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[ONNX] Fix Onnx.Hardsigmoid lowering (#3239) 

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
pull/2882/merge
Vivek Khandelwal 2024-06-21 11:18:14 +05:30 committed by GitHub
parent 694210f429
commit d29ad4dfbd
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 33 additions and 31 deletions
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28
lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
View File

@ -46,29 +46,31 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
Value constAlpha = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getF64FloatAttr(alpha));
Value constBeta = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getF64FloatAttr(beta));
// Expression: alpha * x + beta
Value alpha_x_plus_beta = rewriter.create<Torch::AtenAddScalarOp>(
binder.getLoc(), resultType, tensorOperand, constBeta,
/*alpha=*/constAlpha);
Value alphaMulX = rewriter.create<Torch::AtenMulScalarOp>(
binder.getLoc(), resultType, tensorOperand, constAlpha);
Value constOne = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getType<Torch::FloatType>(),
rewriter.getF64FloatAttr(1.0));
Value alphaMulXPlusBeta = rewriter.create<Torch::AtenAddScalarOp>(
binder.getLoc(), resultType, alphaMulX, constBeta,
/*alpha=*/constOne);
// Expression: min(1, alpha * x + beta)
Value constantOne = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(1));
Value oneTensor = createRank0Tensor(rewriter, binder.getLoc(),
resultType, constantOne);
Value oneTensor =
createRank0Tensor(rewriter, binder.getLoc(), resultType, constOne);
Value minExpression = rewriter.create<Torch::AtenMinimumOp>(
binder.getLoc(), resultType, oneTensor, alpha_x_plus_beta);
binder.getLoc(), resultType, oneTensor, alphaMulXPlusBeta);
// Expression: max(0, min(1, alpha * x + beta))
Value constantZero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(0));
Value zeroTensor = createRank0Tensor(rewriter, binder.getLoc(),
resultType, constantZero);
Value constZero = rewriter.create<Torch::ConstantFloatOp>(
binder.getLoc(), rewriter.getF64FloatAttr(0.0));
Value zeroTensor =
createRank0Tensor(rewriter, binder.getLoc(), resultType, constZero);
rewriter.replaceOpWithNewOp<Torch::AtenMaximumOp>(
binder.op, resultType, zeroTensor, minExpression);
return success();

2
projects/pt1/e2e_testing/xfail_sets.py
View File

@ -2204,8 +2204,6 @@ ONNX_XFAIL_SET = {
"ElementwiseLog2IntModule_basic",
"FlipModuleStaticShape_basic",
"FlipNegativeIndexModule_basic",
"HardsigmoidModule_basic",
"HardsigmoidRandomModule_basic",
"PixelShuffleModuleStaticRank4Float32_basic",
"ReflectionPad1dModule2dInput_Right",
"ReflectionPad1dModule2dInput_basic",

