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501a8be55c
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3825 - previously, there is 1 benchmark that measures the reconstruction error and then measures the performance of the distance computation for the scalar quantizer. Split it up into distance benchmark and accuracy benchmark. - add performance benchmarks for encode and decode as well - refactor the benchmarks to accept `n` and `d` as command line arguments. We run the benchmarks with `n` = 2000 and `d` = 128 to start. Happy to expand it to d=`256` and a higher `n` if we think it's better. - refactor the targets file so we can create servicelab experiments based on different parameters Planning to use the benchmarks here to test my simd refactor changes (and expand the benchmarks when necessary). Reviewed By: mnorris11 Differential Revision: D62049857 fbshipit-source-id: 7e4cbfe27af6da09616b2e7c82d77480c8ddecd6
83 lines
2.1 KiB
C++
83 lines
2.1 KiB
C++
/**
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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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*/
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#include <omp.h>
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#include <cstdio>
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#include <benchmark/benchmark.h>
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#include <faiss/impl/ScalarQuantizer.h>
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#include <faiss/utils/distances.h>
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#include <faiss/utils/random.h>
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#include <faiss/utils/utils.h>
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using namespace faiss;
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static void bench(benchmark::State& state) {
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int d = 128;
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int n = 2000;
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state.SetLabel(faiss::get_compile_options());
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std::vector<float> x(d * n);
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float_rand(x.data(), d * n, 12345);
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// make sure it's idempotent
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ScalarQuantizer sq(d, ScalarQuantizer::QT_6bit);
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omp_set_num_threads(1);
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sq.train(n, x.data());
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size_t code_size = sq.code_size;
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state.counters["code_size"] = sq.code_size;
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// encode
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std::vector<uint8_t> codes(code_size * n);
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sq.compute_codes(x.data(), codes.data(), n);
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// decode
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std::vector<float> x2(d * n);
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sq.decode(codes.data(), x2.data(), n);
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state.counters["sql2_recons_error"] =
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fvec_L2sqr(x.data(), x2.data(), n * d) / n;
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// encode again
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std::vector<uint8_t> codes2(code_size * n);
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sq.compute_codes(x2.data(), codes2.data(), n);
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size_t ndiff = 0;
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for (size_t i = 0; i < codes.size(); i++) {
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if (codes[i] != codes2[i])
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ndiff++;
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}
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state.counters["ndiff_for_idempotence"] = ndiff;
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state.counters["code_size_two"] = codes.size();
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std::unique_ptr<ScalarQuantizer::SQDistanceComputer> dc(
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sq.get_distance_computer());
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dc->codes = codes.data();
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dc->code_size = sq.code_size;
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state.counters["code_size_three"] = dc->code_size;
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for (auto _ : state) {
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float sum_dis = 0;
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for (int i = 0; i < n; i++) {
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dc->set_query(&x[i * d]);
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for (int j = 0; j < n; j++) {
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benchmark::DoNotOptimize(sum_dis += (*dc)(j));
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}
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}
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}
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}
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// I think maybe n and d should be input arguments
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// for things to really make sense, idk.
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BENCHMARK(bench)->Iterations(20);
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BENCHMARK_MAIN();
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