Dana Zlotnik | d5c496d | 2021-11-28 14:46:12 +0200 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2021-2022 Arm Limited. |
| 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | #ifndef SRC_CORE_NEON_KERNELS_ELEMENTWISE_IMPL_H |
| 25 | #define SRC_CORE_NEON_KERNELS_ELEMENTWISE_IMPL_H |
| 26 | |
| 27 | #include "src/core/NEON/NEAsymm.h" |
| 28 | |
| 29 | namespace arm_compute |
| 30 | { |
| 31 | namespace cpu |
| 32 | { |
| 33 | template <ArithmeticOperation op, typename VectorType> |
| 34 | typename VectorType::type elementwise_arithm_op(const typename VectorType::type &a, const typename VectorType::type &b) |
| 35 | { |
| 36 | using vec_type = typename VectorType::type; |
| 37 | using scalar_type = typename VectorType::scalar_type; |
| 38 | using tag_type = typename VectorType::tag_type; |
| 39 | |
| 40 | vec_type res = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{}); |
| 41 | |
| 42 | switch(op) |
| 43 | { |
| 44 | case ArithmeticOperation::MAX: |
| 45 | res = wrapper::vmax(a, b); |
| 46 | break; |
| 47 | case ArithmeticOperation::MIN: |
| 48 | res = wrapper::vmin(a, b); |
| 49 | break; |
| 50 | case ArithmeticOperation::SQUARED_DIFF: |
| 51 | { |
| 52 | const vec_type tmp = wrapper::vsub(a, b); |
| 53 | res = wrapper::vmul(tmp, tmp); |
| 54 | break; |
| 55 | } |
| 56 | case ArithmeticOperation::PRELU: |
| 57 | { |
| 58 | const vec_type zero = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{}); |
| 59 | const vec_type tmp = wrapper::vmul(a, b); |
| 60 | const auto gt = wrapper::vcgt(a, zero); |
| 61 | |
| 62 | res = wrapper::vbsl(gt, a, tmp); |
| 63 | break; |
| 64 | } |
| 65 | |
| 66 | default: |
| 67 | ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); |
| 68 | } |
| 69 | |
| 70 | return res; |
| 71 | } |
Dana Zlotnik | a538ae5 | 2022-02-21 13:12:41 +0200 | [diff] [blame^] | 72 | |
Dana Zlotnik | d5c496d | 2021-11-28 14:46:12 +0200 | [diff] [blame] | 73 | template <ArithmeticOperation op, typename ScalarType, typename VectorType> |
| 74 | typename VectorType::type elementwise_arithm_op_broadcast(const typename VectorType::type &a, const ScalarType &broadcast_value, const bool reorder) |
| 75 | { |
| 76 | using tag_type = typename VectorType::tag_type; |
| 77 | using vec_type = typename VectorType::type; |
| 78 | |
| 79 | vec_type broadcast_vector = wrapper::vdup_n(broadcast_value, tag_type{}); |
| 80 | return elementwise_arithm_op<op, VectorType>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector); |
| 81 | } |
| 82 | |
| 83 | template <typename InputScalarType, typename OutputScalarType, typename InputVectorType> |
| 84 | void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, |
| 85 | OutputScalarType (*scalar_func)(const InputScalarType &, const InputScalarType &), |
| 86 | int (*broadcast_func)(int, int, int, const InputScalarType *, const InputScalarType &, OutputScalarType *, const bool), |
| 87 | int (*neon_func)(int, int, int, const InputScalarType *, const InputScalarType *, OutputScalarType *)) |
| 88 | { |
| 89 | // Create input windows |
| 90 | Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| 91 | Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape()); |
| 92 | |
| 93 | // Clear X Dimension on execution window as we handle manually |
| 94 | Window win = window; |
| 95 | win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 96 | |
| 97 | const int window_step_x = std::min(16 / static_cast<int>(sizeof(OutputScalarType)), 8); |
| 98 | const auto window_start_x = static_cast<int>(window.x().start()); |
| 99 | const auto window_end_x = static_cast<int>(window.x().end()); |
| 100 | const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x(); |
| 101 | |
| 102 | if(is_broadcast_across_x) |
| 103 | { |
| 104 | const bool is_broadcast_input_2 = input2_win.x().step() == 0; |
| 105 | Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; |
| 106 | Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; |
| 107 | const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1; |
| 108 | const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; |
| 109 | |
| 110 | // Clear X Dimension on execution window as we handle manually |
| 111 | non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 112 | |
| 113 | Iterator broadcast_input(broadcast_tensor, broadcast_win); |
| 114 | Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); |
| 115 | Iterator output(out, win); |
| 116 | |
| 117 | execute_window_loop(win, [&](const Coordinates &) |
| 118 | { |
| 119 | auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); |
| 120 | const auto non_broadcast_input_ptr = reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr()); |
| 121 | const InputScalarType broadcast_value = *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr()); |
| 122 | |
| 123 | int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_value, output_ptr, !is_broadcast_input_2); |
| 124 | for(; x < window_end_x; ++x) |
| 125 | { |
| 126 | const auto a = *(non_broadcast_input_ptr + x); |
| 127 | *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? broadcast_value : a, !is_broadcast_input_2 ? a : broadcast_value); |
| 128 | } |
| 129 | }, |
| 130 | broadcast_input, non_broadcast_input, output); |
| 131 | } |
| 132 | else |
| 133 | { |
| 134 | // Clear X Dimension on execution window as we handle manually |
| 135 | input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 136 | input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 137 | |
| 138 | Iterator input1(in1, input1_win); |
| 139 | Iterator input2(in2, input2_win); |
| 140 | Iterator output(out, win); |
| 141 | |
| 142 | execute_window_loop(win, [&](const Coordinates &) |
| 143 | { |
| 144 | auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); |
| 145 | const auto input1_ptr = reinterpret_cast<const InputScalarType *>(input1.ptr()); |
| 146 | const auto input2_ptr = reinterpret_cast<const InputScalarType *>(input2.ptr()); |
| 147 | |
| 148 | int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr); |
| 149 | for(; x < window_end_x; ++x) |
| 150 | { |
| 151 | const auto a = *(input1_ptr + x); |
| 152 | const auto b = *(input2_ptr + x); |
| 153 | *(output_ptr + x) = (*scalar_func)(a, b); |
| 154 | } |
| 155 | }, |
| 156 | input1, input2, output); |
| 157 | } |
