Delete mythos-fineweb-dense.py

mythos-fineweb-dense.py

@@ -1,1060 +0,0 @@
-#!/usr/bin/env python3
-"""
-SpiderPortal v5-Dense: English pretraining on FineWeb-Edu with AdamW.
-
-Architecture: RDT (2 prelude + 6 recurrent + 2 coda) with:
-- MLA (Multi-Latent Attention): 10.7x KV cache compression + sliding window
-- Engram conditional memory at recurrent layers 1 and 4
-- Dense FFN (all params active, MoE conversion in Phase 2)
-- LTI Injection + ACT Halting + LoRA Adapter
-- 32k context (extendable to 256k at inference via YaRN)
-
-Config: hidden_size=2048, 6 recurrent layers, 32 experts (Phase 2), top-2 routing
-
-Single GPU:
-    python mythos-fineweb-dense.py
-
-Multi-GPU:
-    torchrun --nproc_per_node=$(python -c "import torch; print(torch.cuda.device_count())") mythos-fineweb-dense.py
-"""
-import os
-import math
-import time
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.distributed as dist
-from loguru import logger
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-    ShardingStrategy,
-    MixedPrecision,
-    FullStateDictConfig,
-    StateDictType,
-)
-from torch.distributed.fsdp.wrap import ModuleWrapPolicy
-from torch.utils.data import IterableDataset, DataLoader, get_worker_info
-from contextlib import nullcontext
-from dataclasses import dataclass, field
-from typing import Optional, Tuple, Dict, List
-from torch.nn import CrossEntropyLoss
-from datasets import load_dataset
-from transformers import AutoTokenizer
-
-
-# ---------------------------------------------------------------------------
-# SpiderPortal Model Architecture (Dense + MLA + Engram)
-# ---------------------------------------------------------------------------
-
-@dataclass
-class SpiderPortalConfig:
-    vocab_size: int = 50257
-    hidden_size: int = 2048
-    num_hidden_layers: int = 6
-    num_attention_heads: int = 16
-    num_key_value_heads: int = 4
-    intermediate_size: int = 8192
-    hidden_act: str = "silu"
-    num_experts: int = 32
-    num_experts_per_tok: int = 2
-    num_shared_experts: int = 1
-    router_aux_loss_coef: float = 0.05
-    max_loop_iters: int = 4
-    act_threshold: float = 0.5
-    max_position_embeddings: int = 32768
-    rope_theta: float = 10000000.0
-    rope_scaling: dict = None
-    sliding_window: int = 4096
-    attention_dropout: float = 0.0
-    rms_norm_eps: float = 1e-6
-    initializer_range: float = 0.02
-    use_cache: bool = True
-    tie_word_embeddings: bool = True
-    prelude_layers: int = 2
-    coda_layers: int = 2
-    lora_rank: int = 128
-    loop_embed_dim: int = 128
-    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
-    model_type: str = "spiderportal"
-    torch_dtype: str = "bfloat16"
-
-    # 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)
-    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 = 65537  # prime number for hash table
-    engram_conv_kernel: int = 4
-    engram_conv_dilation: int = 3
-    engram_dim: int = 128  # per-head embedding dimension
-
-
-def loop_index_embedding(h, loop_t, loop_dim, theta=10000.0):
-    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)
-
-
-class SpiderPortalRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight.to(input_dtype) * hidden_states.to(input_dtype)
-
-
-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
-
-
-# ---------------------------------------------------------------------------
-# MLA: Multi-Latent Attention (DeepSeek-V2 style) + Sliding Window
-# ---------------------------------------------------------------------------
-
-class SpiderPortalMLA(nn.Module):
-    """Multi-Latent Attention with compressed KV cache and sliding window.
-
-    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=4096 → local attention range
-    """
-    def __init__(self, config):
-        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', None)
-
-        # 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 = SpiderPortalRMSNorm(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 = SpiderPortalRMSNorm(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
-        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 = 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)
-
-    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 _make_sliding_window_mask(self, q_len, kv_len, device, dtype):
-        """Create a sliding window causal mask."""
