Merge 864bd67437 into 393e387e9a

README.md

@@ -1926,26 +1926,16 @@ python llmexport.py --path /path/to/模型路径/ --export mnn --hqq --dst_path
 
 ## 🫶支持者
 
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 </picture>
 
 ## 🎉 MiniMind 相关成果

README_en.md

@@ -1926,26 +1926,16 @@ Thanks also to the following papers and projects:
 
 ## 🫶Supporters
 
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 ## 🎉 MiniMind Related Achievements

trainer/opd_utils.py

@@ -0,0 +1,148 @@
+"""Core tensor utilities for Generalized Knowledge Distillation (GKD)."""
+
+from typing import Dict, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+
+def build_completion_mask(
+    completion_ids: Tensor,
+    base_mask: Optional[Tensor],
+    eos_token_id: Optional[int],
+    pad_token_id: Optional[int] = None,
+) -> Tensor:
+    """Keep valid completion tokens up to and including the first EOS.
+
+    MiniMind's native ``generate`` pads finished rows with repeated EOS tokens
+    until every row is complete. Those repeated tokens are valid model inputs,
+    but they must not contribute to the distillation objective.
+    """
+    if completion_ids.ndim != 2:
+        raise ValueError(f"completion_ids must be 2D, got {tuple(completion_ids.shape)}")
+    if base_mask is None:
+        mask = torch.ones_like(completion_ids, dtype=torch.bool)
+    else:
+        if base_mask.shape != completion_ids.shape:
+            raise ValueError(
+                f"base_mask shape {tuple(base_mask.shape)} does not match "
+                f"completion_ids shape {tuple(completion_ids.shape)}"
+            )
+        mask = base_mask.bool().clone()
+
+    # Some tokenizers intentionally use EOS as PAD. In that case the first EOS
+    # still belongs to the completion and is handled by the cumulative mask.
+    if pad_token_id is not None and pad_token_id != eos_token_id:
+        mask &= completion_ids.ne(pad_token_id)
+
+    if eos_token_id is not None:
+        eos_hits = completion_ids.eq(eos_token_id) & mask
+        eos_seen_before = eos_hits.long().cumsum(dim=1) - eos_hits.long()
+        mask &= eos_seen_before.eq(0)
+    return mask
+
+
+def _masked_mean(values: Tensor, mask: Tensor) -> Tensor:
+    mask = mask.to(device=values.device, dtype=values.dtype)
+    return (values * mask).sum() / mask.sum().clamp_min(1.0)
+
+
+def generalized_jsd_loss(
+    student_logits: Tensor,
+    teacher_logits: Tensor,
+    completion_mask: Tensor,
+    beta: float = 0.5,
+    temperature: float = 1.0,
+) -> Tuple[Tensor, Dict[str, Tensor]]:
+    """Compute the exact full-vocabulary generalized JSD from GKD.
+
+    With teacher distribution ``p`` and student distribution ``q``, the
+    mixture is ``m = beta * p + (1 - beta) * q`` and the objective is
+    ``beta * KL(p || m) + (1 - beta) * KL(q || m)``. Its limiting cases are
+    forward KL at ``beta=0`` and reverse KL at ``beta=1``.
+
+    The teacher is always treated as a fixed target. Only completion tokens
+    selected by ``completion_mask`` contribute to the token-mean loss.
+    """
+    if student_logits.ndim != 3 or teacher_logits.ndim != 3:
+        raise ValueError("student_logits and teacher_logits must be [batch, time, vocab]")
+    if student_logits.shape != teacher_logits.shape:
+        raise ValueError(
+            "student and teacher logits must have identical shapes, got "
+            f"{tuple(student_logits.shape)} and {tuple(teacher_logits.shape)}"
+        )
+    if completion_mask.shape != student_logits.shape[:2]:
+        raise ValueError(
+            f"completion_mask shape {tuple(completion_mask.shape)} does not match "
+            f"logits shape {tuple(student_logits.shape[:2])}"
+        )
+    if not 0.0 <= beta <= 1.0:
+        raise ValueError(f"beta must be in [0, 1], got {beta}")
+    if temperature <= 0:
+        raise ValueError(f"temperature must be positive, got {temperature}")
+
+    # Float32 log-softmax keeps the divergence stable under fp16/bf16 model
+    # autocast, while preserving gradients back to the student parameters.
+    student_logps = F.log_softmax(student_logits.float() / temperature, dim=-1)
+    teacher_logps = F.log_softmax(
+        teacher_logits.detach().float() / temperature, dim=-1
+    )
+    student_probs = student_logps.exp()
+    teacher_probs = teacher_logps.exp()
+
+    forward_kl = (teacher_probs * (teacher_logps - student_logps)).sum(dim=-1)
+    reverse_kl = (student_probs * (student_logps - teacher_logps)).sum(dim=-1)
+
+    if beta == 0.0:
+        per_token_loss = forward_kl
+    elif beta == 1.0:
+        per_token_loss = reverse_kl
+    else:
+        beta_tensor = student_logps.new_tensor(beta)
+        mixture_logps = torch.logsumexp(
+            torch.stack(
+                (
+                    student_logps + torch.log1p(-beta_tensor),
+                    teacher_logps + torch.log(beta_tensor),
+                )
+            ),
+            dim=0,
+        )
+        teacher_to_mixture = (
+            teacher_probs * (teacher_logps - mixture_logps)
+        ).sum(dim=-1)
+        student_to_mixture = (
+            student_probs * (student_logps - mixture_logps)
+        ).sum(dim=-1)
+        per_token_loss = (
+            beta_tensor * teacher_to_mixture
+            + (1.0 - beta_tensor) * student_to_mixture
+        )
+
+    mask = completion_mask.bool()
+    loss = _masked_mean(per_token_loss, mask)
+    top1_agreement = _masked_mean(
+        student_logps.detach()
+        .argmax(dim=-1)
+        .eq(teacher_logps.argmax(dim=-1))
+        .float(),
+        mask,
+    )
+    metrics = {
+        "divergence": loss.detach(),
+        "forward_kl": _masked_mean(forward_kl.detach(), mask),
+        "reverse_kl": _masked_mean(reverse_kl.detach(), mask),
+        "top1_agreement": top1_agreement.detach(),
+        "valid_tokens": mask.sum().detach(),
+    }
+    return loss, metrics
+
+
+def use_on_policy_batch(lmbda: float, random_value: float) -> bool:
+    """Return the Algorithm 1 branch for a pre-sampled value in ``[0, 1)``."""
+    if not 0.0 <= lmbda <= 1.0:
+        raise ValueError(f"lmbda must be in [0, 1], got {lmbda}")
+    if not 0.0 <= random_value < 1.0:
+        raise ValueError(f"random_value must be in [0, 1), got {random_value}")
+    return random_value < lmbda

