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node_state.cc 10 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 "hybrid/executor/node_state.h"
  17. #include <chrono>
  18. #include "framework/common/debug/log.h"
  19. #include "graph/compute_graph.h"
  20. #include "graph/utils/tensor_utils.h"
  21. #include "hybrid_execution_context.h"
  22. #include "subgraph_context.h"
  23. namespace ge {
  24. namespace hybrid {
  25. namespace {
  26. // 5s * 120, wait for 10m
  27. constexpr auto kWaitInternal = 5;
  28. constexpr auto kMaxWaitTimes = 120;
  29. }
  30. ShapeInferenceState::ShapeInferenceState(const NodeItem &node_item) : node_item(node_item) {
  31. this->num_pending_shapes_ = node_item.num_inputs - node_item.num_static_input_shapes;
  32. GELOGD("[%s] ShapeInferenceState created, pending shape count = %d",
  33. node_item.NodeName().c_str(),
  34. this->num_pending_shapes_);
  35. input_tensor_desc.resize(node_item.num_inputs);
  36. for (int i = 0; i < node_item.num_inputs; ++i) {
  37. node_item.GetInputDesc(i, input_tensor_desc[i]);
  38. }
  39. output_tensor_desc.resize(node_item.num_outputs);
  40. for (int i = 0; i < node_item.num_outputs; ++i) {
  41. node_item.GetOutputDesc(i, output_tensor_desc[i]);
  42. }
  43. }
  44. Status ShapeInferenceState::UpdateInputShape(int idx, const GeTensorDesc &target) {
  45. if (node_item.IsInputShapeStatic(idx)) {
  46. GELOGD("[%s] Trying to update static shape, idx = %d. old shape = [%s], new shape = [%s]",
  47. node_item.NodeName().c_str(),
  48. idx,
  49. node_item.MutableInputDesc(idx)->GetShape().ToString().c_str(),
  50. target.GetShape().ToString().c_str());
  51. return SUCCESS;
  52. }
  53. std::lock_guard<std::mutex> lk(mu_);
  54. auto &input_desc = input_tensor_desc[idx];
  55. GeShape shape = target.GetShape();
  56. input_desc.SetShape(shape);
  57. input_desc.SetOriginShape(target.GetOriginShape());
  58. int64_t tensor_size = -1;
  59. (void) TensorUtils::GetSize(target, tensor_size);
  60. if (tensor_size <= 0) {
  61. Format format = input_desc.GetFormat();
  62. DataType data_type = input_desc.GetDataType();
  63. if (TensorUtils::CalcTensorMemSize(shape, format, data_type, tensor_size) != GRAPH_SUCCESS) {
  64. GELOGE(FAILED, "[%s] Calculate tensor memory size failed.", node_item.NodeName().c_str());
  65. return FAILED;
  66. }
  67. }
  68. GELOGD("[%s] Update input shape [%d] with Shape: [%s] and OriginalShape: [%s], size = %ld",
  69. node_item.NodeName().c_str(),
  70. idx,
  71. shape.ToString().c_str(),
  72. target.GetOriginShape().ToString().c_str(),
  73. tensor_size);
  74. (void) TensorUtils::SetSize(input_desc, tensor_size);
  75. if (--num_pending_shapes_ <= 0) {
  76. ready_cv_.notify_all();
  77. }
  78. return SUCCESS;
  79. }
  80. void ShapeInferenceState::UpdateInputShapeFuture(int idx, ShapeFuture &&future) {
  81. if (node_item.IsInputShapeStatic(idx)) {
  82. GELOGD("[%s] Trying to update constant shape, idx = %d", node_item.NodeName().c_str(), idx);
  83. return;
  84. }
  85. GELOGD("[%s] Update input shape [%d] with ShapeFuture.", node_item.NodeName().c_str(), idx);
  86. std::lock_guard<std::mutex> lk(mu_);
  87. shape_futures.emplace_back(idx, std::move(future));
  88. if (--num_pending_shapes_ == 0) {
  89. ready_cv_.notify_all();
  90. }
  91. }
  92. Status ShapeInferenceState::AwaitShapesReady(const GraphExecutionContext &context) {
  93. if (!node_item.is_dynamic) {
  94. return SUCCESS;
