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single_op_model.cc 19 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 "single_op/single_op_model.h"
  17. #include <atomic>
  18. #include <memory>
  19. #include <string>
  20. #include <vector>
  21. #include "framework/common/debug/ge_log.h"
  22. #include "graph/debug/ge_attr_define.h"
  23. #include "graph/load/model_manager/model_utils.h"
  24. #include "graph/utils/attr_utils.h"
  25. #include "graph/utils/graph_utils.h"
  26. #include "graph/utils/tensor_utils.h"
  27. #include "runtime/rt.h"
  28. #include "task/aicpu_task_builder.h"
  29. #include "task/aicpu_kernel_task_builder.h"
  30. #include "task/tbe_task_builder.h"
  31. static std::atomic<std::uint64_t> aicpu_kernel_id(0);
  32. using domi::TaskDef;
  33. using std::unique_ptr;
  34. using std::vector;
  35. namespace ge {
  36. namespace {
  37. const size_t kDataOutputNum = 1;
  38. } // namespace
  39. SingleOpModel::SingleOpModel(const std::string &model_name, const void *model_data, uint32_t model_size)
  40. : model_name_(model_name), ori_model_data_(model_data), ori_model_size_(model_size) {}
  41. Status SingleOpModel::Init() {
  42. GE_CHK_STATUS_RET_NOLOG(InitModel());
  43. return LoadAllNodes();
  44. }
  45. Status SingleOpModel::InitModel() {
  46. ge::ModelData model;
  47. model.model_len = ori_model_size_;
  48. model.model_data = const_cast<void *>(ori_model_data_);
  49. auto ret = model_helper_.LoadModel(model);
  50. if (ret != SUCCESS) {
  51. GELOGE(ret, "LoadModel failed");
  52. return ret;
  53. }
  54. return SUCCESS;
  55. }
  56. void SingleOpModel::ParseOpModelParams(ModelHelper &model_helper, SingleOpModelParam &param) {
  57. int64_t value = 0;
  58. bool ret = false;
  59. std::shared_ptr<ge::GeModel> model = model_helper.GetGeModel();
  60. GE_CHECK_NOTNULL_JUST_RETURN(model);
  61. ret = ge::AttrUtils::GetInt(model, ATTR_MODEL_MEMORY_SIZE, value);
  62. param.memory_size = ret ? static_cast<uint64_t>(value) : 0;
  63. ret = ge::AttrUtils::GetInt(model, ATTR_MODEL_ZERO_COPY_MEMORY_SIZE, value);
  64. param.zero_copy_mem_size = ret ? static_cast<uint64_t>(value) : 0;
  65. ret = ge::AttrUtils::GetInt(model, ATTR_MODEL_WEIGHT_SIZE, value);
  66. param.weight_size = ret ? static_cast<uint64_t>(value) : 0;
  67. ret = ge::AttrUtils::GetInt(model, MODEL_ATTR_TASK_GEN_BASE_ADDR, value);
  68. param.base_addr = ret ? static_cast<uint64_t>(value) : 0;
  69. ret = ge::AttrUtils::GetInt(model, MODEL_ATTR_TASK_GEN_WEIGHT_ADDR, value);
  70. param.weight_addr = ret ? static_cast<uint64_t>(value) : 0;
  71. ret = ge::AttrUtils::GetInt(model, ATTR_MODEL_CORE_TYPE, value);
  72. param.core_type = ret ? value : 0;
  73. GELOGI("ParseOpModelParams(), total_memory_size:%lu, zero_copy_size:%lu, weight_size:%lu. core_type = %lu",
  74. param.memory_size, param.zero_copy_mem_size, param.weight_size, param.core_type);
  75. }
  76. Status SingleOpModel::InitModelMem(StreamResource &res) {
  77. ParseOpModelParams(model_helper_, model_params_);
  78. if (model_params_.memory_size > model_params_.zero_copy_mem_size) {
  79. const string purpose("malloc feature map memory on model execute.");
  80. GELOGI("total memory: %lu, zero_copy_mem: %lu", model_params_.memory_size, model_params_.zero_copy_mem_size);
  81. model_params_.mem_base =
