Pctablet505 fix map coordinates #21473
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Corrected indentation in doc string
Fixed issue with passing a single image without batch dimension.
…scale.py Co-authored-by: Jyotinder Singh <[email protected]>
Test case for unbatched inputs
Testcase for checking both unbatched and batched single image inputs.
There was a bug, and it was causing cycle in graph.
removed the use of tree.map_structure
…s-team#21254)" (keras-team#21329) This reverts commit 81821e0.
Enhanced the _can_use_flash_attention function to provide more detailed error messages when flash attention compatibility checks fail. Changes: - Replace generic exception catching with specific error propagation - When raise_error=True, directly re-raise original exceptions from check_layout() and check_is_flash_attention() functions - Preserve detailed error context from JAX internal validation functions - Maintain existing behavior when raise_error=False (returns False) This improves debugging experience by surfacing specific technical details about tensor layout incompatibilities, cuDNN version requirements, and other flash attention compatibility issues. Relates to keras-hub PR keras-team#2257 and addresses flash attention debugging needs.
… debugging" This reverts commit 7a0c547.
…sh_attention` Changes: - Add missing q_offsets=None and kv_offsets=None parameters to check_layout() call to match updated JAX function signature - Replace bare `except:` with `except Exception as e:` and `raise e` to preserve detailed error messages from JAX validation functions - Maintain existing fallback behavior when raise_error=False This resolves compatibility issues with newer JAX versions and improves debugging experience by surfacing specific technical details about flash attention compatibility failures.
Simplified the check for `flasth_attention` by removing redundant checks that are already done in `_can_use_flash_attention`.
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Summary of Changes
Hello @pctablet505, 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 addresses a reported issue related to map coordinates by implementing robust type casting within the image processing backend. The primary goal is to ensure data type consistency throughout tensor operations, specifically within the process_coordinates function, to prevent runtime errors and improve numerical stability. The changes involve strategic placement of tf.cast operations to align tensor dtypes before further computations.
Highlights
- Data Type Consistency: Introduced explicit type casting for the
gatheredtensor toweights[0].dtypeearly in theprocess_coordinatesfunction (line 710) to prevent potential type mismatches during subsequent operations. - Fill Value Handling: Ensured that the
fill_valueis also explicitly cast to the correct computationdtype(weights[0].dtype) at line 714 before being used in thetf.whereconditional assignment, maintaining type consistency. - Code Simplification: Streamlined the calculation of the contribution added to
outputs(line 717) by directly using the already castgatheredtensor, removing an intermediatecontributionvariable and a redundant cast.
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## master #21473 +/- ##
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- Coverage 82.80% 77.19% -5.62%
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Files 565 565
Lines 55505 55508 +3
Branches 8662 8662
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- Hits 45962 42849 -3113
- Misses 7429 10599 +3170
+ Partials 2114 2060 -54
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fixes #21420
we need to do cast before we do further processing.