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per_device instead of per_gpu/error thrown when argument unknown (#4618)

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* per_device instead of per_gpu/error thrown when argument unknown

* [docs] Restore examples.md symlink

* Correct absolute links so that symlink to the doc works correctly

* Update src/transformers/hf_argparser.py

Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Warning + reorder

* Docs

* Style

* not for squad

Co-authored-by: Julien Chaumond <chaumond@gmail.com>
This commit is contained in:
Lysandre Debut
2020-05-27 11:36:55 -04:00
committed by GitHub
parent 1381b6d01d
commit 6a17688021
10 changed files with 73 additions and 686 deletions
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3
src/transformers/hf_argparser.py
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@@ -126,6 +126,9 @@ class HfArgumentParser(ArgumentParser):
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")
return (*outputs,)
def parse_json_file(self, json_file: str) -> Tuple[DataClass, ...]:

2
src/transformers/trainer.py
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@@ -416,7 +416,7 @@ class Trainer:
logger.info("***** Running training *****")
logger.info(" Num examples = %d", self.num_examples(train_dataloader))
logger.info(" Num Epochs = %d", num_train_epochs)
logger.info(" Instantaneous batch size per device = %d", self.args.per_gpu_train_batch_size)
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", t_total)

40
src/transformers/training_args.py
View File

@@ -58,8 +58,28 @@ class TrainingArguments:
default=False, metadata={"help": "Run evaluation during training at each logging step."},
)
per_gpu_train_batch_size: int = field(default=8, metadata={"help": "Batch size per GPU/CPU for training."})
per_gpu_eval_batch_size: int = field(default=8, metadata={"help": "Batch size per GPU/CPU for evaluation."})
per_device_train_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
)
per_gpu_train_batch_size: Optional[int] = field(
default=None,
metadata={
"help": "Deprecated, the use of `--per_device_train_batch_size` is preferred. "
"Batch size per GPU/TPU core/CPU for training."
},
)
per_gpu_eval_batch_size: Optional[int] = field(
default=None,
metadata={
"help": "Deprecated, the use of `--per_device_eval_batch_size` is preferred."
"Batch size per GPU/TPU core/CPU for evaluation."
},
)
gradient_accumulation_steps: int = field(
default=1,
metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."},
@@ -115,11 +135,23 @@ class TrainingArguments:
@property
def train_batch_size(self) -> int:
return self.per_gpu_train_batch_size * max(1, self.n_gpu)
if self.per_gpu_train_batch_size:
logger.warning(
"Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
"version. Using `--per_device_train_batch_size` is preferred."
)
per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
return per_device_batch_size * max(1, self.n_gpu)
@property
def eval_batch_size(self) -> int:
return self.per_gpu_eval_batch_size * max(1, self.n_gpu)
if self.per_gpu_eval_batch_size:
logger.warning(
"Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
"version. Using `--per_device_eval_batch_size` is preferred."
)
per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
return per_device_batch_size * max(1, self.n_gpu)
@cached_property
@torch_required