mirror of https://github.com/milvus-io/milvus.git
Merge remote-tracking branch 'source/0.5.1' into 0.5.1
Former-commit-id: f29536b8ff8ac23aa9f629da28825c1efcedcf0bpull/191/head
starlord
commit
52b6307027
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@ -86,5 +86,6 @@ install(TARGETS test_gpuresource DESTINATION unittest)
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install(TARGETS test_customized_index DESTINATION unittest)
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#add_subdirectory(faiss_ori)
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#add_subdirectory(faiss_benchmark)
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add_subdirectory(test_nsg)
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@ -0,0 +1,24 @@
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include_directories(${INDEX_SOURCE_DIR}/thirdparty)
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include_directories(${INDEX_SOURCE_DIR}/include)
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include_directories(/usr/local/cuda/include)
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include_directories(/usr/local/hdf5/include)
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link_directories(/usr/local/cuda/lib64)
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link_directories(/usr/local/hdf5/lib)
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set(unittest_libs
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gtest gmock gtest_main gmock_main)
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set(depend_libs
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faiss openblas lapack hdf5
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arrow ${ARROW_PREFIX}/lib/libjemalloc_pic.a
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)
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set(basic_libs
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cudart cublas
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gomp gfortran pthread
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)
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add_executable(test_faiss_benchmark faiss_benchmark_test.cpp)
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target_link_libraries(test_faiss_benchmark ${depend_libs} ${unittest_libs} ${basic_libs})
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install(TARGETS test_faiss_benchmark DESTINATION unittest)
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@ -0,0 +1,25 @@
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### To run this FAISS benchmark, please follow these steps:
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#### Step 1:
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Download the HDF5 source from:
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https://support.hdfgroup.org/ftp/HDF5/releases/
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and build/install to "/usr/local/hdf5".
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#### Step 2:
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Download HDF5 data files from:
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https://github.com/erikbern/ann-benchmarks
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#### Step 3:
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Update 'milvus/core/src/index/unittest/CMakeLists.txt',
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uncomment "#add_subdirectory(faiss_benchmark)".
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#### Step 4:
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Build Milvus with unittest enabled: "./build.sh -t Release -u",
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binary 'test_faiss_benchmark' will be generated.
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#### Step 5:
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Put HDF5 data files into the same directory with binary 'test_faiss_benchmark'.
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#### Step 6:
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Run test binary 'test_faiss_benchmark'.
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@ -0,0 +1,604 @@
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// Licensed to the Apache Software Foundation (ASF) under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing,
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// software distributed under the License is distributed on an
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// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations
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// under the License.
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#include <gtest/gtest.h>
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#include <cassert>
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#include <cmath>
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#include <cstdio>
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#include <cstdlib>
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#include <cstring>
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#include <faiss/AutoTune.h>
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#include <faiss/Index.h>
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#include <faiss/IndexIVF.h>
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#include <faiss/gpu/GpuAutoTune.h>
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#include <faiss/gpu/GpuIndexFlat.h>
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#include <faiss/gpu/GpuIndexIVFSQHybrid.h>
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#include <faiss/gpu/StandardGpuResources.h>
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#include <faiss/index_io.h>
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#include <faiss/utils.h>
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#include <hdf5.h>
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#include <sys/stat.h>
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#include <sys/time.h>
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#include <sys/types.h>
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#include <unistd.h>
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#include <vector>
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/*****************************************************
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* To run this test, please download the HDF5 from
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* https://support.hdfgroup.org/ftp/HDF5/releases/
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* and install it to /usr/local/hdf5 .
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*****************************************************/
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double
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elapsed() {
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struct timeval tv;
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gettimeofday(&tv, nullptr);
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return tv.tv_sec + tv.tv_usec * 1e-6;
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}
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void*
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hdf5_read(const char* file_name, const char* dataset_name, H5T_class_t dataset_class, size_t& d_out, size_t& n_out) {
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hid_t file, dataset, datatype, dataspace, memspace;
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H5T_class_t t_class; /* data type class */
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H5T_order_t order; /* data order */
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size_t size; /* size of the data element stored in file */
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hsize_t dimsm[3]; /* memory space dimensions */
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hsize_t dims_out[2]; /* dataset dimensions */
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hsize_t count[2]; /* size of the hyperslab in the file */
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hsize_t offset[2]; /* hyperslab offset in the file */
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hsize_t count_out[3]; /* size of the hyperslab in memory */
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hsize_t offset_out[3]; /* hyperslab offset in memory */
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int rank;
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void* data_out; /* output buffer */
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/* Open the file and the dataset. */
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file = H5Fopen(file_name, H5F_ACC_RDONLY, H5P_DEFAULT);
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dataset = H5Dopen2(file, dataset_name, H5P_DEFAULT);
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/*
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* Get datatype and dataspace handles and then query
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* dataset class, order, size, rank and dimensions.
