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runtime_model.cc 17 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 "ge_runtime/runtime_model.h"
  17. #include <set>
  18. #include "./model_context.h"
  19. #include "./task/task.h"
  20. #include "common/ge_inner_error_codes.h"
  21. #include "common/types.h"
  22. #include "common/util.h"
  23. #include "framework/common/debug/ge_log.h"
  24. #include "framework/common/op/op_parser_util.h"
  25. #include "graph/types.h"
  26. #include "task/task_factory.h"
  27. namespace ge {
  28. namespace model_runner {
  29. RuntimeModel::~RuntimeModel() {
  30. GELOGI("RuntimeModel destructor start");
  31. // Release task first, hccl task hold stream
  32. task_list_.clear();
  33. // Unbind rtModel from all task related streams
  34. RtModelUnbindStream();
  35. // Release all task related streams
  36. RtStreamDestory();
  37. // Release rtlabel resource
  38. RtLabelDestory();
  39. // Release rtEvent resourece
  40. RtEventDestory();
  41. GELOGI("Do RtModelDestory");
  42. // Release all rt_model
  43. RtModelDestory();
  44. }
  45. bool RuntimeModel::InitStream(std::shared_ptr<DavinciModel> &davinci_model) {
  46. if (davinci_model == nullptr) {
  47. GELOGE(PARAM_INVALID, "Davinci model is null.");
  48. return false;
  49. }
  50. std::set<int64_t> wait_active_streams;
  51. std::set<int64_t> force_copy_streams;
  52. for (const auto &stream_id : davinci_model->GetWaitActiveStreams()) {
  53. GELOGI("stream id %u is wait active stream.", stream_id);
  54. (void)wait_active_streams.insert(stream_id);
  55. }
  56. for (const auto &stream_id : davinci_model->GetForceCopyStreams()) {
  57. GELOGI("stream id %u is force copy stream.", stream_id);
  58. (void)force_copy_streams.insert(stream_id);
  59. }
  60. GELOGI("stream number:%u", davinci_model->GetStreamNum());
  61. for (uint32_t i = 0; i < davinci_model->GetStreamNum(); ++i) {
  62. rtStream_t stream = nullptr;
  63. uint32_t flag = (force_copy_streams.find(i) != force_copy_streams.end())
  64. ? (RT_STREAM_PERSISTENT | RT_STREAM_FORCE_COPY)
  65. : (RT_STREAM_PERSISTENT);
  66. rtError_t rt_ret = rtStreamCreateWithFlags(&stream, davinci_model->GetPriority(), flag);
  67. if (rt_ret != RT_ERROR_NONE) {
  68. GELOGE(RT_FAILED, "Call rt api rtStreamCreate failed, ret: 0x%X", rt_ret);
  69. return false;
  70. }
  71. GELOGI("rtStreamCreateWithFlags end.");
  72. stream_list_.emplace_back(stream);
  73. // Bind rt_model_handle_ to all task related streams
  74. flag = (wait_active_streams.find(i) != wait_active_streams.end()) ? (static_cast<uint32_t>(RT_INVALID_FLAG))
  75. : (static_cast<uint32_t>(RT_HEAD_STREAM));
  76. rt_ret = rtModelBindStream(rt_model_handle_, stream, flag);
  77. if (rt_ret != RT_ERROR_NONE) {
  78. GELOGE(RT_FAILED, "Call rt api rtModelBindStream failed, ret: 0x%X", rt_ret);
  79. return false;
  80. }
  81. GELOGI("stream index:%u, stream:%p.", i, stream);
  82. }
  83. return true;
  84. }
  85. bool RuntimeModel::InitEvent(uint32_t event_num) {
  86. GELOGI("event number:%u.", event_num);
  87. for (uint32_t i = 0; i < event_num; ++i) {
  88. rtEvent_t rt_event;
  89. rtError_t rt_ret = rtEventCreate(&rt_event);
  90. if (rt_ret != RT_ERROR_NONE) {
  91. GELOGE(RT_FAILED, "Call rt api rtEventCreate failed, i; %u; ret: 0x%X", i, rt_ret);
  92. return false;
  93. }
  94. event_list_.push_back(rt_event);
  95. }
  96. return true;
  97. }
  98. bool RuntimeModel::InitLabel(std::shared_ptr<DavinciModel> &davinci_model) {
  99. GELOGI("batch number:%u.", davinci_model->GetBatchNum());
  100. label_list_.resize(davinci_model->GetBatchNum());
