You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.

model_utils.cc 29 kB

5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
4 years ago
5 years ago
4 years ago
4 years ago
5 years ago
4 years ago
5 years ago
4 years ago
4 years ago
5 years ago
4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
4 years ago
5 years ago
5 years ago
4 years ago
5 years ago
4 years ago
4 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
4 years ago
5 years ago
4 years ago
5 years ago
5 years ago
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623
  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 "framework/common/debug/log.h"
  19. #include "framework/common/op/ge_op_utils.h"
  20. #include "graph/utils/tensor_utils.h"
  21. #include "graph/manager/graph_var_manager.h"
  22. #include "external/graph/types.h"
  23. #include "graph/build/memory/block_mem_assigner.h"
  24. #include "common/math/math_util.h"
  25. #define VALIDATE_MEM_RANGE(OP, TOTAL_SIZE, OFFSET, SIZE) \
  26. do { \
  27. if (ge::CheckInt64AddOverflow((OFFSET), (SIZE)) != SUCCESS) { \
  28. GELOGE(PARAM_INVALID, "Int64 %ld and %ld addition can result in overflow!", \
  29. static_cast<int64_t>(OFFSET), static_cast<int64_t>(SIZE)); \
  30. return {}; \
  31. } \
  32. int64_t range = (OFFSET) + (SIZE); \
  33. if ((TOTAL_SIZE) < static_cast<uint64_t>(range)) { \
  34. REPORT_INNER_ERROR("E19999", \
  35. "Node:%s(%s) memory out of range, offset:%ld, size:%ld, exceed total size:%lu.", \
  36. OP->GetName().c_str(), OP->GetType().c_str(), (OFFSET), (SIZE), (TOTAL_SIZE)); \
  37. GELOGE(OUT_OF_MEMORY, \
  38. "[Check][Param]Node:%s(%s) memory out of range, offset:%ld, size:%ld, exceed total size:%lu.", \
  39. OP->GetName().c_str(), OP->GetType().c_str(), (OFFSET), (SIZE), (TOTAL_SIZE)); \
  40. return {}; \
  41. } \
  42. } while (0)
  43. namespace ge {
  44. ///
  45. /// @ingroup ge
  46. /// @brief Get input size.
  47. /// @return vector<int64_t>
  48. ///
  49. vector<int64_t> ModelUtils::GetInputSize(ConstOpDescPtr op_desc) {
  50. vector<int64_t> v_input_size;
  51. GE_CHECK_NOTNULL_EXEC(op_desc, return v_input_size);
  52. const size_t inputs_size = op_desc->GetAllInputsSize();
  53. for (size_t i = 0; i < inputs_size; ++i) {
  54. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(i);
  55. if (tensor_desc == nullptr) {
  56. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  57. continue;
  58. }
  59. int64_t tensor_size = 0;
  60. GE_IF_BOOL_EXEC(
  61. TensorUtils::GetSize(*tensor_desc, tensor_size) != GRAPH_SUCCESS,
  62. GELOGI("Get size from TensorDesc failed, op : %s, input index : %zu", op_desc->GetName().c_str(), i);
  63. continue);
  64. GELOGI("GetInputSize op: %s, index: %zu, size:%ld", op_desc->GetName().c_str(), i, tensor_size);
  65. v_input_size.push_back(tensor_size);
  66. }
  67. return v_input_size;
  68. }
  69. ///
  70. /// @ingroup ge
  71. /// @brief Get output size.
