e49c282879
* perf: KB load-once cache, compiled index, lazy loading, parallel resolution (#38) Closes #130, #131, #132, #133, #134. - Loader node routes through a process-wide KnowledgeBaseLoader cache so the KB loads once per (path, version) and is reused across run() calls. - Normalized lookup table compiled once per KB (per NSFW gating) and reused across resolves instead of rebuilt per resolve_scene. normalize_concept moved to knowledge_base (re-exported from the resolver). - Incremental per-category lazy file loading behind KnowledgeBaseLoader. - Optional parallel per-concept resolution (thread pool), merged in exact discovery order; deterministic and threshold-gated (default sequential). - Performance regression guard (generous ceilings + relative ratio). Refreshed docs/benchmarks.md (resolution 57.6us -> 17.8us). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * docs(changelog): note V2 performance improvements (#38) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: kunail0804 <kunail0804@gmail.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
"""Parallel resolution determinism (issue #133, epic #38)."""
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from __future__ import annotations
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from compiler.common.config import Config
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from compiler.resolver.illustrious_resolver import resolve_scene
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from schemas.models import Scene
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from tests.regression.golden_scenes import load_reference_knowledge_base
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# A mix of exact hits, alias hits, head-noun reductions, and unknowns.
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_BASE = [
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"long hair",
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"blue eyes",
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"thighhighs",
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"jacket",
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"open white shirt",
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"white summer dress",
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"smile",
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"sitting",
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"classroom",
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"sunset",
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"quantum flux inverter",
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]
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def _big_scene(n: int) -> Scene:
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concepts = [_BASE[i % len(_BASE)] for i in range(n - 5)] + [
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f"unique_unknown_{i}" for i in range(5)
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]
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return Scene.from_json(
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{
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"characters": [
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{
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"id": 0,
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"identity": concepts,
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"appearance": [],
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"clothing": [],
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"accessories": [],
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"pose": [],
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"expression": [],
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"actions": [],
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}
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],
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"interactions": [],
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"objects": [],
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"environment": [],
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"camera": [],
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"lighting": [],
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"metadata": {"schema_version": "1.0"},
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}
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)
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def _resolve(scene, threshold):
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return resolve_scene(
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scene, load_reference_knowledge_base(), Config(), parallel_threshold=threshold
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)
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def test_parallel_matches_sequential_exactly() -> None:
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scene = _big_scene(80) # above the default threshold
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sequential = _resolve(scene, 0) # forced sequential
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parallel = _resolve(scene, 1) # forced parallel
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assert [t.tag for t in parallel.data] == [t.tag for t in sequential.data]
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assert [m.code for m in parallel.warnings] == [m.code for m in sequential.warnings]
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def test_parallel_is_deterministic_across_runs() -> None:
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scene = _big_scene(90)
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first = [t.tag for t in _resolve(scene, 1).data]
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second = [t.tag for t in _resolve(scene, 1).data]
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assert first == second
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def test_many_scene_sizes_match_sequential() -> None:
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kb = load_reference_knowledge_base()
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for n in (1, 10, 63, 64, 65, 128):
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scene = _big_scene(n)
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seq = resolve_scene(scene, kb, Config(), parallel_threshold=0)
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par = resolve_scene(scene, kb, Config(), parallel_threshold=1)
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assert [t.tag for t in par.data] == [t.tag for t in seq.data], n
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