bench/prelu.cc - platform/external/XNNPACK - Gitiles

 // 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), rng);
   auto f32wrng = std::bind(std::uniform_real_distribution<float>(0.25f, 0.75f), 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(),
     -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity(),
     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), rng);
   auto f32wrng = std::bind(std::uniform_real_distribution<float>(0.25f, 0.75f), 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
	// 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), rng);
	auto f32wrng = std::bind(std::uniform_real_distribution<float>(0.25f, 0.75f), 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(),
	-std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity(),
	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), rng);
	auto f32wrng = std::bind(std::uniform_real_distribution<float>(0.25f, 0.75f), 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