examples/train_qwen_titans_babilong_v3.py

"""
Qwen3 + Titans Deep Integration (v3) - BABILong QA1 (32k)

Key improvements over v1/v2:
1. Deep Attention Integration: Memory participates in attention K/V computation
2. Cross-chunk gradient flow: Support detach_mem_state=False for better long-term learning
3. Memory-Augmented Attention: Memory as additional context source
4. Dual-path architecture: Both attention-level and layer-level memory integration
5. Better memory state management with optional gradient flow
"""

import os
import sys

# =============================================================================
# CRITICAL: Disable torchao BEFORE importing transformers to avoid version conflicts
# =============================================================================
os.environ["TRANSFORMERS_NO_TORCHAO"] = "1"

# Mock torchao to prevent import errors
class _MockTorchAO:
    def __getattr__(self, name):
        return _MockTorchAO()
    def __call__(self, *args, **kwargs):
        return _MockTorchAO()

sys.modules['torchao'] = _MockTorchAO()
sys.modules['torchao.quantization'] = _MockTorchAO()

import json
import math
import argparse
import logging
import weakref
from contextlib import nullcontext
from dataclasses import dataclass, asdict, field
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, repeat

# add repo root to sys.path
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__)


# =============================================================================
# Configuration
# =============================================================================

@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_v3"

    # training
    num_epochs: int = 10  # Aligned with v1/v2
    batch_size: int = 1  # Reduced for 32k length
    gradient_accumulation_steps: int = 16  # Increased to maintain effective batch size
    max_grad_norm: float = 1.0

    # learning rates (separate for different components)
    lr_memory: float = 1e-4
    lr_memory_attention: float = 5e-5  # NEW: for attention-integrated memory
    lr_pretrained: float = 5e-6
    weight_decay: float = 0.01
    warmup_steps: int = 100

    # streaming / memory
    chunk_size: int = 4096  # Reduced for memory efficiency
    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  # Same as v1/v2
    memory_layer_stride: int = 8  # Same as v1/v2
    memory_fp32: bool = True

    # NEW: v3 specific options
    detach_mem_state: bool = True  # True for memory efficiency, set False for gradient flow
    deep_memory_integration: bool = False  # Disable by default for memory efficiency
    memory_as_context: bool = False  # Disable by default for memory efficiency
    num_memory_tokens: int = 16  # Number of virtual memory tokens in attention
    memory_gate_bias: float = -2.0  # Initial bias for memory gate (conservative)
    use_momentum: bool = True
    momentum_order: int = 1

    # gradient flow control
    gradient_checkpoint_memory: bool = False  # Checkpoint memory computation
    cross_chunk_gradient_steps: int = 2  # Allow gradient through N chunks back

    # 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

    # data
    max_length: int = 32768
    answer_reserve_tokens: int = 64
    label_prefix_tokens: int = 0
    max_samples: Optional[int] = 500  # Same as v1/v2 experiments (450 train, 50 eval)

    # distributed
    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


# =============================================================================
# Dataset
# =============================================================================

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}


# =============================================================================
# Memory-Augmented Attention Module (NEW in v3)
# =============================================================================

class MemoryAugmentedAttention(nn.Module):
    """
    Deep integration of memory into attention mechanism.
    Memory provides additional context that enhances hidden states.
    """
    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
        num_memory_tokens: int = 16,
        memory_dim_head: int = 64,
        memory_fp32: bool = True,
        gate_bias: float = -2.0,
    ):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_attention_heads
        self.head_dim = hidden_size // num_attention_heads
        self.memory_fp32 = memory_fp32

        # Memory transformation: projects and mixes memory with hidden states
        self.memory_transform = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size),
        )

        # Learnable memory gate per head
        self.memory_gate = nn.Parameter(torch.full((num_attention_heads, 1, 1), gate_bias))

