#!/usr/bin/env python3 """ train.py One-shot Entropy Minimization Fine-tuning Script """ import argparse import os import random import time from pathlib import Path import psutil import torch import torch.nn.functional as F import pandas as pd import numpy as np from torch.utils.data import Dataset, DataLoader import wandb 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("TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC", "2700") def parse_args(): parser = argparse.ArgumentParser(description="Entropy Minimization Fine-tuning") 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') parser.add_argument('--save_root', type=str, default=None, help='Checkpoint save root directory') parser.add_argument('--effective_batch', type=int, default=64, help='Global batch size') parser.add_argument('--micro_batch_size', type=str, default='auto', help='Micro batch size or "auto"') parser.add_argument('--temperature', type=float, default=0.9, help='Temperature coefficient') parser.add_argument('--learning_rate', type=float, default=2e-5, help='Learning rate') 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('--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') parser.add_argument('--enable_self_distill', action='store_true', help='Enable self-distillation MSE constraint') parser.add_argument('--self_distill_gamma', type=float, default=0.0, help='Self-distillation MSE weight γ') parser.add_argument('--seed', type=int, default=42, help='Random seed') return parser.parse_args() class FTDataset(Dataset): def __init__(self, rows): self.rows = rows def __len__(self): return len(self.rows) def __getitem__(self, idx): return self.rows[idx] def custom_collate(batch): return {"input": [item["input"] for item in batch]} def get_optimal_micro_batch_size(model_name: str, world_size: int = 1) -> int: model_configs = { "1.5B": {"base_batch": 4, "keywords": ["1.5B", "1B"]}, "2B": {"base_batch": 4, "keywords": ["2B"]}, "3B": {"base_batch": 2, "keywords": ["3B"]}, "7B": {"base_batch": 2, "keywords": ["7B"]}, "8B+": {"base_batch": 1, "keywords": ["8B", "9B", "10B", "11B", "12B", "13B", "14B"]}, } model_name_upper = model_name.upper() detected = next((cfg for cfg in model_configs.values() if any(k in model_name_upper for k in cfg["keywords"])), None) base_batch = detected["base_batch"] if detected else 2 if world_size > 1: return min(base_batch + 1, int(base_batch * 1.5)) return base_batch def adjust_batch_config_for_deepspeed(effective_batch: int, micro_batch_size: int, world_size: int): ideal = effective_batch / (micro_batch_size * world_size) if ideal.is_integer(): return effective_batch, micro_batch_size, int(ideal) down = max(1, int(ideal)) up = down + 1 down_eff = micro_batch_size * down * world_size up_eff = micro_batch_size * up * world_size use_up = abs(effective_batch - up_eff) < abs(effective_batch - down_eff) steps = up if use_up else down eff = up_eff if use_up else down_eff return eff, micro_batch_size, steps def apply_chat_template(tokenizer, problem: str) -> str: return tokenizer.apply_chat_template( [{"role": "user", "content": problem}], tokenize=False, add_generation_prompt=True ) def main(): args = parse_args() set_seed(args.seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False 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, "gradient_accumulation_steps": accum_steps, "bf16": {"enabled": True}, "zero_optimization": {"stage": 2, "offload_optimizer": {"device": "cpu"}, "offload_param": {"device": "none"}}, "gradient_clipping": 1.0, } accelerator = Accelerator(mixed_precision="bf16", gradient_accumulation_steps=accum_steps, deepspeed_plugin=DeepSpeedPlugin(hf_ds_config=ds_config)) print = accelerator.print model_path = args.model_path or f"/volume/ailab4sci/models/{args.model_name}" 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 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)) df = pd.read_parquet(args.train_data) train_data = [{"input": apply_chat_template(tokenizer, p)} for p in df["problem"].dropna().tolist()] train_loader = DataLoader(FTDataset(train_data), batch_size=micro_bs, shuffle=True, collate_fn=custom_collate) 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 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) 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) gen_mask = (token_idx.unsqueeze(0) >= prompt_len.unsqueeze(1)) & pad_mask logits = model(seq, attention_mask=pad_mask).logits probs = F.softmax(logits / temp, dim=-1) H_tok = -(probs * torch.log(probs + 1e-12)).sum(-1) loss = (H_tok * gen_mask).sum() / gen_mask.sum().clamp_min(1) if args.enable_self_distill and prev_logits is not None: loss = loss + args.self_distill_gamma * F.mse_loss(logits, prev_logits) prev_logits = logits.detach() accelerator.backward(loss); accelerator.clip_grad_norm_(model.parameters(), 1.0) optimizer.step(); optimizer.zero_grad() if accelerator.is_main_process: 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) accelerator.unwrap_model(model).save_pretrained(ckpt, safe_serialization=True) tokenizer.save_pretrained(ckpt) 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() if __name__ == "__main__": main()