572 lines
21 KiB
C++
572 lines
21 KiB
C++
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// 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_sigmoid_f32(benchmark::State& state) {
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const size_t batch_size = state.range(0);
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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 + XNN_EXTRA_BYTES / sizeof(float));
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std::vector<float> output(batch_size);
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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 sigmoid_op = nullptr;
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status = xnn_create_sigmoid_nc_f32(
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1 /* channels */, 1 /* input stride */, 1 /* output stride */,
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0 /* flags */, &sigmoid_op);
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if (status != xnn_status_success || sigmoid_op == nullptr) {
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state.SkipWithError("failed to create Sigmoid operator");
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return;
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}
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status = xnn_setup_sigmoid_nc_f32(
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sigmoid_op, 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 Sigmoid 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(sigmoid_op, nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to run Sigmoid operator");
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return;
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}
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}
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status = xnn_delete_operator(sigmoid_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to delete Sigmoid 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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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * batch_size * 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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#ifndef XNN_NO_QS8_OPERATORS
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static void xnnpack_sigmoid_qs8(benchmark::State& state) {
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const size_t batch_size = state.range(0);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i8rng = std::bind(
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
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std::ref(rng));
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std::vector<int8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(int8_t));
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std::vector<int8_t> output(batch_size);
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std::generate(input.begin(), input.end(), std::ref(i8rng));
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std::fill(output.begin(), output.end(), INT8_C(0xA5));
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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 sigmoid_op = nullptr;
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status = xnn_create_sigmoid_nc_qs8(
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1 /* channels */, 1 /* input stride */, 1 /* output stride */,
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1 /* input zero point */, 1.0f /* input scale */,
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-128 /* output zero point */, 1.0f / 256.0f /* output scale */,
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std::numeric_limits<int8_t>::min() /* output min */, std::numeric_limits<int8_t>::max() /* output max */,
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0 /* flags */, &sigmoid_op);
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if (status != xnn_status_success || sigmoid_op == nullptr) {
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state.SkipWithError("failed to create Sigmoid operator");
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return;
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}
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status = xnn_setup_sigmoid_nc_qs8(
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sigmoid_op, 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 Sigmoid 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(sigmoid_op, nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to run Sigmoid operator");
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return;
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}
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}
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status = xnn_delete_operator(sigmoid_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to delete Sigmoid 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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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * batch_size * sizeof(int8_t);
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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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#endif // XNN_NO_QS8_OPERATORS
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#ifndef XNN_NO_QU8_OPERATORS
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static void xnnpack_sigmoid_qu8(benchmark::State& state) {
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const size_t batch_size = state.range(0);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto u8rng = std::bind(
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std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
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std::vector<uint8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(uint8_t));
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std::vector<uint8_t> output(batch_size);
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std::generate(input.begin(), input.end(), std::ref(u8rng));
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std::fill(output.begin(), output.end(), UINT8_C(0xA5));
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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 sigmoid_op = nullptr;
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status = xnn_create_sigmoid_nc_qu8(
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1 /* channels */, 1 /* input stride */, 1 /* output stride */,
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128 /* input zero point */, 1.0f /* input scale */,
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0 /* output zero point */, 1.0f / 256.0f /* output scale */,
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std::numeric_limits<uint8_t>::min() /* output min */, std::numeric_limits<uint8_t>::max() /* output max */,
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0 /* flags */, &sigmoid_op);
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if (status != xnn_status_success || sigmoid_op == nullptr) {
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state.SkipWithError("failed to create Sigmoid operator");
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return;
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}
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status = xnn_setup_sigmoid_nc_qu8(
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sigmoid_op, 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 Sigmoid 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(sigmoid_op, nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to run Sigmoid operator");
