Add VectorizedMap op class.
#21516
Add VectorizedMap op class.
#21516
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Also: - fix `Map.compute_output_spec` so that it handles nested inputs - test `map` op with nested inputs - added missing `Deg2Rad.compute_output_spec` - added test verifying that all ops implement `compute_output_spec`.
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Summary of Changes
Hello @hertschuh, 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 primarily introduces a new VectorizedMap operation to the Keras ops library, facilitating efficient parallel execution of functions on symbolic tensors. Alongside this, it refines existing operations by improving their compute_output_spec methods to better support nested data structures and ensures that all operations adhere to the contract of providing a proper output specification for symbolic tracing.
Highlights
- New Operation: Introduced
VectorizedMapas a newOperationsubclass, enabling symbolic execution for parallel mapping of functions over elements, similar totf.vectorized_map. - Improved
MapOp: Enhanced theMapoperation'scompute_output_specto correctly handle nested input structures, ensuring proper shape inference for complex data flows. Deg2RadOutput Spec: Added the missingcompute_output_specimplementation for theDeg2radoperation, improving its symbolic execution capabilities and ensuring correct output shape and dtype inference.- Comprehensive
compute_output_specTesting: Implemented a new test to verify that all Keras operations correctly override and implement theircompute_output_specmethod, promoting robustness in symbolic graph construction.
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Code Review
This pull request introduces the VectorizedMap operation, fixes an issue with Map.compute_output_spec for nested inputs, and improves test coverage. There is an opportunity to improve maintainability by reducing code duplication between the Map and VectorizedMap operations.
| def compute_output_spec(self, elements): | ||
| x = tree.map_structure(lambda t: t[0], elements) | ||
| n = tree.flatten(elements)[0].shape[0] | ||
| y = backend.compute_output_spec(self.function, x) | ||
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| def append_batch_axis(t): | ||
| return KerasTensor( | ||
| shape=(n,) + t.shape, | ||
| dtype=t.dtype, | ||
| sparse=t.sparse, | ||
| ragged=t.ragged, | ||
| ) | ||
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| y = tree.map_structure(append_batch_axis, y) | ||
| return y |
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The logic in this compute_output_spec method is identical to the one in the Map operation. To improve maintainability and reduce code duplication, you can reuse the implementation from Map.
def compute_output_spec(self, elements):
# Reuse the implementation from `Map` to avoid code duplication.
return Map().compute_output_spec(self.function, elements)
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #21516 +/- ##
=======================================
Coverage 82.72% 82.72%
=======================================
Files 567 567
Lines 56214 56245 +31
Branches 8786 8790 +4
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+ Hits 46501 46527 +26
- Misses 7556 7561 +5
Partials 2157 2157
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google-ml-butler
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kokoro:force-run
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It doesn't matter, either way is fine. |
Also:
Map.compute_output_specso that it handles nested inputsmapop with nested inputsDeg2Rad.compute_output_speccompute_output_spec.