configure random seed dtype based on backend #19928
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## master #19928 +/- ##
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- Coverage 79.01% 78.97% -0.04%
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Files 499 499
Lines 46441 46506 +65
Branches 8550 8561 +11
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+ Hits 36694 36730 +36
- Misses 8020 8044 +24
- Partials 1727 1732 +5
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fchollet
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Jun 27, 2024
| @@ -14,15 +14,15 @@ def tf_draw_seed(seed): | |||
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| def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None): | |||
| dtype = dtype or floatx() | |||
| seed = tf_draw_seed(seed) | |||
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You can remove the tf_draw_seed function then
keras/src/backend/jax/core.py
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| @@ -346,6 +346,11 @@ def unstack(x, num=None, axis=0): | |||
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| def random_seed_dtype(): | |||
| # jax random seed uses uint32. | |||
| return standardize_dtype("uint32") | |||
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standardize_dtype will be a no-op here. You can skip it.
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It seems this breaks TF GPU CI due to a strange issue -- TF automatically places |
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seeing below error on running tensorflow distributed training with multiple workers. the issue being seed state getting broadcasted is in a dtype that tensorflow doesn't support:
this pr introduces a seed dtype function to customize for an ideal seed dtype for backends.