531 lines
19 KiB
C++
531 lines
19 KiB
C++
// Copyright 2023 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 <cassert>
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#include <cstddef>
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#include <cstdint>
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#include <limits>
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#include <memory>
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#include <random>
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#include <vector>
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#include "utils.h"
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#include "xnnpack.h"
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#include "xnnpack/datatype.h"
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#include "xnnpack/buffer.h"
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#include "xnnpack/math.h"
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#include <benchmark/benchmark.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/kernels/test_util.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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void init_params(xnn_unary_operator op, xnn_datatype in_type,
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xnn_datatype out_type, xnn_unary_params& params,
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xnn_quantization_params& input_quantization,
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xnn_quantization_params& output_quantization) {
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switch (in_type) {
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case xnn_datatype_qint8:
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input_quantization = {0, 1.0f / 128.0f};
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break;
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case xnn_datatype_quint8:
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input_quantization = {128, 1.0f / 128.0f};
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break;
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default:
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break;
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}
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switch (out_type) {
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case xnn_datatype_qint8:
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output_quantization = {128, 1.0f / 128.0f};
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break;
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case xnn_datatype_quint8:
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output_quantization = {0, 1.0f / 256.0f};
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break;
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default:
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break;
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}
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switch (op) {
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case xnn_unary_clamp:
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params.clamp.min = -10.0f;
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params.clamp.max = 10.0f;
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break;
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case xnn_unary_elu:
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params.elu.alpha = 0.5f;
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break;
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case xnn_unary_leaky_relu:
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params.leaky_relu.negative_slope = 0.5f;
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break;
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case xnn_unary_tanh:
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switch (out_type) {
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case xnn_datatype_qint8:
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output_quantization = {0, 1.0f / 128.0f};
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break;
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case xnn_datatype_quint8:
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output_quantization = {128, 1.0f / 128.0f};
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break;
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default:
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break;
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}
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break;
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case xnn_unary_sigmoid:
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switch (out_type) {
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case xnn_datatype_qint8:
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output_quantization = {-128, 1.0f / 256.0f};
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break;
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case xnn_datatype_quint8:
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output_quantization = {0, 1.0f / 256.0f};
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break;
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default:
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break;
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}
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break;
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case xnn_unary_log:
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case xnn_unary_square_root:
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case xnn_unary_reciprocal_square_root:
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switch (in_type) {
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case xnn_datatype_qint8:
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input_quantization = {-128, 1.0f};
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break;
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case xnn_datatype_quint8:
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input_quantization = {0, 1.0f};
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break;
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default:
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break;
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}
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break;
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default:
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break;
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}
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}
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template <typename In, typename Out>
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static void benchmark_unary_operator(benchmark::State& state,
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xnn_unary_operator op_type) {
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const size_t batch_size = state.range(0);
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state.SetItemsProcessed(batch_size);
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state.SetBytesProcessed(batch_size * (sizeof(In) + sizeof(Out)));
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xnn_unary_params params;
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xnn_quantization_params input_quantization = {0, 1.0f};
