizedTypeGetQuantizedElementType(type)

Returns the element type of the given quantized type as another quantized type.

source

',3))]),t("details",xu,[t("summary",null,[e[2541]||(e[2541]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag-Tuple{}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetSignedFlag")],-1)),e[2542]||(e[2542]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2543]||(e[2543]=l('
julia
mlirQuantizedTypeGetSignedFlag()

Returns the bit flag used to indicate signedness of a quantized type.

source

',3))]),t("details",Fu,[t("summary",null,[e[2544]||(e[2544]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageType")],-1)),e[2545]||(e[2545]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2546]||(e[2546]=l('
julia
mlirQuantizedTypeGetStorageType(type)

Returns the underlying type used to store the values.

source

',3))]),t("details",Pu,[t("summary",null,[e[2547]||(e[2547]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeIntegralWidth")],-1)),e[2548]||(e[2548]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2549]||(e[2549]=l('
julia
mlirQuantizedTypeGetStorageTypeIntegralWidth(type)

Returns the integral bitwidth that the storage type of the given quantized type can represent exactly.

source

',3))]),t("details",Du,[t("summary",null,[e[2550]||(e[2550]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMax")],-1)),e[2551]||(e[2551]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2552]||(e[2552]=l('
julia
mlirQuantizedTypeGetStorageTypeMax(type)

Returns the maximum value that the storage type of the given quantized type can take.

source

',3))]),t("details",Ou,[t("summary",null,[e[2553]||(e[2553]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeGetStorageTypeMin")],-1)),e[2554]||(e[2554]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2555]||(e[2555]=l('
julia
mlirQuantizedTypeGetStorageTypeMin(type)

Returns the minimum value that the storage type of the given quantized type can take.

source

',3))]),t("details",Bu,[t("summary",null,[e[2556]||(e[2556]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeIsCompatibleExpressedType")],-1)),e[2557]||(e[2557]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2558]||(e[2558]=l('
julia
mlirQuantizedTypeIsCompatibleExpressedType(type, candidate)

Returns true if the candidate type is compatible with the given quantized type.

source

',3))]),t("details",Gu,[t("summary",null,[e[2559]||(e[2559]=t("a",{id:"Reactant.MLIR.API.mlirQuantizedTypeIsSigned-Tuple{Any}",href:"#Reactant.MLIR.API.mlirQuantizedTypeIsSigned-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirQuantizedTypeIsSigned")],-1)),e[2560]||(e[2560]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2561]||(e[2561]=l('
julia
mlirQuantizedTypeIsSigned(type)

Returns true if the given type is signed, false otherwise.

source

',3))]),t("details",zu,[t("summary",null,[e[2562]||(e[2562]=t("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGet")],-1)),e[2563]||(e[2563]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2564]||(e[2564]=l('
julia
mlirRankedTensorTypeGet(rank, shape, elementType, encoding)

Creates a tensor type of a fixed rank with the given shape, element type, and optional encoding in the same context as the element type. The type is owned by the context. Tensor types without any specific encoding field should assign mlirAttributeGetNull() to this parameter.

source

',3))]),t("details",wu,[t("summary",null,[e[2565]||(e[2565]=t("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetChecked-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetChecked")],-1)),e[2566]||(e[2566]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2567]||(e[2567]=l('
julia
mlirRankedTensorTypeGetChecked(loc, rank, shape, elementType, encoding)

Same as "mlirRankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",Su,[t("summary",null,[e[2568]||(e[2568]=t("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetEncoding")],-1)),e[2569]||(e[2569]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2570]||(e[2570]=l('
julia
mlirRankedTensorTypeGetEncoding(type)

Gets the 'encoding' attribute from the ranked tensor type, returning a null attribute if none.

source

',3))]),t("details",Nu,[t("summary",null,[e[2571]||(e[2571]=t("a",{id:"Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRankedTensorTypeGetTypeID")],-1)),e[2572]||(e[2572]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2573]||(e[2573]=l('
julia
mlirRankedTensorTypeGetTypeID()

Returns the typeID of an RankedTensor type.

source

',3))]),t("details",Vu,[t("summary",null,[e[2574]||(e[2574]=t("a",{id:"Reactant.MLIR.API.mlirRegionAppendOwnedBlock-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionAppendOwnedBlock-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionAppendOwnedBlock")],-1)),e[2575]||(e[2575]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2576]||(e[2576]=l('
julia
mlirRegionAppendOwnedBlock(region, block)

Takes a block owned by the caller and appends it to the given region.

source

',3))]),t("details",qu,[t("summary",null,[e[2577]||(e[2577]=t("a",{id:"Reactant.MLIR.API.mlirRegionCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirRegionCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionCreate")],-1)),e[2578]||(e[2578]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2579]||(e[2579]=l('
julia
mlirRegionCreate()

Creates a new empty region and transfers ownership to the caller.

source

',3))]),t("details",Uu,[t("summary",null,[e[2580]||(e[2580]=t("a",{id:"Reactant.MLIR.API.mlirRegionDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionDestroy")],-1)),e[2581]||(e[2581]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2582]||(e[2582]=l('
julia
mlirRegionDestroy(region)

Takes a region owned by the caller and destroys it.

source

',3))]),t("details",Qu,[t("summary",null,[e[2583]||(e[2583]=t("a",{id:"Reactant.MLIR.API.mlirRegionEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionEqual")],-1)),e[2584]||(e[2584]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2585]||(e[2585]=l('
julia
mlirRegionEqual(region, other)

Checks whether two region handles point to the same region. This does not perform deep comparison.

source

',3))]),t("details",Wu,[t("summary",null,[e[2586]||(e[2586]=t("a",{id:"Reactant.MLIR.API.mlirRegionGetFirstBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionGetFirstBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionGetFirstBlock")],-1)),e[2587]||(e[2587]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2588]||(e[2588]=l('
julia
mlirRegionGetFirstBlock(region)

Gets the first block in the region.

source

',3))]),t("details",Hu,[t("summary",null,[e[2589]||(e[2589]=t("a",{id:"Reactant.MLIR.API.mlirRegionGetNextInOperation-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionGetNextInOperation-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionGetNextInOperation")],-1)),e[2590]||(e[2590]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2591]||(e[2591]=l('
julia
mlirRegionGetNextInOperation(region)

Returns the region immediately following the given region in its parent operation.

source

',3))]),t("details",Zu,[t("summary",null,[e[2592]||(e[2592]=t("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlock-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlock-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlock")],-1)),e[2593]||(e[2593]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2594]||(e[2594]=l('
julia
mlirRegionInsertOwnedBlock(region, pos, block)

Takes a block owned by the caller and inserts it at pos to the given region. This is an expensive operation that linearly scans the region, prefer insertAfter/Before instead.

source

',3))]),t("details",Ju,[t("summary",null,[e[2595]||(e[2595]=t("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlockAfter")],-1)),e[2596]||(e[2596]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2597]||(e[2597]=l('
julia
mlirRegionInsertOwnedBlockAfter(region, reference, block)

Takes a block owned by the caller and inserts it after the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, prepends the block to the region.

source

',3))]),t("details",Ku,[t("summary",null,[e[2598]||(e[2598]=t("a",{id:"Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionInsertOwnedBlockBefore")],-1)),e[2599]||(e[2599]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2600]||(e[2600]=l('
julia
mlirRegionInsertOwnedBlockBefore(region, reference, block)

Takes a block owned by the caller and inserts it before the (non-owned) reference block in the given region. The reference block must belong to the region. If the reference block is null, appends the block to the region.

source

',3))]),t("details",$u,[t("summary",null,[e[2601]||(e[2601]=t("a",{id:"Reactant.MLIR.API.mlirRegionIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegionIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionIsNull")],-1)),e[2602]||(e[2602]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2603]||(e[2603]=l('
julia
mlirRegionIsNull(region)

Checks whether a region is null.

source

',3))]),t("details",Xu,[t("summary",null,[e[2604]||(e[2604]=t("a",{id:"Reactant.MLIR.API.mlirRegionTakeBody-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRegionTakeBody-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegionTakeBody")],-1)),e[2605]||(e[2605]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2606]||(e[2606]=l('
julia
mlirRegionTakeBody(target, source)

Moves the entire content of the source region to the target region.

source

',3))]),t("details",Yu,[t("summary",null,[e[2607]||(e[2607]=t("a",{id:"Reactant.MLIR.API.mlirRegisterAllDialects-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegisterAllDialects-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllDialects")],-1)),e[2608]||(e[2608]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2609]||(e[2609]=l('
julia
mlirRegisterAllDialects(registry)

Appends all upstream dialects and extensions to the dialect registry.

source

',3))]),t("details",_u,[t("summary",null,[e[2610]||(e[2610]=t("a",{id:"Reactant.MLIR.API.mlirRegisterAllLLVMTranslations-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRegisterAllLLVMTranslations-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllLLVMTranslations")],-1)),e[2611]||(e[2611]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2612]||(e[2612]=l('
julia
mlirRegisterAllLLVMTranslations(context)

Register all translations to LLVM IR for dialects that can support it.

source

',3))]),t("details",eg,[t("summary",null,[e[2613]||(e[2613]=t("a",{id:"Reactant.MLIR.API.mlirRegisterAllPasses-Tuple{}",href:"#Reactant.MLIR.API.mlirRegisterAllPasses-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRegisterAllPasses")],-1)),e[2614]||(e[2614]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2615]||(e[2615]=l('
julia
mlirRegisterAllPasses()

Register all compiler passes of MLIR.

source

',3))]),t("details",tg,[t("summary",null,[e[2616]||(e[2616]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCancelOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCancelOpModification-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCancelOpModification")],-1)),e[2617]||(e[2617]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2618]||(e[2618]=l('
julia
mlirRewriterBaseCancelOpModification(rewriter, op)

This method cancels a pending in-place modification. This can only be called on operations that were provided to a call to startOpModification.

source

',3))]),t("details",sg,[t("summary",null,[e[2619]||(e[2619]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseClearInsertionPoint")],-1)),e[2620]||(e[2620]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2621]||(e[2621]=l('
julia
mlirRewriterBaseClearInsertionPoint(rewriter)

Reset the insertion point to no location. Creating an operation without a set insertion point is an error, but this can still be useful when the current insertion point a builder refers to is being removed.

source

',3))]),t("details",ag,[t("summary",null,[e[2622]||(e[2622]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseClone-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseClone-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseClone")],-1)),e[2623]||(e[2623]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2624]||(e[2624]=l('
julia
mlirRewriterBaseClone(rewriter, op)

Creates a deep copy of the specified operation.

source

',3))]),t("details",ig,[t("summary",null,[e[2625]||(e[2625]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCloneRegionBefore")],-1)),e[2626]||(e[2626]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2627]||(e[2627]=l('
julia
mlirRewriterBaseCloneRegionBefore(rewriter, region, before)

Clone the blocks that belong to "region" before the given position in another region "parent".

source

',3))]),t("details",lg,[t("summary",null,[e[2628]||(e[2628]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCloneWithoutRegions")],-1)),e[2629]||(e[2629]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2630]||(e[2630]=l('
julia
mlirRewriterBaseCloneWithoutRegions(rewriter, op)

Creates a deep copy of this operation but keep the operation regions empty.

source

',3))]),t("details",ng,[t("summary",null,[e[2631]||(e[2631]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseCreateBlockBefore")],-1)),e[2632]||(e[2632]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2633]||(e[2633]=l('
julia
mlirRewriterBaseCreateBlockBefore(rewriter, insertBefore, nArgTypes, argTypes, locations)

Add new block with 'argTypes' arguments and set the insertion point to the end of it. The block is placed before 'insertBefore'. locs contains the locations of the inserted arguments, and should match the size of argTypes.

source

',3))]),t("details",pg,[t("summary",null,[e[2634]||(e[2634]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseEraseBlock-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseEraseBlock-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseEraseBlock")],-1)),e[2635]||(e[2635]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2636]||(e[2636]=l('
julia
mlirRewriterBaseEraseBlock(rewriter, block)

Erases a block along with all operations inside it.

source

',3))]),t("details",rg,[t("summary",null,[e[2637]||(e[2637]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseEraseOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseEraseOp-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseEraseOp")],-1)),e[2638]||(e[2638]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2639]||(e[2639]=l('
julia
mlirRewriterBaseEraseOp(rewriter, op)

Erases an operation that is known to have no uses.

source

',3))]),t("details",og,[t("summary",null,[e[2640]||(e[2640]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseFinalizeOpModification")],-1)),e[2641]||(e[2641]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2642]||(e[2642]=l('
julia
mlirRewriterBaseFinalizeOpModification(rewriter, op)

This method is used to signal the end of an in-place modification of the given operation. This can only be called on operations that were provided to a call to startOpModification.

source

',3))]),t("details",dg,[t("summary",null,[e[2643]||(e[2643]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetBlock")],-1)),e[2644]||(e[2644]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2645]||(e[2645]=l('
julia
mlirRewriterBaseGetBlock(rewriter)

Returns the current block of the rewriter.

source

',3))]),t("details",cg,[t("summary",null,[e[2646]||(e[2646]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetContext")],-1)),e[2647]||(e[2647]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2648]||(e[2648]=l('
julia
mlirRewriterBaseGetContext(rewriter)

Get the MLIR context referenced by the rewriter.

source

',3))]),t("details",bg,[t("summary",null,[e[2649]||(e[2649]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock-Tuple{Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseGetInsertionBlock")],-1)),e[2650]||(e[2650]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2651]||(e[2651]=l('
julia
mlirRewriterBaseGetInsertionBlock(rewriter)

Return the block the current insertion point belongs to. Note that the insertion point is not necessarily the end of the block.

source

',3))]),t("details",hg,[t("summary",null,[e[2652]||(e[2652]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInlineBlockBefore")],-1)),e[2653]||(e[2653]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2654]||(e[2654]=l('
julia
mlirRewriterBaseInlineBlockBefore(rewriter, source, op, nArgValues, argValues)

Inline the operations of block 'source' before the operation 'op'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'

The source block must have no successors. Otherwise, the resulting IR would have unreachable operations.

source

',4))]),t("details",ug,[t("summary",null,[e[2655]||(e[2655]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInlineRegionBefore")],-1)),e[2656]||(e[2656]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2657]||(e[2657]=l('
julia
mlirRewriterBaseInlineRegionBefore(rewriter, region, before)

Move the blocks that belong to "region" before the given position in another region "parent". The two regions must be different. The caller is responsible for creating or updating the operation transferring flow of control to the region and passing it the correct block arguments.

source

',3))]),t("details",gg,[t("summary",null,[e[2658]||(e[2658]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseInsert-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseInsert-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseInsert")],-1)),e[2659]||(e[2659]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2660]||(e[2660]=l('
julia
mlirRewriterBaseInsert(rewriter, op)

Insert the given operation at the current insertion point and return it.

source

',3))]),t("details",yg,[t("summary",null,[e[2661]||(e[2661]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMergeBlocks-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMergeBlocks-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMergeBlocks")],-1)),e[2662]||(e[2662]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2663]||(e[2663]=l('
julia
mlirRewriterBaseMergeBlocks(rewriter, source, dest, nArgValues, argValues)

Inline the operations of block 'source' into the end of block 'dest'. The source block will be deleted and must have no uses. 'argValues' is used to replace the block arguments of 'source'

The dest block must have no successors. Otherwise, the resulting IR would have unreachable operation.

source

',4))]),t("details",mg,[t("summary",null,[e[2664]||(e[2664]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveBlockBefore")],-1)),e[2665]||(e[2665]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2666]||(e[2666]=l('
julia
mlirRewriterBaseMoveBlockBefore(rewriter, block, existingBlock)

Unlink this block and insert it right before existingBlock.

source

',3))]),t("details",kg,[t("summary",null,[e[2667]||(e[2667]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveOpAfter")],-1)),e[2668]||(e[2668]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2669]||(e[2669]=l('
julia
mlirRewriterBaseMoveOpAfter(rewriter, op, existingOp)

Unlink this operation from its current block and insert it right after existingOp which may be in the same or another block in the same function.

source

',3))]),t("details",Rg,[t("summary",null,[e[2670]||(e[2670]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseMoveOpBefore")],-1)),e[2671]||(e[2671]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2672]||(e[2672]=l('
julia
mlirRewriterBaseMoveOpBefore(rewriter, op, existingOp)

Unlink this operation from its current block and insert it right before existingOp which may be in the same or another block in the same function.

source

',3))]),t("details",Ig,[t("summary",null,[e[2673]||(e[2673]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithOperation")],-1)),e[2674]||(e[2674]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2675]||(e[2675]=l('
julia
mlirRewriterBaseReplaceAllOpUsesWithOperation(rewriter, from, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.

source

',3))]),t("details",jg,[t("summary",null,[e[2676]||(e[2676]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllOpUsesWithValueRange")],-1)),e[2677]||(e[2677]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2678]||(e[2678]=l('
julia
mlirRewriterBaseReplaceAllOpUsesWithValueRange(rewriter, from, nTo, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced) and that the from operation is about to be replaced.

source

',3))]),t("details",fg,[t("summary",null,[e[2679]||(e[2679]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesExcept")],-1)),e[2680]||(e[2680]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2681]||(e[2681]=l('
julia
mlirRewriterBaseReplaceAllUsesExcept(rewriter, from, to, exceptedUser)

Find uses of from and replace them with to except if the user is exceptedUser. Also notify the listener about every in-place op modification (for every use that was replaced).

source

',3))]),t("details",Mg,[t("summary",null,[e[2682]||(e[2682]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllUsesWith")],-1)),e[2683]||(e[2683]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2684]||(e[2684]=l('
julia
mlirRewriterBaseReplaceAllUsesWith(rewriter, from, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).

source

',3))]),t("details",Ag,[t("summary",null,[e[2685]||(e[2685]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceAllValueRangeUsesWith")],-1)),e[2686]||(e[2686]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2687]||(e[2687]=l('
julia
mlirRewriterBaseReplaceAllValueRangeUsesWith(rewriter, nValues, from, to)

Find uses of from and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced).

source

',3))]),t("details",Lg,[t("summary",null,[e[2688]||(e[2688]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpUsesWithinBlock")],-1)),e[2689]||(e[2689]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2690]||(e[2690]=l('
julia
mlirRewriterBaseReplaceOpUsesWithinBlock(rewriter, op, nNewValues, newValues, block)

Find uses of from within block and replace them with to. Also notify the listener about every in-place op modification (for every use that was replaced). The optional allUsesReplaced flag is set to "true" if all uses were replaced.

source

',3))]),t("details",Eg,[t("summary",null,[e[2691]||(e[2691]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithOperation")],-1)),e[2692]||(e[2692]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2693]||(e[2693]=l('
julia
mlirRewriterBaseReplaceOpWithOperation(rewriter, op, newOp)

Replace the results of the given (original) operation with the specified new op (replacement). The result types of the two ops must match. The original op is erased.

source

',3))]),t("details",vg,[t("summary",null,[e[2694]||(e[2694]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseReplaceOpWithValues")],-1)),e[2695]||(e[2695]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2696]||(e[2696]=l('
julia
mlirRewriterBaseReplaceOpWithValues(rewriter, op, nValues, values)

Replace the results of the given (original) operation with the specified list of values (replacements). The result types of the given op and the replacements must match. The original op is erased.

source

',3))]),t("details",Tg,[t("summary",null,[e[2697]||(e[2697]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfter")],-1)),e[2698]||(e[2698]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2699]||(e[2699]=l('
julia
mlirRewriterBaseSetInsertionPointAfter(rewriter, op)

Sets the insertion point to the node after the specified operation, which will cause subsequent insertions to go right after it.

source

',3))]),t("details",Cg,[t("summary",null,[e[2700]||(e[2700]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointAfterValue")],-1)),e[2701]||(e[2701]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2702]||(e[2702]=l('
julia
mlirRewriterBaseSetInsertionPointAfterValue(rewriter, value)

Sets the insertion point to the node after the specified value. If value has a defining operation, sets the insertion point to the node after such defining operation. This will cause subsequent insertions to go right after it. Otherwise, value is a BlockArgument. Sets the insertion point to the start of its block.

source

',3))]),t("details",xg,[t("summary",null,[e[2703]||(e[2703]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointBefore")],-1)),e[2704]||(e[2704]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2705]||(e[2705]=l('
julia
mlirRewriterBaseSetInsertionPointBefore(rewriter, op)

Sets the insertion point to the specified operation, which will cause subsequent insertions to go right before it.

source

',3))]),t("details",Fg,[t("summary",null,[e[2706]||(e[2706]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToEnd")],-1)),e[2707]||(e[2707]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2708]||(e[2708]=l('
julia
mlirRewriterBaseSetInsertionPointToEnd(rewriter, block)

Sets the insertion point to the end of the specified block.

source

',3))]),t("details",Pg,[t("summary",null,[e[2709]||(e[2709]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseSetInsertionPointToStart")],-1)),e[2710]||(e[2710]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2711]||(e[2711]=l('
julia
mlirRewriterBaseSetInsertionPointToStart(rewriter, block)

Sets the insertion point to the start of the specified block.

source

',3))]),t("details",Dg,[t("summary",null,[e[2712]||(e[2712]=t("a",{id:"Reactant.MLIR.API.mlirRewriterBaseStartOpModification-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirRewriterBaseStartOpModification-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirRewriterBaseStartOpModification")],-1)),e[2713]||(e[2713]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2714]||(e[2714]=l('
julia
mlirRewriterBaseStartOpModification(rewriter, op)

This method is used to notify the rewriter that an in-place operation modification is about to happen. A call to this function must be followed by a call to either finalizeOpModification or cancelOpModification. This is a minor efficiency win (it avoids creating a new operation and removing the old one) but also often allows simpler code in the client.

source

',3))]),t("details",Og,[t("summary",null,[e[2715]||(e[2715]=t("a",{id:"Reactant.MLIR.API.mlirSetGlobalDebugType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSetGlobalDebugType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSetGlobalDebugType")],-1)),e[2716]||(e[2716]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2717]||(e[2717]=l('
julia
mlirSetGlobalDebugType(type)

Sets the current debug type, similarly to -debug-only=type in the command-line tools. Note that global debug should be enabled for any output to be produced.

source

',3))]),t("details",Bg,[t("summary",null,[e[2718]||(e[2718]=t("a",{id:"Reactant.MLIR.API.mlirSetGlobalDebugTypes-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSetGlobalDebugTypes-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSetGlobalDebugTypes")],-1)),e[2719]||(e[2719]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2720]||(e[2720]=l('
julia
mlirSetGlobalDebugTypes(types, n)

Sets multiple current debug types, similarly to `-debug-only=type1,type2" in the command-line tools. Note that global debug should be enabled for any output to be produced.

source

',3))]),t("details",Gg,[t("summary",null,[e[2721]||(e[2721]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDimSize-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDimSize-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDimSize")],-1)),e[2722]||(e[2722]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2723]||(e[2723]=l('
julia
mlirShapedTypeGetDimSize(type, dim)

Returns the dim-th dimension of the given ranked shaped type.

source

',3))]),t("details",zg,[t("summary",null,[e[2724]||(e[2724]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDynamicSize-Tuple{}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDynamicSize-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDynamicSize")],-1)),e[2725]||(e[2725]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2726]||(e[2726]=l('
julia
mlirShapedTypeGetDynamicSize()

Returns the value indicating a dynamic size in a shaped type. Prefer mlirShapedTypeIsDynamicSize to direct comparisons with this value.

source

',3))]),t("details",wg,[t("summary",null,[e[2727]||(e[2727]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset-Tuple{}",href:"#Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetDynamicStrideOrOffset")],-1)),e[2728]||(e[2728]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2729]||(e[2729]=l('
julia
mlirShapedTypeGetDynamicStrideOrOffset()

Returns the value indicating a dynamic stride or offset in a shaped type. Prefer mlirShapedTypeGetDynamicStrideOrOffset to direct comparisons with this value.

source

',3))]),t("details",Sg,[t("summary",null,[e[2730]||(e[2730]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetElementType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetElementType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetElementType")],-1)),e[2731]||(e[2731]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2732]||(e[2732]=l('
julia
mlirShapedTypeGetElementType(type)

Returns the element type of the shaped type.

source

',3))]),t("details",Ng,[t("summary",null,[e[2733]||(e[2733]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeGetRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeGetRank-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeGetRank")],-1)),e[2734]||(e[2734]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2735]||(e[2735]=l('
julia
mlirShapedTypeGetRank(type)

Returns the rank of the given ranked shaped type.

source

',3))]),t("details",Vg,[t("summary",null,[e[2736]||(e[2736]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeHasRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeHasRank-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeHasRank")],-1)),e[2737]||(e[2737]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2738]||(e[2738]=l('
julia
mlirShapedTypeHasRank(type)

Checks whether the given shaped type is ranked.

source

',3))]),t("details",qg,[t("summary",null,[e[2739]||(e[2739]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeHasStaticShape-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeHasStaticShape-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeHasStaticShape")],-1)),e[2740]||(e[2740]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2741]||(e[2741]=l('
julia
mlirShapedTypeHasStaticShape(type)

Checks whether the given shaped type has a static shape.

source

',3))]),t("details",Ug,[t("summary",null,[e[2742]||(e[2742]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicDim-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicDim-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicDim")],-1)),e[2743]||(e[2743]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2744]||(e[2744]=l('
julia
mlirShapedTypeIsDynamicDim(type, dim)

Checks wither the dim-th dimension of the given shaped type is dynamic.

source

',3))]),t("details",Qg,[t("summary",null,[e[2745]||(e[2745]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicSize-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicSize-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicSize")],-1)),e[2746]||(e[2746]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2747]||(e[2747]=l('
julia
mlirShapedTypeIsDynamicSize(size)

Checks whether the given value is used as a placeholder for dynamic sizes in shaped types.

source

',3))]),t("details",Wg,[t("summary",null,[e[2748]||(e[2748]=t("a",{id:"Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset-Tuple{Any}",href:"#Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirShapedTypeIsDynamicStrideOrOffset")],-1)),e[2749]||(e[2749]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2750]||(e[2750]=l('
julia
mlirShapedTypeIsDynamicStrideOrOffset(val)

Checks whether the given value is used as a placeholder for dynamic strides and offsets in shaped types.

source

',3))]),t("details",Hg,[t("summary",null,[e[2751]||(e[2751]=t("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetIndices-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetIndices-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetIndices")],-1)),e[2752]||(e[2752]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2753]||(e[2753]=l('
julia
mlirSparseElementsAttrGetIndices(attr)

Returns the dense elements attribute containing 64-bit integer indices of non-null elements in the given sparse elements attribute.

source

',3))]),t("details",Zg,[t("summary",null,[e[2754]||(e[2754]=t("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetTypeID")],-1)),e[2755]||(e[2755]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2756]||(e[2756]=l('
julia
mlirSparseElementsAttrGetTypeID()

Returns the typeID of a SparseElements attribute.

source

',3))]),t("details",Jg,[t("summary",null,[e[2757]||(e[2757]=t("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttrGetValues-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttrGetValues-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttrGetValues")],-1)),e[2758]||(e[2758]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2759]||(e[2759]=l('
julia
mlirSparseElementsAttrGetValues(attr)

Returns the dense elements attribute containing the non-null elements in the given sparse elements attribute.

source

',3))]),t("details",Kg,[t("summary",null,[e[2760]||(e[2760]=t("a",{id:"Reactant.MLIR.API.mlirSparseElementsAttribute-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirSparseElementsAttribute-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseElementsAttribute")],-1)),e[2761]||(e[2761]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2762]||(e[2762]=l('
julia
mlirSparseElementsAttribute(shapedType, denseIndices, denseValues)

Creates a sparse elements attribute of the given shape from a list of indices and a list of associated values. Both lists are expected to be dense elements attributes with the same number of elements. The list of indices is expected to contain 64-bit integers. The attribute is created in the same context as the type.

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',3))]),t("details",$g,[t("summary",null,[e[2763]||(e[2763]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGet")],-1)),e[2764]||(e[2764]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2765]||(e[2765]=l('
julia
mlirSparseTensorEncodingAttrGet(ctx, lvlRank, lvlTypes, dimToLvl, lvlTodim, posWidth, crdWidth, explicitVal, implicitVal)

Creates a sparse\\_tensor.encoding attribute with the given parameters.

source

',3))]),t("details",Xg,[t("summary",null,[e[2766]||(e[2766]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetCrdWidth")],-1)),e[2767]||(e[2767]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2768]||(e[2768]=l('
julia
mlirSparseTensorEncodingAttrGetCrdWidth(attr)

Returns the coordinate bitwidth of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",Yg,[t("summary",null,[e[2769]||(e[2769]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetDimToLvl")],-1)),e[2770]||(e[2770]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2771]||(e[2771]=l('
julia
mlirSparseTensorEncodingAttrGetDimToLvl(attr)

Returns the dimension-to-level mapping of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",_g,[t("summary",null,[e[2772]||(e[2772]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetExplicitVal")],-1)),e[2773]||(e[2773]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2774]||(e[2774]=l('
julia
mlirSparseTensorEncodingAttrGetExplicitVal(attr)

Returns the explicit value of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ey,[t("summary",null,[e[2775]||(e[2775]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetImplicitVal")],-1)),e[2776]||(e[2776]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2777]||(e[2777]=l('
julia
mlirSparseTensorEncodingAttrGetImplicitVal(attr)

Returns the implicit value of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ty,[t("summary",null,[e[2778]||(e[2778]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlFmt")],-1)),e[2779]||(e[2779]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2780]||(e[2780]=l('
julia
mlirSparseTensorEncodingAttrGetLvlFmt(attr, lvl)

Returns a specified level-format of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",sy,[t("summary",null,[e[2781]||(e[2781]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlToDim")],-1)),e[2782]||(e[2782]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2783]||(e[2783]=l('
julia
mlirSparseTensorEncodingAttrGetLvlToDim(attr)

Returns the level-to-dimension mapping of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ay,[t("summary",null,[e[2784]||(e[2784]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetLvlType")],-1)),e[2785]||(e[2785]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2786]||(e[2786]=l('
julia
mlirSparseTensorEncodingAttrGetLvlType(attr, lvl)

Returns a specified level-type of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",iy,[t("summary",null,[e[2787]||(e[2787]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingAttrGetPosWidth")],-1)),e[2788]||(e[2788]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2789]||(e[2789]=l('
julia
mlirSparseTensorEncodingAttrGetPosWidth(attr)

Returns the position bitwidth of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ly,[t("summary",null,[e[2790]||(e[2790]=t("a",{id:"Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSparseTensorEncodingGetLvlRank")],-1)),e[2791]||(e[2791]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2792]||(e[2792]=l('
julia
mlirSparseTensorEncodingGetLvlRank(attr)

Returns the level-rank of the sparse\\_tensor.encoding attribute.

source

',3))]),t("details",ny,[t("summary",null,[e[2793]||(e[2793]=t("a",{id:"Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStridedLayoutAttrGetTypeID")],-1)),e[2794]||(e[2794]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2795]||(e[2795]=l('
julia
mlirStridedLayoutAttrGetTypeID()

Returns the typeID of a StridedLayout attribute.

source

',3))]),t("details",py,[t("summary",null,[e[2796]||(e[2796]=t("a",{id:"Reactant.MLIR.API.mlirStringAttrGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringAttrGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGet")],-1)),e[2797]||(e[2797]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2798]||(e[2798]=l('
julia
mlirStringAttrGet(ctx, str)

Creates a string attribute in the given context containing the given string.

source

',3))]),t("details",ry,[t("summary",null,[e[2799]||(e[2799]=t("a",{id:"Reactant.MLIR.API.mlirStringAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirStringAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGetTypeID")],-1)),e[2800]||(e[2800]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2801]||(e[2801]=l('
julia
mlirStringAttrGetTypeID()

Returns the typeID of a String attribute.

source

',3))]),t("details",oy,[t("summary",null,[e[2802]||(e[2802]=t("a",{id:"Reactant.MLIR.API.mlirStringAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirStringAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrGetValue")],-1)),e[2803]||(e[2803]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2804]||(e[2804]=l('
julia
mlirStringAttrGetValue(attr)

Returns the attribute values as a string reference. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",dy,[t("summary",null,[e[2805]||(e[2805]=t("a",{id:"Reactant.MLIR.API.mlirStringAttrTypedGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringAttrTypedGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringAttrTypedGet")],-1)),e[2806]||(e[2806]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2807]||(e[2807]=l('
julia
mlirStringAttrTypedGet(type, str)

Creates a string attribute in the given context containing the given string. Additionally, the attribute has the given type.

source

',3))]),t("details",cy,[t("summary",null,[e[2808]||(e[2808]=t("a",{id:"Reactant.MLIR.API.mlirStringRefCreate-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringRefCreate-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefCreate")],-1)),e[2809]||(e[2809]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2810]||(e[2810]=l('
julia
mlirStringRefCreate(str, length)

Constructs a string reference from the pointer and length. The pointer need not reference to a null-terminated string.

source

',3))]),t("details",by,[t("summary",null,[e[2811]||(e[2811]=t("a",{id:"Reactant.MLIR.API.mlirStringRefCreateFromCString-Tuple{Any}",href:"#Reactant.MLIR.API.mlirStringRefCreateFromCString-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefCreateFromCString")],-1)),e[2812]||(e[2812]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2813]||(e[2813]=l('
julia
mlirStringRefCreateFromCString(str)

Constructs a string reference from a null-terminated C string. Prefer mlirStringRefCreate if the length of the string is known.

source

',3))]),t("details",hy,[t("summary",null,[e[2814]||(e[2814]=t("a",{id:"Reactant.MLIR.API.mlirStringRefEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirStringRefEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirStringRefEqual")],-1)),e[2815]||(e[2815]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2816]||(e[2816]=l('
julia
mlirStringRefEqual(string, other)

Returns true if two string references are equal, false otherwise.

source

',3))]),t("details",uy,[t("summary",null,[e[2817]||(e[2817]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGet-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGet-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGet")],-1)),e[2818]||(e[2818]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2819]||(e[2819]=l('
julia
mlirSymbolRefAttrGet(ctx, symbol, numReferences, references)

Creates a symbol reference attribute in the given context referencing a symbol identified by the given string inside a list of nested references. Each of the references in the list must not be nested.

source

',3))]),t("details",gy,[t("summary",null,[e[2820]||(e[2820]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetLeafReference")],-1)),e[2821]||(e[2821]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2822]||(e[2822]=l('
julia
mlirSymbolRefAttrGetLeafReference(attr)

Returns the string reference to the leaf referenced symbol. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",yy,[t("summary",null,[e[2823]||(e[2823]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetNestedReference")],-1)),e[2824]||(e[2824]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2825]||(e[2825]=l('
julia
mlirSymbolRefAttrGetNestedReference(attr, pos)

Returns pos-th reference nested in the given symbol reference attribute.

source

',3))]),t("details",my,[t("summary",null,[e[2826]||(e[2826]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetNumNestedReferences")],-1)),e[2827]||(e[2827]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2828]||(e[2828]=l('
julia
mlirSymbolRefAttrGetNumNestedReferences(attr)

Returns the number of references nested in the given symbol reference attribute.

source

',3))]),t("details",ky,[t("summary",null,[e[2829]||(e[2829]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetRootReference")],-1)),e[2830]||(e[2830]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2831]||(e[2831]=l('
julia
mlirSymbolRefAttrGetRootReference(attr)

Returns the string reference to the root referenced symbol. The data remains live as long as the context in which the attribute lives.

source

',3))]),t("details",Ry,[t("summary",null,[e[2832]||(e[2832]=t("a",{id:"Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolRefAttrGetTypeID")],-1)),e[2833]||(e[2833]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2834]||(e[2834]=l('
julia
mlirSymbolRefAttrGetTypeID()

Returns the typeID of an SymbolRef attribute.

source

',3))]),t("details",Iy,[t("summary",null,[e[2835]||(e[2835]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableCreate")],-1)),e[2836]||(e[2836]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2837]||(e[2837]=l('
julia
mlirSymbolTableCreate(operation)

Creates a symbol table for the given operation. If the operation does not have the SymbolTable trait, returns a null symbol table.

source

',3))]),t("details",jy,[t("summary",null,[e[2838]||(e[2838]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableDestroy")],-1)),e[2839]||(e[2839]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2840]||(e[2840]=l('
julia
mlirSymbolTableDestroy(symbolTable)

Destroys the symbol table created with mlirSymbolTableCreate. This does not affect the operations in the table.

source

',3))]),t("details",fy,[t("summary",null,[e[2841]||(e[2841]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableErase-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableErase-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableErase")],-1)),e[2842]||(e[2842]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2843]||(e[2843]=l('
julia
mlirSymbolTableErase(symbolTable, operation)

Removes the given operation from the symbol table and erases it.

source

',3))]),t("details",My,[t("summary",null,[e[2844]||(e[2844]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableGetSymbolAttributeName")],-1)),e[2845]||(e[2845]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2846]||(e[2846]=l('
julia
mlirSymbolTableGetSymbolAttributeName()

Returns the name of the attribute used to store symbol names compatible with symbol tables.

source

',3))]),t("details",Ay,[t("summary",null,[e[2847]||(e[2847]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName-Tuple{}",href:"#Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableGetVisibilityAttributeName")],-1)),e[2848]||(e[2848]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2849]||(e[2849]=l('
julia
mlirSymbolTableGetVisibilityAttributeName()

Returns the name of the attribute used to store symbol visibility.

source

',3))]),t("details",Ly,[t("summary",null,[e[2850]||(e[2850]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableInsert-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableInsert-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableInsert")],-1)),e[2851]||(e[2851]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2852]||(e[2852]=l('
julia
mlirSymbolTableInsert(symbolTable, operation)

Inserts the given operation into the given symbol table. The operation must have the symbol trait. If the symbol table already has a symbol with the same name, renames the symbol being inserted to ensure name uniqueness. Note that this does not move the operation itself into the block of the symbol table operation, this should be done separately. Returns the name of the symbol after insertion.

source

',3))]),t("details",Ey,[t("summary",null,[e[2853]||(e[2853]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirSymbolTableIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableIsNull")],-1)),e[2854]||(e[2854]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2855]||(e[2855]=l('
julia
mlirSymbolTableIsNull(symbolTable)

Returns true if the symbol table is null.

source

',3))]),t("details",vy,[t("summary",null,[e[2856]||(e[2856]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableLookup-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableLookup-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableLookup")],-1)),e[2857]||(e[2857]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2858]||(e[2858]=l('
julia
mlirSymbolTableLookup(symbolTable, name)

Looks up a symbol with the given name in the given symbol table and returns the operation that corresponds to the symbol. If the symbol cannot be found, returns a null operation.

source

',3))]),t("details",Ty,[t("summary",null,[e[2859]||(e[2859]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableReplaceAllSymbolUses")],-1)),e[2860]||(e[2860]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2861]||(e[2861]=l('
julia
mlirSymbolTableReplaceAllSymbolUses(oldSymbol, newSymbol, from)

Attempt to replace all uses that are nested within the given operation of the given symbol 'oldSymbol' with the provided 'newSymbol'. This does not traverse into nested symbol tables. Will fail atomically if there are any unknown operations that may be potential symbol tables.

source

',3))]),t("details",Cy,[t("summary",null,[e[2862]||(e[2862]=t("a",{id:"Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirSymbolTableWalkSymbolTables")],-1)),e[2863]||(e[2863]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2864]||(e[2864]=l('
julia
mlirSymbolTableWalkSymbolTables(from, allSymUsesVisible, callback, userData)

Walks all symbol table operations nested within, and including, op. For each symbol table operation, the provided callback is invoked with the op and a boolean signifying if the symbols within that symbol table can be treated as if all uses within the IR are visible to the caller. allSymUsesVisible identifies whether all of the symbol uses of symbols within op are visible.

source

',3))]),t("details",xy,[t("summary",null,[e[2865]||(e[2865]=t("a",{id:"Reactant.MLIR.API.mlirTF32TypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTF32TypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTF32TypeGet")],-1)),e[2866]||(e[2866]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2867]||(e[2867]=l('
julia
mlirTF32TypeGet(ctx)

Creates a TF32 type in the given context. The type is owned by the context.

source

',3))]),t("details",Fy,[t("summary",null,[e[2868]||(e[2868]=t("a",{id:"Reactant.MLIR.API.mlirTransformApplyNamedSequence-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirTransformApplyNamedSequence-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformApplyNamedSequence")],-1)),e[2869]||(e[2869]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2870]||(e[2870]=l('
julia
mlirTransformApplyNamedSequence(payload, transformRoot, transformModule, transformOptions)

Applies the transformation script starting at the given transform root operation to the given payload operation. The module containing the transform root as well as the transform options should be provided. The transform operation must implement TransformOpInterface and the module must be a ModuleOp. Returns the status of the application.

source

',3))]),t("details",Py,[t("summary",null,[e[2871]||(e[2871]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirTransformOptionsCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsCreate")],-1)),e[2872]||(e[2872]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2873]||(e[2873]=l('
julia
mlirTransformOptionsCreate()

Creates a default-initialized transform options object.

source

',3))]),t("details",Dy,[t("summary",null,[e[2874]||(e[2874]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsDestroy")],-1)),e[2875]||(e[2875]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2876]||(e[2876]=l('
julia
mlirTransformOptionsDestroy(transformOptions)

Destroys a transform options object previously created by mlirTransformOptionsCreate.

source

',3))]),t("details",Oy,[t("summary",null,[e[2877]||(e[2877]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsEnableExpensiveChecks")],-1)),e[2878]||(e[2878]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2879]||(e[2879]=l('
julia
mlirTransformOptionsEnableExpensiveChecks(transformOptions, enable)

Enables or disables expensive checks in transform options.

source

',3))]),t("details",By,[t("summary",null,[e[2880]||(e[2880]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsEnforceSingleTopLevelTransformOp")],-1)),e[2881]||(e[2881]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2882]||(e[2882]=l('
julia
mlirTransformOptionsEnforceSingleTopLevelTransformOp(transformOptions, enable)

Enables or disables the enforcement of the top-level transform op being single in transform options.

source

',3))]),t("details",Gy,[t("summary",null,[e[2883]||(e[2883]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsGetEnforceSingleTopLevelTransformOp")],-1)),e[2884]||(e[2884]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2885]||(e[2885]=l('
julia
mlirTransformOptionsGetEnforceSingleTopLevelTransformOp(transformOptions)

Returns true if the enforcement of the top-level transform op being single is enabled in transform options.

source

',3))]),t("details",zy,[t("summary",null,[e[2886]||(e[2886]=t("a",{id:"Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTransformOptionsGetExpensiveChecksEnabled")],-1)),e[2887]||(e[2887]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2888]||(e[2888]=l('
julia
mlirTransformOptionsGetExpensiveChecksEnabled(transformOptions)

Returns true if expensive checks are enabled in transform options.

source

',3))]),t("details",wy,[t("summary",null,[e[2889]||(e[2889]=t("a",{id:"Reactant.MLIR.API.mlirTranslateModuleToLLVMIR-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTranslateModuleToLLVMIR-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTranslateModuleToLLVMIR")],-1)),e[2890]||(e[2890]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2891]||(e[2891]=l('
julia
mlirTranslateModuleToLLVMIR(_module, context)

Translate operation that satisfies LLVM dialect module requirements into an LLVM IR module living in the given context. This translates operations from any dilalect that has a registered implementation of LLVMTranslationDialectInterface.

