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model_utils.cc 26 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/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. #include "graph/types.h"
  23. #define VALIDATE_MEM_RANGE(OP, SIZE, OFFSET) \
  24. do { \
  25. if (SIZE <= static_cast<uint64_t>(OFFSET)) { \
  26. REPORT_INNER_ERROR("E19999", \
  27. "Node:%s(%s) offset:%ld out of range size:%lu, check invalid", \
  28. OP->GetName().c_str(), OP->GetType().c_str(), OFFSET, SIZE); \
  29. GELOGE(OUT_OF_MEMORY, "Node: %s, memory out of range[%lu: %ld]", OP->GetName().c_str(), SIZE, OFFSET); \
  30. return {}; \
  31. } \
  32. } while (0)
  33. namespace ge {
  34. ///
  35. /// @ingroup ge
  36. /// @brief Get input size.
  37. /// @return vector<int64_t>
  38. ///
  39. vector<int64_t> ModelUtils::GetInputSize(ConstOpDescPtr op_desc) {
  40. vector<int64_t> v_input_size;
  41. GE_CHECK_NOTNULL_EXEC(op_desc, return v_input_size);
  42. const size_t inputs_size = op_desc->GetAllInputsSize();
  43. for (size_t i = 0; i < inputs_size; ++i) {
  44. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(i);
  45. if (tensor_desc == nullptr) {
  46. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  47. continue;
  48. }
  49. int64_t tensor_size = 0;
  50. GE_IF_BOOL_EXEC(
  51. TensorUtils::GetSize(*tensor_desc, tensor_size) != GRAPH_SUCCESS,
  52. GELOGI("Get size from TensorDesc failed, op : %s, input index : %zu", op_desc->GetName().c_str(), i);
  53. continue);
  54. GELOGI("GetInputSize op: %s, index: %zu, size:%ld", op_desc->GetName().c_str(), i, tensor_size);
  55. v_input_size.push_back(tensor_size);
  56. }
  57. return v_input_size;
  58. }
  59. ///
  60. /// @ingroup ge
  61. /// @brief Get output size.
  62. /// @return vector<int64_t>
  63. ///
  64. vector<int64_t> ModelUtils::GetOutputSize(ConstOpDescPtr op_desc) {
  65. vector<int64_t> v_output_size;
  66. GE_CHECK_NOTNULL_EXEC(op_desc, return v_output_size);
  67. const size_t outputs_size = op_desc->GetOutputsSize();
  68. const vector<int64_t> v_output_offset = op_desc->GetOutputOffset();
  69. GE_IF_BOOL_EXEC(v_output_offset.size() != outputs_size,
  70. GELOGW("Output param invalid: output_offset=%zu, outputs=%zu.", v_output_offset.size(), outputs_size);
  71. return v_output_size;);
  72. for (size_t i = 0; i < outputs_size; ++i) {
  73. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  74. if (tensor_desc == nullptr) {
  75. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  76. continue;
  77. }
  78. int64_t tensor_size = 0;
  79. GE_IF_BOOL_EXEC(
  80. TensorUtils::GetSize(*tensor_desc, tensor_size) != GRAPH_SUCCESS,
  81. GELOGI("Get size from TensorDesc failed, op : %s, output index : %zu", op_desc->GetName().c_str(), i);
  82. continue);
  83. GELOGI("GetOutputSize op: %s, index: %zu, size:%ld", op_desc->GetName().c_str(), i, tensor_size);
  84. v_output_size.push_back(tensor_size);
  85. }
  86. return v_output_size;
  87. }
  88. ///
  89. /// @ingroup ge
  90. /// @brief Get workspace size.
  91. /// @return vector<int64_t>
  92. ///
  93. vector<int64_t> ModelUtils::GetWorkspaceSize(ConstOpDescPtr op_desc) {
  94. vector<int64_t> v_workspace_size;
  95. GE_CHECK_NOTNULL_EXEC(op_desc, return v_workspace_size);
  96. const vector<int64_t> v_workspace_num = op_desc->GetWorkspace();
  97. const vector<int64_t> v_workspace_bytes = op_desc->GetWorkspaceBytes();
  98. if (v_workspace_num.size() != v_workspace_bytes.size()) {
  99. GELOGW("workspace_num[%zu]!= workspace_bytes[%zu]", v_workspace_num.size(), v_workspace_bytes.size());
  100. return v_workspace_size;
  101. }
  102. for (auto workspace_bytes : v_workspace_bytes) {
  103. v_workspace_size.push_back(workspace_bytes);
  104. }
  105. return v_workspace_size;
  106. }
  107. ///
  108. /// @ingroup ge
  109. /// @brief Get weight size.
