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567e51a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # 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:
```bash
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:
```bash
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
```
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