        # Output projection
        self.memory_output_proj = nn.Linear(hidden_size, hidden_size, bias=False)
        nn.init.zeros_(self.memory_output_proj.weight)  # Start as identity

    def forward(
        self,
        hidden_states: torch.Tensor,
        memory_context: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states: [batch, seq_len, hidden_size] - current hidden states
            memory_context: [batch, seq_len, hidden_size] - retrieved memory
            attention_mask: optional attention mask
        Returns:
            enhanced_hidden: [batch, seq_len, hidden_size]
        """
        batch_size, seq_len, _ = hidden_states.shape

        # Transform memory
        mem_transformed = self.memory_transform(memory_context)

        # Reshape to multi-head format for gating
        mem_heads = rearrange(mem_transformed, 'b n (h d) -> b h n d', h=self.num_heads)

        # Compute memory gate (sigmoid for smooth interpolation)
        gate = torch.sigmoid(self.memory_gate)  # [num_heads, 1, 1]

        # Apply gated memory enhancement
        mem_contribution = mem_heads * gate
        mem_contribution = rearrange(mem_contribution, 'b h n d -> b n (h d)')

        # Project and add to hidden states
        enhanced = hidden_states + self.memory_output_proj(mem_contribution)

        return enhanced


# =============================================================================
# Deep Memory Layer (v3 - integrates memory at multiple levels)
# =============================================================================

class QwenDecoderLayerWithDeepMemory(nn.Module):
    """
    v3: Deep integration of Titans memory into Qwen decoder layer.

    Key differences from v1/v2:
    1. Memory participates in attention computation (not just post-processing)
    2. Support for cross-chunk gradient flow
    3. Dual-path architecture: attention-level + layer-level memory
    4. Better memory state management
    """
    def __init__(
        self,
        base_layer: nn.Module,
        layer_idx: int,
        *,
        hidden_size: int,
        num_attention_heads: int,
        chunk_size: int,
        batch_size: int,
        dim_head: int,
        num_heads: int,
        memory_depth: int,
        memory_fp32: bool,
        detach_mem_state: bool,
        deep_integration: bool,
        memory_as_context: bool,
        num_memory_tokens: int,
        memory_gate_bias: float,
        use_momentum: bool,
        momentum_order: int,
        parent_model: Optional[nn.Module] = None,
    ):
        super().__init__()
        self.layer = base_layer
        self.layer_idx = layer_idx
        self.memory_fp32 = memory_fp32
        self.detach_mem_state = bool(detach_mem_state)
        self.deep_integration = deep_integration
        self.memory_as_context = memory_as_context
        self.memory_state: Optional[NeuralMemState] = None
        self.parent_model_ref = weakref.ref(parent_model) if parent_model is not None else None

        # Chunk counter for gradient flow control
        self._chunk_counter = 0
        self._gradient_steps_back = 2  # Allow gradient through 2 chunks

        # Core Neural Memory module
        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=use_momentum,
            momentum_order=momentum_order,
            qk_rmsnorm=True,
            pre_rmsnorm=True,
            default_step_transform_max_lr=1e-2,
            init_adaptive_step_bias=-4.0,  # Slightly higher than v1/v2 for faster adaptation
            max_grad_norm=1.0,
            spectral_norm_surprises=True,
            use_accelerated_scan=False,
        )

        # Layer-level memory gate (similar to v1/v2)
        self.mem_gate = nn.Sequential(
            nn.Linear(hidden_size * 2, hidden_size),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size),
            nn.Sigmoid(),
        )
        # Initialize gate to be conservative
        nn.init.zeros_(self.mem_gate[-2].weight)
        nn.init.constant_(self.mem_gate[-2].bias, memory_gate_bias)

        # NEW: Deep attention integration
        if deep_integration:
            self.memory_attention = MemoryAugmentedAttention(
                hidden_size=hidden_size,
                num_attention_heads=num_attention_heads,
                num_memory_tokens=num_memory_tokens,
                memory_dim_head=dim_head,
                memory_fp32=memory_fp32,
                gate_bias=memory_gate_bias,
            )
        else:
            self.memory_attention = None