Returns

the generated LLVM IR Module from the translated MLIR module, it is owned by the caller.

source

',5))]),t("details",Sy,[t("summary",null,[e[2892]||(e[2892]=t("a",{id:"Reactant.MLIR.API.mlirTupleTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGet")],-1)),e[2893]||(e[2893]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2894]||(e[2894]=l('
julia
mlirTupleTypeGet(ctx, numElements, elements)

Creates a tuple type that consists of the given list of elemental types. The type is owned by the context.

source

',3))]),t("details",Ny,[t("summary",null,[e[2895]||(e[2895]=t("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetNumTypes-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGetNumTypes-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetNumTypes")],-1)),e[2896]||(e[2896]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2897]||(e[2897]=l('
julia
mlirTupleTypeGetNumTypes(type)

Returns the number of types contained in a tuple.

source

',3))]),t("details",Vy,[t("summary",null,[e[2898]||(e[2898]=t("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTupleTypeGetType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetType")],-1)),e[2899]||(e[2899]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2900]||(e[2900]=l('
julia
mlirTupleTypeGetType(type, pos)

Returns the pos-th type in the tuple type.

source

',3))]),t("details",qy,[t("summary",null,[e[2901]||(e[2901]=t("a",{id:"Reactant.MLIR.API.mlirTupleTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirTupleTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTupleTypeGetTypeID")],-1)),e[2902]||(e[2902]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2903]||(e[2903]=l('
julia
mlirTupleTypeGetTypeID()

Returns the typeID of an Tuple type.

source

',3))]),t("details",Uy,[t("summary",null,[e[2904]||(e[2904]=t("a",{id:"Reactant.MLIR.API.mlirTypeAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGet")],-1)),e[2905]||(e[2905]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2906]||(e[2906]=l('
julia
mlirTypeAttrGet(type)

Creates a type attribute wrapping the given type in the same context as the type.

source

',3))]),t("details",Qy,[t("summary",null,[e[2907]||(e[2907]=t("a",{id:"Reactant.MLIR.API.mlirTypeAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirTypeAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGetTypeID")],-1)),e[2908]||(e[2908]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2909]||(e[2909]=l('
julia
mlirTypeAttrGetTypeID()

Returns the typeID of a Type attribute.

source

',3))]),t("details",Wy,[t("summary",null,[e[2910]||(e[2910]=t("a",{id:"Reactant.MLIR.API.mlirTypeAttrGetValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeAttrGetValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeAttrGetValue")],-1)),e[2911]||(e[2911]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2912]||(e[2912]=l('
julia
mlirTypeAttrGetValue(attr)

Returns the type stored in the given type attribute.

source

',3))]),t("details",Hy,[t("summary",null,[e[2913]||(e[2913]=t("a",{id:"Reactant.MLIR.API.mlirTypeDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeDump")],-1)),e[2914]||(e[2914]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2915]||(e[2915]=l('
julia
mlirTypeDump(type)

Prints the type to the standard error stream.

source

',3))]),t("details",Zy,[t("summary",null,[e[2916]||(e[2916]=t("a",{id:"Reactant.MLIR.API.mlirTypeEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeEqual")],-1)),e[2917]||(e[2917]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2918]||(e[2918]=l('
julia
mlirTypeEqual(t1, t2)

Checks if two types are equal.

source

',3))]),t("details",Jy,[t("summary",null,[e[2919]||(e[2919]=t("a",{id:"Reactant.MLIR.API.mlirTypeGetContext-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetContext-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetContext")],-1)),e[2920]||(e[2920]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2921]||(e[2921]=l('
julia
mlirTypeGetContext(type)

Gets the context that a type was created with.

source

',3))]),t("details",Ky,[t("summary",null,[e[2922]||(e[2922]=t("a",{id:"Reactant.MLIR.API.mlirTypeGetDialect-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetDialect-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetDialect")],-1)),e[2923]||(e[2923]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2924]||(e[2924]=l('
julia
mlirTypeGetDialect(type)

Gets the dialect a type belongs to.

source

',3))]),t("details",$y,[t("summary",null,[e[2925]||(e[2925]=t("a",{id:"Reactant.MLIR.API.mlirTypeGetTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeGetTypeID-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeGetTypeID")],-1)),e[2926]||(e[2926]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2927]||(e[2927]=l('
julia
mlirTypeGetTypeID(type)

Gets the type ID of the type.

source

',3))]),t("details",Xy,[t("summary",null,[e[2928]||(e[2928]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorAllocateTypeID")],-1)),e[2929]||(e[2929]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2930]||(e[2930]=l('
julia
mlirTypeIDAllocatorAllocateTypeID(allocator)

Allocates a type id that is valid for the lifetime of the allocator

source

',3))]),t("details",Yy,[t("summary",null,[e[2931]||(e[2931]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorCreate-Tuple{}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorCreate-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorCreate")],-1)),e[2932]||(e[2932]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2933]||(e[2933]=l('
julia
mlirTypeIDAllocatorCreate()

Creates a type id allocator for dynamic type id creation

source

',3))]),t("details",_y,[t("summary",null,[e[2934]||(e[2934]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDAllocatorDestroy-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDAllocatorDestroy-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDAllocatorDestroy")],-1)),e[2935]||(e[2935]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2936]||(e[2936]=l('
julia
mlirTypeIDAllocatorDestroy(allocator)

Deallocates the allocator and all allocated type ids

source

',3))]),t("details",em,[t("summary",null,[e[2937]||(e[2937]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDCreate-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDCreate-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDCreate")],-1)),e[2938]||(e[2938]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2939]||(e[2939]=l('
julia
mlirTypeIDCreate(ptr)

ptr must be 8 byte aligned and unique to a type valid for the duration of the returned type id's usage

source

',3))]),t("details",tm,[t("summary",null,[e[2940]||(e[2940]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeIDEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDEqual")],-1)),e[2941]||(e[2941]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2942]||(e[2942]=l('
julia
mlirTypeIDEqual(typeID1, typeID2)

Checks if two type ids are equal.

source

',3))]),t("details",sm,[t("summary",null,[e[2943]||(e[2943]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDHashValue-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDHashValue-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDHashValue")],-1)),e[2944]||(e[2944]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2945]||(e[2945]=l('
julia
mlirTypeIDHashValue(typeID)

Returns the hash value of the type id.

source

',3))]),t("details",am,[t("summary",null,[e[2946]||(e[2946]=t("a",{id:"Reactant.MLIR.API.mlirTypeIDIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIDIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIDIsNull")],-1)),e[2947]||(e[2947]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2948]||(e[2948]=l('
julia
mlirTypeIDIsNull(typeID)

Checks whether a type id is null.

source

',3))]),t("details",im,[t("summary",null,[e[2949]||(e[2949]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAAnyQuantizedType")],-1)),e[2950]||(e[2950]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2951]||(e[2951]=l('
julia
mlirTypeIsAAnyQuantizedType(type)

Returns true if the given type is an AnyQuantizedType.

source

',3))]),t("details",lm,[t("summary",null,[e[2952]||(e[2952]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsABF16-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsABF16-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsABF16")],-1)),e[2953]||(e[2953]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2954]||(e[2954]=l('
julia
mlirTypeIsABF16(type)

Checks whether the given type is a bf16 type.

source

',3))]),t("details",nm,[t("summary",null,[e[2955]||(e[2955]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsACalibratedQuantizedType")],-1)),e[2956]||(e[2956]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2957]||(e[2957]=l('
julia
mlirTypeIsACalibratedQuantizedType(type)

Returns true if the given type is a CalibratedQuantizedType.

source

',3))]),t("details",pm,[t("summary",null,[e[2958]||(e[2958]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAComplex-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAComplex-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAComplex")],-1)),e[2959]||(e[2959]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2960]||(e[2960]=l('
julia
mlirTypeIsAComplex(type)

Checks whether the given type is a Complex type.

source

',3))]),t("details",rm,[t("summary",null,[e[2961]||(e[2961]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAF16-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF16-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF16")],-1)),e[2962]||(e[2962]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2963]||(e[2963]=l('
julia
mlirTypeIsAF16(type)

Checks whether the given type is an f16 type.

source

',3))]),t("details",om,[t("summary",null,[e[2964]||(e[2964]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAF32-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF32-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF32")],-1)),e[2965]||(e[2965]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2966]||(e[2966]=l('
julia
mlirTypeIsAF32(type)

Checks whether the given type is an f32 type.

source

',3))]),t("details",dm,[t("summary",null,[e[2967]||(e[2967]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAF64-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAF64-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAF64")],-1)),e[2968]||(e[2968]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2969]||(e[2969]=l('
julia
mlirTypeIsAF64(type)

Checks whether the given type is an f64 type.

source

',3))]),t("details",cm,[t("summary",null,[e[2970]||(e[2970]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat")],-1)),e[2971]||(e[2971]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2972]||(e[2972]=l('
julia
mlirTypeIsAFloat(type)

Checks whether the given type is a floating-point type.

source

',3))]),t("details",bm,[t("summary",null,[e[2973]||(e[2973]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat4E2M1FN")],-1)),e[2974]||(e[2974]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2975]||(e[2975]=l('
julia
mlirTypeIsAFloat4E2M1FN(type)

Checks whether the given type is an f4E2M1FN type.

source

',3))]),t("details",hm,[t("summary",null,[e[2976]||(e[2976]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat6E2M3FN")],-1)),e[2977]||(e[2977]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2978]||(e[2978]=l('
julia
mlirTypeIsAFloat6E2M3FN(type)

Checks whether the given type is an f6E2M3FN type.

source

',3))]),t("details",um,[t("summary",null,[e[2979]||(e[2979]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat6E3M2FN")],-1)),e[2980]||(e[2980]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2981]||(e[2981]=l('
julia
mlirTypeIsAFloat6E3M2FN(type)

Checks whether the given type is an f6E3M2FN type.

source

',3))]),t("details",gm,[t("summary",null,[e[2982]||(e[2982]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E3M4-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E3M4-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E3M4")],-1)),e[2983]||(e[2983]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2984]||(e[2984]=l('
julia
mlirTypeIsAFloat8E3M4(type)

Checks whether the given type is an f8E3M4 type.

source

',3))]),t("details",ym,[t("summary",null,[e[2985]||(e[2985]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3")],-1)),e[2986]||(e[2986]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2987]||(e[2987]=l('
julia
mlirTypeIsAFloat8E4M3(type)

Checks whether the given type is an f8E4M3 type.

source

',3))]),t("details",mm,[t("summary",null,[e[2988]||(e[2988]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3B11FNUZ")],-1)),e[2989]||(e[2989]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2990]||(e[2990]=l('
julia
mlirTypeIsAFloat8E4M3B11FNUZ(type)

Checks whether the given type is an f8E4M3B11FNUZ type.

source

',3))]),t("details",km,[t("summary",null,[e[2991]||(e[2991]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FN")],-1)),e[2992]||(e[2992]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2993]||(e[2993]=l('
julia
mlirTypeIsAFloat8E4M3FN(type)

Checks whether the given type is an f8E4M3FN type.

source

',3))]),t("details",Rm,[t("summary",null,[e[2994]||(e[2994]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E4M3FNUZ")],-1)),e[2995]||(e[2995]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2996]||(e[2996]=l('
julia
mlirTypeIsAFloat8E4M3FNUZ(type)

Checks whether the given type is an f8E4M3FNUZ type.

source

',3))]),t("details",Im,[t("summary",null,[e[2997]||(e[2997]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E5M2-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2")],-1)),e[2998]||(e[2998]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2999]||(e[2999]=l('
julia
mlirTypeIsAFloat8E5M2(type)

Checks whether the given type is an f8E5M2 type.

source

',3))]),t("details",jm,[t("summary",null,[e[3e3]||(e[3e3]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E5M2FNUZ")],-1)),e[3001]||(e[3001]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3002]||(e[3002]=l('
julia
mlirTypeIsAFloat8E5M2FNUZ(type)

Checks whether the given type is an f8E5M2FNUZ type.

source

',3))]),t("details",fm,[t("summary",null,[e[3003]||(e[3003]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFloat8E8M0FNU")],-1)),e[3004]||(e[3004]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3005]||(e[3005]=l('
julia
mlirTypeIsAFloat8E8M0FNU(type)

Checks whether the given type is an f8E8M0FNU type.

source

',3))]),t("details",Mm,[t("summary",null,[e[3006]||(e[3006]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAFunction-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAFunction-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAFunction")],-1)),e[3007]||(e[3007]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3008]||(e[3008]=l('
julia
mlirTypeIsAFunction(type)

Checks whether the given type is a function type.

source

',3))]),t("details",Am,[t("summary",null,[e[3009]||(e[3009]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAIndex-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAIndex-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAIndex")],-1)),e[3010]||(e[3010]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3011]||(e[3011]=l('
julia
mlirTypeIsAIndex(type)

Checks whether the given type is an index type.

source

',3))]),t("details",Lm,[t("summary",null,[e[3012]||(e[3012]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAInteger-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAInteger-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAInteger")],-1)),e[3013]||(e[3013]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3014]||(e[3014]=l('
julia
mlirTypeIsAInteger(type)

Checks whether the given type is an integer type.

source

',3))]),t("details",Em,[t("summary",null,[e[3015]||(e[3015]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsALLVMPointerType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsALLVMPointerType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsALLVMPointerType")],-1)),e[3016]||(e[3016]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3017]||(e[3017]=l('
julia
mlirTypeIsALLVMPointerType(type)

Returns true if the type is an LLVM dialect pointer type.

source

',3))]),t("details",vm,[t("summary",null,[e[3018]||(e[3018]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsALLVMStructType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsALLVMStructType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsALLVMStructType")],-1)),e[3019]||(e[3019]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3020]||(e[3020]=l('
julia
mlirTypeIsALLVMStructType(type)

Returns true if the type is an LLVM dialect struct type.

source

',3))]),t("details",Tm,[t("summary",null,[e[3021]||(e[3021]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAMemRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAMemRef-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAMemRef")],-1)),e[3022]||(e[3022]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3023]||(e[3023]=l('
julia
mlirTypeIsAMemRef(type)

Checks whether the given type is a MemRef type.

source

',3))]),t("details",Cm,[t("summary",null,[e[3024]||(e[3024]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsANone-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsANone-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsANone")],-1)),e[3025]||(e[3025]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3026]||(e[3026]=l('
julia
mlirTypeIsANone(type)

Checks whether the given type is a None type.

source

',3))]),t("details",xm,[t("summary",null,[e[3027]||(e[3027]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAOpaque-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAOpaque-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAOpaque")],-1)),e[3028]||(e[3028]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3029]||(e[3029]=l('
julia
mlirTypeIsAOpaque(type)

Checks whether the given type is an opaque type.

source

',3))]),t("details",Fm,[t("summary",null,[e[3030]||(e[3030]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAQuantizedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAQuantizedType")],-1)),e[3031]||(e[3031]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3032]||(e[3032]=l('
julia
mlirTypeIsAQuantizedType(type)

Returns true if the given type is a quantization dialect type.

source

',3))]),t("details",Pm,[t("summary",null,[e[3033]||(e[3033]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsARankedTensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsARankedTensor-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsARankedTensor")],-1)),e[3034]||(e[3034]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3035]||(e[3035]=l('
julia
mlirTypeIsARankedTensor(type)

Checks whether the given type is a ranked tensor type.

source

',3))]),t("details",Dm,[t("summary",null,[e[3036]||(e[3036]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAShaped-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAShaped-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAShaped")],-1)),e[3037]||(e[3037]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3038]||(e[3038]=l('
julia
mlirTypeIsAShaped(type)

Checks whether the given type is a Shaped type.

source

',3))]),t("details",Om,[t("summary",null,[e[3039]||(e[3039]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsATF32-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATF32-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATF32")],-1)),e[3040]||(e[3040]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3041]||(e[3041]=l('
julia
mlirTypeIsATF32(type)

Checks whether the given type is an TF32 type.

source

',3))]),t("details",Bm,[t("summary",null,[e[3042]||(e[3042]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsATensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATensor-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATensor")],-1)),e[3043]||(e[3043]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3044]||(e[3044]=l('
julia
mlirTypeIsATensor(type)

Checks whether the given type is a Tensor type.

source

',3))]),t("details",Gm,[t("summary",null,[e[3045]||(e[3045]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsATuple-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsATuple-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsATuple")],-1)),e[3046]||(e[3046]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3047]||(e[3047]=l('
julia
mlirTypeIsATuple(type)

Checks whether the given type is a tuple type.

source

',3))]),t("details",zm,[t("summary",null,[e[3048]||(e[3048]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedPerAxisType")],-1)),e[3049]||(e[3049]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3050]||(e[3050]=l('
julia
mlirTypeIsAUniformQuantizedPerAxisType(type)

Returns true if the given type is a UniformQuantizedPerAxisType.

source

',3))]),t("details",wm,[t("summary",null,[e[3051]||(e[3051]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUniformQuantizedType")],-1)),e[3052]||(e[3052]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3053]||(e[3053]=l('
julia
mlirTypeIsAUniformQuantizedType(type)

Returns true if the given type is a UniformQuantizedType.

source

',3))]),t("details",Sm,[t("summary",null,[e[3054]||(e[3054]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUnrankedMemRef")],-1)),e[3055]||(e[3055]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3056]||(e[3056]=l('
julia
mlirTypeIsAUnrankedMemRef(type)

Checks whether the given type is an UnrankedMemRef type.

source

',3))]),t("details",Nm,[t("summary",null,[e[3057]||(e[3057]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAUnrankedTensor-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAUnrankedTensor-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAUnrankedTensor")],-1)),e[3058]||(e[3058]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3059]||(e[3059]=l('
julia
mlirTypeIsAUnrankedTensor(type)

Checks whether the given type is an unranked tensor type.

source

',3))]),t("details",Vm,[t("summary",null,[e[3060]||(e[3060]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsAVector-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsAVector-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsAVector")],-1)),e[3061]||(e[3061]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3062]||(e[3062]=l('
julia
mlirTypeIsAVector(type)

Checks whether the given type is a Vector type.

source

',3))]),t("details",qm,[t("summary",null,[e[3063]||(e[3063]=t("a",{id:"Reactant.MLIR.API.mlirTypeIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirTypeIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeIsNull")],-1)),e[3064]||(e[3064]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3065]||(e[3065]=l('
julia
mlirTypeIsNull(type)

Checks whether a type is null.

source

',3))]),t("details",Um,[t("summary",null,[e[3066]||(e[3066]=t("a",{id:"Reactant.MLIR.API.mlirTypeParseGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirTypeParseGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypeParseGet")],-1)),e[3067]||(e[3067]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3068]||(e[3068]=l('
julia
mlirTypeParseGet(context, type)

Parses a type. The type is owned by the context.

source

',3))]),t("details",Qm,[t("summary",null,[e[3069]||(e[3069]=t("a",{id:"Reactant.MLIR.API.mlirTypePrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirTypePrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirTypePrint")],-1)),e[3070]||(e[3070]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3071]||(e[3071]=l('
julia
mlirTypePrint(type, callback, userData)

Prints a location by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Wm,[t("summary",null,[e[3072]||(e[3072]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet-NTuple{9, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet-NTuple{9, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGet")],-1)),e[3073]||(e[3073]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3074]||(e[3074]=l('
julia
mlirUniformQuantizedPerAxisTypeGet(flags, storageType, expressedType, nDims, scales, zeroPoints, quantizedDimension, storageTypeMin, storageTypeMax)

Creates an instance of UniformQuantizedPerAxisType with the given parameters in the same context as storageType and returns it. scales and zeroPoints point to nDims number of elements. The instance is owned by the context.

source

',3))]),t("details",Hm,[t("summary",null,[e[3075]||(e[3075]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetNumDims")],-1)),e[3076]||(e[3076]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3077]||(e[3077]=l('
julia
mlirUniformQuantizedPerAxisTypeGetNumDims(type)

Returns the number of axes in the given quantized per-axis type.

source

',3))]),t("details",Zm,[t("summary",null,[e[3078]||(e[3078]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetQuantizedDimension")],-1)),e[3079]||(e[3079]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3080]||(e[3080]=l('
julia
mlirUniformQuantizedPerAxisTypeGetQuantizedDimension(type)

Returns the index of the quantized dimension in the given quantized per-axis type.

source

',3))]),t("details",Jm,[t("summary",null,[e[3081]||(e[3081]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetScale")],-1)),e[3082]||(e[3082]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3083]||(e[3083]=l('
julia
mlirUniformQuantizedPerAxisTypeGetScale(type, pos)

Returns pos-th scale of the given quantized per-axis type.

source

',3))]),t("details",Km,[t("summary",null,[e[3084]||(e[3084]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeGetZeroPoint")],-1)),e[3085]||(e[3085]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3086]||(e[3086]=l('
julia
mlirUniformQuantizedPerAxisTypeGetZeroPoint(type, pos)

Returns pos-th zero point of the given quantized per-axis type.

source

',3))]),t("details",$m,[t("summary",null,[e[3087]||(e[3087]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedPerAxisTypeIsFixedPoint")],-1)),e[3088]||(e[3088]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3089]||(e[3089]=l('
julia
mlirUniformQuantizedPerAxisTypeIsFixedPoint(type)

Returns true if the given uniform quantized per-axis type is fixed-point.

source

',3))]),t("details",Xm,[t("summary",null,[e[3090]||(e[3090]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGet-NTuple{7, Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGet-NTuple{7, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGet")],-1)),e[3091]||(e[3091]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3092]||(e[3092]=l('
julia
mlirUniformQuantizedTypeGet(flags, storageType, expressedType, scale, zeroPoint, storageTypeMin, storageTypeMax)

Creates an instance of UniformQuantizedType with the given parameters in the same context as storageType and returns it. The instance is owned by the context.

source

',3))]),t("details",Ym,[t("summary",null,[e[3093]||(e[3093]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGetScale")],-1)),e[3094]||(e[3094]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3095]||(e[3095]=l('
julia
mlirUniformQuantizedTypeGetScale(type)

Returns the scale of the given uniform quantized type.

source

',3))]),t("details",_m,[t("summary",null,[e[3096]||(e[3096]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeGetZeroPoint")],-1)),e[3097]||(e[3097]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3098]||(e[3098]=l('
julia
mlirUniformQuantizedTypeGetZeroPoint(type)

Returns the zero point of the given uniform quantized type.

source

',3))]),t("details",ek,[t("summary",null,[e[3099]||(e[3099]=t("a",{id:"Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUniformQuantizedTypeIsFixedPoint")],-1)),e[3100]||(e[3100]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3101]||(e[3101]=l('
julia
mlirUniformQuantizedTypeIsFixedPoint(type)

Returns true if the given uniform quantized type is fixed-point.

source

',3))]),t("details",tk,[t("summary",null,[e[3102]||(e[3102]=t("a",{id:"Reactant.MLIR.API.mlirUnitAttrGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnitAttrGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnitAttrGet")],-1)),e[3103]||(e[3103]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3104]||(e[3104]=l('
julia
mlirUnitAttrGet(ctx)

Creates a unit attribute in the given context.

source

',3))]),t("details",sk,[t("summary",null,[e[3105]||(e[3105]=t("a",{id:"Reactant.MLIR.API.mlirUnitAttrGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnitAttrGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnitAttrGetTypeID")],-1)),e[3106]||(e[3106]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3107]||(e[3107]=l('
julia
mlirUnitAttrGetTypeID()

Returns the typeID of a Unit attribute.

source

',3))]),t("details",ak,[t("summary",null,[e[3108]||(e[3108]=t("a",{id:"Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet-NTuple{8, Any}",href:"#Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet-NTuple{8, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnmanagedDenseResourceElementsAttrGet")],-1)),e[3109]||(e[3109]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3110]||(e[3110]=l('
julia
mlirUnmanagedDenseResourceElementsAttrGet(shapedType, name, data, dataLength, dataAlignment, dataIsMutable, deleter, userData)

Unlike the typed accessors below, constructs the attribute with a raw data buffer and no type/alignment checking. Use a more strongly typed accessor if possible. If dataIsMutable is false, then an immutable AsmResourceBlob will be created and that passed data contents will be treated as const. If the deleter is non NULL, then it will be called when the data buffer can no longer be accessed (passing userData to it).

source

',3))]),t("details",ik,[t("summary",null,[e[3111]||(e[3111]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGet-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGet-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGet")],-1)),e[3112]||(e[3112]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3113]||(e[3113]=l('
julia
mlirUnrankedMemRefTypeGet(elementType, memorySpace)

Creates an Unranked MemRef type with the given element type and in the given memory space. The type is owned by the context of element type.

source

',3))]),t("details",lk,[t("summary",null,[e[3114]||(e[3114]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetChecked")],-1)),e[3115]||(e[3115]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3116]||(e[3116]=l('
julia
mlirUnrankedMemRefTypeGetChecked(loc, elementType, memorySpace)

Same as "mlirUnrankedMemRefTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",nk,[t("summary",null,[e[3117]||(e[3117]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemRefTypeGetTypeID")],-1)),e[3118]||(e[3118]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3119]||(e[3119]=l('
julia
mlirUnrankedMemRefTypeGetTypeID()

Returns the typeID of an UnrankedMemRef type.

source

',3))]),t("details",pk,[t("summary",null,[e[3120]||(e[3120]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedMemrefGetMemorySpace")],-1)),e[3121]||(e[3121]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3122]||(e[3122]=l('
julia
mlirUnrankedMemrefGetMemorySpace(type)

Returns the memory spcae of the given Unranked MemRef type.

source

',3))]),t("details",rk,[t("summary",null,[e[3123]||(e[3123]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGet-Tuple{Any}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGet-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGet")],-1)),e[3124]||(e[3124]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3125]||(e[3125]=l('
julia
mlirUnrankedTensorTypeGet(elementType)

Creates an unranked tensor type with the given element type in the same context as the element type. The type is owned by the context.

source

',3))]),t("details",ok,[t("summary",null,[e[3126]||(e[3126]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGetChecked")],-1)),e[3127]||(e[3127]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3128]||(e[3128]=l('
julia
mlirUnrankedTensorTypeGetChecked(loc, elementType)

Same as "mlirUnrankedTensorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",dk,[t("summary",null,[e[3129]||(e[3129]=t("a",{id:"Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirUnrankedTensorTypeGetTypeID")],-1)),e[3130]||(e[3130]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3131]||(e[3131]=l('
julia
mlirUnrankedTensorTypeGetTypeID()

Returns the typeID of an UnrankedTensor type.

source

',3))]),t("details",ck,[t("summary",null,[e[3132]||(e[3132]=t("a",{id:"Reactant.MLIR.API.mlirValueDump-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueDump-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueDump")],-1)),e[3133]||(e[3133]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3134]||(e[3134]=l('
julia
mlirValueDump(value)

Prints the value to the standard error stream.

source

',3))]),t("details",bk,[t("summary",null,[e[3135]||(e[3135]=t("a",{id:"Reactant.MLIR.API.mlirValueEqual-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirValueEqual-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueEqual")],-1)),e[3136]||(e[3136]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3137]||(e[3137]=l('
julia
mlirValueEqual(value1, value2)

Returns 1 if two values are equal, 0 otherwise.

source

',3))]),t("details",hk,[t("summary",null,[e[3138]||(e[3138]=t("a",{id:"Reactant.MLIR.API.mlirValueGetFirstUse-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueGetFirstUse-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueGetFirstUse")],-1)),e[3139]||(e[3139]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3140]||(e[3140]=l('
julia
mlirValueGetFirstUse(value)

Returns an op operand representing the first use of the value, or a null op operand if there are no uses.

source

',3))]),t("details",uk,[t("summary",null,[e[3141]||(e[3141]=t("a",{id:"Reactant.MLIR.API.mlirValueGetType-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueGetType-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueGetType")],-1)),e[3142]||(e[3142]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3143]||(e[3143]=l('
julia
mlirValueGetType(value)

Returns the type of the value.

source

',3))]),t("details",gk,[t("summary",null,[e[3144]||(e[3144]=t("a",{id:"Reactant.MLIR.API.mlirValueIsABlockArgument-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueIsABlockArgument-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueIsABlockArgument")],-1)),e[3145]||(e[3145]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3146]||(e[3146]=l('
julia
mlirValueIsABlockArgument(value)

Returns 1 if the value is a block argument, 0 otherwise.

source

',3))]),t("details",yk,[t("summary",null,[e[3147]||(e[3147]=t("a",{id:"Reactant.MLIR.API.mlirValueIsAOpResult-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueIsAOpResult-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueIsAOpResult")],-1)),e[3148]||(e[3148]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3149]||(e[3149]=l('
julia
mlirValueIsAOpResult(value)

Returns 1 if the value is an operation result, 0 otherwise.

source

',3))]),t("details",mk,[t("summary",null,[e[3150]||(e[3150]=t("a",{id:"Reactant.MLIR.API.mlirValueIsNull-Tuple{Any}",href:"#Reactant.MLIR.API.mlirValueIsNull-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueIsNull")],-1)),e[3151]||(e[3151]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3152]||(e[3152]=l('
julia
mlirValueIsNull(value)

Returns whether the value is null.

source

',3))]),t("details",kk,[t("summary",null,[e[3153]||(e[3153]=t("a",{id:"Reactant.MLIR.API.mlirValuePrint-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirValuePrint-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValuePrint")],-1)),e[3154]||(e[3154]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3155]||(e[3155]=l('
julia
mlirValuePrint(value, callback, userData)

Prints a value by sending chunks of the string representation and forwarding userData tocallback`. Note that the callback may be called several times with consecutive chunks of the string.

source

',3))]),t("details",Rk,[t("summary",null,[e[3156]||(e[3156]=t("a",{id:"Reactant.MLIR.API.mlirValuePrintAsOperand-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirValuePrintAsOperand-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValuePrintAsOperand")],-1)),e[3157]||(e[3157]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3158]||(e[3158]=l('
julia
mlirValuePrintAsOperand(value, state, callback, userData)

Prints a value as an operand (i.e., the ValueID).

source

',3))]),t("details",Ik,[t("summary",null,[e[3159]||(e[3159]=t("a",{id:"Reactant.MLIR.API.mlirValueReplaceAllUsesExcept-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirValueReplaceAllUsesExcept-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueReplaceAllUsesExcept")],-1)),e[3160]||(e[3160]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3161]||(e[3161]=l('
julia
mlirValueReplaceAllUsesExcept(of, with, numExceptions, exceptions)

Replace all uses of 'of' value with 'with' value, updating anything in the IR that uses 'of' to use 'with' instead, except if the user is listed in 'exceptions'. The 'exceptions' parameter is an array of MlirOperation pointers with a length of 'numExceptions'.

source

',3))]),t("details",jk,[t("summary",null,[e[3162]||(e[3162]=t("a",{id:"Reactant.MLIR.API.mlirValueReplaceAllUsesOfWith-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirValueReplaceAllUsesOfWith-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueReplaceAllUsesOfWith")],-1)),e[3163]||(e[3163]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3164]||(e[3164]=l('
julia
mlirValueReplaceAllUsesOfWith(of, with)

Replace all uses of 'of' value with the 'with' value, updating anything in the IR that uses 'of' to use the other value instead. When this returns there are zero uses of 'of'.

source

',3))]),t("details",fk,[t("summary",null,[e[3165]||(e[3165]=t("a",{id:"Reactant.MLIR.API.mlirValueSetType-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirValueSetType-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirValueSetType")],-1)),e[3166]||(e[3166]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3167]||(e[3167]=l('
julia
mlirValueSetType(value, type)

Set the type of the value.

source

',3))]),t("details",Mk,[t("summary",null,[e[3168]||(e[3168]=t("a",{id:"Reactant.MLIR.API.mlirVectorTypeGet-Tuple{Any, Any, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeGet-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGet")],-1)),e[3169]||(e[3169]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3170]||(e[3170]=l('
julia
mlirVectorTypeGet(rank, shape, elementType)

Creates a vector type of the shape identified by its rank and dimensions, with the given element type in the same context as the element type. The type is owned by the context.

source

',3))]),t("details",Ak,[t("summary",null,[e[3171]||(e[3171]=t("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetChecked-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeGetChecked-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetChecked")],-1)),e[3172]||(e[3172]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3173]||(e[3173]=l('
julia
mlirVectorTypeGetChecked(loc, rank, shape, elementType)

Same as "mlirVectorTypeGet" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",Lk,[t("summary",null,[e[3174]||(e[3174]=t("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetScalable-NTuple{4, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeGetScalable-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetScalable")],-1)),e[3175]||(e[3175]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3176]||(e[3176]=l('
julia
mlirVectorTypeGetScalable(rank, shape, scalable, elementType)

Creates a scalable vector type with the shape identified by its rank and dimensions. A subset of dimensions may be marked as scalable via the corresponding flag list, which is expected to have as many entries as the rank of the vector. The vector is created in the same context as the element type.

source

',3))]),t("details",Ek,[t("summary",null,[e[3177]||(e[3177]=t("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetScalableChecked-NTuple{5, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeGetScalableChecked-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetScalableChecked")],-1)),e[3178]||(e[3178]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3179]||(e[3179]=l('
julia
mlirVectorTypeGetScalableChecked(loc, rank, shape, scalable, elementType)

Same as "mlirVectorTypeGetScalable" but returns a nullptr wrapping MlirType on illegal arguments, emitting appropriate diagnostics.

source

',3))]),t("details",vk,[t("summary",null,[e[3180]||(e[3180]=t("a",{id:"Reactant.MLIR.API.mlirVectorTypeGetTypeID-Tuple{}",href:"#Reactant.MLIR.API.mlirVectorTypeGetTypeID-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeGetTypeID")],-1)),e[3181]||(e[3181]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3182]||(e[3182]=l('
julia
mlirVectorTypeGetTypeID()

Returns the typeID of an Vector type.

source

',3))]),t("details",Tk,[t("summary",null,[e[3183]||(e[3183]=t("a",{id:"Reactant.MLIR.API.mlirVectorTypeIsDimScalable-Tuple{Any, Any}",href:"#Reactant.MLIR.API.mlirVectorTypeIsDimScalable-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeIsDimScalable")],-1)),e[3184]||(e[3184]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3185]||(e[3185]=l('
julia
mlirVectorTypeIsDimScalable(type, dim)

Checks whether the "dim"-th dimension of the given vector is scalable.

source

',3))]),t("details",Ck,[t("summary",null,[e[3186]||(e[3186]=t("a",{id:"Reactant.MLIR.API.mlirVectorTypeIsScalable-Tuple{Any}",href:"#Reactant.MLIR.API.mlirVectorTypeIsScalable-Tuple{Any}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.API.mlirVectorTypeIsScalable")],-1)),e[3187]||(e[3187]=s()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[3188]||(e[3188]=l('
julia
mlirVectorTypeIsScalable(type)

Checks whether the given vector type is scalable, i.e., has at least one scalable dimension.

source

',3))]),e[3191]||(e[3191]=t("h1",{id:"Other-Functions",tabindex:"-1"},[s("Other Functions "),t("a",{class:"header-anchor",href:"#Other-Functions","aria-label":'Permalink to "Other Functions {#Other-Functions}"'},"​")],-1))])}const wk=n(d,[["render",xk]]);export{zk as __pageData,wk as default}; diff --git a/previews/PR441/assets/api_ops.md.C1YgWHhK.js b/previews/PR441/assets/api_ops.md.B65ZOBdU.js similarity index 94% rename from previews/PR441/assets/api_ops.md.C1YgWHhK.js rename to previews/PR441/assets/api_ops.md.B65ZOBdU.js index aa399ca07..241119126 100644 --- a/previews/PR441/assets/api_ops.md.C1YgWHhK.js +++ b/previews/PR441/assets/api_ops.md.B65ZOBdU.js @@ -1,4 +1,4 @@ -import{_ as l,c as p,j as a,a as i,G as e,a2 as n,B as h,o as r}from"./chunks/framework.B4pBzPBy.js";const f=JSON.parse('{"title":"Reactant.Ops API","description":"","frontmatter":{},"headers":[],"relativePath":"api/ops.md","filePath":"api/ops.md","lastUpdated":null}'),d={name:"api/ops.md"},k={class:"jldocstring custom-block"},o={class:"jldocstring custom-block"},c={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"};function u(F,s,T,R,b,m){const t=h("Badge");return r(),p("div",null,[s[18]||(s[18]=a("h1",{id:"Reactant.Ops-API",tabindex:"-1"},[a("code",null,"Reactant.Ops"),i(" API "),a("a",{class:"header-anchor",href:"#Reactant.Ops-API","aria-label":'Permalink to "`Reactant.Ops` API {#Reactant.Ops-API}"'},"​")],-1)),s[19]||(s[19]=a("p",null,[a("code",null,"Reactant.Ops"),i(" module provides a high-level API to construct MLIR operations without having to directly interact with the different dialects.")],-1)),s[20]||(s[20]=a("p",null,[i("Currently we haven't documented all the functions in "),a("code",null,"Reactant.Ops"),i(".")],-1)),a("details",k,[a("summary",null,[s[0]||(s[0]=a("a",{id:"Reactant.Ops.gather_getindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}}} where {T, N}",href:"#Reactant.Ops.gather_getindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}}} where {T, N}"},[a("span",{class:"jlbinding"},"Reactant.Ops.gather_getindex")],-1)),s[1]||(s[1]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=n('
julia
gather_getindex(src, gather_indices)

Uses MLIR.Dialects.stablehlo.gather to get the values of src at the indices specified by gather_indices. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_slice instead.

source

',3))]),a("details",o,[a("summary",null,[s[3]||(s[3]=a("a",{id:"Reactant.Ops.hlo_call-Tuple{Any, Vararg{Any}}",href:"#Reactant.Ops.hlo_call-Tuple{Any, Vararg{Any}}"},[a("span",{class:"jlbinding"},"Reactant.Ops.hlo_call")],-1)),s[4]||(s[4]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[5]||(s[5]=n(`
julia
Ops.hlo_call(mlir_code::String, args::Vararg{AnyTracedRArray}...; func_name::String="main") -> NTuple{N, AnyTracedRArray}

Given a MLIR module given as a string, calls the function identified by the func_name keyword parameter (default "main") with the provided arguments and return a tuple for each result of the call.

