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 | #include <assert.h> |
| 10 | #include <math.h> |
| 11 | #include <stdbool.h> |
| 12 | #include <stddef.h> |
| 13 | #include <stdint.h> |
| 14 | #include <stdlib.h> |
| 15 | #include <string.h> |
| 16 | |
| 17 | #include <xnnpack.h> |
| 18 | #include <xnnpack/allocator.h> |
| 19 | #include <xnnpack/operator.h> |
| 20 | #include <xnnpack/log.h> |
| 21 | #include <xnnpack/common.h> |
| 22 | #include <xnnpack/compute.h> |
| 23 | #include <xnnpack/math.h> |
| 24 | #include <xnnpack/pack.h> |
| 25 | #include <xnnpack/params.h> |
| 26 | #include <xnnpack/indirection.h> |
| 27 | |
| 28 | |
| 29 | static inline size_t compute_output_dimension( |
| 30 | size_t padded_input_dimension, |
| 31 | size_t kernel_dimension, |
| 32 | size_t dilation_dimension, |
| 33 | size_t subsampling_dimension) |
| 34 | { |
| 35 | const size_t effective_kernel_dimension = (kernel_dimension - 1) * dilation_dimension + 1; |
| 36 | return doz(padded_input_dimension, effective_kernel_dimension) / subsampling_dimension + 1; |
| 37 | } |
| 38 | |
| 39 | static const struct dwconv_parameters* find_dwigemm_ukernel( |
| 40 | size_t kernel_size, |
| 41 | const struct dwconv_parameters* ukernel, |
| 42 | size_t num_ukernels) |
| 43 | { |
| 44 | while (num_ukernels-- != 0) { |
| 45 | if (ukernel->mr == kernel_size) { |
| 46 | return ukernel; |
| 47 | } |
| 48 | ukernel++; |
| 49 | } |
| 50 | return NULL; |
| 51 | } |
| 52 | |
| 53 | enum xnn_status xnn_create_convolution2d_nhwc_q8( |
| 54 | uint32_t input_padding_top, |
| 55 | uint32_t input_padding_right, |
| 56 | uint32_t input_padding_bottom, |
| 57 | uint32_t input_padding_left, |
| 58 | uint32_t kernel_height, |
| 59 | uint32_t kernel_width, |
| 60 | uint32_t subsampling_height, |
| 61 | uint32_t subsampling_width, |
| 62 | uint32_t dilation_height, |
| 63 | uint32_t dilation_width, |
| 64 | uint32_t groups, |
| 65 | size_t group_input_channels, |
| 66 | size_t group_output_channels, |
| 67 | size_t input_pixel_stride, |
| 68 | size_t output_pixel_stride, |
| 69 | uint8_t input_zero_point, |
| 70 | float input_scale, |
| 71 | uint8_t kernel_zero_point, |
| 72 | float kernel_scale, |
| 73 | const uint8_t* kernel, |
| 74 | const int32_t* bias, |
| 75 | uint8_t output_zero_point, |
| 76 | float output_scale, |
| 77 | uint8_t output_min, |
| 78 | uint8_t output_max, |
| 79 | uint32_t flags, |
| 80 | xnn_operator_t* convolution_op_out) |
| 81 | { |
| 82 | xnn_operator_t convolution_op = NULL; |
| 83 | enum xnn_status status = xnn_status_uninitialized; |
| 84 | |
| 85 | if (!xnn_params.initialized) { |
| 86 | xnn_log_error("failed to create Convolution operator: XNNPACK is not initialized"); |
| 87 | goto error; |
| 88 | } |
| 89 | |
| 90 | status = xnn_status_invalid_parameter; |
| 91 | |
| 92 | if (kernel_width == 0 || kernel_height == 0) { |
| 93 | xnn_log_error( |
| 94 | "failed to create Convolution operator with %" PRIu32 "x%" PRIu32 " kernel: kernel dimensions must be non-zero", |
| 95 | kernel_width, kernel_height); |
| 96 | goto error; |
| 97 | } |
| 98 | |
| 99 | if (subsampling_width == 0 || subsampling_height == 0) { |
| 100 | xnn_log_error( |
| 101 | "failed to create Convolution operator with %" PRIu32 "x%" PRIu32 " subsampling: " |
| 102 | "subsampling dimensions must be non-zero", |
| 103 | subsampling_width, subsampling_height); |
| 104 | goto error; |
| 105 | } |
| 106 | |
| 107 | if (dilation_width == 0 || dilation_height == 0) { |
| 108 | xnn_log_error( |
| 109 | "failed to create Convolution operator with %" PRIu32 "x%" PRIu32 " dilation: " |
| 110 | "dilation dimensions must be non-zero", |
| 111 | dilation_width, dilation_height); |
| 112 | goto error; |
| 113 | } |
| 114 | |
| 115 | if (groups == 0) { |
| 116 | xnn_log_error( |
| 117 | "failed to create Convolution operator with %" PRIu32 " groups: number of groups must be non-zero", groups); |
| 118 | goto error; |
| 119 | } |
| 120 | |
| 121 | if (group_input_channels == 0) { |
| 122 | xnn_log_error( |
| 123 | "failed to create Convolution operator with %zu input channels per group: " |
| 124 | "number of channels must be non-zero", |
| 125 | group_input_channels); |
| 126 | goto error; |
| 127 | } |
| 128 | |
| 129 | if (group_output_channels == 0) { |
| 130 | xnn_log_error( |
| 131 | "failed to create Convolution operator with %zu output channels per group: " |
| 132 | "number of channels must be non-zero", |
| 133 | group_output_channels); |
| 134 | goto error; |
| 135 | } |
| 136 | |
| 137 | const size_t input_channels = groups * group_input_channels; |
| 138 | if (input_pixel_stride < input_channels) { |
| 139 | xnn_log_error( |
| 140 | "failed to create Convolution operator with input pixel stride of %zu: " |
| 141 | "stride must be at least as large as the number of input channels (%" PRIu32 "x%zu)", |
| 142 | input_pixel_stride, groups, group_input_channels); |
| 143 | goto error; |
| 144 | } |
| 145 | |
| 146 | const size_t output_channels = groups * group_output_channels; |
| 147 | if (output_pixel_stride < output_channels) { |
| 148 | xnn_log_error( |
| 149 | "failed to create Convolution operator with output pixel stride of %zu: " |
| 150 | "stride must be at least as large as the number of output channels (%" PRIu32 "x%zu)", |
| 151 | output_pixel_stride, groups, group_output_channels); |
| 152 | goto error; |
| 153 | } |
| 154 | |
| 155 | if (input_scale <= 0.0f || !isnormal(input_scale)) { |
| 156 | xnn_log_error( |
| 157 | "failed to create Convolution operator with %.7g input scale: scale must be finite, normalized, and positive", |
| 158 | input_scale); |
| 159 | goto error; |
| 160 | } |
| 161 | |
| 162 | if (kernel_scale <= 0.0f || !isnormal(kernel_scale)) { |
| 163 | xnn_log_error( |
| 164 | "failed to create Convolution operator with %.7g kernel scale: scale must be finite, normalized, and positive", |
| 165 | kernel_scale); |
| 166 | goto error; |
| 167 | } |
| 168 | |
| 169 | if (output_scale <= 0.0f || !isnormal(output_scale)) { |
| 170 | xnn_log_error( |
| 171 | "failed to create Convolution operator with %.7g output scale: scale must be finite, normalized, and positive", |
| 172 | output_scale); |
| 173 | goto error; |
| 174 | } |
| 175 | |
| 176 | if (output_min >= output_max) { |
