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register_prim_ops.cpp
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register_prim_ops.cpp
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#include <c10/util/Optional.h>
#include <c10/util/irange.h>
#include <torch/csrc/jit/runtime/custom_operator.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <torch/csrc/jit/runtime/register_ops_utils.h>
#include <torch/csrc/jit/runtime/slice_indices_adjust.h>
#include <torch/library.h>
#include <algorithm>
#include <bitset>
#include <cctype>
#include <cmath>
#include <exception>
#include <fstream>
#include <iostream>
#include <limits>
#include <memory>
#include <mutex>
#include <ostream>
#include <stdexcept>
#include <string>
#include <typeinfo>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
namespace torch {
namespace jit {
namespace {
std::string stringSlice(
std::string string,
c10::optional<int64_t> start,
c10::optional<int64_t> end,
int64_t step) {
int64_t start_val = start.has_value() ? start.value() : INT64_MAX;
int64_t end_val = end.has_value() ? end.value() : INT64_MAX;
const int64_t num_vals =
slice_indices_adjust(string.size(), &start_val, &end_val, step);
int64_t i = start_val;
std::string result = "";
for (const auto j : c10::irange(num_vals)) {
(void)j; // Suppress unused variable warning
result += string[i];
i += step;
}
return result;
}
// consecutive whitespace are regarded as a single separator,
// the result will contain no empty strings at the start or end
// if the string has leading or trailing whitespace.
c10::List<std::string> splitNoneSeparator(const std::string& string) {
c10::List<std::string> splits;
// whitespaces includes tab, space and
// the delimiters defined in the implementation of splitlines
std::string whitespaces =
" \t\n\r\r\n\v\x0b\f\x0c\x1c\x1d\x1e\x85\u2028\u2029";
std::string::size_type prev_pos = 0;
std::string::size_type pos = 0;
while ((pos = string.find_first_of(whitespaces, pos)) != std::string::npos) {
auto substr = string.substr(prev_pos, pos - prev_pos);
// skip the whitespaces as the Python split() method
if (!substr.empty()) {
splits.emplace_back(substr);
}
pos++;
prev_pos = pos;
}
if (prev_pos != string.size()) {
splits.emplace_back(string.substr(prev_pos));
}
return splits;
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
RegisterOperators reg(
{OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::str(t elem) -> str"),
[](Stack* stack) {
std::stringstream ss;
ss << pop(stack);
push(stack, ss.str());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::list(str t) -> str[]"),
[](Stack* stack) {
auto str = pop(stack).toStringRef();
c10::List<std::string> chars;
chars.reserve(str.size());
for (auto c : str) {
chars.push_back(std::string(1, c));
}
push(stack, std::move(chars));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::cpu(Tensor(a) self) -> Tensor(a|b)"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.cpu());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::layout(Tensor a) -> int"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.layout());
},
aliasAnalysisFromSchema()),
Operator(
prim::tolist,
// This operator has to be unschematized because the return type
// depends on the type hint and input. The implementation of this
// operator below is intended to be as close to the Python
// implementation in torch/csrc/utils/tensor_list.cpp as possible.
[](const Node* /*node*/) -> Operation {
return [](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int elem_ty_val;
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int dim_val;
at::Tensor t;
pop(stack, elem_ty_val);
pop(stack, dim_val);
pop(stack, t);
// If the Tensor is not on the CPU, transfer it.
if (!t.device().is_cpu()) {
t = t.cpu();
}
// Rebuild the output type using elem_ty_val and dim_val. Start
// with the element type corresponding to elem_ty_val.
TypePtr out_ty;
if (elem_ty_val == 0) {
out_ty = IntType::get();
} else if (elem_ty_val == 1) {
out_ty = FloatType::get();
} else if (elem_ty_val == 2) {
out_ty = BoolType::get();
} else if (elem_ty_val == 3) {
out_ty = ComplexType::get();
} else {
TORCH_CHECK(
false,
"Unsupported element type for tolist; only int, float, complex and bool are supported");
}
// Check that type of the Tensor matches that of the annotation.
// Make an exception for the case in which the annotated type is
// float/complex and the Tensor data type is also float/complex;
// the elements will be casted to double/c10::complex<double>
// later.
