tests/datasets/test_streaming.py

#!/usr/bin/env python

# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pytest
import torch

pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")

from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
from lerobot.datasets.utils import safe_shard
from lerobot.utils.constants import ACTION
from tests.fixtures.constants import DUMMY_REPO_ID


def get_frames_expected_order(streaming_ds: StreamingLeRobotDataset) -> list[int]:
    """Replicates the shuffling logic of StreamingLeRobotDataset to get the expected order of indices."""
    rng = np.random.default_rng(streaming_ds.seed)
    buffer_size = streaming_ds.buffer_size
    num_shards = streaming_ds.num_shards

    shards_indices = []
    for shard_idx in range(num_shards):
        shard = streaming_ds.hf_dataset.shard(num_shards, index=shard_idx)
        shard_indices = [item["index"] for item in shard]
        shards_indices.append(shard_indices)

    shard_iterators = {i: iter(s) for i, s in enumerate(shards_indices)}

    buffer_indices_generator = streaming_ds._iter_random_indices(rng, buffer_size)

    frames_buffer = []
    expected_indices = []

    while shard_iterators:  # While there are still available shards
        available_shard_keys = list(shard_iterators.keys())
        if not available_shard_keys:
            break

        # Call _infinite_generator_over_elements with current available shards (key difference!)
        shard_key = next(streaming_ds._infinite_generator_over_elements(rng, available_shard_keys))

        try:
            frame_index = next(shard_iterators[shard_key])

            if len(frames_buffer) == buffer_size:
                i = next(buffer_indices_generator)
                expected_indices.append(frames_buffer[i])
                frames_buffer[i] = frame_index
            else:
                frames_buffer.append(frame_index)

        except StopIteration:
            del shard_iterators[shard_key]  # Remove exhausted shard

    rng.shuffle(frames_buffer)
    expected_indices.extend(frames_buffer)

    return expected_indices


def test_single_frame_consistency(tmp_path, lerobot_dataset_factory):
    """Test if are correctly accessed"""
    ds_num_frames = 400
    ds_num_episodes = 10
    buffer_size = 100

    local_path = tmp_path / "test"
    repo_id = f"{DUMMY_REPO_ID}"

    ds = lerobot_dataset_factory(
        root=local_path,
        repo_id=repo_id,
        total_episodes=ds_num_episodes,
        total_frames=ds_num_frames,
    )

    streaming_ds = iter(StreamingLeRobotDataset(repo_id=repo_id, root=local_path, buffer_size=buffer_size))

    key_checks = []
    for _ in range(ds_num_frames):
        streaming_frame = next(streaming_ds)
        frame_idx = streaming_frame["index"]
        target_frame = ds[frame_idx]

        for key in streaming_frame:
            left = streaming_frame[key]
            right = target_frame[key]

            if isinstance(left, str):
                check = left == right

            elif isinstance(left, torch.Tensor):
                check = torch.allclose(left, right) and left.shape == right.shape

            elif isinstance(left, float):
                check = left == right.item()  # right is a torch.Tensor

            key_checks.append((key, check))

        assert all(t[1] for t in key_checks), (
            f"Checking {list(filter(lambda t: not t[1], key_checks))[0][0]} left and right were found different (frame_idx: {frame_idx})"
        )


@pytest.mark.parametrize(
    "shuffle",
    [False, True],
)
def test_frames_order_over_epochs(tmp_path, lerobot_dataset_factory, shuffle):
    """Test if streamed frames correspond to shuffling operations over in-memory dataset."""
    ds_num_frames = 400
    ds_num_episodes = 10
    buffer_size = 100
    seed = 42
    n_epochs = 3

    local_path = tmp_path / "test"
    repo_id = f"{DUMMY_REPO_ID}"

    lerobot_dataset_factory(
        root=local_path,
        repo_id=repo_id,
        total_episodes=ds_num_episodes,
        total_frames=ds_num_frames,
    )

    streaming_ds = StreamingLeRobotDataset(
        repo_id=repo_id, root=local_path, buffer_size=buffer_size, seed=seed, shuffle=shuffle
    )

