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