  110. /// @return vector<int64_t>
  111. ///
  112. vector<int64_t> ModelUtils::GetWeightSize(ConstOpDescPtr op_desc) {
  113. vector<int64_t> v_weight_size;
  114. GE_CHECK_NOTNULL_EXEC(op_desc, return v_weight_size);
  115. // const op, get weight directly
  116. const string type_name = op_desc->GetType();
  117. if ((type_name == "Const") || (type_name == "Constant")) {
  118. ConstGeTensorPtr weight = nullptr;
  119. if (AttrUtils::GetTensor(*op_desc, ATTR_NAME_WEIGHTS, weight)) {
  120. v_weight_size.push_back(TensorUtils::GetWeightSize(weight));
  121. }
  122. return v_weight_size;
  123. }
  124. // other ops get weight from connected constop
  125. const size_t inputs_size = op_desc->GetAllInputsSize();
  126. const vector<bool> v_is_input_const = op_desc->GetIsInputConst();
  127. for (size_t i = 0; i < inputs_size; ++i) {
  128. if ((i < v_is_input_const.size()) && v_is_input_const[i]) {
  129. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(i);
  130. if (tensor_desc == nullptr) {
  131. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  132. continue;
  133. }
  134. int64_t tensor_size = 0;
  135. (void)TensorUtils::GetSize(*tensor_desc, tensor_size);
  136. v_weight_size.push_back(tensor_size);
  137. }
  138. }
  139. return v_weight_size;
  140. }
  141. ///
  142. /// @ingroup ge
  143. /// @brief Get weights.
  144. /// @return vector<ConstGeTensorPtr>
  145. ///
  146. vector<ConstGeTensorPtr> ModelUtils::GetWeights(ConstOpDescPtr op_desc) {
  147. vector<ConstGeTensorPtr> v_weights;
  148. GE_CHECK_NOTNULL_EXEC(op_desc, return v_weights);
  149. // const op, get weight directly
  150. const string op_type = op_desc->GetType();
  151. if ((op_type == "Const") || (op_type == "Constant")) {
  152. ConstGeTensorPtr weight = nullptr;
  153. if (AttrUtils::GetTensor(*op_desc, ATTR_NAME_WEIGHTS, weight)) {
  154. v_weights.push_back(weight);
  155. }
  156. return v_weights;
  157. }
  158. // other ops get weight from connected constop
  159. const size_t inputs_size = op_desc->GetAllInputsSize();
  160. const vector<bool> v_is_input_const = op_desc->GetIsInputConst();
  161. for (size_t i = 0; i < inputs_size; ++i) {
  162. if ((i < v_is_input_const.size()) && v_is_input_const[i]) {
  163. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(i);
  164. if (tensor_desc == nullptr) {
  165. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  166. continue;
  167. }
  168. ConstGeTensorPtr weight = nullptr;
  169. if (AttrUtils::GetTensor(*tensor_desc, ATTR_NAME_WEIGHTS, weight)) {
  170. v_weights.push_back(weight);
  171. }
  172. }
  173. }
  174. return v_weights;
  175. }
  176. ///
  177. /// @ingroup ge
  178. /// @brief Get AiCpuOp Input descriptor.
