Marat Dukhan | 1d75a54 | 2020-02-03 12:23:01 -0800 | [diff] [blame] | 1 | // Copyright 2020 Google LLC |
| 2 | // |
| 3 | // This source code is licensed under the BSD-style license found in the |
| 4 | // LICENSE file in the root directory of this source tree. |
| 5 | |
| 6 | #include <math.h> |
| 7 | #include <stddef.h> |
| 8 | #include <stdint.h> |
| 9 | #include <stdlib.h> |
| 10 | |
| 11 | #include <xnnpack.h> |
| 12 | #include <xnnpack/allocator.h> |
| 13 | #include <xnnpack/log.h> |
| 14 | #include <xnnpack/math.h> |
| 15 | #include <xnnpack/params.h> |
| 16 | #include <xnnpack/subgraph.h> |
| 17 | |
| 18 | |
| 19 | enum xnn_status xnn_create_subgraph( |
| 20 | uint32_t external_value_ids, |
| 21 | uint32_t flags, |
| 22 | xnn_subgraph_t* subgraph_out) |
| 23 | { |
| 24 | struct xnn_subgraph* subgraph = NULL; |
| 25 | enum xnn_status status = xnn_status_uninitialized; |
| 26 | |
| 27 | if (!xnn_params.initialized) { |
| 28 | xnn_log_error("failed to create subgraph: XNNPACK is not initialized"); |
| 29 | goto error; |
| 30 | } |
| 31 | |
| 32 | status = xnn_status_out_of_memory; |
| 33 | |
| 34 | subgraph = xnn_allocate_zero_memory(sizeof(struct xnn_subgraph)); |
| 35 | if (subgraph == NULL) { |
| 36 | xnn_log_error("failed to allocate %zu bytes for subgraph descriptor", sizeof(struct xnn_subgraph)); |
| 37 | goto error; |
| 38 | } |
| 39 | |
| 40 | subgraph->external_value_ids = external_value_ids; |
| 41 | |
| 42 | subgraph->values = xnn_allocate_zero_memory(external_value_ids * sizeof(struct xnn_value)); |
| 43 | if (subgraph->values == NULL) { |
| 44 | xnn_log_error("failed to allocate %zu bytes for subgraph values", external_value_ids * sizeof(struct xnn_value)); |
| 45 | goto error; |
| 46 | } |
| 47 | for (size_t i = 0; i < external_value_ids; i++) { |
| 48 | subgraph->values[i].id = i; |
| 49 | } |
| 50 | subgraph->num_values = external_value_ids; |
| 51 | subgraph->num_reserved_values = external_value_ids; |
| 52 | |
| 53 | *subgraph_out = subgraph; |
| 54 | return xnn_status_success; |
| 55 | |
| 56 | error: |
| 57 | xnn_delete_subgraph(subgraph); |
| 58 | return status; |
| 59 | } |
| 60 | |
| 61 | |
| 62 | struct xnn_value* xnn_subgraph_new_internal_value(xnn_subgraph_t subgraph) |
| 63 | { |
| 64 | struct xnn_value* values = subgraph->values; |
| 65 | const size_t size = subgraph->num_values; |
| 66 | const size_t capacity = subgraph->num_reserved_values; |
| 67 | if (capacity < size + 1) { |
| 68 | const size_t new_capacity = max(min(capacity * 2, capacity + 512), capacity + 64); |
| 69 | assert(new_capacity >= size + 1); |
| 70 | values = xnn_reallocate_memory(values, new_capacity * sizeof(struct xnn_value)); |
| 71 | if (values == NULL) { |
| 72 | xnn_log_error("failed to allocate %zu bytes for subgraph values", |
| 73 | capacity * sizeof(struct xnn_value)); |
| 74 | return values; |
| 75 | } |
| 76 | |
| 77 | memset(values + size, 0, (new_capacity - size) * sizeof(struct xnn_value)); |
| 78 | subgraph->num_reserved_values = new_capacity; |
| 79 | subgraph->values = values; |
| 80 | } |
| 81 | subgraph->num_values = size + 1; |
| 82 | struct xnn_value* new_value = values + size; |
| 83 | new_value->id = size; |
| 84 | return new_value; |
| 85 | } |
| 86 | |
| 87 | struct xnn_node* xnn_subgraph_new_node(xnn_subgraph_t subgraph) |
| 88 | { |
| 89 | struct xnn_node* nodes = subgraph->nodes; |
| 90 | const size_t size = subgraph->num_nodes; |
| 91 | const size_t capacity = subgraph->num_reserved_nodes; |
| 92 | |
| 93 | if (capacity < size + 1) { |
| 94 | const size_t new_capacity = max(min(capacity * 2, capacity + 512), capacity + 64); |
| 95 | assert(new_capacity >= size + 1); |
| 96 | nodes = xnn_reallocate_memory(nodes, new_capacity * sizeof(struct xnn_node)); |
| 97 | if (nodes == NULL) { |