  101. for (auto &task_info : davinci_model->GetTaskInfoList()) {
  102. if (task_info == nullptr) {
  103. GELOGE(PARAM_INVALID, "task_info is null.");
  104. continue;
  105. }
  106. if (task_info->type() != TaskInfoType::LABEL_SET) {
  107. continue;
  108. }
  109. auto label_set_task_info = std::static_pointer_cast<LabelSetTaskInfo>(task_info);
  110. if (label_set_task_info->stream_id() >= stream_list_.size()) {
  111. GELOGE(PARAM_INVALID, "Invalid stream id.");
  112. return false;
  113. }
  114. rtLabel_t rt_label = nullptr;
  115. rtError_t rt_ret = rtLabelCreateEx(&rt_label, stream_list_[label_set_task_info->stream_id()]);
  116. if (rt_ret != RT_ERROR_NONE) {
  117. GELOGE(RT_FAILED, "Call rt api rtLabelCreate failed, ret: 0x%X", rt_ret);
  118. return false;
  119. }
  120. label_list_[label_set_task_info->label_id()] = rt_label;
  121. }
  122. return true;
  123. }
  124. bool RuntimeModel::InitResource(std::shared_ptr<DavinciModel> &davinci_model) {
  125. GELOGI("InitResource start");
  126. if (davinci_model == nullptr) {
  127. GELOGE(PARAM_INVALID, "davinci model is null");
  128. return false;
  129. }
  130. rtError_t rt_ret = rtModelCreate(&rt_model_handle_, 0);
  131. if (rt_ret != RT_ERROR_NONE) {
  132. GELOGE(RT_FAILED, "Call rt api rtModelCreate failed, ret: 0x%X", rt_ret);
  133. return false;
  134. }
  135. // Create rtStream for rt_model_handle_
  136. rt_ret = rtStreamCreate(&rt_model_stream_, davinci_model->GetPriority());
  137. if (rt_ret != RT_ERROR_NONE) {
  138. GELOGE(RT_FAILED, "Call rt api rtStreamCreate failed, ret: 0x%X", rt_ret);
  139. return false;
  140. }
  141. GELOGI("rtStreamCreate end");
  142. if (!InitStream(davinci_model)) {
  143. return false;
  144. }
  145. if (!InitEvent(davinci_model->GetEventNum())) {
  146. return false;
  147. }
  148. if (!InitLabel(davinci_model)) {
  149. return false;
  150. }
  151. GELOGI("InitResource succ");
  152. return true;
  153. }
  154. void RuntimeModel::GenerateTask(uint32_t device_id, uint64_t session_id, std::shared_ptr<DavinciModel> &davinci_model) {
  155. GELOGI("GenerateTask start.");
  156. if (davinci_model == nullptr) {
  157. GELOGE(PARAM_INVALID, "davinci model is null");
  158. return;
  159. }
  160. auto task_infos = davinci_model->GetTaskInfoList();
  161. ModelContext model_context(device_id, session_id, davinci_model->GetPriority(), rt_model_handle_, rt_model_stream_,
  162. stream_list_, label_list_, event_list_);
  163. for (auto &task_info : task_infos) {
  164. auto task = TaskFactory::GetInstance().Create(model_context, task_info);
  165. task_list_.push_back(task);
  166. }
  167. GELOGI("GenerateTask succ.");
  168. }
  169. bool RuntimeModel::LoadTask() {
  170. GELOGI("LoadTask start.");
  171. for (auto &task : task_list_) {
  172. if (task == nullptr) {
  173. GELOGE(PARAM_INVALID, "task is null.");
  174. continue;
  175. }
  176. bool ret = task->Distribute();
  177. if (!ret) {
  178. GELOGE(FAILED, "task distribute fail.");
  179. return false;
  180. }
  181. uint32_t task_id = 0;
  182. uint32_t stream_id = 0;
  183. rtError_t rt_ret = rtModelGetTaskId(rt_model_handle_, &task_id, &stream_id);
  184. if (rt_ret != RT_ERROR_NONE) {
  185. GELOGE(RT_FAILED, "Call rt api failed, ret: 0x%X.", rt_ret);
  186. return false;
  187. }
  188. task_id_list_.push_back(task_id);
  189. stream_id_list_.push_back(stream_id);
  190. if (task->Args() != nullptr) {
  191. std::shared_ptr<RuntimeInfo> runtime_tuple = nullptr;
  192. GE_MAKE_SHARED(runtime_tuple = std::make_shared<RuntimeInfo>(task_id, stream_id, task->Args()), return false);