  82. res.MallocMemory(purpose, model_params_.memory_size - model_params_.zero_copy_mem_size, false);
  83. if (model_params_.mem_base == nullptr) {
  84. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  85. }
  86. }
  87. if (model_params_.weight_size > 0 && has_weight_) {
  88. const string purpose("malloc weights memory on model execute.");
  89. model_params_.weight_base = res.MallocWeight(purpose, model_params_.weight_size);
  90. if (model_params_.weight_base == nullptr) {
  91. // no need to free memory, for that was handled by StreamResources
  92. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  93. }
  94. auto weight_buffer = model_helper_.GetGeModel()->GetWeight();
  95. GELOGI("To copy weight to device. weight size = %zu", weight_buffer.GetSize());
  96. GE_CHK_RT_RET(rtMemcpy(model_params_.weight_base,
  97. model_params_.weight_size,
  98. weight_buffer.GetData(),
  99. weight_buffer.GetSize(),
  100. RT_MEMCPY_HOST_TO_DEVICE));
  101. }
  102. return SUCCESS;
  103. }
  104. Status SingleOpModel::ParseInputNode(const OpDescPtr &op_desc) {
  105. vector<int64_t> offsets = op_desc->GetOutputOffset();
  106. if (offsets.size() != kDataOutputNum) {
  107. GELOGE(ACL_ERROR_GE_PARAM_INVALID,
  108. "Data op should have only one output, but got %zu", op_desc->GetOutputOffset().size());
  109. return ACL_ERROR_GE_PARAM_INVALID;
  110. }
  111. auto output_desc = op_desc->GetOutputDescPtr(0);
  112. GE_CHECK_NOTNULL(output_desc);
  113. int64_t tensor_size = 0;
  114. (void)TensorUtils::GetSize(*output_desc, tensor_size);
  115. input_offset_list_.emplace_back(offsets[0]);
  116. input_sizes_.emplace_back(tensor_size);
  117. GELOGI("[%s] parse input node: %s, size = %ld, offset = %u", model_name_.c_str(), op_desc->GetName().c_str(),
  118. tensor_size, static_cast<uint32_t>(offsets[0]));
  119. return SUCCESS;
  120. }
  121. void SingleOpModel::ParseOutputNode(const OpDescPtr &op_desc) {
  122. vector<int64_t> offsets = op_desc->GetInputOffset();
  123. for (uint32_t k = 0; k < static_cast<uint32_t>(offsets.size()); ++k) {
  124. auto input_desc = op_desc->GetInputDescPtr(k);
  125. if (input_desc == nullptr) {
  126. continue;
  127. }
  128. int64_t tensor_size = 0;
  129. (void)TensorUtils::GetSize(*input_desc, tensor_size);
  130. output_offset_list_.emplace_back(offsets[k]);
  131. output_sizes_.emplace_back(tensor_size);
  132. GELOGI("[%s] parse output node: %s, size = %ld, offset = %u", model_name_.c_str(), op_desc->GetName().c_str(),
  133. tensor_size, static_cast<uint32_t>(offsets[k]));
  134. }
  135. }
  136. Status SingleOpModel::LoadAllNodes() {
  137. auto ge_model = model_helper_.GetGeModel();
  138. GE_CHECK_NOTNULL(ge_model);
  139. Graph graph = ge_model->GetGraph();
  140. model_id_ = ge_model->GetModelId();
  141. auto compute_graph = GraphUtils::GetComputeGraph(graph);
  142. if (compute_graph == nullptr) {
  143. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "[%s] compute_graph is null", model_name_.c_str());
  144. return ACL_ERROR_GE_INTERNAL_ERROR;
  145. }
  146. auto nodes = compute_graph->GetDirectNode();
  147. size_t model_op_size = nodes.size();
  148. GELOGI("[%s] node size = %zu", model_name_.c_str(), model_op_size);
  149. for (size_t i = 0; i < model_op_size; ++i) {
  150. auto node = nodes.at(i);
