fix(deps): bump keras from 3.14.0 to 3.15.0

[dependabot]

Bumps keras from 3.14.0 to 3.15.0.

Release notes

Sourced from keras's releases.

v3.15.0

Highlights

• Keras-to-Torch Export: New export_torch enables exporting Keras models to native PyTorch nn.Module format, along with LiteRT (TFLite) export support for the PyTorch backend.
• Sliding Window Attention: Added sliding_window parameter to MultiHeadAttention and GroupedQueryAttention for efficient long-context attention.
• Flash / Fused SDPA: Causal-only MHA/GQA now automatically dispatches to Flash Attention (cuDNN SDPA), and the manual attention path correctly applies causal masking.
• Multi-Optimizer Training: New MultiOptimizer supports assigning different optimizers to sub-networks.
• New Math Operations: Added unique, pinv, matrix_rank, fabs, fmax, fmin, erfc, dsplit, percentile, nanpercentile, sobel_edges, and ssim (structural similarity) to keras.ops.
• Security Hardening: Comprehensive hardening of model reloading against HDF5 exploits, tar/zip traversal attacks, insecure deserialization.

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New Features and Operations

Multi-Backend Operations

• New NumPy Operations: Added unique, fabs, fmax, fmin, dsplit, erfc, percentile, nanpercentile in keras.ops.numpy.
• New Linear Algebra Operations: Added pinv (pseudo-inverse) and matrix_rank in keras.ops.linalg.
• New Image Operations: Added sobel_edges for edge detection and ssim (structural similarity) in keras.ops.image.
• Negative Axes in Transpose: keras.ops.transpose now supports negative axis values.

Layers and Attention

• Sliding Window Attention: MultiHeadAttention and GroupedQueryAttention layers support the sliding_window parameter for efficient long-sequence processing.
• Flash Attention Engagement: Causal-only attention in MHA/GQA now uses Flash SDPA for significant speedups.
• Fused Bidirectional LSTM/GRU: JAX backend now fuses Bidirectional LSTM into a single cuDNN call; fused bidirectional GRU added for Torch backend.
• CTC Beam Search Decoder: Added CTC beam search decoding for the Torch backend.

Training and Optimizers

• MultiOptimizer: Supports training sub-networks with different optimizers.
• SKLearn Classifier: Added predict_proba method to SKLearnClassifier.

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Export and Deployment

• Keras-to-Torch Export: Export Keras models to native PyTorch nn.Module via model.export(..., format="torch").
• LiteRT (TFLite) Export for PyTorch: Added LiteRT export support for models using the PyTorch backend.
• LiteRT Compatibility Fix: Fixed LiteRT export for Keras 3 + TF 2.20 + Python 3.13.
• ONNX Export: Support for dict/list inputs in Torch ONNX export; documented static input signature requirement for LiteRT PyTorch export.

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Distribution and Parallelism

• ModelParallel Improvements: Defined contiguous replica-group data shard ID convention; added distribution information (num_processes, num_model_replicas, data_shard_id).
• Initializer Distribution Layout: Initializers can now handle the distribution layout directly with JAX.
• TF Dataset Distribution: Refactored TF dataset distribution with centralized sharding routing; fixed data distribution for model training in JAX.

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OpenVINO Backend Support

... (truncated)

Commits

• 9d1bbf9 Add erfinv tests and TensorFlow CPU fallback for half precision (#23086)
• b34af30 Fix pad_sequences string dtype check (np.bytes_ instead of duplicate np.str_)...
• f512e93 Fix wrong parameter names in ops.select and ops.argpartition docstrings (#23091)
• 915ec7e Support native Grouped-Query Attention (GQA) Key/Value head broadcast. (#23081)
• 49d668d Add JAX multi-process distribution tests (#23105)
• b49fde7 Refactor rematerialization logic into Operation and support more modes (#23107)
• e0bfa2d Add to the list of APIs that should not be part of a reloaded model. (#23115)
• 69d7fd6 Suppress spurious 'Skipping nested container' warning on freshly-saved files ...
• 048d142 Delete temp files after loading model from remote path. (#23113)
• 46f5eac Bump GitHub Actions versions to the latest (#23104)
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[github-actions]

Workflow run and artifacts

Performance Benchmarks

Compared 13 shared benchmarks with a regression threshold of 15%.
Status: 0 regressions, 0 improved, 13 stable, 0 new, 0 missing.
Aggregate shared-benchmark median: 4.243s -> 4.238s (-0.1%).

Workload	Benchmark	Target	Size	Files	Baseline	Current	Change	Status
nested-payload-review	tests/benchmarks/test_picklescan_benchmarks.py::test_picklescan_nested_payload_review[nested_base64]	nested_base64	98 B	1	253.9us	262.8us	+3.5%	stable
nested-payload-review	tests/benchmarks/test_picklescan_benchmarks.py::test_picklescan_nested_payload_review[nested_raw]	nested_raw	78 B	1	233.5us	239.9us	+2.7%	stable
direct-malicious-upload	tests/benchmarks/test_picklescan_benchmarks.py::test_picklescan_direct_malicious_upload	malicious_reduce	52 B	1	184.6us	188.8us	+2.2%	stable
mixed-model-repository	tests/benchmarks/test_scan_benchmarks.py::test_scan_release_candidate_repository	release-candidate	547.3 KiB	32	628.94ms	638.89ms	+1.6%	stable
padded-multi-stream-upload	tests/benchmarks/test_picklescan_benchmarks.py::test_picklescan_padded_multi_stream_upload	multi_stream_padded	4.1 KiB	1	296.7us	298.7us	+0.7%	stable
rejected-basic-auth-candidates	tests/benchmarks/test_scan_benchmarks.py::test_rejected_basic_auth_candidates_scan_linearly	-	371.1 KiB	1	2.401s	2.386s	-0.6%	stable
single-checkpoint-preflight	tests/benchmarks/test_scan_benchmarks.py::test_scan_single_checkpoint_before_load	single_checkpoint.pkl	183.0 KiB	1	107.10ms	106.50ms	-0.6%	stable
nested-payload-review	tests/benchmarks/test_picklescan_benchmarks.py::test_picklescan_nested_payload_review[nested_hex]	nested_hex	130 B	1	260.0us	261.3us	+0.5%	stable
chunked-upload-stream	tests/benchmarks/test_picklescan_benchmarks.py::test_picklescan_chunked_upload_stream	chunked_stream	278.2 KiB	1	117.17ms	117.71ms	+0.5%	stable
clean-training-checkpoint	tests/benchmarks/test_picklescan_benchmarks.py::test_picklescan_clean_training_checkpoint	safe_large	278.2 KiB	1	114.58ms	114.21ms	-0.3%	stable
suspicious-pickle-intake	tests/benchmarks/test_scan_benchmarks.py::test_scan_suspicious_pickle_intake	suspicious-intake	183.8 KiB	4	148.91ms	148.75ms	-0.1%	stable
duplicate-heavy-registry	tests/benchmarks/test_scan_benchmarks.py::test_scan_duplicate_registry_snapshot	registry-snapshot	915.2 KiB	13	584.64ms	585.08ms	+0.1%	stable
warm-cache-rescan	tests/benchmarks/test_scan_benchmarks.py::test_scan_warm_cached_repository_rescan	release-candidate	547.3 KiB	32	139.81ms	139.87ms	+0.0%	stable