Merge 138c367ddb into a3c7b01cc0

trainer/rollout_engine.py

@@ -84,7 +84,7 @@ class TorchRolloutEngine(RolloutEngine):
             ).clone()  # [B*num_gen, P+R]
             prompt_len = prompt_ids.size(1)
             completion_ids = output_ids[:, prompt_len:]  # [B*num_gen, R]
-            full_mask = (output_ids != self.tokenizer.pad_token_id).long()
+            full_mask = torch.cat([attention_mask.repeat_interleave(num_generations, dim=0), attention_mask.new_ones(output_ids.size(0), completion_ids.size(1))], dim=1)
             per_token_logps = compute_per_token_logps(self.policy_model, output_ids, completion_ids.size(1), attention_mask=full_mask)
         completions = self.tokenizer.batch_decode(completion_ids, skip_special_tokens=True)
         return RolloutResult(output_ids, completion_ids, per_token_logps, completions,

trainer/train_agent.py

@@ -106,34 +106,33 @@ def rollout_single(rollout_engine, tokenizer, messages, tools, max_turns=3, max_
     open_thinking = random.random() < thinking_ratio
     for turn in range(max_turns):
         context = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, tools=tools, open_thinking=open_thinking)
-        inputs = tokenizer(context, return_tensors="pt", add_special_tokens=False).to(device)
-        context_ids = inputs["input_ids"][0].tolist()
         if prompt_ids is None:
-            prompt_ids = context_ids
+            prompt_ids = tokenizer(context, add_special_tokens=False)["input_ids"]
+        input_ids = torch.tensor([prompt_ids + response_ids], device=device)
         rollout_result = rollout_engine.rollout(
-            prompt_ids=inputs["input_ids"],
+            prompt_ids=input_ids,
-            attention_mask=inputs["attention_mask"],
+            attention_mask=torch.ones_like(input_ids),
             num_generations=1,
             max_new_tokens=max_new_tokens,
             temperature=0.8,
         )
-        new_ids = rollout_result.completion_ids[0].tolist()
+        valid_len = int(rollout_result.completion_mask[0].sum().item())
-        new_logps = rollout_result.per_token_logps[0].tolist()
+        new_ids = rollout_result.completion_ids[0, :valid_len].tolist()
-        if len(new_ids) != len(new_logps): Logger(f"rollout token/logprob length mismatch: {len(new_ids)} vs {len(new_logps)}")
+        new_logps = rollout_result.per_token_logps[0, :valid_len].tolist()
-        pairs = [(t, lp) for t, lp in zip(new_ids, new_logps) if t != tokenizer.pad_token_id and t != tokenizer.eos_token_id]
+        if len(new_ids) != len(new_logps):
-        new_ids = [t for t, _ in pairs]
+            raise RuntimeError(f"rollout token/logprob length mismatch: {len(new_ids)} vs {len(new_logps)}")
-        new_logps = [lp for _, lp in pairs]
         new_text = rollout_result.completions[0]
         all_outputs.append(new_text)
         response_ids.extend(new_ids)
-        response_mask.extend([1] * len(new_ids))
+        response_mask.extend([int(t != tokenizer.eos_token_id) for t in new_ids])
         response_old_logps.extend(new_logps)
         final_context = context + new_text
         calls = parse_tool_calls(new_text)
         if not calls:
             break
         unfinished = turn == max_turns - 1
-        messages.append({"role": "assistant", "content": new_text})
+        assistant_message = {"role": "assistant", "content": new_text}
+        messages.append(assistant_message)
         for call in calls:
             name, raw = call.get("name", ""), call.get("arguments", {})
             if isinstance(raw, str):
@@ -143,10 +142,17 @@ def rollout_single(rollout_engine, tokenizer, messages, tools, max_turns=3, max_
             result_str = (json.dumps(result, ensure_ascii=False) if result else '{"error": "tool not found"}')[:2048]  # 防止天文数字撑爆tokenizer
             messages.append({"role": "tool", "content": result_str})
 
