#!/usr/bin/env python """Train Fractus 13M engine — fixed version that resumes from checkpoint. Fixes the stalling issue: 1. GC between epochs (clear accumulated graph state) 2. Progress logging every 5000 chunks (so we know it's alive) 3. Resumes from existing checkpoint if available 4. Saves checkpoint after every epoch (not just at the end) """ import argparse, gc, math, os, sys, time sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import torch, torch.nn as nn, torch.nn.functional as F from fractus.continuous_engine import ContinuousThoughtEngine from fractus.tokenizer import FractusTokenizer def main(): p = argparse.ArgumentParser() p.add_argument("--epochs", type=int, default=10) p.add_argument("--chunk-len", type=int, default=16) p.add_argument("--lr", type=float, default=2e-4) p.add_argument("--resume", type=str, default=None) args = p.parse_args() torch.set_num_threads(os.cpu_count() or 6) torch.manual_seed(42) # Load corpus. corpus = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data", "quality_500k.pt") tokens = torch.load(corpus, weights_only=False).long() print(f"Corpus: {len(tokens):,} tokens", flush=True) # Build engine. engine = ContinuousThoughtEngine( vocab_size=50257, d_model=128, n_heads=2, d_head=64, n_levels=2, n_oscillators=8, coupling_rank=4, n_experts=4, top_k=2, expert_d_ff=128, siren_rank=32, ) n_params = sum(p.numel() for p in engine.parameters()) print(f"Engine: {n_params/1e6:.1f}M params", flush=True) # Resume from checkpoint if available. ckpt_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "checkpoints") os.makedirs(ckpt_dir, exist_ok=True) start_epoch = 0 resume_path = args.resume if resume_path is None: # Auto-find latest checkpoint. ckpts = [f for f in os.listdir(ckpt_dir) if f.startswith("fractus_13m_") and f.endswith(".pt")] if ckpts: ckpts.sort() resume_path = os.path.join(ckpt_dir, ckpts[-1]) if resume_path and os.path.exists(resume_path): print(f"Resuming from: {resume_path}", flush=True) ckpt = torch.load(resume_path, weights_only=False) # Fix buffer shape mismatches. sd = ckpt['model_state'] sd['kuramoto_phases'] = engine.kuramoto_phases sd['attn_S'] = engine.attn_S sd['attn_z'] = engine.attn_z sd['thought_state'] = engine.thought_state engine.load_state_dict(sd, strict=False) start_epoch = ckpt.get('epoch', 0) print(f" Resumed from epoch {start_epoch}, loss={ckpt.get('loss','?')}", flush=True) # Optimizer. opt = torch.optim.AdamW(engine.parameters(), lr=args.lr, weight_decay=0.01) sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=args.epochs, eta_min=1e-5) # Skip scheduler to the right position. for _ in range(start_epoch): sched.step() seq = args.chunk_len n_chunks = len(tokens) // seq tok = FractusTokenizer.gpt2_compatible() vocab = 50257 print(f"\nTraining epochs {start_epoch+1}-{args.epochs}, {n_chunks:,} chunks/epoch, seq={seq}", flush=True) print("=" * 70, flush=True) for epoch in range(start_epoch, args.epochs): engine.train() engine.reset_thought(batch_size=1) t0 = time.perf_counter() ep_loss = 0.0 ep_correct = 0 ep_total = 0 for i in range(0, len(tokens) - seq - 1, seq): chunk = tokens[i:i+seq].unsqueeze(0) target = tokens[i+1:i+seq+1].unsqueeze(0) opt.zero_grad() logits = engine.tick_chunk(chunk) loss = F.cross_entropy(logits.reshape(-1, vocab), target.reshape(-1)) loss.backward() torch.nn.utils.clip_grad_norm_(engine.parameters(), 1.0) opt.step() ep_loss += loss.item() * seq ep_correct += (logits.argmax(dim=-1) == target).sum().item() ep_total += seq # Progress every 5000 chunks. if (i // seq) % 5000 == 0 and i > 0: elapsed = time.perf_counter() - t0 tps = i / elapsed print(f" epoch {epoch+1} chunk {i//seq}/{n_chunks} " f"loss={ep_loss/ep_total:.3f} acc={ep_correct/ep_total:.1%} " f"{tps:.0f} tok/s {elapsed/60:.0f}min", flush=True) elapsed = time.perf_counter() - t0 avg_loss = ep_loss / max(ep_total, 1) acc = ep_correct / max(ep_total, 1) ppl = math.exp(min(avg_loss, 20)) sched.step() print(f"\nEpoch {epoch+1}/{args.epochs}: loss={avg_loss:.3f} ppl={ppl:.1f} " f"acc={acc:.1%} {len(tokens)/elapsed:.0f} tok/s {elapsed/60:.1f}min", flush=True) # Save checkpoint every epoch. ckpt_path = os.path.join(ckpt_dir, f"fractus_13m_epoch{epoch+1}.pt") torch.save({ "model_state": engine.state_dict(), "config": {"d_model": 128, "vocab_size": vocab}, "epoch": epoch + 1, "loss": avg_loss, "accuracy": acc, }, ckpt_path) print(f" [ckpt] {ckpt_path} ({os.path.getsize(ckpt_path)/1e6:.0f}MB)", flush=True) # Sample generation. engine.eval() engine.reset_thought(1) prompt = "def fibonacci" pids = tok.encode(prompt)[:16] for tid in pids: engine.tick(torch.tensor([tid])) gen = list(pids) for _ in range(30): logits, _ = engine.tick() l = logits[0] / 0.8 tv, ti = l.topk(40) gen.append(ti[torch.multinomial(F.softmax(tv, dim=-1), 1).item()].item()) print(f" Sample: {tok.decode(gen)[:100]}", flush=True) # GC between epochs (prevent memory accumulation). gc.collect() print(flush=True) print("Training complete.", flush=True) # Final coherence test. print("\n=== COHERENCE TEST ===", flush=True) engine.eval() for prompt in ["What is Python?", "def sort", "The sun is", "Explain AI"]: engine.reset_thought(1) ids = tok.encode(prompt)[:16] for tid in ids: engine.tick(torch.tensor([tid])) gen = list(ids) for _ in range(40): logits, _ = engine.tick() l = logits[0] / 0.8 tv, ti = l.topk(40) gen.append(ti[torch.multinomial(F.softmax(tv, dim=-1), 1).item()].item()) print(f" [{prompt}] -> {tok.decode(gen)[:120]}", flush=True) print("\nDone.", flush=True) if __name__ == "__main__": main()