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*/
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datatype = H5Dget_type(dataset); /* datatype handle */
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t_class = H5Tget_class(datatype);
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assert(t_class == dataset_class || !"Illegal dataset class type");
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order = H5Tget_order(datatype);
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switch (order) {
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case H5T_ORDER_LE:
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printf("Little endian order \n");
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break;
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case H5T_ORDER_BE:
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printf("Big endian order \n");
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break;
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default:
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printf("Illegal endian order \n");
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break;
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}
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size = H5Tget_size(datatype);
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printf("Data size is %d \n", (int)size);
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dataspace = H5Dget_space(dataset); /* dataspace handle */
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rank = H5Sget_simple_extent_ndims(dataspace);
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H5Sget_simple_extent_dims(dataspace, dims_out, NULL);
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n_out = dims_out[0];
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d_out = dims_out[1];
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printf("rank %d, dimensions %lu x %lu \n", rank, n_out, d_out);
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/* Define hyperslab in the dataset. */
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offset[0] = offset[1] = 0;
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count[0] = dims_out[0];
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count[1] = dims_out[1];
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H5Sselect_hyperslab(dataspace, H5S_SELECT_SET, offset, NULL, count, NULL);
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/* Define the memory dataspace. */
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dimsm[0] = dims_out[0];
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dimsm[1] = dims_out[1];
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dimsm[2] = 1;
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memspace = H5Screate_simple(3, dimsm, NULL);
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/* Define memory hyperslab. */
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offset_out[0] = offset_out[1] = offset_out[2] = 0;
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count_out[0] = dims_out[0];
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count_out[1] = dims_out[1];
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count_out[2] = 1;
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H5Sselect_hyperslab(memspace, H5S_SELECT_SET, offset_out, NULL, count_out, NULL);
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/* Read data from hyperslab in the file into the hyperslab in memory and display. */
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switch (t_class) {
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case H5T_INTEGER:
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data_out = new int[dims_out[0] * dims_out[1]];
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H5Dread(dataset, H5T_NATIVE_INT, memspace, dataspace, H5P_DEFAULT, data_out);
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break;
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case H5T_FLOAT:
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data_out = new float[dims_out[0] * dims_out[1]];
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H5Dread(dataset, H5T_NATIVE_FLOAT, memspace, dataspace, H5P_DEFAULT, data_out);
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break;
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default:
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printf("Illegal dataset class type\n");
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break;
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}
