Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 1 | // Copyright 2021 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 <cfloat> |
| 9 | #include <cmath> |
| 10 | #include <functional> |
| 11 | #include <random> |
| 12 | #include <vector> |
| 13 | |
| 14 | #include <xnnpack.h> |
| 15 | |
| 16 | #include <benchmark/benchmark.h> |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 17 | #include <fp16/fp16.h> |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 18 | #include "bench/utils.h" |
| 19 | #ifdef BENCHMARK_TENSORFLOW_LITE |
| 20 | #include "flatbuffers/include/flatbuffers/flatbuffers.h" |
| 21 | #include "tensorflow/lite/interpreter.h" |
| 22 | #include "tensorflow/lite/kernels/register.h" |
| 23 | #include "tensorflow/lite/model.h" |
| 24 | #include "tensorflow/lite/schema/schema_generated.h" |
| 25 | #include "tensorflow/lite/version.h" |
| 26 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 27 | |
| 28 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 29 | void xnnpack_convert_f16_f32(benchmark::State& state) { |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 30 | const size_t batch_size = state.range(0); |
| 31 | |
| 32 | std::random_device random_device; |
| 33 | auto rng = std::mt19937(random_device()); |
| 34 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng)); |
| 35 | auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
| 36 | |
| 37 | std::vector<uint16_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
| 38 | std::generate(input.begin(), input.end(), std::ref(f16rng)); |
| 39 | std::vector<float> output(batch_size); |
| 40 | std::fill(output.begin(), output.end(), std::nanf("")); |
| 41 | |
| 42 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 43 | if (status != xnn_status_success) { |
| 44 | state.SkipWithError("failed to initialize XNNPACK"); |
| 45 | return; |
| 46 | } |
| 47 | |
| 48 | xnn_operator_t convert_op = nullptr; |
| 49 | status = xnn_create_convert_nc_f16_f32( |
| 50 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
| 51 | 0 /* flags */, &convert_op); |
| 52 | if (status != xnn_status_success) { |
| 53 | state.SkipWithError("failed to create F16->F32 Convert operator"); |
| 54 | return; |
| 55 | } |
| 56 | |
| 57 | status = xnn_setup_convert_nc_f16_f32( |
| 58 | convert_op, batch_size, |
| 59 | input.data(), output.data(), |
| 60 | nullptr /* thread pool */); |
| 61 | if (status != xnn_status_success) { |
| 62 | state.SkipWithError("failed to setup F16->F32 Convert operator"); |
| 63 | return; |
| 64 | } |
| 65 | |
| 66 | for (auto _ : state) { |
| 67 | status = xnn_run_operator(convert_op, nullptr /* thread pool */); |
| 68 | if (status != xnn_status_success) { |
| 69 | state.SkipWithError("failed to run F16->F32 Convert operator"); |
| 70 | return; |
| 71 | } |
| 72 | } |
| 73 | |
| 74 | status = xnn_delete_operator(convert_op); |
| 75 | if (status != xnn_status_success) { |
| 76 | state.SkipWithError("failed to delete F16->F32 Convert operator"); |
| 77 | return; |
| 78 | } |
| 79 | convert_op = nullptr; |
| 80 | |
| 81 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 82 | if (cpu_frequency != 0) { |
| 83 | state.counters["cpufreq"] = cpu_frequency; |
| 84 | } |
| 85 | |
| 86 | state.counters["elements"] = |
| 87 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 88 | |
| 89 | const size_t bytes_per_iteration = batch_size * (sizeof(uint16_t) + sizeof(float)); |
| 90 | state.counters["bytes"] = |
| 91 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 92 | } |
| 93 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 94 | void xnnpack_convert_f32_f16(benchmark::State& state) { |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 95 | const size_t batch_size = state.range(0); |
| 96 | |
| 97 | std::random_device random_device; |
| 98 | auto rng = std::mt19937(random_device()); |
| 99 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng)); |
| 100 | |
| 101 | std::vector<float> input(batch_size + XNN_EXTRA_BYTES / sizeof(float)); |
| 102 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 103 | std::vector<uint16_t> output(batch_size); |
| 104 | std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */); |
| 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 convert_op = nullptr; |
| 113 | status = xnn_create_convert_nc_f32_f16( |
| 114 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
| 115 | 0 /* flags */, &convert_op); |
| 116 | if (status != xnn_status_success) { |
| 117 | state.SkipWithError("failed to create F32->F16 Convert operator"); |
| 118 | return; |
| 119 | } |
| 120 | |
| 121 | status = xnn_setup_convert_nc_f32_f16( |
| 122 | convert_op, batch_size, |
| 123 | input.data(), output.data(), |
| 124 | nullptr /* thread pool */); |
| 125 | if (status != xnn_status_success) { |
| 126 | state.SkipWithError("failed to setup F32->F16 Convert operator"); |
| 127 | return; |
| 128 | } |
| 129 | |
| 130 | for (auto _ : state) { |
| 131 | status = xnn_run_operator(convert_op, nullptr /* thread pool */); |
| 132 | if (status != xnn_status_success) { |
| 133 | state.SkipWithError("failed to run F32->F16 Convert operator"); |
| 134 | return; |
| 135 | } |
| 136 | } |
| 137 | |
