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311 lines
10 KiB
311 lines
10 KiB
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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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 <cmath>
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#include <functional>
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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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#ifndef XNN_NO_QU8_OPERATORS
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static void xnnpack_softmax_qu8(benchmark::State& state) {
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const size_t batch_size = static_cast<size_t>(state.range(0));
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const size_t channels = static_cast<size_t>(state.range(1));
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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(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 * channels);
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std::vector<uint8_t> output(batch_size * channels);
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std::generate(input.begin(), input.end(), std::ref(u8rng));
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std::fill(output.begin(), output.end(), 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 softmax_op = nullptr;
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status = xnn_create_softmax_nc_qu8(
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channels, channels /* input stride */, channels /* output stride */,
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1.0f /* input scale */,
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0 /* output zero point */, 1.0f / 256.0f /* output scale */,
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0 /* flags */, &softmax_op);
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if (status != xnn_status_success || softmax_op == nullptr) {
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state.SkipWithError("failed to create SoftMax operator");
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return;
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}
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status = xnn_setup_softmax_nc_qu8(
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softmax_op,
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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 SoftMax 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(softmax_op, nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to run SoftMax operator");
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return;
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}
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}
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status = xnn_delete_operator(softmax_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to delete SoftMax 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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const size_t elements_per_iteration = batch_size * channels;
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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * elements_per_iteration * 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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static void xnnpack_softmax_f32(benchmark::State& state) {
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const size_t batch_size = static_cast<size_t>(state.range(0));
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const size_t channels = static_cast<size_t>(state.range(1));
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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>(-100.0f, 100.0f), std::ref(rng));
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std::vector<float> input(batch_size * channels + XNN_EXTRA_BYTES / sizeof(float));
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std::vector<float> output(batch_size * channels);
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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 softmax_op = nullptr;
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status = xnn_create_softmax_nc_f32(
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channels, channels /* input stride */, channels /* output stride */,
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0 /* flags */, &softmax_op);
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if (status != xnn_status_success || softmax_op == nullptr) {
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state.SkipWithError("failed to create SoftMax operator");
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return;
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}
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status = xnn_setup_softmax_nc_f32(
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softmax_op,
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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 SoftMax 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(softmax_op, nullptr /* thread pool */);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to run SoftMax operator");
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return;
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}
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}
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status = xnn_delete_operator(softmax_op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to delete SoftMax 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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const size_t elements_per_iteration = batch_size * channels;
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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * elements_per_iteration * 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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#endif // XNN_NO_QU8_OPERATORS
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#ifdef BENCHMARK_TENSORFLOW_LITE
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static void tflite_softmax_f32(benchmark::State& state) {
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const size_t batch_size = state.range(0);
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const size_t channels = state.range(1);
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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>(-100.0f, 100.0f), std::ref(rng));
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flatbuffers::FlatBufferBuilder builder;
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flatbuffers::Offset<tflite::OperatorCode> operator_code =
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tflite::CreateOperatorCode(builder, tflite::BuiltinOperator_SOFTMAX);
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flatbuffers::Offset<tflite::SoftmaxOptions> softmax_options =
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tflite::CreateSoftmaxOptions(builder, 1.0f /* beta */);
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flatbuffers::Offset<tflite::Buffer> buffers[1] = {
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tflite::CreateBuffer(builder, builder.CreateVector({})),
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};
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const int32_t input_shape[4] = {
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static_cast<int32_t>(batch_size),
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static_cast<int32_t>(1 /* height */),
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static_cast<int32_t>(1 /* width */),
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static_cast<int32_t>(channels)
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};
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const int32_t output_shape[4] = {
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static_cast<int32_t>(batch_size),
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static_cast<int32_t>(1 /* height */),
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static_cast<int32_t>(1 /* width */),
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static_cast<int32_t>(channels)
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};
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flatbuffers::Offset<tflite::Tensor> tensors[2] = {
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tflite::CreateTensor(builder,
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builder.CreateVector<int32_t>(input_shape, 4),
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tflite::TensorType_FLOAT32),
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tflite::CreateTensor(builder,
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builder.CreateVector<int32_t>(output_shape, 4),
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tflite::TensorType_FLOAT32),
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};
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const int32_t op_inputs[1] = { 0 };
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const int32_t op_outputs[1] = { 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, 1),
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builder.CreateVector<int32_t>(op_outputs, 1),
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tflite::BuiltinOptions_SoftmaxOptions, softmax_options.Union());
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const int32_t graph_inputs[1] = { 0 };
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const int32_t graph_outputs[1] = { 1 };
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flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph(
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builder,
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builder.CreateVector(tensors, 2),
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builder.CreateVector<int32_t>(graph_inputs, 1),
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builder.CreateVector<int32_t>(graph_outputs, 1),
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builder.CreateVector(&op, 1));
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flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Softmax model");
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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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description,
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builder.CreateVector(buffers, 1));
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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::BuiltinOpResolver 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) {
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state.SkipWithError("failed to create TFLite interpreter");
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return;
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}
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if (interpreter == nullptr) {
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state.SkipWithError("TFLite interpreter is null");
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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 * channels,
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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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const size_t elements_per_iteration = batch_size * channels;
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state.counters["elements"] =
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benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
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const size_t bytes_per_iteration = 2 * elements_per_iteration * 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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#endif // BENCHMARK_TENSORFLOW_LITE
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static void CharacteristicArguments(benchmark::internal::Benchmark* b)
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{
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b->ArgNames({"N", "C"});
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// CIFAR-10
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b->Args({1, 10});
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// CIFAR-100 */
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b->Args({1, 100});
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// ImageNet-1K
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b->Args({1, 1000});
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// ImageNet-1K+1
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b->Args({1, 1001});
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// ImageNet-22K
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b->Args({1, 21841});
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// ADE20K
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b->Args({257 * 257, 151});
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}
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#ifndef XNN_NO_QU8_OPERATORS
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BENCHMARK(xnnpack_softmax_qu8)->Apply(CharacteristicArguments)->UseRealTime();
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#endif // XNN_NO_QU8_OPERATORS
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BENCHMARK(xnnpack_softmax_f32)->Apply(CharacteristicArguments)->UseRealTime();
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#ifdef BENCHMARK_TENSORFLOW_LITE
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BENCHMARK(tflite_softmax_f32)->Apply(CharacteristicArguments)->UseRealTime();
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#endif // BENCHMARK_TENSORFLOW_LITE
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#ifndef XNNPACK_BENCHMARK_NO_MAIN
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BENCHMARK_MAIN();
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#endif
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