You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.

node_state.h 5.2 kB

4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192
  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. #ifndef GE_HYBRID_EXECUTOR_NODE_STATE_H_
  17. #define GE_HYBRID_EXECUTOR_NODE_STATE_H_
  18. #include <condition_variable>
  19. #include <future>
  20. #include <mutex>
  21. #include "common/blocking_queue.h"
  22. #include "external/ge/ge_api_error_codes.h"
  23. #include "hybrid/model/node_item.h"
  24. #include "node_done_manager.h"
  25. namespace ge {
  26. namespace hybrid {
  27. class NodeTask;
  28. struct GraphExecutionContext;
  29. class SubgraphContext;
  30. class TaskContext;
  31. struct NodeState;
  32. using NodeStatePtr = std::shared_ptr<NodeState>;
  33. class ShapeFuture {
  34. public:
  35. ShapeFuture(NodeState *src_node, uint32_t src_index, SubgraphContext *subgraph_context);
  36. ~ShapeFuture() = default;
  37. Status Get(GeShape &ori_shape, GeShape &shape);
  38. Status GetTensorDesc(const GeTensorDesc **tensor_desc);
  39. private:
  40. NodeState *src_node_;
  41. uint32_t src_index_;
  42. SubgraphContext *subgraph_context_;
  43. };
  44. struct ShapeInferenceState {
  45. explicit ShapeInferenceState(const NodeItem &node_item);
  46. void InitShapeState();
  47. Status UpdateInputShape(int idx, const GeTensorDesc &tensor_desc);
  48. void UpdateInputShapeFuture(int idx, ShapeFuture &&future);
  49. Status AwaitShapesReady(const GraphExecutionContext &context);
  50. Status UpdateOutputDesc();
  51. const vector<GeTensorDesc> &GetOutputTensorDesc() const;
  52. const NodeItem &node_item;
  53. private:
  54. Status UpdateInputForMerge(const GraphExecutionContext &context);
  55. friend struct NodeState;
  56. std::vector<std::pair<int, ShapeFuture>> shape_futures;
  57. // do not directly update op_desc, in case race condition across pipelines
  58. std::vector<GeTensorDesc> input_tensor_desc;
  59. std::vector<GeTensorDesc> output_tensor_desc;
  60. int num_pending_shapes_ = 0;
  61. std::condition_variable ready_cv_;
  62. std::mutex mu_;
  63. };
  64. // saving sth. dynamic during execution
  65. struct NodeState {
  66. public:
  67. NodeState(const NodeItem &node_item, SubgraphContext *subgraph_context);
  68. ~NodeState() = default;
  69. OpDesc *GetOpDesc() const {
  70. return op_desc_.get();
  71. }
  72. inline const NodeItem *GetNodeItem() const {
  73. return node_item_;
  74. }
  75. inline const string &GetName() const {
  76. return node_item_->NodeName();
  77. }
  78. inline const string &GetType() const {
  79. return node_item_->NodeType();
  80. }
  81. ShapeInferenceState &GetShapeInferenceState() {
  82. return shape_inference_state_;
  83. }
  84. Status UpdateOutputShapes(int index, const GeShape &shape, const GeShape &ori_shape);
  85. inline bool IsShapeDependence() const {
  86. return node_item_->IsControlFlowOp() || node_item_->shape_inference_type >= DEPEND_SHAPE_RANGE;
  87. }
  88. void RunLoopNext();
  89. void RunLoopExit();
  90. Status NodeScheduled(const std::function<void(const NodeItem *)> &ready) const;
  91. void SetScheduleFuture(std::future<Status> &&future);
  92. Status WaitForScheduleDone();
  93. void SetSwitchIndex(int index) {
  94. switch_index_ = index;
  95. }
  96. int GetSwitchIndex() const {
  97. return switch_index_;
  98. }
  99. void SetMergeIndex(int index) {
  100. merge_index_ = index;
  101. }
  102. int GetMergeIndex() const {
  103. return merge_index_;
  104. }
  105. void SetGroup(int group) {
  106. group_ = group;
  107. }
  108. int GetGroup() const {
  109. return group_;
  110. }
  111. const shared_ptr<NodeTask> &GetKernelTask() const {
  112. return kernel_task_;
  113. }
  114. void SetKernelTask(const shared_ptr<NodeTask> &kernel_task) {
  115. kernel_task_ = kernel_task;
  116. }
  117. Status WaitForPrepareDone();
  118. void SetPrepareFuture(std::future<Status> &&prepare_future) {
  119. this->prepare_future_ = std::move(prepare_future);
  120. }
  121. Status AwaitInputTensors(GraphExecutionContext &context) const;
  122. void SetTaskContext(std::shared_ptr<TaskContext> &task_context);
  123. std::shared_ptr<TaskContext> GetTaskContext();
  124. private:
  125. bool IsScheduleReady() const;
  126. void SetDataSchedule(const NodeState &node_state, const std::function<void(const NodeItem *)> &ready);
  127. void SetCtrlSchedule(const NodeState &node_state, const std::function<void(const NodeItem *)> &ready);
  128. void ResetContext(uint64_t loop_count);
  129. const NodeItem *node_item_ = nullptr;
  130. std::shared_ptr<NodeTask> kernel_task_ = nullptr;
  131. std::future<Status> prepare_future_;
  132. OpDescPtr op_desc_;
  133. ShapeInferenceState shape_inference_state_;
  134. SubgraphContext *subgraph_context_;
  135. std::shared_ptr<TaskContext> task_context_ = nullptr;
  136. std::mutex mu_;
  137. std::future<Status> schedule_future_;
  138. uint64_t loop_count_ = 0;
  139. uint32_t ctrl_scheduled_ = 0;
  140. uint32_t data_scheduled_ = 0;
  141. int merge_index_ = -1; // Use for Execute (Reset after Executed).
  142. int switch_index_ = -1; // Use for Schedule (Reset after Prepared).
  143. int group_ = -1;
  144. };
  145. } // namespace hybrid
  146. } // namespace ge
  147. #endif // GE_HYBRID_EXECUTOR_NODE_STATE_H_

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