| 98 | xnn_log_error("failed to allocate %zu bytes for subgraph nodes", |
| 99 | capacity * sizeof(struct xnn_node)); |
| 100 | return nodes; |
| 101 | } |
| 102 | |
| 103 | memset(nodes + size, 0, (new_capacity - size) * sizeof(struct xnn_node)); |
| 104 | subgraph->num_reserved_nodes = new_capacity; |
| 105 | subgraph->nodes = nodes; |
| 106 | } |
| 107 | subgraph->num_nodes = size + 1; |
| 108 | struct xnn_node* new_node = nodes + size; |
| 109 | new_node->id = size; |
| 110 | return new_node; |
| 111 | } |
| 112 | |
| 113 | enum xnn_status xnn_define_convolution_2d( |
| 114 | xnn_subgraph_t subgraph, |
| 115 | uint32_t input_padding_top, |
| 116 | uint32_t input_padding_right, |
| 117 | uint32_t input_padding_bottom, |
| 118 | uint32_t input_padding_left, |
| 119 | uint32_t kernel_height, |
| 120 | uint32_t kernel_width, |
| 121 | uint32_t subsampling_height, |
| 122 | uint32_t subsampling_width, |
| 123 | uint32_t dilation_height, |
| 124 | uint32_t dilation_width, |
| 125 | uint32_t groups, |
| 126 | size_t group_input_channels, |
| 127 | size_t group_output_channels, |
| 128 | float output_min, |
| 129 | float output_max, |
| 130 | uint32_t input_id, |
| 131 | uint32_t filter_id, |
| 132 | uint32_t bias_id, |
| 133 | uint32_t output_id, |
| 134 | uint32_t flags) |
| 135 | { |
| 136 | if (!xnn_params.initialized) { |
| 137 | xnn_log_error("failed to define Convolution operator: XNNPACK is not initialized"); |
| 138 | return xnn_status_uninitialized; |
| 139 | } |
| 140 | |
| 141 | if (kernel_width == 0 || kernel_height == 0) { |
| 142 | xnn_log_error( |
| 143 | "failed to define Convolution operator with %" PRIu32 "x%" PRIu32 " kernel: kernel dimensions must be non-zero", |
| 144 | kernel_width, kernel_height); |
| 145 | return xnn_status_invalid_parameter; |
| 146 | } |
| 147 | |
| 148 | if (subsampling_width == 0 || subsampling_height == 0) { |
| 149 | xnn_log_error( |
| 150 | "failed to define Convolution operator with %" PRIu32 "x%" PRIu32 " subsampling: " |
| 151 | "subsampling dimensions must be non-zero", |
| 152 | subsampling_width, subsampling_height); |
| 153 | return xnn_status_invalid_parameter; |
| 154 | } |
| 155 | |
| 156 | if (dilation_width == 0 || dilation_height == 0) { |
| 157 | xnn_log_error( |
| 158 | "failed to define Convolution operator with %" PRIu32 "x%" PRIu32 " dilation: " |
| 159 | "dilation dimensions must be non-zero", |
| 160 | dilation_width, dilation_height); |
| 161 | return xnn_status_invalid_parameter; |
| 162 | } |
| 163 | |
| 164 | if (groups == 0) { |
| 165 | xnn_log_error( |
| 166 | "failed to define Convolution operator with %" PRIu32 " groups: number of groups must be non-zero", groups); |
| 167 | return xnn_status_invalid_parameter; |
| 168 | } |
| 169 | |
| 170 | if (group_input_channels == 0) { |
| 171 | xnn_log_error( |
| 172 | "failed to define Convolution operator with %zu input channels per group: " |
| 173 | "number of channels must be non-zero", |
| 174 | group_input_channels); |
| 175 | return xnn_status_invalid_parameter; |
| 176 | } |
| 177 | |
| 178 | if (group_output_channels == 0) { |
| 179 | xnn_log_error( |
| 180 | "failed to define Convolution operator with %zu output channels per group: " |
| 181 | "number of channels must be non-zero", |
| 182 | group_output_channels); |
| 183 | return xnn_status_invalid_parameter; |
| 184 | } |
| 185 | |
| 186 | if (isnan(output_min)) { |
| 187 | xnn_log_error( |
| 188 | "failed to define Convolution operator with NaN output lower bound: lower bound must be non-NaN"); |
| 189 | return xnn_status_invalid_parameter; |
| 190 | } |
| 191 | |
| 192 | if (isnan(output_max)) { |
| 193 | xnn_log_error( |
| 194 | "failed to define Convolution operator with NaN output upper bound: upper bound must be non-NaN"); |
| 195 | return xnn_status_invalid_parameter; |
