Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 1 | // Copyright 2020 Google LLC |
| 2 | // |
| 3 | // This source code is licensed under the BSD-style license found in the |
| 4 | // LICENSE file in the root directory of this source tree. |
| 5 | |
| 6 | #include <algorithm> |
| 7 | #include <array> |
| 8 | #include <cmath> |
| 9 | #include <functional> |
| 10 | #include <limits> |
| 11 | #include <random> |
| 12 | #include <vector> |
| 13 | |
| 14 | #include <xnnpack.h> |
| 15 | |
| 16 | #include <benchmark/benchmark.h> |
| 17 | #include "bench/utils.h" |
| 18 | #ifdef BENCHMARK_TENSORFLOW_LITE |
| 19 | #include "flatbuffers/include/flatbuffers/flatbuffers.h" |
| 20 | #include "tensorflow/lite/interpreter.h" |
| 21 | #include "tensorflow/lite/kernels/register.h" |
| 22 | #include "tensorflow/lite/model.h" |
| 23 | #include "tensorflow/lite/schema/schema_generated.h" |
| 24 | #include "tensorflow/lite/version.h" |
| 25 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 26 | |
| 27 | |
| 28 | static void xnnpack_elu_f32(benchmark::State& state) { |
| 29 | const size_t batch_size = state.range(0); |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 30 | |
| 31 | std::random_device random_device; |
| 32 | auto rng = std::mt19937(random_device()); |
| 33 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-20.0f, 20.0f), std::ref(rng)); |
| 34 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 35 | std::vector<float> input(batch_size + XNN_EXTRA_BYTES / sizeof(float)); |
| 36 | std::vector<float> output(batch_size); |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 37 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 38 | std::fill(output.begin(), output.end(), std::nanf("")); |
| 39 | |
| 40 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 41 | if (status != xnn_status_success) { |
| 42 | state.SkipWithError("failed to initialize XNNPACK"); |
| 43 | return; |
| 44 | } |
| 45 | |
| 46 | xnn_operator_t elu_op = nullptr; |
| 47 | status = xnn_create_elu_nc_f32( |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 48 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 49 | 1.0f /* alpha */, 0 /* flags */, &elu_op); |
| 50 | if (status != xnn_status_success || elu_op == nullptr) { |
| 51 | state.SkipWithError("failed to create ELU operator"); |
| 52 | return; |
| 53 | } |
| 54 | |
| 55 | status = xnn_setup_elu_nc_f32( |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 56 | elu_op, batch_size, |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 57 | input.data(), output.data(), |
| 58 | nullptr /* thread pool */); |
| 59 | if (status != xnn_status_success) { |
| 60 | state.SkipWithError("failed to setup ELU operator"); |
| 61 | return; |
| 62 | } |
| 63 | |
| 64 | for (auto _ : state) { |
| 65 | status = xnn_run_operator(elu_op, nullptr /* thread pool */); |
| 66 | if (status != xnn_status_success) { |
| 67 | state.SkipWithError("failed to run ELU operator"); |
| 68 | return; |
| 69 | } |
| 70 | } |
| 71 | |
| 72 | status = xnn_delete_operator(elu_op); |
| 73 | if (status != xnn_status_success) { |
| 74 | state.SkipWithError("failed to delete ELU operator"); |
| 75 | return; |
| 76 | } |
| 77 | |
| 78 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 79 | if (cpu_frequency != 0) { |
| 80 | state.counters["cpufreq"] = cpu_frequency; |
| 81 | } |
| 82 | |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 83 | state.counters["elements"] = |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 84 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 85 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 86 | const size_t bytes_per_iteration = 2 * batch_size * sizeof(float); |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 87 | state.counters["bytes"] = |
| 88 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 89 | } |
| 90 | |
Marat Dukhan | 9084fc8 | 2021-12-31 10:16:09 -0800 | [diff] [blame] | 91 | #ifndef XNN_NO_QS8_OPERATORS |
| 92 | static void xnnpack_elu_qs8(benchmark::State& state) { |
| 93 | const size_t batch_size = state.range(0); |
| 94 | |
| 95 | std::random_device random_device; |
| 96 | auto rng = std::mt19937(random_device()); |
| 97 | auto i8rng = std::bind( |
| 98 | std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), |
| 99 | std::ref(rng)); |
