XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 1 | // Copyright (c) Facebook, Inc. and its affiliates. |
| 2 | // All rights reserved. |
| 3 | // |
| 4 | // Copyright 2019 Google LLC |
| 5 | // |
| 6 | // This source code is licensed under the BSD-style license found in the |
| 7 | // LICENSE file in the root directory of this source tree. |
| 8 | |
| 9 | #pragma once |
| 10 | |
| 11 | #include <gtest/gtest.h> |
| 12 | |
| 13 | #include <algorithm> |
| 14 | #include <cassert> |
| 15 | #include <cmath> |
| 16 | #include <cstddef> |
| 17 | #include <cstdlib> |
| 18 | #include <functional> |
| 19 | #include <random> |
| 20 | #include <vector> |
| 21 | |
| 22 | #include <xnnpack.h> |
| 23 | #include <xnnpack/AlignedAllocator.h> |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 24 | #include <xnnpack/params-init.h> |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 25 | #include <xnnpack/params.h> |
| 26 | #include <xnnpack/requantization.h> |
| 27 | |
| 28 | |
| 29 | class AvgPoolMicrokernelTester { |
| 30 | public: |
| 31 | enum class Variant { |
| 32 | Native, |
| 33 | Scalar, |
| 34 | }; |
| 35 | |
| 36 | inline AvgPoolMicrokernelTester& n(size_t n) { |
| 37 | assert(n != 0); |
| 38 | this->n_ = n; |
| 39 | return *this; |
| 40 | } |
| 41 | |
| 42 | inline size_t n() const { |
| 43 | return this->n_; |
| 44 | } |
| 45 | |
| 46 | inline AvgPoolMicrokernelTester& s(size_t s) { |
| 47 | assert(s != 0); |
| 48 | this->s_ = s; |
| 49 | return *this; |
| 50 | } |
| 51 | |
| 52 | inline size_t s() const { |
| 53 | return this->s_; |
| 54 | } |
| 55 | |
| 56 | inline AvgPoolMicrokernelTester& kh(size_t kh) { |
| 57 | assert(kh != 0); |
| 58 | this->kh_ = kh; |
| 59 | return *this; |
| 60 | } |
| 61 | |
| 62 | inline size_t kh() const { |
| 63 | return this->kh_; |
| 64 | } |
| 65 | |
| 66 | inline AvgPoolMicrokernelTester& kw(size_t kw) { |
| 67 | assert(kw != 0); |
| 68 | this->kw_ = kw; |
| 69 | return *this; |
| 70 | } |
| 71 | |
| 72 | inline size_t kw() const { |
| 73 | return this->kw_; |
| 74 | } |
| 75 | |
| 76 | inline size_t ks() const { |
| 77 | return kh() * kw(); |
| 78 | } |
| 79 | |
| 80 | inline size_t packed_ks() const { |
| 81 | if (ks() <= mr()) { |
| 82 | return mr(); |
| 83 | } else { |
| 84 | return (ks() - mr()) % qr() == 0 ? ks() : ((ks() - mr()) / qr() + 1) * qr() + mr(); |
| 85 | } |
| 86 | } |
| 87 | |
| 88 | inline AvgPoolMicrokernelTester& mr(size_t mr) { |
| 89 | assert(mr != 0); |
| 90 | this->mr_ = mr; |
| 91 | return *this; |
| 92 | } |
| 93 | |
| 94 | inline size_t mr() const { |
| 95 | return this->mr_; |
| 96 | } |
| 97 | |
| 98 | inline AvgPoolMicrokernelTester& qr(size_t qr) { |
| 99 | assert(qr != 0); |
| 100 | this->qr_ = qr; |
| 101 | return *this; |
| 102 | } |
| 103 | |
| 104 | inline size_t qr() const { |
| 105 | return this->qr_; |
| 106 | } |
| 107 | |
| 108 | inline AvgPoolMicrokernelTester& kc(size_t kc) { |
| 109 | assert(kc != 0); |
| 110 | this->kc_ = kc; |
| 111 | return *this; |
| 112 | } |
| 113 | |
| 114 | inline size_t kc() const { |
| 115 | return this->kc_; |
| 116 | } |
| 117 | |
| 118 | inline AvgPoolMicrokernelTester& x_stride(size_t x_stride) { |
| 119 | assert(x_stride != 0); |
| 120 | this->x_stride_ = x_stride; |
| 121 | return *this; |
| 122 | } |
| 123 | |
| 124 | inline size_t x_stride() const { |
| 125 | if (this->x_stride_ == 0) { |
| 126 | return kc(); |
| 127 | } else { |
| 128 | assert(this->x_stride_ >= kc()); |
| 129 | return this->x_stride_; |
| 130 | } |
| 131 | } |
| 132 | |
| 133 | inline AvgPoolMicrokernelTester& y_stride(size_t y_stride) { |
| 134 | assert(y_stride != 0); |
| 135 | this->y_stride_ = y_stride; |
| 136 | return *this; |
| 137 | } |
| 138 | |
| 139 | inline size_t y_stride() const { |
| 140 | if (this->y_stride_ == 0) { |
| 141 | return kc(); |
| 142 | } else { |
| 143 | assert(this->y_stride_ >= kc()); |