| 158 | } |
| 159 | |
| 160 | template <ArithmeticOperation op, typename ScalarType> |
| 161 | inline ScalarType elementwise_arithm_op_scalar(const ScalarType &a, const ScalarType &b) |
| 162 | { |
| 163 | auto res = ScalarType(0); |
| 164 | |
| 165 | switch(op) |
| 166 | { |
| 167 | case ArithmeticOperation::MAX: |
| 168 | res = std::max(a, b); |
| 169 | break; |
| 170 | case ArithmeticOperation::MIN: |
| 171 | res = std::min(a, b); |
| 172 | break; |
| 173 | case ArithmeticOperation::SQUARED_DIFF: |
| 174 | { |
| 175 | res = (a - b) * (a - b); |
| 176 | break; |
| 177 | } |
| 178 | case ArithmeticOperation::PRELU: |
| 179 | { |
| 180 | res = (a > 0 ? a : a * b); |
| 181 | break; |
| 182 | } |
| 183 | case ArithmeticOperation::DIV: |
| 184 | { |
| 185 | res = a / b; |
| 186 | if(std::is_integral<ScalarType>::value) |
| 187 | { |
| 188 | res = (b == 0) ? 0 : res; |
| 189 | if(static_cast<int32_t>(a) % static_cast<int32_t>(b) != 0 && ((a < 0) != (b < 0))) |
| 190 | { |
| 191 | --res; |
| 192 | } |
| 193 | } |
| 194 | break; |
| 195 | } |
| 196 | case ArithmeticOperation::POWER: |
| 197 | { |
| 198 | res = std::pow(a, b); |
| 199 | break; |
| 200 | } |
| 201 | default: |
| 202 | ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); |
| 203 | } |
| 204 | return res; |
| 205 | } |
| 206 | |
| 207 | template <> |
| 208 | inline int32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<int32_t, 4>>(const int32x4_t &a, const int32x4_t &b) |
| 209 | { |
| 210 | return vcvtq_s32_f32(vfloorq_f32(wrapper::vdiv(vcvtq_f32_s32(a), vcvtq_f32_s32(b)))); |
| 211 | } |
| 212 | |
| 213 | template <> |
| 214 | inline float32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b) |
| 215 | { |
| 216 | return wrapper::vdiv(a, b); |
| 217 | } |
| 218 | |
| 219 | template <> |
| 220 | inline float32x4_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b) |
| 221 | { |
| 222 | return wrapper::vpow(a, b); |
| 223 | } |
| 224 | |
| 225 | #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| 226 | template <> |
| 227 | inline float16x8_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<float16_t, 8>>(const float16x8_t &a, const float16x8_t &b) |
| 228 | { |
| 229 | return wrapper::vdiv(a, b); |
| 230 | } |
| 231 | |
| 232 | template <> |
| 233 | inline float16x8_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector<float16_t, 8>>(const float16x8_t &a, const float16x8_t &b) |
| 234 | { |
| 235 | return wrapper::vpow(a, b); |
| 236 | } |
| 237 | #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| 238 | |
| 239 | template <ArithmeticOperation op, typename ScalarType, typename VectorType> |
| 240 | inline int elementwise_arithm_op_loop(int window_start_x, int window_end_x, int window_step_x, |
| 241 | const ScalarType *input1_ptr, const ScalarType *input2_ptr, ScalarType *output_ptr) |
| 242 | { |
| 243 | int x = window_start_x; |
| 244 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 245 | { |
| 246 | const auto a = wrapper::vloadq(input1_ptr + x); |
| 247 | const auto b = wrapper::vloadq(input2_ptr + x); |
| 248 | wrapper::vstore(output_ptr + x, elementwise_arithm_op<op, VectorType>(a, b)); |
| 249 | } |
| 250 | return x; |
| 251 | } |
| 252 | |
| 253 | template <ArithmeticOperation op, typename ScalarType, typename VectorType> |
| 254 | inline int elementwise_arithm_op_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, |
| 255 | const ScalarType *non_broadcast_input_ptr, const ScalarType &broadcast_value, ScalarType *output_ptr, const bool reorder) |
| 256 | { |
| 257 | int x = window_start_x; |
| 258 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 259 | { |
| 260 | const auto a = wrapper::vloadq((non_broadcast_input_ptr + x)); |
| 261 | wrapper::vstore(output_ptr + x, elementwise_arithm_op_broadcast<op, ScalarType, VectorType>(a, broadcast_value, reorder)); |
| 262 | } |
| 263 | return x; |
| 264 | } |
| 265 | |
| 266 | template <ArithmeticOperation op, typename VectorType> |
| 267 | void elementwise_arithm_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| 268 | { |
| 269 | using scalar_type = typename VectorType::scalar_type; |
| 270 | |
| 271 | elementwise_op<scalar_type, scalar_type, VectorType>(in1, in2, out, window, |
| 272 | &elementwise_arithm_op_scalar<op, scalar_type>, |
| 273 | &elementwise_arithm_op_broadcast_loop<op, scalar_type, VectorType>, |
| 274 | &elementwise_arithm_op_loop<op, scalar_type, VectorType>); |
| 275 | } |
| 276 | |
| 277 | template <ComparisonOperation op, typename InputScalarType> |
| 278 | inline uint8_t elementwise_comp_op_scalar(const InputScalarType &a, const InputScalarType &b) |
| 279 | { |
| 280 | bool res = false; |
| 281 | |
| 282 | switch(op) |
| 283 | { |
| 284 | case ComparisonOperation::Equal: |
| 285 | res = (a == b); |
| 286 | break; |
| 287 | case ComparisonOperation::NotEqual: |
| 288 | res = (a != b); |
| 289 | break; |
| 290 | case ComparisonOperation::Greater: |
| 291 | res = (a > b); |
| 292 | break; |
| 293 | case ComparisonOperation::GreaterEqual: |
| 294 | res = (a >= b); |
| 295 | break; |
| 296 | case ComparisonOperation::Less: |
| 297 | res = (a < b); |
| 298 | break; |
| 299 | case ComparisonOperation::LessEqual: |
| 300 | res = (a <= b); |
| 301 | break; |
| 302 | default: |
| 303 | ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); |
| 304 | } |
| 305 | return res ? ~static_cast<uint8_t>(0) : static_cast<uint8_t>(0); |
| 306 | } |
| 307 | |
| 308 | template <ComparisonOperation op, typename InputVectorType, typename OutputVectorType> |
| 309 | inline OutputVectorType elementwise_comp_op(const InputVectorType &a, const InputVectorType &b) |
| 310 | { |
| 311 | OutputVectorType res = { 0, 0, 0, 0 }; |
| 312 | |
| 313 | switch(op) |
| 314 | { |
| 315 | case ComparisonOperation::Equal: |
| 316 | res = wrapper::vceq(a, b); |
| 317 | break; |
| 318 | case ComparisonOperation::NotEqual: |
| 319 | res = wrapper::vnot(wrapper::vceq(a, b)); |
| 320 | break; |
| 321 | case ComparisonOperation::Greater: |
| 322 | res = wrapper::vcgt(a, b); |
| 323 | break; |
| 324 | case ComparisonOperation::GreaterEqual: |
| 325 | res = wrapper::vcge(a, b); |
| 326 | break; |
| 327 | case ComparisonOperation::Less: |
| 328 | res = wrapper::vcgt(b, a); |
| 329 | break; |
| 330 | case ComparisonOperation::LessEqual: |