| 100 | |
| 101 | std::vector<int8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(int8_t)); |
| 102 | std::vector<int8_t> output(batch_size); |
| 103 | std::generate(input.begin(), input.end(), std::ref(i8rng)); |
| 104 | std::fill(output.begin(), output.end(), INT8_C(0xA5)); |
| 105 | |
| 106 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 107 | if (status != xnn_status_success) { |
| 108 | state.SkipWithError("failed to initialize XNNPACK"); |
| 109 | return; |
| 110 | } |
| 111 | |
| 112 | xnn_operator_t elu_op = nullptr; |
| 113 | status = xnn_create_elu_nc_qs8( |
| 114 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
| 115 | 1.0f /* alpha */, |
| 116 | 0 /* input zero point */, 1.0f /* input scale */, |
| 117 | 0 /* output zero point */, 1.0f /* output scale */, |
| 118 | std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max(), |
| 119 | 0 /* flags */, &elu_op); |
| 120 | if (status != xnn_status_success || elu_op == nullptr) { |
| 121 | state.SkipWithError("failed to create ELU operator"); |
| 122 | return; |
| 123 | } |
| 124 | |
| 125 | status = xnn_setup_elu_nc_qs8( |
| 126 | elu_op, batch_size, |
| 127 | input.data(), output.data(), |
| 128 | nullptr /* thread pool */); |
| 129 | if (status != xnn_status_success) { |
| 130 | state.SkipWithError("failed to setup ELU operator"); |
| 131 | return; |
| 132 | } |
| 133 | |
| 134 | for (auto _ : state) { |
| 135 | status = xnn_run_operator(elu_op, nullptr /* thread pool */); |
| 136 | if (status != xnn_status_success) { |
| 137 | state.SkipWithError("failed to run ELU operator"); |
| 138 | return; |
| 139 | } |
| 140 | } |
| 141 | |
| 142 | status = xnn_delete_operator(elu_op); |
| 143 | if (status != xnn_status_success) { |
| 144 | state.SkipWithError("failed to delete ELU operator"); |
| 145 | return; |
| 146 | } |
| 147 | |
| 148 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 149 | if (cpu_frequency != 0) { |
| 150 | state.counters["cpufreq"] = cpu_frequency; |
| 151 | } |
| 152 | |
| 153 | state.counters["elements"] = |
| 154 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 155 | |
| 156 | const size_t bytes_per_iteration = 2 * batch_size * sizeof(int8_t); |
| 157 | state.counters["bytes"] = |
| 158 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 159 | } |
| 160 | #endif // XNN_NO_QS8_OPERATORS |
| 161 | |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 162 | #ifdef BENCHMARK_TENSORFLOW_LITE |
| 163 | static void tflite_elu_f32(benchmark::State& state) { |
| 164 | const size_t batch_size = state.range(0); |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 165 | |
| 166 | std::random_device random_device; |
| 167 | auto rng = std::mt19937(random_device()); |
| 168 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-20.0f, 20.0f), std::ref(rng)); |
| 169 | |
| 170 | flatbuffers::FlatBufferBuilder builder; |
| 171 | const flatbuffers::Offset<tflite::OperatorCode> operator_code = |
| 172 | CreateOperatorCode(builder, tflite::BuiltinOperator_ELU); |
| 173 | |
| 174 | const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
| 175 | tflite::CreateBuffer(builder, builder.CreateVector({})), |
| 176 | }}; |
| 177 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 178 | const std::array<int32_t, 1> shape{{ |
| 179 | static_cast<int32_t>(batch_size) |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 180 | }}; |
| 181 | |
| 182 | const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
| 183 | tflite::CreateTensor(builder, |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 184 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 185 | tflite::TensorType_FLOAT32), |
| 186 | tflite::CreateTensor(builder, |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 187 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 188 | tflite::TensorType_FLOAT32), |
| 189 | }}; |
| 190 | |
| 191 | const std::array<int32_t, 1> op_inputs{{ 0 }}; |
| 192 | const std::array<int32_t, 1> op_outputs{{ 1 }}; |
| 193 | flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator( |
| 194 | builder, |
| 195 | 0 /* opcode_index */, |
| 196 | builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
| 197 | builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
| 198 | |
| 199 | const std::array<int32_t, 1> graph_inputs{{ 0 }}; |