trainer/rollout_engine.py

@@ -52,7 +52,10 @@ class RolloutEngine(ABC):
     tokenizer = None
     
     @abstractmethod
-    def rollout(self, prompt_ids: Tensor, attention_mask: Tensor, num_generations: int, max_new_tokens: int, temperature: float = 0.8) -> RolloutResult:
+    def rollout(self, prompt_ids: Tensor, attention_mask: Tensor, num_generations: int,
+                max_new_tokens: int, temperature: float = 0.8,
+                top_p: Optional[float] = None, top_k: Optional[int] = None,
+                calculate_logps: bool = True) -> RolloutResult:
         pass
     
     @abstractmethod
@@ -68,9 +71,15 @@ class TorchRolloutEngine(RolloutEngine):
         self.device = device
         self.autocast_ctx = autocast_ctx
     
-    def rollout(self, prompt_ids: Tensor, attention_mask: Tensor, num_generations: int, max_new_tokens: int, temperature: float = 0.8) -> RolloutResult:
+    def rollout(self, prompt_ids: Tensor, attention_mask: Tensor, num_generations: int,
+                max_new_tokens: int, temperature: float = 0.8,
+                top_p: Optional[float] = None, top_k: Optional[int] = None,
+                calculate_logps: bool = True) -> RolloutResult:
         model = self.policy_model.module if isinstance(self.policy_model, DistributedDataParallel) else self.policy_model
         ctx = self.autocast_ctx if self.autocast_ctx else nullcontext()
+        sampling_kwargs = {}
+        if top_p is not None: sampling_kwargs['top_p'] = top_p
+        if top_k is not None: sampling_kwargs['top_k'] = top_k
         with torch.no_grad(), ctx:
             output_ids = model.generate(
                 input_ids=prompt_ids.repeat_interleave(num_generations, dim=0),
@@ -81,11 +90,20 @@ class TorchRolloutEngine(RolloutEngine):
                 num_return_sequences=1,
                 pad_token_id=self.tokenizer.pad_token_id,
                 eos_token_id=self.tokenizer.eos_token_id,
+                **sampling_kwargs,
             ).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()
-            per_token_logps = compute_per_token_logps(self.policy_model, output_ids, completion_ids.size(1), attention_mask=full_mask)
+            if calculate_logps:
+                per_token_logps = compute_per_token_logps(
+                    self.policy_model, output_ids, completion_ids.size(1), attention_mask=full_mask
+                )
+            else:
+                per_token_logps = torch.zeros(
+                    output_ids.size(0), completion_ids.size(1),
+                    device=output_ids.device, dtype=torch.float32
+                )
         completions = self.tokenizer.batch_decode(completion_ids, skip_special_tokens=True)
         return RolloutResult(output_ids, completion_ids, per_token_logps, completions,
                              prompt_ids.new_full((output_ids.size(0),), prompt_len),
@@ -104,7 +122,10 @@ class SGLangRolloutEngine(RolloutEngine):
         self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
         self.http = requests
     