TORCH_CHECK(
(out_ty == FloatType::get() && t.is_floating_point()) ||
(out_ty == ComplexType::get() && t.is_complex()) ||
tryScalarTypeFromJitType(out_ty) == t.scalar_type(),
"Output annotation element type and runtime tensor element type must match for tolist()");
// Check that the dimension of the Tensor matches that of the
// annotation.
TORCH_CHECK(
dim_val == t.dim(),
"Output annotation list dimension and runtime tensor dimension must match for tolist()");
// Wrap out_ty in a ListType dim times.
for (int i = 0; i < dim_val; ++i) {
out_ty = ListType::create(out_ty);
}
int64_t dim = t.dim();
auto sizes = t.sizes();
auto strides = t.strides();
size_t element_size = t.element_size();
char* data = static_cast<char*>(t.data_ptr());
auto result = tensorToListRecursive(
data,
0,
dim,
out_ty,
t.scalar_type(),
sizes,
strides,
element_size);
push(stack, std::move(result));
};
},
aliasAnalysisSpecialCase()),
// only used internally in range() translation
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::__range_length(int lo, int hi, int step) -> int"),
[](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int64_t lo, hi, step;
pop(stack, lo, hi, step);
// error handling when step_val = 0 during runtime
if (step == 0) {
throw std::runtime_error("range() arg 3 must not be zero");
}
if (step > 0 && lo < hi) {
push(stack, 1 + (hi - 1 - lo) / step);
} else if (step < 0 && lo > hi) {
push(stack, 1 + (lo - 1 - hi) / (0 - step));
} else {
push(stack, 0);
}
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::__derive_index(int index, int start, int step) -> int"),
[](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int64_t index, start, step;
pop(stack, index, start, step);
push(stack, start + index * step);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::TupleUnpack(Any tup) -> ..."),
[](Stack* stack) { tupleUnpack(*stack); },
aliasAnalysisSpecialCase()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::unchecked_cast(t x) -> t"),
noop,
aliasAnalysisSpecialCase()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::IntImplicit(Tensor a) -> int"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
checkImplicitTensorToNum(a, /*to int*/ true);
push(stack, a.item<int64_t>());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::ComplexImplicit(Tensor a) -> complex"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
checkImplicitTensorToNum(a, /*to int*/ false);
push(stack, a.item<c10::complex<double>>());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::FloatImplicit(Tensor a) -> float"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
checkImplicitTensorToNum(a, /*to int*/ false);
push(stack, a.item<double>());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::ScalarImplicit(Tensor a) -> Scalar"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
checkImplicitTensorToNum(a, /*to int*/ false);
push(stack, a.item());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Bool.Tensor(Tensor a) -> bool"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.is_nonzero());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Bool.int(int a) -> bool"),
[](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int64_t i;
pop(stack, i);
push(stack, (bool)i);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Bool.float(float a) -> bool"),
[](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
double d;
pop(stack, d);
push(stack, (bool)d);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Int.Tensor(Tensor a) -> int"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.item<int64_t>());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Int.bool(bool a) -> int"),
[](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
bool b;
pop(stack, b);
push(stack, static_cast<int64_t>(b));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Int.float(float a) -> int"),
[](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
double d;
pop(stack, d);
push(stack, static_cast<int64_t>(d));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Int.Scalar(Scalar a) -> int"),
[](Stack* stack) {
IValue scalar;
pop(stack, scalar);
if (scalar.isInt()) {
push(stack, std::move(scalar));
} else {
// toScalar() needed to avoid strict type check in IValue::toInt.