| 196 | } |
| 197 | |
| 198 | if (output_min >= output_max) { |
| 199 | xnn_log_error( |
| 200 | "failed to define Convolution operator with [%.7g, %.7g] output range: " |
| 201 | "lower bound must be below upper bound", |
| 202 | output_min, output_max); |
| 203 | return xnn_status_invalid_parameter; |
| 204 | } |
| 205 | |
| 206 | if (input_id >= subgraph->num_values) { |
| 207 | xnn_log_error( |
| 208 | "failed to define Convolution operator with input ID #%" PRIu32 ": invalid Value ID", |
| 209 | input_id); |
| 210 | return xnn_status_invalid_parameter; |
| 211 | } |
| 212 | |
| 213 | if (filter_id >= subgraph->num_values) { |
| 214 | xnn_log_error( |
| 215 | "failed to define Convolution operator with filter ID #%" PRIu32 ": invalid Value ID", |
| 216 | filter_id); |
| 217 | return xnn_status_invalid_parameter; |
| 218 | } |
| 219 | |
| 220 | if (bias_id >= subgraph->num_values) { |
| 221 | xnn_log_error( |
| 222 | "failed to define Convolution operator with bias ID #%" PRIu32 ": invalid Value ID", |
| 223 | bias_id); |
| 224 | return xnn_status_invalid_parameter; |
| 225 | } |
| 226 | |
| 227 | if (output_id >= subgraph->num_values) { |
| 228 | xnn_log_error( |
| 229 | "failed to define Convolution operator with output ID #%" PRIu32 ": invalid Value ID", |
| 230 | output_id); |
| 231 | return xnn_status_invalid_parameter; |
| 232 | } |
| 233 | |
| 234 | struct xnn_node* node = xnn_subgraph_new_node(subgraph); |
| 235 | if (node == NULL) { |
| 236 | return xnn_status_out_of_memory; |
| 237 | } |
| 238 | |
| 239 | node->type = xnn_node_type_convolution_2d; |
| 240 | node->params.convolution_2d.input_padding_top = input_padding_top; |
| 241 | node->params.convolution_2d.input_padding_right = input_padding_right; |
| 242 | node->params.convolution_2d.input_padding_bottom = input_padding_bottom; |
| 243 | node->params.convolution_2d.input_padding_left = input_padding_left; |
| 244 | node->params.convolution_2d.kernel_height = kernel_height; |
| 245 | node->params.convolution_2d.kernel_width = kernel_width; |
| 246 | node->params.convolution_2d.subsampling_height = subsampling_height; |
| 247 | node->params.convolution_2d.subsampling_width = subsampling_width; |
| 248 | node->params.convolution_2d.dilation_height = dilation_height; |
| 249 | node->params.convolution_2d.dilation_width = dilation_width; |
| 250 | node->params.convolution_2d.groups = groups; |
| 251 | node->params.convolution_2d.group_input_channels = group_input_channels; |
| 252 | node->params.convolution_2d.group_output_channels = group_output_channels; |
Marat Dukhan | 54dcb46 | 2020-02-10 11:06:12 -0800 | [diff] [blame] | 253 | node->activation.output_min = output_min; |
| 254 | node->activation.output_max = output_max; |
Marat Dukhan | 1d75a54 | 2020-02-03 12:23:01 -0800 | [diff] [blame] | 255 | node->num_inputs = 3; |
| 256 | node->inputs.raw[0] = input_id; |
| 257 | node->inputs.raw[1] = filter_id; |
| 258 | node->inputs.raw[2] = bias_id; |
| 259 | node->num_outputs = 1; |
| 260 | node->outputs.raw[0] = output_id; |
| 261 | node->flags = flags; |
| 262 | |
| 263 | return xnn_status_success; |
| 264 | }; |
| 265 | |
| 266 | enum xnn_status xnn_define_depthwise_convolution_2d( |
| 267 | xnn_subgraph_t subgraph, |
| 268 | uint32_t input_padding_top, |
| 269 | uint32_t input_padding_right, |
| 270 | uint32_t input_padding_bottom, |
| 271 | uint32_t input_padding_left, |
| 272 | uint32_t kernel_height, |
| 273 | uint32_t kernel_width, |
| 274 | uint32_t subsampling_height, |
| 275 | uint32_t subsampling_width, |
| 276 | uint32_t dilation_height, |
| 277 | uint32_t dilation_width, |
| 278 | uint32_t depth_multiplier, |
| 279 | size_t input_channels, |
| 280 | float output_min, |
| 281 | float output_max, |
| 282 | uint32_t input_id, |
