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model_utils.cc 24 kB

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  1. /**
  2. * Copyright 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 "graph/load/new_model_manager/model_utils.h"
  17. #include <string>
  18. #include "common/debug/log.h"
  19. #include "common/op/ge_op_utils.h"
  20. #include "graph/utils/tensor_utils.h"
  21. #include "graph/manager/graph_var_manager.h"
  22. #define VALIDATE_MEM_RANGE(OP, SIZE, OFFSET) \
  23. do { \
  24. if (SIZE <= static_cast<uint64_t>(OFFSET)) { \
  25. GELOGE(OUT_OF_MEMORY, "Node: %s, memory out of range[%lu: %ld]", OP->GetName().c_str(), SIZE, OFFSET); \
  26. return {}; \
  27. } \
  28. } while (0)
  29. namespace ge {
  30. ///
  31. /// @ingroup ge
  32. /// @brief Get input size.
  33. /// @return vector<int64_t>
  34. ///
  35. vector<int64_t> ModelUtils::GetInputSize(ConstOpDescPtr op_desc) {
  36. vector<int64_t> v_input_size;
  37. GE_CHECK_NOTNULL_EXEC(op_desc, return v_input_size);
  38. const size_t inputs_size = op_desc->GetAllInputsSize();
  39. for (size_t i = 0; i < inputs_size; ++i) {
  40. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(i);
  41. if (tensor_desc == nullptr) {
  42. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  43. continue;
  44. }
  45. int64_t tensor_size = 0;
  46. GE_IF_BOOL_EXEC(
  47. TensorUtils::GetSize(*tensor_desc, tensor_size) != GRAPH_SUCCESS,
  48. GELOGI("Get size from TensorDesc failed, op : %s, input index : %zu", op_desc->GetName().c_str(), i);
  49. continue);
  50. GELOGI("GetInputSize op: %s, index: %zu, size:%ld", op_desc->GetName().c_str(), i, tensor_size);
  51. v_input_size.push_back(tensor_size);
  52. }
  53. return v_input_size;
  54. }
  55. ///
  56. /// @ingroup ge
  57. /// @brief Get output size.
  58. /// @return vector<int64_t>
  59. ///
  60. vector<int64_t> ModelUtils::GetOutputSize(ConstOpDescPtr op_desc) {
  61. vector<int64_t> v_output_size;
  62. GE_CHECK_NOTNULL_EXEC(op_desc, return v_output_size);
  63. const size_t outputs_size = op_desc->GetOutputsSize();
  64. const vector<int64_t> v_output_offset = op_desc->GetOutputOffset();
  65. GE_IF_BOOL_EXEC(v_output_offset.size() != outputs_size,
  66. GELOGW("Output param invalid: output_offset=%zu, outputs=%zu.", v_output_offset.size(), outputs_size);
  67. return v_output_size;);
  68. for (size_t i = 0; i < outputs_size; ++i) {
  69. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  70. if (tensor_desc == nullptr) {
  71. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  72. continue;
  73. }
  74. int64_t tensor_size = 0;
  75. GE_IF_BOOL_EXEC(
  76. TensorUtils::GetSize(*tensor_desc, tensor_size) != GRAPH_SUCCESS,
  77. GELOGI("Get size from TensorDesc failed, op : %s, output index : %zu", op_desc->GetName().c_str(), i);
  78. continue);
  79. GELOGI("GetOutputSize op: %s, index: %zu, size:%ld", op_desc->GetName().c_str(), i, tensor_size);
  80. v_output_size.push_back(tensor_size);
  81. }
  82. return v_output_size;
  83. }
  84. ///
  85. /// @ingroup ge
  86. /// @brief Get workspace size.
