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node_executor.cc 9.5 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/node_executor/node_executor.h"
  17. #include "framework/common/debug/log.h"
  18. #include "graph/utils/node_utils.h"
  19. #include "init/gelib.h"
  20. #include "graph/utils/tensor_utils.h"
  21. #include "hybrid/model/hybrid_model.h"
  22. #include "graph/debug/ge_attr_define.h"
  23. #include "opskernel_manager/ops_kernel_builder_manager.h"
  24. namespace ge {
  25. namespace hybrid {
  26. namespace {
  27. const char *const kEngineNameAiCore = "AIcoreEngine";
  28. const char *const kEngineNameGeLocal = "DNN_VM_GE_LOCAL_OP_STORE";
  29. const char *const kEngineNameAiCpu = "aicpu_ascend_kernel";
  30. const char *const kEngineNameAiCpuTf = "aicpu_tf_kernel";
  31. const char *const kEngineNameHccl = "ops_kernel_info_hccl";
  32. const char *const kEngineNameRts = "DNN_VM_RTS_OP_STORE";
  33. const char *const kEngineNameHostCpu = "DNN_VM_HOST_CPU_OP_STORE";
  34. const char *const kOwnerGraphIsUnknown = "OwnerGraphIsUnknown";
  35. bool IsGraphUnknown(ComputeGraph &graph) {
  36. for (const auto &node : graph.GetDirectNode()) {
  37. bool is_unknown_shape = false;
  38. (void)AttrUtils::GetBool(node->GetOpDesc(), kOwnerGraphIsUnknown, is_unknown_shape);
  39. return is_unknown_shape;
  40. }
  41. return false;
  42. }
  43. }
  44. Status NodeExecutor::PrepareTask(NodeTask &task, TaskContext &context) const {
  45. GE_CHK_STATUS_RET_NOLOG(context.AllocateOutputs());
  46. GE_CHK_STATUS_RET_NOLOG(task.UpdateTilingData(context)); // update op_desc before alloc ws
  47. GE_CHK_STATUS_RET_NOLOG(context.AllocateWorkspaces());
  48. GE_CHK_STATUS_RET_NOLOG(task.UpdateArgs(context));
  49. return SUCCESS;
  50. }
  51. Status NodeExecutor::ExecuteTask(NodeTask &task, TaskContext &context, const std::function<void()> &callback) const {
  52. GE_CHK_STATUS_RET(task.ExecuteAsync(context, callback),
  53. "Failed to execute task. node = %s",
  54. context.GetNodeItem().NodeName().c_str());
  55. return SUCCESS;
  56. }
  57. Status NodeExecutor::LoadTask(const HybridModel &model, const NodePtr &node, shared_ptr<NodeTask> &task) const {
  58. return UNSUPPORTED;
  59. }
  60. Status NodeExecutor::CompileTask(const HybridModel &model, const NodePtr &node, shared_ptr<NodeTask> &task) const {
  61. return UNSUPPORTED;
  62. }
  63. Status NodeExecutorManager::EnsureInitialized() {
  64. GE_CHK_STATUS_RET(InitializeExecutors());
  65. std::lock_guard<std::mutex> lk(mu_);
  66. if (initialized_) {
  67. return SUCCESS;
  68. }
  69. engine_mapping_.emplace(kEngineNameAiCore, NodeExecutorManager::ExecutorType::AICORE);
  70. engine_mapping_.emplace(kEngineNameGeLocal, NodeExecutorManager::ExecutorType::GE_LOCAL);
  71. engine_mapping_.emplace(kEngineNameAiCpuTf, NodeExecutorManager::ExecutorType::AICPU_TF);
  72. engine_mapping_.emplace(kEngineNameAiCpu, NodeExecutorManager::ExecutorType::AICPU_TF);
  73. engine_mapping_.emplace(kEngineNameHccl, NodeExecutorManager::ExecutorType::HCCL);
  74. engine_mapping_.emplace(kEngineNameRts, NodeExecutorManager::ExecutorType::RTS);
  75. engine_mapping_.emplace(kEngineNameHostCpu, NodeExecutorManager::ExecutorType::HOST_CPU);
