Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame^] | 1 | |
| 2 | // Copyright (c) 2020, ARM Limited. |
| 3 | // |
| 4 | // Licensed under the Apache License, Version 2.0 (the "License"); |
| 5 | // you may not use this file except in compliance with the License. |
| 6 | // You may obtain a copy of the License at |
| 7 | // |
| 8 | // http://www.apache.org/licenses/LICENSE-2.0 |
| 9 | // |
| 10 | // Unless required by applicable law or agreed to in writing, software |
| 11 | // distributed under the License is distributed on an "AS IS" BASIS, |
| 12 | // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 13 | // See the License for the specific language governing permissions and |
| 14 | // limitations under the License. |
| 15 | |
| 16 | #include "subgraph_traverser.h" |
| 17 | |
| 18 | using namespace TosaReference; |
| 19 | using namespace Eigen; |
| 20 | using namespace tosa; |
| 21 | |
| 22 | SubgraphTraverser::SubgraphTraverser(TosaSerializationBasicBlock* _block, TosaSerializationHandler* _tsh) |
| 23 | { |
| 24 | block = _block; |
| 25 | tsh = _tsh; |
| 26 | |
| 27 | tensors.clear(); |
| 28 | nodes.clear(); |
| 29 | nextNodeList.clear(); |
| 30 | } |
| 31 | |
| 32 | SubgraphTraverser::~SubgraphTraverser() |
| 33 | { |
| 34 | nextNodeList.clear(); |
| 35 | |
| 36 | for (GraphNode* n : nodes) |
| 37 | { |
| 38 | delete n; |
| 39 | } |
| 40 | nodes.clear(); |
| 41 | |
| 42 | for (TosaReference::Tensor* t : tensors) |
| 43 | { |
| 44 | if (t->is_allocated()) |
| 45 | { |
| 46 | t->deallocate(); |
| 47 | } |
| 48 | delete t; |
| 49 | } |
| 50 | tensors.clear(); |
| 51 | } |
| 52 | |
| 53 | int SubgraphTraverser::getNumInputTensors() const |
| 54 | { |
| 55 | return inputTensors.size(); |
| 56 | } |
| 57 | |
| 58 | TosaReference::Tensor* SubgraphTraverser::getInputTensor(const unsigned int idx) const |
| 59 | { |
| 60 | return inputTensors[idx]; |
| 61 | } |
| 62 | |
| 63 | TosaReference::Tensor* SubgraphTraverser::getInputTensorByName(const std::string name) const |
| 64 | { |
| 65 | for (auto t : inputTensors) |
| 66 | { |
| 67 | if (t->getName() == name) |
| 68 | { |
| 69 | return t; |
| 70 | } |
| 71 | } |
| 72 | |
| 73 | return nullptr; |
| 74 | } |
| 75 | |
| 76 | int SubgraphTraverser::getNumOutputTensors() const |
| 77 | { |
| 78 | return outputTensors.size(); |
| 79 | } |
| 80 | |
| 81 | TosaReference::Tensor* SubgraphTraverser::getOutputTensor(const unsigned int idx) const |
| 82 | { |
| 83 | return outputTensors[idx]; |
| 84 | } |
| 85 | |
| 86 | TosaReference::Tensor* SubgraphTraverser::getOutputTensorByName(const std::string name) const |
| 87 | { |
| 88 | for (auto t : outputTensors) |
| 89 | { |
| 90 | if (t->getName() == name) |
| 91 | { |
| 92 | return t; |
| 93 | } |
| 94 | } |
| 95 | |
| 96 | return nullptr; |
| 97 | } |
| 98 | |
| 99 | int SubgraphTraverser::initializeGraph() |
| 100 | { |
| 101 | char tensor_fullname[1000]; |
| 102 | int idx = 0; |
| 103 | for (auto op : block->GetOperators()) |