-        if self.sliding_window is None or self.sliding_window <= 0:
-            return None
-        mask = torch.full((q_len, kv_len), torch.finfo(dtype).min, device=device, dtype=dtype)
-        for i in range(q_len):
-            start = max(0, i - self.sliding_window + 1)
-            mask[i, start:i + 1] = 0.0
-        return mask
-
-    def forward(self, hidden_states, 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)
-
-        # 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 = cos[position_ids].unsqueeze(1)
-        sin = sin[position_ids].unsqueeze(1)
-
-        q_rope = self._apply_rotary(q_rope, cos, sin)
-        k_rope = self._apply_rotary(k_rope, cos, sin)
-
-        # 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
-        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)
-        past_kv = (k_full, v) if use_cache else None
-
-        # Build attention mask: user mask + sliding window
-        final_mask = attention_mask
-        if self.sliding_window is not None and self.sliding_window > 0:
-            kv_len = k_full.size(2)
-            sw_mask = self._make_sliding_window_mask(q_len, kv_len, hidden_states.device, hidden_states.dtype)
-            if final_mask is not None:
-                final_mask = final_mask + sw_mask
-            else:
-                final_mask = sw_mask
-
-        # Attention with SDPA
-        attn_output = F.scaled_dot_product_attention(
-            q_full, k_full, v,
-            attn_mask=final_mask,
-            dropout_p=self.config.attention_dropout if self.training else 0.0,
-            is_causal=(final_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=50257):
-    """Simulate NFKC + lowercase canonical ID projection."""
-    return token_ids % (vocab_size * 77 // 100)
-
-
-class SpiderPortalEngram(nn.Module):
-    """Conditional memory module via NN-gram lookup.
-
-    Applied only at specific recurrent layers (config.engram_layers).
-    """
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.ngram_orders = config.engram_ngram_orders
-        self.num_heads = 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 * self.d_mem
-
-        self.embed_tables = nn.ParameterDict()
-        for n in self.ngram_orders:
-            for h in range(self.num_heads):
-                key = f"e_{n}_{h}"
-                self.embed_tables[key] = nn.Parameter(
-                    torch.randn(self.table_size, self.d_mem) * 0.02
-                )
-
-        self.register_buffer("hash_seeds", torch.tensor([
-            (h + 1) * 2654435761
-            for _ in self.ngram_orders
-            for h in range(self.num_heads)
-        ], dtype=torch.int64))
-
-        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 = SpiderPortalRMSNorm(config.hidden_size)
-        self.k_norm = SpiderPortalRMSNorm(config.hidden_size)
-
-    def _compute_indices(self, compressed_ids, n, head_idx):
-        """Vectorized NN-gram hash indices for a single (order, head)."""
-        bsz, seq_len = compressed_ids.shape
-        pad = torch.zeros(bsz, n - 1, dtype=compressed_ids.dtype, device=compressed_ids.device)
-        padded = torch.cat([pad, compressed_ids], dim=1)
-
-        indices_list = []
-        for i in range(n):
-            indices_list.append(padded[:, i:i + seq_len])
-        ngrams = torch.stack(indices_list, dim=-1)
-
-        seed = int(self.hash_seeds[head_idx].item())
-        h_val = torch.zeros(bsz, seq_len, dtype=torch.int64, device=compressed_ids.device)
-        for i in range(n):
-            h_val = h_val * 31 + ngrams[:, :, i]
-            h_val = h_val % self.table_size
-        h_val = (h_val * seed) % 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)
-
-        all_parts = []
-        head_counter = 0
-        for n in self.ngram_orders:
-            for h in range(self.num_heads):
-                key = f"e_{n}_{h}"
-                table = self.embed_tables[key]
-                indices = self._compute_indices(compressed, n, head_counter)
-                emb = table[indices.view(-1)]
-                all_parts.append(emb.view(bsz, seq_len, self.d_mem))
-                head_counter += 1
-
-        memory = torch.cat(all_parts, dim=-1)
-        return memory
-
-    def forward(self, hidden_states, token_ids):
-        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 (dense)
-# ---------------------------------------------------------------------------
-
-class SpiderPortalExpert(nn.Module):
-    def __init__(self, config, 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))
-
-
-# ---------------------------------------------------------------------------
-# Prelude/Coda Dense Layer (uses MLA)
-# ---------------------------------------------------------------------------
-
-class SpiderPortalDenseLayer(nn.Module):
-    """Prelude/coda dense layer with MLA attention."""
