mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-18 08:47:05 +00:00
Pepijn
1050c2fb6c
Replace the shard/Backtrackable/decoded-shuffle-buffer internals with an
episode pool: each (rank x worker) consumer keeps episode_pool_size whole
episodes' tabular rows in RAM and emits uniformly random frames across
them. delta_timestamps windows become exact in-RAM slices with correct
boundary padding (the Backtrackable machinery and its lookback/lookahead
ceilings are gone), and video is decoded only when a sample is emitted,
so pool memory stays tabular-sized instead of buffer_size decoded
samples.
- Prefetch-on-admit: when streaming from a remote source, each pooled
episode's video files download to a local cache in the background
(refcounted, since v3 packs several episodes per file; deleted on
eviction), so decode-on-exit reads local bytes instead of paying
network seek latency.
- Per-consumer RNG derived from (seed, epoch, rank, worker): consumers
decorrelated, runs reproducible, epochs reshuffle automatically.
- Deterministic fast-forward resume: load_state_dict takes the trainer's
{batches_consumed, batch_size}; each worker re-derives its own skip
from the DataLoader's round-robin batch assignment and replays
tabular-only (no decode). Exact within an epoch, works with
num_workers > 0, and the same state file serves every rank. Replaces
the per-shard HF state_dict approach, which lived in worker processes
and could not be captured from the trainer.
- Shard-cap default removed (max_num_shards=None uses every parquet
shard); runtime warnings for non-divisible world sizes (datasets
degrades to read-everything splitting) and workers left without
shards.
- episode_pool_size replaces buffer_size (deprecated, ignored with a
warning); decoder cache sized to the pool working set, capped at 128.
Legacy order-replication tests asserted the old buffer algorithm
step-by-step and are rewritten as behavior contracts (exactly-once
coverage, per-seed determinism, epoch reshuffle). Value-level parity
tests against the map-style dataset pass unchanged.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
325 lines
10 KiB
Python
325 lines
10 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import torch
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pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
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from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
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from lerobot.datasets.utils import safe_shard
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from lerobot.utils.constants import ACTION
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from tests.fixtures.constants import DUMMY_REPO_ID
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def test_single_frame_consistency(tmp_path, lerobot_dataset_factory):
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"""Test if are correctly accessed"""
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ds_num_frames = 400
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ds_num_episodes = 10
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buffer_size = 100
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local_path = tmp_path / "test"
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repo_id = f"{DUMMY_REPO_ID}"
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ds = lerobot_dataset_factory(
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root=local_path,
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repo_id=repo_id,
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total_episodes=ds_num_episodes,
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total_frames=ds_num_frames,
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)
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streaming_ds = iter(StreamingLeRobotDataset(repo_id=repo_id, root=local_path, buffer_size=buffer_size))
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key_checks = []
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for _ in range(ds_num_frames):
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streaming_frame = next(streaming_ds)
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frame_idx = streaming_frame["index"]
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target_frame = ds[frame_idx]
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for key in streaming_frame:
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left = streaming_frame[key]
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right = target_frame[key]
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if isinstance(left, str):
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check = left == right
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elif isinstance(left, torch.Tensor):
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check = torch.allclose(left, right) and left.shape == right.shape
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elif isinstance(left, float):
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check = left == right.item() # right is a torch.Tensor
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key_checks.append((key, check))
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assert all(t[1] for t in key_checks), (
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f"Checking {list(filter(lambda t: not t[1], key_checks))[0][0]} left and right were found different (frame_idx: {frame_idx})"
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)
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@pytest.mark.parametrize(
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"shuffle",
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[False, True],
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)
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def test_frames_order_over_epochs(tmp_path, lerobot_dataset_factory, shuffle):
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"""Each epoch covers every frame exactly once; shuffle reshuffles across epochs."""
