mirror of
https://github.com/facebookresearch/faiss.git
synced 2025-06-03 01:25:53 +08:00
6d0bc58db6
Summary: IndexPQ and IndexIVFPQ implementations with AVX shuffle instructions. The training and computing of the codes does not change wrt. the original PQ versions but the code layout is "packed" so that it can be used efficiently by the SIMD computation kernels. The main changes are: - new IndexPQFastScan and IndexIVFPQFastScan objects - simdib.h for an abstraction above the AVX2 intrinsics - BlockInvertedLists for invlists that are 32-byte aligned and where codes are not sequential - pq4_fast_scan.h/.cpp: for packing codes and look-up tables + optmized distance comptuation kernels - simd_result_hander.h: SIMD version of result collection in heaps / reservoirs Misc changes: - added contrib.inspect_tools to access fields in C++ objects - moved .h and .cpp code for inverted lists to an invlists/ subdirectory, and made a .h/.cpp for InvertedListsIOHook - added a new inverted lists type with 32-byte aligned codes (for consumption by SIMD) - moved Windows-specific intrinsics to platfrom_macros.h Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1542 Test Plan: ``` buck test mode/opt -j 4 //faiss/tests/:test_fast_scan_ivf //faiss/tests/:test_fast_scan buck test mode/opt //faiss/manifold/... ``` Reviewed By: wickedfoo Differential Revision: D25175439 Pulled By: mdouze fbshipit-source-id: ad1a40c0df8c10f4b364bdec7172e43d71b56c34
90 lines
2.1 KiB
Python
90 lines
2.1 KiB
Python
#! /usr/bin/env python2
|
|
|
|
# Copyright (c) Facebook, Inc. and its 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))
|