-    def __init__(self, config):
-        super().__init__()
-        self.self_attn = SpiderPortalMLA(config)
-        dense_intermediate = config.hidden_size * 4 // 3
-        self.ffn = SpiderPortalExpert(config, intermediate_size=dense_intermediate)
-        self.input_layernorm = SpiderPortalRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = SpiderPortalRMSNorm(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 Dense Layer (uses MLA + optional Engram)
-# ---------------------------------------------------------------------------
-
-class SpiderPortalRecurrentDenseLayer(nn.Module):
-    """Recurrent layer with MLA attention and optional Engram memory."""
-    def __init__(self, config, layer_idx, has_engram=False):
-        super().__init__()
-        self.layer_idx = layer_idx
-        self.has_engram = has_engram
-        self.self_attn = SpiderPortalMLA(config)
-        if has_engram:
-            self.engram = SpiderPortalEngram(config)
-        self.ffn = SpiderPortalExpert(config, intermediate_size=config.intermediate_size)
-        self.input_layernorm = SpiderPortalRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = SpiderPortalRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_engram_layernorm = SpiderPortalRMSNorm(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)
-            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 = self.ffn(ffn_input)
-        hidden_states = hidden_states + ffn_output
-        return hidden_states, 0.0, past_kv
-
-
-# ---------------------------------------------------------------------------
-# LTI Injection, ACT Halting, LoRA Adapter
-# ---------------------------------------------------------------------------
-
-class LTIInjection(nn.Module):
-    def __init__(self, config):
-        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):
-    def __init__(self, config):
-        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):
-    def __init__(self, config):
-        super().__init__()
-        rank = config.lora_rank
-        self.down = nn.Linear(config.hidden_size, rank, bias=False)
-        self.B = nn.Parameter(torch.randn(rank, config.hidden_size) * 0.02)
-        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 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 Model
-# ---------------------------------------------------------------------------
-
-class SpiderPortalDenseModel(nn.Module):
-    """Full RDT model with MLA attention + Engram memory at layers 1,4.
-
-    Architecture:
-        2x Prelude (MLA + dense FFN)
-        6x Recurrent (MLA + Engram@L1,L4 + dense FFN) — with gradient checkpointing
-        2x Coda (MLA + dense FFN)
-        LTI Injection + ACT Halting + LoRA Adapter
-    """
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.prelude_layers = nn.ModuleList([SpiderPortalDenseLayer(config) for _ in range(config.prelude_layers)])
-        self.recurrent_layers = nn.ModuleList([
-            SpiderPortalRecurrentDenseLayer(config, i, has_engram=(i in config.engram_layers))
-            for i in range(config.num_hidden_layers)
-        ])
-        self.coda_layers = nn.ModuleList([SpiderPortalDenseLayer(config) for _ in range(config.coda_layers)])
-        self.norm = SpiderPortalRMSNorm(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
-    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):
-        n_loops = n_loops or self.config.max_loop_iters
-        input_embedding = input_embedding if input_embedding is not None else hidden_states
-        for layer in self.prelude_layers:
-            hidden_states, _ = layer(hidden_states, attention_mask=attention_mask, position_ids=position_ids)
-        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)
-        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
-                )
-                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
-        for layer in self.coda_layers:
-            hidden_states, _ = layer(hidden_states, attention_mask=attention_mask, position_ids=position_ids)
-        hidden_states = self.norm(hidden_states)
-        return hidden_states, 0.0, new_past_key_values
-
-
-class SpiderPortalForConditionalGeneration(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.model = SpiderPortalDenseModel(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 _init_weights(self, module):
-        if isinstance(module, nn.Linear):
-            if hasattr(self, 'model') and module is self.model.injection.B:
-                return
-            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 forward(self, input_ids, attention_mask=None, position_ids=None, labels=None, n_loops=None, use_cache=False):
-        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()
-        if attention_mask is None:
-            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
-        causal_mask = torch.full((attention_mask.size(0), 1, attention_mask.size(1), attention_mask.size(1)), 0.0, dtype=hidden_states.dtype, device=hidden_states.device)
-        causal_mask = causal_mask.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(2), torch.finfo(hidden_states.dtype).min)
-        causal_mask = causal_mask.triu(1)
-        hidden_states, aux_loss, past_kv = self.model(
-            hidden_states, input_embedding=input_embedding,
-            attention_mask=causal_mask, position_ids=position_ids,
-            use_cache=use_cache, n_loops=n_loops, token_ids=input_ids
-        )
-        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}
-    def get_num_params(self):
-        total = sum(p.numel() for p in self.parameters())
-        return {"total": total, "trainable": total}
-
-
-# ---------------------------------------------------------------------------
-# Dataset
-# ---------------------------------------------------------------------------
-
-class FineWebEduDataset(IterableDataset):
-    def __init__(self, tokenizer, seq_len: int, subset: str, rank: int, world_size: int):
-        self.tokenizer = tokenizer
-        self.seq_len = seq_len
-        self.subset = subset
-        self.rank = rank
-        self.world_size = world_size
-    def __iter__(self):
-        worker = get_worker_info()
-        num_workers = worker.num_workers if worker else 1
-        worker_id = worker.id if worker else 0
-        total_shards = self.world_size * num_workers
-        shard_index = self.rank * num_workers + worker_id
-        ds = load_dataset(
-            "HuggingFaceFW/fineweb-edu",
-            name=self.subset,
-            split="train",
-            streaming=True,
-        ).shard(num_shards=total_shards, index=shard_index)
-        buf = []
-        for sample in ds:
-            buf.extend(self.tokenizer.encode(sample["text"]))
-            while len(buf) >= self.seq_len + 1:
-                chunk = buf[: self.seq_len + 1]
-                buf = buf[self.seq_len + 1 :]
-                yield (
-                    torch.tensor(chunk[:-1], dtype=torch.long),
-                    torch.tensor(chunk[1:], dtype=torch.long),
-                )
-
-
-# ---------------------------------------------------------------------------
-# LR schedule
-# ---------------------------------------------------------------------------
-
-def get_lr(step: int, warmup: int, total: int, max_lr: float, min_lr: float) -> float:
-    if step < warmup:
-        return max_lr * step / warmup
-    if step >= total:
-        return min_lr
-    decay = (step - warmup) / (total - warmup)
-    return min_lr + 0.5 * (max_lr - min_lr) * (1.0 + math.cos(math.pi * decay))
-
-
-# ---------------------------------------------------------------------------
-# Checkpointing
-# ---------------------------------------------------------------------------
-
-def save_weights_only(model, step, epoch, ckpt_dir, ddp):
-    if ddp:
-        with FSDP.state_dict_type(
-            model,
-            StateDictType.FULL_STATE_DICT,
-            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
-        ):
-            model_state = model.state_dict()
-    else:
-        model_state = model.state_dict()
-    ckpt_path = os.path.join(ckpt_dir, f"spiderportal-v5-dense-ep{epoch}-step{step}.pt")
-    tmp_path = ckpt_path + ".tmp"
-    torch.save(model_state, tmp_path)
-    os.replace(tmp_path, ckpt_path)
-    size_mb = os.path.getsize(ckpt_path) / (1024 * 1024)
-    return ckpt_path, size_mb
-
-
-def save_full_checkpoint(model, optimizer, step, epoch, cfg, vocab_size, ckpt_dir, ddp, master, ckpt_name="full"):
-    if ddp:
-        with FSDP.state_dict_type(
-            model,
-            StateDictType.FULL_STATE_DICT,
-            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
-        ):
-            model_state = model.state_dict()
-            optim_state = FSDP.optim_state_dict(model, optimizer)
-    else:
-        model_state = model.state_dict()
-        optim_state = optimizer.state_dict()
-    if not master:
