mirror of
https://github.com/huggingface/lerobot.git
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e963e5a0c4
* 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 commit9c2af818ff) * fix(rl): add time limit processor to environment pipeline (cherry picked from commitcd105f65cb) * fix(rl): clarify discrete gripper action mapping in GripperVelocityToJoint for SO100 (cherry picked from commit494f469a2b) * fix(rl): update neutral gripper action (cherry picked from commit9c9064e5be) * fix(rl): merge environment and action-processor info in transition processing (cherry picked from commit30e1886b64) * fix(rl): mirror gym_manipulator in actor (cherry picked from commitd2a046dfc5) * fix(rl): postprocess action in actor (cherry picked from commitc2556439e5) * fix(rl): improve action processing for discrete and continuous actions (cherry picked from commitf887ab3f6a) * fix(rl): enhance intervention handling in actor and learner (cherry picked from commitef8bfffbd7) * Revert "perf(observation_processor): add CUDA support for image processing" This reverts commit38b88c414c. * 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>
171 lines
4.4 KiB
Python
171 lines
4.4 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 threading
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import time
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from queue import Queue
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import pytest
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pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
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from torch.multiprocessing import Queue as TorchMPQueue # noqa: E402
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from lerobot.rl.queue import get_last_item_from_queue # noqa: E402
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def test_get_last_item_single_item():
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"""Test getting the last item when queue has only one item."""
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queue = Queue()
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queue.put("single_item")
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result = get_last_item_from_queue(queue)
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assert result == "single_item"
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assert queue.empty()
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def test_get_last_item_multiple_items():
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"""Test getting the last item when queue has multiple items."""
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queue = Queue()
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items = ["first", "second", "third", "fourth", "last"]
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for item in items:
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queue.put(item)
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result = get_last_item_from_queue(queue)
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assert result == "last"
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assert queue.empty()
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def test_get_last_item_multiple_items_with_torch_queue():
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"""Test getting the last item when queue has multiple items."""
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queue = TorchMPQueue()
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items = ["first", "second", "third", "fourth", "last"]
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for item in items:
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queue.put(item)
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result = get_last_item_from_queue(queue)
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assert result == "last"
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assert queue.empty()
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def test_get_last_item_different_types():
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"""Test with different data types in the queue."""
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queue = Queue()
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items = [1, 2.5, "string", {"key": "value"}, [1, 2, 3], ("tuple", "data")]
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for item in items:
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queue.put(item)
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result = get_last_item_from_queue(queue)
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assert result == ("tuple", "data")
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assert queue.empty()
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def test_get_last_item_maxsize_queue():
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"""Test with a queue that has a maximum size."""
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queue = Queue(maxsize=5)
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# Fill the queue
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for i in range(5):
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queue.put(i)
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# Give the queue time to fill
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time.sleep(0.1)
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result = get_last_item_from_queue(queue)
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assert result == 4
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assert queue.empty()
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def test_get_last_item_with_none_values():
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"""Test with None values in the queue."""
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queue = Queue()
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items = [1, None, 2, None, 3]
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for item in items:
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queue.put(item)
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# Give the queue time to fill
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time.sleep(0.1)
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result = get_last_item_from_queue(queue)
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assert result == 3
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assert queue.empty()
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def test_get_last_item_blocking_timeout():
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"""Test get_last_item_from_queue returns None on timeout."""
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queue = Queue()
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result = get_last_item_from_queue(queue, block=True, timeout=0.1)
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assert result is None
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def test_get_last_item_non_blocking_empty():
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"""Test get_last_item_from_queue with block=False on an empty queue returns None."""
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queue = Queue()
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result = get_last_item_from_queue(queue, block=False)
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assert result is None
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def test_get_last_item_non_blocking_success():
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"""Test get_last_item_from_queue with block=False on a non-empty queue."""
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queue = Queue()
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items = ["first", "second", "last"]
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for item in items:
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queue.put(item)
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# Give the queue time to fill
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time.sleep(0.1)
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result = get_last_item_from_queue(queue, block=False)
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assert result == "last"
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assert queue.empty()
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def test_get_last_item_blocking_waits_for_item():
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"""Test that get_last_item_from_queue waits for an item if block=True."""
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queue = Queue()
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result = []
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def producer():
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queue.put("item1")
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queue.put("item2")
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def consumer():
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# This will block until the producer puts the first item
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item = get_last_item_from_queue(queue, block=True, timeout=0.2)
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result.append(item)
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producer_thread = threading.Thread(target=producer)
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consumer_thread = threading.Thread(target=consumer)
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producer_thread.start()
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consumer_thread.start()
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producer_thread.join()
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consumer_thread.join()
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assert result == ["item2"]
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assert queue.empty()
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