| 331 | res = wrapper::vcge(b, a); |
| 332 | break; |
| 333 | default: |
| 334 | ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); |
| 335 | } |
| 336 | |
| 337 | return res; |
| 338 | } |
| 339 | |
| 340 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType, typename OutputVectorType> |
| 341 | inline OutputVectorType elementwise_comp_op_broadcast(const InputVectorType &a, const InputScalarType &broadcast_value, const bool reorder) |
| 342 | { |
| 343 | InputVectorType broadcast_vector = wrapper::vdup_n(broadcast_value, wrapper::traits::vector_128_tag()); |
| 344 | return elementwise_comp_op<op, InputVectorType, OutputVectorType>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector); |
| 345 | } |
| 346 | |
| 347 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> |
| 348 | inline int elementwise_comp_op_broadcast_8_loop(int window_start_x, int window_end_x, int window_step_x, |
| 349 | const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder) |
| 350 | { |
| 351 | int x = window_start_x; |
| 352 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 353 | { |
| 354 | const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint8x16_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder); |
| 355 | wrapper::vstore(output_ptr + x, a); |
| 356 | } |
| 357 | return x; |
| 358 | } |
| 359 | |
| 360 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> |
| 361 | inline int elementwise_comp_op_broadcast_16_loop(int window_start_x, int window_end_x, int window_step_x, |
| 362 | const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder) |
| 363 | { |
| 364 | int x = window_start_x; |
| 365 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 366 | { |
| 367 | const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint16x8_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder); |
| 368 | wrapper::vstore(output_ptr + x, wrapper::vmovn(a)); |
| 369 | } |
| 370 | return x; |
| 371 | } |
| 372 | |
| 373 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> |
| 374 | inline int elementwise_comp_op_broadcast_32_loop(int window_start_x, int window_end_x, int window_step_x, |
| 375 | const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder) |
| 376 | { |
| 377 | int x = window_start_x; |
| 378 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 379 | { |
| 380 | const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq(non_broadcast_input_ptr + x), broadcast_value, reorder); |
| 381 | const auto b = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq(non_broadcast_input_ptr + x + 4), broadcast_value, reorder); |
| 382 | wrapper::vstore(output_ptr + x, wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(a), wrapper::vmovn(b)))); |
| 383 | } |
| 384 | if(x <= window_end_x - 4) |
| 385 | { |
| 386 | const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder); |
| 387 | for(int i = 0; i < 4; i++) |
| 388 | { |
| 389 | *(output_ptr + x + i) = wrapper::vgetlane(a, i); |
| 390 | } |
| 391 | x = +4; |
| 392 | } |
| 393 | return x; |
| 394 | } |
| 395 | |
| 396 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> |
| 397 | inline int elementwise_comp_op_8_loop(int window_start_x, int window_end_x, int window_step_x, |
| 398 | const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr) |
| 399 | { |
| 400 | int x = window_start_x; |
| 401 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 402 | { |
| 403 | const auto a = wrapper::vloadq(input1_ptr + x); |
| 404 | const auto b = wrapper::vloadq(input2_ptr + x); |
| 405 | const auto res = elementwise_comp_op<op, InputVectorType, uint8x16_t>(a, b); |
| 406 | wrapper::vstore(output_ptr + x, res); |
| 407 | } |
| 408 | return x; |
| 409 | } |
| 410 | |
| 411 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> |
| 412 | inline int elementwise_comp_op_16_loop(int window_start_x, int window_end_x, int window_step_x, |
| 413 | const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr) |
| 414 | { |
| 415 | int x = window_start_x; |
| 416 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 417 | { |
| 418 | const auto a = wrapper::vloadq(input1_ptr + x); |
| 419 | const auto b = wrapper::vloadq(input2_ptr + x); |
| 420 | const auto res = elementwise_comp_op<op, InputVectorType, uint16x8_t>(a, b); |
| 421 | wrapper::vstore(output_ptr + x, wrapper::vmovn(res)); |
| 422 | } |
| 423 | return x; |
| 424 | } |
| 425 | |
| 426 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> |
| 427 | inline int elementwise_comp_op_32_loop(int window_start_x, int window_end_x, int window_step_x, |
| 428 | const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr) |
| 429 | { |
| 430 | int x = window_start_x; |
| 431 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 432 | { |
| 433 | auto a = wrapper::vloadq(input1_ptr + x); |
| 434 | auto b = wrapper::vloadq(input2_ptr + x); |
| 435 | const auto res = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b); |
| 436 | a = wrapper::vloadq(input1_ptr + x + 4); |
| 437 | b = wrapper::vloadq(input2_ptr + x + 4); |
| 438 | const auto res2 = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b); |
| 439 | wrapper::vstore(output_ptr + x, wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(res), wrapper::vmovn(res2)))); |
| 440 | } |
| 441 | if(x <= window_end_x - 4) |
| 442 | { |
| 443 | const auto a = wrapper::vloadq(input1_ptr + x); |
| 444 | const auto b = wrapper::vloadq(input2_ptr + x); |
| 445 | const auto res = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b); |
| 446 | for(int i = 0; i < 4; i++) |
| 447 | { |
| 448 | *(output_ptr + x + i) = wrapper::vgetlane(res, i); |
| 449 | } |
| 450 | x = +4; |
| 451 | } |
| 452 | return x; |
| 453 | } |
| 454 | |
| 455 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> |
| 456 | void elementwise_comp_op_8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| 457 | { |
| 458 | elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window, |
| 459 | &elementwise_comp_op_scalar<op, InputScalarType>, |
| 460 | &elementwise_comp_op_broadcast_8_loop<op, InputScalarType, InputVectorType>, |
| 461 | &elementwise_comp_op_8_loop<op, InputScalarType, InputVectorType>); |
| 462 | } |
| 463 | |
| 464 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> |