| 138 | status = xnn_delete_operator(convert_op); |
| 139 | if (status != xnn_status_success) { |
| 140 | state.SkipWithError("failed to delete F32->F16 Convert operator"); |
| 141 | return; |
| 142 | } |
| 143 | convert_op = nullptr; |
| 144 | |
| 145 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 146 | if (cpu_frequency != 0) { |
| 147 | state.counters["cpufreq"] = cpu_frequency; |
| 148 | } |
| 149 | |
| 150 | state.counters["elements"] = |
| 151 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 152 | |
| 153 | const size_t bytes_per_iteration = batch_size * (sizeof(float) + sizeof(uint16_t)); |
| 154 | state.counters["bytes"] = |
| 155 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 156 | } |
| 157 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 158 | void xnnpack_convert_f32_qs8(benchmark::State& state) { |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 159 | const size_t batch_size = state.range(0); |
| 160 | |
| 161 | std::random_device random_device; |
| 162 | auto rng = std::mt19937(random_device()); |
| 163 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng)); |
| 164 | |
| 165 | std::vector<float> input(batch_size + XNN_EXTRA_BYTES / sizeof(float)); |
| 166 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 167 | std::vector<int8_t> output(batch_size); |
| 168 | std::fill(output.begin(), output.end(), 0); |
| 169 | |
| 170 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 171 | if (status != xnn_status_success) { |
| 172 | state.SkipWithError("failed to initialize XNNPACK"); |
| 173 | return; |
| 174 | } |
| 175 | |
| 176 | xnn_operator_t convert_op = nullptr; |
| 177 | status = xnn_create_convert_nc_f32_qs8( |
| 178 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
| 179 | 1.0f / 128.0f /* scale */, 1 /* zero point */, |
| 180 | std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max(), |
| 181 | 0 /* flags */, &convert_op); |
| 182 | if (status != xnn_status_success) { |
| 183 | state.SkipWithError("failed to create F32->QS8 Convert operator"); |
| 184 | return; |
| 185 | } |
| 186 | |
| 187 | status = xnn_setup_convert_nc_f32_qs8( |
| 188 | convert_op, batch_size, |
| 189 | input.data(), output.data(), |
| 190 | nullptr /* thread pool */); |
| 191 | if (status != xnn_status_success) { |
| 192 | state.SkipWithError("failed to setup F32->QS8 Convert operator"); |
| 193 | return; |
| 194 | } |
| 195 | |
| 196 | for (auto _ : state) { |
| 197 | status = xnn_run_operator(convert_op, nullptr /* thread pool */); |
| 198 | if (status != xnn_status_success) { |
| 199 | state.SkipWithError("failed to run F32->QS8 Convert operator"); |
| 200 | return; |
| 201 | } |
| 202 | } |
| 203 | |
| 204 | status = xnn_delete_operator(convert_op); |
| 205 | if (status != xnn_status_success) { |
| 206 | state.SkipWithError("failed to delete F32->QS8 Convert operator"); |
| 207 | return; |
| 208 | } |
| 209 | convert_op = nullptr; |
| 210 | |
| 211 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 212 | if (cpu_frequency != 0) { |
| 213 | state.counters["cpufreq"] = cpu_frequency; |
| 214 | } |
| 215 | |
| 216 | state.counters["elements"] = |
| 217 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 218 | |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 219 | const size_t bytes_per_iteration = batch_size * (sizeof(float) + sizeof(int8_t)); |
| 220 | state.counters["bytes"] = |
| 221 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 222 | } |
| 223 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 224 | void xnnpack_convert_f32_qu8(benchmark::State& state) { |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 225 | const size_t batch_size = state.range(0); |
| 226 | |
| 227 | std::random_device random_device; |
| 228 | auto rng = std::mt19937(random_device()); |
| 229 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng)); |
| 230 | |
| 231 | std::vector<float> input(batch_size + XNN_EXTRA_BYTES / sizeof(float)); |
| 232 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 233 | std::vector<uint8_t> output(batch_size); |
| 234 | std::fill(output.begin(), output.end(), 0); |
| 235 | |
| 236 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 237 | if (status != xnn_status_success) { |
| 238 | state.SkipWithError("failed to initialize XNNPACK"); |
| 239 | return; |
| 240 | } |
| 241 | |
| 242 | xnn_operator_t convert_op = nullptr; |
| 243 | status = xnn_create_convert_nc_f32_qu8( |
| 244 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
| 245 | 1.0f / 128.0f /* scale */, 127 /* zero point */, |
| 246 | std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max(), |
| 247 | 0 /* flags */, &convert_op); |
| 248 | if (status != xnn_status_success) { |
| 249 | state.SkipWithError("failed to create F32->QU8 Convert operator"); |
| 250 | return; |
| 251 | } |
| 252 | |
| 253 | status = xnn_setup_convert_nc_f32_qu8( |
| 254 | convert_op, batch_size, |
| 255 | input.data(), output.data(), |
| 256 | nullptr /* thread pool */); |
| 257 | if (status != xnn_status_success) { |
| 258 | state.SkipWithError("failed to setup F32->QU8 Convert operator"); |
| 259 | return; |
| 260 | } |
| 261 | |
| 262 | for (auto _ : state) { |
