mirror of
https://github.com/huggingface/lerobot.git
synced 2026-08-08 17:39:44 +00:00
Haoming Song
ff7cc3de1d
Two post-merge CI failures on main, both from #4010. Benchmark Integration Tests (Libero) — `accelerate launch` exports whole groups of variables unconditionally (the five ACCELERATE_DYNAMO_* it writes default the backend to "no"), so matching on prefixes refused launches that configure nothing, contradicting the documented flow where accelerate is supported as a plain launcher. The guard now watches only the three switches that hand a subsystem to the environment. GPU Tests — `test_metrics_tracker_reduce_across_ranks_invokes_all_reduce` compared the captured reduction buffer against a CPU tensor, so the assert raised "Expected all tensors to be on the same device" wherever CUDA is available. The expected tensor is built on the buffer's device instead.
242 lines
8.6 KiB
Python
242 lines
8.6 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 pytest
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import torch
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import lerobot.utils.logging_utils as logging_utils
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from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
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@pytest.fixture
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def mock_metrics():
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return {"loss": AverageMeter("loss", ":.3f"), "accuracy": AverageMeter("accuracy", ":.2f")}
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def test_average_meter_initialization():
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meter = AverageMeter("loss", ":.2f")
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assert meter.name == "loss"
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assert meter.fmt == ":.2f"
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assert meter.val == 0.0
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assert meter.avg == 0.0
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assert meter.sum == 0.0
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assert meter.count == 0.0
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def test_average_meter_update():
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meter = AverageMeter("accuracy")
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meter.update(5, n=2)
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assert meter.val == 5
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assert meter.sum == 10
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assert meter.count == 2
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assert meter.avg == 5
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def test_average_meter_reset():
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meter = AverageMeter("loss")
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meter.update(3, 4)
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meter.reset()
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assert meter.val == 0.0
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assert meter.avg == 0.0
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assert meter.sum == 0.0
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assert meter.count == 0.0
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def test_average_meter_str():
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meter = AverageMeter("metric", ":.1f")
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meter.update(4.567, 3)
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assert str(meter) == "metric:4.6"
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def test_metrics_tracker_initialization(mock_metrics):
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tracker = MetricsTracker(
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batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=10
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)
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assert tracker.steps == 10
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assert tracker.samples == 10 * 32
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assert tracker.episodes == tracker.samples / (1000 / 50)
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assert tracker.epochs == tracker.samples / 1000
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assert "loss" in tracker.metrics
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assert "accuracy" in tracker.metrics
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def test_metrics_tracker_step(mock_metrics):
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tracker = MetricsTracker(
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batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=5
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)
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tracker.step()
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assert tracker.steps == 6
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assert tracker.samples == 6 * 32
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assert tracker.episodes == tracker.samples / (1000 / 50)
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assert tracker.epochs == tracker.samples / 1000
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def test_metrics_tracker_initialization_with_dp_world(mock_metrics):
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tracker = MetricsTracker(
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batch_size=32,
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num_frames=1000,
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num_episodes=50,
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metrics=mock_metrics,
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initial_step=10,
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dp_world_size=2,
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)
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assert tracker.steps == 10
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assert tracker.samples == 10 * 32 * 2
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assert tracker.episodes == tracker.samples / (1000 / 50)
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assert tracker.epochs == tracker.samples / 1000
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def test_metrics_tracker_step_with_dp_world(mock_metrics):
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tracker = MetricsTracker(
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batch_size=32,
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num_frames=1000,
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num_episodes=50,
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metrics=mock_metrics,
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initial_step=5,
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dp_world_size=2,
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)
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tracker.step()
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assert tracker.steps == 6
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assert tracker.samples == (5 * 32 * 2) + (32 * 2)
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assert tracker.episodes == tracker.samples / (1000 / 50)
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assert tracker.epochs == tracker.samples / 1000
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def test_metrics_tracker_getattr(mock_metrics):
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
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assert tracker.loss == mock_metrics["loss"]
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assert tracker.accuracy == mock_metrics["accuracy"]
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with pytest.raises(AttributeError):
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_ = tracker.non_existent_metric
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def test_metrics_tracker_setattr(mock_metrics):
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
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tracker.loss = 2.0
