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// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#include <algorithm>
#include <cfloat>
#include <cmath>
#include <functional>
#include <random>
#include <vector>
#include <cpuinfo.h>
#include <benchmark/benchmark.h>
#include "bench/dwconv.h"
#include "bench/utils.h"
#include <xnnpack/AlignedAllocator.h>
#include <xnnpack/common.h>
#include <xnnpack/dwconv.h>
#include <xnnpack/indirection.h>
#include <xnnpack/operator.h>
#include <xnnpack/pack.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
static void DWConvCHWBenchmark(benchmark::State& state,
xnn_f32_dwconv_spchw_ukernel_function dwconv,
uint32_t it, uint32_t ot, uint32_t kh, uint32_t kw, uint32_t pw, uint32_t s)
{
if (!cpuinfo_initialize()) {
state.SkipWithError("cpuinfo initialization failed");
return;
}
const size_t input_height = state.range(0);
const size_t input_width = state.range(1);
const size_t kernel_height = state.range(2);
const size_t kernel_width = state.range(3);
const size_t padding_height = state.range(4);
const size_t padding_width = state.range(5);
const size_t subsampling = state.range(6);
const size_t dilation = state.range(7);
const size_t channels = state.range(8);
if (kernel_height != kh) {
state.SkipWithError("kernel height mismatch");
return;
}
if (kernel_width != kw) {
state.SkipWithError("kernel width mismatch");
return;
}
if (subsampling != s) {
state.SkipWithError("subsampling mismatch");
return;
}
if (padding_width % 2 != 0 || padding_width / 2 != pw) {
state.SkipWithError("padding width mismatch");
return;
}
if (dilation != 1) {
state.SkipWithError("unsupported dilation");
return;
}
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;
const size_t inputSize = (input_height + padding_height) * input_width;
const size_t kernel_size = kernel_height * kernel_width;
const size_t output_size = output_height * output_width;
std::vector<float> input(inputSize * channels + 2 * it);
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::vector<float> bias(channels);
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
std::vector<float> kernel(channels * kernel_size);
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
const size_t w_elements = (kernel_size + 1) * channels;
const size_t o_elements = output_size * channels;
const size_t num_buffers = 1 +
benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
sizeof(float) * (w_elements + o_elements));
std::vector<float, AlignedAllocator<float, 32>> packed_weights(w_elements * num_buffers);
std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
for (size_t c = 0; c < channels; c++) {
packed_weights[c * kernel_size + c] = bias[c];
for (size_t i = 0; i < kernel_size; i++) {
packed_weights[c * kernel_size + c + 1 + i] = kernel[c * kernel_size + i];
}
}
for (size_t n = 1; n < num_buffers; n++) {
std::copy(packed_weights.cbegin(), packed_weights.cbegin() + w_elements, packed_weights.begin() + n * w_elements);
}
std::vector<float> output(o_elements * num_buffers);
std::fill(output.begin(), output.end(), std::nanf(""));
xnn_f32_spchw_params spchw_params =
xnn_init_f32_spchw_params(input_width, -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity());
size_t buffer_index = 0;
for (auto _ : state) {
state.PauseTiming();
benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(float));
buffer_index = (buffer_index + 1) % num_buffers;
state.ResumeTiming();
for (uint32_t channel = 0; channel < channels; channel++) {
dwconv(
output_height, input_width,
input.data() + channel * inputSize,
packed_weights.data() + channel * (kernel_size + 1) + buffer_index * w_elements,
output.data() + channel * output_size + buffer_index * o_elements,
it * sizeof(float), ot * sizeof(float),
input_width * sizeof(float), output_width * sizeof(float),
&spchw_params);