  87. /// @return vector<int64_t>
  88. ///
  89. vector<int64_t> ModelUtils::GetWorkspaceSize(ConstOpDescPtr op_desc) {
  90. vector<int64_t> v_workspace_size;
  91. GE_CHECK_NOTNULL_EXEC(op_desc, return v_workspace_size);
  92. const vector<int64_t> v_workspace_num = op_desc->GetWorkspace();
  93. const vector<int64_t> v_workspace_bytes = op_desc->GetWorkspaceBytes();
  94. if (v_workspace_num.size() != v_workspace_bytes.size()) {
  95. GELOGW("workspace_num[%zu]!= workspace_bytes[%zu]", v_workspace_num.size(), v_workspace_bytes.size());
  96. return v_workspace_size;
  97. }
  98. for (auto workspace_bytes : v_workspace_bytes) {
  99. v_workspace_size.push_back(workspace_bytes);
  100. }
  101. return v_workspace_size;
  102. }
  103. ///
  104. /// @ingroup ge
  105. /// @brief Get weight size.
  106. /// @return vector<int64_t>
  107. ///
  108. vector<int64_t> ModelUtils::GetWeightSize(ConstOpDescPtr op_desc) {
  109. vector<int64_t> v_weight_size;
  110. GE_CHECK_NOTNULL_EXEC(op_desc, return v_weight_size);
  111. // const op, get weight directly
  112. const string type_name = op_desc->GetType();
  113. if ((type_name == "Const") || (type_name == "Constant")) {
  114. ConstGeTensorPtr weight = nullptr;
  115. if (AttrUtils::GetTensor(*op_desc, ATTR_NAME_WEIGHTS, weight)) {
  116. v_weight_size.push_back(TensorUtils::GetWeightSize(weight));
  117. }
  118. return v_weight_size;
  119. }
  120. // other ops get weight from connected constop
  121. const size_t inputs_size = op_desc->GetAllInputsSize();
  122. const vector<bool> v_is_input_const = op_desc->GetIsInputConst();
  123. for (size_t i = 0; i < inputs_size; ++i) {
  124. if ((i < v_is_input_const.size()) && v_is_input_const[i]) {
  125. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(i);
  126. if (tensor_desc == nullptr) {
  127. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  128. continue;
  129. }
  130. int64_t tensor_size = 0;
  131. (void)TensorUtils::GetSize(*tensor_desc, tensor_size);
  132. v_weight_size.push_back(tensor_size);
  133. }
  134. }
  135. return v_weight_size;
  136. }
  137. ///
  138. /// @ingroup ge
  139. /// @brief Get weights.
  140. /// @return vector<ConstGeTensorPtr>
  141. ///
  142. vector<ConstGeTensorPtr> ModelUtils::GetWeights(ConstOpDescPtr op_desc) {
  143. vector<ConstGeTensorPtr> v_weights;
  144. GE_CHECK_NOTNULL_EXEC(op_desc, return v_weights);
  145. // const op, get weight directly
  146. const string op_type = op_desc->GetType();
  147. if ((op_type == "Const") || (op_type == "Constant")) {
  148. ConstGeTensorPtr weight = nullptr;
  149. if (AttrUtils::GetTensor(*op_desc, ATTR_NAME_WEIGHTS, weight)) {
  150. v_weights.push_back(weight);
  151. }
  152. return v_weights;
  153. }
  154. // other ops get weight from connected constop
  155. const size_t inputs_size = op_desc->GetAllInputsSize();
  156. const vector<bool> v_is_input_const = op_desc->GetIsInputConst();
  157. for (size_t i = 0; i < inputs_size; ++i) {
  158. if ((i < v_is_input_const.size()) && v_is_input_const[i]) {
  159. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(i);
  160. if (tensor_desc == nullptr) {
  161. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  162. continue;
  163. }
  164. ConstGeTensorPtr weight = nullptr;
  165. if (AttrUtils::GetTensor(*tensor_desc, ATTR_NAME_WEIGHTS, weight)) {
  166. v_weights.push_back(weight);
  167. }
  168. }
  169. }
  170. return v_weights;
  171. }
  172. ///
  173. /// @ingroup ge
  174. /// @brief Get AiCpuOp Input descriptor.