-    def rollout(self, prompt_ids: Tensor, attention_mask: Tensor, num_generations: int, max_new_tokens: int, temperature: float = 0.8) -> RolloutResult:
+    def rollout(self, prompt_ids: Tensor, attention_mask: Tensor, num_generations: int,
+                max_new_tokens: int, temperature: float = 0.8,
+                top_p: Optional[float] = None, top_k: Optional[int] = None,
+                calculate_logps: bool = True) -> RolloutResult:
         # 去除左侧 padding tokens，只保留有效 token
         input_ids_list = []
         for ids, mask in zip(prompt_ids, attention_mask):
@@ -119,8 +140,10 @@ class SGLangRolloutEngine(RolloutEngine):
                 "max_new_tokens": max_new_tokens,
                 "stop_token_ids": [self.tokenizer.eos_token_id] if self.tokenizer.eos_token_id else [],
             },
-            "return_logprob": True,
+            "return_logprob": calculate_logps,
         }
+        if top_p is not None: payload['sampling_params']['top_p'] = top_p
+        if top_k is not None: payload['sampling_params']['top_k'] = top_k if top_k > 0 else -1
         
         resp = self.http.post(f"{self.base_url}/generate", json=payload, timeout=self.timeout)
         resp.raise_for_status()

trainer/train_opd.py

@@ -0,0 +1,755 @@
+import os
+import sys
+
+__package__ = "trainer"
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+import datasets  # noqa: F401  # Windows pyarrow/torch DLL conflict workaround (issue #771)
+import argparse
+import gc
+import json
+import math
+import random
+import warnings
+from contextlib import nullcontext
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+from datasets import load_dataset
+from torch import optim
+from torch.nn.parallel import DistributedDataParallel
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from torch.utils.data import DataLoader, Dataset, DistributedSampler
+from transformers import AutoTokenizer
+
+from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
+from trainer.opd_utils import (
+    build_completion_mask,
+    generalized_jsd_loss,
+    use_on_policy_batch,
+)
+from trainer.rollout_engine import create_rollout_engine
+from trainer.trainer_utils import (
+    Logger,
+    SkipBatchSampler,
+    init_distributed_mode,
+    is_main_process,
+    lm_checkpoint,
+    setup_seed,
+)
+
+warnings.filterwarnings("ignore")
+REPO_ROOT = Path(__file__).resolve().parents[1]
+
+
+class OPDDataset(Dataset):
+    """Raw conversational samples used by both GKD data branches."""
+
+    def __init__(self, data_path):
+        self.samples = load_dataset("json", data_files=str(data_path), split="train")
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, index):
+        sample = self.samples[index]
+        conversations = sample.get("conversations", sample.get("messages"))
+        if not isinstance(conversations, list) or not conversations:
+            raise ValueError(
+                "OPD data requires a non-empty 'conversations' or 'messages' list"
+            )
+        return {"conversations": [dict(message) for message in conversations]}
+
+
+def collate_conversations(batch):
+    return {"conversations": [item["conversations"] for item in batch]}
+
+
+def resolve_device(device):
+    if device != "auto":
+        return device
+    if torch.cuda.is_available():
+        return "cuda:0"
+    if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
+        return "mps"
+    return "cpu"
+
+
+def resolve_path(value):
+    path = Path(value).expanduser()
+    if path.is_absolute():
+        return path.resolve()
+    return (REPO_ROOT / path).resolve()
+
+
+def validate_args(parsed_args):
+    if not 0.0 <= parsed_args.lmbda <= 1.0:
+        raise ValueError("--lmbda must be in [0, 1]")
+    if not 0.0 <= parsed_args.beta <= 1.0:
+        raise ValueError("--beta must be in [0, 1]")
+    if parsed_args.temperature <= 0 or parsed_args.distill_temperature <= 0:
+        raise ValueError("sampling and distillation temperatures must be positive")
+    if not 0.0 < parsed_args.rollout_top_p <= 1.0:
+        raise ValueError("--rollout_top_p must be in (0, 1]")
+    if parsed_args.rollout_top_k < 0:
+        raise ValueError("--rollout_top_k must be zero (disabled) or positive")
+    if parsed_args.num_generations < 1 or parsed_args.max_gen_len < 1:
+        raise ValueError("--num_generations and --max_gen_len must be positive")
+    if parsed_args.max_seq_len < 1 or parsed_args.accumulation_steps < 1:
+        raise ValueError("--max_seq_len and --accumulation_steps must be positive")
+    if parsed_args.max_train_steps < 0:
+        raise ValueError("--max_train_steps must be zero or positive")
+    if parsed_args.log_interval < 1 or parsed_args.save_interval < 1:
+        raise ValueError("log/save intervals must be positive")
+    if parsed_args.rollout_top_p < 1.0 or parsed_args.rollout_top_k > 0:
+        Logger(
+            "[OPD WARNING] top-p/top-k truncation changes the behavior policy; "
+            "top_p=1 and top_k=0 match the GKD baseline"
+        )
+
+
+def _torch_load(path):
+    try:
+        return torch.load(path, map_location="cpu", weights_only=True)
+    except TypeError:
+        return torch.load(path, map_location="cpu")
+
+
+def _load_transformers_state_dict(model_dir):
+    single_safetensors = model_dir / "model.safetensors"
+    safetensors_index = model_dir / "model.safetensors.index.json"
