[ONNX] Fix Onnx.Hardsigmoid lowering

Signed-Off By: Vivek Khandelwal <[email protected]>
llvm · Apr 26, 2024 · ac1ca74 · ac1ca74
1 parent 7030eac
commit ac1ca74
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Showing 3 changed files with 18 additions and 9 deletions.
diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
@@ -52,17 +52,22 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
             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 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>(

diff --git a/projects/pt1/e2e_testing/xfail_sets.py b/projects/pt1/e2e_testing/xfail_sets.py
@@ -2183,8 +2183,6 @@
     "ElementwiseSeluModule_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
@@ -627,7 +627,9 @@ 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: %[[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: %[[INT_1:.*]] = torch.constant.int 1
   // CHECK: %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]] = torch.prim.ListConstruct  : () -> !torch.list<int>
   // CHECK: %[[NONE_FOR_ONE:.*]] = torch.constant.none
@@ -652,7 +654,9 @@ 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: %[[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: %[[INT_1:.*]] = torch.constant.int 1
     // CHECK: %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]] = torch.prim.ListConstruct  : () -> !torch.list<int>
     // CHECK: %[[NONE_FOR_ONE:.*]] = torch.constant.none
@@ -676,7 +680,9 @@ 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: %[[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: %[[INT_1:.*]] = torch.constant.int 1
     // CHECK: %[[TENSOR_DIMENSION_LIST_FOR_ONE:.*]] = torch.prim.ListConstruct  : () -> !torch.list<int>
     // CHECK: %[[NONE_FOR_ONE:.*]] = torch.constant.none