| 104 | { |
| 105 | // translated TosaSerializationOperator to GraphNode |
| 106 | DType in_dtype = DType_UNKNOWN, out_dtype = DType_UNKNOWN, weight_dtype = DType_UNKNOWN; |
| 107 | uint32_t in_rank = 0, out_rank = 0, weight_rank = 0; |
| 108 | for (auto name : op->GetInputTensorNames()) |
| 109 | { |
| 110 | |
| 111 | TosaSerializationTensor* ts = block->GetTensorByName(name); |
| 112 | ASSERT_MSG(ts, "SubgraphTraverser: fail to get tensor %s from TosaSerializationHandler", name.c_str()); |
| 113 | |
| 114 | if (ts->HasUsage(Usage_WEIGHT)) |
| 115 | { |
| 116 | weight_dtype = ts->GetDtype(); |
| 117 | weight_rank = ts->GetShape().size(); |
| 118 | } |
| 119 | else if (ts->HasUsage(Usage_INDEX)) |
| 120 | { |
| 121 | // do nothing, but this will prevent tensor's dtype/rank being wrongly used as template argument when initializing this op |
| 122 | } |
| 123 | else if (ts->HasUsage(Usage_ACTIVATION)) |
| 124 | { |
| 125 | if (ts->GetShape().size() >= in_rank) |
| 126 | { |
| 127 | in_dtype = ts->GetDtype(); |
| 128 | in_rank = ts->GetShape().size(); |
| 129 | } |
| 130 | } |
| 131 | } |
| 132 | |
| 133 | for (auto name : op->GetOutputTensorNames()) |
| 134 | { |
| 135 | |
| 136 | TosaSerializationTensor* ts = block->GetTensorByName(name); |
| 137 | ASSERT_MSG(ts, "SubgraphTraverser: fail to get tensor %s from TosaSerializationHandler", name.c_str()); |
| 138 | |
| 139 | out_dtype = ts->GetDtype(); |
| 140 | out_rank = ts->GetShape().size(); |
| 141 | } |
| 142 | |
| 143 | DEBUG_INFO(GT, "Creating operator id_%03u, %8s, %lu input tensors, %lu output tensors", idx, |
| 144 | EnumNamesOp()[op->GetOp()], op->GetInputTensorNames().size(), op->GetOutputTensorNames().size()); |
| 145 | |
| 146 | GraphNode* cn = OpFactory::newOp(tsh, op->GetOp(), op->GetAttribute(), op->GetQInfo(), idx, in_dtype, in_rank, |
| 147 | out_dtype, out_rank, weight_dtype, weight_rank); |
| 148 | if (!cn) |
| 149 | { |
| 150 | if (weight_dtype == DType_UNKNOWN && weight_rank == 0) |
| 151 | { |
| 152 | fprintf(g_func_debug.func_debug_file, |
| 153 | "OpFactory could not allocate op %8s input=(%s rank %d) -> (%s rank %d)", |
| 154 | EnumNamesOp()[op->GetOp()], EnumNamesDType()[in_dtype], in_rank, EnumNamesDType()[out_dtype], |
| 155 | out_rank); |
| 156 | } |
| 157 | else |
| 158 | { |
| 159 | fprintf(g_func_debug.func_debug_file, |
| 160 | "OpFactory could not allocate op %8s input=(%s rank %d), weight=(%s rank %d) -> (%s rank %d)", |
| 161 | EnumNamesOp()[op->GetOp()], EnumNamesDType()[in_dtype], in_rank, EnumNamesDType()[weight_dtype], |
| 162 | weight_rank, EnumNamesDType()[out_dtype], out_rank); |
| 163 | } |
| 164 | |
| 165 | for (auto ts : op->GetInputTensors()) |
| 166 | { |
| 167 | fprintf(g_func_debug.func_debug_file, "Input: %s\n", ts->GetName().c_str()); |
| 168 | } |
| 169 | |
| 170 | for (auto ts : op->GetOutputTensors()) |
| 171 | { |
| 172 | fprintf(g_func_debug.func_debug_file, "Output: %s\n", ts->GetName().c_str()); |
| 173 | } |
