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single_op_model.cc 18 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/new_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_sessionid(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 = res.MallocMemory(purpose, model_params_.memory_size - model_params_.zero_copy_mem_size);
  82. if (model_params_.mem_base == nullptr) {
  83. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  84. }
  85. }
  86. if (model_params_.weight_size > 0 && has_weight_) {
  87. const string purpose("malloc weights memory on model execute.");
  88. model_params_.weight_base = res.MallocWeight(purpose, model_params_.weight_size);
  89. if (model_params_.weight_base == nullptr) {
  90. // no need to free memory, for that was handled by StreamResources
  91. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  92. }
  93. auto weight_buffer = model_helper_.GetGeModel()->GetWeight();
  94. GELOGI("To copy weight to device. weight size = %zu", weight_buffer.GetSize());
  95. GE_CHK_RT_RET(rtMemcpy(model_params_.weight_base,
  96. model_params_.weight_size,
  97. weight_buffer.GetData(),
  98. weight_buffer.GetSize(),
  99. RT_MEMCPY_HOST_TO_DEVICE));
  100. }
  101. return SUCCESS;
  102. }
  103. Status SingleOpModel::ParseInputNode(const OpDescPtr &op_desc) {
  104. vector<int64_t> offsets = op_desc->GetOutputOffset();
  105. if (offsets.size() != kDataOutputNum) {
  106. GELOGE(ACL_ERROR_GE_PARAM_INVALID,
  107. "Data op should have only one output, but got %zu", op_desc->GetOutputOffset().size());
  108. return ACL_ERROR_GE_PARAM_INVALID;
  109. }
  110. auto output_desc = op_desc->GetOutputDescPtr(0);
  111. GE_CHECK_NOTNULL(output_desc);
  112. int64_t tensor_size = 0;
  113. (void)TensorUtils::GetSize(*output_desc, tensor_size);
  114. input_offset_list_.emplace_back(offsets[0]);
  115. input_sizes_.emplace_back(tensor_size);
  116. GELOGI("[%s] parse input node: %s, size = %ld, offset = %u", model_name_.c_str(), op_desc->GetName().c_str(),
  117. tensor_size, static_cast<uint32_t>(offsets[0]));
  118. return SUCCESS;
  119. }
  120. void SingleOpModel::ParseOutputNode(const OpDescPtr &op_desc) {
  121. vector<int64_t> offsets = op_desc->GetInputOffset();
  122. for (uint32_t k = 0; k < static_cast<uint32_t>(offsets.size()); ++k) {
  123. auto input_desc = op_desc->GetInputDescPtr(k);
  124. if (input_desc == nullptr) {
  125. continue;
  126. }
  127. int64_t tensor_size = 0;
  128. (void)TensorUtils::GetSize(*input_desc, tensor_size);
  129. output_offset_list_.emplace_back(offsets[k]);
  130. output_sizes_.emplace_back(tensor_size);
  131. GELOGI("[%s] parse output node: %s, size = %ld, offset = %u", model_name_.c_str(), op_desc->GetName().c_str(),
  132. tensor_size, static_cast<uint32_t>(offsets[k]));
  133. }
  134. }
  135. Status SingleOpModel::LoadAllNodes() {
  136. auto ge_model = model_helper_.GetGeModel();
  137. GE_CHECK_NOTNULL(ge_model);
  138. Graph graph = ge_model->GetGraph();
  139. auto compute_graph = GraphUtils::GetComputeGraph(graph);
  140. if (compute_graph == nullptr) {
  141. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "[%s] compute_graph is null", model_name_.c_str());
