SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 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_DYNAMIC_FUSION_SKETCH_UTILS_DEPENDENCYGRAPH |
| 25 | #define SRC_DYNAMIC_FUSION_SKETCH_UTILS_DEPENDENCYGRAPH |
| 26 | |
| 27 | #include "arm_compute/core/Error.h" |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 28 | #include <cstdint> |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 29 | #include <map> |
| 30 | #include <set> |
| 31 | #include <tuple> |
| 32 | #include <vector> |
| 33 | |
| 34 | namespace arm_compute |
| 35 | { |
| 36 | namespace experimental |
| 37 | { |
| 38 | namespace dynamic_fusion |
| 39 | { |
| 40 | namespace |
| 41 | { |
| 42 | template <typename T> |
| 43 | bool is_in(const T &v, const std::vector<T> &vec) |
| 44 | { |
| 45 | return std::find(std::begin(vec), std::end(vec), v) != std::end(vec); |
| 46 | } |
| 47 | } // namespace |
| 48 | |
| 49 | /** A multi-input (tensors), multi-output (tensors) acyclic directed graph |
| 50 | * Represented as a doubly-linked adjacency list with the differentiation between source and destination |
| 51 | */ |
| 52 | class DependencyGraph |
| 53 | { |
| 54 | public: |
| 55 | using Id = int32_t; |
| 56 | using TensorId = Id; |
| 57 | using OperatorId = Id; |
| 58 | /** Adjacency list |
| 59 | * |
| 60 | */ |
| 61 | using AdjList = std::map<Id, std::vector<Id>>; |
| 62 | |
| 63 | /** A pack of operator including its input and output tensors, used by traversing through the graph in topological order |
| 64 | * |
| 65 | */ |
| 66 | struct OpPack |
| 67 | { |
| 68 | OperatorId op{}; |
| 69 | std::vector<TensorId> inputs{}; |
| 70 | std::vector<TensorId> outputs{}; |
| 71 | friend bool operator==(const OpPack &opp0, const OpPack &opp1) |
| 72 | { |
| 73 | return std::make_tuple( |
| 74 | opp0.op, opp0.inputs, opp0.outputs) |
| 75 | == std::make_tuple( |
| 76 | opp1.op, opp1.inputs, opp1.outputs); |
| 77 | } |
| 78 | }; |
| 79 | |
| 80 | public: |
| 81 | DependencyGraph() = default; |
| 82 | friend std::ostream &operator<<(std::ostream &os, const DependencyGraph &); |
| 83 | |
| 84 | /** Try adding an operator (without actually adding it), while keeping the graph as a "linear sequence" / list |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 85 | * |
| 86 | * Rule: If the new operator is not the first operator, at least one input tensor must be |
| 87 | * the output tensor of the last non-output operator. All other input tensors must be |
| 88 | * the global input of the graph (i.e. not the output of any operator). |
| 89 | * |
| 90 | * Rule: The output tensor of the new operator must not be the input tensor of any previously |
| 91 | * added operator. |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 92 | * |
| 93 | * PRECONDITION: The current graph is already linear |
| 94 | * |
| 95 | * @return true If the operator can be added while keeping the graph as a linear sequence |
| 96 | * @return false Otherwise |
| 97 | */ |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 98 | bool try_add_operator_as_linear(OperatorId op, const std::vector<TensorId> &inputs, const std::vector<TensorId> &outputs, bool is_output = false) const |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 99 | { |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 100 | ARM_COMPUTE_UNUSED(op, is_output); |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 101 | if(all_ops().empty()) |