+        marker = f"<|agent_observation_{id(messages)}_{len(response_ids)}|>"
+        assistant_message["content"] += marker
+        marked_context = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=not unfinished, tools=tools, open_thinking=open_thinking)
+        assistant_message["content"] = new_text
+        _, found, observation = marked_context.partition(marker)
+        if not found:
+            raise RuntimeError("chat template did not preserve the assistant content boundary")
+        obs_delta = tokenizer(observation, add_special_tokens=False)["input_ids"]
+        if new_ids and new_ids[-1] == tokenizer.eos_token_id and obs_delta[:1] == [tokenizer.eos_token_id]:
+            obs_delta = obs_delta[1:]
         observe_context = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=not unfinished, tools=tools, open_thinking=open_thinking)
-        observe_ids = tokenizer(observe_context, return_tensors="pt", add_special_tokens=False)["input_ids"][0].tolist()
-        current_len = len(prompt_ids) + len(response_ids)
-        obs_delta = observe_ids[current_len:]
         response_ids.extend(obs_delta)
         response_mask.extend([0] * len(obs_delta))
         response_old_logps.extend([0.0] * len(obs_delta))
@@ -268,7 +274,7 @@ def rl_train_epoch(epoch, loader, iters, rollout_engine, ref_model, reward_model
         prompt_lens = torch.tensor([prompt_len for _, _, prompt_len, _ in packed_samples], device=args.device)
         full_response_masks = torch.tensor([mask + [0] * (max_len - len(mask)) for _, mask, _, _ in packed_samples], device=args.device, dtype=torch.float32)
         old_per_token_logps = torch.tensor([old_logps + [0.0] * ((max_len - 1) - len(old_logps)) for _, _, _, old_logps in packed_samples], device=args.device, dtype=torch.float32)
-        full_mask = (input_ids != tokenizer.pad_token_id).long()
+        full_mask = (torch.arange(max_len, device=args.device).unsqueeze(0) < seq_lens.unsqueeze(1)).long()
 
         rewards = calculate_rewards(prompts, completions, gt_batch, tools_batch, args.num_generations, reward_model, device=args.device, turn_outputs_batch=turn_outputs_batch, unfinished_batch=unfinished_batch)
 