| 263 | status = xnn_run_operator(convert_op, nullptr /* thread pool */); |
| 264 | if (status != xnn_status_success) { |
| 265 | state.SkipWithError("failed to run F32->QU8 Convert operator"); |
| 266 | return; |
| 267 | } |
| 268 | } |
| 269 | |
| 270 | status = xnn_delete_operator(convert_op); |
| 271 | if (status != xnn_status_success) { |
| 272 | state.SkipWithError("failed to delete F32->QU8 Convert operator"); |
| 273 | return; |
| 274 | } |
| 275 | convert_op = nullptr; |
| 276 | |
| 277 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 278 | if (cpu_frequency != 0) { |
| 279 | state.counters["cpufreq"] = cpu_frequency; |
| 280 | } |
| 281 | |
| 282 | state.counters["elements"] = |
| 283 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 284 | |
| 285 | const size_t bytes_per_iteration = batch_size * (sizeof(float) + sizeof(uint8_t)); |
| 286 | state.counters["bytes"] = |
| 287 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 288 | } |
| 289 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 290 | void xnnpack_convert_qs8_f32(benchmark::State& state) { |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 291 | const size_t batch_size = state.range(0); |
| 292 | |
| 293 | std::random_device random_device; |
| 294 | auto rng = std::mt19937(random_device()); |
| 295 | auto i8rng = std::bind( |
| 296 | std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), |
| 297 | std::ref(rng)); |
| 298 | |
| 299 | std::vector<int8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(int8_t)); |
| 300 | std::generate(input.begin(), input.end(), std::ref(i8rng)); |
| 301 | std::vector<float> output(batch_size); |
| 302 | std::fill(output.begin(), output.end(), std::nanf("")); |
| 303 | |
| 304 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 305 | if (status != xnn_status_success) { |
| 306 | state.SkipWithError("failed to initialize XNNPACK"); |
| 307 | return; |
| 308 | } |
| 309 | |
| 310 | xnn_operator_t convert_op = nullptr; |
| 311 | status = xnn_create_convert_nc_qs8_f32( |
| 312 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
| 313 | 1.0f / 255.0f /* scale */, -128 /* zero point */, |
| 314 | 0 /* flags */, &convert_op); |
| 315 | if (status != xnn_status_success) { |
| 316 | state.SkipWithError("failed to create QS8->F32 Convert operator"); |
| 317 | return; |
| 318 | } |
| 319 | |
| 320 | status = xnn_setup_convert_nc_qs8_f32( |
| 321 | convert_op, batch_size, |
| 322 | input.data(), output.data(), |
| 323 | nullptr /* thread pool */); |
| 324 | if (status != xnn_status_success) { |
| 325 | state.SkipWithError("failed to setup QS8->F32 Convert operator"); |
| 326 | return; |
| 327 | } |
| 328 | |
| 329 | for (auto _ : state) { |
| 330 | status = xnn_run_operator(convert_op, nullptr /* thread pool */); |
| 331 | if (status != xnn_status_success) { |
| 332 | state.SkipWithError("failed to run QS8->F32 Convert operator"); |
| 333 | return; |
| 334 | } |
| 335 | } |
| 336 | |
| 337 | status = xnn_delete_operator(convert_op); |
| 338 | if (status != xnn_status_success) { |
| 339 | state.SkipWithError("failed to delete QS8->F32 Convert operator"); |
| 340 | return; |
| 341 | } |
| 342 | convert_op = nullptr; |
| 343 | |
| 344 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 345 | if (cpu_frequency != 0) { |
| 346 | state.counters["cpufreq"] = cpu_frequency; |
| 347 | } |
| 348 | |
| 349 | state.counters["elements"] = |
| 350 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 351 | |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 352 | const size_t bytes_per_iteration = batch_size * (sizeof(int8_t) + sizeof(float)); |
| 353 | state.counters["bytes"] = |
| 354 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 355 | } |
| 356 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 357 | void xnnpack_convert_qu8_f32(benchmark::State& state) { |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 358 | const size_t batch_size = state.range(0); |
| 359 | |
| 360 | std::random_device random_device; |
| 361 | auto rng = std::mt19937(random_device()); |
| 362 | auto u8rng = std::bind( |
| 363 | std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()), |
| 364 | std::ref(rng)); |
| 365 | |
| 366 | std::vector<uint8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
| 367 | std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| 368 | std::vector<float> output(batch_size); |
| 369 | std::fill(output.begin(), output.end(), std::nanf("")); |
| 370 | |
| 371 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 372 | if (status != xnn_status_success) { |
| 373 | state.SkipWithError("failed to initialize XNNPACK"); |
| 374 | return; |
| 375 | } |
| 376 | |
| 377 | xnn_operator_t convert_op = nullptr; |
| 378 | status = xnn_create_convert_nc_qu8_f32( |
| 379 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
| 380 | 1.0f / 128.0f /* scale */, 128 /* zero point */, |
| 381 | 0 /* flags */, &convert_op); |
| 382 | if (status != xnn_status_success) { |
| 383 | state.SkipWithError("failed to create QU8->F32 Convert operator"); |
| 384 | return; |
| 385 | } |
| 386 | |
| 387 | status = xnn_setup_convert_nc_qu8_f32( |
| 388 | convert_op, batch_size, |
| 389 | input.data(), output.data(), |
