diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
index 8919df43a..2712f0964 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
@@ -1292,14 +1292,6 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
       });
   patterns.onOp(
       "Conv", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
-        std::string autoPad;
-        if (binder.customOpNameStringAttr(autoPad, "auto_pad", "NOTSET"))
-          return failure();
-        if (autoPad != "NOTSET") {
-          // TODO: Add support for `auto_pad` != "NOTSET"
-          return rewriter.notifyMatchFailure(
-              binder.op, "unsupported conversion: auto_pad != NOTSET");
-        }
         Torch::ValueTensorType resultType;
         Value input, weight;
         int64_t group;
@@ -1349,20 +1341,6 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
           defaultStrides.push_back(1);
           defaultDilations.push_back(1);
         }
-        // Padding for the beginning and ending along each spatial axis, it can
-        // take any value greater than or equal to 0. The value represent the
-        // number of pixels added to the beginning and end part of the
-        // corresponding axis. pads format should be as follow [x1_begin,
-        // x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added
-        // at the beginning of axis i and xi_end, the number of pixels added at
-        // the end of axis i.
-        if (binder.s64IntegerArrayAttr(padding, "pads", defaultPadding)) {
-          return failure();
-        }
-        if (padding.size() != rank - 2 && padding.size() != 2 * (rank - 2)) {
-          return rewriter.notifyMatchFailure(
-              binder.op, "padding list size does not match the number of axes");
-        }
         if (binder.s64IntegerArrayAttr(dilations, "dilations",
                                        defaultDilations)) {
           return failure();
@@ -1379,6 +1357,46 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
           return rewriter.notifyMatchFailure(
               binder.op, "strides list size does not match the number of axes");
         }
+        std::string autoPad;
+        if (binder.customOpNameStringAttr(autoPad, "auto_pad", "NOTSET"))
+          return failure();
+        auto inputTensorType = cast<Torch::ValueTensorType>(input.getType());
+        // Padding for the beginning and ending along each spatial axis, it can
+        // take any value greater than or equal to 0. The value represent the
+        // number of pixels added to the beginning and end part of the
+        // corresponding axis. pads format should be as follow [x1_begin,
+        // x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added
+        // at the beginning of axis i and xi_end, the number of pixels added at
+        // the end of axis i.
+        if (autoPad == "NOTSET") {
+          if (binder.s64IntegerArrayAttr(padding, "pads", defaultPadding)) {
+            return failure();
+          }
+        } else if (autoPad == "VALID") {
+          padding = defaultPadding;
+        } else {
+          const bool isSameLower = autoPad == "SAME_LOWER";
+          const unsigned spatialRank = rank - 2;
+          ArrayRef<int64_t> inputShape = inputTensorType.getSizes();
+          padding.resize_for_overwrite(2 * spatialRank);
+          for (unsigned dimIdx = 0; dimIdx < spatialRank; dimIdx++) {
+            const int64_t dilatedKernelSize =
+                dilations[dimIdx] * (weightShape[dimIdx + 2] - 1) + 1;
+            int64_t totalPad = ((inputShape[dimIdx + 2] + strides[dimIdx] - 1) /
+                                    strides[dimIdx] -
+                                1) *
+                                   strides[dimIdx] +
+                               dilatedKernelSize - inputShape[dimIdx + 2];
+            totalPad = totalPad >= 0 ? totalPad : 0;