| 177 | xnn_log_error( |
| 178 | "failed to create Convolution operator with [%" PRIu8 ", %" PRIu8 "] output range: " |
| 179 | "range min must be below range max", |
| 180 | output_min, output_max); |
| 181 | goto error; |
| 182 | } |
| 183 | |
Marat Dukhan | dd69f0b | 2019-10-04 19:40:03 -0700 | [diff] [blame^] | 184 | if ((flags & XNN_FLAG_DEPTHWISE_CONVOLUTION) != 0 && group_input_channels != 1) { |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 185 | xnn_log_error( |
| 186 | "failed to create Depthwise Convolution operator with %zu input channels per group: " |
| 187 | "Depthwise Convolution must have exactly 1 input channel per group", |
| 188 | group_input_channels); |
| 189 | goto error; |
| 190 | } |
| 191 | |
| 192 | status = xnn_status_unsupported_parameter; |
| 193 | |
| 194 | const uint32_t effective_kernel_height = (kernel_height - 1) * dilation_height + 1; |
| 195 | const uint32_t effective_kernel_width = (kernel_width - 1) * dilation_width + 1; |
| 196 | |
| 197 | if (input_padding_top >= effective_kernel_height) { |
| 198 | xnn_log_info( |
| 199 | "inefficiency in Convolution operator with %" PRIu32 "x%" PRIu32 " effective kernel and %" PRIu32 "+%" PRIu32 " height padding: " |
| 200 | "input top padding is greater or equal to effective kernel height", |
| 201 | effective_kernel_width, effective_kernel_height, input_padding_top, input_padding_bottom); |
| 202 | } |
| 203 | |
| 204 | if (input_padding_bottom >= effective_kernel_height) { |
| 205 | xnn_log_info( |
| 206 | "inefficiency in Convolution operator with %" PRIu32 "x%" PRIu32 " effective kernel and %" PRIu32 "+%" PRIu32 " height padding: " |
| 207 | "input bottom padding is greater or equal to effective kernel height", |
| 208 | effective_kernel_width, effective_kernel_height, input_padding_top, input_padding_bottom); |
| 209 | } |
| 210 | |
| 211 | if (input_padding_right >= effective_kernel_width) { |
| 212 | xnn_log_info( |
| 213 | "inefficiency in Convolution operator with %" PRIu32 "x%" PRIu32 " effective kernel and %" PRIu32 "+%" PRIu32 " width padding: " |
| 214 | "input right padding is greater or equal to effective kernel width", |
| 215 | effective_kernel_width, effective_kernel_height, input_padding_left, input_padding_right); |
| 216 | } |
| 217 | |
| 218 | if (input_padding_left >= effective_kernel_width) { |
| 219 | xnn_log_info( |
| 220 | "inefficiency in Convolution operator with %" PRIu32 "x%" PRIu32 " effective kernel and %" PRIu32 "+%" PRIu32 " width padding: " |
| 221 | "input left padding is greater or equal to effective kernel width", |
| 222 | effective_kernel_width, effective_kernel_height, input_padding_left, input_padding_right); |
| 223 | } |
| 224 | |
| 225 | const float convolution_scale = input_scale * kernel_scale / output_scale; |
| 226 | if (convolution_scale >= 1.0f) { |
| 227 | xnn_log_error( |
| 228 | "failed to create Convolution operator with %.7g input scale, %.7g kernel scale, and %.7g output scale: " |
| 229 | "convolution scale %.7g is greater or equal to 1.0", |
| 230 | input_scale, kernel_scale, output_scale, convolution_scale); |
| 231 | goto error; |
| 232 | } |
| 233 | |
| 234 | status = xnn_status_out_of_memory; |
| 235 | |
| 236 | convolution_op = xnn_allocate_zero_memory(sizeof(struct xnn_operator)); |
| 237 | if (convolution_op == NULL) { |
| 238 | xnn_log_error("failed to allocate %zu bytes for Convolution operator descriptor", sizeof(struct xnn_operator)); |
| 239 | goto error; |
| 240 | } |
| 241 | |
| 242 | const size_t kernel_size = kernel_height * kernel_width; |
| 243 | |
| 244 | enum xnn_ukernel_type ukernel_type = xnn_ukernel_type_none; |
| 245 | const struct dwconv_parameters* dwconv_parameters = NULL; |
| 246 | const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0; |
| 247 | if (group_input_channels == 1 && group_output_channels == 1 && groups > 1 && |
| 248 | (dwconv_parameters = find_dwigemm_ukernel(kernel_size, xnn_params.q8.dwconv, XNN_MAX_Q8_DWCONV_UKERNELS)) != NULL) |
| 249 | { |
| 250 | ukernel_type = xnn_ukernel_type_dwconv; |
| 251 | } else if (kernel_size == 1 && subsampling_height == 1 && subsampling_width == 1 && !any_padding) { |
| 252 | ukernel_type = xnn_ukernel_type_gemm; |
| 253 | } else { |
| 254 | ukernel_type = xnn_ukernel_type_igemm; |
| 255 | } |
| 256 | |
| 257 | size_t zero_size = 0; |
| 258 | switch (ukernel_type) { |
| 259 | case xnn_ukernel_type_dwconv: |
| 260 | { |
| 261 | assert(dwconv_parameters != NULL); |
| 262 | assert(dwconv_parameters->mr == kernel_size); |
| 263 | |
| 264 | const uint32_t c_stride = round_up_po2(groups, dwconv_parameters->cr); |
| 265 | const size_t packed_weights_size = (sizeof(uint8_t) * kernel_size + sizeof(int32_t)) * c_stride; |
| 266 | convolution_op->packed_weights = xnn_allocate_memory(packed_weights_size); |
| 267 | if (convolution_op->packed_weights == NULL) { |
| 268 | xnn_log_error("failed to allocate %zu bytes for packed weights", packed_weights_size); |
| 269 | goto error; |
| 270 | } |
| 271 | |
Marat Dukhan | dd69f0b | 2019-10-04 19:40:03 -0700 | [diff] [blame^] | 272 | if (flags & XNN_FLAG_DEPTHWISE_CONVOLUTION) { |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 273 | xnn_pack_q8_dwconv_hwg_w( |
| 274 | kernel_height, kernel_width, |
| 275 | groups, dwconv_parameters->cr, |
| 276 | input_zero_point, kernel_zero_point, |
| 277 | kernel, bias, convolution_op->packed_weights); |
| 278 | } else { |
| 279 | xnn_pack_q8_dwconv_ghw_w( |
| 280 | kernel_height, kernel_width, |
| 281 | groups, dwconv_parameters->cr, |
| 282 | input_zero_point, kernel_zero_point, |
| 283 | kernel, bias, convolution_op->packed_weights); |
| 284 | } |
| 285 | |
| 286 | convolution_op->ukernel.dwconv = (struct xnn_ukernel_dwconv) { |
| 287 | .unipass_function = dwconv_parameters->up, |
| 288 | .mr = dwconv_parameters->mr, |
| 289 | .qr = dwconv_parameters->qr, |
| 290 | }; |
| 291 | |
| 292 | zero_size = sizeof(uint8_t) * c_stride + XNN_EXTRA_BYTES; |
| 293 | break; |
| 294 | } |
| 295 | case xnn_ukernel_type_gemm: |
| 296 | case xnn_ukernel_type_igemm: |
| 297 | { |
| 298 | const uint32_t nr = xnn_params.q8.gemm.nr; |
| 299 | const uint32_t kr = UINT32_C(1) << xnn_params.q8.gemm.log2_kr; |
| 300 | const uint32_t n_stride = round_up(group_output_channels, nr); |
