examples/train_qwen_titans_babilong.py

"""
Qwen3 + Titans training on BABILong QA1 (32k).

Key ideas:
- Fixed-length 32k samples for DDP/FSDP stability.
- Stream long sequences by chunk (default 8k).
- Insert Titans memory modules into Qwen layers (stride configurable).
"""

import os
import json
import math
import argparse
import logging
import weakref
from contextlib import nullcontext
from dataclasses import dataclass, asdict
from typing import Optional, Dict, Any, List, Tuple, Callable

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.data import Dataset, DataLoader
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.nn.parallel import DistributedDataParallel as DDP
from tqdm import tqdm

from einops import rearrange

# add repo root to sys.path
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

# Titans components
from titans_pytorch import NeuralMemory, MemoryMLP
from titans_pytorch.neural_memory import NeuralMemState

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)


@dataclass
class TrainingConfig:
    # paths
    model_path: str = "/data/huangyifei/huggingface_cache/hub/models--Qwen--Qwen3-4B-Instruct-2507/snapshots/cdbee75f17c01a7cc42f958dc650907174af0554"
    data_path: str = "/data/yty/BABILong/babilong-train-5k-samples/data/qa1/32k.json"
    output_dir: str = "./outputs/qwen_titans_babilong"

    # training
    num_epochs: int = 10
    batch_size: int = 2
    gradient_accumulation_steps: int = 8
    max_grad_norm: float = 1.0

    # learning rates
    lr_memory: float = 1e-4
    lr_pretrained: float = 5e-6
    weight_decay: float = 0.01
    warmup_steps: int = 100

    # streaming / memory
    chunk_size: int = 8192
    use_memory: bool = True
    memory_chunk_size: int = 128
    memory_batch_size: int = 128
    memory_heads: int = 8
    memory_dim_head: int = 64
    memory_depth: int = 1
    memory_layer_stride: int = 8
    memory_fp32: bool = True
    detach_mem_state: bool = True
    freeze_base_model: bool = False  # 冻结 Qwen base，只训练记忆模块

    # evaluation / logging
    eval_steps: int = 200
    eval_topk: int = 0
    logging_steps: int = 10
    log_every_batches: int = 80
    final_eval_print_examples: int = 10
    debug_data_samples: int = 0
    debug_label_batches: int = 0
    debug_eval_stats: bool = False
    debug_grad_norm: bool = False

    # precision
    bf16: bool = True
    fp16: bool = False
    use_tf32: bool = True
    gradient_checkpointing: bool = False
    chunkwise_backward: bool = True
    chunkwise_backward: bool = True

    # data
    max_length: int = 32768
    answer_reserve_tokens: int = 64
    label_prefix_tokens: int = 0
    max_samples: Optional[int] = 500  # 快速实验用 500，完整训练可设置更大值

    # fsdp
    use_fsdp: bool = False
    fsdp_use_orig_params: bool = True
    ddp_find_unused_parameters: bool = False

    # checkpoint
    save_full_checkpoint: bool = True
    final_ckpt_name: str = "final_memory_checkpoint.pt"
    final_full_ckpt_name: str = "final_full_checkpoint.pt"

    seed: int = 42


class BABILongDataset(Dataset):
    def __init__(
        self,
        data_path: str,
        tokenizer,
        max_length: int = 32768,
        answer_reserve_tokens: int = 64,
        label_prefix_tokens: int = 0,
        max_samples: Optional[int] = None,
    ):
        self.tokenizer = tokenizer
        self.max_length = max_length
        self.answer_reserve_tokens = answer_reserve_tokens
        self.label_prefix_tokens = int(label_prefix_tokens)

        logger.info(f"Loading dataset: {data_path}")
        with open(data_path, "r") as f:
            self.data = json.load(f)

        if max_samples:
            self.data = self.data[:max_samples]

        logger.info(f"Dataset size: {len(self.data)}")

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        item = self.data[idx]
        text = f"{item['input']}\n\nQuestion: {item['question']}\nAnswer:"
        target = item["target"]

        pad_id = self.tokenizer.pad_token_id or 0
        reserve = int(self.answer_reserve_tokens)

        prompt_ids = self.tokenizer(
            text,
            max_length=max(self.max_length - reserve, 1),
            truncation=True,
            add_special_tokens=True,
            return_tensors="pt",
        ).input_ids.squeeze(0)

        answer_ids = self.tokenizer(
            f" {target}",
            add_special_tokens=False,
            return_tensors="pt",
        ).input_ids.squeeze(0)

        available = max(self.max_length - prompt_ids.numel(), 0)
        answer_ids = answer_ids[:available]

        input_ids = torch.cat([prompt_ids, answer_ids], dim=0)[: self.max_length]

        labels = torch.full_like(input_ids, fill_value=-100)
        if answer_ids.numel() > 0:
            start = prompt_ids.numel()
            end = min(start + answer_ids.numel(), labels.numel())
            labels[start:end] = input_ids[start:end]
            if self.label_prefix_tokens > 0:
                prefix = min(start, self.label_prefix_tokens)
                if prefix > 0:
                    labels[start - prefix:start] = input_ids[start - prefix:start]

        seq_len = input_ids.numel()
        if seq_len < self.max_length:
            pad_len = self.max_length - seq_len
            input_ids = F.pad(input_ids, (0, pad_len), value=int(pad_id))
            labels = F.pad(labels, (0, pad_len), value=-100)
            attention_mask = torch.cat(
                [torch.ones(seq_len, dtype=torch.long), torch.zeros(pad_len, dtype=torch.long)],
                dim=0,
            )
        else:
            attention_mask = torch.ones(self.max_length, dtype=torch.long)

        return {
            "input_ids": input_ids.to(dtype=torch.long),
            "labels": labels.to(dtype=torch.long),
            "attention_mask": attention_mask,
        }


def collate_fn(batch):
    keys = batch[0].keys()
    return {k: torch.stack([b[k] for b in batch], dim=0) for k in keys}


def _get_raw_dataset_item(dataset, idx: int) -> Optional[Dict[str, Any]]:
    base = dataset
    true_idx = idx
    if isinstance(dataset, torch.utils.data.Subset):
        base = dataset.dataset
        true_idx = dataset.indices[idx]
    if isinstance(base, BABILongDataset) and hasattr(base, "data"):
        try:
            return base.data[true_idx]
        except Exception:
            return None
    return None


def log_dataset_debug_stats(dataset, tokenizer, name: str, num_samples: int) -> None:
    if num_samples <= 0:
        return
    total = len(dataset)
    if total <= 0:
        logger.warning(f"[DATA DEBUG] {name}: empty dataset")
        return

    n = min(int(num_samples), total)
    zero_label = 0
    total_label_tokens = 0
    total_loss_tokens = 0
    total_attn_tokens = 0

    for i in range(n):
        sample = dataset[i]
        labels = sample["labels"]
        attn = sample["attention_mask"]

        label_mask = labels != -100
        label_tokens = int(label_mask.sum().item())
        loss_tokens = int((labels[1:] != -100).sum().item()) if labels.numel() > 1 else 0
        attn_tokens = int(attn.sum().item())

        total_label_tokens += label_tokens
        total_loss_tokens += loss_tokens
        total_attn_tokens += attn_tokens
        if label_tokens == 0:
            zero_label += 1

        if i < min(3, n):
            label_pos = label_mask.nonzero(as_tuple=False).view(-1)
            first_label = int(label_pos[0].item()) if label_pos.numel() > 0 else -1
            last_label = int(label_pos[-1].item()) if label_pos.numel() > 0 else -1

            decoded = ""
            if tokenizer is not None and label_pos.numel() > 0:
                answer_ids = labels[label_pos].tolist()
                decoded = tokenizer.decode(answer_ids, skip_special_tokens=True).strip()
                if len(decoded) > 200:
                    decoded = decoded[:200] + "..."