| 465 | void elementwise_comp_op_16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| 466 | { |
| 467 | elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window, |
| 468 | &elementwise_comp_op_scalar<op, InputScalarType>, |
| 469 | &elementwise_comp_op_broadcast_16_loop<op, InputScalarType, InputVectorType>, |
| 470 | &elementwise_comp_op_16_loop<op, InputScalarType, InputVectorType>); |
| 471 | } |
| 472 | |
| 473 | template <ComparisonOperation op, typename InputScalarType, typename InputVectorType> |
| 474 | void elementwise_comp_op_32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| 475 | { |
| 476 | elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window, |
| 477 | &elementwise_comp_op_scalar<op, InputScalarType>, |
| 478 | &elementwise_comp_op_broadcast_32_loop<op, InputScalarType, InputVectorType>, |
| 479 | &elementwise_comp_op_32_loop<op, InputScalarType, InputVectorType>); |
| 480 | } |
| 481 | |
| 482 | inline float32x4x4_t load_quantized(const uint8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale) |
| 483 | { |
| 484 | qasymm8x16_t x = vld1q_u8(input1_ptr); |
| 485 | const float32x4x4_t out = |
| 486 | { |
| 487 | { |
| 488 | vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale), |
| 489 | vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale), |
| 490 | vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale), |
| 491 | vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale), |
| 492 | } |
| 493 | }; |
| 494 | return out; |
| 495 | } |
| 496 | |
| 497 | inline float32x4x4_t load_quantized_signed(const int8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale) |
| 498 | { |
| 499 | qasymm8x16_signed_t x = vld1q_s8(input1_ptr); |
| 500 | const float32x4x4_t out = |
| 501 | { |
| 502 | { |
| 503 | vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(x)))), offset)), scale), |
| 504 | vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_low_s8(x)))), offset)), scale), |
| 505 | vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_high_s8(x)))), offset)), scale), |
| 506 | vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_high_s8(x)))), offset)), scale), |
| 507 | } |
| 508 | }; |
| 509 | return out; |
| 510 | } |
| 511 | |
| 512 | inline void store_quantized(uint8_t *output_ptr, const uint32x4x4_t &out) |
| 513 | { |
| 514 | const uint8x8_t pa = vqmovn_u16(vcombine_u16(vqmovn_u32(out.val[0]), vqmovn_u32(out.val[1]))); |
| 515 | const uint8x8_t pb = vqmovn_u16(vcombine_u16(vqmovn_u32(out.val[2]), vqmovn_u32(out.val[3]))); |
| 516 | vst1q_u8(output_ptr, vcombine_u8(pa, pb)); |
| 517 | } |
| 518 | |
| 519 | inline void store_quantized(uint8_t *output_ptr, const int32x4x4_t &out) |
| 520 | { |
| 521 | const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1]))); |
| 522 | const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3]))); |
| 523 | vst1q_u8(output_ptr, vcombine_u8(pa, pb)); |
| 524 | } |
| 525 | |
| 526 | inline void store_quantized(uint8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset, const float32x4_t &invscale) |
| 527 | { |
| 528 | int32x4x4_t out = |
| 529 | { |
| 530 | { |
| 531 | vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)), |
| 532 | vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)), |
| 533 | vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)), |
| 534 | vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)), |
| 535 | } |
| 536 | }; |
| 537 | store_quantized(output_ptr, out); |
| 538 | } |
| 539 | |
| 540 | inline void store_quantized_signed(int8_t *output_ptr, const int32x4x4_t &out) |
| 541 | { |
| 542 | const int8x8_t pa = vqmovn_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1]))); |
| 543 | const int8x8_t pb = vqmovn_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3]))); |
| 544 | vst1q_s8(output_ptr, vcombine_s8(pa, pb)); |
| 545 | } |
| 546 | |
| 547 | inline void store_quantized_signed(int8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset, const float32x4_t &invscale) |
| 548 | { |
| 549 | int32x4x4_t out = |
| 550 | { |
| 551 | { |
| 552 | vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)), |
| 553 | vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)), |
| 554 | vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)), |
| 555 | vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)), |
| 556 | } |
| 557 | }; |
| 558 | store_quantized_signed(output_ptr, out); |
| 559 | } |
| 560 | |
| 561 | template <ArithmeticOperation op> |
| 562 | inline uint8_t elementwise_arithm_op_quantized_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo) |
| 563 | { |
| 564 | return quantize_qasymm8(elementwise_arithm_op_scalar<op>(a, b), qinfo); |
| 565 | } |
| 566 | |
| 567 | template <ArithmeticOperation op> |
| 568 | inline int8_t elementwise_arithm_op_quantized_signed_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo) |
| 569 | { |
| 570 | return quantize_qasymm8_signed(elementwise_arithm_op_scalar<op>(a, b), qinfo); |
| 571 | } |
| 572 | |
| 573 | template <ArithmeticOperation op> |
| 574 | float32x4x4_t elementwise_arithm_op(const float32x4x4_t &a, const float32x4x4_t &b) |
| 575 | { |
| 576 | using neon_vector_float = wrapper::traits::neon_vector<float, 4>; |
| 577 | float32x4x4_t out = |
| 578 | { |
| 579 | { |
| 580 | elementwise_arithm_op<op, neon_vector_float>(a.val[0], b.val[0]), |
| 581 | elementwise_arithm_op<op, neon_vector_float>(a.val[1], b.val[1]), |
| 582 | elementwise_arithm_op<op, neon_vector_float>(a.val[2], b.val[2]), |
| 583 | elementwise_arithm_op<op, neon_vector_float>(a.val[3], b.val[3]), |
| 584 | } |
| 585 | }; |
| 586 | return out; |
| 587 | } |
| 588 | |
| 589 | template <ComparisonOperation op> |
| 590 | inline uint8_t elementwise_comp_op_quantized_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo) |
| 591 | { |
| 592 | ARM_COMPUTE_UNUSED(qinfo); |
| 593 | return elementwise_comp_op_scalar<op>(a, b); |
| 594 | } |
| 595 | |
| 596 | template <ComparisonOperation op> |
| 597 | inline uint32x4x4_t elementwise_comp_op(const float32x4x4_t &a, const float32x4x4_t &b) |
| 598 | { |
| 599 | uint32x4x4_t out = |
| 600 | { |
| 601 | { |
| 602 | elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[0], b.val[0]), |
| 603 | elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[1], b.val[1]), |
