Add num workers cli arg (#7322)
* Add dataloader_num_workers to TrainingArguments This argument is meant to be used to set the number of workers for the PyTorch DataLoader. * Pass num_workers argument on DataLoader init
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@@ -352,6 +352,7 @@ class Trainer:
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sampler=train_sampler,
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collate_fn=self.data_collator,
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drop_last=self.args.dataloader_drop_last,
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num_workers=self.args.dataloader_num_workers,
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)
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def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.sampler.Sampler]:
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@@ -391,6 +392,7 @@ class Trainer:
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batch_size=self.args.eval_batch_size,
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collate_fn=self.data_collator,
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drop_last=self.args.dataloader_drop_last,
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num_workers=self.args.dataloader_num_workers,
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)
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def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
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@@ -122,6 +122,8 @@ class TrainingArguments:
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eval_steps (:obj:`int`, `optional`):
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Number of update steps between two evaluations if :obj:`evaluation_strategy="steps"`. Will default to the
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same value as :obj:`logging_steps` if not set.
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dataloader_num_workers (:obj:`int`, `optional`, defaults to 0):
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Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.
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past_index (:obj:`int`, `optional`, defaults to -1):
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Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can
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make use of the past hidden states for their predictions. If this argument is set to a positive int, the
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@@ -259,6 +261,10 @@ class TrainingArguments:
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default=False, metadata={"help": "Drop the last incomplete batch if it is not divisible by the batch size."}
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)
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eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
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dataloader_num_workers: int = field(
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default=0,
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metadata={"help": "Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process."}
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)
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past_index: int = field(
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default=-1,
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