            raw_item = _get_raw_dataset_item(dataset, i)
            target_chars = None
            target_tokens = None
            if raw_item is not None and tokenizer is not None:
                target_text = str(raw_item.get("target", ""))
                target_chars = len(target_text)
                target_ids = tokenizer(
                    f" {target_text}",
                    add_special_tokens=False,
                    return_tensors="pt",
                ).input_ids.squeeze(0)
                target_tokens = int(target_ids.numel())

            logger.info(
                f"[DATA DEBUG] {name} sample {i}: attn_tokens={attn_tokens}, "
                f"label_tokens={label_tokens}, loss_tokens={loss_tokens}, "
                f"label_span=[{first_label},{last_label}]"
            )
            if target_chars is not None or decoded:
                logger.info(
                    f"[DATA DEBUG] {name} target_chars={target_chars}, "
                    f"target_tokens={target_tokens}, decoded_answer={repr(decoded)}"
                )

    avg_label = total_label_tokens / max(n, 1)
    avg_loss = total_loss_tokens / max(n, 1)
    avg_attn = total_attn_tokens / max(n, 1)
    logger.info(
        f"[DATA DEBUG] {name} summary: samples={n}, zero_label_samples={zero_label}, "
        f"avg_label_tokens={avg_label:.2f}, avg_loss_tokens={avg_loss:.2f}, avg_attn_tokens={avg_attn:.2f}"
    )


class QwenDecoderLayerWithTitansMemory(nn.Module):
    def __init__(
        self,
        base_layer: nn.Module,
        *,
        hidden_size: int,
        chunk_size: int,
        batch_size: int,
        dim_head: int,
        num_heads: int,
        memory_depth: int,
        memory_fp32: bool,
        detach_mem_state: bool,
        parent_model: Optional[nn.Module] = None,
    ):
        super().__init__()
        self.layer = base_layer
        self.memory_fp32 = memory_fp32
        self.detach_mem_state = bool(detach_mem_state)
        self.memory_state: Optional[NeuralMemState] = None
        self.parent_model_ref = weakref.ref(parent_model) if parent_model is not None else None

        memory_model = MemoryMLP(
            dim=dim_head,
            depth=memory_depth,
            expansion_factor=2.0,
        )

        self.neural_memory = NeuralMemory(
            dim=hidden_size,
            chunk_size=chunk_size,
            batch_size=batch_size,
            dim_head=dim_head,
            heads=num_heads,
            model=memory_model,
            momentum=True,
            momentum_order=1,
            qk_rmsnorm=True,
            pre_rmsnorm=True,
            default_step_transform_max_lr=1e-2,
            init_adaptive_step_bias=-6.0,
            max_grad_norm=1.0,
            spectral_norm_surprises=True,
            use_accelerated_scan=False,
        )

        self.mem_gate = nn.Sequential(
            nn.Linear(hidden_size * 2, hidden_size),
            nn.Sigmoid(),
        )

        try:
            layer_device = next(base_layer.parameters()).device
            layer_dtype = next(base_layer.parameters()).dtype
        except StopIteration:
            layer_device = None
            layer_dtype = None

        if layer_device is not None:
            mem_dtype = torch.float32 if memory_fp32 else layer_dtype
            self.neural_memory = self.neural_memory.to(device=layer_device, dtype=mem_dtype)
            if layer_dtype is not None:
                self.mem_gate = self.mem_gate.to(device=layer_device, dtype=layer_dtype)
            else:
                self.mem_gate = self.mem_gate.to(device=layer_device)

    def reset_memory_state(self):
        self.memory_state = None

    def _get_store_mask(self, hidden_states: torch.Tensor) -> Optional[torch.Tensor]:
        parent_model = self.parent_model_ref() if self.parent_model_ref is not None else None
        if parent_model is None or not hasattr(parent_model, "_mem_store_mask"):
            return None
        store_mask = getattr(parent_model, "_mem_store_mask")
        if store_mask is None:
            return None
        store_mask = store_mask.to(device=hidden_states.device).bool()
        if store_mask.shape[:2] != hidden_states.shape[:2]:
            return None
        return store_mask

    def forward(self, *args, **kwargs):
        outputs = self.layer(*args, **kwargs)

        if isinstance(outputs, (tuple, list)):
            hidden_states = outputs[0]
            rest = outputs[1:]
        else:
            hidden_states = outputs
            rest = None

        full_store_mask = self._get_store_mask(hidden_states)
        mem_inp = hidden_states.float() if self.memory_fp32 else hidden_states

        store_seq = None
        store_mask = full_store_mask
        if store_mask is not None:
            store_seq = mem_inp
            if store_mask.shape[1] > 0 and not store_mask[:, 0].any():
                store_seq = store_seq[:, 1:]
                store_mask = store_mask[:, 1:]

            store_chunk = self.neural_memory.store_chunk_size
            remainder = store_seq.shape[1] % store_chunk
            if remainder != 0:
                store_seq = store_seq[:, :-remainder]
                store_mask = store_mask[:, :-remainder]

            if store_mask is not None and store_seq is not None:
                if store_mask.shape[1] != store_seq.shape[1]:
                    min_len = min(store_mask.shape[1], store_seq.shape[1])
                    store_seq = store_seq[:, :min_len]
                    store_mask = store_mask[:, :min_len]

            if store_seq.shape[1] == 0:
                store_seq = None
                store_mask = None

        mem_ctx = (
            torch.amp.autocast(device_type=hidden_states.device.type, enabled=False)
            if self.memory_fp32
            else nullcontext()
        )
        with mem_ctx:
            retrieved, next_state = self.neural_memory(
                mem_inp,
                store_seq=store_seq,
                state=self.memory_state,
                store_mask=store_mask,
                detach_mem_state=self.detach_mem_state,
            )
        self.memory_state = next_state

        if retrieved is not None:
            retrieved = retrieved.to(dtype=hidden_states.dtype)
            if full_store_mask is not None and full_store_mask.shape[:2] == retrieved.shape[:2]:
                retrieved = retrieved * full_store_mask.unsqueeze(-1).to(dtype=retrieved.dtype)
            gate = self.mem_gate(torch.cat([hidden_states, retrieved], dim=-1))
            hidden_states = hidden_states + gate * retrieved

        if rest is None:
            return hidden_states
        return (hidden_states, *rest)


class QwenTitansForBABILong(nn.Module):
    def __init__(self, qwen_model, config: TrainingConfig):
        super().__init__()
        self.qwen = qwen_model
        self.config = config
        self.hidden_size = qwen_model.config.hidden_size
        self.use_memory = bool(getattr(config, "use_memory", True))

        if self.use_memory:
            self.memory_layer_stride = int(getattr(config, "memory_layer_stride", 4))
            self.memory_layer_indices = [
                idx for idx in range(len(self.qwen.model.layers)) if idx % self.memory_layer_stride == 0
            ]

            for layer_idx in self.memory_layer_indices:
                base_layer = self.qwen.model.layers[layer_idx]
                wrapped = QwenDecoderLayerWithTitansMemory(
                    base_layer,
                    hidden_size=self.hidden_size,
                    chunk_size=config.memory_chunk_size,
                    batch_size=config.memory_batch_size,
                    dim_head=config.memory_dim_head,
                    num_heads=config.memory_heads,
                    memory_depth=config.memory_depth,
                    memory_fp32=config.memory_fp32,
                    detach_mem_state=config.detach_mem_state,
                    parent_model=self.qwen.model,
                )
                self.qwen.model.layers[layer_idx] = wrapped
        else:
            self.memory_layer_stride = 0
            self.memory_layer_indices = []

        if self.use_memory:
            logger.info("[QwenTitansForBABILong] Initialized")
            logger.info(f"  - hidden_size: {self.hidden_size}")
            logger.info(f"  - chunk_size: {config.chunk_size}")
            logger.info(f"  - memory_layer_stride: {self.memory_layer_stride}")
            logger.info(f"  - memory_layers: {self.memory_layer_indices}")
        else:
            logger.info("[QwenTitansForBABILong] Initialized (memory disabled)")
            logger.info(f"  - hidden_size: {self.hidden_size}")
            logger.info(f"  - chunk_size: {config.chunk_size}")