  142. return ACL_ERROR_GE_INTERNAL_ERROR;
  143. }
  144. auto nodes = compute_graph->GetDirectNode();
  145. size_t model_op_size = nodes.size();
  146. GELOGI("[%s] node size = %zu", model_name_.c_str(), model_op_size);
  147. for (size_t i = 0; i < model_op_size; ++i) {
  148. auto node = nodes.at(i);
  149. auto op_desc = node->GetOpDesc();
  150. GE_CHECK_NOTNULL(op_desc);
  151. op_list_[i] = node;
  152. auto op_type = op_desc->GetType();
  153. GELOGI("[%s] node[%zu] = %s, type = %s", model_name_.c_str(), i, node->GetName().c_str(), op_type.c_str());
  154. if (op_type == DATA_TYPE || op_type == AIPP_DATA_TYPE) {
  155. data_ops_.emplace_back(op_desc);
  156. continue;
  157. }
  158. if (op_type == CONSTANT || op_type == CONSTANTOP) {
  159. has_weight_ = true;
  160. continue;
  161. }
  162. if (op_type == NETOUTPUT) {
  163. netoutput_op_ = op_desc;
  164. continue;
  165. }
  166. ge_model->GetTBEKernelStore().LoadTBEKernelBinToOpDesc(op_desc);
  167. ge_model->GetCustAICPUKernelStore().LoadCustAICPUKernelBinToOpDesc(op_desc);
  168. }
  169. return SUCCESS;
  170. }
  171. Status SingleOpModel::ParseInputsAndOutputs() {
  172. for (auto &op_desc : data_ops_) {
  173. GE_CHK_STATUS_RET_NOLOG(ParseInputNode(op_desc));
  174. }
  175. ParseOutputNode(netoutput_op_);
  176. return SUCCESS;
  177. }
  178. Status SingleOpModel::SetInputsAndOutputs(SingleOp &single_op) {
  179. int arg_index = 0;
  180. for (size_t i = 0; i < input_offset_list_.size(); ++i) {
  181. auto *addr = model_params_.mem_base + input_offset_list_[i];
  182. model_params_.addr_mapping_.emplace(reinterpret_cast<uintptr_t>(addr), arg_index++);
  183. single_op.input_sizes_.emplace_back(input_sizes_[i]);
  184. single_op.input_addr_list_.emplace_back(addr);
  185. }
  186. for (size_t i = 0; i < output_offset_list_.size(); ++i) {
  187. auto *addr = model_params_.mem_base + output_offset_list_[i];
  188. model_params_.addr_mapping_.emplace(reinterpret_cast<uintptr_t>(addr), arg_index++);
  189. single_op.output_sizes_.emplace_back(output_sizes_[i]);
  190. single_op.output_addr_list_.emplace_back(addr);
  191. }
  192. single_op.args_.resize(arg_index);
  193. return SUCCESS;
  194. }
  195. Status SingleOpModel::BuildTaskList(SingleOp &single_op) {
  196. auto ge_model = model_helper_.GetGeModel();
  197. GE_CHECK_NOTNULL(ge_model);
  198. auto tasks = ge_model->GetModelTaskDefPtr()->task();
  199. for (int i = 0; i < tasks.size(); ++i) {
  200. const TaskDef &task_def = tasks[i];
  201. GELOGI("[%s] Task[%d], type = %u, DebugString = %s", model_name_.c_str(), i, task_def.type(),
  202. task_def.DebugString().c_str());
  203. auto task_type = static_cast<rtModelTaskType_t>(task_def.type());
  204. if (task_type == RT_MODEL_TASK_KERNEL) {
  205. const domi::KernelDef &kernel_def = task_def.kernel();
  206. const auto &context = kernel_def.context();
  207. auto kernel_type = static_cast<cce::ccKernelType>(context.kernel_type());
  208. if (kernel_type == cce::ccKernelType::TE) {
  209. GELOGD("Building TBE task");
  210. TbeOpTask *tbe_task = nullptr;