| 604 | elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[2], b.val[2]), |
| 605 | elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[3], b.val[3]) |
| 606 | } |
| 607 | }; |
| 608 | return out; |
| 609 | } |
| 610 | |
| 611 | template <ArithmeticOperation op> |
| 612 | inline int elementwise_arithm_op_quantized_loop(int window_start_x, int window_end_x, int window_step_x, |
| 613 | const uint8_t *input1_ptr, const uint8_t *input2_ptr, uint8_t *output_ptr, |
| 614 | int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2, |
| 615 | float32x4_t voffseto, float32x4_t invvscaleo) |
| 616 | { |
| 617 | int x = window_start_x; |
| 618 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 619 | { |
| 620 | // Get inputs and compute output |
| 621 | const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1); |
| 622 | const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2); |
| 623 | const float32x4x4_t rf = elementwise_arithm_op<op>(af, bf); |
| 624 | store_quantized(output_ptr + x, rf, voffseto, invvscaleo); |
| 625 | } |
| 626 | return x; |
| 627 | } |
| 628 | |
| 629 | template <ArithmeticOperation op> |
| 630 | inline int elementwise_arithm_op_quantized_singed_loop(int window_start_x, int window_end_x, int window_step_x, |
| 631 | const int8_t *input1_ptr, const int8_t *input2_ptr, int8_t *output_ptr, |
| 632 | int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2, |
| 633 | float32x4_t voffseto, float32x4_t invvscaleo) |
| 634 | { |
| 635 | int x = window_start_x; |
| 636 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 637 | { |
| 638 | // Get inputs and compute output |
| 639 | const float32x4x4_t af = load_quantized_signed(input1_ptr + x, voffset1, vscale1); |
| 640 | const float32x4x4_t bf = load_quantized_signed(input2_ptr + x, voffset2, vscale2); |
| 641 | const float32x4x4_t rf = elementwise_arithm_op<op>(af, bf); |
| 642 | store_quantized_signed(output_ptr + x, rf, voffseto, invvscaleo); |
| 643 | } |
| 644 | return x; |
| 645 | } |
| 646 | |
| 647 | template <ArithmeticOperation op> |
| 648 | inline int elementwise_arithm_op_quantized_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, |
| 649 | const uint8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr, |
| 650 | int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast, |
| 651 | float32x4_t voffseto, float32x4_t invvscaleo, bool reorder) |
| 652 | { |
| 653 | int x = window_start_x; |
| 654 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 655 | { |
| 656 | const float32x4x4_t af = load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast); |
| 657 | const float32x4x4_t rf = elementwise_arithm_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector); |
| 658 | store_quantized(output_ptr + x, rf, voffseto, invvscaleo); |
| 659 | } |
| 660 | return x; |
| 661 | } |
| 662 | template <ArithmeticOperation op> |
| 663 | inline int elementwise_arithm_op_quantized_signed_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, |
| 664 | const int8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, int8_t *output_ptr, |
| 665 | int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast, |
| 666 | float32x4_t voffseto, float32x4_t invvscaleo, bool reorder) |
| 667 | { |
| 668 | int x = window_start_x; |
| 669 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 670 | { |
| 671 | const float32x4x4_t af = load_quantized_signed(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast); |
| 672 | const float32x4x4_t rf = elementwise_arithm_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector); |
| 673 | store_quantized_signed(output_ptr + x, rf, voffseto, invvscaleo); |
| 674 | } |
| 675 | return x; |
| 676 | } |
| 677 | |
| 678 | template <ComparisonOperation op> |
| 679 | inline int elementwise_comp_op_quantized_loop(int window_start_x, int window_end_x, int window_step_x, |
| 680 | const uint8_t *input1_ptr, const uint8_t *input2_ptr, uint8_t *output_ptr, |
| 681 | int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2, |
| 682 | float32x4_t voffseto, float32x4_t invvscaleo) |
| 683 | { |
| 684 | ARM_COMPUTE_UNUSED(voffseto, invvscaleo); |
| 685 | int x = window_start_x; |
| 686 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 687 | { |
| 688 | const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1); |
| 689 | const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2); |
| 690 | const uint32x4x4_t rf = elementwise_comp_op<op>(af, bf); |
| 691 | store_quantized(output_ptr + x, rf); |
| 692 | } |
| 693 | return x; |
| 694 | } |
| 695 | |
| 696 | template <ComparisonOperation op> |
| 697 | inline int elementwise_comp_op_quantized_signed_loop(int window_start_x, int window_end_x, int window_step_x, |
| 698 | const int8_t *input1_ptr, const int8_t *input2_ptr, uint8_t *output_ptr, |
| 699 | int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2, |
| 700 | float32x4_t voffseto, float32x4_t invvscaleo) |
| 701 | { |
| 702 | ARM_COMPUTE_UNUSED(voffseto, invvscaleo); |
| 703 | int x = window_start_x; |
| 704 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 705 | { |
| 706 | const float32x4x4_t af = load_quantized_signed(input1_ptr + x, voffset1, vscale1); |
| 707 | const float32x4x4_t bf = load_quantized_signed(input2_ptr + x, voffset2, vscale2); |
| 708 | const uint32x4x4_t rf = elementwise_comp_op<op>(af, bf); |
| 709 | store_quantized(output_ptr + x, rf); |
| 710 | } |
| 711 | return x; |
| 712 | } |
| 713 | |
| 714 | template <ComparisonOperation op> |
| 715 | inline int elementwise_comp_op_quantized_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, |
| 716 | const uint8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr, |
| 717 | int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast, |
| 718 | float32x4_t voffseto, float32x4_t invvscaleo, bool reorder) |
| 719 | { |
| 720 | ARM_COMPUTE_UNUSED(voffseto, invvscaleo); |
| 721 | int x = window_start_x; |
| 722 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 723 | { |
| 724 | const float32x4x4_t af = load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast); |
| 725 | const uint32x4x4_t rf = elementwise_comp_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector); |
| 726 | store_quantized(output_ptr + x, rf); |
| 727 | } |
| 728 | return x; |