  95. }
  96. std::unique_lock<std::mutex> lk(mu_);
  97. if (num_pending_shapes_ > 0) {
  98. GELOGD("[%s] Await pending shape or shape future start.", node_item.NodeName().c_str());
  99. int try_count = 0;
  100. bool wait_success = false;
  101. while (try_count++ < kMaxWaitTimes) {
  102. if (ready_cv_.wait_for(lk, std::chrono::seconds(kWaitInternal), [&]() { return num_pending_shapes_ == 0; })) {
  103. GELOGD("[%s] Await pending shape or shape future end.", node_item.NodeName().c_str());
  104. wait_success = true;
  105. break;
  106. }
  107. if (context.is_eos_) {
  108. GELOGD("[%s] Await pending shape cancelled due to end of sequence", node_item.NodeName().c_str());
  109. return END_OF_SEQUENCE;
  110. }
  111. if (context.GetStatus() != SUCCESS) {
  112. GELOGE(FAILED, "[%s] Await pending shape cancelled", node_item.NodeName().c_str());
  113. break;
  114. }
  115. }
  116. if (!wait_success) {
  117. GELOGE(FAILED, "[%s] Wait for shape timeout.", node_item.NodeName().c_str());
  118. return FAILED;
  119. }
  120. }
  121. for (size_t i = 0; i < input_tensor_desc.size(); ++i) {
  122. auto dst_tensor_desc = node_item.op_desc->MutableInputDesc(i);
  123. if (dst_tensor_desc == nullptr) {
  124. continue;
  125. }
  126. auto &tensor_desc = input_tensor_desc[i];
  127. int64_t tensor_size = -1;
  128. (void) TensorUtils::GetSize(tensor_desc, tensor_size);
  129. dst_tensor_desc->SetShape(tensor_desc.MutableShape());
  130. dst_tensor_desc->SetOriginShape(tensor_desc.GetOriginShape());
  131. (void) TensorUtils::SetSize(*dst_tensor_desc, tensor_size);
  132. }
  133. for (auto &p : shape_futures) {
  134. auto idx = p.first;
  135. auto &future = p.second;
  136. RECORD_SHAPE_INFERENCE_EVENT(&context, node_item.NodeName().c_str(), "[AwaitShape] [idx = %u] Start", idx);
  137. const GeTensorDesc* src_tensor_desc = nullptr;
  138. GE_CHK_STATUS_RET_NOLOG(future.GetTensorDesc(&src_tensor_desc));
  139. GE_CHECK_NOTNULL(src_tensor_desc);
  140. RECORD_SHAPE_INFERENCE_EVENT(&context, node_item.NodeName().c_str(), "[AwaitShape] [idx = %u] End", idx);
  141. auto input_desc = node_item.MutableInputDesc(idx);
  142. GE_CHECK_NOTNULL(input_desc);
  143. int64_t tensor_size = -1;
  144. (void) TensorUtils::GetSize(*src_tensor_desc, tensor_size);
  145. GELOGD("[%s] Update input shape [%u] with shape: [%s] and ori_shape: [%s], index = %zu",
  146. node_item.NodeName().c_str(),
  147. idx,
  148. src_tensor_desc->GetShape().ToString().c_str(),
  149. src_tensor_desc->GetOriginShape().ToString().c_str(),
  150. tensor_size);
  151. input_desc->SetShape(src_tensor_desc->GetShape());
  152. input_desc->SetOriginShape(src_tensor_desc->GetOriginShape());
  153. (void) TensorUtils::SetSize(*input_desc, tensor_size);
  154. }
  155. return SUCCESS;
  156. }
  157. const vector<GeTensorDesc> &ShapeInferenceState::GetOutputTensorDesc() const {
  158. return output_tensor_desc;
  159. }
  160. Status ShapeInferenceState::UpdateOutputDesc() {
  161. for (size_t i = 0; i < output_tensor_desc.size(); ++i) {
  162. auto src_tensor_desc = node_item.MutableOutputDesc(i);
  163. GE_CHECK_NOTNULL(src_tensor_desc);
  164. auto &dst_tensor_desc = output_tensor_desc[i];
  165. dst_tensor_desc.SetShape(src_tensor_desc->MutableShape());
  166. dst_tensor_desc.SetOriginShape(src_tensor_desc->GetOriginShape());
  167. int64_t tensor_size = -1;
  168. (void) TensorUtils::GetSize(*src_tensor_desc, tensor_size);