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assert tracker.loss.val == 2.0
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def test_metrics_tracker_str(mock_metrics):
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
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tracker.loss.update(3.456, 1)
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tracker.accuracy.update(0.876, 1)
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output = str(tracker)
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assert "loss:3.456" in output
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assert "accuracy:0.88" in output
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def test_metrics_tracker_to_dict(mock_metrics):
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
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tracker.loss.update(5, 2)
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metrics_dict = tracker.to_dict()
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assert isinstance(metrics_dict, dict)
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assert metrics_dict["loss"] == 5 # average value
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assert metrics_dict["steps"] == tracker.steps
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def test_metrics_tracker_reset_averages(mock_metrics):
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
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tracker.loss.update(10, 3)
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tracker.accuracy.update(0.95, 5)
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tracker.reset_averages()
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assert tracker.loss.avg == 0.0
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assert tracker.accuracy.avg == 0.0
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def test_average_meter_invalid_reduction():
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with pytest.raises(ValueError):
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AverageMeter("loss", reduction="median")
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def test_average_meter_reduction_stored():
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meter = AverageMeter("updt_s", reduction="max")
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assert meter.reduction == "max"
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def test_metrics_tracker_reduce_across_ranks_outside_distributed():
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metrics = {"update_s": AverageMeter("update_s", reduction="max")}
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=metrics)
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tracker.update_s = 0.5
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tracker.reduce_across_ranks() # no-op without an initialized process group
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assert tracker.update_s.avg == 0.5
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def test_metrics_tracker_reduce_across_ranks_invokes_all_reduce(monkeypatch):
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captured = {}
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def fake_all_reduce(tensor, op):
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captured["op"] = op
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captured["values"] = tensor.clone()
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# Pretend the slowest rank reported 0.9 instead of this rank's 0.4.
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tensor.fill_(0.9)
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monkeypatch.setattr(logging_utils.dist, "is_initialized", lambda: True)
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monkeypatch.setattr(logging_utils.dist, "get_world_size", lambda: 4)
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monkeypatch.setattr(logging_utils.dist, "all_reduce", fake_all_reduce)
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metrics = {
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"loss": AverageMeter("loss"), # reduction="none" -> not touched
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"update_s": AverageMeter("update_s", reduction="max"),
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}
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=metrics)
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tracker.loss = 1.0
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tracker.update_s = 0.4
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tracker.reduce_across_ranks()
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assert captured["op"] == logging_utils.dist.ReduceOp.MAX
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assert torch.allclose(captured["values"], torch.tensor([0.4], device=captured["values"].device))
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assert tracker.update_s.avg == pytest.approx(0.9)
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# Metrics without a reduction stay untouched.
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assert tracker.loss.avg == 1.0
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# Invariant: avg == sum / count must hold after reduce, so subsequent .update() calls
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# accumulate against the cluster view rather than the stale per-rank sum.
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meter = tracker.update_s
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assert meter.sum / meter.count == pytest.approx(meter.avg)
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def test_metrics_tracker_update_metrics_registers_and_averages():
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics={})
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tracker.update_metrics({"latent_loss": 0.2, "action_loss": 0.4})
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tracker.update_metrics({"latent_loss": 0.4, "action_loss": 0.6})
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# New keys are auto-registered as mean-reduced meters and averaged over the window.
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assert tracker.metrics["latent_loss"].reduction == "mean"
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assert tracker.metrics["latent_loss"].avg == pytest.approx(0.3)
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assert tracker.metrics["action_loss"].avg == pytest.approx(0.5)
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assert tracker.to_dict()["latent_loss"] == pytest.approx(0.3)
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def test_metrics_tracker_update_metrics_skips_non_numeric():
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics={})
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tracker.update_metrics({"loss": 0.5, "head_mode": "sparse", "enabled": True})
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# strings and bools ignored
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assert "loss" in tracker.metrics
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assert "head_mode" not in tracker.metrics
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assert "enabled" not in tracker.metrics
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def test_metrics_tracker_update_metrics_does_not_override_caller_meter():
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# A policy that echoes "loss" in its output dict must not overwrite the caller-owned,
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# already-aggregated loss meter.
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metrics = {"loss": AverageMeter("loss", ":.3f", reduction="mean")}
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tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=metrics)
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tracker.loss = 1.0 # caller-set optimized loss
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tracker.update_metrics({"loss": 99.0, "latent_loss": 0.2})
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assert tracker.metrics["loss"].avg == pytest.approx(1.0) # snapshot ignored
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assert tracker.metrics["latent_loss"].avg == pytest.approx(0.2)
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