Fix Torch output_padding constraint for ConvTranspose layers (#21852)

* Fix Conv3DTranspose padding conversion for torch backend

* Fix Conv3DTranspose padding conversion for torch backend

Author: MalyalaKarthik66

keras/src/backend/common/backend_utils.py

@@ -96,13 +96,13 @@ def _convert_conv_transpose_padding_args_from_keras_to_torch(
         )
 
     if torch_output_padding >= stride:
-        raise ValueError(
-            f"The padding arguments (padding={padding}) and "
-            f"output_padding={output_padding}) lead to a Torch "
-            f"output_padding ({torch_output_padding}) that is greater than "
-            f"strides ({stride}). This is not supported. You can change the "
-            f"padding arguments, kernel or stride, or run on another backend. "
+        warnings.warn(
+            f"Torch backend requires output_padding < stride. "
+            f"Clamping output_padding {torch_output_padding} -> {stride - 1} "
+            f"for stride {stride}.",
+            UserWarning,
         )
+        torch_output_padding = stride - 1
 
     return torch_padding, torch_output_padding
 
@@ -184,6 +184,22 @@ def compute_conv_transpose_padding_args_for_torch(
         torch_paddings.append(torch_padding)
         torch_output_paddings.append(torch_output_padding)
 
+    # --- FIX FOR TORCH CONSTRAINT: output_padding < stride ---
+    corrected_output_paddings = []
+    for s, op in zip(
+        strides
+        if isinstance(strides, (list, tuple))
+        else [strides] * num_spatial_dims,
+        torch_output_paddings,
+    ):
+        max_allowed = max(0, s - 1)
+        if op > max_allowed:
+            corrected_output_paddings.append(max_allowed)
+        else:
+            corrected_output_paddings.append(op)
+
+    torch_output_paddings = corrected_output_paddings
+
     return torch_paddings, torch_output_paddings
 
 

keras/src/backend/common/backend_utils_test.py

@@ -170,6 +170,25 @@ def test_valid_padding_with_output_padding(self):
         self.assertEqual(torch_padding, 0)
         self.assertEqual(torch_output_padding, 1)
 
+    def test_output_padding_clamped_for_torch_constraint(self):
+        """Test that output_padding is clamped
+        when >= stride (Torch constraint).
+        """
+        (
+            torch_paddings,
+            torch_output_paddings,
+        ) = compute_conv_transpose_padding_args_for_torch(
+            input_shape=(1, 8, 8, 8, 16),  # any shape
+            kernel_shape=(2, 2, 2, 16, 32),  # Keras kernel shape
+            strides=1,
+            padding="same",
+            output_padding=1,  # Keras wants this
+            dilation_rate=1,
+        )
+        # Torch expects output_padding < stride (1)
+        # so output_padding should be clamped to 0
+        self.assertEqual(torch_output_paddings, [0, 0, 0])
+
 
 class GetOutputShapeGivenTFPaddingTest(test_case.TestCase):
     def test_valid_padding_without_output_padding(self):

keras/src/layers/convolutional/conv_transpose_test.py

@@ -825,21 +825,30 @@ def test_conv1d_transpose_consistency(
 
         # Special cases for Torch
         if backend.backend() == "torch":
-            # The following set of arguments lead to Torch output padding to be
-            # greater than strides, which is not supported by Torch.
-            # An error is raised.
+            # Args that cause output_padding >= strides
+            # are clamped with a warning.
             if (kernel_size, strides, padding, output_padding) in [
                 (2, 1, "same", None),
                 (4, 1, "same", None),
             ]:
-                with pytest.raises(ValueError):
+                clamped_output_padding = strides - 1  # usually 0 when stride=1
+                expected_res = np_conv1d_transpose(
+                    x=input,
+                    kernel_weights=kernel_weights,
+                    bias_weights=np.zeros(shape=(1,)),
+                    strides=strides,
+                    padding=padding,
+                    output_padding=clamped_output_padding,
+                    data_format=backend.config.image_data_format(),
+                    dilation_rate=1,
+                )
+                with pytest.warns(UserWarning):
                     kc_res = kc_layer(input)
+                self.assertAllClose(expected_res, kc_res, atol=1e-5)
                 return
 
-            # When both torch_padding and torch_output_padding are greater
-            # than 0, Torch outputs are inconsistent with the ones from
-            # Tensorflow. A warning is raised, and we expect the results to be
-            # different.
+            # torch_padding > 0 and torch_output_padding > 0 case
+            # Torch output differs from TF.
             (
                 torch_padding,
                 torch_output_padding,