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fix: accelerator and transformers lib upgrade issue of e2e models #2643

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fix: accelerator and transformers lib upgrade issue of e2e models #2643

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4 changes: 2 additions & 2 deletions torchbenchmark/e2e_models/hf_bert/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from accelerate.utils.dataclasses import DeepSpeedPlugin
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import AdamW
from torch.utils.data import DataLoader
from torchbenchmark.tasks import NLP
from torchbenchmark.util.e2emodel import E2EBenchmarkModel
Expand All @@ -22,7 +23,6 @@
preprocess_dataset,
)
from transformers import (
AdamW,
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
Expand Down Expand Up @@ -172,7 +172,7 @@ def prep(self, hf_args):
# 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).
self.data_collator = DataCollatorWithPadding(
tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)
tokenizer, pad_to_multiple_of=(8 if accelerator.mixed_precision == "fp16" else None)
)

train_dataloader = DataLoader(
Expand Down
2 changes: 1 addition & 1 deletion torchbenchmark/e2e_models/hf_t5/__init__.py
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Expand Up @@ -256,7 +256,7 @@ def prep(self, hf_args):
tokenizer,
model=model,
label_pad_token_id=label_pad_token_id,
pad_to_multiple_of=8 if accelerator.use_fp16 else None,
pad_to_multiple_of=8 if accelerator.mixed_precision == "fp16" else None,
)

train_dataloader = DataLoader(
Expand Down