Debug Tools

Puzzle Eval-Stack Oracle Replay

The previous root-level debug_puzzle.py now lives in this directory. Run it from the repository root:

MUJOCO_GL=egl python debug/debug_puzzle.py --config-name=puzzle

PushT Multi-Step Latent Rollout Drift

debug_pusht_rollout.py freezes a trained HyperbolicJEPA checkpoint and replays ground-truth PushT actions from the offline dataset. It compares each predicted latent against the encoded real future frame for blocks 1...5.

The script writes:

rollout_drift.png: Euclidean, tangent-space, and Lorentz error curves.
rollout_drift_summary.json: run metadata and aggregated metrics.
rollout_drift_summary.csv: aggregated metrics for plotting or tables.
rollout_drift_per_sequence.csv: per-window metrics for deeper analysis.

Each curve contains two modes:

autoregressive: append each predicted latent and continue rollout.
teacher_forced: restart each one-step prediction from real encoded frames.

Interpretation:

Low teacher-forced error with rapidly rising autoregressive error indicates compounding rollout drift.
High error from block 1 indicates a one-step dynamics or action-protocol issue.
Similar low curves with poor planning SR point toward candidate sampling or goal-cost alignment instead of dynamics quality.

Example:

MUJOCO_GL=egl python debug/debug_pusht_rollout.py \
  --policy /data_nvme/user/zliu681/le-wm-main/lewm_cache/pusht/hyperbolic_pusht/lewm_hyperbolic_pusht_epoch_10 \
  --dataset pusht/pusht_expert_train \
  --device auto \
  --num-sequences 512 \
  --context-blocks 3 \
  --max-blocks 5 \
  --frameskip 5