chore: import upstream snapshot with attribution

2026-07-13 13:05:14 +08:00
commit 2a547be7fe
7904 changed files with 1000926 additions and 0 deletions
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+"""Per-call overhead micro-benchmarks for the rr.log() pipeline.
+
+See README.md for usage.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pytest
+
+import rerun as rr
+import rerun_bindings as bindings
+from rerun._log import _log_components
+
+ARCHETYPE_CASES = [
+    pytest.param(lambda: rr.Scalars(42.0), id="Scalars"),
+    pytest.param(
+        lambda: rr.Points3D([[1, 2, 3]], colors=[0xFF0000FF], radii=[0.1]),
+        id="Points3D",
+    ),
+    pytest.param(
+        lambda: rr.Transform3D(translation=[1, 2, 3], mat3x3=np.eye(3, dtype=np.float32)),
+        id="Transform3D",
+    ),
+    pytest.param(
+        lambda: rr.Boxes3D(half_sizes=[[1, 2, 3]], colors=[0xFF0000FF]),
+        id="Boxes3D",
+    ),
+]
+
+
+def _init() -> None:
+    """Common setup: init rerun + memory recording."""
+    rr.init("rerun_example_micro_benchmark", spawn=False)
+    rr.memory_recording()
+
+
+@pytest.mark.parametrize("make_archetype", ARCHETYPE_CASES)
+def test_bench_micro_construct(benchmark: Any, make_archetype: Any) -> None:
+    _init()
+    benchmark(make_archetype)
+
+
+@pytest.mark.parametrize("make_archetype", ARCHETYPE_CASES)
+def test_bench_micro_as_component_batches(benchmark: Any, make_archetype: Any) -> None:
+    _init()
+    archetype = make_archetype()
+    benchmark(archetype.as_component_batches)
+
+
+@pytest.mark.parametrize("make_archetype", ARCHETYPE_CASES)
+def test_bench_micro_log_components(benchmark: Any, make_archetype: Any) -> None:
+    _init()
+    batches = make_archetype().as_component_batches()
+    benchmark(_log_components, "test_entity", batches)
+
+
+@pytest.mark.parametrize("make_archetype", ARCHETYPE_CASES)
+def test_bench_micro_log_arrow_msg(benchmark: Any, make_archetype: Any) -> None:
+    _init()
+    batches = make_archetype().as_component_batches()
+    instanced = {b.component_descriptor(): b.as_arrow_array() for b in batches if b.as_arrow_array() is not None}
+    benchmark(bindings.log_arrow_msg, "test_entity", components=instanced, static_=False, recording=None)
+
+
+@pytest.mark.parametrize("make_archetype", ARCHETYPE_CASES)
+def test_bench_micro_log(benchmark: Any, make_archetype: Any) -> None:
+    _init()
+    archetype = make_archetype()
+    benchmark(rr.log, "test_entity", archetype)
+
+
+def test_bench_micro_set_time(benchmark: Any) -> None:
+    _init()
+    benchmark(rr.set_time, "frame", sequence=42)