  169. (void) TensorUtils::SetSize(dst_tensor_desc, tensor_size);
  170. }
  171. return SUCCESS;
  172. }
  173. ShapeFuture::ShapeFuture(NodeState *src_node,
  174. uint32_t src_index,
  175. SubgraphContext *subgraph_context)
  176. : src_node_(src_node), src_index_(src_index), subgraph_context_(subgraph_context) {
  177. }
  178. NodeState::NodeState(const NodeItem &node_item, SubgraphContext *subgraph_context)
  179. : node_item_(&node_item), shape_inference_state_(node_item), subgraph_context_(subgraph_context) {
  180. this->op_desc_ = node_item.node->GetOpDesc();
  181. }
  182. Status NodeState::AwaitInputTensors(GraphExecutionContext &context) const {
  183. for (auto &src_node : node_item_->dependents_for_execution) {
  184. GELOGD("[%s] Start to wait for data dependent node: [%s]",
  185. node_item_->NodeName().c_str(),
  186. src_node->GetName().c_str());
  187. RECORD_EXECUTION_EVENT(&context,
  188. node_item_->NodeName().c_str(),
  189. "[AwaitNodeDone] [%s] Start",
  190. src_node->GetName().c_str());
  191. HYBRID_CHK_STATUS_RET(subgraph_context_->Await(src_node),
  192. "[%s] Await node [%s] failed.",
  193. GetName().c_str(),
  194. src_node->GetName().c_str());
  195. RECORD_EXECUTION_EVENT(&context,
  196. node_item_->NodeName().c_str(),
  197. "[AwaitNodeDone] [%s] End",
  198. src_node->GetName().c_str());
  199. GELOGD("[%s] Done waiting node.", src_node->GetName().c_str());
  200. }
  201. return SUCCESS;
  202. }
  203. Status NodeState::WaitForPrepareDone() {
  204. if (prepare_future_.valid()) {
  205. GELOGD("[%s] Start to wait for prepare future.", GetName().c_str());
  206. GE_CHK_STATUS_RET(prepare_future_.get(),
  207. "[%s] PreRun failed.", GetName().c_str());
  208. }
  209. return SUCCESS;
  210. }
  211. Status NodeState::UpdateOutputShapes(int index, const GeShape &shape, const GeShape &ori_shape) {
  212. auto self_tensor_desc = op_desc_->MutableOutputDesc(index);
  213. GE_CHECK_NOTNULL(self_tensor_desc);
  214. self_tensor_desc->SetShape(shape);
  215. self_tensor_desc->SetOriginShape(ori_shape);
  216. return SUCCESS;
  217. }
  218. void NodeState::SetTaskContext(std::shared_ptr<TaskContext> &task_context) {
  219. task_context_ = task_context;
  220. }
  221. std::shared_ptr<TaskContext> NodeState::GetTaskContext() {
  222. return task_context_;
  223. }
  224. Status ShapeFuture::Get(GeShape &ori_shape, GeShape &shape) {
  225. GELOGD("Start to wait node: %s for getting shape", src_node_->GetName().c_str());
  226. HYBRID_CHK_STATUS_RET(subgraph_context_->Await(src_node_->GetNodeItem()->node), "cancelled");
  227. auto &output_desc = src_node_->GetShapeInferenceState().GetOutputTensorDesc().at(src_index_);
  228. shape = output_desc.GetShape();
  229. ori_shape = output_desc.GetOriginShape();
  230. GELOGD("Get shape from %s:%u. shape = [%s]", src_node_->GetName().c_str(), src_index_, shape.ToString().c_str());
  231. return SUCCESS;
  232. }
  233. Status ShapeFuture::GetTensorDesc(const GeTensorDesc **tensor_desc) {
  234. GE_CHECK_NOTNULL(tensor_desc);
  235. GELOGD("Start to wait node: %s for getting shape", src_node_->GetName().c_str());
  236. HYBRID_CHK_STATUS_RET(subgraph_context_->Await(src_node_->GetNodeItem()->node), "cancelled");
  237. *tensor_desc = &src_node_->GetShapeInferenceState().GetOutputTensorDesc().at(src_index_);
  238. return SUCCESS;
  239. }
  240. } // namespace hybrid
  241. } // 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