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/* Close/release resources. */
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H5Tclose(datatype);
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H5Dclose(dataset);
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H5Sclose(dataspace);
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H5Sclose(memspace);
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H5Fclose(file);
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return data_out;
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}
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std::string
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get_index_file_name(const std::string& ann_test_name, const std::string& index_key, int32_t data_loops) {
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size_t pos = index_key.find_first_of(',', 0);
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std::string file_name = ann_test_name;
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file_name = file_name + "_" + index_key.substr(0, pos) + "_" + index_key.substr(pos + 1);
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file_name = file_name + "_" + std::to_string(data_loops) + ".index";
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return file_name;
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}
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bool
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parse_ann_test_name(const std::string& ann_test_name, size_t& dim, faiss::MetricType& metric_type) {
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size_t pos1, pos2;
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if (ann_test_name.empty())
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return false;
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pos1 = ann_test_name.find_first_of('-', 0);
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if (pos1 == std::string::npos)
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return false;
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pos2 = ann_test_name.find_first_of('-', pos1 + 1);
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if (pos2 == std::string::npos)
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return false;
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dim = std::stoi(ann_test_name.substr(pos1 + 1, pos2 - pos1 - 1));
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std::string metric_str = ann_test_name.substr(pos2 + 1);
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if (metric_str == "angular") {
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metric_type = faiss::METRIC_INNER_PRODUCT;
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} else if (metric_str == "euclidean") {
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metric_type = faiss::METRIC_L2;
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} else {
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return false;
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}
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return true;
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}
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int32_t
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GetResultHitCount(const faiss::Index::idx_t* ground_index, const faiss::Index::idx_t* index, size_t ground_k, size_t k,
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size_t nq, int32_t index_add_loops) {
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assert(ground_k <= k);
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int hit = 0;
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for (int i = 0; i < nq; i++) {
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// count the num of results exist in ground truth result set
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// each result replicates INDEX_ADD_LOOPS times
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for (int j_c = 0; j_c < ground_k; j_c++) {
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int r_c = index[i * k + j_c];
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int j_g = 0;
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for (; j_g < ground_k / index_add_loops; j_g++) {
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if (ground_index[i * ground_k + j_g] == r_c) {
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hit++;
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continue;
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}
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}
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}