trainer/train_dapo.py

@@ -0,0 +1,421 @@
+import os
+import sys
+
+__package__ = "trainer"
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import argparse
+import re
+import warnings
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+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.nn.utils import clip_grad_norm_
+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, args):
+    """
+    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。
+    输入的 prompts/responses 已经是展开后的（batch_size * group_size）条目。
+    """
+    def reasoning_model_reward(rewards):
+        # 1. 结构化格式奖励
+        pattern  = r"^<think>\n.*?\n</think>\n<answer>\n.*?\n</answer>$"
+        pattern2 = r"^<think>\n.*?\n</think>\n\n<answer>\n.*?\n</answer>$"
+        matches_pattern  = [re.match(pattern,  r, re.S) for r in responses]
+        matches_pattern2 = [re.match(pattern2, r, re.S) for r in responses]
+        format_rewards = [0.5 if (m1 or m2) else 0.0
+                         for m1, m2 in zip(matches_pattern, matches_pattern2)]
+        rewards += torch.tensor(format_rewards, device=args.device)
+
+        # 2. 标签存在奖励
+        def mark_num(text):
+            reward = 0
+            for tag in ["<think>", "</think>", "<answer>", "</answer>"]:
+                if text.count(tag) == 1:
+                    reward += 0.25
+            return reward
+
+        mark_rewards = [mark_num(r) for r 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)
+
+    # 3. 超长惩罚 (Overlong Punishment - DAPO 特性)
+    len_penalty = []
+    for resp in responses:
+        curr_len = len(resp)
+        if curr_len > args.length_threshold:
+            penalty = -args.length_penalty * ((curr_len - args.length_threshold) / 100)
+            len_penalty.append(max(penalty, -1.0))
+        else:
+            len_penalty.append(0.0)
+    rewards += torch.tensor(len_penalty, device=args.device)
+
+    # 4. 奖励模型评分
+    with torch.no_grad():
+        reward_model_scores = []
+        for prompt, response in zip(prompts, responses):
+            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)
+            scale = 3.0
+            score = max(min(score, scale), -scale)
+
+            if args.reasoning == 1:
+                answer_match = re.search(r'<answer>(.*?)</answer>', 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 dapo_train_epoch(epoch, loader, iters, old_actor_model, ref_model,
+                     actor_scheduler, reward_model, reward_tokenizer,
+                     start_step=0, wandb=None):
+    actor_model.train()
+
+    for step, batch in enumerate(loader, start=start_step + 1):
+        prompts    = batch["prompt"]
+        batch_size = len(prompts)
+        G          = args.group_size
+
+        # 每个 prompt 重复 G 次，展开为 (batch_size * G) 条输入
+        prompts_expanded = [p for p in prompts for _ in range(G)]
+
+        enc = tokenizer(
+            prompts_expanded,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=args.max_seq_len,
+            padding_side="left"
+        ).to(args.device)
+        prompt_length = enc.input_ids.shape[1]
+
+        with torch.no_grad():
+            model_for_gen = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
+            gen_out = model_for_gen.generate(
+                input_ids=enc.input_ids,
+                attention_mask=enc.attention_mask,
+                max_new_tokens=args.max_gen_len,
+                do_sample=True,
+                temperature=0.8,
+                pad_token_id=tokenizer.pad_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+            )
+
+        # gen_out: (batch_size * G, seq_len)
+        responses_text = [
+            tokenizer.decode(gen_out[i, prompt_length:], skip_special_tokens=True)
+            for i in range(len(prompts_expanded))
+        ]
+        rewards_flat = calculate_rewards(prompts_expanded, responses_text, reward_model, reward_tokenizer, args)
+        # rewards_flat: (batch_size * G,)
+
+        # ------------------------------------------------------------------
+        # advantage = (r - mean_group) / std_group
+        # ------------------------------------------------------------------
+        rewards_grouped = rewards_flat.view(batch_size, G)
+        mean_r          = rewards_grouped.mean(dim=1, keepdim=True)
+        std_r           = rewards_grouped.std(dim=1, keepdim=True).clamp(min=1e-8)
+        advantages      = ((rewards_grouped - mean_r) / std_r).view(-1)  # (batch_size * G,)
+
+        # ------------------------------------------------------------------
+        # Dynamic Sampling
+        # ------------------------------------------------------------------
+        if args.use_dynamic_sampling == 1:
+            if (std_r < 1e-6).all().item():
+                if is_main_process():
+                    Logger(f"Step {step}: All groups have zero reward variance, skipping...")
+                continue
+
+        # ------------------------------------------------------------------
+        # Actor forward
+        # ------------------------------------------------------------------
+        full_mask = (gen_out != tokenizer.pad_token_id).long()
+
+        with autocast_ctx:
+            res      = actor_model(input_ids=gen_out, attention_mask=full_mask)
+            logits   = res.logits
+            aux_loss = res.aux_loss if lm_config.use_moe else torch.tensor(0.0, device=args.device)
+
+        labels      = gen_out[:, 1:].clone()
+        logp_tokens = F.log_softmax(logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1)
+
+        seq_len    = gen_out.size(1) - 1
+        resp_mask  = torch.arange(seq_len, device=gen_out.device).unsqueeze(0) >= prompt_length - 1
+        final_mask = resp_mask & (~labels.eq(tokenizer.pad_token_id))
+
+        # Token-level 平均 log-prob（防止长回复主导梯度）
+        token_count = final_mask.sum(dim=1).clamp(min=1).float()
+        actor_logp  = (logp_tokens * final_mask).sum(dim=1) / token_count
+
+        with torch.no_grad():
+            old_logits      = old_actor_model(input_ids=gen_out, attention_mask=full_mask).logits
+            old_logp_tokens = F.log_softmax(old_logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1)
+            old_logp        = (old_logp_tokens * final_mask).sum(dim=1) / token_count
+
+            ref_logits      = ref_model(input_ids=gen_out, attention_mask=full_mask).logits
+            ref_logp_tokens = F.log_softmax(ref_logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1)
+            ref_logp        = (ref_logp_tokens * final_mask).sum(dim=1) / token_count
+
+        kl     = (actor_logp - old_logp).mean()
+        kl_ref = (actor_logp - ref_logp).mean()
+        ratio  = torch.exp(actor_logp - old_logp)
+
+        # ------------------------------------------------------------------
+        # Decoupled Clip
+        #   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)]
+        #   Adv ≤ 0：只限下界，防止负向更新反弹
+        # ------------------------------------------------------------------
+        surr1 = ratio * advantages
+        clipped_ratio = torch.where(
+            advantages > 0,