  72. /// @return vector<int64_t>
  73. ///
  74. vector<int64_t> ModelUtils::GetOutputSize(ConstOpDescPtr op_desc) {
  75. vector<int64_t> v_output_size;
  76. GE_CHECK_NOTNULL_EXEC(op_desc, return v_output_size);
  77. const size_t outputs_size = op_desc->GetOutputsSize();
  78. const vector<int64_t> v_output_offset = op_desc->GetOutputOffset();
  79. GE_IF_BOOL_EXEC(v_output_offset.size() != outputs_size,
  80. GELOGW("Output param invalid: output_offset=%zu, outputs=%zu.", v_output_offset.size(), outputs_size);
  81. return v_output_size;);
  82. for (size_t i = 0; i < outputs_size; ++i) {
  83. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  84. if (tensor_desc == nullptr) {
  85. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  86. continue;
  87. }
  88. int64_t tensor_size = 0;
  89. GE_IF_BOOL_EXEC(
  90. TensorUtils::GetSize(*tensor_desc, tensor_size) != GRAPH_SUCCESS,
  91. GELOGI("Get size from TensorDesc failed, op : %s, output index : %zu", op_desc->GetName().c_str(), i);
  92. continue);
  93. GELOGI("GetOutputSize op: %s, index: %zu, size:%ld", op_desc->GetName().c_str(), i, tensor_size);
  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. REPORT_INNER_ERROR("E19999", "Attr:%s, memory_type.size:%zu != input_desc.size:%zu, op:%s(%s), check invalid",
  273. ATTR_NAME_INPUT_MEM_TYPE_LIST.c_str(), v_memory_type.size(), inputs_size,
  274. op_desc->GetName().c_str(), op_desc->GetType().c_str());
  275. GELOGE(PARAM_INVALID, "[Check][Param] Attr:%s, memory_type.size:%zu != input_desc.size:%zu, op:%s(%s)",
  276. ATTR_NAME_INPUT_MEM_TYPE_LIST.c_str(), v_memory_type.size(), inputs_size,
  277. op_desc->GetName().c_str(), op_desc->GetType().c_str());
  278. return v_input_data_addr;
  279. }
  280. for (size_t i = 0; i < op_desc->GetAllInputsSize(); ++i) {
  281. const GeTensorDescPtr tensor_desc = op_desc->MutableInputDesc(static_cast<uint32_t>(i));
  282. GE_IF_BOOL_EXEC(tensor_desc == nullptr, GELOGD("Op: %s, Index: %zu, has no input", op_desc->GetName().c_str(), i);
  283. continue;)
  284. int64_t tensor_size = 0;
  285. GE_CHK_STATUS_EXEC(TensorUtils::GetSize(*tensor_desc, tensor_size), return {});
  286. if ((i < v_is_input_const.size()) && v_is_input_const[i]) {
  287. // Add weights address to input
  288. int64_t data_offset = 0;
  289. GE_CHK_STATUS(TensorUtils::GetDataOffset(*tensor_desc, data_offset));
  290. int64_t weight_size = 0;
  291. // The reason why GetTensorSizeInBytes is used here is that the weight is allocated based on the size of
  292. // TensorData in function AdjustConstWeightSize. and the size is zero when the tensor is empty.
  293. GE_CHK_STATUS(TensorUtils::GetTensorSizeInBytes(*tensor_desc, weight_size));
  294. VALIDATE_MEM_RANGE(op_desc, model_param.weight_size, data_offset, weight_size);
  295. uint8_t *weight_addr = model_param.weight_base + data_offset;
  296. v_input_data_addr.push_back(weight_addr);
  297. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[C] name[%s] input[%zu] memaddr[%p]", model_param.graph_id,
  298. op_desc->GetName().c_str(), i, weight_addr);
  299. non_const_index++;
  300. continue;
  301. }
  302. GE_IF_BOOL_EXEC(non_const_index >= v_input_offset.size(), break);
  303. int64_t input_offset = v_input_offset[non_const_index];
  304. non_const_index++;
  305. int64_t inner_offset = 0;
  306. (void)ge::AttrUtils::GetInt(op_desc->MutableInputDesc(i), ATTR_NAME_INNER_OFFSET, inner_offset);
  307. GE_IF_BOOL_EXEC(model_param.var_size != 0
  308. && ge::VarManager::Instance(session_id)->IsVarAddr(input_offset - inner_offset),
  309. uint8_t *variable_addr = nullptr;
  310. GE_CHK_STATUS_EXEC(GetVarAddr(model_param, op_desc, input_offset - inner_offset,
  311. tensor_size + inner_offset, variable_addr), return {});
  312. variable_addr += inner_offset;
  313. v_input_data_addr.push_back(variable_addr);
  314. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[V] name[%s] input[%lu] memaddr[%p]",
  315. model_param.graph_id, op_desc->GetName().c_str(), i, variable_addr);
  316. continue);
  317. int64_t mem_type;
  318. bool tensor_has_mem_type = ge::AttrUtils::GetInt(tensor_desc, ATTR_NAME_TENSOR_MEM_TYPE, mem_type);
  319. // feature maps
  320. void *mem_addr = nullptr;
  321. if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_L1) { // fusion
  322. mem_addr = reinterpret_cast<uint8_t *>(static_cast<intptr_t>(input_offset));
  323. v_input_data_addr.push_back(mem_addr);
  324. } else if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_TS_4G) {
  325. // The input size and peer output size may be not consecutive, therefore, the tensor_size is not been checked.