| 144 | return this->y_stride_; |
| 145 | } |
| 146 | } |
| 147 | |
| 148 | inline AvgPoolMicrokernelTester& x_scale(float x_scale) { |
| 149 | assert(x_scale > 0.0f); |
| 150 | assert(std::isnormal(x_scale)); |
| 151 | this->x_scale_ = x_scale; |
| 152 | return *this; |
| 153 | } |
| 154 | |
| 155 | inline float x_scale() const { |
| 156 | return this->x_scale_; |
| 157 | } |
| 158 | |
| 159 | inline AvgPoolMicrokernelTester& x_zero_point(uint8_t x_zero_point) { |
| 160 | this->x_zero_point_ = x_zero_point; |
| 161 | return *this; |
| 162 | } |
| 163 | |
| 164 | inline uint8_t x_zero_point() const { |
| 165 | return this->x_zero_point_; |
| 166 | } |
| 167 | |
| 168 | inline AvgPoolMicrokernelTester& y_scale(float y_scale) { |
| 169 | assert(y_scale > 0.0f); |
| 170 | assert(std::isnormal(y_scale)); |
| 171 | this->y_scale_ = y_scale; |
| 172 | return *this; |
| 173 | } |
| 174 | |
| 175 | inline float y_scale() const { |
| 176 | return this->y_scale_; |
| 177 | } |
| 178 | |
| 179 | inline AvgPoolMicrokernelTester& y_zero_point(uint8_t y_zero_point) { |
| 180 | this->y_zero_point_ = y_zero_point; |
| 181 | return *this; |
| 182 | } |
| 183 | |
| 184 | inline uint8_t y_zero_point() const { |
| 185 | return this->y_zero_point_; |
| 186 | } |
| 187 | |
| 188 | inline AvgPoolMicrokernelTester& qmin(uint8_t qmin) { |
| 189 | this->qmin_ = qmin; |
| 190 | return *this; |
| 191 | } |
| 192 | |
| 193 | inline uint8_t qmin() const { |
| 194 | return this->qmin_; |
| 195 | } |
| 196 | |
| 197 | inline AvgPoolMicrokernelTester& qmax(uint8_t qmax) { |
| 198 | this->qmax_ = qmax; |
| 199 | return *this; |
| 200 | } |
| 201 | |
| 202 | inline uint8_t qmax() const { |
| 203 | return this->qmax_; |
| 204 | } |
| 205 | |
| 206 | inline AvgPoolMicrokernelTester& iterations(size_t iterations) { |
| 207 | this->iterations_ = iterations; |
| 208 | return *this; |
| 209 | } |
| 210 | |
| 211 | inline size_t iterations() const { |
| 212 | return this->iterations_; |
| 213 | } |
| 214 | |
| 215 | void Test(xnn_q8_avgpool_up_ukernel_function avgpool, Variant variant = Variant::Native) const { |
| 216 | std::random_device random_device; |
| 217 | auto rng = std::mt19937(random_device()); |
| 218 | auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng); |
| 219 | |
Marat Dukhan | bd8a962 | 2019-12-06 01:05:35 -0800 | [diff] [blame] | 220 | std::vector<const uint8_t*> indirect_x(packed_ks() + (n() - 1) * s() * kh()); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 221 | std::vector<uint8_t> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
| 222 | |
| 223 | std::vector<uint8_t> zero(kc() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
| 224 | std::vector<uint8_t> y((n() - 1) * y_stride() + kc()); |
| 225 | std::vector<uint8_t> y_ref(n() * kc()); |
| 226 | std::vector<float> y_fp(n() * kc()); |
| 227 | std::vector<int32_t> y_acc(n() * kc()); |
| 228 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 229 | std::generate(x.begin(), x.end(), std::ref(u8rng)); |
| 230 | std::fill(y.begin(), y.end(), 0xA5); |
| 231 | |
| 232 | for (size_t i = 0; i < indirect_x.size(); i++) { |
| 233 | indirect_x[i] = x.data() + i * x_stride(); |
| 234 | } |
| 235 | std::shuffle(indirect_x.begin(), indirect_x.end(), rng); |
| 236 | |
| 237 | // Prepare quantization parameters. |
| 238 | xnn_q8_avgpool_params quantization_params = { }; |
| 239 | switch (variant) { |
| 240 | case Variant::Native: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 241 | quantization_params = xnn_init_q8_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 242 | -int32_t(x_zero_point()) * int32_t(ks()), |
| 243 | x_scale() / (y_scale() * float(ks())), |
| 244 | y_zero_point(), qmin(), qmax()); |
| 245 | break; |
| 246 | case Variant::Scalar: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 247 | quantization_params = xnn_init_scalar_q8_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 248 | -int32_t(x_zero_point()) * int32_t(ks()), |
