fp8-ready-spider.py

#!/usr/bin/env python3
"""Spider: MoE + RDT (Recurrent-Depth Transformer) architecture v5.

Canonical architecture ported from mythos-fineweb-moe.py (SpiderPortal v5-Dense)
with the following adaptations per Phase 02 decisions:

- Full Spider rebrand (no SpiderPortal/SpiderPortal prefix) per D-07
- Byte-level vocab: 272 tokens (256 bytes + 16 specials) per D-06
- MLA (Multi-Latent Attention) with compressed KV cache per D-10
- Engram conditional memory at recurrent layers 1 and 4
- MoE: 16 routed experts + 1 shared expert, top-1 routing
- Sliding window attention (sliding_window=8192) with 256k context (YaRN factor=8.0)
- Weight-tied embeddings per v5 canonical config (tie_word_embeddings=True)
- LTI Injection + ACT Halting + LoRA Adapter for RDT loops
- BoundaryPredictor + downsample/upsample for FlexiToken integration
- 272-token byte-level vocab with sentinel tokens for multimodal (D-11)

Architecture: RDT (2 prelude + 6 recurrent + 2 coda) with:
  - 2x Prelude (MLA + dense FFN)
  - 6x Recurrent (MLA + Engram@L1,L4 + MoE) -- with gradient checkpointing
  - 2x Coda (MLA + dense FFN)
  - LTI Injection + ACT Halting + LoRA Adapter

Config: hidden_size=2048, 6 recurrent layers, 32 experts, top-2 routing
"""

import math
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss


# ============================================================================
# Spider Configuration
# ============================================================================

@dataclass
class SpiderConfig:
    """Spider model configuration (hidden_size=2048, byte-level vocab).

    Based on mythos-fineweb-moe.py SpiderPortalConfig with byte-level
    tokenization, MLA attention, and Engram memory.
    """
    # Core architecture
    vocab_size: int = 272  # 256 bytes + 16 specials (D-06)
    hidden_size: int = 2048
    num_hidden_layers: int = 6  # recurrent layers
    num_attention_heads: int = 16
    num_key_value_heads: int = 4  # not used directly in MLA but kept for compat
    intermediate_size: int = 1024
    hidden_act: str = "silu"

    # MoE configuration (D-20, D-21: shared-projection MoE)
    num_experts: int = 32
    num_experts_per_tok: int = 2
    num_shared_experts: int = 1
    router_aux_loss_coef: float = 0.05
    shared_intermediate_size: int = 6144
    expert_core_rank: int = 256
    shared_expert_intermediate_size: int = 7424
    prelude_coda_intermediate_size: int = 4096

    # RDT configuration
    max_loop_iters: int = 16
    act_threshold: float = 0.5
    prelude_layers: int = 2
    coda_layers: int = 2
    lora_rank: int = 128
    loop_embed_dim: int = 128

    # MLA parameters (DeepSeek-V2 style, scaled for hidden_size=2048)
    kv_lora_rank: int = 128
    q_lora_rank: int = 256
    qk_rope_head_dim: int = 64
    qk_nope_head_dim: int = 64
    v_head_dim: int = 64

    # Engram parameters (DeepSeek conditional memory, offloaded to CPU)
    engram_layers: List[int] = field(default_factory=lambda: [1, 4])
    engram_ngram_orders: Tuple[int, ...] = (2, 3)
    engram_hash_heads: int = 4
    engram_table_size: int = 8191 # prime, sized for byte vocab=272
    engram_conv_kernel: int = 4
    engram_conv_dilation: int = 3
    engram_dim: int = 128 # per-head embedding dimension
    engram_offload: bool = True # offload embed table to CPU (DeepSeek style)

    # Attention / RoPE
    max_position_embeddings: int = 262144  # 256k context
    rope_theta: float = 10000000.0
    rope_scaling: Optional[Dict] = field(default_factory=lambda: {
        "type": "yarn",
        "factor": 8.0,
        "original_max_position_embeddings": 32768,
    })
    sliding_window: int = 8192  # local attention window
    attention_dropout: float = 0.0
    rms_norm_eps: float = 1e-6
    initializer_range: float = 0.02

    # Embeddings / head
    tie_word_embeddings: bool = True  # per v5 canonical config

    # Multimodal
    vision_hidden_size: int = 2048
    audio_hidden_size: int = 512
    vision_num_frames: int = 60
    vision_tokens_per_frame: int = 256
    vision_temporal_tokens: int = 64
    vision_temporal_layers: int = 2

    # Metadata
    model_type: str = "spider"
    torch_dtype: str = "bfloat16"

    # BoundaryPredictor (for FlexiToken integration)
    bp_d_inner: int = 8192

    @property
    def head_dim(self):
        return self.qk_nope_head_dim + self.qk_rope_head_dim  # 128


def spider_flexitokens_997m() -> SpiderConfig:
    """Spider-FLEXITOKENS 995.1M config per D-20."""
    return SpiderConfig()


# ============================================================================
# Sentinel Token Vocabulary (D-06, D-11)
# ============================================================================

# 272-token vocab: 256 bytes + 16 specials
# Sentinel tokens at indices 259-264 mark modality region boundaries
SENTINEL_TOKENS = {
    'PAD': 256, 'BOS': 257, 'EOS': 258,
    'IMG_START': 259, 'IMG_END': 260,
    'AUD_START': 261, 'AUD_END': 262,
    'VID_START': 263, 'VID_END': 264,
    'MASK': 265, 'im_start': 266, 'im_end': 267,
    'prefix': 268, 'suffix': 269, 'middle': 270,
    'THINK': 271,
}

# Sentinel pairs for modality regions (start_id, end_id)
_SENTINEL_PAIRS = [
    (SENTINEL_TOKENS['IMG_START'], SENTINEL_TOKENS['IMG_END']),  # (259, 260)
    (SENTINEL_TOKENS['AUD_START'], SENTINEL_TOKENS['AUD_END']),  # (261, 262)
    (SENTINEL_TOKENS['VID_START'], SENTINEL_TOKENS['VID_END']),  # (263, 264)
]

# Set of modality sentinel token IDs (259-264 only)
_MODALITY_SENTINEL_IDS = {259, 260, 261, 262, 263, 264}

# Reverse mapping (computed once at module level, per IN-01)
_TOKEN_NAMES_BY_ID = {v: k for k, v in SENTINEL_TOKENS.items()}


def is_sentinel_token(token_id: int) -> bool:
    """Return True if token_id is one of the 6 modality sentinel tokens (259-264).

    These are the sentinel tokens that mark modality region boundaries:
    IMG_START/END, AUD_START/END, VID_START/END.
    Other special tokens (PAD, BOS, EOS, MASK, etc.) are NOT modality sentinels.
    """
    return token_id in _MODALITY_SENTINEL_IDS


def create_modality_mask(input_ids: torch.Tensor, strict: bool = True) -> torch.Tensor:
    """Create boolean mask (B×L) marking sentinel and modality token positions.

    Per D-11: Sentinel-gated passthrough ensures modality tokens bypass the
    BoundaryPredictor entirely. This mask marks positions where:
    - Sentinel tokens (IMG_START/END, AUD_START/END, VID_START/END) appear
    - Modality tokens (between sentinel pairs) appear

    The BoundaryPredictor uses this mask to force boundary=1.0 at these
    positions, ensuring no boundary merging across modality boundaries.

