mirror of
https://github.com/jingyaogong/minimind.git
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jingyaogong
328 lines
19 KiB
Python
Executable File
328 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 math
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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.nn.functional as F
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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, LMForRewardModel
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from trainer.rollout_engine import create_rollout_engine, compute_per_token_logps
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warnings.filterwarnings('ignore')
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def rep_penalty(text, n=3, cap=0.5):
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toks = re.findall(r"\w+|[^\w\s]", text.lower())
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grams = [tuple(toks[i:i + n]) for i in range(len(toks) - n + 1)]
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return min(cap, (len(grams) - len(set(grams))) * cap * 2 / len(grams)) if grams else 0.0
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def calculate_rewards(prompts, responses, reward_model):
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rewards = torch.zeros(len(responses), device=args.device)
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with torch.no_grad():
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reward_model_scores = []
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batch_size = len(prompts)
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for i in range(batch_size):
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for j in range(args.num_generations):
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response_idx = i * args.num_generations + j
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response = responses[response_idx]
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prompt = prompts[i]
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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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answer = response
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rewards[response_idx] += 0.5 if 20 <= len(response.strip()) <= 800 else -0.5
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if '</think>' in response:
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thinking_content, answer_content = response.split('</think>', 1)
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rewards[response_idx] += 1.0 if 20 <= len(thinking_content.strip()) <= 300 else -0.5
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rewards[response_idx] += 0.25 if response.count('</think>') == 1 else -0.25
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answer = answer_content.strip()
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rewards[response_idx] -= rep_penalty(answer)
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score = reward_model.get_score(messages, answer)
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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 grpo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, reward_model, start_step=0, wandb=None, use_sglang=False):
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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)
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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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rollout_result = rollout_engine.rollout(
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prompt_ids=prompt_inputs["input_ids"],
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attention_mask=prompt_inputs["attention_mask"],
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num_generations=args.num_generations,
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max_new_tokens=args.max_gen_len,
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temperature=0.8,
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)
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outputs = rollout_result.output_ids
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completion_ids = rollout_result.completion_ids
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completions = rollout_result.completions
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old_per_token_logps = rollout_result.per_token_logps.to(args.device)
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full_mask = (outputs != tokenizer.pad_token_id).long()
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model_unwrapped = model.module if isinstance(model, DistributedDataParallel) else model
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with autocast_ctx:
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res = model_unwrapped(outputs, attention_mask=full_mask)
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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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logits = res.logits[:, :-1, :]
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per_token_logps = F.log_softmax(logits, dim=-1).gather(2, outputs[:, 1:].unsqueeze(-1)).squeeze(-1)[:, -completion_ids.size(1):]
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with torch.no_grad():
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ref_per_token_logps = compute_per_token_logps(ref_model, outputs, completion_ids.size(1), attention_mask=full_mask)
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rewards = calculate_rewards(prompts, completions, reward_model).to(args.device) # [B*num_gen]
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if args.debug_mode and is_main_process() and step % args.debug_interval == 0:
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for i in range(len(prompts)):
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Logger(f"[DEBUG] step={step}, sample[{i}]")
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Logger('-'*100)
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Logger(f"{'=' * 30} [DEBUG] sample[{i}] CONTEXT_BEGIN {'=' * 30}")
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Logger(prompts[i])
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Logger(f"{'=' * 31} [DEBUG] sample[{i}] CONTEXT_END {'=' * 31}")
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for j in range(args.num_generations):
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idx = i * args.num_generations + j
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Logger(f"{'=' * 28} [DEBUG] gen[{j}] RESPONSE_BEGIN {'=' * 28}")
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Logger(completions[idx])
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Logger(f"{'=' * 29} [DEBUG] gen[{j}] RESPONSE_END {'=' * 29}")
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Logger(f"[DEBUG] gen[{j}] reward={rewards[idx].item():.4f}")
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Logger('='*100)
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grouped_rewards = rewards.view(-1, args.num_generations) # [B, num_gen]
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mean_r = grouped_rewards.mean(dim=1).repeat_interleave(args.num_generations) # [B*num_gen]
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std_r = grouped_rewards.std(dim=1, unbiased=False).repeat_interleave(args.num_generations) # [B*num_gen]
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advantages = (rewards - mean_r) / (std_r + 1e-4) # [B*num_gen]
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is_eos = completion_ids == tokenizer.eos_token_id # [B*num_gen, R]
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eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=args.device)
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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*num_gen, R]
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kl_div = ref_per_token_logps - per_token_logps
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per_token_kl = torch.exp(kl_div) - kl_div - 1 # [B*num_gen, R]
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ratio = torch.exp(per_token_logps - old_per_token_logps) # [B*num_gen, R]
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if args.loss_type == "cispo":
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clamped_ratio = torch.clamp(ratio, max=args.epsilon_high).detach()
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per_token_loss = -(clamped_ratio * advantages.unsqueeze(1) * per_token_logps - args.beta * per_token_kl)
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else:
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clipped_ratio = torch.clamp(ratio, 1 - args.epsilon, 1 + args.epsilon)
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per_token_loss1 = ratio * advantages.unsqueeze(1)
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per_token_loss2 = clipped_ratio * advantages.unsqueeze(1)
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per_token_loss = -(torch.min(per_token_loss1, per_token_loss2) - args.beta * per_token_kl)
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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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if step % 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_ref_val = ((ref_per_token_logps - per_token_logps) * completion_mask).sum().item() / completion_mask.sum().item()