  211. auto ret = BuildKernelTask(task_def.kernel(), &tbe_task);
  212. if (ret != SUCCESS) {
  213. return ret;
  214. }
  215. single_op.arg_table_.resize(single_op.input_sizes_.size() + single_op.output_sizes_.size());
  216. ParseArgTable(tbe_task, single_op);
  217. single_op.tasks_.emplace_back(tbe_task);
  218. } else if (kernel_type == cce::ccKernelType::AI_CPU || kernel_type == cce::ccKernelType::CUST_AI_CPU) {
  219. GELOGD("Building AICPU_CC task");
  220. OpTask *task = nullptr;
  221. auto ret = BuildCpuKernelTask(task_def.kernel(), &task);
  222. if (ret != SUCCESS) {
  223. return ret;
  224. }
  225. single_op.tasks_.emplace_back(task);
  226. } else {
  227. GELOGE(ACL_ERROR_GE_OP_KERNEL_TYPE_INVALID, "Only TBE, AI_CPU, CUST_AI_CPU kernel are supported, but got %u", context.kernel_type());
  228. return ACL_ERROR_GE_OP_KERNEL_TYPE_INVALID;
  229. }
  230. } else if (task_type == RT_MODEL_TASK_KERNEL_EX) {
  231. GELOGD("Building AICPU_TF task");
  232. AiCpuTask *aicpu_task = nullptr;
  233. bool depend_compute_flag = false;
  234. uint64_t singleop_sessionid = aicpu_sessionid++;
  235. GELOGI("Build singleOp, sessionId = %lu", singleop_sessionid);
  236. auto ret = BuildKernelExTask(task_def.kernel_ex(), &aicpu_task, false, depend_compute_flag, singleop_sessionid);
  237. if (ret != SUCCESS) {
  238. return ret;
  239. }
  240. single_op.tasks_.emplace_back(aicpu_task);
  241. single_op.SetSessionID(singleop_sessionid);
  242. } else {
  243. // skip
  244. GELOGD("Skip task type: %d", static_cast<int>(task_type));
  245. }
  246. }
  247. return SUCCESS;
  248. }
  249. void SingleOpModel::ParseArgTable(TbeOpTask *task, SingleOp &op) {
  250. if (task == nullptr) {
  251. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "tbe op task is nullptr");
  252. return;
  253. }
  254. // args: addr1, addr2, addr3 ...
  255. auto *args = const_cast<uintptr_t *>(reinterpret_cast<const uintptr_t *>(task->GetArgs()));
  256. size_t arg_size = task->GetArgSize();
  257. for (size_t i = 0; i < arg_size / sizeof(void *); ++i) {
  258. uintptr_t *ptr_to_addr = args + i;
  259. uintptr_t addr = *ptr_to_addr;
  260. auto iter = model_params_.addr_mapping_.find(addr);
  261. if (iter != model_params_.addr_mapping_.end()) {
  262. int arg_index = iter->second;
  263. GELOGI("%s args[%zu] mapped to user designated args[%d]", task->GetStubName().c_str(), i, arg_index);
  264. op.arg_table_[iter->second].emplace_back(ptr_to_addr);
  265. }
  266. }
  267. }
  268. Status SingleOpModel::BuildKernelTask(const domi::KernelDef &kernel_def, TbeOpTask **task) {
  269. GE_CHECK_NOTNULL(task);
  270. const auto &context = kernel_def.context();
  271. auto iter = op_list_.find(context.op_index());
  272. if (iter == op_list_.end()) {
  273. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "op desc not found. op index = %u", context.op_index());
  274. return ACL_ERROR_GE_INTERNAL_ERROR;
  275. }
  276. auto *tbe_task = new (std::nothrow) TbeOpTask();
  277. if (tbe_task == nullptr) {
  278. GELOGE(ACL_ERROR_GE_MEMORY_ALLOCATION, "create tbe op task failed");
  279. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  280. }
