diff --git a/lib/Conversion/TorchToMhlo/Linear.cpp b/lib/Conversion/TorchToMhlo/Linear.cpp
index d9475e8efa8..09a3bc9343e 100644
--- a/lib/Conversion/TorchToMhlo/Linear.cpp
+++ b/lib/Conversion/TorchToMhlo/Linear.cpp
@@ -379,6 +379,171 @@ class ConvertAtenLinearOp : public ConvertAtenMatmulBaseOp<AtenOpT> {
 
 } // namespace
 
+// AtenConvolutionOp
+namespace {
+class ConvertAtenConvlutionOp : public OpConversionPattern<AtenConvolutionOp> {
+public:
+  using OpConversionPattern<AtenConvolutionOp>::OpConversionPattern;
+  using OpAdaptor = typename AtenConvolutionOp::Adaptor;
+
+  LogicalResult
+  matchAndRewrite(AtenConvolutionOp op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    Value input = adaptor.input();
+    Value weight = adaptor.weight();
+
+    // The input shape is [N, C, H, W]
+    auto inputTy = input.getType().template cast<RankedTensorType>();
+    // The weight shape is [OC, (IC // groups), KH, KW]
+    // If tranposed is set to true, the weight shape changes to [IC, (OC //
+    // groups), KH, KW]
+    auto weightTy = weight.getType().template cast<RankedTensorType>();
+    auto outTy = getTypeConverter()
+                     ->convertType(op.getType())
+                     .template cast<RankedTensorType>();
+
+    if (!inputTy || !weightTy || !outTy) {
+      return op.emitError("input, weight and output must be ranked tensors");
+    }
+
+    if (inputTy.getRank() < 3)
+      return op.emitError("only input with at least 3 dims valid");
+
+    SmallVector<int64_t> stride;
+    if (!matchPattern(op.stride(), m_TorchConstantIntList(stride))) {
+      return rewriter.notifyMatchFailure(op,
+                                         "non-const stride list unsupported");
+    }
+
+    SmallVector<int64_t> padding;
+    if (!matchPattern(op.padding(), m_TorchConstantIntList(padding))) {
+      return rewriter.notifyMatchFailure(op,
+                                         "non-const padding list unsupported");
+    }
+
+    SmallVector<int64_t> dilation;
+    if (!matchPattern(op.dilation(), m_TorchConstantIntList(dilation))) {
+      return rewriter.notifyMatchFailure(op,
+                                         "non-const dilation list unsupported");
+    }
+    SmallVector<int64_t> outputPadding;
+    if (!matchPattern(op.output_padding(),
+                      m_TorchConstantIntList(outputPadding))) {
+      return rewriter.notifyMatchFailure(
+          op, "non-const output_padding list unsupported");
+    }
+    // Just ignore the outputPadding attribute
+    for (int64_t item : outputPadding) {
+      if (item != 0)
+        return rewriter.notifyMatchFailure(
+            op, "only zero output_padding list supported");
+    }
+
+    int64_t groups;
+    if (!matchPattern(op.groups(), m_TorchConstantInt(&groups))) {
+      return rewriter.notifyMatchFailure(op, "non-int groups unsupported");
+    }
+
+    bool transposed;
+    if (!matchPattern(op.transposed(), m_TorchConstantBool(&transposed))) {
+      return rewriter.notifyMatchFailure(op, "non-bool transposed unsupported");
+    }
+    if (transposed) {
+      return rewriter.notifyMatchFailure(
+          op, "only param tranposed of value 'false' supported!");
+    }
+
+    assert(padding.size() == dilation.size() &&
+           padding.size() == stride.size() &&
+           padding.size() == static_cast<size_t>(inputTy.getRank()) - 2);
+    int64_t nSpatialDims = padding.size();
+
+    // Get mhlo::ConvolutionOp attributes
+    DenseIntElementsAttr mhloWindowStride = DenseIntElementsAttr::get(
+        RankedTensorType::get({static_cast<long int>(stride.size())},
+                              rewriter.getI64Type()),
+        stride);
+    std::vector<int64_t> mhloPaddingVec;
+    for (size_t i = 0; i < padding.size(); i++) {
+      mhloPaddingVec.emplace_back(padding[i]);
+      mhloPaddingVec.emplace_back(padding[i]);
+    }
+
+    DenseIntElementsAttr mhloPadding = DenseIntElementsAttr::get(
+        RankedTensorType::get(
+            {static_cast<long int>(padding.size()), static_cast<long int>(2)},
+            rewriter.getI64Type()),
+        mhloPaddingVec);
+
+    DenseIntElementsAttr mhloRhsDilation = DenseIntElementsAttr::get(
+        RankedTensorType::get({static_cast<long int>(dilation.size())},
+                              rewriter.getI64Type()),
+        dilation);
+
+    SmallVector<int64_t> spatialDimensions;
+    for (int64_t i = 2; i < inputTy.getRank(); i++) {
+      spatialDimensions.emplace_back(i);
+    }
+    mhlo::ConvDimensionNumbersAttr dimensionNumbers =
