diff --git a/TrainingExtensions/torch/src/python/aimet_torch/v2/seq_mse.py b/TrainingExtensions/torch/src/python/aimet_torch/v2/seq_mse.py
index f94a7744079..6ce8ac3aaf0 100644
--- a/TrainingExtensions/torch/src/python/aimet_torch/v2/seq_mse.py
+++ b/TrainingExtensions/torch/src/python/aimet_torch/v2/seq_mse.py
@@ -322,8 +322,10 @@ def _compute_loss(cls,
             x_blocks = torch.split(x, block_size, dim=-1)
             xq_blocks = torch.split(xq, block_size, dim=-1)
         else:
-            x_blocks = torch.split(x, block_size, dim=-3)
-            xq_blocks = torch.split(xq, block_size, dim=-3)
+            assert isinstance(quant_module, torch.nn.Conv2d)
+            groups = quant_module.groups
+            x_blocks = torch.split(x, block_size * groups, dim=-3)
+            xq_blocks = torch.split(xq, block_size * groups, dim=-3)
 
         block_losses = []
         for idx, x_block in enumerate(x_blocks):
diff --git a/TrainingExtensions/torch/test/python/v2/test_seq_mse_.py b/TrainingExtensions/torch/test/python/v2/test_seq_mse_.py
index 0ca66c664ba..769d2ce8b55 100644
--- a/TrainingExtensions/torch/test/python/v2/test_seq_mse_.py
+++ b/TrainingExtensions/torch/test/python/v2/test_seq_mse_.py
@@ -334,3 +334,35 @@ def test_handle_grouped_block_quantizers(self):
 
         assert torch.equal(out, out_3)
         assert not torch.equal(out, out_2)
+
+    @pytest.mark.parametrize('kwargs', [
+        dict(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=2),
+        dict(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=1),
+        dict(in_channels=16, out_channels=16, kernel_size=(3, 3), dilation=2),
+        dict(in_channels=16, out_channels=16, kernel_size=(3, 3), groups=16),
+        dict(in_channels=16, out_channels=16, kernel_size=(3, 3), groups=4),
+    ])
+    def test_non_default_conv(self, kwargs):
+        """
+        When: Run sequential MSE with conv2d with non-default arguments
+              (stride, padding, dilation, groups, ...)
+        Then: Shouldn't raise runtime error
+        """
+        model = torch.nn.Sequential(
+            torch.nn.Conv2d(**kwargs),
+        )
+        dummy_input = torch.randn(1, 16, 100, 100)
+        data_loader = (dummy_input,) * 2
+        sim = QuantizationSimModel(model, dummy_input, default_param_bw=4,
+                                   quant_scheme=QuantScheme.post_training_tf)
+        qconv = sim.model[0]
+        qconv.param_quantizers['weight'].min.copy_(-1)
+        qconv.param_quantizers['weight'].max.copy_(1)
+        sim.compute_encodings(lambda m: m(dummy_input))
+
+        params = SeqMseParams(num_batches=2, inp_symmetry='asym', loss_fn='mse')
+        apply_seq_mse(model, sim, data_loader, params)
+
+        # sanity check
+        assert qconv.param_quantizers['weight'].min != -1
+        assert qconv.param_quantizers['weight'].max != 1