    first_epoch_indices = [frame["index"] for frame in streaming_ds]
    expected_indices = get_frames_expected_order(streaming_ds)

    assert first_epoch_indices == expected_indices, "First epoch indices do not match expected indices"

    expected_indices = get_frames_expected_order(streaming_ds)
    for _ in range(n_epochs):
        streaming_indices = [frame["index"] for frame in streaming_ds]
        frames_match = all(
            s_index == e_index for s_index, e_index in zip(streaming_indices, expected_indices, strict=True)
        )

        if shuffle:
            assert not frames_match
        else:
            assert frames_match


@pytest.mark.parametrize(
    "shuffle",
    [False, True],
)
def test_frames_order_with_shards(tmp_path, lerobot_dataset_factory, shuffle):
    """Test if streamed frames correspond to shuffling operations over in-memory dataset with multiple shards."""
    ds_num_frames = 100
    ds_num_episodes = 10
    buffer_size = 10

    seed = 42
    n_epochs = 3
    data_file_size_mb = 0.001

    chunks_size = 1

    local_path = tmp_path / "test"
    repo_id = f"{DUMMY_REPO_ID}-ciao"

    lerobot_dataset_factory(
        root=local_path,
        repo_id=repo_id,
        total_episodes=ds_num_episodes,
        total_frames=ds_num_frames,
        data_files_size_in_mb=data_file_size_mb,
        chunks_size=chunks_size,
    )

    streaming_ds = StreamingLeRobotDataset(
        repo_id=repo_id,
        root=local_path,
        buffer_size=buffer_size,
        seed=seed,
        shuffle=shuffle,
        max_num_shards=4,
    )

    first_epoch_indices = [frame["index"] for frame in streaming_ds]
    expected_indices = get_frames_expected_order(streaming_ds)

    assert first_epoch_indices == expected_indices, "First epoch indices do not match expected indices"

    for _ in range(n_epochs):
        streaming_indices = [
            frame["index"] for frame in streaming_ds
        ]  # NOTE: this is the same as first_epoch_indices
        frames_match = all(
            s_index == e_index for s_index, e_index in zip(streaming_indices, expected_indices, strict=True)
        )
        if shuffle:
            assert not frames_match
        else:
            assert frames_match


@pytest.mark.parametrize(
    "state_deltas, action_deltas",
    [
        ([-1, -0.5, -0.20, 0], [0, 1, 2, 3]),
        ([-1, -0.5, -0.20, 0], [-1.5, -1, -0.5, -0.20, -0.10, 0]),
        ([-2, -1, -0.5, 0], [0, 1, 2, 3]),
        ([-2, -1, -0.5, 0], [-1.5, -1, -0.5, -0.20, -0.10, 0]),
    ],
)
def test_frames_with_delta_consistency(tmp_path, lerobot_dataset_factory, state_deltas, action_deltas):
    ds_num_frames = 500
    ds_num_episodes = 10
    buffer_size = 100

    seed = 42

    local_path = tmp_path / "test"
    repo_id = f"{DUMMY_REPO_ID}-ciao"
    camera_key = "phone"

    delta_timestamps = {
        camera_key: state_deltas,
        "state": state_deltas,
        ACTION: action_deltas,
    }

    ds = lerobot_dataset_factory(
        root=local_path,
        repo_id=repo_id,
        total_episodes=ds_num_episodes,
        total_frames=ds_num_frames,
        delta_timestamps=delta_timestamps,
    )

    streaming_ds = iter(
        StreamingLeRobotDataset(
            repo_id=repo_id,
            root=local_path,
            buffer_size=buffer_size,
            seed=seed,
            shuffle=False,
            delta_timestamps=delta_timestamps,
        )
    )

    for i in range(ds_num_frames):
        streaming_frame = next(streaming_ds)
        frame_idx = streaming_frame["index"]
        target_frame = ds[frame_idx]

        assert set(streaming_frame.keys()) == set(target_frame.keys()), (
            f"Keys differ between streaming frame and target one. Differ at: {set(streaming_frame.keys()) - set(target_frame.keys())}"
        )

