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subgraph_context.cc 7.9 kB

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  1. /**
  2. * Copyright 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 "subgraph_context.h"
  17. #include "hybrid/executor/hybrid_model_executor.h"
  18. namespace ge {
  19. namespace hybrid {
  20. SubgraphContext::SubgraphContext(const GraphItem *graph_item, const GraphExecutionContext *execution_context)
  21. : graph_item_(graph_item), execution_context_(execution_context) {
  22. }
  23. SubgraphContext::~SubgraphContext() {
  24. if (mmRWLockDestroy(&rw_lock_) != EN_OK) {
  25. REPORT_CALL_ERROR("E19999", "Destroy rw_lock failed");
  26. GELOGE(INTERNAL_ERROR, "[RWLock][Destroy] Destroy rw_lock failed");
  27. }
  28. }
  29. Status SubgraphContext::Init() {
  30. GE_CHECK_NOTNULL(graph_item_);
  31. GELOGD("[%s] Start to init subgraph context. total inputs = %d, total outputs = %d",
  32. graph_item_->GetName().c_str(),
  33. graph_item_->TotalInputs(),
  34. graph_item_->TotalOutputs());
  35. all_inputs_.resize(static_cast<unsigned long>(graph_item_->TotalInputs()));
  36. all_outputs_.resize(static_cast<unsigned long>(graph_item_->TotalOutputs()));
  37. if (mmRWLockInit(&rw_lock_) != EN_OK) {
  38. REPORT_CALL_ERROR("E19999", "Init rw_lock failed");
  39. GELOGE(INTERNAL_ERROR, "[RWLock][Init] Init rw_lock failed");
  40. return INTERNAL_ERROR;
  41. }
  42. return SUCCESS;
  43. }
  44. void SubgraphContext::SetGroup(int group) {
  45. group_ = group;
  46. }
  47. void SubgraphContext::ResetContext(const NodePtr &node) {
  48. node_done_manager_.Reset(node);
  49. }
  50. NodeStatePtr SubgraphContext::GetOrCreateNodeState(const NodeItem *node_item) {
  51. GELOGD("[%s] lock for read", node_item->NodeName().c_str());
  52. if (mmRWLockRDLock(&rw_lock_) != EN_OK) {
  53. REPORT_CALL_ERROR("E19999", "[Node:%s] Lock for read failed", node_item->NodeName().c_str());
  54. GELOGE(INTERNAL_ERROR, "[RWLock][Lock][Node:%s] Lock for read failed", node_item->NodeName().c_str());
  55. return nullptr;
  56. }
  57. const auto &iter = node_states_.find(node_item);
  58. if (iter != node_states_.end()) {
  59. auto state = iter->second;
  60. GELOGD("[%s] unlock for read", node_item->NodeName().c_str());
  61. if (mmRDLockUnLock(&rw_lock_) != EN_OK) {
  62. REPORT_CALL_ERROR("E19999", "[Node:%s] Unlock for read failed", node_item->NodeName().c_str());
  63. GELOGE(INTERNAL_ERROR, "[RWLock][Unlock][Node:%s] Unlock for read failed", node_item->NodeName().c_str());
  64. return nullptr;
  65. }
  66. return state;
  67. }
  68. GELOGD("[%s] unlock for read", node_item->NodeName().c_str());
  69. if (mmRDLockUnLock(&rw_lock_) != EN_OK) {
  70. REPORT_CALL_ERROR("E19999", "[Node:%s] Unlock for read failed", node_item->NodeName().c_str());
  71. GELOGE(INTERNAL_ERROR, "[RWLock][Unlock][Node:%s] Unlock for read failed", node_item->NodeName().c_str());
  72. return nullptr;
  73. }
  74. GELOGD("[%s] lock for write", node_item->NodeName().c_str());
  75. if (mmRWLockWRLock(&rw_lock_) != EN_OK) {
  76. REPORT_CALL_ERROR("E19999", "[Node:%s] Lock for write failed", node_item->NodeName().c_str());
  77. GELOGE(INTERNAL_ERROR, "[RWLock][Lock][Node:%s] Lock for write failed", node_item->NodeName().c_str());
  78. return nullptr;
  79. }
  80. auto &node_state = node_states_[node_item];
  81. if (node_state == nullptr) {
  82. const auto &guard = node_item->MutexGuard("GetOrCreateNodeState");
  83. node_state = std::move(std::unique_ptr<NodeState>(new(std::nothrow)NodeState(*node_item, this)));
  84. node_state->SetGroup(group_);
  85. (void)guard;
  86. }
  87. GELOGD("[%s] unlock for write", node_item->NodeName().c_str());
  88. if (mmWRLockUnLock(&rw_lock_) != EN_OK) {
  89. REPORT_CALL_ERROR("E19999", "[Node:%s] Unlock for write failed", node_item->NodeName().c_str());
  90. GELOGE(INTERNAL_ERROR, "[RWLock][Unlock][Node:%s] Unlock for write failed", node_item->NodeName().c_str());
  91. return nullptr;
  92. }
  93. return node_state;