    Args:
        input_ids: Token IDs of shape [B, L] with values in 0-271 range.
        strict: If True, raise on mismatched sentinel pairs (training mode).
            If False, skip mismatched pairs gracefully (generation mode).

    Returns:
        Boolean tensor of shape [B, L], True at sentinel+modality positions.

    Raises:
        ValueError: If strict=True and sentinel pairs are mismatched.
    """
    B, L = input_ids.shape
    mask = torch.zeros(B, L, dtype=torch.bool, device=input_ids.device)

    # Mark direct sentinel token positions
    for sid in _MODALITY_SENTINEL_IDS:
        mask |= (input_ids == sid)

    # Mark regions between sentinel pairs (inclusive of sentinels)
    for start_id, end_id in _SENTINEL_PAIRS:
        for b in range(B):
            starts = (input_ids[b] == start_id).nonzero(as_tuple=True)[0]
            ends = (input_ids[b] == end_id).nonzero(as_tuple=True)[0]

            # T-02-04 mitigation: validate sentinel pairs are matched (strict mode only)
            if strict and len(starts) != len(ends):
                raise ValueError(
                    f"Batch {b}: mismatched sentinel pairs — "
                    f"{len(starts)} {_TOKEN_NAMES_BY_ID[start_id]}(s) vs "
                    f"{len(ends)} {_TOKEN_NAMES_BY_ID[end_id]}(s). "
                    f"Every {_TOKEN_NAMES_BY_ID[start_id]} must have a matching "
                    f"{_TOKEN_NAMES_BY_ID[end_id]}."
                )

            # Match pairs min(starts, ends) — skip unmatched in non-strict mode
            n_pairs = min(len(starts), len(ends))
            for i in range(n_pairs):
                s, e = starts[i].item(), ends[i].item()
                if s > e:
                    if strict:
                        raise ValueError(
                            f"Batch {b}: {_TOKEN_NAMES_BY_ID[start_id]} at position {s} "
                            f"appears after {_TOKEN_NAMES_BY_ID[end_id]} at position {e}. "
                            f"Sentinel pairs must be properly ordered."
                        )
                    continue
                mask[b, s:e + 1] = True

    return mask


# ============================================================================
# RMSNorm
# ============================================================================

class SpiderRMSNorm(nn.Module):
    """RMS normalization (bf16-only, no dtype conversions)."""

    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size, dtype=torch.float32))  # IN-02: RMSNorm weight is float32 per convention
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states


# ============================================================================
# MLA: Multi-Latent Attention (DeepSeek-V2 style)
# ============================================================================

class SpiderMLA(nn.Module):
    """Multi-Latent Attention with compressed KV cache.

    For hidden_size=2048, num_heads=16:
    - qk_nope_head_dim=64, qk_rope_head_dim=64 -> total head_dim=128
    - kv_lora_rank=128 -> 10.7x compression vs full 2048-dim KV
    - v_head_dim=64 -> value projection
        - sliding_window=8192 -> local attention window
    """

    def __init__(self, config: SpiderConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.kv_lora_rank = config.kv_lora_rank
        self.q_lora_rank = config.q_lora_rank
        self.qk_rope_head_dim = config.qk_rope_head_dim
        self.qk_nope_head_dim = config.qk_nope_head_dim
        self.v_head_dim = config.v_head_dim
        self.head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
        self.sliding_window = getattr(config, 'sliding_window', 0)

        # Q projection: optional low-rank -> full Q
        if self.q_lora_rank > 0:
            self.q_a_proj = nn.Linear(config.hidden_size, self.q_lora_rank, bias=False)
            self.q_a_layernorm = SpiderRMSNorm(self.q_lora_rank)
            self.q_b_proj = nn.Linear(self.q_lora_rank, self.num_heads * self.head_dim, bias=False)
        else:
            self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=False)

        # KV compression: hidden -> kv_lora_rank (shared latent)
        self.kv_a_proj_with_mqa = nn.Linear(
            config.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=False,
        )
        self.kv_a_layernorm = SpiderRMSNorm(self.kv_lora_rank)
        # Decompress: kv_lora_rank -> nope heads + v heads
        self.kv_b_proj = nn.Linear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
        )
        # Output projection: [hidden_size, num_heads * v_head_dim]
        # Per D-08 and MLA architecture: o_proj maps from num_heads*v_head_dim back to hidden_size
        self.o_proj = nn.Linear(self.num_heads * self.v_head_dim, config.hidden_size, bias=False)

        # RoPE frequencies
        rope_scaling = getattr(config, 'rope_scaling', None)
        if rope_scaling and rope_scaling.get("type") == "yarn":
            factor = rope_scaling.get("factor", 1.0)
            orig_max_pos = rope_scaling.get(
                "original_max_position_embeddings", config.max_position_embeddings
            )
            inv_freq = self._compute_yarn_inv_freq(
                self.qk_rope_head_dim, config.rope_theta, factor, orig_max_pos
            )
        else:
            inv_freq = 1.0 / (
                config.rope_theta
                ** (torch.arange(0, self.qk_rope_head_dim, 2).float() / self.qk_rope_head_dim)
            )
        self.register_buffer("inv_freq", inv_freq, persistent=False)

    @staticmethod
    def _compute_yarn_inv_freq(head_dim, rope_theta, factor, orig_max, beta_fast=32.0, beta_slow=1.0):
        dim = head_dim
        orig_inv_freq = 1.0 / (rope_theta ** (torch.arange(0, dim, 2).float() / dim))
        pos_freqs = torch.arange(0, dim, 2).float() / dim
        beta = (pos_freqs * math.log(rope_theta) / math.log(orig_max))
        scale = torch.where(
            beta < beta_slow, torch.ones_like(beta),
            torch.where(
                beta > beta_fast, torch.ones_like(beta) / factor,
                1.0 - (beta - beta_slow) / (beta_fast - beta_slow) * (1.0 - 1.0 / factor)
            )
        )
        return orig_inv_freq * scale

    def _rotate_half(self, x):
        x1 = x[..., :x.shape[-1] // 2]
        x2 = x[..., x.shape[-1] // 2:]
        return torch.cat((-x2, x1), dim=-1)

    def _apply_rotary(self, x, cos, sin):
        return (x * cos) + (self._rotate_half(x) * sin)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask=None,
        position_ids=None,
        past_key_value=None,
        use_cache=False,
    ):
        bsz, q_len, _ = hidden_states.size()

        # Q projection
        if self.q_lora_rank > 0:
            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
        else:
            q = self.q_proj(hidden_states)
        q = q.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        q_nope, q_rope = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)

        # KV: compress to latent, then decompress
        kv_hidden = self.kv_a_proj_with_mqa(hidden_states)
        kv_latent, k_rope = torch.split(
            kv_hidden, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
        )
        kv_latent_norm = self.kv_a_layernorm(kv_latent)
        kv_b_out = self.kv_b_proj(kv_latent_norm)
        k_nope, v = torch.split(
            kv_b_out,
            [self.num_heads * self.qk_nope_head_dim, self.num_heads * self.v_head_dim],
            dim=-1,
        )

        k_nope = k_nope.view(bsz, q_len, self.num_heads, self.qk_nope_head_dim).transpose(1, 2)
        v = v.view(bsz, q_len, self.num_heads, self.v_head_dim).transpose(1, 2)
        k_rope = k_rope.unsqueeze(1)  # [B, 1, L, qk_rope_head_dim]