  151. auto op_desc = node->GetOpDesc();
  152. GE_CHECK_NOTNULL(op_desc);
  153. op_list_[i] = node;
  154. auto op_type = op_desc->GetType();
  155. GELOGI("[%s] node[%zu] = %s, type = %s", model_name_.c_str(), i, node->GetName().c_str(), op_type.c_str());
  156. if (op_type == DATA_TYPE || op_type == AIPP_DATA_TYPE) {
  157. data_ops_.emplace_back(op_desc);
  158. continue;
  159. }
  160. if (op_type == CONSTANT || op_type == CONSTANTOP) {
  161. has_weight_ = true;
  162. continue;
  163. }
  164. if (op_type == NETOUTPUT) {
  165. netoutput_op_ = op_desc;
  166. continue;
  167. }
  168. ge_model->GetTBEKernelStore().LoadTBEKernelBinToOpDesc(op_desc);
  169. ge_model->GetCustAICPUKernelStore().LoadCustAICPUKernelBinToOpDesc(op_desc);
  170. }
  171. return SUCCESS;
  172. }
  173. Status SingleOpModel::ParseInputsAndOutputs() {
  174. for (auto &op_desc : data_ops_) {
  175. GE_CHK_STATUS_RET_NOLOG(ParseInputNode(op_desc));
  176. }
  177. ParseOutputNode(netoutput_op_);
  178. return SUCCESS;
  179. }
  180. Status SingleOpModel::SetInputsAndOutputs(SingleOp &single_op) {
  181. int arg_index = 0;
  182. for (size_t i = 0; i < input_offset_list_.size(); ++i) {
  183. auto *addr = model_params_.mem_base + input_offset_list_[i];
  184. model_params_.addr_mapping_.emplace(reinterpret_cast<uintptr_t>(addr), arg_index++);
  185. single_op.input_sizes_.emplace_back(input_sizes_[i]);
  186. single_op.input_addr_list_.emplace_back(addr);
  187. }
  188. for (size_t i = 0; i < output_offset_list_.size(); ++i) {
  189. auto *addr = model_params_.mem_base + output_offset_list_[i];
  190. model_params_.addr_mapping_.emplace(reinterpret_cast<uintptr_t>(addr), arg_index++);
  191. single_op.output_sizes_.emplace_back(output_sizes_[i]);
  192. single_op.output_addr_list_.emplace_back(addr);
  193. }
  194. single_op.args_.resize(arg_index);
  195. return SUCCESS;
  196. }
  197. Status SingleOpModel::BuildTaskList(StreamResource *stream_resource, SingleOp &single_op) {
  198. auto ge_model = model_helper_.GetGeModel();
  199. GE_CHECK_NOTNULL(ge_model);
  200. single_op.arg_table_.resize(single_op.input_sizes_.size() + single_op.output_sizes_.size());
  201. auto tasks = ge_model->GetModelTaskDefPtr()->task();
  202. for (int i = 0; i < tasks.size(); ++i) {
  203. const TaskDef &task_def = tasks[i];
  204. GELOGI("[%s] Task[%d], type = %u, DebugString = %s", model_name_.c_str(), i, task_def.type(),
  205. task_def.DebugString().c_str());
  206. auto task_type = static_cast<rtModelTaskType_t>(task_def.type());
  207. if (task_type == RT_MODEL_TASK_KERNEL) {
  208. const domi::KernelDef &kernel_def = task_def.kernel();
  209. const auto &context = kernel_def.context();
  210. auto kernel_type = static_cast<ccKernelType>(context.kernel_type());
  211. if (kernel_type == ccKernelType::TE) {
  212. GELOGD("Building TBE task");
  213. TbeOpTask *tbe_task = nullptr;
  214. auto ret = BuildKernelTask(task_def.kernel(), &tbe_task);
  215. if (ret != SUCCESS) {
  216. return ret;
  217. }
  218. ParseArgTable(tbe_task, single_op);
  219. tbe_task->SetModelArgs(model_name_, model_id_);
  220. if (tbe_task->tiling_buffer_ != nullptr) {
  221. tbe_task->stream_resource_ = stream_resource;
  222. }
  223. single_op.tasks_.emplace_back(tbe_task);
  224. } else if (kernel_type == ccKernelType::AI_CPU || kernel_type == ccKernelType::CUST_AI_CPU) {
