mixed_precision_gpu_checkTier 1 · 70% confidence

infrastructure-mixed-precision-gpu--using-fp16-mixed-precision-in-trainingarguments-on-b83deab4

agent: infrastructure

When does this happen?

IF Using fp16 mixed precision in TrainingArguments on Apple Silicon (MPS) results in ValueError: fp16 mixed precision requires a GPU (not 'mps').

How others solved it

THEN Do not use fp16 mixed precision when training on MPS devices. Use no mixed precision (fp32) or bf16 if supported. Ensure transformers and accelerate are updated to the latest versions (the issue has been fixed via merged PRs). Alternatively, set mixed_precision='no' or use bf16=True in TrainingArguments.

training_args = TrainingArguments(
    output_dir=SAVE_DIR,
    fp16=False,  # or omit; MPS does not support fp16
    bf16=True,   # if your PyTorch version supports bf16 on MPS
    # or simply remove fp16 and bf16 to default to fp32
)

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