+            torch.clamp(ratio, max=1.0 + args.clip_epsilon_high),
+            torch.clamp(ratio, min=1.0 - args.clip_epsilon_low),
+        )
+        surr2       = clipped_ratio * advantages
+        policy_loss = -torch.min(surr1, surr2).mean()
+
+        loss = (policy_loss + args.kl_coef * kl_ref + aux_loss) / args.accumulation_steps
+        loss.backward()
+
+        if (step + 1) % args.accumulation_steps == 0:
+            clip_grad_norm_(actor_model.parameters(), args.grad_clip)
+            actor_optimizer.step()
+            actor_scheduler.step()
+            actor_optimizer.zero_grad()
+
+        if is_main_process():
+            response_ids = gen_out[:, enc.input_ids.shape[1]:]
+            is_eos       = (response_ids == tokenizer.eos_token_id)
+            eos_indices  = torch.argmax(is_eos.int(), dim=1)
+            lengths = torch.where(
+                is_eos.any(dim=1),
+                eos_indices + 1,
+                torch.tensor(response_ids.shape[1], device=is_eos.device)
+            )
+            avg_len    = lengths.float().mean()
+            reward_std = rewards_flat.std().item()
+
+            if wandb is not None:
+                wandb.log({
+                    "actor_loss":       policy_loss.item(),
+                    "reward":           rewards_flat.mean().item(),
+                    "reward_std":       reward_std,
+                    "kl":               kl.item(),
+                    "kl_ref":           kl_ref.item(),
+                    "avg_response_len": avg_len.item(),
+                    "actor_lr":         actor_optimizer.param_groups[0]['lr'],
+                })
+
+            Logger(
+                f"Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), "
+                f"Loss: {policy_loss.item():.4f}, Reward: {rewards_flat.mean().item():.4f}, "
+                f"KL: {kl.item():.4f}, Len: {avg_len.item():.2f}"
+            )
+
+        if (step + 1) % args.update_old_actor_freq == 0:
+            raw_actor = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
+            raw_actor = getattr(raw_actor, '_orig_mod', raw_actor)
+            old_actor_model.load_state_dict({k: v.detach().cpu() for k, v in raw_actor.state_dict().items()})
+            old_actor_model.to(args.device)
+
+        if (step % args.save_interval == 0 or step == iters - 1) and is_main_process():
+            actor_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'
+            raw_actor = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
+            raw_actor = getattr(raw_actor, '_orig_mod', raw_actor)
+            torch.save({k: v.half().cpu() for k, v in raw_actor.state_dict().items()}, ckp)
+            lm_checkpoint(
+                lm_config, weight=args.save_weight, model=actor_model,
+                optimizer=actor_optimizer, epoch=epoch, step=step,
+                wandb=wandb, save_dir='../checkpoints', scheduler=actor_scheduler,
+            )
+            actor_model.train()
+
+        del enc, gen_out, responses_text, rewards_flat, rewards_grouped
+        del advantages, full_mask, logits, labels, logp_tokens, final_mask, token_count
+        del actor_logp, old_logits, old_logp, ref_logits, ref_logp
+        del kl, kl_ref, ratio, surr1, surr2, clipped_ratio, policy_loss, loss
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="MiniMind DAPO (Decoupled Clip and Dynamic Sampling Policy Optimization)"
+    )
+    parser.add_argument("--save_dir",              type=str,   default="../out",                      help="模型保存目录")
+    parser.add_argument('--save_weight',            type=str,   default='dapo',                  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="prompt 级别的 batch size（实际前向 = batch_size * group_size）")
+    parser.add_argument("--learning_rate",         type=float, default=8e-8,                          help="Actor 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=8,                             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',            type=int,   default=512,                           help="隐藏层维度")
+    parser.add_argument('--num_hidden_layers',      type=int,   default=8,                             help="隐藏层数量")
+    parser.add_argument('--use_moe',                type=int,   default=0,  choices=[0, 1],            help="是否使用 MoE 架构（0=否，1=是）")
+    parser.add_argument('--max_seq_len',            type=int,   default=66,                            help="Prompt 最大长度")
+    parser.add_argument("--max_gen_len",            type=int,   default=1024,                          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")
+
+    # DAPO 核心参数
+    parser.add_argument("--clip_epsilon_low",      type=float, default=0.1,  help="Decoupled Clip 下界")
+    parser.add_argument("--clip_epsilon_high",     type=float, default=0.2,  help="Decoupled Clip 上界")
+    parser.add_argument("--group_size",            type=int,   default=8,    help="每个 prompt 的采样回复数")
+    parser.add_argument("--use_dynamic_sampling",  type=int,   default=1,   choices=[0, 1], help="是否开启动态采样过滤")
+    parser.add_argument("--length_penalty",        type=float, default=0.1,  help="长度惩罚系数")
+    parser.add_argument("--length_threshold",      type=int,   default=1200, help="触发长度惩罚的字符阈值")
+    parser.add_argument("--kl_coef",               type=float, default=0.02, help="KL 散度惩罚系数（相对 ref_model）")
+    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）")
+    parser.add_argument("--update_old_actor_freq", type=int,   default=4,   help="更新 old_actor_model 的频率（步数）")
+    parser.add_argument("--reward_model_path",     type=str,   default="../../internlm2-1_8b-reward", help="Reward 模型路径")
+    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）")
+    parser.add_argument("--use_wandb",             action="store_true",                     help="是否使用 wandb/swanlab")
+    parser.add_argument("--wandb_project",         type=str,   default="MiniMind-DAPO",     help="wandb 项目名")
+    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）")
+    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,
+        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 (SwanLab) ==========
+    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.init(
+            project=args.wandb_project,
+            name=f"MiniMind-DAPO-{args.learning_rate}",
+            id=wandb_id,
+            resume=resume,
+        )
+
+    # ========== 5. 初始化模型 ==========
+    base_weight = "reason" if args.reasoning == 1 else "full_sft"
+
+    actor_model, tokenizer = init_model(lm_config, base_weight, device=args.device)
+    if args.use_compile == 1:
+        actor_model = torch.compile(actor_model)
+
+    old_actor_model, _ = init_model(lm_config, base_weight, device=args.device)
+    old_actor_model = old_actor_model.eval().requires_grad_(False)
+
+    ref_model, _ = init_model(lm_config, base_weight, device=args.device)
+    ref_model = ref_model.eval().requires_grad_(False)
+
+    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)
+
+    # ========== 6. 数据、优化器、调度器 ==========
+    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()