  76. initialized_ = true;
  77. GELOGI("Initializing NodeExecutors successfully");
  78. return SUCCESS;
  79. }
  80. NodeExecutorManager::ExecutorType NodeExecutorManager::ResolveExecutorType(Node &node) const {
  81. auto op_type = node.GetType();
  82. if (op_type == PARTITIONEDCALL) {
  83. const auto &subgraph = NodeUtils::GetSubgraph(node, 0);
  84. if (subgraph != nullptr && IsGraphUnknown(*subgraph)) {
  85. GELOGD("node %s was marked as unknown shape in node executor.", node.GetName().c_str());
  86. return ExecutorType::DYNAMIC_SUBGRAPH;
  87. }
  88. bool is_dynamic = false;
  89. (void) NodeUtils::GetNodeUnknownShapeStatus(node, is_dynamic);
  90. if (is_dynamic) {
  91. return ExecutorType::DYNAMIC_SUBGRAPH;
  92. }
  93. return ExecutorType::COMPILED_SUBGRAPH;
  94. }
  95. // rts kernel store is assigned to NetOutput
  96. if (op_type == NETOUTPUT || op_type == VARIABLE) {
  97. return ExecutorType::GE_LOCAL;
  98. }
  99. if (IsControlOp(op_type)) {
  100. return ExecutorType::CONTROL_OP;
  101. }
  102. auto op_desc = node.GetOpDesc(); // checked before
  103. const auto &lib_name = op_desc->GetOpKernelLibName();
  104. auto it = engine_mapping_.find(lib_name);
  105. if (it == engine_mapping_.end()) {
  106. GELOGE(UNSUPPORTED, "KernelLib not supported. node = %s, lib_name = %s", node.GetName().c_str(), lib_name.c_str());
  107. return ExecutorType::RESERVED;
  108. }
  109. return it->second;
  110. }
  111. Status NodeExecutorManager::GetExecutor(Node &node, const NodeExecutor **executor) const {
  112. auto executor_type = ResolveExecutorType(node);
  113. const auto it = executors_.find(executor_type);
  114. if (it == executors_.end()) {
  115. GELOGE(INTERNAL_ERROR, "Failed to get executor by type: %d.", executor_type);
  116. return INTERNAL_ERROR;
  117. }
  118. GELOGD("[%s] Set node executor by type: %d.", node.GetName().c_str(), executor_type);
  119. *executor = it->second.get();
  120. return SUCCESS;
  121. }
  122. void NodeExecutorManager::RegisterExecutorBuilder(NodeExecutorManager::ExecutorType executor_type,
  123. const std::function<NodeExecutor *()> &builder) {
  124. builders_.emplace(executor_type, builder);
  125. }
  126. Status NodeExecutorManager::CalcOpRunningParam(Node &node) const {
  127. auto op_desc = node.GetOpDesc();
  128. GE_CHECK_NOTNULL(op_desc);
  129. if (op_desc->GetType() == PARTITIONEDCALL) {
  130. GELOGD("[%s] Skipping CalcOpRunningParam for PartitionedCall.", node.GetName().c_str());
  131. return SUCCESS;
  132. }
  133. for (size_t i = 0; i < node.GetOpDesc()->GetOutputsSize(); ++i) {
  134. GeTensorDescPtr output_tensor = op_desc->MutableOutputDesc(static_cast<uint32_t>(i));
  135. TensorUtils::SetSize(*(output_tensor.get()), 0);
  136. }
  137. // calc hccl output size independent, hccl ops kernel manager should GetSize for
  138. // input which is the output size of input-op, but sometimes return error
  139. // when multi-thread
  140. if (op_desc->GetOpKernelLibName() == kEngineNameHccl) {
  141. for (size_t i = 0; i < op_desc->GetOutputsSize(); ++i) {
  142. GeTensorDesc output_tensor = op_desc->GetOutputDesc(static_cast<uint32_t>(i));