julia
julia> Reactant.@jit(
+import{_ as l,c as p,j as a,a as i,G as t,a2 as n,B as h,o as r}from"./chunks/framework.B4pBzPBy.js";const f=JSON.parse('{"title":"Reactant.Ops API","description":"","frontmatter":{},"headers":[],"relativePath":"api/ops.md","filePath":"api/ops.md","lastUpdated":null}'),d={name:"api/ops.md"},k={class:"jldocstring custom-block"},o={class:"jldocstring custom-block"},c={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"};function u(F,s,T,b,R,m){const e=h("Badge");return r(),p("div",null,[s[18]||(s[18]=a("h1",{id:"Reactant.Ops-API",tabindex:"-1"},[a("code",null,"Reactant.Ops"),i(" API "),a("a",{class:"header-anchor",href:"#Reactant.Ops-API","aria-label":'Permalink to "`Reactant.Ops` API {#Reactant.Ops-API}"'},"​")],-1)),s[19]||(s[19]=a("p",null,[a("code",null,"Reactant.Ops"),i(" module provides a high-level API to construct MLIR operations without having to directly interact with the different dialects.")],-1)),s[20]||(s[20]=a("p",null,[i("Currently we haven't documented all the functions in "),a("code",null,"Reactant.Ops"),i(".")],-1)),a("details",k,[a("summary",null,[s[0]||(s[0]=a("a",{id:"Reactant.Ops.gather_getindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}}} where {T, N}",href:"#Reactant.Ops.gather_getindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}}} where {T, N}"},[a("span",{class:"jlbinding"},"Reactant.Ops.gather_getindex")],-1)),s[1]||(s[1]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=n('
julia
gather_getindex(src, gather_indices)
Uses MLIR.Dialects.stablehlo.gather to get the values of src at the indices specified by gather_indices. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_slice instead.
source
',3))]),a("details",o,[a("summary",null,[s[3]||(s[3]=a("a",{id:"Reactant.Ops.hlo_call-Tuple{Any, Vararg{Any}}",href:"#Reactant.Ops.hlo_call-Tuple{Any, Vararg{Any}}"},[a("span",{class:"jlbinding"},"Reactant.Ops.hlo_call")],-1)),s[4]||(s[4]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[5]||(s[5]=n(`
julia
Ops.hlo_call(mlir_code::String, args::Vararg{AnyTracedRArray}...; func_name::String="main") -> NTuple{N, AnyTracedRArray}
Given a MLIR module given as a string, calls the function identified by the func_name keyword parameter (default "main") with the provided arguments and return a tuple for each result of the call.
julia
julia> Reactant.@jit(
           Ops.hlo_call(
               """
               module {
@@ -12,22 +12,22 @@ import{_ as l,c as p,j as a,a as i,G as e,a2 as n,B as h,o as r}from"./chunks/fr
               Reactant.to_rarray(Float32[1, 2, 3]),
           )
        )
-(ConcreteRArray{Float32, 1}(Float32[2.0, 4.0, 6.0]),)
source
`,4))]),a("details",c,[a("summary",null,[s[6]||(s[6]=a("a",{id:"Reactant.Ops.randexp-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T",href:"#Reactant.Ops.randexp-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T"},[a("span",{class:"jlbinding"},"Reactant.Ops.randexp")],-1)),s[7]||(s[7]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[8]||(s[8]=n(`
julia
randexp(
+(ConcreteRArray{Float32, 1}(Float32[2.0, 4.0, 6.0]),)
source
`,4))]),a("details",c,[a("summary",null,[s[6]||(s[6]=a("a",{id:"Reactant.Ops.randexp-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T",href:"#Reactant.Ops.randexp-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T"},[a("span",{class:"jlbinding"},"Reactant.Ops.randexp")],-1)),s[7]||(s[7]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[8]||(s[8]=n(`
julia
randexp(
     ::Type{T},
     seed::TracedRArray{UInt64,1},
     shape;
     algorithm::String="DEFAULT",
     location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
-)
Generate a random array of type T with the given shape and seed from an exponential distribution with rate 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",E,[a("summary",null,[s[9]||(s[9]=a("a",{id:"Reactant.Ops.randn-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T",href:"#Reactant.Ops.randn-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T"},[a("span",{class:"jlbinding"},"Reactant.Ops.randn")],-1)),s[10]||(s[10]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[11]||(s[11]=n(`
julia
randn(
+)
Generate a random array of type T with the given shape and seed from an exponential distribution with rate 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",E,[a("summary",null,[s[9]||(s[9]=a("a",{id:"Reactant.Ops.randn-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T",href:"#Reactant.Ops.randn-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T"},[a("span",{class:"jlbinding"},"Reactant.Ops.randn")],-1)),s[10]||(s[10]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[11]||(s[11]=n(`
julia
randn(
     ::Type{T},
     seed::TracedRArray{UInt64,1},
     shape;
     algorithm::String="DEFAULT",
     location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
-)
Generate a random array of type T with the given shape and seed from a standard normal distribution of mean 0 and standard deviation 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",g,[a("summary",null,[s[12]||(s[12]=a("a",{id:"Reactant.Ops.rng_bit_generator-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T<:Integer",href:"#Reactant.Ops.rng_bit_generator-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T<:Integer"},[a("span",{class:"jlbinding"},"Reactant.Ops.rng_bit_generator")],-1)),s[13]||(s[13]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[14]||(s[14]=n(`
julia
rng_bit_generator(
+)
Generate a random array of type T with the given shape and seed from a standard normal distribution of mean 0 and standard deviation 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",g,[a("summary",null,[s[12]||(s[12]=a("a",{id:"Reactant.Ops.rng_bit_generator-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T<:Integer",href:"#Reactant.Ops.rng_bit_generator-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T<:Integer"},[a("span",{class:"jlbinding"},"Reactant.Ops.rng_bit_generator")],-1)),s[13]||(s[13]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[14]||(s[14]=n(`
julia
rng_bit_generator(
     ::Type{T},
     seed::TracedRArray{UInt64,1},
     shape;
     algorithm::String="DEFAULT",
     location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
-)
Generate a random array of type T with the given shape and seed from a uniform random distribution between 0 and 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",y,[a("summary",null,[s[15]||(s[15]=a("a",{id:"Reactant.Ops.scatter_setindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}, Reactant.TracedRArray{T, 1}}} where {T, N}",href:"#Reactant.Ops.scatter_setindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}, Reactant.TracedRArray{T, 1}}} where {T, N}"},[a("span",{class:"jlbinding"},"Reactant.Ops.scatter_setindex")],-1)),s[16]||(s[16]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[17]||(s[17]=n('
julia
scatter_setindex(dest, scatter_indices, updates)
Uses MLIR.Dialects.stablehlo.scatter to set the values of dest at the indices specified by scatter_indices to the values in updates. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_update_slice instead.
source
',3))])])}const A=l(d,[["render",u]]);export{f as __pageData,A as default};
+)

Generate a random array of type T with the given shape and seed from a uniform random distribution between 0 and 1. Returns a NamedTuple with the following fields:

Arguments

source

`,6))]),a("details",y,[a("summary",null,[s[15]||(s[15]=a("a",{id:"Reactant.Ops.scatter_setindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}, Reactant.TracedRArray{T, 1}}} where {T, N}",href:"#Reactant.Ops.scatter_setindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}, Reactant.TracedRArray{T, 1}}} where {T, N}"},[a("span",{class:"jlbinding"},"Reactant.Ops.scatter_setindex")],-1)),s[16]||(s[16]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[17]||(s[17]=n('
julia
scatter_setindex(dest, scatter_indices, updates)

Uses MLIR.Dialects.stablehlo.scatter to set the values of dest at the indices specified by scatter_indices to the values in updates. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_update_slice instead.

source

',3))])])}const A=l(d,[["render",u]]);export{f as __pageData,A as default}; diff --git a/previews/PR441/assets/api_ops.md.C1YgWHhK.lean.js b/previews/PR441/assets/api_ops.md.B65ZOBdU.lean.js similarity index 94% rename from previews/PR441/assets/api_ops.md.C1YgWHhK.lean.js rename to previews/PR441/assets/api_ops.md.B65ZOBdU.lean.js index aa399ca07..241119126 100644 --- a/previews/PR441/assets/api_ops.md.C1YgWHhK.lean.js +++ b/previews/PR441/assets/api_ops.md.B65ZOBdU.lean.js @@ -1,4 +1,4 @@ -import{_ as l,c as p,j as a,a as i,G as e,a2 as n,B as h,o as r}from"./chunks/framework.B4pBzPBy.js";const f=JSON.parse('{"title":"Reactant.Ops API","description":"","frontmatter":{},"headers":[],"relativePath":"api/ops.md","filePath":"api/ops.md","lastUpdated":null}'),d={name:"api/ops.md"},k={class:"jldocstring custom-block"},o={class:"jldocstring custom-block"},c={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"};function u(F,s,T,R,b,m){const t=h("Badge");return r(),p("div",null,[s[18]||(s[18]=a("h1",{id:"Reactant.Ops-API",tabindex:"-1"},[a("code",null,"Reactant.Ops"),i(" API "),a("a",{class:"header-anchor",href:"#Reactant.Ops-API","aria-label":'Permalink to "`Reactant.Ops` API {#Reactant.Ops-API}"'},"​")],-1)),s[19]||(s[19]=a("p",null,[a("code",null,"Reactant.Ops"),i(" module provides a high-level API to construct MLIR operations without having to directly interact with the different dialects.")],-1)),s[20]||(s[20]=a("p",null,[i("Currently we haven't documented all the functions in "),a("code",null,"Reactant.Ops"),i(".")],-1)),a("details",k,[a("summary",null,[s[0]||(s[0]=a("a",{id:"Reactant.Ops.gather_getindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}}} where {T, N}",href:"#Reactant.Ops.gather_getindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}}} where {T, N}"},[a("span",{class:"jlbinding"},"Reactant.Ops.gather_getindex")],-1)),s[1]||(s[1]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=n('
julia
gather_getindex(src, gather_indices)

Uses MLIR.Dialects.stablehlo.gather to get the values of src at the indices specified by gather_indices. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_slice instead.

source

',3))]),a("details",o,[a("summary",null,[s[3]||(s[3]=a("a",{id:"Reactant.Ops.hlo_call-Tuple{Any, Vararg{Any}}",href:"#Reactant.Ops.hlo_call-Tuple{Any, Vararg{Any}}"},[a("span",{class:"jlbinding"},"Reactant.Ops.hlo_call")],-1)),s[4]||(s[4]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[5]||(s[5]=n(`
julia
Ops.hlo_call(mlir_code::String, args::Vararg{AnyTracedRArray}...; func_name::String="main") -> NTuple{N, AnyTracedRArray}

Given a MLIR module given as a string, calls the function identified by the func_name keyword parameter (default "main") with the provided arguments and return a tuple for each result of the call.

julia
julia> Reactant.@jit(
+import{_ as l,c as p,j as a,a as i,G as t,a2 as n,B as h,o as r}from"./chunks/framework.B4pBzPBy.js";const f=JSON.parse('{"title":"Reactant.Ops API","description":"","frontmatter":{},"headers":[],"relativePath":"api/ops.md","filePath":"api/ops.md","lastUpdated":null}'),d={name:"api/ops.md"},k={class:"jldocstring custom-block"},o={class:"jldocstring custom-block"},c={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"};function u(F,s,T,b,R,m){const e=h("Badge");return r(),p("div",null,[s[18]||(s[18]=a("h1",{id:"Reactant.Ops-API",tabindex:"-1"},[a("code",null,"Reactant.Ops"),i(" API "),a("a",{class:"header-anchor",href:"#Reactant.Ops-API","aria-label":'Permalink to "`Reactant.Ops` API {#Reactant.Ops-API}"'},"​")],-1)),s[19]||(s[19]=a("p",null,[a("code",null,"Reactant.Ops"),i(" module provides a high-level API to construct MLIR operations without having to directly interact with the different dialects.")],-1)),s[20]||(s[20]=a("p",null,[i("Currently we haven't documented all the functions in "),a("code",null,"Reactant.Ops"),i(".")],-1)),a("details",k,[a("summary",null,[s[0]||(s[0]=a("a",{id:"Reactant.Ops.gather_getindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}}} where {T, N}",href:"#Reactant.Ops.gather_getindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}}} where {T, N}"},[a("span",{class:"jlbinding"},"Reactant.Ops.gather_getindex")],-1)),s[1]||(s[1]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=n('
julia
gather_getindex(src, gather_indices)
Uses MLIR.Dialects.stablehlo.gather to get the values of src at the indices specified by gather_indices. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_slice instead.
source
',3))]),a("details",o,[a("summary",null,[s[3]||(s[3]=a("a",{id:"Reactant.Ops.hlo_call-Tuple{Any, Vararg{Any}}",href:"#Reactant.Ops.hlo_call-Tuple{Any, Vararg{Any}}"},[a("span",{class:"jlbinding"},"Reactant.Ops.hlo_call")],-1)),s[4]||(s[4]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[5]||(s[5]=n(`
julia
Ops.hlo_call(mlir_code::String, args::Vararg{AnyTracedRArray}...; func_name::String="main") -> NTuple{N, AnyTracedRArray}
Given a MLIR module given as a string, calls the function identified by the func_name keyword parameter (default "main") with the provided arguments and return a tuple for each result of the call.
julia
julia> Reactant.@jit(
           Ops.hlo_call(
               """
               module {
@@ -12,22 +12,22 @@ import{_ as l,c as p,j as a,a as i,G as e,a2 as n,B as h,o as r}from"./chunks/fr
               Reactant.to_rarray(Float32[1, 2, 3]),
           )
        )
-(ConcreteRArray{Float32, 1}(Float32[2.0, 4.0, 6.0]),)
source
`,4))]),a("details",c,[a("summary",null,[s[6]||(s[6]=a("a",{id:"Reactant.Ops.randexp-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T",href:"#Reactant.Ops.randexp-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T"},[a("span",{class:"jlbinding"},"Reactant.Ops.randexp")],-1)),s[7]||(s[7]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[8]||(s[8]=n(`
julia
randexp(
+(ConcreteRArray{Float32, 1}(Float32[2.0, 4.0, 6.0]),)
source
`,4))]),a("details",c,[a("summary",null,[s[6]||(s[6]=a("a",{id:"Reactant.Ops.randexp-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T",href:"#Reactant.Ops.randexp-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T"},[a("span",{class:"jlbinding"},"Reactant.Ops.randexp")],-1)),s[7]||(s[7]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[8]||(s[8]=n(`
julia
randexp(
     ::Type{T},
     seed::TracedRArray{UInt64,1},
     shape;
     algorithm::String="DEFAULT",
     location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
-)
Generate a random array of type T with the given shape and seed from an exponential distribution with rate 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",E,[a("summary",null,[s[9]||(s[9]=a("a",{id:"Reactant.Ops.randn-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T",href:"#Reactant.Ops.randn-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T"},[a("span",{class:"jlbinding"},"Reactant.Ops.randn")],-1)),s[10]||(s[10]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[11]||(s[11]=n(`
julia
randn(
+)
Generate a random array of type T with the given shape and seed from an exponential distribution with rate 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",E,[a("summary",null,[s[9]||(s[9]=a("a",{id:"Reactant.Ops.randn-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T",href:"#Reactant.Ops.randn-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T"},[a("span",{class:"jlbinding"},"Reactant.Ops.randn")],-1)),s[10]||(s[10]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[11]||(s[11]=n(`
julia
randn(
     ::Type{T},
     seed::TracedRArray{UInt64,1},
     shape;
     algorithm::String="DEFAULT",
     location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
-)
Generate a random array of type T with the given shape and seed from a standard normal distribution of mean 0 and standard deviation 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",g,[a("summary",null,[s[12]||(s[12]=a("a",{id:"Reactant.Ops.rng_bit_generator-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T<:Integer",href:"#Reactant.Ops.rng_bit_generator-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T<:Integer"},[a("span",{class:"jlbinding"},"Reactant.Ops.rng_bit_generator")],-1)),s[13]||(s[13]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[14]||(s[14]=n(`
julia
rng_bit_generator(
+)
Generate a random array of type T with the given shape and seed from a standard normal distribution of mean 0 and standard deviation 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",g,[a("summary",null,[s[12]||(s[12]=a("a",{id:"Reactant.Ops.rng_bit_generator-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T<:Integer",href:"#Reactant.Ops.rng_bit_generator-Union{Tuple{T}, Tuple{Type{T}, Reactant.TracedRArray{UInt64, 1}, Any}} where T<:Integer"},[a("span",{class:"jlbinding"},"Reactant.Ops.rng_bit_generator")],-1)),s[13]||(s[13]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[14]||(s[14]=n(`
julia
rng_bit_generator(
     ::Type{T},
     seed::TracedRArray{UInt64,1},
     shape;
     algorithm::String="DEFAULT",
     location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
-)
Generate a random array of type T with the given shape and seed from a uniform random distribution between 0 and 1. Returns a NamedTuple with the following fields:
  • output_state: The state of the random number generator after the operation.
  • output: The generated array.
Arguments
  • T: The type of the generated array.
  • seed: The seed for the random number generator.
  • shape: The shape of the generated array.
  • algorithm: The algorithm to use for generating the random numbers. Defaults to "DEFAULT". Other options include "PHILOX" and "THREE_FRY".
source
`,6))]),a("details",y,[a("summary",null,[s[15]||(s[15]=a("a",{id:"Reactant.Ops.scatter_setindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}, Reactant.TracedRArray{T, 1}}} where {T, N}",href:"#Reactant.Ops.scatter_setindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}, Reactant.TracedRArray{T, 1}}} where {T, N}"},[a("span",{class:"jlbinding"},"Reactant.Ops.scatter_setindex")],-1)),s[16]||(s[16]=i()),e(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[17]||(s[17]=n('
julia
scatter_setindex(dest, scatter_indices, updates)
Uses MLIR.Dialects.stablehlo.scatter to set the values of dest at the indices specified by scatter_indices to the values in updates. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_update_slice instead.
source
',3))])])}const A=l(d,[["render",u]]);export{f as __pageData,A as default};
+)

Generate a random array of type T with the given shape and seed from a uniform random distribution between 0 and 1. Returns a NamedTuple with the following fields:

Arguments

source

`,6))]),a("details",y,[a("summary",null,[s[15]||(s[15]=a("a",{id:"Reactant.Ops.scatter_setindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}, Reactant.TracedRArray{T, 1}}} where {T, N}",href:"#Reactant.Ops.scatter_setindex-Union{Tuple{N}, Tuple{T}, Tuple{Reactant.TracedRArray{T, N}, Reactant.TracedRArray{Int64, 2}, Reactant.TracedRArray{T, 1}}} where {T, N}"},[a("span",{class:"jlbinding"},"Reactant.Ops.scatter_setindex")],-1)),s[16]||(s[16]=i()),t(e,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[17]||(s[17]=n('
julia
scatter_setindex(dest, scatter_indices, updates)

Uses MLIR.Dialects.stablehlo.scatter to set the values of dest at the indices specified by scatter_indices to the values in updates. If the indices are contiguous it is recommended to directly use MLIR.Dialects.stablehlo.dynamic_update_slice instead.

source

',3))])])}const A=l(d,[["render",u]]);export{f as __pageData,A as default}; diff --git a/previews/PR441/assets/api_stablehlo.md.rWlP7YUr.js b/previews/PR441/assets/api_stablehlo.md.BG0YL1kY.js similarity index 93% rename from previews/PR441/assets/api_stablehlo.md.rWlP7YUr.js rename to previews/PR441/assets/api_stablehlo.md.BG0YL1kY.js index 96cad3ae1..0e4811cb0 100644 --- a/previews/PR441/assets/api_stablehlo.md.rWlP7YUr.js +++ b/previews/PR441/assets/api_stablehlo.md.BG0YL1kY.js @@ -1,48 +1,48 @@ -import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const yt=JSON.parse('{"title":"StableHLO Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/stablehlo.md","filePath":"api/stablehlo.md","lastUpdated":null}'),c={name:"api/stablehlo.md"},d={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},x={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},I={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},_={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},w={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},H={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},q={class:"jldocstring custom-block"},P={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Q={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"},te={class:"jldocstring custom-block"},ae={class:"jldocstring custom-block"},se={class:"jldocstring custom-block"},le={class:"jldocstring custom-block"},ne={class:"jldocstring custom-block"},oe={class:"jldocstring custom-block"},ie={class:"jldocstring custom-block"},pe={class:"jldocstring custom-block"},re={class:"jldocstring custom-block"},ce={class:"jldocstring custom-block"},de={class:"jldocstring custom-block"},be={class:"jldocstring custom-block"},ue={class:"jldocstring custom-block"},he={class:"jldocstring custom-block"},me={class:"jldocstring custom-block"},ge={class:"jldocstring custom-block"},Re={class:"jldocstring custom-block"},fe={class:"jldocstring custom-block"},xe={class:"jldocstring custom-block"},Le={class:"jldocstring custom-block"},Ie={class:"jldocstring custom-block"},Me={class:"jldocstring custom-block"},ye={class:"jldocstring custom-block"},je={class:"jldocstring custom-block"},ke={class:"jldocstring custom-block"},ve={class:"jldocstring custom-block"},De={class:"jldocstring custom-block"},_e={class:"jldocstring custom-block"},Te={class:"jldocstring custom-block"},Ve={class:"jldocstring custom-block"},Oe={class:"jldocstring custom-block"},Ee={class:"jldocstring custom-block"},Se={class:"jldocstring custom-block"},Ce={class:"jldocstring custom-block"},we={class:"jldocstring custom-block"},ze={class:"jldocstring custom-block"},He={class:"jldocstring custom-block"},Ae={class:"jldocstring custom-block"},qe={class:"jldocstring custom-block"},Pe={class:"jldocstring custom-block"},Ne={class:"jldocstring custom-block"},Fe={class:"jldocstring custom-block"},Be={class:"jldocstring custom-block"},Ue={class:"jldocstring custom-block"},We={class:"jldocstring custom-block"},Ge={class:"jldocstring custom-block"},Xe={class:"jldocstring custom-block"},$e={class:"jldocstring custom-block"},Ye={class:"jldocstring custom-block"},Je={class:"jldocstring custom-block"},Ke={class:"jldocstring custom-block"},Qe={class:"jldocstring custom-block"},Ze={class:"jldocstring custom-block"},et={class:"jldocstring custom-block"},tt={class:"jldocstring custom-block"},at={class:"jldocstring custom-block"},st={class:"jldocstring custom-block"},lt={class:"jldocstring custom-block"},nt={class:"jldocstring custom-block"},ot={class:"jldocstring custom-block"},it={class:"jldocstring custom-block"},pt={class:"jldocstring custom-block"},rt={class:"jldocstring custom-block"},ct={class:"jldocstring custom-block"},dt={class:"jldocstring custom-block"},bt={class:"jldocstring custom-block"},ut={class:"jldocstring custom-block"},ht={class:"jldocstring custom-block"},mt={class:"jldocstring custom-block"};function gt(Rt,e,ft,xt,Lt,It){const s=p("Badge");return r(),i("div",null,[e[339]||(e[339]=t("h1",{id:"StableHLO-Dialect",tabindex:"-1"},[a("StableHLO Dialect "),t("a",{class:"header-anchor",href:"#StableHLO-Dialect","aria-label":'Permalink to "StableHLO Dialect {#StableHLO-Dialect}"'},"​")],-1)),e[340]||(e[340]=t("p",null,[a("Refer to the "),t("a",{href:"https://openxla.org/stablehlo",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),t("details",d,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.abs-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.abs-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.abs")],-1)),e[1]||(e[1]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=n('

abs

Performs element-wise abs operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#abs

Example

mlir
%result = stablehlo.abs %operand : tensor<3xi32>

source

',6))]),t("details",b,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.add-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.add-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.add")],-1)),e[4]||(e[4]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=n('

add

Performs element-wise addition of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#add

Example

mlir
%result = stablehlo.add %lhs, %rhs : tensor<2x2xi32>

source

',6))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.after_all-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.after_all-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.after_all")],-1)),e[7]||(e[7]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=n('

after_all

Ensures that the operations producing the inputs are executed before any operations that depend on result.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all

Example

mlir
%result = stablehlo.after_all %input0, %input1 : !stablehlo.token

source

',6))]),t("details",h,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_gather-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_gather-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_gather")],-1)),e[10]||(e[10]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=n(`

all_gather

Within each process group in the process grid, concatenates the values of the operand tensor from each process along all_gather_dim and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_gather