trainer/train_grpo.py

@@ -91,6 +91,7 @@ def grpo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, reward_mod
         prompt_lens = rollout_result.prompt_lens.to(args.device)
         full_mask = (outputs != tokenizer.pad_token_id).long()
         logp_pos = prompt_lens.unsqueeze(1) - 1 + torch.arange(completion_ids.size(1), device=args.device).unsqueeze(0)
+        full_mask.scatter_(1, logp_pos + 1, rollout_result.completion_mask.to(args.device, dtype=full_mask.dtype))
 
         rewards = calculate_rewards(prompts, completions, reward_model).to(args.device)  # [B*num_gen]
 

trainer/train_ppo.py

@@ -120,6 +120,7 @@ def ppo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, actor_sched
         resp_idx = torch.arange(resp_labels.size(1), device=gen_out.device).unsqueeze(0)
         logp_pos = prompt_lens.unsqueeze(1) - 1 + resp_idx
         resp_pad_mask = rollout_result.completion_mask.to(args.device).bool()
+        full_mask.scatter_(1, logp_pos + 1, resp_pad_mask.to(full_mask.dtype))
         resp_lengths = resp_pad_mask.sum(dim=1); valid_resp = resp_lengths > 0; eos_mask = resp_labels.eq(tokenizer.eos_token_id) & resp_pad_mask
         has_eos = eos_mask.any(dim=1); eos_pos = torch.argmax(eos_mask.int(), dim=1)
         resp_lengths = torch.where(has_eos, eos_pos + 1, resp_lengths).long().clamp(min=1)