test / bench /f16-igemm.cc
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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 <benchmark/benchmark.h>
#include <fp16/fp16.h>
#include "bench/conv.h"
#include "bench/utils.h"
#include <xnnpack.h>
#include <xnnpack/aligned-allocator.h>
#include <xnnpack/common.h>
#include <xnnpack/igemm.h>
#include <xnnpack/indirection.h>
#include <xnnpack/microfnptr.h>
#include <xnnpack/microparams-init.h>
#include <xnnpack/operator.h>
#include <xnnpack/pack.h>
static void f16_igemm(benchmark::State& state,
xnn_f16_igemm_minmax_ukernel_fn igemm,
xnn_init_f16_minmax_params_fn init_params,
uint32_t mr, uint32_t nr, uint32_t kr, uint32_t sr,
benchmark::utils::IsaCheckFunction isa_check = nullptr)
{
if (isa_check != nullptr && !isa_check(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 * sr);
std::vector<uint16_t> a(input_height * input_width * input_pixel_stride + XNN_EXTRA_BYTES / sizeof(uint16_t));
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 + XNN_EXTRA_BYTES / sizeof(uint16_t));
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, 64>> 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, XNN_LOG2_SIZEOF_HALF);
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(), UINT16_C(0x7E00) /* NaN */);
// Prepare minmax parameters.
xnn_f16_minmax_params params;
init_params(&params, UINT16_C(0xFC00) /* -inf */, UINT16_C(0x7C00) /* 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);
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_PLATFORM_JIT
static void f16_igemm(benchmark::State& state,
xnn_jit_igemm_code_generator_fn generator,
xnn_init_f16_minmax_params_fn init_params,
uint32_t mr, uint32_t nr, uint32_t kr, uint32_t sr,
benchmark::utils::IsaCheckFunction isa_check = nullptr)
{
if (isa_check != nullptr && !isa_check(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 * sr);
std::vector<uint16_t> a(input_height * input_width * input_pixel_stride + XNN_EXTRA_BYTES / sizeof(uint16_t));
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 + XNN_EXTRA_BYTES / sizeof(uint16_t));
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, 64>> 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, XNN_LOG2_SIZEOF_HALF);
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(), UINT16_C(0x7E00) /* NaN */);
// Prepare minmax parameters.
xnn_f16_minmax_params params;
init_params(&params, UINT16_C(0xFC00) /* -inf */, UINT16_C(0x7C00) /* inf */);
jit_gemm_params jit_params = {};
jit_params.f16_minmax.min = UINT16_C(0xFC00); /* -inf */
jit_params.f16_minmax.max = UINT16_C(0x7C00); /* inf */
xnn_code_buffer code_buffer;
xnn_allocate_code_memory(&code_buffer, XNN_DEFAULT_CODE_BUFFER_SIZE);
generator(&code_buffer,
mr,
group_output_channels % nr,
group_input_channels * sizeof(uint16_t),
kernel_size * mr * sizeof(void *),
&jit_params);
xnn_finalize_code_memory(&code_buffer);
auto igemm = reinterpret_cast<xnn_f16_igemm_minmax_ukernel_fn>(code_buffer.start);
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);
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);
}
}
}
xnn_release_code_memory(&code_buffer);
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);
}
#endif // XNN_PLATFORM_JIT
#if XNN_ARCH_ARM64 && XNN_ENABLE_ASSEMBLY
static void f16_igemm_6x16__asm_aarch64_neonfp16arith_cortex_a55(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_6x16__asm_aarch64_neonfp16arith_cortex_a55,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_6x16__asm_aarch64_neonfp16arith_cortex_a55r0(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_6x16__asm_aarch64_neonfp16arith_cortex_a55r0,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_6x16__asm_aarch64_neonfp16arith_cortex_a75(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_6x16__asm_aarch64_neonfp16arith_cortex_a75,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_6x16__asm_aarch64_neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_6x16__asm_aarch64_neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_4x16__asm_aarch64_neonfp16arith_ld32(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_4x16__asm_aarch64_neonfp16arith_ld32,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/4, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_4x16__asm_aarch64_neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_4x16__asm_aarch64_neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/4, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_1x16__asm_aarch64_neonfp16arith_ld32(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_1x16__asm_aarch64_neonfp16arith_ld32,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/1, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_1x16__asm_aarch64_neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_1x16__asm_aarch64_neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/1, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
BENCHMARK_CONV(f16_igemm_6x16__asm_aarch64_neonfp16arith_cortex_a55)
BENCHMARK_CONV(f16_igemm_6x16__asm_aarch64_neonfp16arith_cortex_a55r0)
BENCHMARK_CONV(f16_igemm_6x16__asm_aarch64_neonfp16arith_cortex_a75)
BENCHMARK_CONV(f16_igemm_6x16__asm_aarch64_neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_4x16__asm_aarch64_neonfp16arith_ld32)
BENCHMARK_CONV(f16_igemm_4x16__asm_aarch64_neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_1x16__asm_aarch64_neonfp16arith_ld32)
BENCHMARK_CONV(f16_igemm_1x16__asm_aarch64_neonfp16arith_ld64)
#endif // XNN_ARCH_ARM64 && XNN_ENABLE_ASSEMBLY
#if XNN_ENABLE_ARM_FP16_VECTOR && (XNN_ARCH_ARM || XNN_ARCH_ARM64)