| 249 | x_scale() / (y_scale() * float(ks())), |
| 250 | y_zero_point(), qmin(), qmax()); |
| 251 | break; |
| 252 | } |
| 253 | const xnn_q8_avgpool_params scalar_quantization_params = |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 254 | xnn_init_scalar_q8_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 255 | -int32_t(x_zero_point()) * int32_t(ks()), |
| 256 | x_scale() / (y_scale() * float(ks())), |
| 257 | y_zero_point(), qmin(), qmax()); |
| 258 | |
| 259 | // Compute reference results. |
| 260 | for (size_t i = 0; i < n(); i++) { |
| 261 | for (size_t k = 0; k < kc(); k++) { |
| 262 | int32_t acc = scalar_quantization_params.scalar.bias; |
| 263 | for (size_t j = 0; j < ks(); j++) { |
| 264 | acc += indirect_x[i * s() * kh() + j][k]; |
| 265 | } |
| 266 | y_acc[i * kc() + k] = acc; |
| 267 | y_ref[i * kc() + k] = xnn_avgpool_quantize(acc, scalar_quantization_params); |
| 268 | y_fp[i * kc() + k] = float(acc) * (x_scale() / (y_scale() * float(ks()))) + float(y_zero_point()); |
| 269 | y_fp[i * kc() + k] = std::min<float>(y_fp[i * kc() + k], float(qmax())); |
| 270 | y_fp[i * kc() + k] = std::max<float>(y_fp[i * kc() + k], float(qmin())); |
| 271 | } |
| 272 | } |
| 273 | |
| 274 | // Call optimized micro-kernel. |
| 275 | avgpool(n(), ks(), kc(), |
| 276 | indirect_x.data(), zero.data(), y.data(), |
| 277 | kh() * s() * sizeof(void*), |
| 278 | (y_stride() - kc()) * sizeof(uint8_t), |
| 279 | &quantization_params); |
| 280 | |
| 281 | // Verify results. |
| 282 | for (size_t i = 0; i < n(); i++) { |
| 283 | for (size_t k = 0; k < kc(); k++) { |
| 284 | ASSERT_LE(uint32_t(y[i * y_stride() + k]), uint32_t(qmax())) |
| 285 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 286 | ASSERT_GE(uint32_t(y[i * y_stride() + k]), uint32_t(qmin())) |
| 287 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 288 | ASSERT_NEAR(float(int32_t(y[i * y_stride() + k])), y_fp[i * kc() + k], 0.5f) |
| 289 | << "at pixel " << i << ", channel " << k << ", n = " << n() |
| 290 | << ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc() |
| 291 | << ", acc = " << y_acc[i * kc() + k]; |
| 292 | ASSERT_EQ(uint32_t(y_ref[i * kc() + k]), uint32_t(y[i * y_stride() + k])) |
| 293 | << "at pixel " << i << ", channel " << k << ", n = " << n() |
| 294 | << ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc() |
| 295 | << ", acc = " << y_acc[i * kc() + k]; |
| 296 | } |
| 297 | } |
| 298 | } |
| 299 | } |
| 300 | |
| 301 | void Test(xnn_q8_avgpool_mp_ukernel_function avgpool, Variant variant = Variant::Native) const { |
| 302 | std::random_device random_device; |
| 303 | auto rng = std::mt19937(random_device()); |
| 304 | auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng); |
| 305 | |
Marat Dukhan | bd8a962 | 2019-12-06 01:05:35 -0800 | [diff] [blame] | 306 | std::vector<const uint8_t*> indirect_x(packed_ks() + (n() - 1) * s() * kh()); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 307 | std::vector<uint8_t> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
Marat Dukhan | 9594db0 | 2019-12-05 14:32:37 -0800 | [diff] [blame] | 308 | std::vector<int32_t, AlignedAllocator<int32_t, 64>> buf(kc() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 309 | |
| 310 | std::vector<uint8_t> zero(kc() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
| 311 | std::vector<uint8_t> y((n() - 1) * y_stride() + kc()); |
| 312 | std::vector<uint8_t> y_ref(n() * kc()); |
| 313 | std::vector<float> y_fp(n() * kc()); |
| 314 | std::vector<int32_t> y_acc(n() * kc()); |
| 315 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 316 | std::generate(x.begin(), x.end(), std::ref(u8rng)); |
| 317 | std::fill(y.begin(), y.end(), 0xA5); |
| 318 | |
| 319 | for (size_t i = 0; i < indirect_x.size(); i++) { |