| 390 | nullptr /* thread pool */); |
| 391 | if (status != xnn_status_success) { |
| 392 | state.SkipWithError("failed to setup QU8->F32 Convert operator"); |
| 393 | return; |
| 394 | } |
| 395 | |
| 396 | for (auto _ : state) { |
| 397 | status = xnn_run_operator(convert_op, nullptr /* thread pool */); |
| 398 | if (status != xnn_status_success) { |
| 399 | state.SkipWithError("failed to run QU8->F32 Convert operator"); |
| 400 | return; |
| 401 | } |
| 402 | } |
| 403 | |
| 404 | status = xnn_delete_operator(convert_op); |
| 405 | if (status != xnn_status_success) { |
| 406 | state.SkipWithError("failed to delete QU8->F32 Convert operator"); |
| 407 | return; |
| 408 | } |
| 409 | convert_op = nullptr; |
| 410 | |
| 411 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 412 | if (cpu_frequency != 0) { |
| 413 | state.counters["cpufreq"] = cpu_frequency; |
| 414 | } |
| 415 | |
| 416 | state.counters["elements"] = |
| 417 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 418 | |
| 419 | const size_t bytes_per_iteration = batch_size * (sizeof(uint8_t) + sizeof(float)); |
| 420 | state.counters["bytes"] = |
| 421 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 422 | } |
| 423 | |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 424 | #ifdef BENCHMARK_TENSORFLOW_LITE |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 425 | void tflite_convert_f16_f32(benchmark::State& state) { |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 426 | const size_t batch_size = state.range(0); |
| 427 | |
| 428 | std::random_device random_device; |
| 429 | auto rng = std::mt19937(random_device()); |
| 430 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng)); |
| 431 | auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
| 432 | |
| 433 | flatbuffers::FlatBufferBuilder builder; |
| 434 | flatbuffers::Offset<tflite::OperatorCode> operator_code = |
| 435 | CreateOperatorCode(builder, tflite::BuiltinOperator_DEQUANTIZE); |
| 436 | |
| 437 | std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
| 438 | tflite::CreateBuffer(builder, builder.CreateVector({})), |
| 439 | }}; |
| 440 | |
| 441 | const std::array<int32_t, 1> shape{{ |
| 442 | static_cast<int32_t>(batch_size) |
| 443 | }}; |
| 444 | |
| 445 | const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
| 446 | tflite::CreateTensor(builder, |
| 447 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 448 | tflite::TensorType_FLOAT16), |
| 449 | tflite::CreateTensor(builder, |
| 450 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 451 | tflite::TensorType_FLOAT32) |
| 452 | }}; |
| 453 | |
| 454 | const std::array<int32_t, 1> op_inputs{{0}}; |
| 455 | const std::array<int32_t, 1> op_outputs{{1}}; |
| 456 | flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(builder, |
| 457 | 0 /* opcode_index */, |
| 458 | builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
| 459 | builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
| 460 | |
| 461 | const std::array<int32_t, 1> graph_inputs{{0}}; |
| 462 | const std::array<int32_t, 1> graph_outputs{{1}}; |
| 463 | flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
| 464 | builder, |
| 465 | builder.CreateVector(tensors.data(), tensors.size()), |
| 466 | builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
| 467 | builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
| 468 | builder.CreateVector(&op, 1)); |
| 469 | |
| 470 | flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Dequantize model"); |
| 471 | |
| 472 | flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
| 473 | TFLITE_SCHEMA_VERSION, |
| 474 | builder.CreateVector(&operator_code, 1), |
| 475 | builder.CreateVector(&subgraph, 1), |
| 476 | description, |
| 477 | builder.CreateVector(buffers.data(), buffers.size())); |
| 478 | |
| 479 | builder.Finish(model_buffer); |
| 480 | |
| 481 | const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
| 482 | tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
| 483 | tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
| 484 | std::unique_ptr<tflite::Interpreter> interpreter; |
| 485 | if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
| 486 | state.SkipWithError("failed to create TFLite interpreter"); |
| 487 | return; |
| 488 | } |
| 489 | interpreter->SetNumThreads(1); |
| 490 | |
| 491 | if (interpreter->AllocateTensors() != kTfLiteOk) { |
| 492 | state.SkipWithError("failed to allocate tensors"); |
| 493 | return; |
| 494 | } |
| 495 | |
| 496 | uint16_t* input_data = reinterpret_cast<uint16_t*>(interpreter->tensor(0)->data.data); |
| 497 | std::generate(input_data, input_data + batch_size, std::ref(f16rng)); |
| 498 | |
| 499 | for (auto _ : state) { |
| 500 | if (interpreter->Invoke() != kTfLiteOk) { |
| 501 | state.SkipWithError("failed to invoke TFLite interpreter"); |
| 502 | return; |
| 503 | } |
| 504 | } |
| 505 | |
| 506 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 507 | if (cpu_frequency != 0) { |
| 508 | state.counters["cpufreq"] = cpu_frequency; |
| 509 | } |
| 510 | |
| 511 | state.counters["elements"] = |
| 512 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 513 | |