        self._memory_layers = [
            layer for layer in self.qwen.model.layers if isinstance(layer, QwenDecoderLayerWithTitansMemory)
        ]
        self.qwen.model._mem_store_mask = None

    def _split_into_chunks(self, tensor, chunk_size):
        seq_len = tensor.shape[1]
        chunks = []
        for start in range(0, seq_len, chunk_size):
            end = min(start + chunk_size, seq_len)
            chunks.append((start, end, tensor[:, start:end]))
        return chunks

    def reset_memory_states(self):
        for layer in self._memory_layers:
            layer.reset_memory_state()

    def _set_mem_store_mask(
        self,
        chunk_ids: torch.Tensor,
        chunk_mask: Optional[torch.Tensor],
        chunk_start: int,
    ) -> None:
        if not self.use_memory:
            self.qwen.model._mem_store_mask = None
            return
        if chunk_mask is None:
            if chunk_start > 0:
                store_mask = torch.ones_like(chunk_ids, dtype=torch.bool)
                store_mask[:, 0] = False
            else:
                store_mask = None
        else:
            store_mask = chunk_mask.to(device=chunk_ids.device).bool()
            if chunk_start > 0:
                store_mask[:, 0] = False
        self.qwen.model._mem_store_mask = store_mask

    def get_memory_modules(self) -> List[nn.Module]:
        if not self._memory_layers:
            return []
        modules = []
        for layer in self._memory_layers:
            modules.append(layer.neural_memory)
            modules.append(layer.mem_gate)
        return modules

    def forward(
        self,
        input_ids: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        return_pred_tokens: bool = False,
        topk: int = 0,
        chunkwise_backward: bool = False,
        loss_scale: Optional[float] = None,
        backward_fn: Optional[Callable[[torch.Tensor], None]] = None,
        chunk_start: Optional[int] = None,
        chunk_end: Optional[int] = None,
        reset_mem_state: bool = False,
    ) -> Dict[str, torch.Tensor]:
        if chunk_start is not None or chunk_end is not None:
            start = 0 if chunk_start is None else int(chunk_start)
            end = int(chunk_end) if chunk_end is not None else None
            return self._forward_single_chunk(
                input_ids=input_ids,
                attention_mask=attention_mask,
                labels=labels,
                chunk_start=start,
                chunk_end=end,
                reset_mem_state=reset_mem_state,
            )
        batch_size, seq_len = input_ids.shape
        chunk_size = self.config.chunk_size
        chunks = self._split_into_chunks(input_ids, chunk_size)

        self.reset_memory_states()
        loss_fct_sum = nn.CrossEntropyLoss(reduction="sum")
        total_loss_sum = None
        total_loss_tokens = 0
        topk_correct = None
        topk_total = None

        pred_tokens_by_sample: List[List[int]] = [[] for _ in range(batch_size)]
        target_tokens_by_sample: List[List[int]] = [[] for _ in range(batch_size)]
        if topk and topk > 0:
            device = input_ids.device
            topk_correct = torch.tensor(0.0, device=device, dtype=torch.float32)
            topk_total = torch.tensor(0.0, device=device, dtype=torch.float32)

        for start, end, _ in chunks:
            proc_start = max(0, start - 1)
            chunk_ids = input_ids[:, proc_start:end]
            chunk_labels = labels[:, proc_start:end] if labels is not None else None
            chunk_mask = attention_mask[:, proc_start:end] if attention_mask is not None else None

            self._set_mem_store_mask(chunk_ids, chunk_mask, start)
            hidden_full = self._process_chunk(chunk_ids, chunk_mask)
            if self.use_memory:
                self.qwen.model._mem_store_mask = None

            if chunk_labels is not None and (chunk_labels != -100).any():
                chunk_labels_local = chunk_labels.to(device=hidden_full.device)
                shift_hidden = hidden_full[:, :-1, :].contiguous()
                shift_labels = chunk_labels_local[:, 1:].contiguous()

                valid = shift_labels != -100
                if valid.any():
                    hs = shift_hidden[valid]
                    targets = shift_labels[valid]

                    hs = torch.nan_to_num(hs.float(), nan=0.0, posinf=0.0, neginf=0.0)

                    logits = self.qwen.lm_head(hs)
                    logits = logits.float()
                    logits = torch.nan_to_num(logits, nan=0.0, posinf=0.0, neginf=0.0)
                    targets = targets.to(device=logits.device)

                    chunk_loss_sum = loss_fct_sum(logits, targets)
                    if total_loss_sum is None:
                        total_loss_sum = chunk_loss_sum
                    else:
                        total_loss_sum = total_loss_sum + chunk_loss_sum
                    total_loss_tokens += targets.numel()

                    if topk and topk > 0:
                        k = min(int(topk), logits.shape[-1])
                        topk_ids = torch.topk(logits, k=k, dim=-1).indices
                        correct = (topk_ids == targets.unsqueeze(-1)).any(dim=-1)
                        topk_correct = topk_correct + correct.float().sum()
                        topk_total = topk_total + torch.tensor(float(targets.numel()), device=topk_total.device)

                    if return_pred_tokens:
                        idx = valid.nonzero(as_tuple=False)
                        pred_flat = torch.argmax(logits, dim=-1).detach().to("cpu", dtype=torch.long).tolist()
                        tgt_flat = targets.detach().to("cpu", dtype=torch.long).tolist()
                        b_idx_flat = idx[:, 0].detach().to("cpu", dtype=torch.long).tolist()

                        for i, b_idx in enumerate(b_idx_flat):
                            pred_tokens_by_sample[b_idx].append(int(pred_flat[i]))
                            target_tokens_by_sample[b_idx].append(int(tgt_flat[i]))

        if total_loss_sum is None or total_loss_tokens == 0:
            device = next(self.qwen.parameters()).device
            loss = torch.zeros((), device=device, dtype=torch.float32)
        else:
            loss = total_loss_sum / total_loss_tokens

        out: Dict[str, torch.Tensor] = {"loss": loss}
        if return_pred_tokens:
            lengths = torch.tensor([len(x) for x in target_tokens_by_sample], dtype=torch.long)
            max_len = int(lengths.max().item()) if lengths.numel() > 0 else 0
            if max_len > 0:
                pred_mat = torch.full((batch_size, max_len), -1, dtype=torch.long)
                tgt_mat = torch.full((batch_size, max_len), -1, dtype=torch.long)
                for b in range(batch_size):
                    L = int(lengths[b].item())
                    if L > 0:
                        pred_mat[b, :L] = torch.tensor(pred_tokens_by_sample[b], dtype=torch.long)
                        tgt_mat[b, :L] = torch.tensor(target_tokens_by_sample[b], dtype=torch.long)
            else:
                pred_mat = torch.empty((batch_size, 0), dtype=torch.long)
                tgt_mat = torch.empty((batch_size, 0), dtype=torch.long)
            out["pred_ids"] = pred_mat
            out["target_ids"] = tgt_mat
            out["target_lengths"] = lengths
        if topk and topk > 0 and topk_correct is not None and topk_total is not None:
            out["topk_correct"] = topk_correct
            out["topk_total"] = topk_total
        return out

    def _forward_single_chunk(
        self,
        input_ids: torch.Tensor,
        attention_mask: Optional[torch.Tensor],
        labels: Optional[torch.Tensor],
        chunk_start: int,
        chunk_end: Optional[int],
        reset_mem_state: bool,
    ) -> Dict[str, torch.Tensor]:
        if reset_mem_state:
            self.reset_memory_states()

        seq_len = input_ids.shape[1]
        end = chunk_end if chunk_end is not None else min(chunk_start + self.config.chunk_size, seq_len)
        end = min(int(end), seq_len)
        start = max(0, int(chunk_start))

        proc_start = max(0, start - 1)
        chunk_ids = input_ids[:, proc_start:end]
        chunk_labels = labels[:, proc_start:end] if labels is not None else None
        chunk_mask = attention_mask[:, proc_start:end] if attention_mask is not None else None

        self._set_mem_store_mask(chunk_ids, chunk_mask, start)
        hidden_full = self._process_chunk(chunk_ids, chunk_mask)
        if self.use_memory:
            self.qwen.model._mem_store_mask = None

