Suport keras.op.view() to view the same data bitwise at a new dtype

[wenyi-guo]

Support view() on backeds numpy, jax, tensorflow, pytorch.

[gemini-code-assist]

Summary of Changes

Hello @wenyi-guo, 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 integrates a view() operation into keras.ops, allowing users to reinterpret the underlying data of a tensor as a new data type without altering its bitwise content. This feature is crucial for advanced data manipulation and interoperability, providing a consistent API across various backends like NumPy, JAX, TensorFlow, and PyTorch, albeit with some backend-specific limitations for the type argument.

Highlights

• New keras.ops.view() function: Introduces a new operation to create a bitwise view of tensor data with a different dtype without copying the underlying data.
• Multi-backend support: The view() function is implemented for NumPy, JAX, TensorFlow, and PyTorch backends, providing consistent functionality across the Keras ecosystem.
• type argument limitation: The type argument for view() is currently only fully supported by the NumPy backend; TensorFlow and PyTorch backends will raise a NotImplementedError if this argument is used.

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[gemini-code-assist]

Code Review

This pull request adds support for keras.op.view across multiple backends. The implementation is mostly correct, but there are a few issues to address. The most critical one is an incorrect shape inference logic in the symbolic View operation, which fails to account for shape changes when the data type's item size changes. This could lead to errors in model construction. I've also pointed out that the test suite for this new operation is not comprehensive enough, as it misses this important shape-changing scenario. Finally, there's a minor simplification possible in the NumPy backend implementation. Addressing these points will make the new feature more robust and maintainable.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 82.08955% with 12 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.63%. Comparing base (47fcb39) to head (28c460f).
⚠️ Report is 9 commits behind head on master.

Files with missing lines	Patch %	Lines
keras/src/ops/numpy.py	76.92%	3 Missing and 3 partials ⚠️
keras/src/backend/tensorflow/numpy.py	84.61%	2 Missing and 2 partials ⚠️
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   #21763      +/-   ##
==========================================
- Coverage   82.69%   82.63%   -0.06%     
==========================================
  Files         573      577       +4     
  Lines       58888    59316     +428     
  Branches     9218     9300      +82     
==========================================
+ Hits        48696    49018     +322     
- Misses       7845     7910      +65     
- Partials     2347     2388      +41     

Flag	Coverage Δ
keras	82.46% <82.08%> (-0.04%)	⬇️
keras-jax	63.32% <41.79%> (+0.08%)	⬆️
keras-numpy	57.56% <41.79%> (-0.17%)	⬇️
keras-openvino	34.29% <16.41%> (-0.11%)	⬇️
keras-tensorflow	64.11% <70.14%> (+0.09%)	⬆️
keras-torch	63.62% <43.28%> (+0.06%)	⬆️

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[hertschuh]

Thanks for the PR!

A few things for consistency with other ops.

[hertschuh]

Last nitpick about the unit test.

[hertschuh]

I missed a couple small things earlier.