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336 lines
13 KiB
336 lines
13 KiB
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// Copyright 2019 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#include <algorithm>
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#include <cfloat>
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#include <chrono>
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#include <cmath>
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#include <functional>
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#include <limits>
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#include <mutex>
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#include <random>
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#include <vector>
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#include <benchmark/benchmark.h>
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#ifdef BENCHMARK_GEMMLOWP
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#include "gemmlowp/public/gemmlowp.h"
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#endif // BENCHMARK_GEMMLOWP
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#ifdef BENCHMARK_RUY
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#include "ruy/ruy.h"
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#endif // BENCHMARK_RUY
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#include "bench/gemm.h"
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#include "bench/utils.h"
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#include <xnnpack/AlignedAllocator.h>
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#include <xnnpack/common.h>
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#include <xnnpack/gemm.h>
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#include <xnnpack/pack.h>
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#include <xnnpack/params-init.h>
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#include <xnnpack/params.h>
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static void GEMMBenchmark(benchmark::State& state,
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xnn_qu8_gemm_ukernel_function gemm,
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size_t mr, size_t nr, size_t kr, size_t sr,
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benchmark::utils::IsaCheckFunction isa_check = nullptr)
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{
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const size_t mc = state.range(0);
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const size_t nc = state.range(1);
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const size_t kc = state.range(2);
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const size_t nc_stride = benchmark::utils::RoundUp(nc, nr);
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const size_t kc_stride = benchmark::utils::RoundUp(kc, kr);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
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auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
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std::vector<uint8_t> a(mc * kc);
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std::generate(a.begin(), a.end(), std::ref(u8rng));
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std::vector<uint8_t> k(nc * kc);
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std::generate(k.begin(), k.end(), std::ref(u8rng));
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std::vector<int32_t> b(nc);
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std::generate(b.begin(), b.end(), std::ref(i32rng));
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const size_t w_elements = kc_stride * nc_stride + nc_stride * sizeof(int32_t) / sizeof(uint8_t);
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const size_t c_elements = mc * nc;
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const size_t num_buffers = 1 +
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benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
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sizeof(uint8_t) * (w_elements + c_elements));
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std::vector<uint8_t, AlignedAllocator<uint8_t, 32>> w(w_elements * num_buffers);
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std::fill(w.begin(), w.end(), 0);
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const xnn_qu8_packing_params packing_params = { 127, 127 };
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xnn_pack_qu8_gemm_goi_w(1 /* groups */, nc, kc, nr, kr, sr, k.data(), b.data(), w.data(), &packing_params);
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std::vector<uint8_t> c(c_elements * num_buffers);
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std::fill(c.begin(), c.end(), 0xA5);
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union xnn_qu8_gemm_params quantization_params =
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xnn_init_qu8_gemm_params(127, 0.75f, 127, 1, 254);
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size_t buffer_index = 0;
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for (auto _ : state) {
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// Use circular buffers (exceeding cache size) and prefetch to control cache state:
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// - A is always in L1 cache (if fits, otherwise L2, L3, etc)
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// - W is not in cache (for any cache level)
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// - C is not in cache (for any cache level)
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state.PauseTiming();
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benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(uint8_t));
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buffer_index = (buffer_index + 1) % num_buffers;
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state.ResumeTiming();
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for (uint32_t m = 0; m < mc; m += mr) {
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const uint32_t mb = min(mc - m, mr);
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for (uint32_t n = 0; n < nc; n += nr) {
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const uint32_t nb = min(nc - n, nr);
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gemm(
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mb, nb, kc * sizeof(uint8_t),
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a.data() + m * kc, kc * sizeof(uint8_t),
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w.data() + (w_elements * buffer_index + n * (kc_stride + sizeof(int32_t))) / sizeof(uint8_t),
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c.data() + (mc * buffer_index + m) * nc + n, nc * sizeof(uint8_t), nr * sizeof(uint8_t),
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&quantization_params);
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}
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}
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}
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
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if (cpu_frequency != 0) {
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state.counters["cpufreq"] = cpu_frequency;
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}
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state.counters["OPS"] = benchmark::Counter(
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uint64_t(state.iterations()) * 2 * mc * nc * kc, benchmark::Counter::kIsRate);
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}
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#ifdef BENCHMARK_GEMMLOWP
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struct GemmlowpOutputPipeline {
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typedef gemmlowp::VectorMap<const int32_t, gemmlowp::VectorShape::Col> ColVectorMap;
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typedef std::tuple<
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gemmlowp::OutputStageBiasAddition<ColVectorMap>,
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gemmlowp::OutputStageQuantizeDownInt32ToUint8ScaleByFixedPoint,
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gemmlowp::OutputStageClamp,
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gemmlowp::OutputStageSaturatingCastToUint8>
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Pipeline;
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static Pipeline Make(