}
}
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
state.counters["FLOPS"] = benchmark::Counter(
uint64_t(state.iterations()) * 2 * output_size * channels * kernel_size,
benchmark::Counter::kIsRate);
state.counters["BYTES"] = benchmark::Counter(
uint64_t(state.iterations()) * (output_size + inputSize + kernel_size + 1 /* bias */) * channels * sizeof(float),
benchmark::Counter::kIsRate);
}
static void DWConvHWoTCTBenchmark(benchmark::State& state,
xnn_f32_dwconv_spchw_ukernel_function dwconv,
uint32_t it, uint32_t ot, uint32_t kh, uint32_t kw, uint32_t pw, uint32_t s)
{
if (!cpuinfo_initialize()) {
state.SkipWithError("cpuinfo initialization failed");
return;
}
const size_t input_height = state.range(0);
const size_t input_width = state.range(1);
const size_t kernel_height = state.range(2);
const size_t kernel_width = state.range(3);
const size_t padding_height = state.range(4);
const size_t padding_width = state.range(5);
const size_t subsampling = state.range(6);
const size_t dilation = state.range(7);
const size_t channels = state.range(8);
if (kernel_height != kh) {
state.SkipWithError("kernel height mismatch");
return;
}
if (kernel_width != kw) {
state.SkipWithError("kernel width mismatch");
return;
}
if (subsampling != s) {
state.SkipWithError("subsampling mismatch");
return;
}
if (padding_width % 2 != 0 || padding_width / 2 != pw) {
state.SkipWithError("padding width mismatch");
return;
}
if (dilation != 1) {
state.SkipWithError("unsupported dilation");
return;
}
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;
const size_t inputSize = (input_height + padding_height) * input_width;
const size_t kernel_size = kernel_height * kernel_width;
const size_t output_size = output_height * output_width;
std::vector<float> input(input_height * benchmark::utils::RoundUp<size_t>(input_width, it) * channels);
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::vector<float> bias(channels);
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
std::vector<float> kernel(channels * kernel_size);
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
const size_t w_elements = (kernel_size + 1) * channels;
const size_t o_elements = output_height * benchmark::utils::RoundUp<size_t>(output_width, ot) * channels;
const size_t num_buffers = 1 +
benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
sizeof(float) * (w_elements + o_elements));
std::vector<float, AlignedAllocator<float, 32>> packed_weights(w_elements * num_buffers);
std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
for (size_t c = 0; c < channels; c++) {
packed_weights[c * kernel_size + c] = bias[c];
for (size_t i = 0; i < kernel_size; i++) {
packed_weights[c * kernel_size + c + 1 + i] = kernel[c * kernel_size + i];
}
}
for (size_t n = 1; n < num_buffers; n++) {
std::copy(packed_weights.cbegin(), packed_weights.cbegin() + w_elements, packed_weights.begin() + n * w_elements);
}
std::vector<float> output(o_elements * num_buffers);
std::fill(output.begin(), output.end(), std::nanf(""));
xnn_f32_spchw_params spchw_params =
xnn_init_f32_spchw_params(input_width, -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity());
size_t buffer_index = 0;
for (auto _ : state) {
state.PauseTiming();
benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(float));
buffer_index = (buffer_index + 1) % num_buffers;
state.ResumeTiming();
for (uint32_t channel = 0; channel < channels; channel++) {
dwconv(
output_height, input_width,
input.data() + channel * it,
packed_weights.data() + channel * (kernel_size + 1) + buffer_index * w_elements,
output.data() + channel * ot + buffer_index * o_elements,
it * channels * sizeof(float), ot * channels * sizeof(float),
benchmark::utils::RoundUp<size_t>(input_width, it) * channels * sizeof(float),
