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graph_builder_utils.cc 1.8 kB

5 years ago
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  1. /**
  2. * Copyright 2019-2020 Huawei Technologies Co., Ltd
  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 "graph_builder_utils.h"
  17. #include "graph/utils/graph_utils.h"
  18. namespace ge {
  19. namespace ut {
  20. NodePtr GraphBuilder::AddNode(const std::string &name, const std::string &type, int in_cnt, int out_cnt, Format format,
  21. DataType data_type, std::vector<int64_t> shape) {
  22. auto tensor_desc = std::make_shared<GeTensorDesc>();
  23. tensor_desc->SetShape(GeShape(std::move(shape)));
  24. tensor_desc->SetFormat(format);
  25. tensor_desc->SetDataType(data_type);
  26. auto op_desc = std::make_shared<OpDesc>(name, type);
  27. for (int i = 0; i < in_cnt; ++i) {
  28. op_desc->AddInputDesc(tensor_desc->Clone());
  29. }
  30. for (int i = 0; i < out_cnt; ++i) {
  31. op_desc->AddOutputDesc(tensor_desc->Clone());
  32. }
  33. return graph_->AddNode(op_desc);
  34. }
  35. void GraphBuilder::AddDataEdge(NodePtr &src_node, int src_idx, NodePtr &dst_node, int dst_idx) {
  36. GraphUtils::AddEdge(src_node->GetOutDataAnchor(src_idx), dst_node->GetInDataAnchor(dst_idx));
  37. }
  38. void GraphBuilder::AddControlEdge(NodePtr &src_node, NodePtr &dst_node) {
  39. GraphUtils::AddEdge(src_node->GetOutControlAnchor(), dst_node->GetInControlAnchor());
  40. }
  41. } // namespace ut
  42. } // namespace ge

图引擎模块(GE)是MindSpore的一个子模块,其代码由C++实现,位于前端模块ME和底层硬件之间,起到承接作用。图引擎模块以ME下发的图作为输入,然后进行一系列的深度图优化操作,最后输出一张可以在底层硬件上高效运行的图。GE针对昇腾AI处理器的硬件结构特点,做了特定的优化工作,以此来充分发挥出昇腾AI处理器的强大算力。在进行模型训练/推理时,GE会被自动调用而用户并不感知。GE主要由GE API和GE Core两部分组成,详细的架构图如下所示