Port HIL SERL (#644)
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Eugene Mironov <helper2424@gmail.com> Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com> Co-authored-by: Ke Wang <superwk1017@gmail.com> Co-authored-by: Yoel Chornton <yoel.chornton@gmail.com> Co-authored-by: imstevenpmwork <steven.palma@huggingface.co> Co-authored-by: Simon Alibert <simon.alibert@huggingface.co>
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tests/rl/test_actor.py
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208
tests/rl/test_actor.py
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#!/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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from concurrent import futures
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from unittest.mock import patch
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import pytest
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import torch
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from torch.multiprocessing import Event, Queue
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from lerobot.common.utils.transition import Transition
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from tests.utils import require_package
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def create_learner_service_stub():
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import grpc
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from lerobot.common.transport import services_pb2, services_pb2_grpc
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class MockLearnerService(services_pb2_grpc.LearnerServiceServicer):
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def __init__(self):
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self.ready_call_count = 0
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self.should_fail = False
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def Ready(self, request, context): # noqa: N802
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self.ready_call_count += 1
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if self.should_fail:
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context.set_code(grpc.StatusCode.UNAVAILABLE)
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context.set_details("Service unavailable")
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raise grpc.RpcError("Service unavailable")
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return services_pb2.Empty()
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"""Fixture to start a LearnerService gRPC server and provide a connected stub."""
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servicer = MockLearnerService()
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# Create a gRPC server and add our servicer to it.
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=4))
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services_pb2_grpc.add_LearnerServiceServicer_to_server(servicer, server)
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port = server.add_insecure_port("[::]:0") # bind to a free port chosen by OS
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server.start() # start the server (non-blocking call):contentReference[oaicite:1]{index=1}
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# Create a client channel and stub connected to the server's port.
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channel = grpc.insecure_channel(f"localhost:{port}")
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return services_pb2_grpc.LearnerServiceStub(channel), servicer, channel, server
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def close_service_stub(channel, server):
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channel.close()
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server.stop(None)
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@require_package("grpc")
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def test_establish_learner_connection_success():
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from lerobot.scripts.rl.actor import establish_learner_connection
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"""Test successful connection establishment."""
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stub, _servicer, channel, server = create_learner_service_stub()
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shutdown_event = Event()
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# Test successful connection
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result = establish_learner_connection(stub, shutdown_event, attempts=5)
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assert result is True
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close_service_stub(channel, server)
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@require_package("grpc")
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def test_establish_learner_connection_failure():
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from lerobot.scripts.rl.actor import establish_learner_connection
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"""Test connection failure."""
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stub, servicer, channel, server = create_learner_service_stub()
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servicer.should_fail = True
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shutdown_event = Event()
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# Test failed connection
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with patch("time.sleep"): # Speed up the test
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result = establish_learner_connection(stub, shutdown_event, attempts=2)
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assert result is False
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close_service_stub(channel, server)
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@require_package("grpc")
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def test_push_transitions_to_transport_queue():
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from lerobot.common.transport.utils import bytes_to_transitions
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from lerobot.scripts.rl.actor import push_transitions_to_transport_queue
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from tests.transport.test_transport_utils import assert_transitions_equal
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"""Test pushing transitions to transport queue."""
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# Create mock transitions
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transitions = []
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for i in range(3):
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transition = Transition(
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state={"observation": torch.randn(3, 64, 64), "state": torch.randn(10)},
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action=torch.randn(5),
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reward=torch.tensor(1.0 + i),
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done=torch.tensor(False),
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truncated=torch.tensor(False),
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next_state={"observation": torch.randn(3, 64, 64), "state": torch.randn(10)},
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complementary_info={"step": torch.tensor(i)},
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)
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transitions.append(transition)
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transitions_queue = Queue()
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# Test pushing transitions
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push_transitions_to_transport_queue(transitions, transitions_queue)
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# Verify the data can be retrieved
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serialized_data = transitions_queue.get()
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assert isinstance(serialized_data, bytes)
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deserialized_transitions = bytes_to_transitions(serialized_data)
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assert len(deserialized_transitions) == len(transitions)
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for i, deserialized_transition in enumerate(deserialized_transitions):
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assert_transitions_equal(deserialized_transition, transitions[i])
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@require_package("grpc")
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@pytest.mark.timeout(3) # force cross-platform watchdog
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def test_transitions_stream():
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from lerobot.scripts.rl.actor import transitions_stream
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"""Test transitions stream functionality."""
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shutdown_event = Event()
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transitions_queue = Queue()
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# Add test data to queue
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test_data = [b"transition_data_1", b"transition_data_2", b"transition_data_3"]
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for data in test_data:
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transitions_queue.put(data)
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# Collect streamed data
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streamed_data = []
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stream_generator = transitions_stream(shutdown_event, transitions_queue, 0.1)
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# Process a few items
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for i, message in enumerate(stream_generator):
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streamed_data.append(message)
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if i >= len(test_data) - 1:
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shutdown_event.set()
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break
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# Verify we got messages
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assert len(streamed_data) == len(test_data)
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assert streamed_data[0].data == b"transition_data_1"
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assert streamed_data[1].data == b"transition_data_2"
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assert streamed_data[2].data == b"transition_data_3"
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@require_package("grpc")
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@pytest.mark.timeout(3) # force cross-platform watchdog
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def test_interactions_stream():
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from lerobot.common.transport.utils import bytes_to_python_object, python_object_to_bytes
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from lerobot.scripts.rl.actor import interactions_stream
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"""Test interactions stream functionality."""
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shutdown_event = Event()
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interactions_queue = Queue()
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# Create test interaction data (similar structure to what would be sent)
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test_interactions = [
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{"episode_reward": 10.5, "step": 1, "policy_fps": 30.2},
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{"episode_reward": 15.2, "step": 2, "policy_fps": 28.7},
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{"episode_reward": 8.7, "step": 3, "policy_fps": 29.1},
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]
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# Serialize the interaction data as it would be in practice
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test_data = [
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interactions_queue.put(python_object_to_bytes(interaction)) for interaction in test_interactions
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]
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# Collect streamed data
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streamed_data = []
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stream_generator = interactions_stream(shutdown_event, interactions_queue, 0.1)
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# Process the items
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for i, message in enumerate(stream_generator):
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streamed_data.append(message)
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if i >= len(test_data) - 1:
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shutdown_event.set()
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break
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# Verify we got messages
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assert len(streamed_data) == len(test_data)
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# Verify the messages can be deserialized back to original data
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for i, message in enumerate(streamed_data):
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deserialized_interaction = bytes_to_python_object(message.data)
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assert deserialized_interaction == test_interactions[i]
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