ZacharyZhang-NY
71733d86d6
A review of the p2-1 record found seven places where the prose had drifted away from the reports it cites. None were product defects; all were record keeping, and record keeping is what a memory system is judged on. The quantization row mis-transcribed nine latencies, and the two worst made dimension truncation look twice as fast as the full scan when the report measures six percent. The extrapolation table scaled latency by atoms and bytes by vectors under one heading that named neither switch. The picture index stats existed only in prose, so the harness now reads the index shape off the artifact and times the encoder on a sample, which reproduces the hand-timed build within seven percent. The dense arms named a store that holds no vectors, so the report names the store the vectors came from and refuses to run where a dense leg would rank an empty index. The frozen baseline was migrated in place by the product CLI while the picture index was built, so its pinned mtime, digest and schema version are re-pinned and the step that moved it is named. The sqlite_vec import added two mypy errors; the fix is the missing stub declaration, not an inline ignore, and the count is back to the recorded baseline of twenty-four.
426 lines
18 KiB
Python
426 lines
18 KiB
Python
"""What each retrieval leg is actually worth, on the frozen suite, asked as its holder.
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The product fuses three legs with reciprocal rank fusion: a sparse page, a dense vector leg, and a
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picture leg that only speaks up when the words come back thin. Each one costs latency and a model
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snapshot, and the suite is the only honest referee. This harness turns each leg on and off over the
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same probes and prints recall, first-hit quality, latency, and per-modality recall in one table, so
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a default like `dense_embeddings_enabled=False` is a measurement rather than a preference.
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Run: python -m bench.leg_check [--probes 300]
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Writes: bench/reports/LEGS.md, bench/reports/leg_check.json
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"""
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from __future__ import annotations
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import argparse
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import json
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import shutil
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import statistics
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import sys
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import time
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from pathlib import Path
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from typing import Any
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from bench.metrics import aggregate, score_query
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from bench.query_set import load_queries, scale_probes
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from bench.report import git_commit
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from bench.run import latency
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from bench.scratch import copy_store
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from bench.systems import machine_facts
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DEFAULT_HOME = Path("bench/runs/dense_scale")
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DEFAULT_SCRATCH = Path("bench/runs/leg_check")
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DEFAULT_OBJECTS = Path("bench/runs/baseline/objects")
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DEFAULT_CLIP_INDEX = Path("bench/runs/leg_check_index")
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DEFAULT_CLIP_MODEL = Path("bench/models/chinese-clip-vit-base-patch16")
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DEFAULT_EMBEDDING_MODEL = Path("bench/models/paraphrase-multilingual-MiniLM-L12-v2")
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ENCODE_SAMPLE = 200
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# The page the product asks for by default; measure() checks it against Settings, so "as shipped" cannot rot into a hypothesis.
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SHIPPED_TOP_K = 24
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# (label, atoms asked, dense leg, picture leg); the picture leg answers only on a thin page, which is the product's rule.
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CONFIGS: list[dict[str, Any]] = [
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{"label": "words only", "top_k": 10, "dense": False, "visual": False},
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{"label": "words only (the configured 24)", "top_k": SHIPPED_TOP_K, "dense": False, "visual": False},
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{"label": "words only, wider page", "top_k": 30, "dense": False, "visual": False},
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{"label": "+ dense leg", "top_k": 10, "dense": True, "visual": False},
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{"label": "+ dense leg, wider page", "top_k": 30, "dense": True, "visual": False},
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{"label": "+ picture leg", "top_k": 10, "dense": False, "visual": True},
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{"label": "+ picture leg, wider page", "top_k": 30, "dense": False, "visual": True},
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]
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def prepare(
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home: Path,
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scratch: Path,
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objects: Path,
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clip_index: Path,
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*,
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dense: bool,
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visual: bool,
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clip_model: Path,
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embedding_model: Path,
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) -> Path:
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"""Build a scratch store that can run every leg: vectors, the picture index, and the bytes.
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The picture index and the CLIP snapshot are 48 minutes of encoding, so they are carried in from
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wherever they were built rather than rebuilt per row.
