test / bench /convert.cc
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// Copyright 2021 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 <cfloat>
#include <cmath>
#include <functional>
#include <limits>
#include <memory>
#include <random>
#include <vector>
#include <xnnpack.h>
#include <benchmark/benchmark.h>
#include <fp16/fp16.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_convert_f16_f32(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng));
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
std::vector<uint16_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(uint16_t));
std::generate(input.begin(), input.end(), std::ref(f16rng));
std::vector<float> output(batch_size);
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 convert_op = nullptr;
status = xnn_create_convert_nc_f16_f32(
1 /* channels */, 1 /* input stride */, 1 /* output stride */,
0 /* flags */, &convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create F16->F32 Convert operator");
return;
}
status = xnn_reshape_convert_nc_f16_f32(convert_op, batch_size, /*threadpool=*/nullptr);
if (status != xnn_status_success) {
state.SkipWithError("failed to reshape F16->F32 Convert operator");
return;
}
status = xnn_setup_convert_nc_f16_f32(convert_op, input.data(), output.data());
if (status != xnn_status_success) {
state.SkipWithError("failed to setup F16->F32 Convert operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(convert_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run F16->F32 Convert operator");
return;
}
}
status = xnn_delete_operator(convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete F16->F32 Convert operator");
return;
}
convert_op = nullptr;
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 = batch_size * (sizeof(uint16_t) + sizeof(float));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
void xnnpack_convert_f32_f16(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng));
std::vector<float> input(batch_size + XNN_EXTRA_BYTES / sizeof(float));
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::vector<uint16_t> output(batch_size);
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
xnn_operator_t convert_op = nullptr;
status = xnn_create_convert_nc_f32_f16(
1 /* channels */, 1 /* input stride */, 1 /* output stride */,
0 /* flags */, &convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create F32->F16 Convert operator");
return;
}
status = xnn_reshape_convert_nc_f32_f16(convert_op, batch_size, /*threadpool=*/nullptr);
if (status != xnn_status_success) {
state.SkipWithError("failed to reshape F32->F16 Convert operator");
return;
}
status = xnn_setup_convert_nc_f32_f16(convert_op, input.data(), output.data());
if (status != xnn_status_success) {
state.SkipWithError("failed to setup F32->F16 Convert operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(convert_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run F32->F16 Convert operator");
return;
}
}
status = xnn_delete_operator(convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete F32->F16 Convert operator");
return;
}
convert_op = nullptr;
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 = batch_size * (sizeof(float) + sizeof(uint16_t));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
void xnnpack_convert_f32_qs8(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng));
std::vector<float> input(batch_size + XNN_EXTRA_BYTES / sizeof(float));
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::vector<int8_t> output(batch_size);
std::fill(output.begin(), output.end(), 0);
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
xnn_operator_t convert_op = nullptr;
status = xnn_create_convert_nc_f32_qs8(
1 /* channels */, 1 /* input stride */, 1 /* output stride */,
1.0f / 128.0f /* scale */, 1 /* zero point */,
std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max(),
0 /* flags */, &convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create F32->QS8 Convert operator");
return;
}
status = xnn_reshape_convert_nc_f32_qs8(convert_op, batch_size, /*threadpool=*/nullptr);
if (status != xnn_status_success) {
state.SkipWithError("failed to reshape F32->QS8 Convert operator");
return;
}
status = xnn_setup_convert_nc_f32_qs8(convert_op, input.data(), output.data());
if (status != xnn_status_success) {
state.SkipWithError("failed to setup F32->QS8 Convert operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(convert_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run F32->QS8 Convert operator");
return;
}
}
status = xnn_delete_operator(convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete F32->QS8 Convert operator");
return;
}
convert_op = nullptr;
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 = batch_size * (sizeof(float) + sizeof(int8_t));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
void xnnpack_convert_f32_qu8(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng));
std::vector<float> input(batch_size + XNN_EXTRA_BYTES / sizeof(float));
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::vector<uint8_t> output(batch_size);
std::fill(output.begin(), output.end(), 0);
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
xnn_operator_t convert_op = nullptr;
status = xnn_create_convert_nc_f32_qu8(
1 /* channels */, 1 /* input stride */, 1 /* output stride */,
1.0f / 128.0f /* scale */, 127 /* zero point */,
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max(),
0 /* flags */, &convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create F32->QU8 Convert operator");
return;
}
status = xnn_reshape_convert_nc_f32_qu8(convert_op, batch_size, /*threadpool=*/nullptr);
if (status != xnn_status_success) {
state.SkipWithError("failed to reshape F32->QU8 Convert operator");
return;
}
status = xnn_setup_convert_nc_f32_qu8(convert_op, input.data(), output.data());
if (status != xnn_status_success) {
