mirror of
https://github.com/jingyaogong/minimind.git
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349 lines
19 KiB
Python
Executable File
349 lines
19 KiB
Python
Executable File
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 gc
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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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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.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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class AutoAdaptiveValueTracker:
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"""SPO自适应价值追踪器"""
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def __init__(self, rho_mode='kl', rho_const=0.9, D_half=0.06, clip_lower=0.5, clip_upper=0.96):
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self.rho_mode = rho_mode
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self.rho_const = rho_const
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self.D_half = D_half
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self.clip_lower = clip_lower
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self.clip_upper = clip_upper
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N_init = 1.0 / (1.0 - self.clip_lower)
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self.alpha = 0.5 * N_init
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self.beta = 0.5 * N_init
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self.old_mean_logprob = None
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def get_baselines(self, batch_size):
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baseline = self.alpha / (self.alpha + self.beta)
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return torch.full((batch_size,), baseline, dtype=torch.float32)
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def compute_rho(self, cur_mean_logprob):
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if self.rho_mode == 'constant':
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return self.rho_const
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if self.old_mean_logprob is None:
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return self.rho_const
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kl = abs(self.old_mean_logprob - cur_mean_logprob)
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rho = 2 ** (-kl / self.D_half)
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return max(min(rho, self.clip_upper), self.clip_lower)
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def update(self, rewards, cur_logprobs=None, response_masks=None):
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if cur_logprobs is not None and response_masks is not None:
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mean_logprob = ((cur_logprobs * response_masks).sum() / response_masks.sum()).item()
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rho = self.compute_rho(mean_logprob)
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self.old_mean_logprob = mean_logprob
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else:
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rho = self.rho_const
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scale = 3.0
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normalized_rewards = (rewards + scale) / (2 * scale)
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avg_normalized_reward = normalized_rewards.mean().item()
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self.alpha = rho * self.alpha + avg_normalized_reward
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self.beta = rho * self.beta + (1 - avg_normalized_reward)
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return rho
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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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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 or 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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def mark_num(text):
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reward = 0
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if text.count("<think>") == 1: reward += 0.25
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if text.count("</think>") == 1: reward += 0.25
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if text.count("<answer>") == 1: reward += 0.25
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if text.count("</answer>") == 1: 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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if args.reasoning == 1:
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rewards = reasoning_model_reward(rewards)
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with torch.no_grad():
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reward_model_scores = []
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scale = 3.0
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for i, (prompt, response) in enumerate(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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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 spo_train_epoch(epoch, loader, iters, ref_model, reward_model, reward_tokenizer, value_tracker, start_step=0, wandb=None):
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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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prompt_inputs = tokenizer(prompts, return_tensors="pt", padding=True, return_token_type_ids=False,
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padding_side="left", add_special_tokens=False).to(args.device) # input_ids: [B, P], attention_mask: [B, P]
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if args.max_seq_len:
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prompt_inputs["input_ids"] = prompt_inputs["input_ids"][:, -args.max_seq_len:]
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prompt_inputs["attention_mask"] = prompt_inputs["attention_mask"][:, -args.max_seq_len:]
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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 = model.module if isinstance(model, DistributedDataParallel) else model
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outputs = model_for_gen.generate(
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**prompt_inputs, max_new_tokens=args.max_gen_len, do_sample=True, temperature=0.8,
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num_return_sequences=1, pad_token_id=tokenizer.pad_token_id) # [B, P+R]
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completion_ids = outputs[:, prompt_inputs["input_ids"].size(1):] # [B, R]
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def get_per_token_logps(mdl, input_ids, n_keep):
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input_ids = input_ids.detach().clone() if input_ids.is_inference() else input_ids
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logits = mdl(input_ids, logits_to_keep=n_keep + 1).logits[:, :-1, :]
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per_token_logps = []
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for logits_row, ids_row in zip(logits, input_ids[:, -n_keep:]):
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ids_row = ids_row.detach().clone() if ids_row.is_inference() else ids_row
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per_token_logps.append(torch.gather(logits_row.log_softmax(dim=-1), 1, ids_row.unsqueeze(1)).squeeze(1))
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return torch.stack(per_token_logps)
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with autocast_ctx:
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per_token_logps = get_per_token_logps(model, outputs, completion_ids.size(1)) # [B, R]
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res = model(outputs) if lm_config.use_moe else None
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aux_loss = res.aux_loss if res is not None else torch.tensor(0.0, device=args.device)
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with torch.no_grad():
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ref_per_token_logps = get_per_token_logps(ref_model, outputs, completion_ids.size(1)) # [B, R]
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completions = tokenizer.batch_decode(completion_ids, skip_special_tokens=True) # list[str], length B
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rewards = calculate_rewards(prompts, completions, reward_model, reward_tokenizer).to(args.device) # [B]
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baselines = value_tracker.get_baselines(len(prompts)).to(args.device) # [B]
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scale = 3.0
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# Un-normalize baselines to be in the same scale as raw rewards [-3, 3]
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unnormalized_baselines = baselines * (2 * scale) - scale # [B]
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advantages = rewards - unnormalized_baselines # [B]
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# 直接使用 baseline 提供的优势估计，只做裁剪防止梯度爆炸。不再做 batch 内归一化，因为 baseline 已经提供了跨 batch 的稳定基线
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advantages = advantages.clamp(-5.0, 5.0)
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is_eos = completion_ids == tokenizer.eos_token_id # [B, R]