  179. /// @return vector<::tagCcAICPUTensor>
  180. ///
  181. vector<::tagCcAICPUTensor> ModelUtils::GetInputDescs(ConstOpDescPtr op_desc) {
  182. // AiCpuOp::GetInputDescs
  183. vector<::opTensor_t> v_input_descs;
  184. GE_CHECK_NOTNULL_EXEC(op_desc, return v_input_descs);
  185. const size_t inputs_size = op_desc->GetAllInputsSize();
  186. const vector<bool> v_is_input_const = op_desc->GetIsInputConst();
  187. for (size_t i = 0; i < inputs_size; ++i) {
  188. if ((i < v_is_input_const.size()) && v_is_input_const[i]) { // skip Const input node
  189. continue;
  190. }
  191. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(i);
  192. if (tensor_desc == nullptr) {
  193. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  194. continue;
  195. }
  196. uint32_t dim_cnt = 0;
  197. GE_CHK_BOOL_EXEC_WARN(TensorUtils::GetRealDimCnt(*tensor_desc, dim_cnt) == GRAPH_SUCCESS, continue,
  198. "Get dim_cnt failed");
  199. opTensor_t tmp;
  200. uint32_t tmp_fmt = tensor_desc->GetFormat();
  201. tmp.format = tagOpTensorFormat(tmp_fmt);
  202. tmp.dim_cnt = static_cast<int32_t>(dim_cnt);
  203. uint32_t tmp_type = tensor_desc->GetDataType();
  204. tmp.data_type = tagOpDataType(tmp_type);
  205. for (int32_t j = 0; j < 4; j++) { // 4 dims
  206. tmp.dim[j] = (j < tmp.dim_cnt ? tensor_desc->GetShape().GetDim(j) : 1);
  207. }
  208. v_input_descs.push_back(tmp);
  209. }
  210. return v_input_descs;
  211. }
  212. ///
  213. /// @ingroup ge
  214. /// @brief Get AiCpuOp Output descriptor.
  215. /// @return vector<::tagCcAICPUTensor>
  216. ///
  217. vector<::tagCcAICPUTensor> ModelUtils::GetOutputDescs(ConstOpDescPtr op_desc) {
  218. // AiCpuOp::GetOutputDescs
  219. vector<::opTensor_t> v_output_descs;
  220. GE_CHECK_NOTNULL_EXEC(op_desc, return v_output_descs);
  221. // init op output opTensor_t struct
  222. const size_t output_num = op_desc->GetOutputsSize();
  223. for (size_t i = 0; i < output_num; ++i) {
  224. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  225. if (tensor_desc == nullptr) {
  226. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  227. continue;
  228. }
  229. uint32_t dim_cnt = 0;
  230. GE_CHK_BOOL_EXEC_WARN(TensorUtils::GetRealDimCnt(*tensor_desc, dim_cnt) == GRAPH_SUCCESS, continue,
  231. "Get dim_cnt failed");
  232. opTensor_t tmp;
  233. uint32_t tmp_fmt = tensor_desc->GetFormat();
  234. tmp.format = tagOpTensorFormat(tmp_fmt);
  235. tmp.dim_cnt = static_cast<int32_t>(dim_cnt);
  236. uint32_t tmp_type = tensor_desc->GetDataType();
  237. tmp.data_type = tagOpDataType(tmp_type);
  238. for (int32_t j = 0; j < 4; j++) { // 4 dims
  239. tmp.dim[j] = (j < tmp.dim_cnt ? tensor_desc->GetShape().GetDim(j) : 1);
  240. }
  241. v_output_descs.push_back(tmp);
  242. }
  243. return v_output_descs;
  244. }
  245. ///
  246. /// @ingroup ge
  247. /// @brief Get input data address.
  248. /// @return vector<void*>
  249. ///
  250. vector<void *> ModelUtils::GetInputDataAddrs(const RuntimeParam &model_param, ConstOpDescPtr op_desc) {
  251. vector<void *> v_input_data_addr; // init as:buf_base + op_def_->input(i));
  252. GE_CHECK_NOTNULL_EXEC(op_desc, return v_input_data_addr);
  253. uint64_t session_id = model_param.session_id;
  254. const size_t inputs_size = op_desc->GetInputsSize();
  255. const vector<int64_t> v_input_offset = op_desc->GetInputOffset();
  256. const string op_type = op_desc->GetType();
  257. size_t non_const_index = 0;
  258. const vector<bool> v_is_input_const = op_desc->GetIsInputConst();
  259. vector<int64_t> v_memory_type;
  260. bool has_mem_type_attr = ge::AttrUtils::GetListInt(op_desc, ATTR_NAME_INPUT_MEM_TYPE_LIST, v_memory_type);
  261. if (has_mem_type_attr && (v_memory_type.size() != inputs_size)) {
  262. REPORT_INNER_ERROR("E19999", "Attr:%s, memory_type.size:%zu != input_desc.size:%zu, op:%s(%s), check invalid",
