diff --git a/lib/Conversion/TorchToLinalg/Linear.cpp b/lib/Conversion/TorchToLinalg/Linear.cpp
index ccb876a05ce1..c2a2c8cd3349 100644
--- a/lib/Conversion/TorchToLinalg/Linear.cpp
+++ b/lib/Conversion/TorchToLinalg/Linear.cpp
@@ -747,9 +747,33 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
                                                       indices);
       };
 
+      auto expandGroupsOut = [&](Value tensor) {
+        auto inType = tensor.getType().cast<RankedTensorType>();
+        auto inShape = inType.getShape();
+
+        SmallVector<int64_t> outShape(inShape.begin(), inShape.end());
+        outShape.insert(outShape.begin() + 1,
+                        (inShape[1] == kUnknownSize ? kUnknownSize
+                                                    : inShape[1] / groupSize));
+        outShape[2] = groupSize;
+
+        SmallVector<ReassociationIndices> indices;
+        for (auto i = 0; i <= (long)inShape.size(); i++) {
+          if (i == 1) {
+            indices.push_back({i, ++i});
+            continue;
+          }
+          indices.push_back({i});
+        }
+
+        auto retType = inType.clone(outShape);
+        return rewriter.create<tensor::ExpandShapeOp>(loc, retType, tensor,
+                                                      indices);
+      };
+
       Value paddedInputExpanded = expandGroups(paddedInput, 1);
       Value weightExpanded = expandGroups(weight, 0);
-      Value outputTensorExpanded = expandGroups(outputTensor, 1);
+      Value outputTensorExpanded = expandGroupsOut(outputTensor);
 
       // TODO: add 1D and 3D case
       conv = rewriter
diff --git a/python/torch_mlir_e2e_test/test_suite/conv.py b/python/torch_mlir_e2e_test/test_suite/conv.py
index 832a4e3314e8..cc48b8c9ef75 100644
--- a/python/torch_mlir_e2e_test/test_suite/conv.py
+++ b/python/torch_mlir_e2e_test/test_suite/conv.py
@@ -268,20 +268,20 @@ def __init__(self):
     @export
     @annotate_args([
         None,
-        ([-1, -1, -1, -1], torch.float32, True),
-        ([-1, -1, -1, -1], torch.float32, True),
+        ([1, 32, 4, 4], torch.float32, True),
+        ([32, 4, 1, 1], torch.float32, True),
     ])
     def forward(self, inputVec, weight):
         return torch.ops.aten.convolution(inputVec,
                                           weight,
                                           bias=None,
-                                          stride=[3, 3],
-                                          padding=[2, 2],
+                                          stride=[1, 1],
+                                          padding=[1, 1],
                                           dilation=[1, 1],
                                           transposed=False,
                                           output_padding=[0, 0],
-                                          groups=3)
+                                          groups=8)
 
 @register_test_case(module_factory=lambda: ConvolutionModule2DGroups())
 def ConvolutionModule2DGroups_basic(module, tu: TestUtils):
-    module.forward(torch.randn(3, 9, 10, 10), torch.randn(3, 3, 2, 2))
+    module.forward(torch.ones(1, 32, 4, 4), torch.randn(32, 4, 1, 1))