        # NEW: Pre-attention memory projection (for memory-as-context)
        if memory_as_context:
            self.memory_context_proj = nn.Sequential(
                nn.Linear(hidden_size, hidden_size),
                nn.SiLU(),
                nn.Linear(hidden_size, hidden_size),
            )
            nn.init.zeros_(self.memory_context_proj[-1].weight)
            nn.init.zeros_(self.memory_context_proj[-1].bias)
        else:
            self.memory_context_proj = None

        # Move to appropriate device/dtype
        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)
                if self.memory_attention is not None:
                    self.memory_attention = self.memory_attention.to(device=layer_device, dtype=layer_dtype)
                if self.memory_context_proj is not None:
                    self.memory_context_proj = self.memory_context_proj.to(device=layer_device, dtype=layer_dtype)

    def reset_memory_state(self):
        self.memory_state = None
        self._chunk_counter = 0

    def set_gradient_steps_back(self, steps: int):
        """Control how many chunks back gradient can flow."""
        self._gradient_steps_back = steps

    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 _should_detach_state(self) -> bool:
        """Determine if memory state should be detached based on chunk counter."""
        if self.detach_mem_state:
            return True
        # Allow gradient flow through recent chunks
        self._chunk_counter += 1
        return self._chunk_counter > self._gradient_steps_back

    def forward(self, *args, **kwargs):
        # Get original layer output
        outputs = self.layer(*args, **kwargs)

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

        # Get store mask
        full_store_mask = self._get_store_mask(hidden_states)

        # Prepare memory input
        mem_inp = hidden_states.float() if self.memory_fp32 else hidden_states

        # Prepare store sequence and mask
        store_seq = None
        store_mask = full_store_mask
        if store_mask is not None:
            store_seq = mem_inp
            # Skip first token if not the first chunk
            if store_mask.shape[1] > 0 and not store_mask[:, 0].any():
                store_seq = store_seq[:, 1:]
                store_mask = store_mask[:, 1:]

            # Align to chunk size
            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

        # Memory computation context
        mem_ctx = (
            torch.amp.autocast(device_type=hidden_states.device.type, enabled=False)
            if self.memory_fp32
            else nullcontext()
        )

        # Determine if we should detach memory state
        should_detach = self._should_detach_state()

        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=should_detach,
            )
        self.memory_state = next_state

        if retrieved is not None:
            retrieved = retrieved.to(dtype=hidden_states.dtype)

            # Apply store mask to retrieved memory
            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)

            # ===== v3 Deep Integration =====

            # Path 1: Memory-augmented attention (if enabled)
            if self.memory_attention is not None:
                hidden_states = self.memory_attention(
                    hidden_states=hidden_states,
                    memory_context=retrieved,
                    attention_mask=None,  # Could pass attention mask if needed
                )

            # Path 2: Memory as context projection (if enabled)
            if self.memory_context_proj is not None:
                context_enhancement = self.memory_context_proj(retrieved)
                hidden_states = hidden_states + context_enhancement

            # Path 3: Layer-level gated fusion (always active)
            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)


# =============================================================================
# Main Model Wrapper
# =============================================================================

class QwenTitansForBABILongV3(nn.Module):
    """
    v3: Qwen3 with deep Titans memory integration.
    """
    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.num_attention_heads = qwen_model.config.num_attention_heads
        self.use_memory = bool(getattr(config, "use_memory", True))