  263. ATTR_NAME_INPUT_MEM_TYPE_LIST.c_str(), v_memory_type.size(), inputs_size,
  264. op_desc->GetName().c_str(), op_desc->GetType().c_str());
  265. GELOGE(PARAM_INVALID, "Fusion: check input size failed, op: %s, input v_memory_type size: %zu input numbers: %zu",
  266. op_desc->GetName().c_str(), v_memory_type.size(), inputs_size);
  267. return v_input_data_addr;
  268. }
  269. for (size_t i = 0; i < op_desc->GetAllInputsSize(); ++i) {
  270. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(static_cast<uint32_t>(i));
  271. GE_IF_BOOL_EXEC(tensor_desc == nullptr, GELOGD("Op: %s, Index: %zu, has no input", op_desc->GetName().c_str(), i);
  272. continue;)
  273. if ((i < v_is_input_const.size()) && v_is_input_const[i]) {
  274. // TBE: add weights address to input
  275. int64_t tensor_size = 0;
  276. GE_CHK_STATUS(TensorUtils::GetSize(*tensor_desc, tensor_size));
  277. if (tensor_size) {
  278. int64_t data_offset = 0;
  279. GE_CHK_STATUS(TensorUtils::GetDataOffset(*tensor_desc, data_offset));
  280. VALIDATE_MEM_RANGE(op_desc, model_param.weight_size, data_offset);
  281. uint8_t *weight_addr = model_param.weight_base + data_offset;
  282. v_input_data_addr.push_back(weight_addr);
  283. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[C] name[%s] input[%zu] memaddr[%p]", model_param.graph_id,
  284. op_desc->GetName().c_str(), i, weight_addr);
  285. }
  286. non_const_index++;
  287. continue;
  288. }
  289. GE_IF_BOOL_EXEC(non_const_index >= v_input_offset.size(), break);
  290. int64_t input_offset = v_input_offset[non_const_index];
  291. non_const_index++;
  292. GE_IF_BOOL_EXEC(model_param.var_size != 0 && ge::VarManager::Instance(session_id)->IsVarAddr(input_offset),
  293. uint8_t *variable_addr = nullptr;
  294. GE_CHK_STATUS_EXEC(GetVarAddr(model_param, op_desc, input_offset, variable_addr), return {});
  295. v_input_data_addr.push_back(variable_addr);
  296. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[V] name[%s] input[%lu] memaddr[%p]",
  297. model_param.graph_id, op_desc->GetName().c_str(), i, variable_addr);
  298. continue);
  299. int64_t mem_type;
  300. bool tensor_has_mem_type = ge::AttrUtils::GetInt(tensor_desc, ATTR_NAME_TENSOR_MEM_TYPE, mem_type);
  301. // feature maps
  302. void *mem_addr = nullptr;
  303. if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_L1) { // fusion
  304. mem_addr = reinterpret_cast<uint8_t *>(static_cast<intptr_t>(input_offset));
  305. v_input_data_addr.push_back(mem_addr);
  306. } else if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_TS_4G) {
  307. int64_t tensor_size = 0;
  308. GE_CHK_STATUS_EXEC(TensorUtils::GetSize(*tensor_desc, tensor_size), return {});
  309. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, input_offset);
  310. mem_addr = model_param.ts_mem_mall->Acquire(input_offset, static_cast<uint64_t>(tensor_size));
  311. v_input_data_addr.push_back(mem_addr);
  312. } else if (tensor_has_mem_type && mem_type == RT_MEMORY_P2P_DDR) {
  313. uint8_t *p2p_mem_addr = model_param.memory_infos.at(RT_MEMORY_P2P_DDR).memory_base + v_input_offset[i];
  314. v_input_data_addr.push_back(p2p_mem_addr);
  315. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[P] name[%s] input[%zu] memaddr[%p]", model_param.graph_id,
  316. op_desc->GetName().c_str(), i, p2p_mem_addr);
  317. continue;
  318. } else {
  319. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, input_offset);
  320. mem_addr = model_param.mem_base + input_offset;
  321. v_input_data_addr.push_back(mem_addr);
  322. }
  323. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[F] name[%s] input[%zu] memaddr[%p]", model_param.graph_id,
  324. op_desc->GetName().c_str(), i, mem_addr);
  325. }
  326. return v_input_data_addr;
  327. }
  328. ///
  329. /// @ingroup ge
  330. /// @brief Get variable address.
