Remove batch truncation for fractional batch sizes. #2363
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…robatches instead.
chaserogo
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aireenmei
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Oct 2, 2025
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| sharded_iter = self._base_iter() | ||
| if self.microbatch_size_to_run: | ||
| self.local_iterator = itertools.chain.from_iterable( |
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This can be simplified:
def microbatch_generator():
for global_batch in sharded_iter:
yield from self.explode_to_micro(global_batch)
if self.microbatch_size_to_run:
self.local_iterator = microbatch_generator()
else:
self.local_iterator = sharded_iter
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Description
When running with a
per_device_batch_size < 1, rather than splitting larger batches from the data loader into the smaller batch sizes for training, the larger batch is truncated. This PR changes it so that instead the larger batch is split into smaller batches which are looped over instead, so that no data is discarded.Tests
Please describe how you tested this change, and include any instructions and/or
commands to reproduce.
Checklist
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