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node_executor.cc 9.1 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. }
  36. Status NodeExecutor::PrepareTask(NodeTask &task, TaskContext &context) const {
  37. GE_CHK_STATUS_RET_NOLOG(context.AllocateOutputs());
  38. GE_CHK_STATUS_RET_NOLOG(task.UpdateTilingData(context)); // update op_desc before alloc ws
  39. GE_CHK_STATUS_RET_NOLOG(context.AllocateWorkspaces());
  40. GE_CHK_STATUS_RET_NOLOG(task.UpdateArgs(context));
  41. return SUCCESS;
  42. }
  43. Status NodeExecutor::ExecuteTask(NodeTask &task, TaskContext &context, const std::function<void()> &callback) const {
  44. GE_CHK_STATUS_RET(task.ExecuteAsync(context, callback),
  45. "Failed to execute task. node = %s",
  46. context.GetNodeItem().NodeName().c_str());
  47. return SUCCESS;
  48. }
  49. Status NodeExecutor::LoadTask(const HybridModel &model, const NodePtr &node, shared_ptr<NodeTask> &task) const {
  50. return UNSUPPORTED;
  51. }
  52. Status NodeExecutor::CompileTask(const HybridModel &model, const NodePtr &node, shared_ptr<NodeTask> &task) const {
  53. return UNSUPPORTED;
  54. }
  55. Status NodeExecutorManager::EnsureInitialized() {
  56. GE_CHK_STATUS_RET(InitializeExecutors());
  57. std::lock_guard<std::mutex> lk(mu_);
  58. if (initialized_) {
  59. return SUCCESS;
  60. }
  61. engine_mapping_.emplace(kEngineNameAiCore, NodeExecutorManager::ExecutorType::AICORE);
  62. engine_mapping_.emplace(kEngineNameGeLocal, NodeExecutorManager::ExecutorType::GE_LOCAL);
  63. engine_mapping_.emplace(kEngineNameAiCpuTf, NodeExecutorManager::ExecutorType::AICPU_TF);
  64. engine_mapping_.emplace(kEngineNameAiCpu, NodeExecutorManager::ExecutorType::AICPU_TF);
  65. engine_mapping_.emplace(kEngineNameHccl, NodeExecutorManager::ExecutorType::HCCL);
  66. engine_mapping_.emplace(kEngineNameRts, NodeExecutorManager::ExecutorType::RTS);
  67. engine_mapping_.emplace(kEngineNameHostCpu, NodeExecutorManager::ExecutorType::HOST_CPU);
  68. initialized_ = true;
  69. GELOGI("Initializing NodeExecutors successfully");
  70. return SUCCESS;
  71. }
  72. NodeExecutorManager::ExecutorType NodeExecutorManager::ResolveExecutorType(Node &node) const {
  73. auto op_type = node.GetType();
  74. if (op_type == PARTITIONEDCALL) {
  75. const auto &subgraph = NodeUtils::GetSubgraph(node, 0);
  76. if (subgraph != nullptr && subgraph->GetGraphUnknownFlag()) {
  77. return ExecutorType::DYNAMIC_SUBGRAPH;
  78. }
  79. bool is_dynamic = false;
  80. (void)NodeUtils::GetNodeUnknownShapeStatus(node, is_dynamic);
  81. if (is_dynamic) {
  82. return ExecutorType::DYNAMIC_SUBGRAPH;
  83. }
  84. return ExecutorType::COMPILED_SUBGRAPH;
  85. }
  86. // rts kernel store is assigned to NetOutput
  87. if (op_type == NETOUTPUT || op_type == VARIABLE) {
  88. return ExecutorType::GE_LOCAL;
  89. }
  90. if (IsControlOp(op_type)) {
  91. return ExecutorType::CONTROL_OP;
  92. }
  93. auto op_desc = node.GetOpDesc(); // checked before
  94. const auto &lib_name = op_desc->GetOpKernelLibName();
  95. auto it = engine_mapping_.find(lib_name);
  96. if (it == engine_mapping_.end()) {
  97. GELOGE(UNSUPPORTED, "KernelLib not supported. node = %s, lib_name = %s", node.GetName().c_str(), lib_name.c_str());
  98. return ExecutorType::RESERVED;
  99. }
  100. return it->second;
  101. }
  102. Status NodeExecutorManager::GetExecutor(Node &node, const NodeExecutor **executor) const {
  103. auto executor_type = ResolveExecutorType(node);
  104. const auto it = executors_.find(executor_type);
  105. if (it == executors_.end()) {
  106. GELOGE(INTERNAL_ERROR, "Failed to get executor by type: %d.", executor_type);
  107. return INTERNAL_ERROR;
  108. }
  109. GELOGD("[%s] Set node executor by type: %d.", node.GetName().c_str(), executor_type);
  110. *executor = it->second.get();
  111. return SUCCESS;
  112. }
  113. void NodeExecutorManager::RegisterExecutorBuilder(NodeExecutorManager::ExecutorType executor_type,
  114. const std::function<NodeExecutor *()> &builder) {
  115. builders_.emplace(executor_type, builder);
  116. }
  117. Status NodeExecutorManager::CalcOpRunningParam(Node &node) const {