  326. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, input_offset, static_cast<int64_t>(0));
  327. mem_addr = model_param.ts_mem_mall->Acquire(input_offset, static_cast<uint64_t>(tensor_size));
  328. v_input_data_addr.push_back(mem_addr);
  329. } else if (tensor_has_mem_type && mem_type == RT_MEMORY_P2P_DDR) {
  330. uint8_t *p2p_mem_addr = model_param.memory_infos.at(RT_MEMORY_P2P_DDR).memory_base + v_input_offset[i];
  331. v_input_data_addr.push_back(p2p_mem_addr);
  332. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[P] name[%s] input[%zu] memaddr[%p]", model_param.graph_id,
  333. op_desc->GetName().c_str(), i, p2p_mem_addr);
  334. continue;
  335. } else {
  336. // The input size and peer output size may be not consecutive, therefore, the tensor_size is not been checked.
  337. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, input_offset, static_cast<int64_t>(0));
  338. mem_addr = model_param.mem_base + input_offset;
  339. v_input_data_addr.push_back(mem_addr);
  340. }
  341. GELOGI("[IMAS]GetInputDataAddrs graph_%u type[F] name[%s] input[%zu] memaddr[%p]", model_param.graph_id,
  342. op_desc->GetName().c_str(), i, mem_addr);
  343. }
  344. return v_input_data_addr;
  345. }
  346. ///
  347. /// @ingroup ge
  348. /// @brief Get variable address.
  349. /// @return Status
  350. ///
  351. Status ModelUtils::GetVarAddr(const RuntimeParam &model_param, const ConstOpDescPtr &op_desc, int64_t offset,
  352. int64_t tensor_size, uint8_t *&var_addr) {
  353. rtMemType_t mem_type = ge::VarManager::Instance(model_param.session_id)->GetVarMemType(offset);
  354. switch (mem_type) {
  355. case RT_MEMORY_RDMA_HBM:
  356. if (offset < 0) {
  357. REPORT_INNER_ERROR("E19999", "Param offset:%ld < 0, check invalid", offset);
  358. GELOGE(PARAM_INVALID, "[Check][Param] Param offset:%ld cannot be negative", offset);
  359. return PARAM_INVALID;
  360. }
  361. var_addr = reinterpret_cast<uint8_t *>(static_cast<uintptr_t>(offset));
  362. break;
  363. case RT_MEMORY_HBM:
  364. VALIDATE_MEM_RANGE(op_desc, model_param.var_size, offset - model_param.logic_var_base, tensor_size);
  365. var_addr = model_param.var_base + offset - model_param.logic_var_base;
  366. break;
  367. default:
  368. REPORT_INNER_ERROR("E19999", "Get mem_type:%d for offset:%ld is unsupported, check invalid", mem_type, offset);
  369. GELOGE(PARAM_INVALID, "[Check][Param] Get mem_type:%d for offset:%ld is unsupported, check invalid",
  370. mem_type, offset);
  371. return PARAM_INVALID;
  372. }
  373. GE_CHECK_NOTNULL(var_addr);
  374. return SUCCESS;
  375. }
  376. ///
  377. /// @ingroup ge
  378. /// @brief Get output data address.
