Fix global_step when gradient accumulation > 1 #832
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Borda
reviewed
Feb 14, 2020
| @@ -426,7 +426,9 @@ def run_training_epoch(self): | |||
| # logs user requested information to logger | |||
| self.log_metrics(batch_step_metrics, grad_norm_dic) | |||
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| self.global_step += 1 | |||
| # progress global step according to grads progress | |||
| if (self.batch_idx + 1) % self.accumulate_grad_batches == 0: | |||
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can we clarify what is the meaning of these two variables?
could you pls add comment to the trainer init starting with #: so it is generated also in the documentation...
Thx
hanbyul-kim
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Feb 16, 2020
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What does this PR do?
Fix
global_stepupdate when gradient accumulation is > 1.global_stepwill be updated only after all parts of the batch are done.Fixes #831
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