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graph_context.cc 3.9 kB

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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/manager/graph_context.h"
  17. #include "graph/utils/graph_utils.h"
  18. #include "graph/utils/op_desc_utils.h"
  19. #include "graph/utils/tensor_utils.h"
  20. namespace ge {
  21. GraphContext::GraphContext(const GraphNodePtr &graph_node) {
  22. if (graph_node == nullptr) {
  23. GELOGE(GE_GRAPH_PARAM_NULLPTR, "graphNode is NULL!");
  24. return;
  25. }
  26. compute_graph_ = graph_node->GetComputeGraph();
  27. current_graph_id_ = graph_node->GetGraphId();
  28. if (compute_graph_ == nullptr) {
  29. std::shared_ptr<const ge::Graph> graph = graph_node->GetGraph();
  30. if (graph == nullptr) {
  31. GELOGE(GE_GRAPH_OPTIMIZE_COMPUTE_GRAPH_NULL, "compute_graph by graphNode is NULL!");
  32. return;
  33. }
  34. compute_graph_ = GraphUtils::GetComputeGraph(*graph);
  35. return;
  36. }
  37. }
  38. Status GraphContext::SetComputeGraph(const GraphNodePtr &graph_node) {
  39. if (graph_node == nullptr) {
  40. REPORT_INNER_ERROR("E19999", "Param graph_node is nullptr, check invalid");
  41. GELOGE(GE_GRAPH_PARAM_NULLPTR, "graphNode is NULL!");
  42. return GE_GRAPH_PARAM_NULLPTR;
  43. }
  44. compute_graph_ = graph_node->GetComputeGraph();
  45. current_graph_id_ = graph_node->GetGraphId();
  46. if (compute_graph_ == nullptr) {
  47. std::shared_ptr<const ge::Graph> graph = graph_node->GetGraph();
  48. if (graph == nullptr) {
  49. REPORT_INNER_ERROR("E19999", "Param graph in graph_node is nullptr, check invalid");
  50. GELOGE(GE_GRAPH_OPTIMIZE_COMPUTE_GRAPH_NULL, "compute_graph by graphNode is NULL!");
  51. return GE_GRAPH_OPTIMIZE_COMPUTE_GRAPH_NULL;
  52. }
  53. compute_graph_ = GraphUtils::GetComputeGraph(*graph);
  54. return SUCCESS;
  55. }
  56. return SUCCESS;
  57. }
  58. Status GraphContext::Initialize(const std::map<std::string, std::string> &options) const { return SUCCESS; }
  59. Status GraphContext::Finalize() const { return SUCCESS; }
  60. Status GraphContext::GetVariableTensor(const std::string &var_data_name, GeTensor &returned_tensor) {
  61. if (var_data_name.empty()) {
  62. REPORT_INNER_ERROR("E19999", "Param var_data_name is empty, check invalid");
  63. GELOGE(GE_GRAPH_EMPTY_STRING_NAME, "Variable data name is empty!");
  64. return GE_GRAPH_EMPTY_STRING_NAME;
  65. }
  66. if (GetVarNodeTensorTable().empty()) {
  67. REPORT_INNER_ERROR("E19999", "VarNodeTensorTable is empty, var_data_name:%s, check invalid",
  68. var_data_name.c_str());
  69. GELOGE(GE_GRAPH_EMPTY_VARIABLE_TENSOR_TABLE, "VarNodeTensorTable is empty!");
  70. return GE_GRAPH_EMPTY_VARIABLE_TENSOR_TABLE;
  71. }
  72. for (auto &var_record : GetVarNodeTensorTable()) {
  73. if (var_data_name == std::get<0>(var_record.first)) {
  74. returned_tensor.SetTensorDesc(var_record.second.GetTensorDesc());
  75. auto ret = returned_tensor.SetData(var_record.second.GetData());
  76. if (ret != SUCCESS) {
  77. REPORT_INNER_ERROR("E19999", "SetData to tensor fail, var_data_name:%s",
  78. var_data_name.c_str());
  79. GELOGE(ret, "Set Tensor data failed!");
  80. return ret;
  81. }
  82. return SUCCESS;
  83. }
  84. }
  85. REPORT_INNER_ERROR("E19999", "VarRecord with data_name:%s does not exist, check invalid",
  86. var_data_name.c_str());
  87. GELOGE(GE_GRAPH_VARIABLE_DOES_NOT_EXIST, "VarRecord with data_name %s does NOT exist!", var_data_name.c_str());
  88. return GE_GRAPH_VARIABLE_DOES_NOT_EXIST;
  89. }
  90. } // 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