Fix stateful RNN to raise ValueError on batch size mismatch

[El3ssar]

This PR fixes a silent failure when calling stateful RNN layers (SimpleRNN, GRU, LSTM) with an input that doesn't match the fixed batch size.

Previously:

• SimpleRNN would silently broadcast the input across all internal states.
• GRU and LSTM would crash in CuDNN but still mutate internal state before failing.

Now:

• A ValueError is raised early in RNN.call() if the batch size of the input doesn't match the expected value from the internal state.
• This avoids state corruption and aligns the behavior across all RNN variants.

Fixes #21183

Manually tested by calling a stateful model with incorrect batch sizes and verifying that the new ValueError is raised as expected.

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Codecov Report

Attention: Patch coverage is 0% with 5 lines in your changes missing coverage. Please review.

Project coverage is 34.56%. Comparing base (f5171b3) to head (3180ed4). Report is 124 commits behind head on master.

Files with missing lines	Patch %	Lines
keras/src/layers/rnn/rnn.py	0.00%	5 Missing :warning:

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Flag	BASE (f5171b3)	HEAD (3180ed4)
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keras-numpy	1	0
keras-torch	1	0
keras-tensorflow	1	0
keras-jax	1	0

Additional details and impacted files

@@             Coverage Diff             @@
##           master   #21249       +/-   ##
===========================================
- Coverage   82.60%   34.56%   -48.04%     
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  Files         564      567        +3     
  Lines       54543    56219     +1676     
  Branches     8472     8788      +316     
===========================================
- Hits        45054    19431    -25623     
- Misses       7402    35910    +28508     
+ Partials     2087      878     -1209     

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keras	34.56% <0.00%> (-47.86%)	:arrow_down:
keras-jax	?
keras-numpy	?
keras-openvino	34.56% <0.00%> (+1.56%)	:arrow_up:
keras-tensorflow	?
keras-torch	?

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[keerthanakadiri]

Hi @El3ssar, Can you please sign the CLA? Thanks !

[El3ssar]

Thanks for the PR! Please add a simple test case for the fix (using self.assertRaisesRegex)

Gladly, where do I put the test?

[fchollet]

You can put the test in keras/src/layers/rnn/rnn_test.py alongside test_statefulness_two_states. You can target layer = layers.RNN(TwoStatesRNNCell(2), stateful=True) as the RNN layer.

[El3ssar]

@fchollet I've added the requested test in rnn_test.py using TwoStatesRNNCell. It fails without the fix and passes with it.

[github-actions[bot]]

This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.

[github-actions[bot]]

This PR was closed because it has been inactive for 28 days. Please reopen if you'd like to work on this further.