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advantages_mean_val = advantages.mean().item()
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advantages_std_val = advantages.std().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'Reward: {avg_reward_val:.4f}, KL_ref: {kl_ref_val:.4f}, '
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f'Adv Std: {advantages_std_val:.4f}, Adv Mean: {advantages_mean_val:.4f}, '
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f'Actor Loss: {policy_loss_val:.4f}, 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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"reward": avg_reward_val,
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"kl_ref": kl_ref_val,
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"advantages_std": advantages_std_val,
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"advantages_mean": advantages_mean_val,
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"policy_loss": policy_loss_val,
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"avg_response_len": avg_len_val,
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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) 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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raw_model = model.module if isinstance(model, DistributedDataParallel) else model
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raw_model = getattr(raw_model, '_orig_mod', raw_model)
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state_dict = raw_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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if step % args.save_interval == 0 or step == iters: rollout_engine.update_policy(model)
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del prompt_inputs, outputs, completion_ids, per_token_logps, ref_per_token_logps
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del completions, rewards, grouped_rewards, mean_r, std_r, advantages, completion_mask
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if step > start_step and step % 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 __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind GRPO (Group Relative Policy Optimization)")
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parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录")
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parser.add_argument('--save_weight', default='grpo', 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=3e-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=1, help="梯度累积步数")
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parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值")
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parser.add_argument("--log_interval", type=int, default=1, help="日志打印间隔")
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parser.add_argument("--save_interval", type=int, default=10, help="模型保存间隔")
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parser.add_argument('--hidden_size', default=768, 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=768, type=int, 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.jsonl", help="RLAIF数据路径")
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parser.add_argument("--num_generations", type=int, default=6, help="每个prompt生成的样本数")
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parser.add_argument("--beta", type=float, default=0.1, help="KL惩罚系数")
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parser.add_argument("--loss_type", type=str, default="cispo", choices=["grpo", "cispo"], help="loss类型")
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parser.add_argument("--epsilon", type=float, default=0.2, help="GRPO的PPO clip epsilon")
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parser.add_argument("--epsilon_high", type=float, default=5.0, help="epsilon上界")
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parser.add_argument('--from_weight', default='full_sft', type=str, help="基于哪个权重训练")
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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-GRPO", help="wandb项目名")
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parser.add_argument("--use_compile", default=0, type=int, choices=[0, 1], help="是否使用torch.compile加速（0=否，1=是）")
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parser.add_argument("--debug_mode", action="store_true", help="是否打印训练调试采样")
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parser.add_argument("--debug_interval", type=int, default=20, help="debug模式下每隔多少step打印一次采样")
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parser.add_argument("--thinking_ratio", type=float, default=0.9, help="按概率开启thinking（0.0~1.0）")
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parser.add_argument("--rollout_engine", type=str, default="torch", choices=["torch", "sglang"], help="rollout引擎类型")
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parser.add_argument("--sglang_base_url", type=str, default="http://localhost:8998", help="SGLang服务器URL")
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parser.add_argument("--sglang_model_path", type=str, default="../model", help="SGLang tokenizer路径")
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parser.add_argument("--sglang_shared_path", type=str, default="./sglang_ckpt_grpo", help="SGLang共享存储路径")
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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-GRPO-Epoch-{args.epochs}-BS-{args.batch_size}-LR-{args.learning_rate}"
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wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume)
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# ========== 5. 初始化模型和数据 ==========
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base_weight = args.from_weight
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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 = LMForRewardModel(args.reward_model_path, device=args.device, dtype=torch.float16)
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# Rollout引擎（可插拔替换，只负责 policy 推理）
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rollout_engine = create_rollout_engine(
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engine_type=args.rollout_engine,
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policy_model=model,
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tokenizer=tokenizer,
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device=args.device,
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autocast_ctx=autocast_ctx,
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sglang_base_url=args.sglang_base_url,
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sglang_model_path=args.sglang_model_path,
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sglang_shared_path=args.sglang_shared_path,
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)
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# 数据和优化器
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train_ds = RLAIFDataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len, thinking_ratio=args.thinking_ratio)
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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 = math.ceil(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. 编译和分布式包装 ==========
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if args.use_compile == 1:
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model = torch.compile(model)
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Logger('torch.compile enabled')
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rollout_engine.update_policy(model)
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if dist.is_initialized():
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model = DistributedDataParallel(model, device_ids=[local_rank])
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rollout_engine.update_policy(model)
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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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setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
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skip = start_step if (epoch == start_epoch and start_step > 0) else 0
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batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
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loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
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if skip > 0:
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Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
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grpo_train_epoch(epoch, loader, len(loader) + skip, rollout_engine, ref_model, reward_model, start_step, wandb, use_sglang = (args.rollout_engine == "sglang"))
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else:
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grpo_train_epoch(epoch, loader, len(loader), rollout_engine, ref_model, reward_model, 0, wandb, use_sglang = (args.rollout_engine == "sglang"))
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||
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# ========== 9. 清理分布进程 ==========
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if dist.is_initialized(): dist.destroy_process_group() |