-        return None, 0
-    os.makedirs(ckpt_dir, exist_ok=True)
-    final_path = os.path.join(ckpt_dir, f"spiderportal-v5-dense-{ckpt_name}.pt")
-    tmp_path = final_path + ".tmp"
-    torch.save(
-        {
-            "step": step,
-            "epoch": epoch,
-            "model_state_dict": model_state,
-            "optimizer_state_dict": optim_state,
-            "cfg": cfg,
-            "vocab_size": vocab_size,
-        },
-        tmp_path,
-    )
-    os.replace(tmp_path, final_path)
-    size_mb = os.path.getsize(final_path) / (1024 * 1024)
-    return final_path, size_mb
-
-
-def load_checkpoint(model, optimizer, path, ddp):
-    ckpt = torch.load(path, map_location="cpu", weights_only=False)
-    if ddp:
-        with FSDP.state_dict_type(
-            model,
-            StateDictType.FULL_STATE_DICT,
-            FullStateDictConfig(offload_to_cpu=True, rank0_only=False),
-        ):
-            model.load_state_dict(ckpt["model_state_dict"])
-            optim_state = FSDP.optim_state_dict_to_load(
-                model=model,
-                optim=optimizer,
-                optim_state_dict=ckpt["optimizer_state_dict"],
-            )
-            optimizer.load_state_dict(optim_state)
-    else:
-        model.load_state_dict(ckpt["model_state_dict"])
-        optimizer.load_state_dict(ckpt["optimizer_state_dict"])
-    return int(ckpt["step"]), int(ckpt.get("epoch", 0))
-
-
-# ---------------------------------------------------------------------------
-# Main
-# ---------------------------------------------------------------------------
-
-def main():
-    # ------------------------------------------------------------------
-    # Distributed init
-    # ------------------------------------------------------------------
-    ddp = int(os.environ.get("RANK", -1)) != -1
-    if ddp:
-        dist.init_process_group("nccl")
-        rank = int(os.environ["RANK"])
-        local_rank = int(os.environ["LOCAL_RANK"])
-        world_size = int(os.environ["WORLD_SIZE"])
-        device = f"cuda:{local_rank}"
-        torch.cuda.set_device(device)
-    else:
-        rank = local_rank = 0
-        world_size = 1
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    master = rank == 0
-    if master:
-        logger.info(
-            f"GPUs: {torch.cuda.device_count()}  |  World size: {world_size}  |  Device: {device}"
-        )
-
-    # ------------------------------------------------------------------
-    # Tokenizer
-    # ------------------------------------------------------------------
-    tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    tokenizer.pad_token = tokenizer.eos_token
-    vocab_size = tokenizer.vocab_size
-    if master:
-        logger.info(f"Tokenizer: gpt2  |  Vocab size: {vocab_size:,}")
-
-    # ------------------------------------------------------------------
-    # Hyperparameters
-    # ------------------------------------------------------------------
-    seq_len = 2048
-    micro_batch = 16
-    target_tokens = 20_000_000_000
-    grad_accum = 2
-    global_batch_tok = world_size * micro_batch * grad_accum * seq_len
-    total_steps = target_tokens // global_batch_tok
-    warmup_steps = 200
-    lr = 3e-4
-    wd = 0.1
-    log_every = 10
-    ckpt_every = 500
-    ckpt_dir = "checkpoints-dense"
-    dataset_subset = "sample-10BT"
-
-    if master:
-        logger.info(
-            f"[DENSE MLA+Engram] hidden=2048 | layers=6 | seq_len={seq_len} | micro_batch={micro_batch} | grad_accum={grad_accum} | "
-            f"global_batch_tokens={global_batch_tok:,} | total_steps={total_steps:,}"
-        )
-        logger.info(
-            f"Attention: MLA (kv_lora_rank=128, sliding_window=4096) | "
-            f"Engram: layers [1,4] | Context: 32k | "
-            f"Gradient checkpointing: enabled"
-        )
-
-    # ------------------------------------------------------------------
-    # Model
-    # ------------------------------------------------------------------
-    cfg = SpiderPortalConfig(
-        hidden_size=2048, num_hidden_layers=6, num_attention_heads=16,
-        num_key_value_heads=4, intermediate_size=8192,
-        num_experts=32, num_experts_per_tok=2, num_shared_experts=1,
-        router_aux_loss_coef=0.05, max_loop_iters=4,
-        prelude_layers=2, coda_layers=2, lora_rank=128,
-        rope_theta=10000000.0,
-        rope_scaling=None,
-        max_position_embeddings=32768, sliding_window=4096,