| 102 | { |
| 103 | return true; |
| 104 | } |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 105 | |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 106 | // If the new operator is not the first operator, at least one input tensor must be |
| 107 | // the output tensor of the last non-output operator. All other input tensors must be |
| 108 | // the global input of the graph (i.e. not the output of any operator). |
| 109 | if(_last_op_available) |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 110 | { |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 111 | auto use_input_from_last_op = false; |
| 112 | |
| 113 | for(auto src_tensor : inputs) |
| 114 | { |
| 115 | const auto src_ops = _adj_src_ops.find(src_tensor); |
| 116 | |
| 117 | if(src_ops != _adj_src_ops.end()) |
| 118 | { |
| 119 | ARM_COMPUTE_ERROR_ON(src_ops->second.size() > 1); |
| 120 | |
| 121 | if(!src_ops->second.empty()) |
| 122 | { |
| 123 | const auto src_op = src_ops->second[0]; |
| 124 | |
| 125 | if(src_op == _last_op) |
| 126 | { |
| 127 | if(use_input_from_last_op) |
| 128 | { |
| 129 | // To be safe, we also forbid using the output tensor |
| 130 | // of the last operator twice. |
| 131 | return false; |
| 132 | } |
| 133 | |
| 134 | use_input_from_last_op = true; |
| 135 | } |
| 136 | else |
| 137 | { |
| 138 | // The input tensor of this operator must not be the output tensor |
| 139 | // of any other operator except the last non-output operator. |
| 140 | return false; |
| 141 | } |
| 142 | } |
| 143 | } |
| 144 | } |
| 145 | |
| 146 | if(!use_input_from_last_op) |
| 147 | { |
| 148 | // At least one input tensor must be the output tensor of the last non-output operator. |
| 149 | return false; |
| 150 | } |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 151 | } |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 152 | |
| 153 | // The output tensor of the new operator must not be the input tensor of any previously |
| 154 | // added operator. |
| 155 | for(auto dst_tensor : outputs) |
| 156 | { |
| 157 | if(_adj_dst_ops.find(dst_tensor) != _adj_dst_ops.end()) |
| 158 | { |
| 159 | return false; |
| 160 | } |
| 161 | } |
| 162 | |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 163 | return true; |
| 164 | } |
| 165 | /** Add an operator, while keeping the graph as a "linear sequence" |
| 166 | * |
| 167 | * PRECONDITION: The current graph is already linear |
| 168 | * INVARIANT: The list can only grow from head to tail |
| 169 | * INVARIANT: POSTCONDITION: The graph is linear |
| 170 | */ |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 171 | void add_operator_as_linear(OperatorId op, const std::vector<TensorId> &inputs, const std::vector<TensorId> &outputs, bool is_output = false) |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 172 | { |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 173 | const auto success = add_operator(op, inputs, outputs, is_output); |
SiCong Li | fd76611 | 2022-11-09 16:01:44 +0000 | [diff] [blame] | 174 | ARM_COMPUTE_UNUSED(success); |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 175 | ARM_COMPUTE_ERROR_ON(!success); |
| 176 | } |
| 177 | /** Add a new operator |
| 178 | * Return invalid if it violates the DAG invariant |
| 179 | * Invalid operation will not change the graph |
| 180 | * |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 181 | * @param[in] op Operator to add |
