dibit_trainer.py

#!/usr/bin/env python3
"""
DIBIT TRANSFORMER - 2-bit tokens
Vocab = 4 (00, 01, 10, 11)
Each byte = 4 tokens (vs 8 for bit-level)
Better context efficiency while still pure binary!
"""

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

# DIBIT CONFIG - 2-bit tokens
CONFIG = {
    "d": 512,        # good size
    "layers": 12,    
    "heads": 8,
    "vocab": 4,      # 00, 01, 10, 11
    "ctx": 4096,     # 1024 bytes of context (2x more than bit-level!)
}

LR = 3e-4
UPDATE_EVERY = 512  # dibits between updates (128 bytes worth)
PRINT_EVERY = 50000  # dibits

class DibitAttention(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 DibitBlock(nn.Module):
    def __init__(self, d, h):
        super().__init__()
        self.ln1, self.ln2 = nn.LayerNorm(d), nn.LayerNorm(d)
        self.attn = DibitAttention(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 DibitTransformer(nn.Module):
    """Transformer with vocab=4 (00, 01, 10, 11)"""
    def __init__(self, cfg):
        super().__init__()
        d, L, h = cfg["d"], cfg["layers"], cfg["heads"]
        self.emb = nn.Embedding(4, d)  # 4 embeddings for dibits
        self.blocks = nn.ModuleList([DibitBlock(d, h) for _ in range(L)])
        self.ln = nn.LayerNorm(d)
        self.head = nn.Linear(d, 4, bias=False)  # predict 00, 01, 10, or 11
        
    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_dibits(byte_val):
    """Convert byte to 4 dibits (2-bit chunks, MSB first)
    e.g., 0b11100100 -> [3, 2, 1, 0] (11, 10, 01, 00)
    """
    return [
        (byte_val >> 6) & 0b11,  # bits 7-6
        (byte_val >> 4) & 0b11,  # bits 5-4
        (byte_val >> 2) & 0b11,  # bits 3-2
        byte_val & 0b11,         # bits 1-0
    ]

def dibits_to_byte(dibits):
    """Convert 4 dibits back to byte"""
    return (dibits[0] << 6) | (dibits[1] << 4) | (dibits[2] << 2) | dibits[3]

class DibitTrainer:
    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.dibits_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 4 dibits and absorb"""
        dibits = byte_to_dibits(byte_val)
        for dibit in dibits:
            self.buffer.append(dibit)
            self.dibits_seen += 1
            
            if len(self.buffer) >= UPDATE_EVERY + 1 and self.dibits_seen % UPDATE_EVERY == 0:
                self._update()
        
        self.bytes_seen += 1
        
        if self.dibits_seen % PRINT_EVERY == 0:
            self._print_stats()
            
        if self.bytes_seen % 500000 == 0 and self.bytes_seen > 0:
            self._save()
    
    def _update(self):
        tokens = list(self.buffer)
        x = torch.tensor(tokens[:-1], device=DEVICE, dtype=torch.long).unsqueeze(0)
        y = torch.tensor(tokens[1:], device=DEVICE, dtype=torch.long).unsqueeze(0)
        
        self.model.train()
        logits = self.model(x)
        loss = F.cross_entropy(
            logits[:, -UPDATE_EVERY:].reshape(-1, 4),
            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
        bytes_per_sec = self.bytes_seen / elapsed if elapsed > 0 else 0
        avg_loss = self.total_loss / max(1, self.updates)
        
        # For dibits: random is log(4)/log(2) = 2.0 bits per dibit
        # Entropy in bits per dibit
        entropy_per_dibit = avg_loss / math.log(2)
        # Convert to bits per byte (4 dibits per byte)
        bpb = entropy_per_dibit * 4
        # Random byte = 8 bits, so compression vs random
        compression = (1.0 - bpb/8) * 100
        
        print(f"[{elapsed:.0f}s] {self.bytes_seen/1000:.1f}KB | {bytes_per_sec/1000:.2f} KB/s | "
              f"loss={avg_loss:.4f} | bpb={bpb:.2f} | 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(),
            "dibits": self.dibits_seen,
            "bytes": self.bytes_seen,
            "loss": avg_loss,
        }
        torch.save(ckpt, f"/workspace/dibit_ckpt_{kb}kb.pt")
        print(f"[SAVED] dibit_ckpt_{kb}kb.pt", flush=True)

def main():
    print(f"DIBIT TRANSFORMER - Vocab = 4 (00, 01, 10, 11)", flush=True)
    print(f"Config: {CONFIG}", flush=True)
    print(f"Device: {DEVICE}", flush=True)
    
    model = DibitTransformer(CONFIG)
    params = model.count_params()
    print(f"Parameters: {params:,} ({params/1e6:.2f}M)", flush=True)
    print(f"Vocab: 4 (2-bit tokens: 00, 01, 10, 11)", flush=True)
    print(f"Each byte = 4 dibit tokens", flush=True)
    print(f"Context: {CONFIG['ctx']} dibits = {CONFIG['ctx']//4} bytes", flush=True)
    
    trainer = DibitTrainer(model)
    
    print(f"Listening for bytes (converting to dibits)...", flush=True)
    
    while True:
        byte = sys.stdin.buffer.read(1)
        if not byte:
            break
        trainer.ingest_byte(byte[0])
    
    print(f"Stream ended. Total: {trainer.bytes_seen:,} bytes = {trainer.dibits_seen:,} dibits", flush=True)

if __name__ == "__main__":
    main()