优化finetune.py为混合精度训练. #33
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zhangfaen
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Jan 24, 2025
finetune.py
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| outputs = model(**inputs, labels=labels) | ||
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| # Automatic Mixed Precision (AMP) context manager for efficient training. | ||
| # Inside this context, `torch.is_autocast_enabled()` returns True; outside the context, it returns False. |
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Inside this context, torch.is_autocast_enabled() returns True; outside the context, it returns False.
This line should be changed to below
AMP context manager can be embeded by another AMP context manager, the behavior is shown by below code snippet.
# import torch
# from torch.amp import autocast
# with autocast(device_type='cuda', dtype=torch.bfloat16):
# print(torch.is_autocast_enabled()) # True
# with autocast(device_type='cuda', dtype=torch.bfloat16):
# print(torch.is_autocast_enabled()) # True
# with autocast(device_type='cuda', dtype=torch.bfloat16, enabled=False):
# print(torch.is_autocast_enabled()) # False
# print(torch.is_autocast_enabled()) # True
2.将finetune_distributed.py分布式训练方式改为deepspeed,同时相应开启混合训练. 3.更新requirements.txt到最新版本
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1.优化finetune.py为混合精度训练.
2.将finetune_distributed.py分布式训练方式改为deepspeed,同时相应开启混合训练.
3.更新requirements.txt到最新版本