diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
index 873e02ed9..f7fac5380 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
@@ -2300,15 +2300,14 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
             binder.tensorResultType(resultType)) {
           return failure();
         }
+
+        Location loc = binder.getLoc();
         Value a0 = rewriter.create<Torch::ConstantFloatOp>(
-            binder.getLoc(),
-            rewriter.getFloatAttr(rewriter.getF64Type(), 0.42));
+            loc, rewriter.getF64FloatAttr(0.42));
         Value a1 = rewriter.create<Torch::ConstantFloatOp>(
-            binder.getLoc(),
-            rewriter.getFloatAttr(rewriter.getF64Type(), -0.5));
+            loc, rewriter.getF64FloatAttr(-0.5));
         Value a2 = rewriter.create<Torch::ConstantFloatOp>(
-            binder.getLoc(),
-            rewriter.getFloatAttr(rewriter.getF64Type(), 0.08));
+            loc, rewriter.getF64FloatAttr(0.08));
 
         auto windowFunctionResult =
             windowFunctionImpl(binder, rewriter, size, a0, a1, a2, resultType,
@@ -2332,13 +2331,45 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
             binder.tensorResultType(resultType)) {
           return failure();
         }
+
+        Location loc = binder.getLoc();
         Value a0 = rewriter.create<Torch::ConstantFloatOp>(
-            binder.getLoc(), rewriter.getFloatAttr(rewriter.getF64Type(), 0.5));
+            loc, rewriter.getF64FloatAttr(0.5));
         Value a1 = rewriter.create<Torch::ConstantFloatOp>(
-            binder.getLoc(),
-            rewriter.getFloatAttr(rewriter.getF64Type(), -0.5));
+            loc, rewriter.getF64FloatAttr(-0.5));
         Value a2 = rewriter.create<Torch::ConstantFloatOp>(
-            binder.getLoc(), rewriter.getFloatAttr(rewriter.getF64Type(), 0.0));
+            loc, rewriter.getF64FloatAttr(0.0));
+
+        auto windowFunctionResult =
+            windowFunctionImpl(binder, rewriter, size, a0, a1, a2, resultType,
+                               output_datatype, periodic);
+
+        if (failed(windowFunctionResult))
+          return failure();
+
+        return success();
+      });
+
+  patterns.onOp(
+      "HammingWindow", 17,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Value size;
+        Torch::ValueTensorType resultType;
+        int64_t periodic, output_datatype;
+        if (binder.tensorOperand(size) ||
+            binder.s64IntegerAttr(output_datatype, "output_datatype", 1) ||
+            binder.s64IntegerAttr(periodic, "periodic", 1) ||
+            binder.tensorResultType(resultType)) {
+          return failure();
+        }
+
+        Location loc = binder.getLoc();
+        Value a0 = rewriter.create<Torch::ConstantFloatOp>(
+            loc, rewriter.getF64FloatAttr(0.543478));
+        Value a1 = rewriter.create<Torch::ConstantFloatOp>(
+            loc, rewriter.getF64FloatAttr(-0.456522));
+        Value a2 = rewriter.create<Torch::ConstantFloatOp>(
+            loc, rewriter.getF64FloatAttr(0.0));
 
         auto windowFunctionResult =
             windowFunctionImpl(binder, rewriter, size, a0, a1, a2, resultType,
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
index 47ed8948c..e8266c04f 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
@@ -2153,3 +2153,83 @@ func.func @test_hannwindow_symmetric(%arg0: !torch.vtensor<[],si32>) -> !torch.v
   %0 = torch.operator "onnx.HannWindow"(%arg0) {torch.onnx.periodic = 0 : si64} : (!torch.vtensor<[],si32>) -> !torch.vtensor<[10],f32>
   return %0 : !torch.vtensor<[10],f32>
 }
+
+// -----
+
+// CHECK-LABEL: func.func @test_hammingwindow_symmetric
+func.func @test_hammingwindow_symmetric(%arg0: !torch.vtensor<[],si32>) -> !torch.vtensor<[10],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK-DAG: %[[A0:.+]] = torch.constant.float 5.434780e-01