| 514 | const size_t bytes_per_iteration = batch_size * (sizeof(uint16_t) + sizeof(float)); |
| 515 | state.counters["bytes"] = |
| 516 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 517 | |
| 518 | interpreter.reset(); |
| 519 | } |
| 520 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 521 | void tflite_convert_f32_qs8(benchmark::State& state) { |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 522 | const size_t batch_size = state.range(0); |
| 523 | |
| 524 | std::random_device random_device; |
| 525 | auto rng = std::mt19937(random_device()); |
| 526 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng)); |
| 527 | |
| 528 | flatbuffers::FlatBufferBuilder builder; |
| 529 | flatbuffers::Offset<tflite::OperatorCode> operator_code = |
| 530 | CreateOperatorCode(builder, tflite::BuiltinOperator_QUANTIZE); |
| 531 | |
| 532 | std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
| 533 | tflite::CreateBuffer(builder, builder.CreateVector({})), |
| 534 | }}; |
| 535 | |
| 536 | const std::array<int32_t, 1> shape{{ |
| 537 | static_cast<int32_t>(batch_size) |
| 538 | }}; |
| 539 | |
| 540 | const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
| 541 | tflite::CreateTensor(builder, |
| 542 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 543 | tflite::TensorType_FLOAT32), |
| 544 | tflite::CreateTensor(builder, |
| 545 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 546 | tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */, |
| 547 | tflite::CreateQuantizationParameters(builder, |
| 548 | 0 /*min*/, 0 /*max*/, |
| 549 | builder.CreateVector<float>({1.0f / 128.0f /* scale */}), |
| 550 | builder.CreateVector<int64_t>({1 /* zero point */}))) |
| 551 | }}; |
| 552 | |
| 553 | const std::array<int32_t, 1> op_inputs{{0}}; |
| 554 | const std::array<int32_t, 1> op_outputs{{1}}; |
| 555 | flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(builder, |
| 556 | 0 /* opcode_index */, |
| 557 | builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
| 558 | builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
| 559 | |
| 560 | const std::array<int32_t, 1> graph_inputs{{0}}; |
| 561 | const std::array<int32_t, 1> graph_outputs{{1}}; |
| 562 | flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
| 563 | builder, |
| 564 | builder.CreateVector(tensors.data(), tensors.size()), |
| 565 | builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
| 566 | builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
| 567 | builder.CreateVector(&op, 1)); |
| 568 | |
| 569 | flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Quantize model"); |
| 570 | |
| 571 | flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
| 572 | TFLITE_SCHEMA_VERSION, |
| 573 | builder.CreateVector(&operator_code, 1), |
| 574 | builder.CreateVector(&subgraph, 1), |
| 575 | description, |
| 576 | builder.CreateVector(buffers.data(), buffers.size())); |
| 577 | |
| 578 | builder.Finish(model_buffer); |
| 579 | |
| 580 | const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
| 581 | tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
| 582 | tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
| 583 | std::unique_ptr<tflite::Interpreter> interpreter; |
| 584 | if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
| 585 | state.SkipWithError("failed to create TFLite interpreter"); |
| 586 | return; |
| 587 | } |
| 588 | interpreter->SetNumThreads(1); |
| 589 | |
| 590 | if (interpreter->AllocateTensors() != kTfLiteOk) { |
| 591 | state.SkipWithError("failed to allocate tensors"); |
| 592 | return; |
| 593 | } |
| 594 | |
| 595 | std::generate( |
| 596 | interpreter->typed_tensor<float>(0), |
| 597 | interpreter->typed_tensor<float>(0) + batch_size, |
| 598 | std::ref(f32rng)); |
| 599 | |
| 600 | for (auto _ : state) { |
| 601 | if (interpreter->Invoke() != kTfLiteOk) { |
| 602 | state.SkipWithError("failed to invoke TFLite interpreter"); |
| 603 | return; |
| 604 | } |
| 605 | } |
| 606 | |
| 607 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 608 | if (cpu_frequency != 0) { |
| 609 | state.counters["cpufreq"] = cpu_frequency; |
| 610 | } |
| 611 | |
| 612 | state.counters["elements"] = |
| 613 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 614 | |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 615 | const size_t bytes_per_iteration = batch_size * (sizeof(float) + sizeof(int8_t)); |
| 616 | state.counters["bytes"] = |
| 617 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 618 | |
| 619 | interpreter.reset(); |
| 620 | } |
| 621 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 622 | void tflite_convert_f32_qu8(benchmark::State& state) { |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 623 | const size_t batch_size = state.range(0); |
| 624 | |
| 625 | std::random_device random_device; |
| 626 | auto rng = std::mt19937(random_device()); |
| 627 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng)); |
| 628 | |
| 629 | flatbuffers::FlatBufferBuilder builder; |
| 630 | flatbuffers::Offset<tflite::OperatorCode> operator_code = |
| 631 | CreateOperatorCode(builder, tflite::BuiltinOperator_QUANTIZE); |
| 632 | |
| 633 | std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