| 729 | } |
| 730 | |
| 731 | template <ComparisonOperation op> |
| 732 | inline int elementwise_comp_op_quantized_signed_broadcast_loop(int window_start_x, int window_end_x, int window_step_x, |
| 733 | const int8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr, |
| 734 | int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast, |
| 735 | float32x4_t voffseto, float32x4_t invvscaleo, bool reorder) |
| 736 | { |
| 737 | ARM_COMPUTE_UNUSED(voffseto, invvscaleo); |
| 738 | int x = window_start_x; |
| 739 | for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| 740 | { |
| 741 | const float32x4x4_t af = load_quantized_signed(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast); |
| 742 | const uint32x4x4_t rf = elementwise_comp_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector); |
| 743 | store_quantized(output_ptr + x, rf); |
| 744 | } |
| 745 | return x; |
| 746 | } |
| 747 | |
| 748 | inline void elementwise_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, |
| 749 | uint8_t (*scalar_func)(const float &, const float &, UniformQuantizationInfo), |
| 750 | int (*broadcast_func)(int, int, int, const uint8_t *, float32x4x4_t, uint8_t *, int32x4_t, float32x4_t, |
| 751 | float32x4_t, float32x4_t, const bool), |
| 752 | int (*neon_func)(int, int, int, const uint8_t *, const uint8_t *, uint8_t *, |
| 753 | int32x4_t, int32x4_t, float32x4_t, float32x4_t, |
| 754 | float32x4_t, float32x4_t)) |
| 755 | { |
| 756 | // Create input windows |
| 757 | Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| 758 | Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape()); |
| 759 | |
| 760 | // Clear X Dimension on execution window as we handle manually |
| 761 | Window win = window; |
| 762 | win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 763 | |
| 764 | const int window_step_x = 16; |
| 765 | const auto window_start_x = static_cast<int>(window.x().start()); |
| 766 | const auto window_end_x = static_cast<int>(window.x().end()); |
| 767 | const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x(); |
| 768 | |
| 769 | const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform(); |
| 770 | |
| 771 | // Output quantization info (add 0.5 to round toward the nearest integer - 0.5 rounds away from zero) |
| 772 | const float32x4_t voffseto = vdupq_n_f32(output_qinfo.offset + 0.5f); |
| 773 | const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale); |
| 774 | |
| 775 | if(is_broadcast_across_x) |
| 776 | { |
| 777 | // Select the broadcast input on the X axis |
| 778 | const bool is_broadcast_input_2 = input2_win.x().step() == 0; |
| 779 | Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; |
| 780 | Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; |
| 781 | const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1; |
| 782 | const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; |
| 783 | |
| 784 | const UniformQuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info().uniform(); |
| 785 | const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform(); |
| 786 | |
| 787 | const int32x4_t voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset); |
| 788 | const float32x4_t vscale_non_broadcast = vdupq_n_f32(non_broadcast_qinfo.scale); |
| 789 | |
| 790 | // Clear X Dimension on execution window as we handle manually |
| 791 | non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 792 | |
| 793 | Iterator broadcast_input(broadcast_tensor, broadcast_win); |
| 794 | Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); |
| 795 | Iterator output(out, win); |
| 796 | |
| 797 | execute_window_loop(win, [&](const Coordinates &) |
| 798 | { |
| 799 | const auto non_broadcast_input_ptr = reinterpret_cast<const uint8_t *>(non_broadcast_input.ptr()); |
| 800 | const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr()); |
| 801 | |
| 802 | const uint8_t broadcast_value = *reinterpret_cast<const uint8_t *>(broadcast_input.ptr()); |
| 803 | const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_u8(broadcast_value), broadcast_qinfo); |
| 804 | |
| 805 | int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr, |
| 806 | voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2); |
| 807 | for(; x < window_end_x; ++x) |
| 808 | { |
| 809 | const float afs = dequantize_qasymm8(*(non_broadcast_input_ptr + x), non_broadcast_qinfo); |
| 810 | const float bfs = dequantize_qasymm8(broadcast_value, broadcast_qinfo); |
| 811 | *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo); |
| 812 | } |
| 813 | }, |
| 814 | broadcast_input, non_broadcast_input, output); |
| 815 | } |
| 816 | else |
| 817 | { |
| 818 | const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform(); |
| 819 | const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform(); |
| 820 | |
| 821 | // Input1 quantization info |
| 822 | const int32x4_t voffset1 = vdupq_n_s32(input1_qinfo.offset); |
| 823 | const float32x4_t vscale1 = vdupq_n_f32(input1_qinfo.scale); |
| 824 | |
| 825 | // Input2 quantization info |
| 826 | const int32x4_t voffset2 = vdupq_n_s32(input2_qinfo.offset); |
| 827 | const float32x4_t vscale2 = vdupq_n_f32(input2_qinfo.scale); |
| 828 | |
| 829 | // Clear X Dimension on execution window as we handle manually |
| 830 | input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 831 | input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 832 | |
| 833 | Iterator input1(in1, input1_win); |
| 834 | Iterator input2(in2, input2_win); |
| 835 | Iterator output(out, win); |
| 836 | |
| 837 | execute_window_loop(win, [&](const Coordinates &) |
| 838 | { |
| 839 | const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr()); |
| 840 | const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr()); |
| 841 | const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr()); |
| 842 | |
| 843 | int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2, |
| 844 | vscale1, vscale2, voffseto, invvscaleo); |
| 845 | for(; x < window_end_x; ++x) |
| 846 | { |
| 847 | const float afs = dequantize_qasymm8(*(input1_ptr + x), input1_qinfo); |
| 848 | const float bfs = dequantize_qasymm8(*(input2_ptr + x), input2_qinfo); |
| 849 | *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo); |
| 850 | } |
| 851 | }, |