        loss_fct_sum = nn.CrossEntropyLoss(reduction="sum")
        total_loss_sum = None
        total_loss_tokens = 0

        if chunk_labels is not None and (chunk_labels != -100).any():
            chunk_labels_local = chunk_labels.to(device=hidden_full.device)
            shift_hidden = hidden_full[:, :-1, :].contiguous()
            shift_labels = chunk_labels_local[:, 1:].contiguous()

            valid = shift_labels != -100
            if valid.any():
                hs = shift_hidden[valid]
                targets = shift_labels[valid]

                hs = torch.nan_to_num(hs.float(), nan=0.0, posinf=0.0, neginf=0.0)
                logits = self.qwen.lm_head(hs)
                logits = logits.float()
                logits = torch.nan_to_num(logits, nan=0.0, posinf=0.0, neginf=0.0)
                targets = targets.to(device=logits.device)

                total_loss_sum = loss_fct_sum(logits, targets)
                total_loss_tokens = targets.numel()

        if total_loss_sum is None:
            # 创建一个有梯度图的零 loss，通过 hidden_full 建立连接
            # 这对 DDP 至关重要：确保所有 rank 的 backward 调用一致
            total_loss_sum = (hidden_full.float().sum() * 0.0)

        return {
            "loss_sum": total_loss_sum,
            "loss_tokens": total_loss_tokens,
            "has_grad": True,  # 现在总是有梯度图
        }

    def _process_chunk(
        self,
        chunk_ids: torch.Tensor,
        chunk_attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if hasattr(self.qwen.model, "embed_tokens"):
            token_embeds = self.qwen.model.embed_tokens(chunk_ids)
        else:
            token_embeds = self.qwen.get_input_embeddings()(chunk_ids)

        outputs = self.qwen.model(
            inputs_embeds=token_embeds,
            attention_mask=chunk_attention_mask,
            use_cache=False,
            output_hidden_states=False,
            return_dict=True,
        )
        return outputs.last_hidden_state

    def freeze_base_model(self):
        """
        冻结 Qwen base 模型的大部分参数，保留记忆模块和独立的 lm_head 可训练。
        
        可训练的参数包括：
        - neural_memory: Q/K/V projections, adaptive lr 等（Memory MLP 通过 Surprise 前向更新）
        - mem_gate: 控制记忆输出和原始输出的混合
        - lm_head: 独立的输出层（解开 tied weights）
        
        冻结的参数：
        - qwen.model.embed_tokens (保持输入分布不变！)
        - qwen.model.layers (除了 neural_memory 和 mem_gate)
        - qwen.model.norm
        """
        frozen_count = 0
        trainable_count = 0
        lm_head_count = 0
        
        # 关键：解开 tied weights！
        # 如果 lm_head 和 embed_tokens 共享权重，需要创建独立的 lm_head
        if hasattr(self.qwen, 'lm_head') and hasattr(self.qwen.model, 'embed_tokens'):
            lm_head_weight = self.qwen.lm_head.weight
            embed_weight = self.qwen.model.embed_tokens.weight
            has_tied_weights = lm_head_weight.data_ptr() == embed_weight.data_ptr()
            
            if has_tied_weights:
                logger.info("[freeze_base_model] Detected tied weights - untying lm_head from embed_tokens")
                # 创建独立的 lm_head 权重（复制当前权重）
                new_lm_head = nn.Linear(
                    self.qwen.lm_head.in_features,
                    self.qwen.lm_head.out_features,
                    bias=self.qwen.lm_head.bias is not None,
                    device=lm_head_weight.device,
                    dtype=lm_head_weight.dtype,
                )
                # 复制权重
                with torch.no_grad():
                    new_lm_head.weight.copy_(lm_head_weight)
                    if self.qwen.lm_head.bias is not None and new_lm_head.bias is not None:
                        new_lm_head.bias.copy_(self.qwen.lm_head.bias)
                # 替换 lm_head
                self.qwen.lm_head = new_lm_head
                logger.info(f"[freeze_base_model] Created independent lm_head: {new_lm_head.weight.shape}")
        
        for name, param in self.named_parameters():
            # 判断是否是需要保持可训练的参数
            is_memory = "neural_memory" in name or "mem_gate" in name
            is_lm_head = "lm_head" in name
            
            if is_memory:
                param.requires_grad = True
                trainable_count += 1
            elif is_lm_head:
                # 独立的 lm_head 可训练
                param.requires_grad = True
                trainable_count += 1
                lm_head_count += 1
                logger.info(f"[freeze_base_model] lm_head param: {name}")
            else:
                # 冻结其他所有参数，包括 embed_tokens！
                param.requires_grad = False
                frozen_count += 1
        
        logger.info(f"[freeze_base_model] Frozen: {frozen_count}, Trainable: {trainable_count} (lm_head: {lm_head_count})")
        return self

    def get_param_groups(self, lr_memory: float, lr_pretrained: float, weight_decay: float):
        memory_params = []
        pretrained_params = []

        for name, param in self.named_parameters():
            if not param.requires_grad:
                continue
            if "neural_memory" in name or "mem_gate" in name:
                memory_params.append(param)
            else:
                pretrained_params.append(param)

        param_groups = []
        if len(memory_params) > 0:
            param_groups.append(
                {"params": memory_params, "lr": lr_memory, "weight_decay": weight_decay, "name": "memory_module"}
            )
        if len(pretrained_params) > 0:
            param_groups.append(
                {"params": pretrained_params, "lr": lr_pretrained, "weight_decay": weight_decay, "name": "pretrained"}
            )
        logger.info(f"Param groups: memory={len(memory_params)}, pretrained={len(pretrained_params)}")
        return param_groups


def init_distributed() -> tuple[bool, int, int, int]:
    if "RANK" not in os.environ or "WORLD_SIZE" not in os.environ:
        return False, 0, 0, 1

    rank = int(os.environ["RANK"])
    world_size = int(os.environ["WORLD_SIZE"])
    local_rank = int(os.environ.get("LOCAL_RANK", 0))

    if not dist.is_available():
        raise RuntimeError("torch.distributed not available")

    if not dist.is_initialized():
        dist.init_process_group(backend="nccl", init_method="env://")

    torch.cuda.set_device(local_rank)
    return True, rank, local_rank, world_size


def cleanup_distributed():
    if dist.is_available() and dist.is_initialized():
        dist.barrier()
        dist.destroy_process_group()


def unwrap_model(model: nn.Module) -> nn.Module:
    if hasattr(model, "module"):
        return model.module
    if hasattr(model, "_fsdp_wrapped_module"):
        wrapped = getattr(model, "_fsdp_wrapped_module", None)
        if wrapped is not None and hasattr(wrapped, "module"):
            return wrapped.module
    return model


def is_fsdp_model(model: nn.Module) -> bool:
    try:
        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
        return isinstance(model, FSDP)
    except Exception:
        return False


class Trainer:
    def __init__(
        self,
        model: QwenTitansForBABILong,
        train_dataloader: DataLoader,
        eval_dataloader: DataLoader,
        config: TrainingConfig,
        rank: int = 0,
        world_size: int = 1,
        is_distributed: bool = False,
        tokenizer=None,
    ):
        self.model = model
        self.train_dataloader = train_dataloader
        self.eval_dataloader = eval_dataloader
        self.config = config
        self.device = next(model.parameters()).device
        self.rank = rank
        self.world_size = world_size
        self.is_distributed = is_distributed
        self.is_main_process = (rank == 0)
        self.tokenizer = tokenizer

        base_model = unwrap_model(self.model)
        param_groups = base_model.get_param_groups(
            lr_memory=config.lr_memory,
            lr_pretrained=config.lr_pretrained,
            weight_decay=config.weight_decay,
        )
        self.optimizer = AdamW(param_groups)

        total_steps = math.ceil(
            (len(train_dataloader) * config.num_epochs) / max(config.gradient_accumulation_steps, 1)
        )
        self.scheduler = CosineAnnealingLR(self.optimizer, T_max=total_steps, eta_min=1e-7)