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ds_num_frames = 400
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ds_num_episodes = 10
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seed = 42
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n_epochs = 3
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local_path = tmp_path / "test"
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repo_id = f"{DUMMY_REPO_ID}"
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lerobot_dataset_factory(
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root=local_path,
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repo_id=repo_id,
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total_episodes=ds_num_episodes,
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total_frames=ds_num_frames,
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)
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streaming_ds = StreamingLeRobotDataset(
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repo_id=repo_id, root=local_path, episode_pool_size=4, seed=seed, shuffle=shuffle
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)
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epochs = [[int(frame["index"]) for frame in streaming_ds] for _ in range(n_epochs)]
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for epoch_indices in epochs:
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assert sorted(epoch_indices) == list(range(ds_num_frames)), "epoch did not cover every frame once"
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if shuffle:
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assert epochs[0] != epochs[1], "shuffle did not reshuffle across epochs"
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assert epochs[0] != list(range(ds_num_frames)), "shuffle left the stream in sequential order"
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else:
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assert epochs[0] == epochs[1] == epochs[2], "unshuffled epochs must repeat the same order"
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@pytest.mark.parametrize(
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"shuffle",
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[False, True],
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)
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def test_frames_order_with_shards(tmp_path, lerobot_dataset_factory, shuffle):
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"""Multi-shard streams keep exactly-once coverage and deterministic per-seed order."""
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ds_num_frames = 100
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ds_num_episodes = 10
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seed = 42
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data_file_size_mb = 0.001
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chunks_size = 1
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local_path = tmp_path / "test"
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repo_id = f"{DUMMY_REPO_ID}-ciao"
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lerobot_dataset_factory(
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root=local_path,
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repo_id=repo_id,
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total_episodes=ds_num_episodes,
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total_frames=ds_num_frames,
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data_files_size_in_mb=data_file_size_mb,
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chunks_size=chunks_size,
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)
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def make_ds():
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return StreamingLeRobotDataset(
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repo_id=repo_id,
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root=local_path,
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episode_pool_size=3,
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seed=seed,
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shuffle=shuffle,
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max_num_shards=4,
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)
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first = [int(frame["index"]) for frame in make_ds()]
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again = [int(frame["index"]) for frame in make_ds()]
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assert sorted(first) == list(range(ds_num_frames)), "epoch did not cover every frame once"
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assert first == again, "same seed must reproduce the same order"
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@pytest.mark.parametrize(
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"state_deltas, action_deltas",
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[
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([-1, -0.5, -0.20, 0], [0, 1, 2, 3]),
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([-1, -0.5, -0.20, 0], [-1.5, -1, -0.5, -0.20, -0.10, 0]),
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([-2, -1, -0.5, 0], [0, 1, 2, 3]),
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([-2, -1, -0.5, 0], [-1.5, -1, -0.5, -0.20, -0.10, 0]),
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],
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)
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def test_frames_with_delta_consistency(tmp_path, lerobot_dataset_factory, state_deltas, action_deltas):
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ds_num_frames = 500
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ds_num_episodes = 10
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buffer_size = 100
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seed = 42
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local_path = tmp_path / "test"
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repo_id = f"{DUMMY_REPO_ID}-ciao"
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camera_key = "phone"
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delta_timestamps = {
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camera_key: state_deltas,
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"state": state_deltas,
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ACTION: action_deltas,
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}
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ds = lerobot_dataset_factory(
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root=local_path,
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repo_id=repo_id,
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total_episodes=ds_num_episodes,
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total_frames=ds_num_frames,
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delta_timestamps=delta_timestamps,
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)
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streaming_ds = iter(
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StreamingLeRobotDataset(
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repo_id=repo_id,
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root=local_path,
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buffer_size=buffer_size,
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seed=seed,
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shuffle=False,
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delta_timestamps=delta_timestamps,
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)
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)
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for i in range(ds_num_frames):
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streaming_frame = next(streaming_ds)
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frame_idx = streaming_frame["index"]
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target_frame = ds[frame_idx]
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assert set(streaming_frame.keys()) == set(target_frame.keys()), (
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f"Keys differ between streaming frame and target one. Differ at: {set(streaming_frame.keys()) - set(target_frame.keys())}"
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)
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key_checks = []
