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hybrid_davinci_model.cc 6.8 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 <memory>
  17. #include "hybrid_davinci_model.h"
  18. #include "hybrid/model/hybrid_model.h"
  19. #include "hybrid/executor/hybrid_model_async_executor.h"
  20. #include "hybrid/node_executor/node_executor.h"
  21. namespace ge {
  22. namespace hybrid {
  23. class HybridDavinciModel::Impl {
  24. public:
  25. explicit Impl(GeRootModelPtr ge_model) : model_(std::move(ge_model)), executor_(&model_) {
  26. }
  27. ~Impl() {
  28. NodeExecutorManager::GetInstance().FinalizeExecutors();
  29. }
  30. Status Init() {
  31. GE_CHK_STATUS_RET(NodeExecutorManager::GetInstance().EnsureInitialized(), "Failed to initialize executors");
  32. GE_CHK_STATUS_RET(model_.Init(), "Failed to init model.")
  33. GE_CHK_STATUS_RET(executor_.Init(), "Failed to init model executor.")
  34. return SUCCESS;
  35. }
  36. Status Execute(const std::vector<DataBuffer> &inputs,
  37. const std::vector<GeTensorDesc> &input_desc,
  38. std::vector<DataBuffer> &outputs,
  39. std::vector<GeTensorDesc> &output_desc,
  40. rtStream_t stream) {
  41. return executor_.Execute(inputs, input_desc, outputs, output_desc);
  42. }
  43. Status Execute(const vector<GeTensor> &inputs, vector<GeTensor> &outputs) {
  44. return executor_.Execute(inputs, outputs);
  45. }
  46. Status ModelRunStart() {
  47. return executor_.Start(listener_);
  48. }
  49. Status ModelRunStop() {
  50. return executor_.Stop();
  51. }
  52. Status EnqueueData(const std::shared_ptr<InputDataWrapper> &data) {
  53. return executor_.EnqueueData(data);
  54. }
  55. void SetListener(const shared_ptr<ModelListener> &listener) {
  56. listener_ = listener;
  57. }
  58. void SetModelId(uint32_t model_id) {
  59. executor_.SetModelId(model_id);
  60. model_.SetModelId(model_id);
  61. }
  62. void SetDeviceId(uint32_t device_id) {
  63. model_.SetDeviceId(device_id);
  64. executor_.SetDeviceId(device_id);
  65. }
  66. uint64_t GetSessionId() {
  67. return model_.GetSessionId();
  68. }
  69. Status GetDynamicBatchInfo(std::vector<std::vector<int64_t>> &batch_info, int32_t &dynamic_type) {
  70. return model_.GetDynamicBatchInfo(batch_info, dynamic_type);
  71. }
  72. void GetUserDesignateShapeOrder(std::vector<std::string> &user_input_shape_order) {
  73. model_.GetUserDesignateShapeOrder(user_input_shape_order);
  74. }
  75. void GetModelAttr(std::vector<std::string> &dynamic_output_shape_info) {
  76. model_.GetModelAttr(dynamic_output_shape_info);
  77. }
  78. Status GetInputOutputDescInfo(vector<InputOutputDescInfo> &input_desc,
  79. vector<InputOutputDescInfo> &output_desc,
  80. std::vector<uint32_t> &input_formats,
  81. std::vector<uint32_t> &output_formats) {
  82. return model_.GetInputOutputDescInfo(input_desc, output_desc, input_formats, output_formats);
  83. }
  84. void SetModelDescVersion(bool is_new_model_desc) {
  85. model_.SetModelDescVersion(is_new_model_desc);
  86. }
  87. private:
  88. std::shared_ptr<ModelListener> listener_;
  89. HybridModel model_;
  90. HybridModelAsyncExecutor executor_;
  91. };
  92. HybridDavinciModel::~HybridDavinciModel() {
  93. delete impl_;
  94. }
  95. std::unique_ptr<HybridDavinciModel> HybridDavinciModel::Create(const GeRootModelPtr &ge_root_model) {
