From 7389f64deed57db032127d8194832a4f3d7a211b Mon Sep 17 00:00:00 2001
From: vanking <128895735+vanking20000918@users.noreply.github.com>
Date: Mon, 2 Feb 2026 22:16:52 +0800
Subject: [PATCH] =?UTF-8?q?[add]=20add=20DAPO=20argorithm=20(Decoupled=20C?=
=?UTF-8?q?lip=20and=C2=A0Dynamic=20sAmpling=C2=A0Policy=C2=A0Optimization?=
=?UTF-8?q?)?=
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I mainly add two points into original grpo algorithm according to this [paper](https://arxiv.org/pdf/2503.14476): Clip-Higher & Dynamic Sampling
---
train_dapo.py | 351 ++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 351 insertions(+)
create mode 100644 train_dapo.py
diff --git a/train_dapo.py b/train_dapo.py
new file mode 100644
index 0000000..9adb800
--- /dev/null
+++ b/train_dapo.py
@@ -0,0 +1,351 @@
+import os
+import sys
+
+__package__ = "trainer"
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import argparse
+import re
+import gc
+import warnings
+import torch
+import torch.distributed as dist
+from transformers import AutoTokenizer
+from contextlib import nullcontext
+from torch import optim
+from torch.nn.parallel import DistributedDataParallel
+from torch.utils.data import DataLoader, DistributedSampler
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from transformers import AutoModel
+from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
+from dataset.lm_dataset import RLAIFDataset
+from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model
+
+warnings.filterwarnings('ignore')
+
+
+def calculate_rewards(prompts, responses, reward_model, reward_tokenizer):
+ """整合所有奖励函数计算总奖励"""
+ def reasoning_model_reward(rewards):
+ pattern = r"^\n.*?\n\n\n.*?\n$"
+ pattern2 = r"^\n.*?\n\n\n\n.*?\n$"
+ matches_pattern = [re.match(pattern, response, re.S) for response in responses]
+ matches_pattern2 = [re.match(pattern2, response, re.S) for response in responses]
+
+ format_rewards = []
+ for match_pattern, match_pattern2 in zip(matches_pattern, matches_pattern2):
+ if match_pattern or match_pattern2:
+ format_rewards.append(0.5)
+ else:
+ format_rewards.append(0.0)
+ rewards += torch.tensor(format_rewards, device=args.device)
+
+ def mark_num(text):
+ reward = 0
+ if text.count("") == 1: reward += 0.25
+ if text.count("") == 1: reward += 0.25
+ if text.count("") == 1: reward += 0.25
+ if text.count("") == 1: reward += 0.25
+ return reward
+
+ mark_rewards = [mark_num(response) for response in responses]
+ rewards += torch.tensor(mark_rewards, device=args.device)
+ return rewards
+
+ rewards = torch.zeros(len(responses), device=args.device)
+ if args.reasoning == 1:
+ rewards = reasoning_model_reward(rewards)
+
+ with torch.no_grad():
+ reward_model_scores = []
+ batch_size = len(prompts)
+ scale = 3.0
+
+ for i in range(batch_size):
+ for j in range(args.num_generations):
+ response_idx = i * args.num_generations + j
+ response = responses[response_idx]
+ prompt = prompts[i]
+
+ pattern = r"<\|im_start\|>(system|user|assistant)\s+(.*?)<\|im_end\|>"
+ matches = re.findall(pattern, prompt, re.DOTALL)
+ messages = [{"role": role, "content": content.strip()} for role, content in matches]
+
+ tmp_chat = messages + [{"role": "assistant", "content": response}]
+ score = reward_model.get_score(reward_tokenizer, tmp_chat)
+ score = max(min(score, scale), -scale)
+
+ if args.reasoning == 1:
+ answer_match = re.search(r'(.*?)', response, re.DOTALL)
+ if answer_match:
+ answer_content = answer_match.group(1).strip()
+ tmp_chat = messages + [{"role": "assistant", "content": answer_content}]
+ answer_score = reward_model.get_score(reward_tokenizer, tmp_chat)
+ answer_score = max(min(answer_score, scale), -scale)
+ score = score * 0.4 + answer_score * 0.6
+
+ reward_model_scores.append(score)
+
+ reward_model_scores = torch.tensor(reward_model_scores, device=args.device)
+ rewards += reward_model_scores
+
+ return rewards
+
+
+def get_per_token_logps(mdl, input_ids, n_keep):
+ """计算每个token的log概率"""
+ # 确保在计算新策略logp时保留梯度，计算旧策略时detach
