diff options
Diffstat (limited to 'reference_model/src/subgraph_traverser.cc')
| -rw-r--r-- | reference_model/src/subgraph_traverser.cc | 649 |
1 files changed, 649 insertions, 0 deletions
diff --git a/reference_model/src/subgraph_traverser.cc b/reference_model/src/subgraph_traverser.cc new file mode 100644 index 0000000..789bcae --- /dev/null +++ b/reference_model/src/subgraph_traverser.cc @@ -0,0 +1,649 @@ + +// Copyright (c) 2020, ARM Limited. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "subgraph_traverser.h" + +using namespace TosaReference; +using namespace Eigen; +using namespace tosa; + +SubgraphTraverser::SubgraphTraverser(TosaSerializationBasicBlock* _block, TosaSerializationHandler* _tsh) +{ + block = _block; + tsh = _tsh; + + tensors.clear(); + nodes.clear(); + nextNodeList.clear(); +} + +SubgraphTraverser::~SubgraphTraverser() +{ + nextNodeList.clear(); + + for (GraphNode* n : nodes) + { + delete n; + } + nodes.clear(); + + for (TosaReference::Tensor* t : tensors) + { + if (t->is_allocated()) + { + t->deallocate(); + } + delete t; + } + tensors.clear(); +} + +int SubgraphTraverser::getNumInputTensors() const +{ + return inputTensors.size(); +} + +TosaReference::Tensor* SubgraphTraverser::getInputTensor(const unsigned int idx) const +{ + return inputTensors[idx]; +} + +TosaReference::Tensor* SubgraphTraverser::getInputTensorByName(const std::string name) const +{ + for (auto t : inputTensors) + { + if (t->getName() == name) + { + return t; + } + } + + return nullptr; +} + +int SubgraphTraverser::getNumOutputTensors() const +{ + return outputTensors.size(); +} + +TosaReference::Tensor* SubgraphTraverser::getOutputTensor(const unsigned int idx) const +{ + return outputTensors[idx]; +} + +TosaReference::Tensor* SubgraphTraverser::getOutputTensorByName(const std::string name) const +{ + for (auto t : outputTensors) + { + if (t->getName() == name) + { + return t; + } + } + + return nullptr; +} + +int SubgraphTraverser::initializeGraph() +{ + char tensor_fullname[1000]; + int idx = 0; + for (auto op : block->GetOperators()) + { + // translated TosaSerializationOperator to GraphNode + DType in_dtype = DType_UNKNOWN, out_dtype = DType_UNKNOWN, weight_dtype = DType_UNKNOWN; + uint32_t in_rank = 0, out_rank = 0, weight_rank = 0; + for (auto name : op->GetInputTensorNames()) + { + + TosaSerializationTensor* ts = block->GetTensorByName(name); + ASSERT_MSG(ts, "SubgraphTraverser: fail to get tensor %s from TosaSerializationHandler", name.c_str()); + + if (ts->HasUsage(Usage_WEIGHT)) + { + weight_dtype = ts->GetDtype(); + weight_rank = ts->GetShape().size(); + } + else if (ts->HasUsage(Usage_INDEX)) + { + // do nothing, but this will prevent tensor's dtype/rank being wrongly used as template argument when initializing this op + } + else if (ts->HasUsage(Usage_ACTIVATION)) + { + if (ts->GetShape().size() >= in_rank) + { + in_dtype = ts->GetDtype(); + in_rank = ts->GetShape().size(); + } + } + } + + for (auto name : op->GetOutputTensorNames()) + { + + TosaSerializationTensor* ts = block->GetTensorByName(name); + ASSERT_MSG(ts, "SubgraphTraverser: fail to get tensor %s from TosaSerializationHandler", name.c_str()); + + out_dtype = ts->GetDtype(); + out_rank = ts->GetShape().size(); + } + + DEBUG_INFO(GT, "Creating operator id_%03u, %8s, %lu input tensors, %lu output tensors", idx, + EnumNamesOp()[op->GetOp()], op->GetInputTensorNames().size(), op->GetOutputTensorNames().size()); + + GraphNode* cn = OpFactory::newOp(tsh, op->GetOp(), op->GetAttribute(), op->GetQInfo(), idx, in_dtype, in_rank, + out_dtype, out_rank, weight_dtype, weight_rank); + if (!cn) + { + if (weight_dtype == DType_UNKNOWN && weight_rank == 0) + { + fprintf(g_func_debug.func_debug_file, + "OpFactory could not allocate op %8s input=(%s rank %d) -> (%s rank %d)", + EnumNamesOp()[op->GetOp()], EnumNamesDType()[in_dtype], in_rank, EnumNamesDType()[out_dtype], + out_rank); + } + else + { + fprintf(g_func_debug.func_debug_file, + "OpFactory could not allocate op %8s input=(%s rank %d), weight=(%s rank %d) -> (%s rank %d)", + EnumNamesOp()[op->GetOp()], EnumNamesDType()[in_dtype], in_rank, EnumNamesDType()[weight_dtype], + weight_rank, EnumNamesDType()[out_dtype], out_rank); + } + + for (auto ts : op->GetInputTensors()) + { + fprintf(g_func_debug.func_debug_file, "Input: %s\n", ts->GetName().c_str()); + } + + for (auto ts : op->GetOutputTensors()) + { + fprintf(g_func_debug.func_debug_file, "Output: %s\n", ts->GetName().c_str()); + } + FATAL_ERROR("Unsupported operation type or rank."); + } + + for (auto name : op->GetInputTensorNames()) + { + cn->addInputName(name); + } + + for (auto name : op->GetOutputTensorNames()) + { + cn->addOutputName(name); + } + + addNode(cn); + + // if node doesn't have any inputs (i.e. CONST) + // it should be ready for evaluation + if (op->GetInputTensorNames().empty() && !cn->getOnNextNodeList()) + { + addToNextNodeList(cn); + } + + idx++; + } + + for (auto ts : block->GetTensors()) + { + + bool is_const = false; + if (ts->HasUsage(Usage_WEIGHT)) + { + is_const = true; + } + + DEBUG_INFO(GT, "Creating tensor %s", ts->GetName().c_str()); + TosaReference::Tensor* ct = + TensorFactory::newTensor(ts->GetName(), ts->GetDtype(), ts->GetUsage(), ts->GetFormat(), ts->GetShape(), + is_const, ts->GetShape().size()); + + if (ts->GetNpyFilePtr()) + { + if (ct->allocate()) + { + FATAL_ERROR("Fail to allocate Eigen tensor %s", ct->getName().c_str()); + } + + bzero(tensor_fullname, sizeof(tensor_fullname)); + snprintf(tensor_fullname, sizeof(tensor_fullname), "%s/%s", g_func_config.subgraph_dir, + ts->GetNpyFilePtr()->c_str()); + if (ct->readFromNpyFile(tensor_fullname)) + { + FATAL_ERROR("Cannot read input data into graph tensor %s from block %s", ct->getName().c_str(), + block->GetName().c_str()); + } + } + + // update this->tensors + addTensor(ct); + } + + DEBUG_INFO(GT, "Enumerating block %s graph inputs", block->GetName().c_str()); + for (auto& input_name : block->GetInputs()) + { + TosaReference::Tensor* ct = findTensorByName(input_name); + DEBUG_INFO(GT, "input tensor name=%s", input_name.c_str()); + if (ct) + { + ct->setIsSubgraphInput(); + inputTensors.push_back(ct); + } + else + { + FATAL_ERROR("loadGraphJson: Fail to find input tensor by name %s", input_name.c_str()); + } + } + + DEBUG_INFO(GT, "Enumerating block %s graph outputs", block->GetName().c_str()); + for (auto& output_name : block->GetOutputs()) + { + TosaReference::Tensor* ct = findTensorByName(output_name); + DEBUG_INFO(GT, "output tensor name=%s\n", output_name.c_str()); + if (ct) + { + ct->setIsSubgraphOutput(); + outputTensors.push_back(ct); + } + else + { + FATAL_ERROR("loadGraphJson: Fail to find output tensor by name %s", output_name.c_str()); + } + } + + if (DEBUG_ENABLED(DEBUG_VERB_HIGH, GT)) + { + dumpNextNodeList(g_func_debug.func_debug_file); + } + + return 0; +} + +int SubgraphTraverser::isFullyEvaluated() const +{ + return nextNodeList.empty(); +} + +GraphNode* SubgraphTraverser::getNextNode() +{ + GraphNode* nextNode = nextNodeList.front(); + ASSERT_MSG(nextNode, "SubgraphTraverser::getNextNode(): called with empty next node list"); + ASSERT_MSG(nextNode->getOnNextNodeList(), + "SubgraphTraverser::getNextNode(): internal state error: node is not listed as being on next node list"); + + nextNodeList.pop_front(); + + nextNode->clearOnNextNodeList(); + return nextNode; +} + +int SubgraphTraverser::addToNextNodeList(GraphNode* nextNode) +{ + ASSERT_MSG(nextNode, "SubgraphTraverser::addToNextNodeList(): called with no node"); + ASSERT_MSG(!nextNode->getOnNextNodeList(), + "SubgraphTraverser::addToNextNodeList(): internal state error: node is already on next node list"); + + nextNode->setOnNextNodeList(); + nextNodeList.push_back(nextNode); + + return 0; +} + +int