        if self.use_memory:
            self.memory_layer_stride = int(getattr(config, "memory_layer_stride", 6))
            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 = QwenDecoderLayerWithDeepMemory(
                    base_layer,
                    layer_idx=layer_idx,
                    hidden_size=self.hidden_size,
                    num_attention_heads=self.num_attention_heads,
                    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,
                    deep_integration=config.deep_memory_integration,
                    memory_as_context=config.memory_as_context,
                    num_memory_tokens=config.num_memory_tokens,
                    memory_gate_bias=config.memory_gate_bias,
                    use_momentum=config.use_momentum,
                    momentum_order=config.momentum_order,
                    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("[QwenTitansForBABILongV3] Initialized with DEEP memory integration")
            logger.info(f"  - hidden_size: {self.hidden_size}")
            logger.info(f"  - num_attention_heads: {self.num_attention_heads}")
            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}")
            logger.info(f"  - deep_memory_integration: {config.deep_memory_integration}")
            logger.info(f"  - memory_as_context: {config.memory_as_context}")
            logger.info(f"  - detach_mem_state: {config.detach_mem_state}")
            logger.info(f"  - cross_chunk_gradient_steps: {config.cross_chunk_gradient_steps}")
        else:
            logger.info("[QwenTitansForBABILongV3] Initialized (memory disabled)")

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

        # Set gradient steps for cross-chunk gradient flow
        for layer in self._memory_layers:
            layer.set_gradient_steps_back(config.cross_chunk_gradient_steps)

    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)
            if layer.memory_attention is not None:
                modules.append(layer.memory_attention)
            if layer.memory_context_proj is not None:
                modules.append(layer.memory_context_proj)
        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,
        chunk_start: Optional[int] = None,
        chunk_end: Optional[int] = None,
        reset_mem_state: bool = False,
    ) -> Dict[str, torch.Tensor]:

        # Single chunk forward (for chunkwise backward)
        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,
            )

        # Full sequence forward
        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:
            total_loss_sum = (hidden_full.float().sum() * 0.0)

        return {
            "loss_sum": total_loss_sum,
            "loss_tokens": total_loss_tokens,
            "has_grad": True,
        }

    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 get_param_groups(self, config: TrainingConfig):
        """
        v3: Three parameter groups with different learning rates.
        """
        memory_core_params = []  # Neural memory core
        memory_attention_params = []  # Memory-augmented attention
        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_core_params.append(param)
            elif "memory_attention" in name or "memory_context_proj" in name:
                memory_attention_params.append(param)
            else:
                pretrained_params.append(param)

        param_groups = []
        if len(memory_core_params) > 0:
            param_groups.append({
                "params": memory_core_params,
                "lr": config.lr_memory,
                "weight_decay": config.weight_decay,
                "name": "memory_core"
            })
        if len(memory_attention_params) > 0:
            param_groups.append({
                "params": memory_attention_params,
                "lr": config.lr_memory_attention,
                "weight_decay": config.weight_decay,
                "name": "memory_attention"
            })
        if len(pretrained_params) > 0:
            param_groups.append({
                "params": pretrained_params,
                "lr": config.lr_pretrained,
                "weight_decay": config.weight_decay,
                "name": "pretrained"
            })

        logger.info(f"Param groups: memory_core={len(memory_core_params)}, "
                   f"memory_attention={len(memory_attention_params)}, "
                   f"pretrained={len(pretrained_params)}")
        return param_groups


# =============================================================================
# Distributed Training Utilities
# =============================================================================

def init_distributed() -> tuple:
    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


# =============================================================================
# Trainer
# =============================================================================

class Trainer:
    def __init__(
        self,
        model: QwenTitansForBABILongV3,
        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(config)
        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("=" * 60)
            logger.info("Starting v3 training with deep memory integration")
            logger.info("=" * 60)

        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()}

                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"]
                                if raw_loss_sum is None:
                                    raw_loss_sum = chunk_loss_sum.detach()
                                else:
                                    raw_loss_sum = raw_loss_sum + chunk_loss_sum.detach()

                                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_core") or 0.0
                        lr_mem_attn = self._get_group_lr("memory_attention") or 0.0
                        lr_pre = self._get_group_lr("pretrained") or 0.0
                        grad_note = ""
                        if self.config.debug_grad_norm and grad_norm is not None:
                            grad_note = f" | grad_norm={float(grad_norm):.4f}"
                        logger.info(
                            f"Step {self.global_step} | loss={epoch_loss / max(num_batches, 1):.4f} | "
                            f"lr_mem={lr_mem:.2e} | lr_mem_attn={lr_mem_attn:.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)
        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)