        self.scaler = torch.cuda.amp.GradScaler(enabled=config.fp16)
        self.global_step = 0

    def _get_group_lr(self, group_name: str) -> Optional[float]:
        for group in self.optimizer.param_groups:
            if group.get("name") == group_name:
                return group.get("lr")
        return None

    def train(self):
        self.model.train()
        if self.is_main_process:
            logger.info("Start training")

        last_epoch_loss = None
        for epoch in range(self.config.num_epochs):
            sampler = getattr(self.train_dataloader, "sampler", None)
            if sampler is not None and hasattr(sampler, "set_epoch"):
                sampler.set_epoch(epoch)
            if self.is_main_process:
                logger.info(f"Epoch {epoch + 1}/{self.config.num_epochs}")

            epoch_loss = 0.0
            num_batches = 0

            pbar = self.train_dataloader
            if self.is_main_process:
                pbar = tqdm(
                    self.train_dataloader,
                    desc=f"Epoch {epoch + 1}/{self.config.num_epochs}",
                    leave=False,
                    dynamic_ncols=True,
                )
            for step, batch in enumerate(pbar):
                batch = {k: v.to(self.device) for k, v in batch.items()}
                if (
                    self.config.debug_label_batches > 0
                    and self.is_main_process
                    and step < int(self.config.debug_label_batches)
                ):
                    labels = batch.get("labels")
                    if labels is not None:
                        label_tokens = int((labels != -100).sum().item())
                        loss_tokens = int((labels[:, 1:] != -100).sum().item()) if labels.size(1) > 1 else 0
                        attn_tokens = int(batch["attention_mask"].sum().item())
                        logger.info(
                            f"[BATCH DEBUG] epoch={epoch + 1} step={step + 1}: "
                            f"attn_tokens={attn_tokens}, label_tokens={label_tokens}, loss_tokens={loss_tokens}"
                        )
                    else:
                        logger.info(f"[BATCH DEBUG] epoch={epoch + 1} step={step + 1}: labels missing")

                ga = max(self.config.gradient_accumulation_steps, 1)
                sync_gradients = ((step + 1) % ga == 0)
                amp_enabled = self.config.fp16 or self.config.bf16
                amp_dtype = torch.float16 if self.config.fp16 else torch.bfloat16
                with torch.amp.autocast(device_type=self.device.type, enabled=amp_enabled, dtype=amp_dtype):
                    if self.config.chunkwise_backward:
                        labels = batch.get("labels")
                        if labels is not None:
                            total_tokens = int((labels[:, 1:] != -100).sum().item())
                        else:
                            total_tokens = 0
                        loss_scale = 0.0 if total_tokens == 0 else (1.0 / total_tokens / ga)

                        seq_len = batch["input_ids"].shape[1]
                        chunk_size = int(self.config.chunk_size)
                        chunk_ranges = [
                            (start, min(start + chunk_size, seq_len))
                            for start in range(0, seq_len, chunk_size)
                        ]
                        raw_loss_sum = None

                        for idx, (start, end) in enumerate(chunk_ranges):
                            is_last_chunk = (idx == len(chunk_ranges) - 1)
                            sync_chunk = sync_gradients and is_last_chunk
                            chunk_ctx = (
                                self.model.no_sync
                                if (self.is_distributed and not sync_chunk)
                                else nullcontext
                            )
                            with chunk_ctx():
                                outputs = self.model(
                                    input_ids=batch["input_ids"],
                                    attention_mask=batch["attention_mask"],
                                    labels=labels,
                                    chunk_start=start,
                                    chunk_end=end,
                                    reset_mem_state=(idx == 0),
                                )
                                chunk_loss_sum = outputs["loss_sum"]
                                chunk_loss_tokens = int(outputs.get("loss_tokens", 0))
                                if raw_loss_sum is None:
                                    raw_loss_sum = chunk_loss_sum.detach()
                                else:
                                    raw_loss_sum = raw_loss_sum + chunk_loss_sum.detach()
                                
                                # DDP 关键：所有 rank 必须执行相同的 backward 调用序列
                                # 即使 loss_scale=0 或 chunk 无有效 token，也要调用 backward
                                # 以确保 allreduce 同步
                                scaled_loss = chunk_loss_sum * float(loss_scale)
                                if self.config.fp16:
                                    self.scaler.scale(scaled_loss).backward()
                                else:
                                    scaled_loss.backward()

                        if raw_loss_sum is None or total_tokens == 0:
                            raw_loss = torch.zeros((), device=self.device, dtype=torch.float32)
                        else:
                            raw_loss = raw_loss_sum / total_tokens
                        loss = raw_loss / ga
                    else:
                        ctx = self.model.no_sync if (self.is_distributed and not sync_gradients) else nullcontext
                        with ctx():
                            outputs = self.model(
                                input_ids=batch["input_ids"],
                                attention_mask=batch["attention_mask"],
                                labels=batch["labels"],
                            )
                            raw_loss = outputs["loss"]
                            loss = raw_loss / ga

                        if self.config.fp16:
                            self.scaler.scale(loss).backward()
                        else:
                            loss.backward()

                epoch_loss += raw_loss.detach().float().item()
                num_batches += 1

                if sync_gradients:
                    grad_norm = None
                    if self.config.fp16:
                        self.scaler.unscale_(self.optimizer)
                        grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.config.max_grad_norm)
                        self.scaler.step(self.optimizer)
                        self.scaler.update()
                    else:
                        grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.config.max_grad_norm)
                        self.optimizer.step()

                    self.scheduler.step()
                    self.optimizer.zero_grad(set_to_none=True)
                    self.global_step += 1

                    if self.is_main_process:
                        avg_loss = epoch_loss / max(num_batches, 1)
                        pbar.set_postfix(
                            {
                                "gstep": self.global_step,
                                "loss": f"{avg_loss:.4f}",
                            }
                        )

                    if self.global_step % self.config.logging_steps == 0 and self.is_main_process:
                        lr_mem = self._get_group_lr("memory_module")
                        lr_pre = self._get_group_lr("pretrained")
                        if lr_pre is None and self.optimizer.param_groups:
                            lr_pre = self.optimizer.param_groups[0]["lr"]
                        grad_note = ""
                        if self.config.debug_grad_norm and grad_norm is not None:
                            grad_note = f" | grad_norm={float(grad_norm):.4f}"
                        if lr_mem is None:
                            lr_label = f"lr={lr_pre:.2e}" if lr_pre is not None else "lr=NA"
                            logger.info(
                                f"Step {self.global_step} | loss={epoch_loss / max(num_batches, 1):.4f} | "
                                f"{lr_label}{grad_note}"
                            )
                        else:
                            logger.info(
                                f"Step {self.global_step} | loss={epoch_loss / max(num_batches, 1):.4f} | "
                                f"lr_mem={lr_mem:.2e} | lr_pre={lr_pre:.2e}{grad_note}"
                            )

                    if self.global_step % self.config.eval_steps == 0:
                        eval_metrics = self.evaluate()
                        if self.is_main_process:
                            logger.info(
                                f"Step {self.global_step}: "
                                f"eval_loss={eval_metrics['loss']:.4f}, "
                                f"em_acc={eval_metrics['em_acc'] * 100:.2f}%, "
                                f"tok_acc={eval_metrics['tok_acc'] * 100:.2f}%"
                            )
                        self.model.train()

            avg_epoch_loss = epoch_loss / max(num_batches, 1)
            if self.is_distributed:
                t = torch.tensor(avg_epoch_loss, device=self.device, dtype=torch.float32)
                dist.all_reduce(t, op=dist.ReduceOp.SUM)
                avg_epoch_loss = (t / self.world_size).item()

            if self.is_main_process:
                logger.info(f"Epoch {epoch + 1} done, avg loss={avg_epoch_loss:.4f}")
            last_epoch_loss = avg_epoch_loss