| 182 | * @param[in] inputs Input tensors to the operator |
| 183 | * @param[in] outputs Output tensors to the operator |
| 184 | * @param[in] is_output Whether this is an output operator |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 185 | */ |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 186 | bool add_operator(OperatorId op, const std::vector<TensorId> &inputs, const std::vector<TensorId> &outputs, bool is_output = false) |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 187 | { |
| 188 | if(operator_exists(op)) |
| 189 | { |
| 190 | return false; |
| 191 | } |
| 192 | _adj_src_tensors[op] = {}; |
| 193 | _adj_dst_tensors[op] = {}; |
| 194 | for(auto in_tensor : inputs) |
| 195 | { |
| 196 | // Linking input tensor to operator node will never create a cycle / loop because we guarantee |
| 197 | // each op is newly created, so every <input, op> pair / edge is new |
| 198 | link_input(op, in_tensor); |
| 199 | } |
| 200 | for(auto out_tensor : outputs) |
| 201 | { |
| 202 | // If there exists a back path from op's output tensor to op already, then linking the two will create a loop / cycle |
| 203 | if(path_exists_from_tensor_to_op(out_tensor, op)) |
| 204 | { |
| 205 | remove_operator(op); |
| 206 | return false; |
| 207 | } |
| 208 | else |
| 209 | { |
| 210 | link_output(op, out_tensor); |
| 211 | } |
| 212 | } |
| 213 | |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 214 | if(!is_output) |
| 215 | { |
| 216 | _last_op_available = true; |
| 217 | _last_op = op; |
| 218 | } |
| 219 | |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 220 | return true; |
| 221 | } |
| 222 | |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 223 | /** Build a sequence of operators from the acyclic graph of operators. |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 224 | * |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 225 | * The graph will be visited in depth-first strategy. The operator can only be added to |
| 226 | * the sequence when all operators that supply the input tensors have been added. Otherwise, |
| 227 | * the operator will be ignored and later visited again. In other words, the dependency between |
| 228 | * operators will be preserved in the sequence. |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 229 | */ |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 230 | std::vector<OpPack> build_operators_sequence() const |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 231 | { |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 232 | std::vector<OpPack> ops_seq; |
| 233 | std::set<Id> done_ops; |
| 234 | std::set<Id> done_tensors; |
| 235 | |
| 236 | const auto input_tensors = global_src_tensors(); |
| 237 | |
| 238 | for(auto tensor : input_tensors) |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 239 | { |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 240 | done_tensors.insert(tensor); |
| 241 | |
| 242 | for(auto op : _adj_dst_ops.at(tensor)) |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 243 | { |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 244 | build_operators_sequence_from_op(op, ops_seq, done_ops, done_tensors); |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 245 | } |
| 246 | } |
| 247 | |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 248 | return ops_seq; |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 249 | } |
