mirror of
https://github.com/facebookresearch/faiss.git
synced 2026-10-11 22:50:00 +00:00
Taksh
faeac8636c
Summary: - Update C++ language level from C++17 to C++20 in `CONTRIBUTING.md` to match the actual CMake configuration (`CMAKE_CXX_STANDARD 20` in the root `CMakeLists.txt`) - Remove outdated "progressively dropping python2 support" note from `contrib/README.md` (Python 2 reached end-of-life in January 2020 and Faiss requires Python 3) - Update shebangs from `python2` to `python3` in three benchmark scripts: `benchs/kmeans_mnist.py`, `benchs/bench_gpu_1bn.py`, and `benchs/bench_vector_ops.py` Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5045 Test Plan: - [ ] No functional code changes; only documentation and shebangs are modified - [ ] Verified C++20 is the actual standard by checking `CMakeLists.txt` (`set(CMAKE_CXX_STANDARD 20)`) and `INSTALL.md` (which already references "a C++20 compiler") 🤖 Generated with [Claude Code](https://claude.com/claude-code) Reviewed By: mnorris11 Differential Revision: D99691731 Pulled By: alibeklfc fbshipit-source-id: 579cf21054cdf6bdaea27f5abb1c56e0b709a922
90 lines
2.1 KiB
Python
90 lines
2.1 KiB
Python
#! /usr/bin/env python3
|
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
#
|
|
# This source code is licensed under the MIT license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
|
|
from __future__ import print_function
|
|
import numpy as np
|
|
import time
|
|
import faiss
|
|
import sys
|
|
|
|
|
|
# Get command-line arguments
|
|
|
|
k = int(sys.argv[1])
|
|
ngpu = int(sys.argv[2])
|
|
|
|
# Load Leon's file format
|
|
|
|
|
|
def load_mnist(fname):
|
|
print("load", fname)
|
|
f = open(fname)
|
|
|
|
header = np.fromfile(f, dtype='int8', count=4*4)
|
|
header = header.reshape(4, 4)[:, ::-1].copy().view('int32')
|
|
print(header)
|
|
nim, xd, yd = [int(x) for x in header[1:]]
|
|
|
|
data = np.fromfile(f, count=nim * xd * yd,
|
|
dtype='uint8')
|
|
|
|
print(data.shape, nim, xd, yd)
|
|
data = data.reshape(nim, xd, yd)
|
|
return data
|
|
|
|
basedir = "/path/to/mnist/data"
|
|
|
|
x = load_mnist(basedir + 'mnist8m/mnist8m-patterns-idx3-ubyte')
|
|
|
|
print("reshape")
|
|
|
|
x = x.reshape(x.shape[0], -1).astype('float32')
|
|
|
|
|
|
def train_kmeans(x, k, ngpu):
|
|
"Runs kmeans on one or several GPUs"
|
|
d = x.shape[1]
|
|
clus = faiss.Clustering(d, k)
|
|
clus.verbose = True
|
|
clus.niter = 20
|
|
|
|
# otherwise the kmeans implementation sub-samples the training set
|
|
clus.max_points_per_centroid = 10000000
|
|
|
|
res = [faiss.StandardGpuResources() for i in range(ngpu)]
|
|
|
|
flat_config = []
|
|
for i in range(ngpu):
|
|
cfg = faiss.GpuIndexFlatConfig()
|
|
cfg.useFloat16 = False
|
|
cfg.device = i
|
|
flat_config.append(cfg)
|
|
|
|
if ngpu == 1:
|
|
index = faiss.GpuIndexFlatL2(res[0], d, flat_config[0])
|
|
else:
|
|
indexes = [faiss.GpuIndexFlatL2(res[i], d, flat_config[i])
|
|
for i in range(ngpu)]
|
|
index = faiss.IndexReplicas()
|
|
for sub_index in indexes:
|
|
index.addIndex(sub_index)
|
|
|
|
# perform the training
|
|
clus.train(x, index)
|
|
centroids = faiss.vector_float_to_array(clus.centroids)
|
|
|
|
obj = faiss.vector_float_to_array(clus.obj)
|
|
print("final objective: %.4g" % obj[-1])
|
|
|
|
return centroids.reshape(k, d)
|
|
|
|
print("run")
|
|
t0 = time.time()
|
|
train_kmeans(x, k, ngpu)
|
|
t1 = time.time()
|
|
|
|
print("total runtime: %.3f s" % (t1 - t0))
|