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[ONNX] Fix Onnx.Selu lowering and canonicalizer for IntImplicit op #3221

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Apr 29, 2024
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[ONNX] Fix Onnx.Selu lowering and canonicalizer for IntImplicit op #3221

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35 changes: 30 additions & 5 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -847,15 +847,21 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(

patterns.onOp(
"Selu", 6, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
// y = gamma * (alpha * e^x - alpha) for x <= 0, y = gamma * x for x > 0
Torch::ValueTensorType resultType;
float alpha, gamma;
Value operand;
// Refer https://onnx.ai/onnx/operators/onnx__Selu.html for the default
// alpha and gamma values.
if (binder.tensorOperand(operand) ||
binder.f32FloatAttr(alpha, "alpha") ||
binder.f32FloatAttr(gamma, "gamma") ||
binder.f32FloatAttr(alpha, "alpha", 1.67326) ||
binder.f32FloatAttr(gamma, "gamma", 1.0507) ||
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binder.tensorResultType(resultType))
return failure();

Torch::ValueTensorType inputType =
operand.getType().cast<Torch::ValueTensorType>();

Value vAlpha = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), alpha));
Expand All @@ -864,12 +870,31 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), gamma));

Value vInputScale = rewriter.create<Torch::ConstantFloatOp>(
Value cstOne = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getFloatAttr(rewriter.getF64Type(), 1.0));

rewriter.replaceOpWithNewOp<Torch::AtenEluOp>(
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binder.op, resultType, operand, vAlpha, vScale, vInputScale);
Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
Value zeroTensor = rewriter.create<Torch::AtenZerosLikeOp>(
binder.getLoc(), resultType, operand, cstNone, cstNone, cstNone,
cstNone, cstNone);
Value exp = rewriter.create<Torch::AtenExpOp>(binder.getLoc(),
resultType, operand);
Value expMulAlpha = rewriter.create<Torch::AtenMulScalarOp>(
binder.getLoc(), resultType, exp, vAlpha);
Value expMulAlphaSubAlpha = rewriter.create<Torch::AtenSubScalarOp>(
binder.getLoc(), resultType, expMulAlpha, vAlpha, cstOne);
Value neg = rewriter.create<Torch::AtenMulScalarOp>(
binder.getLoc(), resultType, expMulAlphaSubAlpha, vScale);
Value pos = rewriter.create<Torch::AtenMulScalarOp>(
binder.getLoc(), resultType, operand, vScale);
Type compareType = inputType.getWithSizesAndDtype(
inputType.getOptionalSizes(), rewriter.getI1Type());
Value xLessThanZero = rewriter.create<Torch::AtenLtTensorOp>(
binder.getLoc(), compareType, operand, zeroTensor);

rewriter.replaceOpWithNewOp<Torch::AtenWhereSelfOp>(
binder.op, resultType, xLessThanZero, neg, pos);
return success();
});
patterns.onOp("ReduceL1", 1,
Expand Down
19 changes: 14 additions & 5 deletions lib/Dialect/Torch/IR/TorchOps.cpp
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Expand Up @@ -140,18 +140,27 @@ static Value getScalarIntValue(Value input, Location loc,
return nullptr;

Type inputDtype = inputTensorType.getOptionalDtype();
if (!inputDtype || !inputDtype.isInteger(64))
if (!inputDtype || !(inputDtype.isInteger(64) || inputDtype.isInteger(1)))
return nullptr;

std::optional<unsigned> inputRank = getTensorRank(input);
if (!inputRank || *inputRank != 0)
return nullptr;

if (auto valueTensorLiteralOp = input.getDefiningOp<ValueTensorLiteralOp>()) {
auto val = cast<DenseIntElementsAttr>(valueTensorLiteralOp.getValue())
.getSplatValue<int64_t>();
return rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(val));
if (inputDtype.isInteger(64)) {
auto val = valueTensorLiteralOp.getValue()
.cast<DenseIntElementsAttr>()
.getSplatValue<int64_t>();
return rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(val));
} else {
auto val = valueTensorLiteralOp.getValue()
.cast<DenseIntElementsAttr>()
.getSplatValue<bool>();
return rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(val));
}
} else if (auto primNumToTensorScalarOp =
input.getDefiningOp<PrimNumToTensorScalarOp>()) {
return primNumToTensorScalarOp.getA();
Expand Down
3 changes: 0 additions & 3 deletions projects/pt1/e2e_testing/xfail_sets.py
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Expand Up @@ -2124,7 +2124,6 @@
"ElementwiseAtenFloorDivideTensorNegativeModule_basic",
"ElementwiseLog10IntModule_basic",
"ElementwiseLog2IntModule_basic",
"ElementwiseSeluModule_basic",
"FlipModuleStaticShape_basic",
"FlipNegativeIndexModule_basic",
"HardsigmoidModule_basic",
Expand Down Expand Up @@ -2637,8 +2636,6 @@
"CopyWithDifferentDTypesModule_basic",
"CosineSimilarityStaticBroadcastModule_basic",
"CumsumInputDtypeInt32Module_basic",
"DropoutTrainModule_basic",
"DropoutTrainStaticShapeModule_basic",
"ElementwiseAcosIntModule_basic",
"ElementwiseAsinIntModule_basic",
"ElementwiseAtanTensorIntModule_basic",
Expand Down
16 changes: 12 additions & 4 deletions test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
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Expand Up @@ -582,10 +582,18 @@ func.func @test_softmax_negative_axis(%arg0: !torch.vtensor<[3,4,5],f32>) -> !to

// CHECK-LABEL: func.func @test_selu
func.func @test_selu(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.opset_version = 6 : si64} {
// CHECK-DAG: %[[F1:.+]] = torch.constant.float 1
// CHECK-DAG: %[[F2:.+]] = torch.constant.float 2
// CHECK-DAG: %[[F3:.+]] = torch.constant.float 3
// CHECK: %[[ELU:.+]] = torch.aten.elu %arg0, %[[F2]], %[[F3]], %[[F1]]
// CHECK: %[[F2:.+]] = torch.constant.float 2.000000e+00
// CHECK: %[[F3:.+]] = torch.constant.float 3.000000e+00
// CHECK: %[[F1:.+]] = torch.constant.float 1.000000e+00
// CHECK: %[[NONE:.+]] = torch.constant.none
// CHECK: %[[ZEROS:.+]] = torch.aten.zeros_like %arg0, %none, %none, %none, %none, %none : !torch.vtensor<[3,4,5],f32>, !torch.none, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[EXP:.+]] = torch.aten.exp %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[MUL:.+]] = torch.aten.mul.Scalar %[[EXP]], %[[F2]] : !torch.vtensor<[3,4,5],f32>, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[SUB:.+]] = torch.aten.sub.Scalar %[[MUL]], %[[F2]], %[[F1]] : !torch.vtensor<[3,4,5],f32>, !torch.float, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[MUL_1:.+]] = torch.aten.mul.Scalar %[[SUB]], %[[F3]] : !torch.vtensor<[3,4,5],f32>, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[MUL_2:.+]] = torch.aten.mul.Scalar %arg0, %[[F3]] : !torch.vtensor<[3,4,5],f32>, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[LT:.+]] = torch.aten.lt.Tensor %arg0, %[[ZEROS]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],i1>
// CHECK: torch.aten.where.self %[[LT]], %[[MUL_1]], %[[MUL_2]] : !torch.vtensor<[3,4,5],i1>, !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.Selu"(%arg0) {torch.onnx.alpha = 2.000000e+00 : f32, torch.onnx.gamma = 3.000000e+00 : f32} : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>
}
Expand Down
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