  379. /// @return vector<void*>
  380. ///
  381. vector<void *> ModelUtils::GetOutputDataAddrs(const RuntimeParam &model_param, ConstOpDescPtr op_desc) {
  382. vector<void *> v_output_data_addr; // init as:buf_base + op_def_->output(i)
  383. GE_CHECK_NOTNULL_EXEC(op_desc, return v_output_data_addr);
  384. uint64_t session_id = model_param.session_id;
  385. const size_t outputs_size = op_desc->GetOutputsSize();
  386. const vector<int64_t> v_output_offset = op_desc->GetOutputOffset();
  387. GE_IF_BOOL_EXEC(v_output_offset.size() != outputs_size,
  388. GELOGW("Output param invalid: output_offset=%zu, outputs=%zu.", v_output_offset.size(), outputs_size);
  389. return v_output_data_addr);
  390. vector<int64_t> v_memory_type;
  391. bool has_mem_type_attr = ge::AttrUtils::GetListInt(op_desc, ATTR_NAME_OUTPUT_MEM_TYPE_LIST, v_memory_type);
  392. if (has_mem_type_attr && (v_memory_type.size() != outputs_size)) {
  393. REPORT_INNER_ERROR("E19999", "Attr:%s, memory_type.size:%zu != output_desc.size:%zu, op:%s(%s), check invalid",
  394. ATTR_NAME_OUTPUT_MEM_TYPE_LIST.c_str(), v_memory_type.size(), outputs_size,
  395. op_desc->GetName().c_str(), op_desc->GetType().c_str());
  396. GELOGE(PARAM_INVALID, "[Check][Param] Attr:%s, memory_type.size:%zu != output_desc.size:%zu, op:%s(%s)",
  397. ATTR_NAME_OUTPUT_MEM_TYPE_LIST.c_str(), v_memory_type.size(), outputs_size,
  398. op_desc->GetName().c_str(), op_desc->GetType().c_str());
  399. return v_output_data_addr;
  400. }
  401. for (size_t i = 0; i < outputs_size; ++i) {
  402. const GeTensorDescPtr tensor_desc = op_desc->MutableOutputDesc(i);
  403. if (tensor_desc == nullptr) {
  404. GELOGW("Op: %s, Index: %zu, Tensor Desc is null", op_desc->GetName().c_str(), i);
  405. continue;
  406. }
  407. int32_t calc_type = 0;
  408. bool ret = ge::AttrUtils::GetInt(tensor_desc, ATTR_NAME_MEMORY_SIZE_CALC_TYPE, calc_type);
  409. if (ret && (calc_type == static_cast<int32_t>(ge::MemorySizeCalcType::ALWAYS_EMPTY))) {
  410. GELOGD("%s is an optional output, the address don't need to be saved.", tensor_desc->GetName().c_str());
  411. continue;
  412. }
  413. int64_t inner_offset = 0;
  414. (void)ge::AttrUtils::GetInt(op_desc->MutableOutputDesc(i), ATTR_NAME_INNER_OFFSET, inner_offset);
  415. int64_t tensor_size = 0;
  416. GE_CHK_STATUS_EXEC(TensorUtils::GetSize(*tensor_desc, tensor_size), return {});
  417. GE_IF_BOOL_EXEC(model_param.var_size != 0
  418. && ge::VarManager::Instance(session_id)->IsVarAddr(v_output_offset[i] - inner_offset),
  419. uint8_t *variable_addr = nullptr;
  420. GE_CHK_STATUS_EXEC(GetVarAddr(model_param, op_desc, v_output_offset[i] - inner_offset,
  421. tensor_size + inner_offset, variable_addr), return {});
  422. variable_addr += inner_offset;
  423. v_output_data_addr.push_back(variable_addr);
  424. GELOGI("[IMAS]GetOutputDataAddrs graph_%u type[V] name[%s] output[%zu] memaddr[%p]",
  425. model_param.graph_id, op_desc->GetName().c_str(), i, variable_addr);
  426. continue);
  427. int64_t mem_type;
  428. bool tensor_has_mem_type = ge::AttrUtils::GetInt(tensor_desc, ATTR_NAME_TENSOR_MEM_TYPE, mem_type);
  429. // feature maps
  430. void *mem_addr = nullptr;
  431. if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_L1) { // fusion
  432. mem_addr = reinterpret_cast<uint8_t *>(static_cast<intptr_t>(v_output_offset[i]));
  433. v_output_data_addr.push_back(mem_addr);
  434. } else if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_TS_4G) {
  435. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, v_output_offset[i], tensor_size);
  436. mem_addr = model_param.ts_mem_mall->Acquire(v_output_offset[i], static_cast<uint64_t>(tensor_size));
  437. v_output_data_addr.push_back(mem_addr);
  438. } else if (tensor_has_mem_type && mem_type == RT_MEMORY_P2P_DDR) {
  439. uint8_t *p2p_mem_addr = model_param.memory_infos.at(RT_MEMORY_P2P_DDR).memory_base + v_output_offset[i];
  440. v_output_data_addr.push_back(p2p_mem_addr);
  441. GELOGI("[IMAS]GetOutputDataAddrs graph_%u type[P] name[%s] output[%zu] memaddr[%p]", model_param.graph_id,
  442. op_desc->GetName().c_str(), i, p2p_mem_addr);
  443. continue;
  444. } else {
  445. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, v_output_offset[i], tensor_size);
  446. mem_addr = static_cast<uint8_t *>(model_param.mem_base + v_output_offset[i]);
  447. v_output_data_addr.push_back(mem_addr);
  448. }
  449. GELOGI("[IMAS]GetOutputDataAddrs graph_%u type[F] name[%s] output[%zu] memaddr[%p]", model_param.graph_id,
  450. op_desc->GetName().c_str(), i, mem_addr);
  451. }
  452. return v_output_data_addr;
  453. }
  454. ///
  455. /// @ingroup ge
  456. /// @brief Get workspace data address.