  225. GELOGD("Building AICPU_CC task");
  226. OpTask *task = nullptr;
  227. uint64_t singleop_kernel_id = aicpu_kernel_id++;
  228. GELOGI("Build singleOp CCTask, kernel_id = %lu", singleop_kernel_id);
  229. auto ret = BuildCpuKernelTask(task_def.kernel(), &task, singleop_kernel_id);
  230. if (ret != SUCCESS) {
  231. return ret;
  232. }
  233. task->SetModelArgs(model_name_, model_id_);
  234. ParseArgTable(task, single_op);
  235. single_op.tasks_.emplace_back(task);
  236. } else {
  237. GELOGE(ACL_ERROR_GE_OP_KERNEL_TYPE_INVALID,
  238. "Only TBE, AI_CPU, CUST_AI_CPU kernel are supported, but got %u", context.kernel_type());
  239. return ACL_ERROR_GE_OP_KERNEL_TYPE_INVALID;
  240. }
  241. } else if (task_type == RT_MODEL_TASK_KERNEL_EX) {
  242. GELOGD("Building AICPU_TF task");
  243. AiCpuTask *aicpu_task = nullptr;
  244. bool depend_compute_flag = false;
  245. uint64_t singleop_kernel_id = aicpu_kernel_id++;
  246. GELOGI("Build singleOp TfTask, kernel_id = %lu", singleop_kernel_id);
  247. auto ret = BuildKernelExTask(task_def.kernel_ex(), &aicpu_task, false, depend_compute_flag, singleop_kernel_id);
  248. if (ret != SUCCESS) {
  249. return ret;
  250. }
  251. aicpu_task->SetModelArgs(model_name_, model_id_);
  252. ParseArgTable(aicpu_task, single_op);
  253. single_op.tasks_.emplace_back(aicpu_task);
  254. } else {
  255. // skip
  256. GELOGD("Skip task type: %d", static_cast<int>(task_type));
  257. }
  258. }
  259. return SUCCESS;
  260. }
  261. void SingleOpModel::ParseArgTable(OpTask *task, SingleOp &op) {
  262. if (task == nullptr) {
  263. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "tbe op task is nullptr");
  264. return;
  265. }
  266. // args: addr1, addr2, addr3 ...
  267. uintptr_t *arg_base = nullptr;
  268. size_t arg_num = 0;
  269. task->GetIoAddr(arg_base, arg_num);
  270. for (size_t i = 0; i < arg_num; ++i) {
  271. uintptr_t *ptr_to_addr = arg_base + i;
  272. uintptr_t addr = *ptr_to_addr;
  273. auto iter = model_params_.addr_mapping_.find(addr);
  274. if (iter != model_params_.addr_mapping_.end()) {
  275. int arg_index = iter->second;
  276. GELOGI("%s args[%zu] mapped to user designated args[%d]", task->GetOpdesc()->GetName().c_str(), i, arg_index);
  277. op.arg_table_[iter->second].emplace_back(ptr_to_addr);
  278. }
  279. }
  280. }
  281. Status SingleOpModel::BuildKernelTask(const domi::KernelDef &kernel_def, TbeOpTask **task) {
  282. GE_CHECK_NOTNULL(task);
  283. const auto &context = kernel_def.context();
  284. auto iter = op_list_.find(context.op_index());
  285. if (iter == op_list_.end()) {
  286. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "op desc not found. op index = %u", context.op_index());
  287. return ACL_ERROR_GE_INTERNAL_ERROR;
  288. }
  289. auto *tbe_task = new (std::nothrow) TbeOpTask();
  290. if (tbe_task == nullptr) {
  291. GELOGE(ACL_ERROR_GE_MEMORY_ALLOCATION, "create tbe op task failed");
  292. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  293. }
  294. auto builder = TbeTaskBuilder(model_name_, iter->second, kernel_def);
  295. auto ret = builder.BuildTask(*tbe_task, model_params_);
  296. if (ret != SUCCESS) {
  297. delete tbe_task;
  298. tbe_task = nullptr;
  299. return ret;
  300. }
  301. *task = tbe_task;
