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158 lines
6.5 KiB
158 lines
6.5 KiB
// 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 <cmath>
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#include <functional>
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#include <random>
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#include <vector>
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#include <benchmark/benchmark.h>
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#include "bench/dconv.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/conv.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 DConv3X3S2P1Benchmark(benchmark::State& state,
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xnn_f32_conv_hwc_ukernel_function conv,
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uint32_t output_channels_tile,
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benchmark::utils::IsaCheckFunction isa_check = nullptr)
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{
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if (isa_check && !isa_check(state)) {
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return;
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}
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const size_t input_height = state.range(0);
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const size_t input_width = state.range(1);
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const size_t output_channels = 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 f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), std::ref(rng));
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const size_t input_channels = 3;
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const size_t kernel_size = 3;
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const size_t padding = 1;
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const size_t subsampling = 2;
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const size_t output_height = (input_height + 2 * padding - kernel_size) / subsampling + 1;
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const size_t output_width = (input_width + 2 * padding - kernel_size) / subsampling + 1;
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std::vector<float> input(input_height * input_width * input_channels + XNN_EXTRA_BYTES / sizeof(float));
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std::generate(input.begin(), input.end(), std::ref(f32rng));
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std::vector<float> kernel(output_channels * kernel_size * kernel_size * input_channels);
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std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
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std::vector<float> bias(output_channels);
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std::generate(bias.begin(), bias.end(), std::ref(f32rng));
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std::vector<float, AlignedAllocator<float, 32>> zero(input_channels * input_width + XNN_EXTRA_BYTES / sizeof(float));
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const size_t weights_elements = (kernel_size * kernel_size * input_channels + 1) *
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benchmark::utils::RoundUp<size_t>(output_channels, output_channels_tile);
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const size_t output_elements = output_height * output_width * output_channels;
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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(float) * (weights_elements + output_elements));
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std::vector<float, AlignedAllocator<float, 32>> packed_weights(weights_elements * num_buffers);
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std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
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xnn_pack_f32_dconv_oki_w(
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output_channels, input_channels, output_channels_tile,
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kernel_size /* kernel height */, kernel_size /* kernel width */,
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kernel.data(), bias.data(), packed_weights.data(), nullptr);
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for (size_t n = 1; n < num_buffers; n++) {
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std::copy(packed_weights.cbegin(),
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packed_weights.cbegin() + weights_elements,
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packed_weights.begin() + n * weights_elements);
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}
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std::vector<float> output(output_elements * num_buffers);
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std::fill(output.begin(), output.end(), std::nanf(""));
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xnn_f32_minmax_params params =
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xnn_init_f32_minmax_params(-std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity());
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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(input.data(), input.size() * sizeof(float));
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buffer_index = (buffer_index + 1) % num_buffers;
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state.ResumeTiming();
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conv(
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input_height, input_width,
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0 /* output_y_start */, output_height /* output_y_end */,
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input.data(), zero.data(),
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packed_weights.data() + buffer_index * weights_elements,
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output.data() + buffer_index * output_elements,
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padding, output_channels,
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output_channels * output_width * sizeof(float),
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output_channels * sizeof(float),
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¶ms);
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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["FLOPS"] = benchmark::Counter(
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uint64_t(state.iterations()) * 2 *
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output_height * output_width *
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input_channels * output_channels *
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kernel_size * kernel_size,
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benchmark::Counter::kIsRate);
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}
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#if XNN_ARCH_ARM64
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static void f32_conv_hwc_3x3s2p1c3x8__neonfma_2x1(benchmark::State& state, const char* net) {
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DConv3X3S2P1Benchmark(state, xnn_f32_conv_hwc_ukernel_3x3s2p1c3x8__neonfma_2x1, 8, benchmark::utils::CheckNEONFMA);
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}
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static void f32_conv_hwc_3x3s2p1c3x4__neonfma_2x1(benchmark::State& state, const char* net) {
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DConv3X3S2P1Benchmark(state, xnn_f32_conv_hwc_ukernel_3x3s2p1c3x4__neonfma_2x1, 4, benchmark::utils::CheckNEONFMA);
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}
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static void f32_conv_hwc_3x3s2p1c3x8__neonfma_2x2(benchmark::State& state, const char* net) {
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DConv3X3S2P1Benchmark(state, xnn_f32_conv_hwc_ukernel_3x3s2p1c3x8__neonfma_2x2, 8, benchmark::utils::CheckNEONFMA);
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}
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static void f32_conv_hwc_3x3s2p1c3x4__neonfma_2x2(benchmark::State& state, const char* net) {
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DConv3X3S2P1Benchmark(state, xnn_f32_conv_hwc_ukernel_3x3s2p1c3x4__neonfma_2x2, 4, benchmark::utils::CheckNEONFMA);
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}
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BENCHMARK_DCONV(f32_conv_hwc_3x3s2p1c3x8__neonfma_2x1);
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BENCHMARK_DCONV(f32_conv_hwc_3x3s2p1c3x4__neonfma_2x1);
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BENCHMARK_DCONV(f32_conv_hwc_3x3s2p1c3x8__neonfma_2x2);
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BENCHMARK_DCONV(f32_conv_hwc_3x3s2p1c3x4__neonfma_2x2);
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#endif // XNN_ARCH_ARM64
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#if XNN_ARCH_ARM || XNN_ARCH_ARM64
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static void f32_conv_hwc_3x3s2p1c3x8__neon_2x1(benchmark::State& state, const char* net) {
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DConv3X3S2P1Benchmark(state, xnn_f32_conv_hwc_ukernel_3x3s2p1c3x8__neon_2x1, 8, benchmark::utils::CheckNEON);
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}
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static void f32_conv_hwc_3x3s2p1c3x4__neon_2x1(benchmark::State& state, const char* net) {
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DConv3X3S2P1Benchmark(state, xnn_f32_conv_hwc_ukernel_3x3s2p1c3x4__neon_2x1, 4, benchmark::utils::CheckNEON);
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}
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static void f32_conv_hwc_3x3s2p1c3x8__neon_2x2(benchmark::State& state, const char* net) {
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DConv3X3S2P1Benchmark(state, xnn_f32_conv_hwc_ukernel_3x3s2p1c3x8__neon_2x2, 8, benchmark::utils::CheckNEON);
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}
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static void f32_conv_hwc_3x3s2p1c3x4__neon_2x2(benchmark::State& state, const char* net) {
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DConv3X3S2P1Benchmark(state, xnn_f32_conv_hwc_ukernel_3x3s2p1c3x4__neon_2x2, 4, benchmark::utils::CheckNEON);
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}
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BENCHMARK_DCONV(f32_conv_hwc_3x3s2p1c3x8__neon_2x1);
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BENCHMARK_DCONV(f32_conv_hwc_3x3s2p1c3x4__neon_2x1);
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BENCHMARK_DCONV(f32_conv_hwc_3x3s2p1c3x8__neon_2x2);
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BENCHMARK_DCONV(f32_conv_hwc_3x3s2p1c3x4__neon_2x2);
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#endif // XNN_ARCH_ARM || XNN_ARCH_ARM64
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#ifndef XNNPACK_BENCHMARK_NO_MAIN
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BENCHMARK_MAIN();
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#endif
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