+  // CHECK-DAG: %[[A1:.+]] = torch.constant.float -4.565220e-01
+  // CHECK-DAG: %[[A2:.+]] = torch.constant.float 0.000000e+00
+  // CHECK-DAG: %[[ZERO:.+]] = torch.constant.float 0.000000e+00
+  // CHECK-DAG: %[[ONE:.+]] = torch.constant.float 1.000000e+00
+  // CHECK-DAG: %[[TWO:.+]] = torch.constant.float 2.000000e+00
+  // CHECK-DAG: %[[TAU:.+]] = torch.constant.float 6.2831853071795862
+  // CHECK-DAG: %[[NONE:.+]] = torch.constant.none
+  // CHECK-DAG: %[[FALSE:.+]] = torch.constant.bool false
+  // CHECK-DAG: %[[INT6_0:.+]] = torch.constant.int 6
+  // CHECK-DAG: %[[CAST_0:.+]] = torch.aten.to.dtype %arg0, %[[INT6_0]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[],si32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[SYMMSIZE:.+]] = torch.aten.sub.Scalar %[[CAST_0]], %[[ONE]], %[[ONE]] : !torch.vtensor<[],f32>, !torch.float, !torch.float -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[PERIODIC:.+]] = torch.constant.float 0.000000e+00
+  // CHECK-DAG: %[[SYMMETRIC:.+]] = torch.constant.float 1.000000e+00
+  // CHECK-DAG: %[[PERIODICCOMP:.+]] = torch.aten.mul.Scalar %[[CAST_0]], %[[PERIODIC]] : !torch.vtensor<[],f32>, !torch.float -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[SYMMETRICCOMP:.+]] = torch.aten.mul.Scalar %[[SYMMSIZE]], %[[SYMMETRIC]] : !torch.vtensor<[],f32>, !torch.float -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[SIZEFP:.+]] = torch.aten.add.Tensor %[[SYMMETRICCOMP]], %[[PERIODICCOMP]], %[[ONE]] : !torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.float -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[RANGELIM:.+]] = torch.aten.item %arg0 : !torch.vtensor<[],si32> -> !torch.int
+  // CHECK-DAG: %[[ARANGE:.+]] = torch.aten.arange.start_step %[[ZERO]], %[[RANGELIM]], %[[ONE]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.float, !torch.int, !torch.float, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[RANGETIMESTAU:.+]] = torch.aten.mul.Scalar %[[ARANGE]], %[[TAU]] : !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[RANGEANGULAR:.+]] = torch.aten.div.Tensor %[[RANGETIMESTAU]], %[[SIZEFP]] : !torch.vtensor<[10],f32>, !torch.vtensor<[],f32> -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[TWORANGEANGULAR:.+]] = torch.aten.mul.Scalar %[[RANGEANGULAR]], %[[TWO]] : !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[COSRANGEANGULAR:.+]] = torch.aten.cos %[[RANGEANGULAR]] : !torch.vtensor<[10],f32> -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[TWOCOSRANGEANGULAR:.+]] = torch.aten.cos %[[TWORANGEANGULAR]] : !torch.vtensor<[10],f32> -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[A1COMP:.+]] = torch.aten.mul.Scalar %[[COSRANGEANGULAR]], %[[A1]] : !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[A2COMP:.+]] = torch.aten.mul.Scalar %[[TWOCOSRANGEANGULAR]], %[[A2]] : !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[RES:.+]] = torch.aten.add.Scalar %[[A1COMP]], %[[A0]], %[[ONE]] : !torch.vtensor<[10],f32>, !torch.float, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[RESULT:.+]] = torch.aten.add.Tensor %[[RES]], %[[A2COMP]], %[[ONE]] : !torch.vtensor<[10],f32>, !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[INT6_1:.+]] = torch.constant.int 6
+  // CHECK: %[[CAST_1:.+]] = torch.aten.to.dtype %[[RESULT]], %[[INT6_1]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[10],f32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[10],f32>
+  // CHECK: return %[[CAST_1]] : !torch.vtensor<[10],f32>
+
+  %0 = torch.operator "onnx.HammingWindow"(%arg0) {torch.onnx.periodic = 0 : si64} : (!torch.vtensor<[],si32>) -> !torch.vtensor<[10],f32>
+  return %0 : !torch.vtensor<[10],f32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_hammingwindow