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xnn_quantization_params output_quantization = {0, 1.0f};
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init_params(op_type, xnn_datatype_of<In>(), xnn_datatype_of<Out>(),
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params, input_quantization, output_quantization);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32dist = std::uniform_real_distribution<float>(
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std::max<float>(std::numeric_limits<In>::lowest(), -128.0f),
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std::min<float>(std::numeric_limits<In>::max(), 127.0f));
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xnnpack::Buffer<In> input(batch_size + XNN_EXTRA_BYTES / sizeof(In));
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xnnpack::Buffer<Out> output(batch_size);
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
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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 op = nullptr;
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status = xnn_create_unary_elementwise_nc(
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op_type, xnn_datatype_of<In>(), xnn_datatype_of<Out>(), ¶ms,
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&input_quantization, &output_quantization, 0 /* flags */, &op);
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if (status != xnn_status_success || op == nullptr) {
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state.SkipWithError("failed to create operator");
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return;
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}
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status = xnn_reshape_unary_elementwise_nc(op, batch_size,
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/*channels=*/1, /*input_stride=*/1,
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/*output_stride=*/1,
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/*threadpool=*/nullptr);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to reshape operator");
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return;
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}
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status = xnn_setup_unary_elementwise_nc(op, input.data(), output.data());
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if (status != xnn_status_success) {
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state.SkipWithError("failed to setup 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(op, /*threadpool=*/nullptr);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to run operator");
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return;
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}
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}
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status = xnn_delete_operator(op);
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if (status != xnn_status_success) {
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state.SkipWithError("failed to delete 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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}
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template <typename In, typename Out>
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static void benchmark_convert(benchmark::State& state) {
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benchmark_unary_operator<In, Out>(state, xnn_unary_convert);
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}
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#ifdef BENCHMARK_TENSORFLOW_LITE
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tflite::BuiltinOperator xnn_unary_operator_to_tflite(xnn_unary_operator op) {
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switch (op) {
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case xnn_unary_convert:
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return tflite::BuiltinOperator_CAST;
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case xnn_unary_clamp:
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return tflite::BuiltinOperator_STABLEHLO_CLAMP;
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case xnn_unary_abs:
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return tflite::BuiltinOperator_ABS;
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case xnn_unary_bankers_rounding:
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return tflite::BuiltinOperator_ROUND;
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case xnn_unary_ceiling:
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return tflite::BuiltinOperator_CEIL;
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case xnn_unary_elu:
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return tflite::BuiltinOperator_ELU;
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case xnn_unary_exp:
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return tflite::BuiltinOperator_EXP;
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case xnn_unary_floor:
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return tflite::BuiltinOperator_FLOOR;
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case xnn_unary_gelu:
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return tflite::BuiltinOperator_GELU;
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case xnn_unary_hardswish:
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return tflite::BuiltinOperator_HARD_SWISH;
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case xnn_unary_leaky_relu:
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return tflite::BuiltinOperator_LEAKY_RELU;
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case xnn_unary_log:
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return tflite::BuiltinOperator_LOG;
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case xnn_unary_negate:
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return tflite::BuiltinOperator_NEG;
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case xnn_unary_sigmoid:
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return tflite::BuiltinOperator_LOGISTIC;
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case xnn_unary_square:
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return tflite::BuiltinOperator_SQUARE;
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case xnn_unary_square_root:
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return tflite::BuiltinOperator_SQRT;
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case xnn_unary_reciprocal_square_root:
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return tflite::BuiltinOperator_RSQRT;
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case xnn_unary_tanh:
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return tflite::BuiltinOperator_TANH;
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case xnn_unary_cube_root:
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return tflite::BuiltinOperator_STABLEHLO_CBRT;
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case xnn_unary_cosine:
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return tflite::BuiltinOperator_COS;
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case xnn_unary_sine:
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return tflite::BuiltinOperator_SIN;
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case xnn_unary_sign:
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return tflite::BuiltinOperator_SIGN;
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default:
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XNN_UNREACHABLE;
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return tflite::BuiltinOperator_CUSTOM;
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}
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}
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template <typename T>
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struct TypeToTfliteType {
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using type = T;
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static constexpr auto tensor_type =
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tflite::TensorTypeFor<typename xnnpack::unwrap_quantized<T>::type>::value;
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};
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template <>
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struct TypeToTfliteType<xnn_float16> {
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using type = TfLiteFloat16;
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static constexpr auto tensor_type = tflite::TensorType_FLOAT16;
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};
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template <>
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struct TypeToTfliteType<xnn_bfloat16> {
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using type = TfLiteBFloat16;
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static constexpr auto tensor_type = tflite::TensorType_BFLOAT16;
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};
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template <typename In, typename Out, class BuildInQuantization,
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class BuildOutQuantization>
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static void benchmark_tflite_unary_operator(
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benchmark::State& state, BuildInQuantization in_quantization,
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BuildOutQuantization out_quantization, tflite::BuiltinOperator op_code) {
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const size_t batch_size = state.range(0);
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flatbuffers::FlatBufferBuilder builder;
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const flatbuffers::Offset<tflite::OperatorCode> operator_code =
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CreateOperatorCode(builder, op_code);
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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{{static_cast<int32_t>(batch_size)}};
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const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{
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tflite::CreateTensor(
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builder, builder.CreateVector<int32_t>(shape.data(), shape.size()),
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TypeToTfliteType<In>::tensor_type,
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/*buffer=*/0, /*name=*/0, in_quantization(builder)),
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tflite::CreateTensor(
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builder, builder.CreateVector<int32_t>(shape.data(), shape.size()),
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TypeToTfliteType<Out>::tensor_type,
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/*buffer=*/0, /*name=*/0, out_quantization(builder)),
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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, 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, 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(
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builder, TFLITE_SCHEMA_VERSION, builder.CreateVector(&operator_code, 1),
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builder.CreateVector(&subgraph, 1), builder.CreateString("Abs 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::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32dist = std::uniform_real_distribution<float>(
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std::max<float>(std::numeric_limits<In>::lowest(), -128.0f),
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std::min<float>(std::numeric_limits<In>::max(), 127.0f));
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In* input_ptr = reinterpret_cast<In*>(interpreter->tensor(0)->data.raw);
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std::generate(input_ptr, input_ptr + batch_size,
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[&]() { return f32dist(rng); });
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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"] = benchmark::Counter(
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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,
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benchmark::Counter::kIsRate);
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interpreter.reset();
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}
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static flatbuffers::Offset<tflite::QuantizationParameters> no_quantization(
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flatbuffers::FlatBufferBuilder& builder) {
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return 0;
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}
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static auto CreateTfLiteQuantizationParameters(
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flatbuffers::FlatBufferBuilder& builder,
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const xnn_quantization_params& params) {
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return tflite::CreateQuantizationParameters(
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builder, 0 /*min*/, 0 /*max*/,
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builder.CreateVector<float>({params.scale}),
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builder.CreateVector<int64_t>({params.zero_point}));
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}
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template <typename In, typename Out>
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static void benchmark_tflite_unary_operator(benchmark::State& state,
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xnn_unary_operator op) {
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xnn_unary_params params;
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xnn_quantization_params input_quantization = {0, 1.0f};
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xnn_quantization_params output_quantization = {0, 1.0f};
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init_params(op, xnn_datatype_of<In>(), xnn_datatype_of<Out>(),
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params, input_quantization, output_quantization);
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auto in_quantization = [=](flatbuffers::FlatBufferBuilder& builder) {
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return CreateTfLiteQuantizationParameters(builder, input_quantization);
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};
