Add broadcast of strides and kernel_dilation to nn.ConvTranspose

flax/linen/linear.py

@@ -738,15 +738,16 @@ class ConvTranspose(Module):
       kernel size can be passed as an integer, which will be interpreted as a
       tuple of the single integer. For all other cases, it must be a sequence of
       integers.
-    strides: a sequence of `n` integers, representing the inter-window strides.
+    strides: an integer or a sequence of `n` integers, representing the
+      inter-window strides.
     padding: either the string `'SAME'`, the string `'VALID'`, the string
       `'CIRCULAR'` (periodic boundary conditions), or a sequence of `n` `(low,
       high)` integer pairs that give the padding to apply before and after each
       spatial dimension. A single int is interpreted as applying the same
       padding in all dims and assign a single int in a sequence causes the same
       padding to be used on both sides.
-    kernel_dilation: ``None``, or a sequence of ``n`` integers, giving the
-      dilation factor to apply in each spatial dimension of the convolution
+    kernel_dilation: ``None``, or an integer or a sequence of ``n`` integers,
+      giving the dilation factor to apply in each spatial dimension of the convolution
       kernel. Convolution with kernel dilation is also known as 'atrous
       convolution'.
     use_bias: whether to add a bias to the output (default: True).
@@ -804,6 +805,17 @@ def __call__(self, inputs: Array) -> Array:
     else:
       kernel_size = tuple(self.kernel_size)
 
+    def maybe_broadcast(
+      x: Optional[Union[int, Sequence[int]]],
+    ) -> Tuple[int, ...]:
+      if x is None:
+        # backward compatibility with using None as sentinel for
+        # broadcast 1
+        x = 1
+      if isinstance(x, int):
+        return (x,) * len(kernel_size)
+      return tuple(x)
+
     # Combine all input batch dimensions into a single leading batch axis.
     num_batch_dimensions = inputs.ndim - (len(kernel_size) + 1)
     if num_batch_dimensions != 1:
@@ -814,11 +826,8 @@ def __call__(self, inputs: Array) -> Array:
       ]
       inputs = jnp.reshape(inputs, flat_input_shape)
 
-    strides: Tuple[int, ...]
-    if self.strides is None:
-      strides = (1,) * (inputs.ndim - 2)
-    else:
-      strides = tuple(self.strides)
+    strides = maybe_broadcast(self.strides)
+    kernel_dilation = maybe_broadcast(self.kernel_dilation)
 
     in_features = jnp.shape(inputs)[-1]
     if self.transpose_kernel:
@@ -857,7 +866,7 @@ def __call__(self, inputs: Array) -> Array:
       kernel,
       strides,
       padding_lax,
-      rhs_dilation=self.kernel_dilation,
+      rhs_dilation=kernel_dilation,
       transpose_kernel=self.transpose_kernel,
       precision=self.precision,
     )

tests/linen/linen_linear_test.py

@@ -980,7 +980,7 @@ def test_transpose_kernel_conv_transpose(self, use_bias):
     conv_module = nn.ConvTranspose(
       features=4,
       use_bias=use_bias,
-      strides=(2, 2),
+      strides=2,
       kernel_size=(6, 6),
       padding='CIRCULAR',
       transpose_kernel=True,