| 320 | indirect_x[i] = x.data() + i * x_stride(); |
| 321 | } |
| 322 | std::shuffle(indirect_x.begin(), indirect_x.end(), rng); |
| 323 | |
| 324 | // Prepare quantization parameters. |
| 325 | xnn_q8_avgpool_params quantization_params = { }; |
| 326 | switch (variant) { |
| 327 | case Variant::Native: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 328 | quantization_params = xnn_init_q8_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 329 | -int32_t(x_zero_point()) * int32_t(ks()), |
| 330 | x_scale() / (y_scale() * float(ks())), |
| 331 | y_zero_point(), qmin(), qmax()); |
| 332 | break; |
| 333 | case Variant::Scalar: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 334 | quantization_params = xnn_init_scalar_q8_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 335 | -int32_t(x_zero_point()) * int32_t(ks()), |
| 336 | x_scale() / (y_scale() * float(ks())), |
| 337 | y_zero_point(), qmin(), qmax()); |
| 338 | break; |
| 339 | } |
| 340 | const xnn_q8_avgpool_params scalar_quantization_params = |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 341 | xnn_init_scalar_q8_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 342 | -int32_t(x_zero_point()) * int32_t(ks()), |
| 343 | x_scale() / (y_scale() * float(ks())), |
| 344 | y_zero_point(), qmin(), qmax()); |
| 345 | |
| 346 | // Compute reference results. |
| 347 | for (size_t i = 0; i < n(); i++) { |
| 348 | for (size_t k = 0; k < kc(); k++) { |
| 349 | int32_t acc = scalar_quantization_params.scalar.bias; |
| 350 | for (size_t j = 0; j < ks(); j++) { |
| 351 | acc += indirect_x[i * s() * kh() + j][k]; |
| 352 | } |
| 353 | y_acc[i * kc() + k] = acc; |
| 354 | y_ref[i * kc() + k] = xnn_avgpool_quantize(acc, scalar_quantization_params); |
| 355 | y_fp[i * kc() + k] = float(acc) * (x_scale() / (y_scale() * float(ks()))) + float(y_zero_point()); |
| 356 | y_fp[i * kc() + k] = std::min<float>(y_fp[i * kc() + k], float(qmax())); |
| 357 | y_fp[i * kc() + k] = std::max<float>(y_fp[i * kc() + k], float(qmin())); |
| 358 | } |
| 359 | } |
| 360 | |
| 361 | // Call optimized micro-kernel. |
| 362 | avgpool(n(), ks(), kc(), |
| 363 | indirect_x.data(), zero.data(), buf.data(), y.data(), |
| 364 | (kh() * s() - (packed_ks() - qr())) * sizeof(void*), |
| 365 | (y_stride() - kc()) * sizeof(uint8_t), |
| 366 | &quantization_params); |
| 367 | |
| 368 | // Verify results. |
| 369 | for (size_t i = 0; i < n(); i++) { |
| 370 | for (size_t k = 0; k < kc(); k++) { |
| 371 | ASSERT_LE(uint32_t(y[i * y_stride() + k]), uint32_t(qmax())) |
| 372 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 373 | ASSERT_GE(uint32_t(y[i * y_stride() + k]), uint32_t(qmin())) |
| 374 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 375 | ASSERT_NEAR(float(int32_t(y[i * y_stride() + k])), y_fp[i * kc() + k], 0.5f) |
| 376 | << "at pixel " << i << ", channel " << k << ", n = " << n() |
| 377 | << ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc() |
| 378 | << ", acc = " << y_acc[i * kc() + k]; |
| 379 | ASSERT_EQ(uint32_t(y_ref[i * kc() + k]), uint32_t(y[i * y_stride() + k])) |
| 380 | << "at pixel " << i << ", channel " << k << ", n = " << n() |
| 381 | << ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc() |
| 382 | << ", acc = " << y_acc[i * kc() + k]; |
| 383 | } |
| 384 | } |
| 385 | } |
| 386 | } |
| 387 | |
| 388 | void Test(xnn_f32_avgpool_up_ukernel_function avgpool, Variant variant = Variant::Native) const { |
| 389 | std::random_device random_device; |
| 390 | auto rng = std::mt19937(random_device()); |
| 391 | auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng); |
| 392 | |
Marat Dukhan | bd8a962 | 2019-12-06 01:05:35 -0800 | [diff] [blame] | 393 | std::vector<const float*> indirect_x(packed_ks() + (n() - 1) * s() * kh()); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 394 | std::vector<float> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(float)); |