            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)

        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()

        return {"loss": avg_loss, "tok_acc": tok_acc, "em_acc": em_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)

        # Save memory-related parameters
        memory_sd = {
            name: p.detach().cpu()
            for name, p in base_model.named_parameters()
            if ("neural_memory" in name) or ("mem_gate" in name) or
               ("memory_attention" in name) or ("memory_context_proj" 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) or
                   ("memory_attention" in k) or ("memory_context_proj" 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()


# =============================================================================
# Main
# =============================================================================

def main():
    from transformers import AutoModelForCausalLM, AutoTokenizer

    parser = argparse.ArgumentParser(description="Qwen3 + Titans v3 Deep Integration Training")
    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_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("--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")

    # v3 specific arguments
    parser.add_argument("--detach_mem_state", action="store_true",
                       help="Detach memory state (disable cross-chunk gradients)")
    parser.add_argument("--no_deep_integration", action="store_true",
                       help="Disable deep attention integration")
    parser.add_argument("--no_memory_as_context", action="store_true",
                       help="Disable memory-as-context projection")
    parser.add_argument("--cross_chunk_gradient_steps", type=int, default=None,
                       help="Number of chunks to allow gradient flow through")
    parser.add_argument("--memory_depth", type=int, default=None)
    parser.add_argument("--num_memory_tokens", type=int, default=None)

    parser.add_argument("--debug_grad_norm", action="store_true")
    args = parser.parse_args()

    config = TrainingConfig()

    # Apply arguments
    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_v3"
    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.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

    # v3 specific
    if args.detach_mem_state:
        config.detach_mem_state = True
    if args.no_deep_integration:
        config.deep_memory_integration = False
    if args.no_memory_as_context:
        config.memory_as_context = False
    if args.cross_chunk_gradient_steps is not None:
        config.cross_chunk_gradient_steps = int(args.cross_chunk_gradient_steps)
    if args.memory_depth is not None:
        config.memory_depth = int(args.memory_depth)
    if args.num_memory_tokens is not None:
        config.num_memory_tokens = int(args.num_memory_tokens)
    if args.debug_grad_norm:
        config.debug_grad_norm = True

    is_distributed, rank, local_rank, world_size = init_distributed()
    is_main = (rank == 0)

    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):
        if not config.ddp_find_unused_parameters:
            config.ddp_find_unused_parameters = True
            if is_main:
                logger.warning("Enabling DDP find_unused_parameters.")

    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; 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("=" * 70)
        logger.info("Qwen3-4B + Titans v3 DEEP INTEGRATION Training")
        logger.info("=" * 70)
        logger.info(f"distributed={is_distributed}, world_size={world_size}")
        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"num_epochs={config.num_epochs}")
        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"memory_depth={config.memory_depth}")
            logger.info(f"deep_memory_integration={config.deep_memory_integration}")
            logger.info(f"memory_as_context={config.memory_as_context}")
            logger.info(f"detach_mem_state={config.detach_mem_state}")
            logger.info(f"cross_chunk_gradient_steps={config.cross_chunk_gradient_steps}")
            logger.info(f"num_memory_tokens={config.num_memory_tokens}")

    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 detection (torchao already disabled at top of file)
    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
        if hasattr(_import_utils, "is_torchao_available"):
            _import_utils.is_torchao_available = _disabled
        if hasattr(_import_utils, "is_torchvision_available"):
            _import_utils.is_torchvision_available = _disabled
    except Exception as e:
        if is_main:
            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),
    )

    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 = QwenTitansForBABILongV3(qwen_model, config)
    model.to(device)

    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, QwenDecoderLayerWithDeepMemory}
            )

            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,
            )

    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")

        memory_sd = ckpt.get("memory_state_dict", {})
        if len(memory_sd) > 0:
            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()