| 174 | FATAL_ERROR("Unsupported operation type or rank."); |
| 175 | } |
| 176 | |
| 177 | for (auto name : op->GetInputTensorNames()) |
| 178 | { |
| 179 | cn->addInputName(name); |
| 180 | } |
| 181 | |
| 182 | for (auto name : op->GetOutputTensorNames()) |
| 183 | { |
| 184 | cn->addOutputName(name); |
| 185 | } |
| 186 | |
| 187 | addNode(cn); |
| 188 | |
| 189 | // if node doesn't have any inputs (i.e. CONST) |
| 190 | // it should be ready for evaluation |
| 191 | if (op->GetInputTensorNames().empty() && !cn->getOnNextNodeList()) |
| 192 | { |
| 193 | addToNextNodeList(cn); |
| 194 | } |
| 195 | |
| 196 | idx++; |
| 197 | } |
| 198 | |
| 199 | for (auto ts : block->GetTensors()) |
| 200 | { |
| 201 | |
| 202 | bool is_const = false; |
| 203 | if (ts->HasUsage(Usage_WEIGHT)) |
| 204 | { |
| 205 | is_const = true; |
| 206 | } |
| 207 | |
| 208 | DEBUG_INFO(GT, "Creating tensor %s", ts->GetName().c_str()); |
| 209 | TosaReference::Tensor* ct = |
| 210 | TensorFactory::newTensor(ts->GetName(), ts->GetDtype(), ts->GetUsage(), ts->GetFormat(), ts->GetShape(), |
| 211 | is_const, ts->GetShape().size()); |
| 212 | |
| 213 | if (ts->GetNpyFilePtr()) |
| 214 | { |
| 215 | if (ct->allocate()) |
| 216 | { |
| 217 | FATAL_ERROR("Fail to allocate Eigen tensor %s", ct->getName().c_str()); |
| 218 | } |
| 219 | |
| 220 | bzero(tensor_fullname, sizeof(tensor_fullname)); |
| 221 | snprintf(tensor_fullname, sizeof(tensor_fullname), "%s/%s", g_func_config.subgraph_dir, |
| 222 | ts->GetNpyFilePtr()->c_str()); |
| 223 | if (ct->readFromNpyFile(tensor_fullname)) |
| 224 | { |
| 225 | FATAL_ERROR("Cannot read input data into graph tensor %s from block %s", ct->getName().c_str(), |
| 226 | block->GetName().c_str()); |
| 227 | } |
| 228 | } |
| 229 | |
| 230 | // update this->tensors |
| 231 | addTensor(ct); |
| 232 | } |
| 233 | |
| 234 | DEBUG_INFO(GT, "Enumerating block %s graph inputs", block->GetName().c_str()); |
| 235 | for (auto& input_name : block->GetInputs()) |
| 236 | { |
| 237 | TosaReference::Tensor* ct = findTensorByName(input_name); |
| 238 | DEBUG_INFO(GT, "input tensor name=%s", input_name.c_str()); |
| 239 | if (ct) |
| 240 | { |
| 241 | ct->setIsSubgraphInput(); |
| 242 | inputTensors.push_back(ct); |
| 243 | } |
| 244 | else |
| 245 | { |
| 246 | FATAL_ERROR("loadGraphJson: Fail to find input tensor by name %s", input_name.c_str()); |
| 247 | } |
| 248 | } |
| 249 | |
| 250 | DEBUG_INFO(GT, "Enumerating block %s graph outputs", block->GetName().c_str()); |
| 251 | for (auto& output_name : block->GetOutputs()) |
| 252 | { |
| 253 | TosaReference::Tensor* ct = findTensorByName(output_name); |
| 254 | DEBUG_INFO(GT, "output tensor name=%s\n", output_name.c_str()); |
| 255 | if (ct) |
| 256 | { |
| 257 | ct->setIsSubgraphOutput(); |
| 258 | outputTensors.push_back(ct); |
| 259 | } |
| 260 | else |
| 261 | { |
| 262 | FATAL_ERROR("loadGraphJson: Fail to find output tensor by name %s", output_name.c_str()); |
| 263 | } |
| 264 | } |
| 265 | |