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return;
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}
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}
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status = xnn_delete_operator(sigmoid_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to delete Sigmoid 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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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * batch_size * sizeof(uint8_t);
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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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#endif // XNN_NO_QU8_OPERATORS
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#ifdef BENCHMARK_TENSORFLOW_LITE
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static void tflite_sigmoid_f32(benchmark::State& state) {
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const size_t batch_size = state.range(0);
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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_LOGISTIC);
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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, 1> shape{{
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static_cast<int32_t>(batch_size)
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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>(shape.data(), shape.size()),
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tflite::TensorType_FLOAT32),
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tflite::CreateTensor(builder,
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builder.CreateVector<int32_t>(shape.data(), 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("Sigmoid 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::BuiltinOpResolverWithoutDefaultDelegates 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 || interpreter == nullptr) {
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state.SkipWithError("failed to create TFLite interpreter");
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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,
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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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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * batch_size * 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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static void tflite_sigmoid_qs8(benchmark::State& state) {
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const size_t batch_size = state.range(0);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i8rng = std::bind(
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
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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_LOGISTIC);
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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, 1> shape{{
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static_cast<int32_t>(batch_size)
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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>(shape.data(), shape.size()),
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tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */,
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tflite::CreateQuantizationParameters(builder,
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0 /*min*/, 0 /*max*/,
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builder.CreateVector<float>({1.0f /* scale */}),
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builder.CreateVector<int64_t>({1 /* zero point */}))),
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tflite::CreateTensor(builder,
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builder.CreateVector<int32_t>(shape.data(), shape.size()),
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tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */,
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tflite::CreateQuantizationParameters(builder,
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0 /*min*/, 0 /*max*/,
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builder.CreateVector<float>({1.0f / 256.0f /* scale */}),
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builder.CreateVector<int64_t>({-128 /* zero point */}))),
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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("Sigmoid 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::BuiltinOpResolverWithoutDefaultDelegates 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 || interpreter == nullptr) {
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state.SkipWithError("failed to create TFLite interpreter");
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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<int8_t>(0),
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interpreter->typed_tensor<int8_t>(0) + batch_size,
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std::ref(i8rng));
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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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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * batch_size * sizeof(int8_t);
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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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static void tflite_sigmoid_qu8(benchmark::State& state) {
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const size_t batch_size = state.range(0);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto u8rng = std::bind(
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std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()),
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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_LOGISTIC);
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|
|
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>, 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 /* scale */}),
|
|
builder.CreateVector<int64_t>({128 /* 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>({1.0f / 256.0f /* scale */}),
|
|
builder.CreateVector<int64_t>({0 /* 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 }};
|
|
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("Sigmoid 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::generate(
|
|
interpreter->typed_tensor<uint8_t>(0),
|
|
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();
|
|
}
|
|
#endif // BENCHMARK_TENSORFLOW_LITE
|
|
|
|
BENCHMARK(xnnpack_sigmoid_f32)
|
|
->Apply(benchmark::utils::UnaryElementwiseParameters<float, float>)
|
|
->UseRealTime();
|
|
#ifndef XNN_NO_QS8_OPERATORS
|
|
BENCHMARK(xnnpack_sigmoid_qs8)
|
|
->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, int8_t>)
|
|
->UseRealTime();
|
|
#endif // XNN_NO_QS8_OPERATORS
|
|
#ifndef XNN_NO_QU8_OPERATORS
|
|
BENCHMARK(xnnpack_sigmoid_qu8)
|
|
->Apply(benchmark::utils::UnaryElementwiseParameters<uint8_t, uint8_t>)
|
|
->UseRealTime();
|
|
#endif // XNN_NO_QU8_OPERATORS
|
|
|
|
#ifdef BENCHMARK_TENSORFLOW_LITE
|
|
BENCHMARK(tflite_sigmoid_f32)
|
|
->Apply(benchmark::utils::UnaryElementwiseParameters<float, float>)
|
|
->UseRealTime();
|
|
BENCHMARK(tflite_sigmoid_qs8)
|
|
->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, int8_t>)
|
|
->UseRealTime();
|
|
BENCHMARK(tflite_sigmoid_qu8)
|
|
->Apply(benchmark::utils::UnaryElementwiseParameters<uint8_t, uint8_t>)
|
|
->UseRealTime();
|
|
#endif // BENCHMARK_TENSORFLOW_LITE
|
|
|
|
#ifndef XNNPACK_BENCHMARK_NO_MAIN
|
|
BENCHMARK_MAIN();
|
|
#endif
|