| 283 | uint32_t filter_id, |
| 284 | uint32_t bias_id, |
| 285 | uint32_t output_id, |
| 286 | uint32_t flags) |
| 287 | { |
| 288 | if (!xnn_params.initialized) { |
| 289 | xnn_log_error("failed to define Depthwise Convolution operator: XNNPACK is not initialized"); |
| 290 | return xnn_status_uninitialized; |
| 291 | } |
| 292 | |
| 293 | if (kernel_width == 0 || kernel_height == 0) { |
| 294 | xnn_log_error( |
| 295 | "failed to define Depthwise Convolution operator with %" PRIu32 "x%" PRIu32 " kernel: kernel dimensions must be non-zero", |
| 296 | kernel_width, kernel_height); |
| 297 | return xnn_status_invalid_parameter; |
| 298 | } |
| 299 | |
| 300 | if (subsampling_width == 0 || subsampling_height == 0) { |
| 301 | xnn_log_error( |
| 302 | "failed to define Depthwise Convolution operator with %" PRIu32 "x%" PRIu32 " subsampling: " |
| 303 | "subsampling dimensions must be non-zero", |
| 304 | subsampling_width, subsampling_height); |
| 305 | return xnn_status_invalid_parameter; |
| 306 | } |
| 307 | |
| 308 | if (dilation_width == 0 || dilation_height == 0) { |
| 309 | xnn_log_error( |
| 310 | "failed to define Depthwise Convolution operator with %" PRIu32 "x%" PRIu32 " dilation: " |
| 311 | "dilation dimensions must be non-zero", |
| 312 | dilation_width, dilation_height); |
| 313 | return xnn_status_invalid_parameter; |
| 314 | } |
| 315 | |
| 316 | if (depth_multiplier == 0) { |
| 317 | xnn_log_error( |
| 318 | "failed to define Depthwise Convolution operator with %" PRIu32 " depth multiplier: " |
| 319 | "depth multiplier must be non-zero", |
| 320 | depth_multiplier); |
| 321 | return xnn_status_invalid_parameter; |
| 322 | } |
| 323 | |
| 324 | if (input_channels == 0) { |
| 325 | xnn_log_error( |
| 326 | "failed to define Depthwise Convolution operator with %zu input channels: " |
| 327 | "number of channels must be non-zero", |
| 328 | input_channels); |
| 329 | return xnn_status_invalid_parameter; |
| 330 | } |
| 331 | |
| 332 | if (isnan(output_min)) { |
| 333 | xnn_log_error( |
| 334 | "failed to define Depthwise Convolution operator with NaN output lower bound: lower bound must be non-NaN"); |
| 335 | return xnn_status_invalid_parameter; |
| 336 | } |
| 337 | |
| 338 | if (isnan(output_max)) { |
| 339 | xnn_log_error( |
| 340 | "failed to define Depthwise Convolution operator with NaN output upper bound: upper bound must be non-NaN"); |
| 341 | return xnn_status_invalid_parameter; |
| 342 | } |
| 343 | |
| 344 | if (output_min >= output_max) { |
| 345 | xnn_log_error( |
| 346 | "failed to define Depthwise Convolution operator with [%.7g, %.7g] output range: " |
| 347 | "lower bound must be below upper bound", |
| 348 | output_min, output_max); |
| 349 | return xnn_status_invalid_parameter; |
| 350 | } |
| 351 | |
| 352 | if (input_id >= subgraph->num_values) { |
| 353 | xnn_log_error( |
| 354 | "failed to define Depthwise Convolution operator with input ID #%" PRIu32 ": invalid Value ID", |
| 355 | input_id); |
| 356 | return xnn_status_invalid_parameter; |
| 357 | } |
| 358 | |
| 359 | if (filter_id >= subgraph->num_values) { |
| 360 | xnn_log_error( |
| 361 | "failed to define Depthwise Convolution operator with filter ID #%" PRIu32 ": invalid Value ID", |
| 362 | filter_id); |
| 363 | return xnn_status_invalid_parameter; |
| 364 | } |
| 365 | |
| 366 | if (bias_id >= subgraph->num_values) { |
| 367 | xnn_log_error( |
| 368 | "failed to define Depthwise Convolution operator with bias ID #%" PRIu32 ": invalid Value ID", |
| 369 | bias_id); |
| 370 | return xnn_status_invalid_parameter; |
| 371 | } |
| 372 | |
| 373 | if (output_id >= subgraph->num_values) { |
| 374 | xnn_log_error( |
| 375 | "failed to define Depthwise Convolution operator with output ID #%" PRIu32 ": invalid Value ID", |
