chore: import upstream snapshot with attribution

tests/utils/test_logging_utils.py

@@ -0,0 +1,235 @@
+#!/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.
+
+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)