  143. Format format = output_tensor.GetFormat();
  144. DataType data_type = output_tensor.GetDataType();
  145. GeShape output_shape = output_tensor.GetShape();
  146. int64_t output_mem_size = 0;
  147. GE_CHK_STATUS_RET(TensorUtils::CalcTensorMemSize(output_shape, format, data_type, output_mem_size),
  148. "hccl calc tensor mem size failed.");
  149. output_mem_size = ((output_mem_size +
  150. MEMORY_ALIGN_RATIO * MEMORY_ALIGN_SIZE - 1) / MEMORY_ALIGN_SIZE) * MEMORY_ALIGN_SIZE;
  151. TensorUtils::SetSize(output_tensor, output_mem_size);
  152. GE_CHK_STATUS_RET(op_desc->UpdateOutputDesc(static_cast<uint32_t>(i), output_tensor),
  153. "hccl update output size failed.");
  154. GELOGD("%s output desc[%u], dim_size: %zu, mem_size: %ld.", node.GetName().c_str(), i,
  155. output_tensor.GetShape().GetDimNum(), output_mem_size);
  156. }
  157. return SUCCESS;
  158. }
  159. return OpsKernelBuilderManager::Instance().CalcOpRunningParam(node);
  160. }
  161. Status NodeExecutorManager::InitializeExecutors() {
  162. std::lock_guard<std::mutex> lk(mu_);
  163. if (executor_initialized_) {
  164. ++ref_count_;
  165. GELOGI("Executor is already initialized. add ref count to [%d]", ref_count_);
  166. return SUCCESS;
  167. }
  168. GELOGI("Start to Initialize NodeExecutors");
  169. for (auto &it : builders_) {
  170. auto engine_type = it.first;
  171. auto build_fn = it.second;
  172. GE_CHECK_NOTNULL(build_fn);
  173. auto executor = std::unique_ptr<NodeExecutor>(build_fn());
  174. if (executor == nullptr) {
  175. GELOGE(INTERNAL_ERROR, "Failed to create executor for engine type = %d", engine_type);
  176. return INTERNAL_ERROR;
  177. }
  178. GELOGD("Executor of engine type = %d was created successfully", engine_type);
  179. auto ret = executor->Initialize();
  180. if (ret != SUCCESS) {
  181. GELOGE(ret, "Failed to initialize NodeExecutor of type = %d, clear executors", engine_type);
  182. for (auto &executor_it : executors_) {
  183. executor_it.second->Finalize();
  184. }
  185. executors_.clear();
  186. return ret;
  187. }
  188. executors_.emplace(engine_type, std::move(executor));
  189. }
  190. ++ref_count_;
  191. executor_initialized_ = true;
  192. GELOGI("Initializing NodeExecutors successfully.");
  193. return SUCCESS;
  194. }
  195. void NodeExecutorManager::FinalizeExecutors() {
  196. std::lock_guard<std::mutex> lk(mu_);
  197. if (!executor_initialized_) {
  198. GELOGD("No need for finalizing for not initialized.");
  199. return;
  200. }
  201. if (--ref_count_ > 0) {
  202. GELOGD("Ref count = %d, do not finalize executors.", ref_count_);
  203. return;
  204. }
  205. GELOGD("Start to invoke Finalize on executors.");
  206. for (auto &it : executors_) {
  207. it.second->Finalize();
  208. }
  209. executors_.clear();
  210. executor_initialized_ = false;
  211. GELOGD("Done invoking Finalize successfully.");
  212. }
  213. NodeExecutorRegistrar::NodeExecutorRegistrar(NodeExecutorManager::ExecutorType executor_type,
  214. NodeExecutor *(*builder)()) {
  215. NodeExecutorManager::GetInstance().RegisterExecutorBuilder(executor_type, builder);
  216. }
  217. } // namespace hybrid
  218. } // 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