Revert hyperbolic trigonometric decompositions (#3271)

We should be using the `torch` path and handling decomposition in the
`math` dialect.

lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp

@@ -300,29 +300,17 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
                       binder.op, resultType, operand);
                   return success();
                 });
-  patterns.onOp(
-      "Asinh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
-        Torch::ValueTensorType resultType;
-        Value operand;
-        if (binder.tensorOperand(operand) ||
-            binder.tensorResultType(resultType))
-          return failure();
-
-        // log(x + sqrt(x**2 + 1))
-        Value square = rewriter.create<Torch::AtenSquareOp>(
-            binder.getLoc(), resultType, operand);
-        Value cstOne = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getI64IntegerAttr(1));
-        Value add0 = rewriter.create<Torch::AtenAddScalarOp>(
-            binder.getLoc(), resultType, square, cstOne, cstOne);
-        Value sqrt = rewriter.create<Torch::AtenSqrtOp>(binder.getLoc(),
-                                                        resultType, add0);
-        Value add1 = rewriter.create<Torch::AtenAddTensorOp>(
-            binder.getLoc(), resultType, operand, sqrt, cstOne);
-        rewriter.replaceOpWithNewOp<Torch::AtenLogOp>(binder.op, resultType,
-                                                      add1);
-        return success();
-      });
+  patterns.onOp("Asinh", 9,
+                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+                  Torch::ValueTensorType resultType;
+                  Value operand;
+                  if (binder.tensorOperand(operand) ||
+                      binder.tensorResultType(resultType))
+                    return failure();
+                  rewriter.replaceOpWithNewOp<Torch::AtenAsinhOp>(
+                      binder.op, resultType, operand);
+                  return success();
+                });
   patterns.onOp("Atan", 7,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
                   Torch::ValueTensorType resultType;
@@ -334,33 +322,17 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
                       binder.op, resultType, operand);
                   return success();
                 });
-  patterns.onOp(
-      "Atanh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
-        Torch::ValueTensorType resultType;
-        Value operand;
-        if (binder.tensorOperand(operand) ||
-            binder.tensorResultType(resultType))
-          return failure();
-
-        // 1/2 * log((1 + x) / (1 - x))
-        Value cstOne = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getI64IntegerAttr(1));
-        Value add = rewriter.create<Torch::AtenAddScalarOp>(
-            binder.getLoc(), resultType, operand, cstOne, cstOne);
-        Value neg = rewriter.create<Torch::AtenNegOp>(binder.getLoc(),
-                                                      resultType, operand);
-        Value sub = rewriter.create<Torch::AtenAddScalarOp>(
-            binder.getLoc(), resultType, neg, cstOne, cstOne);
-        Value div = rewriter.create<Torch::AtenDivTensorOp>(
-            binder.getLoc(), resultType, add, sub);
-        Value log =
-            rewriter.create<Torch::AtenLogOp>(binder.getLoc(), resultType, div);
-        Value cstTwo = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getI64IntegerAttr(2));
-        rewriter.replaceOpWithNewOp<Torch::AtenDivScalarOp>(
-            binder.op, resultType, log, cstTwo);
-        return success();
-      });
+  patterns.onOp("Atanh", 9,
+                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+                  Torch::ValueTensorType resultType;
+                  Value operand;
+                  if (binder.tensorOperand(operand) ||
+                      binder.tensorResultType(resultType))
+                    return failure();
+                  rewriter.replaceOpWithNewOp<Torch::AtenAtanhOp>(
+                      binder.op, resultType, operand);
+                  return success();
+                });
   patterns.onOp("Acos", 7,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
                   Torch::ValueTensorType resultType;
@@ -372,29 +344,17 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
                       binder.op, resultType, operand);
                   return success();
                 });
-  patterns.onOp(
-      "Acosh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
-        Torch::ValueTensorType resultType;
-        Value operand;
-        if (binder.tensorOperand(operand) ||
-            binder.tensorResultType(resultType))
-          return failure();
-
-        // log(x + sqrt(x**2 - 1))
-        Value square = rewriter.create<Torch::AtenSquareOp>(
-            binder.getLoc(), resultType, operand);
-        Value cstOne = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getI64IntegerAttr(1));
-        Value sub = rewriter.create<Torch::AtenSubScalarOp>(
-            binder.getLoc(), resultType, square, cstOne, cstOne);
-        Value sqrt = rewriter.create<Torch::AtenSqrtOp>(binder.getLoc(),
-                                                        resultType, sub);
-        Value add = rewriter.create<Torch::AtenAddTensorOp>(
-            binder.getLoc(), resultType, operand, sqrt, cstOne);
-        rewriter.replaceOpWithNewOp<Torch::AtenLogOp>(binder.op, resultType,
-                                                      add);
-        return success();
-      });
+  patterns.onOp("Acosh", 9,
+                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+                  Torch::ValueTensorType resultType;
+                  Value operand;
+                  if (binder.tensorOperand(operand) ||
+                      binder.tensorResultType(resultType))
+                    return failure();
+                  rewriter.replaceOpWithNewOp<Torch::AtenAcoshOp>(
+                      binder.op, resultType, operand);
+                  return success();
+                });
   patterns.onOp("BatchNormalization", 15,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
                   Torch::ValueTensorType resultType;
@@ -1490,31 +1450,17 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
                       binder.op, resultType, operand);
                   return success();
                 });
-  patterns.onOp(
-      "Cosh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
-        Torch::ValueTensorType resultType;
-        Value operand;
-        if (binder.tensorOperand(operand) ||
-            binder.tensorResultType(resultType))
-          return failure();
-
-        // 1/2 * (exp(x) + exp(-x))
-        Value x = rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType,
-                                                    operand);
-        Value neg = rewriter.create<Torch::AtenNegOp>(binder.getLoc(),
-                                                      resultType, operand);
-        Value y =
-            rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType, neg);
-        Value cstOne = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getI64IntegerAttr(1));
-        Value z = rewriter.create<Torch::AtenAddTensorOp>(
-            binder.getLoc(), resultType, x, y, cstOne);
-        Value cstTwo = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getI64IntegerAttr(2));
-        rewriter.replaceOpWithNewOp<Torch::AtenDivScalarOp>(
-            binder.op, resultType, z, cstTwo);
-        return success();
-      });
+  patterns.onOp("Cosh", 9,
+                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+                  Torch::ValueTensorType resultType;
+                  Value operand;
+                  if (binder.tensorOperand(operand) ||
+                      binder.tensorResultType(resultType))
+                    return failure();
+                  rewriter.replaceOpWithNewOp<Torch::AtenCoshOp>(
+                      binder.op, resultType, operand);
+                  return success();
+                });
   patterns.onOp(
       "CumSum", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
         Location loc = binder.getLoc();

lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp

@@ -1439,31 +1439,18 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                   return success();
                 });
 
-  patterns.onOp(
-      "Sinh", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
-        Torch::ValueTensorType resultType;
-        Value operand;
-        if (binder.tensorOperand(operand) ||
-            binder.tensorResultType(resultType))
-          return failure();
+  patterns.onOp("Sinh", 9,
+                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+                  Torch::ValueTensorType resultType;
+                  Value operand;
+                  if (binder.tensorOperand(operand) ||
+                      binder.tensorResultType(resultType))
+                    return failure();
 
-        // 1/2 * (exp(x) – exp(-x))
-        Value x = rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType,
-                                                    operand);
-        Value neg = rewriter.create<Torch::AtenNegOp>(binder.getLoc(),
-                                                      resultType, operand);
-        Value y =
-            rewriter.create<Torch::AtenExpOp>(binder.getLoc(), resultType, neg);
-        Value cstOne = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getI64IntegerAttr(1));
-        Value z = rewriter.create<Torch::AtenSubTensorOp>(
-            binder.getLoc(), resultType, x, y, cstOne);
-        Value cstTwo = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getI64IntegerAttr(2));
-        rewriter.replaceOpWithNewOp<Torch::AtenDivScalarOp>(
-            binder.op, resultType, z, cstTwo);
-        return success();
-      });
+                  rewriter.replaceOpWithNewOp<Torch::AtenSinhOp>(
+                      binder.op, resultType, operand);
+                  return success();
+                });
 
   // split with fixed-size parts
   // Arguments:

test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir

@@ -201,14 +201,7 @@ func.func @test_atan(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,
 
 // CHECK-LABEL: @test_atanh
 func.func @test_atanh(%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 = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
-  // CHECK: %[[C1:.*]] = torch.constant.int 1
-  // CHECK: %[[ADD:.*]] = torch.aten.add.Scalar %arg0, %[[C1]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[NEG:.*]] = torch.aten.neg %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[SUB:.*]] = torch.aten.add.Scalar %[[NEG]], %[[C1]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[DIV:.*]] = torch.aten.div.Tensor %[[ADD]], %[[SUB]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[LOG:.*]] = torch.aten.log %[[DIV]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[C2:.*]] = torch.constant.int 2
-  // CHECK: torch.aten.div.Scalar %[[LOG]], %[[C2]] : !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
+  // CHECK: torch.aten.atanh %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
   %0 = torch.operator "onnx.Atanh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
   return %0 : !torch.vtensor<[3,4,5],f32>
 }
@@ -672,13 +665,7 @@ func.func @test_cos(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5
 
 // CHECK-LABEL: @test_cosh_example
 func.func @test_cosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
-  // CHECK: %[[X:.+]] = torch.aten.exp %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[NEG:.+]] = torch.aten.neg %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[Y:.+]] = torch.aten.exp %[[NEG]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[C1:.+]] = torch.constant.int 1
-  // CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %[[X]], %[[Y]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
-  // CHECK: %[[C2:.+]] = torch.constant.int 2
-  // CHECK: torch.aten.div.Scalar %[[ADD]], %[[C2]] : !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
+  // CHECK: torch.aten.cosh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
   %0 = torch.operator "onnx.Cosh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
   return %0 : !torch.vtensor<[3],f32>
 }
@@ -687,13 +674,7 @@ func.func @test_cosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[
 