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}
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return hit;
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}
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void
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test_ann_hdf5(const std::string& ann_test_name, const std::string& index_key, int32_t index_add_loops,
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const std::vector<size_t>& nprobes, int32_t search_loops) {
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double t0 = elapsed();
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const std::string ann_file_name = ann_test_name + ".hdf5";
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faiss::MetricType metric_type;
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size_t dim;
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if (!parse_ann_test_name(ann_test_name, dim, metric_type)) {
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printf("Invalid ann test name: %s\n", ann_test_name.c_str());
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return;
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}
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faiss::Index* index;
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size_t d;
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std::string index_file_name = get_index_file_name(ann_test_name, index_key, index_add_loops);
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try {
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index = faiss::read_index(index_file_name.c_str());
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d = dim;
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} catch (...) {
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printf("Cannot read index file: %s\n", index_file_name.c_str());
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printf("[%.3f s] Loading train set\n", elapsed() - t0);
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size_t nb;
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float* xb = (float*)hdf5_read(ann_file_name.c_str(), "train", H5T_FLOAT, d, nb);
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assert(d == dim || !"dataset does not have correct dimension");
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printf("[%.3f s] Preparing index \"%s\" d=%ld\n", elapsed() - t0, index_key.c_str(), d);
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index = faiss::index_factory(d, index_key.c_str(), metric_type);
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printf("[%.3f s] Training on %ld vectors\n", elapsed() - t0, nb);
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index->train(nb, xb);
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printf("[%.3f s] Loading database\n", elapsed() - t0);
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// add index multiple times to get ~1G data set
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for (int i = 0; i < index_add_loops; i++) {
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printf("[%.3f s] Indexing database, size %ld*%ld\n", elapsed() - t0, nb, d);
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index->add(nb, xb);
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}
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|
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faiss::write_index(index, index_file_name.c_str());
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delete[] xb;
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}
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|
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size_t nq;
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float* xq;
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{
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printf("[%.3f s] Loading queries\n", elapsed() - t0);
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size_t d2;
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xq = (float*)hdf5_read(ann_file_name.c_str(), "test", H5T_FLOAT, d2, nq);
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assert(d == d2 || !"query does not have same dimension as train set");
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}
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|
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size_t k; // nb of results per query in the GT
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faiss::Index::idx_t* gt; // nq * k matrix of ground-truth nearest-neighbors