| 301 | const uint32_t k_stride = round_up_po2(group_input_channels, kr); |
| 302 | |
| 303 | const size_t packed_group_weights_size = |
| 304 | (sizeof(uint8_t) * kernel_size * k_stride + sizeof(int32_t)) * n_stride; |
| 305 | convolution_op->packed_weights = xnn_allocate_memory(packed_group_weights_size * groups); |
| 306 | if (convolution_op->packed_weights == NULL) { |
| 307 | xnn_log_error("failed to allocate %zu bytes for packed weights", packed_group_weights_size * groups); |
| 308 | goto error; |
| 309 | } |
| 310 | memset(convolution_op->packed_weights, kernel_zero_point, packed_group_weights_size * groups); |
| 311 | |
| 312 | switch (ukernel_type) { |
| 313 | case xnn_ukernel_type_gemm: |
| 314 | xnn_pack_q8_gemm_goi_w( |
| 315 | groups, group_output_channels, group_input_channels, |
| 316 | nr, kr, |
| 317 | input_zero_point, kernel_zero_point, |
| 318 | kernel, bias, convolution_op->packed_weights); |
| 319 | convolution_op->ukernel.gemm = (struct xnn_ukernel_gemm) { |
| 320 | .mr = xnn_params.q8.gemm.mr, |
| 321 | .nr = nr, |
| 322 | .kr = kr, |
| 323 | .default_function = xnn_params.q8.gemm.gemm, |
| 324 | }; |
| 325 | break; |
| 326 | case xnn_ukernel_type_igemm: |
Marat Dukhan | dd69f0b | 2019-10-04 19:40:03 -0700 | [diff] [blame^] | 327 | if (flags & XNN_FLAG_DEPTHWISE_CONVOLUTION) { |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 328 | xnn_pack_q8_conv_kgo_w( |
| 329 | groups, group_output_channels, kernel_size, |
| 330 | nr, kr, |
| 331 | input_zero_point, kernel_zero_point, |
| 332 | kernel, bias, convolution_op->packed_weights); |
| 333 | } else { |
| 334 | xnn_pack_q8_conv_goki_w( |
| 335 | groups, group_output_channels, kernel_size, group_input_channels, |
| 336 | nr, kr, |
| 337 | input_zero_point, kernel_zero_point, |
| 338 | kernel, bias, convolution_op->packed_weights); |
| 339 | } |
| 340 | convolution_op->ukernel.igemm = (struct xnn_ukernel_igemm) { |
| 341 | .mr = xnn_params.q8.gemm.mr, |
| 342 | .nr = nr, |
| 343 | .kr = kr, |
| 344 | .default_function = xnn_params.q8.gemm.igemm, |
| 345 | }; |
| 346 | break; |
| 347 | default: |
| 348 | XNN_UNREACHABLE; |
| 349 | } |
| 350 | |
| 351 | zero_size = sizeof(uint8_t) * k_stride + XNN_EXTRA_BYTES; |
| 352 | break; |
| 353 | } |
| 354 | default: |
| 355 | XNN_UNREACHABLE; |
| 356 | } |
| 357 | |
| 358 | if (any_padding) { |
| 359 | void* zero_buffer = xnn_allocate_memory(zero_size); |
| 360 | if (zero_buffer == NULL) { |
| 361 | xnn_log_error("failed to allocate %zu bytes for zero padding", zero_size); |
| 362 | goto error; |
| 363 | } |
| 364 | memset(zero_buffer, input_zero_point, zero_size); |
| 365 | convolution_op->zero_buffer = zero_buffer; |
| 366 | } |
| 367 | |
| 368 | convolution_op->padding_top = input_padding_top; |
| 369 | convolution_op->padding_right = input_padding_right; |
| 370 | convolution_op->padding_bottom = input_padding_bottom; |
| 371 | convolution_op->padding_left = input_padding_left; |
| 372 | |
| 373 | convolution_op->kernel_height = kernel_height; |
| 374 | convolution_op->kernel_width = kernel_width; |
| 375 | convolution_op->stride_height = subsampling_height; |
| 376 | convolution_op->stride_width = subsampling_width; |
| 377 | convolution_op->dilation_height = dilation_height; |
| 378 | convolution_op->dilation_width = dilation_width; |
| 379 | convolution_op->groups = groups; |
| 380 | convolution_op->group_input_channels = group_input_channels; |
| 381 | convolution_op->group_output_channels = group_output_channels; |
| 382 | convolution_op->input_pixel_stride = input_pixel_stride; |
| 383 | convolution_op->output_pixel_stride = output_pixel_stride; |
| 384 | |
| 385 | convolution_op->kernel_zero_point = kernel_zero_point; |
| 386 | |
| 387 | convolution_op->q8_gemm_params = |
| 388 | xnn_compute_q8_gemm_params( |
| 389 | input_zero_point, kernel_zero_point, |
| 390 | convolution_scale, output_zero_point, output_min, output_max); |
| 391 | |
| 392 | convolution_op->type = xnn_operator_type_convolution_q8; |
| 393 | convolution_op->ukernel.type = ukernel_type; |
| 394 | |
| 395 | convolution_op->state = xnn_run_state_invalid; |
| 396 | |
| 397 | *convolution_op_out = convolution_op; |
| 398 | return xnn_status_success; |
| 399 | |
| 400 | error: |
| 401 | xnn_delete_operator(convolution_op); |
| 402 | return status; |
| 403 | } |
| 404 | |
| 405 | enum xnn_status xnn_create_convolution2d_nhwc_f32( |
| 406 | uint32_t input_padding_top, |
| 407 | uint32_t input_padding_right, |
| 408 | uint32_t input_padding_bottom, |
| 409 | uint32_t input_padding_left, |
| 410 | uint32_t kernel_height, |
| 411 | uint32_t kernel_width, |
| 412 | uint32_t subsampling_height, |
| 413 | uint32_t subsampling_width, |
| 414 | uint32_t dilation_height, |
| 415 | uint32_t dilation_width, |
| 416 | uint32_t groups, |
| 417 | size_t group_input_channels, |
| 418 | size_t group_output_channels, |
| 419 | size_t input_pixel_stride, |
| 420 | size_t output_pixel_stride, |
| 421 | const float* kernel, |
| 422 | const float* bias, |
| 423 | float output_min, |
| 424 | float output_max, |
| 425 | uint32_t flags, |
| 426 | xnn_operator_t* convolution_op_out) |
| 427 | { |
| 428 | xnn_operator_t convolution_op = NULL; |
| 429 | enum xnn_status status = xnn_status_uninitialized; |
| 430 | |
| 431 | if (!xnn_params.initialized) { |
| 432 | xnn_log_error("failed to create Convolution operator: XNNPACK is not initialized"); |
| 433 | goto error; |
| 434 | } |
| 435 | |
| 436 | status = xnn_status_invalid_parameter; |
| 437 | |
| 438 | if (kernel_width == 0 || kernel_height == 0) { |
| 439 | xnn_log_error( |
| 440 | "failed to create Convolution operator with %" PRIu32 "x%" PRIu32 " kernel: kernel dimensions must be non-zero", |
| 441 | kernel_width, kernel_height); |
| 442 | goto error; |
| 443 | } |
| 444 | |
| 445 | if (subsampling_width == 0 || subsampling_height == 0) { |
| 446 | xnn_log_error( |
| 447 | "failed to create Convolution operator with %" PRIu32 "x%" PRIu32 " subsampling: " |
| 448 | "subsampling dimensions must be non-zero", |
| 449 | subsampling_width, subsampling_height); |
| 450 | goto error; |
| 451 | } |
| 452 | |
| 453 | if (dilation_width == 0 || dilation_height == 0) { |
| 454 | xnn_log_error( |
| 455 | "failed to create Convolution operator with %" PRIu32 "x%" PRIu32 " dilation: " |