| 852 | input1, input2, output); |
| 853 | } |
| 854 | } |
| 855 | |
| 856 | inline void elementwise_comp_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, |
| 857 | uint8_t (*scalar_func)(const float &, const float &, UniformQuantizationInfo), |
| 858 | int (*broadcast_func)(int, int, int, const int8_t *, float32x4x4_t, uint8_t *, int32x4_t, float32x4_t, |
| 859 | float32x4_t, float32x4_t, const bool), |
| 860 | int (*neon_func)(int, int, int, const int8_t *, const int8_t *, uint8_t *, |
| 861 | int32x4_t, int32x4_t, float32x4_t, float32x4_t, |
| 862 | float32x4_t, float32x4_t)) |
| 863 | { |
| 864 | // Create input windows |
| 865 | Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| 866 | Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape()); |
| 867 | |
| 868 | // Clear X Dimension on execution window as we handle manually |
| 869 | Window win = window; |
| 870 | win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 871 | |
| 872 | const int window_step_x = 16; |
| 873 | const auto window_start_x = static_cast<int>(window.x().start()); |
| 874 | const auto window_end_x = static_cast<int>(window.x().end()); |
| 875 | const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x(); |
| 876 | |
| 877 | const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform(); |
| 878 | |
| 879 | const float32x4_t voffseto = vdupq_n_f32(output_qinfo.offset); |
| 880 | const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale); |
| 881 | |
| 882 | if(is_broadcast_across_x) |
| 883 | { |
| 884 | // Select the broadcast input on the X axis |
| 885 | const bool is_broadcast_input_2 = input2_win.x().step() == 0; |
| 886 | Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; |
| 887 | Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; |
| 888 | const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1; |
| 889 | const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; |
| 890 | |
| 891 | const UniformQuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info().uniform(); |
| 892 | const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform(); |
| 893 | |
| 894 | const int32x4_t voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset); |
| 895 | const float32x4_t vscale_non_broadcast = vdupq_n_f32(non_broadcast_qinfo.scale); |
| 896 | |
| 897 | // Clear X Dimension on execution window as we handle manually |
| 898 | non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 899 | |
| 900 | Iterator broadcast_input(broadcast_tensor, broadcast_win); |
| 901 | Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); |
| 902 | Iterator output(out, win); |
| 903 | |
| 904 | execute_window_loop(win, [&](const Coordinates &) |
| 905 | { |
| 906 | const auto non_broadcast_input_ptr = reinterpret_cast<const int8_t *>(non_broadcast_input.ptr()); |
| 907 | const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr()); |
| 908 | |
| 909 | const int8_t broadcast_value = *reinterpret_cast<const int8_t *>(broadcast_input.ptr()); |
| 910 | const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_s8(broadcast_value), broadcast_qinfo); |
| 911 | |
| 912 | int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr, |
| 913 | voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2); |
| 914 | for(; x < window_end_x; ++x) |
| 915 | { |
| 916 | const float afs = dequantize_qasymm8_signed(*(non_broadcast_input_ptr + x), non_broadcast_qinfo); |
| 917 | const float bfs = dequantize_qasymm8_signed(broadcast_value, broadcast_qinfo); |
| 918 | *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo); |
| 919 | } |
| 920 | }, |
| 921 | broadcast_input, non_broadcast_input, output); |
| 922 | } |
| 923 | else |
| 924 | { |
| 925 | const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform(); |
| 926 | const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform(); |
| 927 | |
| 928 | // Input1 quantization info |
| 929 | const int32x4_t voffset1 = vdupq_n_s32(input1_qinfo.offset); |
| 930 | const float32x4_t vscale1 = vdupq_n_f32(input1_qinfo.scale); |
| 931 | |
| 932 | // Input2 quantization info |
| 933 | const int32x4_t voffset2 = vdupq_n_s32(input2_qinfo.offset); |
| 934 | const float32x4_t vscale2 = vdupq_n_f32(input2_qinfo.scale); |
| 935 | |
| 936 | // Clear X Dimension on execution window as we handle manually |
| 937 | input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 938 | input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 939 | |
| 940 | Iterator input1(in1, input1_win); |
| 941 | Iterator input2(in2, input2_win); |
| 942 | Iterator output(out, win); |
| 943 | |
| 944 | execute_window_loop(win, [&](const Coordinates &) |
| 945 | { |
| 946 | const auto input1_ptr = reinterpret_cast<const int8_t *>(input1.ptr()); |
| 947 | const auto input2_ptr = reinterpret_cast<const int8_t *>(input2.ptr()); |
| 948 | const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr()); |
| 949 | |
| 950 | int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2, |
| 951 | vscale1, vscale2, voffseto, invvscaleo); |
| 952 | for(; x < window_end_x; ++x) |
| 953 | { |
| 954 | const float afs = dequantize_qasymm8_signed(*(input1_ptr + x), input1_qinfo); |
| 955 | const float bfs = dequantize_qasymm8_signed(*(input2_ptr + x), input2_qinfo); |
| 956 | *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo); |
| 957 | } |
| 958 | }, |
| 959 | input1, input2, output); |
| 960 | } |
| 961 | } |
| 962 | |
| 963 | inline void elementwise_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, |
| 964 | int8_t (*scalar_func)(const float &, const float &, UniformQuantizationInfo), |
| 965 | int (*broadcast_func)(int, int, int, const int8_t *, float32x4x4_t, int8_t *, int32x4_t, float32x4_t, |
| 966 | float32x4_t, float32x4_t, const bool), |
| 967 | int (*neon_func)(int, int, int, const int8_t *, const int8_t *, int8_t *, |
| 968 | int32x4_t, int32x4_t, float32x4_t, float32x4_t, |
| 969 | float32x4_t, float32x4_t)) |
| 970 | { |
| 971 | // Create input windows |
| 972 | Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); |
| 973 | Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape()); |
| 974 | |
| 975 | // Clear X Dimension on execution window as we handle manually |
| 976 | Window win = window; |
| 977 | win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 978 | |