state.SkipWithError("failed to setup F32->QU8 Convert operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(convert_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run F32->QU8 Convert operator");
return;
}
}
status = xnn_delete_operator(convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete F32->QU8 Convert operator");
return;
}
convert_op = nullptr;
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 = batch_size * (sizeof(float) + sizeof(uint8_t));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
void xnnpack_convert_qs8(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto i8rng = std::bind(
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
std::ref(rng));
std::vector<int8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(int8_t));
std::generate(input.begin(), input.end(), std::ref(i8rng));
std::vector<int8_t> output(batch_size);
std::fill(output.begin(), output.end(), INT8_C(0xAA));
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
xnn_operator_t convert_op = nullptr;
status = xnn_create_convert_nc_qs8(
1 /* channels */, 1 /* input stride */, 1 /* output stride */,
0.75f /* input scale */, -1 /* input zero point */,
0.5f /* output scale */, 1 /* output zero point */,
0 /* flags */, &convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create QS8 Convert operator");
return;
}
status = xnn_reshape_convert_nc_qs8(convert_op, batch_size, /*threadpool=*/nullptr);
if (status != xnn_status_success) {
state.SkipWithError("failed to reshape QS8 Convert operator");
return;
}
status = xnn_setup_convert_nc_qs8(convert_op, input.data(), output.data());
if (status != xnn_status_success) {
state.SkipWithError("failed to setup QS8 Convert operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(convert_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run QS8 Convert operator");
return;
}
}
status = xnn_delete_operator(convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete QS8 Convert operator");
return;
}
convert_op = nullptr;
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(int8_t);
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
void xnnpack_convert_qs8_f32(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto i8rng = std::bind(
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
std::ref(rng));
std::vector<int8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(int8_t));
std::generate(input.begin(), input.end(), std::ref(i8rng));
std::vector<float> output(batch_size);
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 convert_op = nullptr;
status = xnn_create_convert_nc_qs8_f32(
1 /* channels */, 1 /* input stride */, 1 /* output stride */,
1.0f / 255.0f /* scale */, -128 /* zero point */,
0 /* flags */, &convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create QS8->F32 Convert operator");
return;
}
status = xnn_reshape_convert_nc_qs8_f32(convert_op, batch_size, /*threadpool=*/nullptr);
if (status != xnn_status_success) {
state.SkipWithError("failed to reshape QS8->F32 Convert operator");
return;
}
status = xnn_setup_convert_nc_qs8_f32(convert_op, input.data(), output.data());
if (status != xnn_status_success) {
state.SkipWithError("failed to setup QS8->F32 Convert operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(convert_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run QS8->F32 Convert operator");
return;
}
}
status = xnn_delete_operator(convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete QS8->F32 Convert operator");
return;
}
convert_op = nullptr;
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 = batch_size * (sizeof(int8_t) + sizeof(float));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
void xnnpack_convert_qu8(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(
std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()),
std::ref(rng));
std::vector<uint8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::vector<uint8_t> output(batch_size);
std::fill(output.begin(), output.end(), UINT8_C(0xAA));
xnn_status status = xnn_initialize(nullptr /* allocator */);
if (status != xnn_status_success) {
state.SkipWithError("failed to initialize XNNPACK");
return;
}
xnn_operator_t convert_op = nullptr;
status = xnn_create_convert_nc_qu8(
1 /* channels */, 1 /* input stride */, 1 /* output stride */,
0.75f /* scale */, 125 /* zero point */,
0.5f /* scale */, 130 /* zero point */,
0 /* flags */, &convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create QU8 Convert operator");
return;
}
status = xnn_reshape_convert_nc_qu8(convert_op, batch_size, /*threadpool=*/nullptr);
if (status != xnn_status_success) {
state.SkipWithError("failed to reshape QU8 Convert operator");
return;
}
status = xnn_setup_convert_nc_qu8(convert_op, input.data(), output.data());
if (status != xnn_status_success) {
state.SkipWithError("failed to setup QU8 Convert operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(convert_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run QU8 Convert operator");
return;
}
}
status = xnn_delete_operator(convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete QU8 Convert operator");
return;
}
convert_op = nullptr;
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(uint8_t);
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
void xnnpack_convert_qu8_f32(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(
std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()),
std::ref(rng));
std::vector<uint8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::vector<float> output(batch_size);
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 convert_op = nullptr;
status = xnn_create_convert_nc_qu8_f32(
1 /* channels */, 1 /* input stride */, 1 /* output stride */,
1.0f / 128.0f /* scale */, 128 /* zero point */,
0 /* flags */, &convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to create QU8->F32 Convert operator");
return;
}
status = xnn_reshape_convert_nc_qu8_f32(convert_op, batch_size, /*threadpool=*/nullptr);
