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// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// 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 <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
#ifndef XNN_NO_QU8_OPERATORS
static void xnnpack_softmax_qu8(benchmark::State& state) {
const size_t batch_size = static_cast<size_t>(state.range(0));
const size_t channels = static_cast<size_t>(state.range(1));
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
std::vector<uint8_t> input(batch_size * channels);
std::vector<uint8_t> output(batch_size * channels);
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::fill(output.begin(), output.end(), 0xA5);
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
xnn_operator_t softmax_op = nullptr;
status = xnn_create_softmax_nc_qu8(
channels, channels /* input stride */, channels /* output stride */,
1.0f /* input scale */,
0 /* output zero point */, 1.0f / 256.0f /* output scale */,
0 /* flags */, &softmax_op);
if (status != xnn_status_success || softmax_op == nullptr) {
state.SkipWithError("failed to create SoftMax operator");
return;
}
status = xnn_setup_softmax_nc_qu8(
softmax_op,
batch_size,
input.data(), output.data(),
nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to setup SoftMax operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(softmax_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run SoftMax operator");
return;
}
}
status = xnn_delete_operator(softmax_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete SoftMax operator");
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 * 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 * sizeof(uint8_t);
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
static void xnnpack_softmax_f32(benchmark::State& state) {
const size_t batch_size = static_cast<size_t>(state.range(0));
const size_t channels = static_cast<size_t>(state.range(1));
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-100.0f, 100.0f), std::ref(rng));
std::vector<float> input(batch_size * channels + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> output(batch_size * channels);
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), std::nanf(""));
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
xnn_operator_t softmax_op = nullptr;
status = xnn_create_softmax_nc_f32(
channels, channels /* input stride */, channels /* output stride */,
0 /* flags */, &softmax_op);
if (status != xnn_status_success || softmax_op == nullptr) {
state.SkipWithError("failed to create SoftMax operator");
return;
}
status = xnn_setup_softmax_nc_f32(
softmax_op,
batch_size,
input.data(), output.data(),
nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to setup SoftMax operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(softmax_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run SoftMax operator");
return;
}
}
status = xnn_delete_operator(softmax_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete SoftMax operator");
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 * 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 * sizeof(float);
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
#endif // XNN_NO_QU8_OPERATORS
#ifdef BENCHMARK_TENSORFLOW_LITE
static void tflite_softmax_f32(benchmark::State& state) {
const size_t batch_size = state.range(0);
const size_t channels = state.range(1);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-100.0f, 100.0f), std::ref(rng));
flatbuffers::FlatBufferBuilder builder;
flatbuffers::Offset<tflite::OperatorCode> operator_code =
tflite::CreateOperatorCode(builder, tflite::BuiltinOperator_SOFTMAX);
flatbuffers::Offset<tflite::SoftmaxOptions> softmax_options =
tflite::CreateSoftmaxOptions(builder, 1.0f /* beta */);
flatbuffers::Offset<tflite::Buffer> buffers[1] = {
tflite::CreateBuffer(builder, builder.CreateVector({})),
};
const int32_t input_shape[4] = {
static_cast<int32_t>(batch_size),
static_cast<int32_t>(1 /* height */),
static_cast<int32_t>(1 /* width */),
static_cast<int32_t>(channels)
};
const int32_t output_shape[4] = {
static_cast<int32_t>(batch_size),
static_cast<int32_t>(1 /* height */),
static_cast<int32_t>(1 /* width */),
static_cast<int32_t>(channels)
};
flatbuffers::Offset<tflite::Tensor> tensors[2] = {
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(input_shape, 4),
tflite::TensorType_FLOAT32),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(output_shape, 4),
tflite::TensorType_FLOAT32),
};
const int32_t op_inputs[1] = { 0 };
const int32_t op_outputs[1] = { 1 };
flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(
builder,
0 /* opcode_index */,
builder.CreateVector<int32_t>(op_inputs, 1),
builder.CreateVector<int32_t>(op_outputs, 1),
tflite::BuiltinOptions_SoftmaxOptions, softmax_options.Union());
const int32_t graph_inputs[1] = { 0 };
const int32_t graph_outputs[1] = { 1 };
flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph(
builder,
builder.CreateVector(tensors, 2),
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("Softmax 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, 1));
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 * channels,
std::ref(f32rng));
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 * 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 * sizeof(float);
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
interpreter.reset();
}
#endif // BENCHMARK_TENSORFLOW_LITE
static void CharacteristicArguments(benchmark::internal::Benchmark* b)
{
b->ArgNames({"N", "C"});
// CIFAR-10
b->Args({1, 10});
// CIFAR-100 */
b->Args({1, 100});
// ImageNet-1K
b->Args({1, 1000});
// ImageNet-1K+1
b->Args({1, 1001});
// ImageNet-22K
b->Args({1, 21841});
// ADE20K
b->Args({257 * 257, 151});
}
#ifndef XNN_NO_QU8_OPERATORS
BENCHMARK(xnnpack_softmax_qu8)->Apply(CharacteristicArguments)->UseRealTime();
#endif // XNN_NO_QU8_OPERATORS
BENCHMARK(xnnpack_softmax_f32)->Apply(CharacteristicArguments)->UseRealTime();
#ifdef BENCHMARK_TENSORFLOW_LITE
BENCHMARK(tflite_softmax_f32)->Apply(CharacteristicArguments)->UseRealTime();
#endif // BENCHMARK_TENSORFLOW_LITE
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();
#endif