SubgraphTraverser::evaluateNextNode() +{ + if (isFullyEvaluated()) + return 0; + + GraphNode* currNode = getNextNode(); + + DEBUG_INFO(GT, "Evaluating node_%03lu, %8s, output tensor=%s", currNode->getID(), EnumNamesOp()[currNode->getOp()], + currNode->getOutputNames()[0].c_str()); + + // Sanity check for never-ending loops + if (currNode->getEvalCount() >= MAX_EVAL_COUNT && (currNode->getEvalCount() % MAX_EVAL_COUNT) == 0) + { + WARNING("Node %lu has been evaluated %d times. Loop suspected.", currNode->getID(), currNode->getEvalCount()); + } + + for (auto ct : currNode->getOutputs()) + { + if (!ct->is_allocated()) + if (ct->allocate()) + { + FATAL_ERROR("Fail to allocate Eigen tensor %s", ct->getName().c_str()); + } + } + + if (currNode->eval()) + { + FATAL_ERROR("Error evaluating node: %lu\n", currNode->getID()); + } + + // free input tensor if all of its consumers have all of their outputs ready and it's not block's output + for (auto ct : currNode->getInputs()) + { + bool in_use = false; + for (auto cn : ct->getConsumers()) + { + if (!cn->hasAllOutputsReady()) + { + in_use = true; + } + } + for (auto name : block->GetOutputs()) + { + if (name == ct->getName()) + { + in_use = true; + } + } + if (!in_use) + { + ct->deallocate(); + } + } + + // Search the output tensors of this node to see if + // there are now new ready nodes available from completing this node + for (TosaReference::Tensor* tensor : currNode->getOutputs()) + { + for (GraphNode* node : tensor->getConsumers()) + { + if (!node->getOnNextNodeList() && node->hasAllInputsReady()) + { + addToNextNodeList(node); + } + } + } + + if (DEBUG_ENABLED(DEBUG_VERB_HIGH, GT)) + { + dumpNextNodeList(g_func_debug.func_debug_file); + } + + if (g_func_config.dump_intermediates) + { + currNode->dumpNode(g_func_debug.func_debug_file); + for (auto outs : currNode->getOutputs()) + { + outs->dumpTensorParams(g_func_debug.func_debug_file); + outs->dumpTensor(g_func_debug.func_debug_file); + fprintf(g_func_debug.func_debug_file, "\n"); + } + } + + return 0; +} + +int SubgraphTraverser::dumpNextNodeList(FILE* out) const +{ + + // Dump next node list + fprintf(out, "Next node list\n"); + + if (nextNodeList.empty()) + { + fprintf(out, "<empty>\n"); + } + + for (auto gn : nextNodeList) + { + gn->dumpNode(out); + } + + fprintf(out, "Done.\n"); + return 0; +} + +int SubgraphTraverser::clearAllNodeMarkings() +{ + for (GraphNode* currNode : nodes) + { + currNode->clearNodeMarked(); + } + + return false; +} + +int SubgraphTraverser::addTensor(TosaReference::Tensor* ct) +{ + // Enforce no duplicate tensors/tensor names + // O(N), but the number of tensors is small + for (TosaReference::Tensor* currTensor : tensors) + { + if (ct == currTensor || currTensor->getName() == ct->getName()) + { + FATAL_ERROR("Error: Duplicate tensor or tensor name being added to graph: %s\n", ct->getName().c_str()); + return 1; + } + } + + tensors.push_back(ct); + + if (ct->getIsSubgraphInput()) + { + inputTensors.push_back(ct); + } + + if (ct->getIsSubgraphOutput()) + { + outputTensors.push_back(ct); + } + + return 0; +} +int SubgraphTraverser::addNode(GraphNode* newNode) +{ + // Enforce no duplicate nodes + for (GraphNode* currNode : nodes) + { + if (currNode == newNode) + { + FATAL_ERROR("Error: duplicate node being added to graph"); + return 1; + } + } + + nodes.push_back(newNode); + + return 0; +} + +TosaReference::Tensor* SubgraphTraverser::findTensorByName(const std::string& name) const +{ + for (TosaReference::Tensor* currTensor : tensors) + { + if (currTensor->getName() == name) + { + return currTensor; + } + } + + WARNING("Unable to find tensor with name: %s\n", name.c_str()); + + return nullptr; +} + +int SubgraphTraverser::linkTensorsAndNodes() +{ + // Nodes have a list of input/output tensor names + // For each node, read this list, link up the tensors with their inputs/outputs + for (GraphNode* currNode : nodes) + { + + // Link inputs/consuming nodes + for (std::string& name : currNode->getInputNames()) + { + TosaReference::Tensor* t = findTensorByName(name); + if (!t) + { + FATAL_ERROR("linkTensorsAndNodes: Cannot find tensor %s in node %lu\n", name.c_str(), + currNode->getID()); + return 1; + } + + if (currNode->addInputTensor(t)) + { + FATAL_ERROR("linkTensorsAndNodes: cannot link tensor %s to node %lu\n", name.c_str(), + currNode->getID()); + return 1; + } + + if (t->addConsumer(currNode)) + { + FATAL_ERROR("linkTensorsAndNodes: cannot link consumer node %lu to tensor %s\n", currNode->getID(), + name.c_str()); + return 1; + } + } + + // Link outputs/producing nodes + for (std::string& name : currNode->getOutputNames()) + { + TosaReference::Tensor* t = findTensorByName(name); + if (!t) + { + FATAL_ERROR("linkTensorsAndNodes: Cannot find tensor %s in node %lu\n", name.c_str(), + currNode->getID()); + return 1; + } + + if (currNode->addOutputTensor(t)) + { + FATAL_ERROR("linkTensorsAndNodes: cannot link tensor %s to node %lu\n", name.c_str(), + currNode->getID()); + return 1; + } + + if (t->setProducer(currNode)) + { + FATAL_ERROR("linkTensorsAndNodes: cannot link producer node %lu to tensor tensor %s\n", + currNode->getID(), name.c_str()); + return 1; + } + } + } + + return 0; +} + +int SubgraphTraverser::validateGraph() +{ + // Need to make sure that: + // - each tensor is actually used + // - input and output tesnsors truly are just input and just output + // Graph building already determined that each node has found its input/output tensors + + for (TosaReference::Tensor* currTensor : tensors) + { + + if (!currTensor->getProducer() && currTensor->getConsumers().empty()) + { + WARNING("Graph inconsistency: TosaReference::Tensor %s has no producers or consumers\n", + currTensor->getName().c_str()); + return 1; + } + + if (currTensor->getIsSubgraphInput()) + { + if (currTensor->getProducer() && currTensor->getProducer()->getOp() != Op_PLACEHOLDER) + { + WARNING("Graph inconsistency: TosaReference::Tensor %s is a subgraph input and has a producer\n", + currTensor->getName().c_str()); + return 1; + } + } + + // comment this check out as this is possible when graph have multiple output + // for example: + // %0 = add(%arg0, %arg1) + // %1 = mul(%arg0, %0) + // yields(%0, %1) + //if (currTensor->getIsSubgraphOutput()) { + // if (!currTensor->getConsumers().empty()) { + // WARNING ("Graph inconsistency: TosaReference::Tensor %s is a subgraph output and has a consumer\n", + // currTensor->getName().c_str()); + // return 1; + // } + //} + + if (g_func_config.tosa_profile == 0) + { + DType dtype = currTensor->getDtype(); + + // Float-point disallowed + if (dtype == DType_FLOAT) + { + WARNING("TOSA Base Inference profile selected: All floating point disabled, but %s tensor %s found\n", + EnumNamesDType()[dtype], currTensor->getName().c_str()); + return 1; + } + } + else if (g_func_config.tosa_profile == 1 || g_func_config.tosa_profile == 2) + { + // Do nothing. All FP types allowed + // Currently no implementation difference between Main Inference and Main Training modes + } + else + { + FATAL_ERROR("TOSA profile not recognized: %d", g_func_config.tosa_profile); + } + } + + for (GraphNode* currNode : nodes) + { + if (currNode->checkTensorAttributes()) + { + WARNING("TosaReference::Tensor attribute check failed"); + return 1; + } + } + + if (outputTensors.size() <= 0) + { + DEBUG_MED(GT, "Graph output tensor empty"); + return 0; + } + + return 0; +} + +int SubgraphTraverser::dumpGraph(FILE* out) const +{ + int i = 0; + + fprintf(out, "Full graph dump:\n"); + for (GraphNode* currNode : nodes) + { + fprintf(out, "Node [%d]: ", i++); + currNode->dumpNode(out); + } + + return 0; +} + +int SubgraphTraverser::evaluateAll() +{ + // evaluation loop + while (!isFullyEvaluated()) + { + if (evaluateNextNode()) + { + return 1; + } + } + + return 0; +} |