  193. auto emplace_ret = runtime_info_map_.emplace(task->task_name(), runtime_tuple);
  194. if (!emplace_ret.second) {
  195. GELOGW("Task name exist:%s", task->task_name().c_str());
  196. }
  197. }
  198. }
  199. if (task_list_.empty()) {
  200. GELOGE(FAILED, "Task list is empty");
  201. return false;
  202. }
  203. GELOGI("LoadTask succ.");
  204. return true;
  205. }
  206. bool RuntimeModel::LoadComplete() {
  207. uint32_t task_id = 0;
  208. uint32_t stream_id = 0;
  209. auto rt_ret = rtModelGetTaskId(rt_model_handle_, &task_id, &stream_id);
  210. if (rt_ret != RT_ERROR_NONE) {
  211. GELOGE(RT_FAILED, "Call rtModelGetTaskId failed, ret:0x%X", rt_ret);
  212. return RT_FAILED;
  213. }
  214. task_id_list_.push_back(task_id);
  215. stream_id_list_.push_back(stream_id);
  216. rt_ret = rtModelLoadComplete(rt_model_handle_);
  217. if (rt_ret != RT_ERROR_NONE) {
  218. GELOGE(RT_FAILED, "Call rt api rtModelLoadComplete failed, ret: 0x%X.", rt_ret);
  219. return false;
  220. }
  221. return true;
  222. }
  223. bool RuntimeModel::Load(uint32_t device_id, uint64_t session_id, std::shared_ptr<DavinciModel> &davinci_model) {
  224. bool status = InitResource(davinci_model);
  225. if (!status) {
  226. GELOGE(FAILED, "InitResource failed.");
  227. return status;
  228. }
  229. status = InitDataInfo(davinci_model);
  230. if (!status) {
  231. GELOGE(FAILED, "InitDataInfo failed.");
  232. return status;
  233. }
  234. status = InitOutputInfo(davinci_model);
  235. if (!status) {
  236. GELOGE(FAILED, "InitOutputInfo failed.");
  237. return status;
  238. }
  239. status = InitConstantInfo(davinci_model);
  240. if (!status) {
  241. GELOGE(FAILED, "InitConstantInfo failed.");
  242. return status;
  243. }
  244. GenerateTask(device_id, session_id, davinci_model);
  245. return status;
  246. }
  247. bool RuntimeModel::DistributeTask() {
  248. bool status = LoadTask();
  249. if (!status) {
  250. GELOGE(FAILED, "DistributeTask failed");
  251. return false;
  252. }
  253. return true;
  254. }
  255. bool RuntimeModel::Run() {
  256. GELOGI("Davinci task run start");
  257. rtError_t ret = rtModelExecute(rt_model_handle_, rt_model_stream_, 0);
  258. if (ret != RT_ERROR_NONE) {
  259. GELOGE(RT_FAILED, "Model execute failed, ret = 0x%X", ret);
  260. return false;
  261. }
  262. GELOGI("Run rtModelExecute success, ret = 0x%X", ret);
  263. ret = rtStreamSynchronize(rt_model_stream_);
  264. if (ret != RT_ERROR_NONE) {
  265. if (ret == RT_ERROR_END_OF_SEQUENCE) {
  266. GELOGI("Model stream RT_ERROR_END_OF_SEQUENCE signal received, ret = 0x%X", ret);
  267. return true;
  268. }
  269. GELOGE(RT_FAILED, "Model stream sync failed, ret = 0x%X", ret);
  270. return false;
  271. }
  272. GELOGI("Davinci task run succ.");
  273. return true;
  274. }
  275. void RuntimeModel::RtModelUnbindStream() noexcept {
  276. for (size_t i = 0; i < stream_list_.size(); i++) {
  277. if (rtModelUnbindStream(rt_model_handle_, stream_list_[i]) != RT_ERROR_NONE) {
  278. GELOGE(RT_FAILED, "Unbind stream from model failed! Index: %zu", i);
  279. return;
  280. }
  281. }
  282. }
  283. void RuntimeModel::RtStreamDestory() noexcept {
  284. if (rtStreamDestroy(rt_model_stream_) != RT_ERROR_NONE) {
  285. GELOGE(RT_FAILED, "Destroy stream for rt_model failed!");
  286. return;
  287. }
  288. for (size_t i = 0; i < stream_list_.size(); i++) {
  289. if (rtStreamDestroy(stream_list_[i]) != RT_ERROR_NONE) {
  290. GELOGE(RT_FAILED, "Destroy stream failed! Index: %zu", i);
  291. return;
  292. }
  293. }
  294. }