Example

mlir
%result:2 = "stablehlo.all_gather"(%operand0, %operand1) {
+import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const yt=JSON.parse('{"title":"StableHLO Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/stablehlo.md","filePath":"api/stablehlo.md","lastUpdated":null}'),c={name:"api/stablehlo.md"},d={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},x={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},I={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},_={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},w={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},H={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},q={class:"jldocstring custom-block"},P={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Q={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"},te={class:"jldocstring custom-block"},ae={class:"jldocstring custom-block"},se={class:"jldocstring custom-block"},le={class:"jldocstring custom-block"},ne={class:"jldocstring custom-block"},oe={class:"jldocstring custom-block"},ie={class:"jldocstring custom-block"},pe={class:"jldocstring custom-block"},re={class:"jldocstring custom-block"},ce={class:"jldocstring custom-block"},de={class:"jldocstring custom-block"},be={class:"jldocstring custom-block"},ue={class:"jldocstring custom-block"},he={class:"jldocstring custom-block"},me={class:"jldocstring custom-block"},ge={class:"jldocstring custom-block"},Re={class:"jldocstring custom-block"},fe={class:"jldocstring custom-block"},xe={class:"jldocstring custom-block"},Le={class:"jldocstring custom-block"},Ie={class:"jldocstring custom-block"},Me={class:"jldocstring custom-block"},ye={class:"jldocstring custom-block"},je={class:"jldocstring custom-block"},ke={class:"jldocstring custom-block"},ve={class:"jldocstring custom-block"},De={class:"jldocstring custom-block"},_e={class:"jldocstring custom-block"},Te={class:"jldocstring custom-block"},Ve={class:"jldocstring custom-block"},Oe={class:"jldocstring custom-block"},Ee={class:"jldocstring custom-block"},Se={class:"jldocstring custom-block"},Ce={class:"jldocstring custom-block"},we={class:"jldocstring custom-block"},ze={class:"jldocstring custom-block"},He={class:"jldocstring custom-block"},Ae={class:"jldocstring custom-block"},qe={class:"jldocstring custom-block"},Pe={class:"jldocstring custom-block"},Ne={class:"jldocstring custom-block"},Fe={class:"jldocstring custom-block"},Be={class:"jldocstring custom-block"},Ue={class:"jldocstring custom-block"},We={class:"jldocstring custom-block"},Ge={class:"jldocstring custom-block"},Xe={class:"jldocstring custom-block"},$e={class:"jldocstring custom-block"},Ye={class:"jldocstring custom-block"},Je={class:"jldocstring custom-block"},Ke={class:"jldocstring custom-block"},Qe={class:"jldocstring custom-block"},Ze={class:"jldocstring custom-block"},et={class:"jldocstring custom-block"},tt={class:"jldocstring custom-block"},at={class:"jldocstring custom-block"},st={class:"jldocstring custom-block"},lt={class:"jldocstring custom-block"},nt={class:"jldocstring custom-block"},ot={class:"jldocstring custom-block"},it={class:"jldocstring custom-block"},pt={class:"jldocstring custom-block"},rt={class:"jldocstring custom-block"},ct={class:"jldocstring custom-block"},dt={class:"jldocstring custom-block"},bt={class:"jldocstring custom-block"},ut={class:"jldocstring custom-block"},ht={class:"jldocstring custom-block"},mt={class:"jldocstring custom-block"};function gt(Rt,e,ft,xt,Lt,It){const s=p("Badge");return r(),i("div",null,[e[339]||(e[339]=t("h1",{id:"StableHLO-Dialect",tabindex:"-1"},[a("StableHLO Dialect "),t("a",{class:"header-anchor",href:"#StableHLO-Dialect","aria-label":'Permalink to "StableHLO Dialect {#StableHLO-Dialect}"'},"​")],-1)),e[340]||(e[340]=t("p",null,[a("Refer to the "),t("a",{href:"https://openxla.org/stablehlo",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),t("details",d,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.abs-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.abs-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.abs")],-1)),e[1]||(e[1]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=n('
abs
Performs element-wise abs operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#abs
Example
mlir
%result = stablehlo.abs %operand : tensor<3xi32>
source
',6))]),t("details",b,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.add-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.add-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.add")],-1)),e[4]||(e[4]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=n('
add
Performs element-wise addition of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#add
Example
mlir
%result = stablehlo.add %lhs, %rhs : tensor<2x2xi32>
source
',6))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.after_all-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.after_all-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.after_all")],-1)),e[7]||(e[7]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=n('
after_all
Ensures that the operations producing the inputs are executed before any operations that depend on result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all
Example
mlir
%result = stablehlo.after_all %input0, %input1 : !stablehlo.token
source
',6))]),t("details",h,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_gather-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_gather-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_gather")],-1)),e[10]||(e[10]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=n(`
all_gather
Within each process group in the process grid, concatenates the values of the operand tensor from each process along all_gather_dim and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_gather
Example
mlir
%result:2 = "stablehlo.all_gather"(%operand0, %operand1) {
   all_gather_dim = 1 : i64,
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<2x2xi64>, tensor<2x2xi64>) -> (tensor<2x4xi64>, tensor<2x4xi64>)
source
`,6))]),t("details",m,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_reduce-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_reduce-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_reduce")],-1)),e[13]||(e[13]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=n(`
all_reduce
Within each process group in the process grid, applies a reduction function computation to the values of the operand tensor from each process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result:2 = "stablehlo.all_reduce"(%operand0, %operand0) ({
+} : (tensor<2x2xi64>, tensor<2x2xi64>) -> (tensor<2x4xi64>, tensor<2x4xi64>)
source
`,6))]),t("details",m,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_reduce-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_reduce-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_reduce")],-1)),e[13]||(e[13]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=n(`
all_reduce
Within each process group in the process grid, applies a reduction function computation to the values of the operand tensor from each process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result:2 = "stablehlo.all_reduce"(%operand0, %operand0) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
   %0 = "stablehlo.add"(%arg0, %arg1) : (tensor<i64>, tensor<i64>) -> tensor<i64>
   "stablehlo.return"(%0) : (tensor<i64>) -> ()
 }) {
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<4xi64>, tensor<4xi64>) -> (tensor<4xi64>, tensor<4xi64>)
source
`,6))]),t("details",g,[t("summary",null,[e[15]||(e[15]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_to_all-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_to_all-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_to_all")],-1)),e[16]||(e[16]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=n(`
all_to_all
Within each process group in the process grid, splits the values of the operand tensor along split_dimension into parts, scatters the split parts between the processes, concatenates the scattered parts along concat_dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_to_all
Example
mlir
%result:2 = "stablehlo.all_to_all"(%operand1, %operand2) {
+} : (tensor<4xi64>, tensor<4xi64>) -> (tensor<4xi64>, tensor<4xi64>)
source
`,6))]),t("details",g,[t("summary",null,[e[15]||(e[15]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_to_all-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_to_all-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_to_all")],-1)),e[16]||(e[16]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=n(`
all_to_all
Within each process group in the process grid, splits the values of the operand tensor along split_dimension into parts, scatters the split parts between the processes, concatenates the scattered parts along concat_dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_to_all
Example
mlir
%result:2 = "stablehlo.all_to_all"(%operand1, %operand2) {
   split_dimension = 1 : i64,
   concat_dimension = 0 : i64,
   split_count = 2 : i64,
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
-} : (tensor<2x4xi64>, tensor<2x4xi64>) -> (tensor<4x2xi64>, tensor<4x2xi64>)
source
`,6))]),t("details",R,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.and-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.and-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.and")],-1)),e[19]||(e[19]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=n('
and
Performs element-wise AND of two tensors lhs and rhs and produces a result tensor
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#and
Example
mlir
%result = stablehlo.and %lhs, %rhs : tensor<2x2xi32>
source
',6))]),t("details",f,[t("summary",null,[e[21]||(e[21]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.atan2-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.atan2-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.atan2")],-1)),e[22]||(e[22]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=n('
atan2
Performs element-wise atan2 operation on lhs and rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#atan2
Example
mlir
%result = stablehlo.atan2 %lhs, %rhs : tensor<3xf64>
source
',6))]),t("details",x,[t("summary",null,[e[24]||(e[24]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_grad-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_grad-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_grad")],-1)),e[25]||(e[25]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=n(`
batch_norm_grad
Computes gradients of several inputs of BatchNormTrainingOp backpropagating from grad_output, and produces grad_operand, grad_scale and grad_offset tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_grad
Example
mlir
%grad_operand, %grad_scale, %grad_offset =
+} : (tensor<2x4xi64>, tensor<2x4xi64>) -> (tensor<4x2xi64>, tensor<4x2xi64>)
source
`,6))]),t("details",R,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.and-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.and-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.and")],-1)),e[19]||(e[19]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=n('
and
Performs element-wise AND of two tensors lhs and rhs and produces a result tensor
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#and
Example
mlir
%result = stablehlo.and %lhs, %rhs : tensor<2x2xi32>
source
',6))]),t("details",f,[t("summary",null,[e[21]||(e[21]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.atan2-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.atan2-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.atan2")],-1)),e[22]||(e[22]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=n('
atan2
Performs element-wise atan2 operation on lhs and rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#atan2
Example
mlir
%result = stablehlo.atan2 %lhs, %rhs : tensor<3xf64>
source
',6))]),t("details",x,[t("summary",null,[e[24]||(e[24]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_grad-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_grad-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_grad")],-1)),e[25]||(e[25]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=n(`
batch_norm_grad
Computes gradients of several inputs of BatchNormTrainingOp backpropagating from grad_output, and produces grad_operand, grad_scale and grad_offset tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_grad
Example
mlir
%grad_operand, %grad_scale, %grad_offset =
 "stablehlo.batch_norm_grad"(%operand, %scale, %mean, %variance, %grad_output) {
   epsilon = 0.0 : f32,
   feature_index = 2 : i64
 } : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>,
-     tensor<2x2x2xf64>) -> (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
`,6))]),t("details",L,[t("summary",null,[e[27]||(e[27]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_inference-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_inference-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_inference")],-1)),e[28]||(e[28]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=n(`
batch_norm_inference
Normalizes the operand tensor across all dimensions except for the feature_index dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_inference
Example
mlir
%result = "stablehlo.batch_norm_inference"(%operand, %scale, %offset, %mean, %variance) {
+     tensor<2x2x2xf64>) -> (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
`,6))]),t("details",L,[t("summary",null,[e[27]||(e[27]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_inference-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_inference-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_inference")],-1)),e[28]||(e[28]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=n(`
batch_norm_inference
Normalizes the operand tensor across all dimensions except for the feature_index dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_inference
Example
mlir
%result = "stablehlo.batch_norm_inference"(%operand, %scale, %offset, %mean, %variance) {
   epsilon = 0.0 : f32,
   feature_index = 2 : i64
-} : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>) -> tensor<2x2x2xf64>
source
`,6))]),t("details",I,[t("summary",null,[e[30]||(e[30]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_training-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_training-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_training")],-1)),e[31]||(e[31]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=n(`
batch_norm_training
Computes mean and variance across batch and spatial dimensions and normalizes the operand tensor, for each feature in the feature_index dimension and produces output, batch_mean and batch_var tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_training
Example
mlir
%output, %batch_mean, %batch_var = "stablehlo.batch_norm_training"(%operand, %scale, %offset) {
+} : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>) -> tensor<2x2x2xf64>
source
`,6))]),t("details",I,[t("summary",null,[e[30]||(e[30]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_training-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_training-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_training")],-1)),e[31]||(e[31]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=n(`
batch_norm_training
Computes mean and variance across batch and spatial dimensions and normalizes the operand tensor, for each feature in the feature_index dimension and produces output, batch_mean and batch_var tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_training
Example
mlir
%output, %batch_mean, %batch_var = "stablehlo.batch_norm_training"(%operand, %scale, %offset) {
   epsilon = 0.0 : f32,
   feature_index = 2 : i64
 } : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>) ->
-    (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
`,6))]),t("details",M,[t("summary",null,[e[33]||(e[33]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.bitcast_convert-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.bitcast_convert-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.bitcast_convert")],-1)),e[34]||(e[34]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=n('
bitcast_convert
Performs a bitcast operation on operand tensor and produces a result tensor where the bits of the entire operand tensor are reinterpreted using the type of the result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#bitcast_convert
Example
mlir
%result = stablehlo.bitcast_convert %operand : (tensor<f64>) -> tensor<4xf16>
source
',6))]),t("details",y,[t("summary",null,[e[36]||(e[36]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.broadcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.broadcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.broadcast")],-1)),e[37]||(e[37]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=n('
broadcast
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Broadcast: https://www.tensorflow.org/xla/operation_semantics#broadcast
Example
mlir
%result = stablehlo.broadcast %operand, sizes = [1, 2] : (tensor<3xi32>) -> tensor<1x2x3xi32>
source
',6))]),t("details",j,[t("summary",null,[e[39]||(e[39]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim")],-1)),e[40]||(e[40]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=n('
broadcast_in_dim
Expands the dimensions and/or rank of an input tensor by duplicating the data in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#broadcast_in_dim
Example
mlir
%result = stablehlo.broadcast_in_dim %operand, dims = [2, 1] : (tensor<1x3xi32>) -> tensor<2x3x2xi32>
source
',6))]),t("details",k,[t("summary",null,[e[42]||(e[42]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.case-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.case-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.case")],-1)),e[43]||(e[43]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[44]||(e[44]=n(`
case
Produces the output from executing exactly one function from branches depending on the value of index.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#case
Example
mlir
%result0, %result1 = "stablehlo.case"(%index) ({
+    (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
`,6))]),t("details",M,[t("summary",null,[e[33]||(e[33]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.bitcast_convert-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.bitcast_convert-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.bitcast_convert")],-1)),e[34]||(e[34]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=n('
bitcast_convert
Performs a bitcast operation on operand tensor and produces a result tensor where the bits of the entire operand tensor are reinterpreted using the type of the result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#bitcast_convert
Example
mlir
%result = stablehlo.bitcast_convert %operand : (tensor<f64>) -> tensor<4xf16>
source
',6))]),t("details",y,[t("summary",null,[e[36]||(e[36]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.broadcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.broadcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.broadcast")],-1)),e[37]||(e[37]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=n('
broadcast
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Broadcast: https://www.tensorflow.org/xla/operation_semantics#broadcast
Example
mlir
%result = stablehlo.broadcast %operand, sizes = [1, 2] : (tensor<3xi32>) -> tensor<1x2x3xi32>
source
',6))]),t("details",j,[t("summary",null,[e[39]||(e[39]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim")],-1)),e[40]||(e[40]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=n('
broadcast_in_dim
Expands the dimensions and/or rank of an input tensor by duplicating the data in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#broadcast_in_dim
Example
mlir
%result = stablehlo.broadcast_in_dim %operand, dims = [2, 1] : (tensor<1x3xi32>) -> tensor<2x3x2xi32>
source
',6))]),t("details",k,[t("summary",null,[e[42]||(e[42]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.case-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.case-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.case")],-1)),e[43]||(e[43]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[44]||(e[44]=n(`
case
Produces the output from executing exactly one function from branches depending on the value of index.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#case
Example
mlir
%result0, %result1 = "stablehlo.case"(%index) ({
   stablehlo.return %result_branch0, %result_branch0 : tensor<2xi64>, tensor<2xi64>
 }, {
   stablehlo.return %result_branch1, %result_branch1 : tensor<2xi64>, tensor<2xi64>
-}) : (tensor<i32>) -> (tensor<2xi64>, tensor<2xi64>)
source
`,6))]),t("details",v,[t("summary",null,[e[45]||(e[45]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cbrt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cbrt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cbrt")],-1)),e[46]||(e[46]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[47]||(e[47]=n('
cbrt
Performs element-wise cubic root operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cbrt
Example
mlir
%result = stablehlo.cbrt %operand : tensor<4xf64>
source
',6))]),t("details",D,[t("summary",null,[e[48]||(e[48]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.ceil-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.ceil-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.ceil")],-1)),e[49]||(e[49]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[50]||(e[50]=n('
ceil
Performs element-wise ceil of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#ceil
Example
mlir
%result = stablehlo.ceil %operand : tensor<5xf32>
source
',6))]),t("details",_,[t("summary",null,[e[51]||(e[51]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cholesky-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cholesky-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cholesky")],-1)),e[52]||(e[52]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[53]||(e[53]=n('
cholesky
Computes the Cholesky decomposition of a batch of matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cholesky
Example
mlir
%result = stablehlo.cholesky %a, lower = true : tensor<3x3xf64>
source
',6))]),t("details",T,[t("summary",null,[e[54]||(e[54]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.clamp-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.clamp-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.clamp")],-1)),e[55]||(e[55]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[56]||(e[56]=n('
clamp
Clamps every element of the operand tensor between a minimum and maximum value and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#clamp
Example
mlir
%result = stablehlo.clamp %min, %operand, %max : tensor<3xi32>
source
',6))]),t("details",V,[t("summary",null,[e[57]||(e[57]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.collective_broadcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.collective_broadcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.collective_broadcast")],-1)),e[58]||(e[58]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[59]||(e[59]=n(`
collective_broadcast
Within each process group in the process grid, send the value of the operand tensor from the source process to the target processes and produce a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_broadcast
Example
mlir
%result = "stablehlo.collective_broadcast"(%operand) {
+}) : (tensor<i32>) -> (tensor<2xi64>, tensor<2xi64>)
source
`,6))]),t("details",v,[t("summary",null,[e[45]||(e[45]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cbrt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cbrt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cbrt")],-1)),e[46]||(e[46]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[47]||(e[47]=n('
cbrt
Performs element-wise cubic root operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cbrt
Example
mlir
%result = stablehlo.cbrt %operand : tensor<4xf64>
source
',6))]),t("details",D,[t("summary",null,[e[48]||(e[48]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.ceil-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.ceil-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.ceil")],-1)),e[49]||(e[49]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[50]||(e[50]=n('
ceil
Performs element-wise ceil of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#ceil
Example
mlir
%result = stablehlo.ceil %operand : tensor<5xf32>
source
',6))]),t("details",_,[t("summary",null,[e[51]||(e[51]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cholesky-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cholesky-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cholesky")],-1)),e[52]||(e[52]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[53]||(e[53]=n('
cholesky
Computes the Cholesky decomposition of a batch of matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cholesky
Example
mlir
%result = stablehlo.cholesky %a, lower = true : tensor<3x3xf64>
source
',6))]),t("details",T,[t("summary",null,[e[54]||(e[54]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.clamp-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.clamp-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.clamp")],-1)),e[55]||(e[55]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[56]||(e[56]=n('
clamp
Clamps every element of the operand tensor between a minimum and maximum value and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#clamp
Example
mlir
%result = stablehlo.clamp %min, %operand, %max : tensor<3xi32>
source
',6))]),t("details",V,[t("summary",null,[e[57]||(e[57]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.collective_broadcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.collective_broadcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.collective_broadcast")],-1)),e[58]||(e[58]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[59]||(e[59]=n(`
collective_broadcast
Within each process group in the process grid, send the value of the operand tensor from the source process to the target processes and produce a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_broadcast
Example
mlir
%result = "stablehlo.collective_broadcast"(%operand) {
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<1x2xi64>) -> tensor<1x2xi64>
source
`,6))]),t("details",O,[t("summary",null,[e[60]||(e[60]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.collective_permute-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.collective_permute-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.collective_permute")],-1)),e[61]||(e[61]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[62]||(e[62]=n(`
collective_permute
Within each process group in the process grid, sends the value of the operand tensor from the source process to the target process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_permute
Example
mlir
%result = "stablehlo.collective_permute"(%operand) {
+} : (tensor<1x2xi64>) -> tensor<1x2xi64>
source
`,6))]),t("details",O,[t("summary",null,[e[60]||(e[60]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.collective_permute-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.collective_permute-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.collective_permute")],-1)),e[61]||(e[61]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[62]||(e[62]=n(`
collective_permute
Within each process group in the process grid, sends the value of the operand tensor from the source process to the target process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_permute
Example
mlir
%result = "stablehlo.collective_permute"(%operand) {
   source_target_pairs = dense<[[0, 1], [1, 2]]> : tensor<2x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<2x2xi64>) -> tensor<2x2xi64>
source
`,6))]),t("details",E,[t("summary",null,[e[63]||(e[63]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.compare-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.compare-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.compare")],-1)),e[64]||(e[64]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[65]||(e[65]=n('
compare
Performs element-wise comparison of lhs and rhs tensors according to comparison_direction and compare_type, and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#compare
Example
mlir
%result = stablehlo.compare LT, %lhs, %rhs, FLOAT : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1>
source
',6))]),t("details",S,[t("summary",null,[e[66]||(e[66]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.complex-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.complex-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.complex")],-1)),e[67]||(e[67]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[68]||(e[68]=n('
complex
Performs element-wise conversion to a complex value from a pair of real and imaginary values, lhs and rhs, and produces a result tensor. See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#complex
Example
mlir
%result = stablehlo.complex %lhs, %rhs : tensor<2xcomplex<f64>>
source
',5))]),t("details",C,[t("summary",null,[e[69]||(e[69]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.composite-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.composite-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.composite")],-1)),e[70]||(e[70]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[71]||(e[71]=n(`
composite
Encapsulates an operation made up (composed) of other StableHLO operations, taking inputs and composite_attributes and producing results. The semantics of the op are implemented by the decomposition attribute. The composite op can be replaced with its decomposition without changing program semantics. In cases where inlining the decomposition does not provide the same op semantics, prefer using custom_call.
The version field (defaults to 0) is used to denote when a composite's semantics change.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#composite
Example
mlir
%results = stablehlo.composite "my.op" %input0, %input1 {
+} : (tensor<2x2xi64>) -> tensor<2x2xi64>
source
`,6))]),t("details",E,[t("summary",null,[e[63]||(e[63]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.compare-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.compare-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.compare")],-1)),e[64]||(e[64]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[65]||(e[65]=n('
compare
Performs element-wise comparison of lhs and rhs tensors according to comparison_direction and compare_type, and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#compare
Example
mlir
%result = stablehlo.compare LT, %lhs, %rhs, FLOAT : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1>
source
',6))]),t("details",S,[t("summary",null,[e[66]||(e[66]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.complex-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.complex-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.complex")],-1)),e[67]||(e[67]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[68]||(e[68]=n('
complex
Performs element-wise conversion to a complex value from a pair of real and imaginary values, lhs and rhs, and produces a result tensor. See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#complex
Example
mlir
%result = stablehlo.complex %lhs, %rhs : tensor<2xcomplex<f64>>
source
',5))]),t("details",C,[t("summary",null,[e[69]||(e[69]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.composite-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.composite-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.composite")],-1)),e[70]||(e[70]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[71]||(e[71]=n(`
composite
Encapsulates an operation made up (composed) of other StableHLO operations, taking inputs and composite_attributes and producing results. The semantics of the op are implemented by the decomposition attribute. The composite op can be replaced with its decomposition without changing program semantics. In cases where inlining the decomposition does not provide the same op semantics, prefer using custom_call.
The version field (defaults to 0) is used to denote when a composite's semantics change.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#composite
Example
mlir
%results = stablehlo.composite "my.op" %input0, %input1 {
   composite_attributes = {
     my_attribute = "my_value"
   },
   decomposition = @my_op,
   version = 1 : i32
-} : (tensor<f32>, tensor<f32>) -> tensor<f32>
source
`,7))]),t("details",w,[t("summary",null,[e[72]||(e[72]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.concatenate-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.concatenate-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.concatenate")],-1)),e[73]||(e[73]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[74]||(e[74]=n('
concatenate
Concatenates a variadic number of tensors in inputs along dimension dimension in the same order as the given arguments and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#concatenate
Example
mlir
%result = stablehlo.concatenate %input0, %input1, dim = 0 : (tensor<3x2xi64>, tensor<1x2xi64>) -> tensor<4x2xi64>
source
',6))]),t("details",z,[t("summary",null,[e[75]||(e[75]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.constant-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.constant")],-1)),e[76]||(e[76]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[77]||(e[77]=n('
constant
Produces an output tensor from a constant value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#constant
Example
mlir
%output = stablehlo.constant dense<[[0.0, 1.0], [2.0, 3.0]]> : tensor<2x2xf32>
source
',6))]),t("details",H,[t("summary",null,[e[78]||(e[78]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.convert-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.convert-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.convert")],-1)),e[79]||(e[79]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[80]||(e[80]=n('
convert
Performs an element-wise conversion from one element type to another on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convert
Example
mlir
%result = stablehlo.convert %operand : (tensor<3xi64>) -> tensor<3xcomplex<f64>>
source
',6))]),t("details",A,[t("summary",null,[e[81]||(e[81]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.convolution-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.convolution-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.convolution")],-1)),e[82]||(e[82]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[83]||(e[83]=n(`
convolution
Computes dot products between windows of lhs and slices of rhs and produces result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convolution
Example
mlir
%result = stablehlo.convolution(%lhs, %rhs)
+} : (tensor<f32>, tensor<f32>) -> tensor<f32>
source
`,7))]),t("details",w,[t("summary",null,[e[72]||(e[72]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.concatenate-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.concatenate-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.concatenate")],-1)),e[73]||(e[73]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[74]||(e[74]=n('
concatenate
Concatenates a variadic number of tensors in inputs along dimension dimension in the same order as the given arguments and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#concatenate
Example
mlir
%result = stablehlo.concatenate %input0, %input1, dim = 0 : (tensor<3x2xi64>, tensor<1x2xi64>) -> tensor<4x2xi64>
source
',6))]),t("details",z,[t("summary",null,[e[75]||(e[75]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.constant-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.constant")],-1)),e[76]||(e[76]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[77]||(e[77]=n('
constant
Produces an output tensor from a constant value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#constant
Example
mlir
%output = stablehlo.constant dense<[[0.0, 1.0], [2.0, 3.0]]> : tensor<2x2xf32>
source
',6))]),t("details",H,[t("summary",null,[e[78]||(e[78]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.convert-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.convert-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.convert")],-1)),e[79]||(e[79]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[80]||(e[80]=n('
convert
Performs an element-wise conversion from one element type to another on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convert
Example
mlir
%result = stablehlo.convert %operand : (tensor<3xi64>) -> tensor<3xcomplex<f64>>
source
',6))]),t("details",A,[t("summary",null,[e[81]||(e[81]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.convolution-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.convolution-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.convolution")],-1)),e[82]||(e[82]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[83]||(e[83]=n(`
convolution
Computes dot products between windows of lhs and slices of rhs and produces result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convolution
Example
mlir
%result = stablehlo.convolution(%lhs, %rhs)
   dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
   window = {
     stride = [4, 4],
@@ -55,24 +55,24 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
     batch_group_count = 1 : i64,
     precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
   } :
-(tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>) -> tensor<1x2x2x1xi64>
source
`,6))]),t("details",q,[t("summary",null,[e[84]||(e[84]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cosine-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cosine-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cosine")],-1)),e[85]||(e[85]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[86]||(e[86]=n('
cosine
Performs element-wise cosine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cosine
Example
mlir
%result = stablehlo.cosine %operand : tensor<2xf32>
source
',6))]),t("details",P,[t("summary",null,[e[87]||(e[87]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.count_leading_zeros-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.count_leading_zeros-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.count_leading_zeros")],-1)),e[88]||(e[88]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[89]||(e[89]=n('
count_leading_zeros
Performs element-wise count of the number of leading zero bits in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#count_leading_zeros
Example
mlir
%result = stablehlo.count_leading_zeros %operand : tensor<2x2xi64>
source
',6))]),t("details",N,[t("summary",null,[e[90]||(e[90]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.create_token-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.create_token-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.create_token")],-1)),e[91]||(e[91]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=n('
create_token
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AfterAllOp with 0 inputs: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all
Example
mlir
%output = stablehlo.create_token : !stablehlo.token
source
',6))]),t("details",F,[t("summary",null,[e[93]||(e[93]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cross_replica_sum-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cross_replica_sum-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cross_replica_sum")],-1)),e[94]||(e[94]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[95]||(e[95]=n(`
cross_replica_sum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AllReduceOp with channel_id = 0, use_global_device_ids = false and computation implementing addition: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result = "stablehlo.cross-replica-sum"(%operand) {
+(tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>) -> tensor<1x2x2x1xi64>
source
`,6))]),t("details",q,[t("summary",null,[e[84]||(e[84]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cosine-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cosine-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cosine")],-1)),e[85]||(e[85]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[86]||(e[86]=n('
cosine
Performs element-wise cosine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cosine
Example
mlir
%result = stablehlo.cosine %operand : tensor<2xf32>
source
',6))]),t("details",P,[t("summary",null,[e[87]||(e[87]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.count_leading_zeros-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.count_leading_zeros-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.count_leading_zeros")],-1)),e[88]||(e[88]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[89]||(e[89]=n('
count_leading_zeros
Performs element-wise count of the number of leading zero bits in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#count_leading_zeros
Example
mlir
%result = stablehlo.count_leading_zeros %operand : tensor<2x2xi64>
source
',6))]),t("details",N,[t("summary",null,[e[90]||(e[90]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.create_token-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.create_token-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.create_token")],-1)),e[91]||(e[91]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=n('
create_token
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AfterAllOp with 0 inputs: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all
Example
mlir
%output = stablehlo.create_token : !stablehlo.token
source
',6))]),t("details",F,[t("summary",null,[e[93]||(e[93]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cross_replica_sum-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cross_replica_sum-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cross_replica_sum")],-1)),e[94]||(e[94]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[95]||(e[95]=n(`
cross_replica_sum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AllReduceOp with channel_id = 0, use_global_device_ids = false and computation implementing addition: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result = "stablehlo.cross-replica-sum"(%operand) {
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
-} : (tensor<4xf32>) -> tensor<4xf32>
source
`,6))]),t("details",B,[t("summary",null,[e[96]||(e[96]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.custom_call-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.custom_call-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.custom_call")],-1)),e[97]||(e[97]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[98]||(e[98]=n(`
custom_call
Encapsulates an implementation-defined operation call_target_name that takes inputs and called_computations and produces results.
Depending on the API version there are two ways to pass extra bits of static information to the external function:
  1. Use API_VERSION_TYPED_FFI which allows passing a dictionary attribute.
  2. Use a previous API version with a StringAttr to encode backend config.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#custom_call
Example
mlir
%results = stablehlo.custom_call @foo(%input0) {
+} : (tensor<4xf32>) -> tensor<4xf32>
source
`,6))]),t("details",B,[t("summary",null,[e[96]||(e[96]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.custom_call-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.custom_call-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.custom_call")],-1)),e[97]||(e[97]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[98]||(e[98]=n(`
custom_call
Encapsulates an implementation-defined operation call_target_name that takes inputs and called_computations and produces results.
Depending on the API version there are two ways to pass extra bits of static information to the external function:
  1. Use API_VERSION_TYPED_FFI which allows passing a dictionary attribute.
  2. Use a previous API version with a StringAttr to encode backend config.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#custom_call
Example
mlir
%results = stablehlo.custom_call @foo(%input0) {
   backend_config = {bar = 42 : i32},
   api_version = 4 : i32,
   called_computations = [@foo]
-} : (tensor<f64>) -> tensor<f64>
source
`,8))]),t("details",U,[t("summary",null,[e[99]||(e[99]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.divide-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.divide-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.divide")],-1)),e[100]||(e[100]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[101]||(e[101]=n('
divide
Performs element-wise division of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#divide
Example
mlir
%result = stablehlo.divide %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",W,[t("summary",null,[e[102]||(e[102]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dot-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dot-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dot")],-1)),e[103]||(e[103]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[104]||(e[104]=n('
dot
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Dot: https://www.tensorflow.org/xla/operation_semantics#dot
Example
mlir
%0 = stablehlo.dot %arg0, %arg1 : (tensor<1x2xi32>, tensor<2x1xi32>) -> tensor<1x1xi32>
source
',6))]),t("details",G,[t("summary",null,[e[105]||(e[105]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dot_general-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dot_general-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dot_general")],-1)),e[106]||(e[106]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[107]||(e[107]=n(`
dot_general
Computes dot products between slices of lhs and slices of rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dot_general
Example
mlir
%result = stablehlo.dot_general %lhs, %rhs,
+} : (tensor<f64>) -> tensor<f64>
source
`,8))]),t("details",U,[t("summary",null,[e[99]||(e[99]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.divide-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.divide-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.divide")],-1)),e[100]||(e[100]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[101]||(e[101]=n('
divide
Performs element-wise division of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#divide
Example
mlir
%result = stablehlo.divide %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",W,[t("summary",null,[e[102]||(e[102]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dot-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dot-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dot")],-1)),e[103]||(e[103]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[104]||(e[104]=n('
dot
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Dot: https://www.tensorflow.org/xla/operation_semantics#dot
Example
mlir
%0 = stablehlo.dot %arg0, %arg1 : (tensor<1x2xi32>, tensor<2x1xi32>) -> tensor<1x1xi32>
source
',6))]),t("details",G,[t("summary",null,[e[105]||(e[105]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dot_general-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dot_general-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dot_general")],-1)),e[106]||(e[106]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[107]||(e[107]=n(`
dot_general
Computes dot products between slices of lhs and slices of rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dot_general
Example
mlir
%result = stablehlo.dot_general %lhs, %rhs,
   batching_dims = [0] x [0],
   contracting_dims = [2] x [1],
   precision = [DEFAULT, DEFAULT],
   algorithm = <lhs_precision_type = tf32, rhs_precision_type = tf32, accumulation_type = f32, lhs_component_count = 1, rhs_component_count = 1, num_primitive_operations = 1, allow_imprecise_accumulation = false>
-  : (tensor<2x2x2xi64>, tensor<2x2x2xi64>) -> tensor<2x2x2xi64>
source
`,6))]),t("details",X,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim")],-1)),e[109]||(e[109]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=n(`
dynamic_broadcast_in_dim
This operation is functionally identical to broadcast_in_dim op, but the result shape is specified dynamically via output_dimensions.
It also accepts optional attributes to express static knowledge about the expanding behavior of dimensions. If not specified, all dimensions are assumed to be possibly expanding. The sets of dimensions that are known to be expanding and the set of dimensions that are known to be non-expanding must be disjoint and they must be a subset of the operand's dimensions.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_broadcast_in_dim
Example
mlir
%operand = stablehlo.constant dense<[[1, 2, 3]]> : tensor<1x3xi64>
+  : (tensor<2x2x2xi64>, tensor<2x2x2xi64>) -> tensor<2x2x2xi64>
source
`,6))]),t("details",X,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim")],-1)),e[109]||(e[109]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=n(`
dynamic_broadcast_in_dim
This operation is functionally identical to broadcast_in_dim op, but the result shape is specified dynamically via output_dimensions.
It also accepts optional attributes to express static knowledge about the expanding behavior of dimensions. If not specified, all dimensions are assumed to be possibly expanding. The sets of dimensions that are known to be expanding and the set of dimensions that are known to be non-expanding must be disjoint and they must be a subset of the operand's dimensions.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_broadcast_in_dim
Example
mlir
%operand = stablehlo.constant dense<[[1, 2, 3]]> : tensor<1x3xi64>
 %output_dimensions = stablehlo.constant dense<[2, 3, 2]> : tensor<3xi64>
 %result = "stablehlo.dynamic_broadcast_in_dim"(%operand, %output_dimensions) {
   broadcast_dimensions = array<i64: 2, 1>,
   known_expanding_dimensions = array<i64: 0>,
   known_nonexpanding_dimensions = array<i64: 1>
-} : (tensor<1x3xi64>, tensor<3xi64>) -> tensor<2x3x2xi64>
source
`,7))]),t("details",$,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_conv-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_conv-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_conv")],-1)),e[112]||(e[112]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=n(`
dynamic_conv
This operation is functionally identical to convolution op, but the padding is specified dynamically via padding.
Example
mlir
%padding = stablehlo.constant dense<2> : tensor<2x2xi64>
+} : (tensor<1x3xi64>, tensor<3xi64>) -> tensor<2x3x2xi64>
source
`,7))]),t("details",$,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_conv-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_conv-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_conv")],-1)),e[112]||(e[112]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=n(`
dynamic_conv
This operation is functionally identical to convolution op, but the padding is specified dynamically via padding.
Example
mlir
%padding = stablehlo.constant dense<2> : tensor<2x2xi64>
 %result = "stablehlo.dynamic_conv"(%lhs, %rhs, %padding) {
   window_strides = array<i64: 4, 4>,
   lhs_dilation = array<i64: 2, 2>,
@@ -82,7 +82,7 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
   batch_group_count = 1 : i64,
   feature_group_count = 1 : i64,
   precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
-} : (tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>, tensor<2x2xi64>) -> tensor<1x2x2x1xi64>
source
`,5))]),t("details",Y,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_gather")],-1)),e[115]||(e[115]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=n(`
dynamic_gather
This operation is functionally identical to gather op, with the slice_sizes specified dynamically as an operand.
Example
mlir
%slice_sizes = stablehlo.constant dense<[1, 2, 2]> : tensor<3xi64>
+} : (tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>, tensor<2x2xi64>) -> tensor<1x2x2x1xi64>
source
`,5))]),t("details",Y,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_gather")],-1)),e[115]||(e[115]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=n(`
dynamic_gather
This operation is functionally identical to gather op, with the slice_sizes specified dynamically as an operand.
Example
mlir
%slice_sizes = stablehlo.constant dense<[1, 2, 2]> : tensor<3xi64>
 %result = "stablehlo.dynamic_gather"(%operand, %start_indices, %slice_sizes) {
   dimension_numbers = #stablehlo.gather<
     offset_dims = [2, 3],
@@ -90,18 +90,18 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
     start_index_map = [0, 2],
     index_vector_dim = 2>,
   indices_are_sorted = false
-} : (tensor<3x4x2xi64>, tensor<2x3x2xi64>, tensor<3xi64>) -> tensor<2x3x2x2xi64>
source
`,5))]),t("details",J,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_iota-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_iota-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_iota")],-1)),e[118]||(e[118]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[119]||(e[119]=n(`
dynamic_iota
This operation is functionally identical to iota op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_iota
Example
mlir
%output_shape = stablehlo.constant dense<[4, 5]> : tensor<2xi64>
-%0 = stablehlo.dynamic_iota %output_shape, dim = 0 : (tensor<2xi64>) -> tensor<4x5xi64>
source
`,6))]),t("details",K,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_pad-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_pad-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_pad")],-1)),e[121]||(e[121]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=n(`
dynamic_pad
This operation is functionally identical to pad https://github.com/openxla/stablehlo/pull/2306#discussion_r1595669709 op, but with edge_padding_low,edge_padding_highandinterior_padding specified dynamically as values.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_pad
Example
mlir
%edge_padding_low = stablehlo.constant dense<[0, 1]> : tensor<2xi32>
+} : (tensor<3x4x2xi64>, tensor<2x3x2xi64>, tensor<3xi64>) -> tensor<2x3x2x2xi64>
source
`,5))]),t("details",J,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_iota-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_iota-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_iota")],-1)),e[118]||(e[118]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[119]||(e[119]=n(`
dynamic_iota
This operation is functionally identical to iota op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_iota
Example
mlir
%output_shape = stablehlo.constant dense<[4, 5]> : tensor<2xi64>
+%0 = stablehlo.dynamic_iota %output_shape, dim = 0 : (tensor<2xi64>) -> tensor<4x5xi64>
source
`,6))]),t("details",K,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_pad-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_pad-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_pad")],-1)),e[121]||(e[121]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=n(`
dynamic_pad
This operation is functionally identical to pad https://github.com/openxla/stablehlo/pull/2306#discussion_r1595669709 op, but with edge_padding_low,edge_padding_highandinterior_padding specified dynamically as values.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_pad
Example
mlir
%edge_padding_low = stablehlo.constant dense<[0, 1]> : tensor<2xi32>
 %edge_padding_high = stablehlo.constant dense<[2, 1]> : tensor<2xi32>
 %interior_padding = stablehlo.constant dense<[1, 2]> : tensor<2xi32>
 %result = stablehlo.dynamic_pad %operand, %padding_value,
             %edge_padding_low, %edge_padding_high, %interior_padding
-            : (tensor<2x3xi64>, tensor<i64>, tensor<2xi64>, tensor<2xi64>, tensor<2xi64>) -> tensor<5x9xi64>
source
`,6))]),t("details",Q,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_reshape-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_reshape-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_reshape")],-1)),e[124]||(e[124]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=n(`
dynamic_reshape
This operation is functionally identical to reshape op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_reshape
Example
mlir
%output_shape = stablehlo.constant dense<[3, 2]> : tensor<2xi64>
-%result = stablehlo.dynamic_reshape %operand, %output_shape : (tensor<2x3xi64>, tensor<2xi64>) -> tensor<3x2xi64>
source
`,6))]),t("details",Z,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_slice-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_slice-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_slice")],-1)),e[127]||(e[127]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=n(`
dynamic_slice
Extracts a slice from the operand using dynamically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_slice
Example
mlir
%result = stablehlo.dynamic_slice %operand, %start_indices0, %start_indices1, sizes = [2, 2]
-  : (tensor<4x4xi32>, tensor<i64>, tensor<i64>) -> tensor<2x2xi32>
source
`,6))]),t("details",ee,[t("summary",null,[e[129]||(e[129]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice")],-1)),e[130]||(e[130]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[131]||(e[131]=n(`
dynamic_update_slice
Produces a result tensor which is equal to the operand tensor except that the slice starting at start_indices is updated with the values in update.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_update_slice
Example
mlir
%result = stablehlo.dynamic_update_slice %operand, %update, %start_indices0, %start_indices1
-  : (tensor<4x4xi32>, tensor<2x2xi32>, tensor<i64>, tensor<i64>) -> tensor<4x4xi32>
source
`,6))]),t("details",te,[t("summary",null,[e[132]||(e[132]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.einsum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.einsum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.einsum")],-1)),e[133]||(e[133]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[134]||(e[134]=n(`
einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.einsum"(%lhs, %rhs) {
+            : (tensor<2x3xi64>, tensor<i64>, tensor<2xi64>, tensor<2xi64>, tensor<2xi64>) -> tensor<5x9xi64>
source
`,6))]),t("details",Q,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_reshape-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_reshape-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_reshape")],-1)),e[124]||(e[124]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=n(`
dynamic_reshape
This operation is functionally identical to reshape op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_reshape
Example
mlir
%output_shape = stablehlo.constant dense<[3, 2]> : tensor<2xi64>
+%result = stablehlo.dynamic_reshape %operand, %output_shape : (tensor<2x3xi64>, tensor<2xi64>) -> tensor<3x2xi64>
source
`,6))]),t("details",Z,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_slice-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_slice-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_slice")],-1)),e[127]||(e[127]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=n(`
dynamic_slice
Extracts a slice from the operand using dynamically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_slice
Example
mlir
%result = stablehlo.dynamic_slice %operand, %start_indices0, %start_indices1, sizes = [2, 2]
+  : (tensor<4x4xi32>, tensor<i64>, tensor<i64>) -> tensor<2x2xi32>
source
`,6))]),t("details",ee,[t("summary",null,[e[129]||(e[129]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice")],-1)),e[130]||(e[130]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[131]||(e[131]=n(`
dynamic_update_slice
Produces a result tensor which is equal to the operand tensor except that the slice starting at start_indices is updated with the values in update.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_update_slice
Example
mlir
%result = stablehlo.dynamic_update_slice %operand, %update, %start_indices0, %start_indices1
+  : (tensor<4x4xi32>, tensor<2x2xi32>, tensor<i64>, tensor<i64>) -> tensor<4x4xi32>
source
`,6))]),t("details",te,[t("summary",null,[e[132]||(e[132]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.einsum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.einsum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.einsum")],-1)),e[133]||(e[133]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[134]||(e[134]=n(`
einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.einsum"(%lhs, %rhs) {
   einsum_config = "ab,bc->ac"
-} : (tensor<4x16xf32>, tensor<16x4xf32>) -> tensor<4x4xf32>
source
`,6))]),t("details",ae,[t("summary",null,[e[135]||(e[135]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.exponential-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.exponential-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.exponential")],-1)),e[136]||(e[136]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[137]||(e[137]=n('
exponential
Performs element-wise exponential operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential
Example
mlir
%result = stablehlo.exponential %operand : tensor<2x2xf64>
source
',6))]),t("details",se,[t("summary",null,[e[138]||(e[138]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.exponential_minus_one-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.exponential_minus_one-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.exponential_minus_one")],-1)),e[139]||(e[139]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[140]||(e[140]=n('
exponential_minus_one
Performs element-wise exponential minus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential_minus_one
Example
mlir
%result = stablehlo.exponential_minus_one %operand : tensor<2xf64>
source
',6))]),t("details",le,[t("summary",null,[e[141]||(e[141]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.fft-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.fft-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.fft")],-1)),e[142]||(e[142]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[143]||(e[143]=n('
fft
Performs the forward and inverse Fourier transforms for real and complex inputs/outputs.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#fft
Example
mlir
%result = stablehlo.fft %operand, type = FFT, length = [4] : (tensor<4xcomplex<f32>>) -> tensor<4xcomplex<f32>>
source
',6))]),t("details",ne,[t("summary",null,[e[144]||(e[144]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.floor-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.floor-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.floor")],-1)),e[145]||(e[145]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[146]||(e[146]=n('
floor
Performs element-wise floor of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#floor
Example
mlir
%result = stablehlo.floor %operand : tensor<2xf32>
source
',6))]),t("details",oe,[t("summary",null,[e[147]||(e[147]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.gather")],-1)),e[148]||(e[148]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[149]||(e[149]=n(`
gather
Gathers slices from operand tensor from offsets specified in start_indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather
Example
mlir
%result = "stablehlo.gather"(%operand, %start_indices) {
+} : (tensor<4x16xf32>, tensor<16x4xf32>) -> tensor<4x4xf32>
source
`,6))]),t("details",ae,[t("summary",null,[e[135]||(e[135]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.exponential-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.exponential-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.exponential")],-1)),e[136]||(e[136]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[137]||(e[137]=n('
exponential
Performs element-wise exponential operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential
Example
mlir
%result = stablehlo.exponential %operand : tensor<2x2xf64>
source
',6))]),t("details",se,[t("summary",null,[e[138]||(e[138]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.exponential_minus_one-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.exponential_minus_one-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.exponential_minus_one")],-1)),e[139]||(e[139]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[140]||(e[140]=n('
exponential_minus_one
Performs element-wise exponential minus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential_minus_one
Example
mlir
%result = stablehlo.exponential_minus_one %operand : tensor<2xf64>
source
',6))]),t("details",le,[t("summary",null,[e[141]||(e[141]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.fft-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.fft-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.fft")],-1)),e[142]||(e[142]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[143]||(e[143]=n('
fft
Performs the forward and inverse Fourier transforms for real and complex inputs/outputs.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#fft
Example
mlir
%result = stablehlo.fft %operand, type = FFT, length = [4] : (tensor<4xcomplex<f32>>) -> tensor<4xcomplex<f32>>
source
',6))]),t("details",ne,[t("summary",null,[e[144]||(e[144]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.floor-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.floor-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.floor")],-1)),e[145]||(e[145]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[146]||(e[146]=n('
floor
Performs element-wise floor of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#floor
Example
mlir
%result = stablehlo.floor %operand : tensor<2xf32>
source
',6))]),t("details",oe,[t("summary",null,[e[147]||(e[147]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.gather")],-1)),e[148]||(e[148]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[149]||(e[149]=n(`
gather
Gathers slices from operand tensor from offsets specified in start_indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather
Example
mlir
%result = "stablehlo.gather"(%operand, %start_indices) {
   dimension_numbers = #stablehlo.gather<
     offset_dims = [3, 4],
     collapsed_slice_dims = [1],
@@ -111,27 +111,27 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
     index_vector_dim = 3>,
   slice_sizes = array<i64: 1, 1, 2, 2>,
   indices_are_sorted = false
-} : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>) -> tensor<2x2x3x2x2xi64>
source
`,6))]),t("details",ie,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.get_dimension_size-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.get_dimension_size-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.get_dimension_size")],-1)),e[151]||(e[151]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=n('
get_dimension_size
Produces the size of the given dimension of the operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_dimension_size
Example
mlir
%result = stablehlo.get_dimension_size %operand, dim = 1 : (tensor<2x3xi64>) -> tensor<i32>
source
',6))]),t("details",pe,[t("summary",null,[e[153]||(e[153]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.get_tuple_element-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.get_tuple_element-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.get_tuple_element")],-1)),e[154]||(e[154]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[155]||(e[155]=n('
get_tuple_element
Extracts element at index position of the operand tuple and produces a result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_tuple_element
Example
mlir
%result = stablehlo.get_tuple_element %operand[0] : (tuple<tensor<2xf64>, tuple<tensor<i64>>>) -> tensor<2xf64>
source
',6))]),t("details",re,[t("summary",null,[e[156]||(e[156]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.if_-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.if_-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.if_")],-1)),e[157]||(e[157]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[158]||(e[158]=n('
if_
Produces the output from executing exactly one branch from true_branch or false_branch depending on the value of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#if
Example
%result = "stablehlo.if"(%pred) ({ "stablehlo.return"(%result_true_branch) : (tensor<i32>) -> () }, { "stablehlo.return"(%result_false_branch) : (tensor<i32>) -> () }) : (tensor<i1>) -> tensor<i32>
source
',6))]),t("details",ce,[t("summary",null,[e[159]||(e[159]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.imag-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.imag-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.imag")],-1)),e[160]||(e[160]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[161]||(e[161]=n('
imag
Extracts the imaginary part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#imag
Example
mlir
%result = stablehlo.imag %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
',6))]),t("details",de,[t("summary",null,[e[162]||(e[162]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.infeed-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.infeed-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.infeed")],-1)),e[163]||(e[163]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[164]||(e[164]=n(`
infeed
Reads data from the infeed and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#infeed
Example
mlir
%results0:2 = "stablehlo.infeed"(%token) :
-    (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
`,6))]),t("details",be,[t("summary",null,[e[165]||(e[165]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.iota-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.iota-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.iota")],-1)),e[166]||(e[166]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[167]||(e[167]=n('
iota
Fills an output tensor with values in increasing order starting from zero along the iota_dimension dimension.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#iota
Example
mlir
%output = stablehlo.iota dim = 0 : tensor<4x5xi32>
source
',6))]),t("details",ue,[t("summary",null,[e[168]||(e[168]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.is_finite-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.is_finite-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.is_finite")],-1)),e[169]||(e[169]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[170]||(e[170]=n('
is_finite
Performs element-wise check whether the value in x is finite (i.e. is neither +Inf, -Inf, nor NaN) and produces a y tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#is_finite
Example
mlir
%y = stablehlo.is_finite %x : (tensor<7xf64>) -> tensor<7xi1>
source
',6))]),t("details",he,[t("summary",null,[e[171]||(e[171]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.log-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.log-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.log")],-1)),e[172]||(e[172]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[173]||(e[173]=n('
log
Performs element-wise logarithm operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log
Example
mlir
%result = stablehlo.log %operand : tensor<2x2xf64>
source
',6))]),t("details",me,[t("summary",null,[e[174]||(e[174]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.log_plus_one-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.log_plus_one-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.log_plus_one")],-1)),e[175]||(e[175]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[176]||(e[176]=n('
log_plus_one
Performs element-wise logarithm plus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log_plus_one
Example
mlir
%result = stablehlo.log_plus_one %operand : tensor<5xf64>
source
',6))]),t("details",ge,[t("summary",null,[e[177]||(e[177]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.logistic-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.logistic-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.logistic")],-1)),e[178]||(e[178]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[179]||(e[179]=n('
logistic
Performs element-wise logistic operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#logistic
Example
mlir
%result = stablehlo.logistic %operand : tensor<2x2xf64>
source
',6))]),t("details",Re,[t("summary",null,[e[180]||(e[180]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.map-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.map-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.map")],-1)),e[181]||(e[181]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[182]||(e[182]=n(`
map
Applies a map function computation to inputs along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#map
Example
mlir
%result = "stablehlo.map"(%input0, %input1) ({
+} : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>) -> tensor<2x2x3x2x2xi64>
source
`,6))]),t("details",ie,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.get_dimension_size-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.get_dimension_size-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.get_dimension_size")],-1)),e[151]||(e[151]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=n('
get_dimension_size
Produces the size of the given dimension of the operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_dimension_size
Example
mlir
%result = stablehlo.get_dimension_size %operand, dim = 1 : (tensor<2x3xi64>) -> tensor<i32>
source
',6))]),t("details",pe,[t("summary",null,[e[153]||(e[153]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.get_tuple_element-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.get_tuple_element-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.get_tuple_element")],-1)),e[154]||(e[154]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[155]||(e[155]=n('
get_tuple_element
Extracts element at index position of the operand tuple and produces a result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_tuple_element
Example
mlir
%result = stablehlo.get_tuple_element %operand[0] : (tuple<tensor<2xf64>, tuple<tensor<i64>>>) -> tensor<2xf64>
source
',6))]),t("details",re,[t("summary",null,[e[156]||(e[156]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.if_-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.if_-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.if_")],-1)),e[157]||(e[157]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[158]||(e[158]=n('
if_
Produces the output from executing exactly one branch from true_branch or false_branch depending on the value of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#if
Example
%result = "stablehlo.if"(%pred) ({ "stablehlo.return"(%result_true_branch) : (tensor<i32>) -> () }, { "stablehlo.return"(%result_false_branch) : (tensor<i32>) -> () }) : (tensor<i1>) -> tensor<i32>
source
',6))]),t("details",ce,[t("summary",null,[e[159]||(e[159]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.imag-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.imag-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.imag")],-1)),e[160]||(e[160]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[161]||(e[161]=n('
imag
Extracts the imaginary part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#imag
Example
mlir
%result = stablehlo.imag %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
',6))]),t("details",de,[t("summary",null,[e[162]||(e[162]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.infeed-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.infeed-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.infeed")],-1)),e[163]||(e[163]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[164]||(e[164]=n(`
infeed
Reads data from the infeed and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#infeed
Example
mlir
%results0:2 = "stablehlo.infeed"(%token) :
+    (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
`,6))]),t("details",be,[t("summary",null,[e[165]||(e[165]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.iota-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.iota-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.iota")],-1)),e[166]||(e[166]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[167]||(e[167]=n('
iota
Fills an output tensor with values in increasing order starting from zero along the iota_dimension dimension.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#iota
Example
mlir
%output = stablehlo.iota dim = 0 : tensor<4x5xi32>
source
',6))]),t("details",ue,[t("summary",null,[e[168]||(e[168]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.is_finite-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.is_finite-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.is_finite")],-1)),e[169]||(e[169]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[170]||(e[170]=n('
is_finite
Performs element-wise check whether the value in x is finite (i.e. is neither +Inf, -Inf, nor NaN) and produces a y tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#is_finite
Example
mlir
%y = stablehlo.is_finite %x : (tensor<7xf64>) -> tensor<7xi1>
source
',6))]),t("details",he,[t("summary",null,[e[171]||(e[171]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.log-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.log-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.log")],-1)),e[172]||(e[172]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[173]||(e[173]=n('
log
Performs element-wise logarithm operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log
Example
mlir
%result = stablehlo.log %operand : tensor<2x2xf64>
source
',6))]),t("details",me,[t("summary",null,[e[174]||(e[174]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.log_plus_one-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.log_plus_one-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.log_plus_one")],-1)),e[175]||(e[175]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[176]||(e[176]=n('
log_plus_one
Performs element-wise logarithm plus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log_plus_one
Example
mlir
%result = stablehlo.log_plus_one %operand : tensor<5xf64>
source
',6))]),t("details",ge,[t("summary",null,[e[177]||(e[177]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.logistic-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.logistic-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.logistic")],-1)),e[178]||(e[178]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[179]||(e[179]=n('
logistic
Performs element-wise logistic operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#logistic
Example
mlir
%result = stablehlo.logistic %operand : tensor<2x2xf64>
source
',6))]),t("details",Re,[t("summary",null,[e[180]||(e[180]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.map-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.map-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.map")],-1)),e[181]||(e[181]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[182]||(e[182]=n(`
map
Applies a map function computation to inputs along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#map
Example
mlir
%result = "stablehlo.map"(%input0, %input1) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.multiply %arg0, %arg1 : tensor<i64>
     stablehlo.return %0 : tensor<i64>
 }) {
   dimensions = array<i64: 0, 1>
-} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>
source
`,6))]),t("details",fe,[t("summary",null,[e[183]||(e[183]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.maximum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.maximum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.maximum")],-1)),e[184]||(e[184]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[185]||(e[185]=n('
maximum
Performs element-wise max operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#maximum
Example
mlir
%result = stablehlo.maximum %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",xe,[t("summary",null,[e[186]||(e[186]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.minimum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.minimum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.minimum")],-1)),e[187]||(e[187]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[188]||(e[188]=n('
minimum
Performs element-wise min operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#minimum
Example
mlir
%result = stablehlo.minimum %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",Le,[t("summary",null,[e[189]||(e[189]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.multiply-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.multiply-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.multiply")],-1)),e[190]||(e[190]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[191]||(e[191]=n('
multiply
Performs element-wise product of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#multiply
Example
mlir
%result = stablehlo.multiply %lhs, %rhs : tensor<2xi32>
source
',6))]),t("details",Ie,[t("summary",null,[e[192]||(e[192]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.negate-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.negate-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.negate")],-1)),e[193]||(e[193]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[194]||(e[194]=n('
negate
Performs element-wise negation of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#negate
Example
mlir
%result = stablehlo.negate %operand : tensor<2x3xi32>
source
',6))]),t("details",Me,[t("summary",null,[e[195]||(e[195]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.not-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.not-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.not")],-1)),e[196]||(e[196]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[197]||(e[197]=n('
not
Performs element-wise NOT of tensor operand of type integer and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#not
Example
mlir
%result = stablehlo.not %operand : tensor<5x3x1xi1>
source
',6))]),t("details",ye,[t("summary",null,[e[198]||(e[198]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.optimization_barrier-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.optimization_barrier-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.optimization_barrier")],-1)),e[199]||(e[199]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[200]||(e[200]=n('
optimization_barrier
Ensures that the operations that produce the operand are executed before any operations that depend on the result and prevents compiler transformations from moving operations across the barrier. Other than that, the operation is an identity, i.e. result = operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#optimization_barrier
Example
mlir
%result0, %result1 = stablehlo.optimization_barrier %operand0, %operand1 : tensor<f32>, tensor<f32>
source
',6))]),t("details",je,[t("summary",null,[e[201]||(e[201]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.or-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.or-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.or")],-1)),e[202]||(e[202]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[203]||(e[203]=n('
or
Performs element-wise OR of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#or
Example
mlir
%result = stablehlo.or %lhs, %rhs : tensor<2xi1>
source
',6))]),t("details",ke,[t("summary",null,[e[204]||(e[204]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.outfeed-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.outfeed-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.outfeed")],-1)),e[205]||(e[205]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[206]||(e[206]=n(`
outfeed
Writes inputs to the outfeed and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#outfeed
Example
mlir
%result = "stablehlo.outfeed"(%input0, %token) :
-    (tensor<2x2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
`,6))]),t("details",ve,[t("summary",null,[e[207]||(e[207]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.pad-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.pad-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.pad")],-1)),e[208]||(e[208]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[209]||(e[209]=n(`
pad
Expands operand by padding around the tensor as well as between the elements of the tensor with the given padding_value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#pad
Example
mlir
%0 = stablehlo.pad %arg0, %arg1, low = [0, 1], high = [2, 1], interior = [1, 2]
-  : (tensor<2x3xi32>, tensor<i32>) -> tensor<5x9xi32>
source
`,6))]),t("details",De,[t("summary",null,[e[210]||(e[210]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.partition_id-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.partition_id-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.partition_id")],-1)),e[211]||(e[211]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[212]||(e[212]=n('
partition_id
Produces partition_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#partition_id
Example
mlir
%result = stablehlo.partition_id : tensor<ui32>
source
',6))]),t("details",_e,[t("summary",null,[e[213]||(e[213]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.popcnt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.popcnt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.popcnt")],-1)),e[214]||(e[214]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[215]||(e[215]=n('
popcnt
Performs element-wise count of the number of bits set in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#popcnt
Example
mlir
%result = stablehlo.popcnt %operand : tensor<4xi64>
source
',6))]),t("details",Te,[t("summary",null,[e[216]||(e[216]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.power-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.power-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.power")],-1)),e[217]||(e[217]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[218]||(e[218]=n('
power
Performs element-wise exponentiation of lhs tensor by rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#power
Example
mlir
%result = stablehlo.power %lhs, %rhs : tensor<6xf64>
source
',6))]),t("details",Ve,[t("summary",null,[e[219]||(e[219]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.real-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.real-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.real")],-1)),e[220]||(e[220]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[221]||(e[221]=n('
real
Extracts the real part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#real
Example
mlir
%result = stablehlo.real %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
',6))]),t("details",Oe,[t("summary",null,[e[222]||(e[222]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice-NTuple{4, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice-NTuple{4, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice")],-1)),e[223]||(e[223]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[224]||(e[224]=n(`
real_dynamic_slice
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as SliceOp except that start_indices, limit_indices and strides are specified dynamically: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.real_dynamic_slice %operand,
+} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>
source
`,6))]),t("details",fe,[t("summary",null,[e[183]||(e[183]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.maximum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.maximum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.maximum")],-1)),e[184]||(e[184]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[185]||(e[185]=n('
maximum
Performs element-wise max operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#maximum
Example
mlir
%result = stablehlo.maximum %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",xe,[t("summary",null,[e[186]||(e[186]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.minimum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.minimum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.minimum")],-1)),e[187]||(e[187]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[188]||(e[188]=n('
minimum
Performs element-wise min operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#minimum
Example
mlir
%result = stablehlo.minimum %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",Le,[t("summary",null,[e[189]||(e[189]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.multiply-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.multiply-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.multiply")],-1)),e[190]||(e[190]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[191]||(e[191]=n('
multiply
Performs element-wise product of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#multiply
Example
mlir
%result = stablehlo.multiply %lhs, %rhs : tensor<2xi32>
source
',6))]),t("details",Ie,[t("summary",null,[e[192]||(e[192]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.negate-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.negate-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.negate")],-1)),e[193]||(e[193]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[194]||(e[194]=n('
negate
Performs element-wise negation of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#negate
Example
mlir
%result = stablehlo.negate %operand : tensor<2x3xi32>
source
',6))]),t("details",Me,[t("summary",null,[e[195]||(e[195]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.not-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.not-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.not")],-1)),e[196]||(e[196]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[197]||(e[197]=n('
not
Performs element-wise NOT of tensor operand of type integer and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#not
Example
mlir
%result = stablehlo.not %operand : tensor<5x3x1xi1>
source
',6))]),t("details",ye,[t("summary",null,[e[198]||(e[198]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.optimization_barrier-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.optimization_barrier-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.optimization_barrier")],-1)),e[199]||(e[199]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[200]||(e[200]=n('
optimization_barrier
Ensures that the operations that produce the operand are executed before any operations that depend on the result and prevents compiler transformations from moving operations across the barrier. Other than that, the operation is an identity, i.e. result = operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#optimization_barrier
Example
mlir
%result0, %result1 = stablehlo.optimization_barrier %operand0, %operand1 : tensor<f32>, tensor<f32>
source
',6))]),t("details",je,[t("summary",null,[e[201]||(e[201]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.or-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.or-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.or")],-1)),e[202]||(e[202]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[203]||(e[203]=n('
or
Performs element-wise OR of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#or
Example
mlir
%result = stablehlo.or %lhs, %rhs : tensor<2xi1>
source
',6))]),t("details",ke,[t("summary",null,[e[204]||(e[204]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.outfeed-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.outfeed-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.outfeed")],-1)),e[205]||(e[205]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[206]||(e[206]=n(`
outfeed
Writes inputs to the outfeed and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#outfeed
Example
mlir
%result = "stablehlo.outfeed"(%input0, %token) :
+    (tensor<2x2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
`,6))]),t("details",ve,[t("summary",null,[e[207]||(e[207]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.pad-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.pad-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.pad")],-1)),e[208]||(e[208]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[209]||(e[209]=n(`
pad
Expands operand by padding around the tensor as well as between the elements of the tensor with the given padding_value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#pad
Example
mlir
%0 = stablehlo.pad %arg0, %arg1, low = [0, 1], high = [2, 1], interior = [1, 2]
+  : (tensor<2x3xi32>, tensor<i32>) -> tensor<5x9xi32>
source
`,6))]),t("details",De,[t("summary",null,[e[210]||(e[210]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.partition_id-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.partition_id-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.partition_id")],-1)),e[211]||(e[211]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[212]||(e[212]=n('
partition_id
Produces partition_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#partition_id
Example
mlir
%result = stablehlo.partition_id : tensor<ui32>
source
',6))]),t("details",_e,[t("summary",null,[e[213]||(e[213]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.popcnt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.popcnt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.popcnt")],-1)),e[214]||(e[214]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[215]||(e[215]=n('
popcnt
Performs element-wise count of the number of bits set in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#popcnt
Example
mlir
%result = stablehlo.popcnt %operand : tensor<4xi64>
source
',6))]),t("details",Te,[t("summary",null,[e[216]||(e[216]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.power-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.power-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.power")],-1)),e[217]||(e[217]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[218]||(e[218]=n('
power
Performs element-wise exponentiation of lhs tensor by rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#power
Example
mlir
%result = stablehlo.power %lhs, %rhs : tensor<6xf64>
source
',6))]),t("details",Ve,[t("summary",null,[e[219]||(e[219]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.real-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.real-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.real")],-1)),e[220]||(e[220]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[221]||(e[221]=n('
real
Extracts the real part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#real
Example
mlir
%result = stablehlo.real %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
',6))]),t("details",Oe,[t("summary",null,[e[222]||(e[222]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice-NTuple{4, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice-NTuple{4, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice")],-1)),e[223]||(e[223]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[224]||(e[224]=n(`
real_dynamic_slice
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as SliceOp except that start_indices, limit_indices and strides are specified dynamically: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.real_dynamic_slice %operand,
             %start_indices, %limit_indices, %strides
-       : (tensor<256x?xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<256x?xf32>
source
`,6))]),t("details",Ee,[t("summary",null,[e[225]||(e[225]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.recv-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.recv-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.recv")],-1)),e[226]||(e[226]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[227]||(e[227]=n(`
recv
Receives data from a channel with channel_id and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#recv
Example
mlir
%results:2 = "stablehlo.recv"(%token) {
+       : (tensor<256x?xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<256x?xf32>
source
`,6))]),t("details",Ee,[t("summary",null,[e[225]||(e[225]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.recv-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.recv-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.recv")],-1)),e[226]||(e[226]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[227]||(e[227]=n(`
recv
Receives data from a channel with channel_id and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#recv
Example
mlir
%results:2 = "stablehlo.recv"(%token) {
   channel_handle = #stablehlo.channel_handle<handle = 1, type = 3>,
   is_host_transfer = true
-} : (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
`,6))]),t("details",Se,[t("summary",null,[e[228]||(e[228]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce")],-1)),e[229]||(e[229]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[230]||(e[230]=n(`
reduce
Applies a reduction function body to inputs and init_values along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce
Example
mlir
%result = "stablehlo.reduce"(%input, %init_value) ({
+} : (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
`,6))]),t("details",Se,[t("summary",null,[e[228]||(e[228]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce")],-1)),e[229]||(e[229]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[230]||(e[230]=n(`
reduce
Applies a reduction function body to inputs and init_values along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce
Example
mlir
%result = "stablehlo.reduce"(%input, %init_value) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
     stablehlo.return %0 : tensor<i64>
 }) {
   dimensions = array<i64: 1>
-} : (tensor<1x6xi64>, tensor<i64>) -> tensor<1xi64>
source
`,6))]),t("details",Ce,[t("summary",null,[e[231]||(e[231]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_precision-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_precision-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_precision")],-1)),e[232]||(e[232]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[233]||(e[233]=n('
reduce_precision
Performs element-wise conversion of operand to another floating-point type that uses exponent_bits and mantissa_bits and back to the original floating-point type and produces an output tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_precision
Example
mlir
%output = stablehlo.reduce_precision %operand, format = e5m10 : tensor<6xf64>
source
',6))]),t("details",we,[t("summary",null,[e[234]||(e[234]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_scatter-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_scatter-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_scatter")],-1)),e[235]||(e[235]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[236]||(e[236]=n('
reduce_scatter
Within each process group in the process grid, performs reduction, using computations, over the values of the operand tensor from each process, splits the reduction result along scatter_dimension into parts, and scatters the split parts between the processes to produce the result.
See:\nhttps://github.com/openxla/stablehlo/blob/main/docs/spec#reduce_scatter\n\nExample:\n```mlir\n%result = "stablehlo.reduce_scatter"(%operand) ({
^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension = 1 : i64, replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>, channel_handle = #stablehlo.channel_handle<handle = 0, type = 0> } : (tensor<2x4xi64>) -> tensor<2x2xi64> ```
source
',5))]),t("details",ze,[t("summary",null,[e[237]||(e[237]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_window-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_window-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_window")],-1)),e[238]||(e[238]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[239]||(e[239]=n(`
reduce_window
Applies a reduction function body to windows of inputs and init_values and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_window
Example
mlir
%result = "stablehlo.reduce_window"(%input, %init_value) ({
+} : (tensor<1x6xi64>, tensor<i64>) -> tensor<1xi64>
source
`,6))]),t("details",Ce,[t("summary",null,[e[231]||(e[231]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_precision-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_precision-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_precision")],-1)),e[232]||(e[232]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[233]||(e[233]=n('
reduce_precision
Performs element-wise conversion of operand to another floating-point type that uses exponent_bits and mantissa_bits and back to the original floating-point type and produces an output tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_precision
Example
mlir
%output = stablehlo.reduce_precision %operand, format = e5m10 : tensor<6xf64>
source
',6))]),t("details",we,[t("summary",null,[e[234]||(e[234]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_scatter-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_scatter-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_scatter")],-1)),e[235]||(e[235]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[236]||(e[236]=n('
reduce_scatter
Within each process group in the process grid, performs reduction, using computations, over the values of the operand tensor from each process, splits the reduction result along scatter_dimension into parts, and scatters the split parts between the processes to produce the result.
See:\nhttps://github.com/openxla/stablehlo/blob/main/docs/spec#reduce_scatter\n\nExample:\n```mlir\n%result = "stablehlo.reduce_scatter"(%operand) ({
^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension = 1 : i64, replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>, channel_handle = #stablehlo.channel_handle<handle = 0, type = 0> } : (tensor<2x4xi64>) -> tensor<2x2xi64> ```
source
',5))]),t("details",ze,[t("summary",null,[e[237]||(e[237]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_window-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_window-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_window")],-1)),e[238]||(e[238]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[239]||(e[239]=n(`
reduce_window
Applies a reduction function body to windows of inputs and init_values and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_window
Example
mlir
%result = "stablehlo.reduce_window"(%input, %init_value) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
     stablehlo.return %0 : tensor<i64>
@@ -141,7 +141,7 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
   base_dilations = array<i64: 2, 1>,
   window_dilations = array<i64: 3, 1>,
   padding = dense<[[2, 1], [0, 0]]> : tensor<2x2xi64>
-} : (tensor<3x2xi64>, tensor<i64>) -> tensor<2x2xi64>
source
`,6))]),t("details",He,[t("summary",null,[e[240]||(e[240]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.remainder-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.remainder-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.remainder")],-1)),e[241]||(e[241]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=n('
remainder
Performs element-wise remainder of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#remainder
Example
mlir
%result = stablehlo.remainder %lhs, %rhs : tensor<4xi64>
source
',6))]),t("details",Ae,[t("summary",null,[e[243]||(e[243]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.replica_id-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.replica_id-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.replica_id")],-1)),e[244]||(e[244]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[245]||(e[245]=n('
replica_id
Produces replica_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#replica_id
Example
mlir
%result = stablehlo.replica_id : tensor<ui32>
source
',6))]),t("details",qe,[t("summary",null,[e[246]||(e[246]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reshape-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reshape-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reshape")],-1)),e[247]||(e[247]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[248]||(e[248]=n('
reshape
Performs reshape of operand tensor to a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reshape
Example
mlir
%result = stablehlo.reshape %operand : (tensor<2xf32>) -> tensor<1x2xf32>
source
',6))]),t("details",Pe,[t("summary",null,[e[249]||(e[249]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reverse-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reverse-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reverse")],-1)),e[250]||(e[250]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[251]||(e[251]=n('
reverse
Reverses the order of elements in the operand along the specified dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reverse
Example
mlir
%result = stablehlo.reverse %operand, dims = [1] : tensor<3x2xi32>
source
',6))]),t("details",Ne,[t("summary",null,[e[252]||(e[252]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rng-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rng-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rng")],-1)),e[253]||(e[253]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=n('
rng
Generates random numbers using the rng_distribution algorithm and produces a result tensor of a given shape shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng
Example
mlir
%result = stablehlo.rng %a, %b, %shape, distribution = NORMAL : (tensor<i32>, tensor<i32>, tensor<2xi64>) -> tensor<3x3xi32>
source
',6))]),t("details",Fe,[t("summary",null,[e[255]||(e[255]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rng_bit_generator-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rng_bit_generator-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rng_bit_generator")],-1)),e[256]||(e[256]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[257]||(e[257]=n('
rng_bit_generator
Returns an output filled with uniform random data and an updated output state output_state given an initial state initial_state using the pseudorandom number generator algorithm rng_algorithm.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng_bit_generator
Example
mlir
%output_state, %output = stablehlo.rng_bit_generator %initial_state, algorithm = THREE_FRY : (tensor<2xui64>) -> (tensor<2xui64>, tensor<2x2xui64>)
source
',6))]),t("details",Be,[t("summary",null,[e[258]||(e[258]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.round_nearest_afz-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.round_nearest_afz-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.round_nearest_afz")],-1)),e[259]||(e[259]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=n('
round_nearest_afz
Performs element-wise rounding towards the nearest integer, breaking ties away from zero, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_afz
Example
mlir
%result = stablehlo.round_nearest_afz %operand : tensor<5xf64>
source
',6))]),t("details",Ue,[t("summary",null,[e[261]||(e[261]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.round_nearest_even-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.round_nearest_even-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.round_nearest_even")],-1)),e[262]||(e[262]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[263]||(e[263]=n('
round_nearest_even
Performs element-wise rounding towards the nearest integer, breaking ties towards the even integer, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_even
Example
mlir
%result = stablehlo.round_nearest_even %operand : tensor<5xf64>
source
',6))]),t("details",We,[t("summary",null,[e[264]||(e[264]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rsqrt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rsqrt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rsqrt")],-1)),e[265]||(e[265]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[266]||(e[266]=n('
rsqrt
Performs element-wise reciprocal square root operation on operand tensor and produces a result tensor, implementing the rSqrt operation from the IEEE-754 specification.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rsqrt
Example
mlir
%result = stablehlo.rsqrt %operand : tensor<2x2xf32>
source
',6))]),t("details",Ge,[t("summary",null,[e[267]||(e[267]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.scatter-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.scatter-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.scatter")],-1)),e[268]||(e[268]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[269]||(e[269]=n('
scatter
Produces results tensors which are equal to inputs tensors except that several slices specified by scatter_indices are updated with the values updates using update_computation.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter
Example: mlir %result = "stablehlo.scatter"(%input, %scatter_indices, %update) ({ ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension_numbers = #stablehlo.scatter< update_window_dims = [3, 4], inserted_window_dims = [1], input_batching_dims = [0], scatter_indices_batching_dims = [1], scatter_dims_to_operand_dims = [2, 1], index_vector_dim = 3>, indices_are_sorted = false, unique_indices = false } : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>, tensor<2x2x3x2x2xi64>) -> tensor<2x3x4x2xi64>
source
',5))]),t("details",Xe,[t("summary",null,[e[270]||(e[270]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.select")],-1)),e[271]||(e[271]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[272]||(e[272]=n('
select
Produces a result tensor where each element is selected from on_true or on_false tensor based on the value of the corresponding element of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select
Example
mlir
%result = stablehlo.select %pred, %on_true, %on_false : tensor<2x2xi1>, tensor<2x2xi32>
source
',6))]),t("details",$e,[t("summary",null,[e[273]||(e[273]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.select_and_scatter-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.select_and_scatter-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.select_and_scatter")],-1)),e[274]||(e[274]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[275]||(e[275]=n(`
select_and_scatter
Scatters the values from the source tensor using scatter based on the outcome of reduce_window of the input tensor using select and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select_and_scatter
Example
mlir
%result = "stablehlo.select_and_scatter"(%operand, %source, %init_value) ({
+} : (tensor<3x2xi64>, tensor<i64>) -> tensor<2x2xi64>
source
`,6))]),t("details",He,[t("summary",null,[e[240]||(e[240]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.remainder-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.remainder-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.remainder")],-1)),e[241]||(e[241]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=n('
remainder
Performs element-wise remainder of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#remainder
Example
mlir
%result = stablehlo.remainder %lhs, %rhs : tensor<4xi64>
source
',6))]),t("details",Ae,[t("summary",null,[e[243]||(e[243]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.replica_id-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.replica_id-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.replica_id")],-1)),e[244]||(e[244]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[245]||(e[245]=n('
replica_id
Produces replica_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#replica_id
Example
mlir
%result = stablehlo.replica_id : tensor<ui32>
source
',6))]),t("details",qe,[t("summary",null,[e[246]||(e[246]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reshape-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reshape-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reshape")],-1)),e[247]||(e[247]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[248]||(e[248]=n('
reshape
Performs reshape of operand tensor to a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reshape
Example
mlir
%result = stablehlo.reshape %operand : (tensor<2xf32>) -> tensor<1x2xf32>
source
',6))]),t("details",Pe,[t("summary",null,[e[249]||(e[249]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reverse-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reverse-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reverse")],-1)),e[250]||(e[250]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[251]||(e[251]=n('
reverse
Reverses the order of elements in the operand along the specified dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reverse
Example
mlir
%result = stablehlo.reverse %operand, dims = [1] : tensor<3x2xi32>
source
',6))]),t("details",Ne,[t("summary",null,[e[252]||(e[252]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rng-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rng-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rng")],-1)),e[253]||(e[253]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=n('
rng
Generates random numbers using the rng_distribution algorithm and produces a result tensor of a given shape shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng
Example
mlir
%result = stablehlo.rng %a, %b, %shape, distribution = NORMAL : (tensor<i32>, tensor<i32>, tensor<2xi64>) -> tensor<3x3xi32>
source
',6))]),t("details",Fe,[t("summary",null,[e[255]||(e[255]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rng_bit_generator-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rng_bit_generator-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rng_bit_generator")],-1)),e[256]||(e[256]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[257]||(e[257]=n('
rng_bit_generator
Returns an output filled with uniform random data and an updated output state output_state given an initial state initial_state using the pseudorandom number generator algorithm rng_algorithm.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng_bit_generator
Example
mlir
%output_state, %output = stablehlo.rng_bit_generator %initial_state, algorithm = THREE_FRY : (tensor<2xui64>) -> (tensor<2xui64>, tensor<2x2xui64>)
source
',6))]),t("details",Be,[t("summary",null,[e[258]||(e[258]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.round_nearest_afz-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.round_nearest_afz-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.round_nearest_afz")],-1)),e[259]||(e[259]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=n('
round_nearest_afz
Performs element-wise rounding towards the nearest integer, breaking ties away from zero, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_afz
Example
mlir
%result = stablehlo.round_nearest_afz %operand : tensor<5xf64>
source
',6))]),t("details",Ue,[t("summary",null,[e[261]||(e[261]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.round_nearest_even-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.round_nearest_even-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.round_nearest_even")],-1)),e[262]||(e[262]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[263]||(e[263]=n('
round_nearest_even
Performs element-wise rounding towards the nearest integer, breaking ties towards the even integer, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_even
Example
mlir
%result = stablehlo.round_nearest_even %operand : tensor<5xf64>
source
',6))]),t("details",We,[t("summary",null,[e[264]||(e[264]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rsqrt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rsqrt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rsqrt")],-1)),e[265]||(e[265]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[266]||(e[266]=n('
rsqrt
Performs element-wise reciprocal square root operation on operand tensor and produces a result tensor, implementing the rSqrt operation from the IEEE-754 specification.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rsqrt
Example
mlir
%result = stablehlo.rsqrt %operand : tensor<2x2xf32>
source
',6))]),t("details",Ge,[t("summary",null,[e[267]||(e[267]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.scatter-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.scatter-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.scatter")],-1)),e[268]||(e[268]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[269]||(e[269]=n('
scatter
Produces results tensors which are equal to inputs tensors except that several slices specified by scatter_indices are updated with the values updates using update_computation.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter
Example: mlir %result = "stablehlo.scatter"(%input, %scatter_indices, %update) ({ ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension_numbers = #stablehlo.scatter< update_window_dims = [3, 4], inserted_window_dims = [1], input_batching_dims = [0], scatter_indices_batching_dims = [1], scatter_dims_to_operand_dims = [2, 1], index_vector_dim = 3>, indices_are_sorted = false, unique_indices = false } : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>, tensor<2x2x3x2x2xi64>) -> tensor<2x3x4x2xi64>
source
',5))]),t("details",Xe,[t("summary",null,[e[270]||(e[270]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.select")],-1)),e[271]||(e[271]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[272]||(e[272]=n('
select
Produces a result tensor where each element is selected from on_true or on_false tensor based on the value of the corresponding element of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select
Example
mlir
%result = stablehlo.select %pred, %on_true, %on_false : tensor<2x2xi1>, tensor<2x2xi32>
source
',6))]),t("details",$e,[t("summary",null,[e[273]||(e[273]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.select_and_scatter-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.select_and_scatter-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.select_and_scatter")],-1)),e[274]||(e[274]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[275]||(e[275]=n(`
select_and_scatter
Scatters the values from the source tensor using scatter based on the outcome of reduce_window of the input tensor using select and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select_and_scatter
Example
mlir
%result = "stablehlo.select_and_scatter"(%operand, %source, %init_value) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.compare GE, %arg0, %arg1 : (tensor<i64>, tensor<i64>) -> tensor<i1>
     stablehlo.return %0 : tensor<i1>
@@ -153,12 +153,12 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
   window_dimensions = dense<[3, 1]> : tensor<2xi64>,
   window_strides = dense<[2, 1]> : tensor<2xi64>,
   padding = dense<[[0, 1], [0, 0]]> : tensor<2x2xi64>
-} : (tensor<4x2xi64>, tensor<2x2xi64>, tensor<i64>) -> tensor<4x2xi64>
source
`,6))]),t("details",Ye,[t("summary",null,[e[276]||(e[276]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.send-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.send-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.send")],-1)),e[277]||(e[277]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[278]||(e[278]=n(`
send
Sends inputs to a channel channel_id and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#send
Example
mlir
%result = "stablehlo.send"(%operand, %token) {
+} : (tensor<4x2xi64>, tensor<2x2xi64>, tensor<i64>) -> tensor<4x2xi64>
source
`,6))]),t("details",Ye,[t("summary",null,[e[276]||(e[276]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.send-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.send-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.send")],-1)),e[277]||(e[277]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[278]||(e[278]=n(`
send
Sends inputs to a channel channel_id and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#send
Example
mlir
%result = "stablehlo.send"(%operand, %token) {
   channel_handle = #stablehlo.channel_handle<handle = 1, type = 2>,
   is_host_transfer = true
-} : (tensor<2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
`,6))]),t("details",Je,[t("summary",null,[e[279]||(e[279]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.set_dimension_size-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.set_dimension_size-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.set_dimension_size")],-1)),e[280]||(e[280]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[281]||(e[281]=n('
set_dimension_size
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as XLA's SetDimensionSize: https://www.tensorflow.org/xla/operation_semantics#setdimensionsize
Example
mlir
%0 = stablehlo.set_dimension_size %arg0, %arg1, dim = 1 : (tensor<4x2xf32>, tensor<i32>) -> tensor<4x2xf32>
source
',6))]),t("details",Ke,[t("summary",null,[e[282]||(e[282]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_left-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_left-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_left")],-1)),e[283]||(e[283]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[284]||(e[284]=n('
shift_left
Performs element-wise left-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_left
Example
mlir
%result = stablehlo.shift_left %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",Qe,[t("summary",null,[e[285]||(e[285]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic")],-1)),e[286]||(e[286]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[287]||(e[287]=n('
shift_right_arithmetic
Performs element-wise arithmetic right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_arithmetic
Example
mlir
%result = stablehlo.shift_right_arithmetic %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",Ze,[t("summary",null,[e[288]||(e[288]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_right_logical-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_right_logical-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_right_logical")],-1)),e[289]||(e[289]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[290]||(e[290]=n('
shift_right_logical
Performs element-wise logical right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_logical
Example
mlir
%result = stablehlo.shift_right_logical %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",et,[t("summary",null,[e[291]||(e[291]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sign-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.sign-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sign")],-1)),e[292]||(e[292]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[293]||(e[293]=n('
sign
Returns the sign of the operand element-wise and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sign
Example
mlir
%result = stablehlo.sign %operand : tensor<5xf64>
source
',6))]),t("details",tt,[t("summary",null,[e[294]||(e[294]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sine-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.sine-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sine")],-1)),e[295]||(e[295]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[296]||(e[296]=n('
sine
Performs element-wise sine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sine
Example
mlir
%result = stablehlo.sine %operand : tensor<2xf32>
source
',6))]),t("details",at,[t("summary",null,[e[297]||(e[297]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.slice-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.slice-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.slice")],-1)),e[298]||(e[298]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[299]||(e[299]=n('
slice
Extracts a slice from the operand using statically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.slice %operand [1:3, 4:8:2]\n   : (tensor<3x8xi64>) -> tensor<2x2xi64>\n\n// Same in generic form: the `1:3` above is mapped to the first entry in\n// `start_indices` and `limit_indices`, while `strides` is implicitly 1.\n// The `4:8:2` above is parsed into the second entry of `start_indices`,\n// `limit_indices` and `strides` respectively.\n%result = "stablehlo.slice" (%operand) {\n  start_indices = array<i64: 1, 4>,\n  limit_indices = array<i64: 3, 8>,\n  strides = array<i64: 1, 2>\n} : (tensor<3x8xi64>) -> tensor<2x2xi64>
source
',6))]),t("details",st,[t("summary",null,[e[300]||(e[300]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sort-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.sort-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sort")],-1)),e[301]||(e[301]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[302]||(e[302]=n(`
sort
Sorts a variadic number of tensors in inputs together, according to a custom comparator, along the given dimension and produces a variadic number of tensors as results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sort
Example
mlir