+    pytorch_bin = model_dir / "pytorch_model.bin"
+    if single_safetensors.exists() or safetensors_index.exists():
+        try:
+            from safetensors.torch import load_file
+        except ImportError as exc:
+            raise ImportError(
+                "Loading a Transformers safetensors model requires safetensors"
+            ) from exc
+    if single_safetensors.exists():
+        return load_file(str(single_safetensors), device="cpu")
+    if safetensors_index.exists():
+        index = json.loads(safetensors_index.read_text(encoding="utf-8"))
+        state_dict = {}
+        for shard_name in sorted(set(index["weight_map"].values())):
+            state_dict.update(load_file(str(model_dir / shard_name), device="cpu"))
+        return state_dict
+    if pytorch_bin.exists():
+        return _torch_load(pytorch_bin)
+    raise FileNotFoundError(
+        f"No model.safetensors, model.safetensors.index.json, or pytorch_model.bin in {model_dir}"
+    )
+
+
+def resolve_model_source(source, config):
+    direct = Path(source).expanduser()
+    if not direct.is_absolute():
+        direct = REPO_ROOT / direct
+    if direct.exists():
+        return direct.resolve()
+    moe_suffix = "_moe" if config.use_moe else ""
+    weight_path = REPO_ROOT / "out" / f"{source}_{config.hidden_size}{moe_suffix}.pth"
+    if weight_path.exists():
+        return weight_path.resolve()
+    raise FileNotFoundError(
+        f"Model source {source!r} was not found directly or as {weight_path}"
+    )
+
+
+def load_state_dict(source, config):
+    source = resolve_model_source(source, config)
+    state_dict = (
+        _load_transformers_state_dict(source) if source.is_dir() else _torch_load(source)
+    )
+    if isinstance(state_dict, dict) and isinstance(state_dict.get("model"), dict):
+        state_dict = state_dict["model"]
+    if not isinstance(state_dict, dict) or not state_dict:
+        raise ValueError(f"Invalid or empty state dict from {source}")
+    for prefix in ("module.", "_orig_mod."):
+        if all(key.startswith(prefix) for key in state_dict):
+            state_dict = {key[len(prefix):]: value for key, value in state_dict.items()}
+    if "lm_head.weight" not in state_dict and "model.embed_tokens.weight" in state_dict:
+        state_dict["lm_head.weight"] = state_dict["model.embed_tokens.weight"]
+    if "model.embed_tokens.weight" not in state_dict and "lm_head.weight" in state_dict:
+        state_dict["model.embed_tokens.weight"] = state_dict["lm_head.weight"]
+    return state_dict, source
+
+
+def validate_directory_config(source, config, label):
+    source_path = Path(source).expanduser()
+    if not source_path.is_absolute():
+        source_path = REPO_ROOT / source_path
+    config_path = source_path / "config.json" if source_path.is_dir() else None
+    if not config_path or not config_path.exists():
+        return
+    saved = json.loads(config_path.read_text(encoding="utf-8"))
+    expected = {
+        "hidden_size": config.hidden_size,
+        "num_hidden_layers": config.num_hidden_layers,
+        "vocab_size": config.vocab_size,
+        "num_attention_heads": config.num_attention_heads,
+        "num_key_value_heads": config.num_key_value_heads,
+    }
+    mismatches = {
+        key: (saved.get(key), value)
+        for key, value in expected.items()
+        if saved.get(key) is not None and saved.get(key) != value
+    }
+    if mismatches:
+        raise ValueError(f"{label} config does not match CLI architecture: {mismatches}")
+
+
+def load_native_model(config, source, device, label, model_dtype=None):
+    validate_directory_config(source, config, label)
+    state_dict, resolved_source = load_state_dict(source, config)
+    loaded_model = MiniMindForCausalLM(config)
+    loaded_model.load_state_dict(state_dict, strict=True)
+    del state_dict
+    gc.collect()
+    if model_dtype is None:
+        loaded_model = loaded_model.to(device)
+    else:
+        loaded_model = loaded_model.to(device=device, dtype=model_dtype)
+    Logger(f"Loaded {label} strictly from {resolved_source}")
+    return loaded_model
+
+
+def load_tokenizer(tokenizer_path, vocab_size):
+    tokenizer = AutoTokenizer.from_pretrained(
+        str(resolve_path(tokenizer_path)), trust_remote_code=True
+    )
+    if len(tokenizer) != vocab_size:
+        raise ValueError(
+            f"Tokenizer size {len(tokenizer)} does not match model vocab {vocab_size}"
+        )
+    if tokenizer.pad_token_id is None or tokenizer.eos_token_id is None:
+        raise ValueError("OPD requires tokenizer.pad_token_id and tokenizer.eos_token_id")
+    tokenizer.padding_side = "left"
+    tokenizer.truncation_side = "left"
+    return tokenizer
+
+
+def _parse_json_field(value):
+    if not isinstance(value, str):
+        return value
+    try:
+        return json.loads(value)
+    except json.JSONDecodeError:
+        return value
+
+
+def render_sample(tokenizer, conversations):
+    """Render one prompt and its optional fixed completion."""
+    messages = []
+    tools = None
+    for raw_message in conversations:
+        message = {key: value for key, value in dict(raw_message).items() if value is not None}
+        if message.get("tools"):
+            parsed_tools = _parse_json_field(message["tools"])
+            if isinstance(parsed_tools, list):
