mirror of
https://github.com/jingyaogong/minimind.git
synced 2026-01-13 19:57:20 +08:00
374 lines
18 KiB
Python
374 lines
18 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 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 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 transformers import AutoTokenizer, 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 torch.nn.utils import clip_grad_norm_
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from torch.optim.lr_scheduler import CosineAnnealingLR
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def Logger(content):
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if not ddp or dist.get_rank() == 0:
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print(content)
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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) # 训练推理模型时使用
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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: int, wandb_run, old_actor_model, ref_model, actor_scheduler, critic_scheduler):
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actor_model.train()
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critic_model.train()
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is_master = (not ddp) or dist.get_rank() == 0
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for step, batch in enumerate(train_loader):
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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).to(args.device) # input_ids: [B, P], attention_mask: [B, P]
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prompt_lengths = enc.attention_mask.sum(dim=1) # [B]
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with torch.no_grad():
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gen_out = actor_model.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_lengths[i]:], 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.sum(dim=1) - 1 # [B]
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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_lengths.unsqueeze(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_master:
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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_run is not None:
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wandb_run.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}, Step: {step + 1}/{len(train_loader)}, "
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f"Actor Loss: {actor_loss_val:.4f}, Critic Loss: {critic_loss_val:.4f}, "
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f"Reward: {reward_val:.4f}, KL: {kl_val:.4f}, KL_ref: {kl_ref_val:.4f}, "
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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, torch.nn.parallel.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 + 1) % args.save_interval == 0 or step == iter_per_epoch - 1) and (not ddp or dist.get_rank() == 0):
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actor_model.eval()
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/ppo_actor_{lm_config.hidden_size}{moe_path}.pth'
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if isinstance(actor_model, torch.nn.parallel.DistributedDataParallel):
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state_dict = actor_model.module.state_dict()
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else:
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state_dict = actor_model.state_dict()
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state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存
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torch.save(state_dict, ckp)
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actor_model.train()
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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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# self.model 返回的是一个元组，第一个元素是 last_hidden_state
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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 init_model(lm_config):
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tokenizer = AutoTokenizer.from_pretrained('../model/', padding_side='left')
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/{"reason" if args.reasoning == 1 else "full_sft"}_{lm_config.hidden_size}{moe_path}.pth'
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state_dict = torch.load(ckp, map_location=args.device)
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actor_model = MiniMindForCausalLM(lm_config)
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actor_model.load_state_dict(state_dict, strict=False)
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actor_model = actor_model.to(args.device)
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old_actor_model = MiniMindForCausalLM(lm_config)
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old_actor_model.load_state_dict(state_dict, strict=False)
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old_actor_model = old_actor_model.eval().requires_grad_(False).to(args.device)
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ref_model = MiniMindForCausalLM(lm_config)
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ref_model.load_state_dict(state_dict, strict=False)
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ref_model = ref_model.eval().requires_grad_(False).to(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_name = "../../internlm2-1_8b-reward"
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reward_model = AutoModel.from_pretrained(
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reward_name, device_map="cuda", torch_dtype=torch.float32, trust_remote_code=True
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).to(args.device).eval().requires_grad_(False)
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reward_tokenizer = AutoTokenizer.from_pretrained(reward_name, trust_remote_code=True)
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Logger(f'Actor模型总参数量：{sum(p.numel() for p in actor_model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
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Logger(f'Critic模型总参数量：{sum(p.numel() for p in critic_model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
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return actor_model, old_actor_model, ref_model, critic_model, reward_model, tokenizer, reward_tokenizer
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def init_distributed_mode():
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if not ddp: return
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global ddp_local_rank, DEVICE
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dist.init_process_group(backend="nccl")
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ddp_local_rank = int(os.environ["LOCAL_RANK"])
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DEVICE = f"cuda:{ddp_local_rank}"
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torch.cuda.set_device(DEVICE)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--out_dir", type=str, default="../out")
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parser.add_argument("--epochs", type=int, default=1)
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parser.add_argument("--batch_size", type=int, default=2)
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parser.add_argument("--learning_rate", type=float, default=8e-8)
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parser.add_argument("--critic_learning_rate", type=float, default=8e-8)
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
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parser.add_argument("--dtype", type=str, default="bfloat16")
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parser.add_argument("--use_wandb", action="store_true")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-PPO")
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parser.add_argument("--num_workers", type=int, default=1)
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parser.add_argument("--ddp", action="store_true")
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parser.add_argument("--accumulation_steps", type=int, default=1)
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parser.add_argument("--grad_clip", type=float, default=1.0)
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parser.add_argument("--log_interval", type=int, default=1)
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parser.add_argument("--save_interval", type=int, default=10)
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parser.add_argument('--hidden_size', default=512, type=int)
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parser.add_argument('--num_hidden_layers', default=8, type=int)
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parser.add_argument('--use_moe', default=False, type=bool)
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parser.add_argument('--max_seq_len', default=66, type=int)
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parser.add_argument("--max_gen_len", type=int, default=1536)
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parser.add_argument("--data_path", type=str, default="../dataset/rlaif-mini.jsonl")
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parser.add_argument("--clip_epsilon", type=float, default=0.1)
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parser.add_argument("--vf_coef", type=float, default=0.5)
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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, help='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="频率：每处理n个batch后更新old_actor_model")
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args = parser.parse_args()
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lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers,
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use_moe=args.use_moe)
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args.save_dir = os.path.join(args.out_dir)
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os.makedirs(args.save_dir, exist_ok=True)
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os.makedirs(args.out_dir, exist_ok=True)
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ddp = int(os.environ.get("RANK", -1)) != -1
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ddp_local_rank, DEVICE = 0, "cuda:0"
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base_seed = 1337
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torch.manual_seed(base_seed)
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torch.cuda.manual_seed(base_seed)
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if ddp:
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init_distributed_mode()
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args.device = torch.device(DEVICE)
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rank = dist.get_rank()
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torch.manual_seed(base_seed + rank)
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# 同时设置 CUDA 的随机种子
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torch.cuda.manual_seed(base_seed + rank)
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if args.use_wandb and (not ddp or ddp_local_rank == 0):
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import swanlab as wandb
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wandb.init(project=args.wandb_project)
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else:
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wandb = None
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# 初始化所有模型
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actor_model, old_actor_model, ref_model, critic_model, reward_model, tokenizer, reward_tokenizer = init_model(lm_config=lm_config)
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# 准备数据集和数据加载器
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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 ddp else None
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train_loader = DataLoader(train_ds, batch_size=args.batch_size, pin_memory=True,
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drop_last=False, shuffle=False,
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num_workers=args.num_workers, sampler=train_sampler)
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# 初始化优化器
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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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iter_per_epoch = len(train_loader)
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total_optimizer_steps = (iter_per_epoch // 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)
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critic_scheduler = CosineAnnealingLR(critic_optimizer, T_max=total_optimizer_steps,
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eta_min=args.critic_learning_rate / 10)
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# 如果使用分布式训练，包装模型
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if ddp:
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actor_model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
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critic_model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
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actor_model = DistributedDataParallel(actor_model, device_ids=[ddp_local_rank])
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||
critic_model = DistributedDataParallel(critic_model, device_ids=[ddp_local_rank])
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# old_actor_model 不需要DDP包装，因为它只在主进程上用于计算，并且不进行梯度更新
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old_actor_model.to(args.device)
|
||
|
||
for epoch in range(args.epochs):
|
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ppo_train_epoch(epoch, wandb, old_actor_model, ref_model, actor_scheduler, critic_scheduler)
|
||
|
||
if ddp:
|
||
dist.destroy_process_group()
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