| 395 | |
| 396 | std::vector<float> zero(kc() + XNN_EXTRA_BYTES / sizeof(float)); |
| 397 | std::vector<float> y((n() - 1) * y_stride() + kc()); |
| 398 | std::vector<float> y_ref(n() * kc()); |
| 399 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 400 | std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| 401 | std::fill(y.begin(), y.end(), std::nanf("")); |
| 402 | |
| 403 | for (size_t i = 0; i < indirect_x.size(); i++) { |
| 404 | indirect_x[i] = x.data() + i * x_stride(); |
| 405 | } |
| 406 | std::shuffle(indirect_x.begin(), indirect_x.end(), rng); |
| 407 | |
| 408 | // Compute reference results, without clamping. |
| 409 | for (size_t i = 0; i < n(); i++) { |
| 410 | for (size_t k = 0; k < kc(); k++) { |
| 411 | float acc = 0.0f; |
| 412 | for (size_t j = 0; j < ks(); j++) { |
| 413 | acc += indirect_x[i * s() * kh() + j][k]; |
| 414 | } |
| 415 | y_ref[i * kc() + k] = acc / float(ks()); |
| 416 | } |
| 417 | } |
| 418 | |
| 419 | // Compute clamping parameters. |
| 420 | const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend()); |
| 421 | const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend()); |
| 422 | const float accumulated_range = accumulated_max - accumulated_min; |
| 423 | const float y_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
| 424 | const float y_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
| 425 | |
| 426 | // Clamp reference results. |
| 427 | for (float& y_value : y_ref) { |
| 428 | y_value = std::max(std::min(y_value, y_max), y_min); |
| 429 | } |
| 430 | |
| 431 | // Prepare output parameters. |
| 432 | xnn_f32_avgpool_params params = { }; |
| 433 | switch (variant) { |
| 434 | case Variant::Native: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 435 | params = xnn_init_f32_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 436 | 1.0f / float(ks()), y_min, y_max); |
| 437 | break; |
| 438 | case Variant::Scalar: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 439 | params = xnn_init_scalar_f32_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 440 | 1.0f / float(ks()), y_min, y_max); |
| 441 | break; |
| 442 | } |
| 443 | |
| 444 | // Call optimized micro-kernel. |
| 445 | avgpool(n(), ks(), kc(), |
| 446 | indirect_x.data(), zero.data(), y.data(), |
| 447 | kh() * s() * sizeof(void*), |
| 448 | (y_stride() - kc()) * sizeof(float), |
| 449 | ¶ms); |
| 450 | |
| 451 | // Verify results. |
| 452 | for (size_t i = 0; i < n(); i++) { |
| 453 | for (size_t k = 0; k < kc(); k++) { |
| 454 | ASSERT_LE(y[i * y_stride() + k], y_max) |
| 455 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 456 | ASSERT_GE(y[i * y_stride() + k], y_min) |
| 457 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 458 | ASSERT_NEAR(y[i * y_stride() + k], y_ref[i * kc() + k], std::abs(y_ref[i * kc() + k]) * 1.0e-6) |
| 459 | << "at pixel " << i << ", channel " << k << ", n = " << n() |
| 460 | << ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc(); |
| 461 | } |
| 462 | } |
| 463 | } |
| 464 | } |
| 465 | |
| 466 | void Test(xnn_f32_avgpool_mp_ukernel_function avgpool, Variant variant = Variant::Native) const { |
| 467 | std::random_device random_device; |
| 468 | auto rng = std::mt19937(random_device()); |
| 469 | auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng); |
| 470 | |
Marat Dukhan | bd8a962 | 2019-12-06 01:05:35 -0800 | [diff] [blame] | 471 | std::vector<const float*> indirect_x(packed_ks() + (n() - 1) * s() * kh()); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 472 | std::vector<float> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(float)); |
Marat Dukhan | 9594db0 | 2019-12-05 14:32:37 -0800 | [diff] [blame] | 473 | std::vector<float, AlignedAllocator<float, 64>> buf(kc() + XNN_EXTRA_BYTES / sizeof(float)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 474 | |
