398 lines
15 KiB
C++
398 lines
15 KiB
C++
// Copyright 2023 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 <cassert>
|
|
#include <cstddef>
|
|
#include <cstdint>
|
|
#include <limits>
|
|
#include <memory>
|
|
#include <random>
|
|
#include <vector>
|
|
|
|
#include "utils.h"
|
|
#include "xnnpack.h"
|
|
#include "xnnpack/datatype.h"
|
|
#include "xnnpack/buffer.h"
|
|
#include "xnnpack/math.h"
|
|
#include <benchmark/benchmark.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/kernels/test_util.h"
|
|
#include "tensorflow/lite/model.h"
|
|
#include "tensorflow/lite/schema/schema_generated.h"
|
|
#include "tensorflow/lite/version.h"
|
|
#endif // BENCHMARK_TENSORFLOW_LITE
|
|
|
|
void init_params(xnn_binary_operator op_type, xnn_datatype datatype,
|
|
xnn_binary_params& params,
|
|
xnn_quantization_params& input_quantization,
|
|
xnn_quantization_params& output_quantization) {
|
|
switch (op_type) {
|
|
case xnn_datatype_qint8:
|
|
input_quantization = {0, 1.0f / 128.0f};
|
|
output_quantization = {128, 1.0f / 128.0f};
|
|
break;
|
|
case xnn_datatype_quint8:
|
|
input_quantization = {128, 1.0f / 128.0f};
|
|
output_quantization = {0, 1.0f / 256.0f};
|
|
break;
|
|
default:
|
|
input_quantization = {0, 1.0f};
|
|
output_quantization = {0, 1.0f};
|
|
break;
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
static void benchmark_binary_operator(benchmark::State& state,
|
|
xnn_binary_operator op_type) {
|
|
const size_t batch_size = state.range(0);
|
|
|
|
state.SetItemsProcessed(batch_size);
|
|
state.SetBytesProcessed(batch_size * (sizeof(T) * 3));
|
|
|
|
xnn_binary_params params;
|
|
xnn_quantization_params input_quantization;
|
|
xnn_quantization_params output_quantization;
|
|
init_params(op_type, xnn_datatype_of<T>(), params, input_quantization,
|
|
output_quantization);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32dist = std::uniform_real_distribution<float>(
|
|
std::max<float>(std::numeric_limits<T>::lowest(), -128.0f),
|
|
std::min<float>(std::numeric_limits<T>::max(), 127.0f));
|
|
|
|
xnnpack::Buffer<T> input1(batch_size + XNN_EXTRA_BYTES / sizeof(T));
|
|
xnnpack::Buffer<T> input2(batch_size + XNN_EXTRA_BYTES / sizeof(T));
|
|
xnnpack::Buffer<T> output(batch_size);
|
|
std::generate(input1.begin(), input1.end(), [&]() { return f32dist(rng); });
|
|
std::generate(input2.begin(), input2.end(), [&]() { return f32dist(rng); });
|
|
|
|
xnn_status status = xnn_initialize(nullptr /* allocator */);
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to initialize XNNPACK");
|
|
return;
|
|
}
|
|
|
|
xnn_operator_t op = nullptr;
|
|
status = xnn_create_binary_elementwise_nd(
|
|
op_type, xnn_datatype_of<T>(), &input_quantization, &input_quantization,
|
|
&output_quantization, /*flags*/ 0, &op);
|
|
if (status != xnn_status_success || op == nullptr) {
|
|
state.SkipWithError("failed to create operator");
|
|
return;
|
|
}
|
|
|
|
const size_t input_shape[] = {batch_size};
|
|
status =
|
|
xnn_reshape_binary_elementwise_nd(op, /*num_input1_dims*/ 1, input_shape,
|
|
/*num_input2_dims*/ 1, input_shape,
|
|
/*threadpool=*/nullptr);
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to reshape operator");
|
|
return;
|
|
}
|
|
|
|
status = xnn_setup_binary_elementwise_nd(op, input1.data(), input2.data(),
|
|
output.data());
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to setup operator");