  175. /// @return vector<::tagCcAICPUTensor>
  176. ///
  177. vector<::tagCcAICPUTensor> ModelUtils::GetInputDescs(ConstOpDescPtr op_desc) {
  178. // AiCpuOp::GetInputDescs
  179. vector<::opTensor_t> v_input_descs;
  180. GE_CHECK_NOTNULL_EXEC(op_desc, return v_input_descs);
  181. const size_t inputs_size = op_desc->GetAllInputsSize();
  182. const vector<bool> v_is_input_const = op_desc->GetIsInputConst();
  183. for (size_t i = 0; i < inputs_size; ++i) {
  184. if ((i < v_is_input_const.size()) && v_is_input_const[i]) { // skip Const input node
  185. continue;
  186. }
  187. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(i);
  188. if (tensor_desc == nullptr) {
  189. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  190. continue;
  191. }
  192. uint32_t dim_cnt = 0;
  193. GE_CHK_BOOL_EXEC_WARN(TensorUtils::GetRealDimCnt(*tensor_desc, dim_cnt) == GRAPH_SUCCESS, continue,
  194. "Get dim_cnt failed");
  195. opTensor_t tmp;
  196. uint32_t tmp_fmt = tensor_desc->GetFormat();
  197. tmp.format = tagOpTensorFormat(tmp_fmt);
  198. tmp.dim_cnt = static_cast<int32_t>(dim_cnt);
  199. uint32_t tmp_type = tensor_desc->GetDataType();
  200. tmp.data_type = tagOpDataType(tmp_type);
  201. for (int32_t j = 0; j < 4; j++) { // 4 dims
  202. tmp.dim[j] = (j < tmp.dim_cnt ? tensor_desc->GetShape().GetDim(j) : 1);
  203. }
  204. v_input_descs.push_back(tmp);
  205. }
  206. return v_input_descs;
  207. }
  208. ///
  209. /// @ingroup ge
  210. /// @brief Get AiCpuOp Output descriptor.
  211. /// @return vector<::tagCcAICPUTensor>
  212. ///
  213. vector<::tagCcAICPUTensor> ModelUtils::GetOutputDescs(ConstOpDescPtr op_desc) {
  214. // AiCpuOp::GetOutputDescs
  215. vector<::opTensor_t> v_output_descs;
  216. GE_CHECK_NOTNULL_EXEC(op_desc, return v_output_descs);
  217. // init op output opTensor_t struct
  218. const size_t output_num = op_desc->GetOutputsSize();
  219. for (size_t i = 0; i < output_num; ++i) {
  220. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  221. if (tensor_desc == nullptr) {
  222. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  223. continue;
  224. }
  225. uint32_t dim_cnt = 0;
  226. GE_CHK_BOOL_EXEC_WARN(TensorUtils::GetRealDimCnt(*tensor_desc, dim_cnt) == GRAPH_SUCCESS, continue,
  227. "Get dim_cnt failed");
  228. opTensor_t tmp;
  229. uint32_t tmp_fmt = tensor_desc->GetFormat();
  230. tmp.format = tagOpTensorFormat(tmp_fmt);
  231. tmp.dim_cnt = static_cast<int32_t>(dim_cnt);
  232. uint32_t tmp_type = tensor_desc->GetDataType();
  233. tmp.data_type = tagOpDataType(tmp_type);
  234. for (int32_t j = 0; j < 4; j++) { // 4 dims
  235. tmp.dim[j] = (j < tmp.dim_cnt ? tensor_desc->GetShape().GetDim(j) : 1);
  236. }
  237. v_output_descs.push_back(tmp);
  238. }
  239. return v_output_descs;
  240. }
  241. ///
  242. /// @ingroup ge
  243. /// @brief Get input data address.
