Benchmarks for rounding operators
PiperOrigin-RevId: 318152280
diff --git a/bench/bankers-rounding.cc b/bench/bankers-rounding.cc
new file mode 100644
index 0000000..6fec22d
--- /dev/null
+++ b/bench/bankers-rounding.cc
@@ -0,0 +1,220 @@
+// 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_bankers_rounding_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), 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 bankers_rounding_op = nullptr;
+ status = xnn_create_bankers_rounding_nc_f32(
+ channels, channels /* input stride */, channels /* output stride */,
+ 0 /* flags */, &bankers_rounding_op);
+ if (status != xnn_status_success || bankers_rounding_op == nullptr) {
+ state.SkipWithError("failed to create Bankers' Rounding operator");
+ return;
+ }
+
+ status = xnn_setup_bankers_rounding_nc_f32(
+ bankers_rounding_op,
+ batch_size,
+ input.data(), output.data(),
+ nullptr /* thread pool */);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to setup Bankers' Rounding operator");
+ return;
+ }
+
+ for (auto _ : state) {
+ status = xnn_run_operator(bankers_rounding_op, nullptr /* thread pool */);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to run Bankers' Rounding operator");
+ return;
+ }
+ }
+
+ status = xnn_delete_operator(bankers_rounding_op);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to delete Bankers' Rounding 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_bankers_rounding_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), rng);
+
+ flatbuffers::FlatBufferBuilder builder;
+ const flatbuffers::Offset<tflite::OperatorCode> operator_code =
+ CreateOperatorCode(builder, tflite::BuiltinOperator_ROUND);
+
+ 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("Round 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_bankers_rounding_f32)->Apply(CharacteristicArguments)->UseRealTime();
+
+#ifdef BENCHMARK_TENSORFLOW_LITE
+ BENCHMARK(tflite_bankers_rounding_f32)->Apply(CharacteristicArguments)->UseRealTime();
+#endif // BENCHMARK_TENSORFLOW_LITE
+
+#ifndef XNNPACK_BENCHMARK_NO_MAIN
+BENCHMARK_MAIN();
+#endif