From b97354623b2eb5cae1f422ce3a4e79f6723e49ab Mon Sep 17 00:00:00 2001 From: goodnight Date: Sun, 21 Jun 2026 12:00:00 +0100 Subject: [PATCH 1/4] add quantization-faceoff --- Quantization-FaceOff/.env.example | 9 + Quantization-FaceOff/.gitignore | 12 + Quantization-FaceOff/README.md | 131 ++ Quantization-FaceOff/assets/faceoff.png | Bin 0 -> 100190 bytes .../assets/qdrant-baseline.png | Bin 0 -> 30299 bytes Quantization-FaceOff/assets/qdrant-bq.png | Bin 0 -> 31450 bytes .../assets/qdrant-collections.png | Bin 0 -> 47770 bytes Quantization-FaceOff/assets/qdrant-pq.png | Bin 0 -> 31429 bytes Quantization-FaceOff/assets/qdrant-sq.png | Bin 0 -> 31221 bytes Quantization-FaceOff/assets/rescore.png | Bin 0 -> 62857 bytes Quantization-FaceOff/assets/rescore_bq.png | Bin 0 -> 63700 bytes Quantization-FaceOff/assets/rescore_pq.png | Bin 0 -> 62916 bytes Quantization-FaceOff/docker-compose.yml | 12 + Quantization-FaceOff/pyproject.toml | 19 + Quantization-FaceOff/qfo/__init__.py | 0 Quantization-FaceOff/qfo/bench/__init__.py | 0 Quantization-FaceOff/qfo/bench/eval.py | 70 + .../qfo/bench/rescore_sweep.py | 75 + Quantization-FaceOff/qfo/bench/throughput.py | 66 + Quantization-FaceOff/qfo/config.py | 53 + Quantization-FaceOff/qfo/demo.py | 52 + Quantization-FaceOff/qfo/pipeline/__init__.py | 0 Quantization-FaceOff/qfo/pipeline/add_bq.py | 31 + Quantization-FaceOff/qfo/pipeline/ingest.py | 59 + Quantization-FaceOff/qfo/run_all.py | 37 + Quantization-FaceOff/qfo/telemetry.py | 103 + Quantization-FaceOff/qfo/viz/__init__.py | 0 Quantization-FaceOff/qfo/viz/report.py | 77 + Quantization-FaceOff/qfo/viz/rescore_chart.py | 61 + Quantization-FaceOff/uv.lock | 1714 +++++++++++++++++ 30 files changed, 2581 insertions(+) create mode 100644 Quantization-FaceOff/.env.example create mode 100644 Quantization-FaceOff/.gitignore create mode 100644 Quantization-FaceOff/README.md create mode 100644 Quantization-FaceOff/assets/faceoff.png create mode 100644 Quantization-FaceOff/assets/qdrant-baseline.png create mode 100644 Quantization-FaceOff/assets/qdrant-bq.png create mode 100644 Quantization-FaceOff/assets/qdrant-collections.png create mode 100644 Quantization-FaceOff/assets/qdrant-pq.png create mode 100644 Quantization-FaceOff/assets/qdrant-sq.png create mode 100644 Quantization-FaceOff/assets/rescore.png create mode 100644 Quantization-FaceOff/assets/rescore_bq.png create mode 100644 Quantization-FaceOff/assets/rescore_pq.png create mode 100644 Quantization-FaceOff/docker-compose.yml create mode 100644 Quantization-FaceOff/pyproject.toml create mode 100644 Quantization-FaceOff/qfo/__init__.py create mode 100644 Quantization-FaceOff/qfo/bench/__init__.py create mode 100644 Quantization-FaceOff/qfo/bench/eval.py create mode 100644 Quantization-FaceOff/qfo/bench/rescore_sweep.py create mode 100644 Quantization-FaceOff/qfo/bench/throughput.py create mode 100644 Quantization-FaceOff/qfo/config.py create mode 100644 Quantization-FaceOff/qfo/demo.py create mode 100644 Quantization-FaceOff/qfo/pipeline/__init__.py create mode 100644 Quantization-FaceOff/qfo/pipeline/add_bq.py create mode 100644 Quantization-FaceOff/qfo/pipeline/ingest.py create mode 100644 Quantization-FaceOff/qfo/run_all.py create mode 100644 Quantization-FaceOff/qfo/telemetry.py create mode 100644 Quantization-FaceOff/qfo/viz/__init__.py create mode 100644 Quantization-FaceOff/qfo/viz/report.py create mode 100644 Quantization-FaceOff/qfo/viz/rescore_chart.py create mode 100644 Quantization-FaceOff/uv.lock diff --git a/Quantization-FaceOff/.env.example b/Quantization-FaceOff/.env.example new file mode 100644 index 0000000..98a7733 --- /dev/null +++ b/Quantization-FaceOff/.env.example @@ -0,0 +1,9 @@ +# Copy to .env and fill in. (.env is gitignored.) + +# Weights & Biases - required: eval / sweeps / throughput log runs here. +# Get a key at https://wandb.ai/authorize +WANDB_API_KEY=your_wandb_key_here + +# HuggingFace - optional. The dbpedia dataset is public, so this is usually not +# needed; set it only if you hit rate limits or use a gated mirror. +HF_TOKEN= diff --git a/Quantization-FaceOff/.gitignore b/Quantization-FaceOff/.gitignore new file mode 100644 index 0000000..e74feae --- /dev/null +++ b/Quantization-FaceOff/.gitignore @@ -0,0 +1,12 @@ +.env +.venv/ +__pycache__/ +*.pyc +data/ +wandb/ +results/ +.idea/ + +# docs stay local — only README.md ships +*.md +!README.md diff --git a/Quantization-FaceOff/README.md b/Quantization-FaceOff/README.md new file mode 100644 index 0000000..33aef6f --- /dev/null +++ b/Quantization-FaceOff/README.md @@ -0,0 +1,131 @@ +# Qdrant Quantization Face-Off + +How much do you actually pay - in recall and latency - to shrink your vectors 4× or 16× in RAM? + +This benchmarks four native Qdrant configurations head-to-head on **high-dimensional text embeddings**, measuring the real tradeoff: **recall vs. latency vs. memory** - and how rescoring buys recall back. + +- **Baseline** - HNSW, uncompressed `float32` +- **SQ** - Scalar Quantization, `int8` (4× smaller) +- **PQ** - Product Quantization, ×16 compression (16× smaller) +- **BQ** - Binary Quantization, 1 bit/dim (32× smaller) + +Dataset: [`dbpedia-entities-openai-1M`](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M) - OpenAI `ada-002` embeddings, **1536-d, cosine**, real Wikipedia article titles/text as payload. Qdrant runs in local Docker (not Cloud - network jitter would corrupt latency numbers). All runs logged to Weights & Biases. + +## Results + +![faceoff](assets/faceoff.png) + +99,000 vectors indexed, 1,000 held-out queries, Recall@10 vs. exact-NN ground truth: + +| config | Recall@10 | p50 | p95 | p99 | vectors in RAM | compression | +|---|---|---|---|---|---|---| +| **baseline** f32 | **0.995** | 12.1 ms | 32.3 ms | 56.7 ms | 608 MB | 1× | +| **SQ** int8 | 0.895 | **9.5 ms** | 28.7 ms | 31.5 ms | 152 MB | **4×** | +| **PQ** x16 | 0.845 | 11.3 ms | 26.9 ms | 33.3 ms | **38 MB** | **16×** | +| **BQ** x32 | 0.888 | 12.7 ms | 25.3 ms | 29.3 ms | **19 MB** | **32×** | + +*Recall is out-of-the-box (default search). Memory = RAM-resident quantized-vector size (computed); SQ/PQ/BQ keep the f32 originals on disk for rescoring. p99 is noisy on a single laptop.* + +## Verdict + +- **SQ int8 - the easy default.** 4× less RAM, ~0.90 recall out of the box, median latency as fast as baseline. +- **PQ x16 / BQ x32 - when RAM is the wall.** Raw recall drops (0.845 / 0.672), but rescoring buys it straight back. +- **Baseline** is the recall ceiling (0.995) and the memory floor to beat. + +### Rescoring is the real lever + +Quantized search is fast but approximate. Qdrant can **oversample** candidates on the cheap codes, then **rescore** them against the full-precision originals on disk - one search param. Recall climbs back to baseline: + +| config | RAM | raw recall | + rescore (sweet spot) | +|---|---|---|---| +| PQ x16 | 38 MB | 0.845 | **0.989** @ 2× oversampling | +| BQ x32 | 19 MB | 0.672 | **0.990** @ 8× oversampling | + +**The takeaway: BQ + 8× rescore = baseline-grade recall (0.990) at 32× less RAM.