[MLIR][TORCH] Add e2e support for aten.pow.Scalar op

Signed-Off By: Vivek Khandelwal <[email protected]>

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -660,6 +660,18 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     divTensorMode.emitError("invalid rounding mode");
     return nullptr;
   }
+
+  if (auto pow = dyn_cast<AtenPowScalarOp>(op)) {
+    Type dtype = pow.getType().cast<ValueTensorType>().getDtype();
+    if (!dtype.isa<mlir::FloatType>()) {
+      pow.emitError("unimplemented: non-floating point dtype");
+      return nullptr;
+    }
+    Value selfPromoted = convertScalarToDtype(b, loc, operands[0], dtype);
+    Value expPromoted = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
+    return b.create<math::PowFOp>(loc, selfPromoted, expPromoted);
+  }
+
   if (auto pow = dyn_cast<AtenPowTensorScalarOp>(op)) {
     if (!pow.getType()
              .cast<ValueTensorType>()
@@ -1162,20 +1174,20 @@ class ConvertElementwiseOp : public ConversionPattern {
              AtenLerpTensorOp, AtenSigmoidOp, AtenExpOp, AtenExpm1Op,
              AtenMinimumOp, AtenMaximumOp, AtenToDtypeOp, AtenClampOp,
              AtenRsubScalarOp, AtenMulScalarOp, AtenLogOp, AtenErfOp,
-             AtenSqrtOp, AtenFloorOp, AtenPowTensorScalarOp,
+             AtenSqrtOp, AtenFloorOp, AtenPowScalarOp, AtenPowTensorScalarOp,
              AtenPowTensorTensorOp, AtenLog2Op, AtenLog1pOp, AtenRsqrtOp,
              AtenDivScalarOp, AtenRemainderScalarOp, AtenAbsOp,
              AtenReciprocalOp, AtenBitwiseAndTensorOp, AtenBitwiseOrTensorOp,
              AtenBitwiseXorTensorOp, AtenGtScalarOp, AtenGeScalarOp,
              AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp, AtenWhereSelfOp,
-             AtenCeilOp, AtenGtTensorOp, AtenGeTensorOp, AtenEqTensorOp, AtenNeTensorOp,
-             AtenLtTensorOp, AtenLeTensorOp, AtenSubScalarOp, AtenAddScalarOp,
-             AtenThresholdOp, AtenThresholdBackwardOp, AtenHardtanhBackwardOp,
-             AtenCloneOp, AtenSinOp, AtenCosOp, AtenNeScalarOp, AtenNegOp,
-             AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenLogicalAndOp,
-             AtenLogicalXorOp, AtenLogicalNotOp, AtenTriuOp, AtenTrilOp,
-             AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp, AtenFillTensorOp,
-             AtenAtanOp, AtenRealOp, AtenImagOp>(op))
+             AtenCeilOp, AtenGtTensorOp, AtenGeTensorOp, AtenEqTensorOp,
+             AtenNeTensorOp, AtenLtTensorOp, AtenLeTensorOp, AtenSubScalarOp,
+             AtenAddScalarOp, AtenThresholdOp, AtenThresholdBackwardOp,
+             AtenHardtanhBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp,
+             AtenNeScalarOp, AtenNegOp, AtenMaskedFillTensorOp, AtenLogicalOrOp,
+             AtenLogicalAndOp, AtenLogicalXorOp, AtenLogicalNotOp, AtenTriuOp,
+             AtenTrilOp, AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp,
+             AtenFillTensorOp, AtenAtanOp, AtenRealOp, AtenImagOp>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
 
