path: root/research/flossing/diagnose_hrm_separate.py

"""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()