  94. }
  95. Status SubgraphContext::SetInput(int index, const TensorValue &tensor) {
  96. if (static_cast<size_t>(index) >= all_inputs_.size()) {
  97. GELOGE(INTERNAL_ERROR,
  98. "[Check][Param:index]input index out of range. all input num = %zu, input index = %d",
  99. all_inputs_.size(), index);
  100. REPORT_INNER_ERROR("E19999", "input param index out of range, all input num = %zu, input index = %d.",
  101. all_inputs_.size(), index);
  102. return INTERNAL_ERROR;
  103. }
  104. all_inputs_[index] = tensor;
  105. return SUCCESS;
  106. }
  107. Status SubgraphContext::SetInput(const NodeItem &node_item, int input_index, const TensorValue &tensor) {
  108. auto index = node_item.input_start + input_index;
  109. return SetInput(index, tensor);
  110. }
  111. Status SubgraphContext::SetOutput(const NodeItem &node_item, int output_index, const TensorValue &tensor) {
  112. auto index = node_item.output_start + output_index;
  113. if ((output_index >= node_item.num_outputs) || (static_cast<size_t>(index) >= all_outputs_.size())) {
  114. GELOGE(INTERNAL_ERROR, "[Check][Param:output_index]output index out of range. all output num = %zu,"
  115. "node_item = %s, output index = %d.",
  116. all_outputs_.size(), node_item.DebugString().c_str(), output_index);
  117. REPORT_INNER_ERROR("E19999", "output index out of range. all output num = %zu, node_item = %s, output index = %d.",
  118. all_outputs_.size(), node_item.DebugString().c_str(), output_index);
  119. return INTERNAL_ERROR;
  120. }
  121. all_outputs_[index] = tensor;
  122. return SUCCESS;
  123. }
  124. Status SubgraphContext::GetInput(int index, TensorValue &tensor) {
  125. GE_CHECK_GE(all_inputs_.size(), index + 1U);
  126. tensor = all_inputs_[index];
  127. return SUCCESS;
  128. }
  129. Status SubgraphContext::GetOutputs(std::vector<TensorValue> &outputs) {
  130. if (graph_item_->IsDynamic()) {
  131. GELOGD("[%s] graph is dynamic, get outputs from net output input tensors", graph_item_->GetName().c_str());
  132. // get from net output inputs
  133. auto output_node = graph_item_->GetOutputNode();
  134. if (output_node != nullptr) {
  135. for (int i = 0; i < output_node->num_inputs; ++i) {
  136. TensorValue tensor;
  137. GE_CHK_STATUS_RET_NOLOG(GetInput(output_node->input_start + i, tensor));
  138. GELOGD("[%s] Adding output tensor by input index [%d], tensor = %s",
  139. graph_item_->GetName().c_str(),
  140. output_node->input_start + i,
  141. tensor.DebugString().c_str());
  142. outputs.emplace_back(std::move(tensor));
  143. }
  144. }
  145. } else {
  146. GELOGD("[%s] graph is non-dynamic, get outputs from subgraph outputs", graph_item_->GetName().c_str());
  147. for (auto &tensor : all_outputs_) {
  148. GELOGD("[%s] Adding output tensor: %s", graph_item_->GetName().c_str(), tensor.DebugString().c_str());
  149. outputs.emplace_back(tensor);
  150. }
  151. }
  152. return SUCCESS;
  153. }
  154. Status SubgraphContext::Await(const NodePtr &node) {
  155. if (node_done_manager_.Await(node)) {
  156. return SUCCESS;
  157. }
  158. if (execution_context_->is_eos_) {
  159. return END_OF_SEQUENCE;
  160. }
  161. return FAILED;
  162. }
  163. void SubgraphContext::OnError(Status error) {
  164. if (error != END_OF_SEQUENCE) {
  165. GELOGE(error, "[Check][Param:error][%s] Error:%d occurred while executing graph.",
  166. graph_item_->GetName().c_str(), error);
  167. REPORT_INNER_ERROR("E19999", "[%s] Error:%d occurred while executing graph.",
  168. graph_item_->GetName().c_str(), error);
  169. }
  170. node_done_manager_.Destroy();
  171. }
  172. void SubgraphContext::NodeDone(const NodePtr &node) {
  173. node_done_manager_.NodeDone(node);
  174. }
  175. void SubgraphContext::Reset() {
  176. node_done_manager_.Reset();
  177. if (mmRWLockWRLock(&rw_lock_) == EN_OK) {
  178. node_states_.clear();
  179. (void)mmWRLockUnLock(&rw_lock_);
  180. }
  181. }
  182. } // namespace hybrid
  183. } // 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