+} : (tensor<2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
`,6))]),t("details",Je,[t("summary",null,[e[279]||(e[279]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.set_dimension_size-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.set_dimension_size-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.set_dimension_size")],-1)),e[280]||(e[280]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[281]||(e[281]=n('
set_dimension_size
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as XLA's SetDimensionSize: https://www.tensorflow.org/xla/operation_semantics#setdimensionsize
Example
mlir
%0 = stablehlo.set_dimension_size %arg0, %arg1, dim = 1 : (tensor<4x2xf32>, tensor<i32>) -> tensor<4x2xf32>
source
',6))]),t("details",Ke,[t("summary",null,[e[282]||(e[282]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_left-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_left-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_left")],-1)),e[283]||(e[283]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[284]||(e[284]=n('
shift_left
Performs element-wise left-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_left
Example
mlir
%result = stablehlo.shift_left %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",Qe,[t("summary",null,[e[285]||(e[285]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic")],-1)),e[286]||(e[286]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[287]||(e[287]=n('
shift_right_arithmetic
Performs element-wise arithmetic right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_arithmetic
Example
mlir
%result = stablehlo.shift_right_arithmetic %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",Ze,[t("summary",null,[e[288]||(e[288]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_right_logical-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_right_logical-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_right_logical")],-1)),e[289]||(e[289]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[290]||(e[290]=n('
shift_right_logical
Performs element-wise logical right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_logical
Example
mlir
%result = stablehlo.shift_right_logical %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",et,[t("summary",null,[e[291]||(e[291]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sign-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.sign-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sign")],-1)),e[292]||(e[292]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[293]||(e[293]=n('
sign
Returns the sign of the operand element-wise and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sign
Example
mlir
%result = stablehlo.sign %operand : tensor<5xf64>
source
',6))]),t("details",tt,[t("summary",null,[e[294]||(e[294]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sine-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.sine-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sine")],-1)),e[295]||(e[295]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[296]||(e[296]=n('
sine
Performs element-wise sine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sine
Example
mlir
%result = stablehlo.sine %operand : tensor<2xf32>
source
',6))]),t("details",at,[t("summary",null,[e[297]||(e[297]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.slice-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.slice-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.slice")],-1)),e[298]||(e[298]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[299]||(e[299]=n('
slice
Extracts a slice from the operand using statically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.slice %operand [1:3, 4:8:2]\n   : (tensor<3x8xi64>) -> tensor<2x2xi64>\n\n// Same in generic form: the `1:3` above is mapped to the first entry in\n// `start_indices` and `limit_indices`, while `strides` is implicitly 1.\n// The `4:8:2` above is parsed into the second entry of `start_indices`,\n// `limit_indices` and `strides` respectively.\n%result = "stablehlo.slice" (%operand) {\n  start_indices = array<i64: 1, 4>,\n  limit_indices = array<i64: 3, 8>,\n  strides = array<i64: 1, 2>\n} : (tensor<3x8xi64>) -> tensor<2x2xi64>
source
',6))]),t("details",st,[t("summary",null,[e[300]||(e[300]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sort-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.sort-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sort")],-1)),e[301]||(e[301]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[302]||(e[302]=n(`
sort
Sorts a variadic number of tensors in inputs together, according to a custom comparator, along the given dimension and produces a variadic number of tensors as results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sort
Example
mlir

 
-[source](https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/StableHLO.jl#L4195-L4215)
+[source](https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/StableHLO.jl#L4195-L4215)
 
 </details>
 
@@ -176,17 +176,17 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
 **Example**
 
 \`\`\`mlir
-%result = stablehlo.sqrt %operand : tensor<2x2xf32>
source
`,6))]),t("details",lt,[t("summary",null,[e[303]||(e[303]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.subtract-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.subtract-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.subtract")],-1)),e[304]||(e[304]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[305]||(e[305]=n('
subtract
Performs element-wise subtraction of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#subtract
Example
mlir
%result = stablehlo.subtract %lhs, %rhs : tensor<2xi32>
source
',6))]),t("details",nt,[t("summary",null,[e[306]||(e[306]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tan-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.tan-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tan")],-1)),e[307]||(e[307]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[308]||(e[308]=n('
tan
Performs element-wise tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tan
Example
mlir
%result = stablehlo.tan %operand : tensor<2x2xf64>
source
',6))]),t("details",ot,[t("summary",null,[e[309]||(e[309]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tanh-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.tanh-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tanh")],-1)),e[310]||(e[310]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[311]||(e[311]=n('
tanh
Performs element-wise hyperbolic tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tanh
Example
mlir
%result = stablehlo.tanh %operand : tensor<2xf32>
source
',6))]),t("details",it,[t("summary",null,[e[312]||(e[312]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.torch_index_select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.torch_index_select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.torch_index_select")],-1)),e[313]||(e[313]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[314]||(e[314]=n(`
torch_index_select
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as PyTorch's index_select, augmented with support for batch dimensions: https://pytorch.org/docs/stable/generated/torch.index_select.html.
The batch_dims attribute specifies the number of major batch dimensions (0 or more) that act like a multidimensional loop over both the operand and the index.
Example
mlir
%result = "stablehlo.torch_index_select"(%operand, %index) {
+%result = stablehlo.sqrt %operand : tensor<2x2xf32>
source
`,6))]),t("details",lt,[t("summary",null,[e[303]||(e[303]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.subtract-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.subtract-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.subtract")],-1)),e[304]||(e[304]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[305]||(e[305]=n('
subtract
Performs element-wise subtraction of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#subtract
Example
mlir
%result = stablehlo.subtract %lhs, %rhs : tensor<2xi32>
source
',6))]),t("details",nt,[t("summary",null,[e[306]||(e[306]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tan-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.tan-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tan")],-1)),e[307]||(e[307]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[308]||(e[308]=n('
tan
Performs element-wise tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tan
Example
mlir
%result = stablehlo.tan %operand : tensor<2x2xf64>
source
',6))]),t("details",ot,[t("summary",null,[e[309]||(e[309]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tanh-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.tanh-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tanh")],-1)),e[310]||(e[310]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[311]||(e[311]=n('
tanh
Performs element-wise hyperbolic tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tanh
Example
mlir
%result = stablehlo.tanh %operand : tensor<2xf32>
source
',6))]),t("details",it,[t("summary",null,[e[312]||(e[312]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.torch_index_select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.torch_index_select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.torch_index_select")],-1)),e[313]||(e[313]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[314]||(e[314]=n(`
torch_index_select
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as PyTorch's index_select, augmented with support for batch dimensions: https://pytorch.org/docs/stable/generated/torch.index_select.html.
The batch_dims attribute specifies the number of major batch dimensions (0 or more) that act like a multidimensional loop over both the operand and the index.
Example
mlir
%result = "stablehlo.torch_index_select"(%operand, %index) {
   dim = 2 : i64,
   batch_dims = 1 : i64
-} : (tensor<8x128x3072x64xf32>, tensor<8x16x1024xi32>) -> tensor<8x128x16x1024x64xf32>
source
`,7))]),t("details",pt,[t("summary",null,[e[315]||(e[315]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.transpose-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.transpose-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.transpose")],-1)),e[316]||(e[316]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[317]||(e[317]=n('
transpose
Permutes the dimensions of operand tensor using permutation and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#transpose
Example
mlir
%0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<1x2x3xi32>) -> tensor<3x2x1xi32>
source
',6))]),t("details",rt,[t("summary",null,[e[318]||(e[318]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.triangular_solve-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.triangular_solve-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.triangular_solve")],-1)),e[319]||(e[319]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[320]||(e[320]=n(`
triangular_solve
Solves batches of systems of linear equations with lower or upper triangular coefficient matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#triangular_solve
Example
mlir
%result = "stablehlo.triangular_solve"(%a, %b) {
+} : (tensor<8x128x3072x64xf32>, tensor<8x16x1024xi32>) -> tensor<8x128x16x1024x64xf32>
source
`,7))]),t("details",pt,[t("summary",null,[e[315]||(e[315]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.transpose-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.transpose-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.transpose")],-1)),e[316]||(e[316]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[317]||(e[317]=n('
transpose
Permutes the dimensions of operand tensor using permutation and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#transpose
Example
mlir
%0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<1x2x3xi32>) -> tensor<3x2x1xi32>
source
',6))]),t("details",rt,[t("summary",null,[e[318]||(e[318]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.triangular_solve-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.triangular_solve-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.triangular_solve")],-1)),e[319]||(e[319]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[320]||(e[320]=n(`
triangular_solve
Solves batches of systems of linear equations with lower or upper triangular coefficient matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#triangular_solve
Example
mlir
%result = "stablehlo.triangular_solve"(%a, %b) {
   left_side = true,
   lower = true,
   unit_diagonal = false,
   transpose_a = #stablehlo<transpose NO_TRANSPOSE>
-} : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
source
`,6))]),t("details",ct,[t("summary",null,[e[321]||(e[321]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tuple-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.tuple-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tuple")],-1)),e[322]||(e[322]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[323]||(e[323]=n('
tuple
Produces a result tuple from values val.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tuple
Example
mlir
%result = stablehlo.tuple %val0, %val1 : tuple<tensor<2xf64>, tuple<tensor<i64>>>
source
',6))]),t("details",dt,[t("summary",null,[e[324]||(e[324]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.unary_einsum-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.unary_einsum-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.unary_einsum")],-1)),e[325]||(e[325]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[326]||(e[326]=n(`
unary_einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.unary_einsum"(%operand) {
+} : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
source
`,6))]),t("details",ct,[t("summary",null,[e[321]||(e[321]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tuple-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.tuple-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tuple")],-1)),e[322]||(e[322]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[323]||(e[323]=n('
tuple
Produces a result tuple from values val.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tuple
Example
mlir
%result = stablehlo.tuple %val0, %val1 : tuple<tensor<2xf64>, tuple<tensor<i64>>>
source
',6))]),t("details",dt,[t("summary",null,[e[324]||(e[324]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.unary_einsum-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.unary_einsum-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.unary_einsum")],-1)),e[325]||(e[325]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[326]||(e[326]=n(`
unary_einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.unary_einsum"(%operand) {
   einsum_config = "ab->a"
-} : (tensor<4x16xf32>) -> tensor<4xf32>
source
`,6))]),t("details",bt,[t("summary",null,[e[327]||(e[327]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.uniform_dequantize-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.uniform_dequantize-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.uniform_dequantize")],-1)),e[328]||(e[328]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[329]||(e[329]=n('
uniform_dequantize
Performs element-wise conversion of quantized tensor operand to a floating-point tensor result according to the quantization parameters defined by the operand type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_dequantize
Example
mlir
%result = stablehlo.uniform_dequantize %operand : (tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>) -> tensor<2xf32>
source
',6))]),t("details",ut,[t("summary",null,[e[330]||(e[330]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.uniform_quantize-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.uniform_quantize-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.uniform_quantize")],-1)),e[331]||(e[331]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[332]||(e[332]=n('
uniform_quantize
Performs element-wise conversion of floating-point tensor or quantized tensor operand to a quantized tensor result according to the quantization parameters defined by the result type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_quantize
Example
mlir
%result = stablehlo.uniform_quantize %operand : (tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>
source
',6))]),t("details",ht,[t("summary",null,[e[333]||(e[333]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.while_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.while_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.while_")],-1)),e[334]||(e[334]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[335]||(e[335]=n(`
while_
Produces the output from executing body function 0 or more times while the cond function outputs true.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#while
Example
mlir
%results0, %results1 = stablehlo.while(%arg0 = %init_i, %arg1 = %init_sum) : tensor<i64>, tensor<i64>
+} : (tensor<4x16xf32>) -> tensor<4xf32>
source
`,6))]),t("details",bt,[t("summary",null,[e[327]||(e[327]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.uniform_dequantize-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.uniform_dequantize-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.uniform_dequantize")],-1)),e[328]||(e[328]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[329]||(e[329]=n('
uniform_dequantize
Performs element-wise conversion of quantized tensor operand to a floating-point tensor result according to the quantization parameters defined by the operand type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_dequantize
Example
mlir
%result = stablehlo.uniform_dequantize %operand : (tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>) -> tensor<2xf32>
source
',6))]),t("details",ut,[t("summary",null,[e[330]||(e[330]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.uniform_quantize-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.uniform_quantize-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.uniform_quantize")],-1)),e[331]||(e[331]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[332]||(e[332]=n('
uniform_quantize
Performs element-wise conversion of floating-point tensor or quantized tensor operand to a quantized tensor result according to the quantization parameters defined by the result type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_quantize
Example
mlir
%result = stablehlo.uniform_quantize %operand : (tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>
source
',6))]),t("details",ht,[t("summary",null,[e[333]||(e[333]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.while_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.while_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.while_")],-1)),e[334]||(e[334]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[335]||(e[335]=n(`
while_
Produces the output from executing body function 0 or more times while the cond function outputs true.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#while
Example
mlir
%results0, %results1 = stablehlo.while(%arg0 = %init_i, %arg1 = %init_sum) : tensor<i64>, tensor<i64>
 cond {
   %cond = stablehlo.compare LT, %arg0, %ten : (tensor<i64>, tensor<i64>) -> tensor<i1>
   stablehlo.return %cond : tensor<i1>
@@ -194,4 +194,4 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
   %new_sum = stablehlo.add %arg1, %one : tensor<i64>
   %new_i = stablehlo.add %arg0, %one : tensor<i64>
   stablehlo.return %new_i, %new_sum : tensor<i64>, tensor<i64>
-}
source
`,6))]),t("details",mt,[t("summary",null,[e[336]||(e[336]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.xor-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.xor-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.xor")],-1)),e[337]||(e[337]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[338]||(e[338]=n('
xor
Performs element-wise XOR of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#xor
Example
mlir
%result = stablehlo.xor %lhs, %rhs : tensor<2xi32>
source
',6))])])}const jt=o(c,[["render",gt]]);export{yt as __pageData,jt as default};
+}

source

`,6))]),t("details",mt,[t("summary",null,[e[336]||(e[336]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.xor-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.xor-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.xor")],-1)),e[337]||(e[337]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[338]||(e[338]=n('

xor

Performs element-wise XOR of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#xor

