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3 Commits
c6a855929d ... cc82f38b75

Author SHA1 Message Date
Neil Ge cc82f38b75
Merge 9cb07dd1ec into a3c7b01cc0 2026-09-10 21:11:19 -04:00
neil.ge 9cb07dd1ec perf: enable fused AdamW in pretrain training 
Same fused AdamW optimization as the full SFT script, using the
existing device_type variable so the CPU-only path is unaffected.

Co-authored-by: TRAE CLI <traecli@bytedance.com>
 2026-09-05 19:16:51 +08:00
neil.ge 859c8ee2a3 perf: enable fused AdamW in full SFT training 
Use PyTorch's fused AdamW implementation to reduce optimizer kernel
launch overhead and speed up the training step on CUDA devices.
Guard with device check so the CPU-only fallback path is unaffected.

Co-authored-by: TRAE CLI <traecli@bytedance.com>
 2026-09-05 18:37:04 +08:00
2 changed files with 2 additions and 2 deletions
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2
trainer/train_full_sft.py
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@ -137,7 +137,7 @@ if __name__ == "__main__":
train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate, fused=('cuda' in args.device))
# ========== 6. 从ckp恢复状态 ==========
start_epoch, start_step = 0, 0

2
trainer/train_pretrain.py
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@ -136,7 +136,7 @@ if __name__ == "__main__":
train_ds = PretrainDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate, fused=(device_type == 'cuda'))
# ========== 6. 从ckp恢复状态 ==========
start_epoch, start_step = 0, 0