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lerobot-clone/tests/utils/test_process.py

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#!/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 multiprocessing
import os
import signal
import threading
from unittest.mock import patch
import pytest
RL stack refactoring (#3075) * refactor: RL stack refactoring — RLAlgorithm, RLTrainer, DataMixer, and SAC restructuring * chore: clarify torch.compile disabled note in SACAlgorithm * fix(teleop): keyboard EE teleop not registering special keys and losing intervention state Fixes #2345 Co-authored-by: jpizarrom <jpizarrom@gmail.com> * fix: remove leftover normalization calls from reward classifier predict_reward Fixes #2355 * fix: add thread synchronization to ReplayBuffer to prevent race condition between add() and sample() * refactor: update SACAlgorithm to pass action_dim to _init_critics and fix encoder reference * perf: remove redundant CPU→GPU→CPU transition move in learner * Fix: add kwargs in reward classifier __init__() * fix: include IS_INTERVENTION in complementary_info sent to learner for offline replay buffer * fix: add try/finally to control_loop to ensure image writer cleanup on exit * fix: use string key for IS_INTERVENTION in complementary_info to avoid torch.load serialization error * fix: skip tests that require grpc if not available * fix(tests): ensure tensor stats comparison accounts for reshaping in normalization tests * fix(tests): skip tests that require grpc if not available * refactor(rl): expose public API in rl/__init__ and use relative imports in sub-packages * fix(config): update vision encoder model name to lerobot/resnet10 * fix(sac): clarify torch.compile status * refactor(rl): update shutdown_event type hints from 'any' to 'Any' for consistency and clarity * refactor(sac): simplify optimizer return structure * perf(rl): use async iterators in OnlineOfflineMixer.get_iterator * refactor(sac): decouple algorithm hyperparameters from policy config * update losses names in tests * fix docstring * remove unused type alias * fix test for flat dict structure * refactor(policies): rename policies/sac → policies/gaussian_actor * refactor(rl/sac): consolidate hyperparameter ownership and clean up discrete critic * perf(observation_processor): add CUDA support for image processing * fix(rl): correctly wire HIL-SERL gripper penalty through processor pipeline (cherry picked from commit 9c2af818ff4bfef2603348e0609aa249c3ff62b1) * fix(rl): add time limit processor to environment pipeline (cherry picked from commit cd105f65cb213c4a9c9768926cc3304ca52eb5f4) * fix(rl): clarify discrete gripper action mapping in GripperVelocityToJoint for SO100 (cherry picked from commit 494f469a2b9dfb792dde6d9d79d8646ef4fcff54) * fix(rl): update neutral gripper action (cherry picked from commit 9c9064e5befe82e981286c6562194f524e16045e) * fix(rl): merge environment and action-processor info in transition processing (cherry picked from commit 30e1886b6466b8753ec41b3016c09a17dd3e960b) * fix(rl): mirror gym_manipulator in actor (cherry picked from commit d2a046dfc5b6f79df34577aa45f32403d897c0a3) * fix(rl): postprocess action in actor (cherry picked from commit c2556439e550ee3fe5bae6060c57cf227101fcaf) * fix(rl): improve action processing for discrete and continuous actions (cherry picked from commit f887ab3f6ace140c4ea6b6186c26473d785b0727) * fix(rl): enhance intervention handling in actor and learner (cherry picked from commit ef8bfffbd72e9d0951de576553f89c7c281315de) * Revert "perf(observation_processor): add CUDA support for image processing" This reverts commit 38b88c414cdc1f53ebaab3211e688fe87522b732. * refactor(rl): make algorithm a nested config so all SAC hyperparameters are JSON-addressable * refactor(rl): add make_algorithm_config function for RLAlgorithmConfig instantiation * refactor(rl): add type property to RLAlgorithmConfig for better clarity * refactor(rl): make RLAlgorithmConfig an abstract base class for better extensibility * refactor(tests): remove grpc import checks from test files for cleaner code * fix(tests): gate RL tests on the `datasets` extra * refactor: simplify docstrings for clarity and conciseness across multiple files * fix(rl): update gripper position key and handle action absence during reset * fix(rl): record pre-step observation so (obs, action, next.reward) align in gym_manipulator dataset * refactor: clean up import statements * chore: address reviewer comments * chore: improve visual stats reshaping logic and update docstring for clarity * refactor: enforce mandatory config_class and name attributes in RLAlgorithm * refactor: implement NotImplementedError for abstract methods in RLAlgorithm and DataMixer * refactor: replace build_algorithm with make_algorithm for SACAlgorithmConfig and update related tests * refactor: add require_package calls for grpcio and gym-hil in relevant modules * refactor(rl): move grpcio guards to runtime entry points * feat(rl): consolidate HIL-SERL checkpoint into HF-style components Make `RLAlgorithmConfig` and `RLAlgorithm` `HubMixin`s, add abstract `state_dict()` / `load_state_dict()` for critic ensemble, target nets and `log_alpha`, and persist them as a sibling `algorithm/` component next to `pretrained_model/`. Replace the pickled `training_state.pt` with an enriched `training_step.json` carrying `step` and `interaction_step`, so resume restores actor + critics + target nets + temperature + optimizers + RNG + counters from HF-standard files. * refactor(rl): move actor weight-sync wire format from policy to algorithm * refactor(rl): update type hints for learner and actor functions * refactor(rl): hoist grpcio guard to module top in actor/learner * chore(rl): manage import pattern in actor (#3564) * chore(rl): manage import pattern in actor * chore(rl): optional grpc imports in learner; quote grpc ServicerContext types --------- Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> * update uv.lock * chore(doc): update doc --------- Co-authored-by: jpizarrom <jpizarrom@gmail.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
2026-05-12 15:49:54 +02:00
pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
feat(rollout): decouple policy deployment from data recording with new `lerobot-rollout` CLI (#3413) * feat(scripts): lerobot-rollout * fix(rollout) require dataset in dagger + use duration too * fix(docs): dagger num_episodes * test(rollout): fix expectations * fix(rollout): features check * fix(rollout): device and task propagation + feature pos + warn fps + move rename_map config * docs(rollout): edit rename_map instructions * chore(rollout): multiple minor improvements * chore(rollout): address coments + minor improvements * fix(rollout): enable default * fix(tests): default value RTCConfig * fix(rollout): robot_observation_processor and notify_observation at policy frequency instead of interpolator rate Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): prevent relativeactions with sync inference engine Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): rtc reanchor to non normalized state Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): fixing the episode length to use hwc (#3469) also reducing default length to 5 minutes * feat(rollout): go back to initial position is now a config * fix(rollout): properly propagating video_files_size_in_mb to lerobot_dataset (#3470) * chore(rollout): note about dagger correction stage * chore(docs): update comments and docstring * fix(test): move rtc relative out of rollout module * fix(rollout): address the review comments --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co>
2026-04-28 00:57:35 +02:00
from lerobot.utils.process import ProcessSignalHandler # noqa: E402
# Fixture to reset shutdown_event_counter and original signal handlers before and after each test
@pytest.fixture(autouse=True)
def reset_globals_and_handlers():
# Store original signal handlers
original_handlers = {
sig: signal.getsignal(sig)
for sig in [signal.SIGINT, signal.SIGTERM, signal.SIGHUP, signal.SIGQUIT]
if hasattr(signal, sig.name)
}
yield
# Restore original signal handlers
for sig, handler in original_handlers.items():
signal.signal(sig, handler)
def test_setup_process_handlers_event_with_threads():
"""Test that setup_process_handlers returns the correct event type."""
handler = ProcessSignalHandler(use_threads=True)
shutdown_event = handler.shutdown_event
assert isinstance(shutdown_event, threading.Event), "Should be a threading.Event"
assert not shutdown_event.is_set(), "Event should initially be unset"
def test_setup_process_handlers_event_with_processes():
"""Test that setup_process_handlers returns the correct event type."""
handler = ProcessSignalHandler(use_threads=False)
shutdown_event = handler.shutdown_event
assert isinstance(shutdown_event, type(multiprocessing.Event())), "Should be a multiprocessing.Event"
assert not shutdown_event.is_set(), "Event should initially be unset"
@pytest.mark.parametrize("use_threads", [True, False])
@pytest.mark.parametrize(
"sig",
[
signal.SIGINT,
signal.SIGTERM,
# SIGHUP and SIGQUIT are not reliably available on all platforms (e.g. Windows)
pytest.param(
signal.SIGHUP,
marks=pytest.mark.skipif(not hasattr(signal, "SIGHUP"), reason="SIGHUP not available"),
),
pytest.param(
signal.SIGQUIT,
marks=pytest.mark.skipif(not hasattr(signal, "SIGQUIT"), reason="SIGQUIT not available"),
),
],
)
def test_signal_handler_sets_event(use_threads, sig):
"""Test that the signal handler sets the event on receiving a signal."""
handler = ProcessSignalHandler(use_threads=use_threads)
shutdown_event = handler.shutdown_event
assert handler.counter == 0
os.kill(os.getpid(), sig)
# In some environments, the signal might take a moment to be handled.
shutdown_event.wait(timeout=1.0)
assert shutdown_event.is_set(), f"Event should be set after receiving signal {sig}"
# Ensure the internal counter was incremented
assert handler.counter == 1
@pytest.mark.parametrize("use_threads", [True, False])
@patch("sys.exit")
def test_force_shutdown_on_second_signal(mock_sys_exit, use_threads):
"""Test that a second signal triggers a force shutdown."""
handler = ProcessSignalHandler(use_threads=use_threads)
os.kill(os.getpid(), signal.SIGINT)
# Give a moment for the first signal to be processed
import time
time.sleep(0.1)
os.kill(os.getpid(), signal.SIGINT)
time.sleep(0.1)
assert handler.counter == 2
mock_sys_exit.assert_called_once_with(1)