| 200 | const std::array<int32_t, 1> graph_outputs{{ 1 }}; |
| 201 | const flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
| 202 | builder, |
| 203 | builder.CreateVector(tensors.data(), tensors.size()), |
| 204 | builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
| 205 | builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
| 206 | builder.CreateVector(&op, 1)); |
| 207 | |
| 208 | const flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
| 209 | TFLITE_SCHEMA_VERSION, |
| 210 | builder.CreateVector(&operator_code, 1), |
| 211 | builder.CreateVector(&subgraph, 1), |
| 212 | builder.CreateString("ELU model"), |
| 213 | builder.CreateVector(buffers.data(), buffers.size())); |
| 214 | |
| 215 | builder.Finish(model_buffer); |
| 216 | |
| 217 | const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
Chao Mei | f9fdaa7 | 2021-05-18 23:04:34 -0700 | [diff] [blame] | 218 | tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 219 | tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
| 220 | std::unique_ptr<tflite::Interpreter> interpreter; |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 221 | if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 222 | state.SkipWithError("failed to create TFLite interpreter"); |
| 223 | return; |
| 224 | } |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 225 | interpreter->SetNumThreads(1); |
| 226 | |
| 227 | if (interpreter->AllocateTensors() != kTfLiteOk) { |
| 228 | state.SkipWithError("failed to allocate tensors"); |
| 229 | return; |
| 230 | } |
| 231 | |
| 232 | std::generate( |
| 233 | interpreter->typed_tensor<float>(0), |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 234 | interpreter->typed_tensor<float>(0) + batch_size, |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 235 | std::ref(f32rng)); |
| 236 | |
| 237 | for (auto _ : state) { |
| 238 | if (interpreter->Invoke() != kTfLiteOk) { |
| 239 | state.SkipWithError("failed to invoke TFLite interpreter"); |
| 240 | return; |
| 241 | } |
| 242 | } |
| 243 | |
| 244 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 245 | if (cpu_frequency != 0) { |
| 246 | state.counters["cpufreq"] = cpu_frequency; |
| 247 | } |
| 248 | |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 249 | state.counters["elements"] = |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 250 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 251 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 252 | const size_t bytes_per_iteration = 2 * batch_size * sizeof(float); |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 253 | state.counters["bytes"] = |
| 254 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 255 | |
| 256 | interpreter.reset(); |
| 257 | } |
Marat Dukhan | 9084fc8 | 2021-12-31 10:16:09 -0800 | [diff] [blame] | 258 | |
| 259 | static void tflite_elu_qs8(benchmark::State& state) { |
| 260 | const size_t batch_size = state.range(0); |
| 261 | |
| 262 | std::random_device random_device; |
| 263 | auto rng = std::mt19937(random_device()); |
| 264 | auto i8rng = std::bind( |
| 265 | std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), |
| 266 | std::ref(rng)); |
| 267 | |
| 268 | flatbuffers::FlatBufferBuilder builder; |
| 269 | const flatbuffers::Offset<tflite::OperatorCode> operator_code = |
| 270 | CreateOperatorCode(builder, tflite::BuiltinOperator_ELU); |
| 271 | |
| 272 | const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
| 273 | tflite::CreateBuffer(builder, builder.CreateVector({})), |
| 274 | }}; |
| 275 | |
| 276 | const std::array<int32_t, 1> shape{{ |
| 277 | static_cast<int32_t>(batch_size) |
| 278 | }}; |
| 279 | |
| 280 | const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
| 281 | tflite::CreateTensor(builder, |
| 282 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 283 | tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */, |
| 284 | tflite::CreateQuantizationParameters(builder, |
| 285 | 0 /*min*/, 0 /*max*/, |
| 286 | builder.CreateVector<float>({1.0f /* scale */}), |
| 287 | builder.CreateVector<int64_t>({1 /* zero point */}))), |
| 288 | tflite::CreateTensor(builder, |
| 289 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 290 | tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */, |