** You don't choose between small and accurate - you get both. (Binary is lossier than PQ, so it needs a wider oversampling net - but it gets there.) + +![rescore sweeps](assets/rescore_bq.png) + +## Throughput + +QPS under 16 concurrent clients, 2,000 queries each (single laptop - CPU-bound, so the spread is modest; real multi-core hardware widens it): + +| config | QPS | mean latency under load | +|---|---|---| +| baseline f32 | 156 | 102 ms | +| SQ int8 | 159 | 100 ms | +| PQ x16 | 140 | 114 ms | +| **BQ x32** | **163** | **98 ms** | + +Smaller vectors (BQ/SQ) scan faster; PQ pays a codebook-distance overhead. + +## What it saves (RAM → $) + +RAM is the cost driver of a vector database. Scaling the resident quantized-vector footprint to **10M vectors at 1536-d**, at an illustrative **$5 / GB-month** of RAM: + +| config | RAM @ 10M | est. $/month | vs baseline | +|---|---|---|---| +| baseline f32 | 61.4 GB | ~$307 | - | +| SQ int8 | 15.4 GB | ~$77 | 4× cheaper | +| PQ x16 | 3.8 GB | ~$19 | 16× cheaper | +| **BQ x32** | **1.9 GB** | **~$10** | **32× cheaper** | + +The f32 originals stay on disk (cheap) and are only touched to rescore. So you size your **RAM** instance to the quantized footprint: **BQ + 8× rescore = ~$10/mo for baseline-grade recall on 10M vectors, vs ~$307/mo uncompressed.** (Numbers illustrative; plug your own $/GB.) + +## Quickstart + +Requires Docker + [uv](https://docs.astral.sh/uv/). Copy `.env.example` to `.env` and add your `WANDB_API_KEY` (optional `HF_TOKEN`). + +```bash +docker compose up -d # local Qdrant v1.18.2, storage persisted to ./data +uv sync # install deps + the qfo package (editable) +uv run python -m qfo.run_all # the whole faceoff in one command +uv run python -m qfo.demo einstein # then try search-by-example +``` + +`run_all` runs these in order (or run any individually with `uv run python -m [args]`): + +| step | does | +|---|---| +| `qfo.data.dbpedia` | download + cache dataset (curl, resumable) | +| `qfo.pipeline.ingest` | build baseline / sq / pq collections (+ payload) | +| `qfo.pipeline.add_bq` | add the bq (binary) collection | +| `qfo.data.ground_truth` | exact-NN ground truth for the query set | +| `qfo.bench.eval` | recall + latency -> `results/` + W&B | +| `qfo.bench.rescore_sweep pq` / `bq` | oversampling/rescore sweeps | +| `qfo.bench.throughput` | QPS under concurrency | +| `qfo.viz.report` | dashboard chart -> `assets/` | +| `qfo.viz.rescore_chart pq` / `bq` | sweep charts | + +Run everything from the project root: the `qfo` package is importable (installed via `uv sync`), and scripts read/write `data/`, `results/`, `assets/` relative to root. + +Collections persist across reboots (bind-mounted volume) - re-run `docker compose up -d` and the data is still there, no re-ingest. + +## Layout + +Code is the `qfo` package; run modules with `python -m qfo.<...>` from the project root. Data, config, and outputs stay in root (`docker-compose.yml`, `data/`, `results/`, `assets/`). + +``` +qfo/ + run_all.py run the entire pipeline in one command + config.py dataset + collection configs (scale/quant knobs) + telemetry.py RAM/latency helpers + W&B wiring (run groups) + data/ + dbpedia.py loader: curl shard download, cache, query holdout + ground_truth.py exact-NN ground truth via brute-force search + pipeline/ + ingest.py create baseline/sq/pq, upload vectors + payload + add_bq.py add one collection (e.g. bq) without touching others + bench/ + eval.py Recall@K + latency percentiles -> results/ + W&B + rescore_sweep.py oversampling/rescore sweep (pq / bq) + throughput.py QPS per config under concurrency + viz/ + report.py the faceoff dashboard chart + rescore_chart.py rescore-sweep chart + demo.py search-by-example (find similar articles) +``` + +## Notes + +- **Scale knob:** `DATASET_N` in `config.py` (default 100k). 1M needs ~25 GB disk (3 collections keep f32 originals) - use a sequential per-collection ingest if disk-bound. +- **Per-config RAM:** the table uses computed sizes. 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z&feY~nXK`|KN4Bq^>tKrcvzDTbhO?xrl9Ud4FTWv=X=A=p{^cGg8S~8{E&D1a_ohs z$f=B`SY?!eEbj>?B>^RePIZd3vN-tTtzbjUYGC+=M?FXif`V}M;^v-?`il`bZ9d1b zqLNRU=0O&Tg#Hz*%SdYUcg`Q~NDk^dcZ!oK38O$)8C@PV&nF)cP=}X5Sic zQ2W4vCR5e3_Cn;ZynE31x;Od4b4S;korLk zti~ZkFy>4bG<~A0)iAFT(A9y^TQyA6-%ki$H_RT^@rk)~mACU_gt+apESxJ;GYYl! zapRXfj9k+M?=}Ic- zHBT>_!dbYXP+?c(W@}z$faI(Rfg@Ti?&ttmwpEH1fNCLL>YBqnvGpQ{MXlWju4KNq z9zbeK^jqh3>g=1.18 required for TurboQuant. +services: + qdrant: + image: qdrant/qdrant:v1.18.2 + container_name: qfo-qdrant + ports: + - "6333:6333" # REST + - "6334:6334" # gRPC + volumes: + - ./data/qdrant_storage:/qdrant/storage + # defaults are fine for a single-node bench; add mem limits only if profiling the container itself. diff --git a/Quantization-FaceOff/pyproject.toml b/Quantization-FaceOff/pyproject.toml new file mode 100644 index 0000000..079ca0b --- /dev/null +++ b/Quantization-FaceOff/pyproject.toml @@ -0,0 +1,19 @@ +[project] +name = "quantization-faceoff" +version = "0.1.0" +requires-python = ">=3.13" +dependencies = [ + "datasets>=5.0.0", + "matplotlib>=3.11.0", + "numpy>=2.5.0", + "python-dotenv>=1.2.2", + "qdrant-client>=1.18.0", + "wandb>=0.28.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["qfo"] diff --git a/Quantization-FaceOff/qfo/__init__.py b/Quantization-FaceOff/qfo/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Quantization-FaceOff/qfo/bench/__init__.py b/Quantization-FaceOff/qfo/bench/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Quantization-FaceOff/qfo/bench/eval.py b/Quantization-FaceOff/qfo/bench/eval.py new file mode 100644 index 0000000..003c780 --- /dev/null +++ b/Quantization-FaceOff/qfo/bench/eval.py @@ -0,0 +1,70 @@ +"""Evaluation suite. Query each collection, compute Recall@K vs exact-NN ground truth, +log per-config metrics to W&B, dump results.json + +One pass per collection captures BOTH retrieved ids (recall) and latency (no double query). +""" +import json +import time + +import numpy as np +from qdrant_client import QdrantClient + +from qfo.config import QDRANT_URL, COLLECTIONS, K, results_path +from qfo.data.dbpedia import load_dbpedia +from qfo.data.ground_truth import GT_PATH +from qfo.telemetry import container_rss_mb, pctiles, log_run + + +def search_collect(client, name, queries, k): + """Returns (ids[N,k] int64, latencies_ms[N]). Missing slots = -1.""" + ids = np.full((len(queries), k), -1, dtype=np.int64) + lats = np.empty(len(queries), dtype=np.float64) + for i, q in enumerate(queries): + t = time.perf_counter() + res = client.query_points(collection_name=name, query=q.tolist(), limit=k) + lats[i] = (time.perf_counter() - t) * 1000.0 + got = [p.id for p in res.points] + ids[i, : len(got)] = got + return ids, lats + + +def recall_at_k(retrieved, gt, k): + """mean |retrieved_k ∩ gt_k| / k. gt holds top-GT_DEPTH; compare against gt[:, :k].""" + hits = 0 + for r, g in zip(retrieved, gt): + hits += len(set(r.tolist()) & set(g[:k].tolist())) + return hits / (len(retrieved) * k) + + +def main(n_queries=None): + _base, _payload, queries = load_dbpedia() + with open(GT_PATH) as f: + gt = np.array(json.load(f), dtype=np.int64) + if n_queries: + queries, gt = queries[:n_queries], gt[:n_queries] + + client = QdrantClient(url=QDRANT_URL, timeout=120) + results = {} + for name in COLLECTIONS: + search_collect(client, name, queries[:200], K) # warm cache, discard + ids, lats = search_collect(client, name, queries, K) # measured pass + m = { + "recall_at_k": recall_at_k(ids, gt, K), + "k": K, + "rss_mb": container_rss_mb(), + "n_queries": len(queries), + **pctiles(lats), + } + results[name] = m + log_run(name, m, group="faceoff") + print(name, m) + + with open(results_path("results.json"), "w") as f: + json.dump(results, f, indent=2) + print("eval done -> results/results.json") + + +if __name__ == "__main__": + # money-path check + assert abs(recall_at_k(np.array([[1, 2, 3]]), np.array([[1, 2, 9, 8]]), 3) - 2 / 3) < 1e-9 + main() diff --git a/Quantization-FaceOff/qfo/bench/rescore_sweep.py b/Quantization-FaceOff/qfo/bench/rescore_sweep.py new file mode 100644 index 0000000..7dd42db --- /dev/null +++ b/Quantization-FaceOff/qfo/bench/rescore_sweep.py @@ -0,0 +1,75 @@ +"""Rescoring / oversampling sweep on the PQ collection. + +PQ search on quantized codes is fast but lossy. Qdrant can oversample (pull N x more +candidates with the cheap quantized vectors) then rescore them with the full-precision +originals on disk. This sweep shows PQ recall climbing back toward baseline as we +oversample — the production lever that makes 16x compression usable. + +Read-only on the PQ collection. No drops. +""" +import json +import sys +import time + +import numpy as np +from qdrant_client import QdrantClient, models + +from qfo.config import QDRANT_URL, K, results_path +from qfo.data.dbpedia import load_dbpedia +from qfo.data.ground_truth import GT_PATH +from qfo.bench.eval import recall_at_k +from qfo.telemetry import pctiles, log_run + +COLL = sys.argv[1] if len(sys.argv) > 1 else "pq" + +# (label, rescore, oversampling) +SWEEP = [ + ("no rescore", False, 1.0), + ("rescore 1x", True, 1.0), + ("rescore 2x", True, 2.0), + ("rescore 4x", True, 4.0), + ("rescore 8x", True, 8.0), +] + + +def run_one(client, queries, k, rescore, oversampling): + params = models.SearchParams( + quantization=models.QuantizationSearchParams( + rescore=rescore, oversampling=oversampling + ) + ) + ids = np.full((len(queries), k), -1, dtype=np.int64) + lats = np.empty(len(queries)) + for i, q in enumerate(queries): + t = time.perf_counter() + res = client.query_points(COLL, query=q.tolist(), limit=k, search_params=params) + lats[i] = (time.perf_counter() - t) * 1000.0 + got = [p.id for p in res.points] + ids[i, : len(got)] = got + return ids, lats + + +def main(): + _b, _p, queries = load_dbpedia() + with open(GT_PATH) as f: + gt = np.array(json.load(f), dtype=np.int64) + client = QdrantClient(url=QDRANT_URL, timeout=120) + + rows = [] + for label, rescore, oversampling in SWEEP: + ids, lats = run_one(client, queries, K, rescore, oversampling) + m = {"label": label, "rescore": rescore, "oversampling": oversampling, + "recall_at_k": recall_at_k(ids, gt, K), **pctiles(lats)} + rows.append(m) + print(f"{label:12} recall={m['recall_at_k']:.3f} p50={m['latency_p50_ms']:.1f}ms") + log_run(f"{COLL}-{label.replace(' ', '_')}", m, group=f"rescore-{COLL}") + + out = results_path(f"rescore_sweep_{COLL}.json") + with open(out, "w") as f: + json.dump(rows, f, indent=2) + print(f"rescore sweep done -> {out}") + return rows + + +if __name__ == "__main__": + main() diff --git a/Quantization-FaceOff/qfo/bench/throughput.py b/Quantization-FaceOff/qfo/bench/throughput.py new file mode 100644 index 0000000..0f026c2 --- /dev/null +++ b/Quantization-FaceOff/qfo/bench/throughput.py @@ -0,0 +1,66 @@ +"""Throughput (QPS) under concurrency per config — not single-query latency. +Fires N queries across CONCURRENCY worker threads, measures queries/second + mean +latency under load. Read-only on the collections. Logs to W&B group 'throughput'. +""" +import json +import threading +import time +from concurrent.futures import ThreadPoolExecutor + +import numpy as np +from qdrant_client import QdrantClient + +from qfo.config import QDRANT_URL, COLLECTIONS, K, results_path +from qfo.data.dbpedia import load_dbpedia +from qfo.telemetry import log_run + +CONCURRENCY = 16 +N = 2000 + +# one client per worker thread — a shared client's socket isn't thread-safe (WinError 10038) +_local = threading.local() + + +def _client(): + if not hasattr(_local, "c"): + _local.c = QdrantClient(url=QDRANT_URL, timeout=120) + return _local.c + + +def main(): + _b, _p, queries = load_dbpedia() + base = [v.tolist() for v in queries] + qs = [base[i % len(base)] for i in range(N)] # cycle to reach N + + results = {} + for name in COLLECTIONS: + _client().query_points(name, query=qs[0], limit=K) # warm + + def one(vec, _name=name): + t = time.perf_counter() + _client().query_points(_name, query=vec, limit=K) + return (time.perf_counter() - t) * 1000.0 + + t0 = time.perf_counter() + with ThreadPoolExecutor(max_workers=CONCURRENCY) as ex: + lats = list(ex.map(one, qs)) + elapsed = time.perf_counter() - t0 + + m = { + "qps": N / elapsed, + "concurrency": CONCURRENCY, + "n": N, + "mean_latency_ms": float(np.mean(lats)), + "p95_latency_ms": float(np.percentile(lats, 95)), + } + results[name] = m + log_run(f"{name}-qps", m, group="throughput") + print(f"{name:9} {m['qps']:.0f} qps mean {m['mean_latency_ms']:.1f}ms (conc={CONCURRENCY})") + + with open(results_path("throughput.json"), "w") as f: + json.dump(results, f, indent=2) + print("throughput -> results/throughput.json") + + +if __name__ == "__main__": + main() diff --git a/Quantization-FaceOff/qfo/config.py b/Quantization-FaceOff/qfo/config.py new file mode 100644 index 0000000..58a2215 --- /dev/null +++ b/Quantization-FaceOff/qfo/config.py @@ -0,0 +1,53 @@ +"""Shared config for the quantization faceoff. +Dataset: dbpedia-entities-openai-1M (OpenAI ada-002, 1536-d, cosine) high-dim text +embeddings with title/text payload, so the quantization tradeoff is dramatic and the +Qdrant UI shows real content. +""" +import os + +from qdrant_client import models + +QDRANT_URL = "http://localhost:6333" + +RESULTS_DIR = "results" # benchmark output JSONs land here + + +def results_path(name): + """Path under the results dir, ensuring the dir exists.""" + os.makedirs(RESULTS_DIR, exist_ok=True) + return os.path.join(RESULTS_DIR, name) + +# dbpedia-openai facts +DIM = 1536 +DISTANCE = models.Distance.COSINE + +# 100k = ~600MB embeddings, fast + tells the full story. Fixed at 100k: 1M needs +# ~25GB disk (3 collections keep f32 originals) which doesn't fit my 15GB budget +DATASET_N = 100_000 +N_QUERY = 1_000 # held out from the tail; NOT inserted into base +K = 10 # Recall@K +GT_DEPTH = 100 # exact-NN depth to store as ground truth +BATCH_SIZE = 500 # upsert batch (kept modest; 1536-d float vectors are large) +DATA_DIR = "data/dbpedia" + +# 3 configs under test. quantization=None => baseline f32. +COLLECTIONS = { + "baseline": None, + "sq": models.ScalarQuantization( + scalar=models.ScalarQuantizationConfig( + type=models.ScalarType.INT8, + always_ram=True, + ) + ), + # PQ; TurboQuant query-side optimizations apply automatically on 1.18+ + "pq": models.ProductQuantization( + product=models.ProductQuantizationConfig( + compression=models.CompressionRatio.X16, + always_ram=True, + ) + ), + # Binary: 1 bit/dim = 32x smaller. Very lossy raw; needs rescore+oversampling + "bq": models.BinaryQuantization( + binary=models.BinaryQuantizationConfig(always_ram=True) + ), +} diff --git a/Quantization-FaceOff/qfo/demo.py b/Quantization-FaceOff/qfo/demo.py new file mode 100644 index 0000000..66fd555 --- /dev/null +++ b/Quantization-FaceOff/qfo/demo.py @@ -0,0 +1,52 @@ +"""Tangible