-        tie_word_embeddings=True,
-        kv_lora_rank=128, q_lora_rank=256,
-        qk_rope_head_dim=64, qk_nope_head_dim=64, v_head_dim=64,
-        engram_layers=[1, 4],
-        engram_ngram_orders=(2, 3),
-        engram_hash_heads=4,
-        engram_table_size=65537,
-        engram_dim=128,
-    )
-    cfg.vocab_size = vocab_size
-
-    bf16_ok = torch.cuda.is_available() and torch.cuda.is_bf16_supported()
-    amp_dtype = torch.bfloat16 if bf16_ok else torch.float16
-
-    model = SpiderPortalForConditionalGeneration(cfg)
-
-    if ddp:
-        mp_policy = MixedPrecision(
-            param_dtype=amp_dtype,
-            reduce_dtype=amp_dtype,
-            buffer_dtype=amp_dtype,
-        )
-        wrap_policy = ModuleWrapPolicy({SpiderPortalDenseLayer, SpiderPortalRecurrentDenseLayer})
-        model = FSDP(
-            model,
-            sharding_strategy=ShardingStrategy.FULL_SHARD,
-            mixed_precision=mp_policy,
-            auto_wrap_policy=wrap_policy,
-            device_id=local_rank,
-        )
-    else:
-        model = model.to(device)
-        amp_ctx = (
-            torch.amp.autocast(device_type="cuda", dtype=amp_dtype)
-            if "cuda" in device
-            else nullcontext()
-        )
-
-    amp_ctx = nullcontext() if ddp else amp_ctx
-
-    if master:
-        n_params = sum(p.numel() for p in model.parameters())
-        engram_params = sum(p.numel() for n, p in model.named_parameters() if 'engram' in n)
-        mla_params = sum(p.numel() for n, p in model.named_parameters() if 'self_attn' in n)
-        embed_params = sum(p.numel() for n, p in model.named_parameters() if 'embed_tokens' in n or 'lm_head' in n)
-        ffn_params = sum(p.numel() for n, p in model.named_parameters() if 'ffn' in n or 'gate_proj' in n or 'up_proj' in n or 'down_proj' in n)
-        other_params = n_params - engram_params - mla_params - embed_params - ffn_params
-        logger.info(
-            f"Parameters: {n_params:,} (all active) | "
-            f"Embeddings: {embed_params:,} | MLA: {mla_params:,} | "
-            f"FFN: {ffn_params:,} | Engram: {engram_params:,} | "
-            f"Other: {other_params:,} | AMP dtype: {amp_dtype}"
-        )
-
-    # ------------------------------------------------------------------
-    # Optimizer — dual optimizer for Engram embeddings
-    # ------------------------------------------------------------------
-    engram_params_list = [p for n, p in model.named_parameters() if 'engram' in n and 'embed_tables' in n]
-    backbone_params = [p for n, p in model.named_parameters() if 'engram' not in n or 'embed_tables' not in n]
-
-    optimizer = torch.optim.AdamW(
-        backbone_params, lr=lr, weight_decay=wd, betas=(0.9, 0.95), fused=True
-    )
-    if engram_params_list:
-        engram_optimizer = torch.optim.Adam(
-            engram_params_list, lr=lr * 5, betas=(0.9, 0.95), eps=1e-8
-        )
-    else:
-        engram_optimizer = None
-
-    # ------------------------------------------------------------------
-    # Resume from latest checkpoint
-    # ------------------------------------------------------------------
-    start_step = 0
-    start_epoch = 1
-    best_loss = float("inf")
-    existing_ckpts = [f for f in os.listdir(ckpt_dir) if f.startswith("spiderportal-v5-dense-ep") and f.endswith(".pt") and "-step" not in f] if os.path.isdir(ckpt_dir) else []
-    if existing_ckpts:
-        latest = os.path.join(ckpt_dir, sorted(existing_ckpts)[-1])
-        if master:
-            logger.info(f"Resuming from checkpoint: {latest}")
-        start_step, start_epoch = load_checkpoint(model, optimizer, latest, ddp)
-        if master:
-            logger.success(f"Resumed at step {start_step}, epoch {start_epoch}")
-
-    # ------------------------------------------------------------------
-    # Dataset + DataLoader
-    # ------------------------------------------------------------------
-    dataset = FineWebEduDataset(tokenizer, seq_len, dataset_subset, rank, world_size)
-    loader = DataLoader(dataset, batch_size=micro_batch, num_workers=8, pin_memory=True, prefetch_factor=2)
-
-    # ------------------------------------------------------------------
-    # Training loop
-    # ------------------------------------------------------------------
-    if master:
-        os.makedirs(ckpt_dir, exist_ok=True)
-
-    model.train()
-    data_iter = iter(loader)
-    t0 = time.perf_counter()
-    step = start_step
-    epoch = start_epoch
