Stabilize CTCLoss by initializing log_beta values to -inf, fixing comparisons, zeroing padding gradients #21392
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Summary: as discussed at pytorch#21244, we found some values in log_beta are not properly initialized. This diff will 1) initialize all log_beta to -inf; 2) fix a tricky compare condition; 3) zero all the gradient elements corresponding to padding to zero. Offline experiments show that this diff can fix previous seen NaN loss. Differential Revision: D15637977 fbshipit-source-id: 8bc35098aade6aa2035f71f499250883f09b0f25
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yqwangustc
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Jun 5, 2019
t-vi
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Jun 5, 2019
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Ideally, we'd add a test adding NaNs in the padded part. Other than that, I'm super-thrilled!
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Summary: Pull Request resolved: pytorch/pytorch#21392 as discussed at pytorch/pytorch#21244, we found some values in log_beta are not properly initialized. This diff will 1) initialize all log_beta to -inf; 2) fix a tricky compare condition; 3) zero all the gradient elements corresponding to padding to zero. Offline experiments show that this diff can fix previous seen NaN loss. Differential Revision: D15637977 fbshipit-source-id: 477008a5e11aae946bd2aa401ab7e0c513421af0
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This pull request has been merged in b460a19. |
This was referenced Jun 7, 2019
This was referenced Jun 12, 2019
soumith
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Summary:
as discussed at #21244, we
found some values in log_beta are not properly initialized. This diff will 1)
initialize all log_beta to -inf; 2) fix a tricky compare condition; 3) zero all
the gradient elements corresponding to padding to zero.
Offline experiments show that this diff can fix previous seen NaN loss.
Differential Revision: D15637977