| 250 | |
| 251 | /** Strict equality comparison (all internal ids and order of insertion matter). |
| 252 | * In the future this may be replaced with a topological comparison, allowing equivalent graphs with different internal ids to be equal |
| 253 | * |
| 254 | * |
| 255 | * @param[in] g0 |
| 256 | * @param[in] g1 |
| 257 | * @return true If the same |
| 258 | * @return false Otherwise |
| 259 | */ |
| 260 | friend bool operator==(const DependencyGraph &g0, const DependencyGraph &g1) |
| 261 | { |
| 262 | // Do not compare id allocators |
| 263 | return std::make_tuple( |
| 264 | g0._adj_src_tensors, g0._adj_dst_tensors, g0._adj_src_ops, g0._adj_dst_ops) |
| 265 | == std::make_tuple( |
| 266 | g1._adj_src_tensors, g1._adj_dst_tensors, g1._adj_src_ops, g1._adj_dst_ops); |
| 267 | } |
| 268 | std::vector<OperatorId> src_ops_from_tensor(TensorId tensor) const |
| 269 | { |
| 270 | return _adj_src_ops.at(tensor); |
| 271 | } |
| 272 | std::vector<OperatorId> dst_ops_from_tensor(TensorId tensor) const |
| 273 | { |
| 274 | return _adj_dst_ops.at(tensor); |
| 275 | } |
| 276 | /** Get all tensors |
| 277 | * |
| 278 | * @return std::vector<TensorId> |
| 279 | */ |
| 280 | std::vector<TensorId> all_tensors() const |
| 281 | { |
| 282 | std::vector<TensorId> tensors{}; |
| 283 | std::transform(std::begin(_adj_src_ops), std::end(_adj_src_ops), std::back_inserter(tensors), [](const auto & it) |
| 284 | { |
| 285 | return it.first; |
| 286 | }); |
| 287 | return tensors; |
| 288 | } |
| 289 | /** Get source tensors of the whole graph |
| 290 | * |
| 291 | * @return std::vector<TensorId> |
| 292 | */ |
| 293 | std::vector<TensorId> global_src_tensors() const |
| 294 | { |
| 295 | std::vector<TensorId> tensors; |
| 296 | for(auto tensor_src_ops : _adj_src_ops) |
| 297 | { |
| 298 | if(tensor_src_ops.second.empty()) |
| 299 | { |
| 300 | tensors.push_back(tensor_src_ops.first); |
| 301 | } |
| 302 | } |
| 303 | return tensors; |
| 304 | } |
| 305 | /** Get destination tensors of the whole graph |
| 306 | * |
| 307 | * @return std::vector<TensorId> |
| 308 | */ |
| 309 | std::vector<TensorId> global_dst_tensors() const |
| 310 | { |
| 311 | std::vector<TensorId> tensors; |
| 312 | for(auto tensor_dst_ops : _adj_dst_ops) |
| 313 | { |
| 314 | if(tensor_dst_ops.second.empty()) |
| 315 | { |
| 316 | tensors.push_back(tensor_dst_ops.first); |
| 317 | } |
| 318 | } |
| 319 | return tensors; |
| 320 | } |
Viet-Hoa Do | b84e253 | 2022-12-13 13:09:10 +0000 | [diff] [blame] | 321 | /** Get intermediate tensors of the whole graph. |
| 322 | * |
| 323 | * @return std::vector<TensorId> |
| 324 | */ |
| 325 | std::vector<TensorId> intermediate_tensors() const |
| 326 | { |
| 327 | std::vector<TensorId> tensors; |
| 328 | |
| 329 | // If a tensor is used to connect the input of an operator and the output of another operator, |
| 330 | // it is not allocated in the memory. The tensor exists as a temporary variable only. |
| 331 | for(auto src_tensor : _adj_src_ops) |
| 332 | { |
| 333 | if(!src_tensor.second.empty()) |
| 334 | { |
| 335 | const auto dst_tensor = _adj_dst_ops.find(src_tensor.first); |
| 336 | if(dst_tensor != _adj_dst_ops.end()) |
| 337 | { |
| 338 | if(!dst_tensor->second.empty()) |
| 339 | { |
| 340 | tensors.push_back(src_tensor.first); |
| 341 | } |
| 342 | } |
| 343 | } |
| 344 | } |
| 345 | |
| 346 | return tensors; |
