• Lucas Hosseini's avatar
    Facebook sync (#573) · 76bec0b5
    Lucas Hosseini authored
    Features:
    
    - automatic tracking of C++ references in Python
    - non-intel platforms supported -- some functions optimized for ARM
    - override nprobe for concurrent searches
    - support for floating-point quantizers in binary indexes
    Bug fixes:
    
    - no more segfaults in python (I know it's the same as the first feature but it's important!)
    - fix GpuIndexIVFFlat issues for float32 with 64 / 128 dims
    - fix sharding of flat indexes on GPU with index_cpu_to_gpu_multiple
    76bec0b5
bench_vector_ops.py 2.1 KB
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD+Patents license found in the
# LICENSE file in the root directory of this source tree.

#! /usr/bin/env python2

import numpy as np
import faiss
import time

swig_ptr = faiss.swig_ptr

if False:
    a = np.arange(10, 14).astype('float32')
    b = np.arange(20, 24).astype('float32')

    faiss.fvec_inner_product (swig_ptr(a), swig_ptr(b), 4)

    1/0

xd = 100
yd = 1000000

np.random.seed(1234)

faiss.omp_set_num_threads(1)

print 'xd=%d yd=%d' % (xd, yd)

print 'Running inner products test..'
for d in 3, 4, 12, 36, 64:

    x = faiss.rand(xd * d).reshape(xd, d)
    y = faiss.rand(yd * d).reshape(yd, d)

    distances = np.empty((xd, yd), dtype='float32')

    t0 = time.time()
    for i in xrange(xd):
        faiss.fvec_inner_products_ny(swig_ptr(distances[i]),
                                     swig_ptr(x[i]),
                                     swig_ptr(y),
                                     d, yd)
    t1 = time.time()

    # sparse verification
    ntry = 100
    num, denom = 0, 0
    for t in range(ntry):
        xi = np.random.randint(xd)
        yi = np.random.randint(yd)
        num += abs(distances[xi, yi] - np.dot(x[xi], y[yi]))
        denom += abs(distances[xi, yi])

    print 'd=%d t=%.3f s diff=%g' % (d, t1 - t0, num / denom)


print 'Running L2sqr test..'
for d in 3, 4, 12, 36, 64:

    x = faiss.rand(xd * d).reshape(xd, d)
    y = faiss.rand(yd * d).reshape(yd, d)

    distances = np.empty((xd, yd), dtype='float32')

    t0 = time.time()
    for i in xrange(xd):
        faiss.fvec_L2sqr_ny(swig_ptr(distances[i]),
                            swig_ptr(x[i]),
                            swig_ptr(y),
                            d, yd)
    t1 = time.time()

    # sparse verification
    ntry = 100
    num, denom = 0, 0
    for t in range(ntry):
        xi = np.random.randint(xd)
        yi = np.random.randint(yd)
        num += abs(distances[xi, yi] - np.sum((x[xi] - y[yi]) ** 2))
        denom += abs(distances[xi, yi])

    print 'd=%d t=%.3f s diff=%g' % (d, t1 - t0, num / denom)