| 266 | if (DEBUG_ENABLED(DEBUG_VERB_HIGH, GT)) |
| 267 | { |
| 268 | dumpNextNodeList(g_func_debug.func_debug_file); |
| 269 | } |
| 270 | |
| 271 | return 0; |
| 272 | } |
| 273 | |
| 274 | int SubgraphTraverser::isFullyEvaluated() const |
| 275 | { |
| 276 | return nextNodeList.empty(); |
| 277 | } |
| 278 | |
| 279 | GraphNode* SubgraphTraverser::getNextNode() |
| 280 | { |
| 281 | GraphNode* nextNode = nextNodeList.front(); |
| 282 | ASSERT_MSG(nextNode, "SubgraphTraverser::getNextNode(): called with empty next node list"); |
| 283 | ASSERT_MSG(nextNode->getOnNextNodeList(), |
| 284 | "SubgraphTraverser::getNextNode(): internal state error: node is not listed as being on next node list"); |
| 285 | |
| 286 | nextNodeList.pop_front(); |
| 287 | |
| 288 | nextNode->clearOnNextNodeList(); |
| 289 | return nextNode; |
| 290 | } |
| 291 | |
| 292 | int SubgraphTraverser::addToNextNodeList(GraphNode* nextNode) |
| 293 | { |
| 294 | ASSERT_MSG(nextNode, "SubgraphTraverser::addToNextNodeList(): called with no node"); |
| 295 | ASSERT_MSG(!nextNode->getOnNextNodeList(), |
| 296 | "SubgraphTraverser::addToNextNodeList(): internal state error: node is already on next node list"); |
| 297 | |
| 298 | nextNode->setOnNextNodeList(); |
| 299 | nextNodeList.push_back(nextNode); |
| 300 | |
| 301 | return 0; |
| 302 | } |
| 303 | |
| 304 | int SubgraphTraverser::evaluateNextNode() |
| 305 | { |
| 306 | if (isFullyEvaluated()) |
| 307 | return 0; |
| 308 | |
| 309 | GraphNode* currNode = getNextNode(); |
| 310 | |
| 311 | DEBUG_INFO(GT, "Evaluating node_%03lu, %8s, output tensor=%s", currNode->getID(), EnumNamesOp()[currNode->getOp()], |
| 312 | currNode->getOutputNames()[0].c_str()); |
| 313 | |
| 314 | // Sanity check for never-ending loops |
| 315 | if (currNode->getEvalCount() >= MAX_EVAL_COUNT && (currNode->getEvalCount() % MAX_EVAL_COUNT) == 0) |
| 316 | { |
| 317 | WARNING("Node %lu has been evaluated %d times. Loop suspected.", currNode->getID(), currNode->getEvalCount()); |
| 318 | } |
| 319 | |
| 320 | for (auto ct : currNode->getOutputs()) |
| 321 | { |
| 322 | if (!ct->is_allocated()) |
| 323 | if (ct->allocate()) |
| 324 | { |
| 325 | FATAL_ERROR("Fail to allocate Eigen tensor %s", ct->getName().c_str()); |
| 326 | } |
| 327 | } |
| 328 | |
| 329 | if (currNode->eval()) |
| 330 | { |
| 331 | FATAL_ERROR("Error evaluating node: %lu\n", currNode->getID()); |
| 332 | } |
| 333 | |
| 334 | // free input tensor if all of its consumers have all of their outputs ready and it's not block's output |
| 335 | for (auto ct : currNode->getInputs()) |
| 336 | { |
| 337 | bool in_use = false; |
| 338 | for (auto cn : ct->getConsumers()) |
| 339 | { |
| 340 | if (!cn->hasAllOutputsReady()) |
| 341 | { |
| 342 | in_use = true; |
| 343 | } |
| 344 | } |
| 345 | for (auto name : block->GetOutputs()) |
| 346 | { |
| 347 | if (name == ct->getName()) |
| 348 | { |
| 349 | in_use = true; |
| 350 | } |
| 351 | } |
| 352 | if (!in_use) |
| 353 | { |
