Better support for resuming training #8878
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LysandreJik
approved these changes
Dec 1, 2020
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| logger.info(" Num examples = %d", num_examples) | ||
| logger.info(" Num Epochs = %d", num_train_epochs) | ||
| logger.info(" Instantaneous batch size per device = %d", self.args.per_device_train_batch_size) | ||
| logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", total_train_batch_size) | ||
| logger.info(" Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps) | ||
| logger.info(" Total optimization steps = %d", max_steps) | ||
| logger.info(f" Num examples = {num_examples}") | ||
| logger.info(f" Num Epochs = {num_train_epochs}") | ||
| logger.info(f" Instantaneous batch size per device = {self.args.per_device_train_batch_size}") | ||
| logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}") | ||
| logger.info(f" Gradient Accumulation steps = {self.args.gradient_accumulation_steps}") | ||
| logger.info(f" Total optimization steps = {max_steps}") |
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What does this PR do?
This PR adds two things linked to resuming training:
It brings full reproducibility when resuming an interrupted training from a checkpoint (i.e., resuming a training from a checkpoint will give the exact same results as a training from the beginning with the same seeding). This was not currently the case because the dataloader shuffle was not triggered
epochs_already_trainedtimes, so the shuffle of the dataloader was the same as epoch 0. So the full reproducibility was only there for trainings resumed from an early checkpoint (during the first epoch).It also adds the option to ignore that data skipping which can take a very long time on a large dataset. This will go faster but yield different results from a training from scratch.
Fixes #8874 and #8876