+ if not mdl.training:
+ with torch.no_grad():
+ logits = mdl(input_ids, logits_to_keep=n_keep + 1).logits[:, :-1, :]
+ else:
+ logits = mdl(input_ids, logits_to_keep=n_keep + 1).logits[:, :-1, :]
+
+ per_token_logps = []
+ for logits_row, ids_row in zip(logits, input_ids[:, -n_keep:]):
+ # 即使是inference模式，如果ids_row需要梯度(通常不需要)，也需注意。这里假设ids_row是生成的idx
+ per_token_logps.append(torch.gather(logits_row.log_softmax(dim=-1), 1, ids_row.unsqueeze(1)).squeeze(1))
+ return torch.stack(per_token_logps)
+
+
+def grpo_train_epoch(epoch, loader, iters, ref_model, reward_model, reward_tokenizer, start_step=0, wandb=None):
+ for step, batch in enumerate(loader, start=start_step + 1):
+ prompts = batch['prompt'] # list[str], length B
+ prompt_inputs = tokenizer(prompts, return_tensors="pt", padding=True, return_token_type_ids=False,
+ padding_side="left", add_special_tokens=False).to(args.device)
+
+ if args.max_seq_len:
+ prompt_inputs["input_ids"] = prompt_inputs["input_ids"][:, -args.max_seq_len:]
+ prompt_inputs["attention_mask"] = prompt_inputs["attention_mask"][:, -args.max_seq_len:]
+
+ # ========== 采样 (Sampling) ==========
+ with torch.no_grad():
+ model_for_gen = model.module if isinstance(model, DistributedDataParallel) else model
+ outputs = model_for_gen.generate(
+ **prompt_inputs, max_new_tokens=args.max_gen_len, do_sample=True, temperature=0.8,
+ num_return_sequences=args.num_generations, pad_token_id=tokenizer.pad_token_id)
+
+ completion_ids = outputs[:, prompt_inputs["input_ids"].size(1):]
+
+ # 计算旧策略的Log Probs (Old Policy) - Detached
+ with torch.no_grad():
+ old_per_token_logps = get_per_token_logps(model, outputs, completion_ids.size(1))
+ ref_per_token_logps = get_per_token_logps(ref_model, outputs, completion_ids.size(1))
+
+ completions = tokenizer.batch_decode(completion_ids, skip_special_tokens=True)
+ rewards = calculate_rewards(prompts, completions, reward_model, reward_tokenizer).to(args.device)
+
+ # ========== 优势计算 (Advantage Computation) ==========
+ grouped_rewards = rewards.view(-1, args.num_generations) # [B, num_gen]
+ mean_r = grouped_rewards.mean(dim=1)
+ std_r = grouped_rewards.std(dim=1)
+
+ # DAPO Feature: Dynamic Sampling (过滤掉 reward 方差为 0 的组)
+ # 如果 std 为 0，说明该组所有样本奖励相同，无法提供相对优势，视为无效样本
+ valid_group_mask = std_r > 0.3 # 如果取小值1e-6在本项目中偏小，没有起到过滤效果，实际测试std_r均值绝大部分位于(0.3, 0.9)区间
+
+ # 将 valid_group_mask 扩展到 [B * num_gen]
+ valid_sample_mask = valid_group_mask.repeat_interleave(args.num_generations)
+
+ # 标准 GRPO 优势计算
+ mean_r = mean_r.repeat_interleave(args.num_generations)
+ std_r = std_r.repeat_interleave(args.num_generations)
+ advantages = torch.clamp((rewards - mean_r) / (std_r + 1e-4), -10, 10)
+
+ # 仅对有效样本进行 Normalize (可选，防止全0拉低均值，这里保持简单全局归一化)
+ if valid_sample_mask.sum() > 0:
+ advantages = (advantages - advantages[valid_sample_mask].mean()) / (advantages[valid_sample_mask].std() + 1e-8)
+ else:
+ advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
+
+ # 处理 Padding Mask
+ is_eos = completion_ids == tokenizer.eos_token_id
+ eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=args.device)
+ eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)]
+ completion_mask = (torch.arange(is_eos.size(1), device=args.device).expand(is_eos.size(0), -1) <= eos_idx.unsqueeze(1)).int()
+
+ # 结合 Dynamic Sampling Mask
+ loss_mask = completion_mask * valid_sample_mask.unsqueeze(1).int()
+
+ # ========== 策略优化 (Policy Optimization) ==========
+ # DAPO/PPO Inner Loop
+ for _ in range(args.ppo_epochs):
+ per_token_logps = get_per_token_logps(model, outputs, completion_ids.size(1))
+
+ # 计算 Ratio
+ ratio = torch.exp(per_token_logps - old_per_token_logps) # [B*num_gen, R]
+
+ # KL Divergence Penalty (近似)
+ kl_div = ref_per_token_logps - per_token_logps
+ per_token_kl = torch.exp(kl_div) - kl_div - 1
+
+ # DAPO Feature: Decoupled Clip (解耦裁剪)
+ # Clip-Higher: 使用不同的 epsilon_low 和 epsilon_high
+ surr1 = ratio * advantages.unsqueeze(1)
+ surr2 = torch.clamp(ratio, 1.0 - args.clip_ratio_low, 1.0 + args.clip_ratio_high) * advantages.unsqueeze(1)