| 347 | } |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 348 | /** Get all root ops. Root ops can also be referred to as "src ops" of the whole graph |
| 349 | * |
| 350 | * @return std::vector<OperatorId> |
| 351 | */ |
| 352 | std::vector<OperatorId> get_root_ops() const |
| 353 | { |
| 354 | std::vector<OperatorId> ops{}; |
| 355 | const auto op_list = all_ops(); |
| 356 | |
| 357 | for(auto op : op_list) |
| 358 | { |
| 359 | if(src_ops(op).empty()) |
| 360 | { |
| 361 | ops.emplace_back(op); |
| 362 | } |
| 363 | } |
| 364 | return ops; |
| 365 | } |
| 366 | |
| 367 | private: |
| 368 | void link_input(OperatorId op, TensorId in_tensor) |
| 369 | { |
| 370 | ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| 371 | if(!tensor_exists(in_tensor)) |
| 372 | { |
| 373 | insert_new_tensor(in_tensor); |
| 374 | } |
| 375 | ARM_COMPUTE_ERROR_ON(are_connected(op, in_tensor)); // Prevent repetitive linking |
| 376 | _adj_src_tensors[op].push_back(in_tensor); |
| 377 | _adj_dst_ops[in_tensor].push_back(op); |
| 378 | } |
| 379 | void link_output(OperatorId op, TensorId out_tensor) |
| 380 | { |
| 381 | ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| 382 | if(!tensor_exists(out_tensor)) |
| 383 | { |
| 384 | insert_new_tensor(out_tensor); |
| 385 | } |
| 386 | ARM_COMPUTE_ERROR_ON(are_connected(op, out_tensor)); // Prevent repetitive linking |
| 387 | _adj_dst_tensors[op].push_back(out_tensor); |
| 388 | _adj_src_ops[out_tensor].push_back(op); |
| 389 | } |
| 390 | |
| 391 | std::vector<OperatorId> src_ops(OperatorId op) const |
| 392 | { |
| 393 | ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| 394 | std::vector<OperatorId> ops{}; |
| 395 | for(TensorId src_tensor : src_tensors(op)) |
| 396 | { |
| 397 | ops.insert(ops.end(), std::begin(_adj_src_ops.at(src_tensor)), std::end(_adj_src_ops.at(src_tensor))); |
| 398 | } |
| 399 | return ops; |
| 400 | } |
| 401 | std::vector<OperatorId> dst_ops(OperatorId op) const |
| 402 | { |
| 403 | ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| 404 | std::vector<OperatorId> ops{}; |
| 405 | for(TensorId dst_tensor : _adj_dst_tensors.at(op)) |
| 406 | { |
| 407 | ops.insert(ops.end(), std::begin(_adj_dst_ops.at(dst_tensor)), std::end(_adj_dst_ops.at(dst_tensor))); |
| 408 | } |
| 409 | return ops; |
| 410 | } |
| 411 | |
| 412 | /** Get source tensors to an operator |
| 413 | * |
| 414 | * @param[in] op |
| 415 | * @return std::vector<TensorId> |
| 416 | */ |
| 417 | std::vector<TensorId> src_tensors(OperatorId op) const |
| 418 | { |
| 419 | ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| 420 | return _adj_src_tensors.at(op); |
| 421 | } |
| 422 | /** Get destination tensors to an operator |
| 423 | * |
| 424 | * @param[in] op |
| 425 | * @return std::vector<TensorId> |
| 426 | */ |
| 427 | std::vector<TensorId> dst_tensors(OperatorId op) const |
| 428 | { |
| 429 | ARM_COMPUTE_ERROR_ON(!operator_exists(op)); |
| 430 | return _adj_dst_tensors.at(op); |
| 431 | } |
| 432 | /** Get all operators |
| 433 | * |
| 434 | * @return std::vector<OperatorId> |
| 435 | */ |
| 436 | std::vector<OperatorId> all_ops() const |
| 437 | { |
| 438 | std::vector<OperatorId> ops{}; |
| 439 | std::transform(std::begin(_adj_src_tensors), std::end(_adj_src_tensors), std::back_inserter(ops), [](const auto & it) |
| 440 | { |
| 441 | return it.first; |
| 442 | }); |
| 443 | return ops; |
| 444 | } |
