mirror of
https://github.com/jingyaogong/minimind.git
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365 lines
20 KiB
Python
365 lines
20 KiB
Python
import os
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import sys
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__package__ = "trainer"
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import argparse
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import re
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import warnings
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import torch
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import torch.distributed as dist
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import torch.nn.functional as F
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from transformers import AutoTokenizer
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from contextlib import nullcontext
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from torch import optim, nn
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from torch.nn.parallel import DistributedDataParallel
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from torch.utils.data import DataLoader, DistributedSampler
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from torch.nn.utils import clip_grad_norm_
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from torch.optim.lr_scheduler import CosineAnnealingLR
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from transformers import AutoModel
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from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
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from dataset.lm_dataset import RLAIFDataset
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from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model
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warnings.filterwarnings('ignore')
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# 自定义的Critic模型，继承自MiniMindLM
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class CriticModel(MiniMindForCausalLM):
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def __init__(self, params):
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super().__init__(params)
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# 替换lm_head为输出单一价值的线性层
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self.value_head = nn.Linear(params.hidden_size, 1)
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def forward(self, input_ids=None, attention_mask=None, **kwargs):
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# 使用基础模型获取隐藏状态
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outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
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hidden_states = self.model.norm(outputs[0])
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# 使用value_head获取价值估计
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values = self.value_head(hidden_states).squeeze(-1)
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return values
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def calculate_rewards(prompts, responses, reward_model, reward_tokenizer):
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"""整合所有奖励函数计算总奖励"""
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def reasoning_model_reward(rewards):
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# 1. 格式奖励（仅针对训练推理模型时使用）
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pattern = r"^<think>\n.*?\n</think>\n<answer>\n.*?\n</answer>$"
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pattern2 = r"^<think>\n.*?\n</think>\n\n<answer>\n.*?\n</answer>$"
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matches_pattern = [re.match(pattern, response, re.S) for response in responses]
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matches_pattern2 = [re.match(pattern2, response, re.S) for response in responses]
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format_rewards = []
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for match_pattern, match_pattern2 in zip(matches_pattern, matches_pattern2):
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if match_pattern:
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format_rewards.append(0.5)
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elif match_pattern2:
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format_rewards.append(0.5)
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else:
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format_rewards.append(0.0)
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rewards += torch.tensor(format_rewards, device=args.device)
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# 2. 标记奖励（防止严格奖励稀疏，仅针对训练推理模型时使用）
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def mark_num(text):
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reward = 0
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if text.count("<think>") == 1:
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reward += 0.25
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if text.count("</think>") == 1:
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reward += 0.25
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if text.count("<answer>") == 1:
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reward += 0.25
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if text.count("</answer>") == 1:
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reward += 0.25
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return reward
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mark_rewards = [mark_num(response) for response in responses]
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rewards += torch.tensor(mark_rewards, device=args.device)
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return rewards
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rewards = torch.zeros(len(responses), device=args.device)
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# 格式奖励
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if args.reasoning == 1:
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rewards = reasoning_model_reward(rewards)
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# 使用reward model计算整个response的奖励
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with torch.no_grad():
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reward_model_scores = []
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for prompt, response in zip(prompts, responses):
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pattern = r"<\|im_start\|>(system|user|assistant)\s+(.*?)<\|im_end\|>"
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matches = re.findall(pattern, prompt, re.DOTALL)
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messages = [{"role": role, "content": content.strip()} for role, content in matches]
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tmp_chat = messages + [{"role": "assistant", "content": response}]
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score = reward_model.get_score(reward_tokenizer, tmp_chat)
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scale = 3.0
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score = max(min(score, scale), -scale)
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# 当args.reasoning=1时，额外计算<answer>内容的奖励
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if args.reasoning == 1:
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answer_match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)
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if answer_match:
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answer_content = answer_match.group(1).strip()
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# 对answer内容单独计算reward
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tmp_chat = messages + [{"role": "assistant", "content": answer_content}]
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answer_score = reward_model.get_score(reward_tokenizer, tmp_chat)
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answer_score = max(min(answer_score, scale), -scale)
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score = score * 0.4 + answer_score * 0.6
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reward_model_scores.append(score)
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reward_model_scores = torch.tensor(reward_model_scores, device=args.device)
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rewards += reward_model_scores
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return rewards
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def ppo_train_epoch(epoch, loader, iters, old_actor_model, ref_model, actor_scheduler, critic_scheduler, reward_model, reward_tokenizer, start_step=0, wandb=None):
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actor_model.train()
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critic_model.train()
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for step, batch in enumerate(loader, start=start_step + 1):
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prompts = batch["prompt"] # list[str], length B
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enc = tokenizer(prompts, return_tensors="pt", padding=True, truncation=True,
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max_length=args.max_seq_len, padding_side="left").to(args.device) # input_ids: [B, P], attention_mask: [B, P]
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prompt_length = enc.input_ids.shape[1]
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with torch.no_grad():
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# DDP 模型需要使用 .module 访问 generate 方法
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model_for_gen = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
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gen_out = model_for_gen.generate(
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input_ids=enc.input_ids, attention_mask=enc.attention_mask,
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max_new_tokens=args.max_gen_len, do_sample=True, temperature=0.8,
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pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id) # [B, P+R]
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responses_text = [tokenizer.decode(gen_out[i, prompt_length:], skip_special_tokens=True) for i in range(len(prompts))]
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rewards = calculate_rewards(prompts, responses_text, reward_model, reward_tokenizer) # [B]
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full_mask = (gen_out != tokenizer.pad_token_id).long() # [B, P+R]
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values_seq = critic_model(input_ids=gen_out, attention_mask=full_mask) # [B, P+R]
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last_indices = (full_mask * torch.arange(full_mask.size(1), device=gen_out.device)).argmax(dim=1)
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values = values_seq[torch.arange(values_seq.size(0), device=values_seq.device), last_indices] # [B]
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advantages = rewards - values.detach() # [B]
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logits = actor_model(input_ids=gen_out, attention_mask=full_mask).logits # [B, P+R, V]
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labels = gen_out[:, 1:].clone() # [B, P+R-1]
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logp_tokens = F.log_softmax(logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1) # [B, P+R-1]
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seq_len = gen_out.size(1) - 1
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resp_mask = torch.arange(seq_len, device=gen_out.device).unsqueeze(0) >= prompt_length - 1
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final_mask = resp_mask & (~labels.eq(tokenizer.pad_token_id)) # [B, P+R-1]
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actor_logp = (logp_tokens * final_mask).sum(dim=1) # [B]
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with torch.no_grad():
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old_logits = old_actor_model(input_ids=gen_out, attention_mask=full_mask).logits # [B, P+R, V]
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old_logp_tokens = F.log_softmax(old_logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1) # [B, P+R-1]
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old_logp = (old_logp_tokens * final_mask).sum(dim=1) # [B]
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ref_logits = ref_model(input_ids=gen_out, attention_mask=full_mask).logits # [B, P+R, V]
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ref_logp_tokens = F.log_softmax(ref_logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1) # [B, P+R-1]
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ref_logp = (ref_logp_tokens * final_mask).sum(dim=1) # [B]
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kl = (actor_logp - old_logp).mean() # scalar
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kl_ref = (actor_logp - ref_logp).mean() # scalar
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ratio = torch.exp(actor_logp - old_logp) # [B]
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surr1 = ratio * advantages # [B]
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surr2 = torch.clamp(ratio, 1.0 - args.clip_epsilon, 1.0 + args.clip_epsilon) * advantages # [B]
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policy_loss = -torch.min(surr1, surr2).mean() # scalar
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value_loss = F.mse_loss(values, rewards) # scalar
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loss = policy_loss + args.vf_coef * value_loss + args.kl_coef * kl_ref # scalar
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loss.backward()
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if (step + 1) % args.accumulation_steps == 0:
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clip_grad_norm_(actor_model.parameters(), args.grad_clip)
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clip_grad_norm_(critic_model.parameters(), args.grad_clip)
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actor_optimizer.step()
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critic_optimizer.step()
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actor_scheduler.step()
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critic_scheduler.step()
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actor_optimizer.zero_grad()
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critic_optimizer.zero_grad()
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if is_main_process():
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response_ids = gen_out[:, enc.input_ids.shape[1]:]
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is_eos = (response_ids == tokenizer.eos_token_id)
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eos_indices = torch.argmax(is_eos.int(), dim=1)
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has_eos = is_eos.any(dim=1)
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lengths = torch.where(has_eos, eos_indices + 1, torch.tensor(response_ids.shape[1], device=is_eos.device))
