bit_trainer.py

#!/usr/bin/env python3
"""
BIT-LEVEL TRANSFORMER - The Ultimate Zero-Overhead Model
Vocab = 2 (just 0 and 1)
No tokenization. No bytes. Pure binary.

Each byte becomes 8 tokens (bits).
Model learns ALL structure from raw bits.
"""

import sys
import math
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import deque

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cuda.matmul.allow_tf32 = True

# BIT-LEVEL CONFIG - ABSOLUTE UNIT
CONFIG = {
    "d": 768,        # GPT-2 small size
    "layers": 12,    # DEEP for bit pattern learning
    "heads": 12,
    "vocab": 2,      # JUST 0 AND 1!
    "ctx": 4096,     # 512 bytes of context
}

LR = 3e-4           # learning rate
UPDATE_EVERY = 2048  # bits between updates (256 bytes worth) - BIGGER BATCHES
PRINT_EVERY = 100000   # bits

class BitAttention(nn.Module):
    def __init__(self, d, h):
        super().__init__()
        self.h, self.dk = h, d // h
        self.qkv = nn.Linear(d, 3 * d, bias=False)
        self.proj = nn.Linear(d, d, bias=False)
        
    def forward(self, x, mask=None):
        B, N, D = x.shape
        qkv = self.qkv(x).view(B, N, 3, self.h, self.dk).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        att = (q @ k.transpose(-1, -2)) / math.sqrt(self.dk)
        if mask is not None:
            att = att + mask
        return self.proj((F.softmax(att, -1) @ v).transpose(1, 2).reshape(B, N, D))

class BitBlock(nn.Module):
    def __init__(self, d, h):
        super().__init__()
        self.ln1, self.ln2 = nn.LayerNorm(d), nn.LayerNorm(d)
        self.attn = BitAttention(d, h)
        self.ff = nn.Sequential(nn.Linear(d, 4*d), nn.GELU(), nn.Linear(4*d, d))
        
    def forward(self, x, mask):
        x = x + self.attn(self.ln1(x), mask)
        return x + self.ff(self.ln2(x))

class BitTransformer(nn.Module):
    """Transformer with vocab=2 (just 0 and 1)"""
    def __init__(self, cfg):
        super().__init__()
        d, L, h = cfg["d"], cfg["layers"], cfg["heads"]
        self.emb = nn.Embedding(2, d)  # ONLY 2 EMBEDDINGS!
        self.blocks = nn.ModuleList([BitBlock(d, h) for _ in range(L)])
        self.ln = nn.LayerNorm(d)
        self.head = nn.Linear(d, 2, bias=False)  # predict 0 or 1
        
    def forward(self, x):
        B, N = x.shape
        mask = torch.triu(torch.ones(N, N, device=x.device), 1) * -1e9
        h = self.emb(x)
        for block in self.blocks:
            h = block(h, mask)
        return self.head(self.ln(h))
    
    def count_params(self):
        return sum(p.numel() for p in self.parameters())

def byte_to_bits(byte_val):
    """Convert byte to 8 bits (MSB first)"""
    return [(byte_val >> (7 - i)) & 1 for i in range(8)]

def bits_to_byte(bits):
    """Convert 8 bits back to byte"""
    val = 0
    for i, b in enumerate(bits[:8]):
        val |= (b << (7 - i))
    return val

class BitTrainer:
    def __init__(self, model, lr=LR):
        self.model = model.to(DEVICE)
        self.opt = torch.optim.AdamW(model.parameters(), lr=lr)
        self.ctx_size = CONFIG["ctx"]
        self.buffer = deque(maxlen=self.ctx_size + 1)
        
        self.bits_seen = 0
        self.bytes_seen = 0
        self.total_loss = 0.0
        self.updates = 0
        self.start_time = time.time()
        
    def ingest_byte(self, byte_val):
        """Convert byte to 8 bits and absorb"""
        bits = byte_to_bits(byte_val)
        for bit in bits:
            self.buffer.append(bit)
            self.bits_seen += 1
            
            if len(self.buffer) >= UPDATE_EVERY + 1 and self.bits_seen % UPDATE_EVERY == 0:
                self._update()
        
        self.bytes_seen += 1
        
        if self.bits_seen % PRINT_EVERY == 0:
            self._print_stats()
            
        if self.bytes_seen % 500000 == 0 and self.bytes_seen > 0:
            self._save()
    
    def _update(self):
        bits = list(self.buffer)
        x = torch.tensor(bits[:-1], device=DEVICE, dtype=torch.long).unsqueeze(0)
        y = torch.tensor(bits[1:], device=DEVICE, dtype=torch.long).unsqueeze(0)
        
        self.model.train()
        logits = self.model(x)
        loss = F.cross_entropy(
            logits[:, -UPDATE_EVERY:].reshape(-1, 2),
            y[:, -UPDATE_EVERY:].reshape(-1)
        )
        
        self.opt.zero_grad()
        loss.backward()
        torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
        self.opt.step()
        
        self.total_loss += loss.item()
        self.updates += 1
    
    def _print_stats(self):
        elapsed = time.time() - self.start_time
        bits_per_sec = self.bits_seen / elapsed if elapsed > 0 else 0
        bytes_per_sec = self.bytes_seen / elapsed if elapsed > 0 else 0
        avg_loss = self.total_loss / max(1, self.updates)
        
        # For bits: random is 1.0 (coin flip), lower = learning
        # Entropy in bits per bit
        entropy = avg_loss / math.log(2)
        compression = (1.0 - entropy) * 100  # % compression vs random
        
        print(f"[{elapsed:.0f}s] {self.bytes_seen/1000:.1f}KB | {bytes_per_sec/1000:.1f} KB/s | "
              f"loss={avg_loss:.4f} | entropy={entropy:.3f} bit/bit | "
              f"compression={compression:.1f}%", flush=True)
    
    def _save(self):
        avg_loss = self.total_loss / max(1, self.updates)
        kb = self.bytes_seen // 1000
        ckpt = {
            "model": self.model.state_dict(),
            "bits": self.bits_seen,
            "bytes": self.bytes_seen,
            "loss": avg_loss,
        }
        torch.save(ckpt, f"/workspace/bit_ckpt_{kb}kb.pt")
        print(f"[SAVED] bit_ckpt_{kb}kb.pt", flush=True)

def main():
    print(f"BIT-LEVEL TRANSFORMER - Vocab = 2 (just 0 and 1)", flush=True)
    print(f"Config: {CONFIG}", flush=True)
    print(f"Device: {DEVICE}", flush=True)
    
    model = BitTransformer(CONFIG)
    params = model.count_params()
    print(f"Parameters: {params:,} ({params/1e6:.2f}M)", flush=True)
    print(f"Vocab: 2 (literally just 0 and 1)", flush=True)
    print(f"Each byte = 8 bit tokens", flush=True)
    
    trainer = BitTrainer(model)
    
    print(f"Listening for bytes (FAST batch mode)...", flush=True)
    
    # Read in large chunks for speed
    CHUNK_SIZE = 8192  # 8KB chunks = 65536 bits
    while True:
        chunk = sys.stdin.buffer.read(CHUNK_SIZE)
        if not chunk:
            break
        for byte in chunk:
            trainer.ingest_byte(byte)
    
    print(f"Stream ended. Total: {trainer.bytes_seen:,} bytes = {trainer.bits_seen:,} bits", flush=True)

if __name__ == "__main__":
    main()