From d29ad4dfbd1a4f1b9d40293fbb76426786af9916 Mon Sep 17 00:00:00 2001 From: Vivek Khandelwal Date: Fri, 21 Jun 2024 11:18:14 +0530 Subject: [PATCH] [ONNX] Fix Onnx.Hardsigmoid lowering (#3239) Signed-Off By: Vivek Khandelwal --- .../TorchOnnxToTorch/DefaultDomainGtoP.cpp | 28 ++++++++------- projects/pt1/e2e_testing/xfail_sets.py | 2 -- .../TorchOnnxToTorch/simple_ops_g_to_p.mlir | 34 ++++++++++--------- 3 files changed, 33 insertions(+), 31 deletions(-) diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp index 5485f931d..547170cd5 100644 --- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp +++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp @@ -46,29 +46,31 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP( Value constAlpha = rewriter.create( binder.getLoc(), rewriter.getType(), rewriter.getF64FloatAttr(alpha)); - Value constBeta = rewriter.create( binder.getLoc(), rewriter.getType(), rewriter.getF64FloatAttr(beta)); // Expression: alpha * x + beta - Value alpha_x_plus_beta = rewriter.create( - binder.getLoc(), resultType, tensorOperand, constBeta, - /*alpha=*/constAlpha); + Value alphaMulX = rewriter.create( + binder.getLoc(), resultType, tensorOperand, constAlpha); + Value constOne = rewriter.create( + binder.getLoc(), rewriter.getType(), + rewriter.getF64FloatAttr(1.0)); + Value alphaMulXPlusBeta = rewriter.create( + binder.getLoc(), resultType, alphaMulX, constBeta, + /*alpha=*/constOne); // Expression: min(1, alpha * x + beta) - Value constantOne = rewriter.create( - 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( - 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( - binder.getLoc(), rewriter.getI64IntegerAttr(0)); - Value zeroTensor = createRank0Tensor(rewriter, binder.getLoc(), - resultType, constantZero); + Value constZero = rewriter.create( + binder.getLoc(), rewriter.getF64FloatAttr(0.0)); + Value zeroTensor = + createRank0Tensor(rewriter, binder.getLoc(), resultType, constZero); rewriter.replaceOpWithNewOp( binder.op, resultType, zeroTensor, minExpression); return success(); diff --git a/projects/pt1/e2e_testing/xfail_sets.py b/projects/pt1/e2e_testing/xfail_sets.py index 9d21e2dd8..ce5e1c5a7 100644 --- a/projects/pt1/e2e_testing/xfail_sets.py +++ b/projects/pt1/e2e_testing/xfail_sets.py @@ -2204,8 +2204,6 @@ ONNX_XFAIL_SET = { "ElementwiseLog2IntModule_basic", "FlipModuleStaticShape_basic", "FlipNegativeIndexModule_basic", - "HardsigmoidModule_basic", - "HardsigmoidRandomModule_basic", "PixelShuffleModuleStaticRank4Float32_basic", "ReflectionPad1dModule2dInput_Right", "ReflectionPad1dModule2dInput_basic", diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir index 8ed1a9a91..be07ac634 100644 --- a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir +++ b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir @@ -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 // 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, !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, !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 // 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, !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, !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 // 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, !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, !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 // 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, !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, !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 // 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, !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, !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 // 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, !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, !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>