| 634 | tflite::CreateBuffer(builder, builder.CreateVector({})), |
| 635 | }}; |
| 636 | |
| 637 | const std::array<int32_t, 1> shape{{ |
| 638 | static_cast<int32_t>(batch_size) |
| 639 | }}; |
| 640 | |
| 641 | const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
| 642 | tflite::CreateTensor(builder, |
| 643 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 644 | tflite::TensorType_FLOAT32), |
| 645 | tflite::CreateTensor(builder, |
| 646 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 647 | tflite::TensorType_UINT8, 0 /* buffer */, 0 /* name */, |
| 648 | tflite::CreateQuantizationParameters(builder, |
| 649 | 0 /*min*/, 0 /*max*/, |
| 650 | builder.CreateVector<float>({1.0f / 128.0f /* scale */}), |
| 651 | builder.CreateVector<int64_t>({127 /* zero point */}))) |
| 652 | }}; |
| 653 | |
| 654 | const std::array<int32_t, 1> op_inputs{{0}}; |
| 655 | const std::array<int32_t, 1> op_outputs{{1}}; |
| 656 | flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(builder, |
| 657 | 0 /* opcode_index */, |
| 658 | builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
| 659 | builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
| 660 | |
| 661 | const std::array<int32_t, 1> graph_inputs{{0}}; |
| 662 | const std::array<int32_t, 1> graph_outputs{{1}}; |
| 663 | flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
| 664 | builder, |
| 665 | builder.CreateVector(tensors.data(), tensors.size()), |
| 666 | builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
| 667 | builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
| 668 | builder.CreateVector(&op, 1)); |
| 669 | |
| 670 | flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Quantize model"); |
| 671 | |
| 672 | flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
| 673 | TFLITE_SCHEMA_VERSION, |
| 674 | builder.CreateVector(&operator_code, 1), |
| 675 | builder.CreateVector(&subgraph, 1), |
| 676 | description, |
| 677 | builder.CreateVector(buffers.data(), buffers.size())); |
| 678 | |
| 679 | builder.Finish(model_buffer); |
| 680 | |
| 681 | const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
| 682 | tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
| 683 | tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
| 684 | std::unique_ptr<tflite::Interpreter> interpreter; |
| 685 | if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
| 686 | state.SkipWithError("failed to create TFLite interpreter"); |
| 687 | return; |
| 688 | } |
| 689 | interpreter->SetNumThreads(1); |
| 690 | |
| 691 | if (interpreter->AllocateTensors() != kTfLiteOk) { |
| 692 | state.SkipWithError("failed to allocate tensors"); |
| 693 | return; |
| 694 | } |
| 695 | |
| 696 | std::generate( |
| 697 | interpreter->typed_tensor<float>(0), |
| 698 | interpreter->typed_tensor<float>(0) + batch_size, |
| 699 | std::ref(f32rng)); |
| 700 | |
| 701 | for (auto _ : state) { |
| 702 | if (interpreter->Invoke() != kTfLiteOk) { |
| 703 | state.SkipWithError("failed to invoke TFLite interpreter"); |
| 704 | return; |
| 705 | } |
| 706 | } |
| 707 | |
| 708 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 709 | if (cpu_frequency != 0) { |
| 710 | state.counters["cpufreq"] = cpu_frequency; |
| 711 | } |
| 712 | |
| 713 | state.counters["elements"] = |
| 714 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 715 | |
| 716 | const size_t bytes_per_iteration = batch_size * (sizeof(float) + sizeof(uint8_t)); |
| 717 | state.counters["bytes"] = |
| 718 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 719 | |
| 720 | interpreter.reset(); |
| 721 | } |
| 722 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 723 | void tflite_convert_qs8_f32(benchmark::State& state) { |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 724 | const size_t batch_size = state.range(0); |
| 725 | |
| 726 | std::random_device random_device; |
| 727 | auto rng = std::mt19937(random_device()); |
| 728 | auto i8rng = std::bind( |
| 729 | std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), |
| 730 | std::ref(rng)); |
| 731 | |
| 732 | flatbuffers::FlatBufferBuilder builder; |
| 733 | flatbuffers::Offset<tflite::OperatorCode> operator_code = |
| 734 | CreateOperatorCode(builder, tflite::BuiltinOperator_DEQUANTIZE); |
| 735 | |
| 736 | std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
| 737 | tflite::CreateBuffer(builder, builder.CreateVector({})), |
| 738 | }}; |
| 739 | |
| 740 | const std::array<int32_t, 1> shape{{ |
| 741 | static_cast<int32_t>(batch_size) |
| 742 | }}; |
| 743 | |
| 744 | const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
| 745 | tflite::CreateTensor(builder, |
| 746 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 747 | tflite::TensorType_INT8, 0 /* buffer */, 0 /* name */, |
| 748 | tflite::CreateQuantizationParameters(builder, |
| 749 | 0 /*min*/, 0 /*max*/, |
| 750 | builder.CreateVector<float>({1.0f / 255.0f /* scale */}), |
| 751 | builder.CreateVector<int64_t>({-128 /* zero point */}))), |
| 752 | tflite::CreateTensor(builder, |
| 753 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 754 | tflite::TensorType_FLOAT32) |
| 755 | }}; |
| 756 | |
| 757 | const std::array<int32_t, 1> op_inputs{{0}}; |