  244. /// @return vector<void*>
  245. ///
  246. vector<void *> ModelUtils::GetInputDataAddrs(const RuntimeParam &model_param, ConstOpDescPtr op_desc) {
  247. vector<void *> v_input_data_addr; // init as:buf_base + op_def_->input(i));
  248. GE_CHECK_NOTNULL_EXEC(op_desc, return v_input_data_addr);
  249. uint64_t session_id = model_param.session_id;
  250. const size_t inputs_size = op_desc->GetInputsSize();
  251. const vector<int64_t> v_input_offset = op_desc->GetInputOffset();
  252. const string op_type = op_desc->GetType();
  253. size_t non_const_index = 0;
  254. const vector<bool> v_is_input_const = op_desc->GetIsInputConst();
  255. vector<int64_t> v_memory_type;
  256. bool has_mem_type_attr = ge::AttrUtils::GetListInt(op_desc, ATTR_NAME_INPUT_MEM_TYPE_LIST, v_memory_type);
  257. if (has_mem_type_attr && (v_memory_type.size() != inputs_size)) {
  258. GELOGE(PARAM_INVALID, "Fusion: check input size failed, op: %s, input v_memory_type size: %zu input numbers: %zu",
  259. op_desc->GetName().c_str(), v_memory_type.size(), inputs_size);
  260. return v_input_data_addr;
  261. }
  262. for (size_t i = 0; i < op_desc->GetAllInputsSize(); ++i) {
  263. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(static_cast<uint32_t>(i));
  264. GE_IF_BOOL_EXEC(tensor_desc == nullptr, GELOGD("Op: %s, Index: %zu, has no input", op_desc->GetName().c_str(), i);
  265. continue;)
  266. if ((i < v_is_input_const.size()) && v_is_input_const[i] && (op_type != NETOUTPUT)) {
  267. // TBE: add weights address to input
  268. int64_t tensor_size = 0;
  269. GE_CHK_STATUS(TensorUtils::GetSize(*tensor_desc, tensor_size));
  270. if (tensor_size) {
  271. int64_t data_offset = 0;
  272. GE_CHK_STATUS(TensorUtils::GetDataOffset(*tensor_desc, data_offset));
  273. VALIDATE_MEM_RANGE(op_desc, model_param.weight_size, data_offset);
  274. uint8_t *weight_addr = model_param.weight_base + data_offset;
  275. v_input_data_addr.push_back(weight_addr);
  276. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[C] name[%s] input[%zu] memaddr[%p]", model_param.graph_id,
  277. op_desc->GetName().c_str(), i, weight_addr);
  278. }
  279. non_const_index++;
  280. continue;
  281. }
  282. GE_IF_BOOL_EXEC(non_const_index >= v_input_offset.size(), break);
  283. int64_t input_offset = v_input_offset[non_const_index];
  284. non_const_index++;
  285. GE_IF_BOOL_EXEC(model_param.var_size != 0 && ge::VarManager::Instance(session_id)->IsVarAddr(input_offset),
  286. uint8_t *variable_addr = nullptr;
  287. GE_CHK_STATUS_EXEC(GetVarAddr(model_param, op_desc, input_offset, variable_addr), return {});
  288. v_input_data_addr.push_back(variable_addr);
  289. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[V] name[%s] input[%lu] memaddr[%p]",
  290. model_param.graph_id, op_desc->GetName().c_str(), i, variable_addr);
  291. continue);
  292. int64_t mem_type;
  293. bool tensor_has_mem_type = ge::AttrUtils::GetInt(tensor_desc, ATTR_NAME_TENSOR_MEM_TYPE, mem_type);
  294. // feature maps
  295. void *mem_addr = nullptr;
  296. if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_L1) { // fusion
  297. mem_addr = reinterpret_cast<uint8_t *>(static_cast<intptr_t>(input_offset));
  298. v_input_data_addr.push_back(mem_addr);
  299. } else if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_TS_4G) {
  300. int64_t tensor_size = 0;
  301. GE_CHK_STATUS_EXEC(TensorUtils::GetSize(*tensor_desc, tensor_size), return {});
  302. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, input_offset);
  303. mem_addr = model_param.ts_mem_mall->Acquire(input_offset, static_cast<uint64_t>(tensor_size));
  304. v_input_data_addr.push_back(mem_addr);
  305. } else if (tensor_has_mem_type && mem_type == RT_MEMORY_P2P_DDR) {
  306. uint8_t *p2p_mem_addr = model_param.memory_infos.at(RT_MEMORY_P2P_DDR).memory_base + v_input_offset[i];
  307. v_input_data_addr.push_back(p2p_mem_addr);
  308. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[P] name[%s] input[%zu] memaddr[%p]", model_param.graph_id,
  309. op_desc->GetName().c_str(), i, p2p_mem_addr);
  310. continue;
  311. } else {
  312. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, input_offset);
  313. mem_addr = model_param.mem_base + input_offset;
  314. v_input_data_addr.push_back(mem_addr);
  315. }
  316. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[F] name[%s] input[%zu] memaddr[%p]", model_param.graph_id,
  317. op_desc->GetName().c_str(), i, mem_addr);
  318. }
  319. return v_input_data_addr;
  320. }
  321. ///
  322. /// @ingroup ge
  323. /// @brief Get variable address.
