421 lines
21 KiB
Python
421 lines
21 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
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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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def calculate_rewards(prompts, responses, reward_model, reward_tokenizer, args):
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"""
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计算复杂奖励，包含格式奖励、模型打分以及 DAPO 特有的长度惩罚。
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输入的 prompts/responses 已经是展开后的（batch_size * group_size）条目。
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"""
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def reasoning_model_reward(rewards):
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# 1. 结构化格式奖励
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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, r, re.S) for r in responses]
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matches_pattern2 = [re.match(pattern2, r, re.S) for r in responses]
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format_rewards = [0.5 if (m1 or m2) else 0.0
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for m1, m2 in zip(matches_pattern, matches_pattern2)]
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rewards += torch.tensor(format_rewards, device=args.device)
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# 2. 标签存在奖励
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def mark_num(text):
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reward = 0
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for tag in ["<think>", "</think>", "<answer>", "</answer>"]:
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if text.count(tag) == 1:
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reward += 0.25
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return reward
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mark_rewards = [mark_num(r) for r 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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if args.reasoning == 1:
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rewards = reasoning_model_reward(rewards)
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# 3. 超长惩罚 (Overlong Punishment - DAPO 特性)
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len_penalty = []
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for resp in responses:
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curr_len = len(resp)
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if curr_len > args.length_threshold:
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penalty = -args.length_penalty * ((curr_len - args.length_threshold) / 100)
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len_penalty.append(max(penalty, -1.0))
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else:
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len_penalty.append(0.0)
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rewards += torch.tensor(len_penalty, device=args.device)
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# 4. 奖励模型评分
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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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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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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 dapo_train_epoch(epoch, loader, iters, old_actor_model, ref_model,
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actor_scheduler, reward_model, reward_tokenizer,
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start_step=0, wandb=None):
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actor_model.train()
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for step, batch in enumerate(loader, start=start_step + 1):
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prompts = batch["prompt"]
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batch_size = len(prompts)
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G = args.group_size
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# 每个 prompt 重复 G 次，展开为 (batch_size * G) 条输入
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prompts_expanded = [p for p in prompts for _ in range(G)]
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enc = tokenizer(
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prompts_expanded,
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=args.max_seq_len,
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padding_side="left"
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).to(args.device)
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prompt_length = enc.input_ids.shape[1]
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with torch.no_grad():
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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,
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attention_mask=enc.attention_mask,
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max_new_tokens=args.max_gen_len,
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do_sample=True,
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temperature=0.8,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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)
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# gen_out: (batch_size * G, seq_len)
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responses_text = [
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tokenizer.decode(gen_out[i, prompt_length:], skip_special_tokens=True)
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for i in range(len(prompts_expanded))
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]
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rewards_flat = calculate_rewards(prompts_expanded, responses_text, reward_model, reward_tokenizer, args)
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# rewards_flat: (batch_size * G,)
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# ------------------------------------------------------------------
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# advantage = (r - mean_group) / std_group
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# ------------------------------------------------------------------
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rewards_grouped = rewards_flat.view(batch_size, G)
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mean_r = rewards_grouped.mean(dim=1, keepdim=True)
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std_r = rewards_grouped.std(dim=1, keepdim=True).clamp(min=1e-8)
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advantages = ((rewards_grouped - mean_r) / std_r).view(-1) # (batch_size * G,)
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# ------------------------------------------------------------------
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# Dynamic Sampling
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# ------------------------------------------------------------------
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if args.use_dynamic_sampling == 1:
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if (std_r < 1e-6).all().item():
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if is_main_process():
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Logger(f"Step {step}: All groups have zero reward variance, skipping...")
