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Fix issue with concatenate, masking and symbolic inputs #21611

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Merged
merged 1 commit into from
Sep 7, 2025
Merged

Fix issue with concatenate, masking and symbolic inputs #21611

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11 changes: 6 additions & 5 deletions keras/src/layers/merging/concatenate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -145,12 +145,13 @@ def compute_mask(self, inputs, mask=None):
# Input is unmasked. Append all 1s to masks,
masks.append(ops.ones_like(input_i, dtype="bool"))
elif mask_i.ndim < input_i.ndim:
# Mask is smaller than the input, expand it
masks.append(
ops.broadcast_to(
ops.expand_dims(mask_i, axis=-1), ops.shape(input_i)
)
# Broadcast mask shape to match in a way where we capture the
# input as a symbolic input in the op graph.
mask_i = ops.logical_or(
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So you're doing an ops.logical_or just to do the broadcast.

Do you understand why this works and ops.broadcast_to doesn't?

In other words, should we try to fix ops.broadcast_to? Is it a bug in tf.broadcast_to?

ops.expand_dims(mask_i, axis=-1),
ops.zeros_like(input_i, dtype="bool"),
)
masks.append(mask_i)
else:
masks.append(mask_i)
concatenated = ops.concatenate(masks, axis=self.axis)
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12 changes: 12 additions & 0 deletions keras/src/layers/merging/merging_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -340,6 +340,18 @@ def test_concatenate_with_mask(self):
)
self.assertAllClose(output._keras_mask, [[1, 1, 1, 1]])

def test_concatenate_with_mask_symbolic(self):
input1 = layers.Input((4, 2))
input2 = layers.Input((4, 2))
mask = layers.Masking()
output = layers.Concatenate(axis=1)([mask(input1), input2])
model = models.Model(
inputs=[input1, input2], outputs=output._keras_mask
)
x1 = backend.convert_to_tensor([[[0, 0], [1, 2], [0, 0], [3, 4]]])
x2 = backend.convert_to_tensor([[[0, 0], [0, 0], [1, 2], [3, 4]]])
self.assertAllClose(model([x1, x2]), [[0, 1, 0, 1, 1, 1, 1, 1]])

def test_concatenate_errors(self):
# This should work
x1 = np.ones((1, 1, 1, 1, 5))
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