static void f16_igemm_1x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_1x8__neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/1, /*nr=*/8, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_4x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_4x8__neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/4, /*nr=*/8, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_6x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_6x8__neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/8, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_8x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_8x8__neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/8, /*nr=*/8, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_1x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_1x16__neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/1, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_4x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_4x16__neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/4, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_6x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_6x16__neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_8x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_8x16__neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/8, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
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_ENABLE_ARM_FP16_VECTOR && (XNN_ARCH_ARM || XNN_ARCH_ARM64)
#if XNN_ARCH_X86 || XNN_ARCH_X86_64
static void f16_igemm_1x8__avx2_broadcast(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_1x8__avx2_broadcast,
xnn_init_f16_minmax_avx_params,
/*mr=*/1, /*nr=*/8, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckAVX2);
}
static void f16_igemm_4x8__avx2_broadcast(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_4x8__avx2_broadcast,
xnn_init_f16_minmax_avx_params,
/*mr=*/4, /*nr=*/8, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckAVX2);
}
static void f16_igemm_5x8__avx2_broadcast(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_5x8__avx2_broadcast,
xnn_init_f16_minmax_avx_params,
/*mr=*/5, /*nr=*/8, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckAVX2);
}
static void f16_igemm_6x8__avx2_broadcast(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_6x8__avx2_broadcast,
xnn_init_f16_minmax_avx_params,
/*mr=*/6, /*nr=*/8, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckAVX2);
}
static void f16_igemm_7x8__avx2_broadcast(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_7x8__avx2_broadcast,
xnn_init_f16_minmax_avx_params,
/*mr=*/7, /*nr=*/8, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckAVX2);
}
static void f16_igemm_1x16__avx2_broadcast(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_1x16__avx2_broadcast,
xnn_init_f16_minmax_avx_params,
/*mr=*/1, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckAVX2);
}
static void f16_igemm_3x16__avx2_broadcast(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_3x16__avx2_broadcast,
xnn_init_f16_minmax_avx_params,
/*mr=*/3, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckAVX2);
}
static void f16_igemm_4x16__avx2_broadcast(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_4x16__avx2_broadcast,
xnn_init_f16_minmax_avx_params,
/*mr=*/4, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckAVX2);
}
static void f16_igemm_5x16__avx2_broadcast(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_f16_igemm_minmax_ukernel_5x16__avx2_broadcast,
xnn_init_f16_minmax_avx_params,
/*mr=*/5, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckAVX2);
}
BENCHMARK_CONV(f16_igemm_1x8__avx2_broadcast)
BENCHMARK_CONV(f16_igemm_4x8__avx2_broadcast)
BENCHMARK_CONV(f16_igemm_5x8__avx2_broadcast)
BENCHMARK_CONV(f16_igemm_6x8__avx2_broadcast)
BENCHMARK_CONV(f16_igemm_7x8__avx2_broadcast)
BENCHMARK_CONV(f16_igemm_1x16__avx2_broadcast)
BENCHMARK_CONV(f16_igemm_3x16__avx2_broadcast)
BENCHMARK_CONV(f16_igemm_4x16__avx2_broadcast)
BENCHMARK_CONV(f16_igemm_5x16__avx2_broadcast)
#endif // XNN_ARCH_X86 || XNN_ARCH_X86_64
#if XNN_ARCH_ARM64 && XNN_PLATFORM_JIT
static void f16_igemm_6x16_6x16__jit_aarch64_neonfp16arith_cortex_a55(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_generate_f16_igemm_ukernel_6x16__aarch64_neonfp16arith_cortex_a55,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_6x16_6x16__jit_aarch64_neonfp16arith_cortex_a55r0(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_generate_f16_igemm_ukernel_6x16__aarch64_neonfp16arith_cortex_a55r0,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_6x16_5x16__jit_aarch64_neonfp16arith_cortex_a55r0(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_generate_f16_igemm_ukernel_6x16__aarch64_neonfp16arith_cortex_a55r0,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/5, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_6x16_6x16__jit_aarch64_neonfp16arith_cortex_a75(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_generate_f16_igemm_ukernel_6x16__aarch64_neonfp16arith_cortex_a75,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_6x16_6x16__jit_aarch64_neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_generate_f16_igemm_ukernel_6x16__aarch64_neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/6, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_4x16_4x16__jit_aarch64_neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_generate_f16_igemm_ukernel_4x16__aarch64_neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/4, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
static void f16_igemm_1x16_1x16__jit_aarch64_neonfp16arith_ld64(benchmark::State& state, const char* net) {
f16_igemm(state,
xnn_generate_f16_igemm_ukernel_1x16__aarch64_neonfp16arith_ld64,
xnn_init_f16_minmax_fp16arith_params,
/*mr=*/1, /*nr=*/16, /*kr=*/1, /*sr=*/1,
benchmark::utils::CheckNEONFP16ARITH);
}
BENCHMARK_CONV(f16_igemm_6x16_6x16__jit_aarch64_neonfp16arith_cortex_a55)
BENCHMARK_CONV(f16_igemm_6x16_6x16__jit_aarch64_neonfp16arith_cortex_a55)
BENCHMARK_CONV(f16_igemm_6x16_6x16__jit_aarch64_neonfp16arith_cortex_a55r0)
BENCHMARK_CONV(f16_igemm_6x16_5x16__jit_aarch64_neonfp16arith_cortex_a55r0)
BENCHMARK_CONV(f16_igemm_6x16_6x16__jit_aarch64_neonfp16arith_cortex_a75)
BENCHMARK_CONV(f16_igemm_6x16_6x16__jit_aarch64_neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_4x16_4x16__jit_aarch64_neonfp16arith_ld64)
BENCHMARK_CONV(f16_igemm_1x16_1x16__jit_aarch64_neonfp16arith_ld64)
#endif // XNN_ARCH_ARM64 && XNN_PLATFORM_JIT
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();
#endif