search demo: pick an article, find the most similar ones. + +No embedding model needed (we'd need OpenAI ada-002 to embed fresh text) — instead we +search BY EXAMPLE: take a stored article's vector and find its neighbors. Uses BQ x32 + +8x rescore (the recommended config) so you see 32x-compressed search returning real, +relevant titles. + +Usage: + uv run python search_demo.py # random seed article + uv run python search_demo.py einstein # first article whose title contains 'einstein' +""" +import random +import sys + +from qdrant_client import QdrantClient, models + +from qfo.config import QDRANT_URL, K +from qfo.data.dbpedia import load_dbpedia + +RESCORE = models.SearchParams( + quantization=models.QuantizationSearchParams(rescore=True, oversampling=8.0) +) + + +def main(): + base, payload, _q = load_dbpedia() + client = QdrantClient(url=QDRANT_URL, timeout=60) + + arg = " ".join(sys.argv[1:]).strip() + if arg: + hits = [i for i, p in enumerate(payload) if arg.lower() in p["title"].lower()] + if not hits: + print(f"no article title contains {arg!r}") + return + seed = hits[0] + else: + seed = random.randrange(len(base)) + + print(f"\nQuery article [{seed}]: {payload[seed]['title']}") + print(f" {payload[seed]['text'][:160]}...\n") + + res = client.query_points("bq", query=base[seed].tolist(), limit=K + 1, search_params=RESCORE) + neighbors = [p for p in res.points if p.id != seed][:K] + assert neighbors, "no neighbors returned" + + print(f"Most similar (BQ x32 + rescore 8x), top {len(neighbors)}:") + for p in neighbors: + print(f" {p.score:.3f} {payload[p.id]['title']}") + + +if __name__ == "__main__": + main() diff --git a/Quantization-FaceOff/qfo/pipeline/__init__.py b/Quantization-FaceOff/qfo/pipeline/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Quantization-FaceOff/qfo/pipeline/add_bq.py b/Quantization-FaceOff/qfo/pipeline/add_bq.py new file mode 100644 index 0000000..10b90d4 --- /dev/null +++ b/Quantization-FaceOff/qfo/pipeline/add_bq.py @@ -0,0 +1,31 @@ +"""Build a single collection by name without touching the others (no drops to the +existing baseline/sq/pq). Usage: uv run python add_bq.py [name] default: bq + +Logic stays under __main__ + main(): upload_collection(parallel>1) uses process spawn +on Windows, which re-imports this module — unguarded top-level code would re-run it. +""" +import sys + +from qdrant_client import QdrantClient + +from qfo.config import QDRANT_URL, BATCH_SIZE, COLLECTIONS +from qfo.data.dbpedia import load_dbpedia +from qfo.pipeline.ingest import make_collection, wait_green + + +def main(): + name = sys.argv[1] if len(sys.argv) > 1 else "bq" + base, payload, _q = load_dbpedia() + client = QdrantClient(url=QDRANT_URL, timeout=120) + print(f"[{name}] create + upload {len(base)} vectors") + make_collection(client, name, COLLECTIONS[name]) + client.upload_collection( + collection_name=name, vectors=base, payload=payload, + ids=list(range(len(base))), batch_size=BATCH_SIZE, parallel=4, + ) + wait_green(client, name) + print(f"[{name}] green, count={client.count(name).count}") + + +if __name__ == "__main__": + main() diff --git a/Quantization-FaceOff/qfo/pipeline/ingest.py b/Quantization-FaceOff/qfo/pipeline/ingest.py new file mode 100644 index 0000000..30ba033 --- /dev/null +++ b/Quantization-FaceOff/qfo/pipeline/ingest.py @@ -0,0 +1,59 @@ +"""Create the 3 collections (baseline / sq / pq) and load the dbpedia embeddings into each. +Waits for optimizer to reach green before exiting (isolation guardrail).""" +import time + +from qdrant_client import QdrantClient, models + +from qfo.config import ( + QDRANT_URL, DIM, DISTANCE, BATCH_SIZE, COLLECTIONS, +) +from qfo.data.dbpedia import load_dbpedia + + +def make_collection(client, name, quant): + if client.collection_exists(name): + client.delete_collection(name) + client.create_collection( + collection_name=name, + vectors_config=models.VectorParams(size=DIM, distance=DISTANCE), + quantization_config=quant, # None => baseline f32 + ) + + +def wait_green(client, name, timeout=3600): + """Block until indexing/optimization finishes. Raises on timeout.""" + deadline = time.time() + timeout + while time.time() < deadline: + info = client.get_collection(name) + if info.status == models.CollectionStatus.GREEN: + return + time.sleep(5) + raise TimeoutError(f"{name} not green after {timeout}s (status={info.status})") + + +def main(): + base, payload, _queries = load_dbpedia() + ids = list(range(len(base))) + client = QdrantClient(url=QDRANT_URL, timeout=120) + + for name, quant in COLLECTIONS.items(): + print(f"[{name}] create + upload {len(base)} vectors") + make_collection(client, name, quant) + client.upload_collection( + collection_name=name, + vectors=base, + payload=payload, # title/text so the UI shows real content + ids=ids, + batch_size=BATCH_SIZE, + parallel=4, + ) + wait_green(client, name) + cnt = client.count(name).count + assert cnt == len(base), f"{name}: {cnt} != {len(base)}" + print(f"[{name}] green, count={cnt}") + + print("ingest done: 3 collections green.") + + +if __name__ == "__main__": + main() diff --git a/Quantization-FaceOff/qfo/run_all.py b/Quantization-FaceOff/qfo/run_all.py new file mode 100644 index 0000000..cc397e1 --- /dev/null +++ b/Quantization-FaceOff/qfo/run_all.py @@ -0,0 +1,37 @@ +"""Run the whole faceoff end to end with one command: + + uv run python -m qfo.run_all + +Each step runs as `python -m qfo.` in a fresh process (clean state, mirrors the +manual commands). Stops at the first failure. + +Note: ingest + add_bq rebuild the collections from scratch (drop + re-upload). +""" +import subprocess +import sys + +# (module, args, description) +STEPS = [ + ("qfo.data.dbpedia", [], "build + cache dataset"), + ("qfo.pipeline.ingest", [], "create baseline/sq/pq + upload payload"), + ("qfo.pipeline.add_bq", [], "create bq (binary) collection"), + ("qfo.data.ground_truth", [], "exact-NN ground truth"), + ("qfo.bench.eval", [], "recall + latency -> results/ + W&B"), + ("qfo.bench.rescore_sweep", ["pq"], "PQ oversampling/rescore sweep"), + ("qfo.bench.rescore_sweep", ["bq"], "BQ sweep"), + ("qfo.bench.throughput", [], "QPS under concurrency"), + ("qfo.viz.report", [], "dashboard chart -> assets/"), + ("qfo.viz.rescore_chart", ["pq"], "PQ sweep chart"), + ("qfo.viz.rescore_chart", ["bq"], "BQ sweep chart"), +] + + +def main(): + for i, (mod, args, desc) in enumerate(STEPS, 1): + print(f"\n=== [{i}/{len(STEPS)}] {mod} {' '.join(args)} - {desc} ===", flush=True) + subprocess.run([sys.executable, "-m", mod, *args], check=True) + print("\nfaceoff complete. charts in assets/, metrics in results/ + W&B.") + + +if __name__ == "__main__": + main() diff --git a/Quantization-FaceOff/qfo/telemetry.py b/Quantization-FaceOff/qfo/telemetry.py new file mode 100644 index 0000000..514bfd4 --- /dev/null +++ b/Quantization-FaceOff/qfo/telemetry.py @@ -0,0 +1,103 @@ +"""Telemetry primitives + W&B wiring. No custom metric store, log straight to W&B. + +- container RSS via `docker stats` (the RAM we profile; quantized vectors sit here with always_ram). +- per-query latency -> p50/p95/p99. +- cold-start vs warm-cache loops. +One W&B run per config so the project dashboard compares them (table / parallel-coords). +""" +import os +import subprocess +import time + +import numpy as np +import wandb +from