  331. /// @return Status
  332. ///
  333. Status ModelUtils::GetVarAddr(const RuntimeParam &model_param, const ConstOpDescPtr &op_desc, int64_t offset,
  334. uint8_t *&var_addr) {
  335. rtMemType_t mem_type = ge::VarManager::Instance(model_param.session_id)->GetVarMemType(offset);
  336. switch (mem_type) {
  337. case RT_MEMORY_RDMA_HBM:
  338. if (offset < 0) {
  339. REPORT_INNER_ERROR("E19999", "Param offset:%ld < 0, check invalid", offset);
  340. GELOGE(PARAM_INVALID, "rdma var addr is invalid, addr=%p",
  341. reinterpret_cast<uint8_t *>(static_cast<uintptr_t>(offset)));
  342. return PARAM_INVALID;
  343. }
  344. var_addr = reinterpret_cast<uint8_t *>(static_cast<uintptr_t>(offset));
  345. break;
  346. case RT_MEMORY_HBM:
  347. VALIDATE_MEM_RANGE(op_desc, model_param.var_size, offset - model_param.logic_var_base);
  348. var_addr = model_param.var_base + offset - model_param.logic_var_base;
  349. break;
  350. default:
  351. REPORT_INNER_ERROR("E19999", "Get mem_type:%d for offset:%ld is unsupported, check invalid",
  352. mem_type, offset);
  353. GELOGE(PARAM_INVALID, "unsupported memory type %u", mem_type);
  354. return PARAM_INVALID;
  355. }
  356. GE_CHECK_NOTNULL(var_addr);
  357. return SUCCESS;
  358. }
  359. ///
  360. /// @ingroup ge
  361. /// @brief Get output data address.
  362. /// @return vector<void*>
  363. ///
  364. vector<void *> ModelUtils::GetOutputDataAddrs(const RuntimeParam &model_param, ConstOpDescPtr op_desc) {
  365. vector<void *> v_output_data_addr; // init as:buf_base + op_def_->output(i)
  366. GE_CHECK_NOTNULL_EXEC(op_desc, return v_output_data_addr);
  367. uint64_t session_id = model_param.session_id;
  368. const size_t outputs_size = op_desc->GetOutputsSize();
  369. const vector<int64_t> v_output_offset = op_desc->GetOutputOffset();
  370. GE_IF_BOOL_EXEC(v_output_offset.size() != outputs_size,
  371. GELOGW("Output param invalid: output_offset=%zu, outputs=%zu.", v_output_offset.size(), outputs_size);
  372. return v_output_data_addr);
  373. vector<int64_t> v_memory_type;
  374. bool has_mem_type_attr = ge::AttrUtils::GetListInt(op_desc, ATTR_NAME_OUTPUT_MEM_TYPE_LIST, v_memory_type);
  375. if (has_mem_type_attr && (v_memory_type.size() != outputs_size)) {
  376. REPORT_INNER_ERROR("E19999", "Attr:%s, memory_type.size:%zu != output_desc.size:%zu, op:%s(%s), check invalid",
  377. ATTR_NAME_OUTPUT_MEM_TYPE_LIST.c_str(), v_memory_type.size(), outputs_size,
  378. op_desc->GetName().c_str(), op_desc->GetType().c_str());
  379. GELOGE(PARAM_INVALID,
  380. "Fusion: check output size failed, op: %s, output v_memory_type size: %lu output numbers: %zu",
  381. op_desc->GetName().c_str(), v_memory_type.size(), outputs_size);
  382. return v_output_data_addr;
  383. }
  384. for (size_t i = 0; i < outputs_size; ++i) {
  385. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  386. if (tensor_desc == nullptr) {
  387. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  388. continue;
  389. }
  390. int32_t calc_type = 0;
  391. bool ret = ge::AttrUtils::GetInt(tensor_desc, ATTR_NAME_MEMORY_SIZE_CALC_TYPE, calc_type);
  392. if (ret && (calc_type == static_cast<int32_t>(ge::MemorySizeCalcType::ALWAYS_EMPTY))) {
  393. GELOGD("%s is an optional output, the address don't need to be saved.", tensor_desc->GetName().c_str());
  394. continue;
  395. }
  396. GE_IF_BOOL_EXEC(model_param.var_size != 0 && ge::VarManager::Instance(session_id)->IsVarAddr(v_output_offset[i]),
  397. uint8_t *variable_addr = nullptr;
