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start using training_args.parallel_mode (#8882)

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Stas Bekman 2020-12-01 11:40:36 -08:00 committed by GitHub
parent b08843cf4d
commit 379005c9d2
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2 changed files with 4 additions and 2 deletions
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3
examples/seq2seq/finetune_trainer.py
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@ -11,6 +11,7 @@ from seq2seq_trainer import Seq2SeqTrainer
from seq2seq_training_args import Seq2SeqTrainingArguments
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, HfArgumentParser, MBartTokenizer, set_seed
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
Seq2SeqDataCollator,
Seq2SeqDataset,
@ -132,7 +133,7 @@ def main():
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED),
training_args.fp16,
)
# Set the verbosity to info of the Transformers logger (on main process only):

3
examples/seq2seq/seq2seq_trainer.py
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@ -18,6 +18,7 @@ from transformers.optimization import (
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
logger = logging.get_logger(__name__)
@ -123,7 +124,7 @@ class Seq2SeqTrainer(Trainer):
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size,
distributed=(self.args.local_rank != -1),
distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),
)
return (