dotenv import load_dotenv + +load_dotenv() # WANDB_API_KEY from .env + +CONTAINER = "qfo-qdrant" +PROJECT = "quantization-faceoff" + + +def _to_mb(s): + """'1.23GiB' / '512MiB' / '900B' -> megabytes (binary units).""" + s = s.strip() + for unit, mult in (("GiB", 1024), ("MiB", 1), ("KiB", 1 / 1024), + ("GB", 1000), ("MB", 1), ("kB", 1 / 1000), ("B", 1 / 1e6)): + if s.endswith(unit): + return float(s[: -len(unit)]) * mult + return float(s) # bare number = bytes? treat as MB-less fallback + + +def container_rss_mb(container=CONTAINER): + """Resident memory of the Qdrant container, in MB.""" + out = subprocess.run( + ["docker", "stats", "--no-stream", "--format", "{{.MemUsage}}", container], + capture_output=True, text=True, check=True, + ).stdout + return _to_mb(out.split("/")[0]) # "used / limit" + + +def search_latencies_ms(client, name, queries, k): + """One query at a time (latency, not throughput). Returns ms array.""" + lats = np.empty(len(queries), dtype=np.float64) + for i, q in enumerate(queries): + t = time.perf_counter() + client.query_points(collection_name=name, query=q.tolist(), limit=k) + lats[i] = (time.perf_counter() - t) * 1000.0 + return lats + + +def pctiles(lats): + return {f"latency_p{p}_ms": float(np.percentile(lats, p)) for p in (50, 95, 99)} + + +def profile_config(client, name, queries, k): + """Cold (first pass) + warm (second pass) latency + RAM for one collection.""" + cold = search_latencies_ms(client, name, queries, k) + warm = search_latencies_ms(client, name, queries, k) + rss = container_rss_mb() + return { + "rss_mb": rss, + **{f"cold_{m}": v for m, v in pctiles(cold).items()}, + **{f"warm_{m}": v for m, v in pctiles(warm).items()}, + "n_queries": len(queries), + } + + +def log_run(config_name, metrics, group="dbpedia", extra_config=None): + """One W&B run per quant config. reinit so we can loop configs in one process. + `group` keeps the workspace tidy: group by it in the W&B UI to separate the main + faceoff from the rescore sweeps.""" + run = wandb.init( + project=PROJECT, + name=config_name, + group=group, + config={"quant": config_name, "family": group, **(extra_config or {})}, + reinit=True, + ) + wandb.log(metrics) + wandb.summary.update(metrics) + run.finish() + + +if __name__ == "__main__": + # cheap unit check for the parser (the only non-trivial logic here) + assert abs(_to_mb("1.0GiB") - 1024) < 1e-6 + assert abs(_to_mb("512MiB") - 512) < 1e-6 + assert _to_mb("900B") < 1 + print("_to_mb OK") + + # live smoke: needs collections green + valid WANDB_API_KEY + from qdrant_client import QdrantClient + from qfo.config import QDRANT_URL, COLLECTIONS, K + from qfo.data.dbpedia import load_dbpedia + + _b, _p, queries = load_dbpedia() + queries = queries[:200] # smoke slice + client = QdrantClient(url=QDRANT_URL, timeout=120) + for name in COLLECTIONS: + m = profile_config(client, name, queries, K) + print(name, m) + log_run(name, m) + print("smoke done -> check W&B project:", PROJECT) diff --git a/Quantization-FaceOff/qfo/viz/__init__.py b/Quantization-FaceOff/qfo/viz/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Quantization-FaceOff/qfo/viz/report.py b/Quantization-FaceOff/qfo/viz/report.py new file mode 100644 index 0000000..830f42e --- /dev/null +++ b/Quantization-FaceOff/qfo/viz/report.py @@ -0,0 +1,77 @@ +"""Build the faceoff dashboard (one 2x2 PNG) from results.json + computed memory. + +Memory here is the RAM-resident quantized-vector size (exact math) — the thing +quantization actually shrinks. Measured per-config RAM is in Qdrant's dashboard memory panel. +""" +import json +import os + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +from qfo.config import DATASET_N, N_QUERY, DIM, results_path + +OUT = "assets" +LABEL = {"baseline": "f32", "sq": "SQ int8", "pq": "PQ x16", "bq": "BQ x32"} +COMPRESS = {"baseline": 1, "sq": 4, "pq": 16, "bq": 32} + + +def main(): + os.makedirs(OUT, exist_ok=True) + with open(results_path("results.json")) as f: + R = json.load(f) + + n_base = DATASET_N - N_QUERY + mem_mb = {k: n_base * DIM * 4 / 1e6 / COMPRESS[k] for k in R} # resident vector bytes + names = list(R.keys()) + labels = [LABEL.get(n, n) for n in names] + palette = ["#4C72B0", "#DD8452", "#55A868", "#8172B3"] + colors = [palette[i % len(palette)] for i in range(len(names))] + + fig, ax = plt.subplots(2, 2, figsize=(12, 9)) + fig.suptitle(f"Qdrant Quantization Face-Off — dbpedia 1536-d, {n_base:,} vectors", fontsize=14, weight="bold") + + # recall@10 + ax[0, 0].bar(labels, [R[n]["recall_at_k"] for n in names], color=colors) + ax[0, 0].set_title("Recall@10 (higher = better)") + ax[0, 0].set_ylim(0, 1.05) + for i, n in enumerate(names): + ax[0, 0].text(i, R[n]["recall_at_k"] + 0.01, f"{R[n]['recall_at_k']:.3f}", ha="center") + + # latency percentiles + x = range(len(names)) + w = 0.25 + for j, (p, c) in enumerate([("p50", "#8172B3"), ("p95", "#CCB974"), ("p99", "#C44E52")]): + ax[0, 1].bar([i + (j - 1) * w for i in x], + [R[n][f"latency_{p}_ms"] for n in names], w, label=p, color=c) + ax[0, 1].set_title("Latency (ms, lower = better)") + ax[0, 1].set_xticks(list(x)); ax[0, 1].set_xticklabels(labels); ax[0, 1].legend() + + # memory (resident quantized vectors) + ax[1, 0].bar(labels, [mem_mb[n] for n in names], color=colors) + ax[1, 0].set_title("Vectors in RAM (MB, computed — lower = better)") + ax[1, 0].set_ylim(0, max(mem_mb.values()) * 1.25) # headroom so labels clear the title + for i, n in enumerate(names): + ax[1, 0].text(i, mem_mb[n] + max(mem_mb.values()) * 0.02, + f"{mem_mb[n]:.0f} MB ({COMPRESS[n]}x)", ha="center") + + # tradeoff: recall vs p50 latency, bubble size = memory + for i, n in enumerate(names): + ax[1, 1].scatter(R[n]["latency_p50_ms"], R[n]["recall_at_k"], + s=mem_mb[n] * 2, color=colors[i], alpha=0.7, label=labels[i]) + ax[1, 1].annotate(labels[i], (R[n]["latency_p50_ms"], R[n]["recall_at_k"]), + textcoords="offset points", xytext=(8, 0)) + ax[1, 1].set_title("Tradeoff: recall vs p50 latency (bubble = RAM)") + ax[1, 1].set_xlabel("p50 latency (ms)"); ax[1, 1].set_ylabel("recall@10") + + plt.tight_layout(rect=[0, 0, 1, 0.97]) + path = os.path.join(OUT, "faceoff.png") + plt.savefig(path, dpi=120) + print("wrote", path) + for n in names: + print(f"{LABEL.get(n, n):8} recall={R[n]['recall_at_k']:.3f} p50={R[n]['latency_p50_ms']:.1f}ms ram={mem_mb[n]:.0f}MB ({COMPRESS[n]}x)") + + +if __name__ == "__main__": + main() diff --git a/Quantization-FaceOff/qfo/viz/rescore_chart.py b/Quantization-FaceOff/qfo/viz/rescore_chart.py new file mode 100644 index 0000000..3068384 --- /dev/null +++ b/Quantization-FaceOff/qfo/viz/rescore_chart.py @@ -0,0 +1,61 @@ +"""Chart a rescore sweep: quantized recall climbing toward baseline as oversampling +rises, with p50 latency on the second axis. +Usage: python -m qfo.viz.rescore_chart [collection] default: pq +Reads results/rescore_sweep_.json + results/results.json -> assets/rescore_.png +""" +import json +import os +import sys + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +from qfo.config import results_path + +COMPRESS = {"pq": "16x", "bq": "32x", "sq": "4x"} + + +def main(): + coll = sys.argv[1] if len(sys.argv) > 1 else "pq" + + with open(results_path(f"rescore_sweep_{coll}.json")) as f: + rows = json.load(f) + with open(results_path("results.json")) as f: + base_recall = json.load(f)["baseline"]["recall_at_k"] + + os_pts = [r for r in rows if r["rescore"]] + nr = next(r for r in rows if not r["rescore"]) + xs = [r["oversampling"] for r in os_pts] + rec = [r["recall_at_k"] for r in os_pts] + lat = [r["latency_p50_ms"] for r in os_pts] + + fig, ax1 = plt.subplots(figsize=(9, 5.5)) + ax1.set_title(f"{coll.upper()} {COMPRESS.get(coll, '')}: rescoring buys back recall for a few ms", weight="bold") + + ax1.axhline(base_recall, ls="--", color="#4C72B0", label=f"baseline f32 recall ({base_recall:.3f})") + ax1.plot(xs, rec, "-o", color="#55A868", lw=2, label=f"{coll.upper()} recall (rescored)") + ax1.scatter([1], [nr["recall_at_k"]], color="#C44E52", marker="X", s=110, zorder=5, + label=f"{coll.upper()} no rescore ({nr['recall_at_k']:.3f})") + for x, r in zip(xs, rec): + ax1.annotate(f"{r:.3f}", (x, r), textcoords="offset points", xytext=(0, 8), ha="center") + ax1.set_xlabel("oversampling factor"); ax1.set_ylabel("Recall@10") + ax1.set_xticks(xs); ax1.set_ylim(min(nr["recall_at_k"], min(rec)) - 0.03, 1.01) + + ax2 = ax1.twinx() + ax2.plot(xs, lat, "--s", color="#888", alpha=0.8, label="p50 latency") + ax2.set_ylabel("p50 latency (ms)") + + l1, lab1 = ax1.get_legend_handles_labels() + l2, lab2 = ax2.get_legend_handles_labels() + ax1.legend(l1 + l2, lab1 + lab2, loc="center right", fontsize=9) + + plt.tight_layout() + os.makedirs("assets", exist_ok=True) + out = f"assets/rescore_{coll}.png" + plt.savefig(out, dpi=120) + print("wrote", out) + + +if __name__ == "__main__": + main() diff --git a/Quantization-FaceOff/uv.lock b/Quantization-FaceOff/uv.lock new file mode 100644 index 0000000..b8c13a9 --- /dev/null +++ b/Quantization-FaceOff/uv.lock @@ -0,0 +1,1714 @@ +version = 1 +revision = 3 +requires-python = ">=3.13" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < 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-40,6 +40,7 @@ New to Qdrant? Here's how to get started: 3. **Multimodal Search?** Check out [Gemini Embeddings](Gemini-embeddings/) - **✅ Ready!** 4. **Efficient Embeddings?** Learn about [Matryoshka MRL](Matryoshka-MRL/) - **✅ Ready!** 5. **Vector Compression?** Read about [Google TurboQuant](TurboQuant/) - **✅ Ready!** +6. **Quantization tradeoffs?** Compare them in the [Quantization Face-Off](Quantization-FaceOff/) - **✅ Ready!**

@@ -62,7 +63,6 @@ New to Qdrant? Here's how to get started: - **[TurboQuant](TurboQuant/)** - High-recall vector quantization and memory-efficient search ### 🚧 **Coming Soon** -- **[Quantization](quantization/)** - Vector quantization techniques (in development) - **[RAG Applications](RAG/)** - LangChain & LlamaIndex tutorials (in development) - **[Agent Workflows](Agents/)** - LangGraph implementations (in development) - **[Image Recommendations](img_recom/)** - Computer vision search systems (planned) @@ -75,6 +75,7 @@ New to Qdrant? Here's how to get started: | Status | Project | Description | Key Features | | --- | --- | --- | --- | +| ✅ | **[Quantization Face-Off](Quantization-FaceOff/)** | Head-to-head benchmark of Qdrant quantization (f32 / SQ / PQ / BQ) on 1M-scale text embeddings | Recall vs latency vs RAM, rescoring sweeps, cost analysis, W&B telemetry | | ✅ | **[Chonkie Integration](chonkie/)** | Text chunking with seamless Qdrant integration | Multiple chunking strategies, performance comparison | | 🚧 | **Image Recommendations** | Image similarity search systems (in development) | Computer vision, embeddings, similarity search | | 🚧 | **Agent Workflows** | AI agent implementations (planned) | LangGraph, agentic RAG, vector memory | From 6e76c179bb011e058361d36ea59aa09de927ff7e Mon Sep 17 00:00:00 2001 From: goodnight Date: Sun, 21 Jun 2026 12:10:00 +0100 Subject: [PATCH 3/4] fix ruff lint --- Quantization-FaceOff/qfo/telemetry.py | 1 - Quantization-FaceOff/qfo/viz/report.py | 7 +++++-- Quantization-FaceOff/qfo/viz/rescore_chart.py | 6 ++++-- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/Quantization-FaceOff/qfo/telemetry.py b/Quantization-FaceOff/qfo/telemetry.py index 514bfd4..9c46cbd 100644 --- a/Quantization-FaceOff/qfo/telemetry.py +++ b/Quantization-FaceOff/qfo/telemetry.py @@ -5,7 +5,6 @@ - cold-start vs warm-cache loops. One W&B run per config so the project dashboard compares them (table / parallel-coords). """ -import os import subprocess import time diff --git a/Quantization-FaceOff/qfo/viz/report.py b/Quantization-FaceOff/qfo/viz/report.py index 830f42e..cfb6dde 100644 --- a/Quantization-FaceOff/qfo/viz/report.py +++ b/Quantization-FaceOff/qfo/viz/report.py @@ -46,7 +46,9 @@ def main(): ax[0, 1].bar([i + (j - 1) * w for i in x], [R[n][f"latency_{p}_ms"] for n in names], w, label=p, color=c) ax[0, 1].set_title("Latency (ms, lower = better)") - ax[0, 1].set_xticks(list(x)); ax[0, 1].set_xticklabels(labels); ax[0, 1].legend() + ax[0, 1].set_xticks(list(x)) + ax[0, 1].set_xticklabels(labels) + ax[0, 1].legend() # memory (resident quantized vectors) ax[1, 0].bar(labels, [mem_mb[n] for n in names], color=colors) @@ -63,7 +65,8 @@ def main(): ax[1, 1].annotate(labels[i], (R[n]["latency_p50_ms"], R[n]["recall_at_k"]), textcoords="offset points", xytext=(8, 0)) ax[1, 1].set_title("Tradeoff: recall vs p50 latency (bubble = RAM)") - ax[1, 1].set_xlabel("p50 latency (ms)"); ax[1, 1].set_ylabel("recall@10") + ax[1, 1].set_xlabel("p50 latency (ms)") + ax[1, 1].set_ylabel("recall@10") plt.tight_layout(rect=[0, 0, 1, 0.97]) path = os.path.join(OUT, "faceoff.png") diff --git a/Quantization-FaceOff/qfo/viz/rescore_chart.py b/Quantization-FaceOff/qfo/viz/rescore_chart.py index 3068384..e131f48 100644 --- a/Quantization-FaceOff/qfo/viz/rescore_chart.py +++ b/Quantization-FaceOff/qfo/viz/rescore_chart.py @@ -39,8 +39,10 @@ def main(): label=f"{coll.upper()} no rescore ({nr['recall_at_k']:.3f})") for x, r in zip(xs, rec): ax1.annotate(f"{r:.3f}", (x, r), textcoords="offset points", xytext=(0, 8), ha="center") - ax1.set_xlabel("oversampling factor"); ax1.set_ylabel("Recall@10") - ax1.set_xticks(xs); ax1.set_ylim(min(nr["recall_at_k"], min(rec)) - 0.03, 1.01) + ax1.set_xlabel("oversampling factor") + ax1.set_ylabel("Recall@10") + ax1.set_xticks(xs) + ax1.set_ylim(min(nr["recall_at_k"], min(rec)) - 0.03, 1.01) ax2 = ax1.twinx() ax2.plot(xs, lat, "--s", color="#888", alpha=0.8, label="p50 latency") From 0236441a38ee515a8754132528932aab28f14123 Mon Sep 17 00:00:00 2001 From: goodnight Date: Sun, 21 Jun 2026 12:15:00 +0100 Subject: [PATCH 4/4] format with black --- Quantization-FaceOff/qfo/bench/eval.py | 8 ++- .../qfo/bench/rescore_sweep.py | 14 ++++- Quantization-FaceOff/qfo/bench/throughput.py | 5 +- Quantization-FaceOff/qfo/config.py | 10 +-- Quantization-FaceOff/qfo/demo.py | 5 +- Quantization-FaceOff/qfo/pipeline/add_bq.py | 9 ++- Quantization-FaceOff/qfo/pipeline/ingest.py | 9 ++- Quantization-FaceOff/qfo/run_all.py | 5 +- Quantization-FaceOff/qfo/telemetry.py | 16 ++++- Quantization-FaceOff/qfo/viz/report.py | 63 ++++++++++++++----- Quantization-FaceOff/qfo/viz/rescore_chart.py | 33 ++++++++-- 11 files changed, 138 insertions(+), 39 deletions(-) diff --git a/Quantization-FaceOff/qfo/bench/eval.py b/Quantization-FaceOff/qfo/bench/eval.py index 003c780..5f5c17a 100644 --- a/Quantization-FaceOff/qfo/bench/eval.py +++ b/Quantization-FaceOff/qfo/bench/eval.py @@ -3,6 +3,7 @@ One pass per collection captures BOTH