push(stack, static_cast<int64_t>(scalar.toScalar().toInt()));
}
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Int.str(str a) -> int"),
[](Stack* stack) {
auto s = pop(stack).toString();
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
std::string::size_type sz;
int64_t val = static_cast<int64_t>(c10::stoll(s->string(), &sz));
if (sz == s->string().size()) {
push(stack, val);
} else {
std::stringstream error_str;
error_str << "invalid literal for int() "
<< "with base 10: '" << s->string() << "'";
throw std::runtime_error(error_str.str());
}
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Float.Tensor(Tensor a) -> float"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.item<double>());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Float.Scalar(Scalar a) -> float"),
[](Stack* stack) {
IValue scalar;
pop(stack, scalar);
if (scalar.isDouble()) {
push(stack, std::move(scalar));
} else if (scalar.isComplexDouble()) {
push(stack, scalar.toComplexDouble().real());
} else {
push(stack, static_cast<double>(scalar.toInt()));
}
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Float.int(int a) -> float"),
[](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int64_t i;
pop(stack, i);
push(stack, (float)i);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Float.bool(bool a) -> float"),
[](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
bool b;
pop(stack, b);
push(stack, (float)b);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Float.str(str a) -> float"),
[](Stack* stack) {
auto s = pop(stack).toString();
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
std::string::size_type sz;
double b = c10::stod(s->string(), &sz);
if (sz == s->string().size()) {
push(stack, b);
} else {
std::stringstream error_str;
error_str << "could not convert string "
<< "to float: '" << s->string() << "'";
throw std::runtime_error(error_str.str());
}
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Complex.Scalar(Scalar a) -> complex"),
[](Stack* stack) {
IValue scalar;
pop(stack, scalar);
if (scalar.isComplexDouble()) {
push(stack, std::move(scalar));
} else if (scalar.isDouble()) {
push(stack, c10::complex<double>(scalar.toDouble(), 0));
} else {
push(stack, c10::complex<double>(scalar.toInt(), 0));
}
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::Complex.Tensor_Tensor(Tensor a, Tensor b) -> complex"),
[](Stack* stack) {
at::Tensor a, b;
pop(stack, a, b);
push(
stack, c10::complex<double>(a.item<double>(), b.item<double>()));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::format(str self, ...) -> str"),
[](Stack* stack) {
size_t num_inputs = pop(stack).toInt();
format(*stack, num_inputs);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::einsum.sublist(Tensor a, ...) -> Tensor"),
[](Stack* stack) {
size_t num_inputs = pop(stack).toInt();
einsum(*stack, num_inputs);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::NumToTensor.Scalar(Scalar a) -> Tensor"),
[](Stack* stack) {
at::Scalar s;
pop(stack, s);
push(stack, at::scalar_to_tensor(s));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::RaiseException(str msg) -> ()"),
[](Stack* stack) { throw JITException(pop(stack).toStringRef()); },
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Size(int[] sizes) -> int[]"),
[](Stack* stack) {},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::size(Tensor self) -> int[]"),
[](Stack* stack) {
auto t = std::move(pop(stack)).toTensor();
pack(stack, t.sizes().vec());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::EnumName(AnyEnumType enum) -> str"),
[](Stack* stack) {
IValue e = pop(stack);
push(stack, e.toEnumHolder()->name());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::EnumValue.int(AnyEnumType enum) -> int"),
[](Stack* stack) {
IValue e = pop(stack);
push(stack, e.toEnumHolder()->value());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"prim::EnumValue.float(AnyEnumType enum) -> float"),
[](Stack* stack) {
IValue e = pop(stack);
push(stack, e.toEnumHolder()->value());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::EnumValue.str(AnyEnumType enum) -> str"),
[](Stack* stack) {
IValue e = pop(stack);
push(stack, e.toEnumHolder()->value());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
// note the compiler knows to type TupleIndex more accurately than it
// is listed here.
TORCH_SELECTIVE_SCHEMA("prim::TupleIndex(Any tup, int i) -> Any"),
[](Stack* stack) {
int64_t index = pop(stack).toInt();
auto tuple = pop(stack).toTuple();
auto norm_index = normalizeIndex(index, tuple->elements().size());
if (norm_index < 0 ||
norm_index > static_cast<int64_t>(tuple->elements().size())) {
throw std::out_of_range("Tuple list index out of range");
}
stack->emplace_back(tuple->elements()[norm_index]);
},
aliasAnalysisSpecialCase()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::ne.int_list(int[] a, int[] b) -> bool"),
listNe<int64_t>,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"prim::unchecked_unwrap_optional(t(a)? optional) -> t(a)"),
noop,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::device(Tensor a) -> Device"),
[](Stack* stack) { push(stack, pop(stack).toTensor().device()); },
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::dtype(Tensor a) -> int"),
[](Stack* stack) {
at::Tensor a;
pop(stack, a);
push(stack, static_cast<int64_t>(a.scalar_type()));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::__not__(bool self) -> bool"),
