mirror of
https://github.com/facebookresearch/faiss.git
synced 2026-10-12 02:13:36 +00:00
Tia Lim
0f0f728166
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5349 # this is a no-op change Ran `black -l 80` over all tracked Python files under `fbcode/faiss/`, using line length 80 to match faiss's documented coding style (`CONTRIBUTING.md`: "80 character line length (both for C++ and Python)"). 172 files were reformatted; 28 were already compliant. This is a pure-formatting change. `black` runs its AST-equivalence safety check on every file, so no identifiers, string/numeric values, operators, or control flow were altered — only whitespace, indentation, line wrapping, blank lines, trailing commas, and string-quote normalization (single to double quotes). `fbcode/faiss/` is intentionally excluded from Meta's standard Python autoformatter (`arc f` / `arc lint -a` report no formatter for these paths) because it mirrors to public GitHub, so the Python had drifted out of any consistent style. The C++ is already clang-format-clean (0 of 807 files change), so this effort is Python-only. This diff brings the Python to canonical `black` formatting. Excluded `demos/index_pq_flat_separate_codes_from_codebook.py`: it contains a Jupyter `!rm` shell-magic line and is not valid Python, so `black` cannot parse it. This is the base of a 3-diff stack: (1) this black reformat; (2) `.flake8` alignment (max-line-length 80 + black-compatible ignores E203/W503); (3) genuine lint fixes (F401/F403/F841/B011). Reviewed By: mnorris11 Differential Revision: D109736099 fbshipit-source-id: 9a054635766ed11aa3760a788395babd3d198abf
111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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"""Benchmark `faiss.SuperKMeans` against vanilla `faiss.Clustering` on the same
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input. Inputs may be a .fvecs file or a synthetic Gaussian mixture.
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Examples:
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# Synthetic data
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python bench_super_kmeans.py --n 10000 --d 128 --k 64 --niter 10
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# Real .fvecs (e.g., SIFT base)
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python bench_super_kmeans.py --fvecs /path/to/sift_base.fvecs --k 1024 \\
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--niter 10
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"""
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import argparse
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import sys
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import time
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import faiss
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import numpy as np
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from datasets import fvecs_read
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def gaussian_mixture(n, d, k, seed):
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"""k centers in [-1, 1]^d, ~n/k samples per center from N(center, 0.1)."""
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rng = np.random.RandomState(seed)
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centers = rng.uniform(-1.0, 1.0, size=(k, d)).astype("float32")
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cluster = np.arange(n) % k
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noise = rng.normal(0.0, 0.1, size=(n, d)).astype("float32")
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return centers[cluster] + noise
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def run_vanilla(x, d, k, niter, seed):
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quant = faiss.IndexFlatL2(d)
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cl = faiss.Clustering(d, k)
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cl.seed = seed
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cl.niter = niter
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cl.verbose = False
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t0 = time.perf_counter()
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cl.train(x, quant)
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dt = time.perf_counter() - t0
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final_obj = cl.iteration_stats.at(cl.iteration_stats.size() - 1).obj
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return dt, final_obj, cl.iteration_stats.size()
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def run_super(x, d, k, niter, seed):
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p = faiss.SuperKMeansParameters()
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p.seed = seed
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p.niter = niter
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p.verbose = False
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sc = faiss.SuperKMeans(d, k, p)
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t0 = time.perf_counter()
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sc.train(x)
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dt = time.perf_counter() - t0
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final_obj = sc.iteration_stats.at(sc.iteration_stats.size() - 1).obj
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iter_sum = sum(
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sc.iteration_stats.at(i).time for i in range(sc.iteration_stats.size())
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)
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setup = dt - iter_sum
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rates = faiss.vector_to_array(sc.gemm_pruning_rates)
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return dt, final_obj, sc.iteration_stats.size(), iter_sum, setup, rates
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def main():
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ap = argparse.ArgumentParser(description=__doc__)
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src = ap.add_mutually_exclusive_group()
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src.add_argument("--fvecs", help="path to a .fvecs file")
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ap.add_argument("--n", type=int, default=10000, help="synthetic n")
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ap.add_argument("--d", type=int, default=128, help="synthetic d")
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ap.add_argument("--k", type=int, default=64, help="number of centroids")
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ap.add_argument("--niter", type=int, default=10, help="iterations")
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ap.add_argument("--seed", type=int, default=42, help="RNG seed")
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args = ap.parse_args()
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if args.fvecs:
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x = fvecs_read(args.fvecs)
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n, d = x.shape
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print("=== bench_super_kmeans ===")
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print(
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f"fvecs={args.fvecs} n={n} d={d} k={args.k} "
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f"niter={args.niter} seed={args.seed}"
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)
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else:
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n, d = args.n, args.d
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x = gaussian_mixture(n, d, args.k, args.seed)
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print("=== bench_super_kmeans ===")
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print(f"n={n} d={d} k={args.k} niter={args.niter} seed={args.seed}")
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print("\n--- faiss.Clustering ---")
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v_dt, v_obj, v_iters = run_vanilla(x, d, args.k, args.niter, args.seed)
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print(f" wall_time={v_dt:.3f}s final_obj={v_obj:g} iters={v_iters}")
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print("\n--- faiss.SuperKMeans ---")
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s_dt, s_obj, s_iters, iter_sum, setup, rates = run_super(
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x, d, args.k, args.niter, args.seed
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)
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print(
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f" wall_time={s_dt:.3f}s final_obj={s_obj:g} iters={s_iters} "
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f"iter_sum={iter_sum:.3f}s setup={setup:.3f}s"
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)
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print(" gemm_pruning_rates:", " ".join(f"{r:.3f}" for r in rates))
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print()
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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