  295. void RuntimeModel::RtLabelDestory() noexcept {
  296. for (size_t i = 0; i < label_list_.size(); i++) {
  297. if (label_list_[i] == nullptr) {
  298. continue;
  299. }
  300. if (rtLabelDestroy(label_list_[i]) != RT_ERROR_NONE) {
  301. GELOGE(RT_FAILED, "Destroy label failed! Index: %zu.", i);
  302. return;
  303. }
  304. }
  305. }
  306. void RuntimeModel::RtModelDestory() noexcept {
  307. rtError_t ret = rtModelDestroy(rt_model_handle_);
  308. if (ret != RT_ERROR_NONE) {
  309. GELOGE(RT_FAILED, "Call rt api failed, ret: 0x%X", ret);
  310. return;
  311. }
  312. }
  313. void RuntimeModel::RtEventDestory() noexcept {
  314. for (size_t i = 0; i < event_list_.size(); i++) {
  315. if (rtEventDestroy(event_list_[i]) != RT_ERROR_NONE) {
  316. GELOGE(RT_FAILED, "Destroy event failed! Index: %zu", i);
  317. return;
  318. }
  319. }
  320. }
  321. bool RuntimeModel::InitDataInfo(std::shared_ptr<DavinciModel> &davinci_model) { return true; }
  322. bool RuntimeModel::InitOutputInfo(std::shared_ptr<DavinciModel> &davinci_model) {
  323. if (davinci_model == nullptr) {
  324. GELOGE(PARAM_INVALID, "davinci model is null");
  325. return false;
  326. }
  327. output_info_list_ = davinci_model->GetOutputInfoList();
  328. return true;
  329. }
  330. bool RuntimeModel::CopyInputData(const InputData &input_data) {
  331. if (input_data.blobs.size() != data_info_list_.size()) {
  332. GELOGE(PARAM_INVALID, "The input data list size (%zu) does not match the model input list size (%zu)",
  333. input_data.blobs.size(), data_info_list_.size());
  334. return false;
  335. }
  336. for (const auto &data_info : data_info_list_) {
  337. if (data_info == nullptr) {
  338. GELOGE(PARAM_INVALID, "data info is null.");
  339. return false;
  340. }
  341. bool ret = CopyInputDataToModel(input_data.blobs, data_info);
  342. if (!ret) {
  343. GELOGE(FAILED, "Copy input data to model ret fail, data_info: %s, model id: %u", data_info->name.c_str(),
  344. input_data.model_id);
  345. return false;
  346. }
  347. }
  348. return true;
  349. }
  350. bool RuntimeModel::CopyInputDataToModel(const std::vector<DataBuffer> &data, const std::shared_ptr<OpInfo> &data_info) {
  351. return true;
  352. }
  353. bool RuntimeModel::CopyHostData(const std::vector<DataBuffer> &data, const std::shared_ptr<OpInfo> &data_info) const {
  354. GELOGI("Start CopyHostData.");
  355. if (data.empty()) {
  356. GELOGE(PARAM_INVALID, "data buffer is empty.");
  357. return false;
  358. }
  359. if (data_info == nullptr) {
  360. GELOGE(PARAM_INVALID, "data info is null.");
  361. return false;
  362. }
  363. void *host_data_addr = data[data_info->index].data;
  364. uint32_t copy_size = data[data_info->index].length;
  365. GELOGD("data output tensor is aipp tensor,copy data only.");
  366. const std::vector<uintptr_t> &outputs = data_info->output_addrs;
  367. if (outputs.empty()) {
  368. GELOGE(PARAM_INVALID, "Output addrs is empty.");
  369. return false;
  370. }
  371. // Copy input data to data nodes
  372. void *data_out_addr = reinterpret_cast<void *>(outputs[0]);
  373. rtError_t rt_ret = rtMemcpy(data_out_addr, copy_size, host_data_addr, copy_size, RT_MEMCPY_HOST_TO_DEVICE);
  374. if (rt_ret != RT_ERROR_NONE) {
  375. GELOGE(RT_FAILED, "Call rt api failed, ret: 0x%X", rt_ret);
  376. return false;
  377. }
  378. return true;
  379. }
  380. bool RuntimeModel::CopyTransData(const std::vector<DataBuffer> &data, const std::shared_ptr<OpInfo> &data_info) {
  381. return true;
  382. }
  383. bool RuntimeModel::InitConstantInfo(std::shared_ptr<DavinciModel> &davinci_model) {
