stream_trainer.py

#!/usr/bin/env python3
"""
WIRE-SPEED TRANSFORMER - Learns directly from network stream
No batching. No epochs. Just continuous absorption.

Receives tokenized data via stdin from Rust feeder.
Updates weights after every micro-batch (configurable, default 32 tokens).
"""

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

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

# Tiny model for wire-speed updates
CONFIG = {
    "d": 256,       # embedding dim
    "layers": 4,    # transformer layers  
    "heads": 8,     # attention heads
    "rank": 32,     # attention rank (from n.py's tuneable attention)
    "vocab": 128256,   # DeepSeek V3.2 vocab
    "ctx": 512,     # context window
}

LR = 1e-4
UPDATE_EVERY = 32  # tokens between weight updates (micro-batch)
PRINT_EVERY = 10000  # tokens between stats

# ─────────────────── Model (simplified from n.py) ───────────────────
class TuneableAttention(nn.Module):
    def __init__(self, d, h, r):
        super().__init__()
        self.h, self.dk, self.r = h, d // h, r
        self.qkv = nn.Linear(d, 3 * d, bias=False)
        self.U = nn.Parameter(torch.randn(self.dk, r) * 0.02)
        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)
        q, k, v = qkv.unbind(2)  # B, N, h, dk
        
        # Project Q and K through U for tuneable rank
        q = (q @ self.U)  # B, N, h, r
        k = (k @ self.U)  # B, N, h, r
        
        # Attention
        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
        att = (q @ k.transpose(-1, -2)) / math.sqrt(self.r)
        if mask is not None:
            att = att + mask
        att = F.softmax(att, dim=-1)
        out = (att @ v).transpose(1, 2).reshape(B, N, D)
        return self.proj(out)

class Block(nn.Module):
    def __init__(self, d, h, r):
        super().__init__()
        self.ln1 = nn.LayerNorm(d)
        self.attn = TuneableAttention(d, h, r)
        self.ln2 = nn.LayerNorm(d)
        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)
        x = x + self.ff(self.ln2(x))
        return x

class StreamingTransformer(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        d, L, h, r, V = cfg["d"], cfg["layers"], cfg["heads"], cfg["rank"], cfg["vocab"]
        self.emb = nn.Embedding(V, d)
        self.blocks = nn.ModuleList([Block(d, h, r) for _ in range(L)])
        self.ln = nn.LayerNorm(d)
        self.head = nn.Linear(d, V, bias=False)
        # Weight tying
        self.head.weight = self.emb.weight
        
    def forward(self, x):
        B, N = x.shape
        # Causal mask
        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())

# ─────────────────── Online Trainer ───────────────────
class WireSpeedTrainer:
    def __init__(self, model, lr=LR):
        self.model = model.to(DEVICE)
        self.opt = torch.optim.AdamW(model.parameters(), lr=lr, betas=(0.9, 0.95))
        self.ctx_size = CONFIG["ctx"]
        
        # Rolling buffer for context
        self.buffer = deque(maxlen=self.ctx_size + 1)
        
        # Stats
        self.tokens_seen = 0
        self.total_loss = 0.0
        self.updates = 0
        self.start_time = time.time()
        
    def ingest_token(self, token_id):
        """Absorb a single token. Update weights when buffer fills."""
        self.buffer.append(token_id)
        self.tokens_seen += 1
        
        # Update every N tokens when we have enough context
        if len(self.buffer) >= UPDATE_EVERY + 1 and self.tokens_seen % UPDATE_EVERY == 0:
            self._update()
            
        # Print stats
        if self.tokens_seen % PRINT_EVERY == 0:
            self._print_stats()
    
    def _update(self):
        """Single gradient step on current buffer."""
        # Convert buffer to tensor
        tokens = list(self.buffer)
        x = torch.tensor(tokens[:-1], device=DEVICE).unsqueeze(0)  # input
        y = torch.tensor(tokens[1:], device=DEVICE).unsqueeze(0)   # target
        
        # Forward
        self.model.train()
        logits = self.model(x)
        
        # Loss on last UPDATE_EVERY positions only (most recent)
        loss = F.cross_entropy(
            logits[:, -UPDATE_EVERY:].reshape(-1, CONFIG["vocab"]),
            y[:, -UPDATE_EVERY:].reshape(-1)
        )
        
        # Backward
        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
        tok_per_sec = self.tokens_seen / elapsed if elapsed > 0 else 0
        avg_loss = self.total_loss / max(1, self.updates)
        
        print(f"[{elapsed:.0f}s] {self.tokens_seen:,} tok | {tok_per_sec:.0f} tok/s | "
              f"loss={avg_loss:.4f} | updates={self.updates}", flush=True)

# ─────────────────── Main ───────────────────
def main():
    print(f"Wire-Speed Transformer", flush=True)
    print(f"Config: {CONFIG}", flush=True)
    print(f"Device: {DEVICE}", flush=True)
    
    model = StreamingTransformer(CONFIG)
    params = model.count_params()
    print(f"Parameters: {params:,} ({params/1e6:.1f}M)", flush=True)
    
    trainer = WireSpeedTrainer(model)
    
    print(f"Listening for tokens on stdin...", flush=True)
    print(f"Update every {UPDATE_EVERY} tokens, print every {PRINT_EVERY}", flush=True)
    
    # Read token IDs from stdin (one per line from Rust feeder)
    for line in sys.stdin:
        try:
            token_id = int(line.strip())
            if 0 <= token_id < CONFIG["vocab"]:
                trainer.ingest_token(token_id)
        except ValueError:
            continue  # Skip malformed lines
    
    print(f"Stream ended. Total tokens: {trainer.tokens_seen:,}", flush=True)

if __name__ == "__main__":
    main()