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"""
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copy_home, _ = copy_store(home, scratch)
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if visual:
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for name in ("clip_image_index.npy", "clip_image_index_atoms.json"):
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source = clip_index / name
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if not source.exists():
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raise SystemExit(
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f"{source}: the picture index has not been built; run `ely-eye index-visual` "
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"with ELY_EYE_CLIP_MODEL_DIR set, or point --clip-index at its cache"
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)
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(copy_home / "cache").mkdir(parents=True, exist_ok=True)
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shutil.copy2(source, copy_home / "cache" / name)
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if not (clip_model / "onnx" / "model_fp16.onnx").exists():
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raise SystemExit(f"{clip_model / 'onnx' / 'model_fp16.onnx'}: the CLIP snapshot is missing")
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if not objects.is_dir():
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raise SystemExit(f"{objects}: the picture leg needs the object store to check image bytes")
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link = copy_home / "objects"
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if not link.exists() and not link.is_symlink():
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link.symlink_to(objects.resolve())
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if dense and not (embedding_model / "config.json").exists():
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raise SystemExit(f"{embedding_model}: the embedding snapshot is missing; the dense leg cannot run")
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return copy_home
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def _dense_vectors(store: Path) -> int:
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"""Vectors the source store already carries, read without opening it for write."""
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import sqlite3
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import sqlite_vec
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from ely_eye import vector_index
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if vector_index.TABLE != "vec_atoms":
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raise SystemExit(f"the dense table is now {vector_index.TABLE}; update this harness's count")
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if not store.is_file():
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raise SystemExit(f"{store}: no product database")
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with sqlite3.connect(f"file:{store}?mode=ro", uri=True) as connection:
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connection.enable_load_extension(True)
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sqlite_vec.load(connection)
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connection.enable_load_extension(False)
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if not connection.execute("SELECT 1 FROM sqlite_master WHERE name = 'vec_atoms'").fetchone():
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return 0
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return int(connection.execute("SELECT count(*) FROM vec_atoms").fetchone()[0])
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def picture_facts(clip_index: Path) -> dict[str, Any]:
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"""What the picture index really holds, read off the artifact instead of remembered in prose."""
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import numpy as np
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vectors_path = clip_index / "clip_image_index.npy"
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atoms_path = clip_index / "clip_image_index_atoms.json"
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vectors = np.load(vectors_path)
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with atoms_path.open(encoding="utf-8") as handle:
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try:
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atoms = json.load(handle)
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except json.JSONDecodeError as error:
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raise ValueError(f"{atoms_path}: the picture index's atom list is unreadable ({error})") from error
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if len(atoms) != int(vectors.shape[0]):
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raise SystemExit(
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f"{clip_index}: the picture index disagrees with itself — {len(atoms)} atom ids for "
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f"{vectors.shape[0]} vectors"
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)
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return {
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"path": str(clip_index),
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"atoms": len(atoms),
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"dim": int(vectors.shape[-1]),
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"bytes": int(vectors_path.stat().st_size),
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}
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def encode_rate(clip_model: Path, objects: Path, count: int) -> dict[str, Any]:
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"""Pictures per second the encoder manages, measured on a sample instead of remembered."""
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import time
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from ely_eye.clip_encoder import ClipEncoder
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from PIL import Image
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suffixes = {".png", ".jpg", ".jpeg"}
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sample = sorted(p for p in objects.rglob("*") if p.suffix.lower() in suffixes)[:count]
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if len(sample) < count:
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raise SystemExit(f"{objects}: only {len(sample)} pictures to time the encoder on")
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encoder = ClipEncoder(clip_model)
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began = time.perf_counter()
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rows = 0
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for start in range(0, len(sample), 8):
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batch = [Image.open(path).convert("RGB") for path in sample[start : start + 8]]
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rows += len(encoder.encode_images(batch))
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seconds = time.perf_counter() - began
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if rows != len(sample):
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raise SystemExit(f"the encoder returned {rows} vectors for {len(sample)} pictures")
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return {"pictures": rows, "seconds": seconds, "per_second": rows / seconds}
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def _schema(home: Path) -> int:
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import sqlite3
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with sqlite3.connect(home / "data" / "ely_eye.sqlite") as connection:
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return int(connection.execute("PRAGMA user_version").fetchone()[0])
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def rank_all(service: Any, ids: dict[str, str], probes: list[Any], top_k: int) -> tuple[list[list[str]], list[int], list[float]]:
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"""Ask the product as each probe's own holder and collapse atoms to sources by first hit."""
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from ely_eye.schemas import SearchFilters
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rankings: list[list[str]] = []
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distinct: list[int] = []
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timings: list[float] = []
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for index, query in enumerate(probes):
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began = time.perf_counter()
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hits = service.search(query.text, top_k=top_k, filters=SearchFilters(as_holder=query.holder)).hits
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timings.append(time.perf_counter() - began)
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sources: list[str] = []
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for hit in hits:
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source = ids[hit.atom.source_id]
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if source not in sources:
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sources.append(source)
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distinct.append(len(sources))
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rankings.append(sources)
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if (index + 1) % 100 == 0:
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print(f" {index + 1}/{len(probes)}", flush=True)
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return rankings, distinct, timings
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def measure(home: Path, scratch: Path, objects: Path, clip_index: Path, probes: int, models: dict[str, Path]) -> dict[str, Any]:
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"""Score every configuration on the same probes, one warm service per configuration."""
