datareader : sampling with moving weights #1174
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| next_name = self._next_dataset_name() | ||
| try: | ||
| batch = next(self._pipeline_iters[next_name]) | ||
| except StopIteration: |
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when would we expect this to occur? when the pipeline has epoched through all data?
| # If we read less than `num_accumulate` batches, it means we reached end | ||
| # of data. | ||
| if self._options.drop_remainder and len(batches) != num_accumulate: | ||
| batches.clear() |
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hm. a bit confused here. some clarifying questions:
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when would this condition be True? isn't that case handled by the above exception handling?
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dumb Q: what does
batches.clear()do? (just empty the batch List?) -
at a conceptual level, seems like this logic is used to determine if we should halt training once we reach end of data (or when any of the pipelines reaches end of data), if
self._options.drop_remainderis set to True (and if False, we just continue epoching the data). is this understanding correct?
| num_batches = _min_num_batches(local_num_batches, self._gang) | ||
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| if num_batches != local_num_batches: | ||
| batches = batches[:num_batches] |
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does this imply that batches is a global list of batches across all ranks? i thought this was a local list...
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this is the general fs2 logic to work with pipelines epoch's end : for some rank the data will be returned but others should skip their turn.
Probably for that scheduled mixture we dont need that since we're dealing with infinite loop here.
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| def _next_dataset_name(self) -> str: | ||
| weights = np.array( | ||
| [scheduler(self._step) for scheduler in self._weights_schedulers.values()], |
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i like the setup of making this callable. instead of having schedulers for each data pipeline, we could have one master scheduler function that rights the list of weights. but either works, i suppose this option you've implemented here gives us more flexibility.
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