|
|
return;
|
|
}
|
|
|
|
for (auto _ : state) {
|
|
status = xnn_run_operator(op, /*threadpool=*/nullptr);
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to run operator");
|
|
return;
|
|
}
|
|
}
|
|
|
|
status = xnn_delete_operator(op);
|
|
if (status != xnn_status_success) {
|
|
state.SkipWithError("failed to delete operator");
|
|
return;
|
|
}
|
|
|
|
const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
|
|
if (cpu_frequency != 0) {
|
|
state.counters["cpufreq"] = cpu_frequency;
|
|
}
|
|
}
|
|
|
|
#ifdef BENCHMARK_TENSORFLOW_LITE
|
|
tflite::BuiltinOperator xnn_binary_operator_to_tflite(xnn_binary_operator op) {
|
|
switch (op) {
|
|
case xnn_binary_add:
|
|
return tflite::BuiltinOperator_STABLEHLO_ADD;
|
|
case xnn_binary_subtract:
|
|
return tflite::BuiltinOperator_STABLEHLO_SUBTRACT;
|
|
case xnn_binary_multiply:
|
|
return tflite::BuiltinOperator_STABLEHLO_MULTIPLY;
|
|
case xnn_binary_divide:
|
|
return tflite::BuiltinOperator_STABLEHLO_DIVIDE;
|
|
case xnn_binary_maximum:
|
|
return tflite::BuiltinOperator_STABLEHLO_MAXIMUM;
|
|
case xnn_binary_minimum:
|
|
return tflite::BuiltinOperator_STABLEHLO_MINIMUM;
|
|
case xnn_binary_copysign:
|
|
return tflite::BuiltinOperator_CUSTOM; // No corresponding TFlite op
|
|
case xnn_binary_squared_difference:
|
|
return tflite::BuiltinOperator_SQUARED_DIFFERENCE;
|
|
case xnn_binary_prelu:
|
|
return tflite::BuiltinOperator_PRELU;
|
|
case xnn_binary_modulus:
|
|
return tflite::BuiltinOperator_CUSTOM; // No corresponding TFlite op
|
|
case xnn_binary_atan2:
|
|
return tflite::BuiltinOperator_ATAN2;
|
|
case xnn_binary_pow:
|
|
return tflite::BuiltinOperator_STABLEHLO_POWER;
|
|
case xnn_binary_bitwise_and:
|
|
return tflite::BuiltinOperator_STABLEHLO_AND;
|
|
case xnn_binary_bitwise_or:
|
|
return tflite::BuiltinOperator_STABLEHLO_OR;
|
|
case xnn_binary_bitwise_xor:
|
|
return tflite::BuiltinOperator_BITWISE_XOR;
|
|
case xnn_binary_shift_left:
|
|
return tflite::BuiltinOperator_STABLEHLO_SHIFT_LEFT;
|
|
case xnn_binary_shift_right_logical:
|
|
// TODO: BuiltinOperator_RIGHT_SHIFT is logical only for unsigned types
|
|
return tflite::BuiltinOperator_RIGHT_SHIFT;
|
|
case xnn_binary_shift_right_arithmetic:
|
|
// TODO: BuiltinOperator_RIGHT_SHIFT is arithmetic only for unsigned types
|
|
return tflite::BuiltinOperator_RIGHT_SHIFT;
|
|
default:
|
|
XNN_UNREACHABLE;
|
|
return tflite::BuiltinOperator_CUSTOM;
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
struct TypeToTfliteType {
|
|
using type = T;
|
|
static constexpr auto tensor_type =
|
|
tflite::TensorTypeFor<typename xnnpack::unwrap_quantized<T>::type>::value;
|
|
};
|
|
template <>
|
|
struct TypeToTfliteType<xnn_float16> {
|
|
using type = TfLiteFloat16;
|
|
static constexpr auto tensor_type = tflite::TensorType_FLOAT16;
|
|
};
|
|
template <>
|
|
struct TypeToTfliteType<xnn_bfloat16> {
|
|
using type = TfLiteBFloat16;
|
|
static constexpr auto tensor_type = tflite::TensorType_BFLOAT16;
|
|
};
|
|
|
|
template <typename T, class BuildInQuantization, class BuildOutQuantization>
|
|
static void benchmark_tflite_binary_operator(
|
|
benchmark::State& state, BuildInQuantization in_quantization,
|
|
BuildOutQuantization out_quantization, tflite::BuiltinOperator op_code) {
|
|
const size_t batch_size = state.range(0);
|
|
|
|
flatbuffers::FlatBufferBuilder builder;