| 445 | /** Remove an operator from graph. |
| 446 | * |
| 447 | * @param[in] op |
| 448 | */ |
| 449 | void remove_operator(OperatorId op) |
| 450 | { |
| 451 | for(auto src_tensor : _adj_src_tensors.at(op)) |
| 452 | { |
| 453 | auto &dst_ops = _adj_dst_ops.at(src_tensor); |
| 454 | dst_ops.erase( |
| 455 | std::remove(std::begin(dst_ops), std::end(dst_ops), op), |
| 456 | std::end(dst_ops)); |
| 457 | } |
| 458 | for(auto dst_tensor : _adj_dst_tensors.at(op)) |
| 459 | { |
| 460 | auto &src_ops = _adj_src_ops.at(dst_tensor); |
| 461 | src_ops.erase( |
| 462 | std::remove(std::begin(src_ops), std::end(src_ops), op), |
| 463 | std::end(src_ops)); |
| 464 | } |
| 465 | // Remove any isolated tensors |
| 466 | // An isolated tensor is one where both its _adj_src_ops and _adj_dst_ops are empty |
| 467 | for(auto t : all_tensors()) |
| 468 | { |
| 469 | if(_adj_src_ops.at(t).empty() && _adj_dst_ops.at(t).empty()) |
| 470 | { |
| 471 | _adj_src_ops.erase(t); |
| 472 | _adj_dst_ops.erase(t); |
| 473 | } |
| 474 | } |
| 475 | _adj_src_tensors.erase(op); |
| 476 | _adj_dst_tensors.erase(op); |
| 477 | } |
| 478 | void insert_new_tensor(TensorId tensor) |
| 479 | { |
| 480 | _adj_src_ops[tensor] = {}; |
| 481 | _adj_dst_ops[tensor] = {}; |
| 482 | } |
| 483 | bool tensor_exists(TensorId tensor) const |
| 484 | { |
| 485 | return _adj_src_ops.find(tensor) != _adj_src_ops.end() && _adj_dst_ops.find(tensor) != _adj_dst_ops.end(); |
| 486 | } |
| 487 | bool operator_exists(OperatorId op) const |
| 488 | { |
| 489 | return _adj_src_tensors.find(op) != _adj_src_tensors.end() && _adj_dst_tensors.find(op) != _adj_dst_tensors.end(); |
| 490 | } |
| 491 | bool is_src_tensor_of(OperatorId op, TensorId tensor) const |
| 492 | { |
| 493 | if(!operator_exists(op) || !tensor_exists(tensor)) |
| 494 | { |
| 495 | return false; |
| 496 | } |
| 497 | const auto op_inputs = src_tensors(op); |
| 498 | return std::find(op_inputs.begin(), op_inputs.end(), tensor) != op_inputs.end(); |
| 499 | } |
| 500 | bool is_dst_tensor_of(OperatorId op, TensorId tensor) const |
| 501 | { |
| 502 | if(!operator_exists(op) || !tensor_exists(tensor)) |
| 503 | { |
| 504 | return false; |
| 505 | } |
| 506 | const auto op_outputs = dst_tensors(op); |
| 507 | return std::find(op_outputs.begin(), op_outputs.end(), tensor) != op_outputs.end(); |
| 508 | } |
| 509 | bool are_connected(OperatorId op, TensorId tensor) const |
| 510 | { |
| 511 | return is_src_tensor_of(op, tensor) || is_dst_tensor_of(op, tensor); |
| 512 | } |
| 513 | /** If op is the destination / leaf operator of the whole graph |
| 514 | * |
| 515 | * @param[in] op |
| 516 | * @return true |
| 517 | * @return false |
| 518 | */ |
| 519 | bool is_dst_op(OperatorId op) const |
| 520 | { |
| 521 | return dst_ops(op).empty(); |
| 522 | } |
| 523 | std::vector<OperatorId> get_dst_ops() const |
| 524 | { |
| 525 | std::vector<OperatorId> ops{}; |
| 526 | const auto op_list = all_ops(); |
| 527 | |
| 528 | for(auto op : op_list) |
| 529 | { |
| 530 | if(is_dst_op(op)) |
| 531 | { |
| 532 | ops.emplace_back(op); |
| 533 | } |
| 534 | } |
| 535 | return ops; |
| 536 | } |
| 537 | bool path_exists_from_tensor_to_op(TensorId src_tensor, OperatorId dst_op) const |
| 538 | { |
| 539 | if(!tensor_exists(src_tensor) || !operator_exists(dst_op)) |
| 540 | { |
| 541 | return false; |
| 542 | } |
| 543 | for(auto child_op : dst_ops_from_tensor(src_tensor)) |
