Marat Dukhan | af2ba00 | 2021-10-24 14:21:41 -0700 | [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 | #pragma once |
| 7 | |
| 8 | #include <gtest/gtest.h> |
| 9 | |
| 10 | #include <algorithm> |
| 11 | #include <cassert> |
| 12 | #include <cmath> |
| 13 | #include <cstddef> |
| 14 | #include <cstdlib> |
| 15 | #include <functional> |
| 16 | #include <random> |
| 17 | #include <vector> |
| 18 | |
| 19 | #include <fp16.h> |
| 20 | |
| 21 | #include <xnnpack.h> |
| 22 | |
| 23 | |
| 24 | class ConvertOperatorTester { |
| 25 | public: |
| 26 | inline ConvertOperatorTester& channels(size_t channels) { |
| 27 | assert(channels != 0); |
| 28 | this->channels_ = channels; |
| 29 | return *this; |
| 30 | } |
| 31 | |
| 32 | inline size_t channels() const { |
| 33 | return this->channels_; |
| 34 | } |
| 35 | |
| 36 | inline ConvertOperatorTester& input_stride(size_t input_stride) { |
| 37 | assert(input_stride != 0); |
| 38 | this->input_stride_ = input_stride; |
| 39 | return *this; |
| 40 | } |
| 41 | |
| 42 | inline size_t input_stride() const { |
| 43 | if (this->input_stride_ == 0) { |
| 44 | return this->channels_; |
| 45 | } else { |
| 46 | assert(this->input_stride_ >= this->channels_); |
| 47 | return this->input_stride_; |
| 48 | } |
| 49 | } |
| 50 | |
| 51 | inline ConvertOperatorTester& output_stride(size_t output_stride) { |
| 52 | assert(output_stride != 0); |
| 53 | this->output_stride_ = output_stride; |
| 54 | return *this; |
| 55 | } |
| 56 | |
| 57 | inline size_t output_stride() const { |
| 58 | if (this->output_stride_ == 0) { |
| 59 | return this->channels_; |
| 60 | } else { |
| 61 | assert(this->output_stride_ >= this->channels_); |
| 62 | return this->output_stride_; |
| 63 | } |
| 64 | } |
| 65 | |
| 66 | inline ConvertOperatorTester& batch_size(size_t batch_size) { |
| 67 | assert(batch_size != 0); |
| 68 | this->batch_size_ = batch_size; |
| 69 | return *this; |
| 70 | } |
| 71 | |
| 72 | inline size_t batch_size() const { |
| 73 | return this->batch_size_; |
| 74 | } |
| 75 | |
Marat Dukhan | ed2d776 | 2021-12-03 23:51:19 -0800 | [diff] [blame] | 76 | inline ConvertOperatorTester& scale(float scale) { |
| 77 | assert(scale >= 0.0f); |
| 78 | assert(isnormal(scale)); |
| 79 | this->scale_ = scale; |
| 80 | return *this; |
| 81 | } |
| 82 | |
| 83 | inline float scale() const { |
| 84 | return this->scale_; |
| 85 | } |
| 86 | |
| 87 | inline ConvertOperatorTester& zero_point(int16_t zero_point) { |
Marat Dukhan | ed2d776 | 2021-12-03 23:51:19 -0800 | [diff] [blame] | 88 | this->zero_point_ = zero_point; |
| 89 | return *this; |
| 90 | } |
| 91 | |
| 92 | inline int16_t zero_point() const { |
| 93 | return this->zero_point_; |
| 94 | } |
| 95 | |
| 96 | inline ConvertOperatorTester& qmin(int16_t qmin) { |
Marat Dukhan | ed2d776 | 2021-12-03 23:51:19 -0800 | [diff] [blame] | 97 | this->qmin_ = qmin; |
| 98 | return *this; |
| 99 | } |
| 100 | |
| 101 | inline int16_t qmin() const { |
| 102 | return this->qmin_; |
| 103 | } |
| 104 | |
| 105 | inline ConvertOperatorTester& qmax(int16_t qmax) { |
Marat Dukhan | ed2d776 | 2021-12-03 23:51:19 -0800 | [diff] [blame] | 106 | this->qmax_ = qmax; |
| 107 | return *this; |
| 108 | } |
| 109 | |
| 110 | inline int16_t qmax() const { |
| 111 | return this->qmax_; |
| 112 | } |