| 979 | const int window_step_x = 16; |
| 980 | const auto window_start_x = static_cast<int>(window.x().start()); |
| 981 | const auto window_end_x = static_cast<int>(window.x().end()); |
| 982 | const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x(); |
| 983 | |
| 984 | const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform(); |
| 985 | |
| 986 | const float32x4_t voffseto = vdupq_n_f32(output_qinfo.offset); |
| 987 | const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale); |
| 988 | |
| 989 | if(is_broadcast_across_x) |
| 990 | { |
| 991 | // Select the broadcast input on the X axis |
| 992 | const bool is_broadcast_input_2 = input2_win.x().step() == 0; |
| 993 | Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; |
| 994 | Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; |
| 995 | const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1; |
| 996 | const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; |
| 997 | |
| 998 | const UniformQuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info().uniform(); |
| 999 | const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform(); |
| 1000 | |
| 1001 | const int32x4_t voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset); |
| 1002 | const float32x4_t vscale_non_broadcast = vdupq_n_f32(non_broadcast_qinfo.scale); |
| 1003 | |
| 1004 | // Clear X Dimension on execution window as we handle manually |
| 1005 | non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 1006 | |
| 1007 | Iterator broadcast_input(broadcast_tensor, broadcast_win); |
| 1008 | Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); |
| 1009 | Iterator output(out, win); |
| 1010 | |
| 1011 | execute_window_loop(win, [&](const Coordinates &) |
| 1012 | { |
| 1013 | const auto non_broadcast_input_ptr = reinterpret_cast<const int8_t *>(non_broadcast_input.ptr()); |
| 1014 | const auto output_ptr = reinterpret_cast<int8_t *>(output.ptr()); |
| 1015 | |
| 1016 | const int8_t broadcast_value = *reinterpret_cast<const int8_t *>(broadcast_input.ptr()); |
| 1017 | const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_s8(broadcast_value), broadcast_qinfo); |
| 1018 | |
| 1019 | int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr, |
| 1020 | voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2); |
| 1021 | for(; x < window_end_x; ++x) |
| 1022 | { |
| 1023 | const float afs = dequantize_qasymm8_signed(*(non_broadcast_input_ptr + x), non_broadcast_qinfo); |
| 1024 | const float bfs = dequantize_qasymm8_signed(broadcast_value, broadcast_qinfo); |
| 1025 | *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo); |
| 1026 | } |
| 1027 | }, |
| 1028 | broadcast_input, non_broadcast_input, output); |
| 1029 | } |
| 1030 | else |
| 1031 | { |
| 1032 | const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform(); |
| 1033 | const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform(); |
| 1034 | |
| 1035 | // Input1 quantization info |
| 1036 | const int32x4_t voffset1 = vdupq_n_s32(input1_qinfo.offset); |
| 1037 | const float32x4_t vscale1 = vdupq_n_f32(input1_qinfo.scale); |
| 1038 | |
| 1039 | // Input2 quantization info |
| 1040 | const int32x4_t voffset2 = vdupq_n_s32(input2_qinfo.offset); |
| 1041 | const float32x4_t vscale2 = vdupq_n_f32(input2_qinfo.scale); |
| 1042 | |
| 1043 | // Clear X Dimension on execution window as we handle manually |
| 1044 | input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 1045 | input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 1046 | |
| 1047 | Iterator input1(in1, input1_win); |
| 1048 | Iterator input2(in2, input2_win); |
| 1049 | Iterator output(out, win); |
| 1050 | |
| 1051 | execute_window_loop(win, [&](const Coordinates &) |
| 1052 | { |
| 1053 | const auto input1_ptr = reinterpret_cast<const int8_t *>(input1.ptr()); |
| 1054 | const auto input2_ptr = reinterpret_cast<const int8_t *>(input2.ptr()); |
| 1055 | const auto output_ptr = reinterpret_cast<int8_t *>(output.ptr()); |
| 1056 | |
| 1057 | int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2, |
| 1058 | vscale1, vscale2, voffseto, invvscaleo); |
| 1059 | for(; x < window_end_x; ++x) |
| 1060 | { |
| 1061 | const float afs = dequantize_qasymm8_signed(*(input1_ptr + x), input1_qinfo); |
| 1062 | const float bfs = dequantize_qasymm8_signed(*(input2_ptr + x), input2_qinfo); |
| 1063 | *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo); |
| 1064 | } |
| 1065 | }, |
| 1066 | input1, input2, output); |
| 1067 | } |
| 1068 | } |
| 1069 | |
| 1070 | template <ArithmeticOperation op> |
| 1071 | void elementwise_arithm_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| 1072 | { |
| 1073 | elementwise_op_quantized(in1, in2, out, window, &elementwise_arithm_op_quantized_scalar<op>, |
| 1074 | &elementwise_arithm_op_quantized_broadcast_loop<op>, |
| 1075 | &elementwise_arithm_op_quantized_loop<op>); |
| 1076 | } |
| 1077 | |
| 1078 | template <ArithmeticOperation op> |
| 1079 | void elementwise_arithm_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| 1080 | { |
| 1081 | elementwise_op_quantized_signed(in1, in2, out, window, &elementwise_arithm_op_quantized_signed_scalar<op>, |
| 1082 | &elementwise_arithm_op_quantized_signed_broadcast_loop<op>, |
| 1083 | &elementwise_arithm_op_quantized_singed_loop<op>); |
| 1084 | } |
| 1085 | |
| 1086 | template <ComparisonOperation op> |
| 1087 | void elementwise_comp_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| 1088 | { |
| 1089 | elementwise_op_quantized(in1, in2, out, window, &elementwise_comp_op_quantized_scalar<op>, |
| 1090 | &elementwise_comp_op_quantized_broadcast_loop<op>, |
| 1091 | &elementwise_comp_op_quantized_loop<op>); |
| 1092 | } |
| 1093 | |
| 1094 | template <ComparisonOperation op> |
| 1095 | void elementwise_comp_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) |
| 1096 | { |
| 1097 | elementwise_comp_quantized_signed(in1, in2, out, window, &elementwise_comp_op_quantized_scalar<op>, |
| 1098 | &elementwise_comp_op_quantized_signed_broadcast_loop<op>, |
| 1099 | &elementwise_comp_op_quantized_signed_loop<op>); |
| 1100 | } |
| 1101 | } // namespace cpu |
| 1102 | } // namespace arm_compute |
| 1103 | |
| 1104 | #endif /* SRC_CORE_NEON_KERNELS_ELEMENTWISE_IMPL_H */ |