+
+ # DAPO Loss: Min(Surr1, Surr2) - Beta * KL
+ per_token_loss = -torch.min(surr1, surr2) + args.beta * per_token_kl
+
+ # 聚合 Loss (Token-level mean)
+ loss = ((per_token_loss * loss_mask).sum(dim=1) / (loss_mask.sum(dim=1) + 1e-8)).mean() / args.accumulation_steps
+
+ loss.backward()
+
+ if (step + 1) % args.accumulation_steps == 0:
+ if args.grad_clip > 0:
+ torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+ optimizer.step()
+ scheduler.step()
+ optimizer.zero_grad()
+ torch.cuda.empty_cache()
+
+ if step % args.log_interval == 0 or step == iters:
+ policy_loss_val = loss.item() * args.accumulation_steps # 还原数值
+ avg_reward_val = rewards[valid_sample_mask].mean().item() if valid_sample_mask.sum() > 0 else 0.0
+ avg_len_val = completion_mask.sum(dim=1).float().mean().item()
+ current_lr = optimizer.param_groups[0]['lr']
+
+ valid_ratio = valid_group_mask.float().mean().item()
+
+ Logger(f'Epoch: {epoch+1}, Step: {step}/{iters}, '
+ f'Loss: {policy_loss_val:.6f}, Reward: {avg_reward_val:.6f}, '
+ f'Valid Batch Ratio: {valid_ratio:.2f}, Avg Len: {avg_len_val:.2f}, LR: {current_lr:.2e}')
+
+ if wandb and is_main_process():
+ wandb.log({
+ "policy_loss": policy_loss_val,
+ "reward": avg_reward_val,
+ "valid_group_ratio": valid_ratio,
+ "avg_response_len": avg_len_val,
+ "advantages_mean": advantages[valid_sample_mask].mean().item() if valid_sample_mask.sum() > 0 else 0,
+ "learning_rate": current_lr,
+ "mean_std": std_r.mean().item()
+ })
+
+ if (step % args.save_interval == 0 or step == iters - 1) and is_main_process():
+ model.eval()
+ moe_suffix = '_moe' if lm_config.use_moe else ''
+ ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
+ state_dict = model.module.state_dict() if isinstance(model, DistributedDataParallel) else model.state_dict()
+ torch.save({k: v.half().cpu() for k, v in state_dict.items()}, ckp)
+ lm_checkpoint(lm_config, weight=args.save_weight, model=model, optimizer=optimizer,
+ epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints', scheduler=scheduler)
+ model.train()
+ del state_dict
+
+ del prompt_inputs, outputs, completion_ids, per_token_logps, ref_per_token_logps, old_per_token_logps
+ del completions, rewards, grouped_rewards, mean_r, std_r, advantages, completion_mask
+ torch.cuda.empty_cache()
+ gc.collect()
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description="MiniMind DAPO (Decoupled Advantage Policy Optimization)")
+ parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录")
+ parser.add_argument('--save_weight', default='dapo', type=str, help="保存权重的前缀名")
+ parser.add_argument("--epochs", type=int, default=1, help="训练轮数")
+ parser.add_argument("--batch_size", type=int, default=2, help="batch size")
+ parser.add_argument("--learning_rate", type=float, default=8e-8, help="初始学习率")
+ parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+ parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
+ parser.add_argument("--num_workers", type=int, default=1, help="数据加载线程数")
+ parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
+ parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值")
+ parser.add_argument("--log_interval", type=int, default=1, help="日志打印间隔")
+ parser.add_argument("--save_interval", type=int, default=10, help="模型保存间隔")
+ parser.add_argument('--hidden_size', default=512, type=int, help="隐藏层维度")
+ parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量")
+ parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构（0=否，1=是）")
+ parser.add_argument('--max_seq_len', default=66, type=int, help="Prompt最大长度")
+ parser.add_argument("--max_gen_len", type=int, default=1536, help="生成的最大长度")
+ parser.add_argument("--data_path", type=str, default="../dataset/rlaif-mini.jsonl", help="RLAIF数据路径")
+ parser.add_argument("--num_generations", type=int, default=8, help="每个prompt生成的样本数")
+ parser.add_argument("--beta", type=float, default=0.01, help="KL惩罚系数")
+ parser.add_argument("--reasoning", type=int, default=1, choices=[0, 1], help='推理模型类型')