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avg_len = lengths.float().mean()
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actor_loss_val = policy_loss.item()
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critic_loss_val = value_loss.item()
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reward_val = rewards.mean().item()
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kl_val = kl.item()
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kl_ref_val = kl_ref.item()
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avg_len_val = avg_len.item()
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actor_lr = actor_optimizer.param_groups[0]['lr']
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critic_lr = critic_optimizer.param_groups[0]['lr']
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if wandb is not None:
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wandb.log({
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"actor_loss": actor_loss_val,
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"critic_loss": critic_loss_val,
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"reward": reward_val,
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"kl": kl_val,
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"kl_ref": kl_ref_val,
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"avg_response_len": avg_len_val,
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"actor_lr": actor_lr,
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})
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Logger(f"Epoch: {epoch+1}, Step: {step}/{iters}, "
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f"Actor Loss: {actor_loss_val:.6f}, Critic Loss: {critic_loss_val:.6f}, "
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f"Reward: {reward_val:.6f}, KL: {kl_val:.6f}, KL_ref: {kl_ref_val:.6f}, "
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f"Avg Response Len: {avg_len_val:.2f}, Actor LR: {actor_lr:.2e}, Critic LR: {critic_lr:.2e}")
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if (step + 1) % args.update_old_actor_freq == 0:
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state_dict = actor_model.module.state_dict() if isinstance(actor_model, DistributedDataParallel) else actor_model.state_dict()
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old_actor_model.load_state_dict({k: v.detach().cpu() for k, v in state_dict.items()})
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old_actor_model.to(args.device)
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if (step % args.save_interval == 0 or step == iters - 1) and is_main_process():
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actor_model.eval()
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moe_suffix = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
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actor_state = actor_model.module.state_dict() if isinstance(actor_model, DistributedDataParallel) else actor_model.state_dict()
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torch.save({k: v.half().cpu() for k, v in actor_state.items()}, ckp)
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# 使用 lm_checkpoint 保存完整状态（包括 critic）
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lm_checkpoint(lm_config, weight=args.save_weight, model=actor_model, optimizer=actor_optimizer,
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epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints',
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scheduler=actor_scheduler, critic_model=critic_model,
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critic_optimizer=critic_optimizer, critic_scheduler=critic_scheduler)
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actor_model.train()
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del actor_state
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del enc, gen_out, responses_text, rewards, full_mask, values_seq, values, advantages
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del logits, labels, logp_tokens, final_mask, actor_logp, old_logits, old_logp, ref_logits, ref_logp
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del kl, kl_ref, ratio, surr1, surr2, policy_loss, value_loss, loss
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind PPO (Proximal Policy Optimization)")
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parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录")
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parser.add_argument('--save_weight', default='ppo_actor', type=str, help="保存权重的前缀名")
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parser.add_argument("--epochs", type=int, default=1, help="训练轮数")
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parser.add_argument("--batch_size", type=int, default=2, help="batch size")
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parser.add_argument("--learning_rate", type=float, default=8e-8, help="Actor学习率")
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parser.add_argument("--critic_learning_rate", type=float, default=8e-8, help="Critic学习率")
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
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parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
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parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
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parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值")
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parser.add_argument("--log_interval", type=int, default=1, help="日志打印间隔")
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parser.add_argument("--save_interval", type=int, default=10, help="模型保存间隔")
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parser.add_argument('--hidden_size', default=512, type=int, help="隐藏层维度")
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parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量")
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parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构（0=否，1=是）")
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parser.add_argument('--max_seq_len', default=66, type=int, help="Prompt最大长度")
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parser.add_argument("--max_gen_len", type=int, default=1536, help="生成的最大长度")
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parser.add_argument("--data_path", type=str, default="../dataset/rlaif-mini.jsonl", help="RLAIF数据路径")
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parser.add_argument("--clip_epsilon", type=float, default=0.1, help="PPO裁剪参数")
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parser.add_argument("--vf_coef", type=float, default=0.5, help="Value function系数")
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parser.add_argument("--kl_coef", type=float, default=0.02, help="KL散度惩罚系数")
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parser.add_argument("--reasoning", type=int, default=1, choices=[0, 1], help='推理模型类型（0=普通模型，1=推理模型）')
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parser.add_argument("--update_old_actor_freq", type=int, default=4, help="更新old_actor_model的频率")
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parser.add_argument("--reward_model_path", type=str, default="../../internlm2-1_8b-reward", help="Reward模型路径")
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parser.add_argument('--from_resume', default=0, type=int, choices=[0, 1], help="是否自动检测&续训（0=否，1=是）")