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auto out_quantization = [=](flatbuffers::FlatBufferBuilder& builder) {
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return CreateTfLiteQuantizationParameters(builder, output_quantization);
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};
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constexpr bool is_quantized_in = xnnpack::is_quantized<In>::value;
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constexpr bool is_quantized_out = xnnpack::is_quantized<Out>::value;
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if (!is_quantized_in && !is_quantized_out) {
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tflite::BuiltinOperator op_code = xnn_unary_operator_to_tflite(op);
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return benchmark_tflite_unary_operator<In, Out>(state, no_quantization,
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no_quantization, op_code);
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} else if (is_quantized_in && !is_quantized_out) {
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assert(op == xnn_unary_convert);
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return benchmark_tflite_unary_operator<In, Out>(
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state, in_quantization, no_quantization,
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tflite::BuiltinOperator_DEQUANTIZE);
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} else if (!is_quantized_in && is_quantized_out) {
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assert(op == xnn_unary_convert);
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return benchmark_tflite_unary_operator<In, Out>(
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state, no_quantization, out_quantization,
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tflite::BuiltinOperator_QUANTIZE);
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} else if (is_quantized_in && is_quantized_out) {
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tflite::BuiltinOperator op_code;
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if (op == xnn_unary_convert) {
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op_code = tflite::BuiltinOperator_QUANTIZE;
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} else {
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op_code = xnn_unary_operator_to_tflite(op);
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}
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return benchmark_tflite_unary_operator<In, Out>(state, in_quantization,
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out_quantization, op_code);
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}
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}
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template <typename In, typename Out>
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static void benchmark_tflite_convert(benchmark::State& state) {
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benchmark_tflite_unary_operator<In, Out>(state, xnn_unary_convert);
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}
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#define BENCHMARK_OP_TYPE(op, type_name, type) \
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void xnnpack_##op##_##type_name(benchmark::State& state) { \
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benchmark_unary_operator<type, type>(state, xnn_unary_##op); \
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} \
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void tflite_##op##_##type_name(benchmark::State& state) { \
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benchmark_tflite_unary_operator<type, type>(state, xnn_unary_##op); \
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} \
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BENCHMARK(xnnpack_##op##_##type_name) \
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->Apply(benchmark::utils::UnaryElementwiseParameters<type, type>) \
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->UseRealTime(); \
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BENCHMARK(tflite_##op##_##type_name) \
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->Apply(benchmark::utils::UnaryElementwiseParameters<type, type>) \
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->UseRealTime();
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#define BENCHMARK_CONVERT(name, in, out) \
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BENCHMARK_TEMPLATE(benchmark_convert, in, out) \
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->Apply(benchmark::utils::UnaryElementwiseParameters<in, out>) \
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->Name("xnnpack_convert_" #name) \
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->UseRealTime(); \
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BENCHMARK_TEMPLATE(benchmark_tflite_convert, in, out) \
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->Apply(benchmark::utils::UnaryElementwiseParameters<in, out>) \
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->Name("tflite_convert_" #name) \
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->UseRealTime()
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#else // BENCHMARK_TENSORFLOW_LITE
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#define BENCHMARK_OP_TYPE(op, type_name, type) \
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void xnnpack_##op##_##type_name(benchmark::State& state) { \
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|
benchmark_unary_operator<type, type>(state, xnn_unary_##op); \
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|
} \
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|
BENCHMARK(xnnpack_##op##_##type_name) \
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|
->Apply(benchmark::utils::UnaryElementwiseParameters<type, type>) \
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|
->UseRealTime();
|
|
|
|
#define BENCHMARK_CONVERT(name, in, out) \
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|
BENCHMARK_TEMPLATE(benchmark_convert, in, out) \
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|
->Apply(benchmark::utils::UnaryElementwiseParameters<in, out>) \
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|
->Name("xnnpack_" #name) \
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|
->UseRealTime()
|
|
|
|
#endif // BENCHMARK_TENSORFLOW_LITE
|
|
|
|
#define BENCHMARK_OP(op) \
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|
BENCHMARK_OP_TYPE(op, f32, float) \
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|
BENCHMARK_OP_TYPE(op, f16, xnn_float16) \
|
|
BENCHMARK_OP_TYPE(op, bf16, xnn_bfloat16) \
|
|
BENCHMARK_OP_TYPE(op, qs8, xnnpack::quantized<int8_t>) \
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|
BENCHMARK_OP_TYPE(op, qu8, xnnpack::quantized<uint8_t>)
|
|
|
|
BENCHMARK_OP(clamp);
|
|
BENCHMARK_OP(abs);
|
|
BENCHMARK_OP(bankers_rounding);
|
|
BENCHMARK_OP(ceiling);
|
|
BENCHMARK_OP(elu);
|
|
BENCHMARK_OP(exp);
|
|
BENCHMARK_OP(floor);
|
|
BENCHMARK_OP(gelu);
|
|
BENCHMARK_OP(hardswish);
|
|
BENCHMARK_OP(leaky_relu);
|
|
BENCHMARK_OP(log);
|
|
BENCHMARK_OP(negate);
|
|
BENCHMARK_OP(sigmoid);
|
|
BENCHMARK_OP(square);
|
|
BENCHMARK_OP(square_root);
|
|
BENCHMARK_OP(reciprocal_square_root);
|
|
BENCHMARK_OP(tanh);
|
|
BENCHMARK_OP(cube_root);
|
|
BENCHMARK_OP(cosine);
|
|
BENCHMARK_OP(sine);
|
|
// Missing in TFlite?