+                tools = parsed_tools
+        if message.get("tool_calls"):
+            message["tool_calls"] = _parse_json_field(message["tool_calls"])
+        messages.append(message)
+
+    has_completion = messages[-1].get("role") == "assistant" and any(
+        messages[-1].get(field) for field in ("content", "reasoning_content", "tool_calls")
+    )
+    prompt_messages = messages[:-1] if messages[-1].get("role") == "assistant" else messages
+    template_kwargs = {"tools": tools} if tools else {}
+    prompt_text = tokenizer.apply_chat_template(
+        prompt_messages,
+        tokenize=False,
+        add_generation_prompt=True,
+        **template_kwargs,
+    )
+    full_text = None
+    if has_completion:
+        full_text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=False,
+            **template_kwargs,
+        )
+    return prompt_text, full_text
+
+
+def tokenize_prompts(tokenizer, prompt_texts, max_seq_len, device):
+    encoded = tokenizer(
+        prompt_texts,
+        add_special_tokens=False,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=max_seq_len,
+        return_token_type_ids=False,
+    )
+    return {key: value.to(device) for key, value in encoded.items()}
+
+
+def build_off_policy_batch(
+    tokenizer, prompt_texts, full_texts, max_seq_len, max_gen_len, device
+):
+    """Tokenize fixed prompt-completion pairs and mask all prompt tokens."""
+    sequences = []
+    label_masks = []
+    for prompt_text, full_text in zip(prompt_texts, full_texts):
+        if full_text is None:
+            raise ValueError(
+                "Off-policy GKD selected a sample without a fixed assistant completion; "
+                "use --lmbda 1 for prompt-only data"
+            )
+        prompt_ids = tokenizer(prompt_text, add_special_tokens=False).input_ids
+        full_ids = tokenizer(full_text, add_special_tokens=False).input_ids
+        if full_ids[:len(prompt_ids)] != prompt_ids:
+            raise ValueError(
+                "The rendered prompt is not a token prefix of the fixed conversation; "
+                "the tokenizer/chat template is incompatible with prompt-completion GKD"
+            )
+        completion_ids = full_ids[len(prompt_ids):]
+        prompt_ids = prompt_ids[-max_seq_len:]
+        completion_ids = completion_ids[:max_gen_len]
+        if not completion_ids:
+            raise ValueError("A fixed completion became empty after tokenization/truncation")
+        sequences.append(prompt_ids + completion_ids)
+        label_masks.append([False] * len(prompt_ids) + [True] * len(completion_ids))
+
+    max_length = max(len(sequence) for sequence in sequences)
+    input_ids = []
+    attention_mask = []
+    labels = []
+    for sequence, label_mask in zip(sequences, label_masks):
+        padding = max_length - len(sequence)
+        input_ids.append(sequence + [tokenizer.pad_token_id] * padding)
+        attention_mask.append([1] * len(sequence) + [0] * padding)
+        labels.append(
+            [token if keep else -100 for token, keep in zip(sequence, label_mask)]
+            + [-100] * padding
+        )
+    return (
+        torch.tensor(input_ids, dtype=torch.long, device=device),
+        torch.tensor(attention_mask, dtype=torch.long, device=device),
+        torch.tensor(labels, dtype=torch.long, device=device),
+    )
+
+
+def distributed_mean(value):
+    result = value.detach().float().clone()
+    if dist.is_initialized():
+        dist.all_reduce(result, op=dist.ReduceOp.SUM)
+        result /= dist.get_world_size()
+    return result.item()
+
+
+def choose_on_policy(lmbda, device):
+    if dist.is_initialized():
+        draw = torch.zeros((), device=device)
+        if dist.get_rank() == 0:
+            draw.fill_(random.random())
+        dist.broadcast(draw, src=0)
+        random_value = draw.item()
+    else:
+        random_value = random.random()
+    return use_on_policy_batch(lmbda, random_value)
+
+
+def save_student(lm_config, epoch, step, wandb):
+    if not is_main_process():
+        return
+    model.eval()
+    raw_model = model.module if isinstance(model, DistributedDataParallel) else model
+    raw_model = getattr(raw_model, "_orig_mod", raw_model)
+    moe_suffix = "_moe" if lm_config.use_moe else ""
+    output_path = resolve_path(args.save_dir) / (
+        f"{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth"
+    )
+    state_dict = raw_model.state_dict()
+    torch.save({key: value.half().cpu() for key, value in state_dict.items()}, output_path)
+    lm_checkpoint(
+        lm_config,
+        weight=args.save_weight,
+        model=model,
+        optimizer=optimizer,
+        epoch=epoch,
+        step=step,
+        wandb=wandb,
+        save_dir=str(resolve_path(args.checkpoint_dir)),
+        scheduler=scheduler,
+        scaler=scaler,
+    )
+    model.train()
+    del state_dict
+
+
+def gkd_train_epoch(
+    epoch, loader, iters, rollout_engine, teacher_model, lm_config,
+    start_step=0, wandb=None
+):
+    teacher_model.eval().requires_grad_(False)
+    model.train()
+    on_policy_batches = 0
+    processed_batches = 0
+
+    for step, batch in enumerate(loader, start=start_step + 1):
+        if args.max_train_steps > 0 and processed_batches >= args.max_train_steps:
+            break
+        processed_batches += 1
+        on_policy = choose_on_policy(args.lmbda, args.device)