  384. // Const no input, only 1 output, and this output has no data
  385. // weight data copy to output mem
  386. if (davinci_model == nullptr) {
  387. GELOGE(PARAM_INVALID, "Davinci model is null.");
  388. return false;
  389. }
  390. constant_info_list_ = davinci_model->GetConstantInfoList();
  391. for (const auto &constant : constant_info_list_) {
  392. if (constant == nullptr) {
  393. GELOGE(PARAM_INVALID, "constant is null");
  394. continue;
  395. }
  396. if (constant->output_tensors.empty()) {
  397. GELOGE(PARAM_INVALID, "Output tensors is empty");
  398. return false;
  399. }
  400. if (constant->weight_tensors.empty()) {
  401. GELOGE(PARAM_INVALID, "Weight tensors is empty");
  402. return false;
  403. }
  404. if (constant->output_tensors[0].size < constant->weight_data.size()) {
  405. GELOGE(PARAM_INVALID, "Output size:%u is less than weight data size:%zu", constant->output_tensors[0].size,
  406. constant->weight_data.size());
  407. return false;
  408. }
  409. if (constant->weight_data.empty()) {
  410. GELOGW("Const op:%s has no weight data.", constant->name.c_str());
  411. continue;
  412. }
  413. if (constant->weight_tensors[0].datatype == DT_STRING) {
  414. /// If tensor is a scaler, it's shape size if zero, according ge_tensor.cc.
  415. /// The logic of GetShapeSize is wrong, the scaler tensor's GetShapeSize is zero
  416. /// and that of unknown shape is zero too.
  417. /// Unknown shape will not appear here, so we can use zero judge a tensor is scaler or not.
  418. int64_t elem_num =
  419. (constant->weight_tensors[0].GetShapeSize() == 0) ? 1 : constant->weight_tensors[0].GetShapeSize();
  420. if (constant->weight_data.size() < sizeof(uint64_t)) {
  421. GELOGE(FAILED, "weight_data size is smaller than sizeof(uint64_t)");
  422. return false;
  423. }
  424. uint64_t *buff = reinterpret_cast<uint64_t *>(const_cast<char *>(constant->weight_data.data()));
  425. int64_t offset = elem_num * 8;
  426. uintptr_t hbm_raw_data_base_addr = reinterpret_cast<uintptr_t>(constant->output_addrs[0]) + offset;
  427. for (int64_t i = elem_num - 1; i >= 0; --i) {
  428. buff[i] = hbm_raw_data_base_addr + (buff[i] - buff[0]);
  429. }
  430. }
  431. rtError_t rt_ret = rtMemcpy(reinterpret_cast<void *>(constant->output_addrs[0]), constant->output_tensors[0].size,
  432. constant->weight_data.data(), constant->weight_data.size(), RT_MEMCPY_HOST_TO_DEVICE);
  433. if (rt_ret != RT_ERROR_NONE) {
  434. GELOGE(RT_FAILED, "rtGetFunctionByName failed, ret: 0x%X", rt_ret);
  435. return false;
  436. }
  437. }
  438. return true;
  439. }
  440. bool RuntimeModel::GetInputOutputDescInfo(bool zero_copy, std::vector<InputOutputDescInfo> *input_desc,
  441. std::vector<InputOutputDescInfo> *output_desc,
  442. std::vector<uint32_t> *input_format, std::vector<uint32_t> *output_format) {
  443. return true;
  444. }
  445. bool RuntimeModel::GetInputDescInfo(std::vector<InputOutputDescInfo> *input_desc, std::vector<uint32_t> *formats) {
  446. return true;
  447. }
  448. bool RuntimeModel::GetOutputDescInfo(std::vector<InputOutputDescInfo> *output_desc, std::vector<uint32_t> *formats) {
  449. return true;
  450. }
  451. void RuntimeModel::CreateOutput(uint32_t index, const OpInfo &op_info, InputOutputDescInfo *output,
  452. uint32_t *format_result) {}
  453. const std::vector<uint32_t> &RuntimeModel::GetTaskIdList() const { return task_id_list_; }
  454. const std::vector<uint32_t> &RuntimeModel::GetStreamIdList() const { return stream_id_list_; }
  455. } // namespace model_runner
  456. } // 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