scripts/check_dataloader.py

"""Pull one fully-transformed sample through the real openpi data pipeline. Fails in seconds.

    uv run python scripts/check_dataloader.py pi05_g1_pickplace

Run this BEFORE compute_norm_stats.py. It exercises exactly the path that norm-stat computation
and training use -- LeRobotDataset construction, delta_timestamps, repack, the policy transforms,
the delta-action transform and the model transforms -- but on a single sample with no workers, so
a wiring error surfaces immediately instead of inside a DataLoader worker after a minute of setup.

The class of bug this catches: `action_sequence_keys` defaults to ("actions",) while a LeRobot
dataset's column may be "action". That name is fed raw into delta_timestamps, so it is resolved
against the hf_dataset BEFORE any repack renames it, and the failure looks like

    KeyError: Column actions not in the dataset. Current columns: [... 'action' ...]

buried in a torch worker traceback.
"""
import sys

import numpy as np


def main() -> int:
    if len(sys.argv) < 2:
        print(__doc__)
        return 2
    name = sys.argv[1]

    from openpi.training import config as _config
    from openpi.training import data_loader as _data_loader

    cfg = _config.get_config(name)
    data_config = cfg.data.create(cfg.assets_dirs, cfg.model)

    print(f"config              {cfg.name}")
    print(f"repo_id             {data_config.repo_id}")
    print(f"action_sequence_keys{'':<0} {tuple(data_config.action_sequence_keys)}")
    print(f"action_horizon      {cfg.model.action_horizon}")
    print(f"use_quantile_norm   {data_config.use_quantile_norm}")
    print(f"prompt_from_task    {data_config.prompt_from_task}")

    # Raw dataset first: this is where an action_sequence_keys mismatch blows up.
    ds = _data_loader.create_torch_dataset(data_config, cfg.model.action_horizon, cfg.model)
    print(f"\nraw dataset         {len(ds)} samples")
    raw = ds[0]
    for k, v in sorted(raw.items()):
        if hasattr(v, "shape"):
            print(f"  {k:<44} {tuple(v.shape)}")
    if isinstance(raw.get("prompt"), str) or isinstance(raw.get("task"), str):
        print(f"  prompt/task{'':<33} {raw.get('prompt') or raw.get('task')!r}")

    # Then the full transform stack. skip_norm_stats=True so this works before Step 8.
    tds = _data_loader.transform_dataset(ds, data_config, skip_norm_stats=True)
    item = tds[0]
    print("\ntransformed sample (what the model actually receives):")
    for k, v in sorted(item.items()):
        if isinstance(v, dict):
            for kk, vv in sorted(v.items()):
                print(f"  {k}/{kk:<38} {getattr(vv, 'shape', vv)}")
        elif hasattr(v, "shape"):
            print(f"  {k:<44} {tuple(np.asarray(v).shape)}  {np.asarray(v).dtype}")
        else:
            print(f"  {k:<44} {v!r}")

    problems = []
    st, ac = np.asarray(item["state"]), np.asarray(item["actions"])
    if st.shape != (cfg.model.action_dim,):
        problems.append(f"state is {st.shape}, expected ({cfg.model.action_dim},)")
    if ac.shape != (cfg.model.action_horizon, cfg.model.action_dim):
        problems.append(f"actions is {ac.shape}, expected "
                        f"({cfg.model.action_horizon}, {cfg.model.action_dim})")
    if not np.all(np.isfinite(st)) or not np.all(np.isfinite(ac)):
        problems.append("non-finite values in state/actions")
    if np.all(ac == 0):
        problems.append("every action is exactly 0 -- the action column is probably not wired")

    print()
    if problems:
        for p in problems:
            print("FAIL", p)
        return 1
    print("PASS -- the pipeline produces well-formed model inputs. Safe to run compute_norm_stats.py.")
    return 0


if __name__ == "__main__":
    sys.exit(main())