  324. /// @return Status
  325. ///
  326. Status ModelUtils::GetVarAddr(const RuntimeParam &model_param, const ConstOpDescPtr &op_desc, int64_t offset,
  327. uint8_t *&var_addr) {
  328. rtMemType_t mem_type = ge::VarManager::Instance(model_param.session_id)->GetVarMemType(offset);
  329. switch (mem_type) {
  330. case RT_MEMORY_RDMA_HBM:
  331. if (offset < 0) {
  332. GELOGE(PARAM_INVALID, "rdma var addr is invalid, addr=%p", reinterpret_cast<uint8_t *>(offset));
  333. return PARAM_INVALID;
  334. }
  335. var_addr = reinterpret_cast<uint8_t *>(offset);
  336. break;
  337. case RT_MEMORY_HBM:
  338. VALIDATE_MEM_RANGE(op_desc, model_param.var_size, offset - model_param.logic_var_base);
  339. var_addr = model_param.var_base + offset - model_param.logic_var_base;
  340. break;
  341. default:
  342. GELOGE(PARAM_INVALID, "unsupported memory type %u", mem_type);
  343. return PARAM_INVALID;
  344. }
  345. GE_CHECK_NOTNULL(var_addr);
  346. return SUCCESS;
  347. }
  348. ///
  349. /// @ingroup ge
  350. /// @brief Get output data address.
  351. /// @return vector<void*>
  352. ///
  353. vector<void *> ModelUtils::GetOutputDataAddrs(const RuntimeParam &model_param, ConstOpDescPtr op_desc) {
  354. vector<void *> v_output_data_addr; // init as:buf_base + op_def_->output(i)
  355. GE_CHECK_NOTNULL_EXEC(op_desc, return v_output_data_addr);
  356. uint64_t session_id = model_param.session_id;
  357. const size_t outputs_size = op_desc->GetOutputsSize();
  358. const vector<int64_t> v_output_offset = op_desc->GetOutputOffset();
  359. GE_IF_BOOL_EXEC(v_output_offset.size() != outputs_size,
  360. GELOGW("Output param invalid: output_offset=%zu, outputs=%zu.", v_output_offset.size(), outputs_size);
  361. return v_output_data_addr);
  362. vector<int64_t> v_memory_type;
  363. bool has_mem_type_attr = ge::AttrUtils::GetListInt(op_desc, ATTR_NAME_OUTPUT_MEM_TYPE_LIST, v_memory_type);
  364. if (has_mem_type_attr && (v_memory_type.size() != outputs_size)) {
  365. GELOGE(PARAM_INVALID,
  366. "Fusion: check output size failed, op: %s, output v_memory_type size: %lu output numbers: %zu",
  367. op_desc->GetName().c_str(), v_memory_type.size(), outputs_size);
  368. return v_output_data_addr;
  369. }
  370. for (size_t i = 0; i < outputs_size; ++i) {
  371. GE_IF_BOOL_EXEC(model_param.var_size != 0 && ge::VarManager::Instance(session_id)->IsVarAddr(v_output_offset[i]),
  372. uint8_t *variable_addr = nullptr;
  373. GE_CHK_STATUS_EXEC(GetVarAddr(model_param, op_desc, v_output_offset[i], variable_addr), return {});
  374. v_output_data_addr.push_back(variable_addr);
  375. GELOGI("[IMAS]GetOutputDataAddrs graph_%u type[V] name[%s] output[%zu] memaddr[%p]",
  376. model_param.graph_id, op_desc->GetName().c_str(), i, variable_addr);
  377. continue);
  378. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  379. if (tensor_desc == nullptr) {
  380. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  381. continue;
  382. }
  383. int64_t mem_type;
  384. bool tensor_has_mem_type = ge::AttrUtils::GetInt(tensor_desc, ATTR_NAME_TENSOR_MEM_TYPE, mem_type);