| 376 | output_id); |
| 377 | return xnn_status_invalid_parameter; |
| 378 | } |
| 379 | |
| 380 | struct xnn_node* node = xnn_subgraph_new_node(subgraph); |
| 381 | if (node == NULL) { |
| 382 | return xnn_status_out_of_memory; |
| 383 | } |
| 384 | |
| 385 | node->type = xnn_node_type_depthwise_convolution_2d; |
| 386 | node->params.depthwise_convolution_2d.input_padding_top = input_padding_top; |
| 387 | node->params.depthwise_convolution_2d.input_padding_right = input_padding_right; |
| 388 | node->params.depthwise_convolution_2d.input_padding_bottom = input_padding_bottom; |
| 389 | node->params.depthwise_convolution_2d.input_padding_left = input_padding_left; |
| 390 | node->params.depthwise_convolution_2d.kernel_height = kernel_height; |
| 391 | node->params.depthwise_convolution_2d.kernel_width = kernel_width; |
| 392 | node->params.depthwise_convolution_2d.subsampling_height = subsampling_height; |
| 393 | node->params.depthwise_convolution_2d.subsampling_width = subsampling_width; |
| 394 | node->params.depthwise_convolution_2d.dilation_height = dilation_height; |
| 395 | node->params.depthwise_convolution_2d.dilation_width = dilation_width; |
| 396 | node->params.depthwise_convolution_2d.depth_multiplier = depth_multiplier; |
| 397 | node->params.depthwise_convolution_2d.input_channels = input_channels; |
Marat Dukhan | 54dcb46 | 2020-02-10 11:06:12 -0800 | [diff] [blame] | 398 | node->activation.output_min = output_min; |
| 399 | node->activation.output_max = output_max; |
Marat Dukhan | 1d75a54 | 2020-02-03 12:23:01 -0800 | [diff] [blame] | 400 | node->num_inputs = 3; |
| 401 | node->inputs.raw[0] = input_id; |
| 402 | node->inputs.raw[1] = filter_id; |
| 403 | node->inputs.raw[2] = bias_id; |
| 404 | node->num_outputs = 1; |
| 405 | node->outputs.raw[0] = output_id; |
| 406 | node->flags = flags; |
| 407 | |
| 408 | return xnn_status_success; |
| 409 | }; |
| 410 | |
Marat Dukhan | 54dcb46 | 2020-02-10 11:06:12 -0800 | [diff] [blame] | 411 | enum xnn_status xnn_define_add2( |
| 412 | xnn_subgraph_t subgraph, |
| 413 | float output_min, |
| 414 | float output_max, |
| 415 | uint32_t input1_id, |
| 416 | uint32_t input2_id, |
| 417 | uint32_t output_id, |
| 418 | uint32_t flags) |
| 419 | { |
| 420 | if (!xnn_params.initialized) { |
| 421 | xnn_log_error("failed to define Add2 operator: XNNPACK is not initialized"); |
| 422 | return xnn_status_uninitialized; |
| 423 | } |
| 424 | |
| 425 | if (isnan(output_min)) { |
| 426 | xnn_log_error( |
| 427 | "failed to define Add2 operator with NaN output lower bound: lower bound must be non-NaN"); |
| 428 | return xnn_status_invalid_parameter; |
| 429 | } |
| 430 | |
| 431 | if (isnan(output_max)) { |
| 432 | xnn_log_error( |
| 433 | "failed to define Add2 operator with NaN output upper bound: upper bound must be non-NaN"); |
| 434 | return xnn_status_invalid_parameter; |
| 435 | } |
| 436 | |
| 437 | if (output_min >= output_max) { |
| 438 | xnn_log_error( |
| 439 | "failed to define Add2 operator with [%.7g, %.7g] output range: " |
| 440 | "lower bound must be below upper bound", |
| 441 | output_min, output_max); |
| 442 | return xnn_status_invalid_parameter; |
| 443 | } |
| 444 | |
| 445 | if (input1_id >= subgraph->num_values) { |
| 446 | xnn_log_error( |
| 447 | "failed to define Add2 operator with the first input ID #%" PRIu32 ": invalid Value ID", |
| 448 | input1_id); |
| 449 | return xnn_status_invalid_parameter; |
| 450 | } |
| 451 | |
| 452 | if (input2_id >= subgraph->num_values) { |
| 453 | xnn_log_error( |
| 454 | "failed to define Add2 operator with the second input ID #%" PRIu32 ": invalid Value ID", |
| 455 | input2_id); |
| 456 | return xnn_status_invalid_parameter; |
| 457 | } |
| 458 | |
| 459 | if (output_id >= subgraph->num_values) { |
| 460 | xnn_log_error( |