| 456 | "dilation dimensions must be non-zero", |
| 457 | dilation_width, dilation_height); |
| 458 | goto error; |
| 459 | } |
| 460 | |
| 461 | if (groups == 0) { |
| 462 | xnn_log_error( |
| 463 | "failed to create Convolution operator with %" PRIu32 " groups: number of groups must be non-zero", groups); |
| 464 | goto error; |
| 465 | } |
| 466 | |
| 467 | if (group_input_channels == 0) { |
| 468 | xnn_log_error( |
| 469 | "failed to create Convolution operator with %zu input channels per group: " |
| 470 | "number of channels must be non-zero", |
| 471 | group_input_channels); |
| 472 | goto error; |
| 473 | } |
| 474 | |
| 475 | if (group_output_channels == 0) { |
| 476 | xnn_log_error( |
| 477 | "failed to create Convolution operator with %zu output channels per group: " |
| 478 | "number of channels must be non-zero", |
| 479 | group_output_channels); |
| 480 | goto error; |
| 481 | } |
| 482 | |
| 483 | const size_t input_channels = groups * group_input_channels; |
| 484 | if (input_pixel_stride < input_channels) { |
| 485 | xnn_log_error( |
| 486 | "failed to create Convolution operator with input pixel stride of %zu: " |
| 487 | "stride must be at least as large as the number of input channels (%" PRIu32 "x%zu)", |
| 488 | input_pixel_stride, groups, group_input_channels); |
| 489 | goto error; |
| 490 | } |
| 491 | |
| 492 | const size_t output_channels = groups * group_output_channels; |
| 493 | if (output_pixel_stride < output_channels) { |
| 494 | xnn_log_error( |
| 495 | "failed to create Convolution operator with output pixel stride of %zu: " |
| 496 | "stride must be at least as large as the number of output channels (%" PRIu32 "x%zu)", |
| 497 | output_pixel_stride, groups, group_output_channels); |
| 498 | goto error; |
| 499 | } |
| 500 | |
| 501 | if (isnan(output_min)) { |
| 502 | xnn_log_error( |
| 503 | "failed to create Convolution operator with NaN output lower bound: lower bound must be non-NaN"); |
| 504 | goto error; |
| 505 | } |
| 506 | |
| 507 | if (isnan(output_max)) { |
| 508 | xnn_log_error( |
| 509 | "failed to create Convolution operator with NaN output upper bound: upper bound must be non-NaN"); |
| 510 | goto error; |
| 511 | } |
| 512 | |
| 513 | if (output_min >= output_max) { |
| 514 | xnn_log_error( |
| 515 | "failed to create Convolution operator with [%.7g, %.7g] output range: " |
| 516 | "lower bound must be below upper bound", |
| 517 | output_min, output_max); |
| 518 | goto error; |
| 519 | } |
| 520 | |
Marat Dukhan | dd69f0b | 2019-10-04 19:40:03 -0700 | [diff] [blame^] | 521 | if ((flags & XNN_FLAG_DEPTHWISE_CONVOLUTION) != 0 && group_input_channels != 1) { |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 522 | xnn_log_error( |
| 523 | "failed to create Depthwise Convolution operator with %zu input channels per group: " |
| 524 | "Depthwise Convolution must have exactly 1 input channel per group", |
| 525 | group_input_channels); |
| 526 | goto error; |
| 527 | } |
| 528 | |
| 529 | const uint32_t effective_kernel_height = (kernel_height - 1) * dilation_height + 1; |
| 530 | const uint32_t effective_kernel_width = (kernel_width - 1) * dilation_width + 1; |
| 531 | |
| 532 | if (input_padding_top >= effective_kernel_height) { |
| 533 | xnn_log_info( |
| 534 | "inefficiency in Convolution operator with %" PRIu32 "x%" PRIu32 " effective kernel and %" PRIu32 "+%" PRIu32 " height padding: " |
| 535 | "input top padding is greater or equal to effective kernel height", |
| 536 | effective_kernel_width, effective_kernel_height, input_padding_top, input_padding_bottom); |
| 537 | } |
| 538 | |
| 539 | if (input_padding_bottom >= effective_kernel_height) { |
| 540 | xnn_log_info( |
| 541 | "inefficiency in Convolution operator with %" PRIu32 "x%" PRIu32 " effective kernel and %" PRIu32 "+%" PRIu32 " height padding: " |
| 542 | "input bottom padding is greater or equal to effective kernel height", |
| 543 | effective_kernel_width, effective_kernel_height, input_padding_top, input_padding_bottom); |
| 544 | } |
| 545 | |
| 546 | if (input_padding_right >= effective_kernel_width) { |
| 547 | xnn_log_info( |
| 548 | "inefficiency in Convolution operator with %" PRIu32 "x%" PRIu32 " effective kernel and %" PRIu32 "+%" PRIu32 " width padding: " |
| 549 | "input right padding is greater or equal to effective kernel width", |
| 550 | effective_kernel_width, effective_kernel_height, input_padding_left, input_padding_right); |
| 551 | } |
| 552 | |
| 553 | if (input_padding_left >= effective_kernel_width) { |
| 554 | xnn_log_info( |
| 555 | "inefficiency in Convolution operator with %" PRIu32 "x%" PRIu32 " effective kernel and %" PRIu32 "+%" PRIu32 " width padding: " |
| 556 | "input left padding is greater or equal to effective kernel width", |
| 557 | effective_kernel_width, effective_kernel_height, input_padding_left, input_padding_right); |
| 558 | } |
| 559 | |
| 560 | status = xnn_status_out_of_memory; |
| 561 | |
| 562 | convolution_op = xnn_allocate_zero_memory(sizeof(struct xnn_operator)); |
| 563 | if (convolution_op == NULL) { |
| 564 | xnn_log_error("failed to allocate %zu bytes for Convolution operator descriptor", sizeof(struct xnn_operator)); |
| 565 | goto error; |
| 566 | } |
| 567 | |
| 568 | const size_t kernel_size = kernel_height * kernel_width; |
| 569 | |
| 570 | enum xnn_ukernel_type ukernel_type = xnn_ukernel_type_none; |
| 571 | const struct dwconv_parameters* dwconv_parameters = NULL; |
| 572 | const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0; |
| 573 | const bool unit_subsampling = (subsampling_width | subsampling_height) == 1; |
| 574 | if (group_input_channels == 1 && group_output_channels == 1 && kernel_size == 1 && unit_subsampling && !any_padding) { |
| 575 | ukernel_type = xnn_ukernel_type_vmulcaddc; |
| 576 | } else if (group_input_channels == 1 && group_output_channels == 1 && (dwconv_parameters = |
| 577 | find_dwigemm_ukernel(kernel_size, xnn_params.f32.dwconv, XNN_MAX_F32_DWCONV_UKERNELS)) != NULL) |
| 578 | { |
| 579 | ukernel_type = xnn_ukernel_type_dwconv; |
| 580 | } else if (kernel_size == 1 && unit_subsampling && !any_padding) { |
| 581 | ukernel_type = xnn_ukernel_type_gemm; |
| 582 | } else { |