        key_checks = []
        for key in streaming_frame:
            left = streaming_frame[key]
            right = target_frame[key]

            if isinstance(left, str):
                check = left == right

            elif isinstance(left, torch.Tensor):
                if (
                    key not in ds.meta.camera_keys
                    and "is_pad" not in key
                    and f"{key}_is_pad" in streaming_frame
                ):
                    # comparing frames only on non-padded regions. Padding is applied to last-valid broadcasting
                    left = left[~streaming_frame[f"{key}_is_pad"]]
                    right = right[~target_frame[f"{key}_is_pad"]]

                check = torch.allclose(left, right) and left.shape == right.shape

            key_checks.append((key, check))

        assert all(t[1] for t in key_checks), (
            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})"
        )


@pytest.mark.parametrize(
    "state_deltas, action_deltas",
    [
        ([-1, -0.5, -0.20, 0], [0, 1, 2, 3, 10, 20]),
        ([-1, -0.5, -0.20, 0], [-20, -1.5, -1, -0.5, -0.20, -0.10, 0]),
        ([-2, -1, -0.5, 0], [0, 1, 2, 3, 10, 20]),
        ([-2, -1, -0.5, 0], [-20, -1.5, -1, -0.5, -0.20, -0.10, 0]),
    ],
)
def test_frames_with_delta_consistency_with_shards(
    tmp_path, lerobot_dataset_factory, state_deltas, action_deltas
):
    ds_num_frames = 100
    ds_num_episodes = 10
    buffer_size = 10
    data_file_size_mb = 0.001
    chunks_size = 1

    seed = 42

    local_path = tmp_path / "test"
    repo_id = f"{DUMMY_REPO_ID}-ciao"
    camera_key = "phone"

    delta_timestamps = {
        camera_key: state_deltas,
        "state": state_deltas,
        ACTION: action_deltas,
    }

    ds = lerobot_dataset_factory(
        root=local_path,
        repo_id=repo_id,
        total_episodes=ds_num_episodes,
        total_frames=ds_num_frames,
        delta_timestamps=delta_timestamps,
        data_files_size_in_mb=data_file_size_mb,
        chunks_size=chunks_size,
    )
    streaming_ds = StreamingLeRobotDataset(
        repo_id=repo_id,
        root=local_path,
        buffer_size=buffer_size,
        seed=seed,
        shuffle=False,
        delta_timestamps=delta_timestamps,
        max_num_shards=4,
    )

    iter(streaming_ds)

    num_shards = 4
    shards_indices = []
    for shard_idx in range(num_shards):
        shard = safe_shard(streaming_ds.hf_dataset, shard_idx, num_shards)
        shard_indices = [item["index"] for item in shard]
        shards_indices.append(shard_indices)

    streaming_ds = iter(streaming_ds)

    for i in range(ds_num_frames):
        streaming_frame = next(streaming_ds)
        frame_idx = streaming_frame["index"]
        target_frame = ds[frame_idx]

        assert set(streaming_frame.keys()) == set(target_frame.keys()), (
            f"Keys differ between streaming frame and target one. Differ at: {set(streaming_frame.keys()) - set(target_frame.keys())}"
        )

        key_checks = []
        for key in streaming_frame:
            left = streaming_frame[key]
            right = target_frame[key]

            if isinstance(left, str):
                check = left == right

            elif isinstance(left, torch.Tensor):
                if (
                    key not in ds.meta.camera_keys
                    and "is_pad" not in key
                    and f"{key}_is_pad" in streaming_frame
                ):
                    # comparing frames only on non-padded regions. Padding is applied to last-valid broadcasting
                    left = left[~streaming_frame[f"{key}_is_pad"]]
                    right = right[~target_frame[f"{key}_is_pad"]]

                check = torch.allclose(left, right) and left.shape == right.shape

            elif isinstance(left, float):
                check = left == right.item()  # right is a torch.Tensor

            key_checks.append((key, check))

        assert all(t[1] for t in key_checks), (
            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})"
        )