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227 lines
7.6 KiB
227 lines
7.6 KiB
// Copyright 2020 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#include <algorithm>
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#include <array>
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#include <cmath>
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#include <functional>
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#include <limits>
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#include <random>
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#include <vector>
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#include <xnnpack.h>
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#include <benchmark/benchmark.h>
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#include "bench/utils.h"
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#ifdef BENCHMARK_TENSORFLOW_LITE
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#include "flatbuffers/include/flatbuffers/flatbuffers.h"
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/kernels/register.h"
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#include "tensorflow/lite/model.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/version.h"
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#endif // BENCHMARK_TENSORFLOW_LITE
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static void xnnpack_ceiling_f32(benchmark::State& state) {
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const size_t batch_size = state.range(0);
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const size_t channels = state.range(1);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32rng = std::bind(std::uniform_real_distribution<float>(-10.0f, 10.0f), std::ref(rng));
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std::vector<float> input(batch_size * channels);
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std::vector<float> output(batch_size * channels);
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std::generate(input.begin(), input.end(), std::ref(f32rng));
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std::fill(output.begin(), output.end(), std::nanf(""));
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xnn_status status = xnn_initialize(nullptr /* allocator */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to initialize XNNPACK");
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return;
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}
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xnn_operator_t ceiling_op = nullptr;
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status = xnn_create_ceiling_nc_f32(
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channels, channels /* input stride */, channels /* output stride */,
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0 /* flags */, &ceiling_op);
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if (status != xnn_status_success || ceiling_op == nullptr) {
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state.SkipWithError("failed to create Ceiling operator");
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return;
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}
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status = xnn_setup_ceiling_nc_f32(
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ceiling_op,
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batch_size,
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input.data(), output.data(),
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nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to setup Ceiling operator");
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return;
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}
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for (auto _ : state) {
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status = xnn_run_operator(ceiling_op, nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to run Ceiling operator");
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return;
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}
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}
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status = xnn_delete_operator(ceiling_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to delete Ceiling operator");
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return;
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}
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
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if (cpu_frequency != 0) {
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state.counters["cpufreq"] = cpu_frequency;
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}
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const size_t elements_per_iteration = batch_size * channels;
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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * elements_per_iteration * sizeof(float);
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state.counters["bytes"] =
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benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
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}
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#ifdef BENCHMARK_TENSORFLOW_LITE
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static void tflite_ceiling_f32(benchmark::State& state) {
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const size_t batch_size = state.range(0);
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const size_t channels = state.range(1);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32rng = std::bind(std::uniform_real_distribution<float>(-10.0f, 10.0f), std::ref(rng));
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flatbuffers::FlatBufferBuilder builder;
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const flatbuffers::Offset<tflite::OperatorCode> operator_code =
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CreateOperatorCode(builder, tflite::BuiltinOperator_CEIL);
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const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{
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tflite::CreateBuffer(builder, builder.CreateVector({})),
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}};
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const std::array<int32_t, 4> input_shape{{
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static_cast<int32_t>(batch_size),
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static_cast<int32_t>(1 /* height */),
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static_cast<int32_t>(1 /* width */),
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static_cast<int32_t>(channels)
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}};
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const std::array<int32_t, 4> output_shape{{
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static_cast<int32_t>(batch_size),
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static_cast<int32_t>(1 /* height */),
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static_cast<int32_t>(1 /* width */),
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static_cast<int32_t>(channels)
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}};
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const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{
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tflite::CreateTensor(builder,
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builder.CreateVector<int32_t>(input_shape.data(), input_shape.size()),
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tflite::TensorType_FLOAT32),
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tflite::CreateTensor(builder,
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builder.CreateVector<int32_t>(output_shape.data(), output_shape.size()),
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tflite::TensorType_FLOAT32),
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}};
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const std::array<int32_t, 1> op_inputs{{ 0 }};
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const std::array<int32_t, 1> op_outputs{{ 1 }};
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flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(
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builder,
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0 /* opcode_index */,
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builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
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builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size()));
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const std::array<int32_t, 1> graph_inputs{{ 0 }};
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const std::array<int32_t, 1> graph_outputs{{ 1 }};
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const flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph(
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builder,
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builder.CreateVector(tensors.data(), tensors.size()),
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builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()),
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builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()),
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builder.CreateVector(&op, 1));
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const flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
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TFLITE_SCHEMA_VERSION,
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builder.CreateVector(&operator_code, 1),
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builder.CreateVector(&subgraph, 1),
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builder.CreateString("Ceil model"),
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builder.CreateVector(buffers.data(), buffers.size()));
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builder.Finish(model_buffer);
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const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer());
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tflite::ops::builtin::BuiltinOpResolver resolver;
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tflite::InterpreterBuilder interpreterBuilder(model, resolver);
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std::unique_ptr<tflite::Interpreter> interpreter;
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if (interpreterBuilder(&interpreter) != kTfLiteOk) {
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state.SkipWithError("failed to create TFLite interpreter");
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return;
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}
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if (interpreter == nullptr) {
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state.SkipWithError("TFLite interpreter is null");
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return;
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}
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interpreter->SetNumThreads(1);
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if (interpreter->AllocateTensors() != kTfLiteOk) {
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state.SkipWithError("failed to allocate tensors");
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return;
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}
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std::generate(
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interpreter->typed_tensor<float>(0),
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interpreter->typed_tensor<float>(0) + batch_size * channels,
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std::ref(f32rng));
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for (auto _ : state) {
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if (interpreter->Invoke() != kTfLiteOk) {
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state.SkipWithError("failed to invoke TFLite interpreter");
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return;
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}
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}
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
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if (cpu_frequency != 0) {
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state.counters["cpufreq"] = cpu_frequency;
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}
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const size_t elements_per_iteration = batch_size * channels;
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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * elements_per_iteration * sizeof(float);
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state.counters["bytes"] =
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benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
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interpreter.reset();
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}
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#endif // BENCHMARK_TENSORFLOW_LITE
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static void CharacteristicArguments(benchmark::internal::Benchmark* b)
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{
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b->ArgNames({"N", "C"});
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int32_t c = 16;
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for (int32_t n = 224; n >= 7; n /= 2) {
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b->Args({n * n, c});
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c *= 2;
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}
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}
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BENCHMARK(xnnpack_ceiling_f32)->Apply(CharacteristicArguments)->UseRealTime();
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#ifdef BENCHMARK_TENSORFLOW_LITE
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BENCHMARK(tflite_ceiling_f32)->Apply(CharacteristicArguments)->UseRealTime();
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#endif // BENCHMARK_TENSORFLOW_LITE
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#ifndef XNNPACK_BENCHMARK_NO_MAIN
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BENCHMARK_MAIN();
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#endif
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