| 583 | ukernel_type = xnn_ukernel_type_igemm; |
| 584 | } |
| 585 | |
| 586 | size_t zero_size = 0; |
| 587 | switch (ukernel_type) { |
| 588 | case xnn_ukernel_type_vmulcaddc: |
| 589 | { |
| 590 | const uint32_t c_stride = round_up_po2(groups, xnn_params.f32.vmulcaddc.cr); |
| 591 | const size_t packed_weights_size = 2 * sizeof(float) * c_stride; |
| 592 | convolution_op->packed_weights = xnn_allocate_memory(packed_weights_size); |
| 593 | if (convolution_op->packed_weights == NULL) { |
| 594 | xnn_log_error("failed to allocate %zu bytes for packed weights", packed_weights_size); |
| 595 | goto error; |
| 596 | } |
| 597 | |
| 598 | xnn_pack_f32_vmulcaddc_w( |
| 599 | groups, xnn_params.f32.vmulcaddc.cr, |
| 600 | kernel, bias, convolution_op->packed_weights); |
| 601 | |
| 602 | convolution_op->ukernel.vmulcaddc = (struct xnn_ukernel_vmulcaddc) { |
| 603 | .function = xnn_params.f32.vmulcaddc.ukernel, |
| 604 | .mr = xnn_params.f32.vmulcaddc.mr, |
| 605 | }; |
| 606 | break; |
| 607 | } |
| 608 | case xnn_ukernel_type_dwconv: |
| 609 | { |
| 610 | assert(dwconv_parameters != NULL); |
| 611 | assert(dwconv_parameters->mr == kernel_size); |
| 612 | |
| 613 | const uint32_t c_stride = round_up_po2(groups, dwconv_parameters->cr); |
| 614 | const size_t packed_weights_size = (kernel_size + 1) * sizeof(float) * c_stride; |
| 615 | convolution_op->packed_weights = xnn_allocate_memory(packed_weights_size); |
| 616 | if (convolution_op->packed_weights == NULL) { |
| 617 | xnn_log_error("failed to allocate %zu bytes for packed weights", packed_weights_size); |
| 618 | goto error; |
| 619 | } |
| 620 | |
Marat Dukhan | dd69f0b | 2019-10-04 19:40:03 -0700 | [diff] [blame^] | 621 | if (flags & XNN_FLAG_DEPTHWISE_CONVOLUTION) { |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 622 | xnn_pack_f32_dwconv_hwg_w( |
| 623 | kernel_height, kernel_width, |
| 624 | groups, dwconv_parameters->cr, |
| 625 | kernel, bias, convolution_op->packed_weights); |
| 626 | } else { |
| 627 | xnn_pack_f32_dwconv_ghw_w( |
| 628 | kernel_height, kernel_width, |
| 629 | groups, dwconv_parameters->cr, |
| 630 | kernel, bias, convolution_op->packed_weights); |
| 631 | } |
| 632 | |
| 633 | convolution_op->ukernel.dwconv = (struct xnn_ukernel_dwconv) { |
| 634 | .unipass_function = dwconv_parameters->up, |
| 635 | .mr = dwconv_parameters->mr, |
| 636 | .qr = dwconv_parameters->qr, |
| 637 | }; |
| 638 | |
| 639 | zero_size = sizeof(float) * c_stride; |
| 640 | break; |
| 641 | } |
| 642 | case xnn_ukernel_type_gemm: |
| 643 | case xnn_ukernel_type_igemm: |
| 644 | { |
| 645 | const uint32_t nr = xnn_params.f32.gemm.nr; |
| 646 | const uint32_t kr = UINT32_C(1) << xnn_params.f32.gemm.log2_kr; |
| 647 | const uint32_t sr = UINT32_C(1) << xnn_params.f32.gemm.log2_sr; |
| 648 | const uint32_t n_stride = round_up(group_output_channels, nr); |
| 649 | const uint32_t k_stride = round_up_po2(group_input_channels, kr); |
| 650 | |
| 651 | const size_t packed_group_weights_size = (kernel_size * k_stride + 1) * sizeof(float) * n_stride; |
| 652 | convolution_op->packed_weights = xnn_allocate_memory(packed_group_weights_size * groups); |
| 653 | if (convolution_op->packed_weights == NULL) { |
| 654 | xnn_log_error("failed to allocate %zu bytes for packed weights", packed_group_weights_size * groups); |
| 655 | goto error; |
| 656 | } |
| 657 | memset(convolution_op->packed_weights, 0, packed_group_weights_size * groups); |
| 658 | |
| 659 | switch (ukernel_type) { |
| 660 | case xnn_ukernel_type_gemm: |
| 661 | xnn_pack_f32_gemm_goi_w( |
| 662 | groups, group_output_channels, group_input_channels, |
| 663 | nr, kr, sr, |
| 664 | kernel, bias, convolution_op->packed_weights); |
| 665 | convolution_op->ukernel.gemm = (struct xnn_ukernel_gemm) { |
| 666 | .mr = xnn_params.f32.gemm.mr, |
| 667 | .nr = nr, |
| 668 | .kr = kr, |
| 669 | .default_function = xnn_params.f32.gemm.gemm, |
| 670 | .mr1_function = xnn_params.f32.gemm.gemm1, |
| 671 | }; |
| 672 | break; |
| 673 | case xnn_ukernel_type_igemm: |
Marat Dukhan | dd69f0b | 2019-10-04 19:40:03 -0700 | [diff] [blame^] | 674 | if (flags & XNN_FLAG_DEPTHWISE_CONVOLUTION) { |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 675 | xnn_pack_f32_conv_kgo_w( |
| 676 | groups, group_output_channels, kernel_size, |
| 677 | nr, kr, |
| 678 | kernel, bias, convolution_op->packed_weights); |
| 679 | } else { |
| 680 | xnn_pack_f32_conv_goki_w( |
| 681 | groups, group_output_channels, kernel_size, group_input_channels, |
| 682 | nr, kr, sr, |
| 683 | kernel, bias, convolution_op->packed_weights); |
| 684 | } |
| 685 | convolution_op->ukernel.igemm = (struct xnn_ukernel_igemm) { |
| 686 | .mr = xnn_params.f32.gemm.mr, |
| 687 | .nr = nr, |
| 688 | .kr = kr, |
| 689 | .default_function = xnn_params.f32.gemm.igemm, |
| 690 | .mr1_function = xnn_params.f32.gemm.igemm1, |
| 691 | }; |
| 692 | break; |
| 693 | default: |
| 694 | XNN_UNREACHABLE; |
| 695 | } |
| 696 | |
| 697 | zero_size = sizeof(float) * k_stride; |
| 698 | break; |
| 699 | } |
| 700 | default: |
| 701 | XNN_UNREACHABLE; |
| 702 | } |
| 703 | |
| 704 | if (any_padding) { |
| 705 | void* zero_buffer = xnn_allocate_zero_memory(zero_size); |
| 706 | if (zero_buffer == NULL) { |
| 707 | xnn_log_error("failed to allocate %zu bytes for zero padding", zero_size); |
| 708 | goto error; |
| 709 | } |
| 710 | convolution_op->zero_buffer = zero_buffer; |
| 711 | } |
| 712 | |
| 713 | convolution_op->padding_top = input_padding_top; |
| 714 | convolution_op->padding_right = input_padding_right; |
| 715 | convolution_op->padding_bottom = input_padding_bottom; |
| 716 | convolution_op->padding_left = input_padding_left; |
| 717 | |
| 718 | convolution_op->kernel_height = kernel_height; |
| 719 | convolution_op->kernel_width = kernel_width; |
| 720 | convolution_op->stride_height = subsampling_height; |
| 721 | convolution_op->stride_width = subsampling_width; |
| 722 | convolution_op->dilation_height = dilation_height; |
| 723 | convolution_op->dilation_width = dilation_width; |
| 724 | convolution_op->groups = groups; |
| 725 | convolution_op->group_input_channels = group_input_channels; |
| 726 | convolution_op->group_output_channels = group_output_channels; |
| 727 | convolution_op->input_pixel_stride = input_pixel_stride; |