|
|
const flatbuffers::Offset<tflite::OperatorCode> operator_code =
|
|
CreateOperatorCode(builder, op_code);
|
|
|
|
const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{
|
|
tflite::CreateBuffer(builder, builder.CreateVector({})),
|
|
}};
|
|
|
|
const std::array<int32_t, 1> shape{{static_cast<int32_t>(batch_size)}};
|
|
|
|
const std::array<flatbuffers::Offset<tflite::Tensor>, 3> tensors{{
|
|
tflite::CreateTensor(
|
|
builder, builder.CreateVector<int32_t>(shape.data(), shape.size()),
|
|
TypeToTfliteType<T>::tensor_type,
|
|
/*buffer=*/0, /*name=*/0, in_quantization(builder)),
|
|
tflite::CreateTensor(
|
|
builder, builder.CreateVector<int32_t>(shape.data(), shape.size()),
|
|
TypeToTfliteType<T>::tensor_type,
|
|
/*buffer=*/0, /*name=*/0, in_quantization(builder)),
|
|
tflite::CreateTensor(
|
|
builder, builder.CreateVector<int32_t>(shape.data(), shape.size()),
|
|
TypeToTfliteType<T>::tensor_type,
|
|
/*buffer=*/0, /*name=*/0, out_quantization(builder)),
|
|
}};
|
|
|
|
const std::array<int32_t, 2> op_inputs{{0, 1}};
|
|
const std::array<int32_t, 1> op_outputs{{2}};
|
|
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, 2> graph_inputs{{0, 1}};
|
|
const std::array<int32_t, 1> graph_outputs{{2}};
|
|
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("Binary model"),
|
|
builder.CreateVector(buffers.data(), buffers.size()));
|
|
|
|
builder.Finish(model_buffer);
|
|
|
|
const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer());
|
|
tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver;
|
|
tflite::InterpreterBuilder interpreterBuilder(model, resolver);
|
|
std::unique_ptr<tflite::Interpreter> interpreter;
|
|
if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) {
|
|
state.SkipWithError("failed to create TFLite interpreter");
|
|
return;
|
|
}
|
|
interpreter->SetNumThreads(1);
|
|
|
|
if (interpreter->AllocateTensors() != kTfLiteOk) {
|
|
state.SkipWithError("failed to allocate tensors");
|
|
return;
|
|
}
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32dist = std::uniform_real_distribution<float>(
|
|
std::max<float>(std::numeric_limits<T>::lowest(), -128.0f),
|
|
std::min<float>(std::numeric_limits<T>::max(), 127.0f));
|
|
T* input_ptr = reinterpret_cast<T*>(interpreter->tensor(0)->data.raw);
|
|
std::generate(input_ptr, input_ptr + batch_size,
|
|
[&]() { return f32dist(rng); });
|
|
|
|
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;
|
|
}
|
|
|
|
state.counters["elements"] = benchmark::Counter(
|
|
uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate);
|
|
|
|
const size_t bytes_per_iteration = 2 * batch_size * sizeof(float);
|
|
state.counters["bytes"] =
|
|
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration,
|
|
benchmark::Counter::kIsRate);
|
|
|
|
interpreter.reset();
|
|
}
|
|
|
|
static flatbuffers::Offset<tflite::QuantizationParameters> no_quantization(
|
|
flatbuffers::FlatBufferBuilder& builder) {
|
|
return 0;
|
|
}
|
|
|
|
static auto CreateTfLiteQuantizationParameters(
|
|
flatbuffers::FlatBufferBuilder& builder,
|
|
const xnn_quantization_params& params) {
|
|
return tflite::CreateQuantizationParameters(
|
|
builder, 0 /*min*/, 0 /*max*/,
|
|
builder.CreateVector<float>({params.scale}),
|
|
builder.CreateVector<int64_t>({params.zero_point}));
|
|
}
|
|
|
|
template <typename T>
|
|