| 544 | { |
| 545 | if(path_exists_from_op_to_op(child_op, dst_op)) |
| 546 | { |
| 547 | return true; |
| 548 | } |
| 549 | } |
| 550 | return false; |
| 551 | } |
| 552 | |
| 553 | bool path_exists_from_op_to_op(OperatorId src_op, OperatorId dst_op) const |
| 554 | { |
| 555 | if(!operator_exists(src_op) || !operator_exists(dst_op)) |
| 556 | { |
| 557 | return false; |
| 558 | } |
| 559 | if(src_op == dst_op) |
| 560 | { |
| 561 | return true; |
| 562 | } |
| 563 | if(is_in(src_op, get_dst_ops())) |
| 564 | { |
| 565 | return false; |
| 566 | } |
| 567 | for(auto child_tensor : dst_tensors(src_op)) |
| 568 | { |
| 569 | if(path_exists_from_tensor_to_op(child_tensor, dst_op)) |
| 570 | { |
| 571 | return true; |
| 572 | } |
| 573 | } |
| 574 | return false; |
| 575 | } |
| 576 | |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 577 | void build_operators_sequence_from_op( |
| 578 | Id op, |
| 579 | std::vector<OpPack> &ops_seq, |
| 580 | std::set<Id> &done_ops, |
| 581 | std::set<Id> &done_tensors) const |
| 582 | { |
| 583 | while(true) |
| 584 | { |
| 585 | // If the operator has been added to the sequence, ignore it. |
| 586 | if(done_ops.find(op) != done_ops.end()) |
| 587 | { |
| 588 | return; |
| 589 | } |
| 590 | |
| 591 | // If not all the input tensors of the operator are available, this operator cannot be |
| 592 | // added to the sequence for now. It will be visited again after the source operator |
| 593 | // is added to the sequence. |
| 594 | const auto src_tensors = _adj_src_tensors.at(op); |
| 595 | |
| 596 | for(auto src : src_tensors) |
| 597 | { |
| 598 | if(done_tensors.find(src) == done_tensors.end()) |
| 599 | { |
| 600 | return; |
| 601 | } |
| 602 | } |
| 603 | |
| 604 | // This operator is ready to be added to the sequence. |
| 605 | const auto dst_tensors = _adj_dst_tensors.at(op); |
| 606 | |
| 607 | done_ops.insert(op); |
| 608 | |
| 609 | OpPack pack{ op, src_tensors, dst_tensors }; |
| 610 | ops_seq.push_back(pack); |
| 611 | |
| 612 | done_tensors.insert(dst_tensors.begin(), dst_tensors.end()); |
| 613 | |
| 614 | // Visit all the sink operators. |
| 615 | // Call this function recursively unless there is only one sink. |
| 616 | if(dst_tensors.size() == 1 && _adj_dst_ops.at(dst_tensors[0]).size() == 1) |
| 617 | { |
| 618 | op = _adj_dst_ops.at(dst_tensors[0])[0]; |
| 619 | } |
| 620 | else |
| 621 | { |
| 622 | for(auto dst_tensor : dst_tensors) |
| 623 | { |
| 624 | const auto dst_ops = _adj_dst_ops.at(dst_tensor); |
| 625 | |
| 626 | for(auto dst_op : dst_ops) |
| 627 | { |
| 628 | build_operators_sequence_from_op(dst_op, ops_seq, done_ops, done_tensors); |
| 629 | } |
| 630 | } |
| 631 | |
| 632 | return; |
| 633 | } |
| 634 | } |
| 635 | } |
| 636 | |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 637 | private: |
| 638 | AdjList _adj_src_tensors{}; |
| 639 | AdjList _adj_dst_tensors{}; |
| 640 | AdjList _adj_src_ops{}; |
| 641 | AdjList _adj_dst_ops{}; |
Viet-Hoa Do | 04f4620 | 2022-12-14 14:49:56 +0000 | [diff] [blame] | 642 | |
| 643 | bool _last_op_available{ false }; |
| 644 | OperatorId _last_op{ 0 }; |
SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame] | 645 | }; |
| 646 | |
| 647 | } // namespace dynamic_fusion |
| 648 | } // namespace experimental |
| 649 | } // namespace arm_compute |
| 650 | #endif /* SRC_DYNAMIC_FUSION_SKETCH_UTILS_DEPENDENCYGRAPH */ |