[](Stack* stack) { push(stack, !pop(stack).toBool()); },
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::__is__(t1 self, t2 obj) -> bool"),
[](Stack* stack) {
IValue self, obj;
pop(stack, self, obj);
push(stack, self.is(obj));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::__isnot__(t1 self, t2 obj) -> bool"),
[](Stack* stack) {
IValue self, obj;
pop(stack, self, obj);
push(stack, !self.is(obj));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::element_size(Tensor self) -> int"),
[](Stack* stack) {
at::Tensor arg = pop(stack).toTensor();
push(stack, arg.element_size());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::numel(Tensor self) -> int"),
[](Stack* stack) {
at::Tensor arg = pop(stack).toTensor();
push(stack, arg.numel());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::dim(Tensor self) -> int"),
[](Stack* stack) {
at::Tensor arg = pop(stack).toTensor();
push(stack, arg.dim());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::get_device(Tensor self) -> int"),
[](Stack* stack) {
RECORD_FUNCTION("get_device", std::vector<c10::IValue>());
auto result =
at::get_device((std::move(peek(stack, 0, 1))).toTensor());
drop(stack, 1);
pack(stack, result);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::storage_offset(Tensor self) -> int"),
[](Stack* stack) {
RECORD_FUNCTION("storage_offset", std::vector<c10::IValue>());
auto result =
((std::move(peek(stack, 0, 1))).toTensor()).storage_offset();
drop(stack, 1);
pack(stack, result);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::is_contiguous(Tensor self) -> bool"),
[](Stack* stack) {
RECORD_FUNCTION("is_contiguous", std::vector<c10::IValue>());
auto result =
((std::move(peek(stack, 0, 1))).toTensor()).is_contiguous();
drop(stack, 1);
pack(stack, result);
},
aliasAnalysisFromSchema()),
// these ops are generic over the list element type.
// CREATING GENERIC_LIST_OPS
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::select.t(t[](a) list, int idx) -> t(*)"),
listSelect,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::__getitem__.t(t[](a) list, int idx) -> t(*)"),
listSelect,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::append.t(t[](a!) self, t(c -> *) el) -> t[](a!)"),
listAppend,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::reverse.t(t[](a!) self) -> ()"),
listReverse,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::extend.t(t[](a!) self, t[] other) -> ()"),
listExtend,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::copy.t(t[](a) self) -> t[]"),
listCopy,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::_set_item.t(t [](a!) l, int idx, t(b -> *) el) -> t[](a!)"),
listSetItem,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::clear.t(t[](a!) self) -> ()"),
listClear,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::Delete.t(t[](a!) self, int idx) -> ()"),
listDelete,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::insert.t(t[](a!) self, int idx, t(b -> *) el) -> ()"),
listInsert,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::pop.t(t[](a!) self, int idx=-1) -> t(*)"),
listPop,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::add.t(t[] a, t[] b) -> t[]"),
listAdd,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::add_.t(t[](a!) self, t[] b) -> t[]"),
listInplaceAdd,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::slice.t(t[] l, int? start=None, int? end=None, int step=1) -> t[]"),
listSlice,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::list.t(t[] l) -> t[]"),
listList,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::mul.left_t(t[] l, int n) -> t[]"),
listMulIntLeft,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::mul.right_(int n, t[] l) -> t[]"),
listMulIntRight,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::mul_.t(t[](a!) l, int n) -> t[](a!)"),
listMulIntLeftInPlace,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::len.t(t[] a) -> int"),
listLen,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::eq.int_list(int[] a, int[] b) -> bool"),
listEq<int64_t>,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::eq.device(Device a, Device b) -> bool"),
[](Stack* stack) {
auto a = pop(stack).toDevice();
auto b = pop(stack).toDevice();
push(stack, a == b);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::ne.device(Device a, Device b) -> bool"),
[](Stack* stack) {
auto a = pop(stack).toDevice();
auto b = pop(stack).toDevice();
push(stack, a != b);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::eq.bool(bool a, bool b) -> bool"),
[](Stack* stack) {
auto a = pop(stack);
auto b = pop(stack);
push(stack, a == b);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::ne.bool(bool a, bool b) -> bool"),
[](Stack* stack) {
auto a = pop(stack);
auto b = pop(stack);
push(stack, a != b);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::Uninitialized() -> Any"),
[](Stack* stack) { push(stack, IValue::uninitialized()); },
aliasAnalysisSpecialCase()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::Print(...) -> ()"),
[](Stack* stack) {
auto num_inputs = pop(stack).toInt();
std::stringstream ss;
bool first = true;
for (const IValue& i : last(stack, num_inputs)) {
if (!first)
ss << " ";
first = false;
ss << i;
}
drop(stack, num_inputs);
ss << std::endl;
auto* handler = getPrintHandler();
TORCH_INTERNAL_ASSERT(handler);
handler(ss.str());
},
aliasAnalysisSpecialCase()),
// This is an alternative to aten::cat op that takes variable number of
// parameters as input.
