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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 <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;
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
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;
}
}
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
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