  457. /// @return vector<void*>
  458. ///
  459. vector<void *> ModelUtils::GetWorkspaceDataAddrs(const RuntimeParam &model_param, ConstOpDescPtr op_desc) {
  460. vector<void *> v_workspace_data_addr;
  461. GE_CHECK_NOTNULL_EXEC(op_desc, return v_workspace_data_addr);
  462. const vector<int64_t> v_workspace_offset = op_desc->GetWorkspace();
  463. const vector<int64_t> v_workspace_bytes = op_desc->GetWorkspaceBytes();
  464. if (v_workspace_offset.size() != v_workspace_bytes.size()) {
  465. GELOGW("v_workspace_offset.size()[%zu] != v_workspace_bytes.size()[%zu]", v_workspace_offset.size(),
  466. v_workspace_bytes.size());
  467. return v_workspace_data_addr;
  468. }
  469. vector<bool> workspace_reuse_flag;
  470. bool has_workspace_reuse = ge::AttrUtils::GetListBool(op_desc, "workspace_reuse_flag", workspace_reuse_flag);
  471. vector<int64_t> v_memory_type;
  472. vector<int64_t> workspace_memory_type;
  473. bool has_mem_type_attr = ge::AttrUtils::GetListInt(op_desc, TVM_ATTR_NAME_WORKSPACE_TYPE, v_memory_type);
  474. bool has_mem_type_workspace =
  475. ge::AttrUtils::GetListInt(op_desc, ATTR_NAME_WORKSPACE_TYPE_LIST, workspace_memory_type);
  476. vector<int32_t> workspace_no_reuse_scope;
  477. bool has_workspace_no_reuse_scope =
  478. ge::AttrUtils::GetListInt(op_desc, ATTR_NAME_WORKSPACE_MEMORY_NO_REUSE_SCOPE, workspace_no_reuse_scope);
  479. for (size_t i = 0; i < v_workspace_bytes.size(); ++i) {
  480. // Temporary solution, the aicpu workspace of multiple images cannot be shared.
  481. bool aicpu_work_space = (has_workspace_reuse && i < workspace_reuse_flag.size() && !workspace_reuse_flag[i] &&
  482. !model_param.is_single_op);
  483. if (aicpu_work_space) {
  484. void *mem_addr = model_param.aicpu_mem_mall->Acquire(v_workspace_offset[i], v_workspace_bytes[i]);
  485. v_workspace_data_addr.push_back(mem_addr);
  486. GELOGI(
  487. "[IMAS]GetWorkspaceDataAddrs graph_%u type[F] name[%s] aicpu workspace[%zu] offset[%ld] bytes[%ld] "
  488. "memaddr[%p]",
  489. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i], v_workspace_bytes[i], mem_addr);
  490. continue;
  491. } else if (has_mem_type_workspace && workspace_memory_type[i] == RT_MEMORY_P2P_DDR) {
  492. int64_t p2p_workspace_offset = v_workspace_offset[i];
  493. int64_t p2p_workspace_bytes = v_workspace_bytes[i];
  494. uint8_t *p2p_mem_addr = p2p_workspace_bytes == 0
  495. ? nullptr
  496. : model_param.memory_infos.at(RT_MEMORY_P2P_DDR).memory_base + p2p_workspace_offset;
  497. v_workspace_data_addr.push_back(p2p_mem_addr);
  498. GELOGI(
  499. "[IMAS]GetWorkspaceDataAddrs graph_%u type[P] name[%s] p2p workspace[%zu] offset[%ld] bytes[%ld] "
  500. "memaddr[%p]",
  501. model_param.graph_id, op_desc->GetName().c_str(), i, p2p_workspace_offset, p2p_workspace_bytes, p2p_mem_addr);
  502. continue;
  503. }
  504. if (has_mem_type_attr && v_memory_type[i] == RT_MEMORY_L1) {
  505. v_workspace_data_addr.push_back(reinterpret_cast<uint8_t *>(static_cast<intptr_t>(v_workspace_offset[i])));