| 354 | ct->deallocate(); |
| 355 | } |
| 356 | } |
| 357 | |
| 358 | // Search the output tensors of this node to see if |
| 359 | // there are now new ready nodes available from completing this node |
| 360 | for (TosaReference::Tensor* tensor : currNode->getOutputs()) |
| 361 | { |
| 362 | for (GraphNode* node : tensor->getConsumers()) |
| 363 | { |
| 364 | if (!node->getOnNextNodeList() && node->hasAllInputsReady()) |
| 365 | { |
| 366 | addToNextNodeList(node); |
| 367 | } |
| 368 | } |
| 369 | } |
| 370 | |
| 371 | if (DEBUG_ENABLED(DEBUG_VERB_HIGH, GT)) |
| 372 | { |
| 373 | dumpNextNodeList(g_func_debug.func_debug_file); |
| 374 | } |
| 375 | |
| 376 | if (g_func_config.dump_intermediates) |
| 377 | { |
| 378 | currNode->dumpNode(g_func_debug.func_debug_file); |
| 379 | for (auto outs : currNode->getOutputs()) |
| 380 | { |
| 381 | outs->dumpTensorParams(g_func_debug.func_debug_file); |
| 382 | outs->dumpTensor(g_func_debug.func_debug_file); |
| 383 | fprintf(g_func_debug.func_debug_file, "\n"); |
| 384 | } |
| 385 | } |
| 386 | |
| 387 | return 0; |
| 388 | } |
| 389 | |
| 390 | int SubgraphTraverser::dumpNextNodeList(FILE* out) const |
| 391 | { |
| 392 | |
| 393 | // Dump next node list |
| 394 | fprintf(out, "Next node list\n"); |
| 395 | |
| 396 | if (nextNodeList.empty()) |
| 397 | { |
| 398 | fprintf(out, "<empty>\n"); |
| 399 | } |
| 400 | |
| 401 | for (auto gn : nextNodeList) |
| 402 | { |
| 403 | gn->dumpNode(out); |
| 404 | } |
| 405 | |
| 406 | fprintf(out, "Done.\n"); |
| 407 | return 0; |
| 408 | } |
| 409 | |
| 410 | int SubgraphTraverser::clearAllNodeMarkings() |
| 411 | { |
| 412 | for (GraphNode* currNode : nodes) |
| 413 | { |
| 414 | currNode->clearNodeMarked(); |
| 415 | } |
| 416 | |
| 417 | return false; |
| 418 | } |
| 419 | |
| 420 | int SubgraphTraverser::addTensor(TosaReference::Tensor* ct) |
| 421 | { |
| 422 | // Enforce no duplicate tensors/tensor names |
| 423 | // O(N), but the number of tensors is small |
| 424 | for (TosaReference::Tensor* currTensor : tensors) |
| 425 | { |
| 426 | if (ct == currTensor || currTensor->getName() == ct->getName()) |
| 427 | { |
| 428 | FATAL_ERROR("Error: Duplicate tensor or tensor name being added to graph: %s\n", ct->getName().c_str()); |
| 429 | return 1; |
| 430 | } |
| 431 | } |
| 432 | |
| 433 | tensors.push_back(ct); |
| 434 | |
| 435 | if (ct->getIsSubgraphInput()) |
| 436 | { |
| 437 | inputTensors.push_back(ct); |
| 438 | } |
| 439 | |
| 440 | if (ct->getIsSubgraphOutput()) |
| 441 | { |
| 442 | outputTensors.push_back(ct); |
| 443 | } |
| 444 | |
| 445 | return 0; |
| 446 | } |
| 447 | int SubgraphTraverser::addNode(GraphNode* newNode) |
| 448 | { |
| 449 | // Enforce no duplicate nodes |
| 450 | for (GraphNode* currNode : nodes) |
| 451 | { |
| 452 | if (currNode == newNode) |
| 453 | { |
| 454 | FATAL_ERROR("Error: duplicate node being added to graph"); |
| 455 | return 1; |
| 456 | } |
| 457 | } |
| 458 | |
| 459 | nodes.push_back(newNode); |
| 460 | |
