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Diffstat (limited to 'runtimes/neurun/src/linear/Linear.cc')
| -rw-r--r-- | runtimes/neurun/src/linear/Linear.cc | 199 |
1 files changed, 0 insertions, 199 deletions
diff --git a/runtimes/neurun/src/linear/Linear.cc b/runtimes/neurun/src/linear/Linear.cc deleted file mode 100644 index 6452bbd49..000000000 --- a/runtimes/neurun/src/linear/Linear.cc +++ /dev/null @@ -1,199 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 <algorithm> - -#include "Linear.h" - -#include "graph/Graph.h" - -#include "graph/operation/LowerInfo.h" -#include "backend/interface/IStageGenerator.h" -#include "backend/interface/IConfig.h" -#include "compiler/SubTensorInfo.h" -#include "compiler/TensorInfo.h" - -#include "util/logging.h" - -namespace neurun -{ -namespace linear -{ - -Linear::Linear(const graph::Graph &graph) : _graph(graph) -{ - // Linearize with topological sort - // - // Topological sort algorithm - // 1. Iterate with DFS - // 2. Append the node to vector when DFS for the node finishes(post order) - // 3. Reverse the order of nodes - - graph::Graph::PostDfsConstIterator().iterate( - graph, [&](const model::operation::Index &index, const model::operation::Node &node) { - const auto lower_info = graph.getLowerInfo(index); - _operations.emplace_back(&node, lower_info); - }); - - std::reverse(std::begin(_operations), std::end(_operations)); -} - -void Linear::accept(model::operation::NodeVisitor &&visitor) const -{ - for (const auto op : _operations) - { - op.node->accept(std::move(visitor)); - } -} - -backend::TensorBuilderSet Linear::planTensors() -{ - using ITensorBuilderPtr = std::shared_ptr<backend::ITensorBuilder>; - using FnOnTensorBuilder = - std::function<void(const model::operand::Index &ind, ITensorBuilderPtr)>; - - const auto &operands = _graph.operands(); - auto iterTensorBuilders = [&operands](const model::operand::Index &ind, FnOnTensorBuilder fn) { - const auto &obj = operands.at(ind); - for (auto backend : obj.lower_info()->def_backends()) - { - auto tensor_builder = backend->tensor_builder(); - fn(ind, tensor_builder); - } - }; - - backend::TensorBuilderSet tensor_builders; - - std::unordered_map<model::operand::Index, uint32_t> uses_map; - std::vector<model::operand::Index> constants; - - _graph.operands().iterate( - [&](const model::operand::Index &ind, const model::operand::Object &obj) { - uses_map[ind] = obj.getUses().size(); - - // If a tensor is a constant, increase the use of the tensor. - // It makes the tensor not be dealloced. - if (obj.getUsage() == model::operand::OperandUsage::CONSTANT) - { - constants.push_back(ind); - uses_map[ind]++; - } - - for (auto backend : obj.lower_info()->def_backends()) - { - bool isSubTensor = false; - auto tensor_builder = backend->tensor_builder(); - - if (backend->config()->SupportSubTensorAlloc()) - { - const auto parentInfo = obj.parent_info(); - if (parentInfo != nullptr) - { - isSubTensor = true; - } - } - - if (isSubTensor) - { - const compiler::SubTensorInfo info(obj); - tensor_builder->registerSubTensorInfo(ind, info); - } - else - { - const auto info = compiler::TensorInfo(obj.shape(), obj.typeInfo()); - tensor_builder->registerTensorInfo(ind, info); - } - - // Prepare tensor builders to be returned - tensor_builders.insert(tensor_builder); - } - }); - - // If a tensor is model output, increase the use of the tensor. - // This aim is same to above one. - for (const auto &ind : _graph.getOutputs()) - { - uses_map[ind]++; - } - - // Allocate constant operands first - VERBOSE(LINEAR) << "TENSORS as CONSTANT" << std::endl; - for (const auto &ind : constants) - { - iterTensorBuilders(ind, [](const model::operand::Index &ind, ITensorBuilderPtr tensor_builder) { - tensor_builder->notifyFirstUse(ind); - }); - } - - // Allocate Model's inputs - VERBOSE(LINEAR) << "TENSORS as MODEL INPUT" << std::endl; - for (const auto &ind : _graph.getInputs()) - { - iterTensorBuilders(ind, [](const model::operand::Index &ind, ITensorBuilderPtr tensor_builder) { - tensor_builder->notifyFirstUse(ind); - }); - } - - // At each operation, - // 1. Scan USE of inputs. Decrease the USE and deallocate if the USE is 0 - // 2. Scan DEF of outputs. If the DEF, allocate it - VERBOSE(LINEAR) << "TENSORS" << std::endl; - for (const auto op : _operations) - { - for (const auto &ind : op.node->getOutputs()) - { - const auto &obj = operands.at(ind); - if (obj.getDef().size()) - { - iterTensorBuilders(ind, - [](const model::operand::Index &ind, ITensorBuilderPtr tensor_builder) { - tensor_builder->notifyFirstUse(ind); - }); - } - } - - for (const auto &ind : op.node->getInputs()) - { - uses_map[ind]--; - if (uses_map[ind] == 0) - { - iterTensorBuilders(ind, - [](const model::operand::Index &ind, ITensorBuilderPtr tensor_builder) { - tensor_builder->notifyLastUse(ind); - }); - } - } - } - - // Now, model outputs should be not deallocated - assert(std::all_of(_graph.getOutputs().begin(), _graph.getOutputs().end(), - [&uses_map](const model::operand::Index &ind) { return uses_map[ind] > 0; })); - - // Set subtensor information - // Todo: move this phase outside as optimization phase - return tensor_builders; -} - -void Linear::iterate(const std::function<void(const Element &element)> &fn) const -{ - for (const auto op : _operations) - { - fn(op); - } -} - -} // namespace linear -} // namespace neurun |