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{
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printf("[%.3f s] Loading ground truth for %ld queries\n", elapsed() - t0, nq);
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// load ground-truth and convert int to long
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size_t nq2;
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int* gt_int = (int*)hdf5_read(ann_file_name.c_str(), "neighbors", H5T_INTEGER, k, nq2);
|
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assert(nq2 == nq || !"incorrect nb of ground truth entries");
|
||||
|
||||
gt = new faiss::Index::idx_t[k * nq];
|
||||
for (int i = 0; i < k * nq; i++) {
|
||||
gt[i] = gt_int[i];
|
||||
}
|
||||
delete[] gt_int;
|
||||
}
|
||||
|
||||
for (auto nprobe : nprobes) {
|
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faiss::ParameterSpace params;
|
||||
|
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std::string nprobe_str = "nprobe=" + std::to_string(nprobe);
|
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params.set_index_parameters(index, nprobe_str.c_str());
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|
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// output buffers
|
||||
#if 1
|
||||
const size_t NQ = 1000, K = 1000;
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faiss::Index::idx_t* I = new faiss::Index::idx_t[NQ * K];
|
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float* D = new float[NQ * K];
|
||||
|
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printf("\n%s | %s | nprobe=%lu\n", ann_test_name.c_str(), index_key.c_str(), nprobe);
|
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printf("======================================================================================\n");
|
||||
for (size_t t_nq = 10; t_nq <= NQ; t_nq *= 10) { // nq = {10, 100, 1000}
|
||||
for (size_t t_k = 100; t_k <= K; t_k *= 10) { // k = {100, 1000}
|
||||
faiss::indexIVF_stats.quantization_time = 0.0;
|
||||
faiss::indexIVF_stats.search_time = 0.0;
|
||||
|
||||
double t_start = elapsed(), t_end;
|
||||
for (int i = 0; i < search_loops; i++) {
|
||||
index->search(t_nq, xq, t_k, D, I);
|
||||
}
|
||||
t_end = elapsed();
|
||||
|
||||
// k = 100 for ground truth
|
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int32_t hit = GetResultHitCount(gt, I, k, t_k, t_nq, index_add_loops);
|
||||
|
||||
printf("nq = %4ld, k = %4ld, elapse = %.4fs (quant = %.4fs, search = %.4fs), R@ = %.4f\n", t_nq, t_k,
|
||||
(t_end - t_start) / search_loops, faiss::indexIVF_stats.quantization_time / 1000 / search_loops,
|
||||
faiss::indexIVF_stats.search_time / 1000 / search_loops,
|
||||
(hit / float(t_nq * k / index_add_loops)));
|
||||
}
|
||||
}
|
||||
printf("======================================================================================\n");
|
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#else
|
||||
printf("[%.3f s] Perform a search on %ld queries\n", elapsed() - t0, nq);
|
||||
|
||||
faiss::Index::idx_t* I = new faiss::Index::idx_t[nq * k];
|
||||
float* D = new float[nq * k];
|
||||
|
||||
index->search(nq, xq, k, D, I);
|
||||
|
||||
printf("[%.3f s] Compute recalls\n", elapsed() - t0);
|
||||
|
||||
// evaluate result by hand.
|
||||
int n_1 = 0, n_10 = 0, n_100 = 0;
|
||||
for (int i = 0; i < nq; i++) {
|
||||
int gt_nn = gt[i * k];
|
||||
for (int j = 0; j < k; j++) {
|
||||
if (I[i * k + j] == gt_nn) {
|
||||
if (j < 1)
|
||||
n_1++;
|
||||
if (j < 10)
|
||||
n_10++;
|
||||
if (j < 100)
|
||||
n_100++;
|
||||
}
|
||||
}
|
||||
}
|
||||
printf("R@1 = %.4f\n", n_1 / float(nq));
|
||||
printf("R@10 = %.4f\n", n_10 / float(nq));
|
||||
printf("R@100 = %.4f\n", n_100 / float(nq));
|
||||
#endif
|
||||
|
||||
printf("[%.3f s] Search test done\n\n", elapsed() - t0);
|
||||
|
||||
delete[] I;
|
||||
delete[] D;
|
||||
}
|
||||
|
||||
delete[] xq;
|
||||
delete[] gt;
|
||||
delete index;
|
||||
}
|
||||
|
||||
#ifdef CUSTOMIZATION
|
||||
void
|
||||
test_ivfsq8h(const std::string& ann_test_name, int32_t index_add_loops, const std::vector<size_t>& nprobes,
|
||||
bool pure_gpu_mode, int32_t search_loops) {
|
||||
double t0 = elapsed();
|
||||
|
||||
const std::string ann_file_name = ann_test_name + ".hdf5";
|
||||
|
||||
faiss::MetricType metric_type;
|
||||
size_t dim;
|
||||
|
||||
if (!parse_ann_test_name(ann_test_name, dim, metric_type)) {
|
||||
printf("Invalid ann test name: %s\n", ann_test_name.c_str());
|
||||
return;
|
||||
}
|
||||
|
||||
faiss::distance_compute_blas_threshold = 800;
|
||||
faiss::gpu::StandardGpuResources res;
|
||||
|
||||
const std::string index_key = "IVF16384,SQ8Hybrid";