            eval_metrics = self.evaluate()
            if self.is_main_process:
                logger.info(
                    f"[EPOCH {epoch + 1} EVAL] "
                    f"eval_loss={eval_metrics['loss']:.4f}, "
                    f"em_acc={eval_metrics['em_acc'] * 100:.2f}%, "
                    f"tok_acc={eval_metrics['tok_acc'] * 100:.2f}%"
                )
            self._append_eval_metrics(
                eval_metrics,
                phase="epoch",
                epoch=int(epoch + 1),
                train_avg_loss=avg_epoch_loss,
            )
            self.model.train()

        if self.is_main_process:
            logger.info("Training done, final evaluation")

        final_eval = self.evaluate(print_examples=int(self.config.final_eval_print_examples))
        if self.is_main_process:
            ppl = float(math.exp(min(20.0, final_eval["loss"])))
            logger.info(
                f"[FINAL EVAL] loss={final_eval['loss']:.4f}, ppl≈{ppl:.3f}, "
                f"em_acc={final_eval['em_acc'] * 100:.2f}%, "
                f"tok_acc={final_eval['tok_acc'] * 100:.2f}%"
            )
            logger.info("Saving final checkpoint")
        self._append_eval_metrics(
            final_eval,
            phase="final",
            epoch=int(self.config.num_epochs),
            train_avg_loss=last_epoch_loss,
        )
        self.save_final_checkpoint()

    @torch.no_grad()
    def evaluate(self, print_examples: int = 0) -> Dict[str, float]:
        self.model.eval()
        total_loss = torch.tensor(0.0, device=self.device, dtype=torch.float32)
        total_batches = torch.tensor(0.0, device=self.device, dtype=torch.float32)

        total_tok_correct = torch.tensor(0.0, device=self.device, dtype=torch.float32)
        total_tok_total = torch.tensor(0.0, device=self.device, dtype=torch.float32)
        total_em_correct = torch.tensor(0.0, device=self.device, dtype=torch.float32)
        total_em_total = torch.tensor(0.0, device=self.device, dtype=torch.float32)
        total_topk_correct = torch.tensor(0.0, device=self.device, dtype=torch.float32)
        total_topk_total = torch.tensor(0.0, device=self.device, dtype=torch.float32)
        printed = 0

        for batch in self.eval_dataloader:
            batch = {k: v.to(self.device) for k, v in batch.items()}
            amp_enabled = self.config.fp16 or self.config.bf16
            amp_dtype = torch.float16 if self.config.fp16 else torch.bfloat16
            with torch.amp.autocast(device_type=self.device.type, enabled=amp_enabled, dtype=amp_dtype):
                outputs = self.model(
                    input_ids=batch["input_ids"],
                    attention_mask=batch["attention_mask"],
                    labels=batch["labels"],
                    return_pred_tokens=True,
                    topk=int(self.config.eval_topk) if self.config.eval_topk else 0,
                )

            if torch.isfinite(outputs["loss"]):
                total_loss += outputs["loss"].detach().float()
                total_batches += 1.0

            pred_ids = outputs.get("pred_ids", None)
            target_ids = outputs.get("target_ids", None)
            lengths = outputs.get("target_lengths", None)
            topk_correct = outputs.get("topk_correct", None)
            topk_total = outputs.get("topk_total", None)
            if topk_correct is not None and topk_total is not None:
                total_topk_correct += topk_correct.detach().float()
                total_topk_total += topk_total.detach().float()
            if (
                pred_ids is not None
                and target_ids is not None
                and lengths is not None
                and pred_ids.ndim == 2
                and target_ids.ndim == 2
                and lengths.ndim == 1
                and pred_ids.shape == target_ids.shape
                and pred_ids.shape[0] == lengths.shape[0]
            ):
                pred_cpu = pred_ids.to("cpu", dtype=torch.long)
                tgt_cpu = target_ids.to("cpu", dtype=torch.long)
                len_cpu = lengths.to("cpu", dtype=torch.long)

                for i in range(int(len_cpu.shape[0])):
                    L = int(len_cpu[i].item())
                    if L <= 0:
                        continue
                    p = pred_cpu[i, :L]
                    t = tgt_cpu[i, :L]

                    total_tok_correct += torch.tensor(float((p == t).sum().item()), device=self.device, dtype=torch.float32)
                    total_tok_total += torch.tensor(float(L), device=self.device, dtype=torch.float32)

                    if self.tokenizer is not None:
                        pred_text = self.tokenizer.decode(p.tolist(), skip_special_tokens=True).strip()
                        tgt_text = self.tokenizer.decode(t.tolist(), skip_special_tokens=True).strip()
                        em = float(pred_text == tgt_text)
                        total_em_correct += torch.tensor(em, device=self.device, dtype=torch.float32)
                        total_em_total += torch.tensor(1.0, device=self.device, dtype=torch.float32)

                        if self.is_main_process and printed < print_examples:
                            logger.info(f"[EVAL SAMPLE] pred={repr(pred_text)} | label={repr(tgt_text)} | match={bool(em)}")
                            printed += 1

        if self.is_distributed:
            dist.all_reduce(total_loss, op=dist.ReduceOp.SUM)
            dist.all_reduce(total_batches, op=dist.ReduceOp.SUM)
            dist.all_reduce(total_tok_correct, op=dist.ReduceOp.SUM)
            dist.all_reduce(total_tok_total, op=dist.ReduceOp.SUM)
            dist.all_reduce(total_em_correct, op=dist.ReduceOp.SUM)
            dist.all_reduce(total_em_total, op=dist.ReduceOp.SUM)
            dist.all_reduce(total_topk_correct, op=dist.ReduceOp.SUM)
            dist.all_reduce(total_topk_total, op=dist.ReduceOp.SUM)

        avg_loss = (total_loss / total_batches.clamp(min=1.0)).item()
        tok_acc = (total_tok_correct / total_tok_total.clamp(min=1.0)).item()
        em_acc = (total_em_correct / total_em_total.clamp(min=1.0)).item()
        topk_acc = (total_topk_correct / total_topk_total.clamp(min=1.0)).item()
        if self.is_main_process:
            if self.config.debug_eval_stats:
                logger.info(
                    "[EVAL DEBUG] total_batches="
                    f"{float(total_batches.item()):.0f}, total_tok_total={float(total_tok_total.item()):.0f}, "
                    f"total_em_total={float(total_em_total.item()):.0f}, "
                    f"total_topk_total={float(total_topk_total.item()):.0f}"
                )
                if total_tok_total.item() == 0:
                    logger.warning("[EVAL DEBUG] No answer tokens found in eval set; acc will be 0.")
            logger.info(f"[EVAL METRIC] token_acc(answer-only) = {tok_acc * 100:.2f}%")
            logger.info(f"[EVAL METRIC] EM/acc(answer-only) = {em_acc * 100:.2f}%")
            if self.config.eval_topk and self.config.eval_topk > 0:
                logger.info(f"[EVAL METRIC] top{int(self.config.eval_topk)}_acc(answer-only) = {topk_acc * 100:.2f}%")
        return {"loss": avg_loss, "tok_acc": tok_acc, "em_acc": em_acc, "topk_acc": topk_acc}

    def _append_eval_metrics(
        self,
        metrics: Dict[str, float],
        *,
        phase: str,
        epoch: Optional[int],
        train_avg_loss: Optional[float],
    ) -> None:
        if not self.is_main_process:
            return
        os.makedirs(self.config.output_dir, exist_ok=True)
        record = {
            "phase": phase,
            "epoch": epoch,
            "global_step": int(self.global_step),
            "train_avg_loss": None if train_avg_loss is None else float(train_avg_loss),
            "eval_loss": float(metrics.get("loss", 0.0)),
            "em_acc_pct": float(metrics.get("em_acc", 0.0) * 100.0),
            "tok_acc_pct": float(metrics.get("tok_acc", 0.0) * 100.0),
        }
        metrics_path = os.path.join(self.config.output_dir, "eval_metrics.jsonl")
        with open(metrics_path, "a") as f:
            f.write(json.dumps(record) + "\n")

