Fix ops.tile shape inference issue on TensorFlow backend

[ssam18]

Summary

Fixes issue #20914 where ops.tile returns all-None shapes when called inside a Layer's call method with concrete integer repeats on the TensorFlow backend.

Problem

When ops.tile was called inside a Layer with concrete integer repeats like [1, 2, 1, 1], the TensorFlow backend was converting those repeats to a tensor, which prevented TensorFlow's shape inference from properly determining the output shape. This resulted in all dimensions being None.

For example:

class TileLayer(layers.Layer):
    def call(self, x):
        return ops.tile(x, [1, 2, 1, 1])

x = Input(shape=(3, 2, 2))
result = TileLayer()(x)
# Before fix: result.shape = (None, None, None, None)
# After fix:  result.shape = (None, 6, 2, 2)

Solution

1. TensorFlow Backend: Modified tile() to detect when repeats contains only concrete integer values and pass them directly to tf.tile as a Python list instead of converting to a tensor. This allows TensorFlow's shape inference to work correctly.

2. Symbolic Ops: Enhanced Tile.compute_output_spec() to handle symbolic repeat values more gracefully by checking if each repeat is a concrete integer before attempting multiplication.

3. Tests: Added regression tests to verify shape inference works correctly both in direct ops.tile calls and when used inside Layer.call().

Testing

• Added test case in NumpyOneInputOpsDynamicShapeTest.test_tile for multi-dimensional inputs
• Added new test test_tile_shape_inference_in_layer specifically for the Layer use case
• Verified both TensorFlow and JAX backends work correctly
• All existing tests continue to pass

Fixes #20914

[gemini-code-assist]

Summary of Changes

Hello @ssam18, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request resolves a critical shape inference bug in ops.tile when operating on the TensorFlow backend, particularly within Keras Layers. Previously, using concrete integer repeat values would lead to undefined output shapes. The changes ensure that tf.tile receives concrete repeat values in a format it can use for accurate shape inference, and the symbolic shape computation logic is also improved to handle these cases correctly.

Highlights

• TensorFlow ops.tile Shape Inference Fix: Addresses an issue where ops.tile on the TensorFlow backend would produce all-None output shapes when used within a Keras Layer with concrete integer repeats.
• Concrete Repeats Handling in TensorFlow Backend: The TensorFlow backend's tile() implementation now detects concrete integer repeats and passes them as a Python list to tf.tile, enabling correct shape inference.
• Improved Symbolic Shape Computation: Enhanced Tile.compute_output_spec() to correctly infer output shapes by explicitly checking if each repeat value is a concrete integer, even when dealing with symbolic inputs.
• New Regression Tests: New test cases have been added to validate the fix for multi-dimensional inputs and specifically for ops.tile usage within Keras Layers, ensuring proper shape inference.

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[gemini-code-assist]

Code Review

This pull request effectively addresses the shape inference issue with ops.tile on the TensorFlow backend, particularly when used within a Layer. The approach of detecting concrete integer repeats and passing them as a Python list to tf.tile is a solid solution that correctly enables static shape inference. The corresponding adjustments in Tile.compute_output_spec to handle symbolic repeats more gracefully are also well-implemented. The inclusion of targeted regression tests is a great addition that ensures the fix is robust and prevents future regressions.

My only suggestion is to refine the exception handling in the TensorFlow backend tile function to be more specific, which will improve maintainability and debugging.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 75.58140% with 21 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.56%. Comparing base (edbf8f5) to head (61e4526).
⚠️ Report is 1 commits behind head on master.

Files with missing lines	Patch %	Lines
keras/src/ops/numpy.py	0.00%	5 Missing and 2 partials ⚠️
keras/src/backend/tensorflow/numpy.py	77.27%	3 Missing and 2 partials ⚠️
keras/api/_tf_keras/keras/initializers/__init__.py	0.00%	3 Missing ⚠️
keras/api/_tf_keras/keras/applications/__init__.py	0.00%	2 Missing ⚠️
...pi/_tf_keras/keras/applications/resnet/__init__.py	0.00%	1 Missing ⚠️
..._tf_keras/keras/applications/resnet_v2/__init__.py	0.00%	1 Missing ⚠️
keras/api/_tf_keras/keras/ops/__init__.py	0.00%	1 Missing ⚠️
keras/api/_tf_keras/keras/ops/numpy/__init__.py	0.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #21860      +/-   ##
==========================================
+ Coverage   82.47%   82.56%   +0.08%     
==========================================
  Files         577      577              
  Lines       59508    59597      +89     
  Branches     9332     9355      +23     
==========================================
+ Hits        49080    49206     +126     
+ Misses       8015     7982      -33     
+ Partials     2413     2409       -4     

Flag	Coverage Δ
keras	82.37% <75.58%> (+0.08%)	⬆️
keras-jax	62.83% <63.95%> (-0.07%)	⬇️
keras-numpy	57.49% <63.95%> (-0.07%)	⬇️
keras-openvino	34.31% <55.81%> (-0.04%)	⬇️
keras-tensorflow	64.39% <75.58%> (+0.26%)	⬆️
keras-torch	63.53% <63.95%> (-0.08%)	⬇️

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