| 461 | return 0; |
| 462 | } |
| 463 | |
| 464 | TosaReference::Tensor* SubgraphTraverser::findTensorByName(const std::string& name) const |
| 465 | { |
| 466 | for (TosaReference::Tensor* currTensor : tensors) |
| 467 | { |
| 468 | if (currTensor->getName() == name) |
| 469 | { |
| 470 | return currTensor; |
| 471 | } |
| 472 | } |
| 473 | |
| 474 | WARNING("Unable to find tensor with name: %s\n", name.c_str()); |
| 475 | |
| 476 | return nullptr; |
| 477 | } |
| 478 | |
| 479 | int SubgraphTraverser::linkTensorsAndNodes() |
| 480 | { |
| 481 | // Nodes have a list of input/output tensor names |
| 482 | // For each node, read this list, link up the tensors with their inputs/outputs |
| 483 | for (GraphNode* currNode : nodes) |
| 484 | { |
| 485 | |
| 486 | // Link inputs/consuming nodes |
| 487 | for (std::string& name : currNode->getInputNames()) |
| 488 | { |
| 489 | TosaReference::Tensor* t = findTensorByName(name); |
| 490 | if (!t) |
| 491 | { |
| 492 | FATAL_ERROR("linkTensorsAndNodes: Cannot find tensor %s in node %lu\n", name.c_str(), |
| 493 | currNode->getID()); |
| 494 | return 1; |
| 495 | } |
| 496 | |
| 497 | if (currNode->addInputTensor(t)) |
| 498 | { |
| 499 | FATAL_ERROR("linkTensorsAndNodes: cannot link tensor %s to node %lu\n", name.c_str(), |
| 500 | currNode->getID()); |
| 501 | return 1; |
| 502 | } |
| 503 | |
| 504 | if (t->addConsumer(currNode)) |
| 505 | { |
| 506 | FATAL_ERROR("linkTensorsAndNodes: cannot link consumer node %lu to tensor %s\n", currNode->getID(), |
| 507 | name.c_str()); |
| 508 | return 1; |
| 509 | } |
| 510 | } |
| 511 | |
| 512 | // Link outputs/producing nodes |
| 513 | for (std::string& name : currNode->getOutputNames()) |
| 514 | { |
| 515 | TosaReference::Tensor* t = findTensorByName(name); |
| 516 | if (!t) |
| 517 | { |
| 518 | FATAL_ERROR("linkTensorsAndNodes: Cannot find tensor %s in node %lu\n", name.c_str(), |
| 519 | currNode->getID()); |
| 520 | return 1; |
| 521 | } |
| 522 | |
| 523 | if (currNode->addOutputTensor(t)) |
| 524 | { |
| 525 | FATAL_ERROR("linkTensorsAndNodes: cannot link tensor %s to node %lu\n", name.c_str(), |
| 526 | currNode->getID()); |
| 527 | return 1; |
| 528 | } |
| 529 | |
| 530 | if (t->setProducer(currNode)) |
| 531 | { |
| 532 | FATAL_ERROR("linkTensorsAndNodes: cannot link producer node %lu to tensor tensor %s\n", |
| 533 | currNode->getID(), name.c_str()); |
| 534 | return 1; |
| 535 | } |
| 536 | } |
| 537 | } |
| 538 | |
| 539 | return 0; |
| 540 | } |
| 541 | |
| 542 | int SubgraphTraverser::validateGraph() |
| 543 | { |
| 544 | // Need to make sure that: |
| 545 | // - each tensor is actually used |
| 546 | // - input and output tesnsors truly are just input and just output |
| 547 | // Graph building already determined that each node has found its input/output tensors |
| 548 | |
| 549 | for (TosaReference::Tensor* currTensor : tensors) |
| 550 | { |
| 551 | |
| 552 | if (!currTensor->getProducer() && currTensor->getConsumers().empty()) |