| 113 | |
Marat Dukhan | af2ba00 | 2021-10-24 14:21:41 -0700 | [diff] [blame] | 114 | inline ConvertOperatorTester& iterations(size_t iterations) { |
| 115 | this->iterations_ = iterations; |
| 116 | return *this; |
| 117 | } |
| 118 | |
| 119 | inline size_t iterations() const { |
| 120 | return this->iterations_; |
| 121 | } |
| 122 | |
| 123 | void TestF16toF32() const { |
| 124 | std::random_device random_device; |
| 125 | auto rng = std::mt19937(random_device()); |
| 126 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), rng); |
| 127 | auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
| 128 | |
| 129 | std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + |
| 130 | (batch_size() - 1) * input_stride() + channels()); |
| 131 | std::vector<float> output((batch_size() - 1) * output_stride() + channels()); |
| 132 | std::vector<float> output_ref(batch_size() * channels()); |
| 133 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 134 | std::generate(input.begin(), input.end(), std::ref(f16rng)); |
| 135 | std::fill(output.begin(), output.end(), std::nanf("")); |
| 136 | |
| 137 | // Compute reference results. |
| 138 | for (size_t i = 0; i < batch_size(); i++) { |
| 139 | for (size_t c = 0; c < channels(); c++) { |
| 140 | output_ref[i * channels() + c] = fp16_ieee_to_fp32_value(input[i * input_stride() + c]); |
| 141 | } |
| 142 | } |
| 143 | |
| 144 | // Create, setup, run, and destroy Convert operator. |
| 145 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| 146 | xnn_operator_t convert_op = nullptr; |
| 147 | |
| 148 | ASSERT_EQ(xnn_status_success, |
| 149 | xnn_create_convert_nc_f16_f32( |
| 150 | channels(), input_stride(), output_stride(), |
| 151 | 0, &convert_op)); |
| 152 | ASSERT_NE(nullptr, convert_op); |
| 153 | |
| 154 | // Smart pointer to automatically delete convert op. |
Marat Dukhan | a0c6168 | 2021-11-10 19:23:41 -0800 | [diff] [blame] | 155 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator); |
Marat Dukhan | af2ba00 | 2021-10-24 14:21:41 -0700 | [diff] [blame] | 156 | |
| 157 | ASSERT_EQ(xnn_status_success, |
| 158 | xnn_setup_convert_nc_f16_f32( |
| 159 | convert_op, |
| 160 | batch_size(), |
| 161 | input.data(), output.data(), |
| 162 | nullptr /* thread pool */)); |
| 163 | |
| 164 | ASSERT_EQ(xnn_status_success, |
| 165 | xnn_run_operator(convert_op, nullptr /* thread pool */)); |
| 166 | |
| 167 | // Verify results. |
| 168 | for (size_t i = 0; i < batch_size(); i++) { |
| 169 | for (size_t c = 0; c < channels(); c++) { |
| 170 | ASSERT_EQ(output_ref[i * channels() + c], output[i * output_stride() + c]) |
| 171 | << "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels(); |
| 172 | } |
| 173 | } |
| 174 | } |
| 175 | } |
| 176 | |
Marat Dukhan | a0c6168 | 2021-11-10 19:23:41 -0800 | [diff] [blame] | 177 | void TestF32toF16() const { |
| 178 | std::random_device random_device; |
| 179 | auto rng = std::mt19937(random_device()); |
| 180 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), rng); |
| 181 | |
| 182 | std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
| 183 | (batch_size() - 1) * input_stride() + channels()); |
| 184 | std::vector<uint16_t> output((batch_size() - 1) * output_stride() + channels()); |
| 185 | std::vector<uint16_t> output_ref(batch_size() * channels()); |