        # RoPE on Q and K rope parts
        if position_ids is None:
            position_ids = torch.arange(q_len, device=hidden_states.device).unsqueeze(0).expand(bsz, -1)
        max_pos = position_ids.max().item() + 1
        seq_len = max(max_pos, q_len)
        t = torch.arange(seq_len, device=hidden_states.device, dtype=self.inv_freq.dtype)
        freqs = torch.outer(t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        cos, sin = emb.cos(), emb.sin()
        cos_full = cos[position_ids].unsqueeze(1)
        sin_full = sin[position_ids].unsqueeze(1)

        q_rope = self._apply_rotary(q_rope, cos_full, sin_full)
        k_rope = self._apply_rotary(k_rope, cos_full, sin_full)

        # Assemble full K
        k_rope_expanded = k_rope.expand(-1, self.num_heads, -1, -1)
        k_full = torch.cat([k_nope, k_rope_expanded], dim=-1)
        q_full = torch.cat([q_nope, q_rope], dim=-1)

        # KV cache
        past_kv = None
        if past_key_value is not None:
            k_full = torch.cat([past_key_value[0], k_full], dim=2)
            v = torch.cat([past_key_value[1], v], dim=2)
        if use_cache:
            past_kv = (k_full, v)

        # Attention with SDPA
        attn_mask = None
        if self.sliding_window > 0 and k_full.shape[2] > self.sliding_window:
            kv_len = k_full.shape[2]
            q_positions = torch.arange(kv_len - q_len, kv_len, device=q_full.device)
            k_positions = torch.arange(kv_len, device=q_full.device)
            diff = q_positions.unsqueeze(1) - k_positions.unsqueeze(0)
            causal = diff >= 0
            window = diff < self.sliding_window
            attn_mask = (causal & window).float().unsqueeze(0).unsqueeze(0)
            attn_mask = attn_mask.masked_fill(attn_mask == 0, float('-inf'))

        attn_output = F.scaled_dot_product_attention(
            q_full, k_full, v,
            attn_mask=attn_mask,
            dropout_p=self.config.attention_dropout if self.training else 0.0,
            is_causal=(attn_mask is None),
        )
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
        return self.o_proj(attn_output), past_kv


# ============================================================================
# Engram: Conditional Memory via Scalable Lookup (DeepSeek style)
# ============================================================================

def _tokenizer_compress(token_ids, vocab_size=272):
    """Simulate NFKC + lowercase canonical ID projection.

    Per D-06: vocab_size=272 for byte-level Spider vocab.
    """
    return token_ids % (vocab_size * 77 // 100)


class SpiderEngram(nn.Module):
    """Conditional memory module via NN-gram lookup.

    Applied only at specific recurrent layers (config.engram_layers).
    Ported from SpiderPortalEngram in mythos-fineweb-moe.py.
    """

    def __init__(self, config: SpiderConfig):
        super().__init__()
        self.config = config
        self.ngram_orders = list(config.engram_ngram_orders)
        self.num_heads_per_order = config.engram_hash_heads
        self.table_size = config.engram_table_size
        self.d_mem = config.engram_dim

        self.total_mem_dim = len(self.ngram_orders) * self.num_heads_per_order * self.d_mem

        # Stacked embedding table with offsets: [orders, heads, table_size, d_mem]
        # Per DeepSeek Engram: static memory, offloaded to CPU, accessed via deterministic hash.
        embed_data = torch.randn(len(self.ngram_orders), self.num_heads_per_order, self.table_size, self.d_mem) * 0.02
        if config.engram_offload:
            self.register_buffer("embed", embed_data, persistent=True)
        else:
            self.embed = nn.Parameter(embed_data)

        seeds = []
        for _order in self.ngram_orders:
            for h in range(self.num_heads_per_order):
                seeds.append((h + 1) * 2654435761)
        self.register_buffer("hash_seeds", torch.tensor(seeds, dtype=torch.int64), persistent=False)

        self.W_k = nn.Linear(self.total_mem_dim, config.hidden_size, bias=False)
        self.W_v = nn.Linear(self.total_mem_dim, config.hidden_size, bias=False)

        self.conv = nn.Conv1d(
            config.hidden_size, config.hidden_size,
            kernel_size=config.engram_conv_kernel,
            padding=config.engram_conv_kernel - 1,
            groups=config.hidden_size,
        )
        self.conv_dilation = config.engram_conv_dilation

        with torch.no_grad():
            self.conv.weight.zero_()
            if self.conv.bias is not None:
                self.conv.bias.zero_()

        self.q_norm = SpiderRMSNorm(config.hidden_size)
        self.k_norm = SpiderRMSNorm(config.hidden_size)

    def _compute_hash(self, compressed, n, head_counter, bsz, seq_len):
        """Compute n-gram hash indices (PyTorch-only path, no Numba/CUDA dependency)."""
        pad = torch.zeros(bsz, n - 1, dtype=compressed.dtype, device=compressed.device)
        padded = torch.cat([pad, compressed], dim=1)
        ngrams = torch.stack([padded[:, i : i + seq_len] for i in range(n)], dim=-1)
        h_val = torch.zeros(bsz, seq_len, dtype=torch.int64, device=compressed.device)
        for i in range(n):
            h_val = h_val * 31 + ngrams[:, :, i].to(torch.int64)
            h_val = h_val % self.table_size
        return h_val

    def _retrieve(self, token_ids):
        """Retrieve memory vectors for a batch of token sequences."""
        bsz, seq_len = token_ids.shape
        compressed = _tokenizer_compress(token_ids)

        # PyTorch fallback (CPU and GPU, no external kernel dependency)
        all_parts = []
        head_counter = 0
        for order_idx, n in enumerate(self.ngram_orders):
            h_val = self._compute_hash(compressed, n, head_counter, bsz, seq_len)
            seeds_slice = self.hash_seeds[head_counter : head_counter + self.num_heads_per_order]
            indices_pt = (h_val.unsqueeze(-1) * seeds_slice.view(1, 1, -1)) % self.table_size
            emb_table = self.embed[order_idx]
            idx = indices_pt.permute(0, 2, 1).unsqueeze(-1).expand(-1, -1, -1, self.d_mem)
            mem = torch.gather(emb_table.unsqueeze(0).expand(bsz, -1, -1, -1), dim=2, index=idx)
            mem = mem.permute(0, 2, 1, 3).reshape(bsz, seq_len, self.num_heads_per_order * self.d_mem)
            all_parts.append(mem)
            head_counter += self.num_heads_per_order
        return torch.cat(all_parts, dim=-1)

    def forward(self, hidden_states, token_ids, layer_id: int):
        mem = self._retrieve(token_ids)

        q = hidden_states
        k = self.W_k(mem)
        v = self.W_v(mem)
        q_norm = self.q_norm(q)
        k_norm = self.k_norm(k)
        alpha = torch.sigmoid(
            (q_norm * k_norm).sum(dim=-1, keepdim=True) / math.sqrt(q.shape[-1])
        )
        v_gated = alpha * v
        v_gated_t = v_gated.transpose(1, 2)
        conv_out = self.conv(v_gated_t)
        conv_out = conv_out[:, :, :v_gated_t.shape[-1]]
        conv_out = conv_out.transpose(1, 2)

        y = F.silu(conv_out) + v_gated
        return y


# ============================================================================
# FFN Expert (SwiGLU)
# ============================================================================

class SpiderExpert(nn.Module):
    """SwiGLU FFN expert for dense layers and MoE shared expert."""

    def __init__(self, config: SpiderConfig, intermediate_size=None):
        super().__init__()
        inter_size = intermediate_size or config.intermediate_size
        self.gate_proj = nn.Linear(config.hidden_size, inter_size, bias=False)
        self.up_proj = nn.Linear(config.hidden_size, inter_size, bias=False)
        self.down_proj = nn.Linear(inter_size, config.hidden_size, bias=False)
        self.act_fn = nn.SiLU()

    def forward(self, hidden_states):
        return self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))


# ============================================================================
# Shared-Projection MoE (D-20, D-21: top-2 routing with shared projections)
# ============================================================================

class SimpleMoE(nn.Module):
    """Mixture of Experts with top-1 routing and shared expert.