Example

mlir
%result = stablehlo.xor %lhs, %rhs : tensor<2xi32>

source

',6))])])}const jt=o(c,[["render",gt]]);export{yt as __pageData,jt as default}; diff --git a/previews/PR441/assets/api_stablehlo.md.rWlP7YUr.lean.js b/previews/PR441/assets/api_stablehlo.md.BG0YL1kY.lean.js similarity index 93% rename from previews/PR441/assets/api_stablehlo.md.rWlP7YUr.lean.js rename to previews/PR441/assets/api_stablehlo.md.BG0YL1kY.lean.js index 96cad3ae1..0e4811cb0 100644 --- a/previews/PR441/assets/api_stablehlo.md.rWlP7YUr.lean.js +++ b/previews/PR441/assets/api_stablehlo.md.BG0YL1kY.lean.js @@ -1,48 +1,48 @@ -import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const yt=JSON.parse('{"title":"StableHLO Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/stablehlo.md","filePath":"api/stablehlo.md","lastUpdated":null}'),c={name:"api/stablehlo.md"},d={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},x={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},I={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},_={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},w={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},H={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},q={class:"jldocstring custom-block"},P={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Q={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"},te={class:"jldocstring custom-block"},ae={class:"jldocstring custom-block"},se={class:"jldocstring custom-block"},le={class:"jldocstring custom-block"},ne={class:"jldocstring custom-block"},oe={class:"jldocstring custom-block"},ie={class:"jldocstring custom-block"},pe={class:"jldocstring custom-block"},re={class:"jldocstring custom-block"},ce={class:"jldocstring custom-block"},de={class:"jldocstring custom-block"},be={class:"jldocstring custom-block"},ue={class:"jldocstring custom-block"},he={class:"jldocstring custom-block"},me={class:"jldocstring custom-block"},ge={class:"jldocstring custom-block"},Re={class:"jldocstring custom-block"},fe={class:"jldocstring custom-block"},xe={class:"jldocstring custom-block"},Le={class:"jldocstring custom-block"},Ie={class:"jldocstring custom-block"},Me={class:"jldocstring custom-block"},ye={class:"jldocstring custom-block"},je={class:"jldocstring custom-block"},ke={class:"jldocstring custom-block"},ve={class:"jldocstring custom-block"},De={class:"jldocstring custom-block"},_e={class:"jldocstring custom-block"},Te={class:"jldocstring custom-block"},Ve={class:"jldocstring custom-block"},Oe={class:"jldocstring custom-block"},Ee={class:"jldocstring custom-block"},Se={class:"jldocstring custom-block"},Ce={class:"jldocstring custom-block"},we={class:"jldocstring custom-block"},ze={class:"jldocstring custom-block"},He={class:"jldocstring custom-block"},Ae={class:"jldocstring custom-block"},qe={class:"jldocstring custom-block"},Pe={class:"jldocstring custom-block"},Ne={class:"jldocstring custom-block"},Fe={class:"jldocstring custom-block"},Be={class:"jldocstring custom-block"},Ue={class:"jldocstring custom-block"},We={class:"jldocstring custom-block"},Ge={class:"jldocstring custom-block"},Xe={class:"jldocstring custom-block"},$e={class:"jldocstring custom-block"},Ye={class:"jldocstring custom-block"},Je={class:"jldocstring custom-block"},Ke={class:"jldocstring custom-block"},Qe={class:"jldocstring custom-block"},Ze={class:"jldocstring custom-block"},et={class:"jldocstring custom-block"},tt={class:"jldocstring custom-block"},at={class:"jldocstring custom-block"},st={class:"jldocstring custom-block"},lt={class:"jldocstring custom-block"},nt={class:"jldocstring custom-block"},ot={class:"jldocstring custom-block"},it={class:"jldocstring custom-block"},pt={class:"jldocstring custom-block"},rt={class:"jldocstring custom-block"},ct={class:"jldocstring custom-block"},dt={class:"jldocstring custom-block"},bt={class:"jldocstring custom-block"},ut={class:"jldocstring custom-block"},ht={class:"jldocstring custom-block"},mt={class:"jldocstring custom-block"};function gt(Rt,e,ft,xt,Lt,It){const s=p("Badge");return r(),i("div",null,[e[339]||(e[339]=t("h1",{id:"StableHLO-Dialect",tabindex:"-1"},[a("StableHLO Dialect "),t("a",{class:"header-anchor",href:"#StableHLO-Dialect","aria-label":'Permalink to "StableHLO Dialect {#StableHLO-Dialect}"'},"​")],-1)),e[340]||(e[340]=t("p",null,[a("Refer to the "),t("a",{href:"https://openxla.org/stablehlo",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),t("details",d,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.abs-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.abs-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.abs")],-1)),e[1]||(e[1]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=n('

abs

Performs element-wise abs operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#abs

Example

mlir
%result = stablehlo.abs %operand : tensor<3xi32>

source

',6))]),t("details",b,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.add-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.add-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.add")],-1)),e[4]||(e[4]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=n('

add

Performs element-wise addition of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#add

Example

mlir
%result = stablehlo.add %lhs, %rhs : tensor<2x2xi32>

source

',6))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.after_all-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.after_all-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.after_all")],-1)),e[7]||(e[7]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=n('

after_all

Ensures that the operations producing the inputs are executed before any operations that depend on result.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all

Example

mlir
%result = stablehlo.after_all %input0, %input1 : !stablehlo.token

source

',6))]),t("details",h,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_gather-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_gather-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_gather")],-1)),e[10]||(e[10]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=n(`

all_gather

Within each process group in the process grid, concatenates the values of the operand tensor from each process along all_gather_dim and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_gather