|
|
//BENCHMARK_OP(count_leading_zeros);
|
|
//BENCHMARK_OP(bitwise_not);
|
|
//BENCHMARK_OP(popcount);
|
|
BENCHMARK_OP(sign);
|
|
|
|
BENCHMARK_CONVERT(qs8_qs8, xnnpack::quantized<int8_t>,
|
|
xnnpack::quantized<int8_t>);
|
|
BENCHMARK_CONVERT(qs8_qu8, xnnpack::quantized<int8_t>,
|
|
xnnpack::quantized<uint8_t>);
|
|
BENCHMARK_CONVERT(qs8_f16, xnnpack::quantized<int8_t>, xnn_float16);
|
|
BENCHMARK_CONVERT(qs8_bf16, xnnpack::quantized<int8_t>, xnn_bfloat16);
|
|
BENCHMARK_CONVERT(qs8_f32, xnnpack::quantized<int8_t>, float);
|
|
|
|
BENCHMARK_CONVERT(qu8_qs8, xnnpack::quantized<uint8_t>,
|
|
xnnpack::quantized<int8_t>);
|
|
BENCHMARK_CONVERT(qu8_qu8, xnnpack::quantized<uint8_t>,
|
|
xnnpack::quantized<uint8_t>);
|
|
BENCHMARK_CONVERT(qu8_f16, xnnpack::quantized<uint8_t>, xnn_float16);
|
|
BENCHMARK_CONVERT(qu8_bf16, xnnpack::quantized<uint8_t>, xnn_bfloat16);
|
|
BENCHMARK_CONVERT(qu8_f32, xnnpack::quantized<uint8_t>, float);
|
|
|
|
BENCHMARK_CONVERT(f16_qs8, xnn_float16, xnnpack::quantized<int8_t>);
|
|
BENCHMARK_CONVERT(f16_qu8, xnn_float16, xnnpack::quantized<uint8_t>);
|
|
// BENCHMARK_CONVERT(f16_f16, xnn_float16, xnn_float16);
|
|
BENCHMARK_CONVERT(f16_bf16, xnn_float16, xnn_bfloat16);
|
|
BENCHMARK_CONVERT(f16_f32, xnn_float16, float);
|
|
|
|
BENCHMARK_CONVERT(bf16_qs8, xnn_bfloat16, xnnpack::quantized<int8_t>);
|
|
BENCHMARK_CONVERT(bf16_qu8, xnn_bfloat16, xnnpack::quantized<uint8_t>);
|
|
BENCHMARK_CONVERT(bf16_f16, xnn_bfloat16, xnn_float16);
|
|
// BENCHMARK_CONVERT(bf16_bf16, xnn_bfloat16, xnn_bfloat16);
|
|
BENCHMARK_CONVERT(bf16_f32, xnn_bfloat16, float);
|
|
|
|
BENCHMARK_CONVERT(f32_qs8, float, xnnpack::quantized<int8_t>);
|
|
BENCHMARK_CONVERT(f32_qu8, float, xnnpack::quantized<uint8_t>);
|
|
BENCHMARK_CONVERT(f32_f16, float, xnn_float16);
|
|
BENCHMARK_CONVERT(f32_bf16, float, xnn_bfloat16);
|
|
// BENCHMARK_CONVERT(f32_f32, float, float);
|
|
|
|
#ifndef XNNPACK_BENCHMARK_NO_MAIN
|
|
BENCHMARK_MAIN();
|
|
#endif
|