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const int32_t* bias_data,
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int output_rows,
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int32_t output_offset,
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int32_t output_multiplier,
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int output_shift,
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int32_t output_activation_min,
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int32_t output_activation_max)
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{
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ColVectorMap bias_vector(bias_data, output_rows);
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gemmlowp::OutputStageBiasAddition<ColVectorMap> bias_addition_stage;
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bias_addition_stage.bias_vector = bias_vector;
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gemmlowp::OutputStageQuantizeDownInt32ToUint8ScaleByFixedPoint quantize_down_stage;
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quantize_down_stage.result_offset_after_shift = output_offset;
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quantize_down_stage.result_fixedpoint_multiplier = output_multiplier;
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quantize_down_stage.result_shift = output_shift;
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gemmlowp::OutputStageClamp clamp_stage;
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clamp_stage.min = output_activation_min;
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clamp_stage.max = output_activation_max;
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gemmlowp::OutputStageSaturatingCastToUint8 saturating_cast_stage;
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return std::make_tuple(bias_addition_stage, quantize_down_stage, clamp_stage, saturating_cast_stage);
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}
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};
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static void GemmlowpBenchmark(benchmark::State& state, uint32_t threads)
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{
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const size_t mc = state.range(0);
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const size_t nc = state.range(1);
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const size_t kc = state.range(2);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
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auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
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std::vector<uint8_t> a(mc * kc);
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std::generate(a.begin(), a.end(), std::ref(u8rng));
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const size_t kElements = nc * kc;
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const size_t bElements = nc;
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const size_t c_elements = mc * nc;
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const size_t num_buffers = 1 +
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benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
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kElements * sizeof(uint8_t) + bElements * sizeof(int32_t) + c_elements * sizeof(uint8_t));
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std::vector<uint8_t> k(kElements * num_buffers);
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std::generate(k.begin(), k.end(), std::ref(u8rng));
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std::vector<int32_t> b(bElements * num_buffers);
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std::generate(b.begin(), b.end(), std::ref(i32rng));
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std::vector<uint8_t> c(c_elements * num_buffers);
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std::fill(c.begin(), c.end(), 0xA5);
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gemmlowp::MultiThreadGemmContext threadingContext;
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threadingContext.set_max_num_threads(threads);
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size_t buffer_index = 0;
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for (auto _ : state) {
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state.PauseTiming();
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benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(uint8_t));
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buffer_index = (buffer_index + 1) % num_buffers;
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state.ResumeTiming();
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gemmlowp::MatrixMap<const uint8_t, gemmlowp::MapOrder::RowMajor> AM(a.data(), mc, kc, kc);
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gemmlowp::MatrixMap<const uint8_t, gemmlowp::MapOrder::ColMajor> BM(k.data() + buffer_index * kElements, kc, nc, kc);
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gemmlowp::MatrixMap<uint8_t, gemmlowp::MapOrder::RowMajor> CM(c.data() + buffer_index * c_elements, mc, nc, nc);
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const auto& outputPipeline = GemmlowpOutputPipeline::Make(b.data() + buffer_index * bElements, nc, 127, 127, 127, 0, 255);
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gemmlowp::GemmWithOutputPipeline<uint8_t, uint8_t, gemmlowp::L8R8WithLhsNonzeroBitDepthParams>(
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&threadingContext, AM, BM, &CM, 127, 127, outputPipeline);
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}
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
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if (cpu_frequency != 0) {
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state.counters["cpufreq"] = cpu_frequency;
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}
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state.counters["OPS"] = benchmark::Counter(
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uint64_t(state.iterations()) * 2 * mc * nc * kc, benchmark::Counter::kIsRate);
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}
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static void gemmlowp_st(benchmark::State& state, const char* net)
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{
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GemmlowpBenchmark(state, 1);
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}
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#endif // BENCHMARK_GEMMLOWP
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#ifdef BENCHMARK_RUY
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static void RuyBenchmark(benchmark::State& state, size_t threads)
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{
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const size_t mc = state.range(0);
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const size_t nc = state.range(1);
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const size_t kc = state.range(2);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
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auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
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const size_t num_buffers = 1 +
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benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
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nc * (sizeof(uint8_t) * (mc + kc) + sizeof(int32_t)));
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std::vector<uint8_t> a(mc * kc);
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std::generate(a.begin(), a.end(), std::ref(u8rng));
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std::vector<uint8_t> k(num_buffers * nc * kc);
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std::generate(k.begin(), k.end(), std::ref(u8rng));
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std::vector<int32_t> b(num_buffers * nc);
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std::generate(b.begin(), b.end(), std::ref(i32rng));
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std::vector<uint8_t> c(num_buffers * nc * mc);
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std::fill(c.begin(), c.end(), std::nanf(""));
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// Note: context must be static to avoid the cost of re-creating it for each benchmark.