+        mhlo::ConvDimensionNumbersAttr::get(
+            /*context=*/rewriter.getContext(), /*inputBatchDimension=*/0,
+            /*inputFeatureDimension=*/1,
+            /*inputSpatialDimensions=*/spatialDimensions,
+            /*kernelInputFeatureDimension=*/1,
+            /*kernelOutputFeatureDimension=*/0,
+            /*kernelSpatialDimensions=*/spatialDimensions,
+            /*outputBatchDimension=*/0, /*outputFeatureDimension=*/1,
+            /*outputSpatialDimensions=*/spatialDimensions);
+
+    IntegerAttr featureGroupCount =
+        IntegerAttr::get(rewriter.getI64Type(), groups);
+    IntegerAttr batchGroupCount = IntegerAttr::get(rewriter.getI64Type(), 1);
+
+    // mhlo::ConvolutionOp's optional attributes, leave them as default
+    DenseIntElementsAttr mhloLhsDilation;
+    DenseElementsAttr windowReversal;
+    ArrayAttr precisionConfig;
+
+    auto mhloConvOp = rewriter.create<mhlo::ConvolutionOp>(
+        op->getLoc(), outTy, input, weight, mhloWindowStride, mhloPadding,
+        mhloLhsDilation, mhloRhsDilation, windowReversal, dimensionNumbers,
+        featureGroupCount, batchGroupCount, precisionConfig);
+
+    auto bias = adaptor.bias();
+
+    // No bias provided
+    if (failed(checkNotNone(rewriter, op, op.bias()))) {
+      rewriter.replaceOp(op, mhloConvOp.getResult());
+      return success();
+    }
+
+    // Handle bias
+    if (!bias.getType().cast<RankedTensorType>()) {
+      return op.emitError("bias provided but not a ranked tensor");
+    }
+
+    auto biasTy = bias.getType().template cast<RankedTensorType>();
+    if (!biasTy.getElementType().isIntOrFloat()) {
+      return op.emitError("only floating-point or integer datatype "
+                          "legalization for bias supported");
+    }
+
+    assert(biasTy.getRank() <= 1);
+
+    // Reshape and promote bias
+    auto inputUnsqzDims =
+        llvm::to_vector<4>(llvm::seq<int64_t>(-nSpatialDims, 0));
+    bias = *mhlo::unsqueezeTensor(rewriter, op, bias, inputUnsqzDims);
+    bias = mhlo::promoteType(rewriter, bias, outTy);
+
+    DenseIntElementsAttr bcastDimensions;
+    rewriter.replaceOpWithNewOp<chlo::BroadcastAddOp>(
+        op, outTy, mhloConvOp.getResult(), bias, bcastDimensions);
+    return success();
+  }
+};
+} // namespace
+
 void mlir::torch::torch_to_mhlo::populateLinearOpPatternsAndLegality(
     TypeConverter &typeConverter, RewritePatternSet &patterns,
     ConversionTarget &target) {
@@ -402,4 +567,10 @@ void mlir::torch::torch_to_mhlo::populateLinearOpPatternsAndLegality(
   patterns.add<ConvertAtenLinearOp<AtenOp>>(typeConverter, context);
   INSERT_LINEAR_ATENOP_PATTERN(AtenLinearOp);
 #undef INSERT_LINEAR_ATEMOP_PATTERN
+
+#define INSERT_CONVOLUTION_ATENOP_PATTERN(AtenOp)                              \
+  target.addIllegalOp<AtenOp>();                                               \
+  patterns.add<ConvertAtenConvlutionOp>(typeConverter, context);
+  INSERT_CONVOLUTION_ATENOP_PATTERN(AtenConvolutionOp);
+#undef INSERT_CONVOLUTION_ATENOP_PATTERN
 }
diff --git a/test/Conversion/TorchToMhlo/linear.mlir b/test/Conversion/TorchToMhlo/linear.mlir
index 18ea97654eb..d39be34e0be 100644
--- a/test/Conversion/TorchToMhlo/linear.mlir
+++ b/test/Conversion/TorchToMhlo/linear.mlir
@@ -266,3 +266,82 @@ func.func @torch.aten.mm$proj(%arg0: !torch.vtensor<[?,256],f32>) -> !torch.vten
   return %1 : !torch.vtensor<[?,256],f32>
 }
 
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.convolution(
+// CHECK-SAME:                                 %[[ARG_0:.*]]: !torch.vtensor<[?,?,?,?],f32>, 
+// CHECK-SAME:                                 %[[ARG_1:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
+// CHECK:           %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
+// CHECK:           %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
+// CHECK:           %[[T_2:.*]] = torch.constant.none
+// CHECK:           %[[T_4:.*]] = torch.constant.int 2
+// CHECK:           %[[T_5:.*]] = torch.constant.int 1
+// CHECK:           %[[T_6:.*]] = torch.constant.int 4
+// CHECK:           %[[T_7:.*]] = torch.constant.int 3
+// CHECK:           %[[T_8:.*]] = torch_c.to_i64 %[[T_7]]
+// CHECK:           %[[T_9:.*]] = torch.prim.ListConstruct %[[T_4]], %[[T_5]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[T_10:.*]] = torch.prim.ListConstruct %[[T_6]], %[[T_4]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[T_11:.*]] = torch.prim.ListConstruct %[[T_7]], %[[T_5]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[T_12:.*]] = torch.prim.ListConstruct  : () -> !torch.list<int>