  302. return SUCCESS;
  303. }
  304. Status SingleOpModel::BuildKernelExTask(const domi::KernelExDef &kernel_def, AiCpuTask **task,
  305. bool dynamic_flag, bool& depend_compute_flag, uint64_t kernel_id) {
  306. auto iter = op_list_.find(kernel_def.op_index());
  307. if (iter == op_list_.end()) {
  308. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "op desc not found. op index = %u", kernel_def.op_index());
  309. return ACL_ERROR_GE_INTERNAL_ERROR;
  310. }
  311. std::unique_ptr<AiCpuTask> aicpu_task(new (std::nothrow) AiCpuTask());
  312. if (aicpu_task == nullptr) {
  313. GELOGE(ACL_ERROR_GE_MEMORY_ALLOCATION, "create aicpu_TF op task failed");
  314. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  315. }
  316. auto builder = AiCpuTaskBuilder(iter->second->GetOpDesc(), kernel_def);
  317. auto ret = builder.BuildTask(*aicpu_task, model_params_, dynamic_flag, kernel_id);
  318. if (ret != SUCCESS) {
  319. GELOGE(ret, "build aicpu_TF op task failed");
  320. return ret;
  321. }
  322. depend_compute_flag = (aicpu_task->GetUnknownType() == DEPEND_COMPUTE);
  323. *task = aicpu_task.release();
  324. return SUCCESS;
  325. }
  326. Status SingleOpModel::BuildCpuKernelTask(const domi::KernelDef &kernel_def, OpTask **task, uint64_t kernel_id) {
  327. const auto &context = kernel_def.context();
  328. auto iter = op_list_.find(context.op_index());
  329. if (iter == op_list_.end()) {
  330. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "op desc not found. op index = %u", context.op_index());
  331. return ACL_ERROR_GE_INTERNAL_ERROR;
  332. }
  333. std::unique_ptr<AiCpuCCTask> aicpucc_task(new (std::nothrow) AiCpuCCTask());
  334. if (aicpucc_task == nullptr) {
  335. GELOGE(ACL_ERROR_GE_MEMORY_ALLOCATION, "create aicpu_CC op task failed");
  336. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  337. }
  338. auto builder = AiCpuCCTaskBuilder(iter->second->GetOpDesc(), kernel_def);
  339. auto ret = builder.BuildTask(*aicpucc_task, kernel_id, model_params_);
  340. if (ret != SUCCESS) {
  341. GELOGE(ret, "build aicpu_CC op task failed");
  342. return ret;
  343. }
  344. *task = aicpucc_task.release();
  345. return SUCCESS;
  346. }
  347. Status SingleOpModel::BuildOp(StreamResource &resource, SingleOp &single_op) {
  348. GE_CHK_STATUS_RET_NOLOG(ParseInputsAndOutputs());
  349. GE_CHK_STATUS_RET_NOLOG(InitModelMem(resource));
  350. single_op.running_param_.reset(new (std::nothrow)SingleOpModelParam(model_params_));
  351. GE_CHECK_NOTNULL(single_op.running_param_);
  352. GE_CHK_STATUS_RET_NOLOG(SetInputsAndOutputs(single_op));
  353. return BuildTaskList(&resource, single_op);
  354. }
  355. Status SingleOpModel::BuildModelTaskKernel(const TaskDef &task_def, DynamicSingleOp &single_op) {
  356. const domi::KernelDef &kernel_def = task_def.kernel();
  357. const auto &context = kernel_def.context();
  358. auto kernel_type = static_cast<ccKernelType>(context.kernel_type());
  359. if (kernel_type == ccKernelType::TE) {
  360. GELOGD("Building TBE task");
  361. TbeOpTask *tbe_task = nullptr;
  362. GE_CHK_STATUS_RET_NOLOG(BuildKernelTask(task_def.kernel(), &tbe_task));
  363. tbe_task->SetModelArgs(model_name_, model_id_);
  364. single_op.op_task_.reset(tbe_task);
  365. } else if (kernel_type == ccKernelType::AI_CPU || kernel_type == ccKernelType::CUST_AI_CPU) {