    This is a self-contained MoE implementation that does not depend on
    torchtitan's MoE. Used by SpiderRecurrentLayer when torchtitan
    is not available (e.g., during weight transfer and testing).
    """

    def __init__(self, config: SpiderConfig):
        super().__init__()
        self.num_experts = config.num_experts
        self.num_experts_per_tok = config.num_experts_per_tok

        # Shared expert
        self.shared_expert = SpiderExpert(config, intermediate_size=config.intermediate_size)

        # Routed experts
        self.experts = nn.ModuleList([
            SpiderExpert(config, intermediate_size=config.intermediate_size)
            for _ in range(config.num_experts)
        ])

        # Router
        self.router = nn.Linear(config.hidden_size, config.num_experts, bias=True)
        self.router.bias = nn.Parameter(torch.zeros(config.num_experts, dtype=torch.float32))

    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        B, L, D = x.shape

        shared_out = self.shared_expert(x)

        router_logits = self.router(x)
        router_probs = F.softmax(router_logits, dim=-1)

        top1_indices = router_probs.argmax(dim=-1)
        top1_probs = router_probs.gather(-1, top1_indices.unsqueeze(-1)).squeeze(-1)

        x_flat = x.reshape(B * L, D)
        top1_flat = top1_indices.reshape(B * L)

        expert_outs = torch.zeros_like(x_flat)
        for e in range(self.num_experts):
            mask = (top1_flat == e)
            if mask.any():
                expert_input = x_flat[mask]
                expert_out = self.experts[e](expert_input)
                expert_outs[mask] = expert_out

        expert_outs = expert_outs.reshape(B, L, D)
        routed_out = expert_outs * top1_probs.unsqueeze(-1)

        z_loss = (router_logits.logsumexp(dim=-1) ** 2).mean()
        return shared_out + routed_out, z_loss


# ============================================================================
# Shared-Projection MoE (D-20, D-21: top-2 routing with shared projections)
# ============================================================================

class SharedProjectionMoE(nn.Module):
    """Mixture of Experts with shared projections and low-rank expert cores.

    Per D-20: 32 experts, top-2 routing, shared_intermediate_size=6144.
    Per D-21: Shared up/down projections computed once per token, rank-192
    expert cores specialize on the shared representation.

    Architecture:
    - shared_up: Linear(hidden, shared_inter) — computed once for all experts
    - shared_down: Linear(shared_inter, hidden) — computed once for all experts
    - W_gate: [num_experts, hidden, expert_core_rank] — per-expert gating
    - W_transform: [num_experts, expert_core_rank, shared_inter] — per-expert transform
    - shared_expert: SpiderExpert(hidden, shared_expert_inter=4096) — always active

    Forward: shared_hidden = SiLU(shared_up(x))
    routed_out = sum(top2_weights * shared_down(core_i(shared_hidden)))
    output = routed_out + shared_expert(x)
    """

    def __init__(self, config: SpiderConfig):
        super().__init__()
        self.num_experts = config.num_experts
        self.num_experts_per_tok = config.num_experts_per_tok
        self.shared_inter = config.shared_intermediate_size
        self.expert_core_rank = config.expert_core_rank
        self.hidden_size = config.hidden_size

        self.shared_up = nn.Linear(config.hidden_size, config.shared_intermediate_size, bias=False)
        self.shared_down = nn.Linear(config.shared_intermediate_size, config.hidden_size, bias=False)

        self.W_gate = nn.Parameter(
            torch.randn(config.num_experts, config.hidden_size, config.expert_core_rank) * 0.02
        )
        self.W_transform = nn.Parameter(
            torch.randn(config.num_experts, config.expert_core_rank, config.shared_intermediate_size) * 0.02
        )

        self.shared_expert = SpiderExpert(config, intermediate_size=config.shared_expert_intermediate_size)

        self.router = nn.Linear(config.hidden_size, config.num_experts, bias=True)
        self.router.bias = nn.Parameter(torch.zeros(config.num_experts, dtype=torch.float32))

    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        B, L, D = x.shape

        shared_hidden = F.silu(self.shared_up(x))

        shared_out = self.shared_expert(x)

        router_logits = self.router(x)
        router_probs = F.softmax(router_logits, dim=-1)

        top2_probs, top2_indices = router_probs.topk(self.num_experts_per_tok, dim=-1)
        top2_probs = top2_probs / top2_probs.sum(dim=-1, keepdim=True)

        x_flat = x.reshape(B * L, D)
        shared_hidden_flat = shared_hidden.reshape(B * L, self.shared_inter)

        routed_out = torch.zeros(B * L, D, device=x.device, dtype=x.dtype)

        for k in range(self.num_experts_per_tok):
            expert_indices = top2_indices[:, :, k].reshape(B * L)
            expert_weights = top2_probs[:, :, k].reshape(B * L)

            for e in range(self.num_experts):
                mask = (expert_indices == e)
                if not mask.any():
                    continue
                expert_input = x_flat[mask]
                expert_sh = shared_hidden_flat[mask]

                gate = expert_input @ self.W_gate[e]
                core = gate @ self.W_transform[e]
                expert_output = self.shared_down(core * expert_sh)

                routed_out[mask] += expert_weights[mask].unsqueeze(-1) * expert_output

        routed_out = routed_out.reshape(B, L, D)

        z_loss = (router_logits.logsumexp(dim=-1) ** 2).mean()

        return shared_out + routed_out, z_loss


# ============================================================================
# Prelude/Coda Dense Layer (uses MLA)
# ============================================================================

class SpiderDenseLayer(nn.Module):
    """Prelude/coda dense layer with MLA attention."""

    def __init__(self, config: SpiderConfig):
        super().__init__()
        self.self_attn = SpiderMLA(config)
        dense_intermediate = config.prelude_coda_intermediate_size
        self.ffn = SpiderExpert(config, intermediate_size=dense_intermediate)
        self.input_layernorm = SpiderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = SpiderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        position_ids=None,
        past_key_value=None,
        use_cache=False,
    ):
        attn_input = self.input_layernorm(hidden_states)
        attn_output, past_kv = self.self_attn(
            attn_input, attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            use_cache=use_cache,
        )
        hidden_states = hidden_states + attn_output
        ffn_input = self.post_attention_layernorm(hidden_states)
        ffn_output = self.ffn(ffn_input)
        hidden_states = hidden_states + ffn_output
        return hidden_states, past_kv


# ============================================================================
# Recurrent Layer (uses MLA + optional Engram + MoE)
# ============================================================================

class SpiderRecurrentLayer(nn.Module):
    """Recurrent layer with MLA attention, optional Engram memory, and MoE."""