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from ely_eye.config import Settings
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from ely_eye.db import Database
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from ely_eye.retrieval import RetrievalService
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shipped = Settings().retrieval_top_k
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if shipped != SHIPPED_TOP_K:
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raise SystemExit(
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f"the product asks for {shipped} atoms per search but this harness pins "
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f"SHIPPED_TOP_K={SHIPPED_TOP_K}; update the rows before trusting the 'as shipped' label"
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)
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chosen = scale_probes(load_queries(), probes)
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answerable = [query for query in chosen if query.gold]
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if not answerable:
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raise SystemExit("the frozen suite carries no answerable query; nothing to score")
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print(f"{len(answerable)} answerable probes", flush=True)
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vectors = _dense_vectors(home / "data" / "ely_eye.sqlite")
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if vectors == 0 and any(config["dense"] for config in CONFIGS):
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raise SystemExit(
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f"{home}: the store carries no dense vectors, so a dense arm here would rank an empty "
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"index and the 24-minute build would hide outside the measured latency. Build them with "
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"python -m bench.dense_scale, or point --home at a store that already has them."
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)
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arms: list[dict[str, Any]] = []
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picture: dict[str, Any] | None = None
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rate: dict[str, Any] | None = None
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schema = 0
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for config in CONFIGS:
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print(f" {config['label']}: dense={config['dense']} visual={config['visual']}", flush=True)
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copy_home = prepare(
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home,
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scratch,
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objects,
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clip_index,
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dense=config["dense"],
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visual=config["visual"],
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clip_model=models["clip"],
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embedding_model=models["embedding"],
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)
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schema = _schema(copy_home)
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if config["visual"]:
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picture = picture_facts(clip_index)
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if rate is None:
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rate = encode_rate(models["clip"], objects, ENCODE_SAMPLE)
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settings = Settings(
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home=copy_home,
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dense_embeddings_enabled=config["dense"],
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embedding_model_dir=models["embedding"] if config["dense"] else None,
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clip_model_dir=models["clip"] if config["visual"] else None,
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)
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database = Database(settings)
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service = RetrievalService(settings=settings, database=database)
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ids = _source_ids(database)
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rankings, distinct, timings = rank_all(service, ids, answerable, int(config["top_k"]))
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scores = [
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score_query(query.query_id, ranking, set(query.gold), query.holder)
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for query, ranking in zip(answerable, rankings, strict=True)
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]
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summary = aggregate(scores)
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timing = latency(timings)
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by_modality: dict[str, list[float]] = {}
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for query, score in zip(answerable, scores, strict=True):
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by_modality.setdefault(query.modality, []).append(score.recall_at_10)
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arms.append(
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{
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"label": config["label"],
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"top_k": config["top_k"],
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"dense": config["dense"],
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"visual": config["visual"],
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"recall_at_10": summary["recall_at_10"],
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"mrr": summary["mrr"],
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"hit_at_10": summary["hit_at_10"],
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"wilson_lower": summary["hit_at_10_wilson_lower"],
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"p50_seconds": timing["p50"],
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"p95_seconds": timing["p95"],
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"median_distinct_sources": statistics.median(distinct),
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"by_modality": {
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modality: sum(rows) / len(rows) for modality, rows in sorted(by_modality.items())
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},
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"modality_counts": {modality: len(rows) for modality, rows in sorted(by_modality.items())},
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}
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)
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print(
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f" recall@10 {summary['recall_at_10']:.3f}, mrr {summary['mrr']:.3f}, "
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f"p50 {timing['p50'] * 1000:.1f}ms",
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flush=True,
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)
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return {
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"commit": git_commit(),
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"schema_version": schema,
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"store": str(scratch / "data" / "ely_eye.sqlite"),
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"source_store": str(home),
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"dense_vectors": vectors,
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"picture_index": picture,
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"encode_rate": rate,
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"probes": len(answerable),
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"arms": arms,
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"machine": machine_facts(),
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}
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def _source_ids(database: Any) -> dict[str, str]:
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"""Store source id to benchmark source id, the same mapping every other arm uses."""