  118. auto op_desc = node.GetOpDesc();
  119. GE_CHECK_NOTNULL(op_desc);
  120. if (op_desc->GetType() == PARTITIONEDCALL) {
  121. GELOGD("[%s] Skipping CalcOpRunningParam for PartitionedCall.", node.GetName().c_str());
  122. return SUCCESS;
  123. }
  124. for (size_t i = 0; i < node.GetOpDesc()->GetOutputsSize(); ++i) {
  125. GeTensorDescPtr output_tensor = op_desc->MutableOutputDesc(static_cast<uint32_t>(i));
  126. TensorUtils::SetSize(*(output_tensor.get()), 0);
  127. }
  128. // calc hccl output size independent, hccl ops kernel manager should GetSize for
  129. // input which is the output size of input-op, but sometimes return error
  130. // when multi-thread
  131. if (op_desc->GetOpKernelLibName() == kEngineNameHccl) {
  132. for (size_t i = 0; i < op_desc->GetOutputsSize(); ++i) {
  133. GeTensorDesc output_tensor = op_desc->GetOutputDesc(static_cast<uint32_t>(i));
  134. Format format = output_tensor.GetFormat();
  135. DataType data_type = output_tensor.GetDataType();
  136. GeShape output_shape = output_tensor.GetShape();
  137. int64_t output_mem_size = 0;
  138. GE_CHK_STATUS_RET(TensorUtils::CalcTensorMemSize(output_shape, format, data_type, output_mem_size),
  139. "hccl calc tensor mem size failed.");
  140. output_mem_size = ((output_mem_size +
  141. MEMORY_ALIGN_RATIO * MEMORY_ALIGN_SIZE - 1) / MEMORY_ALIGN_SIZE) * MEMORY_ALIGN_SIZE;
  142. TensorUtils::SetSize(output_tensor, output_mem_size);
  143. GE_CHK_STATUS_RET(op_desc->UpdateOutputDesc(static_cast<uint32_t>(i), output_tensor),
  144. "hccl update output size failed.");
  145. GELOGD("%s output desc[%u], dim_size: %zu, mem_size: %ld.", node.GetName().c_str(), i,
  146. output_tensor.GetShape().GetDimNum(), output_mem_size);
  147. }
  148. return SUCCESS;
  149. }
  150. return OpsKernelBuilderManager::Instance().CalcOpRunningParam(node);
  151. }
  152. Status NodeExecutorManager::InitializeExecutors() {
  153. std::lock_guard<std::mutex> lk(mu_);
  154. if (executor_initialized_) {
  155. ++ref_count_;
  156. GELOGI("Executor is already initialized. add ref count to [%d]", ref_count_);
  157. return SUCCESS;
  158. }
  159. GELOGI("Start to Initialize NodeExecutors");
  160. for (auto &it : builders_) {
  161. auto engine_type = it.first;
  162. auto build_fn = it.second;
  163. GE_CHECK_NOTNULL(build_fn);
  164. auto executor = std::unique_ptr<NodeExecutor>(build_fn());
  165. if (executor == nullptr) {
  166. GELOGE(INTERNAL_ERROR, "Failed to create executor for engine type = %d", engine_type);
  167. return INTERNAL_ERROR;
  168. }
  169. GELOGD("Executor of engine type = %d was created successfully", engine_type);
  170. auto ret = executor->Initialize();
  171. if (ret != SUCCESS) {
  172. GELOGE(ret, "Failed to initialize NodeExecutor of type = %d, clear executors", engine_type);
  173. for (auto &executor_it : executors_) {
  174. executor_it.second->Finalize();
  175. }
  176. executors_.clear();
  177. return ret;
  178. }
  179. executors_.emplace(engine_type, std::move(executor));
  180. }
  181. ++ref_count_;
  182. executor_initialized_ = true;
  183. GELOGI("Initializing NodeExecutors successfully.");
  184. return SUCCESS;
  185. }
  186. void NodeExecutorManager::FinalizeExecutors() {
  187. std::lock_guard<std::mutex> lk(mu_);
  188. if (!executor_initialized_) {
  189. GELOGD("No need for finalizing for not initialized.");
  190. return;
  191. }
  192. if (--ref_count_ > 0) {
  193. GELOGD("Ref count = %d, do not finalize executors.", ref_count_);
  194. return;
  195. }
  196. GELOGD("Start to invoke Finalize on executors.");
  197. for (auto &it : executors_) {
  198. it.second->Finalize();
  199. }
  200. executors_.clear();
  201. executor_initialized_ = false;
  202. GELOGD("Done invoking Finalize successfully.");
  203. }
  204. NodeExecutorRegistrar::NodeExecutorRegistrar(NodeExecutorManager::ExecutorType executor_type,
  205. NodeExecutor *(*builder)()) {
  206. NodeExecutorManager::GetInstance().RegisterExecutorBuilder(executor_type, builder);
  207. }
  208. } // namespace hybrid
  209. } // 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