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# 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.
"""Unit-tests for the `PolicyServer` core logic.
Monkey-patch the `policy` attribute with a stub so that no real model inference is performed.
"""
from __future__ import annotations
import time
import pytest
import torch
from lerobot.configs.types import PolicyFeature
from lerobot.utils.constants import OBS_STATE
from tests.utils import require_package
# -----------------------------------------------------------------------------
# Test fixtures
# -----------------------------------------------------------------------------
class MockPolicy:
"""A minimal mock for an actual policy, returning zeros.
Refer to tests/policies for tests of the individual policies supported."""
class _Config:
robot_type = "dummy_robot"
@property
def image_features(self) -> dict[str, PolicyFeature]:
"""Empty image features since this test doesn't use images."""
return {}
def predict_action_chunk(self, observation: dict[str, torch.Tensor]) -> torch.Tensor:
"""Return a chunk of 20 dummy actions."""
batch_size = len(observation[OBS_STATE])
return torch.zeros(batch_size, 20, 6)
def __init__(self):
self.config = self._Config()
def to(self, *args, **kwargs):
# The server calls `policy.to(device)`. This stub ignores it.
return self
def model(self, batch: dict) -> torch.Tensor:
# Return a chunk of 20 dummy actions.
batch_size = len(batch["robot_type"])
return torch.zeros(batch_size, 20, 6)
@pytest.fixture
@require_package("grpc")
def policy_server():
"""Fresh `PolicyServer` instance with a stubbed-out policy model."""
# Import only when the test actually runs (after decorator check)
from lerobot.async_inference.configs import PolicyServerConfig
from lerobot.async_inference.policy_server import PolicyServer
test_config = PolicyServerConfig(host="localhost", port=9999)
server = PolicyServer(test_config)
# Replace the real policy with our fast, deterministic stub.
server.policy = MockPolicy()
server.actions_per_chunk = 20
server.device = "cpu"
# Add mock lerobot_features that the observation similarity functions need
server.lerobot_features = {
OBS_STATE: {
"dtype": "float32",
"shape": [6],
"names": ["joint1", "joint2", "joint3", "joint4", "joint5", "joint6"],
}
}
return server
# -----------------------------------------------------------------------------
# Helper utilities for tests
# -----------------------------------------------------------------------------
def _make_obs(state: torch.Tensor, timestep: int = 0, must_go: bool = False):
"""Create a TimedObservation with a given state vector."""
# Import only when needed
from lerobot.async_inference.helpers import TimedObservation
return TimedObservation(
observation={
"joint1": state[0].item() if len(state) > 0 else 0.0,
"joint2": state[1].item() if len(state) > 1 else 0.0,
"joint3": state[2].item() if len(state) > 2 else 0.0,
"joint4": state[3].item() if len(state) > 3 else 0.0,
"joint5": state[4].item() if len(state) > 4 else 0.0,
"joint6": state[5].item() if len(state) > 5 else 0.0,
},
timestamp=time.time(),
timestep=timestep,
must_go=must_go,
)
# -----------------------------------------------------------------------------
# Tests
# -----------------------------------------------------------------------------
def test_time_action_chunk(policy_server):
"""Verify that `_time_action_chunk` assigns correct timestamps and timesteps."""
start_ts = time.time()
start_t = 10
# A chunk of 3 action tensors.
action_tensors = [torch.randn(6) for _ in range(3)]
timed_actions = policy_server._time_action_chunk(start_ts, action_tensors, start_t)
assert len(timed_actions) == 3
# Check timesteps
assert [ta.get_timestep() for ta in timed_actions] == [10, 11, 12]
# Check timestamps
expected_timestamps = [
start_ts,
start_ts + policy_server.config.environment_dt,
start_ts + 2 * policy_server.config.environment_dt,
]
for ta, expected_ts in zip(timed_actions, expected_timestamps, strict=True):
assert abs(ta.get_timestamp() - expected_ts) < 1e-6
def test_maybe_enqueue_observation_must_go(policy_server):
"""An observation with `must_go=True` is always enqueued."""
obs = _make_obs(torch.zeros(6), must_go=True)
assert policy_server._enqueue_observation(obs) is True
assert policy_server.observation_queue.qsize() == 1
assert policy_server.observation_queue.get_nowait() is obs
def test_maybe_enqueue_observation_dissimilar(policy_server):
"""A dissimilar observation (not `must_go`) is enqueued."""
# Set a last predicted observation.
policy_server.last_processed_obs = _make_obs(torch.zeros(6))
# Create a new, dissimilar observation.
new_obs = _make_obs(torch.ones(6) * 5) # High norm difference
assert policy_server._enqueue_observation(new_obs) is True
assert policy_server.observation_queue.qsize() == 1
def test_maybe_enqueue_observation_is_skipped(policy_server):
"""A similar observation (not `must_go`) is skipped."""
# Set a last predicted observation.
policy_server.last_processed_obs = _make_obs(torch.zeros(6))
# Create a new, very similar observation.
new_obs = _make_obs(torch.zeros(6) + 1e-4)
assert policy_server._enqueue_observation(new_obs) is False
assert policy_server.observation_queue.empty() is True
def test_obs_sanity_checks(policy_server):
"""Unit-test the private `_obs_sanity_checks` helper."""
prev = _make_obs(torch.zeros(6), timestep=0)
# Case 1 – timestep already predicted
policy_server._predicted_timesteps.add(1)
obs_same_ts = _make_obs(torch.ones(6), timestep=1)
assert policy_server._obs_sanity_checks(obs_same_ts, prev) is False
# Case 2 – observation too similar
policy_server._predicted_timesteps.clear()
obs_similar = _make_obs(torch.zeros(6) + 1e-4, timestep=2)
assert policy_server._obs_sanity_checks(obs_similar, prev) is False
# Case 3 – genuinely new & dissimilar observation passes
obs_ok = _make_obs(torch.ones(6) * 5, timestep=3)
assert policy_server._obs_sanity_checks(obs_ok, prev) is True
def test_predict_action_chunk(monkeypatch, policy_server):
"""End-to-end test of `_predict_action_chunk` with a stubbed _get_action_chunk."""
# Import only when needed
from lerobot.async_inference.policy_server import PolicyServer
# Force server to act-style policy; patch method to return deterministic tensor
policy_server.policy_type = "act"
# NOTE(Steven): Smelly tests as the Server is a state machine being partially mocked. Adding these processors as a quick fix.
policy_server.preprocessor = lambda obs: obs
policy_server.postprocessor = lambda tensor: tensor
action_dim = 6
batch_size = 1
actions_per_chunk = policy_server.actions_per_chunk
def _fake_get_action_chunk(_self, _obs, _type="act"):
return torch.zeros(batch_size, actions_per_chunk, action_dim)
monkeypatch.setattr(PolicyServer, "_get_action_chunk", _fake_get_action_chunk, raising=True)
obs = _make_obs(torch.zeros(6), timestep=5)
timed_actions = policy_server._predict_action_chunk(obs)
assert len(timed_actions) == actions_per_chunk
assert [ta.get_timestep() for ta in timed_actions] == list(range(5, 5 + actions_per_chunk))
for i, ta in enumerate(timed_actions):
expected_ts = obs.get_timestamp() + i * policy_server.config.environment_dt
assert abs(ta.get_timestamp() - expected_ts) < 1e-6
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