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Replace accelerator.use_fp16 in examples (#33513)

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* Replace `accelerator.use_fp16` in examples

* pad_to_multiple_of=16 for fp8
This commit is contained in:
hlky
2024-09-17 03:13:06 +01:00
committed by GitHub
parent ba1f1dc132
commit 9f196ef2e0
10 changed files with 79 additions and 16 deletions
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11
examples/research_projects/luke/run_luke_ner_no_trainer.py
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@@ -542,9 +542,14 @@ def main():
# Otherwise, `DataCollatorForTokenClassification` will apply dynamic padding for us (by padding to the maximum length of
# the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
# of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
data_collator = DataCollatorForLukeTokenClassification(
tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)
)
# For fp8, we pad to multiple of 16.
if accelerator.mixed_precision == "fp8":
pad_to_multiple_of = 16
elif accelerator.mixed_precision != "no":
pad_to_multiple_of = 8
else:
pad_to_multiple_of = None
data_collator = DataCollatorForLukeTokenClassification(tokenizer, pad_to_multiple_of=pad_to_multiple_of)
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size

9
examples/research_projects/self-training-text-classification/finetuning.py
View File

@@ -704,7 +704,14 @@ def finetune(accelerator, model_name_or_path, train_file, output_dir, **kwargs):
# precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple of
# 8s, which will enable the use of Tensor Cores on NVIDIA hardware with
# compute capability >= 7.5 (Volta).
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None))
# For fp8, we pad to multiple of 16.
if accelerator.mixed_precision == "fp8":
pad_to_multiple_of = 16
elif accelerator.mixed_precision != "no":
pad_to_multiple_of = 8
else:
pad_to_multiple_of = None
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=pad_to_multiple_of)
train_dataloader = DataLoader(
train_dataset,