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continue
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# ------------------------------------------------------------------
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# Actor forward
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# ------------------------------------------------------------------
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full_mask = (gen_out != tokenizer.pad_token_id).long()
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with autocast_ctx:
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res = actor_model(input_ids=gen_out, attention_mask=full_mask)
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logits = res.logits
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aux_loss = res.aux_loss if lm_config.use_moe else torch.tensor(0.0, device=args.device)
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labels = gen_out[:, 1:].clone()
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logp_tokens = F.log_softmax(logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-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))
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# Token-level 平均 log-prob（防止长回复主导梯度）
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token_count = final_mask.sum(dim=1).clamp(min=1).float()
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actor_logp = (logp_tokens * final_mask).sum(dim=1) / token_count
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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
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old_logp_tokens = F.log_softmax(old_logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1)
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old_logp = (old_logp_tokens * final_mask).sum(dim=1) / token_count
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ref_logits = ref_model(input_ids=gen_out, attention_mask=full_mask).logits
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ref_logp_tokens = F.log_softmax(ref_logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1)
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ref_logp = (ref_logp_tokens * final_mask).sum(dim=1) / token_count
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kl = (actor_logp - old_logp).mean()
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kl_ref = (actor_logp - ref_logp).mean()
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ratio = torch.exp(actor_logp - old_logp)
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# ------------------------------------------------------------------
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# Decoupled Clip
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# Adv > 0：只限上界，允许正向大步更新prompts_expanded = [p for p in prompts for _ in range(G)]
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# Adv ≤ 0：只限下界，防止负向更新反弹
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# ------------------------------------------------------------------
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surr1 = ratio * advantages
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clipped_ratio = torch.where(
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advantages > 0,
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torch.clamp(ratio, max=1.0 + args.clip_epsilon_high),
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torch.clamp(ratio, min=1.0 - args.clip_epsilon_low),
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)
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surr2 = clipped_ratio * advantages
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policy_loss = -torch.min(surr1, surr2).mean()
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loss = (policy_loss + args.kl_coef * kl_ref + aux_loss) / args.accumulation_steps
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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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actor_optimizer.step()
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actor_scheduler.step()
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actor_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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lengths = torch.where(
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is_eos.any(dim=1),
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eos_indices + 1,
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torch.tensor(response_ids.shape[1], device=is_eos.device)
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)
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avg_len = lengths.float().mean()
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reward_std = rewards_flat.std().item()
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if wandb is not None:
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wandb.log({
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"actor_loss": policy_loss.item(),
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"reward": rewards_flat.mean().item(),
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"reward_std": reward_std,
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"kl": kl.item(),
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"kl_ref": kl_ref.item(),
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"avg_response_len": avg_len.item(),
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"actor_lr": actor_optimizer.param_groups[0]['lr'],
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})
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Logger(
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f"Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), "
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f"Loss: {policy_loss.item():.4f}, Reward: {rewards_flat.mean().item():.4f}, "
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f"KL: {kl.item():.4f}, Len: {avg_len.item():.2f}"
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)
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if (step + 1) % args.update_old_actor_freq == 0:
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raw_actor = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
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raw_actor = getattr(raw_actor, '_orig_mod', raw_actor)
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old_actor_model.load_state_dict({k: v.detach().cpu() for k, v in raw_actor.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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raw_actor = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
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raw_actor = getattr(raw_actor, '_orig_mod', raw_actor)
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torch.save({k: v.half().cpu() for k, v in raw_actor.state_dict().items()}, ckp)
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lm_checkpoint(
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lm_config, weight=args.save_weight, model=actor_model,
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optimizer=actor_optimizer, epoch=epoch, step=step,
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wandb=wandb, save_dir='../checkpoints', scheduler=actor_scheduler,
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)
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actor_model.train()
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del enc, gen_out, responses_text, rewards_flat, rewards_grouped
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del advantages, full_mask, logits, labels, logp_tokens, final_mask, token_count