| 728 | convolution_op->output_pixel_stride = output_pixel_stride; |
| 729 | |
| 730 | convolution_op->f32_output_params = xnn_compute_f32_output_params(output_min, output_max); |
| 731 | |
| 732 | convolution_op->type = xnn_operator_type_convolution_f32; |
| 733 | convolution_op->ukernel.type = ukernel_type; |
| 734 | |
| 735 | convolution_op->state = xnn_run_state_invalid; |
| 736 | |
| 737 | *convolution_op_out = convolution_op; |
| 738 | return xnn_status_success; |
| 739 | |
| 740 | error: |
| 741 | xnn_delete_operator(convolution_op); |
| 742 | return status; |
| 743 | } |
| 744 | |
| 745 | static enum xnn_status setup_convolution2d_nhwc( |
| 746 | xnn_operator_t convolution_op, |
| 747 | size_t batch_size, |
| 748 | size_t input_height, |
| 749 | size_t input_width, |
| 750 | const void* input, |
| 751 | void* output, |
| 752 | uint32_t log2_input_element_size, |
| 753 | uint32_t log2_filter_element_size, |
| 754 | uint32_t bias_element_size, |
| 755 | uint32_t log2_output_element_size, |
| 756 | const void* params, |
| 757 | size_t num_threads) |
| 758 | { |
| 759 | convolution_op->state = xnn_run_state_invalid; |
| 760 | |
| 761 | if (!xnn_params.initialized) { |
| 762 | xnn_log_error("failed to setup Convolution operator: XNNPACK is not initialized"); |
| 763 | return xnn_status_uninitialized; |
| 764 | } |
| 765 | |
| 766 | if (input_width == 0 || input_height == 0) { |
| 767 | xnn_log_error( |
| 768 | "failed to setup Convolution operator with %zux%zu input: input dimensions must be non-zero", |
| 769 | input_width, input_height); |
| 770 | return xnn_status_invalid_parameter; |
| 771 | } |
| 772 | |
| 773 | if (batch_size == 0) { |
| 774 | convolution_op->state = xnn_run_state_skip; |
| 775 | return xnn_status_success; |
| 776 | } |
| 777 | |
| 778 | convolution_op->batch_size = batch_size; |
| 779 | convolution_op->input_height = input_height; |
| 780 | convolution_op->input_width = input_width; |
| 781 | convolution_op->input = input; |
| 782 | |
| 783 | convolution_op->output_height = compute_output_dimension( |
| 784 | convolution_op->padding_top + input_height + convolution_op->padding_bottom, |
| 785 | convolution_op->kernel_height, |
| 786 | convolution_op->dilation_height, |
| 787 | convolution_op->stride_height); |
| 788 | convolution_op->output_width = compute_output_dimension( |
| 789 | convolution_op->padding_left + input_width + convolution_op->padding_right, |
| 790 | convolution_op->kernel_width, |
| 791 | convolution_op->dilation_width, |
| 792 | convolution_op->stride_width); |
| 793 | convolution_op->output = output; |
| 794 | |
| 795 | switch (convolution_op->ukernel.type) { |
| 796 | case xnn_ukernel_type_gemm: |
| 797 | { |
| 798 | // Convolution maps directly to GEMM and doesn't use indirection buffer. |
| 799 | |
| 800 | const size_t output_height = convolution_op->output_height; |
| 801 | const size_t output_width = convolution_op->output_width; |
| 802 | const size_t output_size = output_height * output_width; |
| 803 | const size_t batch_output_size = batch_size * output_size; |
| 804 | |
| 805 | const size_t groups = convolution_op->groups; |
| 806 | const size_t group_input_channels = convolution_op->group_input_channels; |
| 807 | const size_t w_stride = (round_up_po2(group_input_channels, convolution_op->ukernel.gemm.kr) << log2_filter_element_size) + bias_element_size; |
| 808 | const size_t group_output_channels = convolution_op->group_output_channels; |
| 809 | |
| 810 | uint32_t mr = convolution_op->ukernel.gemm.mr; |
| 811 | const uint32_t nr = convolution_op->ukernel.gemm.nr; |
| 812 | xnn_gemm_ukernel_function gemm_ukernel = convolution_op->ukernel.gemm.default_function; |
| 813 | if (batch_output_size == 1 && convolution_op->ukernel.gemm.mr1_function != NULL) { |
| 814 | mr = 1; |
| 815 | gemm_ukernel = convolution_op->ukernel.gemm.mr1_function; |
| 816 | } |
| 817 | |
| 818 | convolution_op->context.gemm = (struct gemm_context) { |
| 819 | .k_scaled = group_input_channels << log2_input_element_size, |
| 820 | .a = input, |
| 821 | .a_stride = convolution_op->input_pixel_stride << log2_input_element_size, |
| 822 | .packed_w = convolution_op->packed_weights, |
| 823 | .w_stride = w_stride, |
| 824 | .wg_stride = w_stride * round_up(group_output_channels, nr), |
| 825 | .c = output, |
| 826 | .cm_stride = convolution_op->output_pixel_stride << log2_output_element_size, |
| 827 | .cn_stride = nr << log2_output_element_size, |
| 828 | .cg_stride = group_output_channels << log2_output_element_size, |
| 829 | .log2_csize = log2_output_element_size, |
| 830 | .ukernel = gemm_ukernel, |
| 831 | }; |
| 832 | memcpy(&convolution_op->context.gemm.params, params, sizeof(convolution_op->context.gemm.params)); |
| 833 | |
| 834 | size_t nc = group_output_channels; |
| 835 | if (num_threads > 1) { |
| 836 | const size_t num_other_tiles = groups * divide_round_up(batch_output_size, mr); |
| 837 | const size_t target_tiles_per_thread = 5; |
| 838 | const size_t max_nc = divide_round_up(group_output_channels * num_other_tiles, num_threads * target_tiles_per_thread); |
| 839 | if (max_nc < nc) { |
| 840 | nc = min(nc, divide_round_up(nc, max_nc * nr) * nr); |
| 841 | } |
| 842 | } |
| 843 | if (groups == 1) { |
| 844 | convolution_op->compute.type = xnn_parallelization_type_2d_tile_2d; |
| 845 | convolution_op->compute.task_2d_tile_2d = (pthreadpool_task_2d_tile_2d_t) xnn_compute_gemm; |
| 846 | convolution_op->compute.range[0] = batch_output_size; |
| 847 | convolution_op->compute.range[1] = group_output_channels; |
| 848 | convolution_op->compute.tile[0] = mr; |
| 849 | convolution_op->compute.tile[1] = nc; |
| 850 | } else { |
| 851 | convolution_op->compute.type = xnn_parallelization_type_3d_tile_2d; |
| 852 | convolution_op->compute.task_3d_tile_2d = (pthreadpool_task_3d_tile_2d_t) xnn_compute_ggemm; |
| 853 | convolution_op->compute.range[0] = groups; |
| 854 | convolution_op->compute.range[1] = batch_output_size; |
| 855 | convolution_op->compute.range[2] = group_output_channels; |
| 856 | convolution_op->compute.tile[0] = mr; |
| 857 | convolution_op->compute.tile[1] = nc; |
| 858 | } |
| 859 | convolution_op->state = xnn_run_state_ready; |
| 860 | |
| 861 | return xnn_status_success; |
| 862 | } |