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eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=args.device) # [B]
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eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)]
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completion_mask = (torch.arange(is_eos.size(1), device=args.device).expand(is_eos.size(0), -1) <= eos_idx.unsqueeze(1)).int() # [B, R]
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kl_div = ref_per_token_logps - per_token_logps # [B, R]
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per_token_kl = torch.exp(kl_div) - kl_div - 1 # [B, R]
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per_token_loss = -per_token_logps * advantages.unsqueeze(1) + args.beta * per_token_kl # [B, R]
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policy_loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
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loss = (policy_loss + aux_loss) / args.accumulation_steps # scalar
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loss.backward()
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response_masks = completion_mask.float() # [B, R]
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rho = value_tracker.update(rewards, per_token_logps.detach(), response_masks)
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if (step + 1) % args.accumulation_steps == 0:
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if args.grad_clip > 0:
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
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optimizer.step()
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scheduler.step()
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optimizer.zero_grad()
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if step % args.log_interval == 0 or step == iters:
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policy_loss_val = loss.item() * args.accumulation_steps
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current_aux_loss = aux_loss.item()
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avg_reward_val = rewards.mean().item()
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avg_len_val = completion_mask.sum(dim=1).float().mean().item()
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kl_val = ((per_token_kl * completion_mask).sum() / (completion_mask.sum() + 1e-8)).item()
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avg_baseline_val = baselines.mean().item()
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current_lr = optimizer.param_groups[0]['lr']
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Logger(f'Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), '
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f'Actor Loss: {policy_loss_val:.4f}, Aux Loss: {current_aux_loss:.4f}, Reward: {avg_reward_val:.4f}, '
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f'Baseline: {avg_baseline_val:.4f}, KL: {kl_val:.4f}, Rho: {rho:.4f}, '
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f'Avg Response Len: {avg_len_val:.2f}, Learning Rate: {current_lr:.8f}')
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if wandb and is_main_process():
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wandb.log({
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"policy_loss": policy_loss_val,
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"aux_loss": current_aux_loss,
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"reward": avg_reward_val,
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"kl": kl_val,
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"rho": float(rho),
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"baseline": avg_baseline_val,
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"advantages_mean": advantages.mean().item(),
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"learning_rate": current_lr
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})
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if (step % args.save_interval == 0 or step == iters - 1) and is_main_process():
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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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state_dict = model.module.state_dict() if isinstance(model, DistributedDataParallel) else model.state_dict()
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torch.save({k: v.half().cpu() for k, v in state_dict.items()}, ckp)
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lm_checkpoint(lm_config, weight=args.save_weight, model=model, optimizer=optimizer,
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epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints', scheduler=scheduler)
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model.train()
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del state_dict
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del prompt_inputs, outputs, completion_ids, per_token_logps, ref_per_token_logps
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del completions, rewards, advantages, completion_mask, baselines, response_masks
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind SPO (Self-Play 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='spo', 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=1e-7, help="初始学习率")
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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=4, 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("--beta", type=float, default=0.02, help="KL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("--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-SPO", 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,
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max_seq_len=args.max_seq_len + args.max_gen_len, 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-SPO-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（Policy, Ref, Reward）和Value Tracker、数据 ==========
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base_weight = "reason" if args.reasoning == 1 else "full_sft"
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# Policy模型
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model, tokenizer = init_model(lm_config, base_weight, device=args.device)
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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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# 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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# Value Tracker
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value_tracker = AutoAdaptiveValueTracker(rho_mode='kl', rho_const=0.9, D_half=0.06, clip_lower=0.5, clip_upper=0.96)
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train_ds = RLAIFDataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len)
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train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
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optimizer = optim.AdamW(model.parameters(), lr=args.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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scheduler = CosineAnnealingLR(optimizer, T_max=total_optimizer_steps, eta_min=args.learning_rate / 10)
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# ========== 6. 从ckp恢复状态 ==========
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start_epoch, start_step = 0, 0
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if ckp_data:
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model.load_state_dict(ckp_data['model'])
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optimizer.load_state_dict(ckp_data['optimizer'])
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scheduler.load_state_dict(ckp_data['scheduler'])
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start_epoch = ckp_data['epoch']
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start_step = ckp_data.get('step', 0)
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# ========== 7. DDP包模型 ==========
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if dist.is_initialized():
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model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
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model = DistributedDataParallel(model, device_ids=[local_rank])
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# ========== 8. 开始训练 ==========
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for epoch in range(start_epoch, args.epochs):
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train_sampler and train_sampler.set_epoch(epoch)
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if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
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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')
|
||
spo_train_epoch(epoch, loader, len(loader) + start_step + 1, ref_model, reward_model, reward_tokenizer, value_tracker, start_step, wandb)
|
||
else: # 默认从头开始
|
||
loader = DataLoader(train_ds, batch_size=args.batch_size, pin_memory=True,
|
||
drop_last=False, shuffle=(train_sampler is None),
|
||
num_workers=args.num_workers, sampler=train_sampler)
|
||
spo_train_epoch(epoch, loader, len(loader), ref_model, reward_model, reward_tokenizer, value_tracker, 0, wandb)
|
||
|
||
# ========== 9. 清理分布进程 ==========
|
||
if dist.is_initialized(): dist.destroy_process_group() |