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Add ckp_dir and tokenizer path for train_distill_reason.py

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xunuohope1107 2025-02-19 11:52:29 +08:00 committed by GitHub
parent 1cc496d08b
commit f3f651408d
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1 changed files with 5 additions and 3 deletions
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train_distill_reason.py
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@ -35,7 +35,7 @@ def train_epoch(epoch, wandb):
# 思考标签占位符 # 思考标签占位符
start_of_think_ids = tokenizer('<think>').input_ids start_of_think_ids = tokenizer('<think>').input_ids
end_of_think_ids = tokenizer('</think>').input_ids end_of_think_ids = tokenizer('</think>').input_ids
start_of_answer_ids = tokenizer('<answer>').input_ids start_of_answer_ids = tokenizer('</answer>').input_ids
end_of_answer_ids = tokenizer('</answer>').input_ids end_of_answer_ids = tokenizer('</answer>').input_ids
loss_fct = nn.CrossEntropyLoss(reduction='none') loss_fct = nn.CrossEntropyLoss(reduction='none')
start_time = time.time() start_time = time.time()
@ -109,10 +109,10 @@ def train_epoch(epoch, wandb):
def init_model(lm_config): def init_model(lm_config):
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer') tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
model = MiniMindLM(lm_config) model = MiniMindLM(lm_config)
moe_path = '_moe' if lm_config.use_moe else '' moe_path = '_moe' if lm_config.use_moe else ''
ckp = f'./out/rlhf_{lm_config.dim}{moe_path}.pth' ckp = f'{args.ckp_dir}/rlhf_{lm_config.dim}{moe_path}.pth'
state_dict = torch.load(ckp, map_location=args.device) state_dict = torch.load(ckp, map_location=args.device)
model.load_state_dict(state_dict, strict=False) model.load_state_dict(state_dict, strict=False)
Logger(f'LLM总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') Logger(f'LLM总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
@ -135,6 +135,8 @@ def init_distributed_mode():
if __name__ == "__main__": if __name__ == "__main__":
parser = argparse.ArgumentParser(description="MiniMind Distill Reasoning") parser = argparse.ArgumentParser(description="MiniMind Distill Reasoning")
parser.add_argument("--out_dir", type=str, default="out") parser.add_argument("--out_dir", type=str, default="out")
parser.add_argument("--ckp_dir", type=str, default="out", help="Directory to save/load checkpoints")
parser.add_argument("--tokenizer_path", type=str, default="./model/minimind_tokenizer", help="Path to the tokenizer")
parser.add_argument("--epochs", type=int, default=1) parser.add_argument("--epochs", type=int, default=1)
parser.add_argument("--batch_size", type=int, default=8) parser.add_argument("--batch_size", type=int, default=8)
parser.add_argument("--learning_rate", type=float, default=1e-6) parser.add_argument("--learning_rate", type=float, default=1e-6)