+// CHECK:           %[[T_13:.*]] = torch.constant.bool false
+// CHECK:           %[[T_14:.*]] = mhlo.convolution(%[[T_0]], %[[T_1]]) 
+// CHECK-SAME{LITERAL}:                                                 dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {stride = [2, 1], pad = [[4, 4], [2, 2]], rhs_dilate = [3, 1]} {batch_group_count = 1 : i64, feature_group_count = 3 : i64} : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+// CHECK:           %[[T_15:.*]] = torch_c.from_builtin_tensor %[[T_14]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
+// CHECK:           return %[[T_15]] : !torch.vtensor<[?,?,?,?],f32>
+func.func @torch.aten.convolution(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
+  %none = torch.constant.none
+  %int2 = torch.constant.int 2
+  %int1 = torch.constant.int 1
+  %int4 = torch.constant.int 4
+  %int3 = torch.constant.int 3
+  %1 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+  %2 = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
+  %3 = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+  %4 = torch.prim.ListConstruct  : () -> !torch.list<int>
+  %false = torch.constant.bool false
+  %5 = torch.aten.convolution %arg0, %arg1, %none, %1, %2, %3, %false, %4, %int3 : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
+  return %5 : !torch.vtensor<[?,?,?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.convolution$bias(
+// CHECK-SAME:                                           %[[ARG_0:.*]]: !torch.vtensor<[?,?,?,?],f32>, %[[ARG_1:.*]]: !torch.vtensor<[?,?,?,?],f32>, 
+// CHECK-SAME:                                           %[[ARG_2:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
+// CHECK:           %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
+// CHECK:           %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
+// CHECK:           %[[T_2:.*]] = torch_c.to_builtin_tensor %[[ARG_2]] : !torch.vtensor<[?],f32> -> tensor<?xf32>
+// CHECK:           %int2 = torch.constant.int 2
+// CHECK:           %int1 = torch.constant.int 1
+// CHECK:           %int4 = torch.constant.int 4
+// CHECK:           %int3 = torch.constant.int 3
+// CHECK:           %[[T_3:.*]] = torch_c.to_i64 %int3
+// CHECK:           %[[T_4:.*]] = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[T_5:.*]] = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[T_6:.*]] = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[T_7:.*]] = torch.prim.ListConstruct  : () -> !torch.list<int>
+// CHECK:           %false = torch.constant.bool false
+// CHECK:           %[[T_8:.*]] = mhlo.convolution(%[[T_0]], %[[T_1]]) 
+// CHECK{LITERAL}:                                                     dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {stride = [2, 1], pad = [[4, 4], [2, 2]], rhs_dilate = [3, 1]} {batch_group_count = 1 : i64, feature_group_count = 3 : i64} : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+// CHECK:           %[[IDX_0:.*]] = arith.constant 0 : index
+// CHECK:           %[[T_9:.*]] = tensor.dim %[[T_2]], %[[IDX_0]] : tensor<?xf32>
+// CHECK:           %[[T_10:.*]] = arith.index_cast %[[T_9]] : index to i64
+// CHECK:           %[[VAL_0:.*]] = arith.constant 1 : i64
+// CHECK:           %[[T_11:.*]] = tensor.from_elements %[[T_10]], %[[VAL_0]], %[[VAL_0]] : tensor<3xi64>
+// CHECK:           %[[T_12:.*]] = "mhlo.dynamic_reshape"(%[[T_2]], %[[T_11]]) : (tensor<?xf32>, tensor<3xi64>) -> tensor<?x1x1xf32>
+// CHECK:           %[[T_13:.*]] = chlo.broadcast_add %[[T_8]], %[[T_12]] : (tensor<?x?x?x?xf32>, tensor<?x1x1xf32>) -> tensor<?x?x?x?xf32>
+// CHECK:           %[[T_14:.*]] = torch_c.from_builtin_tensor %[[T_13]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
+// CHECK:           return %[[T_14]] : !torch.vtensor<[?,?,?,?],f32>
+func.func @torch.aten.convolution$bias(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?,?],f32>, %arg2: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
+  %int2 = torch.constant.int 2
+  %int1 = torch.constant.int 1
+  %int4 = torch.constant.int 4
+  %int3 = torch.constant.int 3
+  %1 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+  %2 = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
+  %3 = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+  %4 = torch.prim.ListConstruct  : () -> !torch.list<int>
+  %false = torch.constant.bool false
+  %5 = torch.aten.convolution %arg0, %arg1, %arg2, %1, %2, %3, %false, %4, %int3 : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
+  return %5 : !torch.vtensor<[?,?,?,?],f32>
+}
\ No newline at end of file