+ parser.add_argument("--reward_model_path", type=str, default="../../internlm2-1_8b-reward", help="Reward模型路径")
+ parser.add_argument('--from_resume', default=0, type=int, choices=[0, 1], help="是否自动检测&续训")
+ parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb")
+ parser.add_argument("--wandb_project", type=str, default="MiniMind-DAPO", help="wandb项目名")
+
+ # DAPO 特有参数
+ parser.add_argument("--clip_ratio_low", type=float, default=0.2, help="DAPO: 下限裁剪系数 (e.g. 1-0.2)")
+ parser.add_argument("--clip_ratio_high", type=float, default=0.28, help="DAPO: 上限裁剪系数 (e.g. 1+0.28)")
+ parser.add_argument("--ppo_epochs", type=int, default=1, help="DAPO: 每个Batch的更新次数")
+
+ args = parser.parse_args()
+
+ # ========== 1. 初始化环境和随机种子 ==========
+ local_rank = init_distributed_mode()
+ if dist.is_initialized(): args.device = f"cuda:{local_rank}"
+ setup_seed(42 + (dist.get_rank() if dist.is_initialized() else 0))
+
+ # ========== 2. 配置目录、模型参数、检查ckp ==========
+ os.makedirs(args.save_dir, exist_ok=True)
+ lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers,
+ max_seq_len=args.max_seq_len + args.max_gen_len, use_moe=bool(args.use_moe))
+ ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
+
+ # ========== 3. 设置混合精度 ==========
+ device_type = "cuda" if "cuda" in args.device else "cpu"
+ dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
+ autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+
+ # ========== 4. 配wandb ==========
+ wandb = None
+ if args.use_wandb and is_main_process():
+ import swanlab as wandb
+ wandb_id = ckp_data.get('wandb_id') if ckp_data else None
+ resume = 'must' if wandb_id else None
+ wandb_run_name = f"MiniMind-DAPO-Epoch-{args.epochs}-BS-{args.batch_size}-LR-{args.learning_rate}"
+ wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume, mode="local")
+
+ # ========== 5. 初始化模型和数据 ==========
+ base_weight = "reason" if args.reasoning == 1 else "full_sft"
+ # Policy模型
+ model, tokenizer = init_model(lm_config, base_weight, device=args.device)
+ # Reference模型
+ ref_model, _ = init_model(lm_config, base_weight, device=args.device)
+ ref_model = ref_model.eval().requires_grad_(False)
+ # Reward模型
+ reward_model = AutoModel.from_pretrained(
+ args.reward_model_path, torch_dtype=torch.float16, trust_remote_code=True
+ )
+ reward_model = reward_model.to(args.device).eval().requires_grad_(False)
+ reward_tokenizer = AutoTokenizer.from_pretrained(args.reward_model_path, trust_remote_code=True)
+ # 数据和优化器
+ train_ds = RLAIFDataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len)
+ train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
+ optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
+ loader_for_count = DataLoader(train_ds, batch_size=args.batch_size, sampler=train_sampler)
+ iters = len(loader_for_count)
+ total_optimizer_steps = (iters // args.accumulation_steps) * args.epochs
+ scheduler = CosineAnnealingLR(optimizer, T_max=total_optimizer_steps, eta_min=args.learning_rate / 10)
+
+ # ========== 6. 从ckp恢复状态 ==========
+ start_epoch, start_step = 0, 0
+ if ckp_data:
+ model.load_state_dict(ckp_data['model'])
+ optimizer.load_state_dict(ckp_data['optimizer'])
+ scheduler.load_state_dict(ckp_data['scheduler'])
+ start_epoch = ckp_data['epoch']
+ start_step = ckp_data.get('step', 0)
+
+ # ========== 7. DDP包模型 ==========
+ if dist.is_initialized():
+ model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
+ model = DistributedDataParallel(model, device_ids=[local_rank])
+
+ # ========== 8. 开始训练 ==========
+ for epoch in range(start_epoch, args.epochs):
+ train_sampler and train_sampler.set_epoch(epoch)
+ if epoch == start_epoch and start_step > 0:
+ 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')
+ grpo_train_epoch(epoch, loader, len(loader) + start_step + 1, ref_model, reward_model, reward_tokenizer, start_step, wandb)
+ else:
+ 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)
+ grpo_train_epoch(epoch, loader, len(loader), ref_model, reward_model, reward_tokenizer, 0, wandb)