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static ruy::Context context;
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context.set_max_num_threads(threads);
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ruy::Matrix<uint8_t> ruy_a;
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ruy::MakeSimpleLayout(nc, kc, ruy::Order::kRowMajor, ruy_a.mutable_layout());
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ruy_a.set_zero_point(127);
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ruy::Matrix<uint8_t> ruy_b;
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ruy::MakeSimpleLayout(kc, mc, ruy::Order::kColMajor, ruy_b.mutable_layout());
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ruy_b.set_data(a.data());
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ruy_b.set_zero_point(127);
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ruy::Matrix<uint8_t> ruy_c;
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ruy::MakeSimpleLayout(nc, mc, ruy::Order::kColMajor, ruy_c.mutable_layout());
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ruy_c.set_zero_point(127);
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ruy::MulParams<int32_t, uint8_t> mul_params;
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mul_params.set_multiplier_fixedpoint(0x40000000);
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// ruy::Context uses deferred initialization, which affects percieved GEMM performance. Initialization happens during
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// the first GEMM calls, and per Benoit Jacob it takes up to ~250 milliseconds for performance to stabilize.
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// Thus, on the first benchmark, we compute GEMM for 500 milliseconds (to be safe) without recording performance, and
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// keep the ruy::Context object initialized (by being static) between subsequent benchmarks.
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static std::once_flag warmup;
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std::call_once(warmup, [&](){
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auto start = std::chrono::steady_clock::now();
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do {
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ruy_a.set_data(k.data());
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ruy_c.set_data(c.data());
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mul_params.set_bias(b.data());
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ruy::Mul(ruy_a, ruy_b, mul_params, &context, &ruy_c);
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} while (std::chrono::duration<double>(std::chrono::steady_clock::now() - start).count() < 0.5);
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});
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size_t buffer_index = 0;
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for (auto _ : state) {
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// Use circular buffers (exceeding cache size) and prefetch to control cache state:
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// - A is always in L1 cache (if fits, otherwise L2, L3, etc)
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// - K is not in cache (for any cache level)
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// - B is not in cache (for any cache level)
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// - C is not in cache (for any cache level)
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state.PauseTiming();
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benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(uint8_t));
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buffer_index = (buffer_index + 1) % num_buffers;
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state.ResumeTiming();
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ruy_a.set_data(k.data() + buffer_index * nc * kc);
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ruy_c.set_data(c.data() + buffer_index * mc * nc);
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mul_params.set_bias(b.data() + buffer_index * nc);
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ruy::Mul(ruy_a, ruy_b, mul_params, &context, &ruy_c);
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}
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
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if (cpu_frequency != 0) {
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state.counters["cpufreq"] = cpu_frequency;
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}
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state.counters["OPS"] = benchmark::Counter(
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uint64_t(state.iterations()) * 2 * mc * nc * kc, benchmark::Counter::kIsRate);
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}
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static void ruy_st(benchmark::State& state, const char* net)
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{
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RuyBenchmark(state, 1);
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}
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#endif // BENCHMARK_RUY
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#if XNN_ARCH_ARM || XNN_ARCH_ARM64
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static void qu8_gemm_4x8__neon(benchmark::State& state, const char* net) {
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GEMMBenchmark(state, xnn_qu8_gemm_minmax_ukernel_4x8__neon, 4, 8, 1, 1, benchmark::utils::CheckNEON);
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}
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static void qu8_gemm_8x8__neon(benchmark::State& state, const char* net) {
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GEMMBenchmark(state, xnn_qu8_gemm_minmax_ukernel_8x8__neon, 8, 8, 1, 1, benchmark::utils::CheckNEON);
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}
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BENCHMARK_GEMM(qu8_gemm_4x8__neon)
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BENCHMARK_GEMM(qu8_gemm_8x8__neon)
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#endif
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#if XNN_ARCH_X86 || XNN_ARCH_X86_64
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static void qu8_gemm_4x4c2__sse2(benchmark::State& state, const char* net) {
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GEMMBenchmark(state, xnn_qu8_gemm_minmax_ukernel_4x4c2__sse2, 4, 4, 2, 1);
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}
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static void qu8_gemm_2x4c8__sse2(benchmark::State& state, const char* net) {
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GEMMBenchmark(state, xnn_qu8_gemm_minmax_ukernel_2x4c8__sse2, 2, 4, 8, 1);
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}
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BENCHMARK_GEMM(qu8_gemm_4x4c2__sse2)
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BENCHMARK_GEMM(qu8_gemm_2x4c8__sse2)
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#endif
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#ifdef BENCHMARK_RUY
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BENCHMARK_GEMM(ruy_st)
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#endif // BENCHMARK_RUY
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#ifdef BENCHMARK_GEMMLOWP
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BENCHMARK_GEMM(gemmlowp_st)
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#endif // BENCHMARK_GEMMLOWP
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#ifndef XNNPACK_BENCHMARK_NO_MAIN
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BENCHMARK_MAIN();
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#endif
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