| 186 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 187 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 188 | std::fill(output.begin(), output.end(), UINT16_C(0x7E)); |
| 189 | |
| 190 | // Compute reference results. |
| 191 | for (size_t i = 0; i < batch_size(); i++) { |
| 192 | for (size_t c = 0; c < channels(); c++) { |
| 193 | output_ref[i * channels() + c] = fp16_ieee_from_fp32_value(input[i * input_stride() + c]); |
| 194 | } |
| 195 | } |
| 196 | |
| 197 | // Create, setup, run, and destroy Convert operator. |
| 198 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| 199 | xnn_operator_t convert_op = nullptr; |
| 200 | |
| 201 | ASSERT_EQ(xnn_status_success, |
| 202 | xnn_create_convert_nc_f32_f16( |
| 203 | channels(), input_stride(), output_stride(), |
| 204 | 0, &convert_op)); |
| 205 | ASSERT_NE(nullptr, convert_op); |
| 206 | |
| 207 | // Smart pointer to automatically delete convert op. |
| 208 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator); |
| 209 | |
| 210 | ASSERT_EQ(xnn_status_success, |
| 211 | xnn_setup_convert_nc_f32_f16( |
| 212 | convert_op, |
| 213 | batch_size(), |
| 214 | input.data(), output.data(), |
| 215 | nullptr /* thread pool */)); |
| 216 | |
| 217 | ASSERT_EQ(xnn_status_success, |
| 218 | xnn_run_operator(convert_op, nullptr /* thread pool */)); |
| 219 | |
| 220 | // Verify results. |
| 221 | for (size_t i = 0; i < batch_size(); i++) { |
| 222 | for (size_t c = 0; c < channels(); c++) { |
| 223 | ASSERT_EQ(output_ref[i * channels() + c], output[i * output_stride() + c]) |
| 224 | << "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels(); |
| 225 | } |
| 226 | } |
| 227 | } |
| 228 | } |
| 229 | |
Marat Dukhan | ed2d776 | 2021-12-03 23:51:19 -0800 | [diff] [blame] | 230 | void TestF32toQS8() const { |
| 231 | ASSERT_GE(qmin(), std::numeric_limits<int8_t>::min()); |
| 232 | ASSERT_LE(qmax(), std::numeric_limits<int8_t>::max()); |
| 233 | ASSERT_LT(qmin(), qmax()); |
| 234 | |
| 235 | ASSERT_GE(zero_point(), std::numeric_limits<int8_t>::min()); |
| 236 | ASSERT_LE(zero_point(), std::numeric_limits<int8_t>::max()); |
| 237 | |
| 238 | std::random_device random_device; |
| 239 | auto rng = std::mt19937(random_device()); |
| 240 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), rng); |
| 241 | |
| 242 | std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
| 243 | (batch_size() - 1) * input_stride() + channels()); |
| 244 | std::vector<int8_t> output((batch_size() - 1) * output_stride() + channels()); |
| 245 | std::vector<int8_t> output_ref(batch_size() * channels()); |
| 246 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 247 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 248 | std::fill(output.begin(), output.end(), UINT16_C(0x7E)); |
| 249 | |
| 250 | // Compute reference results. |
| 251 | const float inv_scale = 1.0f / scale(); |
| 252 | for (size_t i = 0; i < batch_size(); i++) { |
| 253 | for (size_t c = 0; c < channels(); c++) { |
| 254 | float scaled_input = input[i * input_stride() + c] * inv_scale; |
| 255 | scaled_input = std::min<float>(scaled_input, float(qmax() - zero_point())); |
| 256 | scaled_input = std::max<float>(scaled_input, float(qmin() - zero_point())); |
| 257 | output_ref[i * channels() + c] = int8_t(std::lrintf(scaled_input) + long(zero_point())); |
