"""TRM Sudoku joint Lyapunov diagnostic — TRM version of diagnose_hrm_joint.py. Key differences from HRM: - TRM has ONE shared L_level (H_layers config is "ignored") - z_L update: z_L = L_level(z_L, z_H + input_embeddings) - z_H update: z_H = L_level(z_H, z_L) ← same L_level! - H_cycles=3, L_cycles=6 (vs HRM 2,2) Joint tangent block structure: - L step: v_L_new = J · (v_L + v_H), v_H_new = v_H, J at (z_L + z_H + ie) - H step: v_H_new = J' · (v_H + v_L), v_L_new = v_L, J' at (z_H + z_L) J and J' share weights but evaluated at different points. """ from __future__ import annotations import sys, os, yaml, math, argparse, json, time from pathlib import Path import numpy as np import torch TRM_DIR = Path("/home/yurenh2/rrm/trm") sys.path.insert(0, str(TRM_DIR)) from models.recursive_reasoning.trm import TinyRecursiveReasoningModel_ACTV1 def load_model(ckpt_root: Path, ckpt_name: str, device: str): cfg = yaml.safe_load((ckpt_root / "all_config.yaml").read_text()) arch_cfg = dict(cfg["arch"]) train_meta = json.loads((Path(cfg["data_paths"][0]) / "train" / "dataset.json").read_text()) arch_cfg.update(batch_size=cfg["global_batch_size"], seq_len=train_meta["seq_len"], vocab_size=train_meta["vocab_size"], num_puzzle_identifiers=train_meta["num_puzzle_identifiers"]) model = TinyRecursiveReasoningModel_ACTV1(arch_cfg) sd = torch.load(ckpt_root / ckpt_name, map_location="cpu", weights_only=True) stripped = {k.replace("_orig_mod.", "").replace("model.", ""): v for k, v in sd.items()} missing, unexpected = model.load_state_dict(stripped, strict=False) print(f"[load] missing={len(missing)} unexpected={len(unexpected)}") if missing[:3]: print(f" sample missing: {missing[:3]}") if unexpected[:3]: print(f" sample unexpected: {unexpected[:3]}") model.to(device).eval() return model, cfg, train_meta def load_test_samples(data_path, n_total, shard_id, num_shards, seed): rng = np.random.default_rng(seed) inputs = np.load(data_path / "test" / "all__inputs.npy") labels = np.load(data_path / "test" / "all__labels.npy") pid = np.load(data_path / "test" / "all__puzzle_identifiers.npy") all_idx = rng.choice(len(inputs), size=n_total, replace=False) shard_size = (n_total + num_shards - 1) // num_shards s, e = shard_id * shard_size, min((shard_id + 1) * shard_size, n_total) idx = all_idx[s:e] return { "inputs": torch.from_numpy(inputs[idx].astype(np.int32)), "labels": torch.from_numpy(labels[idx].astype(np.int32)), "puzzle_identifiers": torch.from_numpy(pid[idx].astype(np.int32)), "idx": idx, } def jvp_through(f, x, v): return torch.autograd.functional.jvp(f, x, v=v, create_graph=False, strict=False) def run_diagnose_batch(model, batch, device, k_lyap, t_ons, seed): inner = model.inner cfg = inner.config B = batch["inputs"].shape[0] seq_full = cfg.seq_len + inner.puzzle_emb_len hidden = cfg.hidden_size D = seq_full * hidden z_H = inner.H_init.unsqueeze(0).expand(B, seq_full, hidden).clone().to(inner.forward_dtype) z_L = inner.L_init.unsqueeze(0).expand(B, seq_full, hidden).clone().to(inner.forward_dtype) seq_info = dict(cos_sin=inner.rotary_emb() if hasattr(inner, "rotary_emb") else None) input_embeddings = inner._input_embeddings(batch["inputs"].to(device), batch["puzzle_identifiers"].to(device)) g = torch.Generator(device=device).manual_seed(seed) Q0 = torch.randn(B, 2*D, k_lyap, device=device, dtype=torch.float32, generator=g) Q, _ = torch.linalg.qr(Q0) log_R_sum = torch.zeros(B, k_lyap, device=device, dtype=torch.float32) n_lyap_steps = 0 step_counter = 0 drift_zH_per_step, drift_zL_per_step = [], [] halted_at = torch.zeros(B, dtype=torch.long, device=device) q_halt_hist, q_continue_hist = [], [] for act_step in range(cfg.halt_max_steps): z_H_prev = z_H.detach().clone() z_L_prev = z_L.detach().clone() with torch.enable_grad(): zH, zL = z_H.detach(), z_L.detach() for _h in range(cfg.H_cycles): # L cycles for _l in range(cfg.L_cycles): v_H_j = Q[:, :D, :] v_L_j = Q[:, D:, :] v_comb = v_H_j + v_L_j new_v_L_cols = [] f_L = lambda z: inner.L_level(z, zH + input_embeddings, **seq_info) for i in range(k_lyap): v_i = v_comb[:, :, i].reshape(B, seq_full, hidden).to(inner.forward_dtype) zL_new, Dv = jvp_through(f_L, zL, v_i) new_v_L_cols.append(Dv.reshape(B, D).to(torch.float32)) new_v_L = torch.stack(new_v_L_cols, dim=-1) Q = torch.cat([v_H_j, new_v_L], dim=1) zL = zL_new step_counter += 1 if step_counter % t_ons == 0: Q, R = torch.linalg.qr(Q) log_R_sum = log_R_sum + R.diagonal(dim1=-2, dim2=-1).abs().clamp_min(1e-30).log() n_lyap_steps += 1 # H step (uses SAME L_level!) v_H_j = Q[:, :D, :] v_L_j = Q[:, D:, :] v_comb = v_H_j + v_L_j new_v_H_cols = [] f_H = lambda z: inner.L_level(z, zL, **seq_info) for i in range(k_lyap): v_i = v_comb[:, :, i].reshape(B, seq_full, hidden).to(inner.forward_dtype) zH_new, Dv = jvp_through(f_H, zH, v_i) new_v_H_cols.append(Dv.reshape(B, D).to(torch.float32)) new_v_H = torch.stack(new_v_H_cols, dim=-1) Q = torch.cat([new_v_H, v_L_j], dim=1) zH = zH_new step_counter += 1 if step_counter % t_ons == 0: Q, R = torch.linalg.qr(Q) log_R_sum = log_R_sum + R.diagonal(dim1=-2, dim2=-1).abs().clamp_min(1e-30).log() n_lyap_steps += 1 z_H, z_L = zH, zL drift_zH_per_step.append((z_H - z_H_prev).float().flatten(1).norm(dim=1).cpu()) drift_zL_per_step.append((z_L - z_L_prev).float().flatten(1).norm(dim=1).cpu()) with torch.no_grad(): q_logits = inner.q_head(z_H[:, 0]).float() q_halt, q_continue = q_logits[..., 0], q_logits[..., 1] q_halt_hist.append(q_halt.cpu()); q_continue_hist.append(q_continue.cpu()) new_halt = (q_halt > q_continue) & (halted_at == 0) halted_at[new_halt] = act_step + 1 output = inner.lm_head(z_H)[:, inner.puzzle_emb_len:].float() final_logits = output lyap_spec = (log_R_sum / max(n_lyap_steps, 1)).cpu().numpy() with torch.no_grad(): preds = final_logits.argmax(dim=-1) labels = batch["labels"].to(device) mask = labels > 0 exact = ((preds == labels) | ~mask).all(dim=-1).cpu().float() token_acc = ((preds == labels) & mask).sum(-1).float() / mask.sum(-1).float().clamp_min(1) token_acc = token_acc.cpu() return { "drift_zH": torch.stack(drift_zH_per_step, dim=1).numpy(), "drift_zL": torch.stack(drift_zL_per_step, dim=1).numpy(), "halted_at": halted_at.cpu().numpy(), "q_halt": torch.stack(q_halt_hist, dim=1).numpy(), "q_continue": torch.stack(q_continue_hist, dim=1).numpy(), "lyap_spec": lyap_spec, "exact_correct": exact.numpy(), "token_acc": token_acc.numpy(), } def main(): ap = argparse.ArgumentParser() ap.add_argument("--ckpt-root", required=True) ap.add_argument("--ckpt-name", default="step_13020") ap.add_argument("--n-samples", type=int, default=512) ap.add_argument("--shard-id", type=int, default=0) ap.add_argument("--num-shards", type=int, default=1) ap.add_argument("--batch-size", type=int, default=16) ap.add_argument("--k-lyap", type=int, default=8) ap.add_argument("--t-ons", type=int, default=1) ap.add_argument("--seed", type=int, default=0) ap.add_argument("--out", default="diag_trm.npz") args = ap.parse_args() device = "cuda" model, cfg, train_meta = load_model(Path(args.ckpt_root), args.ckpt_name, device) print(f"loaded {args.ckpt_name}: hidden={model.inner.config.hidden_size}, " f"seq_full={train_meta['seq_len'] + model.inner.puzzle_emb_len}, " f"halt_max_steps={model.inner.config.halt_max_steps}, " f"H={model.inner.config.H_cycles} L={model.inner.config.L_cycles}") test = load_test_samples(Path(cfg["data_paths"][0]), args.n_samples, args.shard_id, args.num_shards, args.seed) n = len(test["inputs"]) print(f"shard {args.shard_id}/{args.num_shards}: {n} samples") res = {k: [] for k in ["drift_zH","drift_zL","halted_at","q_halt","q_continue","lyap_spec","exact_correct","token_acc","idx"]} t0 = time.time() for s in range(0, n, args.batch_size): e = min(s + args.batch_size, n) batch = {k: test[k][s:e].to(device) for k in ["inputs","labels","puzzle_identifiers"]} out = run_diagnose_batch(model, batch, device, args.k_lyap, args.t_ons, args.seed + s) for k, v in out.items(): res[k].append(v) res["idx"].append(test["idx"][s:e]) ls = out["lyap_spec"] print(f" [{e}/{n}] dt={time.time()-t0:.1f}s exact={out['exact_correct'].mean():.3f} " f"λ_1={ls[:,0].mean():+.4f} λ_{args.k_lyap}={ls[:,-1].mean():+.4f}", flush=True) saved = {} for k, v in res.items(): if not v: continue try: saved[k] = np.concatenate(v, 0) except ValueError: saved[k] = np.stack(v, 0) np.savez_compressed(args.out, **saved) succ = saved["exact_correct"] > 0.5 print(f"\nN={len(succ)} acc={succ.mean():.4f}") print(f"{'i':>3} {'all':>10} {'succ':>10} {'fail':>10} {'Δ':>9}") for i in range(saved["lyap_spec"].shape[1]): li = saved["lyap_spec"][:, i] print(f"{i+1:>3} {li.mean():+10.4f} {li[succ].mean():+10.4f} {li[~succ].mean():+10.4f} {li[~succ].mean()-li[succ].mean():+9.4f}") if __name__ == "__main__": main()