  366. GELOGD("Building AICPU_CC task");
  367. OpTask *task = nullptr;
  368. uint64_t dynamic_singleop_kernel_id = aicpu_kernel_id++;
  369. GELOGI("Build dynamic singleOp CCTask, kernel_id = %lu", dynamic_singleop_kernel_id);
  370. GE_CHK_STATUS_RET_NOLOG(BuildCpuKernelTask(task_def.kernel(), &task, dynamic_singleop_kernel_id));
  371. task->SetModelArgs(model_name_, model_id_);
  372. single_op.op_task_.reset(task);
  373. } else {
  374. GELOGE(ACL_ERROR_GE_OP_KERNEL_TYPE_INVALID,
  375. "Only TBE, AI_CPU, CUST_AI_CPU kernel are supported, but got %u", context.kernel_type());
  376. return ACL_ERROR_GE_OP_KERNEL_TYPE_INVALID;
  377. }
  378. return SUCCESS;
  379. }
  380. Status SingleOpModel::BuildTaskListForDynamicOp(DynamicSingleOp &single_op) {
  381. auto ge_model = model_helper_.GetGeModel();
  382. GE_CHECK_NOTNULL(ge_model);
  383. auto tasks = ge_model->GetModelTaskDefPtr()->task();
  384. for (int i = 0; i < tasks.size(); ++i) {
  385. const TaskDef &task_def = tasks[i];
  386. GELOGI("[%s] Task[%d], type = %u, DebugString = %s", model_name_.c_str(), i, task_def.type(),
  387. task_def.DebugString().c_str());
  388. auto task_type = static_cast<rtModelTaskType_t>(task_def.type());
  389. if (task_type == RT_MODEL_TASK_KERNEL) {
  390. if (single_op.op_task_ != nullptr) {
  391. GELOGE(ACL_ERROR_GE_OP_TASK_TYPE_INVALID, "Do not support dynamic op with multiple tasks.");
  392. return ACL_ERROR_GE_OP_TASK_TYPE_INVALID;
  393. }
  394. GE_CHK_STATUS_RET_NOLOG(BuildModelTaskKernel(task_def, single_op));
  395. } else if (task_type == RT_MODEL_TASK_KERNEL_EX) {
  396. if (single_op.op_task_ != nullptr) {
  397. GELOGE(ACL_ERROR_GE_OP_TASK_TYPE_INVALID, "Do not support dynamic op with multiple tasks.");
  398. return ACL_ERROR_GE_OP_TASK_TYPE_INVALID;
  399. }
  400. GELOGD("Building AICPU_TF task");
  401. AiCpuTask *aicpu_task = nullptr;
  402. bool depend_compute_flag = false;
  403. uint64_t dynamic_singleop_kernel_id = aicpu_kernel_id++;
  404. GELOGI("Build dynamic singleOp TfTask, kernel_id = %lu", dynamic_singleop_kernel_id);
  405. GE_CHK_STATUS_RET_NOLOG(BuildKernelExTask(task_def.kernel_ex(), &aicpu_task, true,
  406. depend_compute_flag, dynamic_singleop_kernel_id));
  407. if (depend_compute_flag) {
  408. if (i >= tasks.size() - 1) {
  409. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "The copy task of the fourth operator was not found.");
  410. return ACL_ERROR_GE_PARAM_INVALID;
  411. }
  412. ++i;
  413. const TaskDef &copy_task_def = tasks[i];
  414. GE_CHK_STATUS_RET_NOLOG(aicpu_task->SetMemCopyTask(copy_task_def.kernel_ex()));
  415. }
  416. aicpu_task->SetModelArgs(model_name_, model_id_);
  417. single_op.op_task_.reset(aicpu_task);
  418. } else {
  419. // skip
  420. GELOGD("Skip task type: %d", static_cast<int>(task_type));
  421. }
  422. }
  423. return SUCCESS;
  424. }
  425. Status SingleOpModel::BuildDynamicOp(StreamResource &resource, DynamicSingleOp &single_op) {
  426. single_op.num_inputs_ = data_ops_.size();
  427. single_op.num_outputs_ = netoutput_op_->GetAllInputsSize();
  428. GE_CHK_STATUS_RET_NOLOG(InitModelMem(resource));
  429. model_params_.memory_size = UINT_MAX;
  430. return BuildTaskListForDynamicOp(single_op);
  431. }
  432. } // 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