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del actor_logp, old_logits, old_logp, ref_logits, ref_logp
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del kl, kl_ref, ratio, surr1, surr2, clipped_ratio, policy_loss, loss
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="MiniMind DAPO (Decoupled Clip and Dynamic Sampling Policy Optimization)"
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)
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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', type=str, default='dapo', 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="prompt 级别的 batch size（实际前向 = batch_size * group_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("--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', type=int, default=512, help="隐藏层维度")
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parser.add_argument('--num_hidden_layers', type=int, default=8, help="隐藏层数量")
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parser.add_argument('--use_moe', type=int, default=0, 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', type=int, default=66, help="Prompt 最大长度")
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parser.add_argument("--max_gen_len", type=int, default=1024, 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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# DAPO 核心参数
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parser.add_argument("--clip_epsilon_low", type=float, default=0.1, help="Decoupled Clip 下界")
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parser.add_argument("--clip_epsilon_high", type=float, default=0.2, help="Decoupled Clip 上界")
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parser.add_argument("--group_size", type=int, default=8, help="每个 prompt 的采样回复数")
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parser.add_argument("--use_dynamic_sampling", type=int, default=1, choices=[0, 1], help="是否开启动态采样过滤")
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parser.add_argument("--length_penalty", type=float, default=0.1, help="长度惩罚系数")
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parser.add_argument("--length_threshold", type=int, default=1200, help="触发长度惩罚的字符阈值")
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parser.add_argument("--kl_coef", type=float, default=0.02, help="KL 散度惩罚系数（相对 ref_model）")
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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（步数）")
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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', type=int, default=0, choices=[0, 1], help="是否自动检测并续训（0=否，1=是）")
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parser.add_argument("--use_wandb", action="store_true", help="是否使用 wandb/swanlab")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-DAPO", help="wandb 项目名")
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parser.add_argument("--use_compile", type=int, default=0, choices=[0, 1], help="是否使用 torch.compile 加速（0=否，1=是）")
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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():
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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(
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hidden_size=args.hidden_size,
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num_hidden_layers=args.num_hidden_layers,
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use_moe=bool(args.use_moe)
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)
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ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') \
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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 (SwanLab) ==========
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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.init(
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project=args.wandb_project,
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name=f"MiniMind-DAPO-{args.learning_rate}",
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id=wandb_id,
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resume=resume,
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)
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# ========== 5. 初始化模型 ==========
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base_weight = "reason" if args.reasoning == 1 else "full_sft"
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actor_model, tokenizer = init_model(lm_config, base_weight, device=args.device)
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if args.use_compile == 1:
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actor_model = torch.compile(actor_model)
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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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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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reward_model = AutoModel.from_pretrained(
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args.reward_model_path,
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torch_dtype=torch.float16,
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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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# ========== 6. 数据、优化器、调度器 ==========
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train_ds = RLAIFDataset(args.data_path, tokenizer, max_length=(args.max_seq_len + args.max_gen_len))
|
||
train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
|
||
actor_optimizer = optim.AdamW(actor_model.parameters(), lr=args.learning_rate)
|
||
|
||
iters = len(DataLoader(train_ds, batch_size=args.batch_size, sampler=train_sampler))
|
||
total_steps = (iters // args.accumulation_steps) * args.epochs
|
||
actor_scheduler = CosineAnnealingLR(actor_optimizer, T_max=total_steps, eta_min=args.learning_rate / 10)
|
||
|
||
if ckp_data:
|
||
actor_model.load_state_dict(ckp_data['model'])
|
||
actor_optimizer.load_state_dict(ckp_data['optimizer'])
|
||
actor_scheduler.load_state_dict(ckp_data['scheduler'])
|
||
start_epoch = ckp_data['epoch']
|
||
start_step = ckp_data.get('step', 0)
|
||
else:
|
||
start_epoch, start_step = 0, 0
|
||
|
||
if dist.is_initialized():
|
||
actor_model = DistributedDataParallel(actor_model, device_ids=[local_rank])
|
||
old_actor_model.to(args.device)
|
||
|
||
# ========== 7. 打印配置摘要 ==========
|
||
if is_main_process():
|
||
Logger("=" * 60)
|
||
Logger("DAPO Training Config (Critic-free):")
|
||
Logger(f" group_size = {args.group_size}")
|
||
Logger(f" clip_epsilon_low = {args.clip_epsilon_low}")
|
||
Logger(f" clip_epsilon_high = {args.clip_epsilon_high}")
|
||
Logger(f" use_dynamic_sampling = {args.use_dynamic_sampling}")
|
||
Logger(f" kl_coef = {args.kl_coef}")
|
||
Logger(f" effective_batch = {args.batch_size} prompts × {args.group_size} = {args.batch_size * args.group_size}")
|
||
Logger("=" * 60)
|
||
|
||
# ========== 8. 开始训练 ==========
|
||
for epoch in range(start_epoch, args.epochs):
|
||
train_sampler and train_sampler.set_epoch(epoch)
|
||
setup_seed(42 + epoch)
|
||
indices = torch.randperm(len(train_ds)).tolist()
|
||
skip = start_step if (epoch == start_epoch and start_step > 0) else 0
|
||
loader = DataLoader(
|
||
train_ds,
|
||
batch_sampler=SkipBatchSampler(train_sampler or indices, args.batch_size, skip),
|
||
num_workers=args.num_workers,
|
||
pin_memory=True,
|
||
)
|
||
dapo_train_epoch(
|
||
epoch, loader, len(loader),
|
||
old_actor_model, ref_model,
|
||
actor_scheduler, reward_model, reward_tokenizer,
|
||
skip, wandb,
|
||
)
|
||
|
||
if dist.is_initialized():
|
||
dist.destroy_process_group() |