  385. // feature maps
  386. void *mem_addr = nullptr;
  387. if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_L1) { // fusion
  388. mem_addr = reinterpret_cast<uint8_t *>(static_cast<intptr_t>(v_output_offset[i]));
  389. v_output_data_addr.push_back(mem_addr);
  390. } else if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_TS_4G) {
  391. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  392. GE_CHECK_NOTNULL_EXEC(tensor_desc, return {});
  393. int64_t tensor_size = 0;
  394. GE_CHK_STATUS_EXEC(TensorUtils::GetSize(*tensor_desc, tensor_size), return {});
  395. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, v_output_offset[i]);
  396. mem_addr = model_param.ts_mem_mall->Acquire(v_output_offset[i], static_cast<uint64_t>(tensor_size));
  397. v_output_data_addr.push_back(mem_addr);
  398. } else if (tensor_has_mem_type && mem_type == RT_MEMORY_P2P_DDR) {
  399. uint8_t *p2p_mem_addr = model_param.memory_infos.at(RT_MEMORY_P2P_DDR).memory_base + v_output_offset[i];
  400. v_output_data_addr.push_back(p2p_mem_addr);
  401. GELOGI("[IMAS]GetOutputDataAddrs graph_%u type[P] name[%s] output[%zu] memaddr[%p]", model_param.graph_id,
  402. op_desc->GetName().c_str(), i, p2p_mem_addr);
  403. continue;
  404. } else {
  405. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, v_output_offset[i]);
  406. mem_addr = static_cast<uint8_t *>(model_param.mem_base + v_output_offset[i]);
  407. v_output_data_addr.push_back(mem_addr);
  408. }
  409. GELOGI("[IMAS]GetOutputDataAddrs graph_%u type[F] name[%s] output[%zu] memaddr[%p]", model_param.graph_id,
  410. op_desc->GetName().c_str(), i, mem_addr);
  411. }
  412. return v_output_data_addr;
  413. }
  414. ///
  415. /// @ingroup ge
  416. /// @brief Get workspace data address.
  417. /// @return vector<void*>
  418. ///
  419. vector<void *> ModelUtils::GetWorkspaceDataAddrs(const RuntimeParam &model_param, ConstOpDescPtr op_desc) {
  420. vector<void *> v_workspace_data_addr;
  421. GE_CHECK_NOTNULL_EXEC(op_desc, return v_workspace_data_addr);
  422. const vector<int64_t> v_workspace_offset = op_desc->GetWorkspace();
  423. const vector<int64_t> v_workspace_bytes = op_desc->GetWorkspaceBytes();
  424. if (v_workspace_offset.size() != v_workspace_bytes.size()) {
  425. GELOGW("v_workspace_offset.size()[%zu] != v_workspace_bytes.size()[%zu]", v_workspace_offset.size(),
  426. v_workspace_bytes.size());
  427. return v_workspace_data_addr;
  428. }
  429. vector<bool> workspace_reuse_flag;
  430. bool has_workspace_reuse = ge::AttrUtils::GetListBool(op_desc, "workspace_reuse_flag", workspace_reuse_flag);
  431. vector<int64_t> v_memory_type;
  432. vector<int64_t> workspace_memory_type;
  433. bool has_mem_type_attr = ge::AttrUtils::GetListInt(op_desc, TVM_ATTR_NAME_WORKSPACE_TYPE, v_memory_type);
  434. bool has_mem_type_workspace =
  435. ge::AttrUtils::GetListInt(op_desc, ATTR_NAME_WORKSPACE_TYPE_LIST, workspace_memory_type);
  436. for (size_t i = 0; i < v_workspace_bytes.size(); ++i) {
  437. // Temporary solution, the aicpu workspace of multiple images cannot be shared.
