You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
252 lines
8.5 KiB
252 lines
8.5 KiB
// 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 <xnnpack.h>
|
|
|
|
#include <benchmark/benchmark.h>
|
|
#include "bench/utils.h"
|
|
#ifdef BENCHMARK_TENSORFLOW_LITE
|
|
#include "flatbuffers/include/flatbuffers/flatbuffers.h"
|
|
#include "tensorflow/lite/interpreter.h"
|
|
#include "tensorflow/lite/kernels/register.h"
|
|
#include "tensorflow/lite/model.h"
|
|
#include "tensorflow/lite/schema/schema_generated.h"
|
|
#include "tensorflow/lite/version.h"
|
|
#endif // BENCHMARK_TENSORFLOW_LITE
|
|
|
|
|
|
void xnnpack_prelu_f32(benchmark::State& state, const char* net) {
|
|
const size_t batch_size = state.range(0);
|
|
const size_t height = state.range(1);
|
|
const size_t width = state.range(2);
|
|
const size_t channels = state.range(3);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32irng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng));
|
|
auto f32wrng = std::bind(std::uniform_real_distribution<float>(0.25f, 0.75f), std::ref(rng));
|
|
|
|
std::vector<float> input(batch_size * height * width * channels + XNN_EXTRA_BYTES / sizeof(float));
|
|
std::generate(input.begin(), input.end(), std::ref(f32irng));
|
|
std::vector<float> slope(channels);
|
|
std::generate(slope.begin(), slope.end(), std::ref(f32wrng));
|
|
std::vector<float> output(batch_size * height * width * channels);
|
|
|
|
xnn_status status = xnn_initialize(nullptr /* allocator */);
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to initialize XNNPACK");
|
|
return;
|
|
}
|
|
|
|
xnn_operator_t prelu_op = nullptr;
|
|
status = xnn_create_prelu_nc_f32(
|
|
channels, channels /* input stride */, channels /* output stride */,
|
|
slope.data(),
|
|
0 /* flags */, &prelu_op);
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to create FP32 PReLU operator");
|
|
return;
|
|
}
|
|
|
|
status = xnn_setup_prelu_nc_f32(
|
|
prelu_op,
|
|
batch_size * height * width,
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */);
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to setup FP32 PReLU operator");
|
|
return;
|
|
}
|
|
|
|
for (auto _ : state) {
|
|
status = xnn_run_operator(prelu_op, nullptr /* thread pool */);
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to run FP32 PReLU operator");
|
|
return;
|
|
}
|
|
}
|
|
|
|
status = xnn_delete_operator(prelu_op);
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to delete FP32 PReLU operator");
|
|
return;
|
|
}
|
|
prelu_op = nullptr;
|
|
|
|
const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
|
|
if (cpu_frequency != 0) {
|
|
state.counters["cpufreq"] = cpu_frequency;
|
|
}
|
|
|
|
const size_t elements_per_iteration = batch_size * height * width * channels;
|
|
state.counters["elements"] =
|
|
benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
|
|
|
|
const size_t bytes_per_iteration = (2 * elements_per_iteration + channels) * sizeof(float);
|
|
state.counters["bytes"] =
|
|
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
|
|
}
|
|
|
|
#ifdef BENCHMARK_TENSORFLOW_LITE
|
|
void tflite_prelu_f32(benchmark::State& state, const char* net) {
|
|
const size_t batch_size = state.range(0);
|
|
const size_t height = state.range(1);
|
|
const size_t width = state.range(2);
|
|
const size_t channels = state.range(3);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32irng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng));
|
|
auto f32wrng = std::bind(std::uniform_real_distribution<float>(0.25f, 0.75f), std::ref(rng));
|
|
|
|
std::vector<float> slope(channels);
|
|
std::generate(slope.begin(), slope.end(), std::ref(f32wrng));
|
|
|
|
flatbuffers::FlatBufferBuilder builder;
|
|
flatbuffers::Offset<tflite::OperatorCode> operator_code =
|
|
CreateOperatorCode(builder, tflite::BuiltinOperator_PRELU);
|
|
|
|
flatbuffers::Offset<tflite::Buffer> buffers[2] = {
|
|
tflite::CreateBuffer(builder, builder.CreateVector({})),
|
|
tflite::CreateBuffer(builder, builder.CreateVector(
|
|
reinterpret_cast<const uint8_t*>(slope.data()),
|
|
sizeof(float) * slope.size())),
|
|
};
|
|
|
|
const int32_t input_shape[4] = {