| 475 | std::vector<float> zero(kc() + XNN_EXTRA_BYTES / sizeof(float)); |
| 476 | std::vector<float> y((n() - 1) * y_stride() + kc()); |
| 477 | std::vector<float> y_ref(n() * kc()); |
| 478 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 479 | std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| 480 | std::fill(y.begin(), y.end(), std::nanf("")); |
| 481 | |
| 482 | for (size_t i = 0; i < indirect_x.size(); i++) { |
| 483 | indirect_x[i] = x.data() + i * x_stride(); |
| 484 | } |
| 485 | std::shuffle(indirect_x.begin(), indirect_x.end(), rng); |
| 486 | |
| 487 | // Compute reference results, without clamping. |
| 488 | for (size_t i = 0; i < n(); i++) { |
| 489 | for (size_t k = 0; k < kc(); k++) { |
| 490 | float acc = 0.0f; |
| 491 | for (size_t j = 0; j < ks(); j++) { |
| 492 | acc += indirect_x[i * s() * kh() + j][k]; |
| 493 | } |
| 494 | y_ref[i * kc() + k] = acc / float(ks()); |
| 495 | } |
| 496 | } |
| 497 | |
| 498 | // Compute clamping parameters. |
| 499 | const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend()); |
| 500 | const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend()); |
| 501 | const float accumulated_range = accumulated_max - accumulated_min; |
| 502 | const float y_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
| 503 | const float y_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
| 504 | |
| 505 | // Clamp reference results. |
| 506 | for (float& y_value : y_ref) { |
| 507 | y_value = std::max(std::min(y_value, y_max), y_min); |
| 508 | } |
| 509 | |
| 510 | // Prepare output parameters. |
| 511 | xnn_f32_avgpool_params params = { }; |
| 512 | switch (variant) { |
| 513 | case Variant::Native: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 514 | params = xnn_init_f32_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 515 | 1.0f / float(ks()), y_min, y_max); |
| 516 | break; |
| 517 | case Variant::Scalar: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 518 | params = xnn_init_scalar_f32_avgpool_params( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 519 | 1.0f / float(ks()), y_min, y_max); |
| 520 | break; |
| 521 | } |
| 522 | |
| 523 | // Call optimized micro-kernel. |
| 524 | avgpool(n(), ks(), kc(), |
| 525 | indirect_x.data(), zero.data(), buf.data(), y.data(), |
| 526 | (kh() * s() - (packed_ks() - qr())) * sizeof(void*), |
| 527 | (y_stride() - kc()) * sizeof(float), |
| 528 | ¶ms); |
| 529 | |
| 530 | // Verify results. |
| 531 | for (size_t i = 0; i < n(); i++) { |
| 532 | for (size_t k = 0; k < kc(); k++) { |
| 533 | ASSERT_LE(y[i * y_stride() + k], y_max) |
| 534 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 535 | ASSERT_GE(y[i * y_stride() + k], y_min) |
| 536 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 537 | ASSERT_NEAR(y[i * y_stride() + k], y_ref[i * kc() + k], std::abs(y_ref[i * kc() + k]) * 1.0e-6) |
| 538 | << "at pixel " << i << ", channel " << k << ", n = " << n() |
| 539 | << ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc(); |
| 540 | } |
| 541 | } |
| 542 | } |
| 543 | } |
| 544 | |
| 545 | void Test(xnn_f32_pavgpool_up_ukernel_function pavgpool, Variant variant = Variant::Native) const { |
| 546 | std::random_device random_device; |
| 547 | auto rng = std::mt19937(random_device()); |
| 548 | auto f32irng = std::bind(std::uniform_real_distribution<float>(), rng); |
| 549 | auto f32mrng = std::bind(std::uniform_real_distribution<float>(0.1f, 0.5f), rng); |
| 550 | |
Marat Dukhan | bd8a962 | 2019-12-06 01:05:35 -0800 | [diff] [blame] | 551 | std::vector<const float*> indirect_x(packed_ks() + (n() - 1) * s() * kh()); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 552 | std::vector<float> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(float)); |
| 553 | |
| 554 | std::vector<float> zero(kc() + XNN_EXTRA_BYTES / sizeof(float)); |