Example

mlir
%result:2 = "stablehlo.all_gather"(%operand0, %operand1) {
+import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framework.B4pBzPBy.js";const yt=JSON.parse('{"title":"StableHLO Dialect","description":"","frontmatter":{},"headers":[],"relativePath":"api/stablehlo.md","filePath":"api/stablehlo.md","lastUpdated":null}'),c={name:"api/stablehlo.md"},d={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},x={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},I={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},_={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},w={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},H={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},q={class:"jldocstring custom-block"},P={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Q={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"},te={class:"jldocstring custom-block"},ae={class:"jldocstring custom-block"},se={class:"jldocstring custom-block"},le={class:"jldocstring custom-block"},ne={class:"jldocstring custom-block"},oe={class:"jldocstring custom-block"},ie={class:"jldocstring custom-block"},pe={class:"jldocstring custom-block"},re={class:"jldocstring custom-block"},ce={class:"jldocstring custom-block"},de={class:"jldocstring custom-block"},be={class:"jldocstring custom-block"},ue={class:"jldocstring custom-block"},he={class:"jldocstring custom-block"},me={class:"jldocstring custom-block"},ge={class:"jldocstring custom-block"},Re={class:"jldocstring custom-block"},fe={class:"jldocstring custom-block"},xe={class:"jldocstring custom-block"},Le={class:"jldocstring custom-block"},Ie={class:"jldocstring custom-block"},Me={class:"jldocstring custom-block"},ye={class:"jldocstring custom-block"},je={class:"jldocstring custom-block"},ke={class:"jldocstring custom-block"},ve={class:"jldocstring custom-block"},De={class:"jldocstring custom-block"},_e={class:"jldocstring custom-block"},Te={class:"jldocstring custom-block"},Ve={class:"jldocstring custom-block"},Oe={class:"jldocstring custom-block"},Ee={class:"jldocstring custom-block"},Se={class:"jldocstring custom-block"},Ce={class:"jldocstring custom-block"},we={class:"jldocstring custom-block"},ze={class:"jldocstring custom-block"},He={class:"jldocstring custom-block"},Ae={class:"jldocstring custom-block"},qe={class:"jldocstring custom-block"},Pe={class:"jldocstring custom-block"},Ne={class:"jldocstring custom-block"},Fe={class:"jldocstring custom-block"},Be={class:"jldocstring custom-block"},Ue={class:"jldocstring custom-block"},We={class:"jldocstring custom-block"},Ge={class:"jldocstring custom-block"},Xe={class:"jldocstring custom-block"},$e={class:"jldocstring custom-block"},Ye={class:"jldocstring custom-block"},Je={class:"jldocstring custom-block"},Ke={class:"jldocstring custom-block"},Qe={class:"jldocstring custom-block"},Ze={class:"jldocstring custom-block"},et={class:"jldocstring custom-block"},tt={class:"jldocstring custom-block"},at={class:"jldocstring custom-block"},st={class:"jldocstring custom-block"},lt={class:"jldocstring custom-block"},nt={class:"jldocstring custom-block"},ot={class:"jldocstring custom-block"},it={class:"jldocstring custom-block"},pt={class:"jldocstring custom-block"},rt={class:"jldocstring custom-block"},ct={class:"jldocstring custom-block"},dt={class:"jldocstring custom-block"},bt={class:"jldocstring custom-block"},ut={class:"jldocstring custom-block"},ht={class:"jldocstring custom-block"},mt={class:"jldocstring custom-block"};function gt(Rt,e,ft,xt,Lt,It){const s=p("Badge");return r(),i("div",null,[e[339]||(e[339]=t("h1",{id:"StableHLO-Dialect",tabindex:"-1"},[a("StableHLO Dialect "),t("a",{class:"header-anchor",href:"#StableHLO-Dialect","aria-label":'Permalink to "StableHLO Dialect {#StableHLO-Dialect}"'},"​")],-1)),e[340]||(e[340]=t("p",null,[a("Refer to the "),t("a",{href:"https://openxla.org/stablehlo",target:"_blank",rel:"noreferrer"},"official documentation"),a(" for more details.")],-1)),t("details",d,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.abs-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.abs-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.abs")],-1)),e[1]||(e[1]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=n('
abs
Performs element-wise abs operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#abs
Example
mlir
%result = stablehlo.abs %operand : tensor<3xi32>
source
',6))]),t("details",b,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.add-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.add-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.add")],-1)),e[4]||(e[4]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=n('
add
Performs element-wise addition of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#add
Example
mlir
%result = stablehlo.add %lhs, %rhs : tensor<2x2xi32>
source
',6))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.after_all-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.after_all-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.after_all")],-1)),e[7]||(e[7]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=n('
after_all
Ensures that the operations producing the inputs are executed before any operations that depend on result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all
Example
mlir
%result = stablehlo.after_all %input0, %input1 : !stablehlo.token
source
',6))]),t("details",h,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_gather-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_gather-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_gather")],-1)),e[10]||(e[10]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[11]||(e[11]=n(`
all_gather
Within each process group in the process grid, concatenates the values of the operand tensor from each process along all_gather_dim and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_gather
Example
mlir
%result:2 = "stablehlo.all_gather"(%operand0, %operand1) {
   all_gather_dim = 1 : i64,
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<2x2xi64>, tensor<2x2xi64>) -> (tensor<2x4xi64>, tensor<2x4xi64>)
source
`,6))]),t("details",m,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_reduce-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_reduce-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_reduce")],-1)),e[13]||(e[13]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=n(`
all_reduce
Within each process group in the process grid, applies a reduction function computation to the values of the operand tensor from each process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result:2 = "stablehlo.all_reduce"(%operand0, %operand0) ({
+} : (tensor<2x2xi64>, tensor<2x2xi64>) -> (tensor<2x4xi64>, tensor<2x4xi64>)
source
`,6))]),t("details",m,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_reduce-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_reduce-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_reduce")],-1)),e[13]||(e[13]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=n(`
all_reduce
Within each process group in the process grid, applies a reduction function computation to the values of the operand tensor from each process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result:2 = "stablehlo.all_reduce"(%operand0, %operand0) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
   %0 = "stablehlo.add"(%arg0, %arg1) : (tensor<i64>, tensor<i64>) -> tensor<i64>
   "stablehlo.return"(%0) : (tensor<i64>) -> ()
 }) {
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<4xi64>, tensor<4xi64>) -> (tensor<4xi64>, tensor<4xi64>)
source
`,6))]),t("details",g,[t("summary",null,[e[15]||(e[15]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_to_all-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_to_all-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_to_all")],-1)),e[16]||(e[16]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=n(`
all_to_all
Within each process group in the process grid, splits the values of the operand tensor along split_dimension into parts, scatters the split parts between the processes, concatenates the scattered parts along concat_dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_to_all
Example
mlir
%result:2 = "stablehlo.all_to_all"(%operand1, %operand2) {
+} : (tensor<4xi64>, tensor<4xi64>) -> (tensor<4xi64>, tensor<4xi64>)
source
`,6))]),t("details",g,[t("summary",null,[e[15]||(e[15]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.all_to_all-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.all_to_all-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.all_to_all")],-1)),e[16]||(e[16]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=n(`
all_to_all
Within each process group in the process grid, splits the values of the operand tensor along split_dimension into parts, scatters the split parts between the processes, concatenates the scattered parts along concat_dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_to_all
Example
mlir
%result:2 = "stablehlo.all_to_all"(%operand1, %operand2) {
   split_dimension = 1 : i64,
   concat_dimension = 0 : i64,
   split_count = 2 : i64,
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
-} : (tensor<2x4xi64>, tensor<2x4xi64>) -> (tensor<4x2xi64>, tensor<4x2xi64>)
source
`,6))]),t("details",R,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.and-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.and-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.and")],-1)),e[19]||(e[19]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=n('
and
Performs element-wise AND of two tensors lhs and rhs and produces a result tensor
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#and
Example
mlir
%result = stablehlo.and %lhs, %rhs : tensor<2x2xi32>
source
',6))]),t("details",f,[t("summary",null,[e[21]||(e[21]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.atan2-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.atan2-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.atan2")],-1)),e[22]||(e[22]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=n('
atan2
Performs element-wise atan2 operation on lhs and rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#atan2
Example
mlir
%result = stablehlo.atan2 %lhs, %rhs : tensor<3xf64>
source
',6))]),t("details",x,[t("summary",null,[e[24]||(e[24]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_grad-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_grad-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_grad")],-1)),e[25]||(e[25]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=n(`
batch_norm_grad
Computes gradients of several inputs of BatchNormTrainingOp backpropagating from grad_output, and produces grad_operand, grad_scale and grad_offset tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_grad
Example
mlir
%grad_operand, %grad_scale, %grad_offset =
+} : (tensor<2x4xi64>, tensor<2x4xi64>) -> (tensor<4x2xi64>, tensor<4x2xi64>)
source
`,6))]),t("details",R,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.and-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.and-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.and")],-1)),e[19]||(e[19]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=n('
and
Performs element-wise AND of two tensors lhs and rhs and produces a result tensor
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#and
Example
mlir
%result = stablehlo.and %lhs, %rhs : tensor<2x2xi32>
source
',6))]),t("details",f,[t("summary",null,[e[21]||(e[21]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.atan2-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.atan2-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.atan2")],-1)),e[22]||(e[22]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=n('
atan2
Performs element-wise atan2 operation on lhs and rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#atan2
Example
mlir
%result = stablehlo.atan2 %lhs, %rhs : tensor<3xf64>
source
',6))]),t("details",x,[t("summary",null,[e[24]||(e[24]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_grad-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_grad-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_grad")],-1)),e[25]||(e[25]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=n(`
batch_norm_grad
Computes gradients of several inputs of BatchNormTrainingOp backpropagating from grad_output, and produces grad_operand, grad_scale and grad_offset tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_grad
Example
mlir
%grad_operand, %grad_scale, %grad_offset =
 "stablehlo.batch_norm_grad"(%operand, %scale, %mean, %variance, %grad_output) {
   epsilon = 0.0 : f32,
   feature_index = 2 : i64
 } : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>,
-     tensor<2x2x2xf64>) -> (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
`,6))]),t("details",L,[t("summary",null,[e[27]||(e[27]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_inference-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_inference-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_inference")],-1)),e[28]||(e[28]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=n(`
batch_norm_inference
Normalizes the operand tensor across all dimensions except for the feature_index dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_inference
Example
mlir
%result = "stablehlo.batch_norm_inference"(%operand, %scale, %offset, %mean, %variance) {
+     tensor<2x2x2xf64>) -> (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
`,6))]),t("details",L,[t("summary",null,[e[27]||(e[27]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_inference-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_inference-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_inference")],-1)),e[28]||(e[28]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=n(`
batch_norm_inference
Normalizes the operand tensor across all dimensions except for the feature_index dimension and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_inference
Example
mlir
%result = "stablehlo.batch_norm_inference"(%operand, %scale, %offset, %mean, %variance) {
   epsilon = 0.0 : f32,
   feature_index = 2 : i64
-} : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>) -> tensor<2x2x2xf64>
source
`,6))]),t("details",I,[t("summary",null,[e[30]||(e[30]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_training-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_training-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_training")],-1)),e[31]||(e[31]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=n(`
batch_norm_training
Computes mean and variance across batch and spatial dimensions and normalizes the operand tensor, for each feature in the feature_index dimension and produces output, batch_mean and batch_var tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_training
Example
mlir
%output, %batch_mean, %batch_var = "stablehlo.batch_norm_training"(%operand, %scale, %offset) {
+} : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>) -> tensor<2x2x2xf64>
source
`,6))]),t("details",I,[t("summary",null,[e[30]||(e[30]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.batch_norm_training-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.batch_norm_training-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.batch_norm_training")],-1)),e[31]||(e[31]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=n(`
batch_norm_training
Computes mean and variance across batch and spatial dimensions and normalizes the operand tensor, for each feature in the feature_index dimension and produces output, batch_mean and batch_var tensors.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_training
Example
mlir
%output, %batch_mean, %batch_var = "stablehlo.batch_norm_training"(%operand, %scale, %offset) {
   epsilon = 0.0 : f32,
   feature_index = 2 : i64
 } : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>) ->
-    (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
`,6))]),t("details",M,[t("summary",null,[e[33]||(e[33]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.bitcast_convert-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.bitcast_convert-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.bitcast_convert")],-1)),e[34]||(e[34]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=n('
bitcast_convert
Performs a bitcast operation on operand tensor and produces a result tensor where the bits of the entire operand tensor are reinterpreted using the type of the result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#bitcast_convert
Example
mlir
%result = stablehlo.bitcast_convert %operand : (tensor<f64>) -> tensor<4xf16>
source
',6))]),t("details",y,[t("summary",null,[e[36]||(e[36]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.broadcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.broadcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.broadcast")],-1)),e[37]||(e[37]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=n('
broadcast
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Broadcast: https://www.tensorflow.org/xla/operation_semantics#broadcast
Example
mlir
%result = stablehlo.broadcast %operand, sizes = [1, 2] : (tensor<3xi32>) -> tensor<1x2x3xi32>
source
',6))]),t("details",j,[t("summary",null,[e[39]||(e[39]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim")],-1)),e[40]||(e[40]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=n('
broadcast_in_dim
Expands the dimensions and/or rank of an input tensor by duplicating the data in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#broadcast_in_dim
Example
mlir
%result = stablehlo.broadcast_in_dim %operand, dims = [2, 1] : (tensor<1x3xi32>) -> tensor<2x3x2xi32>
source
',6))]),t("details",k,[t("summary",null,[e[42]||(e[42]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.case-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.case-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.case")],-1)),e[43]||(e[43]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[44]||(e[44]=n(`
case
Produces the output from executing exactly one function from branches depending on the value of index.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#case
Example
mlir
%result0, %result1 = "stablehlo.case"(%index) ({
+    (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)
source
`,6))]),t("details",M,[t("summary",null,[e[33]||(e[33]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.bitcast_convert-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.bitcast_convert-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.bitcast_convert")],-1)),e[34]||(e[34]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=n('
bitcast_convert
Performs a bitcast operation on operand tensor and produces a result tensor where the bits of the entire operand tensor are reinterpreted using the type of the result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#bitcast_convert
Example
mlir
%result = stablehlo.bitcast_convert %operand : (tensor<f64>) -> tensor<4xf16>
source
',6))]),t("details",y,[t("summary",null,[e[36]||(e[36]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.broadcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.broadcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.broadcast")],-1)),e[37]||(e[37]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=n('
broadcast
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Broadcast: https://www.tensorflow.org/xla/operation_semantics#broadcast
Example
mlir
%result = stablehlo.broadcast %operand, sizes = [1, 2] : (tensor<3xi32>) -> tensor<1x2x3xi32>
source
',6))]),t("details",j,[t("summary",null,[e[39]||(e[39]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim")],-1)),e[40]||(e[40]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=n('
broadcast_in_dim
Expands the dimensions and/or rank of an input tensor by duplicating the data in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#broadcast_in_dim
Example
mlir
%result = stablehlo.broadcast_in_dim %operand, dims = [2, 1] : (tensor<1x3xi32>) -> tensor<2x3x2xi32>
source
',6))]),t("details",k,[t("summary",null,[e[42]||(e[42]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.case-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.case-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.case")],-1)),e[43]||(e[43]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[44]||(e[44]=n(`
case
Produces the output from executing exactly one function from branches depending on the value of index.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#case
Example
mlir
%result0, %result1 = "stablehlo.case"(%index) ({
   stablehlo.return %result_branch0, %result_branch0 : tensor<2xi64>, tensor<2xi64>
 }, {
   stablehlo.return %result_branch1, %result_branch1 : tensor<2xi64>, tensor<2xi64>
-}) : (tensor<i32>) -> (tensor<2xi64>, tensor<2xi64>)
source
`,6))]),t("details",v,[t("summary",null,[e[45]||(e[45]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cbrt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cbrt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cbrt")],-1)),e[46]||(e[46]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[47]||(e[47]=n('
cbrt
Performs element-wise cubic root operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cbrt
Example
mlir
%result = stablehlo.cbrt %operand : tensor<4xf64>
source
',6))]),t("details",D,[t("summary",null,[e[48]||(e[48]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.ceil-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.ceil-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.ceil")],-1)),e[49]||(e[49]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[50]||(e[50]=n('
ceil
Performs element-wise ceil of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#ceil
Example
mlir
%result = stablehlo.ceil %operand : tensor<5xf32>
source
',6))]),t("details",_,[t("summary",null,[e[51]||(e[51]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cholesky-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cholesky-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cholesky")],-1)),e[52]||(e[52]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[53]||(e[53]=n('
cholesky
Computes the Cholesky decomposition of a batch of matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cholesky
Example
mlir
%result = stablehlo.cholesky %a, lower = true : tensor<3x3xf64>
source
',6))]),t("details",T,[t("summary",null,[e[54]||(e[54]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.clamp-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.clamp-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.clamp")],-1)),e[55]||(e[55]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[56]||(e[56]=n('
clamp
Clamps every element of the operand tensor between a minimum and maximum value and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#clamp
Example
mlir
%result = stablehlo.clamp %min, %operand, %max : tensor<3xi32>
source
',6))]),t("details",V,[t("summary",null,[e[57]||(e[57]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.collective_broadcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.collective_broadcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.collective_broadcast")],-1)),e[58]||(e[58]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[59]||(e[59]=n(`
collective_broadcast
Within each process group in the process grid, send the value of the operand tensor from the source process to the target processes and produce a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_broadcast
Example
mlir
%result = "stablehlo.collective_broadcast"(%operand) {
+}) : (tensor<i32>) -> (tensor<2xi64>, tensor<2xi64>)
source
`,6))]),t("details",v,[t("summary",null,[e[45]||(e[45]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cbrt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cbrt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cbrt")],-1)),e[46]||(e[46]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[47]||(e[47]=n('
cbrt
Performs element-wise cubic root operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cbrt
Example
mlir
%result = stablehlo.cbrt %operand : tensor<4xf64>
source
',6))]),t("details",D,[t("summary",null,[e[48]||(e[48]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.ceil-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.ceil-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.ceil")],-1)),e[49]||(e[49]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[50]||(e[50]=n('
ceil
Performs element-wise ceil of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#ceil
Example
mlir
%result = stablehlo.ceil %operand : tensor<5xf32>
source
',6))]),t("details",_,[t("summary",null,[e[51]||(e[51]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cholesky-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cholesky-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cholesky")],-1)),e[52]||(e[52]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[53]||(e[53]=n('
cholesky
Computes the Cholesky decomposition of a batch of matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cholesky
Example
mlir
%result = stablehlo.cholesky %a, lower = true : tensor<3x3xf64>
source
',6))]),t("details",T,[t("summary",null,[e[54]||(e[54]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.clamp-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.clamp-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.clamp")],-1)),e[55]||(e[55]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[56]||(e[56]=n('
clamp
Clamps every element of the operand tensor between a minimum and maximum value and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#clamp
Example
mlir
%result = stablehlo.clamp %min, %operand, %max : tensor<3xi32>
source
',6))]),t("details",V,[t("summary",null,[e[57]||(e[57]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.collective_broadcast-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.collective_broadcast-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.collective_broadcast")],-1)),e[58]||(e[58]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[59]||(e[59]=n(`
collective_broadcast
Within each process group in the process grid, send the value of the operand tensor from the source process to the target processes and produce a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_broadcast
Example
mlir
%result = "stablehlo.collective_broadcast"(%operand) {
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<1x2xi64>) -> tensor<1x2xi64>
source
`,6))]),t("details",O,[t("summary",null,[e[60]||(e[60]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.collective_permute-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.collective_permute-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.collective_permute")],-1)),e[61]||(e[61]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[62]||(e[62]=n(`
collective_permute
Within each process group in the process grid, sends the value of the operand tensor from the source process to the target process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_permute
Example
mlir
%result = "stablehlo.collective_permute"(%operand) {
+} : (tensor<1x2xi64>) -> tensor<1x2xi64>
source
`,6))]),t("details",O,[t("summary",null,[e[60]||(e[60]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.collective_permute-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.collective_permute-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.collective_permute")],-1)),e[61]||(e[61]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[62]||(e[62]=n(`
collective_permute
Within each process group in the process grid, sends the value of the operand tensor from the source process to the target process and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_permute
Example
mlir
%result = "stablehlo.collective_permute"(%operand) {
   source_target_pairs = dense<[[0, 1], [1, 2]]> : tensor<2x2xi64>,
   channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
-} : (tensor<2x2xi64>) -> tensor<2x2xi64>
source
`,6))]),t("details",E,[t("summary",null,[e[63]||(e[63]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.compare-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.compare-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.compare")],-1)),e[64]||(e[64]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[65]||(e[65]=n('
compare
Performs element-wise comparison of lhs and rhs tensors according to comparison_direction and compare_type, and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#compare
Example
mlir
%result = stablehlo.compare LT, %lhs, %rhs, FLOAT : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1>
source
',6))]),t("details",S,[t("summary",null,[e[66]||(e[66]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.complex-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.complex-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.complex")],-1)),e[67]||(e[67]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[68]||(e[68]=n('
complex
Performs element-wise conversion to a complex value from a pair of real and imaginary values, lhs and rhs, and produces a result tensor. See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#complex
Example
mlir
%result = stablehlo.complex %lhs, %rhs : tensor<2xcomplex<f64>>
source
',5))]),t("details",C,[t("summary",null,[e[69]||(e[69]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.composite-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.composite-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.composite")],-1)),e[70]||(e[70]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[71]||(e[71]=n(`
composite
Encapsulates an operation made up (composed) of other StableHLO operations, taking inputs and composite_attributes and producing results. The semantics of the op are implemented by the decomposition attribute. The composite op can be replaced with its decomposition without changing program semantics. In cases where inlining the decomposition does not provide the same op semantics, prefer using custom_call.
The version field (defaults to 0) is used to denote when a composite's semantics change.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#composite
Example
mlir
%results = stablehlo.composite "my.op" %input0, %input1 {
+} : (tensor<2x2xi64>) -> tensor<2x2xi64>
source
`,6))]),t("details",E,[t("summary",null,[e[63]||(e[63]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.compare-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.compare-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.compare")],-1)),e[64]||(e[64]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[65]||(e[65]=n('
compare
Performs element-wise comparison of lhs and rhs tensors according to comparison_direction and compare_type, and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#compare
Example
mlir
%result = stablehlo.compare LT, %lhs, %rhs, FLOAT : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1>
source
',6))]),t("details",S,[t("summary",null,[e[66]||(e[66]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.complex-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.complex-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.complex")],-1)),e[67]||(e[67]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[68]||(e[68]=n('
complex
Performs element-wise conversion to a complex value from a pair of real and imaginary values, lhs and rhs, and produces a result tensor. See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#complex
Example
mlir
%result = stablehlo.complex %lhs, %rhs : tensor<2xcomplex<f64>>
source
',5))]),t("details",C,[t("summary",null,[e[69]||(e[69]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.composite-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.composite-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.composite")],-1)),e[70]||(e[70]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[71]||(e[71]=n(`
composite
Encapsulates an operation made up (composed) of other StableHLO operations, taking inputs and composite_attributes and producing results. The semantics of the op are implemented by the decomposition attribute. The composite op can be replaced with its decomposition without changing program semantics. In cases where inlining the decomposition does not provide the same op semantics, prefer using custom_call.
The version field (defaults to 0) is used to denote when a composite's semantics change.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#composite
Example
mlir
%results = stablehlo.composite "my.op" %input0, %input1 {
   composite_attributes = {
     my_attribute = "my_value"
   },
   decomposition = @my_op,
   version = 1 : i32
-} : (tensor<f32>, tensor<f32>) -> tensor<f32>
source
`,7))]),t("details",w,[t("summary",null,[e[72]||(e[72]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.concatenate-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.concatenate-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.concatenate")],-1)),e[73]||(e[73]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[74]||(e[74]=n('
concatenate
Concatenates a variadic number of tensors in inputs along dimension dimension in the same order as the given arguments and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#concatenate
Example
mlir
%result = stablehlo.concatenate %input0, %input1, dim = 0 : (tensor<3x2xi64>, tensor<1x2xi64>) -> tensor<4x2xi64>
source
',6))]),t("details",z,[t("summary",null,[e[75]||(e[75]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.constant-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.constant")],-1)),e[76]||(e[76]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[77]||(e[77]=n('
constant
Produces an output tensor from a constant value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#constant
Example
mlir
%output = stablehlo.constant dense<[[0.0, 1.0], [2.0, 3.0]]> : tensor<2x2xf32>
source
',6))]),t("details",H,[t("summary",null,[e[78]||(e[78]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.convert-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.convert-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.convert")],-1)),e[79]||(e[79]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[80]||(e[80]=n('
convert
Performs an element-wise conversion from one element type to another on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convert
Example
mlir
%result = stablehlo.convert %operand : (tensor<3xi64>) -> tensor<3xcomplex<f64>>
source
',6))]),t("details",A,[t("summary",null,[e[81]||(e[81]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.convolution-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.convolution-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.convolution")],-1)),e[82]||(e[82]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[83]||(e[83]=n(`
convolution
Computes dot products between windows of lhs and slices of rhs and produces result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convolution
Example
mlir
%result = stablehlo.convolution(%lhs, %rhs)
+} : (tensor<f32>, tensor<f32>) -> tensor<f32>
source
`,7))]),t("details",w,[t("summary",null,[e[72]||(e[72]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.concatenate-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.concatenate-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.concatenate")],-1)),e[73]||(e[73]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[74]||(e[74]=n('
concatenate
Concatenates a variadic number of tensors in inputs along dimension dimension in the same order as the given arguments and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#concatenate
Example
mlir
%result = stablehlo.concatenate %input0, %input1, dim = 0 : (tensor<3x2xi64>, tensor<1x2xi64>) -> tensor<4x2xi64>
source
',6))]),t("details",z,[t("summary",null,[e[75]||(e[75]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.constant-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.constant-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.constant")],-1)),e[76]||(e[76]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[77]||(e[77]=n('
constant
Produces an output tensor from a constant value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#constant
Example
mlir
%output = stablehlo.constant dense<[[0.0, 1.0], [2.0, 3.0]]> : tensor<2x2xf32>
source
',6))]),t("details",H,[t("summary",null,[e[78]||(e[78]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.convert-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.convert-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.convert")],-1)),e[79]||(e[79]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[80]||(e[80]=n('
convert
Performs an element-wise conversion from one element type to another on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convert
Example
mlir
%result = stablehlo.convert %operand : (tensor<3xi64>) -> tensor<3xcomplex<f64>>
source
',6))]),t("details",A,[t("summary",null,[e[81]||(e[81]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.convolution-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.convolution-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.convolution")],-1)),e[82]||(e[82]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[83]||(e[83]=n(`
convolution
Computes dot products between windows of lhs and slices of rhs and produces result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convolution
Example
mlir
%result = stablehlo.convolution(%lhs, %rhs)
   dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
   window = {
     stride = [4, 4],
@@ -55,24 +55,24 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
     batch_group_count = 1 : i64,
     precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
   } :
-(tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>) -> tensor<1x2x2x1xi64>
source
`,6))]),t("details",q,[t("summary",null,[e[84]||(e[84]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cosine-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cosine-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cosine")],-1)),e[85]||(e[85]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[86]||(e[86]=n('
cosine
Performs element-wise cosine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cosine
Example
mlir
%result = stablehlo.cosine %operand : tensor<2xf32>
source
',6))]),t("details",P,[t("summary",null,[e[87]||(e[87]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.count_leading_zeros-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.count_leading_zeros-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.count_leading_zeros")],-1)),e[88]||(e[88]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[89]||(e[89]=n('
count_leading_zeros
Performs element-wise count of the number of leading zero bits in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#count_leading_zeros
Example
mlir
%result = stablehlo.count_leading_zeros %operand : tensor<2x2xi64>
source
',6))]),t("details",N,[t("summary",null,[e[90]||(e[90]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.create_token-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.create_token-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.create_token")],-1)),e[91]||(e[91]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=n('
create_token
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AfterAllOp with 0 inputs: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all
Example
mlir
%output = stablehlo.create_token : !stablehlo.token
source
',6))]),t("details",F,[t("summary",null,[e[93]||(e[93]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cross_replica_sum-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cross_replica_sum-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cross_replica_sum")],-1)),e[94]||(e[94]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[95]||(e[95]=n(`
cross_replica_sum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AllReduceOp with channel_id = 0, use_global_device_ids = false and computation implementing addition: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result = "stablehlo.cross-replica-sum"(%operand) {
+(tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>) -> tensor<1x2x2x1xi64>
source
`,6))]),t("details",q,[t("summary",null,[e[84]||(e[84]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cosine-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cosine-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cosine")],-1)),e[85]||(e[85]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[86]||(e[86]=n('
cosine
Performs element-wise cosine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cosine
Example
mlir
%result = stablehlo.cosine %operand : tensor<2xf32>
source
',6))]),t("details",P,[t("summary",null,[e[87]||(e[87]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.count_leading_zeros-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.count_leading_zeros-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.count_leading_zeros")],-1)),e[88]||(e[88]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[89]||(e[89]=n('
count_leading_zeros
Performs element-wise count of the number of leading zero bits in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#count_leading_zeros
Example
mlir
%result = stablehlo.count_leading_zeros %operand : tensor<2x2xi64>
source
',6))]),t("details",N,[t("summary",null,[e[90]||(e[90]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.create_token-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.create_token-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.create_token")],-1)),e[91]||(e[91]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=n('
create_token
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AfterAllOp with 0 inputs: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all
Example
mlir
%output = stablehlo.create_token : !stablehlo.token
source
',6))]),t("details",F,[t("summary",null,[e[93]||(e[93]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.cross_replica_sum-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.cross_replica_sum-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.cross_replica_sum")],-1)),e[94]||(e[94]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[95]||(e[95]=n(`
cross_replica_sum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as AllReduceOp with channel_id = 0, use_global_device_ids = false and computation implementing addition: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce
Example
mlir
%result = "stablehlo.cross-replica-sum"(%operand) {
   replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
-} : (tensor<4xf32>) -> tensor<4xf32>
source
`,6))]),t("details",B,[t("summary",null,[e[96]||(e[96]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.custom_call-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.custom_call-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.custom_call")],-1)),e[97]||(e[97]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[98]||(e[98]=n(`
custom_call
Encapsulates an implementation-defined operation call_target_name that takes inputs and called_computations and produces results.
Depending on the API version there are two ways to pass extra bits of static information to the external function:
  1. Use API_VERSION_TYPED_FFI which allows passing a dictionary attribute.
  2. Use a previous API version with a StringAttr to encode backend config.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#custom_call
Example
mlir
%results = stablehlo.custom_call @foo(%input0) {
+} : (tensor<4xf32>) -> tensor<4xf32>
source
`,6))]),t("details",B,[t("summary",null,[e[96]||(e[96]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.custom_call-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.custom_call-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.custom_call")],-1)),e[97]||(e[97]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[98]||(e[98]=n(`
custom_call
Encapsulates an implementation-defined operation call_target_name that takes inputs and called_computations and produces results.
Depending on the API version there are two ways to pass extra bits of static information to the external function:
  1. Use API_VERSION_TYPED_FFI which allows passing a dictionary attribute.
  2. Use a previous API version with a StringAttr to encode backend config.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#custom_call
Example
mlir
%results = stablehlo.custom_call @foo(%input0) {
   backend_config = {bar = 42 : i32},
   api_version = 4 : i32,
   called_computations = [@foo]
-} : (tensor<f64>) -> tensor<f64>
source
`,8))]),t("details",U,[t("summary",null,[e[99]||(e[99]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.divide-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.divide-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.divide")],-1)),e[100]||(e[100]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[101]||(e[101]=n('
divide
Performs element-wise division of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#divide
Example
mlir
%result = stablehlo.divide %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",W,[t("summary",null,[e[102]||(e[102]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dot-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dot-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dot")],-1)),e[103]||(e[103]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[104]||(e[104]=n('
dot
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Dot: https://www.tensorflow.org/xla/operation_semantics#dot
Example
mlir
%0 = stablehlo.dot %arg0, %arg1 : (tensor<1x2xi32>, tensor<2x1xi32>) -> tensor<1x1xi32>
source
',6))]),t("details",G,[t("summary",null,[e[105]||(e[105]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dot_general-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dot_general-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dot_general")],-1)),e[106]||(e[106]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[107]||(e[107]=n(`
dot_general
Computes dot products between slices of lhs and slices of rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dot_general
Example
mlir
%result = stablehlo.dot_general %lhs, %rhs,
+} : (tensor<f64>) -> tensor<f64>
source
`,8))]),t("details",U,[t("summary",null,[e[99]||(e[99]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.divide-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.divide-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.divide")],-1)),e[100]||(e[100]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[101]||(e[101]=n('
divide
Performs element-wise division of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#divide
Example
mlir
%result = stablehlo.divide %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",W,[t("summary",null,[e[102]||(e[102]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dot-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dot-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dot")],-1)),e[103]||(e[103]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[104]||(e[104]=n('
dot
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as XLA's Dot: https://www.tensorflow.org/xla/operation_semantics#dot
Example
mlir
%0 = stablehlo.dot %arg0, %arg1 : (tensor<1x2xi32>, tensor<2x1xi32>) -> tensor<1x1xi32>
source
',6))]),t("details",G,[t("summary",null,[e[105]||(e[105]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dot_general-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dot_general-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dot_general")],-1)),e[106]||(e[106]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[107]||(e[107]=n(`
dot_general
Computes dot products between slices of lhs and slices of rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dot_general
Example
mlir
%result = stablehlo.dot_general %lhs, %rhs,
   batching_dims = [0] x [0],
   contracting_dims = [2] x [1],
   precision = [DEFAULT, DEFAULT],
   algorithm = <lhs_precision_type = tf32, rhs_precision_type = tf32, accumulation_type = f32, lhs_component_count = 1, rhs_component_count = 1, num_primitive_operations = 1, allow_imprecise_accumulation = false>
-  : (tensor<2x2x2xi64>, tensor<2x2x2xi64>) -> tensor<2x2x2xi64>
source
`,6))]),t("details",X,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim")],-1)),e[109]||(e[109]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=n(`
dynamic_broadcast_in_dim
This operation is functionally identical to broadcast_in_dim op, but the result shape is specified dynamically via output_dimensions.
It also accepts optional attributes to express static knowledge about the expanding behavior of dimensions. If not specified, all dimensions are assumed to be possibly expanding. The sets of dimensions that are known to be expanding and the set of dimensions that are known to be non-expanding must be disjoint and they must be a subset of the operand's dimensions.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_broadcast_in_dim
Example
mlir
%operand = stablehlo.constant dense<[[1, 2, 3]]> : tensor<1x3xi64>
+  : (tensor<2x2x2xi64>, tensor<2x2x2xi64>) -> tensor<2x2x2xi64>
source
`,6))]),t("details",X,[t("summary",null,[e[108]||(e[108]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim")],-1)),e[109]||(e[109]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[110]||(e[110]=n(`
dynamic_broadcast_in_dim
This operation is functionally identical to broadcast_in_dim op, but the result shape is specified dynamically via output_dimensions.
It also accepts optional attributes to express static knowledge about the expanding behavior of dimensions. If not specified, all dimensions are assumed to be possibly expanding. The sets of dimensions that are known to be expanding and the set of dimensions that are known to be non-expanding must be disjoint and they must be a subset of the operand's dimensions.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_broadcast_in_dim
Example
mlir
%operand = stablehlo.constant dense<[[1, 2, 3]]> : tensor<1x3xi64>
 %output_dimensions = stablehlo.constant dense<[2, 3, 2]> : tensor<3xi64>
 %result = "stablehlo.dynamic_broadcast_in_dim"(%operand, %output_dimensions) {
   broadcast_dimensions = array<i64: 2, 1>,
   known_expanding_dimensions = array<i64: 0>,
   known_nonexpanding_dimensions = array<i64: 1>
-} : (tensor<1x3xi64>, tensor<3xi64>) -> tensor<2x3x2xi64>
source
`,7))]),t("details",$,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_conv-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_conv-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_conv")],-1)),e[112]||(e[112]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=n(`
dynamic_conv
This operation is functionally identical to convolution op, but the padding is specified dynamically via padding.
Example
mlir
%padding = stablehlo.constant dense<2> : tensor<2x2xi64>
+} : (tensor<1x3xi64>, tensor<3xi64>) -> tensor<2x3x2xi64>
source
`,7))]),t("details",$,[t("summary",null,[e[111]||(e[111]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_conv-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_conv-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_conv")],-1)),e[112]||(e[112]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[113]||(e[113]=n(`
dynamic_conv
This operation is functionally identical to convolution op, but the padding is specified dynamically via padding.
Example
mlir
%padding = stablehlo.constant dense<2> : tensor<2x2xi64>
 %result = "stablehlo.dynamic_conv"(%lhs, %rhs, %padding) {
   window_strides = array<i64: 4, 4>,
   lhs_dilation = array<i64: 2, 2>,
@@ -82,7 +82,7 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
   batch_group_count = 1 : i64,
   feature_group_count = 1 : i64,
   precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
-} : (tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>, tensor<2x2xi64>) -> tensor<1x2x2x1xi64>
source
`,5))]),t("details",Y,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_gather")],-1)),e[115]||(e[115]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=n(`
dynamic_gather
This operation is functionally identical to gather op, with the slice_sizes specified dynamically as an operand.
Example
mlir
%slice_sizes = stablehlo.constant dense<[1, 2, 2]> : tensor<3xi64>
+} : (tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>, tensor<2x2xi64>) -> tensor<1x2x2x1xi64>
source
`,5))]),t("details",Y,[t("summary",null,[e[114]||(e[114]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_gather")],-1)),e[115]||(e[115]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[116]||(e[116]=n(`
dynamic_gather
This operation is functionally identical to gather op, with the slice_sizes specified dynamically as an operand.
Example
mlir
%slice_sizes = stablehlo.constant dense<[1, 2, 2]> : tensor<3xi64>
 %result = "stablehlo.dynamic_gather"(%operand, %start_indices, %slice_sizes) {
   dimension_numbers = #stablehlo.gather<
     offset_dims = [2, 3],
@@ -90,18 +90,18 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
     start_index_map = [0, 2],
     index_vector_dim = 2>,
   indices_are_sorted = false
-} : (tensor<3x4x2xi64>, tensor<2x3x2xi64>, tensor<3xi64>) -> tensor<2x3x2x2xi64>
source
`,5))]),t("details",J,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_iota-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_iota-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_iota")],-1)),e[118]||(e[118]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[119]||(e[119]=n(`
dynamic_iota
This operation is functionally identical to iota op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_iota
Example
mlir
%output_shape = stablehlo.constant dense<[4, 5]> : tensor<2xi64>
-%0 = stablehlo.dynamic_iota %output_shape, dim = 0 : (tensor<2xi64>) -> tensor<4x5xi64>
source
`,6))]),t("details",K,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_pad-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_pad-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_pad")],-1)),e[121]||(e[121]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=n(`
dynamic_pad
This operation is functionally identical to pad https://github.com/openxla/stablehlo/pull/2306#discussion_r1595669709 op, but with edge_padding_low,edge_padding_highandinterior_padding specified dynamically as values.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_pad
Example
mlir
%edge_padding_low = stablehlo.constant dense<[0, 1]> : tensor<2xi32>
+} : (tensor<3x4x2xi64>, tensor<2x3x2xi64>, tensor<3xi64>) -> tensor<2x3x2x2xi64>
source
`,5))]),t("details",J,[t("summary",null,[e[117]||(e[117]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_iota-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_iota-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_iota")],-1)),e[118]||(e[118]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[119]||(e[119]=n(`
dynamic_iota
This operation is functionally identical to iota op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_iota
Example
mlir
%output_shape = stablehlo.constant dense<[4, 5]> : tensor<2xi64>
+%0 = stablehlo.dynamic_iota %output_shape, dim = 0 : (tensor<2xi64>) -> tensor<4x5xi64>
source
`,6))]),t("details",K,[t("summary",null,[e[120]||(e[120]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_pad-NTuple{5, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_pad-NTuple{5, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_pad")],-1)),e[121]||(e[121]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[122]||(e[122]=n(`
dynamic_pad
This operation is functionally identical to pad https://github.com/openxla/stablehlo/pull/2306#discussion_r1595669709 op, but with edge_padding_low,edge_padding_highandinterior_padding specified dynamically as values.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_pad
Example
mlir
%edge_padding_low = stablehlo.constant dense<[0, 1]> : tensor<2xi32>
 %edge_padding_high = stablehlo.constant dense<[2, 1]> : tensor<2xi32>
 %interior_padding = stablehlo.constant dense<[1, 2]> : tensor<2xi32>
 %result = stablehlo.dynamic_pad %operand, %padding_value,
             %edge_padding_low, %edge_padding_high, %interior_padding
-            : (tensor<2x3xi64>, tensor<i64>, tensor<2xi64>, tensor<2xi64>, tensor<2xi64>) -> tensor<5x9xi64>
source
`,6))]),t("details",Q,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_reshape-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_reshape-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_reshape")],-1)),e[124]||(e[124]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=n(`
dynamic_reshape
This operation is functionally identical to reshape op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_reshape
Example
mlir
%output_shape = stablehlo.constant dense<[3, 2]> : tensor<2xi64>
-%result = stablehlo.dynamic_reshape %operand, %output_shape : (tensor<2x3xi64>, tensor<2xi64>) -> tensor<3x2xi64>
source
`,6))]),t("details",Z,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_slice-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_slice-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_slice")],-1)),e[127]||(e[127]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=n(`
dynamic_slice
Extracts a slice from the operand using dynamically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_slice
Example
mlir
%result = stablehlo.dynamic_slice %operand, %start_indices0, %start_indices1, sizes = [2, 2]
-  : (tensor<4x4xi32>, tensor<i64>, tensor<i64>) -> tensor<2x2xi32>
source
`,6))]),t("details",ee,[t("summary",null,[e[129]||(e[129]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice")],-1)),e[130]||(e[130]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[131]||(e[131]=n(`
dynamic_update_slice
Produces a result tensor which is equal to the operand tensor except that the slice starting at start_indices is updated with the values in update.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_update_slice
Example
mlir
%result = stablehlo.dynamic_update_slice %operand, %update, %start_indices0, %start_indices1
-  : (tensor<4x4xi32>, tensor<2x2xi32>, tensor<i64>, tensor<i64>) -> tensor<4x4xi32>
source
`,6))]),t("details",te,[t("summary",null,[e[132]||(e[132]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.einsum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.einsum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.einsum")],-1)),e[133]||(e[133]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[134]||(e[134]=n(`
einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.einsum"(%lhs, %rhs) {
+            : (tensor<2x3xi64>, tensor<i64>, tensor<2xi64>, tensor<2xi64>, tensor<2xi64>) -> tensor<5x9xi64>
source
`,6))]),t("details",Q,[t("summary",null,[e[123]||(e[123]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_reshape-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_reshape-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_reshape")],-1)),e[124]||(e[124]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[125]||(e[125]=n(`
dynamic_reshape
This operation is functionally identical to reshape op, but the result shape is specified dynamically via output_shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_reshape
Example
mlir
%output_shape = stablehlo.constant dense<[3, 2]> : tensor<2xi64>
+%result = stablehlo.dynamic_reshape %operand, %output_shape : (tensor<2x3xi64>, tensor<2xi64>) -> tensor<3x2xi64>
source
`,6))]),t("details",Z,[t("summary",null,[e[126]||(e[126]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_slice-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_slice-Tuple{Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_slice")],-1)),e[127]||(e[127]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[128]||(e[128]=n(`
dynamic_slice
Extracts a slice from the operand using dynamically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_slice
Example
mlir
%result = stablehlo.dynamic_slice %operand, %start_indices0, %start_indices1, sizes = [2, 2]
+  : (tensor<4x4xi32>, tensor<i64>, tensor<i64>) -> tensor<2x2xi32>
source
`,6))]),t("details",ee,[t("summary",null,[e[129]||(e[129]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice")],-1)),e[130]||(e[130]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[131]||(e[131]=n(`
dynamic_update_slice
Produces a result tensor which is equal to the operand tensor except that the slice starting at start_indices is updated with the values in update.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_update_slice
Example
mlir
%result = stablehlo.dynamic_update_slice %operand, %update, %start_indices0, %start_indices1
+  : (tensor<4x4xi32>, tensor<2x2xi32>, tensor<i64>, tensor<i64>) -> tensor<4x4xi32>
source
`,6))]),t("details",te,[t("summary",null,[e[132]||(e[132]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.einsum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.einsum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.einsum")],-1)),e[133]||(e[133]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[134]||(e[134]=n(`
einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.einsum"(%lhs, %rhs) {
   einsum_config = "ab,bc->ac"
-} : (tensor<4x16xf32>, tensor<16x4xf32>) -> tensor<4x4xf32>
source
`,6))]),t("details",ae,[t("summary",null,[e[135]||(e[135]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.exponential-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.exponential-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.exponential")],-1)),e[136]||(e[136]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[137]||(e[137]=n('
exponential
Performs element-wise exponential operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential
Example
mlir
%result = stablehlo.exponential %operand : tensor<2x2xf64>
source
',6))]),t("details",se,[t("summary",null,[e[138]||(e[138]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.exponential_minus_one-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.exponential_minus_one-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.exponential_minus_one")],-1)),e[139]||(e[139]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[140]||(e[140]=n('
exponential_minus_one
Performs element-wise exponential minus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential_minus_one
Example
mlir
%result = stablehlo.exponential_minus_one %operand : tensor<2xf64>
source
',6))]),t("details",le,[t("summary",null,[e[141]||(e[141]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.fft-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.fft-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.fft")],-1)),e[142]||(e[142]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[143]||(e[143]=n('
fft
Performs the forward and inverse Fourier transforms for real and complex inputs/outputs.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#fft
Example
mlir
%result = stablehlo.fft %operand, type = FFT, length = [4] : (tensor<4xcomplex<f32>>) -> tensor<4xcomplex<f32>>
source
',6))]),t("details",ne,[t("summary",null,[e[144]||(e[144]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.floor-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.floor-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.floor")],-1)),e[145]||(e[145]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[146]||(e[146]=n('
floor
Performs element-wise floor of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#floor
Example
mlir
%result = stablehlo.floor %operand : tensor<2xf32>
source
',6))]),t("details",oe,[t("summary",null,[e[147]||(e[147]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.gather")],-1)),e[148]||(e[148]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[149]||(e[149]=n(`
gather
Gathers slices from operand tensor from offsets specified in start_indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather
Example
mlir
%result = "stablehlo.gather"(%operand, %start_indices) {
+} : (tensor<4x16xf32>, tensor<16x4xf32>) -> tensor<4x4xf32>
source
`,6))]),t("details",ae,[t("summary",null,[e[135]||(e[135]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.exponential-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.exponential-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.exponential")],-1)),e[136]||(e[136]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[137]||(e[137]=n('
exponential
Performs element-wise exponential operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential
Example
mlir
%result = stablehlo.exponential %operand : tensor<2x2xf64>
source
',6))]),t("details",se,[t("summary",null,[e[138]||(e[138]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.exponential_minus_one-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.exponential_minus_one-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.exponential_minus_one")],-1)),e[139]||(e[139]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[140]||(e[140]=n('
exponential_minus_one
Performs element-wise exponential minus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential_minus_one
Example
mlir
%result = stablehlo.exponential_minus_one %operand : tensor<2xf64>
source
',6))]),t("details",le,[t("summary",null,[e[141]||(e[141]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.fft-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.fft-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.fft")],-1)),e[142]||(e[142]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[143]||(e[143]=n('
fft
Performs the forward and inverse Fourier transforms for real and complex inputs/outputs.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#fft
Example
mlir
%result = stablehlo.fft %operand, type = FFT, length = [4] : (tensor<4xcomplex<f32>>) -> tensor<4xcomplex<f32>>
source
',6))]),t("details",ne,[t("summary",null,[e[144]||(e[144]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.floor-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.floor-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.floor")],-1)),e[145]||(e[145]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[146]||(e[146]=n('
floor
Performs element-wise floor of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#floor
Example
mlir
%result = stablehlo.floor %operand : tensor<2xf32>
source
',6))]),t("details",oe,[t("summary",null,[e[147]||(e[147]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.gather-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.gather")],-1)),e[148]||(e[148]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[149]||(e[149]=n(`
gather
Gathers slices from operand tensor from offsets specified in start_indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather
Example
mlir
%result = "stablehlo.gather"(%operand, %start_indices) {
   dimension_numbers = #stablehlo.gather<
     offset_dims = [3, 4],
     collapsed_slice_dims = [1],
@@ -111,27 +111,27 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
     index_vector_dim = 3>,
   slice_sizes = array<i64: 1, 1, 2, 2>,
   indices_are_sorted = false
-} : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>) -> tensor<2x2x3x2x2xi64>
source
`,6))]),t("details",ie,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.get_dimension_size-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.get_dimension_size-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.get_dimension_size")],-1)),e[151]||(e[151]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=n('
get_dimension_size
Produces the size of the given dimension of the operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_dimension_size
Example
mlir
%result = stablehlo.get_dimension_size %operand, dim = 1 : (tensor<2x3xi64>) -> tensor<i32>
source
',6))]),t("details",pe,[t("summary",null,[e[153]||(e[153]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.get_tuple_element-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.get_tuple_element-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.get_tuple_element")],-1)),e[154]||(e[154]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[155]||(e[155]=n('
get_tuple_element
Extracts element at index position of the operand tuple and produces a result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_tuple_element
Example
mlir
%result = stablehlo.get_tuple_element %operand[0] : (tuple<tensor<2xf64>, tuple<tensor<i64>>>) -> tensor<2xf64>
source
',6))]),t("details",re,[t("summary",null,[e[156]||(e[156]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.if_-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.if_-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.if_")],-1)),e[157]||(e[157]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[158]||(e[158]=n('
if_
Produces the output from executing exactly one branch from true_branch or false_branch depending on the value of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#if
Example
%result = "stablehlo.if"(%pred) ({ "stablehlo.return"(%result_true_branch) : (tensor<i32>) -> () }, { "stablehlo.return"(%result_false_branch) : (tensor<i32>) -> () }) : (tensor<i1>) -> tensor<i32>
source
',6))]),t("details",ce,[t("summary",null,[e[159]||(e[159]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.imag-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.imag-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.imag")],-1)),e[160]||(e[160]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[161]||(e[161]=n('
imag
Extracts the imaginary part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#imag
Example
mlir
%result = stablehlo.imag %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
',6))]),t("details",de,[t("summary",null,[e[162]||(e[162]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.infeed-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.infeed-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.infeed")],-1)),e[163]||(e[163]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[164]||(e[164]=n(`
infeed
Reads data from the infeed and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#infeed
Example
mlir
%results0:2 = "stablehlo.infeed"(%token) :
-    (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
`,6))]),t("details",be,[t("summary",null,[e[165]||(e[165]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.iota-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.iota-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.iota")],-1)),e[166]||(e[166]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[167]||(e[167]=n('
iota
Fills an output tensor with values in increasing order starting from zero along the iota_dimension dimension.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#iota
Example
mlir
%output = stablehlo.iota dim = 0 : tensor<4x5xi32>
source
',6))]),t("details",ue,[t("summary",null,[e[168]||(e[168]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.is_finite-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.is_finite-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.is_finite")],-1)),e[169]||(e[169]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[170]||(e[170]=n('
is_finite
Performs element-wise check whether the value in x is finite (i.e. is neither +Inf, -Inf, nor NaN) and produces a y tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#is_finite
Example
mlir
%y = stablehlo.is_finite %x : (tensor<7xf64>) -> tensor<7xi1>
source
',6))]),t("details",he,[t("summary",null,[e[171]||(e[171]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.log-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.log-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.log")],-1)),e[172]||(e[172]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[173]||(e[173]=n('
log
Performs element-wise logarithm operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log
Example
mlir
%result = stablehlo.log %operand : tensor<2x2xf64>
source
',6))]),t("details",me,[t("summary",null,[e[174]||(e[174]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.log_plus_one-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.log_plus_one-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.log_plus_one")],-1)),e[175]||(e[175]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[176]||(e[176]=n('
log_plus_one
Performs element-wise logarithm plus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log_plus_one
Example
mlir
%result = stablehlo.log_plus_one %operand : tensor<5xf64>
source
',6))]),t("details",ge,[t("summary",null,[e[177]||(e[177]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.logistic-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.logistic-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.logistic")],-1)),e[178]||(e[178]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[179]||(e[179]=n('
logistic
Performs element-wise logistic operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#logistic
Example
mlir
%result = stablehlo.logistic %operand : tensor<2x2xf64>
source
',6))]),t("details",Re,[t("summary",null,[e[180]||(e[180]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.map-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.map-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.map")],-1)),e[181]||(e[181]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[182]||(e[182]=n(`
map
Applies a map function computation to inputs along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#map
Example
mlir
%result = "stablehlo.map"(%input0, %input1) ({
+} : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>) -> tensor<2x2x3x2x2xi64>
source
`,6))]),t("details",ie,[t("summary",null,[e[150]||(e[150]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.get_dimension_size-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.get_dimension_size-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.get_dimension_size")],-1)),e[151]||(e[151]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[152]||(e[152]=n('
get_dimension_size
Produces the size of the given dimension of the operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_dimension_size
Example
mlir
%result = stablehlo.get_dimension_size %operand, dim = 1 : (tensor<2x3xi64>) -> tensor<i32>
source
',6))]),t("details",pe,[t("summary",null,[e[153]||(e[153]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.get_tuple_element-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.get_tuple_element-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.get_tuple_element")],-1)),e[154]||(e[154]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[155]||(e[155]=n('
get_tuple_element
Extracts element at index position of the operand tuple and produces a result.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_tuple_element
Example
mlir
%result = stablehlo.get_tuple_element %operand[0] : (tuple<tensor<2xf64>, tuple<tensor<i64>>>) -> tensor<2xf64>
source
',6))]),t("details",re,[t("summary",null,[e[156]||(e[156]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.if_-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.if_-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.if_")],-1)),e[157]||(e[157]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[158]||(e[158]=n('
if_
Produces the output from executing exactly one branch from true_branch or false_branch depending on the value of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#if
Example
%result = "stablehlo.if"(%pred) ({ "stablehlo.return"(%result_true_branch) : (tensor<i32>) -> () }, { "stablehlo.return"(%result_false_branch) : (tensor<i32>) -> () }) : (tensor<i1>) -> tensor<i32>
source
',6))]),t("details",ce,[t("summary",null,[e[159]||(e[159]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.imag-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.imag-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.imag")],-1)),e[160]||(e[160]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[161]||(e[161]=n('
imag
Extracts the imaginary part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#imag
Example
mlir
%result = stablehlo.imag %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
',6))]),t("details",de,[t("summary",null,[e[162]||(e[162]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.infeed-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.infeed-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.infeed")],-1)),e[163]||(e[163]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[164]||(e[164]=n(`
infeed
Reads data from the infeed and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#infeed
Example
mlir
%results0:2 = "stablehlo.infeed"(%token) :
+    (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
`,6))]),t("details",be,[t("summary",null,[e[165]||(e[165]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.iota-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.iota-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.iota")],-1)),e[166]||(e[166]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[167]||(e[167]=n('
iota
Fills an output tensor with values in increasing order starting from zero along the iota_dimension dimension.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#iota
Example
mlir
%output = stablehlo.iota dim = 0 : tensor<4x5xi32>
source
',6))]),t("details",ue,[t("summary",null,[e[168]||(e[168]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.is_finite-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.is_finite-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.is_finite")],-1)),e[169]||(e[169]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[170]||(e[170]=n('
is_finite
Performs element-wise check whether the value in x is finite (i.e. is neither +Inf, -Inf, nor NaN) and produces a y tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#is_finite
Example
mlir
%y = stablehlo.is_finite %x : (tensor<7xf64>) -> tensor<7xi1>
source
',6))]),t("details",he,[t("summary",null,[e[171]||(e[171]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.log-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.log-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.log")],-1)),e[172]||(e[172]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[173]||(e[173]=n('
log
Performs element-wise logarithm operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log
Example
mlir
%result = stablehlo.log %operand : tensor<2x2xf64>
source
',6))]),t("details",me,[t("summary",null,[e[174]||(e[174]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.log_plus_one-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.log_plus_one-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.log_plus_one")],-1)),e[175]||(e[175]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[176]||(e[176]=n('
log_plus_one
Performs element-wise logarithm plus one operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log_plus_one
Example
mlir
%result = stablehlo.log_plus_one %operand : tensor<5xf64>
source
',6))]),t("details",ge,[t("summary",null,[e[177]||(e[177]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.logistic-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.logistic-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.logistic")],-1)),e[178]||(e[178]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[179]||(e[179]=n('
logistic
Performs element-wise logistic operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#logistic
Example
mlir
%result = stablehlo.logistic %operand : tensor<2x2xf64>
source
',6))]),t("details",Re,[t("summary",null,[e[180]||(e[180]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.map-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.map-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.map")],-1)),e[181]||(e[181]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[182]||(e[182]=n(`
map
Applies a map function computation to inputs along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#map
Example
mlir
%result = "stablehlo.map"(%input0, %input1) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.multiply %arg0, %arg1 : tensor<i64>
     stablehlo.return %0 : tensor<i64>
 }) {
   dimensions = array<i64: 0, 1>
-} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>
source
`,6))]),t("details",fe,[t("summary",null,[e[183]||(e[183]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.maximum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.maximum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.maximum")],-1)),e[184]||(e[184]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[185]||(e[185]=n('
maximum
Performs element-wise max operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#maximum
Example
mlir
%result = stablehlo.maximum %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",xe,[t("summary",null,[e[186]||(e[186]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.minimum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.minimum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.minimum")],-1)),e[187]||(e[187]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[188]||(e[188]=n('
minimum
Performs element-wise min operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#minimum
Example
mlir
%result = stablehlo.minimum %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",Le,[t("summary",null,[e[189]||(e[189]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.multiply-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.multiply-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.multiply")],-1)),e[190]||(e[190]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[191]||(e[191]=n('
multiply
Performs element-wise product of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#multiply
Example
mlir
%result = stablehlo.multiply %lhs, %rhs : tensor<2xi32>
source
',6))]),t("details",Ie,[t("summary",null,[e[192]||(e[192]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.negate-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.negate-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.negate")],-1)),e[193]||(e[193]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[194]||(e[194]=n('
negate
Performs element-wise negation of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#negate
Example
mlir
%result = stablehlo.negate %operand : tensor<2x3xi32>
source
',6))]),t("details",Me,[t("summary",null,[e[195]||(e[195]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.not-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.not-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.not")],-1)),e[196]||(e[196]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[197]||(e[197]=n('
not
Performs element-wise NOT of tensor operand of type integer and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#not
Example
mlir
%result = stablehlo.not %operand : tensor<5x3x1xi1>
source
',6))]),t("details",ye,[t("summary",null,[e[198]||(e[198]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.optimization_barrier-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.optimization_barrier-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.optimization_barrier")],-1)),e[199]||(e[199]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[200]||(e[200]=n('
optimization_barrier
Ensures that the operations that produce the operand are executed before any operations that depend on the result and prevents compiler transformations from moving operations across the barrier. Other than that, the operation is an identity, i.e. result = operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#optimization_barrier
Example
mlir
%result0, %result1 = stablehlo.optimization_barrier %operand0, %operand1 : tensor<f32>, tensor<f32>
source
',6))]),t("details",je,[t("summary",null,[e[201]||(e[201]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.or-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.or-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.or")],-1)),e[202]||(e[202]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[203]||(e[203]=n('
or
Performs element-wise OR of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#or
Example
mlir
%result = stablehlo.or %lhs, %rhs : tensor<2xi1>
source
',6))]),t("details",ke,[t("summary",null,[e[204]||(e[204]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.outfeed-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.outfeed-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.outfeed")],-1)),e[205]||(e[205]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[206]||(e[206]=n(`
outfeed
Writes inputs to the outfeed and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#outfeed
Example
mlir
%result = "stablehlo.outfeed"(%input0, %token) :
-    (tensor<2x2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
`,6))]),t("details",ve,[t("summary",null,[e[207]||(e[207]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.pad-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.pad-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.pad")],-1)),e[208]||(e[208]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[209]||(e[209]=n(`
pad
Expands operand by padding around the tensor as well as between the elements of the tensor with the given padding_value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#pad
Example
mlir
%0 = stablehlo.pad %arg0, %arg1, low = [0, 1], high = [2, 1], interior = [1, 2]
-  : (tensor<2x3xi32>, tensor<i32>) -> tensor<5x9xi32>
source
`,6))]),t("details",De,[t("summary",null,[e[210]||(e[210]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.partition_id-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.partition_id-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.partition_id")],-1)),e[211]||(e[211]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[212]||(e[212]=n('
partition_id
Produces partition_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#partition_id
Example
mlir
%result = stablehlo.partition_id : tensor<ui32>
source
',6))]),t("details",_e,[t("summary",null,[e[213]||(e[213]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.popcnt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.popcnt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.popcnt")],-1)),e[214]||(e[214]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[215]||(e[215]=n('
popcnt
Performs element-wise count of the number of bits set in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#popcnt
Example
mlir
%result = stablehlo.popcnt %operand : tensor<4xi64>
source
',6))]),t("details",Te,[t("summary",null,[e[216]||(e[216]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.power-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.power-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.power")],-1)),e[217]||(e[217]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[218]||(e[218]=n('
power
Performs element-wise exponentiation of lhs tensor by rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#power
Example
mlir
%result = stablehlo.power %lhs, %rhs : tensor<6xf64>
source
',6))]),t("details",Ve,[t("summary",null,[e[219]||(e[219]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.real-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.real-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.real")],-1)),e[220]||(e[220]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[221]||(e[221]=n('
real
Extracts the real part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#real
Example
mlir
%result = stablehlo.real %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
',6))]),t("details",Oe,[t("summary",null,[e[222]||(e[222]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice-NTuple{4, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice-NTuple{4, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice")],-1)),e[223]||(e[223]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[224]||(e[224]=n(`
real_dynamic_slice
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as SliceOp except that start_indices, limit_indices and strides are specified dynamically: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.real_dynamic_slice %operand,
+} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>
source
`,6))]),t("details",fe,[t("summary",null,[e[183]||(e[183]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.maximum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.maximum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.maximum")],-1)),e[184]||(e[184]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[185]||(e[185]=n('
maximum
Performs element-wise max operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#maximum
Example
mlir
%result = stablehlo.maximum %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",xe,[t("summary",null,[e[186]||(e[186]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.minimum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.minimum-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.minimum")],-1)),e[187]||(e[187]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[188]||(e[188]=n('
minimum
Performs element-wise min operation on tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#minimum
Example
mlir
%result = stablehlo.minimum %lhs, %rhs : tensor<4xf32>
source
',6))]),t("details",Le,[t("summary",null,[e[189]||(e[189]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.multiply-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.multiply-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.multiply")],-1)),e[190]||(e[190]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[191]||(e[191]=n('
multiply
Performs element-wise product of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#multiply
Example
mlir
%result = stablehlo.multiply %lhs, %rhs : tensor<2xi32>
source
',6))]),t("details",Ie,[t("summary",null,[e[192]||(e[192]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.negate-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.negate-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.negate")],-1)),e[193]||(e[193]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[194]||(e[194]=n('
negate
Performs element-wise negation of operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#negate
Example
mlir
%result = stablehlo.negate %operand : tensor<2x3xi32>
source
',6))]),t("details",Me,[t("summary",null,[e[195]||(e[195]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.not-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.not-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.not")],-1)),e[196]||(e[196]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[197]||(e[197]=n('
not
Performs element-wise NOT of tensor operand of type integer and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#not
Example
mlir
%result = stablehlo.not %operand : tensor<5x3x1xi1>
source
',6))]),t("details",ye,[t("summary",null,[e[198]||(e[198]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.optimization_barrier-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.optimization_barrier-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.optimization_barrier")],-1)),e[199]||(e[199]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[200]||(e[200]=n('
optimization_barrier
Ensures that the operations that produce the operand are executed before any operations that depend on the result and prevents compiler transformations from moving operations across the barrier. Other than that, the operation is an identity, i.e. result = operand.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#optimization_barrier
Example
mlir
%result0, %result1 = stablehlo.optimization_barrier %operand0, %operand1 : tensor<f32>, tensor<f32>
source
',6))]),t("details",je,[t("summary",null,[e[201]||(e[201]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.or-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.or-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.or")],-1)),e[202]||(e[202]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[203]||(e[203]=n('
or
Performs element-wise OR of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#or
Example
mlir
%result = stablehlo.or %lhs, %rhs : tensor<2xi1>
source
',6))]),t("details",ke,[t("summary",null,[e[204]||(e[204]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.outfeed-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.outfeed-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.outfeed")],-1)),e[205]||(e[205]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[206]||(e[206]=n(`
outfeed
Writes inputs to the outfeed and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#outfeed
Example
mlir
%result = "stablehlo.outfeed"(%input0, %token) :
+    (tensor<2x2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
`,6))]),t("details",ve,[t("summary",null,[e[207]||(e[207]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.pad-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.pad-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.pad")],-1)),e[208]||(e[208]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[209]||(e[209]=n(`
pad
Expands operand by padding around the tensor as well as between the elements of the tensor with the given padding_value.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#pad
Example
mlir
%0 = stablehlo.pad %arg0, %arg1, low = [0, 1], high = [2, 1], interior = [1, 2]
+  : (tensor<2x3xi32>, tensor<i32>) -> tensor<5x9xi32>
source
`,6))]),t("details",De,[t("summary",null,[e[210]||(e[210]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.partition_id-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.partition_id-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.partition_id")],-1)),e[211]||(e[211]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[212]||(e[212]=n('
partition_id
Produces partition_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#partition_id
Example
mlir
%result = stablehlo.partition_id : tensor<ui32>
source
',6))]),t("details",_e,[t("summary",null,[e[213]||(e[213]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.popcnt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.popcnt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.popcnt")],-1)),e[214]||(e[214]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[215]||(e[215]=n('
popcnt
Performs element-wise count of the number of bits set in the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#popcnt
Example
mlir
%result = stablehlo.popcnt %operand : tensor<4xi64>
source
',6))]),t("details",Te,[t("summary",null,[e[216]||(e[216]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.power-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.power-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.power")],-1)),e[217]||(e[217]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[218]||(e[218]=n('
power
Performs element-wise exponentiation of lhs tensor by rhs tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#power
Example
mlir
%result = stablehlo.power %lhs, %rhs : tensor<6xf64>
source
',6))]),t("details",Ve,[t("summary",null,[e[219]||(e[219]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.real-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.real-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.real")],-1)),e[220]||(e[220]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[221]||(e[221]=n('
real
Extracts the real part, element-wise, from the operand and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#real
Example
mlir
%result = stablehlo.real %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>
source
',6))]),t("details",Oe,[t("summary",null,[e[222]||(e[222]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice-NTuple{4, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice-NTuple{4, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice")],-1)),e[223]||(e[223]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[224]||(e[224]=n(`
real_dynamic_slice
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as SliceOp except that start_indices, limit_indices and strides are specified dynamically: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.real_dynamic_slice %operand,
             %start_indices, %limit_indices, %strides
-       : (tensor<256x?xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<256x?xf32>
source
`,6))]),t("details",Ee,[t("summary",null,[e[225]||(e[225]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.recv-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.recv-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.recv")],-1)),e[226]||(e[226]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[227]||(e[227]=n(`
recv
Receives data from a channel with channel_id and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#recv
Example
mlir
%results:2 = "stablehlo.recv"(%token) {
+       : (tensor<256x?xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<256x?xf32>
source
`,6))]),t("details",Ee,[t("summary",null,[e[225]||(e[225]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.recv-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.recv-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.recv")],-1)),e[226]||(e[226]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[227]||(e[227]=n(`
recv
Receives data from a channel with channel_id and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#recv
Example
mlir
%results:2 = "stablehlo.recv"(%token) {
   channel_handle = #stablehlo.channel_handle<handle = 1, type = 3>,
   is_host_transfer = true
-} : (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
`,6))]),t("details",Se,[t("summary",null,[e[228]||(e[228]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce")],-1)),e[229]||(e[229]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[230]||(e[230]=n(`
reduce
Applies a reduction function body to inputs and init_values along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce
Example
mlir
%result = "stablehlo.reduce"(%input, %init_value) ({
+} : (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)
source
`,6))]),t("details",Se,[t("summary",null,[e[228]||(e[228]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce")],-1)),e[229]||(e[229]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[230]||(e[230]=n(`
reduce
Applies a reduction function body to inputs and init_values along the dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce
Example
mlir
%result = "stablehlo.reduce"(%input, %init_value) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
     stablehlo.return %0 : tensor<i64>
 }) {
   dimensions = array<i64: 1>
-} : (tensor<1x6xi64>, tensor<i64>) -> tensor<1xi64>
source
`,6))]),t("details",Ce,[t("summary",null,[e[231]||(e[231]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_precision-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_precision-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_precision")],-1)),e[232]||(e[232]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[233]||(e[233]=n('
reduce_precision
Performs element-wise conversion of operand to another floating-point type that uses exponent_bits and mantissa_bits and back to the original floating-point type and produces an output tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_precision
Example
mlir
%output = stablehlo.reduce_precision %operand, format = e5m10 : tensor<6xf64>
source
',6))]),t("details",we,[t("summary",null,[e[234]||(e[234]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_scatter-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_scatter-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_scatter")],-1)),e[235]||(e[235]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[236]||(e[236]=n('
reduce_scatter
Within each process group in the process grid, performs reduction, using computations, over the values of the operand tensor from each process, splits the reduction result along scatter_dimension into parts, and scatters the split parts between the processes to produce the result.
See:\nhttps://github.com/openxla/stablehlo/blob/main/docs/spec#reduce_scatter\n\nExample:\n```mlir\n%result = "stablehlo.reduce_scatter"(%operand) ({
^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension = 1 : i64, replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>, channel_handle = #stablehlo.channel_handle<handle = 0, type = 0> } : (tensor<2x4xi64>) -> tensor<2x2xi64> ```
source
',5))]),t("details",ze,[t("summary",null,[e[237]||(e[237]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_window-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_window-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_window")],-1)),e[238]||(e[238]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[239]||(e[239]=n(`
reduce_window
Applies a reduction function body to windows of inputs and init_values and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_window
Example
mlir
%result = "stablehlo.reduce_window"(%input, %init_value) ({
+} : (tensor<1x6xi64>, tensor<i64>) -> tensor<1xi64>
source
`,6))]),t("details",Ce,[t("summary",null,[e[231]||(e[231]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_precision-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_precision-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_precision")],-1)),e[232]||(e[232]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[233]||(e[233]=n('
reduce_precision
Performs element-wise conversion of operand to another floating-point type that uses exponent_bits and mantissa_bits and back to the original floating-point type and produces an output tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_precision
Example
mlir
%output = stablehlo.reduce_precision %operand, format = e5m10 : tensor<6xf64>
source
',6))]),t("details",we,[t("summary",null,[e[234]||(e[234]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_scatter-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_scatter-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_scatter")],-1)),e[235]||(e[235]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[236]||(e[236]=n('
reduce_scatter
Within each process group in the process grid, performs reduction, using computations, over the values of the operand tensor from each process, splits the reduction result along scatter_dimension into parts, and scatters the split parts between the processes to produce the result.
See:\nhttps://github.com/openxla/stablehlo/blob/main/docs/spec#reduce_scatter\n\nExample:\n```mlir\n%result = "stablehlo.reduce_scatter"(%operand) ({
^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension = 1 : i64, replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>, channel_handle = #stablehlo.channel_handle<handle = 0, type = 0> } : (tensor<2x4xi64>) -> tensor<2x2xi64> ```
source
',5))]),t("details",ze,[t("summary",null,[e[237]||(e[237]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reduce_window-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.reduce_window-Tuple{Vector{Reactant.MLIR.IR.Value}, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reduce_window")],-1)),e[238]||(e[238]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[239]||(e[239]=n(`
reduce_window
Applies a reduction function body to windows of inputs and init_values and produces results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_window
Example
mlir
%result = "stablehlo.reduce_window"(%input, %init_value) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
     stablehlo.return %0 : tensor<i64>
@@ -141,7 +141,7 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
   base_dilations = array<i64: 2, 1>,
   window_dilations = array<i64: 3, 1>,
   padding = dense<[[2, 1], [0, 0]]> : tensor<2x2xi64>
-} : (tensor<3x2xi64>, tensor<i64>) -> tensor<2x2xi64>
source
`,6))]),t("details",He,[t("summary",null,[e[240]||(e[240]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.remainder-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.remainder-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.remainder")],-1)),e[241]||(e[241]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=n('
remainder
Performs element-wise remainder of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#remainder
Example
mlir
%result = stablehlo.remainder %lhs, %rhs : tensor<4xi64>
source
',6))]),t("details",Ae,[t("summary",null,[e[243]||(e[243]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.replica_id-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.replica_id-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.replica_id")],-1)),e[244]||(e[244]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[245]||(e[245]=n('
replica_id
Produces replica_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#replica_id
Example
mlir
%result = stablehlo.replica_id : tensor<ui32>
source
',6))]),t("details",qe,[t("summary",null,[e[246]||(e[246]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reshape-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reshape-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reshape")],-1)),e[247]||(e[247]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[248]||(e[248]=n('
reshape
Performs reshape of operand tensor to a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reshape
Example
mlir
%result = stablehlo.reshape %operand : (tensor<2xf32>) -> tensor<1x2xf32>
source
',6))]),t("details",Pe,[t("summary",null,[e[249]||(e[249]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reverse-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reverse-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reverse")],-1)),e[250]||(e[250]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[251]||(e[251]=n('
reverse
Reverses the order of elements in the operand along the specified dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reverse
Example
mlir
%result = stablehlo.reverse %operand, dims = [1] : tensor<3x2xi32>
source
',6))]),t("details",Ne,[t("summary",null,[e[252]||(e[252]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rng-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rng-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rng")],-1)),e[253]||(e[253]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=n('
rng
Generates random numbers using the rng_distribution algorithm and produces a result tensor of a given shape shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng
Example
mlir
%result = stablehlo.rng %a, %b, %shape, distribution = NORMAL : (tensor<i32>, tensor<i32>, tensor<2xi64>) -> tensor<3x3xi32>
source
',6))]),t("details",Fe,[t("summary",null,[e[255]||(e[255]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rng_bit_generator-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rng_bit_generator-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rng_bit_generator")],-1)),e[256]||(e[256]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[257]||(e[257]=n('
rng_bit_generator
Returns an output filled with uniform random data and an updated output state output_state given an initial state initial_state using the pseudorandom number generator algorithm rng_algorithm.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng_bit_generator
Example
mlir
%output_state, %output = stablehlo.rng_bit_generator %initial_state, algorithm = THREE_FRY : (tensor<2xui64>) -> (tensor<2xui64>, tensor<2x2xui64>)
source
',6))]),t("details",Be,[t("summary",null,[e[258]||(e[258]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.round_nearest_afz-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.round_nearest_afz-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.round_nearest_afz")],-1)),e[259]||(e[259]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=n('
round_nearest_afz
Performs element-wise rounding towards the nearest integer, breaking ties away from zero, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_afz
Example
mlir
%result = stablehlo.round_nearest_afz %operand : tensor<5xf64>
source
',6))]),t("details",Ue,[t("summary",null,[e[261]||(e[261]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.round_nearest_even-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.round_nearest_even-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.round_nearest_even")],-1)),e[262]||(e[262]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[263]||(e[263]=n('
round_nearest_even
Performs element-wise rounding towards the nearest integer, breaking ties towards the even integer, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_even
Example
mlir
%result = stablehlo.round_nearest_even %operand : tensor<5xf64>
source
',6))]),t("details",We,[t("summary",null,[e[264]||(e[264]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rsqrt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rsqrt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rsqrt")],-1)),e[265]||(e[265]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[266]||(e[266]=n('
rsqrt
Performs element-wise reciprocal square root operation on operand tensor and produces a result tensor, implementing the rSqrt operation from the IEEE-754 specification.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rsqrt
Example
mlir
%result = stablehlo.rsqrt %operand : tensor<2x2xf32>
source
',6))]),t("details",Ge,[t("summary",null,[e[267]||(e[267]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.scatter-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.scatter-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.scatter")],-1)),e[268]||(e[268]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[269]||(e[269]=n('
scatter
Produces results tensors which are equal to inputs tensors except that several slices specified by scatter_indices are updated with the values updates using update_computation.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter
Example: mlir %result = "stablehlo.scatter"(%input, %scatter_indices, %update) ({ ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension_numbers = #stablehlo.scatter< update_window_dims = [3, 4], inserted_window_dims = [1], input_batching_dims = [0], scatter_indices_batching_dims = [1], scatter_dims_to_operand_dims = [2, 1], index_vector_dim = 3>, indices_are_sorted = false, unique_indices = false } : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>, tensor<2x2x3x2x2xi64>) -> tensor<2x3x4x2xi64>
source
',5))]),t("details",Xe,[t("summary",null,[e[270]||(e[270]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.select")],-1)),e[271]||(e[271]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[272]||(e[272]=n('
select
Produces a result tensor where each element is selected from on_true or on_false tensor based on the value of the corresponding element of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select
Example
mlir
%result = stablehlo.select %pred, %on_true, %on_false : tensor<2x2xi1>, tensor<2x2xi32>
source
',6))]),t("details",$e,[t("summary",null,[e[273]||(e[273]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.select_and_scatter-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.select_and_scatter-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.select_and_scatter")],-1)),e[274]||(e[274]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[275]||(e[275]=n(`
select_and_scatter
Scatters the values from the source tensor using scatter based on the outcome of reduce_window of the input tensor using select and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select_and_scatter
Example
mlir
%result = "stablehlo.select_and_scatter"(%operand, %source, %init_value) ({
+} : (tensor<3x2xi64>, tensor<i64>) -> tensor<2x2xi64>
source
`,6))]),t("details",He,[t("summary",null,[e[240]||(e[240]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.remainder-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.remainder-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.remainder")],-1)),e[241]||(e[241]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=n('
remainder
Performs element-wise remainder of dividend lhs and divisor rhs tensors and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#remainder
Example
mlir
%result = stablehlo.remainder %lhs, %rhs : tensor<4xi64>
source
',6))]),t("details",Ae,[t("summary",null,[e[243]||(e[243]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.replica_id-Tuple{}",href:"#Reactant.MLIR.Dialects.stablehlo.replica_id-Tuple{}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.replica_id")],-1)),e[244]||(e[244]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[245]||(e[245]=n('
replica_id
Produces replica_id of the current process.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#replica_id
Example
mlir
%result = stablehlo.replica_id : tensor<ui32>
source
',6))]),t("details",qe,[t("summary",null,[e[246]||(e[246]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reshape-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reshape-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reshape")],-1)),e[247]||(e[247]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[248]||(e[248]=n('
reshape
Performs reshape of operand tensor to a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reshape
Example
mlir
%result = stablehlo.reshape %operand : (tensor<2xf32>) -> tensor<1x2xf32>
source
',6))]),t("details",Pe,[t("summary",null,[e[249]||(e[249]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.reverse-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.reverse-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.reverse")],-1)),e[250]||(e[250]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[251]||(e[251]=n('
reverse
Reverses the order of elements in the operand along the specified dimensions and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reverse
Example
mlir
%result = stablehlo.reverse %operand, dims = [1] : tensor<3x2xi32>
source
',6))]),t("details",Ne,[t("summary",null,[e[252]||(e[252]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rng-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rng-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rng")],-1)),e[253]||(e[253]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=n('
rng
Generates random numbers using the rng_distribution algorithm and produces a result tensor of a given shape shape.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng
Example
mlir
%result = stablehlo.rng %a, %b, %shape, distribution = NORMAL : (tensor<i32>, tensor<i32>, tensor<2xi64>) -> tensor<3x3xi32>
source
',6))]),t("details",Fe,[t("summary",null,[e[255]||(e[255]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rng_bit_generator-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rng_bit_generator-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rng_bit_generator")],-1)),e[256]||(e[256]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[257]||(e[257]=n('
rng_bit_generator
Returns an output filled with uniform random data and an updated output state output_state given an initial state initial_state using the pseudorandom number generator algorithm rng_algorithm.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng_bit_generator
Example
mlir
%output_state, %output = stablehlo.rng_bit_generator %initial_state, algorithm = THREE_FRY : (tensor<2xui64>) -> (tensor<2xui64>, tensor<2x2xui64>)
source
',6))]),t("details",Be,[t("summary",null,[e[258]||(e[258]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.round_nearest_afz-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.round_nearest_afz-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.round_nearest_afz")],-1)),e[259]||(e[259]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=n('
round_nearest_afz
Performs element-wise rounding towards the nearest integer, breaking ties away from zero, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_afz
Example
mlir
%result = stablehlo.round_nearest_afz %operand : tensor<5xf64>
source
',6))]),t("details",Ue,[t("summary",null,[e[261]||(e[261]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.round_nearest_even-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.round_nearest_even-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.round_nearest_even")],-1)),e[262]||(e[262]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[263]||(e[263]=n('
round_nearest_even
Performs element-wise rounding towards the nearest integer, breaking ties towards the even integer, on the operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_even
Example
mlir
%result = stablehlo.round_nearest_even %operand : tensor<5xf64>
source
',6))]),t("details",We,[t("summary",null,[e[264]||(e[264]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.rsqrt-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.rsqrt-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.rsqrt")],-1)),e[265]||(e[265]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[266]||(e[266]=n('
rsqrt
Performs element-wise reciprocal square root operation on operand tensor and produces a result tensor, implementing the rSqrt operation from the IEEE-754 specification.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rsqrt
Example
mlir
%result = stablehlo.rsqrt %operand : tensor<2x2xf32>
source
',6))]),t("details",Ge,[t("summary",null,[e[267]||(e[267]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.scatter-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.scatter-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value, Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.scatter")],-1)),e[268]||(e[268]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[269]||(e[269]=n('
scatter
Produces results tensors which are equal to inputs tensors except that several slices specified by scatter_indices are updated with the values updates using update_computation.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter
Example: mlir %result = "stablehlo.scatter"(%input, %scatter_indices, %update) ({ ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension_numbers = #stablehlo.scatter< update_window_dims = [3, 4], inserted_window_dims = [1], input_batching_dims = [0], scatter_indices_batching_dims = [1], scatter_dims_to_operand_dims = [2, 1], index_vector_dim = 3>, indices_are_sorted = false, unique_indices = false } : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>, tensor<2x2x3x2x2xi64>) -> tensor<2x3x4x2xi64>
source
',5))]),t("details",Xe,[t("summary",null,[e[270]||(e[270]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.select")],-1)),e[271]||(e[271]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[272]||(e[272]=n('
select
Produces a result tensor where each element is selected from on_true or on_false tensor based on the value of the corresponding element of pred.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select
Example
mlir
%result = stablehlo.select %pred, %on_true, %on_false : tensor<2x2xi1>, tensor<2x2xi32>
source
',6))]),t("details",$e,[t("summary",null,[e[273]||(e[273]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.select_and_scatter-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.select_and_scatter-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.select_and_scatter")],-1)),e[274]||(e[274]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[275]||(e[275]=n(`
select_and_scatter
Scatters the values from the source tensor using scatter based on the outcome of reduce_window of the input tensor using select and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select_and_scatter
Example
mlir
%result = "stablehlo.select_and_scatter"(%operand, %source, %init_value) ({
   ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
     %0 = stablehlo.compare GE, %arg0, %arg1 : (tensor<i64>, tensor<i64>) -> tensor<i1>
     stablehlo.return %0 : tensor<i1>
@@ -153,12 +153,12 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
   window_dimensions = dense<[3, 1]> : tensor<2xi64>,
   window_strides = dense<[2, 1]> : tensor<2xi64>,
   padding = dense<[[0, 1], [0, 0]]> : tensor<2x2xi64>
-} : (tensor<4x2xi64>, tensor<2x2xi64>, tensor<i64>) -> tensor<4x2xi64>
source
`,6))]),t("details",Ye,[t("summary",null,[e[276]||(e[276]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.send-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.send-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.send")],-1)),e[277]||(e[277]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[278]||(e[278]=n(`
send
Sends inputs to a channel channel_id and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#send
Example
mlir
%result = "stablehlo.send"(%operand, %token) {
+} : (tensor<4x2xi64>, tensor<2x2xi64>, tensor<i64>) -> tensor<4x2xi64>
source
`,6))]),t("details",Ye,[t("summary",null,[e[276]||(e[276]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.send-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.send-Tuple{Vector{Reactant.MLIR.IR.Value}, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.send")],-1)),e[277]||(e[277]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[278]||(e[278]=n(`
send
Sends inputs to a channel channel_id and produces a result token.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#send
Example
mlir
%result = "stablehlo.send"(%operand, %token) {
   channel_handle = #stablehlo.channel_handle<handle = 1, type = 2>,
   is_host_transfer = true
-} : (tensor<2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
`,6))]),t("details",Je,[t("summary",null,[e[279]||(e[279]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.set_dimension_size-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.set_dimension_size-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.set_dimension_size")],-1)),e[280]||(e[280]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[281]||(e[281]=n('
set_dimension_size
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as XLA's SetDimensionSize: https://www.tensorflow.org/xla/operation_semantics#setdimensionsize
Example
mlir
%0 = stablehlo.set_dimension_size %arg0, %arg1, dim = 1 : (tensor<4x2xf32>, tensor<i32>) -> tensor<4x2xf32>
source
',6))]),t("details",Ke,[t("summary",null,[e[282]||(e[282]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_left-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_left-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_left")],-1)),e[283]||(e[283]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[284]||(e[284]=n('
shift_left
Performs element-wise left-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_left
Example
mlir
%result = stablehlo.shift_left %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",Qe,[t("summary",null,[e[285]||(e[285]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic")],-1)),e[286]||(e[286]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[287]||(e[287]=n('
shift_right_arithmetic
Performs element-wise arithmetic right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_arithmetic
Example
mlir
%result = stablehlo.shift_right_arithmetic %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",Ze,[t("summary",null,[e[288]||(e[288]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_right_logical-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_right_logical-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_right_logical")],-1)),e[289]||(e[289]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[290]||(e[290]=n('
shift_right_logical
Performs element-wise logical right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_logical
Example
mlir
%result = stablehlo.shift_right_logical %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",et,[t("summary",null,[e[291]||(e[291]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sign-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.sign-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sign")],-1)),e[292]||(e[292]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[293]||(e[293]=n('
sign
Returns the sign of the operand element-wise and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sign
Example
mlir
%result = stablehlo.sign %operand : tensor<5xf64>
source
',6))]),t("details",tt,[t("summary",null,[e[294]||(e[294]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sine-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.sine-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sine")],-1)),e[295]||(e[295]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[296]||(e[296]=n('
sine
Performs element-wise sine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sine
Example
mlir
%result = stablehlo.sine %operand : tensor<2xf32>
source
',6))]),t("details",at,[t("summary",null,[e[297]||(e[297]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.slice-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.slice-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.slice")],-1)),e[298]||(e[298]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[299]||(e[299]=n('
slice
Extracts a slice from the operand using statically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.slice %operand [1:3, 4:8:2]\n   : (tensor<3x8xi64>) -> tensor<2x2xi64>\n\n// Same in generic form: the `1:3` above is mapped to the first entry in\n// `start_indices` and `limit_indices`, while `strides` is implicitly 1.\n// The `4:8:2` above is parsed into the second entry of `start_indices`,\n// `limit_indices` and `strides` respectively.\n%result = "stablehlo.slice" (%operand) {\n  start_indices = array<i64: 1, 4>,\n  limit_indices = array<i64: 3, 8>,\n  strides = array<i64: 1, 2>\n} : (tensor<3x8xi64>) -> tensor<2x2xi64>
source
',6))]),t("details",st,[t("summary",null,[e[300]||(e[300]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sort-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.sort-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sort")],-1)),e[301]||(e[301]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[302]||(e[302]=n(`
sort
Sorts a variadic number of tensors in inputs together, according to a custom comparator, along the given dimension and produces a variadic number of tensors as results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sort
Example
mlir

