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
92 lines
2.1 KiB
Python
92 lines
2.1 KiB
Python
#! /usr/bin/env python3
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# 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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from __future__ import print_function
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import numpy as np
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import time
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import faiss
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import sys
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# Get command-line arguments
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k = int(sys.argv[1])
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ngpu = int(sys.argv[2])
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# Load Leon's file format
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def load_mnist(fname):
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print("load", fname)
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f = open(fname)
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header = np.fromfile(f, dtype="int8", count=4 * 4)
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header = header.reshape(4, 4)[:, ::-1].copy().view("int32")
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print(header)
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nim, xd, yd = [int(x) for x in header[1:]]
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data = np.fromfile(f, count=nim * xd * yd, dtype="uint8")
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print(data.shape, nim, xd, yd)
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data = data.reshape(nim, xd, yd)
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return data
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basedir = "/path/to/mnist/data"
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x = load_mnist(basedir + "mnist8m/mnist8m-patterns-idx3-ubyte")
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print("reshape")
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x = x.reshape(x.shape[0], -1).astype("float32")
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def train_kmeans(x, k, ngpu):
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"Runs kmeans on one or several GPUs"
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d = x.shape[1]
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clus = faiss.Clustering(d, k)
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clus.verbose = True
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clus.niter = 20
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# otherwise the kmeans implementation sub-samples the training set
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clus.max_points_per_centroid = 10000000
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res = [faiss.StandardGpuResources() for i in range(ngpu)]
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flat_config = []
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for i in range(ngpu):
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cfg = faiss.GpuIndexFlatConfig()
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cfg.useFloat16 = False
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cfg.device = i
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flat_config.append(cfg)
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if ngpu == 1:
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index = faiss.GpuIndexFlatL2(res[0], d, flat_config[0])
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else:
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indexes = [
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faiss.GpuIndexFlatL2(res[i], d, flat_config[i]) for i in range(ngpu)
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]
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index = faiss.IndexReplicas()
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for sub_index in indexes:
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index.addIndex(sub_index)
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# perform the training
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clus.train(x, index)
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centroids = faiss.vector_float_to_array(clus.centroids)
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obj = faiss.vector_float_to_array(clus.obj)
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print("final objective: %.4g" % obj[-1])
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return centroids.reshape(k, d)
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print("run")
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t0 = time.time()
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train_kmeans(x, k, ngpu)
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t1 = time.time()
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print("total runtime: %.3f s" % (t1 - t0))
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