if (status != xnn_status_success) {
state.SkipWithError("failed to reshape QU8->F32 Convert operator");
return;
}
status = xnn_setup_convert_nc_qu8_f32(convert_op, input.data(), output.data());
if (status != xnn_status_success) {
state.SkipWithError("failed to setup QU8->F32 Convert operator");
return;
}
for (auto _ : state) {
status = xnn_run_operator(convert_op, nullptr /* thread pool */);
if (status != xnn_status_success) {
state.SkipWithError("failed to run QU8->F32 Convert operator");
return;
}
}
status = xnn_delete_operator(convert_op);
if (status != xnn_status_success) {
state.SkipWithError("failed to delete QU8->F32 Convert operator");
return;
}
convert_op = nullptr;
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 = batch_size * (sizeof(uint8_t) + sizeof(float));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
#ifdef BENCHMARK_TENSORFLOW_LITE
void tflite_convert_f16_f32(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng));
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
flatbuffers::FlatBufferBuilder builder;
flatbuffers::Offset<tflite::OperatorCode> operator_code =
CreateOperatorCode(builder, tflite::BuiltinOperator_DEQUANTIZE);
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>, 2> tensors{{
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_FLOAT16),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), 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}};
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));
flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Dequantize 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.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;
}
uint16_t* input_data = reinterpret_cast<uint16_t*>(interpreter->tensor(0)->data.data);
std::generate_n(input_data, batch_size, std::ref(f16rng));
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 = batch_size * (sizeof(uint16_t) + sizeof(float));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
interpreter.reset();
}
void tflite_convert_f32_qs8(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng));
flatbuffers::FlatBufferBuilder builder;
flatbuffers::Offset<tflite::OperatorCode> operator_code =
CreateOperatorCode(builder, tflite::BuiltinOperator_QUANTIZE);
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>, 2> tensors{{
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_FLOAT32),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */,
tflite::CreateQuantizationParameters(builder,
0 /*min*/, 0 /*max*/,
builder.CreateVector<float>({1.0f / 128.0f /* scale */}),
builder.CreateVector<int64_t>({1 /* zero point */})))
}};
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}};
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));
flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Quantize 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.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::generate_n(interpreter->typed_tensor<float>(0), batch_size, std::ref(f32rng));
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 = batch_size * (sizeof(float) + sizeof(int8_t));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
interpreter.reset();
}
void tflite_convert_f32_qu8(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng));
flatbuffers::FlatBufferBuilder builder;
flatbuffers::Offset<tflite::OperatorCode> operator_code =
CreateOperatorCode(builder, tflite::BuiltinOperator_QUANTIZE);
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>, 2> tensors{{
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_FLOAT32),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_UINT8, 0 /* buffer */, 0 /* name */,
tflite::CreateQuantizationParameters(builder,
0 /*min*/, 0 /*max*/,
builder.CreateVector<float>({1.0f / 128.0f /* scale */}),
builder.CreateVector<int64_t>({127 /* zero point */})))
}};
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}};
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));
flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Quantize 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.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::generate_n(interpreter->typed_tensor<float>(0), batch_size, std::ref(f32rng));
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 = batch_size * (sizeof(float) + sizeof(uint8_t));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
interpreter.reset();
}
void tflite_convert_qs8(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto i8rng = std::bind(
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
std::ref(rng));
flatbuffers::FlatBufferBuilder builder;
flatbuffers::Offset<tflite::OperatorCode> operator_code =
CreateOperatorCode(builder, tflite::BuiltinOperator_QUANTIZE);
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>, 2> tensors{{
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */,
tflite::CreateQuantizationParameters(builder,
0 /*min*/, 0 /*max*/,
builder.CreateVector<float>({0.75f /* scale */}),
builder.CreateVector<int64_t>({-1 /* zero point */}))),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */,
tflite::CreateQuantizationParameters(builder,
0 /*min*/, 0 /*max*/,
builder.CreateVector<float>({0.5f /* scale */}),
builder.CreateVector<int64_t>({1 /* zero point */}))),
}};
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}};
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));
flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Quantize 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.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::generate_n(interpreter->typed_tensor<int8_t>(0), batch_size, std::ref(i8rng));
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(int8_t);
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
interpreter.reset();
}
void tflite_convert_qs8_f32(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto i8rng = std::bind(
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
