speed up benchmark eval scheduling and docker workflow

2026-05-16 17:20:05 +00:00 · 2026-03-21 06:09:01 +01:00
parent f60d163588
commit 285c500aef
18 changed files with 1451 additions and 235 deletions
@@ -0,0 +1,244 @@
+#!/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.
+
+from lerobot.envs.lazy_vec_env import LazyVectorEnv
+from lerobot.scripts import lerobot_eval
+
+
+class _DummyTaskEnv:
+    def __init__(self):
+        self.close_calls = 0
+
+    def close(self):
+        self.close_calls += 1
+
+
+class _TrackedLazyEnv(LazyVectorEnv):
+    def __init__(self, n_factory_fns: int = 1):
+        super().__init__(lambda fns: None, [lambda: None for _ in range(n_factory_fns)])
+        self.close_calls = 0
+
+    def close(self):
+        self.close_calls += 1
+        super().close()
+
+
+def _fake_metrics():
+    return {
+        "sum_rewards": [1.0],
+        "max_rewards": [1.0],
+        "successes": [True],
+        "video_paths": [],
+    }
+
+
+def test_eval_policy_all_sequential_closes_envs(monkeypatch):
+    def _fake_run_one(task_group, task_id, env, **kwargs):  # noqa: ARG001
+        return task_group, task_id, _fake_metrics()
+
+    monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
+    env_a = _DummyTaskEnv()
+    env_b = _DummyTaskEnv()
+    envs = {"suite": {0: env_a, 1: env_b}}
+
+    result = lerobot_eval.eval_policy_all(
+        envs=envs,
+        policy=None,
+        env_preprocessor=None,
+        env_postprocessor=None,
+        preprocessor=None,
+        postprocessor=None,
+        n_episodes=1,
+        max_parallel_tasks=1,
+    )
+
+    assert env_a.close_calls == 1
+    assert env_b.close_calls == 1
+    assert result["overall"]["n_episodes"] == 2
+
+
+def test_eval_policy_all_threaded_fallback_closes_envs(monkeypatch):
+    def _fake_run_one(task_group, task_id, env, **kwargs):  # noqa: ARG001
+        return task_group, task_id, _fake_metrics()
+
+    monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
+    env_a = _DummyTaskEnv()
+    env_b = _DummyTaskEnv()
+    env_c = _DummyTaskEnv()
+    envs = {"suite": {0: env_a, 1: env_b, 2: env_c}}
+
+    result = lerobot_eval.eval_policy_all(
+        envs=envs,
+        policy=None,
+        env_preprocessor=None,
+        env_postprocessor=None,
+        preprocessor=None,
+        postprocessor=None,
+        n_episodes=1,
+        max_parallel_tasks=2,
+    )
+
+    assert env_a.close_calls == 1
+    assert env_b.close_calls == 1
+    assert env_c.close_calls == 1
+    assert result["overall"]["n_episodes"] == 3
+
+
+def test_eval_policy_all_uses_batched_lazy_mode(monkeypatch):
+    def _run_one_should_not_be_called(*args, **kwargs):
+        raise AssertionError("run_one should not run in batched lazy mode")
+
+    chunk_sizes = []
+
+    def _fake_eval_task_batch(chunk, **kwargs):  # noqa: ARG001
+        chunk_sizes.append(len(chunk))
+        return [(tg, tid, _fake_metrics()) for tg, tid, _ in chunk]
+
+    monkeypatch.setattr(lerobot_eval, "run_one", _run_one_should_not_be_called)
+    monkeypatch.setattr(lerobot_eval, "_eval_task_batch", _fake_eval_task_batch)
+
+    envs = {
+        "suite": {
+            0: LazyVectorEnv(lambda fns: None, [lambda: None]),
+            1: LazyVectorEnv(lambda fns: None, [lambda: None]),
+            2: LazyVectorEnv(lambda fns: None, [lambda: None]),
+        }
+    }
+
+    result = lerobot_eval.eval_policy_all(
+        envs=envs,
+        policy=None,
+        env_preprocessor=None,
+        env_postprocessor=None,
+        preprocessor=None,
+        postprocessor=None,
+        n_episodes=1,
+        max_parallel_tasks=2,
+    )
+
+    assert chunk_sizes == [2, 1]
+    assert result["overall"]["n_episodes"] == 3