-    step_ckpt_files = []
-    tokens_in_epoch = 0
-    tokens_per_epoch = target_tokens
-
-    while step < total_steps:
-        cur_lr = get_lr(step, warmup_steps, total_steps, lr, lr * 0.1)
-        for g in optimizer.param_groups:
-            g["lr"] = cur_lr
-        if engram_optimizer:
-            for g in engram_optimizer.param_groups:
-                g["lr"] = cur_lr * 5
-
-        optimizer.zero_grad()
-        if engram_optimizer:
-            engram_optimizer.zero_grad()
-        loss_accum = 0.0
-
-        for micro_step in range(grad_accum):
-            try:
-                x, y = next(data_iter)
-            except StopIteration:
-                data_iter = iter(loader)
-                x, y = next(data_iter)
-
-            x = x.to(device if not ddp else f"cuda:{local_rank}", non_blocking=True)
-            y = y.to(device if not ddp else f"cuda:{local_rank}", non_blocking=True)
-
-            sync = (
-                nullcontext()
-                if (not ddp or micro_step == grad_accum - 1)
-                else model.no_sync()
-            )
-            with sync, amp_ctx:
-                output = model(x)
-                if isinstance(output, dict):
-                    logits = output["logits"]
-                else:
-                    logits = output
-                loss = nn.functional.cross_entropy(
-                    logits.view(-1, vocab_size), y.view(-1)
-                )
-                loss = loss / grad_accum
-
-            loss.backward()
-            loss_accum += loss.item()
-
-        if ddp:
-            grad_norm = model.clip_grad_norm_(1.0)
-        else:
-            grad_norm = nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
-        optimizer.step()
-        if engram_optimizer:
-            engram_optimizer.step()
-        step += 1
-        tokens_in_epoch += global_batch_tok
-
-        if master and step % log_every == 0:
-            dt = time.perf_counter() - t0
-            tok_per_sec = global_batch_tok * log_every / dt
-            tokens_seen = step * global_batch_tok
-            logger.info(
-                f"Epoch {epoch} | step {step:6d}/{total_steps} | loss {loss_accum:.4f} "
-                f"| gnorm {float(grad_norm):.2f} | lr {cur_lr:.2e} "
-                f"| {tok_per_sec / 1e6:.2f}M tok/s "
-                f"| {tokens_seen / 1e9:.2f}B tokens seen"
-            )
-            t0 = time.perf_counter()
-
-        if step % ckpt_every == 0 and master:
-            ckpt_path, size_mb = save_weights_only(model, step, epoch, ckpt_dir, ddp)
-            step_ckpt_files.append(ckpt_path)
-            logger.info(f"Saved weights-only: {os.path.basename(ckpt_path)} ({size_mb:.0f}MB)")
-
-        if tokens_in_epoch >= tokens_per_epoch:
-            epoch_loss = loss_accum
-            if master:
-                epoch_time = (time.perf_counter() - t0) / 60
-                logger.info(f"Epoch {epoch} complete | loss={epoch_loss:.4f} | Time: {epoch_time:.1f}min")
-
-                for f in step_ckpt_files:
-                    if os.path.exists(f):
-                        os.remove(f)
-                        logger.info(f"  Deleted step checkpoint: {os.path.basename(f)}")
-                step_ckpt_files.clear()
-
-                ckpt_path, size_mb = save_full_checkpoint(model, optimizer, step, epoch, cfg, vocab_size, ckpt_dir, ddp, master, f"ep{epoch}")
-                if ckpt_path:
-                    logger.info(f"Saved epoch checkpoint: {os.path.basename(ckpt_path)} ({size_mb:.0f}MB)")
-
-                if epoch_loss < best_loss:
-                    best_loss = epoch_loss
-                    ckpt_path, size_mb = save_full_checkpoint(model, optimizer, step, epoch, cfg, vocab_size, ckpt_dir, ddp, master, "best")
-                    if ckpt_path:
-                        logger.info(f"Saved best checkpoint: {os.path.basename(ckpt_path)} ({size_mb:.0f}MB)")
-
-            epoch += 1
-            tokens_in_epoch = 0
-
-    if step > start_step and master:
-        ckpt_path, size_mb = save_full_checkpoint(model, optimizer, step, epoch, cfg, vocab_size, ckpt_dir, ddp, master, f"final-ep{epoch}")
-        if ckpt_path:
-            logger.info(f"Saved final checkpoint: {os.path.basename(ckpt_path)} ({size_mb:.0f}MB)")
-
-    if ddp:
-        dist.barrier()
-        dist.destroy_process_group()
-
-    if master:
-        logger.success("Training complete.")
-
-
-if __name__ == "__main__":
-    main()