+func.func @test_hammingwindow(%arg0: !torch.vtensor<[],si32>) -> !torch.vtensor<[10],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK-DAG: %[[A0:.+]] = torch.constant.float 5.434780e-01
+  // CHECK-DAG: %[[A1:.+]] = torch.constant.float -4.565220e-01
+  // CHECK-DAG: %[[A2:.+]] = torch.constant.float 0.000000e+00
+  // CHECK-DAG: %[[ZERO:.+]] = torch.constant.float 0.000000e+00
+  // CHECK-DAG: %[[ONE:.+]] = torch.constant.float 1.000000e+00
+  // CHECK-DAG: %[[TWO:.+]] = torch.constant.float 2.000000e+00
+  // CHECK-DAG: %[[TAU:.+]] = torch.constant.float 6.2831853071795862
+  // CHECK-DAG: %[[NONE:.+]] = torch.constant.none
+  // CHECK-DAG: %[[FALSE:.+]] = torch.constant.bool false
+  // CHECK-DAG: %[[INT6_0:.+]] = torch.constant.int 6
+  // CHECK-DAG: %[[CAST_0:.+]] = torch.aten.to.dtype %arg0, %[[INT6_0]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[],si32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[SYMMSIZE:.+]] = torch.aten.sub.Scalar %[[CAST_0]], %[[ONE]], %[[ONE]] : !torch.vtensor<[],f32>, !torch.float, !torch.float -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[PERIODIC:.+]] = torch.constant.float 1.000000e+00
+  // CHECK-DAG: %[[SYMMETRIC:.+]] = torch.constant.float 0.000000e+00
+  // CHECK-DAG: %[[PERIODICCOMP:.+]] = torch.aten.mul.Scalar %[[CAST_0]], %[[PERIODIC]] : !torch.vtensor<[],f32>, !torch.float -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[SYMMETRICCOMP:.+]] = torch.aten.mul.Scalar %[[SYMMSIZE]], %[[SYMMETRIC]] : !torch.vtensor<[],f32>, !torch.float -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[SIZEFP:.+]] = torch.aten.add.Tensor %[[SYMMETRICCOMP]], %[[PERIODICCOMP]], %[[ONE]] : !torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.float -> !torch.vtensor<[],f32>
+  // CHECK-DAG: %[[RANGELIM:.+]] = torch.aten.item %arg0 : !torch.vtensor<[],si32> -> !torch.int
+  // CHECK-DAG: %[[ARANGE:.+]] = torch.aten.arange.start_step %[[ZERO]], %[[RANGELIM]], %[[ONE]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.float, !torch.int, !torch.float, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[RANGETIMESTAU:.+]] = torch.aten.mul.Scalar %[[ARANGE]], %[[TAU]] : !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[RANGEANGULAR:.+]] = torch.aten.div.Tensor %[[RANGETIMESTAU]], %[[SIZEFP]] : !torch.vtensor<[10],f32>, !torch.vtensor<[],f32> -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[TWORANGEANGULAR:.+]] = torch.aten.mul.Scalar %[[RANGEANGULAR]], %[[TWO]] : !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[COSRANGEANGULAR:.+]] = torch.aten.cos %[[RANGEANGULAR]] : !torch.vtensor<[10],f32> -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[TWOCOSRANGEANGULAR:.+]] = torch.aten.cos %[[TWORANGEANGULAR]] : !torch.vtensor<[10],f32> -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[A1COMP:.+]] = torch.aten.mul.Scalar %[[COSRANGEANGULAR]], %[[A1]] : !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[A2COMP:.+]] = torch.aten.mul.Scalar %[[TWOCOSRANGEANGULAR]], %[[A2]] : !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[RES:.+]] = torch.aten.add.Scalar %[[A1COMP]], %[[A0]], %[[ONE]] : !torch.vtensor<[10],f32>, !torch.float, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[RESULT:.+]] = torch.aten.add.Tensor %[[RES]], %[[A2COMP]], %[[ONE]] : !torch.vtensor<[10],f32>, !torch.vtensor<[10],f32>, !torch.float -> !torch.vtensor<[10],f32>
+  // CHECK-DAG: %[[INT6_1:.+]] = torch.constant.int 6
+  // CHECK: %[[CAST_1:.+]] = torch.aten.to.dtype %[[RESULT]], %[[INT6_1]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[10],f32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[10],f32>
+  // CHECK: return %[[CAST_1]] : !torch.vtensor<[10],f32>
+
+  %0 = torch.operator "onnx.HammingWindow"(%arg0) {torch.onnx.periodic = 1 : si64} : (!torch.vtensor<[],si32>) -> !torch.vtensor<[10],f32>
+  return %0 : !torch.vtensor<[10],f32>
+}