retrieved ids (recall) and latency (no double query). """ + import json import time @@ -46,7 +47,7 @@ def main(n_queries=None): client = QdrantClient(url=QDRANT_URL, timeout=120) results = {} for name in COLLECTIONS: - search_collect(client, name, queries[:200], K) # warm cache, discard + search_collect(client, name, queries[:200], K) # warm cache, discard ids, lats = search_collect(client, name, queries, K) # measured pass m = { "recall_at_k": recall_at_k(ids, gt, K), @@ -66,5 +67,8 @@ def main(n_queries=None): if __name__ == "__main__": # money-path check - assert abs(recall_at_k(np.array([[1, 2, 3]]), np.array([[1, 2, 9, 8]]), 3) - 2 / 3) < 1e-9 + assert ( + abs(recall_at_k(np.array([[1, 2, 3]]), np.array([[1, 2, 9, 8]]), 3) - 2 / 3) + < 1e-9 + ) main() diff --git a/Quantization-FaceOff/qfo/bench/rescore_sweep.py b/Quantization-FaceOff/qfo/bench/rescore_sweep.py index 7dd42db..8607661 100644 --- a/Quantization-FaceOff/qfo/bench/rescore_sweep.py +++ b/Quantization-FaceOff/qfo/bench/rescore_sweep.py @@ -7,6 +7,7 @@ Read-only on the PQ collection. No drops. """ + import json import sys import time @@ -58,10 +59,17 @@ def main(): rows = [] for label, rescore, oversampling in SWEEP: ids, lats = run_one(client, queries, K, rescore, oversampling) - m = {"label": label, "rescore": rescore, "oversampling": oversampling, - "recall_at_k": recall_at_k(ids, gt, K), **pctiles(lats)} + m = { + "label": label, + "rescore": rescore, + "oversampling": oversampling, + "recall_at_k": recall_at_k(ids, gt, K), + **pctiles(lats), + } rows.append(m) - print(f"{label:12} recall={m['recall_at_k']:.3f} p50={m['latency_p50_ms']:.1f}ms") + print( + f"{label:12} recall={m['recall_at_k']:.3f} p50={m['latency_p50_ms']:.1f}ms" + ) log_run(f"{COLL}-{label.replace(' ', '_')}", m, group=f"rescore-{COLL}") out = results_path(f"rescore_sweep_{COLL}.json") diff --git a/Quantization-FaceOff/qfo/bench/throughput.py b/Quantization-FaceOff/qfo/bench/throughput.py index 0f026c2..03345e4 100644 --- a/Quantization-FaceOff/qfo/bench/throughput.py +++ b/Quantization-FaceOff/qfo/bench/throughput.py @@ -2,6 +2,7 @@ Fires N queries across CONCURRENCY worker threads, measures queries/second + mean latency under load. Read-only on the collections. Logs to W&B group 'throughput'. """ + import json import threading import time @@ -55,7 +56,9 @@ def one(vec, _name=name): } results[name] = m log_run(f"{name}-qps", m, group="throughput") - print(f"{name:9} {m['qps']:.0f} qps mean {m['mean_latency_ms']:.1f}ms (conc={CONCURRENCY})") + print( + f"{name:9} {m['qps']:.0f} qps mean {m['mean_latency_ms']:.1f}ms (conc={CONCURRENCY})" + ) with open(results_path("throughput.json"), "w") as f: json.dump(results, f, indent=2) diff --git a/Quantization-FaceOff/qfo/config.py b/Quantization-FaceOff/qfo/config.py index 58a2215..979ea08 100644 --- a/Quantization-FaceOff/qfo/config.py +++ b/Quantization-FaceOff/qfo/config.py @@ -3,6 +3,7 @@ embeddings with title/text payload, so the quantization tradeoff is dramatic and the Qdrant UI shows real content. """ + import os from qdrant_client import models @@ -17,6 +18,7 @@ def results_path(name): os.makedirs(RESULTS_DIR, exist_ok=True) return os.path.join(RESULTS_DIR, name) + # dbpedia-openai facts DIM = 1536 DISTANCE = models.Distance.COSINE @@ -24,10 +26,10 @@ def results_path(name): # 100k = ~600MB embeddings, fast + tells the full story. Fixed at 100k: 1M needs # ~25GB disk (3 collections keep f32 originals) which doesn't fit my 15GB budget DATASET_N = 100_000 -N_QUERY = 1_000 # held out from the tail; NOT inserted into base -K = 10 # Recall@K -GT_DEPTH = 100 # exact-NN depth to store as ground truth -BATCH_SIZE = 500 # upsert batch (kept modest; 1536-d float vectors are large) +N_QUERY = 1_000 # held out from the tail; NOT inserted into base +K = 10 # Recall@K +GT_DEPTH = 100 # exact-NN depth to store as ground truth +BATCH_SIZE = 500 # upsert batch (kept modest; 1536-d float vectors are large) DATA_DIR = "data/dbpedia" # 3 configs under test. quantization=None => baseline f32. diff --git a/Quantization-FaceOff/qfo/demo.py b/Quantization-FaceOff/qfo/demo.py index 66fd555..d77c19e 100644 --- a/Quantization-FaceOff/qfo/demo.py +++ b/Quantization-FaceOff/qfo/demo.py @@ -9,6 +9,7 @@ uv run python search_demo.py # random seed article uv run python search_demo.py einstein # first article whose title contains 'einstein' """ + import random import sys @@ -39,7 +40,9 @@ def main(): print(f"\nQuery article [{seed}]: {payload[seed]['title']}") print(f" {payload[seed]['text'][:160]}...\n") - res = client.query_points("bq", query=base[seed].tolist(), limit=K + 1, search_params=RESCORE) + res = client.query_points( + "bq", query=base[seed].tolist(), limit=K + 1, search_params=RESCORE + ) neighbors = [p for p in res.points if p.id != seed][:K] assert neighbors, "no neighbors returned" diff --git a/Quantization-FaceOff/qfo/pipeline/add_bq.py b/Quantization-FaceOff/qfo/pipeline/add_bq.py index 10b90d4..bbfd380 100644 --- a/Quantization-FaceOff/qfo/pipeline/add_bq.py +++ b/Quantization-FaceOff/qfo/pipeline/add_bq.py @@ -4,6 +4,7 @@ Logic stays under __main__ + main(): upload_collection(parallel>1) uses process spawn on Windows, which re-imports this module — unguarded top-level code would re-run it. """ + import sys from qdrant_client import QdrantClient @@ -20,8 +21,12 @@ def main(): print(f"[{name}] create + upload {len(base)} vectors") make_collection(client, name, COLLECTIONS[name]) client.upload_collection( - collection_name=name, vectors=base, payload=payload, - ids=list(range(len(base))), batch_size=BATCH_SIZE, parallel=4, + collection_name=name, + vectors=base, + payload=payload, + ids=list(range(len(base))), + batch_size=BATCH_SIZE, + parallel=4, ) wait_green(client, name) print(f"[{name}] green, count={client.count(name).count}") diff --git a/Quantization-FaceOff/qfo/pipeline/ingest.py b/Quantization-FaceOff/qfo/pipeline/ingest.py index 30ba033..46efb5c 100644 --- a/Quantization-FaceOff/qfo/pipeline/ingest.py +++ b/Quantization-FaceOff/qfo/pipeline/ingest.py @@ -1,11 +1,16 @@ """Create the 3 collections (baseline / sq / pq) and load the dbpedia embeddings into each. Waits for optimizer to reach green before exiting (isolation guardrail).""" + import time from qdrant_client import QdrantClient, models from qfo.config import ( - QDRANT_URL, DIM, DISTANCE, BATCH_SIZE, COLLECTIONS, + QDRANT_URL, + DIM, + DISTANCE, + BATCH_SIZE, + COLLECTIONS, ) from qfo.data.dbpedia import load_dbpedia @@ -42,7 +47,7 @@ def main(): client.upload_collection( collection_name=name, vectors=base, - payload=payload, # title/text so the UI shows real content + payload=payload, # title/text so the UI shows real content ids=ids, batch_size=BATCH_SIZE, parallel=4, diff --git a/Quantization-FaceOff/qfo/run_all.py b/Quantization-FaceOff/qfo/run_all.py index cc397e1..4df29ec 100644 --- a/Quantization-FaceOff/qfo/run_all.py +++ b/Quantization-FaceOff/qfo/run_all.py @@ -7,6 +7,7 @@ Note: ingest + add_bq rebuild the collections from scratch (drop + re-upload). """ + import subprocess import sys @@ -28,7 +29,9 @@ def main(): for i, (mod, args, desc) in enumerate(STEPS, 1): - print(f"\n=== [{i}/{len(STEPS)}] {mod} {' '.join(args)} - {desc} ===", flush=True) + print( + f"\n=== [{i}/{len(STEPS)}] {mod} {' '.join(args)} - {desc} ===", flush=True + ) subprocess.run([sys.executable, "-m", mod, *args], check=True) print("\nfaceoff complete. charts in assets/, metrics in results/ + W&B.") diff --git a/Quantization-FaceOff/qfo/telemetry.py b/Quantization-FaceOff/qfo/telemetry.py index 9c46cbd..3302e21 100644 --- a/Quantization-FaceOff/qfo/telemetry.py +++ b/Quantization-FaceOff/qfo/telemetry.py @@ -5,6 +5,7 @@ - cold-start vs warm-cache loops. One W&B run per config so the project dashboard compares them (table / parallel-coords). """ + import subprocess import time @@ -21,8 +22,15 @@ def _to_mb(s): """'1.23GiB' / '512MiB' / '900B' -> megabytes (binary units).""" s = s.strip() - for unit, mult in (("GiB", 1024), ("MiB", 1), ("KiB", 1 / 1024), - ("GB", 1000), ("MB", 1), ("kB", 1 / 1000), ("B", 1 / 1e6)): + for unit, mult in ( + ("GiB", 1024), + ("MiB", 1), + ("KiB", 1 / 1024), + ("GB", 1000), + ("MB", 1), + ("kB", 1 / 1000), + ("B", 1 / 1e6), + ): if s.endswith(unit): return float(s[: -len(unit)]) * mult return float(s) # bare number = bytes? treat as MB-less fallback @@ -32,7 +40,9 @@ def container_rss_mb(container=CONTAINER): """Resident memory of the Qdrant container, in MB.""" out = subprocess.run( ["docker", "stats", "--no-stream", "--format", "{{.MemUsage}}", container], - capture_output=True, text=True, check=True, + capture_output=True, + text=True, + check=True, ).stdout return _to_mb(out.split("/")[0]) # "used / limit" diff --git a/Quantization-FaceOff/qfo/viz/report.py b/Quantization-FaceOff/qfo/viz/report.py index cfb6dde..bc92139 100644 --- a/Quantization-FaceOff/qfo/viz/report.py +++ b/Quantization-FaceOff/qfo/viz/report.py @@ -3,10 +3,12 @@ Memory here is the RAM-resident quantized-vector size (exact math) — the thing quantization actually shrinks. Measured per-config RAM is in Qdrant's dashboard memory panel. """ + import json import os import matplotlib + matplotlib.use("Agg") import matplotlib.pyplot as plt @@ -23,28 +25,43 @@ def main(): R = json.load(f) n_base = DATASET_N - N_QUERY - mem_mb = {k: n_base * DIM * 4 / 1e6 / COMPRESS[k] for k in R} # resident vector bytes + mem_mb = { + k: n_base * DIM * 4 / 1e6 / COMPRESS[k] for k in R + } # resident vector bytes names = list(R.keys()) labels = [LABEL.get(n, n) for n in names] palette = ["#4C72B0", "#DD8452", "#55A868", "#8172B3"] colors = [palette[i % len(palette)] for i in range(len(names))] fig, ax = plt.subplots(2, 2, figsize=(12, 9)) - fig.suptitle(f"Qdrant Quantization Face-Off — dbpedia 1536-d, {n_base:,} vectors", fontsize=14, weight="bold") + fig.suptitle( + f"Qdrant Quantization Face-Off — dbpedia 1536-d, {n_base:,} vectors", + fontsize=14, + weight="bold", + ) # recall@10 ax[0, 0].bar(labels, [R[n]["recall_at_k"] for n in names], color=colors) ax[0, 0].set_title("Recall@10 (higher = better)") ax[0, 0].set_ylim(0, 1.05) for i, n in enumerate(names): - ax[0, 0].text(i, R[n]["recall_at_k"] + 0.01, f"{R[n]['recall_at_k']:.3f}", ha="center") + ax[0, 0].text( + i, R[n]["recall_at_k"] + 0.01, f"{R[n]['recall_at_k']:.3f}", ha="center" + ) # latency percentiles x = range(len(names)) w = 0.25 - for j, (p, c) in enumerate([("p50", "#8172B3"), ("p95", "#CCB974"), ("p99", "#C44E52")]): - ax[0, 1].bar([i + (j - 1) * w for i in x], - [R[n][f"latency_{p}_ms"] for n in names], w, label=p, color=c) + for j, (p, c) in enumerate( + [("p50", "#8172B3"), ("p95", "#CCB974"), ("p99", "#C44E52")] + ): + ax[0, 1].bar( + [i + (j - 1) * w for i in x], + [R[n][f"latency_{p}_ms"] for n in names], + w, + label=p, + color=c, + ) ax[0, 1].set_title("Latency (ms, lower = better)") ax[0, 1].set_xticks(list(x)) ax[0, 1].set_xticklabels(labels) @@ -53,17 +70,33 @@ def main(): # memory (resident quantized vectors) ax[1, 0].bar(labels, [mem_mb[n] for n in names], color=colors) ax[1, 0].set_title("Vectors in RAM (MB, computed — lower = better)") - ax[1, 0].set_ylim(0, max(mem_mb.values()) * 1.25) # headroom so labels clear the title + ax[1, 0].set_ylim( + 0, max(mem_mb.values()) * 1.25 + ) # headroom so labels clear the title for i, n in enumerate(names): - ax[1, 0].text(i, mem_mb[n] + max(mem_mb.values()) * 0.02, - f"{mem_mb[n]:.0f} MB ({COMPRESS[n]}x)", ha="center") + ax[1, 0].text( + i, + mem_mb[n] + max(mem_mb.values()) * 0.02, + f"{mem_mb[n]:.0f} MB ({COMPRESS[n]}x)", + ha="center", + ) # tradeoff: recall vs p50 latency, bubble size = memory for i, n in enumerate(names): - ax[1, 1].scatter(R[n]["latency_p50_ms"], R[n]["recall_at_k"], - s=mem_mb[n] * 2, color=colors[i], alpha=0.7, label=labels[i]) - ax[1, 1].annotate(labels[i], (R[n]["latency_p50_ms"], R[n]["recall_at_k"]), - textcoords="offset points", xytext=(8, 0)) + ax[1, 1].scatter( + R[n]["latency_p50_ms"], + R[n]["recall_at_k"], + s=mem_mb[n] * 2, + color=colors[i], + alpha=0.7, + label=labels[i], + ) + ax[1, 1].annotate( + labels[i], + (R[n]["latency_p50_ms"], R[n]["recall_at_k"]), + textcoords="offset points", + xytext=(8, 0), + ) ax[1, 1].set_title("Tradeoff: recall vs p50 latency (bubble = RAM)") ax[1, 1].set_xlabel("p50 latency (ms)") ax[1, 1].set_ylabel("recall@10") @@ -73,7 +106,9 @@ def main(): plt.savefig(path, dpi=120) print("wrote", path) for n in names: - print(f"{LABEL.get(n, n):8} recall={R[n]['recall_at_k']:.3f} p50={R[n]['latency_p50_ms']:.1f}ms ram={mem_mb[n]:.0f}MB ({COMPRESS[n]}x)") + print( + f"{LABEL.get(n, n):8} recall={R[n]['recall_at_k']:.3f} p50={R[n]['latency_p50_ms']:.1f}ms ram={mem_mb[n]:.0f}MB ({COMPRESS[n]}x)" + ) if __name__ == "__main__": diff --git a/Quantization-FaceOff/qfo/viz/rescore_chart.py b/Quantization-FaceOff/qfo/viz/rescore_chart.py index e131f48..144138d 100644 --- a/Quantization-FaceOff/qfo/viz/rescore_chart.py +++ b/Quantization-FaceOff/qfo/viz/rescore_chart.py @@ -3,11 +3,13 @@ Usage: python -m qfo.viz.rescore_chart [collection] default: pq Reads results/rescore_sweep_.json + results/results.json -> assets/rescore_.png """ + import json import os import sys import matplotlib + matplotlib.use("Agg") import matplotlib.pyplot as plt @@ -31,14 +33,33 @@ def main(): lat = [r["latency_p50_ms"] for r in os_pts] fig, ax1 = plt.subplots(figsize=(9, 5.5)) - ax1.set_title(f"{coll.upper()} {COMPRESS.get(coll, '')}: rescoring buys back recall for a few ms", weight="bold") + ax1.set_title( + f"{coll.upper()} {COMPRESS.get(coll, '')}: rescoring buys back recall for a few ms", + weight="bold", + ) - ax1.axhline(base_recall, ls="--", color="#4C72B0", label=f"baseline f32 recall ({base_recall:.3f})") - ax1.plot(xs, rec, "-o", color="#55A868", lw=2, label=f"{coll.upper()} recall (rescored)") - ax1.scatter([1], [nr["recall_at_k"]], color="#C44E52", marker="X", s=110, zorder=5, - label=f"{coll.upper()} no rescore ({nr['recall_at_k']:.3f})") + ax1.axhline( + base_recall, + ls="--", + color="#4C72B0", + label=f"baseline f32 recall ({base_recall:.3f})", + ) + ax1.plot( + xs, rec, "-o", color="#55A868", lw=2, label=f"{coll.upper()} recall (rescored)" + ) + ax1.scatter( + [1], + [nr["recall_at_k"]], + color="#C44E52", + marker="X", + s=110, + zorder=5, + label=f"{coll.upper()} no rescore ({nr['recall_at_k']:.3f})", + ) for x, r in zip(xs, rec): - ax1.annotate(f"{r:.3f}", (x, r), textcoords="offset points", xytext=(0, 8), ha="center") + ax1.annotate( + f"{r:.3f}", (x, r), textcoords="offset points", xytext=(0, 8), ha="center" + ) ax1.set_xlabel("oversampling factor") ax1.set_ylabel("Recall@10") ax1.set_xticks(xs)