  398. GE_CHK_STATUS_EXEC(GetVarAddr(model_param, op_desc, v_output_offset[i], variable_addr), return {});
  399. v_output_data_addr.push_back(variable_addr);
  400. GELOGI("[IMAS]GetOutputDataAddrs graph_%u type[V] name[%s] output[%zu] memaddr[%p]",
  401. model_param.graph_id, op_desc->GetName().c_str(), i, variable_addr);
  402. continue);
  403. int64_t mem_type;
  404. bool tensor_has_mem_type = ge::AttrUtils::GetInt(tensor_desc, ATTR_NAME_TENSOR_MEM_TYPE, mem_type);
  405. // feature maps
  406. void *mem_addr = nullptr;
  407. if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_L1) { // fusion
  408. mem_addr = reinterpret_cast<uint8_t *>(static_cast<intptr_t>(v_output_offset[i]));
  409. v_output_data_addr.push_back(mem_addr);
  410. } else if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_TS_4G) {
  411. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  412. GE_CHECK_NOTNULL_EXEC(tensor_desc, return {});
  413. int64_t tensor_size = 0;
  414. GE_CHK_STATUS_EXEC(TensorUtils::GetSize(*tensor_desc, tensor_size), return {});
  415. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, v_output_offset[i]);
  416. mem_addr = model_param.ts_mem_mall->Acquire(v_output_offset[i], static_cast<uint64_t>(tensor_size));
  417. v_output_data_addr.push_back(mem_addr);
  418. } else if (tensor_has_mem_type && mem_type == RT_MEMORY_P2P_DDR) {
  419. uint8_t *p2p_mem_addr = model_param.memory_infos.at(RT_MEMORY_P2P_DDR).memory_base + v_output_offset[i];
  420. v_output_data_addr.push_back(p2p_mem_addr);
  421. GELOGI("[IMAS]GetOutputDataAddrs graph_%u type[P] name[%s] output[%zu] memaddr[%p]", model_param.graph_id,
  422. op_desc->GetName().c_str(), i, p2p_mem_addr);
  423. continue;
  424. } else {
  425. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, v_output_offset[i]);
  426. mem_addr = static_cast<uint8_t *>(model_param.mem_base + v_output_offset[i]);
  427. v_output_data_addr.push_back(mem_addr);
  428. }
  429. GELOGI("[IMAS]GetOutputDataAddrs graph_%u type[F] name[%s] output[%zu] memaddr[%p]", model_param.graph_id,
  430. op_desc->GetName().c_str(), i, mem_addr);
  431. }
  432. return v_output_data_addr;
  433. }
  434. ///
  435. /// @ingroup ge
  436. /// @brief Get workspace data address.
  437. /// @return vector<void*>
  438. ///
  439. vector<void *> ModelUtils::GetWorkspaceDataAddrs(const RuntimeParam &model_param, ConstOpDescPtr op_desc) {
  440. vector<void *> v_workspace_data_addr;
  441. GE_CHECK_NOTNULL_EXEC(op_desc, return v_workspace_data_addr);
  442. const vector<int64_t> v_workspace_offset = op_desc->GetWorkspace();
  443. const vector<int64_t> v_workspace_bytes = op_desc->GetWorkspaceBytes();
  444. if (v_workspace_offset.size() != v_workspace_bytes.size()) {
  445. GELOGW("v_workspace_offset.size()[%zu] != v_workspace_bytes.size()[%zu]", v_workspace_offset.size(),
  446. v_workspace_bytes.size());
  447. return v_workspace_data_addr;
  448. }
  449. vector<bool> workspace_reuse_flag;
  450. bool has_workspace_reuse = ge::AttrUtils::GetListBool(op_desc, "workspace_reuse_flag", workspace_reuse_flag);
  451. vector<int64_t> v_memory_type;
  452. vector<int64_t> workspace_memory_type;
  453. bool has_mem_type_attr = ge::AttrUtils::GetListInt(op_desc, TVM_ATTR_NAME_WORKSPACE_TYPE, v_memory_type);
  454. bool has_mem_type_workspace =
  455. ge::AttrUtils::GetListInt(op_desc, ATTR_NAME_WORKSPACE_TYPE_LIST, workspace_memory_type);
  456. for (size_t i = 0; i < v_workspace_bytes.size(); ++i) {
  457. // Temporary solution, the aicpu workspace of multiple images cannot be shared.