    def __init__(self, config: SpiderConfig, layer_idx: int, has_engram: bool = False):
        super().__init__()
        self.layer_idx = layer_idx
        self.has_engram = has_engram
        self.self_attn = SpiderMLA(config)
        if has_engram:
            self.engram = SpiderEngram(config)
        self.moe = SharedProjectionMoE(config)
        self.input_layernorm = SpiderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = SpiderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_engram_layernorm = (
            SpiderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
            if has_engram else None
        )

    def forward(
        self,
        hidden_states,
        token_ids=None,
        attention_mask=None,
        position_ids=None,
        past_key_value=None,
        use_cache=False,
    ):
        attn_input = self.input_layernorm(hidden_states)
        attn_output, past_kv = self.self_attn(
            attn_input, attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            use_cache=use_cache,
        )
        hidden_states = hidden_states + attn_output

        if self.has_engram and token_ids is not None:
            engram_out = self.engram(hidden_states, token_ids, layer_id=self.layer_idx)
            hidden_states = hidden_states + engram_out
            if self.post_engram_layernorm is not None:
                hidden_states = self.post_engram_layernorm(hidden_states)

        ffn_input = self.post_attention_layernorm(hidden_states)
        ffn_output, aux_loss = self.moe(ffn_input)
        hidden_states = hidden_states + ffn_output
        return hidden_states, aux_loss, past_kv


# ============================================================================
# BoundaryPredictor (D-04, D-11)
# ============================================================================

class BoundaryPredictor(nn.Module):
    """Boundary predictor for learnable byte-level tokenization.

    2-layer MLP that predicts merge boundaries between tokens.
    Per D-11: When modality_mask is provided, forces boundary=1.0 at
    sentinel and modality token positions, preventing cross-modality merges.

    Architecture: Linear(d_model, d_inner) -> GELU -> Linear(d_inner, 1)
    Uses Gumbel-Softmax straight-through estimator for differentiable
    boundary decisions (ported from FLEXITOKENS fxt.py).
    """

    def __init__(
        self,
        config: SpiderConfig,
        temp: float = 1.0,
        threshold: float = 0.5,
    ):
        super().__init__()
        self.temp = temp
        self.threshold = threshold

        self.boundary_predictor = nn.Sequential(
            nn.Linear(config.hidden_size, config.bp_d_inner),
            nn.GELU(),
            nn.Linear(config.bp_d_inner, 1),
        )

    def forward(self, hidden, modality_mask=None):
        """Predict boundary decisions for token merging.

        Args:
            hidden: Hidden states of shape [B, L, D] (batch-first per D-08).
            modality_mask: Optional boolean tensor [B, L], True at positions
                where sentinel/modality tokens appear. Per D-11,
                forces boundary=1.0 at these positions.

        Returns:
            Tuple of (soft_boundaries, hard_boundaries), each [B, L].
            - soft_boundaries: Differentiable boundary probabilities
            - hard_boundaries: Binary boundary decisions (straight-through)
        """
        boundary_logits = self.boundary_predictor(hidden).squeeze(-1)
        boundary_probs = torch.sigmoid(boundary_logits)

        # Gumbel-Softmax straight-through for differentiable boundary decisions
        bernoulli = torch.distributions.relaxed_bernoulli.RelaxedBernoulli(
            temperature=self.temp,
            probs=boundary_probs,
        )
        soft_boundaries = bernoulli.rsample()

        hard_boundaries = (soft_boundaries > self.threshold).float()
        # Straight-through estimator: gradient flows through soft, forward uses hard
        hard_boundaries = (
            hard_boundaries - soft_boundaries.detach() + soft_boundaries
        )

        # Per D-11: Force boundaries at sentinel/modality positions
        if modality_mask is not None:
            soft_boundaries = soft_boundaries.masked_fill(modality_mask, 1.0)
            hard_boundaries = hard_boundaries.masked_fill(modality_mask, 1.0)

        return soft_boundaries, hard_boundaries


# ============================================================================
# Downsample / Upsample (D-05, D-08, D-11)
# ============================================================================

def _downsample_common(boundaries: torch.Tensor, upsample: bool = False):
    """Common helper for downsample/upsample einsum weight computation.

    Computes the assignment matrix that maps original positions to groups.
    Based on FLEXITOKENS shortening.py, adapted for batch-first (B*L*D) layout.

    Args:
        boundaries: [B, L] binary boundary tensor (1 = new group starts)
        upsample: If True, compute upsample weights; else downsample weights

    Returns:
        Assignment tensor [B, L, S] or None if n_segments == 0
    """
    boundaries = boundaries.clone()
    n_segments = int(boundaries.sum(dim=-1).max().item())

    if upsample:
        n_segments += 1

    if n_segments == 0:
        return None

    tmp = torch.zeros_like(boundaries).unsqueeze(2) + torch.arange(
        start=0, end=n_segments, device=boundaries.device, dtype=boundaries.dtype
    )
    hh1 = boundaries.cumsum(dim=-1)

    if not upsample:
        hh1 -= boundaries  # Subtract current boundary so position belongs to previous group

    foo = tmp - hh1.unsqueeze(-1)

    # WR-01 fix: zero out unused columns for batch items with fewer segments
    # When n_segments is set to the max across the batch, items with fewer
    # segments have unused columns that would produce NaN on normalization.
    item_segment_counts = boundaries.sum(dim=-1)
    for b in range(boundaries.shape[0]):
        item_segs = int(item_segment_counts[b].item())
        if upsample:
            item_segs += 1
        if item_segs < n_segments:
            foo[b, :, item_segs:] = 0

    return foo


def _downsample_final(foo: torch.Tensor, upsample: bool = False) -> torch.Tensor:
    """Normalize assignment weights for downsample/upsample einsum."""
    autoregressive = foo != 0
    lel = 1.0 - foo.float()
    lel[autoregressive] = 0.0
    dim = 2 if upsample else 1
    lel = lel / (lel.sum(dim=dim, keepdim=True) + 1e-9)
    return lel.to(foo.dtype)


def downsample(boundaries: torch.Tensor, hidden: torch.Tensor, null_group: torch.Tensor) -> torch.Tensor:
    """Downsample hidden states using boundary decisions.

    Per D-05: Exact einsum port from FLEXITOKENS shortening.py.
    Per D-08: Batch-first layout [B, L, D].
    Per D-11: Sentinel tokens forced to boundary=1 by modality_mask ->
    downsample treats each sentinel+modality group as a separate merge
    group -> groups appear intact in shortened sequence.

    Args:
        boundaries: [B, L] binary boundary tensor (1 = new group starts)
        hidden: [B, L, D] hidden states (batch-first per D-08)
        null_group: [1, B, D] null group token prepended to output

    Returns:
        shortened_hidden: [S, B, D] shortened sequence (LBD format for
        compatibility with FLEXITOKENS upsample which expects SBD input)
    """
    foo = _downsample_common(boundaries, upsample=False)
    if foo is None:
        return null_group.repeat(1, hidden.size(0), 1)
    else:
        bar = _downsample_final(foo, upsample=False)
        # Einsum: B*L*D @ B*L*S -> B*S*D, then transpose to S*B*D
        shortened_hidden = torch.einsum('bld,bls->bsd', hidden, bar.to(hidden.dtype))
        shortened_hidden = shortened_hidden.permute(1, 0, 2)
        # Prepend null_group: [1, B, D] -> cat along dim=0 -> [S+1, B, D]
        shortened_hidden = torch.cat([null_group, shortened_hidden], dim=0)
        return shortened_hidden


def upsample(boundaries: torch.Tensor, shortened_hidden: torch.Tensor) -> torch.Tensor:
    """Upsample shortened hidden states back to original sequence length.

