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"""Dump per-cell predictions for a TRM-Maze checkpoint (plain forward, no JVP) so we can
analyze WHERE failure errors are (connected detour = coherent stable wrong path vs scattered).
Saves preds, labels, inputs, exact_correct, idx for n test puzzles.
"""
from __future__ import annotations
import sys, argparse
from pathlib import Path
import numpy as np
import torch
sys.path.insert(0, "/home/yurenh2/rrm/research/flossing")
from diagnose_trm_joint_maze import load_model, load_test_samples # att+maze-capable loader
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--ckpt-root", required=True)
ap.add_argument("--ckpt-name", default="step_130200")
ap.add_argument("--data", required=True)
ap.add_argument("--n", type=int, default=512)
ap.add_argument("--batch-size", type=int, default=32)
ap.add_argument("--seed", type=int, default=0)
ap.add_argument("--out", required=True)
args = ap.parse_args()
device = "cuda"
model, cfg, train_meta = load_model(Path(args.ckpt_root), args.ckpt_name, device)
inner = model.inner
test = load_test_samples(Path(args.data), args.n, 0, 1, args.seed)
n = len(test["inputs"])
pe = inner.puzzle_emb_len
preds_all, labels_all, inputs_all, exact_all, idx_all = [], [], [], [], []
ans_drift_full_all, ans_drift_ans_all, ldrift_all = [], [], []
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"]}
B = batch["inputs"].shape[0]
seq_full = inner.config.seq_len + pe
hidden = inner.config.hidden_size
with torch.no_grad():
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)
inp_emb = inner._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
labels = batch["labels"]
ans_mask = (labels != batch["inputs"]) # SOLUTION-SPACE cells: where task requires change
prev = None; prev_zH = None
adrift_full, adrift_ans, ldrift = [], [], [] # answer Hamming drift + LATENT z_H drift
for _ in range(inner.config.halt_max_steps):
for _h in range(inner.config.H_cycles):
for _l in range(inner.config.L_cycles):
z_L = inner.L_level(z_L, z_H + inp_emb, **seq_info)
z_H = inner.L_level(z_H, z_L, **seq_info)
p = inner.lm_head(z_H)[:, pe:].float().argmax(-1) # decode answer THIS step
if prev is None:
adrift_full.append(torch.zeros(B, device=device)); adrift_ans.append(torch.zeros(B, device=device))
ldrift.append(torch.zeros(B, device=device))
else:
adrift_full.append((p != prev).float().sum(-1))
adrift_ans.append(((p != prev) & ans_mask).float().sum(-1))
ldrift.append((z_H - prev_zH).float().flatten(1).norm(dim=1)) # latent z_H drift
prev = p; prev_zH = z_H.detach()
preds = prev
mask = labels > 0
exact = ((preds == labels) | ~mask).all(-1)
preds_all.append(preds.cpu().numpy()); labels_all.append(labels.cpu().numpy())
inputs_all.append(batch["inputs"].cpu().numpy()); exact_all.append(exact.cpu().numpy())
idx_all.append(test["idx"][s:e])
ans_drift_full_all.append(torch.stack(adrift_full, 1).cpu().numpy())
ans_drift_ans_all.append(torch.stack(adrift_ans, 1).cpu().numpy())
ldrift_all.append(torch.stack(ldrift, 1).cpu().numpy())
print(f" [{e}/{n}] exact={exact.float().mean():.3f}", flush=True)
np.savez_compressed(args.out,
preds=np.concatenate(preds_all), labels=np.concatenate(labels_all),
inputs=np.concatenate(inputs_all), exact_correct=np.concatenate(exact_all).astype(np.float32),
idx=np.concatenate(idx_all),
ans_drift_full=np.concatenate(ans_drift_full_all), # (N, steps) decoded-answer Hamming drift
ans_drift_ans=np.concatenate(ans_drift_ans_all),
drift_zH=np.concatenate(ldrift_all)) # (N, steps) over solution-space cells only
print("saved", args.out)
if __name__ == "__main__":
main()
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