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Diffstat (limited to 'research/flossing/diagnose_hrm_separate.py')
| -rw-r--r-- | research/flossing/diagnose_hrm_separate.py | 242 |
1 files changed, 242 insertions, 0 deletions
diff --git a/research/flossing/diagnose_hrm_separate.py b/research/flossing/diagnose_hrm_separate.py new file mode 100644 index 0000000..1157c16 --- /dev/null +++ b/research/flossing/diagnose_hrm_separate.py @@ -0,0 +1,242 @@ +"""HRM diagnostic: separate λ_L (固定 z_H 下 L 子系统) and λ_H (z_L 收敛后 H 子系统), +in addition to the joint λ from diagnose_hrm_joint.py. + +Three orthonormal bases evolved in parallel: +- Q_joint: (B, 2D, k) — joint (v_H, v_L). Block-matrix update per L/H step. +- Q_L: (B, D, k) — only updated during L steps via J_L. Unchanged during H steps. +- Q_H: (B, D, k) — only updated during H steps via J_H. Unchanged during L steps. + +Note: Q_L's evolution uses J_L evaluated at the current trajectory's z_L+z_H+ie, +which means we measure the L sub-system Lyapunov "along the actual z_H trajectory" +(z_H changes between H-cycles). Similarly for Q_H. +""" +from __future__ import annotations +import sys, os, yaml, math, argparse, json, time +from pathlib import Path +import numpy as np +import torch + +HRM_DIR = Path("/home/yurenh2/rrm/hrm") +sys.path.insert(0, str(HRM_DIR)) +from models.hrm.hrm_act_v1 import HierarchicalReasoningModel_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_path"]) / "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"], causal=False) + model = HierarchicalReasoningModel_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()} + model.load_state_dict(stripped, strict=False) + 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(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)) + + # Three independent orthonormal bases + g = torch.Generator(device=device).manual_seed(seed) + Q_joint = torch.linalg.qr(torch.randn(B, 2*D, k_lyap, device=device, dtype=torch.float32, generator=g))[0] + Q_L = torch.linalg.qr(torch.randn(B, D, k_lyap, device=device, dtype=torch.float32, generator=g))[0] + Q_H = torch.linalg.qr(torch.randn(B, D, k_lyap, device=device, dtype=torch.float32, generator=g))[0] + + log_R_joint = torch.zeros(B, k_lyap, device=device, dtype=torch.float32) + log_R_L = torch.zeros(B, k_lyap, device=device, dtype=torch.float32) + log_R_H = torch.zeros(B, k_lyap, device=device, dtype=torch.float32) + n_joint_steps = 0; n_L_steps = 0; n_H_steps = 0 + step_counter_joint = 0; step_counter_L = 0; step_counter_H = 0 + + for act_step in range(cfg.halt_max_steps): + with torch.enable_grad(): + zH, zL = z_H.detach(), z_L.detach() + for _h in range(cfg.H_cycles): + for _l in range(cfg.L_cycles): + # ============ JOINT update (L step) ============ + v_H_j = Q_joint[:, :D, :] + v_L_j = Q_joint[:, D:, :] + v_comb = v_H_j + v_L_j + new_v_L_j_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(f_L, zL, v_i) + new_v_L_j_cols.append(Dv.reshape(B, D).to(torch.float32)) + new_v_L_j = torch.stack(new_v_L_j_cols, dim=-1) + Q_joint = torch.cat([v_H_j, new_v_L_j], dim=1) + + # ============ L-only update ============ + new_v_L_only_cols = [] + for i in range(k_lyap): + v_i = Q_L[:, :, i].reshape(B, seq_full, hidden).to(inner.forward_dtype) + _, Dv = jvp(f_L, zL, v_i) + new_v_L_only_cols.append(Dv.reshape(B, D).to(torch.float32)) + Q_L = torch.stack(new_v_L_only_cols, dim=-1) + + # Q_H untouched