| 461 | "failed to define Add2 operator with output ID #%" PRIu32 ": invalid Value ID", |
| 462 | output_id); |
| 463 | return xnn_status_invalid_parameter; |
| 464 | } |
| 465 | |
| 466 | struct xnn_node* node = xnn_subgraph_new_node(subgraph); |
| 467 | if (node == NULL) { |
| 468 | return xnn_status_out_of_memory; |
| 469 | } |
| 470 | |
| 471 | node->type = xnn_node_type_add2; |
| 472 | node->activation.output_min = output_min; |
| 473 | node->activation.output_max = output_max; |
| 474 | node->num_inputs = 2; |
| 475 | node->inputs.raw[0] = input1_id; |
| 476 | node->inputs.raw[1] = input2_id; |
| 477 | node->num_outputs = 1; |
| 478 | node->outputs.raw[0] = output_id; |
| 479 | node->flags = flags; |
| 480 | |
| 481 | return xnn_status_success; |
| 482 | } |
| 483 | |
| 484 | enum xnn_status xnn_define_multiply2( |
| 485 | xnn_subgraph_t subgraph, |
| 486 | float output_min, |
| 487 | float output_max, |
| 488 | uint32_t input1_id, |
| 489 | uint32_t input2_id, |
| 490 | uint32_t output_id, |
| 491 | uint32_t flags) |
| 492 | { |
| 493 | if (!xnn_params.initialized) { |
| 494 | xnn_log_error("failed to define Multiply2 operator: XNNPACK is not initialized"); |
| 495 | return xnn_status_uninitialized; |
| 496 | } |
| 497 | |
| 498 | if (isnan(output_min)) { |
| 499 | xnn_log_error( |
| 500 | "failed to define Multiply2 operator with NaN output lower bound: lower bound must be non-NaN"); |
| 501 | return xnn_status_invalid_parameter; |
| 502 | } |
| 503 | |
| 504 | if (isnan(output_max)) { |
| 505 | xnn_log_error( |
| 506 | "failed to define Multiply2 operator with NaN output upper bound: upper bound must be non-NaN"); |
| 507 | return xnn_status_invalid_parameter; |
| 508 | } |
| 509 | |
| 510 | if (output_min >= output_max) { |
| 511 | xnn_log_error( |
| 512 | "failed to define Multiply2 operator with [%.7g, %.7g] output range: " |
| 513 | "lower bound must be below upper bound", |
| 514 | output_min, output_max); |
| 515 | return xnn_status_invalid_parameter; |
| 516 | } |
| 517 | |
| 518 | if (input1_id >= subgraph->num_values) { |
| 519 | xnn_log_error( |
| 520 | "failed to define Multiply2 operator with the first input ID #%" PRIu32 ": invalid Value ID", |
| 521 | input1_id); |
| 522 | return xnn_status_invalid_parameter; |
| 523 | } |
| 524 | |
| 525 | if (input2_id >= subgraph->num_values) { |
| 526 | xnn_log_error( |
| 527 | "failed to define Multiply2 operator with the second input ID #%" PRIu32 ": invalid Value ID", |
| 528 | input2_id); |
| 529 | return xnn_status_invalid_parameter; |
| 530 | } |
| 531 | |
| 532 | if (output_id >= subgraph->num_values) { |
| 533 | xnn_log_error( |
| 534 | "failed to define Multiply2 operator with output ID #%" PRIu32 ": invalid Value ID", |
| 535 | output_id); |
| 536 | return xnn_status_invalid_parameter; |
| 537 | } |
| 538 | |
| 539 | struct xnn_node* node = xnn_subgraph_new_node(subgraph); |
| 540 | if (node == NULL) { |
| 541 | return xnn_status_out_of_memory; |
| 542 | } |
| 543 | |
| 544 | node->type = xnn_node_type_multiply2; |
| 545 | node->activation.output_min = output_min; |
| 546 | node->activation.output_max = output_max; |
| 547 | node->num_inputs = 2; |
| 548 | node->inputs.raw[0] = input1_id; |
| 549 | node->inputs.raw[1] = input2_id; |
| 550 | node->num_outputs = 1; |
| 551 | node->outputs.raw[0] = output_id; |
| 552 | node->flags = flags; |
| 553 | |
| 554 | return xnn_status_success; |
| 555 | } |
| 556 | |
Miao Wang | 3fa1f01 | 2020-02-17 22:45:06 +0000 | [diff] [blame] | 557 | enum xnn_status xnn_define_prelu( |
| 558 | xnn_subgraph_t subgraph, |
| 559 | uint32_t input_id, |
| 560 | uint32_t slope_id, |
| 561 | uint32_t output_id, |
| 562 | uint32_t flags) |
| 563 | { |
| 564 | if (!xnn_params.initialized) { |