std::ref(rng));
flatbuffers::FlatBufferBuilder builder;
flatbuffers::Offset<tflite::OperatorCode> operator_code =
CreateOperatorCode(builder, tflite::BuiltinOperator_DEQUANTIZE);
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>, 2> tensors{{
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */,
tflite::CreateQuantizationParameters(builder,
0 /*min*/, 0 /*max*/,
builder.CreateVector<float>({1.0f / 255.0f /* scale */}),
builder.CreateVector<int64_t>({-128 /* zero point */}))),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), 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}};
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));
flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Dequantize 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.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::generate_n(interpreter->typed_tensor<int8_t>(0), batch_size, std::ref(i8rng));
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 = batch_size * (sizeof(int8_t) + sizeof(float));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
interpreter.reset();
}
void tflite_convert_qu8(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(
std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()),
std::ref(rng));
flatbuffers::FlatBufferBuilder builder;
flatbuffers::Offset<tflite::OperatorCode> operator_code =
CreateOperatorCode(builder, tflite::BuiltinOperator_QUANTIZE);
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>, 2> tensors{{
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_UINT8, 0 /* buffer */, 0 /* name */,
tflite::CreateQuantizationParameters(builder,
0 /*min*/, 0 /*max*/,
builder.CreateVector<float>({0.75f /* scale */}),
builder.CreateVector<int64_t>({125 /* zero point */}))),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_UINT8, 0 /* buffer */, 0 /* name */,
tflite::CreateQuantizationParameters(builder,
0 /*min*/, 0 /*max*/,
builder.CreateVector<float>({0.5f /* scale */}),
builder.CreateVector<int64_t>({130 /* zero point */})))
}};
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}};
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));
flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Quantize 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.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::generate_n(interpreter->typed_tensor<uint8_t>(0), batch_size, std::ref(u8rng));
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(uint8_t);
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
interpreter.reset();
}
void tflite_convert_qu8_f32(benchmark::State& state) {
const size_t batch_size = state.range(0);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(
std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()),
std::ref(rng));
flatbuffers::FlatBufferBuilder builder;
flatbuffers::Offset<tflite::OperatorCode> operator_code =
CreateOperatorCode(builder, tflite::BuiltinOperator_DEQUANTIZE);
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>, 2> tensors{{
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), shape.size()),
tflite::TensorType_UINT8, 0 /* buffer */, 0 /* name */,
tflite::CreateQuantizationParameters(builder,
0 /*min*/, 0 /*max*/,
builder.CreateVector<float>({1.0f / 128.0f /* scale */}),
builder.CreateVector<int64_t>({128 /* zero point */}))),
tflite::CreateTensor(builder,
builder.CreateVector<int32_t>(shape.data(), 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}};
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));
flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Dequantize 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.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::generate_n(interpreter->typed_tensor<uint8_t>(0), batch_size, std::ref(u8rng));
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 = batch_size * (sizeof(uint8_t) + sizeof(float));
state.counters["bytes"] =
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
interpreter.reset();
}
#endif // BENCHMARK_TENSORFLOW_LITE
BENCHMARK(xnnpack_convert_f16_f32)
->Apply(benchmark::utils::UnaryElementwiseParameters<uint16_t, float>)
->UseRealTime();
BENCHMARK(xnnpack_convert_f32_f16)
->Apply(benchmark::utils::UnaryElementwiseParameters<float, uint16_t>)
->UseRealTime();
BENCHMARK(xnnpack_convert_f32_qs8)
->Apply(benchmark::utils::UnaryElementwiseParameters<float, int8_t>)
->UseRealTime();
BENCHMARK(xnnpack_convert_f32_qu8)
->Apply(benchmark::utils::UnaryElementwiseParameters<float, uint8_t>)
->UseRealTime();
BENCHMARK(xnnpack_convert_qs8)
->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, int8_t>)
->UseRealTime();
BENCHMARK(xnnpack_convert_qs8_f32)
->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, float>)
->UseRealTime();
BENCHMARK(xnnpack_convert_qu8)
->Apply(benchmark::utils::UnaryElementwiseParameters<uint8_t, uint8_t>)
->UseRealTime();
BENCHMARK(xnnpack_convert_qu8_f32)
->Apply(benchmark::utils::UnaryElementwiseParameters<uint8_t, float>)
->UseRealTime();
#ifdef BENCHMARK_TENSORFLOW_LITE
BENCHMARK(tflite_convert_f16_f32)
->Apply(benchmark::utils::UnaryElementwiseParameters<uint16_t, float>)
->UseRealTime();
BENCHMARK(tflite_convert_f32_qs8)
->Apply(benchmark::utils::UnaryElementwiseParameters<float, int8_t>)
->UseRealTime();
BENCHMARK(tflite_convert_f32_qu8)
->Apply(benchmark::utils::UnaryElementwiseParameters<float, uint8_t>)
->UseRealTime();
BENCHMARK(tflite_convert_qs8)
->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, int8_t>)
->UseRealTime();
BENCHMARK(tflite_convert_qs8_f32)
->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, float>)
->UseRealTime();
BENCHMARK(tflite_convert_qu8)
->Apply(benchmark::utils::UnaryElementwiseParameters<uint8_t, uint8_t>)
->UseRealTime();
BENCHMARK(tflite_convert_qu8_f32)
->Apply(benchmark::utils::UnaryElementwiseParameters<uint8_t, float>)
->UseRealTime();
#endif // BENCHMARK_TENSORFLOW_LITE
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();
#endif