    def save_final_checkpoint(self):
        ckpt_path = os.path.join(self.config.output_dir, self.config.final_ckpt_name)
        base_model = unwrap_model(self.model)
        memory_sd = {
            name: p.detach().cpu()
            for name, p in base_model.named_parameters()
            if ("neural_memory" in name) or ("mem_gate" in name)
        }

        if is_fsdp_model(self.model) and len(memory_sd) == 0:
            from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, StateDictType, FullStateDictConfig
            full_cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
            with FSDP.state_dict_type(self.model, StateDictType.FULL_STATE_DICT, full_cfg):
                full_sd = self.model.state_dict()
            memory_sd = {k: v for k, v in full_sd.items() if ("neural_memory" in k) or ("mem_gate" in k)}

        if self.is_main_process:
            torch.save(
                {"memory_state_dict": memory_sd, "global_step": self.global_step, "config": asdict(self.config)},
                ckpt_path,
            )
            logger.info(f"Saved memory checkpoint: {ckpt_path}")
        if self.is_distributed:
            dist.barrier()

        if self.config.save_full_checkpoint:
            full_ckpt_path = os.path.join(self.config.output_dir, self.config.final_full_ckpt_name)
            if is_fsdp_model(self.model):
                from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, StateDictType, FullStateDictConfig
                full_cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
                with FSDP.state_dict_type(self.model, StateDictType.FULL_STATE_DICT, full_cfg):
                    full_sd = self.model.state_dict()
            else:
                full_sd = unwrap_model(self.model).state_dict()

            if self.is_main_process:
                torch.save(
                    {"model_state_dict": full_sd, "global_step": self.global_step, "config": asdict(self.config)},
                    full_ckpt_path,
                )
                logger.info(f"Saved full checkpoint: {full_ckpt_path}")
            if self.is_distributed:
                dist.barrier()


def main():
    from transformers import AutoModelForCausalLM, AutoTokenizer

    parser = argparse.ArgumentParser()
    parser.add_argument("--fsdp", action="store_true")
    parser.add_argument("--eval_only", action="store_true")
    parser.add_argument("--ckpt_path", type=str, default=None)
    parser.add_argument("--max_eval_samples", type=int, default=None)
    parser.add_argument("--max_samples", type=int, default=None)
    parser.add_argument("--max_length", type=int, default=None)
    parser.add_argument("--output_dir", type=str, default=None)
    parser.add_argument("--num_epochs", type=int, default=None)
    parser.add_argument("--eval_steps", type=int, default=None)
    parser.add_argument("--eval_topk", type=int, default=0)
    parser.add_argument("--batch_size", type=int, default=None)
    parser.add_argument("--gradient_accumulation_steps", type=int, default=None)
    parser.add_argument("--chunk_size", type=int, default=None)
    parser.add_argument("--memory_layer_stride", type=int, default=None)
    parser.add_argument("--no_memory", action="store_true")
    parser.add_argument("--gradient_checkpointing", action="store_true")
    parser.add_argument("--no_chunkwise_backward", action="store_true")
    parser.add_argument("--log_every_batches", type=int, default=80)
    parser.add_argument("--label_prefix_tokens", type=int, default=0)
    parser.add_argument(
        "--no_detach_mem_state",
        action="store_true",
        help="(ignored) kept for backward compatibility; detach_mem_state is forced True",
    )
    parser.add_argument("--debug_data_samples", type=int, default=0)
    parser.add_argument("--debug_label_batches", type=int, default=0)
    parser.add_argument("--debug_eval_stats", action="store_true")
    parser.add_argument("--debug_grad_norm", action="store_true")
    parser.add_argument(
        "--freeze_base_model",
        action="store_true",
        help="冻结 Qwen base 模型，只训练记忆模块 (neural_memory + mem_gate)",
    )
    args = parser.parse_args()

    config = TrainingConfig()
    if args.fsdp:
        config.use_fsdp = True
    if args.no_memory:
        config.use_memory = False
    if args.max_samples is not None:
        config.max_samples = args.max_samples
    if args.max_length is not None:
        config.max_length = int(args.max_length)
    if args.output_dir is not None:
        config.output_dir = args.output_dir
    elif not config.use_memory:
        config.output_dir = "./outputs/qwen_babilong_no_memory"
    if args.num_epochs is not None:
        config.num_epochs = args.num_epochs
    if args.eval_steps is not None:
        config.eval_steps = args.eval_steps
    if args.eval_topk is not None:
        config.eval_topk = int(args.eval_topk)
    if args.batch_size is not None:
        config.batch_size = int(args.batch_size)
    if args.gradient_accumulation_steps is not None:
        config.gradient_accumulation_steps = int(args.gradient_accumulation_steps)
    if args.chunk_size is not None:
        config.chunk_size = int(args.chunk_size)
    if args.memory_layer_stride is not None:
        config.memory_layer_stride = int(args.memory_layer_stride)
    if args.gradient_checkpointing:
        config.gradient_checkpointing = True
    if args.no_chunkwise_backward:
        config.chunkwise_backward = False
    if args.label_prefix_tokens is not None:
        config.label_prefix_tokens = int(args.label_prefix_tokens)
    ignored_no_detach = bool(args.no_detach_mem_state)
    if args.log_every_batches is not None:
        config.log_every_batches = int(args.log_every_batches)
        ga = max(int(config.gradient_accumulation_steps), 1)
        config.logging_steps = max(1, math.ceil(config.log_every_batches / ga))
    if args.debug_data_samples is not None:
        config.debug_data_samples = int(args.debug_data_samples)
    if args.debug_label_batches is not None:
        config.debug_label_batches = int(args.debug_label_batches)
    if args.debug_eval_stats:
        config.debug_eval_stats = True
    if args.debug_grad_norm:
        config.debug_grad_norm = True

    is_distributed, rank, local_rank, world_size = init_distributed()
    is_main = (rank == 0)
    if ignored_no_detach and is_main:
        logger.warning("Ignoring --no_detach_mem_state; plan A keeps detach_mem_state=True.")

    if config.use_fsdp and config.chunkwise_backward:
        if is_main:
            logger.warning("chunkwise_backward is incompatible with FSDP; disabling it.")
        config.chunkwise_backward = False

    if is_distributed and (not config.use_fsdp) and config.gradient_checkpointing:
        config.gradient_checkpointing = False
        if is_main:
            logger.warning("gradient_checkpointing is unstable with DDP here; disabling it.")

    if is_distributed and (not config.use_fsdp) and config.chunkwise_backward:
        if is_main:
            logger.info("DDP chunkwise backward enabled via per-chunk forward/backward.")

    if is_distributed and (not config.use_fsdp):
        if not config.ddp_find_unused_parameters:
            config.ddp_find_unused_parameters = True
            if is_main:
                logger.warning("Enabling DDP find_unused_parameters to avoid unused grad errors.")

    torch.manual_seed(config.seed + rank)

    if torch.cuda.is_available():
        device = torch.device(f"cuda:{local_rank}" if is_distributed else "cuda")
    else:
        device = torch.device("cpu")

    if torch.cuda.is_available() and config.bf16:
        bf16_supported = False
        try:
            bf16_supported = torch.cuda.is_bf16_supported()
        except Exception:
            bf16_supported = False
        if not bf16_supported:
            if is_main:
                logger.warning("bf16 not supported on this GPU/runtime; falling back to fp16.")
            config.bf16 = False
            if not config.fp16:
                config.fp16 = True

    if torch.cuda.is_available() and getattr(config, "use_tf32", False):
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True
        try:
            torch.set_float32_matmul_precision("high")
        except Exception:
            pass