| 565 | xnn_log_error("failed to define PReLU operator: XNNPACK is not initialized"); |
| 566 | return xnn_status_uninitialized; |
| 567 | } |
| 568 | |
| 569 | if (input_id >= subgraph->num_values) { |
| 570 | xnn_log_error( |
| 571 | "failed to define PReLU operator with input ID #%" PRIu32 ": invalid Value ID", |
| 572 | input_id); |
| 573 | return xnn_status_invalid_parameter; |
| 574 | } |
| 575 | |
| 576 | if (slope_id >= subgraph->num_values) { |
| 577 | xnn_log_error( |
| 578 | "failed to define PReLU operator with slope ID #%" PRIu32 ": invalid Value ID", |
| 579 | slope_id); |
| 580 | return xnn_status_invalid_parameter; |
| 581 | } |
| 582 | |
| 583 | if (output_id >= subgraph->num_values) { |
| 584 | xnn_log_error( |
| 585 | "failed to define PReLU operator with output ID #%" PRIu32 ": invalid Value ID", |
| 586 | output_id); |
| 587 | return xnn_status_invalid_parameter; |
| 588 | } |
| 589 | |
| 590 | struct xnn_node* node = xnn_subgraph_new_node(subgraph); |
| 591 | if (node == NULL) { |
| 592 | return xnn_status_out_of_memory; |
| 593 | } |
| 594 | |
| 595 | node->type = xnn_node_type_prelu; |
| 596 | node->num_inputs = 2; |
| 597 | node->inputs.raw[0] = input_id; |
| 598 | node->inputs.raw[1] = slope_id; |
| 599 | node->num_outputs = 1; |
| 600 | node->outputs.raw[0] = output_id; |
| 601 | node->flags = flags; |
| 602 | |
| 603 | return xnn_status_success; |
| 604 | } |
| 605 | |
| 606 | enum xnn_status xnn_define_clamp( |
| 607 | xnn_subgraph_t subgraph, |
| 608 | float output_min, |
| 609 | float output_max, |
| 610 | uint32_t input_id, |
| 611 | uint32_t output_id, |
| 612 | uint32_t flags) |
| 613 | { |
| 614 | if (!xnn_params.initialized) { |
| 615 | xnn_log_error("failed to define Clamp operator: XNNPACK is not initialized"); |
| 616 | return xnn_status_uninitialized; |
| 617 | } |
| 618 | |
| 619 | if (input_id >= subgraph->num_values) { |
| 620 | xnn_log_error( |
| 621 | "failed to define Clamp operator with input ID #%" PRIu32 ": invalid Value ID", |
| 622 | input_id); |
| 623 | return xnn_status_invalid_parameter; |
| 624 | } |
| 625 | |
| 626 | if (output_id >= subgraph->num_values) { |
| 627 | xnn_log_error( |
| 628 | "failed to define Clamp operator with output ID #%" PRIu32 ": invalid Value ID", |
| 629 | output_id); |
| 630 | return xnn_status_invalid_parameter; |
| 631 | } |
| 632 | |
| 633 | struct xnn_node* node = xnn_subgraph_new_node(subgraph); |
| 634 | if (node == NULL) { |
| 635 | return xnn_status_out_of_memory; |
| 636 | } |
| 637 | |
| 638 | node->type = xnn_node_type_clamp; |
| 639 | node->activation.output_min = output_min; |
| 640 | node->activation.output_max = output_max; |
| 641 | node->num_inputs = 1; |
| 642 | node->inputs.raw[0] = input_id; |
| 643 | node->num_outputs = 1; |
| 644 | node->outputs.raw[0] = output_id; |
| 645 | node->flags = flags; |
| 646 | |
| 647 | return xnn_status_success; |
| 648 | } |
| 649 | |
| 650 | enum xnn_status xnn_define_hardswish( |
| 651 | xnn_subgraph_t subgraph, |
| 652 | uint32_t input_id, |
| 653 | uint32_t output_id, |
| 654 | uint32_t flags) |
| 655 | { |
| 656 | if (!xnn_params.initialized) { |
| 657 | xnn_log_error("failed to define HardSwish operator: XNNPACK is not initialized"); |
| 658 | return xnn_status_uninitialized; |
| 659 | } |
| 660 | |
| 661 | if (input_id >= subgraph->num_values) { |
| 662 | xnn_log_error( |
| 663 | "failed to define HardSwish operator with input ID #%" PRIu32 ": invalid Value ID", |
| 664 | input_id); |
| 665 | return xnn_status_invalid_parameter; |
| 666 | } |
| 667 | |
| 668 | if (output_id >= subgraph->num_values) { |
| 669 | xnn_log_error( |
| 670 | "failed to define HardSwish operator with output ID #%" PRIu32 ": invalid Value ID", |
| 671 | output_id); |
| 672 | return xnn_status_invalid_parameter; |
| 673 | } |
| 674 | |