| 555 | std::vector<float> m(kc() + XNN_EXTRA_BYTES / sizeof(float)); |
| 556 | std::vector<float> y((n() - 1) * y_stride() + kc()); |
| 557 | std::vector<float> y_ref(n() * kc()); |
| 558 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 559 | std::generate(x.begin(), x.end(), std::ref(f32irng)); |
| 560 | std::generate(m.begin(), m.end(), std::ref(f32mrng)); |
| 561 | std::fill(y.begin(), y.end(), std::nanf("")); |
| 562 | |
| 563 | for (size_t i = 0; i < indirect_x.size(); i++) { |
| 564 | indirect_x[i] = x.data() + i * x_stride(); |
| 565 | } |
| 566 | std::shuffle(indirect_x.begin(), indirect_x.end(), rng); |
| 567 | |
| 568 | // Compute reference results, without clamping. |
| 569 | for (size_t i = 0; i < n(); i++) { |
| 570 | for (size_t k = 0; k < kc(); k++) { |
| 571 | float acc = 0.0f; |
| 572 | for (size_t j = 0; j < ks(); j++) { |
| 573 | acc += indirect_x[i * s() * kh() + j][k]; |
| 574 | } |
| 575 | y_ref[i * kc() + k] = acc * m[i]; |
| 576 | } |
| 577 | } |
| 578 | |
| 579 | // Compute clamping parameters. |
| 580 | const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend()); |
| 581 | const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend()); |
| 582 | const float accumulated_range = accumulated_max - accumulated_min; |
| 583 | const float y_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
| 584 | const float y_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
| 585 | |
| 586 | // Clamp reference results. |
| 587 | for (float& y_value : y_ref) { |
| 588 | y_value = std::max(std::min(y_value, y_max), y_min); |
| 589 | } |
| 590 | |
| 591 | // Prepare output parameters. |
| 592 | xnn_f32_output_params output_params = { }; |
| 593 | switch (variant) { |
| 594 | case Variant::Native: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 595 | output_params = xnn_init_f32_output_params(y_min, y_max); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 596 | break; |
| 597 | case Variant::Scalar: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 598 | output_params = xnn_init_scalar_f32_output_params(y_min, y_max); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 599 | break; |
| 600 | } |
| 601 | |
| 602 | // Call optimized micro-kernel. |
| 603 | pavgpool(n(), ks(), kc(), |
| 604 | indirect_x.data(), zero.data(), m.data(), y.data(), |
| 605 | kh() * s() * sizeof(void*), |
| 606 | (y_stride() - kc()) * sizeof(float), |
| 607 | &output_params); |
| 608 | |
| 609 | // Verify results. |
| 610 | for (size_t i = 0; i < n(); i++) { |
| 611 | for (size_t k = 0; k < kc(); k++) { |
| 612 | ASSERT_LE(y[i * y_stride() + k], y_max) |
| 613 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 614 | ASSERT_GE(y[i * y_stride() + k], y_min) |
| 615 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 616 | ASSERT_NEAR(y[i * y_stride() + k], y_ref[i * kc() + k], std::abs(y_ref[i * kc() + k]) * 1.0e-6) |
| 617 | << "at pixel " << i << ", channel " << k << ", n = " << n() |
| 618 | << ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc(); |
| 619 | } |
| 620 | } |
| 621 | } |
| 622 | } |
| 623 | |
| 624 | void Test(xnn_f32_pavgpool_mp_ukernel_function pavgpool, Variant variant = Variant::Native) const { |
| 625 | std::random_device random_device; |
| 626 | auto rng = std::mt19937(random_device()); |
| 627 | auto f32irng = std::bind(std::uniform_real_distribution<float>(), rng); |
| 628 | auto f32mrng = std::bind(std::uniform_real_distribution<float>(0.1f, 0.5f), rng); |
| 629 | |
Marat Dukhan | bd8a962 | 2019-12-06 01:05:35 -0800 | [diff] [blame] | 630 | std::vector<const float*> indirect_x(packed_ks() + (n() - 1) * s() * kh()); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 631 | std::vector<float> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(float)); |