| 758 | const std::array<int32_t, 1> op_outputs{{1}}; |
| 759 | flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(builder, |
| 760 | 0 /* opcode_index */, |
| 761 | builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
| 762 | builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
| 763 | |
| 764 | const std::array<int32_t, 1> graph_inputs{{0}}; |
| 765 | const std::array<int32_t, 1> graph_outputs{{1}}; |
| 766 | flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
| 767 | builder, |
| 768 | builder.CreateVector(tensors.data(), tensors.size()), |
| 769 | builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
| 770 | builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
| 771 | builder.CreateVector(&op, 1)); |
| 772 | |
| 773 | flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Dequantize model"); |
| 774 | |
| 775 | flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
| 776 | TFLITE_SCHEMA_VERSION, |
| 777 | builder.CreateVector(&operator_code, 1), |
| 778 | builder.CreateVector(&subgraph, 1), |
| 779 | description, |
| 780 | builder.CreateVector(buffers.data(), buffers.size())); |
| 781 | |
| 782 | builder.Finish(model_buffer); |
| 783 | |
| 784 | const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
| 785 | tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
| 786 | tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
| 787 | std::unique_ptr<tflite::Interpreter> interpreter; |
| 788 | if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
| 789 | state.SkipWithError("failed to create TFLite interpreter"); |
| 790 | return; |
| 791 | } |
| 792 | interpreter->SetNumThreads(1); |
| 793 | |
| 794 | if (interpreter->AllocateTensors() != kTfLiteOk) { |
| 795 | state.SkipWithError("failed to allocate tensors"); |
| 796 | return; |
| 797 | } |
| 798 | |
| 799 | std::generate( |
| 800 | interpreter->typed_tensor<int8_t>(0), |
| 801 | interpreter->typed_tensor<int8_t>(0) + batch_size, |
| 802 | std::ref(i8rng)); |
| 803 | |
| 804 | for (auto _ : state) { |
| 805 | if (interpreter->Invoke() != kTfLiteOk) { |
| 806 | state.SkipWithError("failed to invoke TFLite interpreter"); |
| 807 | return; |
| 808 | } |
| 809 | } |
| 810 | |
| 811 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 812 | if (cpu_frequency != 0) { |
| 813 | state.counters["cpufreq"] = cpu_frequency; |
| 814 | } |
| 815 | |
| 816 | state.counters["elements"] = |
| 817 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 818 | |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 819 | const size_t bytes_per_iteration = batch_size * (sizeof(int8_t) + sizeof(float)); |
| 820 | state.counters["bytes"] = |
| 821 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 822 | |
| 823 | interpreter.reset(); |
| 824 | } |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 825 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 826 | void tflite_convert_qu8_f32(benchmark::State& state) { |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 827 | const size_t batch_size = state.range(0); |
| 828 | |
| 829 | std::random_device random_device; |
| 830 | auto rng = std::mt19937(random_device()); |
| 831 | auto u8rng = std::bind( |
| 832 | std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()), |
| 833 | std::ref(rng)); |
| 834 | |
| 835 | flatbuffers::FlatBufferBuilder builder; |
| 836 | flatbuffers::Offset<tflite::OperatorCode> operator_code = |
| 837 | CreateOperatorCode(builder, tflite::BuiltinOperator_DEQUANTIZE); |
| 838 | |
| 839 | std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
| 840 | tflite::CreateBuffer(builder, builder.CreateVector({})), |
| 841 | }}; |
| 842 | |
| 843 | const std::array<int32_t, 1> shape{{ |
| 844 | static_cast<int32_t>(batch_size) |
| 845 | }}; |
| 846 | |
| 847 | const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
| 848 | tflite::CreateTensor(builder, |
| 849 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 850 | tflite::TensorType_UINT8, 0 /* buffer */, 0 /* name */, |
| 851 | tflite::CreateQuantizationParameters(builder, |
| 852 | 0 /*min*/, 0 /*max*/, |
| 853 | builder.CreateVector<float>({1.0f / 128.0f /* scale */}), |
| 854 | builder.CreateVector<int64_t>({128 /* zero point */}))), |
| 855 | tflite::CreateTensor(builder, |
| 856 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
| 857 | tflite::TensorType_FLOAT32) |
| 858 | }}; |
| 859 | |
| 860 | const std::array<int32_t, 1> op_inputs{{0}}; |
| 861 | const std::array<int32_t, 1> op_outputs{{1}}; |
| 862 | flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(builder, |
| 863 | 0 /* opcode_index */, |
| 864 | builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
| 865 | builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
| 866 | |
| 867 | const std::array<int32_t, 1> graph_inputs{{0}}; |
| 868 | const std::array<int32_t, 1> graph_outputs{{1}}; |
| 869 | flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
| 870 | builder, |
| 871 | builder.CreateVector(tensors.data(), tensors.size()), |
| 872 | builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
| 873 | builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
| 874 | builder.CreateVector(&op, 1)); |
| 875 | |