+
+
+def test_eval_policy_all_disables_batched_lazy_when_n_episodes_not_one(monkeypatch):
+    def _fake_run_one(task_group, task_id, env, **kwargs):  # noqa: ARG001
+        return task_group, task_id, _fake_metrics()
+
+    def _batch_should_not_run(*args, **kwargs):
+        raise AssertionError("_eval_task_batch should not run when n_episodes != 1")
+
+    monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
+    monkeypatch.setattr(lerobot_eval, "_eval_task_batch", _batch_should_not_run)
+
+    env_a = _TrackedLazyEnv()
+    env_b = _TrackedLazyEnv()
+    envs = {"suite": {0: env_a, 1: env_b}}
+
+    result = lerobot_eval.eval_policy_all(
+        envs=envs,
+        policy=None,
+        env_preprocessor=None,
+        env_postprocessor=None,
+        preprocessor=None,
+        postprocessor=None,
+        n_episodes=2,
+        max_parallel_tasks=2,
+    )
+
+    assert env_a.close_calls == 1
+    assert env_b.close_calls == 1
+    assert result["overall"]["n_episodes"] == 2
+
+
+def test_eval_policy_all_disables_batched_lazy_when_batch_size_above_one(monkeypatch):
+    def _fake_run_one(task_group, task_id, env, **kwargs):  # noqa: ARG001
+        return task_group, task_id, _fake_metrics()
+
+    def _batch_should_not_run(*args, **kwargs):
+        raise AssertionError("_eval_task_batch should not run when eval.batch_size > 1")
+
+    monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
+    monkeypatch.setattr(lerobot_eval, "_eval_task_batch", _batch_should_not_run)
+
+    env_a = _TrackedLazyEnv(n_factory_fns=2)
+    env_b = _TrackedLazyEnv(n_factory_fns=2)
+    envs = {"suite": {0: env_a, 1: env_b}}
+
+    result = lerobot_eval.eval_policy_all(
+        envs=envs,
+        policy=None,
+        env_preprocessor=None,
+        env_postprocessor=None,
+        preprocessor=None,
+        postprocessor=None,
+        n_episodes=1,
+        max_parallel_tasks=2,
+    )
+
+    assert env_a.close_calls == 1
+    assert env_b.close_calls == 1
+    assert result["overall"]["n_episodes"] == 2
+
+
+def test_eval_policy_all_applies_instance_sharding(monkeypatch):
+    called = []
+
+    def _fake_run_one(task_group, task_id, env, **kwargs):  # noqa: ARG001
+        called.append(task_id)
+        return task_group, task_id, _fake_metrics()
+
+    monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
+    envs = {"suite": {0: _DummyTaskEnv(), 1: _DummyTaskEnv(), 2: _DummyTaskEnv(), 3: _DummyTaskEnv()}}
+
+    result = lerobot_eval.eval_policy_all(
+        envs=envs,
+        policy=None,
+        env_preprocessor=None,
+        env_postprocessor=None,
+        preprocessor=None,
+        postprocessor=None,
+        n_episodes=1,
+        max_parallel_tasks=1,
+        instance_count=2,
+        instance_id=1,
+    )
+
+    assert called == [1, 3]
+    assert result["overall"]["n_episodes"] == 2
+
+
+def test_aggregate_eval_from_per_task_merges_groups_and_overall():
+    per_task = [
+        {
+            "task_group": "a",
+            "task_id": 0,
+            "metrics": {"sum_rewards": [1.0], "max_rewards": [2.0], "successes": [True], "video_paths": ["v0"]},
+        },
+        {
+            "task_group": "b",
+            "task_id": 1,
+            "metrics": {"sum_rewards": [3.0], "max_rewards": [4.0], "successes": [False], "video_paths": []},
+        },
+    ]
+
+    merged = lerobot_eval._aggregate_eval_from_per_task(per_task, total_eval_s=10.0)
+
+    assert merged["overall"]["n_episodes"] == 2
+    assert merged["overall"]["avg_sum_reward"] == 2.0
+    assert merged["overall"]["pc_success"] == 50.0
+    assert merged["overall"]["eval_s"] == 10.0
+    assert set(merged["per_group"]) == {"a", "b"}
+