benchmark::utils::RoundUp<size_t>(output_width, ot) * channels * sizeof(float),
&spchw_params);
}
}
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
state.counters["FLOPS"] = benchmark::Counter(
uint64_t(state.iterations()) * 2 * output_size * channels * kernel_size,
benchmark::Counter::kIsRate);
state.counters["BYTES"] = benchmark::Counter(
uint64_t(state.iterations()) * (output_size + inputSize + kernel_size + 1 /* bias */) * channels * sizeof(float),
benchmark::Counter::kIsRate);
}
#if XNN_ARCH_ARM64
static void CHW_3x3p1__neonfma(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__neonfma, 4, 4, 3, 3, 1, 1);
}
static void CHW_5x5p2__neonfma(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__neonfma, 4, 4, 5, 5, 2, 1);
}
static void CHW_3x3s2p1__neonfma(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__neonfma, 4, 4, 3, 3, 1, 2);
}
static void CHW_5x5s2p2__neonfma(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__neonfma, 4, 4, 5, 5, 2, 2);
}
static void HWo4C4_3x3p1__neonfma(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__neonfma, 4, 4, 3, 3, 1, 1);
}
static void HWo4C4_5x5p2__neonfma(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__neonfma, 4, 4, 5, 5, 2, 1);
}
static void HWo4C4_3x3s2p1__neonfma(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__neonfma, 4, 4, 3, 3, 1, 2);
}
static void HWo4C4_5x5s2p2__neonfma(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__neonfma, 4, 4, 5, 5, 2, 2);
}
BENCHMARK_DWCONV(CHW_3x3p1__neonfma)
BENCHMARK_DWCONV(CHW_5x5p2__neonfma)
BENCHMARK_DWCONV(CHW_3x3s2p1__neonfma)
BENCHMARK_DWCONV(CHW_5x5s2p2__neonfma)
BENCHMARK_DWCONV(HWo4C4_3x3p1__neonfma)
BENCHMARK_DWCONV(HWo4C4_5x5p2__neonfma)
BENCHMARK_DWCONV(HWo4C4_3x3s2p1__neonfma)
BENCHMARK_DWCONV(HWo4C4_5x5s2p2__neonfma)
#endif // XNN_ARCH_ARM64
#if XNN_ARCH_X86 || XNN_ARCH_X86_64
static void CHW_3x3p1__sse(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__sse, 4, 4, 3, 3, 1, 1);
}
static void CHW_3x3s2p1__sse(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__sse, 4, 4, 3, 3, 1, 2);
}
static void HWo4C4_3x3p1__sse(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__sse, 4, 4, 3, 3, 1, 1);
}
static void HWo4C4_3x3s2p1__sse(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__sse, 4, 4, 3, 3, 1, 2);
}
BENCHMARK_DWCONV(CHW_3x3p1__sse)
BENCHMARK_DWCONV(CHW_3x3s2p1__sse)
BENCHMARK_DWCONV(HWo4C4_3x3p1__sse)
BENCHMARK_DWCONV(HWo4C4_3x3s2p1__sse)
#endif // XNN_ARCH_X86 || XNN_ARCH_X86_64
static void CHW_3x3p1__scalar(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__scalar, 1, 1, 3, 3, 1, 1);
}
static void CHW_5x5p2__scalar(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__scalar, 1, 1, 5, 5, 2, 1);
}
static void CHW_3x3s2p1__scalar(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__scalar, 1, 1, 3, 3, 1, 2);
}
static void CHW_5x5s2p2__scalar(benchmark::State& state, const char* net) {
DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__scalar, 1, 1, 5, 5, 2, 2);
}
static void HWC_3x3p1__scalar(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__scalar, 1, 1, 3, 3, 1, 1);
}
static void HWC_5x5p2__scalar(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__scalar, 1, 1, 5, 5, 2, 1);
}
static void HWC_3x3s2p1__scalar(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__scalar, 1, 1, 3, 3, 1, 2);
}
static void HWC_5x5s2p2__scalar(benchmark::State& state, const char* net) {
DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__scalar, 1, 1, 5, 5, 2, 2);
}
BENCHMARK_DWCONV(CHW_3x3p1__scalar)
BENCHMARK_DWCONV(CHW_5x5p2__scalar)
BENCHMARK_DWCONV(CHW_3x3s2p1__scalar)
BENCHMARK_DWCONV(CHW_5x5s2p2__scalar)
BENCHMARK_DWCONV(HWC_3x3p1__scalar)
BENCHMARK_DWCONV(HWC_5x5p2__scalar)
BENCHMARK_DWCONV(HWC_3x3s2p1__scalar)
BENCHMARK_DWCONV(HWC_5x5s2p2__scalar)
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();
#endif