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for key in streaming_frame:
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left = streaming_frame[key]
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right = target_frame[key]
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if isinstance(left, str):
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check = left == right
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elif isinstance(left, torch.Tensor):
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if (
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key not in ds.meta.camera_keys
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and "is_pad" not in key
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and f"{key}_is_pad" in streaming_frame
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):
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# comparing frames only on non-padded regions. Padding is applied to last-valid broadcasting
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left = left[~streaming_frame[f"{key}_is_pad"]]
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right = right[~target_frame[f"{key}_is_pad"]]
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check = torch.allclose(left, right) and left.shape == right.shape
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key_checks.append((key, check))
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assert all(t[1] for t in key_checks), (
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f"Checking {list(filter(lambda t: not t[1], key_checks))[0][0]} left and right were found different (i: {i}, frame_idx: {frame_idx})"
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)
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@pytest.mark.parametrize(
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"state_deltas, action_deltas",
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[
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([-1, -0.5, -0.20, 0], [0, 1, 2, 3, 10, 20]),
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([-1, -0.5, -0.20, 0], [-20, -1.5, -1, -0.5, -0.20, -0.10, 0]),
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([-2, -1, -0.5, 0], [0, 1, 2, 3, 10, 20]),
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([-2, -1, -0.5, 0], [-20, -1.5, -1, -0.5, -0.20, -0.10, 0]),
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],
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)
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def test_frames_with_delta_consistency_with_shards(
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tmp_path, lerobot_dataset_factory, state_deltas, action_deltas
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):
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ds_num_frames = 100
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ds_num_episodes = 10
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buffer_size = 10
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data_file_size_mb = 0.001
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chunks_size = 1
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seed = 42
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local_path = tmp_path / "test"
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repo_id = f"{DUMMY_REPO_ID}-ciao"
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camera_key = "phone"
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delta_timestamps = {
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camera_key: state_deltas,
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"state": state_deltas,
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ACTION: action_deltas,
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}
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ds = lerobot_dataset_factory(
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root=local_path,
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repo_id=repo_id,
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total_episodes=ds_num_episodes,
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total_frames=ds_num_frames,
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delta_timestamps=delta_timestamps,
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data_files_size_in_mb=data_file_size_mb,
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chunks_size=chunks_size,
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)
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streaming_ds = StreamingLeRobotDataset(
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repo_id=repo_id,
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root=local_path,
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buffer_size=buffer_size,
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seed=seed,
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shuffle=False,
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delta_timestamps=delta_timestamps,
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max_num_shards=4,
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)
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iter(streaming_ds)
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num_shards = 4
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shards_indices = []
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for shard_idx in range(num_shards):
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shard = safe_shard(streaming_ds.hf_dataset, shard_idx, num_shards)
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shard_indices = [item["index"] for item in shard]
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shards_indices.append(shard_indices)
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streaming_ds = iter(streaming_ds)
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for i in range(ds_num_frames):
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streaming_frame = next(streaming_ds)
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frame_idx = streaming_frame["index"]
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target_frame = ds[frame_idx]
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assert set(streaming_frame.keys()) == set(target_frame.keys()), (
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f"Keys differ between streaming frame and target one. Differ at: {set(streaming_frame.keys()) - set(target_frame.keys())}"
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)
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key_checks = []
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for key in streaming_frame:
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left = streaming_frame[key]
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right = target_frame[key]
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if isinstance(left, str):
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check = left == right
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elif isinstance(left, torch.Tensor):
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if (
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key not in ds.meta.camera_keys
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and "is_pad" not in key
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and f"{key}_is_pad" in streaming_frame
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):
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# comparing frames only on non-padded regions. Padding is applied to last-valid broadcasting
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left = left[~streaming_frame[f"{key}_is_pad"]]
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right = right[~target_frame[f"{key}_is_pad"]]
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check = torch.allclose(left, right) and left.shape == right.shape
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elif isinstance(left, float):
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check = left == right.item() # right is a torch.Tensor
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key_checks.append((key, check))
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assert all(t[1] for t in key_checks), (
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f"Checking {list(filter(lambda t: not t[1], key_checks))[0][0]} left and right were found different (i: {i}, frame_idx: {frame_idx})"
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)
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