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
@@ -1697,17 +1709,18 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenLerpTensorOp, AtenSigmoidOp, AtenMinimumOp, AtenAtan2Op,
       AtenMaximumOp, AtenToDtypeOp, AtenClampOp, AtenRsubScalarOp, AtenLogOp,
       AtenErfOp, AtenSqrtOp, AtenFloorOp, AtenCeilOp, AtenPreluOp,
-      AtenPowTensorScalarOp, AtenPowTensorTensorOp, AtenLog2Op, AtenLog1pOp,
-      AtenRsqrtOp, AtenAbsOp, AtenReciprocalOp, AtenBitwiseAndTensorOp,
-      AtenBitwiseOrTensorOp, AtenBitwiseXorTensorOp, AtenGtScalarOp,
-      AtenGeScalarOp, AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp,
-      AtenWhereSelfOp, AtenGtTensorOp, AtenGeTensorOp, AtenEqTensorOp, AtenNeTensorOp,
-      AtenLtTensorOp, AtenLeTensorOp, AtenThresholdOp, AtenThresholdBackwardOp,
-      AtenHardtanhBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp, AtenNeScalarOp,
-      AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenLogicalAndOp, AtenAtanOp,
-      AtenLogicalXorOp, AtenLogicalNotOp, AtenTriuOp, AtenTrilOp,
-      AtenRemainderScalarOp, AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp,
-      AtenFillTensorOp, AtenRealOp, AtenImagOp>();
+      AtenPowScalarOp, AtenPowTensorScalarOp, AtenPowTensorTensorOp, AtenLog2Op,
+      AtenLog1pOp, AtenRsqrtOp, AtenAbsOp, AtenReciprocalOp,
+      AtenBitwiseAndTensorOp, AtenBitwiseOrTensorOp, AtenBitwiseXorTensorOp,
+      AtenGtScalarOp, AtenGeScalarOp, AtenEqScalarOp, AtenLtScalarOp,
+      AtenLeScalarOp, AtenWhereSelfOp, AtenGtTensorOp, AtenGeTensorOp,
+      AtenEqTensorOp, AtenNeTensorOp, AtenLtTensorOp, AtenLeTensorOp,
+      AtenThresholdOp, AtenThresholdBackwardOp, AtenHardtanhBackwardOp,
+      AtenCloneOp, AtenSinOp, AtenCosOp, AtenNeScalarOp, AtenMaskedFillTensorOp,
+      AtenLogicalOrOp, AtenLogicalAndOp, AtenAtanOp, AtenLogicalXorOp,
+      AtenLogicalNotOp, AtenTriuOp, AtenTrilOp, AtenRemainderScalarOp,
+      AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp, AtenFillTensorOp,
+      AtenRealOp, AtenImagOp>();
   patterns.add<ConvertElementwiseOp>(typeConverter, context);
   target.addIllegalOp<AtenNllLossForwardOp>();
   patterns.add<ConvertAtenDetachOp>(typeConverter, context);

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6522,6 +6522,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.pow.Scalar\"(%arg0: !torch.float, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg1) : (!torch.list<int>) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.pow.Tensor_Scalar\"(%arg0: !torch.list<int>, %arg1: !torch.float) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -9747,6 +9751,15 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %6 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.promote_dtypes(%3, %5) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
 "    return %6 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.pow.Scalar\"(%arg0: !torch.number, %arg1: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %none = torch.constant.none\n"
+"    %0:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = torch.prim.ListConstruct %none, %0#0 : (!torch.none, !torch.int) -> !torch.list<optional<int>>\n"
+"    %2 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.get_dtype_of_scalar(%arg0) : (!torch.number) -> !torch.int\n"
+"    %3 = torch.prim.ListConstruct %2, %0#1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
+"    %4 = call @__torch__.torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator.promote_dtypes(%1, %3) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
+"    return %4 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.pow.Tensor_Scalar\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.number) -> !torch.int {\n"
 "    %none = torch.constant.none\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"

python/torch_mlir/__init__.py

@@ -446,7 +446,6 @@ def compile(model: torch.nn.Module,
     if output_type == OutputType.RAW:
         return mb.module
 
-    # mb.module.dump()
     option_string = "{backend-legal-ops=" + ",".join(backend_legal_ops) + \
         " extra-library=" + extra_library_file_name + "}"
     run_pipeline_with_repro_report(

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/abstract_interp_lib_gen.py

@@ -267,6 +267,9 @@ def aten〇remainder〇Scalar〡shape(self: List[int], other: float) -> List[int
 def aten〇floor_divide〇Scalar〡shape(self: List[int], other: float) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇pow〇Scalar〡shape(self: float, exponent: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(exponent)
+
 def aten〇pow〇Tensor_Scalar〡shape(self: List[int], exponent: float) -> List[int]:
     return upstream_shape_functions.unary(self)
 
@@ -2704,6 +2707,12 @@ def aten〇floor_divide〇Scalar〡dtype(self_rank_dtype: Tuple[int, int], other
     dtypes = [self_dtype, get_dtype_of_scalar(other)]
     return promote_dtypes(ranks, dtypes)
 
+def aten〇pow〇Scalar〡dtype(self: Union[int, float, complex], exponent_rank_dtype: Tuple[int, int]) -> int:
+    exponent_rank, exponent_dtype = exponent_rank_dtype
+    ranks: List[Optional[int]] = [None, exponent_rank]
+    dtypes = [get_dtype_of_scalar(self), exponent_dtype]
+    return promote_dtypes(ranks, dtypes)
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, exponent=1) +
                       _check_tensors_with_the_same_dtype(num_of_tensors=1, exponent=1.0))
 def aten〇pow〇Tensor_Scalar〡dtype(self_rank_dtype: Tuple[int, int], exponent: Union[int, float, complex]) -> int:

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -1468,6 +1468,28 @@ def ElementwisePowTensorBroadcastStaticModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwisePowScalarModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([3, 4], torch.float32, True),
+    ])
+    def forward(self, exp):
+        return torch.pow(2.0, exp)
+
+
+@register_test_case(module_factory=lambda: ElementwisePowScalarModule())
+def ElementwisePowScalarModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4))
+
+
+# ==============================================================================
+
+
 class ElementwiseToDtypeF32ToI64Module(torch.nn.Module):
 
     def __init__(self):