+            padding[dimIdx] =
+                isSameLower ? ((totalPad + 1) / 2) : (totalPad / 2);
+            padding[spatialRank + dimIdx] = totalPad - padding[dimIdx];
+          }
+        }
+        if (padding.size() != rank - 2 && padding.size() != 2 * (rank - 2)) {
+          return rewriter.notifyMatchFailure(
+              binder.op, "padding list size does not match the number of axes");
+        }
 
         SmallVector<Value> cstPadding, cstStrides, cstDilations,
             cstOutputPadding;
@@ -1452,8 +1470,7 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
             Value modeVal = rewriter.create<Torch::ConstantStrOp>(
                 binder.getLoc(), rewriter.getStringAttr("constant"));
             Value constantValue;
-            auto inputTensorType =
-                cast<Torch::ValueTensorType>(input.getType());
+
             if (isa<IntegerType>(inputTensorType.getDtype()))
               constantValue = rewriter.create<Torch::ConstantIntOp>(
                   binder.getLoc(), rewriter.getI64IntegerAttr(0));
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
index 10cca7f80..6cc0cf0ec 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
@@ -1062,6 +1062,93 @@ func.func @test_conv_with_asymmetric_padding(%arg0: !torch.vtensor<[1,1,7,5],f32
 
 // -----
 
+// CHECK-LABEL: @test_conv_with_autopad
+func.func @test_conv_with_autopad(%arg0: !torch.vtensor<[1,1,12,7],f32>, %arg1: !torch.vtensor<[1,1,2,3],f32>) -> !torch.vtensor<[1,1,3,3],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[C1:.*]] = torch.constant.int 0
+  // CHECK: %[[C1_0:.*]] = torch.constant.int 1
+  // CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[C1_1:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_2:.*]] = torch.constant.int 1
+  // CHECK: %[[C2:.*]] = torch.constant.int 4
+  // CHECK: %[[C2_0:.*]] = torch.constant.int 3
+  // CHECK: %[[C0:.*]] = torch.constant.int 0
+  // CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1_1]], %[[C1_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C2]], %[[C2_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool false
+  // CHECK: %[[BIAS:.*]] = torch.constant.none
+  // CHECK: %[[GROUPS:.*]] = torch.constant.int 1
+  // CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,12,7],f32>, !torch.vtensor<[1,1,2,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,3,3],f32>
+  %0 = torch.operator "onnx.Conv"(%arg0, %arg1) {torch.onnx.kernel_shape = [2 : si64, 3 : si64], torch.onnx.auto_pad = "SAME_LOWER", torch.onnx.strides = [4 : si64, 3 : si64]} : (!torch.vtensor<[1,1,12,7],f32>, !torch.vtensor<[1,1,2,3],f32>) -> !torch.vtensor<[1,1,3,3],f32>
+  return %0 : !torch.vtensor<[1,1,3,3],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_conv_with_autopad_asymmetric
+func.func @test_conv_with_autopad_asymmetric(%arg0: !torch.vtensor<[1,1,15,9],f32>, %arg1: !torch.vtensor<[1,1,4,4],f32>) -> !torch.vtensor<[1,1,4,3],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[int1:.*]] = torch.constant.int 1
+  // CHECK: %[[int2:.*]] = torch.constant.int 2
+  // CHECK: %[[int0:.*]] = torch.constant.int 0
+  // CHECK: %[[int0_0:.*]] = torch.constant.int 0
+  // CHECK: %[[int1_1:.*]] = torch.constant.int 1
+  // CHECK: %[[int0_2:.*]] = torch.constant.int 0
+  // CHECK: %[[FakePADS:.*]] = torch.prim.ListConstruct %[[int0]], %[[int0_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[OGPADS:.*]] = torch.prim.ListConstruct %[[int1]], %[[int2]], %[[int0_0]], %[[int1_1]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[str:.*]] = torch.constant.str "constant"
+  // CHECK: %[[float0:.*]] = torch.constant.float 0.000
+  // CHECK: %[[PrePad:.*]] = torch.aten.pad %arg0, %[[OGPADS]], %[[str]], %[[float0]] : !torch.vtensor<[1,1,15,9],f32>, !torch.list<int>, !torch.str, !torch.float -> !torch.vtensor<[1,1,16,12],f32>
