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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.
import pytest
import torch
from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
@pytest.fixture
def mock_metrics():
return {"loss": AverageMeter("loss", ":.3f"), "accuracy": AverageMeter("accuracy", ":.2f")}
class MockAccelerator:
def __init__(self, num_processes: int, reduce_fn=None):
self.num_processes = num_processes
self.device = torch.device("cpu")
self._reduce_fn = reduce_fn
def reduce(self, tensor, reduction="mean"):
# In single-process tests we just want a deterministic stand-in for accelerate's reduce.
if self._reduce_fn is not None:
return self._reduce_fn(tensor, reduction)
return tensor
def test_average_meter_initialization():
meter = AverageMeter("loss", ":.2f")
assert meter.name == "loss"
assert meter.fmt == ":.2f"
assert meter.val == 0.0
assert meter.avg == 0.0
assert meter.sum == 0.0
assert meter.count == 0.0
def test_average_meter_update():
meter = AverageMeter("accuracy")
meter.update(5, n=2)
assert meter.val == 5
assert meter.sum == 10
assert meter.count == 2
assert meter.avg == 5
def test_average_meter_reset():
meter = AverageMeter("loss")
meter.update(3, 4)
meter.reset()
assert meter.val == 0.0
assert meter.avg == 0.0
assert meter.sum == 0.0
assert meter.count == 0.0
def test_average_meter_str():
meter = AverageMeter("metric", ":.1f")
meter.update(4.567, 3)
assert str(meter) == "metric:4.6"
def test_metrics_tracker_initialization(mock_metrics):
tracker = MetricsTracker(
batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=10
)
assert tracker.steps == 10
assert tracker.samples == 10 * 32
assert tracker.episodes == tracker.samples / (1000 / 50)
assert tracker.epochs == tracker.samples / 1000
assert "loss" in tracker.metrics
assert "accuracy" in tracker.metrics
def test_metrics_tracker_step(mock_metrics):
tracker = MetricsTracker(
batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=5
)
tracker.step()
assert tracker.steps == 6
assert tracker.samples == 6 * 32
assert tracker.episodes == tracker.samples / (1000 / 50)
assert tracker.epochs == tracker.samples / 1000
def test_metrics_tracker_initialization_with_accelerator(mock_metrics):
tracker = MetricsTracker(
batch_size=32,
num_frames=1000,
num_episodes=50,
metrics=mock_metrics,
initial_step=10,
accelerator=MockAccelerator(num_processes=2),
)
assert tracker.steps == 10
assert tracker.samples == 10 * 32 * 2
assert tracker.episodes == tracker.samples / (1000 / 50)
assert tracker.epochs == tracker.samples / 1000
def test_metrics_tracker_step_with_accelerator(mock_metrics):
tracker = MetricsTracker(
batch_size=32,
num_frames=1000,
num_episodes=50,
metrics=mock_metrics,
initial_step=5,
accelerator=MockAccelerator(num_processes=2),
)
tracker.step()
assert tracker.steps == 6
assert tracker.samples == (5 * 32 * 2) + (32 * 2)
assert tracker.episodes == tracker.samples / (1000 / 50)
assert tracker.epochs == tracker.samples / 1000
def test_metrics_tracker_getattr(mock_metrics):
tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
assert tracker.loss == mock_metrics["loss"]
assert tracker.accuracy == mock_metrics["accuracy"]
with pytest.raises(AttributeError):
_ = tracker.non_existent_metric
def test_metrics_tracker_setattr(mock_metrics):
tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
tracker.loss = 2.0
assert tracker.loss.val == 2.0
def test_metrics_tracker_str(mock_metrics):
tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
tracker.loss.update(3.456, 1)
tracker.accuracy.update(0.876, 1)
output = str(tracker)
assert "loss:3.456" in output
assert "accuracy:0.88" in output
def test_metrics_tracker_to_dict(mock_metrics):
tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
tracker.loss.update(5, 2)
metrics_dict = tracker.to_dict()
assert isinstance(metrics_dict, dict)
assert metrics_dict["loss"] == 5 # average value
assert metrics_dict["steps"] == tracker.steps
def test_metrics_tracker_reset_averages(mock_metrics):
tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
tracker.loss.update(10, 3)
tracker.accuracy.update(0.95, 5)
tracker.reset_averages()
assert tracker.loss.avg == 0.0
assert tracker.accuracy.avg == 0.0
def test_average_meter_invalid_reduction():
with pytest.raises(ValueError):
AverageMeter("loss", reduction="median")
def test_average_meter_reduction_stored():
meter = AverageMeter("updt_s", reduction="max")
assert meter.reduction == "max"
def test_metrics_tracker_reduce_across_ranks_no_accelerator():
metrics = {"update_s": AverageMeter("update_s", reduction="max")}
tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=metrics)
tracker.update_s = 0.5
tracker.reduce_across_ranks() # no-op without accelerator
assert tracker.update_s.avg == 0.5
def test_metrics_tracker_reduce_across_ranks_single_process():
metrics = {"update_s": AverageMeter("update_s", reduction="max")}
tracker = MetricsTracker(
batch_size=32,
num_frames=1000,
num_episodes=50,
metrics=metrics,
accelerator=MockAccelerator(num_processes=1),
)
tracker.update_s = 0.5
tracker.reduce_across_ranks() # no-op when world size is 1
assert tracker.update_s.avg == 0.5
def test_metrics_tracker_reduce_across_ranks_invokes_reduce():
captured = {}
def fake_reduce(tensor, reduction):
captured["reduction"] = reduction
captured["values"] = tensor.clone()
# Pretend the slowest rank reported 0.9 instead of this rank's 0.4.
return torch.tensor([0.9], dtype=tensor.dtype, device=tensor.device)
metrics = {
"loss": AverageMeter("loss"), # reduction="none" -> not touched
"update_s": AverageMeter("update_s", reduction="max"),
}
tracker = MetricsTracker(
batch_size=32,
num_frames=1000,
num_episodes=50,
metrics=metrics,
accelerator=MockAccelerator(num_processes=4, reduce_fn=fake_reduce),
)
tracker.loss = 1.0
tracker.update_s = 0.4
tracker.reduce_across_ranks()
assert captured["reduction"] == "max"
assert torch.allclose(captured["values"], torch.tensor([0.4]))
assert tracker.update_s.avg == pytest.approx(0.9)
# Metrics without a reduction stay untouched.
assert tracker.loss.avg == 1.0
# Invariant: avg == sum / count must hold after reduce, so subsequent .update() calls
# accumulate against the cluster view rather than the stale per-rank sum.
meter = tracker.update_s
assert meter.sum / meter.count == pytest.approx(meter.avg)
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