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from bench.systems import _corpus_ids
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return _corpus_ids(database)
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def write_markdown(report: dict[str, Any], path: Path) -> None:
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machine = report["machine"]
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header = [
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"| leg configuration | atoms asked | recall@10 | MRR | hit@10 | 95% lower | p50 ms | p95 ms | distinct sources |",
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"| --- | --- | --- | --- | --- | --- | --- | --- | --- |",
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]
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rows = []
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for arm in report["arms"]:
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rows.append(
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f"| {arm['label']} | {arm['top_k']} | {arm['recall_at_10']:.3f} | {arm['mrr']:.3f} | "
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f"{arm['hit_at_10']:.3f} | {arm['wilson_lower']:.3f} | {arm['p50_seconds'] * 1000:.1f} | "
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f"{arm['p95_seconds'] * 1000:.1f} | {arm['median_distinct_sources']} |"
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)
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modalities = sorted({modality for arm in report["arms"] for modality in arm["by_modality"]})
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modality_rows = [
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"| leg configuration | " + " | ".join(modalities) + " |",
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"| --- |" + " --- |" * len(modalities),
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]
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for arm in report["arms"]:
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modality_rows.append(
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f"| {arm['label']} | "
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+ " | ".join(f"{arm['by_modality'].get(modality, float('nan')):.3f}" for modality in modalities)
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+ " |"
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)
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counts = report["arms"][0]["modality_counts"]
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path.write_text(
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"\n".join(
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[
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"# LEGS: what each retrieval leg is worth",
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"",
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f"- commit: `{report['commit']}`, schema v{report['schema_version']}",
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(
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f"- store: {report['store']} — a copy of {report['source_store']}, which carries "
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f"{report['dense_vectors']:,} dense vectors built by bench/dense_scale. The build "
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"sits outside the measured latency, and the frozen baseline itself holds no "
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"vectors, so a dense arm there would rank an empty index."
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),
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f"- {report['probes']} answerable queries, each asked as the holder the frozen set assigns it",
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*(
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[]
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if report.get("picture_index") is None
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else [
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(
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f"- picture index: {report['picture_index']['atoms']:,} atoms at "
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f"{report['picture_index']['dim']} dimensions, "
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f"{report['picture_index']['bytes']:,} bytes, read from "
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f"{report['picture_index']['path']}"
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),
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(
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f"- encoder speed: {report['encode_rate']['per_second']:.1f} pictures/s "
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f"measured on {report['encode_rate']['pictures']} pictures, so the "
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f"{report['picture_index']['atoms']:,} pictures in that index cost about "
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f"{report['picture_index']['atoms'] / report['encode_rate']['per_second'] / 60:.1f} "
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"minutes to encode"
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),
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]
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),
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(
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f"- machine: {machine['system']} {machine['release']}, {machine['cpus']} cores, "
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f"{machine['threads_per_query']} threads per query, load {machine['load_average_1_5_15']}"
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),
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"",
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"Recall is scored on the first ten **sources** after atoms are collapsed to their source",
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"by first hit, which is how every other arm in this repo is scored.",
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"",
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*header,
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*rows,
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"",
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"## recall@10 by modality",
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"",
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*modality_rows,
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"",
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f"_Query counts per modality: {', '.join(f'{name} {count}' for name, count in counts.items())}._",
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"",
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(
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"The picture leg is not a switch this harness flips per row: the product runs it only "
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"when the sparse page comes back thin, so these rows measure the product with the leg "
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"available, not a forced always-on fusion."
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),
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"",
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(
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"_Latency is wall time per search on the machine named above with the thread budget "
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"the benchmark pins; a run on a loaded machine is not comparable._"
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),
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"",
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]
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),
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encoding="utf-8",
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)
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--home", type=Path, default=DEFAULT_HOME)
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parser.add_argument("--scratch", type=Path, default=DEFAULT_SCRATCH)
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parser.add_argument("--objects", type=Path, default=DEFAULT_OBJECTS)
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parser.add_argument("--clip-index", type=Path, default=DEFAULT_CLIP_INDEX)
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parser.add_argument("--clip-model", type=Path, default=DEFAULT_CLIP_MODEL)
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parser.add_argument("--embedding-model", type=Path, default=DEFAULT_EMBEDDING_MODEL)
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parser.add_argument("--probes", type=int, default=300)
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parser.add_argument("--out", type=Path, default=Path("bench/reports/LEGS.md"))
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parser.add_argument("--json-out", type=Path, default=Path("bench/reports/leg_check.json"))
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args = parser.parse_args()
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report = measure(
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args.home,
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args.scratch,
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args.objects,
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args.clip_index,
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args.probes,
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{"clip": args.clip_model, "embedding": args.embedding_model},
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)
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args.json_out.parent.mkdir(parents=True, exist_ok=True)
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args.json_out.write_text(json.dumps(report, indent=2, ensure_ascii=False), encoding="utf-8")
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write_markdown(report, args.out)
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print(f"wrote {args.out}")
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if __name__ == "__main__":
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if sys.version_info < (3, 11):
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raise SystemExit("bench needs Python 3.11+")
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main()
|