+} : (tensor<2x2xi64>, !stablehlo.token) -> !stablehlo.token
source
`,6))]),t("details",Je,[t("summary",null,[e[279]||(e[279]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.set_dimension_size-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.set_dimension_size-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.set_dimension_size")],-1)),e[280]||(e[280]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[281]||(e[281]=n('
set_dimension_size
This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.
Informally, this operation does the same thing as XLA's SetDimensionSize: https://www.tensorflow.org/xla/operation_semantics#setdimensionsize
Example
mlir
%0 = stablehlo.set_dimension_size %arg0, %arg1, dim = 1 : (tensor<4x2xf32>, tensor<i32>) -> tensor<4x2xf32>
source
',6))]),t("details",Ke,[t("summary",null,[e[282]||(e[282]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_left-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_left-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_left")],-1)),e[283]||(e[283]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[284]||(e[284]=n('
shift_left
Performs element-wise left-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_left
Example
mlir
%result = stablehlo.shift_left %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",Qe,[t("summary",null,[e[285]||(e[285]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic")],-1)),e[286]||(e[286]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[287]||(e[287]=n('
shift_right_arithmetic
Performs element-wise arithmetic right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_arithmetic
Example
mlir
%result = stablehlo.shift_right_arithmetic %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",Ze,[t("summary",null,[e[288]||(e[288]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.shift_right_logical-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.shift_right_logical-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.shift_right_logical")],-1)),e[289]||(e[289]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[290]||(e[290]=n('
shift_right_logical
Performs element-wise logical right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_logical
Example
mlir
%result = stablehlo.shift_right_logical %lhs, %rhs : tensor<3xi64>
source
',6))]),t("details",et,[t("summary",null,[e[291]||(e[291]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sign-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.sign-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sign")],-1)),e[292]||(e[292]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[293]||(e[293]=n('
sign
Returns the sign of the operand element-wise and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sign
Example
mlir
%result = stablehlo.sign %operand : tensor<5xf64>
source
',6))]),t("details",tt,[t("summary",null,[e[294]||(e[294]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sine-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.sine-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sine")],-1)),e[295]||(e[295]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[296]||(e[296]=n('
sine
Performs element-wise sine operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sine
Example
mlir
%result = stablehlo.sine %operand : tensor<2xf32>
source
',6))]),t("details",at,[t("summary",null,[e[297]||(e[297]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.slice-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.slice-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.slice")],-1)),e[298]||(e[298]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[299]||(e[299]=n('
slice
Extracts a slice from the operand using statically-computed starting indices and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice
Example
mlir
%result = stablehlo.slice %operand [1:3, 4:8:2]\n   : (tensor<3x8xi64>) -> tensor<2x2xi64>\n\n// Same in generic form: the `1:3` above is mapped to the first entry in\n// `start_indices` and `limit_indices`, while `strides` is implicitly 1.\n// The `4:8:2` above is parsed into the second entry of `start_indices`,\n// `limit_indices` and `strides` respectively.\n%result = "stablehlo.slice" (%operand) {\n  start_indices = array<i64: 1, 4>,\n  limit_indices = array<i64: 3, 8>,\n  strides = array<i64: 1, 2>\n} : (tensor<3x8xi64>) -> tensor<2x2xi64>
source
',6))]),t("details",st,[t("summary",null,[e[300]||(e[300]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.sort-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.sort-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.sort")],-1)),e[301]||(e[301]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[302]||(e[302]=n(`
sort
Sorts a variadic number of tensors in inputs together, according to a custom comparator, along the given dimension and produces a variadic number of tensors as results.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sort
Example
mlir

 
-[source](https://github.com/EnzymeAD/Reactant.jl/blob/98bf9dd9d1438c128b598a844ceb9875832f0fd5/src/mlir/Dialects/StableHLO.jl#L4195-L4215)
+[source](https://github.com/EnzymeAD/Reactant.jl/blob/23afeb171cee21067bc614b96db99886156e8dcd/src/mlir/Dialects/StableHLO.jl#L4195-L4215)
 
 </details>
 
@@ -176,17 +176,17 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
 **Example**
 
 \`\`\`mlir
-%result = stablehlo.sqrt %operand : tensor<2x2xf32>
source
`,6))]),t("details",lt,[t("summary",null,[e[303]||(e[303]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.subtract-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.subtract-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.subtract")],-1)),e[304]||(e[304]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[305]||(e[305]=n('
subtract
Performs element-wise subtraction of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#subtract
Example
mlir
%result = stablehlo.subtract %lhs, %rhs : tensor<2xi32>
source
',6))]),t("details",nt,[t("summary",null,[e[306]||(e[306]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tan-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.tan-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tan")],-1)),e[307]||(e[307]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[308]||(e[308]=n('
tan
Performs element-wise tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tan
Example
mlir
%result = stablehlo.tan %operand : tensor<2x2xf64>
source
',6))]),t("details",ot,[t("summary",null,[e[309]||(e[309]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tanh-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.tanh-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tanh")],-1)),e[310]||(e[310]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[311]||(e[311]=n('
tanh
Performs element-wise hyperbolic tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tanh
Example
mlir
%result = stablehlo.tanh %operand : tensor<2xf32>
source
',6))]),t("details",it,[t("summary",null,[e[312]||(e[312]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.torch_index_select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.torch_index_select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.torch_index_select")],-1)),e[313]||(e[313]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[314]||(e[314]=n(`
torch_index_select
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as PyTorch's index_select, augmented with support for batch dimensions: https://pytorch.org/docs/stable/generated/torch.index_select.html.
The batch_dims attribute specifies the number of major batch dimensions (0 or more) that act like a multidimensional loop over both the operand and the index.
Example
mlir
%result = "stablehlo.torch_index_select"(%operand, %index) {
+%result = stablehlo.sqrt %operand : tensor<2x2xf32>
source
`,6))]),t("details",lt,[t("summary",null,[e[303]||(e[303]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.subtract-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.subtract-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.subtract")],-1)),e[304]||(e[304]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[305]||(e[305]=n('
subtract
Performs element-wise subtraction of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#subtract
Example
mlir
%result = stablehlo.subtract %lhs, %rhs : tensor<2xi32>
source
',6))]),t("details",nt,[t("summary",null,[e[306]||(e[306]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tan-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.tan-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tan")],-1)),e[307]||(e[307]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[308]||(e[308]=n('
tan
Performs element-wise tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tan
Example
mlir
%result = stablehlo.tan %operand : tensor<2x2xf64>
source
',6))]),t("details",ot,[t("summary",null,[e[309]||(e[309]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tanh-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.tanh-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tanh")],-1)),e[310]||(e[310]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[311]||(e[311]=n('
tanh
Performs element-wise hyperbolic tangent operation on operand tensor and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tanh
Example
mlir
%result = stablehlo.tanh %operand : tensor<2xf32>
source
',6))]),t("details",it,[t("summary",null,[e[312]||(e[312]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.torch_index_select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.torch_index_select-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.torch_index_select")],-1)),e[313]||(e[313]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[314]||(e[314]=n(`
torch_index_select
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as PyTorch's index_select, augmented with support for batch dimensions: https://pytorch.org/docs/stable/generated/torch.index_select.html.
The batch_dims attribute specifies the number of major batch dimensions (0 or more) that act like a multidimensional loop over both the operand and the index.
Example
mlir
%result = "stablehlo.torch_index_select"(%operand, %index) {
   dim = 2 : i64,
   batch_dims = 1 : i64
-} : (tensor<8x128x3072x64xf32>, tensor<8x16x1024xi32>) -> tensor<8x128x16x1024x64xf32>
source
`,7))]),t("details",pt,[t("summary",null,[e[315]||(e[315]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.transpose-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.transpose-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.transpose")],-1)),e[316]||(e[316]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[317]||(e[317]=n('
transpose
Permutes the dimensions of operand tensor using permutation and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#transpose
Example
mlir
%0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<1x2x3xi32>) -> tensor<3x2x1xi32>
source
',6))]),t("details",rt,[t("summary",null,[e[318]||(e[318]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.triangular_solve-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.triangular_solve-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.triangular_solve")],-1)),e[319]||(e[319]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[320]||(e[320]=n(`
triangular_solve
Solves batches of systems of linear equations with lower or upper triangular coefficient matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#triangular_solve
Example
mlir
%result = "stablehlo.triangular_solve"(%a, %b) {
+} : (tensor<8x128x3072x64xf32>, tensor<8x16x1024xi32>) -> tensor<8x128x16x1024x64xf32>
source
`,7))]),t("details",pt,[t("summary",null,[e[315]||(e[315]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.transpose-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.transpose-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.transpose")],-1)),e[316]||(e[316]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[317]||(e[317]=n('
transpose
Permutes the dimensions of operand tensor using permutation and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#transpose
Example
mlir
%0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<1x2x3xi32>) -> tensor<3x2x1xi32>
source
',6))]),t("details",rt,[t("summary",null,[e[318]||(e[318]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.triangular_solve-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.triangular_solve-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.triangular_solve")],-1)),e[319]||(e[319]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[320]||(e[320]=n(`
triangular_solve
Solves batches of systems of linear equations with lower or upper triangular coefficient matrices.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#triangular_solve
Example
mlir
%result = "stablehlo.triangular_solve"(%a, %b) {
   left_side = true,
   lower = true,
   unit_diagonal = false,
   transpose_a = #stablehlo<transpose NO_TRANSPOSE>
-} : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
source
`,6))]),t("details",ct,[t("summary",null,[e[321]||(e[321]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tuple-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.tuple-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tuple")],-1)),e[322]||(e[322]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[323]||(e[323]=n('
tuple
Produces a result tuple from values val.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tuple
Example
mlir
%result = stablehlo.tuple %val0, %val1 : tuple<tensor<2xf64>, tuple<tensor<i64>>>
source
',6))]),t("details",dt,[t("summary",null,[e[324]||(e[324]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.unary_einsum-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.unary_einsum-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.unary_einsum")],-1)),e[325]||(e[325]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[326]||(e[326]=n(`
unary_einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.unary_einsum"(%operand) {
+} : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
source
`,6))]),t("details",ct,[t("summary",null,[e[321]||(e[321]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.tuple-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.tuple-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.tuple")],-1)),e[322]||(e[322]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[323]||(e[323]=n('
tuple
Produces a result tuple from values val.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tuple
Example
mlir
%result = stablehlo.tuple %val0, %val1 : tuple<tensor<2xf64>, tuple<tensor<i64>>>
source
',6))]),t("details",dt,[t("summary",null,[e[324]||(e[324]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.unary_einsum-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.unary_einsum-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.unary_einsum")],-1)),e[325]||(e[325]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[326]||(e[326]=n(`
unary_einsum
This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.
Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum
Example
mlir
%result = "stablehlo.unary_einsum"(%operand) {
   einsum_config = "ab->a"
-} : (tensor<4x16xf32>) -> tensor<4xf32>
source
`,6))]),t("details",bt,[t("summary",null,[e[327]||(e[327]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.uniform_dequantize-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.uniform_dequantize-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.uniform_dequantize")],-1)),e[328]||(e[328]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[329]||(e[329]=n('
uniform_dequantize
Performs element-wise conversion of quantized tensor operand to a floating-point tensor result according to the quantization parameters defined by the operand type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_dequantize
Example
mlir
%result = stablehlo.uniform_dequantize %operand : (tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>) -> tensor<2xf32>
source
',6))]),t("details",ut,[t("summary",null,[e[330]||(e[330]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.uniform_quantize-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.uniform_quantize-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.uniform_quantize")],-1)),e[331]||(e[331]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[332]||(e[332]=n('
uniform_quantize
Performs element-wise conversion of floating-point tensor or quantized tensor operand to a quantized tensor result according to the quantization parameters defined by the result type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_quantize
Example
mlir
%result = stablehlo.uniform_quantize %operand : (tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>
source
',6))]),t("details",ht,[t("summary",null,[e[333]||(e[333]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.while_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.while_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.while_")],-1)),e[334]||(e[334]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[335]||(e[335]=n(`
while_
Produces the output from executing body function 0 or more times while the cond function outputs true.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#while
Example
mlir
%results0, %results1 = stablehlo.while(%arg0 = %init_i, %arg1 = %init_sum) : tensor<i64>, tensor<i64>
+} : (tensor<4x16xf32>) -> tensor<4xf32>
source
`,6))]),t("details",bt,[t("summary",null,[e[327]||(e[327]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.uniform_dequantize-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.uniform_dequantize-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.uniform_dequantize")],-1)),e[328]||(e[328]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[329]||(e[329]=n('
uniform_dequantize
Performs element-wise conversion of quantized tensor operand to a floating-point tensor result according to the quantization parameters defined by the operand type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_dequantize
Example
mlir
%result = stablehlo.uniform_dequantize %operand : (tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>) -> tensor<2xf32>
source
',6))]),t("details",ut,[t("summary",null,[e[330]||(e[330]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.uniform_quantize-Tuple{Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.uniform_quantize-Tuple{Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.uniform_quantize")],-1)),e[331]||(e[331]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[332]||(e[332]=n('
uniform_quantize
Performs element-wise conversion of floating-point tensor or quantized tensor operand to a quantized tensor result according to the quantization parameters defined by the result type.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_quantize
Example
mlir
%result = stablehlo.uniform_quantize %operand : (tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>
source
',6))]),t("details",ht,[t("summary",null,[e[333]||(e[333]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.while_-Tuple{Vector{Reactant.MLIR.IR.Value}}",href:"#Reactant.MLIR.Dialects.stablehlo.while_-Tuple{Vector{Reactant.MLIR.IR.Value}}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.while_")],-1)),e[334]||(e[334]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[335]||(e[335]=n(`
while_
Produces the output from executing body function 0 or more times while the cond function outputs true.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#while
Example
mlir
%results0, %results1 = stablehlo.while(%arg0 = %init_i, %arg1 = %init_sum) : tensor<i64>, tensor<i64>
 cond {
   %cond = stablehlo.compare LT, %arg0, %ten : (tensor<i64>, tensor<i64>) -> tensor<i1>
   stablehlo.return %cond : tensor<i1>
@@ -194,4 +194,4 @@ import{_ as o,c as i,j as t,a,G as l,a2 as n,B as p,o as r}from"./chunks/framewo
   %new_sum = stablehlo.add %arg1, %one : tensor<i64>
   %new_i = stablehlo.add %arg0, %one : tensor<i64>
   stablehlo.return %new_i, %new_sum : tensor<i64>, tensor<i64>
-}
source
`,6))]),t("details",mt,[t("summary",null,[e[336]||(e[336]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.xor-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.xor-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.xor")],-1)),e[337]||(e[337]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[338]||(e[338]=n('
xor
Performs element-wise XOR of two tensors lhs and rhs and produces a result tensor.
See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#xor
Example
mlir
%result = stablehlo.xor %lhs, %rhs : tensor<2xi32>
source
',6))])])}const jt=o(c,[["render",gt]]);export{yt as __pageData,jt as default};
+}

source

`,6))]),t("details",mt,[t("summary",null,[e[336]||(e[336]=t("a",{id:"Reactant.MLIR.Dialects.stablehlo.xor-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}",href:"#Reactant.MLIR.Dialects.stablehlo.xor-Tuple{Reactant.MLIR.IR.Value, Reactant.MLIR.IR.Value}"},[t("span",{class:"jlbinding"},"Reactant.MLIR.Dialects.stablehlo.xor")],-1)),e[337]||(e[337]=a()),l(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[338]||(e[338]=n('

xor

Performs element-wise XOR of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#xor

Example

mlir
%result = stablehlo.xor %lhs, %rhs : tensor<2xi32>

source

',6))])])}const jt=o(c,[["render",gt]]);export{yt as __pageData,jt as default}; diff --git a/previews/PR441/assets/app.DqFVFsVQ.js b/previews/PR441/assets/app.Lr4HzQqQ.js similarity index 95% rename from previews/PR441/assets/app.DqFVFsVQ.js rename to previews/PR441/assets/app.Lr4HzQqQ.js index efd4af680..c7ee8cdc7 100644 --- a/previews/PR441/assets/app.DqFVFsVQ.js +++ b/previews/PR441/assets/app.Lr4HzQqQ.js @@ -1 +1 @@ -import{R as p}from"./chunks/theme.D5CppCVH.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a5 as S}from"./chunks/framework.B4pBzPBy.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp}; +import{R as p}from"./chunks/theme.CUN7RNBH.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a5 as S}from"./chunks/framework.B4pBzPBy.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp}; diff --git a/previews/PR441/assets/chunks/@localSearchIndexroot.CAtF7Tto.js b/previews/PR441/assets/chunks/@localSearchIndexroot.CAtF7Tto.js deleted file mode 100644 index ea609df50..000000000 --- a/previews/PR441/assets/chunks/@localSearchIndexroot.CAtF7Tto.js +++ /dev/null @@ -1 +0,0 @@ -const 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Trusted by

Scientific Computing

Lux.jl

Machine Learning

Quantum Simulation

Tenet.jl

Tensor Networks

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Lux.jl

Machine Learning

Quantum Simulation

Tenet.jl

Tensor Networks

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diff --git a/previews/PR441/assets/index.md.wUufSJIO.js b/previews/PR441/assets/index.md.BB0E9rM_.js similarity index 93% rename from previews/PR441/assets/index.md.wUufSJIO.js rename to previews/PR441/assets/index.md.BB0E9rM_.js index 8a18600ae..d7c7054c2 100644 --- a/previews/PR441/assets/index.md.wUufSJIO.js +++ b/previews/PR441/assets/index.md.BB0E9rM_.js @@ -1,6 +1,6 @@ import{_ as a,c as s,a2 as t,o as e}from"./chunks/framework.B4pBzPBy.js";const r=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"Reactant.jl Docs","text":"Optimizing Julia Functions with MLIR","tagline":"Optimize Julia Functions With MLIR and XLA for High-Performance Execution on CPU, GPU, TPU and more.","actions":[{"theme":"brand","text":"Tutorials","link":"/tutorials"},{"theme":"alt","text":"API Reference 📚","link":"/api/api"},{"theme":"alt","text":"View on GitHub","link":"https://github.com/EnzymeAD/Reactant.jl"}],"image":{"src":"/logo.svg","alt":"Reactant.jl"}},"features":[{"icon":"🚀","title":"Fast & Device Agnostic","details":"Effortlessly execute your code on CPU, GPU, and TPU with MLIR and XLA.","link":"/introduction"},{"icon":"∂","title":"Built-In MLIR AD","details":"Leverage Enzyme-Powered Automatic Differentiation to Differentiate MLIR Functions","link":"/introduction"},{"icon":"🧩","title":"Composable","details":"Executes and optimizes generic Julia code without requiring special rewriting","link":"/introduction"},{"icon":"⚡","title":"Compiler Optimizations","details":"Fancy MLIR Optimizations seamlessly optimize your Julia code","link":"/introduction"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(h,i,p,k,d,o){return e(),s("div",null,i[0]||(i[0]=[t(`

How to Install Reactant.jl? ​

Its easy to install Reactant.jl. Since Reactant.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:

julia
julia> using Pkg
 julia> Pkg.add("Reactant")

If you want to use the latest unreleased version of Reactant.jl, you can run the following command:

julia
julia> using Pkg
-julia> Pkg.add(url="https://github.com/EnzymeAD/Reactant.jl")

Select an Accelerator Backend ​

julia
using Reactant
+julia> Pkg.add(url="https://github.com/EnzymeAD/Reactant.jl")

Select an Accelerator Backend ​

julia
using Reactant
 Reactant.set_default_backend("cpu")
julia
using Reactant
 Reactant.set_default_backend("gpu")
julia
using Reactant
-Reactant.set_default_backend("tpu")
`,7)]))}const u=a(n,[["render",l]]);export{r as __pageData,u as default}; +Reactant.set_default_backend("tpu")
`,7)]))}const g=a(n,[["render",l]]);export{r as __pageData,g as default}; diff --git a/previews/PR441/assets/index.md.wUufSJIO.lean.js b/previews/PR441/assets/index.md.BB0E9rM_.lean.js similarity index 93% rename from previews/PR441/assets/index.md.wUufSJIO.lean.js rename to previews/PR441/assets/index.md.BB0E9rM_.lean.js index 8a18600ae..d7c7054c2 100644 --- a/previews/PR441/assets/index.md.wUufSJIO.lean.js +++ b/previews/PR441/assets/index.md.BB0E9rM_.lean.js @@ -1,6 +1,6 @@ import{_ as a,c as s,a2 as t,o as e}from"./chunks/framework.B4pBzPBy.js";const r=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"Reactant.jl Docs","text":"Optimizing Julia Functions with MLIR","tagline":"Optimize Julia Functions With MLIR and XLA for High-Performance Execution on CPU, GPU, TPU and more.","actions":[{"theme":"brand","text":"Tutorials","link":"/tutorials"},{"theme":"alt","text":"API Reference 📚","link":"/api/api"},{"theme":"alt","text":"View on GitHub","link":"https://github.com/EnzymeAD/Reactant.jl"}],"image":{"src":"/logo.svg","alt":"Reactant.jl"}},"features":[{"icon":"🚀","title":"Fast & Device Agnostic","details":"Effortlessly execute your code on CPU, GPU, and TPU with MLIR and XLA.","link":"/introduction"},{"icon":"∂","title":"Built-In MLIR AD","details":"Leverage Enzyme-Powered Automatic Differentiation to Differentiate MLIR Functions","link":"/introduction"},{"icon":"🧩","title":"Composable","details":"Executes and optimizes generic Julia code without requiring special rewriting","link":"/introduction"},{"icon":"⚡","title":"Compiler Optimizations","details":"Fancy MLIR Optimizations seamlessly optimize your Julia code","link":"/introduction"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(h,i,p,k,d,o){return e(),s("div",null,i[0]||(i[0]=[t(`

How to Install Reactant.jl? ​

Its easy to install Reactant.jl. Since Reactant.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:

julia
julia> using Pkg
 julia> Pkg.add("Reactant")

If you want to use the latest unreleased version of Reactant.jl, you can run the following command:

julia
julia> using Pkg
-julia> Pkg.add(url="https://github.com/EnzymeAD/Reactant.jl")

Select an Accelerator Backend ​

julia
using Reactant
+julia> Pkg.add(url="https://github.com/EnzymeAD/Reactant.jl")

Select an Accelerator Backend ​

julia
using Reactant
 Reactant.set_default_backend("cpu")
julia
using Reactant
 Reactant.set_default_backend("gpu")
julia
using Reactant
-Reactant.set_default_backend("tpu")
`,7)]))}const u=a(n,[["render",l]]);export{r as __pageData,u as default}; +Reactant.set_default_backend("tpu")
`,7)]))}const g=a(n,[["render",l]]);export{r as __pageData,g as default}; diff --git a/previews/PR441/hashmap.json b/previews/PR441/hashmap.json index 8024559c7..5ea625a44 100644 --- a/previews/PR441/hashmap.json +++ b/previews/PR441/hashmap.json @@ -1 +1 @@ -{"api_affine.md":"DDDiaunG","api_api.md":"EX21q_Ts","api_arith.md":"BX48b7Bc","api_builtin.md":"Dpx_h2Fd","api_chlo.md":"BW-ZonsV","api_enzyme.md":"frKwl5MH","api_func.md":"Wv6kQziA","api_internal.md":"CsBtpNqO","api_mlirc.md":"C4o99U5u","api_ops.md":"C1YgWHhK","api_stablehlo.md":"rWlP7YUr","api_vhlo.md":"myvzTGof","api_xla.md":"DakUC3js","index.md":"wUufSJIO","introduction_index.md":"Cbr4uva1","tutorials_index.md":"OhpPvcwV"} +{"api_affine.md":"BDXHCMIg","api_api.md":"BAu0cnN7","api_arith.md":"CMmO_Sp9","api_builtin.md":"vAmfTwue","api_chlo.md":"Ct7iVV7Y","api_enzyme.md":"DEhmqOoH","api_func.md":"CdLx48lL","api_internal.md":"tz9hnS-V","api_mlirc.md":"C2YJ2Laa","api_ops.md":"B65ZOBdU","api_stablehlo.md":"BG0YL1kY","api_vhlo.md":"myvzTGof","api_xla.md":"DakUC3js","index.md":"BB0E9rM_","introduction_index.md":"Cbr4uva1","tutorials_index.md":"OhpPvcwV"} diff --git a/previews/PR441/index.html b/previews/PR441/index.html index 37d9ce373..2cb0177d5 100644 --- a/previews/PR441/index.html +++ b/previews/PR441/index.html @@ -9,11 +9,11 @@ - + - + - + @@ -23,11 +23,11 @@
Reactant.jl

Appearance

Reactant.jl Docs

Optimizing Julia Functions with MLIR

Optimize Julia Functions With MLIR and XLA for High-Performance Execution on CPU, GPU, TPU and more.

Tutorials
API Reference 📚
View on GitHub
Reactant.jl
🚀

Fast & Device Agnostic

Effortlessly execute your code on CPU, GPU, and TPU with MLIR and XLA.

∂

Built-In MLIR AD

Leverage Enzyme-Powered Automatic Differentiation to Differentiate MLIR Functions

🧩

Composable

Executes and optimizes generic Julia code without requiring special rewriting

⚡

Compiler Optimizations

Fancy MLIR Optimizations seamlessly optimize your Julia code

How to Install Reactant.jl? ​

Its easy to install Reactant.jl. Since Reactant.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:

julia
julia> using Pkg
 julia> Pkg.add("Reactant")

If you want to use the latest unreleased version of Reactant.jl, you can run the following command:

julia
julia> using Pkg
-julia> Pkg.add(url="https://github.com/EnzymeAD/Reactant.jl")

Select an Accelerator Backend ​

julia
using Reactant
+julia> Pkg.add(url="https://github.com/EnzymeAD/Reactant.jl")

Select an Accelerator Backend ​

julia
using Reactant
 Reactant.set_default_backend("cpu")
julia
using Reactant
 Reactant.set_default_backend("gpu")
julia
using Reactant
 Reactant.set_default_backend("tpu")
- + \ No newline at end of file diff --git a/previews/PR441/introduction/index.html b/previews/PR441/introduction/index.html index 166185aa8..558e03d97 100644 --- a/previews/PR441/introduction/index.html +++ b/previews/PR441/introduction/index.html @@ -9,9 +9,9 @@ - + - + @@ -45,7 +45,7 @@ # one can now run the program f(input1, input2)
ConcreteRNumber{Float64}(18.414709848078964)
- + \ No newline at end of file diff --git a/previews/PR441/tutorials/index.html b/previews/PR441/tutorials/index.html index 46edd3273..463c603ea 100644 --- a/previews/PR441/tutorials/index.html +++ b/previews/PR441/tutorials/index.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@
Reactant.jl

Appearance


Trusted by

Scientific Computing

Lux.jl

Machine Learning

Quantum Simulation

Tenet.jl

Tensor Networks

Tutorials ​

We are currently working on adding tutorials to Reactant!! Please check back soon!

- + \ No newline at end of file