 // CHECK-LABEL: @test_cosh
 func.func @test_cosh(%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 = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
-  // CHECK: %[[X:.+]] = torch.aten.exp %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[NEG:.+]] = torch.aten.neg %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[Y:.+]] = torch.aten.exp %[[NEG]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[C1:.+]] = torch.constant.int 1
-  // CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %[[X]], %[[Y]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[C2:.+]] = torch.constant.int 2
-  // CHECK: torch.aten.div.Scalar %[[ADD]], %[[C2]] : !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
+  // CHECK: torch.aten.cosh %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
   %0 = torch.operator "onnx.Cosh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
   return %0 : !torch.vtensor<[3,4,5],f32>
 }
@@ -702,12 +683,7 @@ func.func @test_cosh(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,
 
 // CHECK-LABEL: @test_acosh_example
 func.func @test_acosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
-  // CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[C1:.+]] = torch.constant.int 1
-  // CHECK: %[[SUB:.+]] = torch.aten.sub.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.int, !torch.int -> !torch.vtensor<[3],f32>
-  // CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[SUB]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
-  // CHECK: torch.aten.log %[[ADD]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
+  // CHECK: torch.aten.acosh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
   %0 = torch.operator "onnx.Acosh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
   return %0 : !torch.vtensor<[3],f32>
 }
@@ -716,12 +692,7 @@ func.func @test_acosh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<
 
 // CHECK-LABEL: @test_acosh
 func.func @test_acosh(%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 = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
-  // CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[C1:.+]] = torch.constant.int 1
-  // CHECK: %[[SUB:.+]] = torch.aten.sub.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[SUB]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[ADD:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: torch.aten.log %[[ADD]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
+  // CHECK: torch.aten.acosh %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
   %0 = torch.operator "onnx.Acosh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
   return %0 : !torch.vtensor<[3,4,5],f32>
 }
@@ -748,12 +719,7 @@ func.func @test_asin(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,
 
 // CHECK-LABEL: @test_asinh_example
 func.func @test_asinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
-  // CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[C1:.+]] = torch.constant.int 1
-  // CHECK: %[[ADD:.+]] = torch.aten.add.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.int, !torch.int -> !torch.vtensor<[3],f32>
-  // CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[ADD]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[ADD_0:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
-  // CHECK: torch.aten.log %[[ADD_0]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
+  // CHECK: torch.aten.asinh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
   %0 = torch.operator "onnx.Asinh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
   return %0 : !torch.vtensor<[3],f32>
 }
@@ -762,12 +728,7 @@ func.func @test_asinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<
 
 // CHECK-LABEL: @test_asinh
 func.func @test_asinh(%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 = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
-  // CHECK: %[[SQUARE:.+]] = torch.aten.square %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[C1:.+]] = torch.constant.int 1
-  // CHECK: %[[ADD:.+]] = torch.aten.add.Scalar %[[SQUARE]], %[[C1]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[SQRT:.+]] = torch.aten.sqrt %[[ADD]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: %[[ADD_0:.+]] = torch.aten.add.Tensor %arg0, %[[SQRT]], %[[C1]] : !torch.vtensor<[3,4,5],f32>, !torch.vtensor<[3,4,5],f32>, !torch.int -> !torch.vtensor<[3,4,5],f32>
-  // CHECK: torch.aten.log %[[ADD_0]] : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
+  // CHECK: torch.aten.asinh %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
   %0 = torch.operator "onnx.Asinh"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
   return %0 : !torch.vtensor<[3,4,5],f32>
 }

test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir

@@ -1341,15 +1341,9 @@ func.func @test_reduce_prod_keepdims_random(%arg0: !torch.vtensor<[3,2,2],f32>,
 
 // -----
 
-// CHECK-LABEL: func.func @test_sinh_example
+// CHECK-LABEL: func.func @test_sinh
 func.func @test_sinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 4 : si64, torch.onnx_meta.opset_version = 9 : si64} {
-  // CHECK: %[[X:.+]] = torch.aten.exp %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[NEG:.+]] = torch.aten.neg %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[Y:.+]] = torch.aten.exp %[[NEG]] : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
-  // CHECK: %[[C1:.+]] = torch.constant.int 1
-  // CHECK: %[[SUB:.+]] = torch.aten.sub.Tensor %[[X]], %[[Y]], %[[C1]] : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
-  // CHECK: %[[C2:.+]] = torch.constant.int 2
-  // CHECK: torch.aten.div.Scalar %[[SUB]], %[[C2]] : !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>
+  // CHECK: torch.aten.sinh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
   %0 = torch.operator "onnx.Sinh"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
   return %0 : !torch.vtensor<[3],f32>
 }