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https://github.com/facebookresearch/faiss.git
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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
156 lines
4.0 KiB
Python
156 lines
4.0 KiB
Python
#! /usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its 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 faiss
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import time
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import os
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import argparse
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parser = argparse.ArgumentParser()
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def aa(*args, **kwargs):
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group.add_argument(*args, **kwargs)
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group = parser.add_argument_group('dataset options')
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aa('--dim', type=int, default=64)
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aa('--nb', type=int, default=int(1e6))
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aa('--subset_len', type=int, default=int(1e5))
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aa('--key', default='IVF1000,Flat')
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aa('--nprobe', type=int, default=640)
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aa('--no_intcallback', default=False, action='store_true')
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aa('--twostage', default=False, action='store_true')
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aa('--nt', type=int, default=-1)
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args = parser.parse_args()
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print("args:", args)
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d = args.dim # dimension
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nb = args.nb # database size
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nq = 1000 # nb of queries
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nt = 100000
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subset_len = args.subset_len
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np.random.seed(1234) # make reproducible
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xb = np.random.random((nb, d)).astype('float32')
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xq = np.random.random((nq, d)).astype('float32')
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xt = np.random.random((nt, d)).astype('float32')
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k = 100
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if args.no_intcallback:
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faiss.InterruptCallback.clear_instance()
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if args.nt != -1:
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faiss.omp_set_num_threads(args.nt)
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nprobe = args.nprobe
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key = args.key
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#key = 'IVF1000,Flat'
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# key = 'IVF1000,PQ64'
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# key = 'IVF100_HNSW32,PQ64'
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# faiss.omp_set_num_threads(1)
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pf = 'dim%d_' % d
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if d == 64:
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pf = ''
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basename = '/tmp/base%s%s.index' % (pf, key)
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if os.path.exists(basename):
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print('load', basename)
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index_1 = faiss.read_index(basename)
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else:
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print('train + write', basename)
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index_1 = faiss.index_factory(d, key)
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index_1.train(xt)
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faiss.write_index(index_1, basename)
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print('add')
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index_1.add(xb)
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print('set nprobe=', nprobe)
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faiss.ParameterSpace().set_index_parameter(index_1, 'nprobe', nprobe)
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class ResultHeap:
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""" Combine query results from a sliced dataset """
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def __init__(self, nq, k):
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" nq: number of query vectors, k: number of results per query "
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self.I = np.zeros((nq, k), dtype='int64')
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self.D = np.zeros((nq, k), dtype='float32')
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self.nq, self.k = nq, k
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heaps = faiss.float_maxheap_array_t()
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heaps.k = k
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heaps.nh = nq
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heaps.val = faiss.swig_ptr(self.D)
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heaps.ids = faiss.swig_ptr(self.I)
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heaps.heapify()
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self.heaps = heaps
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def add_batch_result(self, D, I, i0):
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assert D.shape == (self.nq, self.k)
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assert I.shape == (self.nq, self.k)
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I += i0
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self.heaps.addn_with_ids(
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self.k, faiss.swig_ptr(D),
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faiss.swig_ptr(I), self.k)
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def finalize(self):
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self.heaps.reorder()
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stats = faiss.cvar.indexIVF_stats
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stats.reset()
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print('index size', index_1.ntotal,
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'imbalance', index_1.invlists.imbalance_factor())
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start = time.time()
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Dref, Iref = index_1.search(xq, k)
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print('time of searching: %.3f s = %.3f + %.3f ms' % (
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time.time() - start, stats.quantization_time, stats.search_time))
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indexes = {}
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if args.twostage:
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for i in range(0, nb, subset_len):
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index = faiss.read_index(basename)
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faiss.ParameterSpace().set_index_parameter(index, 'nprobe', nprobe)
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print("add %d:%d" %(i, i+subset_len))
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index.add(xb[i:i + subset_len])
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indexes[i] = index
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rh = ResultHeap(nq, k)
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sum_time = tq = ts = 0
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for i in range(0, nb, subset_len):
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if not args.twostage:
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index = faiss.read_index(basename)
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faiss.ParameterSpace().set_index_parameter(index, 'nprobe', nprobe)
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print("add %d:%d" %(i, i+subset_len))
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index.add(xb[i:i + subset_len])
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else:
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index = indexes[i]
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stats.reset()
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start = time.time()
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Di, Ii = index.search(xq, k)
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sum_time = sum_time + time.time() - start
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tq += stats.quantization_time
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ts += stats.search_time
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rh.add_batch_result(Di, Ii, i)
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print('time of searching separately: %.3f s = %.3f + %.3f ms' %
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(sum_time, tq, ts))
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rh.finalize()
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print('diffs: %d / %d' % ((Iref != rh.I).sum(), Iref.size))
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