| 876 | flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Dequantize model"); |
| 877 | |
| 878 | flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
| 879 | TFLITE_SCHEMA_VERSION, |
| 880 | builder.CreateVector(&operator_code, 1), |
| 881 | builder.CreateVector(&subgraph, 1), |
| 882 | description, |
| 883 | builder.CreateVector(buffers.data(), buffers.size())); |
| 884 | |
| 885 | builder.Finish(model_buffer); |
| 886 | |
| 887 | const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
| 888 | tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
| 889 | tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
| 890 | std::unique_ptr<tflite::Interpreter> interpreter; |
| 891 | if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
| 892 | state.SkipWithError("failed to create TFLite interpreter"); |
| 893 | return; |
| 894 | } |
| 895 | interpreter->SetNumThreads(1); |
| 896 | |
| 897 | if (interpreter->AllocateTensors() != kTfLiteOk) { |
| 898 | state.SkipWithError("failed to allocate tensors"); |
| 899 | return; |
| 900 | } |
| 901 | |
| 902 | std::generate( |
| 903 | interpreter->typed_tensor<uint8_t>(0), |
| 904 | interpreter->typed_tensor<uint8_t>(0) + batch_size, |
| 905 | std::ref(u8rng)); |
| 906 | |
| 907 | for (auto _ : state) { |
| 908 | if (interpreter->Invoke() != kTfLiteOk) { |
| 909 | state.SkipWithError("failed to invoke TFLite interpreter"); |
| 910 | return; |
| 911 | } |
| 912 | } |
| 913 | |
| 914 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 915 | if (cpu_frequency != 0) { |
| 916 | state.counters["cpufreq"] = cpu_frequency; |
| 917 | } |
| 918 | |
| 919 | state.counters["elements"] = |
| 920 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 921 | |
| 922 | const size_t bytes_per_iteration = batch_size * (sizeof(uint8_t) + sizeof(float)); |
| 923 | state.counters["bytes"] = |
| 924 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 925 | |
| 926 | interpreter.reset(); |
| 927 | } |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 928 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 929 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 930 | BENCHMARK(xnnpack_convert_f16_f32) |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 931 | ->Apply(benchmark::utils::UnaryElementwiseParameters<uint16_t, float>) |
| 932 | ->UseRealTime(); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 933 | BENCHMARK(xnnpack_convert_f32_f16) |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 934 | ->Apply(benchmark::utils::UnaryElementwiseParameters<float, uint16_t>) |
| 935 | ->UseRealTime(); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 936 | BENCHMARK(xnnpack_convert_f32_qs8) |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 937 | ->Apply(benchmark::utils::UnaryElementwiseParameters<float, int8_t>) |
| 938 | ->UseRealTime(); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 939 | BENCHMARK(xnnpack_convert_f32_qu8) |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 940 | ->Apply(benchmark::utils::UnaryElementwiseParameters<float, uint8_t>) |
| 941 | ->UseRealTime(); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 942 | BENCHMARK(xnnpack_convert_qs8_f32) |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 943 | ->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, float>) |
| 944 | ->UseRealTime(); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 945 | BENCHMARK(xnnpack_convert_qu8_f32) |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 946 | ->Apply(benchmark::utils::UnaryElementwiseParameters<uint8_t, float>) |
| 947 | ->UseRealTime(); |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 948 | |
| 949 | #ifdef BENCHMARK_TENSORFLOW_LITE |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 950 | BENCHMARK(tflite_convert_f16_f32) |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 951 | ->Apply(benchmark::utils::UnaryElementwiseParameters<uint16_t, float>) |
| 952 | ->UseRealTime(); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 953 | BENCHMARK(tflite_convert_f32_qs8) |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 954 | ->Apply(benchmark::utils::UnaryElementwiseParameters<float, int8_t>) |
| 955 | ->UseRealTime(); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 956 | BENCHMARK(tflite_convert_f32_qu8) |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 957 | ->Apply(benchmark::utils::UnaryElementwiseParameters<float, uint8_t>) |
| 958 | ->UseRealTime(); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 959 | BENCHMARK(tflite_convert_qs8_f32) |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 960 | ->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, float>) |
| 961 | ->UseRealTime(); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 962 | BENCHMARK(tflite_convert_qu8_f32) |
Marat Dukhan | 9820234 | 2021-12-14 14:53:05 -0800 | [diff] [blame] | 963 | ->Apply(benchmark::utils::UnaryElementwiseParameters<uint8_t, float>) |
| 964 | ->UseRealTime(); |
Marat Dukhan | 710fb42 | 2021-12-13 16:32:26 -0800 | [diff] [blame] | 965 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 966 | |
| 967 | #ifndef XNNPACK_BENCHMARK_NO_MAIN |
| 968 | BENCHMARK_MAIN(); |
| 969 | #endif |