34
test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
View File

@ -891,21 +891,21 @@ func.func @test_pad_optional_constant(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !
func.func @test_hardsigmoid_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 6 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[ALPHA_FLOAT:.*]] = torch.constant.float 5.000000e-01
// CHECK: %[[BETA_FLOAT:.*]] = torch.constant.float 0.60000002384185791
// CHECK: %[[ALPHA_MULTI_X_PLUS_BETA:.*]] = torch.aten.add.Scalar %arg0, %[[BETA_FLOAT:.*]], %[[ALPHA_FLOAT:.*]] : !torch.vtensor<[3],f32>, !torch.float, !torch.float -> !torch.vtensor<[3],f32>
// CHECK: %[[INT_1:.*]] = torch.constant.int 1
// CHECK: %[[ALPHA_MULTI_X:.*]] = torch.aten.mul.Scalar %arg0, %[[ALPHA_FLOAT]] : !torch.vtensor<[3],f32>, !torch.float -> !torch.vtensor<[3],f32>
// CHECK: %[[F1:.*]] = torch.constant.float 1.000000e+00
// CHECK: %[[ALPHA_MULTI_X_PLUS_BETA:.*]] = torch.aten.add.Scalar %[[ALPHA_MULTI_X]], %[[BETA_FLOAT]], %[[F1]] : !torch.vtensor<[3],f32>, !torch.float, !torch.float -> !torch.vtensor<[3],f32>
// CHECK: %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[NONE_FOR_ONE:.*]] = torch.constant.none
// CHECK: %[[INT_TYPE_FOR_TENSOR_ONE:.*]] = torch.constant.int 6
// CHECK: %[[ONE_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]], %[[INT_1:.*]], %[[INT_TYPE_FOR_TENSOR_ONE:.*]], %[[NONE_FOR_ONE:.*]], %[[NONE_1:.*]], %[[NONE_1:.*]] : !torch.list<int>, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[ONE_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]], %[[F1:.*]], %[[INT_TYPE_FOR_TENSOR_ONE:.*]], %[[NONE_FOR_ONE:.*]], %[[NONE_1:.*]], %[[NONE_1:.*]] : !torch.list<int>, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[MIN_EXPRESSION:.*]] = torch.aten.minimum %[[ONE_TENSOR:.*]], %[[ALPHA_MULTI_X_PLUS_BETA:.*]] : !torch.vtensor<[],f32>, !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: %[[INT_0:.*]] = torch.constant.int 0
// CHECK: %[[F0:.*]] = torch.constant.float 0.000000e+00
// CHECK: %[[TENSOR_DIMENSION_LIST_FOR_ZERO:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[NONE_FOR_ZERO:.*]] = torch.constant.none
// CHECK: %[[INT_TYPE_FOR_TENSOR_ZERO:.*]] = torch.constant.int 6
// CHECK: %[[ZERO_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ZERO:.*]], %[[INT_0:.*]], %[[INT_TYPE_FOR_TENSOR_ZERO:.*]], %[[NONE_FOR_ZERO:.*]], %none_0, %none_0 : !torch.list<int>, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[ZERO_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ZERO:.*]], %[[F0:.*]], %[[INT_TYPE_FOR_TENSOR_ZERO:.*]], %[[NONE_FOR_ZERO:.*]], %none_0, %none_0 : !torch.list<int>, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[RESULT:.*]] = torch.aten.maximum %[[ZERO_TENSOR:.*]], %[[MIN_EXPRESSION:.*]] : !torch.vtensor<[],f32>, !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
// CHECK: return %[[RESULT:.*]] : !torch.vtensor<[3],f32>
%0 = torch.operator "onnx.HardSigmoid"(%arg0) {torch.onnx.alpha = 5.000000e-01 : f32, torch.onnx.beta = 6.000000e-01 : f32} : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
return %0 : !torch.vtensor<[3],f32>
}
@ -916,18 +916,19 @@ func.func @test_hardsigmoid_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vt
func.func @test_hardsigmoid(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 6 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[ALPHA_FLOAT:.*]] = torch.constant.float 5.000000e-01
// CHECK: %[[BETA_FLOAT:.*]] = torch.constant.float 0.60000002384185791
// CHECK: %[[ALPHA_MULTI_X_PLUS_BETA:.*]] = torch.aten.add.Scalar %arg0, %[[BETA_FLOAT:.*]], %[[ALPHA_FLOAT:.*]] : !torch.vtensor<[3,4,5],f32>, !torch.float, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[INT_1:.*]] = torch.constant.int 1
// CHECK: %[[ALPHA_MULTI_X:.*]] = torch.aten.mul.Scalar %arg0, %[[ALPHA_FLOAT]] : !torch.vtensor<[3,4,5],f32>, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[F1:.*]] = torch.constant.float 1.000000e+00
// CHECK: %[[ALPHA_MULTI_X_PLUS_BETA:.*]] = torch.aten.add.Scalar %[[ALPHA_MULTI_X]], %[[BETA_FLOAT]], %[[F1]] : !torch.vtensor<[3,4,5],f32>, !torch.float, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[NONE_FOR_ONE:.*]] = torch.constant.none
// CHECK: %[[INT_TYPE_FOR_TENSOR_ONE:.*]] = torch.constant.int 6