  506. GELOGI("[IMAS]GetWorkspaceDataAddrs graph_%u type[L1] name[%s], mem_addr[workspace index %zu]:0x%lx",
  507. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i]);
  508. } else if (v_workspace_bytes[i] == 0) {
  509. v_workspace_data_addr.push_back(nullptr);
  510. GELOGI("[IMAS]GetWorkspaceDataAddrs graph_%u type[F] name[%s] workspace[%zu] offset[%ld] bytes[%ld] Null addr",
  511. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i], v_workspace_bytes[i]);
  512. } else {
  513. VALIDATE_MEM_RANGE(op_desc, model_param.mem_size, v_workspace_offset[i], v_workspace_bytes[i]);
  514. uint8_t *mem_addr = nullptr;
  515. bool session_scope_memory = (has_workspace_no_reuse_scope) && (i < workspace_no_reuse_scope.size());
  516. if (session_scope_memory) {
  517. mem_addr = model_param.memory_infos.at(kSessionScopeMemory | RT_MEMORY_HBM).memory_base + v_workspace_offset[i];
  518. } else {
  519. mem_addr = model_param.mem_base + v_workspace_offset[i];
  520. }
  521. v_workspace_data_addr.push_back(mem_addr);
  522. GELOGI("[IMAS]GetWorkspaceDataAddrs graph_%u type[F] name[%s] workspace[%zu] offset[%ld] bytes[%ld] memaddr[%p]",
  523. model_param.graph_id, op_desc->GetName().c_str(), i, v_workspace_offset[i], v_workspace_bytes[i],
  524. mem_addr);
  525. }
  526. }
  527. return v_workspace_data_addr;
  528. }
  529. ///
  530. /// @ingroup ge
  531. /// @brief Get runtime memory address.
  532. /// @return Status
  533. ///
  534. Status ModelUtils::GetRtAddress(const RuntimeParam &param, uintptr_t logic_addr, uint8_t *&mem_addr) {
  535. uint8_t *runtime_base_addr = nullptr;
  536. if ((param.logic_mem_base <= logic_addr) && (logic_addr < param.logic_mem_base + param.mem_size)) {
  537. runtime_base_addr = param.mem_base - param.logic_mem_base;
  538. GELOGI("The logic addr:0x%lx is data address, base:0x%lx, size:%lu", logic_addr, param.logic_mem_base,
  539. param.mem_size);
  540. } else if ((param.logic_weight_base <= logic_addr) && (logic_addr < param.logic_weight_base + param.weight_size)) {
  541. runtime_base_addr = param.weight_base - param.logic_weight_base;
  542. GELOGI("The logic addr:0x%lx is weight address, base:0x%lx, size:%lu", logic_addr, param.logic_weight_base,
  543. param.weight_size);
  544. } else if ((param.logic_var_base <= logic_addr) && (logic_addr < param.logic_var_base + param.var_size)) {
  545. runtime_base_addr = param.var_base - param.logic_var_base;
  546. GELOGI("The logic addr:0x%lx is variable address, base:0x%lx, size:%lu", logic_addr, param.logic_var_base,
  547. param.var_size);
  548. } else if (logic_addr != 0) {
  549. mem_addr = nullptr;
  550. REPORT_INNER_ERROR("E19999", "Check param logic addr:0x%lx abnormal", logic_addr);
  551. GELOGE(PARAM_INVALID, "[Check][Param] The logic addr:0x%lx is abnormal", logic_addr);
  552. return PARAM_INVALID;
  553. }
  554. mem_addr = runtime_base_addr + logic_addr;
  555. return SUCCESS;
  556. }
  557. } // 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