| 258 | } |
| 259 | } |
| 260 | |
| 261 | // Create, setup, run, and destroy Convert operator. |
| 262 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| 263 | xnn_operator_t convert_op = nullptr; |
| 264 | |
| 265 | ASSERT_EQ(xnn_status_success, |
| 266 | xnn_create_convert_nc_f32_qs8( |
| 267 | channels(), input_stride(), output_stride(), |
| 268 | scale(), int8_t(zero_point()), int8_t(qmin()), int8_t(qmax()), |
| 269 | 0, &convert_op)); |
| 270 | ASSERT_NE(nullptr, convert_op); |
| 271 | |
| 272 | // Smart pointer to automatically delete convert op. |
| 273 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator); |
| 274 | |
| 275 | ASSERT_EQ(xnn_status_success, |
| 276 | xnn_setup_convert_nc_f32_qs8( |
| 277 | convert_op, |
| 278 | batch_size(), |
| 279 | input.data(), output.data(), |
| 280 | nullptr /* thread pool */)); |
| 281 | |
| 282 | ASSERT_EQ(xnn_status_success, |
| 283 | xnn_run_operator(convert_op, nullptr /* thread pool */)); |
| 284 | |
| 285 | // Verify results. |
| 286 | for (size_t i = 0; i < batch_size(); i++) { |
| 287 | for (size_t c = 0; c < channels(); c++) { |
| 288 | ASSERT_EQ(int32_t(output_ref[i * channels() + c]), int32_t(output[i * output_stride() + c])) |
| 289 | << "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels(); |
| 290 | } |
| 291 | } |
| 292 | } |
| 293 | } |
| 294 | |
| 295 | void TestF32toQU8() const { |
| 296 | ASSERT_GE(qmin(), std::numeric_limits<uint8_t>::min()); |
| 297 | ASSERT_LE(qmax(), std::numeric_limits<uint8_t>::max()); |
| 298 | ASSERT_LT(qmin(), qmax()); |
| 299 | |
| 300 | ASSERT_GE(zero_point(), std::numeric_limits<uint8_t>::min()); |
| 301 | ASSERT_LE(zero_point(), std::numeric_limits<uint8_t>::max()); |
| 302 | |
| 303 | std::random_device random_device; |
| 304 | auto rng = std::mt19937(random_device()); |
| 305 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), rng); |
| 306 | |
| 307 | std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
| 308 | (batch_size() - 1) * input_stride() + channels()); |
| 309 | std::vector<uint8_t> output((batch_size() - 1) * output_stride() + channels()); |
| 310 | std::vector<uint8_t> output_ref(batch_size() * channels()); |
| 311 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 312 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 313 | std::fill(output.begin(), output.end(), UINT16_C(0x7E)); |
| 314 | |
| 315 | // Compute reference results. |
| 316 | const float inv_scale = 1.0f / scale(); |
| 317 | for (size_t i = 0; i < batch_size(); i++) { |
| 318 | for (size_t c = 0; c < channels(); c++) { |
| 319 | float scaled_input = input[i * input_stride() + c] * inv_scale; |
| 320 | scaled_input = std::min<float>(scaled_input, float(qmax() - zero_point())); |
| 321 | scaled_input = std::max<float>(scaled_input, float(qmin() - zero_point())); |
| 322 | output_ref[i * channels() + c] = uint8_t(std::lrintf(scaled_input) + long(zero_point())); |
| 323 | } |
| 324 | } |
| 325 | |
| 326 | // Create, setup, run, and destroy Convert operator. |
| 327 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| 328 | xnn_operator_t convert_op = nullptr; |
| 329 | |
| 330 | ASSERT_EQ(xnn_status_success, |
| 331 | xnn_create_convert_nc_f32_qu8( |
| 332 | channels(), input_stride(), output_stride(), |