  458. if (has_workspace_reuse && i < workspace_reuse_flag.size() && !workspace_reuse_flag[i] &&
  459. !model_param.is_single_op) {
  460. void *mem_addr = model_param.aicpu_mem_mall->Acquire(v_workspace_offset[i], v_workspace_bytes[i]);
  461. v_workspace_data_addr.push_back(mem_addr);
  462. GELOGI(
  463. "[IMAS]GetWorkspaceDataAddrs graph_%u type[F] name[%s] aicpu workspace[%zu] offset[%ld] bytes[%ld] "
  464. "memaddr[%p]",
  465. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i], v_workspace_bytes[i], mem_addr);
  466. continue;
  467. } else if (has_mem_type_workspace && workspace_memory_type[i] == RT_MEMORY_P2P_DDR) {
  468. int64_t p2p_workspace_offset = v_workspace_offset[i];
  469. int64_t p2p_workspace_bytes = v_workspace_bytes[i];
  470. uint8_t *p2p_mem_addr = p2p_workspace_bytes == 0
  471. ? nullptr
  472. : model_param.memory_infos.at(RT_MEMORY_P2P_DDR).memory_base + p2p_workspace_offset;
  473. v_workspace_data_addr.push_back(p2p_mem_addr);
  474. GELOGI(
  475. "[IMAS]GetWorkspaceDataAddrs graph_%u type[P] name[%s] p2p workspace[%zu] offset[%ld] bytes[%ld] "
  476. "memaddr[%p]",
  477. model_param.graph_id, op_desc->GetName().c_str(), i, p2p_workspace_offset, p2p_workspace_bytes, p2p_mem_addr);
  478. continue;
  479. }
  480. if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_L1) {
  481. v_workspace_data_addr.push_back(reinterpret_cast<uint8_t *>(static_cast<intptr_t>(v_workspace_offset[i])));
  482. GELOGI("[IMAS]GetWorkspaceDataAddrs graph_%u type[L1] name[%s], mem_addr[workspace index %zu]:0x%lx",
  483. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i]);
  484. } else if (v_workspace_bytes[i] == 0) {
  485. v_workspace_data_addr.push_back(nullptr);
  486. GELOGI("[IMAS]GetWorkspaceDataAddrs graph_%u type[F] name[%s] workspace[%zu] offset[%ld] bytes[%ld] Null addr",
  487. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i], v_workspace_bytes[i]);
  488. } else {
  489. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, v_workspace_offset[i]);
  490. uint8_t *mem_addr = model_param.mem_base + v_workspace_offset[i];
  491. v_workspace_data_addr.push_back(mem_addr);
  492. GELOGI("[IMAS]GetWorkspaceDataAddrs graph_%u type[F] name[%s] workspace[%zu] offset[%ld] bytes[%ld] memaddr[%p]",
  493. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i], v_workspace_bytes[i],
  494. mem_addr);
  495. }
  496. }
  497. return v_workspace_data_addr;
  498. }
  499. ///
  500. /// @ingroup ge
  501. /// @brief Get runtime memory address.
  502. /// @return Status
  503. ///
  504. Status ModelUtils::GetRtAddress(const RuntimeParam &param, uintptr_t logic_addr, uint8_t *&mem_addr) {
  505. uint8_t *runtime_base_addr = nullptr;
  506. if ((param.logic_mem_base <= logic_addr) && (logic_addr < param.logic_mem_base + param.mem_size)) {
  507. runtime_base_addr = param.mem_base - param.logic_mem_base;
  508. GELOGI("The logic addr:0x%lx is data address, base:0x%lx, size:%lu", logic_addr, param.logic_mem_base,
  509. param.mem_size);
  510. } else if ((param.logic_weight_base <= logic_addr) && (logic_addr < param.logic_weight_base + param.weight_size)) {
  511. runtime_base_addr = param.weight_base - param.logic_weight_base;
  512. GELOGI("The logic addr:0x%lx is weight address, base:0x%lx, size:%lu", logic_addr, param.logic_weight_base,
  513. param.weight_size);
  514. } else if ((param.logic_var_base <= logic_addr) && (logic_addr < param.logic_var_base + param.var_size)) {
  515. runtime_base_addr = param.var_base - param.logic_var_base;
  516. GELOGI("The logic addr:0x%lx is variable address, base:0x%lx, size:%lu", logic_addr, param.logic_var_base,
  517. param.var_size);
  518. } else if (logic_addr != 0) {
  519. mem_addr = nullptr;
  520. REPORT_INNER_ERROR("E19999", "Check param logic addr:0x%lx abnormal", logic_addr);
  521. GELOGE(PARAM_INVALID, "The logic addr:0x%lx is abnormal", logic_addr);
  522. return PARAM_INVALID;
  523. }
  524. mem_addr = runtime_base_addr + logic_addr;
  525. return SUCCESS;
  526. }
  527. } // 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