|
|
static_cast<int32_t>(batch_size),
|
|
static_cast<int32_t>(height),
|
|
static_cast<int32_t>(width),
|
|
static_cast<int32_t>(channels)
|
|
};
|
|
const int32_t output_shape[4] = {
|
|
static_cast<int32_t>(batch_size),
|
|
static_cast<int32_t>(height),
|
|
static_cast<int32_t>(width),
|
|
static_cast<int32_t>(channels)
|
|
};
|
|
const int32_t slope_shape[1] = {
|
|
static_cast<int32_t>(channels)
|
|
};
|
|
|
|
flatbuffers::Offset<tflite::Tensor> tensors[3] = {
|
|
tflite::CreateTensor(builder,
|
|
builder.CreateVector<int32_t>(input_shape, 4),
|
|
tflite::TensorType_FLOAT32),
|
|
tflite::CreateTensor(builder,
|
|
builder.CreateVector<int32_t>(slope_shape, 1),
|
|
tflite::TensorType_FLOAT32,
|
|
1 /* buffer id */),
|
|
tflite::CreateTensor(builder,
|
|
builder.CreateVector<int32_t>(output_shape, 4),
|
|
tflite::TensorType_FLOAT32),
|
|
};
|
|
|
|
const int32_t op_inputs[2] = { 0, 1 };
|
|
const int32_t op_outputs[1] = { 2 };
|
|
flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(
|
|
builder,
|
|
0 /* opcode_index */,
|
|
builder.CreateVector<int32_t>(op_inputs, 2),
|
|
builder.CreateVector<int32_t>(op_outputs, 1));
|
|
|
|
const int32_t graph_inputs[1] = { 0 };
|
|
const int32_t graph_outputs[1] = { 2 };
|
|
flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph(
|
|
builder,
|
|
builder.CreateVector(tensors, 3),
|
|
builder.CreateVector<int32_t>(graph_inputs, 1),
|
|
builder.CreateVector<int32_t>(graph_outputs, 1),
|
|
builder.CreateVector(&op, 1));
|
|
|
|
flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("PReLU model");
|
|
|
|
flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
|
|
TFLITE_SCHEMA_VERSION,
|
|
builder.CreateVector(&operator_code, 1),
|
|
builder.CreateVector(&subgraph, 1),
|
|
description,
|
|
builder.CreateVector(buffers, 2));
|
|
|
|
builder.Finish(model_buffer);
|
|
|
|
const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer());
|
|
tflite::ops::builtin::BuiltinOpResolver resolver;
|
|
tflite::InterpreterBuilder interpreterBuilder(model, resolver);
|
|
std::unique_ptr<tflite::Interpreter> interpreter;
|
|
if (interpreterBuilder(&interpreter) != kTfLiteOk) {
|
|
state.SkipWithError("failed to create TFLite interpreter");
|
|
return;
|
|
}
|
|
if (interpreter == nullptr) {
|
|
state.SkipWithError("TFLite interpreter is null");
|
|
return;
|
|
}
|
|
interpreter->SetNumThreads(1);
|
|
|
|
if (interpreter->AllocateTensors() != kTfLiteOk) {
|
|
state.SkipWithError("failed to allocate tensors");
|
|
return;
|
|
}
|
|
|
|
std::generate(
|
|
interpreter->typed_tensor<float>(0),
|
|
interpreter->typed_tensor<float>(0) + batch_size * height * width * channels,
|
|
std::ref(f32irng));
|
|
|
|
for (auto _ : state) {
|
|
if (interpreter->Invoke() != kTfLiteOk) {
|
|
state.SkipWithError("failed to invoke TFLite interpreter");
|
|
return;
|
|
}
|
|
}
|
|
|
|
const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
|
|
if (cpu_frequency != 0) {
|
|
state.counters["cpufreq"] = cpu_frequency;
|
|
}
|
|
|
|
const size_t elements_per_iteration = batch_size * height * width * channels;
|
|
state.counters["elements"] =
|
|
benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
|
|
|
|
const size_t bytes_per_iteration = (2 * elements_per_iteration + channels) * sizeof(float);
|
|
state.counters["bytes"] =
|
|
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
|
|
|
|
interpreter.reset();
|
|
}
|
|
#endif // BENCHMARK_TENSORFLOW_LITE
|
|
|
|
// Characteristic arguments for ImageNet classification models
|
|
static void ImageNet(benchmark::internal::Benchmark* b)
|
|
{
|
|
b->ArgNames({"N", "H", "W", "C"});
|
|
|
|
int32_t c = 16;
|
|
for (int32_t hw = 224 / 2; hw >= 7; hw /= 2) {
|
|
b->Args({1, hw, hw, c});
|
|
b->Args({1, hw, hw, c * 2});
|
|
c *= 2;
|
|
}
|
|
}
|
|
|
|
BENCHMARK_CAPTURE(xnnpack_prelu_f32, imagenet, "ImageNet 224x224")->Apply(ImageNet)->UseRealTime();
|
|
|
|
#ifdef BENCHMARK_TENSORFLOW_LITE
|
|
BENCHMARK_CAPTURE(tflite_prelu_f32, imagenet, "ImageNet 224x224")->Apply(ImageNet)->UseRealTime();
|
|
#endif // BENCHMARK_TENSORFLOW_LITE
|
|
|
|
#ifndef XNNPACK_BENCHMARK_NO_MAIN
|
|
BENCHMARK_MAIN();
|
|
#endif
|