  281. auto builder = TbeTaskBuilder(model_name_, iter->second, kernel_def);
  282. auto ret = builder.BuildTask(*tbe_task, model_params_);
  283. if (ret != SUCCESS) {
  284. delete tbe_task;
  285. tbe_task = nullptr;
  286. return ret;
  287. }
  288. *task = tbe_task;
  289. return SUCCESS;
  290. }
  291. Status SingleOpModel::BuildKernelExTask(const domi::KernelExDef &kernel_def, AiCpuTask **task,
  292. bool dynamic_flag, bool& depend_compute_flag, uint64_t session_id) {
  293. auto iter = op_list_.find(kernel_def.op_index());
  294. if (iter == op_list_.end()) {
  295. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "op desc not found. op index = %u", kernel_def.op_index());
  296. return ACL_ERROR_GE_INTERNAL_ERROR;
  297. }
  298. std::unique_ptr<AiCpuTask> aicpu_task(new (std::nothrow) AiCpuTask());
  299. if (aicpu_task == nullptr) {
  300. GELOGE(ACL_ERROR_GE_MEMORY_ALLOCATION, "create aicpu_TF op task failed");
  301. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  302. }
  303. auto builder = AiCpuTaskBuilder(iter->second->GetOpDesc(), kernel_def);
  304. auto ret = builder.BuildTask(*aicpu_task, model_params_, dynamic_flag, session_id);
  305. if (ret != SUCCESS) {
  306. GELOGE(ret, "build aicpu_TF op task failed");
  307. return ret;
  308. }
  309. depend_compute_flag = (aicpu_task->GetUnknownType() == DEPEND_COMPUTE);
  310. *task = aicpu_task.release();
  311. return SUCCESS;
  312. }
  313. Status SingleOpModel::BuildCpuKernelTask(const domi::KernelDef &kernel_def, OpTask **task) {
  314. const auto &context = kernel_def.context();
  315. auto iter = op_list_.find(context.op_index());
  316. if (iter == op_list_.end()) {
  317. GELOGE(ACL_ERROR_GE_INTERNAL_ERROR, "op desc not found. op index = %u", context.op_index());
  318. return ACL_ERROR_GE_INTERNAL_ERROR;
  319. }
  320. std::unique_ptr<AiCpuCCTask> aicpucc_task(new (std::nothrow) AiCpuCCTask());
  321. if (aicpucc_task == nullptr) {
  322. GELOGE(ACL_ERROR_GE_MEMORY_ALLOCATION, "create aicpu_CC op task failed");
  323. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  324. }
  325. auto builder = AiCpuCCTaskBuilder(iter->second->GetOpDesc(), kernel_def);
  326. auto ret = builder.BuildTask(*aicpucc_task);
  327. if (ret != SUCCESS) {
  328. GELOGE(ret, "build aicpu_CC op task failed");
  329. return ret;
  330. }
  331. *task = aicpucc_task.release();
  332. return SUCCESS;
  333. }
  334. Status SingleOpModel::BuildOp(StreamResource &resource, SingleOp &single_op) {
  335. GE_CHK_STATUS_RET_NOLOG(ParseInputsAndOutputs());
  336. GE_CHK_STATUS_RET_NOLOG(InitModelMem(resource));
  337. GE_CHK_STATUS_RET_NOLOG(SetInputsAndOutputs(single_op));
  338. return BuildTaskList(single_op);
  339. }
  340. Status SingleOpModel::BuildModelTaskKernel(const TaskDef &task_def, DynamicSingleOp &single_op) {
  341. const domi::KernelDef &kernel_def = task_def.kernel();
  342. const auto &context = kernel_def.context();
  343. auto kernel_type = static_cast<cce::ccKernelType>(context.kernel_type());
  344. if (kernel_type == cce::ccKernelType::TE) {
  345. GELOGD("Building TBE task");
  346. TbeOpTask *tbe_task = nullptr;
