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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 <memory>
#include <numeric>
#include <random>
#include <vector>
#include <cpuinfo.h>
#include <pthreadpool.h>
#include <benchmark/benchmark.h>
#include "bench/utils.h"
#include <xnnpack/aligned-allocator.h>
#include <xnnpack/common.h>
#include <xnnpack/math.h>
#include <xnnpack/math-stubs.h>
struct ComputeErrorContext {
const float* input;
const float* output;
float* error;
};
static void ComputeError(
struct ComputeErrorContext* context,
size_t start,
size_t range)
{
const float* input = context->input;
const float* output = context->output;
float* error = context->error;
for (size_t i = start; i < start + range; i++) {
const double output_ref = std::sqrt(double(input[i]));
const double abs_error = std::abs(output_ref - double(output[i]));
const float output_abs = std::abs(output_ref);
const float output_ulp = uint32_as_float(float_as_uint32(output_abs) + 1) - output_abs;
error[i] = float(abs_error / output_ulp);
}
}
static void SqrtError(benchmark::State& state,
xnn_f32_unary_math_fn sqrt,
benchmark::utils::IsaCheckFunction isa_check = nullptr)
{
if (!cpuinfo_initialize()) {
state.SkipWithError("failed cpuinfo init");
return;
}
if (isa_check != nullptr && !isa_check(state)) {
return;
}
const uint32_t min_input = 0x3F800000;
const uint32_t max_input = 0x41800000;
// Number of elements in one block of inputs/outputs.
// Combining multiple elements in a block reduce function call overhead.
const size_t block_size = 1048576;
// Number of elements in one parallelization tile. Worker threads process this many elements in each task.
const size_t tile_size = 64;
uint32_t num_threads = cpuinfo_get_processors_count();
#if XNN_ARCH_ARM || XNN_ARCH_ARM64
// Use all cores except for the least performant cluster
if (cpuinfo_get_clusters_count() > 1) {
num_threads -= cpuinfo_get_cluster(cpuinfo_get_clusters_count() - 1)->core_count;
}
#endif // XNN_ARCH_ARM || XNN_ARCH_ARM64
std::unique_ptr<pthreadpool, decltype(&pthreadpool_destroy)> threadpool(
pthreadpool_create(num_threads), pthreadpool_destroy);
std::vector<float, AlignedAllocator<float, 64>> x(block_size);
std::vector<float, AlignedAllocator<float, 64>> y(block_size);
std::vector<float> ulp_error(block_size);
float max_ulp_error = 0.0f;
ComputeErrorContext context;
context.input = x.data();
context.output = y.data();
context.error = ulp_error.data();
for (auto _ : state) {
for (uint32_t n = min_input; n < max_input; n += block_size) {
for (uint32_t i = 0; i < block_size; i++) {
x[i] = uint32_as_float(std::min<uint32_t>(n + i, max_input));
}
std::fill(y.begin(), y.end(), std::nanf(""));
sqrt(block_size * sizeof(float), x.data(), y.data());
pthreadpool_parallelize_1d_tile_1d(
threadpool.get(),
reinterpret_cast<pthreadpool_task_1d_tile_1d_t>(ComputeError),
static_cast<void*>(&context),
block_size, tile_size, 0 /* flags */);
max_ulp_error = std::accumulate(ulp_error.cbegin(), ulp_error.cend(), max_ulp_error,
static_cast<const float& (*)(const float&, const float&)>(std::max<float>));
}
}
state.counters["ULPERROR"] = benchmark::Counter(max_ulp_error);
}
#if XNN_ARCH_ARM || XNN_ARCH_ARM64
BENCHMARK_CAPTURE(SqrtError, neonfma_nr1fma,
xnn_math_f32_sqrt__neonfma_nr1fma,
benchmark::utils::CheckNEONFMA)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, neonfma_nr2fma,
xnn_math_f32_sqrt__neonfma_nr2fma,
benchmark::utils::CheckNEONFMA)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, neonfma_nr3fma,
xnn_math_f32_sqrt__neonfma_nr3fma,
benchmark::utils::CheckNEONFMA)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, neonfma_nr2fma1adj,
xnn_math_f32_sqrt__neonfma_nr2fma1adj,
benchmark::utils::CheckNEONFMA)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, neonfma_nr1rsqrts1fma1adj,
xnn_math_f32_sqrt__neonfma_nr1rsqrts1fma1adj,
benchmark::utils::CheckNEONFMA)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, neon_nr1rsqrts,
xnn_math_f32_sqrt__neon_nr1rsqrts,
benchmark::utils::CheckNEON)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, neon_nr2rsqrts,
xnn_math_f32_sqrt__neon_nr2rsqrts,
benchmark::utils::CheckNEON)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, neon_nr3rsqrts,
xnn_math_f32_sqrt__neon_nr3rsqrts,
benchmark::utils::CheckNEON)
->Unit(benchmark::kMillisecond)
->Iterations(1);
#endif // XNN_ARCH_ARM || XNN_ARCH_ARM64
#if XNN_ARCH_X86 || XNN_ARCH_X86_64
BENCHMARK_CAPTURE(SqrtError, avx512f_nr1fma,
xnn_math_f32_sqrt__avx512f_nr1fma,
benchmark::utils::CheckAVX512F)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, avx512f_nr2fma,
xnn_math_f32_sqrt__avx512f_nr2fma,
benchmark::utils::CheckAVX512F)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, avx512f_nr1fma1adj,
xnn_math_f32_sqrt__avx512f_nr1fma1adj,
benchmark::utils::CheckAVX512F)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, fma3_nr1fma,
xnn_math_f32_sqrt__fma3_nr1fma,
benchmark::utils::CheckFMA3)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, fma3_nr2fma,
xnn_math_f32_sqrt__fma3_nr2fma,
benchmark::utils::CheckFMA3)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, fma3_nr1fma1adj,
xnn_math_f32_sqrt__fma3_nr1fma1adj,
benchmark::utils::CheckFMA3)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, sse_nr1mac,
xnn_math_f32_sqrt__sse_nr1mac)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, sse_nr2mac,
xnn_math_f32_sqrt__sse_nr2mac)
->Unit(benchmark::kMillisecond)
->Iterations(1);
BENCHMARK_CAPTURE(SqrtError, sse_hh1mac,
xnn_math_f32_sqrt__sse_hh1mac)
->Unit(benchmark::kMillisecond)
->Iterations(1);
#endif // XNN_ARCH_X86 || XNN_ARCH_X86_64
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();
#endif
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