diff options
| -rw-r--r-- | train.py | 46 |
1 files changed, 25 insertions, 21 deletions
@@ -1,10 +1,3 @@ -#!/usr/bin/env python3 -""" -train.py - -One-shot Entropy Minimization Fine-tuning Script -""" - import argparse import os import random @@ -24,12 +17,12 @@ from accelerate import Accelerator, DeepSpeedPlugin from accelerate.utils import set_seed from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, AdamW -# 设置NCCL超时时间，避免分布式训练超时（设置为45分钟） -os.environ.setdefault("NCCL_TIMEOUT", "2700") # 45分钟 + +os.environ.setdefault("NCCL_TIMEOUT", "2700") os.environ.setdefault("TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC", "2700") def parse_args(): - parser = argparse.ArgumentParser(description="Entropy Minimization Fine-tuning") + parser = argparse.ArgumentParser() parser.add_argument('--model_name', type=str, default='Qwen2.5-Math-7B', help='Model name') parser.add_argument('--model_path', type=str, default=None, help='Local model path') parser.add_argument('--train_data', type=str, default='', help='Training data file path') @@ -41,7 +34,7 @@ def parse_args(): parser.add_argument('--log_steps', type=int, default=1, help='Logging step interval') parser.add_argument('--save_steps', type=int, default=1, help='Checkpoint saving step interval') parser.add_argument('--max_steps', type=int, default=1000, help='Maximum training steps') - parser.add_argument('--sample_temp', type=float, default=None, help='Generation temperature parameter') + parser.add_argument('--sample_temp', type=float, default=0.5, help='Generation temperature parameter') parser.add_argument('--run_name', type=str, default=None, help='Experiment run name') parser.add_argument('--wandb_project', type=str, default='entropy-maximization-ft', help='W&B project name') parser.add_argument('--wandb_name', type=str, default=None, help='W&B run name') @@ -101,12 +94,10 @@ def main(): world_size = int(os.getenv("WORLD_SIZE", "1")) micro_bs = int(args.micro_batch_size) if args.micro_batch_size != 'auto' else get_optimal_micro_batch_size(args.model_name, world_size) eff_bs, micro_bs, accum_steps = adjust_batch_config_for_deepspeed(args.effective_batch, micro_bs, world_size) - temp = args.temperature lr = args.learning_rate save_root = args.save_root or (f"checkpoints/{args.model_name}/{args.run_name}" if args.run_name else f"checkpoints/{args.model_name}") - ds_config = { "train_micro_batch_size_per_gpu": micro_bs, "train_batch_size": eff_bs, @@ -122,7 +113,7 @@ def main(): config = AutoConfig.from_pretrained(model_path); config.use_cache = False model = AutoModelForCausalLM.from_pretrained(model_path, config=config); model.gradient_checkpointing_enable() tokenizer = AutoTokenizer.from_pretrained(model_path, padding_side="left") - if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token + tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token if accelerator.is_main_process: wandb.init(project=args.wandb_project, name=args.run_name or args.wandb_name or args.model_name, config=vars(args)) @@ -133,16 +124,26 @@ def main(): optimizer = AdamW(model.parameters(), lr=lr) model, optimizer, train_loader = accelerator.prepare(model, optimizer, train_loader) - prev_logits = None model.train() + for step, batch in enumerate(train_loader, start=1): if step > args.max_steps: - print(f"达到最大步数 {args.max_steps}，停止训练"); break + print(f"Exceed max step {args.max_steps}, training stopped.") + break + with accelerator.accumulate(model): enc = tokenizer(batch["input"], return_tensors="pt", padding="longest", truncation=True, max_length=2048).to(accelerator.device) + with torch.no_grad(): - gen_ids = accelerator.unwrap_model(model).generate(**enc, max_new_tokens=512, do_sample=True, top_p=0.95, temperature=args.sample_temp if args.sample_temp is not None else temp, synced_gpus=True, pad_token_id=tokenizer.pad_token_id, use_cache=False) + gen_ids = accelerator.unwrap_model(model).generate(**enc, + max_new_tokens=512, do_sample=True, + top_p=0.95, + temperature=args.sample_temp, + synced_gpus=True, + pad_token_id=tokenizer.pad_token_id, + use_cache=False) + seq = torch.cat([enc.input_ids, gen_ids[:, enc.input_ids.shape[1]:]], dim=1)[:, :4096] pad_mask = seq.ne(tokenizer.pad_token_id); prompt_len = pad_mask[:, :enc.input_ids.shape[1]].sum(-1) token_idx = torch.arange(seq.size(1), device=seq.device) @@ -164,7 +165,7 @@ def main(): if step % args.log_steps == 0: print(f"Step {step} | loss={loss.item():.6f}") wandb.log({"step": step, "loss": loss.item()}) - # 根据 save_steps 保存检查点 + if step % args.save_steps == 0: ckpt = Path(save_root) / f"step_{step}" ckpt.mkdir(parents=True, exist_ok=True) @@ -173,9 +174,12 @@ def main(): print(f"Checkpoint saved to {ckpt}") if accelerator.is_main_process: - final=Path(save_root)/"final"; final.mkdir(parents=True,exist_ok=True) - accelerator.unwrap_model(model).save_pretrained(final,safe_serialization=True); tokenizer.save_pretrained(final) - print(f"Final checkpoint saved to {final}"); wandb.finish() + final = Path(save_root) / "final" + final.mkdir(parents=True, exist_ok=True) + accelerator.unwrap_model(model).save_pretrained(final, safe_serialization=True) + tokenizer.save_pretrained(final) + print(f"Final checkpoint saved to {final}") + wandb.finish() if __name__ == "__main__": main()
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