// Format:
// prim::Concat(Tensors..., dim) -> Tensor
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::Concat(...) -> Tensor"),
[](Stack* stack) {
auto num_inputs = pop(stack).toInt();
auto dim = pop(stack).toInt();
std::vector<at::Tensor> inputs(num_inputs - 1);
for (int i = 0; i < num_inputs - 1; ++i) {
inputs[num_inputs - 2 - i] = pop(stack).toTensor();
}
push(stack, at::cat(inputs, dim));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::eq.enum(AnyEnumType a, AnyEnumType b) -> bool"),
[](Stack* stack) {
IValue x = pop(stack);
IValue y = pop(stack);
push(stack, x == y);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::ne.enum(AnyEnumType a, AnyEnumType b) -> bool"),
[](Stack* stack) {
IValue x = pop(stack);
IValue y = pop(stack);
push(stack, x != y);
},
aliasAnalysisFromSchema()),
// We define aten::dequantize in both native_functions.yaml and here,
// however, aten::dequantize.any defined here overrides
// aten::dequantize.tensors in native_functions.yaml. The variants here
// are only for graph mode quantization, and they should be removed once
// we deprecate graph mode quantization, and use the variants in
// native_functions.yaml.
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::dequantize.tensor(Tensor qtensor) -> Tensor"),
[](Stack* stack) {
at::Tensor qtensor;
pop(stack, qtensor);
push(stack, at::dequantize(qtensor));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::dequantize.list(Tensor[] qtensors) -> Tensor[]"),
[](Stack* stack) {
auto qtensors = pop(stack).toTensorVector();
push(stack, at::dequantize(qtensors));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::dequantize.any(Any tensors) -> Any"),
[](Stack* stack) { dequantize(*stack); },
aliasAnalysisFromSchema()),
DEFINE_UNARY_OP_WITH_COMPLEX(aten::log, std::log(a), float, float),
DEFINE_STRING_OP(aten::add, a + b, str),
DEFINE_COMPARISON_OP_WITH_COMPLEX(aten::eq, a == b),
DEFINE_COMPARISON_OP_WITH_COMPLEX(aten::ne, a != b),
DEFINE_GENERIC_OP(
aten::polar,
c10::polar(static_cast<double>(a), static_cast<double>(b)),
c10::polar(static_cast<double>(a), static_cast<double>(b)),
complex,
complex),
DEFINE_INT_FLOAT_OP(
aten::polar,
c10::polar(static_cast<double>(a), static_cast<double>(b)),
complex),
DEFINE_SCALAR_BINARY_OP_AVOID_COLLISION(
aten::polar,
c10::polar(static_cast<double>(a), static_cast<double>(b)),
c10::polar(static_cast<double>(a), static_cast<double>(b)),
Scalar),
DEFINE_COMPARISON_OP(aten::lt, a < b),
DEFINE_COMPARISON_OP(aten::gt, a > b),
DEFINE_COMPARISON_OP(aten::le, a <= b),
DEFINE_COMPARISON_OP(aten::ge, a >= b),
DEFINE_BINARY_OP_WITH_COMPLEX(aten::add, a + b),
DEFINE_BINARY_OP_WITH_COMPLEX(aten::sub, a - b),
DEFINE_BINARY_OP_WITH_COMPLEX(aten::mul, a* b),
DEFINE_BOOL_OP(aten::__and__, a&& b),
DEFINE_BOOL_OP(aten::__or__, a || b),
DEFINE_BOOL_OP(aten::__xor__, a != b),
DEFINE_UNARY_OP(aten::round, round_to_even(a), float, float),
DEFINE_UNARY_OP(aten::floor, floor(a), int, int),
DEFINE_UNARY_OP(aten::ceil, ceil(a), int, int),
DEFINE_UNARY_OP_WITH_COMPLEX(aten::neg, -a, int, float),
DEFINE_UNARY_OP_WITH_COMPLEX(aten::exp, std::exp(a), float, float),
// Pass in two ops for handling int and float separately as % in C++ only
// works for int The modulus calculation is different between C++ and
// Python (on negative), we preserve the python behavior as it's more
// common and match python syntax, hence the conversion.
