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