Updated the DataSpec for the timing abstraction (#146) parts 3 and 4
#178
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For the timing abstraction (#146), the `DataloaderSpec` needed two addition functions -- `get_num_samples_in_batch` and `get_num_tokens_in_batch`. It was getting messy to pass around function pointers in a named tuple, so instead converted `DataloaderSpec` from a NamedTuple into a regular class called `DataSpec`. Custom datasets can inherit the base `DataSpec` class and override functionality as needed. Moved the `DataSpec` class to `composer.core`, as the `DataSpec` is now bound directly to the state. #120 will also need this change. Renamed `train_dataloader` and `eval_dataloader` in the trainer and state to `train_data` and `eval_data`, since it encompasses more than the dataloader. This PR implements part 3 and 4 of the timing abstraction (#146). The implementation differs from the GH issue by adding `num_tokens`, `num_samples`, `get_batch_size`, and `get_num_tokens` to the new `DataSpec` rather than the pytorch dataset class.
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hanlint
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hanlint
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Jan 6, 2022
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As discussed offline, we will leave dataspec for now and leave it attached to the state. A later PR will remove it. For the time being, however, dataspecs can be initialized using a dictionary which serves as kwargs for the class constructor. |
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Feb 23, 2022
…s 3 and 4 (mosaicml#178) For the timing abstraction (mosaicml#146), the DataloaderSpec needed two addition functions -- get_num_samples_in_batch and get_num_tokens_in_batch. Moved the DataSpec class to composer.core, as the DataSpec is now bound directly to the state. mosaicml#120 will also need this change. This PR implements part 3 and 4 of the timing abstraction (mosaicml#146). The implementation differs from the GH issue by adding num_tokens, num_samples, get_batch_size, and get_num_tokens to the new DataSpec rather than the pytorch dataset class.
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For the timing abstraction (#146), the
DataloaderSpecneeded two addition functions --get_num_samples_in_batchandget_num_tokens_in_batch.Moved the
DataSpecclass tocomposer.core, as theDataSpecis now bound directly to the state. #120 will also need this change.This PR implements part 3 and 4 of the timing abstraction (#146). The implementation differs from the GH issue by adding
num_tokens,num_samples,get_batch_size, andget_num_tokensto the newDataSpecrather than the pytorch dataset class.