+  // CHECK: %[[C1_1:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_2:.*]] = torch.constant.int 1
+  // CHECK: %[[C4:.*]] = torch.constant.int 4
+  // CHECK: %[[C4_0:.*]] = torch.constant.int 4
+  // CHECK: %[[C0:.*]] = torch.constant.int 0
+  // CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1_1]], %[[C1_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C4]], %[[C4_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool false
+  // CHECK: %[[BIAS:.*]] = torch.constant.none
+  // CHECK: %[[GROUPS:.*]] = torch.constant.int 1
+  // CHECK: %[[Conv:.*]] = torch.aten.convolution %[[PrePad]], %arg1, %[[BIAS]], %[[STRIDE]], %[[FakePADS]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,16,12],f32>, !torch.vtensor<[1,1,4,4],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,4,3],f32>
+  // CHECK: return %[[Conv]]
+  %0 = torch.operator "onnx.Conv"(%arg0, %arg1) {torch.onnx.kernel_shape = [4 : si64, 4 : si64], torch.onnx.auto_pad = "SAME_UPPER", torch.onnx.strides = [4 : si64, 4 : si64]} : (!torch.vtensor<[1,1,15,9],f32>, !torch.vtensor<[1,1,4,4],f32>) -> !torch.vtensor<[1,1,4,3],f32>
+  return %0 : !torch.vtensor<[1,1,4,3],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_conv_with_autopad_asymmetric_lower
+func.func @test_conv_with_autopad_asymmetric_lower(%arg0: !torch.vtensor<[1,1,15,9],f32>, %arg1: !torch.vtensor<[1,1,4,4],f32>) -> !torch.vtensor<[1,1,4,3],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[int2:.*]] = torch.constant.int 2
+  // CHECK: %[[int1:.*]] = torch.constant.int 1
+  // CHECK: %[[int0:.*]] = torch.constant.int 0
+  // CHECK: %[[int1_0:.*]] = torch.constant.int 1
+  // CHECK: %[[int0_1:.*]] = torch.constant.int 0
+  // CHECK: %[[int0_2:.*]] = torch.constant.int 0
+  // CHECK: %[[FakePADS:.*]] = torch.prim.ListConstruct %[[int0]], %[[int0_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[OGPADS:.*]] = torch.prim.ListConstruct %[[int2]], %[[int1]], %[[int1_0]], %[[int0_1]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[str:.*]] = torch.constant.str "constant"
+  // CHECK: %[[float0:.*]] = torch.constant.float 0.000
+  // CHECK: %[[PrePad:.*]] = torch.aten.pad %arg0, %[[OGPADS]], %[[str]], %[[float0]] : !torch.vtensor<[1,1,15,9],f32>, !torch.list<int>, !torch.str, !torch.float -> !torch.vtensor<[1,1,16,12],f32>
+  // CHECK: %[[C1_1:.*]] = torch.constant.int 1
+  // CHECK: %[[C1_2:.*]] = torch.constant.int 1
+  // CHECK: %[[C4:.*]] = torch.constant.int 4
+  // CHECK: %[[C4_0:.*]] = torch.constant.int 4
+  // CHECK: %[[C0:.*]] = torch.constant.int 0
+  // CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1_1]], %[[C1_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C4]], %[[C4_0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0]] : (!torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool false
+  // CHECK: %[[BIAS:.*]] = torch.constant.none
+  // CHECK: %[[GROUPS:.*]] = torch.constant.int 1
+  // CHECK: %[[Conv:.*]] = torch.aten.convolution %[[PrePad]], %arg1, %[[BIAS]], %[[STRIDE]], %[[FakePADS]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,16,12],f32>, !torch.vtensor<[1,1,4,4],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,4,3],f32>
+  // CHECK: return %[[Conv]]
+  %0 = torch.operator "onnx.Conv"(%arg0, %arg1) {torch.onnx.kernel_shape = [4 : si64, 4 : si64], torch.onnx.auto_pad = "SAME_LOWER", torch.onnx.strides = [4 : si64, 4 : si64]} : (!torch.vtensor<[1,1,15,9],f32>, !torch.vtensor<[1,1,4,4],f32>) -> !torch.vtensor<[1,1,4,3],f32>
+  return %0 : !torch.vtensor<[1,1,4,3],f32>
+}
+
+// -----
+
 // CHECK-LABEL: @test_conv_with_bias_strides_padding
 func.func @test_conv_with_bias_strides_padding(%arg0: !torch.vtensor<[?,?,224,224],f32>, %arg1: !torch.vtensor<[64,3,7,7],f32>, %arg2: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,64,112,112],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: %[[C3:.*]] = torch.constant.int 3