    Per D-05: Exact einsum port from FLEXITOKENS shortening.py.
    Per D-08: Batch-first layout.

    Args:
        boundaries: [B, L] binary boundary tensor
        shortened_hidden: [S, B, D] shortened sequence

    Returns:
        upsampled_hidden: [B, L, D] upsampled sequence
    """
    foo = _downsample_common(boundaries, upsample=True)
    bar = _downsample_final(foo, upsample=True)
    upsampled_hidden = torch.einsum('sbd,bls->bld', shortened_hidden, bar.to(shortened_hidden.dtype))
    return upsampled_hidden


# ============================================================================
# LTI Injection, ACT Halting, LoRA Adapter
# ============================================================================

class LTIInjection(nn.Module):
    """Linear Time-Invariant injection module."""

    def __init__(self, config: SpiderConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.log_A = nn.Parameter(torch.full((config.hidden_size,), -2.0))
        self.delta_t = nn.Parameter(torch.tensor(1.0))
        self.B = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
        with torch.no_grad():
            self.B.weight.data.normal_(mean=0.0, std=0.01)

    def get_A(self):
        return -torch.exp(self.log_A)

    def forward(self, h_t, e):
        A = self.get_A()
        return A * h_t + self.B(e)


class ACTHalting(nn.Module):
    """Adaptive Computation Time halting module."""

    def __init__(self, config: SpiderConfig):
        super().__init__()
        self.halt_predictor = nn.Linear(config.hidden_size, 1)
        self.threshold = config.act_threshold

    def forward(self, hidden_states):
        return torch.sigmoid(self.halt_predictor(hidden_states))


class LoRAAdapter(nn.Module):
    """LoRA adapter for per-loop adaptation in recurrent layers.

    Per CR-01 fix: up-projection (self.B) is initialized to EXACTLY ZERO
    so that LoRA adapter output is zero at initialization -- meaning the
    model starts behaving identically to the base model. This follows
    standard LoRA convention (Hu et al., 2021).
    """

    def __init__(self, config: SpiderConfig):
        super().__init__()
        rank = config.lora_rank
        self.down = nn.Linear(config.hidden_size, rank, bias=False)
        self.B = nn.Parameter(torch.zeros(rank, config.hidden_size, dtype=torch.float32))  # CR-01 fix: zeros, not randn*0.02; IN-02
        self.scale = nn.Embedding(config.max_loop_iters, rank)
        with torch.no_grad():
            self.scale.weight.data.zero_()
            self.down.weight.data.normal_(mean=0.0, std=0.001)

    def forward(self, x, loop_t):
        max_t = self.scale.num_embeddings - 1
        t_idx = min(loop_t, max_t)
        s = self.scale(torch.tensor(t_idx, device=x.device))
        down = self.down(x) * s
        return down @ self.B


def _loop_index_embedding(h, loop_t, loop_dim, theta=10000.0):
    """Sinusoidal loop index embedding for RDT depth differentiation."""
    freqs = 1.0 / (theta ** (torch.arange(0, loop_dim, 2, device=h.device, dtype=h.dtype) / loop_dim))
    angles = loop_t * freqs
    emb = torch.cat([angles.sin(), angles.cos()], dim=-1)[:loop_dim]
    emb_full = torch.zeros(h.shape[-1], device=h.device, dtype=h.dtype)
    emb_full[:loop_dim] = emb
    return h + emb_full.unsqueeze(0).unsqueeze(0)


def _checkpoint(func, *args, **kwargs):
    """Gradient checkpointing wrapper -- saves VRAM at ~20% compute cost."""
    if torch.is_grad_enabled():
        return torch.utils.checkpoint.checkpoint(func, *args, use_reentrant=False, **kwargs)
    return func(*args, **kwargs)


# ============================================================================
# Full Spider Model (with FlexiToken integration)
# ============================================================================

class SpiderModel(nn.Module):
    """Full RDT model with MLA attention + Engram memory + FlexiToken.

    Architecture:
    2x Prelude (MLA + dense FFN)
    6x Recurrent (MLA + Engram@L1,L4 + MoE) -- with gradient checkpointing
    2x Coda (MLA + dense FFN)
    LTI Injection + ACT Halting + LoRA Adapter
    BoundaryPredictor + downsample/upsample for FlexiToken
    """

    def __init__(self, config: SpiderConfig):
        super().__init__()
        self.config = config
        self.prelude_layers = nn.ModuleList([
            SpiderDenseLayer(config) for _ in range(config.prelude_layers)
        ])
        self.recurrent_layers = nn.ModuleList([
            SpiderRecurrentLayer(config, i, has_engram=(i in config.engram_layers))
            for i in range(config.num_hidden_layers)
        ])
        self.coda_layers = nn.ModuleList([
            SpiderDenseLayer(config) for _ in range(config.coda_layers)
        ])
        self.norm = SpiderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.injection = LTIInjection(config)
        self.act_halting = ACTHalting(config)
        self.lora_adapter = LoRAAdapter(config)
        self.loop_embed_dim = config.loop_embed_dim
        self._gradient_checkpointing = False

    def gradient_checkpointing_enable(self):
        self._gradient_checkpointing = True

    def gradient_checkpointing_disable(self):
        self._gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        input_embedding=None,
        attention_mask=None,
        position_ids=None,
        past_key_values=None,
        use_cache=False,
        n_loops=None,
        token_ids=None,
        hard_boundaries=None,
    ):
        n_loops = n_loops or 1
        input_embedding = input_embedding if input_embedding is not None else hidden_states

        # Prelude layers
        for layer in self.prelude_layers:
            if self._gradient_checkpointing and torch.is_grad_enabled():
                hidden_states, _ = _checkpoint(
                    layer, hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                )
            else:
                hidden_states, _ = layer(
                    hidden_states, attention_mask=attention_mask,
                    position_ids=position_ids,
                )

        # FlexiToken: if hard_boundaries provided, downsample before recurrent core
        if hard_boundaries is not None:
            # Apply norm before downsample
            hidden_normed = self.norm(hidden_states)
            null_group = torch.zeros(
                1, hidden_states.shape[0], hidden_states.shape[-1],
                device=hidden_states.device, dtype=hidden_states.dtype,
            )
            shortened = downsample(hard_boundaries, hidden_normed, null_group)
            # shortened: [S, B, D] -> [B, S, D]
            hidden_states = shortened.permute(1, 0, 2)

            # Shorten token_ids to match downsampled sequence length.
            # Take the first token in each boundary group so the Engram
            # hash-based lookup gets a representative token per group.
            # hard_boundaries: [B, L], cumsum gives group index per position.
            # Pick the first position (where boundary=1) of each group.
            if token_ids is not None:
                group_ids = hard_boundaries.cumsum(dim=-1) # [B, L], 1-based group indices
                n_groups = int(group_ids.max().item()) # number of groups
                B = hard_boundaries.shape[0]
                # For each group g (1..n_groups), find the first position where group_ids == g
                short_ids = torch.zeros(B, n_groups, device=token_ids.device, dtype=token_ids.dtype)
                for g in range(1, n_groups + 1):
                    # mask of positions belonging to group g
                    mask = (group_ids == g)
                    # first position in group g
                    first_pos = mask.float().argmax(dim=-1) # [B]
                    short_ids[:, g - 1] = token_ids.gather(1, first_pos.unsqueeze(1)).squeeze(1)
                # Prepend a dummy token (0) for the null_group entry
                null_token = torch.zeros(B, 1, device=token_ids.device, dtype=token_ids.dtype)
                token_ids = torch.cat([null_token, short_ids], dim=1) # [B, S+1]

