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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 <fp16/fp16.h>
#include "bench/conv.h"
#include "bench/utils.h"
#include <xnnpack/AlignedAllocator.h>
#include <xnnpack/common.h>
#include <xnnpack/igemm.h>
#include <xnnpack/indirection.h>
#include <xnnpack/operator.h>
#include <xnnpack/pack.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
static void IGEMMBenchmark(benchmark::State& state,
xnn_f16_igemm_minmax_ukernel_function f16_igemm,
uint32_t mr, uint32_t nr, uint32_t kr, uint32_t sr)
{
if (!cpuinfo_initialize()) {
state.SkipWithError("cpuinfo initialization failed");
}
if (!benchmark::utils::CheckNEONFP16ARITH(state)) {
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 kernel_size = kernel_height * kernel_width;
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 group_input_channels = state.range(8);
const size_t group_output_channels = state.range(9);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
const size_t output_pixel_stride = group_output_channels;
const size_t input_pixel_stride = group_input_channels;
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 padding_left = padding_width / 2;
const size_t padding_top = padding_height / 2;
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 output_size = output_height * output_width;
const size_t mc_stride = benchmark::utils::RoundUp<size_t>(output_size, mr);
const size_t nc_stride = benchmark::utils::RoundUp<size_t>(group_output_channels, nr);
const size_t kc_stride = benchmark::utils::RoundUp<size_t>(group_input_channels, kr);
std::vector<uint16_t> a(input_height * input_width * input_pixel_stride);
std::generate(a.begin(), a.end(), std::ref(f16rng));
std::vector<uint16_t> k(group_output_channels * kernel_height * kernel_width * group_input_channels);
std::generate(k.begin(), k.end(), std::ref(f16rng));
std::vector<uint16_t> b(group_output_channels);
std::generate(b.begin(), b.end(), std::ref(f16rng));
std::vector<uint16_t> z(group_input_channels);
const size_t w_elements = (kernel_size * kc_stride + 1) * nc_stride;
const size_t i_elements = mc_stride * kernel_size;
const size_t c_elements = output_height * output_width * output_pixel_stride;
const size_t num_buffers = 1 +
benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
sizeof(uint16_t) * (w_elements + c_elements) + sizeof(void*) * i_elements);
std::vector<uint16_t, AlignedAllocator<uint16_t, 32>> w(w_elements * num_buffers);
std::fill(w.begin(), w.end(), 0);
xnn_pack_f16_conv_goki_w(
1 /* groups */, group_output_channels, kernel_size, group_input_channels,
nr, kr, sr, k.data(), b.data(), w.data(), 0 /* extra bytes */, nullptr);
for (size_t n = 1; n < num_buffers; n++) {
std::copy(w.cbegin(), w.cbegin() + w_elements, w.begin() + n * w_elements);
}
std::vector<const uint16_t*> i(i_elements * num_buffers);
xnn_operator convolution_op = { };
convolution_op.indirection_buffer = reinterpret_cast<const void**>(i.data());
convolution_op.input = a.data();
convolution_op.input_pixel_stride = input_pixel_stride;
convolution_op.zero_buffer = z.data();
convolution_op.groups = 1;
convolution_op.group_input_channels = group_input_channels;
convolution_op.batch_size = 1;
convolution_op.input_height = input_height;
convolution_op.input_width = input_width;
convolution_op.output_height = output_height;
convolution_op.output_width = output_width;
convolution_op.kernel_height = kernel_height;
convolution_op.kernel_width = kernel_width;
convolution_op.stride_height = subsampling;
convolution_op.stride_width = subsampling;
convolution_op.dilation_height = dilation;
convolution_op.dilation_width = dilation;
convolution_op.padding_top = padding_top;
convolution_op.padding_left = padding_left;
xnn_indirection_init_conv2d(&convolution_op, mr, 1 /* log2(sizeof(uint16_t)) */);
for (size_t n = 1; n < num_buffers; n++) {
std::copy(i.cbegin(), i.cbegin() + i_elements, i.begin() + n * i_elements);
}
std::vector<uint16_t> c(c_elements * num_buffers);
std::fill(c.begin(), c.end(), std::nanf(""));
// Prepare minmax parameters.
xnn_f16_scaleminmax_params params;
xnn_init_f16_scaleminmax_params(
&params,
UINT16_C(0x3C00), /* 1.0 */
UINT16_C(0x7C00), /* inf */
UINT16_C(0xFC00)); /* -inf */
size_t buffer_index = 0;
for (auto _ : state) {
state.PauseTiming();
benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(uint16_t));
buffer_index = (buffer_index + 1) % num_buffers;
state.ResumeTiming();
for (uint32_t m = 0; m < output_size; m += mr) {
const uint32_t mb = min(output_size - m, mr);
for (uint32_t n = 0; n < group_output_channels; n += nr) {
const uint32_t nb = min(group_output_channels - n, nr);
f16_igemm(
mb, nb, group_input_channels * sizeof(uint16_t), kernel_size * mr * sizeof(void*),
reinterpret_cast<const void**>(i.data()) + buffer_index * i_elements + m,
w.data() + buffer_index * w_elements + n * (kc_stride * kernel_size + 1),
c.data() + buffer_index * c_elements + m * group_output_channels + n, group_output_channels * sizeof(uint16_t), nr * sizeof(uint16_t),
0, z.data(), &params);
}
}
}
const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
if (cpu_frequency != 0) {
state.counters["cpufreq"] = cpu_frequency;
}
state.counters["FLOPS"] = benchmark::Counter(
uint64_t(state.iterations()) * 2 *
output_height * output_width *
group_input_channels * group_output_channels *
kernel_height * kernel_width,
benchmark::Counter::kIsRate);
}
#if XNN_ARCH_ARM64
static void f16_igemm_1x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_1x8__neonfp16arith_ld64, 1, 8, 1, 1);
}
static void f16_igemm_4x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_4x8__neonfp16arith_ld64, 4, 8, 1, 1);
}
static void f16_igemm_6x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_6x8__neonfp16arith_ld64, 6, 8, 1, 1);
}
static void f16_igemm_8x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_8x8__neonfp16arith_ld64, 8, 8, 1, 1);
}
static void f16_igemm_1x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_1x16__neonfp16arith_ld64, 1, 16, 1, 1);
}
static void f16_igemm_4x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_4x16__neonfp16arith_ld64, 4, 16, 1, 1);
}
static void f16_igemm_6x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_6x16__neonfp16arith_ld64, 6, 16, 1, 1);
}
static void f16_igemm_8x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_8x16__neonfp16arith_ld64, 8, 16, 1, 1);
}
BENCHMARK_CONV(f16_igemm_1x8__neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_4x8__neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_6x8__neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_8x8__neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_1x16__neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_4x16__neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_6x16__neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_8x16__neonfp16arith_ld64)
#endif /* XNN_ARCH_ARM64 */
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();
#endif