  96. auto instance = std::unique_ptr<HybridDavinciModel>(new (std::nothrow)HybridDavinciModel());
  97. if (instance != nullptr) {
  98. instance->impl_ = new (std::nothrow) HybridDavinciModel::Impl(ge_root_model);
  99. if (instance->impl_ != nullptr) {
  100. return instance;
  101. }
  102. }
  103. return nullptr;
  104. }
  105. Status HybridDavinciModel::Init() {
  106. GE_CHECK_NOTNULL(impl_);
  107. return impl_->Init();
  108. }
  109. Status HybridDavinciModel::Execute(const std::vector<DataBuffer> &inputs,
  110. const std::vector<GeTensorDesc> &input_desc,
  111. std::vector<DataBuffer> &outputs,
  112. std::vector<GeTensorDesc> &output_desc, rtStream_t stream) {
  113. GE_CHECK_NOTNULL(impl_);
  114. return impl_->Execute(inputs, input_desc, outputs, output_desc, stream);
  115. }
  116. Status HybridDavinciModel::Execute(const vector<GeTensor> &inputs, vector<GeTensor> &outputs) {
  117. GE_CHECK_NOTNULL(impl_);
  118. return impl_->Execute(inputs, outputs);
  119. }
  120. Status HybridDavinciModel::ModelRunStart() {
  121. GE_CHECK_NOTNULL(impl_);
  122. return impl_->ModelRunStart();
  123. }
  124. Status HybridDavinciModel::ModelRunStop() {
  125. GE_CHECK_NOTNULL(impl_);
  126. return impl_->ModelRunStop();
  127. }
  128. Status HybridDavinciModel::EnqueueData(const shared_ptr<InputDataWrapper> &data) {
  129. GE_CHECK_NOTNULL(impl_);
  130. return impl_->EnqueueData(data);
  131. }
  132. void HybridDavinciModel::SetListener(const shared_ptr<ModelListener> &listener) {
  133. if (impl_ != nullptr) {
  134. impl_->SetListener(listener);
  135. }
  136. }
  137. void HybridDavinciModel::SetModelId(uint32_t model_id) {
  138. if (impl_ != nullptr) {
  139. impl_->SetModelId(model_id);
  140. }
  141. }
  142. void HybridDavinciModel::SetDeviceId(uint32_t device_id) {
  143. if (impl_ != nullptr) {
  144. impl_->SetDeviceId(device_id);
  145. }
  146. }
  147. Status HybridDavinciModel::GetDynamicBatchInfo(std::vector<std::vector<int64_t>> &batch_info, int32_t &dynamic_type) {
  148. GE_CHECK_NOTNULL(impl_);
  149. return impl_->GetDynamicBatchInfo(batch_info, dynamic_type);
  150. }
  151. void HybridDavinciModel::GetUserDesignateShapeOrder(std::vector<std::string> &user_input_shape_order) {
  152. if (impl_ != nullptr) {
  153. impl_->GetUserDesignateShapeOrder(user_input_shape_order);
  154. }
  155. }
  156. void HybridDavinciModel::GetModelAttr(std::vector<std::string> &dynamic_output_shape_info) {
  157. if (impl_ != nullptr) {
  158. impl_->GetModelAttr(dynamic_output_shape_info);
  159. }
  160. }
  161. Status HybridDavinciModel::GetInputOutputDescInfo(vector<InputOutputDescInfo> &input_desc,
  162. vector<InputOutputDescInfo> &output_desc,
  163. std::vector<uint32_t> &input_formats,
  164. std::vector<uint32_t> &output_formats) {
  165. GE_CHECK_NOTNULL(impl_);
  166. return impl_->GetInputOutputDescInfo(input_desc, output_desc, input_formats, output_formats);
  167. }
  168. void HybridDavinciModel::SetModelDescVersion(bool is_new_model_desc) {
  169. if (impl_ != nullptr) {
  170. impl_->SetModelDescVersion(is_new_model_desc);
  171. }
  172. }
  173. uint64_t HybridDavinciModel::GetSessionId() {
  174. GE_CHECK_NOTNULL(impl_);
  175. return impl_->GetSessionId();
  176. }
  177. } // namespace hybrid
  178. } // 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