            # After downsample, input_embedding must match the shortened sequence length
            input_embedding = hidden_states.clone()

        # Recurrent core with RDT looping
        e = hidden_states.clone()
        B, T_seq, D = hidden_states.shape
        halted = torch.zeros(B, T_seq, device=hidden_states.device, dtype=torch.bool)
        cumulative_p = torch.zeros(B, T_seq, device=hidden_states.device, dtype=hidden_states.dtype)
        h_out = torch.zeros_like(hidden_states)
        total_aux_loss = 0.0
        past_key_values = past_key_values if past_key_values is not None else [None] * len(self.recurrent_layers)

        for t in range(n_loops):
            h_loop = _loop_index_embedding(hidden_states, t, self.loop_embed_dim)
            if t > 0:
                injection = self.injection(hidden_states, input_embedding)
                hidden_states = hidden_states + injection

            new_past_key_values = []
            for i, layer in enumerate(self.recurrent_layers):
                hidden_states, aux_loss, past_kv = _checkpoint(
                    layer, hidden_states,
                    token_ids=token_ids,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values[i] if t == 0 else None,
                    use_cache=use_cache,
                )
                total_aux_loss = total_aux_loss + aux_loss
                new_past_key_values.append(past_kv)

            lora_delta = self.lora_adapter(hidden_states, t)
            hidden_states = hidden_states + lora_delta

            halt_prob = self.act_halting(hidden_states).squeeze(-1)
            still_running = ~halted
            remainder = (1.0 - cumulative_p).clamp(min=0)
            weight = torch.where(
                cumulative_p + halt_prob >= self.config.act_threshold,
                remainder, halt_prob,
            )
            weight = weight * still_running.to(hidden_states.dtype)
            h_out = h_out + weight.unsqueeze(-1) * hidden_states
            cumulative_p = cumulative_p + halt_prob * still_running.to(hidden_states.dtype)
            halted = halted | (cumulative_p >= self.config.act_threshold)
            if halted.all() and not self.training:
                break

        never_halted = (~halted).to(hidden_states.dtype).unsqueeze(-1)
        hidden_states = h_out + never_halted * hidden_states

        # FlexiToken: if hard_boundaries provided, upsample after recurrent core
        if hard_boundaries is not None:
            hidden_states_sbd = hidden_states.permute(1, 0, 2)  # [S, B, D]
            hidden_states = upsample(hard_boundaries, hidden_states_sbd)  # [B, L, D]

        # Coda layers
        for layer in self.coda_layers:
            if self._gradient_checkpointing and torch.is_grad_enabled():
                hidden_states, _ = _checkpoint(
                    layer, hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                )
            else:
                hidden_states, _ = layer(
                    hidden_states, attention_mask=attention_mask,
                    position_ids=position_ids,
                )

        hidden_states = self.norm(hidden_states)
        return hidden_states, total_aux_loss, new_past_key_values


# ============================================================================
# SpiderForConditionalGeneration
# ============================================================================

class SpiderForConditionalGeneration(nn.Module):
    """Spider model with embedding, LM head, and FlexiToken boundary prediction.

    Forward flow:
    1. embed_tokens(input_ids) -> hidden_states
    2. Inject modality features at sentinel positions
    3. Prelude layers
    4. BoundaryPredictor with modality_mask -> boundaries
    5. SpiderModel (downsample -> recurrent -> upsample -> coda)
    6. lm_head -> logits
    """

    def __init__(self, config: SpiderConfig):
        super().__init__()
        self.config = config
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.boundary_predictor = BoundaryPredictor(config)
        self.model = SpiderModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        if config.tie_word_embeddings:
            self.lm_head.weight = self.embed_tokens.weight
        self.apply(self._init_weights)

    def gradient_checkpointing_enable(self):
        self.model.gradient_checkpointing_enable()

    def gradient_checkpointing_disable(self):
        self.model.gradient_checkpointing_disable()

    def enable_input_require_grads(self):
        def _make_inputs_require_grad(module, input, output):
            output.requires_grad_(True)
        self.embed_tokens.register_forward_hook(_make_inputs_require_grad)

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            if hasattr(self, 'model') and module is self.model.injection.B:
                return  # LTI injection B has its own init
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)

    def _inject_modality_features(
        self,
        hidden_states: torch.Tensor,
        input_ids: torch.Tensor,
        features: list,
        modality: str = 'IMG',
    ) -> torch.Tensor:
        """Replace placeholder embeddings with actual encoder features at modality regions.

        Per D-11: Modality tokens (vision, audio, video) are injected at
        sentinel-marked positions. Between sentinel pairs, the initial
        embeddings are placeholders -- this method replaces them with the
        actual encoder features.

        T-02-06 mitigation: Validates feature shape and sentinel pair count.
        """
        start_token = SENTINEL_TOKENS[f'{modality}_START']
        end_token = SENTINEL_TOKENS[f'{modality}_END']

        for b in range(hidden_states.shape[0]):
            starts = (input_ids[b] == start_token).nonzero(as_tuple=True)[0]
            ends = (input_ids[b] == end_token).nonzero(as_tuple=True)[0]

            if len(starts) != len(ends):
                raise ValueError(
                    f"Batch {b}: mismatched {modality} sentinel pairs -- "
                    f"{len(starts)} {_TOKEN_NAMES_BY_ID[start_token]}(s) vs "
                    f"{len(ends)} {_TOKEN_NAMES_BY_ID[end_token]}(s)."
                )
            if len(starts) != len(features):
                raise ValueError(
                    f"Batch {b}: {modality} sentinel pair count ({len(starts)}) "
                    f"doesn't match feature count ({len(features)})."
                )

            for s, e, feat in zip(starts, ends, features):
                num_tokens = e - s - 1
                if feat.shape[0] != num_tokens:
                    raise ValueError(
                        f"Batch {b}: {modality} feature has {feat.shape[0]} tokens "
                        f"but sentinel region has {num_tokens} positions "
                        f"(from pos {s+1} to {e-1})."
                    )
                if feat.shape[1] != hidden_states.shape[-1]:
                    raise ValueError(
                        f"Batch {b}: {modality} feature hidden_size {feat.shape[1]} "
                        f"doesn't match model hidden_size {hidden_states.shape[-1]}."
                    )
                hidden_states[b, s + 1:e] = feat.to(hidden_states.dtype)

        return hidden_states

    def forward(
        self,
        input_ids: torch.Tensor,
        attention_mask=None,
        position_ids=None,
        labels=None,
        n_loops=None,
        use_cache=False,
        vision_features=None,
        audio_features=None,
        video_features=None,
        **kwargs,
    ):
        hidden_states = self.embed_tokens(input_ids)
        model_dtype = next(self.model.parameters()).dtype
        hidden_states = hidden_states.to(model_dtype)
        input_embedding = hidden_states.clone()

        # Inject modality features at sentinel positions
        if vision_features is not None:
            hidden_states = self._inject_modality_features(
                hidden_states, input_ids, vision_features, 'IMG'
            )
        if audio_features is not None:
            hidden_states = self._inject_modality_features(
                hidden_states, input_ids, audio_features, 'AUD'
            )
        if video_features is not None:
            hidden_states = self._inject_modality_features(
                hidden_states, input_ids, video_features, 'VID'
            )