Marat Dukhan | 9594db0 | 2019-12-05 14:32:37 -0800 | [diff] [blame] | 632 | std::vector<float, AlignedAllocator<float, 64>> buf(kc() + XNN_EXTRA_BYTES / sizeof(float)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 633 | |
| 634 | std::vector<float> zero(kc() + XNN_EXTRA_BYTES / sizeof(float)); |
| 635 | std::vector<float> m(kc() + XNN_EXTRA_BYTES / sizeof(float)); |
| 636 | std::vector<float> y((n() - 1) * y_stride() + kc()); |
| 637 | std::vector<float> y_ref(n() * kc()); |
| 638 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 639 | std::generate(x.begin(), x.end(), std::ref(f32irng)); |
| 640 | std::generate(m.begin(), m.end(), std::ref(f32mrng)); |
| 641 | std::fill(y.begin(), y.end(), std::nanf("")); |
| 642 | |
| 643 | for (size_t i = 0; i < indirect_x.size(); i++) { |
| 644 | indirect_x[i] = x.data() + i * x_stride(); |
| 645 | } |
| 646 | std::shuffle(indirect_x.begin(), indirect_x.end(), rng); |
| 647 | |
| 648 | // Compute reference results, without clamping. |
| 649 | for (size_t i = 0; i < n(); i++) { |
| 650 | for (size_t k = 0; k < kc(); k++) { |
| 651 | float acc = 0.0f; |
| 652 | for (size_t j = 0; j < ks(); j++) { |
| 653 | acc += indirect_x[i * s() * kh() + j][k]; |
| 654 | } |
| 655 | y_ref[i * kc() + k] = acc * m[i]; |
| 656 | } |
| 657 | } |
| 658 | |
| 659 | // Compute clamping parameters. |
| 660 | const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend()); |
| 661 | const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend()); |
| 662 | const float accumulated_range = accumulated_max - accumulated_min; |
| 663 | const float y_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range; |
| 664 | const float y_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range; |
| 665 | |
| 666 | // Clamp reference results. |
| 667 | for (float& y_value : y_ref) { |
| 668 | y_value = std::max(std::min(y_value, y_max), y_min); |
| 669 | } |
| 670 | |
| 671 | // Prepare output parameters. |
| 672 | xnn_f32_output_params output_params = { }; |
| 673 | switch (variant) { |
| 674 | case Variant::Native: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 675 | output_params = xnn_init_f32_output_params(y_min, y_max); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 676 | break; |
| 677 | case Variant::Scalar: |
Marat Dukhan | eeaa7bd | 2019-10-25 17:31:25 -0700 | [diff] [blame] | 678 | output_params = xnn_init_scalar_f32_output_params(y_min, y_max); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 679 | break; |
| 680 | } |
| 681 | |
| 682 | // Call optimized micro-kernel. |
| 683 | pavgpool(n(), ks(), kc(), |
| 684 | indirect_x.data(), zero.data(), m.data(), buf.data(), y.data(), |
| 685 | (kh() * s() - (packed_ks() - qr())) * sizeof(void*), |
| 686 | (y_stride() - kc()) * sizeof(float), |
| 687 | &output_params); |
| 688 | |
| 689 | // Verify results. |
| 690 | for (size_t i = 0; i < n(); i++) { |
| 691 | for (size_t k = 0; k < kc(); k++) { |
| 692 | ASSERT_LE(y[i * y_stride() + k], y_max) |
| 693 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 694 | ASSERT_GE(y[i * y_stride() + k], y_min) |
| 695 | << "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc(); |
| 696 | ASSERT_NEAR(y[i * y_stride() + k], y_ref[i * kc() + k], std::abs(y_ref[i * kc() + k]) * 1.0e-6) |
| 697 | << "at pixel " << i << ", channel " << k << ", n = " << n() |
| 698 | << ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc(); |
| 699 | } |
| 700 | } |
| 701 | } |
| 702 | } |
| 703 | |
| 704 | private: |
| 705 | size_t n_{1}; |
| 706 | size_t s_{1}; |
| 707 | size_t kh_{1}; |
| 708 | size_t kw_{1}; |
| 709 | size_t mr_{1}; |
| 710 | size_t qr_{1}; |
| 711 | size_t kc_{1}; |
| 712 | size_t x_stride_{0}; |
| 713 | size_t y_stride_{0}; |
| 714 | float x_scale_{1.25f}; |
| 715 | float y_scale_{0.75f}; |
| 716 | uint8_t x_zero_point_{121}; |
| 717 | uint8_t y_zero_point_{133}; |
| 718 | uint8_t qmin_{0}; |
| 719 | uint8_t qmax_{255}; |
| 720 | size_t iterations_{15}; |
| 721 | }; |