| 863 | case xnn_ukernel_type_igemm: |
| 864 | { |
| 865 | const size_t groups = convolution_op->groups; |
| 866 | const size_t kernel_height = convolution_op->kernel_height; |
| 867 | const size_t kernel_width = convolution_op->kernel_width; |
| 868 | const size_t kernel_size = kernel_height * kernel_width; |
| 869 | const size_t output_height = convolution_op->output_height; |
| 870 | const size_t output_width = convolution_op->output_width; |
| 871 | const size_t output_size = output_height * output_width; |
| 872 | |
| 873 | uint32_t mr = convolution_op->ukernel.igemm.mr; |
| 874 | const uint32_t nr = convolution_op->ukernel.igemm.nr; |
| 875 | xnn_igemm_ukernel_function igemm_ukernel = convolution_op->ukernel.igemm.default_function; |
| 876 | if (output_size == 1 && convolution_op->ukernel.igemm.mr1_function != NULL) { |
| 877 | mr = 1; |
| 878 | igemm_ukernel = convolution_op->ukernel.igemm.mr1_function; |
| 879 | } |
| 880 | |
| 881 | const size_t tiled_output_size = round_up(output_size, mr); |
| 882 | const size_t indirection_buffer_size = sizeof(void*) * kernel_size * tiled_output_size; |
| 883 | |
| 884 | if (input_height != convolution_op->last_input_height || |
| 885 | input_width != convolution_op->last_input_width) |
| 886 | { |
| 887 | const void** indirection_buffer = (const void**) realloc(convolution_op->indirection_buffer, indirection_buffer_size); |
| 888 | if (indirection_buffer == NULL) { |
| 889 | xnn_log_error("failed to allocate %zu bytes for indirection buffer", indirection_buffer_size); |
| 890 | return xnn_status_out_of_memory; |
| 891 | } |
| 892 | convolution_op->indirection_buffer = indirection_buffer; |
| 893 | convolution_op->last_input = input; |
| 894 | convolution_op->last_input_height = input_height; |
| 895 | convolution_op->last_input_width = input_width; |
| 896 | |
| 897 | xnn_indirection_init_conv2d(convolution_op, mr, log2_input_element_size); |
| 898 | } |
| 899 | |
| 900 | const size_t group_input_channels = convolution_op->group_input_channels; |
| 901 | const size_t w_stride = (round_up_po2(group_input_channels, convolution_op->ukernel.igemm.kr) * kernel_size << log2_filter_element_size) + bias_element_size; |
| 902 | const size_t group_output_channels = convolution_op->group_output_channels; |
| 903 | convolution_op->context.igemm = (struct igemm_context) { |
| 904 | .ks = kernel_size, |
| 905 | .ks_scaled = kernel_size * mr * sizeof(void*), |
| 906 | .kc = group_input_channels << log2_input_element_size, |
| 907 | .w_stride = w_stride, |
| 908 | .indirect_a = convolution_op->indirection_buffer, |
| 909 | .a_offset = (size_t) ((uintptr_t) input - (uintptr_t) convolution_op->last_input), |
| 910 | .zero = convolution_op->zero_buffer, |
| 911 | .packed_w = convolution_op->packed_weights, |
| 912 | .c = convolution_op->output, |
| 913 | .cm_stride = convolution_op->output_pixel_stride << log2_output_element_size, |
| 914 | .cn_stride = nr << log2_output_element_size, |
| 915 | .ga_stride = group_input_channels << log2_input_element_size, |
| 916 | .gw_stride = w_stride * round_up(group_output_channels, nr), |
| 917 | .gc_stride = group_output_channels << log2_output_element_size, |
| 918 | .ba_stride = input_height * input_width * convolution_op->input_pixel_stride << log2_input_element_size, |
| 919 | .bc_stride = output_size * convolution_op->output_pixel_stride << log2_output_element_size, |
| 920 | .log2_csize = log2_output_element_size, |
| 921 | .ukernel = igemm_ukernel, |
| 922 | }; |
| 923 | memcpy(&convolution_op->context.igemm.params, params, sizeof(convolution_op->context.igemm.params)); |
| 924 | |
| 925 | size_t nc = group_output_channels; |
| 926 | if (num_threads > 1) { |
| 927 | const size_t num_other_tiles = groups * batch_size * divide_round_up(output_size, mr); |
| 928 | const size_t target_tiles_per_thread = 5; |
| 929 | const size_t max_nc = divide_round_up(group_output_channels * num_other_tiles, num_threads * target_tiles_per_thread); |
| 930 | if (max_nc < nc) { |
| 931 | nc = min(nc, divide_round_up(nc, max_nc * nr) * nr); |
| 932 | } |
| 933 | } |
| 934 | if (groups == 1) { |
| 935 | convolution_op->compute.type = xnn_parallelization_type_3d_tile_2d; |
| 936 | convolution_op->compute.task_3d_tile_2d = (pthreadpool_task_3d_tile_2d_t) xnn_compute_igemm; |
| 937 | convolution_op->compute.range[0] = batch_size; |
| 938 | convolution_op->compute.range[1] = output_size; |
| 939 | convolution_op->compute.range[2] = group_output_channels; |
| 940 | convolution_op->compute.tile[0] = mr; |
| 941 | convolution_op->compute.tile[1] = nc; |
| 942 | } else { |
| 943 | convolution_op->compute.type = xnn_parallelization_type_4d_tile_2d; |
| 944 | convolution_op->compute.task_4d_tile_2d = (pthreadpool_task_4d_tile_2d_t) xnn_compute_gigemm; |
| 945 | convolution_op->compute.range[0] = batch_size; |
| 946 | convolution_op->compute.range[1] = groups; |
| 947 | convolution_op->compute.range[2] = output_size; |
| 948 | convolution_op->compute.range[3] = group_output_channels; |
| 949 | convolution_op->compute.tile[0] = mr; |
| 950 | convolution_op->compute.tile[1] = nc; |
| 951 | } |
| 952 | convolution_op->state = xnn_run_state_ready; |
| 953 | |
| 954 | return xnn_status_success; |
| 955 | } |
| 956 | case xnn_ukernel_type_dwconv: |
| 957 | { |
| 958 | size_t valid_batch_size = 0; |
| 959 | if (input == convolution_op->last_input && |
| 960 | input_height == convolution_op->last_input_height && |
| 961 | input_width == convolution_op->last_input_width) |
| 962 | { |
| 963 | valid_batch_size = convolution_op->valid_batch_size; |
| 964 | if (batch_size <= valid_batch_size) { |
| 965 | convolution_op->compute.range[0] = batch_size * convolution_op->output_height; |
| 966 | convolution_op->state = xnn_run_state_ready; |
| 967 | return xnn_status_success; |
| 968 | } |
| 969 | } |
| 970 | |
| 971 | const size_t kernel_height = convolution_op->kernel_height; |
| 972 | const size_t kernel_width = convolution_op->kernel_width; |
| 973 | const size_t kernel_size = kernel_height * kernel_width; |
| 974 | const size_t output_height = convolution_op->output_height; |
| 975 | const size_t output_width = convolution_op->output_width; |
| 976 | const size_t step_width = convolution_op->dilation_width == 1 ? convolution_op->stride_width : kernel_width; |