+        on_policy_batches += int(on_policy)
+        rendered = [render_sample(tokenizer, item) for item in batch["conversations"]]
+        prompt_texts = [item[0] for item in rendered]
+        full_texts = [item[1] for item in rendered]
+
+        if on_policy:
+            prompt_inputs = tokenize_prompts(
+                tokenizer, prompt_texts, args.max_seq_len, args.device
+            )
+            model.eval()
+            rollout_result = rollout_engine.rollout(
+                prompt_ids=prompt_inputs["input_ids"],
+                attention_mask=prompt_inputs["attention_mask"],
+                num_generations=args.num_generations,
+                max_new_tokens=args.max_gen_len,
+                temperature=args.temperature,
+                top_p=args.rollout_top_p,
+                top_k=args.rollout_top_k,
+                calculate_logps=False,
+            )
+            model.train()
+            input_ids = rollout_result.output_ids.to(args.device)
+            completion_ids = rollout_result.completion_ids.to(args.device)
+            completion_mask = build_completion_mask(
+                completion_ids,
+                rollout_result.completion_mask.to(args.device),
+                eos_token_id=tokenizer.eos_token_id,
+                pad_token_id=tokenizer.pad_token_id,
+            )
+            prompt_mask = prompt_inputs["attention_mask"].repeat_interleave(
+                args.num_generations, dim=0
+            )
+            attention_mask = torch.cat(
+                (prompt_mask, completion_mask.to(prompt_mask.dtype)), dim=1
+            )
+            with autocast_ctx:
+                student_output = model(
+                    input_ids,
+                    attention_mask=attention_mask,
+                    logits_to_keep=completion_ids.size(1) + 1,
+                )
+                student_logits = student_output.logits[:, :-1, :]
+            with torch.no_grad(), autocast_ctx:
+                teacher_logits = teacher_model(
+                    input_ids,
+                    attention_mask=attention_mask,
+                    logits_to_keep=completion_ids.size(1) + 1,
+                ).logits[:, :-1, :]
+            avg_completion_length = completion_mask.sum(dim=1).float().mean()
+            debug_completion = rollout_result.completions[0]
+        else:
+            input_ids, attention_mask, labels = build_off_policy_batch(
+                tokenizer,
+                prompt_texts,
+                full_texts,
+                args.max_seq_len,
+                args.max_gen_len,
+                args.device,
+            )
+            completion_mask = labels[:, 1:].ne(-100)
+            with autocast_ctx:
+                student_output = model(input_ids, attention_mask=attention_mask)
+                student_logits = student_output.logits[:, :-1, :]
+            with torch.no_grad(), autocast_ctx:
+                teacher_logits = teacher_model(
+                    input_ids, attention_mask=attention_mask
+                ).logits[:, :-1, :]
+            avg_completion_length = completion_mask.sum(dim=1).float().mean()
+            debug_completion = full_texts[0]
+
+        gkd_loss, gkd_metrics = generalized_jsd_loss(
+            student_logits,
+            teacher_logits,
+            completion_mask,
+            beta=args.beta,
+            temperature=args.distill_temperature,
+        )
+        aux_loss = student_output.aux_loss if lm_config.use_moe else gkd_loss.new_zeros(())
+        total_loss = gkd_loss + aux_loss
+        scaler.scale(total_loss / args.accumulation_steps).backward()
+
+        should_step = step % args.accumulation_steps == 0 or processed_batches == iters
+        if should_step:
+            scaler.unscale_(optimizer)
+            if args.grad_clip > 0:
+                torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+            scaler.step(optimizer)
+            scaler.update()
+            scheduler.step()
+            optimizer.zero_grad(set_to_none=True)
+
+        if args.debug_mode and is_main_process() and step % args.debug_interval == 0:
+            Logger(f"[OPD DEBUG] on_policy={on_policy}, prompt={prompt_texts[0]!r}")
+            Logger(f"[OPD DEBUG] completion={debug_completion!r}")
+
+        if step % args.log_interval == 0 or processed_batches == iters:
+            values = {
+                "loss/gkd": distributed_mean(gkd_loss),
+                "loss/aux": distributed_mean(aux_loss),
+                "loss/total": distributed_mean(total_loss),
+                "distill/forward_kl": distributed_mean(gkd_metrics["forward_kl"]),
+                "distill/reverse_kl": distributed_mean(gkd_metrics["reverse_kl"]),
+                "distill/top1_agreement": distributed_mean(gkd_metrics["top1_agreement"]),
+                "distill/valid_tokens": distributed_mean(gkd_metrics["valid_tokens"]),
+                "distill/on_policy": float(on_policy),
+                "distill/on_policy_fraction": on_policy_batches / processed_batches,
+                "rollout/avg_completion_length": distributed_mean(avg_completion_length),
+                "train/learning_rate": optimizer.param_groups[0]["lr"],
+            }
+            Logger(
+                f"Epoch:[{epoch + 1}/{args.epochs}]({step}/{start_step + iters}), "