  347. GE_CHK_STATUS_RET_NOLOG(BuildKernelTask(task_def.kernel(), &tbe_task));
  348. single_op.op_task_.reset(tbe_task);
  349. } else if (kernel_type == cce::ccKernelType::AI_CPU || kernel_type == cce::ccKernelType::CUST_AI_CPU) {
  350. GELOGD("Building AICPU_CC task");
  351. OpTask *task = nullptr;
  352. GE_CHK_STATUS_RET_NOLOG(BuildCpuKernelTask(task_def.kernel(), &task));
  353. single_op.op_task_.reset(task);
  354. } else {
  355. GELOGE(ACL_ERROR_GE_OP_KERNEL_TYPE_INVALID,
  356. "Only TBE, AI_CPU, CUST_AI_CPU kernel are supported, but got %u", context.kernel_type());
  357. return ACL_ERROR_GE_OP_KERNEL_TYPE_INVALID;
  358. }
  359. return SUCCESS;
  360. }
  361. Status SingleOpModel::BuildTaskListForDynamicOp(DynamicSingleOp &single_op) {
  362. auto ge_model = model_helper_.GetGeModel();
  363. GE_CHECK_NOTNULL(ge_model);
  364. auto tasks = ge_model->GetModelTaskDefPtr()->task();
  365. for (int i = 0; i < tasks.size(); ++i) {
  366. const TaskDef &task_def = tasks[i];
  367. GELOGI("[%s] Task[%d], type = %u, DebugString = %s", model_name_.c_str(), i, task_def.type(),
  368. task_def.DebugString().c_str());
  369. auto task_type = static_cast<rtModelTaskType_t>(task_def.type());
  370. if (task_type == RT_MODEL_TASK_KERNEL) {
  371. if (single_op.op_task_ != nullptr) {
  372. GELOGE(UNSUPPORTED, "Do not support dynamic op with multiple tasks.");
  373. return UNSUPPORTED;
  374. }
  375. GE_CHK_STATUS_RET_NOLOG(BuildModelTaskKernel(task_def, single_op));
  376. } else if (task_type == RT_MODEL_TASK_KERNEL_EX) {
  377. if (single_op.op_task_ != nullptr) {
  378. GELOGE(ACL_ERROR_GE_OP_TASK_TYPE_INVALID, "Do not support dynamic op with multiple tasks.");
  379. return ACL_ERROR_GE_OP_TASK_TYPE_INVALID;
  380. }
  381. GELOGD("Building AICPU_TF task");
  382. AiCpuTask *aicpu_task = nullptr;
  383. bool depend_compute_flag = false;
  384. uint64_t dynamic_singleop_sessionid = aicpu_sessionid++;
  385. GELOGI("Build dynamic singleOp, sessionId = %lu", dynamic_singleop_sessionid);
  386. GE_CHK_STATUS_RET_NOLOG(BuildKernelExTask(task_def.kernel_ex(), &aicpu_task, true,
  387. depend_compute_flag, dynamic_singleop_sessionid));
  388. if (depend_compute_flag) {
  389. if (i >= tasks.size() - 1) {
  390. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "The copy task of the fourth operator was not found.");
  391. return ACL_ERROR_GE_PARAM_INVALID;
  392. }
  393. ++i;
  394. const TaskDef &copy_task_def = tasks[i];
  395. GE_CHK_STATUS_RET_NOLOG(aicpu_task->SetMemCopyTask(copy_task_def.kernel_ex()));
  396. }
  397. single_op.op_task_.reset(aicpu_task);
  398. single_op.SetSessionID(dynamic_singleop_sessionid);
  399. } else {
  400. // skip
  401. GELOGD("Skip task type: %d", static_cast<int>(task_type));
  402. }
  403. }
  404. return SUCCESS;
  405. }
  406. Status SingleOpModel::BuildDynamicOp(DynamicSingleOp &single_op) {
  407. single_op.num_inputs_ = data_ops_.size();
  408. single_op.num_outputs_ = netoutput_op_->GetAllInputsSize();
  409. ParseOpModelParams(model_helper_, model_params_);
  410. return BuildTaskListForDynamicOp(single_op);
  411. }
  412. } // 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