modeling_mitotic_transformer.py

# modeling_mitotic_transformer.py
"""
Mitotic Transformer with Causal Language Modeling Head

A biologically and cosmologically inspired Transformer architecture based on 
"The Cosmology of the Living Cell (Mother Theory)" by Alis Hasić.

Key concepts:
- Mitosis as core computational operation
- Cytoskeletal Attention (Dark Matter scaffold analogue)
- Osmotic Turgor Decoder (Dark Energy / 70/30 expansion)
- F1-String hierarchical scaling
- Consciousness Module with White-Hole Rendering
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Optional

from transformers import PreTrainedModel
from .configuration_mitotic_transformer import MitoticTransformerConfig


# ──────────────────────────────────────────────────────────────
# Model Components
# ──────────────────────────────────────────────────────────────
class CytoskeletalAttention(nn.Module):
    def __init__(self, d_model: int, n_heads: int):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.qkv = nn.Linear(d_model, 3 * d_model)
        self.filament_weight = nn.Parameter(torch.ones(n_heads))

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        B, N, C = hidden_states.shape
        qkv = self.qkv(hidden_states).chunk(3, dim=-1)
        q, k, v = [t.view(B, N, self.n_heads, self.head_dim).transpose(1, 2) for t in qkv]

        attn = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        attn = attn * self.filament_weight.view(1, self.n_heads, 1, 1)
        attn = F.softmax(attn, dim=-1)

        out = attn @ v
        return out.transpose(1, 2).contiguous().view(B, N, C)

class MitoticBlock(nn.Module):
    def __init__(self, d_model: int, n_heads: int, turgor_pressure: float = 0.70):
        super().__init__()
        self.cyto_attention = CytoskeletalAttention(d_model, n_heads)
        self.norm1 = nn.LayerNorm(d_model)
        self.ffn = nn.Sequential(
            nn.Linear(d_model, 4 * d_model),
            nn.GELU(),
            nn.Linear(4 * d_model, d_model),
        )
        self.norm2 = nn.LayerNorm(d_model)
        self.turgor_pressure = turgor_pressure

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.norm1(hidden_states)
        hidden_states = self.cyto_attention(hidden_states)
        hidden_states = hidden_states + residual

        residual = hidden_states
        hidden_states = self.norm2(hidden_states)
        hidden_states = self.ffn(hidden_states)
        hidden_states = hidden_states + residual

        hidden_states = hidden_states * (1.0 + 0.01 * self.turgor_pressure)
        return hidden_states

class OsmoticTurgorDecoder(nn.Module):
    def __init__(self, d_model: int, turgor_pressure: float = 0.70):
        super().__init__()
        self.turgor_pressure = turgor_pressure
        self.proj = nn.Linear(d_model, d_model)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return self.proj(hidden_states) * (1.0 + self.turgor_pressure)

class ConsciousnessModule(nn.Module):
    def __init__(self, d_model: int):
        super().__init__()
        self.white_hole_proj = nn.Linear(d_model, d_model)
        self.biological_gpu = nn.Sequential(
            nn.Linear(d_model, 2048),
            nn.GELU(),
            nn.Linear(2048, d_model),
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        x = self.white_hole_proj(hidden_states)
        x = self.biological_gpu(x)
        return x


# ──────────────────────────────────────────────────────────────
# Main Model with Causal LM Head
# ──────────────────────────────────────────────────────────────

class MitoticTransformerForCausalLM(PreTrainedModel):
    config_class = MitoticTransformerConfig
    base_model_prefix = "mitotic_transformer"

    supports_gradient_checkpointing = True

    def __init__(self, config: MitoticTransformerConfig):
        super().__init__(config)

        self.embeddings = nn.Embedding(config.vocab_size, config.d_model)
        self.f1_string_layer = nn.Linear(config.d_model, config.d_model)

        self.layers = nn.ModuleList([
            MitoticBlock(config.d_model, config.n_heads, config.turgor_pressure)
            for _ in range(config.n_layers)
        ])

        self.turgor_decoder = OsmoticTurgorDecoder(config.d_model, config.turgor_pressure)
        self.consciousness_module = ConsciousnessModule(config.d_model)
        self.final_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)

        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
        self.lm_head.weight = self.embeddings.weight

        self.is_decoder = True
        self.tie_word_embeddings = True

        self.post_init()

    def forward(self, input_ids, attention_mask=None, labels=None, **kwargs):
        x = self.embeddings(input_ids) * math.sqrt(self.config.kappa_f1)
        x = self.f1_string_layer(x)

        for layer in self.layers:
            if self.training and self.supports_gradient_checkpointing:
                x = torch.utils.checkpoint.checkpoint(layer, x, use_reentrant=False)
            else:
                x = layer(x)

        x = self.turgor_decoder(x)
        x = self.consciousness_module(x)
        x = self.final_norm(x)

        logits = self.lm_head(x)

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

        return {"loss": loss, "logits": logits, "last_hidden_state": x}