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parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-PPO", help="wandb项目名")
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args = parser.parse_args()
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# ========== 1. 初始化环境和随机种子 ==========
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local_rank = init_distributed_mode()
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if dist.is_initialized(): args.device = f"cuda:{local_rank}"
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setup_seed(42 + (dist.get_rank() if dist.is_initialized() else 0))
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# ========== 2. 配置目录、模型参数、检查ckp ==========
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os.makedirs(args.save_dir, exist_ok=True)
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lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=bool(args.use_moe))
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ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
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# ========== 3. 设置混合精度 ==========
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device_type = "cuda" if "cuda" in args.device else "cpu"
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dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
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autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
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# ========== 4. 配wandb ==========
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wandb = None
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if args.use_wandb and is_main_process():
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import swanlab as wandb
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wandb_id = ckp_data.get('wandb_id') if ckp_data else None
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resume = 'must' if wandb_id else None
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wandb_run_name = f"MiniMind-PPO-Epoch-{args.epochs}-BS-{args.batch_size}-LR-{args.learning_rate}"
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wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume)
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# ========== 5. 初始化模型和数据 ==========
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base_weight = "reason" if args.reasoning == 1 else "full_sft"
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# Actor模型
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actor_model, tokenizer = init_model(lm_config, base_weight, device=args.device)
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# Old Actor模型
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old_actor_model, _ = init_model(lm_config, base_weight, device=args.device)
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old_actor_model = old_actor_model.eval().requires_grad_(False)
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# Reference模型
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ref_model, _ = init_model(lm_config, base_weight, device=args.device)
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ref_model = ref_model.eval().requires_grad_(False)
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# Critic模型
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moe_suffix = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/{base_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
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state_dict = torch.load(ckp, map_location=args.device)
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critic_model = CriticModel(lm_config)
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critic_model.load_state_dict(state_dict, strict=False)
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critic_model = critic_model.to(args.device)
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# Reward模型
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reward_model = AutoModel.from_pretrained(
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args.reward_model_path, torch_dtype=torch.float16, trust_remote_code=True
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)
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reward_model = reward_model.to(args.device).eval().requires_grad_(False)
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reward_tokenizer = AutoTokenizer.from_pretrained(args.reward_model_path, trust_remote_code=True)
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# 数据和优化器
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train_ds = RLAIFDataset(args.data_path, tokenizer, max_length=(args.max_seq_len + args.max_gen_len))
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train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
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actor_optimizer = optim.AdamW(actor_model.parameters(), lr=args.learning_rate)
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critic_optimizer = optim.AdamW(critic_model.parameters(), lr=args.critic_learning_rate)
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loader_for_count = DataLoader(train_ds, batch_size=args.batch_size, sampler=train_sampler)
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iters = len(loader_for_count)
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total_optimizer_steps = (iters // args.accumulation_steps) * args.epochs
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actor_scheduler = CosineAnnealingLR(actor_optimizer, T_max=total_optimizer_steps, eta_min=args.learning_rate / 10)
|
||
critic_scheduler = CosineAnnealingLR(critic_optimizer, T_max=total_optimizer_steps, eta_min=args.critic_learning_rate / 10)
|
||
|
||
# ========== 6. 从ckp恢复状态 ==========
|
||
start_epoch, start_step = 0, 0
|
||
if ckp_data:
|
||
actor_model.load_state_dict(ckp_data['model'])
|
||
critic_model.load_state_dict(ckp_data['critic_model'])
|
||
actor_optimizer.load_state_dict(ckp_data['optimizer'])
|
||
critic_optimizer.load_state_dict(ckp_data['critic_optimizer'])
|
||
actor_scheduler.load_state_dict(ckp_data['scheduler'])
|
||
critic_scheduler.load_state_dict(ckp_data['critic_scheduler'])
|
||
start_epoch = ckp_data['epoch']
|
||
start_step = ckp_data.get('step', 0)
|
||
|
||
# ========== 7. DDP包模型 ==========
|
||
if dist.is_initialized():
|
||
actor_model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
|
||
critic_model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
|
||
actor_model = DistributedDataParallel(actor_model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
|
||
critic_model = DistributedDataParallel(critic_model, device_ids=[local_rank], find_unused_parameters=getattr(lm_config, 'use_moe', False))
|
||
old_actor_model.to(args.device)
|
||
|
||
# ========== 8. 开始训练 ==========
|
||
for epoch in range(start_epoch, args.epochs):
|
||
train_sampler and train_sampler.set_epoch(epoch)
|
||
if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
|
||
batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
|
||
loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
|
||
Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
|
||
ppo_train_epoch(epoch, loader, len(loader) + start_step + 1, old_actor_model, ref_model,
|
||
actor_scheduler, critic_scheduler, reward_model, reward_tokenizer, start_step, wandb)
|
||
else: # 默认从头开始
|
||
loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None),
|
||
sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
|
||
ppo_train_epoch(epoch, loader, len(loader), old_actor_model, ref_model,
|
||
actor_scheduler, critic_scheduler, reward_model, reward_tokenizer, 0, wandb)
|