| 553 | { |
| 554 | WARNING("Graph inconsistency: TosaReference::Tensor %s has no producers or consumers\n", |
| 555 | currTensor->getName().c_str()); |
| 556 | return 1; |
| 557 | } |
| 558 | |
| 559 | if (currTensor->getIsSubgraphInput()) |
| 560 | { |
| 561 | if (currTensor->getProducer() && currTensor->getProducer()->getOp() != Op_PLACEHOLDER) |
| 562 | { |
| 563 | WARNING("Graph inconsistency: TosaReference::Tensor %s is a subgraph input and has a producer\n", |
| 564 | currTensor->getName().c_str()); |
| 565 | return 1; |
| 566 | } |
| 567 | } |
| 568 | |
| 569 | // comment this check out as this is possible when graph have multiple output |
| 570 | // for example: |
| 571 | // %0 = add(%arg0, %arg1) |
| 572 | // %1 = mul(%arg0, %0) |
| 573 | // yields(%0, %1) |
| 574 | //if (currTensor->getIsSubgraphOutput()) { |
| 575 | // if (!currTensor->getConsumers().empty()) { |
| 576 | // WARNING ("Graph inconsistency: TosaReference::Tensor %s is a subgraph output and has a consumer\n", |
| 577 | // currTensor->getName().c_str()); |
| 578 | // return 1; |
| 579 | // } |
| 580 | //} |
| 581 | |
| 582 | if (g_func_config.tosa_profile == 0) |
| 583 | { |
| 584 | DType dtype = currTensor->getDtype(); |
| 585 | |
| 586 | // Float-point disallowed |
| 587 | if (dtype == DType_FLOAT) |
| 588 | { |
| 589 | WARNING("TOSA Base Inference profile selected: All floating point disabled, but %s tensor %s found\n", |
| 590 | EnumNamesDType()[dtype], currTensor->getName().c_str()); |
| 591 | return 1; |
| 592 | } |
| 593 | } |
| 594 | else if (g_func_config.tosa_profile == 1 || g_func_config.tosa_profile == 2) |
| 595 | { |
| 596 | // Do nothing. All FP types allowed |
| 597 | // Currently no implementation difference between Main Inference and Main Training modes |
| 598 | } |
| 599 | else |
| 600 | { |
| 601 | FATAL_ERROR("TOSA profile not recognized: %d", g_func_config.tosa_profile); |
| 602 | } |
| 603 | } |
| 604 | |
| 605 | for (GraphNode* currNode : nodes) |
| 606 | { |
| 607 | if (currNode->checkTensorAttributes()) |
| 608 | { |
| 609 | WARNING("TosaReference::Tensor attribute check failed"); |
| 610 | return 1; |
| 611 | } |
| 612 | } |
| 613 | |
| 614 | if (outputTensors.size() <= 0) |
| 615 | { |
| 616 | DEBUG_MED(GT, "Graph output tensor empty"); |
| 617 | return 0; |
| 618 | } |
| 619 | |
| 620 | return 0; |
| 621 | } |
| 622 | |
| 623 | int SubgraphTraverser::dumpGraph(FILE* out) const |
| 624 | { |
| 625 | int i = 0; |
| 626 | |
| 627 | fprintf(out, "Full graph dump:\n"); |
| 628 | for (GraphNode* currNode : nodes) |
| 629 | { |
| 630 | fprintf(out, "Node [%d]: ", i++); |
| 631 | currNode->dumpNode(out); |
| 632 | } |
| 633 | |
| 634 | return 0; |
| 635 | } |
| 636 | |
| 637 | int SubgraphTraverser::evaluateAll() |
| 638 | { |
| 639 | // evaluation loop |
| 640 | while (!isFullyEvaluated()) |
| 641 | { |
| 642 | if (evaluateNextNode()) |
| 643 | { |
| 644 | return 1; |
| 645 | } |
| 646 | } |
| 647 | |
| 648 | return 0; |
| 649 | } |