  438. if (has_workspace_reuse && i < workspace_reuse_flag.size() && !workspace_reuse_flag[i] &&
  439. !model_param.is_single_op) {
  440. void *mem_addr = model_param.aicpu_mem_mall->Acquire(v_workspace_offset[i], v_workspace_bytes[i]);
  441. v_workspace_data_addr.push_back(mem_addr);
  442. GELOGI(
  443. "[IMAS]GetWorkspaceDataAddrs graph_%u type[F] name[%s] aicpu workspace[%zu] offset[%ld] bytes[%ld] "
  444. "memaddr[%p]",
  445. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i], v_workspace_bytes[i], mem_addr);
  446. continue;
  447. } else if (has_mem_type_workspace && workspace_memory_type[i] == RT_MEMORY_P2P_DDR) {
  448. int64_t p2p_workspace_offset = v_workspace_offset[i];
  449. int64_t p2p_workspace_bytes = v_workspace_bytes[i];
  450. uint8_t *p2p_mem_addr = p2p_workspace_bytes == 0
  451. ? nullptr
  452. : model_param.memory_infos.at(RT_MEMORY_P2P_DDR).memory_base + p2p_workspace_offset;
  453. v_workspace_data_addr.push_back(p2p_mem_addr);
  454. GELOGI(
  455. "[IMAS]GetWorkspaceDataAddrs graph_%u type[P] name[%s] p2p workspace[%zu] offset[%ld] bytes[%ld] "
  456. "memaddr[%p]",
  457. model_param.graph_id, op_desc->GetName().c_str(), i, p2p_workspace_offset, p2p_workspace_bytes, p2p_mem_addr);
  458. continue;
  459. }
  460. if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_L1) {
  461. v_workspace_data_addr.push_back(reinterpret_cast<uint8_t *>(static_cast<intptr_t>(v_workspace_offset[i])));
  462. GELOGI("[IMAS]GetWorkspaceDataAddrs graph_%u type[L1] name[%s], mem_addr[workspace index %zu]:0x%lx",
  463. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i]);
  464. } else if (v_workspace_bytes[i] == 0) {
  465. v_workspace_data_addr.push_back(nullptr);
  466. GELOGI("[IMAS]GetWorkspaceDataAddrs graph_%u type[F] name[%s] workspace[%zu] offset[%ld] bytes[%ld] Null addr",
  467. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i], v_workspace_bytes[i]);
  468. } else {
  469. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, v_workspace_offset[i]);
  470. uint8_t *mem_addr = model_param.mem_base + v_workspace_offset[i];
  471. v_workspace_data_addr.push_back(mem_addr);
  472. GELOGI("[IMAS]GetWorkspaceDataAddrs graph_%u type[F] name[%s] workspace[%zu] offset[%ld] bytes[%ld] memaddr[%p]",
  473. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i], v_workspace_bytes[i],
  474. mem_addr);
  475. }
  476. }
  477. return v_workspace_data_addr;
  478. }
  479. ///
  480. /// @ingroup ge
  481. /// @brief Get runtime memory address.
  482. /// @return Status
  483. ///
  484. Status ModelUtils::GetRtAddress(const RuntimeParam &param, uintptr_t logic_addr, uint8_t *&mem_addr) {
  485. uint8_t *runtime_base_addr = nullptr;
  486. if ((param.logic_mem_base <= logic_addr) && (logic_addr < param.logic_mem_base + param.mem_size)) {
  487. runtime_base_addr = param.mem_base - param.logic_mem_base;
  488. GELOGI("The logic addr:0x%lx is data address, base:0x%lx, size:%lu", logic_addr, param.logic_mem_base,
  489. param.mem_size);
  490. } else if ((param.logic_weight_base <= logic_addr) && (logic_addr < param.logic_weight_base + param.weight_size)) {
  491. runtime_base_addr = param.weight_base - param.logic_weight_base;
  492. GELOGI("The logic addr:0x%lx is weight address, base:0x%lx, size:%lu", logic_addr, param.logic_weight_base,
  493. param.weight_size);
  494. } else if ((param.logic_var_base <= logic_addr) && (logic_addr < param.logic_var_base + param.var_size)) {
  495. runtime_base_addr = param.var_base - param.logic_var_base;
  496. GELOGI("The logic addr:0x%lx is variable address, base:0x%lx, size:%lu", logic_addr, param.logic_var_base,
  497. param.var_size);
  498. } else if (logic_addr != 0) {
  499. mem_addr = nullptr;
  500. GELOGE(PARAM_INVALID, "The logic addr:0x%lx is abnormal", logic_addr);
  501. return PARAM_INVALID;
  502. }
  503. mem_addr = runtime_base_addr + logic_addr;
  504. return SUCCESS;
  505. }
  506. } // 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