fix: use pads arg for torch evaluation

as we weren't supporting padding, we neglected to use them in the plain
evaluation, but this result in a confusing error message for the user
when trying to use padding. This fixes the problem by using padding
properly during evaluation, and leaves the error up to the compiler.

concrete/onnx/convolution.py

@@ -618,6 +618,17 @@ def _evaluate_conv(
     )
     torch_conv_func = cast(Callable, torch_conv_func)
 
+    n_dim = x.ndim - 2  # remove batch_size and channel dims
+    torch_padding = []
+    for dim in range(n_dim):
+        if pads[dim] != pads[n_dim + dim]:
+            raise ValueError(
+                f"padding should be the same for the beginning of the dimension and its end, but "
+                f"got {pads[dim]} in the beginning, and {pads[n_dim + dim]} at the end for "
+                f"dimension {dim}"
+            )
+        torch_padding.append(pads[dim])
+
     dtype = (
         torch.float64
         if np.issubdtype(x.dtype, np.floating)
@@ -630,6 +641,7 @@ def _evaluate_conv(
         torch.tensor(weight, dtype=dtype),
         torch.tensor(bias, dtype=dtype),
         stride=strides,
+        padding=torch_padding,
         dilation=dilations,
         groups=group,
     ).numpy()

tests/execution/test_convolution.py

@@ -92,6 +92,20 @@ def test_conv2d(input_shape, weight_shape, group, strides, dilations, has_bias,
             ValueError,
             "auto_pad should be in {'NOTSET'}, but got 'VALID'",
         ),
+        pytest.param(
+            (1, 1, 1, 4),
+            (1, 1, 2, 2),
+            (1,),
+            (1, 0, 2, 0),
+            (1, 1),
+            (1, 1),
+            None,
+            1,
+            "NOTSET",
+            RuntimeError,
+            "padding should be the same for the beginning of the dimension and its end, but got "
+            "1 in the beginning, and 2 at the end for dimension 0",
+        ),
         pytest.param(
             (1, 1, 4),
             (1, 1, 2),
@@ -398,7 +412,14 @@ def test_bad_conv_compilation(
     with pytest.raises(expected_error) as excinfo:
         function.compile(inputset, configuration)
 
-    assert str(excinfo.value) == expected_message
+    # Get the root cause error
+    current_error = excinfo.value
+    cause = current_error.__cause__
+    while cause:
+        current_error = cause
+        cause = current_error.__cause__
+
+    assert str(current_error) == expected_message
 
 
 @pytest.mark.parametrize(