    if is_main:
        logger.info("=" * 60)
        logger.info("Qwen3-4B + Titans training (DDP/FSDP)")
        logger.info("=" * 60)
        logger.info(f"distributed={is_distributed}, world_size={world_size}, use_fsdp={config.use_fsdp}")
        logger.info(f"mode={'EVAL_ONLY' if args.eval_only else 'TRAIN'}")
        logger.info(f"model_path={config.model_path}")
        logger.info(f"data_path={config.data_path}")
        logger.info(f"output_dir={config.output_dir}")
        logger.info(f"max_samples={config.max_samples}")
        logger.info(f"max_length={config.max_length}")
        logger.info(f"chunk_size={config.chunk_size}")
        logger.info(f"use_memory={config.use_memory}")
        if config.use_memory:
            logger.info(f"memory_layer_stride={config.memory_layer_stride}")
        logger.info(f"chunkwise_backward={config.chunkwise_backward}")
        logger.info(f"label_prefix_tokens={config.label_prefix_tokens}")
        logger.info(f"detach_mem_state={config.detach_mem_state}")
        logger.info(f"freeze_base_model={config.freeze_base_model}")
        if config.eval_topk:
            logger.info(f"eval_topk={config.eval_topk}")

    tokenizer = AutoTokenizer.from_pretrained(config.model_path, trust_remote_code=True)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    # disable flash-attn / torchao / torchvision detection
    try:
        import transformers
        from transformers.utils import import_utils as _import_utils

        def _disabled(*args, **kwargs):
            return False

        _import_utils.is_flash_attn_2_available = _disabled
        if hasattr(transformers, "utils") and hasattr(transformers.utils, "is_flash_attn_2_available"):
            transformers.utils.is_flash_attn_2_available = _disabled

        _import_utils.is_torchao_available = _disabled
        if hasattr(transformers, "utils") and hasattr(transformers.utils, "is_torchao_available"):
            transformers.utils.is_torchao_available = _disabled

        _import_utils.is_torchvision_available = _disabled
        if hasattr(transformers, "utils") and hasattr(transformers.utils, "is_torchvision_available"):
            transformers.utils.is_torchvision_available = _disabled
    except Exception as e:
        logger.warning(f"Disable checks failed (ignored): {e}")

    torch_dtype = torch.bfloat16 if config.bf16 else (torch.float16 if config.fp16 else torch.float32)

    qwen_model = AutoModelForCausalLM.from_pretrained(
        config.model_path,
        torch_dtype=torch_dtype,
        device_map=None,
        trust_remote_code=True,
        attn_implementation="sdpa",
        low_cpu_mem_usage=True,
    )
    qwen_model.to(device)
    qwen_model.config.use_cache = False
    if config.gradient_checkpointing and hasattr(qwen_model, "gradient_checkpointing_enable"):
        qwen_model.gradient_checkpointing_enable()

    train_dataset = BABILongDataset(
        config.data_path,
        tokenizer,
        max_length=config.max_length,
        answer_reserve_tokens=config.answer_reserve_tokens,
        label_prefix_tokens=config.label_prefix_tokens,
        max_samples=config.max_samples,
    )

    train_size = int(0.9 * len(train_dataset))
    eval_size = len(train_dataset) - train_size
    train_dataset, eval_dataset = torch.utils.data.random_split(
        train_dataset,
        [train_size, eval_size],
        generator=torch.Generator().manual_seed(config.seed),
    )

    if is_main and config.debug_data_samples > 0:
        log_dataset_debug_stats(train_dataset, tokenizer, "train", config.debug_data_samples)
        log_dataset_debug_stats(eval_dataset, tokenizer, "eval", config.debug_data_samples)

    train_sampler = None
    eval_sampler = None
    if is_distributed:
        from torch.utils.data.distributed import DistributedSampler
        train_sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=rank, shuffle=True, seed=config.seed)
        eval_sampler = DistributedSampler(eval_dataset, num_replicas=world_size, rank=rank, shuffle=False)

    train_dataloader = DataLoader(
        train_dataset,
        batch_size=config.batch_size,
        shuffle=(train_sampler is None),
        sampler=train_sampler,
        collate_fn=collate_fn,
        num_workers=0,
    )
    eval_dataloader = DataLoader(
        eval_dataset,
        batch_size=config.batch_size,
        shuffle=False,
        sampler=eval_sampler,
        collate_fn=collate_fn,
        num_workers=0,
    )

    model = QwenTitansForBABILong(qwen_model, config)
    model.to(device)

    # 处理 freeze_base_model 参数
    if args.freeze_base_model:
        config.freeze_base_model = True
    
    if config.freeze_base_model:
        if not config.use_memory:
            if is_main:
                logger.error("--freeze_base_model requires memory module (--no_memory is incompatible)")
            raise ValueError("freeze_base_model requires use_memory=True")
        model.freeze_base_model()
        if is_main:
            logger.info("=" * 40)
            logger.info("FREEZE MODE: Training memory + independent lm_head")
            logger.info("  - Trainable: neural_memory, mem_gate, lm_head (untied)")
            logger.info("  - Frozen: embed_tokens, transformer layers, norm")
            logger.info("=" * 40)

    if is_distributed:
        if config.use_fsdp:
            from functools import partial
            from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, MixedPrecision
            from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
            from transformers.models.qwen3.modeling_qwen3 import Qwen3DecoderLayer

            mp_policy = MixedPrecision(param_dtype=torch_dtype, reduce_dtype=torch_dtype, buffer_dtype=torch_dtype)
            auto_wrap = partial(transformer_auto_wrap_policy, transformer_layer_cls={Qwen3DecoderLayer, QwenDecoderLayerWithTitansMemory})

            model = FSDP(
                model,
                auto_wrap_policy=auto_wrap,
                mixed_precision=mp_policy,
                device_id=torch.cuda.current_device(),
                use_orig_params=config.fsdp_use_orig_params,
                ignored_modules=model.get_memory_modules(),
            )
        else:
            model = DDP(
                model,
                device_ids=[local_rank],
                output_device=local_rank,
                find_unused_parameters=config.ddp_find_unused_parameters,
            )
            if config.gradient_checkpointing:
                try:
                    model._set_static_graph()
                    if is_main:
                        logger.warning("DDP static graph enabled for gradient checkpointing.")
                except Exception as e:
                    if is_main:
                        logger.warning(f"DDP static graph enable failed (ignored): {e}")

    trainer = Trainer(
        model=model,
        train_dataloader=train_dataloader,
        eval_dataloader=eval_dataloader,
        config=config,
        rank=rank,
        world_size=world_size,
        is_distributed=is_distributed,
        tokenizer=tokenizer,
    )

    if args.eval_only:
        ckpt_path = args.ckpt_path or os.path.join(config.output_dir, config.final_ckpt_name)
        if is_main:
            logger.info(f"eval_only: loading checkpoint: {ckpt_path}")
        ckpt = torch.load(ckpt_path, map_location="cpu")
        has_full = isinstance(ckpt, dict) and ("model_state_dict" in ckpt)
        if has_full:
            full_sd = ckpt["model_state_dict"]
            if is_fsdp_model(model):
                from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, StateDictType, FullStateDictConfig
                full_cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
                with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, full_cfg):
                    sd_to_load = full_sd if is_main else {}
                    model.load_state_dict(sd_to_load, strict=False)
            else:
                unwrap_model(model).load_state_dict(full_sd, strict=False)

        memory_sd = ckpt.get("memory_state_dict", ckpt if isinstance(ckpt, dict) else {})
        memory_sd = {k: v for k, v in memory_sd.items() if ("neural_memory" in k) or ("mem_gate" in k)}
        if len(memory_sd) > 0:
            if is_fsdp_model(model):
                from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, StateDictType, FullStateDictConfig
                full_cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
                with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, full_cfg):
                    sd_to_load = memory_sd if is_main else {}
                    model.load_state_dict(sd_to_load, strict=False)
            else:
                unwrap_model(model).load_state_dict(memory_sd, strict=False)

        eval_metrics = trainer.evaluate()
        if is_main:
            ppl = float(math.exp(min(20.0, eval_metrics["loss"])))
            logger.info(
                f"[EVAL] loss={eval_metrics['loss']:.4f}, ppl≈{ppl:.3f}, "
                f"em_acc={eval_metrics['em_acc'] * 100:.2f}%, "
                f"tok_acc={eval_metrics['tok_acc'] * 100:.2f}%"
            )
        cleanup_distributed()
        return

    trainer.train()
    cleanup_distributed()


if __name__ == "__main__":
    main()