| 291 | tflite::CreateQuantizationParameters(builder, |
| 292 | 0 /*min*/, 0 /*max*/, |
| 293 | builder.CreateVector<float>({1.0f /* scale */}), |
| 294 | builder.CreateVector<int64_t>({1 /* zero point */}))), |
| 295 | }}; |
| 296 | |
| 297 | const std::array<int32_t, 1> op_inputs{{ 0 }}; |
| 298 | const std::array<int32_t, 1> op_outputs{{ 1 }}; |
| 299 | flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator( |
| 300 | builder, |
| 301 | 0 /* opcode_index */, |
| 302 | builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
| 303 | builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
| 304 | |
| 305 | const std::array<int32_t, 1> graph_inputs{{ 0 }}; |
| 306 | const std::array<int32_t, 1> graph_outputs{{ 1 }}; |
| 307 | const flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
| 308 | builder, |
| 309 | builder.CreateVector(tensors.data(), tensors.size()), |
| 310 | builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
| 311 | builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
| 312 | builder.CreateVector(&op, 1)); |
| 313 | |
| 314 | const flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
| 315 | TFLITE_SCHEMA_VERSION, |
| 316 | builder.CreateVector(&operator_code, 1), |
| 317 | builder.CreateVector(&subgraph, 1), |
| 318 | builder.CreateString("ELU model"), |
| 319 | builder.CreateVector(buffers.data(), buffers.size())); |
| 320 | |
| 321 | builder.Finish(model_buffer); |
| 322 | |
| 323 | const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
| 324 | tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
| 325 | tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
| 326 | std::unique_ptr<tflite::Interpreter> interpreter; |
| 327 | if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
| 328 | state.SkipWithError("failed to create TFLite interpreter"); |
| 329 | return; |
| 330 | } |
| 331 | interpreter->SetNumThreads(1); |
| 332 | |
| 333 | if (interpreter->AllocateTensors() != kTfLiteOk) { |
| 334 | state.SkipWithError("failed to allocate tensors"); |
| 335 | return; |
| 336 | } |
| 337 | |
| 338 | std::generate( |
| 339 | interpreter->typed_tensor<int8_t>(0), |
| 340 | interpreter->typed_tensor<int8_t>(0) + batch_size, |
| 341 | std::ref(i8rng)); |
| 342 | |
| 343 | for (auto _ : state) { |
| 344 | if (interpreter->Invoke() != kTfLiteOk) { |
| 345 | state.SkipWithError("failed to invoke TFLite interpreter"); |
| 346 | return; |
| 347 | } |
| 348 | } |
| 349 | |
| 350 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 351 | if (cpu_frequency != 0) { |
| 352 | state.counters["cpufreq"] = cpu_frequency; |
| 353 | } |
| 354 | |
| 355 | state.counters["elements"] = |
| 356 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 357 | |
| 358 | const size_t bytes_per_iteration = 2 * batch_size * sizeof(int8_t); |
| 359 | state.counters["bytes"] = |
| 360 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 361 | |
| 362 | interpreter.reset(); |
| 363 | } |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 364 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 365 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 366 | BENCHMARK(xnnpack_elu_f32) |
| 367 | ->Apply(benchmark::utils::UnaryElementwiseParameters<float, float>) |
| 368 | ->UseRealTime(); |
Marat Dukhan | 9084fc8 | 2021-12-31 10:16:09 -0800 | [diff] [blame] | 369 | #ifndef XNN_NO_QS8_OPERATORS |
| 370 | BENCHMARK(xnnpack_elu_qs8) |
| 371 | ->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, int8_t>) |
| 372 | ->UseRealTime(); |
| 373 | #endif // XNN_NO_QS8_OPERATORS |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 374 | |
| 375 | #ifdef BENCHMARK_TENSORFLOW_LITE |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 376 | BENCHMARK(tflite_elu_f32) |
| 377 | ->Apply(benchmark::utils::UnaryElementwiseParameters<float, float>) |
| 378 | ->UseRealTime(); |
Marat Dukhan | 9084fc8 | 2021-12-31 10:16:09 -0800 | [diff] [blame] | 379 | BENCHMARK(tflite_elu_qs8) |
| 380 | ->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, int8_t>) |
| 381 | ->UseRealTime(); |
Marat Dukhan | b6bd4bc | 2020-12-01 17:01:40 -0800 | [diff] [blame] | 382 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 383 | |
| 384 | #ifndef XNNPACK_BENCHMARK_NO_MAIN |
| 385 | BENCHMARK_MAIN(); |
| 386 | #endif |