RB2 train: add --compile (raw_model state_dict)

harvest/rb2/train_gpt_ref.py

@@ -77,6 +77,8 @@ def get_args():
                    help="Qwen3 QK-Norm: RMSNorm per-head na Q,K przed-attention (stabilnosc z Muon/high-LR)")
     p.add_argument("--events-jsonl", default=None,
                    help="jesli podane: emituj events.jsonl (fabryka-track sidecar-format: update/evaluation/checkpoint/end)")
+    p.add_argument("--compile", action="store_true",
+                   help="torch.compile model (2-3x throughput; state_dict zapisywany bez _orig_mod prefix via raw_model)")
     return p.parse_args()
 
 
@@ -434,16 +436,19 @@ def main():
             except queue.Empty:
                 pass
 
-    model = GPT(a.vocab, a.n_layer, a.n_embd, a.n_head, a.block, a).to(device)
-    nparam = sum(p.numel() for p in model.parameters())
+    raw_model = GPT(a.vocab, a.n_layer, a.n_embd, a.n_head, a.block, a).to(device)
+    nparam = sum(p.numel() for p in raw_model.parameters())
     log(f"model GPT-ref: {nparam/1e6:.1f}M param (L{a.n_layer} d{a.n_embd} h{a.n_head})")
-    opt = build_optimizer(a, model)
+    opt = build_optimizer(a, raw_model)
     log(f"optimizer={a.optimizer}" + (f" muon_lr={a.muon_lr} (mult={a.muon_lr/a.lr:.1f}x)" if a.optimizer == "muon" else ""))
+    model = torch.compile(raw_model) if a.compile else raw_model
+    if a.compile:
+        log("torch.compile enabled")
 
     start_step = 0
     if a.resume and os.path.exists(ckpt_path):
         ck = torch.load(ckpt_path, map_location=device)
-        model.load_state_dict(ck["model"]); opt.load_state_dict(ck["opt"]); start_step = ck["step"]
+        raw_model.load_state_dict(ck["model"]); opt.load_state_dict(ck["opt"]); start_step = ck["step"]
         log(f"RESUME @ {start_step}")
 
     def lr_at(s):
@@ -500,7 +505,7 @@ def main():
             log(f"  >> VAL loss {vloss:.4f} @ {step+1}")
             emit("evaluation", step + 1, metrics={"loss": vloss})
         if (step + 1) % a.ckpt_every == 0 and not a.synthetic:
-            torch.save({"model": model.state_dict(), "opt": opt.state_dict(), "step": step + 1,
+            torch.save({"model": raw_model.state_dict(), "opt": opt.state_dict(), "step": step + 1,
                         "config": {"vocab": a.vocab, "n_layer": a.n_layer, "n_embd": a.n_embd,
                                    "n_head": a.n_head, "block": a.block, "norm": a.norm,
                                    "norm_eps": a.norm_eps, "pos": a.pos, "rope_theta": a.rope_theta,