DEFINE_GENERIC_OP(
aten::remainder,
(b + (a % b)) % b,
fmod((b + fmod(a, b)), b),
int,
float),
DEFINE_INT_FLOAT_OP(aten::remainder, fmod((b + fmod(a, b)), b), float),
DEFINE_SCALAR_BINARY_OP(
aten::remainder,
(b + (a % b)) % b,
fmod((b + fmod(a, b)), b),
Scalar),
// NB: This is the python truediv operation
DEFINE_GENERIC_OP_WITH_COMPLEX(
aten::div,
static_cast<double>(a) / static_cast<double>(b),
a / b,
a / b,
float,
float,
complex),
DEFINE_SCALAR_BINARY_OP(
aten::div,
static_cast<double>(a) / static_cast<double>(b),
a / b,
float),
DEFINE_GENERIC_OP(
aten::floordiv,
floordiv(a, b),
std::floor(a / b),
int,
float),
DEFINE_INT_FLOAT_OP(aten::floordiv, std::floor(a / b), float),
DEFINE_SCALAR_BINARY_OP(
aten::floordiv,
floordiv(a, b),
std::floor(a / b),
Scalar),
// int ** int produces a float, because negative exponents produce float
// results
DEFINE_GENERIC_OP_WITH_COMPLEX(
aten::pow,
static_cast<double>(pow(a, b)),
static_cast<double>(pow(a, b)),
static_cast<c10::complex<double>>(pow(a, b)),
float,
float,
complex),
DEFINE_INT_FLOAT_OP(aten::pow, pow(a, b), float),
DEFINE_FLOAT_COMPLEX_OP(aten::pow, pow(a, b), complex),
DEFINE_SCALAR_BINARY_OP_AVOID_COLLISION(
aten::pow,
static_cast<double>(pow(a, b)),
static_cast<double>(pow(a, b)),
float),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::pow.int_to_int(int a, int b) -> int"),
[](Stack* stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int64_t a, b;
pop(stack, a, b);
push(stack, pow(a, b));
},
aliasAnalysisFromSchema()),
// min and max are in prim:: because there is a difference between
// the python builtin 'min' and 'torch.min'
DEFINE_BINARY_OP(prim::min, a < b ? a : b),
DEFINE_BINARY_OP(prim::max, a > b ? a : b),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("prim::type(Device self) -> str"),
[](Stack* stack) {
auto d = pop(stack);
push(
stack,
DeviceTypeName(d.toDevice().type(), /* lower_case=*/true));
},
aliasAnalysisFromSchema()),
// tensor length op (size of 1st dimension)
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::len.Tensor(Tensor t) -> int"),
[](Stack* stack) {
at::Tensor t = pop(stack).toTensor();
if (t.dim() == 0) {
AT_ERROR("len() of a 0-d tensor");
}
push(stack, t.sizes()[0]);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::ord(str string) -> int"),
[](Stack* stack) {
auto string = pop(stack).toStringRef();
TORCH_CHECK(
string.size() == 1,
"String for ord() must be 1 character, found ",
string.size());
uint8_t ord = string.at(0);
push(stack, int64_t(ord));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::lower(str self) -> str"),
[](Stack* stack) {
auto string = pop(stack).toStringRef();
std::stringstream ss;
for (char c : string) {
ss << static_cast<char>(::tolower(c));
}
push(stack, ss.str());
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::__contains__.int_list(int[] l, int item) -> bool"),
listContains<int64_t>,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::__contains__.str_list(str[] l, str item) -> bool"),
listContains<std::string>,
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::len.str(str s) -> int"),
[](Stack* stack) {
auto string = pop(stack).toStringRef();
push(stack, static_cast<int64_t>(string.size()));
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA("aten::dict() -> Dict(str, Tensor)"),
[](Stack* stack) {
auto dict =
c10::impl::GenericDict(StringType::get(), TensorType::get());
push(stack, dict);
},
aliasAnalysisFromSchema()),
OperatorGenerator(
TORCH_SELECTIVE_SCHEMA(
"aten::__getitem__.str(str s, int index) -> str"),
[](Stack* stack) {
auto index = pop(stack).toInt();
auto string = pop(stack).toStringRef();
auto norm_index = normalizeIndex(index, string.size());
char c = string.at(norm_index);
push(stack, std::string(&c, 1));
},