+                f"GKD Loss: {values['loss/gkd']:.4f}, "
+                f"Forward KL: {values['distill/forward_kl']:.4f}, "
+                f"Reverse KL: {values['distill/reverse_kl']:.4f}, "
+                f"Top1 Agree: {values['distill/top1_agreement']:.4f}, "
+                f"On Policy: {int(on_policy)}, LR: {values['train/learning_rate']:.8f}"
+            )
+            if wandb and is_main_process():
+                wandb.log(values, step=epoch * iters + step)
+
+        if should_step and (step % args.save_interval == 0 or processed_batches == iters):
+            save_student(lm_config, epoch, step, wandb)
+
+        del input_ids, attention_mask, completion_mask
+        del student_output, student_logits, teacher_logits
+        del gkd_loss, gkd_metrics, aux_loss, total_loss
+        if on_policy:
+            del prompt_inputs, completion_ids, prompt_mask, rollout_result
+        else:
+            del labels
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="MiniMind Generalized/On-Policy Knowledge Distillation"
+    )
+    parser.add_argument("--data_path", type=str, default="dataset/rlaif.jsonl")
+    parser.add_argument("--student_model", type=str, default="full_sft")
+    parser.add_argument("--teacher_model", type=str, default="full_sft")
+    parser.add_argument("--tokenizer_path", type=str, default="model")
+    parser.add_argument("--save_dir", type=str, default="out")
+    parser.add_argument("--checkpoint_dir", type=str, default="checkpoints")
+    parser.add_argument("--save_weight", type=str, default="opd")
+    parser.add_argument("--epochs", type=int, default=1)
+    parser.add_argument("--batch_size", type=int, default=2)
+    parser.add_argument("--learning_rate", type=float, default=1e-6)
+    parser.add_argument("--device", type=str, default="auto")
+    parser.add_argument(
+        "--dtype", type=str, default="bfloat16",
+        choices=["float32", "float16", "bfloat16"]
+    )
+    parser.add_argument("--num_workers", type=int, default=4)
+    parser.add_argument("--accumulation_steps", type=int, default=1)
+    parser.add_argument("--grad_clip", type=float, default=1.0)
+    parser.add_argument("--log_interval", type=int, default=1)
+    parser.add_argument("--save_interval", type=int, default=100)
+    parser.add_argument("--max_seq_len", type=int, default=768)
+    parser.add_argument("--max_gen_len", type=int, default=256)
+    parser.add_argument("--max_train_steps", type=int, default=0)
+    parser.add_argument(
+        "--lmbda", type=float, default=1.0,
+        help="fraction of student-generated batches; 1.0 is pure OPD"
+    )
+    parser.add_argument(
+        "--beta", type=float, default=0.5,
+        help="generalized JSD coefficient; 0=forward KL, 1=reverse KL"
+    )
+    parser.add_argument("--temperature", type=float, default=1.0)
+    parser.add_argument("--distill_temperature", type=float, default=1.0)
+    parser.add_argument("--rollout_top_p", type=float, default=1.0)
+    parser.add_argument("--rollout_top_k", type=int, default=0)
+    parser.add_argument("--num_generations", type=int, default=1)
+    parser.add_argument("--student_hidden_size", type=int, default=768)
+    parser.add_argument("--student_num_layers", type=int, default=8)
+    parser.add_argument("--student_use_moe", type=int, default=0, choices=[0, 1])
+    parser.add_argument("--teacher_hidden_size", type=int, default=768)
+    parser.add_argument("--teacher_num_layers", type=int, default=8)
+    parser.add_argument("--teacher_use_moe", type=int, default=1, choices=[0, 1])
+    parser.add_argument("--from_resume", type=int, default=0, choices=[0, 1])
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--use_wandb", action="store_true")
+    parser.add_argument("--wandb_project", type=str, default="MiniMind-OPD")
+    parser.add_argument("--wandb_run_name", type=str, default="")
+    parser.add_argument(
+        "--wandb_mode", type=str, default="cloud",
+        choices=["cloud", "local", "disabled"]
+    )
+    parser.add_argument("--wandb_logdir", type=str, default="swanlog")
+    parser.add_argument("--use_compile", type=int, default=0, choices=[0, 1])
+    parser.add_argument("--debug_mode", action="store_true")
+    parser.add_argument("--debug_interval", type=int, default=20)
+    args = parser.parse_args()
+    validate_args(args)
+
+    local_rank = init_distributed_mode()
+    if dist.is_initialized():
+        args.device = f"cuda:{local_rank}"
+    else:
+        args.device = resolve_device(args.device)
+    rank = dist.get_rank() if dist.is_initialized() else 0
+    setup_seed(args.seed + rank)
+    resolve_path(args.save_dir).mkdir(parents=True, exist_ok=True)
+    resolve_path(args.checkpoint_dir).mkdir(parents=True, exist_ok=True)
+
+    max_position_embeddings = max(32768, args.max_seq_len + args.max_gen_len)
+    student_config = MiniMindConfig(
+        hidden_size=args.student_hidden_size,
+        num_hidden_layers=args.student_num_layers,
+        use_moe=bool(args.student_use_moe),
+        max_position_embeddings=max_position_embeddings,
+    )
+    teacher_config = MiniMindConfig(
+        hidden_size=args.teacher_hidden_size,
+        num_hidden_layers=args.teacher_num_layers,
+        use_moe=bool(args.teacher_use_moe),
+        max_position_embeddings=max_position_embeddings,
+    )
+    if student_config.vocab_size != teacher_config.vocab_size:
+        raise ValueError("GKD requires student and teacher to share the same vocabulary")
+    tokenizer = load_tokenizer(args.tokenizer_path, student_config.vocab_size)
+
+    device_type = args.device.split(":", 1)[0]
+    mixed_dtype = {
+        "float32": torch.float32,
+        "float16": torch.float16,
+        "bfloat16": torch.bfloat16,
+    }[args.dtype]
+    if device_type == "mps" and mixed_dtype == torch.bfloat16:
+        Logger("[OPD WARNING] MPS uses float16 for the frozen teacher")
+        mixed_dtype = torch.float16
+    teacher_dtype = mixed_dtype if device_type in {"cuda", "mps"} else torch.float32
+    autocast_ctx = (
+        torch.cuda.amp.autocast(dtype=mixed_dtype)
+        if device_type == "cuda" and mixed_dtype != torch.float32
+        else nullcontext()
+    )
+
+    model = load_native_model(
+        student_config, args.student_model, args.device, "student"
+    )
+    teacher_model = load_native_model(
+        teacher_config,
+        args.teacher_model,
+        args.device,
+        "teacher",
+        model_dtype=teacher_dtype,
+    ).eval().requires_grad_(False)
+    Logger(f"Student params: {sum(p.numel() for p in model.parameters()) / 1e6:.3f}M")
+    Logger(f"Teacher params: {sum(p.numel() for p in teacher_model.parameters()) / 1e6:.3f}M")
+
+    checkpoint = (
+        lm_checkpoint(
+            student_config,
+            weight=args.save_weight,
+            save_dir=str(resolve_path(args.checkpoint_dir)),
+        )
+        if args.from_resume == 1 else None
+    )
+    train_ds = OPDDataset(resolve_path(args.data_path))
+    train_sampler = (
+        DistributedSampler(train_ds, shuffle=True, seed=args.seed)
+        if dist.is_initialized() else None
+    )
+    count_loader = DataLoader(
+        train_ds,
+        batch_size=args.batch_size,
+        sampler=train_sampler,
+        collate_fn=collate_conversations,
+    )
+    full_iters = len(count_loader)
+    iters = min(full_iters, args.max_train_steps) if args.max_train_steps > 0 else full_iters
+    optimizer_steps_per_epoch = math.ceil(iters / args.accumulation_steps)
+    total_optimizer_steps = max(1, optimizer_steps_per_epoch * args.epochs)
+    optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
+    scheduler = CosineAnnealingLR(
+        optimizer, T_max=total_optimizer_steps, eta_min=args.learning_rate / 10
+    )
+    scaler = torch.cuda.amp.GradScaler(
+        enabled=(device_type == "cuda" and mixed_dtype == torch.float16)
+    )
+
+    start_epoch, start_step = 0, 0
+    if checkpoint:
+        model.load_state_dict(checkpoint["model"], strict=True)
+        optimizer.load_state_dict(checkpoint["optimizer"])
+        if "scheduler" in checkpoint:
+            scheduler.load_state_dict(checkpoint["scheduler"])
+        if "scaler" in checkpoint:
+            scaler.load_state_dict(checkpoint["scaler"])
+        start_epoch = checkpoint["epoch"]
+        start_step = checkpoint.get("step", 0)
+        Logger(f"Resuming GKD from epoch={start_epoch}, step={start_step}")
+
+    if args.use_compile == 1:
+        model = torch.compile(model)
+        Logger("torch.compile enabled")
+    if dist.is_initialized():
+        model = DistributedDataParallel(model, device_ids=[local_rank])
+    rollout_engine = create_rollout_engine(
+        engine_type="torch",
+        policy_model=model,
+        tokenizer=tokenizer,
+        device=args.device,
+        autocast_ctx=autocast_ctx,
+    )
+
+    wandb = None
+    if args.use_wandb and is_main_process():
+        import swanlab as wandb
+        wandb_id = checkpoint.get("wandb_id") if checkpoint else None
+        wandb.init(
+            project=args.wandb_project,
+            name=args.wandb_run_name or (
+                f"GKD-L{args.lmbda}-B{args.beta}-BS{args.batch_size}x"
+                f"{args.accumulation_steps}-LR{args.learning_rate}"
+            ),
+            config=vars(args),
+            mode=args.wandb_mode,
+            logdir=str(resolve_path(args.wandb_logdir)),
+            id=wandb_id if args.wandb_mode == "cloud" else None,
+            resume="must" if wandb_id and args.wandb_mode == "cloud" else None,
+        )
+
+    optimizer.zero_grad(set_to_none=True)
+    for epoch in range(start_epoch, args.epochs):
+        if train_sampler:
+            train_sampler.set_epoch(epoch)
+        setup_seed(args.seed + epoch + rank)
+        indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step if epoch == start_epoch and start_step > 0 else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        loader = DataLoader(
+            train_ds,
+            batch_sampler=batch_sampler,
+            num_workers=args.num_workers,
+            pin_memory=(device_type == "cuda"),
+            collate_fn=collate_conversations,
+        )
+        if skip > 0:
+            Logger(f"Epoch [{epoch + 1}/{args.epochs}]: skipping {skip} completed batches")
+        gkd_train_epoch(
+            epoch,
+            loader,
+            iters,
+            rollout_engine,
+            teacher_model,
+            student_config,
+            start_step=skip,
+            wandb=wandb,
+        )
+        start_step = 0
+
+    if wandb and is_main_process() and hasattr(wandb, "finish"):
+        wandb.finish()
+    if dist.is_initialized():
+        dist.barrier()
+        dist.destroy_process_group()