during L step (since H_level wasn't applied) + zL = zL_new + + step_counter_joint += 1; step_counter_L += 1 + if step_counter_joint % t_ons == 0: + Q_joint, Rj = torch.linalg.qr(Q_joint) + log_R_joint = log_R_joint + Rj.diagonal(dim1=-2, dim2=-1).abs().clamp_min(1e-30).log() + n_joint_steps += 1 + if step_counter_L % t_ons == 0: + Q_L, Rl = torch.linalg.qr(Q_L) + log_R_L = log_R_L + Rl.diagonal(dim1=-2, dim2=-1).abs().clamp_min(1e-30).log() + n_L_steps += 1 + + # ============ JOINT update (H step) ============ + v_H_j = Q_joint[:, :D, :] + v_L_j = Q_joint[:, D:, :] + v_comb = v_H_j + v_L_j + new_v_H_j_cols = [] + f_H = lambda z: inner.H_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(f_H, zH, v_i) + new_v_H_j_cols.append(Dv.reshape(B, D).to(torch.float32)) + new_v_H_j = torch.stack(new_v_H_j_cols, dim=-1) + Q_joint = torch.cat([new_v_H_j, v_L_j], dim=1) + + # ============ H-only update ============ + new_v_H_only_cols = [] + for i in range(k_lyap): + v_i = Q_H[:, :, i].reshape(B, seq_full, hidden).to(inner.forward_dtype) + _, Dv = jvp(f_H, zH, v_i) + new_v_H_only_cols.append(Dv.reshape(B, D).to(torch.float32)) + Q_H = torch.stack(new_v_H_only_cols, dim=-1) + + # Q_L untouched during H step + zH = zH_new + + step_counter_joint += 1; step_counter_H += 1 + if step_counter_joint % t_ons == 0: + Q_joint, Rj = torch.linalg.qr(Q_joint) + log_R_joint = log_R_joint + Rj.diagonal(dim1=-2, dim2=-1).abs().clamp_min(1e-30).log() + n_joint_steps += 1 + if step_counter_H % t_ons == 0: + Q_H, Rh = torch.linalg.qr(Q_H) + log_R_H = log_R_H + Rh.diagonal(dim1=-2, dim2=-1).abs().clamp_min(1e-30).log() + n_H_steps += 1 + + z_H, z_L = zH, zL + + with torch.no_grad(): + output = inner.lm_head(z_H)[:, inner.puzzle_emb_len:].float() + final_logits = output + + lyap_joint = (log_R_joint / max(n_joint_steps, 1)).cpu().numpy() + lyap_L = (log_R_L / max(n_L_steps, 1)).cpu().numpy() + lyap_H = (log_R_H / max(n_H_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 { + "lyap_joint": lyap_joint, + "lyap_L": lyap_L, + "lyap_H": lyap_H, + "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_26040") + ap.add_argument("--n-samples", type=int, default=1024) + 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=32) + 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_separate.npz") + args = ap.parse_args() + + device = "cuda" + model, cfg, train_meta = load_model(Path(args.ckpt_root), args.ckpt_name, device) + test = load_test_samples(Path(cfg["data_path"]), 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") + print(f"H_cycles={model.inner.config.H_cycles} L_cycles={model.inner.config.L_cycles} halt={model.inner.config.halt_max_steps}") + + res = {k: [] for k in ["lyap_joint","lyap_L","lyap_H","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]) + print(f" [{e}/{n}] dt={time.time()-t0:.1f}s exact={out['exact_correct'].mean():.3f} " + f"λj1={out['lyap_joint'][:,0].mean():+.3f} " + f"λL1={out['lyap_L'][:,0].mean():+.3f} " + f"λH1={out['lyap_H'][:,0].mean():+.3f}", 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}") + for name in ["lyap_joint", "lyap_L", "lyap_H"]: + ls = saved[name] + print(f"\n{name}:") + print(f" i mean_succ mean_fail Δ") + for i in range(ls.shape[1]): + ms, mf = ls[succ,i].mean(), ls[~succ,i].mean() + print(f" {i+1} {ms:+8.4f} {mf:+8.4f} {mf-ms:+8.4f}") + print(f"\nsaved → {args.out}") + + +if __name__ == "__main__": + main() |