diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
index bbd5005c44e3..547170cd5d8c 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
@@ -46,7 +46,6 @@ 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));
@@ -62,18 +61,16 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
             /*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, 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();
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
index 58d33db41306..be07ac634de2 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
@@ -894,20 +894,18 @@ func.func @test_hardsigmoid_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vt
   // 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
   // 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>
 }
@@ -921,17 +919,16 @@ func.func @test_hardsigmoid(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtenso
     // 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
     // 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>
@@ -947,17 +944,16 @@ func.func @test_hardsigmoid_default(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torc
     // 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
     // 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>