// CHECK: %[[ONE_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]], %[[INT_1:.*]], %[[INT_TYPE_FOR_TENSOR_ONE:.*]], %[[NONE_FOR_ONE:.*]], %[[NONE_1:.*]], %[[NONE_1:.*]] : !torch.list<int>, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[ONE_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]], %[[F1:.*]], %[[INT_TYPE_FOR_TENSOR_ONE:.*]], %[[NONE_FOR_ONE:.*]], %[[NONE_1:.*]], %[[NONE_1:.*]] : !torch.list<int>, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[MIN_EXPRESSION:.*]] = torch.aten.minimum %[[ONE_TENSOR:.*]], %[[ALPHA_MULTI_X_PLUS_BETA:.*]] : !torch.vtensor<[],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[INT_0:.*]] = torch.constant.int 0
// CHECK: %[[F0:.*]] = torch.constant.float 0.000000e+00
// CHECK: %[[TENSOR_DIMENSION_LIST_FOR_ZERO:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[NONE_FOR_ZERO:.*]] = torch.constant.none
// CHECK: %[[INT_TYPE_FOR_TENSOR_ZERO:.*]] = torch.constant.int 6
// CHECK: %[[ZERO_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ZERO:.*]], %[[INT_0:.*]], %[[INT_TYPE_FOR_TENSOR_ZERO:.*]], %[[NONE_FOR_ZERO:.*]], %none_0, %none_0 : !torch.list<int>, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[ZERO_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ZERO:.*]], %[[F0:.*]], %[[INT_TYPE_FOR_TENSOR_ZERO:.*]], %[[NONE_FOR_ZERO:.*]], %none_0, %none_0 : !torch.list<int>, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[RESULT:.*]] = torch.aten.maximum %[[ZERO_TENSOR:.*]], %[[MIN_EXPRESSION:.*]] : !torch.vtensor<[],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: return %[[RESULT:.*]] : !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.HardSigmoid"(%arg0) {torch.onnx.alpha = 5.000000e-01 : f32, torch.onnx.beta = 6.000000e-01 : f32} : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
@ -940,18 +941,19 @@ func.func @test_hardsigmoid(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtenso
func.func @test_hardsigmoid_default(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 6 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[ALPHA_FLOAT:.*]] = torch.constant.float 0.20000000298023224
// CHECK: %[[BETA_FLOAT:.*]] = torch.constant.float 5.000000e-01
// CHECK: %[[ALPHA_MULTI_X_PLUS_BETA:.*]] = torch.aten.add.Scalar %arg0, %[[BETA_FLOAT:.*]], %[[ALPHA_FLOAT:.*]] : !torch.vtensor<[3,4,5],f32>, !torch.float, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[INT_1:.*]] = torch.constant.int 1
// CHECK: %[[ALPHA_MULTI_X:.*]] = torch.aten.mul.Scalar %arg0, %[[ALPHA_FLOAT]] : !torch.vtensor<[3,4,5],f32>, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[F1:.*]] = torch.constant.float 1.000000e+00
// CHECK: %[[ALPHA_MULTI_X_PLUS_BETA:.*]] = torch.aten.add.Scalar %[[ALPHA_MULTI_X]], %[[BETA_FLOAT]], %[[F1]] : !torch.vtensor<[3,4,5],f32>, !torch.float, !torch.float -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[NONE_FOR_ONE:.*]] = torch.constant.none
// CHECK: %[[INT_TYPE_FOR_TENSOR_ONE:.*]] = torch.constant.int 6
// CHECK: %[[ONE_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]], %[[INT_1:.*]], %[[INT_TYPE_FOR_TENSOR_ONE:.*]], %[[NONE_FOR_ONE:.*]], %[[NONE_1:.*]], %[[NONE_1:.*]] : !torch.list<int>, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[ONE_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]], %[[F1:.*]], %[[INT_TYPE_FOR_TENSOR_ONE:.*]], %[[NONE_FOR_ONE:.*]], %[[NONE_1:.*]], %[[NONE_1:.*]] : !torch.list<int>, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[MIN_EXPRESSION:.*]] = torch.aten.minimum %[[ONE_TENSOR:.*]], %[[ALPHA_MULTI_X_PLUS_BETA:.*]] : !torch.vtensor<[],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
// CHECK: %[[INT_0:.*]] = torch.constant.int 0
// CHECK: %[[F0:.*]] = torch.constant.float 0.000000e+00
// CHECK: %[[TENSOR_DIMENSION_LIST_FOR_ZERO:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[NONE_FOR_ZERO:.*]] = torch.constant.none
// CHECK: %[[INT_TYPE_FOR_TENSOR_ZERO:.*]] = torch.constant.int 6
// CHECK: %[[ZERO_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ZERO:.*]], %[[INT_0:.*]], %[[INT_TYPE_FOR_TENSOR_ZERO:.*]], %[[NONE_FOR_ZERO:.*]], %none_0, %none_0 : !torch.list<int>, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: %[[ZERO_TENSOR:.*]] = torch.aten.full %[[TENSOR_DIMENSION_LIST_FOR_ZERO:.*]], %[[F0:.*]], %[[INT_TYPE_FOR_TENSOR_ZERO:.*]], %[[NONE_FOR_ZERO:.*]], %none_0, %none_0 : !torch.list<int>, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
// CHECK: torch.aten.maximum %[[ZERO_TENSOR:.*]], %[[MIN_EXPRESSION:.*]] : !torch.vtensor<[],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
%0 = torch.operator "onnx.HardSigmoid"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>