| 977 | const size_t step_height = kernel_size + (output_width * step_width - 1) * kernel_height; |
| 978 | const size_t indirection_buffer_size = sizeof(void*) * batch_size * output_height * step_height; |
| 979 | |
| 980 | const void** indirection_buffer = |
| 981 | (const void**) realloc(convolution_op->indirection_buffer, indirection_buffer_size); |
| 982 | if (indirection_buffer == NULL) { |
| 983 | xnn_log_error("failed to allocate %zu bytes for indirection buffer", indirection_buffer_size); |
| 984 | return xnn_status_out_of_memory; |
| 985 | } |
| 986 | convolution_op->indirection_buffer = indirection_buffer; |
| 987 | |
| 988 | xnn_indirection_init_dwconv2d(convolution_op, valid_batch_size, step_height, step_width, log2_input_element_size); |
| 989 | |
| 990 | const size_t groups = convolution_op->groups; |
| 991 | convolution_op->context.dwconv = (struct dwconv_context) { |
| 992 | .groups = groups, |
| 993 | .indirection_buffer = convolution_op->indirection_buffer, |
| 994 | .indirection_buffer_row_stride = step_height, |
| 995 | .indirection_buffer_col_stride = kernel_height * step_width * sizeof(void*), |
| 996 | .packed_weights = convolution_op->packed_weights, |
| 997 | .output = convolution_op->output, |
| 998 | .output_width = output_width, |
| 999 | .output_row_stride = output_width * convolution_op->output_pixel_stride << log2_output_element_size, |
| 1000 | .output_col_increment = (convolution_op->output_pixel_stride - groups) << log2_output_element_size, |
| 1001 | .unipass_ukernel = convolution_op->ukernel.dwconv.unipass_function, |
| 1002 | }; |
| 1003 | memcpy(&convolution_op->context.dwconv.params, params, sizeof(convolution_op->context.dwconv.params)); |
| 1004 | |
| 1005 | convolution_op->compute.type = xnn_parallelization_type_1d; |
| 1006 | convolution_op->compute.task_1d = (pthreadpool_task_1d_t) xnn_compute_dwconv_unipass; |
| 1007 | convolution_op->compute.range[0] = batch_size * output_height; |
| 1008 | convolution_op->state = xnn_run_state_ready; |
| 1009 | |
| 1010 | convolution_op->last_input = input; |
| 1011 | convolution_op->last_input_height = input_height; |
| 1012 | convolution_op->last_input_width = input_width; |
| 1013 | convolution_op->valid_batch_size = max(valid_batch_size, batch_size); |
| 1014 | |
| 1015 | return xnn_status_success; |
| 1016 | } |
| 1017 | case xnn_ukernel_type_vmulcaddc: |
| 1018 | { |
| 1019 | const size_t batch_output_size = batch_size * convolution_op->output_height * convolution_op->output_width; |
| 1020 | |
| 1021 | convolution_op->context.vmulcaddc = (struct vmulcaddc_context) { |
| 1022 | .n = convolution_op->groups << log2_input_element_size, |
| 1023 | .x = input, |
| 1024 | .x_stride = convolution_op->input_pixel_stride << log2_input_element_size, |
| 1025 | .w = convolution_op->packed_weights, |
| 1026 | .y = output, |
| 1027 | .y_stride = convolution_op->output_pixel_stride << log2_output_element_size, |
| 1028 | .ukernel = convolution_op->ukernel.vmulcaddc.function, |
| 1029 | }; |
| 1030 | memcpy(&convolution_op->context.vmulcaddc.params, params, sizeof(convolution_op->context.vmulcaddc.params)); |
| 1031 | |
| 1032 | size_t mc = batch_output_size; |
| 1033 | if (num_threads > 1) { |
| 1034 | const size_t target_tiles_per_thread = 5; |
| 1035 | const size_t max_mc = divide_round_up(batch_output_size, num_threads * target_tiles_per_thread); |
| 1036 | if (max_mc < mc) { |
| 1037 | const uint32_t mr = convolution_op->ukernel.vmulcaddc.mr; |
| 1038 | mc = min(mc, divide_round_up(mc, max_mc * mr) * mr); |
| 1039 | } |
| 1040 | } |
| 1041 | convolution_op->compute.type = xnn_parallelization_type_1d_tile_1d; |
| 1042 | convolution_op->compute.task_1d_tile_1d = (pthreadpool_task_1d_tile_1d_t) xnn_compute_vmulcaddc; |
| 1043 | convolution_op->compute.range[0] = batch_output_size; |
| 1044 | convolution_op->compute.tile[0] = mc; |
| 1045 | convolution_op->state = xnn_run_state_ready; |
| 1046 | |
| 1047 | return xnn_status_success; |
| 1048 | } |
| 1049 | default: |
| 1050 | XNN_UNREACHABLE; |
| 1051 | } |
| 1052 | } |
| 1053 | |
| 1054 | enum xnn_status xnn_setup_convolution2d_nhwc_q8( |
| 1055 | xnn_operator_t convolution_op, |
| 1056 | size_t batch_size, |
| 1057 | size_t input_height, |
| 1058 | size_t input_width, |
| 1059 | const uint8_t* input, |
| 1060 | uint8_t* output, |
| 1061 | pthreadpool_t threadpool) |
| 1062 | { |
| 1063 | if (convolution_op->type != xnn_operator_type_convolution_q8) { |
| 1064 | xnn_log_error("failed to setup Convolution (Q8) operator: operator type mismatch"); |
| 1065 | return xnn_status_invalid_parameter; |
| 1066 | } |
| 1067 | |
| 1068 | return setup_convolution2d_nhwc( |
| 1069 | convolution_op, |
| 1070 | batch_size, input_height, input_width, |
| 1071 | input, output, |
| 1072 | 0 /* log2(sizeof(input element)) = log2(sizeof(uint8_t)) */, |
| 1073 | 0 /* log2(sizeof(filter element)) = log2(sizeof(uint8_t)) */, |
| 1074 | sizeof(int32_t) /* sizeof(bias element) */, |
| 1075 | 0 /* log2(sizeof(output element)) = log2(sizeof(uint8_t)) */, |
| 1076 | &convolution_op->q8_gemm_params, |
| 1077 | pthreadpool_get_threads_count(threadpool)); |
| 1078 | } |
| 1079 | |
| 1080 | enum xnn_status xnn_setup_convolution2d_nhwc_f32( |
| 1081 | xnn_operator_t convolution_op, |
| 1082 | size_t batch_size, |
| 1083 | size_t input_height, |
| 1084 | size_t input_width, |
| 1085 | const float* input, |
| 1086 | float* output, |
| 1087 | pthreadpool_t threadpool) |
| 1088 | { |
| 1089 | if (convolution_op->type != xnn_operator_type_convolution_f32) { |
| 1090 | xnn_log_error("failed to setup Convolution (F32) operator: operator type mismatch"); |
| 1091 | return xnn_status_invalid_parameter; |
| 1092 | } |
| 1093 | |
| 1094 | return setup_convolution2d_nhwc( |
| 1095 | convolution_op, |
| 1096 | batch_size, input_height, input_width, |
| 1097 | input, output, |
| 1098 | 2 /* log2(sizeof(input element)) = log2(sizeof(float)) */, |
| 1099 | 2 /* log2(sizeof(filter element)) = log2(sizeof(float)) */, |
| 1100 | sizeof(float) /* sizeof(bias element) */, |
| 1101 | 2 /* log2(sizeof(output element)) = log2(sizeof(float)) */, |
| 1102 | &convolution_op->f32_output_params, |
| 1103 | pthreadpool_get_threads_count(threadpool)); |
| 1104 | } |