        # Create modality mask and predict boundaries
        modality_mask = create_modality_mask(input_ids, strict=(labels is not None))
        soft_boundaries, hard_boundaries = self.boundary_predictor(
            hidden_states, modality_mask=modality_mask
        )

        # Run model with FlexiToken boundaries
        hidden_states, aux_loss, past_kv = self.model(
            hidden_states,
            input_embedding=input_embedding,
            attention_mask=None,
            position_ids=position_ids,
            use_cache=use_cache,
            n_loops=n_loops,
            token_ids=input_ids,
            hard_boundaries=hard_boundaries,
        )

        logits = self.lm_head(hidden_states)
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

        return {
            "loss": loss,
            "logits": logits,
            "aux_loss": aux_loss,
            "past_key_values": past_kv,
            "soft_boundaries": soft_boundaries,
            "hard_boundaries": hard_boundaries,
        }

    @torch.inference_mode()
    def generate(
        self,
        input_ids: torch.Tensor,
        max_new_tokens: int = 100,
        temperature: float = 1.0,
        top_k: Optional[int] = None,
        n_loops: int = 1,
        use_cache: bool = True,
        boundary_mode: str = 'adaptive',
    ) -> torch.Tensor:
        """Token-level generation with compressed-prefix KV cache per D-28.

        Strategy: Encode the prefix through prelude + BP + downsample to get
        a compressed KV cache, then autoregressively decode byte-by-byte using
        that cached prefix. The speedup comes from the prefix being shorter in
        the KV cache (~3.3x fewer entries for English text).

        Flow:
        1. Embed prefix → prelude layers → BP → downsample → recurrent core
           → collect KV cache for compressed prefix
        2. Coda + lm_head on last position → sample first new byte
        3. For each subsequent byte: embed → recurrent (with KV cache) → coda
           → lm_head → sample → append
        4. Stop at max_new_tokens or EOS

        Args:
            input_ids: Prefix token IDs [B, L] (byte values 0-255 + BOS/EOS)
            max_new_tokens: Maximum number of new bytes to generate
            temperature: Sampling temperature (0 = greedy, 1.0 = default)
            top_k: If set, only sample from top-k logits
            n_loops: Number of recurrent loops during generation
            use_cache: Use KV cache for incremental decoding
            boundary_mode: 'adaptive' (threshold) or 'fixed' (top-k) for BP

        Returns:
            Generated token IDs [B, N] where N ≤ max_new_tokens
        """
        B = input_ids.shape[0]
        device = input_ids.device
        model_dtype = next(self.model.parameters()).dtype

        # --- Step 1: Encode prefix and collect KV cache ---
        hidden_states = self.embed_tokens(input_ids).to(model_dtype)

        # Prelude layers (byte-level, no compression)
        for layer in self.model.prelude_layers:
            hidden_states, _ = layer(hidden_states)

        # Boundary prediction on prefix (strict=False for generation)
        modality_mask = create_modality_mask(input_ids, strict=False)
        soft_boundaries, hard_boundaries = self.boundary_predictor(
            hidden_states, modality_mask=modality_mask
        )

        # Apply boundary mode
        if boundary_mode == 'adaptive':
            hard_boundaries = (soft_boundaries > 0.5).float()
            hard_boundaries = hard_boundaries - soft_boundaries.detach() + soft_boundaries
        elif boundary_mode == 'fixed':
            k = max(1, int(soft_boundaries.shape[-1] / 3.3))
            topk_vals, topk_idx = soft_boundaries.topk(k, dim=-1)
            hard_boundaries = torch.zeros_like(soft_boundaries)
            hard_boundaries.scatter_(-1, topk_idx, 1.0)
            hard_boundaries = hard_boundaries - soft_boundaries.detach() + soft_boundaries

        # Downsample prefix for compressed KV cache
        hidden_normed = self.model.norm(hidden_states)
        null_group = torch.zeros(
            1, B, hidden_states.shape[-1], device=device, dtype=hidden_states.dtype
        )
        shortened = downsample(hard_boundaries, hidden_normed, null_group)
        hidden_states = shortened.permute(1, 0, 2)  # [B, S, D]
        input_embedding = hidden_states.clone()

        # Run through recurrent core + coda (hard_boundaries=None skips downsample/upsample)
        hidden_states, _, past_key_values = self.model(
            hidden_states,
            input_embedding=input_embedding,
            use_cache=use_cache,
            n_loops=n_loops,
            hard_boundaries=None,
        )

        # Get logits for last position of prefix (norm + lm_head only, coda already applied)
        logits = self.lm_head(hidden_states[:, -1:, :])  # [B, 1, vocab]
        next_token = self._sample_token(logits, temperature, top_k)  # [B, 1]

        generated = [next_token]

        # --- Step 2: Autoregressive byte-level decoding with KV cache ---
        for _ in range(max_new_tokens - 1):
            # Check EOS
            if (next_token == SENTINEL_TOKENS['EOS']).all():
                break

            # Embed the last generated token
            hidden_states = self.embed_tokens(next_token).to(model_dtype)  # [B, 1, D]
            input_embedding = hidden_states.clone()

            if use_cache:
                # Incremental forward: 1 new token, cached prefix in past_key_values
                hidden_states, _, past_key_values = self.model(
                    hidden_states,
                    input_embedding=input_embedding,
                    past_key_values=past_key_values,
                    use_cache=True,
                    n_loops=n_loops,
                    hard_boundaries=None,
                )
            else:
                # Naive: re-run full forward from scratch (no KV cache)
                all_ids = torch.cat([input_ids, torch.cat(generated, dim=1)], dim=1)
                output = self.forward(
                    all_ids, n_loops=n_loops, use_cache=False,
                )
                logits_full = output['logits']
                next_logits = logits_full[:, -1, :] / max(temperature, 1e-8)
                if top_k is not None and top_k > 0:
                    v, _ = torch.topk(next_logits, min(top_k, next_logits.size(-1)))
                    next_logits = next_logits.masked_fill(next_logits < v[:, [-1]], float('-inf'))
                if temperature < 1e-8:
                    next_token = next_logits.argmax(dim=-1, keepdim=True)
                else:
                    probs = torch.softmax(next_logits, dim=-1)
                    next_token = torch.multinomial(probs, num_samples=1)
                generated.append(next_token)
                continue

            # lm_head on last position (coda + norm already applied by self.model)
            logits = self.lm_head(hidden_states[:, -1:, :]) # [B, 1, vocab]
            next_token = self._sample_token(logits, temperature, top_k)
            generated.append(next_token)

        return torch.cat(generated, dim=1)  # [B, N]

    @staticmethod
    def _sample_token(logits: torch.Tensor, temperature: float, top_k: Optional[int]) -> torch.Tensor:
        """Sample next token from logits with temperature and top-k."""
        logits = logits.squeeze(1)  # [B, vocab]
        if temperature < 1e-8:
            return logits.argmax(dim=-1, keepdim=True)  # greedy
        logits = logits / temperature
        if top_k is not None and top_k > 0:
            v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
            logits = logits.masked_fill(logits < v[:, [-1]], float('-inf'))
        probs = torch.softmax(logits, dim=-1)
        return torch.multinomial(probs, num_samples=1)  # [B, 1]

    def get_num_params(self):
        total = sum(p.numel() for p in self.parameters())
        return {"total": total, "trainable": total}