| 333 | scale(), uint8_t(zero_point()), uint8_t(qmin()), uint8_t(qmax()), |
| 334 | 0, &convert_op)); |
| 335 | ASSERT_NE(nullptr, convert_op); |
| 336 | |
| 337 | // Smart pointer to automatically delete convert op. |
| 338 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator); |
| 339 | |
| 340 | ASSERT_EQ(xnn_status_success, |
| 341 | xnn_setup_convert_nc_f32_qu8( |
| 342 | convert_op, |
| 343 | batch_size(), |
| 344 | input.data(), output.data(), |
| 345 | nullptr /* thread pool */)); |
| 346 | |
| 347 | ASSERT_EQ(xnn_status_success, |
| 348 | xnn_run_operator(convert_op, nullptr /* thread pool */)); |
| 349 | |
| 350 | // Verify results. |
| 351 | for (size_t i = 0; i < batch_size(); i++) { |
| 352 | for (size_t c = 0; c < channels(); c++) { |
| 353 | ASSERT_EQ(uint32_t(output_ref[i * channels() + c]), uint32_t(output[i * output_stride() + c])) |
| 354 | << "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels(); |
| 355 | } |
| 356 | } |
| 357 | } |
| 358 | } |
| 359 | |
Marat Dukhan | f92206b | 2021-12-10 17:02:07 -0800 | [diff] [blame^] | 360 | void TestQS8toF32() const { |
| 361 | ASSERT_GE(zero_point(), std::numeric_limits<int8_t>::min()); |
| 362 | ASSERT_LE(zero_point(), std::numeric_limits<int8_t>::max()); |
| 363 | |
| 364 | std::random_device random_device; |
| 365 | auto rng = std::mt19937(random_device()); |
| 366 | auto i8rng = std::bind( |
| 367 | std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), |
| 368 | std::ref(rng)); |
| 369 | |
| 370 | std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) + |
| 371 | (batch_size() - 1) * input_stride() + channels()); |
| 372 | std::vector<float> output((batch_size() - 1) * output_stride() + channels()); |
| 373 | std::vector<float> output_ref(batch_size() * channels()); |
| 374 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 375 | std::generate(input.begin(), input.end(), std::ref(i8rng)); |
| 376 | std::fill(output.begin(), output.end(), std::nanf("")); |
| 377 | |
| 378 | // Compute reference results. |
| 379 | for (size_t i = 0; i < batch_size(); i++) { |
| 380 | for (size_t c = 0; c < channels(); c++) { |
| 381 | output_ref[i * channels() + c] = float(input[i * input_stride() + c] - zero_point()) * scale(); |
| 382 | } |
| 383 | } |
| 384 | |
| 385 | // Create, setup, run, and destroy Convert operator. |
| 386 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| 387 | xnn_operator_t convert_op = nullptr; |
| 388 | |
| 389 | ASSERT_EQ(xnn_status_success, |
| 390 | xnn_create_convert_nc_qs8_f32( |
| 391 | channels(), input_stride(), output_stride(), |
| 392 | scale(), int8_t(zero_point()), |
| 393 | 0, &convert_op)); |
| 394 | ASSERT_NE(nullptr, convert_op); |
| 395 | |
| 396 | // Smart pointer to automatically delete convert op. |
| 397 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator); |
| 398 | |
| 399 | ASSERT_EQ(xnn_status_success, |
| 400 | xnn_setup_convert_nc_qs8_f32( |
| 401 | convert_op, |
| 402 | batch_size(), |
| 403 | input.data(), output.data(), |
| 404 | nullptr /* thread pool */)); |
| 405 | |
| 406 | ASSERT_EQ(xnn_status_success, |
| 407 | xnn_run_operator(convert_op, nullptr /* thread pool */)); |
| 408 | |
| 409 | // Verify results. |
| 410 | for (size_t i = 0; i < batch_size(); i++) { |
