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// Copyright 2020 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 <array>
#include <cmath>
#include <functional>
#include <limits>
#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
static void xnnpack_floor_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>(-10.0f, 10.0f), std::ref(rng));
std::vector<float> input(batch_size * channels);
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 floor_op = nullptr;
status = xnn_create_floor_nc_f32(
channels, channels /* input stride */, channels /* output stride */,
0 /* flags */, &floor_op);
if (status != xnn_status_success || floor_op == nullptr) {
state.SkipWithError("failed to create Floor operator");
return;
}
status = xnn_setup_floor_nc_f32(
floor_op,
batch_size,
input.data(), output.data(),
nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to setup Floor operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(floor_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run Floor operator");
return;
}
}
status = xnn_delete_operator(floor_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete Floor operator");
return;
}
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
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);
}
#ifdef BENCHMARK_TENSORFLOW_LITE
static void tflite_floor_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>(-10.0f, 10.0f), std::ref(rng));
flatbuffers::FlatBufferBuilder builder;
const flatbuffers::Offset<tflite::OperatorCode> operator_code =
CreateOperatorCode(builder, tflite::BuiltinOperator_FLOOR);
const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{
tflite::CreateBuffer(builder, builder.CreateVector({})),
}};
const std::array<int32_t, 4> input_shape{{
static_cast<int32_t>(batch_size),
static_cast<int32_t>(1 /* height */),
static_cast<int32_t>(1 /* width */),
static_cast<int32_t>(channels)
}};
const std::array<int32_t, 4> output_shape{{
static_cast<int32_t>(batch_size),
static_cast<int32_t>(1 /* height */),
static_cast<int32_t>(1 /* width */),
static_cast<int32_t>(channels)
}};
const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(input_shape.data(), input_shape.size()),
tflite::TensorType_FLOAT32),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(output_shape.data(), output_shape.size()),
tflite::TensorType_FLOAT32),
}};
const std::array<int32_t, 1> op_inputs{{ 0 }};
const std::array<int32_t, 1> op_outputs{{ 1 }};
flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(
builder,
0 /* opcode_index */,
builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size()));
const std::array<int32_t, 1> graph_inputs{{ 0 }};
const std::array<int32_t, 1> graph_outputs{{ 1 }};
const flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph(
builder,
builder.CreateVector(tensors.data(), tensors.size()),
builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()),
builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()),
builder.CreateVector(&op, 1));
const flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
TFLITE_SCHEMA_VERSION,
builder.CreateVector(&operator_code, 1),
builder.CreateVector(&subgraph, 1),
builder.CreateString("Floor model"),
builder.CreateVector(buffers.data(), buffers.size()));
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;
}
}
state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
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"});
int32_t c = 16;
for (int32_t n = 224; n >= 7; n /= 2) {
b->Args({n * n, c});
c *= 2;
}
}
BENCHMARK(xnnpack_floor_f32)->Apply(CharacteristicArguments)->UseRealTime();
#ifdef BENCHMARK_TENSORFLOW_LITE
BENCHMARK(tflite_floor_f32)->Apply(CharacteristicArguments)->UseRealTime();
#endif // BENCHMARK_TENSORFLOW_LITE
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();
#endif
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