|
||||
|
||||
faiss::Index* cpu_index = nullptr;
|
||||
size_t d;
|
||||
|
||||
std::string index_file_name = get_index_file_name(ann_test_name, index_key, index_add_loops);
|
||||
try {
|
||||
cpu_index = faiss::read_index(index_file_name.c_str());
|
||||
d = dim;
|
||||
} catch (...) {
|
||||
printf("Cannot read index file: %s\n", index_file_name.c_str());
|
||||
|
||||
printf("[%.3f s] Loading train set\n", elapsed() - t0);
|
||||
|
||||
size_t nb;
|
||||
float* xb = (float*)hdf5_read(ann_file_name.c_str(), "train", H5T_FLOAT, d, nb);
|
||||
assert(d == dim || !"dataset does not have correct dimension");
|
||||
|
||||
printf("[%.3f s] Preparing index \"%s\" d=%ld\n", elapsed() - t0, index_key.c_str(), d);
|
||||
|
||||
faiss::Index* ori_index = faiss::index_factory(d, index_key.c_str(), metric_type);
|
||||
|
||||
auto device_index = faiss::gpu::index_cpu_to_gpu(&res, 0, ori_index);
|
||||
|
||||
printf("[%.3f s] Training on %ld vectors\n", elapsed() - t0, nb);
|
||||
|
||||
device_index->train(nb, xb);
|
||||
|
||||
printf("[%.3f s] Loading database\n", elapsed() - t0);
|
||||
|
||||
for (int i = 0; i < index_add_loops; i++) {
|
||||
printf("[%.3f s] Indexing database, size %ld*%ld\n", elapsed() - t0, nb, d);
|
||||
device_index->add(nb, xb);
|
||||
}
|
||||
|
||||
cpu_index = faiss::gpu::index_gpu_to_cpu(device_index);
|
||||
faiss::write_index(cpu_index, index_file_name.c_str());
|
||||
|
||||
delete[] xb;
|
||||
}
|
||||
|
||||
faiss::IndexIVF* cpu_ivf_index = dynamic_cast<faiss::IndexIVF*>(cpu_index);
|
||||
if (cpu_ivf_index != nullptr) {
|
||||
cpu_ivf_index->to_readonly();
|
||||
}
|
||||
|
||||
faiss::gpu::GpuClonerOptions option;
|
||||
option.allInGpu = true;
|
||||
|
||||
faiss::IndexComposition index_composition;
|
||||
index_composition.index = cpu_index;
|
||||
index_composition.quantizer = nullptr;
|
||||
index_composition.mode = 1;
|
||||
|
||||
double copy_time = elapsed();
|
||||
auto index = faiss::gpu::index_cpu_to_gpu(&res, 0, &index_composition, &option);
|
||||
delete index;
|
||||
|
||||
if (pure_gpu_mode) {
|
||||
index_composition.mode = 2; // 0: all data, 1: copy quantizer, 2: copy data
|
||||
index = faiss::gpu::index_cpu_to_gpu(&res, 0, &index_composition, &option);
|
||||
}
|
||||
|
||||
copy_time = elapsed() - copy_time;
|
||||
printf("[%.3f s] Copy quantizer completed, cost %f s\n", elapsed() - t0, copy_time);
|
||||
|
||||
size_t nq;
|
||||
float* xq;
|
||||
{
|
||||
printf("[%.3f s] Loading queries\n", elapsed() - t0);
|
||||
|
||||
size_t d2;
|
||||
xq = (float*)hdf5_read(ann_file_name.c_str(), "test", H5T_FLOAT, d2, nq);
|
||||
assert(d == d2 || !"query does not have same dimension as train set");
|
||||
}
|
||||
|
||||
size_t k;
|
||||
faiss::Index::idx_t* gt;
|
||||
{
|
||||
printf("[%.3f s] Loading ground truth for %ld queries\n", elapsed() - t0, nq);
|
||||
|
||||
size_t nq2;
|
||||
int* gt_int = (int*)hdf5_read(ann_file_name.c_str(), "neighbors", H5T_INTEGER, k, nq2);
|
||||
assert(nq2 == nq || !"incorrect nb of ground truth entries");
|
||||
|
||||
gt = new faiss::Index::idx_t[k * nq];
|
||||
for (uint64_t i = 0; i < k * nq; ++i) {
|
||||
gt[i] = gt_int[i];
|
||||
}
|
||||
delete[] gt_int;
|
||||
}
|
||||
|
||||
const size_t NQ = 1000, K = 1000;
|
||||
if (!pure_gpu_mode) {
|
||||
for (auto nprobe : nprobes) {
|
||||
auto ivf_index = dynamic_cast<faiss::IndexIVF*>(cpu_index);
|
||||
ivf_index->nprobe = nprobe;
|
||||
|
||||
auto is_gpu_flat_index = dynamic_cast<faiss::gpu::GpuIndexFlat*>(ivf_index->quantizer);
|
||||
if (is_gpu_flat_index == nullptr) {
|
||||
delete ivf_index->quantizer;
|
||||
ivf_index->quantizer = index_composition.quantizer;
|
||||
}
|
||||
|
||||
int64_t* I = new faiss::Index::idx_t[NQ * K];
|
||||
float* D = new float[NQ * K];
|
||||
|
||||
printf("\n%s | %s-MIX | nprobe=%lu\n", ann_test_name.c_str(), index_key.c_str(), nprobe);
|
||||
printf("======================================================================================\n");
|
||||
for (size_t t_nq = 10; t_nq <= NQ; t_nq *= 10) { // nq = {10, 100, 1000}
|
||||
for (size_t t_k = 100; t_k <= K; t_k *= 10) { // k = {100, 1000}
|
||||
faiss::indexIVF_stats.quantization_time = 0.0;
|
||||
faiss::indexIVF_stats.search_time = 0.0;
|
||||
|
||||
double t_start = elapsed(), t_end;
|
||||
for (int32_t i = 0; i < search_loops; i++) {
|
||||
cpu_index->search(t_nq, xq, t_k, D, I);
|
||||
}
|
||||
t_end = elapsed();
|
||||
|
||||
// k = 100 for ground truth
|
||||
int32_t hit = GetResultHitCount(gt, I, k, t_k, t_nq, index_add_loops);
|
||||
|
||||
printf("nq = %4ld, k = %4ld, elapse = %.4fs (quant = %.4fs, search = %.4fs), R@ = %.4f\n", t_nq,