| 411 | for (size_t c = 0; c < channels(); c++) { |
| 412 | ASSERT_EQ(output_ref[i * channels() + c], output[i * output_stride() + c]) |
| 413 | << "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels(); |
| 414 | } |
| 415 | } |
| 416 | } |
| 417 | } |
| 418 | |
| 419 | void TestQU8toF32() const { |
| 420 | ASSERT_GE(zero_point(), std::numeric_limits<uint8_t>::min()); |
| 421 | ASSERT_LE(zero_point(), std::numeric_limits<uint8_t>::max()); |
| 422 | |
| 423 | std::random_device random_device; |
| 424 | auto rng = std::mt19937(random_device()); |
| 425 | auto u8rng = std::bind( |
| 426 | std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()), |
| 427 | std::ref(rng)); |
| 428 | |
| 429 | std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + |
| 430 | (batch_size() - 1) * input_stride() + channels()); |
| 431 | std::vector<float> output((batch_size() - 1) * output_stride() + channels()); |
| 432 | std::vector<float> output_ref(batch_size() * channels()); |
| 433 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 434 | std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| 435 | std::fill(output.begin(), output.end(), std::nanf("")); |
| 436 | |
| 437 | // Compute reference results. |
| 438 | for (size_t i = 0; i < batch_size(); i++) { |
| 439 | for (size_t c = 0; c < channels(); c++) { |
| 440 | output_ref[i * channels() + c] = float(input[i * input_stride() + c] - zero_point()) * scale(); |
| 441 | } |
| 442 | } |
| 443 | |
| 444 | // Create, setup, run, and destroy Convert operator. |
| 445 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| 446 | xnn_operator_t convert_op = nullptr; |
| 447 | |
| 448 | ASSERT_EQ(xnn_status_success, |
| 449 | xnn_create_convert_nc_qu8_f32( |
| 450 | channels(), input_stride(), output_stride(), |
| 451 | scale(), uint8_t(zero_point()), |
| 452 | 0, &convert_op)); |
| 453 | ASSERT_NE(nullptr, convert_op); |
| 454 | |
| 455 | // Smart pointer to automatically delete convert op. |
| 456 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator); |
| 457 | |
| 458 | ASSERT_EQ(xnn_status_success, |
| 459 | xnn_setup_convert_nc_qu8_f32( |
| 460 | convert_op, |
| 461 | batch_size(), |
| 462 | input.data(), output.data(), |
| 463 | nullptr /* thread pool */)); |
| 464 | |
| 465 | ASSERT_EQ(xnn_status_success, |
| 466 | xnn_run_operator(convert_op, nullptr /* thread pool */)); |
| 467 | |
| 468 | // Verify results. |
| 469 | for (size_t i = 0; i < batch_size(); i++) { |
| 470 | for (size_t c = 0; c < channels(); c++) { |
| 471 | ASSERT_EQ(output_ref[i * channels() + c], output[i * output_stride() + c]) |
| 472 | << "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels(); |
| 473 | } |
| 474 | } |
| 475 | } |
| 476 | } |
| 477 | |
Marat Dukhan | af2ba00 | 2021-10-24 14:21:41 -0700 | [diff] [blame] | 478 | private: |
| 479 | size_t batch_size_{1}; |
| 480 | size_t channels_{1}; |
| 481 | size_t input_stride_{0}; |
| 482 | size_t output_stride_{0}; |
Marat Dukhan | ed2d776 | 2021-12-03 23:51:19 -0800 | [diff] [blame] | 483 | float scale_{150.0f}; |
| 484 | int16_t zero_point_{1}; |
| 485 | int16_t qmin_{std::numeric_limits<int16_t>::min()}; |
| 486 | int16_t qmax_{std::numeric_limits<int16_t>::max()}; |
Marat Dukhan | af2ba00 | 2021-10-24 14:21:41 -0700 | [diff] [blame] | 487 | size_t iterations_{15}; |
| 488 | }; |