| 675 | struct xnn_node* node = xnn_subgraph_new_node(subgraph); |
| 676 | if (node == NULL) { |
| 677 | return xnn_status_out_of_memory; |
| 678 | } |
| 679 | |
| 680 | node->type = xnn_node_type_hardswish; |
| 681 | node->num_inputs = 1; |
| 682 | node->inputs.raw[0] = input_id; |
| 683 | node->num_outputs = 1; |
| 684 | node->outputs.raw[0] = output_id; |
| 685 | node->flags = flags; |
| 686 | |
| 687 | return xnn_status_success; |
| 688 | } |
| 689 | |
| 690 | enum xnn_status xnn_define_sigmoid( |
| 691 | xnn_subgraph_t subgraph, |
| 692 | uint32_t input_id, |
| 693 | uint32_t output_id, |
| 694 | uint32_t flags) |
| 695 | { |
| 696 | if (!xnn_params.initialized) { |
| 697 | xnn_log_error("failed to define Sigmoid operator: XNNPACK is not initialized"); |
| 698 | return xnn_status_uninitialized; |
| 699 | } |
| 700 | |
| 701 | if (input_id >= subgraph->num_values) { |
| 702 | xnn_log_error( |
| 703 | "failed to define Sigmoid operator with input ID #%" PRIu32 ": invalid Value ID", |
| 704 | input_id); |
| 705 | return xnn_status_invalid_parameter; |
| 706 | } |
| 707 | |
| 708 | if (output_id >= subgraph->num_values) { |
| 709 | xnn_log_error( |
| 710 | "failed to define Sigmoid operator with output ID #%" PRIu32 ": invalid Value ID", |
| 711 | output_id); |
| 712 | return xnn_status_invalid_parameter; |
| 713 | } |
| 714 | |
| 715 | struct xnn_node* node = xnn_subgraph_new_node(subgraph); |
| 716 | if (node == NULL) { |
| 717 | return xnn_status_out_of_memory; |
| 718 | } |
| 719 | |
| 720 | node->type = xnn_node_type_sigmoid; |
| 721 | node->num_inputs = 1; |
| 722 | node->inputs.raw[0] = input_id; |
| 723 | node->num_outputs = 1; |
| 724 | node->outputs.raw[0] = output_id; |
| 725 | node->flags = flags; |
| 726 | |
| 727 | return xnn_status_success; |
| 728 | } |
| 729 | |
| 730 | enum xnn_status xnn_define_softmax( |
| 731 | xnn_subgraph_t subgraph, |
| 732 | uint32_t input_id, |
| 733 | uint32_t output_id, |
| 734 | uint32_t flags) |
| 735 | { |
| 736 | if (!xnn_params.initialized) { |
| 737 | xnn_log_error("failed to define SoftMax operator: XNNPACK is not initialized"); |
| 738 | return xnn_status_uninitialized; |
| 739 | } |
| 740 | |
| 741 | if (input_id >= subgraph->num_values) { |
| 742 | xnn_log_error( |
| 743 | "failed to define SoftMax operator with input ID #%" PRIu32 ": invalid Value ID", |
| 744 | input_id); |
| 745 | return xnn_status_invalid_parameter; |
| 746 | } |
| 747 | |
| 748 | if (output_id >= subgraph->num_values) { |
| 749 | xnn_log_error( |
| 750 | "failed to define SoftMax operator with output ID #%" PRIu32 ": invalid Value ID", |
| 751 | output_id); |
| 752 | return xnn_status_invalid_parameter; |
| 753 | } |
| 754 | |
| 755 | struct xnn_node* node = xnn_subgraph_new_node(subgraph); |
| 756 | if (node == NULL) { |
| 757 | return xnn_status_out_of_memory; |
| 758 | } |
| 759 | |
| 760 | node->type = xnn_node_type_softmax; |
| 761 | node->num_inputs = 1; |
| 762 | node->inputs.raw[0] = input_id; |
| 763 | node->num_outputs = 1; |
| 764 | node->outputs.raw[0] = output_id; |
| 765 | node->flags = flags; |
| 766 | |
| 767 | return xnn_status_success; |
| 768 | } |
| 769 | |
Marat Dukhan | 1d75a54 | 2020-02-03 12:23:01 -0800 | [diff] [blame] | 770 | enum xnn_status xnn_delete_subgraph( |
| 771 | xnn_subgraph_t subgraph) |
| 772 | { |
| 773 | if (subgraph != NULL) { |
| 774 | memset(subgraph->nodes, 0, sizeof(struct xnn_node) * subgraph->num_nodes); |
| 775 | xnn_release_memory(subgraph->nodes); |
| 776 | |
| 777 | memset(subgraph->values, 0, sizeof(struct xnn_value) * subgraph->num_values); |
| 778 | xnn_release_memory(subgraph->values); |
| 779 | |
| 780 | memset(subgraph, 0, sizeof(struct xnn_subgraph)); |
| 781 | xnn_release_memory(subgraph); |
| 782 | } |
| 783 | return xnn_status_success; |
| 784 | } |