|
||||
t_k, (t_end - t_start) / search_loops,
|
||||
faiss::indexIVF_stats.quantization_time / 1000 / search_loops,
|
||||
faiss::indexIVF_stats.search_time / 1000 / search_loops,
|
||||
(hit / float(t_nq * k / index_add_loops)));
|
||||
}
|
||||
}
|
||||
printf("======================================================================================\n");
|
||||
|
||||
printf("[%.3f s] Search test done\n\n", elapsed() - t0);
|
||||
|
||||
delete[] I;
|
||||
delete[] D;
|
||||
}
|
||||
} else {
|
||||
std::shared_ptr<faiss::Index> gpu_index_ivf_ptr = std::shared_ptr<faiss::Index>(index);
|
||||
|
||||
for (auto nprobe : nprobes) {
|
||||
faiss::gpu::GpuIndexIVFSQHybrid* gpu_index_ivf_hybrid =
|
||||
dynamic_cast<faiss::gpu::GpuIndexIVFSQHybrid*>(gpu_index_ivf_ptr.get());
|
||||
gpu_index_ivf_hybrid->setNumProbes(nprobe);
|
||||
|
||||
int64_t* I = new faiss::Index::idx_t[NQ * K];
|
||||
float* D = new float[NQ * K];
|
||||
|
||||
printf("\n%s | %s-GPU | nprobe=%lu\n", ann_test_name.c_str(), index_key.c_str(), nprobe);
|
||||
printf("======================================================================================\n");
|
||||
for (size_t t_nq = 10; t_nq <= NQ; t_nq *= 10) { // nq = {10, 100, 1000}
|
||||
for (size_t t_k = 100; t_k <= K; t_k *= 10) { // k = {100, 1000}
|
||||
faiss::indexIVF_stats.quantization_time = 0.0;
|
||||
faiss::indexIVF_stats.search_time = 0.0;
|
||||
|
||||
double t_start = elapsed(), t_end;
|
||||
for (int32_t i = 0; i < search_loops; i++) {
|
||||
gpu_index_ivf_ptr->search(nq, xq, k, D, I);
|
||||
}
|
||||
t_end = elapsed();
|
||||
|
||||
// k = 100 for ground truth
|
||||
int32_t hit = GetResultHitCount(gt, I, k, t_k, t_nq, index_add_loops);
|
||||
|
||||
printf("nq = %4ld, k = %4ld, elapse = %.4fs (quant = %.4fs, search = %.4fs), R@ = %.4f\n", t_nq,
|
||||
t_k, (t_end - t_start) / search_loops,
|
||||
faiss::indexIVF_stats.quantization_time / 1000 / search_loops,
|
||||
faiss::indexIVF_stats.search_time / 1000 / search_loops,
|
||||
(hit / float(t_nq * k / index_add_loops)));
|
||||
}
|
||||
}
|
||||
printf("======================================================================================\n");
|
||||
|
||||
printf("[%.3f s] Search test done\n\n", elapsed() - t0);
|
||||
|
||||
delete[] I;
|
||||
delete[] D;
|
||||
}
|
||||
}
|
||||
|
||||
delete[] xq;
|
||||
delete[] gt;
|
||||
delete cpu_index;
|
||||
}
|
||||
#endif
|
||||
|
||||
/************************************************************************************
|
||||
* https://github.com/erikbern/ann-benchmarks
|
||||
*
|
||||
* Dataset Dimensions Train_size Test_size Neighbors Distance Download
|
||||
* Fashion-
|
||||
* MNIST 784 60,000 10,000 100 Euclidean HDF5 (217MB)
|
||||
* GIST 960 1,000,000 1,000 100 Euclidean HDF5 (3.6GB)
|
||||
* GloVe 100 1,183,514 10,000 100 Angular HDF5 (463MB)
|
||||
* GloVe 200 1,183,514 10,000 100 Angular HDF5 (918MB)
|
||||
* MNIST 784 60,000 10,000 100 Euclidean HDF5 (217MB)
|
||||
* NYTimes 256 290,000 10,000 100 Angular HDF5 (301MB)
|
||||
* SIFT 128 1,000,000 10,000 100 Euclidean HDF5 (501MB)
|
||||
*************************************************************************************/
|
||||
|
||||
TEST(FAISSTEST, BENCHMARK) {
|
||||
std::vector<size_t> param_nprobes = {8, 128};
|
||||
const int32_t SEARCH_LOOPS = 5;
|
||||
const int32_t SIFT_INSERT_LOOPS = 2; // insert twice to get ~1G data set
|
||||
const int32_t GLOVE_INSERT_LOOPS = 1;
|
||||
|
||||
test_ann_hdf5("sift-128-euclidean", "IVF4096,Flat", SIFT_INSERT_LOOPS, param_nprobes, SEARCH_LOOPS);
|
||||
test_ann_hdf5("sift-128-euclidean", "IVF16384,SQ8", SIFT_INSERT_LOOPS, param_nprobes, SEARCH_LOOPS);
|
||||
#ifdef CUSTOMIZATION
|
||||
test_ann_hdf5("sift-128-euclidean", "IVF16384,SQ8Hybrid", SIFT_INSERT_LOOPS, param_nprobes, SEARCH_LOOPS);
|
||||
test_ivfsq8h("sift-128-euclidean", SIFT_INSERT_LOOPS, param_nprobes, false, SEARCH_LOOPS);
|
||||
test_ivfsq8h("sift-128-euclidean", SIFT_INSERT_LOOPS, param_nprobes, true, SEARCH_LOOPS);
|
||||
#endif
|
||||
|
||||
test_ann_hdf5("glove-200-angular", "IVF4096,Flat", GLOVE_INSERT_LOOPS, param_nprobes, SEARCH_LOOPS);
|
||||
test_ann_hdf5("glove-200-angular", "IVF16384,SQ8", GLOVE_INSERT_LOOPS, param_nprobes, SEARCH_LOOPS);
|
||||
#ifdef CUSTOMIZATION
|
||||
test_ann_hdf5("glove-200-angular", "IVF16384,SQ8Hybrid", GLOVE_INSERT_LOOPS, param_nprobes, SEARCH_LOOPS);
|
||||
test_ivfsq8h("glove-200-angular", GLOVE_INSERT_LOOPS, param_nprobes, false, SEARCH_LOOPS);
|
||||
test_ivfsq8h("glove-200-angular", GLOVE_INSERT_LOOPS, param_nprobes, true, SEARCH_LOOPS);
|
||||
#endif
|
||||
}
|
||||
Loading…
Reference in New Issue