static void benchmark_tflite_binary_operator(benchmark::State& state,
|
|
xnn_binary_operator op) {
|
|
xnn_binary_params params;
|
|
xnn_quantization_params input_quantization;
|
|
xnn_quantization_params output_quantization;
|
|
init_params(op, xnn_datatype_of<T>(), params, input_quantization,
|
|
output_quantization);
|
|
auto in_quantization = [=](flatbuffers::FlatBufferBuilder& builder) {
|
|
return CreateTfLiteQuantizationParameters(builder, input_quantization);
|
|
};
|
|
auto out_quantization = [=](flatbuffers::FlatBufferBuilder& builder) {
|
|
return CreateTfLiteQuantizationParameters(builder, output_quantization);
|
|
};
|
|
|
|
tflite::BuiltinOperator op_code = xnn_binary_operator_to_tflite(op);
|
|
if (op_code == tflite::BuiltinOperator_CUSTOM) {
|
|
state.SkipWithMessage("no corresponding TFLite operator");
|
|
return;
|
|
}
|
|
if (!xnnpack::is_quantized<T>::value) {
|
|
return benchmark_tflite_binary_operator<T>(state, no_quantization,
|
|
no_quantization, op_code);
|
|
} else {
|
|
return benchmark_tflite_binary_operator<T>(state, in_quantization,
|
|
out_quantization, op_code);
|
|
}
|
|
}
|
|
|
|
#define BENCHMARK_OP_TYPE(op, type_name, type) \
|
|
void xnnpack_##op##_##type_name(benchmark::State& state) { \
|
|
benchmark_binary_operator<type>(state, xnn_binary_##op); \
|
|
} \
|
|
void tflite_##op##_##type_name(benchmark::State& state) { \
|
|
benchmark_tflite_binary_operator<type>(state, xnn_binary_##op); \
|
|
} \
|
|
BENCHMARK(xnnpack_##op##_##type_name) \
|
|
->Apply(benchmark::utils::BinaryElementwiseParameters<type, type>) \
|
|
->UseRealTime(); \
|
|
BENCHMARK(tflite_##op##_##type_name) \
|
|
->Apply(benchmark::utils::BinaryElementwiseParameters<type, type>) \
|
|
->UseRealTime();
|
|
|
|
#else // BENCHMARK_TENSORFLOW_LITE
|
|
|
|
#define BENCHMARK_OP_TYPE(op, type_name, type) \
|
|
void xnnpack_##op##_##type_name(benchmark::State& state) { \
|
|
benchmark_binary_operator<type>(state, xnn_binary_##op); \
|
|
} \
|
|
BENCHMARK(xnnpack_##op##_##type_name) \
|
|
->Apply(benchmark::utils::BinaryElementwiseParameters<type, type>) \
|
|
->UseRealTime();
|
|
|
|
#endif // BENCHMARK_TENSORFLOW_LITE
|
|
|
|
#define BENCHMARK_OP_REAL(op) \
|
|
BENCHMARK_OP_TYPE(op, f32, float) \
|
|
BENCHMARK_OP_TYPE(op, f16, xnn_float16) \
|
|
BENCHMARK_OP_TYPE(op, bf16, xnn_bfloat16) \
|
|
BENCHMARK_OP_TYPE(op, qs8, xnnpack::quantized<int8_t>) \
|
|
BENCHMARK_OP_TYPE(op, qu8, xnnpack::quantized<uint8_t>)
|
|
|
|
#define BENCHMARK_OP_INTEGRAL(op) BENCHMARK_OP_TYPE(op, s32, int32_t)
|
|
|
|
#define BENCHMARK_OP_ALL(op) \
|
|
BENCHMARK_OP_REAL(op) \
|
|
BENCHMARK_OP_INTEGRAL(op)
|
|
|
|
BENCHMARK_OP_ALL(add);
|
|
BENCHMARK_OP_ALL(subtract);
|
|
BENCHMARK_OP_ALL(multiply);
|
|
BENCHMARK_OP_ALL(divide);
|
|
BENCHMARK_OP_ALL(maximum);
|
|
BENCHMARK_OP_ALL(minimum);
|
|
// BENCHMARK_OP_ALL(copysign); // Missing in TFLite
|
|
BENCHMARK_OP_REAL(squared_difference);
|
|
BENCHMARK_OP_REAL(prelu);
|
|
// BENCHMARK_OP_ALL(modulus); // Missing in TFLite
|
|
BENCHMARK_OP_REAL(atan2);
|
|
BENCHMARK_OP_ALL(pow);
|
|
BENCHMARK_OP_INTEGRAL(bitwise_and);
|
|
BENCHMARK_OP_INTEGRAL(bitwise_or);
|
|
BENCHMARK_OP_INTEGRAL(bitwise_xor);
|
|
BENCHMARK_OP_INTEGRAL(shift_left);
|
|
BENCHMARK_OP_INTEGRAL(shift_right_logical);
|
|
BENCHMARK_OP_INTEGRAL(shift_right_arithmetic);
|
|
|
|
#ifndef XNNPACK_BENCHMARK_NO_MAIN
|
|
BENCHMARK_MAIN();
|
|
#endif
|