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single_op.cc 13 kB

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  1. /**
  2. * Copyright 2019-2020 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 "single_op/single_op.h"
  17. #include "common/fmk_types.h"
  18. #include "common/ge_types.h"
  19. #include "common/math/math_util.h"
  20. #include "common/profiling/profiling_manager.h"
  21. #include "framework/common/debug/ge_log.h"
  22. #include "framework/common/util.h"
  23. #include "graph/load/new_model_manager/model_utils.h"
  24. #include "runtime/mem.h"
  25. #include "single_op/single_op_manager.h"
  26. #include "graph/load/new_model_manager/model_manager.h"
  27. namespace ge {
  28. namespace {
  29. const size_t kDataMemAlignSize = 32;
  30. const size_t kDataMemAlignUnit = 2;
  31. size_t GetAlignedSize(size_t size) {
  32. size_t aligned_size = (size + kDataMemAlignUnit * kDataMemAlignSize - 1) / kDataMemAlignSize * kDataMemAlignSize;
  33. return aligned_size;
  34. }
  35. Status ProfilingTaskInfo(OpTask *op_task) {
  36. if (!ProfilingManager::Instance().ProfilingModelExecuteOn()) {
  37. return SUCCESS;
  38. }
  39. string model_name;
  40. string op_name;
  41. uint32_t model_id;
  42. uint32_t block_dim;
  43. if (op_task->GetProfilingArgs(model_name, op_name, model_id, block_dim) != SUCCESS) {
  44. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Get profiling data of task failed");
  45. return ACL_ERROR_GE_PARAM_INVALID;
  46. }
  47. GELOGD("ProfilingReport of op[%s] model[%s] start.", op_name.c_str(), model_name.c_str());
  48. std::vector<TaskDescInfo> task_desc_info;
  49. uint32_t task_id = 0;
  50. uint32_t stream_id = 0;
  51. if (rtGetTaskIdAndStreamID(&task_id, &stream_id) != RT_ERROR_NONE) {
  52. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Get task_id and stream_id failed.");
  53. return ACL_ERROR_GE_PARAM_INVALID;
  54. }
  55. TaskDescInfo tmp_task_desc_info;
  56. tmp_task_desc_info.model_name = model_name;
  57. tmp_task_desc_info.op_name = op_name;
  58. tmp_task_desc_info.block_dim = block_dim;
  59. tmp_task_desc_info.task_id = task_id;
  60. tmp_task_desc_info.stream_id = stream_id;
  61. GELOGD("GetTaskDescInfo of op [%s] end, task_id[%u], stream_id[%u]", op_name.c_str(), task_id, stream_id);
  62. task_desc_info.emplace_back(tmp_task_desc_info);
  63. std::vector<ComputeGraphDescInfo> compute_graph_info;
  64. auto &profiling_manager = ProfilingManager::Instance();
  65. profiling_manager.ReportProfilingData(model_id, task_desc_info, compute_graph_info,
  66. !profiling_manager.IsAclApiMode());
  67. return SUCCESS;
  68. }
  69. } // namespace
  70. SingleOp::SingleOp(std::mutex *stream_mutex, rtStream_t stream) : stream_mutex_(stream_mutex), stream_(stream) {
  71. }
  72. FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY SingleOp::~SingleOp() {
  73. for (auto task : tasks_) {
  74. delete task;
  75. task = nullptr;
  76. }
  77. }
  78. Status SingleOp::ValidateArgs(const std::vector<DataBuffer> &inputs, const std::vector<DataBuffer> &outputs) {
  79. auto num_inputs = inputs.size();
  80. if (num_inputs != input_sizes_.size()) {
  81. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Input num mismatch. model expect %zu, but given %zu", input_addr_list_.size(),
  82. inputs.size());
  83. return ACL_ERROR_GE_PARAM_INVALID;
  84. }
  85. for (size_t i = 0; i < num_inputs; ++i) {
  86. // preventing from read out of bound
  87. size_t aligned_size = GetAlignedSize(inputs[i].length);
  88. GELOGI("Input [%zu], aligned_size:%zu, inputs.length:%lu, input_sizes_:%zu",
  89. i, aligned_size, inputs[i].length, input_sizes_[i]);
  90. if (aligned_size < input_sizes_[i]) {
  91. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Input size mismatch. index = %zu, model expect %zu,"
  92. " but given %zu(after align)", i, input_sizes_[i], aligned_size);
  93. return ACL_ERROR_GE_PARAM_INVALID;
  94. }
  95. }
  96. auto num_outputs = outputs.size();
  97. if (num_outputs != output_sizes_.size()) {
  98. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "output num mismatch. model expect %zu, but given %zu",
  99. output_sizes_.size(), outputs.size());
  100. return ACL_ERROR_GE_PARAM_INVALID;
  101. }
  102. for (size_t i = 0; i < num_outputs; ++i) {
  103. // preventing from write out of bound
  104. size_t aligned_size = GetAlignedSize(outputs[i].length);
  105. GELOGI("Output [%zu], aligned_size:%zu, outputs.length:%lu, output_sizes_:%zu",
  106. i, aligned_size, outputs[i].length, output_sizes_[i]);
  107. if (aligned_size < output_sizes_[i]) {
  108. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Output size mismatch. index = %zu, model expect %zu,"
  109. "but given %zu(after align)", i, output_sizes_[i], aligned_size);
  110. return ACL_ERROR_GE_PARAM_INVALID;
  111. }
  112. }
  113. return SUCCESS;
  114. }
  115. Status SingleOp::GetArgs(const std::vector<DataBuffer> &inputs, const std::vector<DataBuffer> &outputs) {
  116. size_t arg_index = 0;
  117. for (auto &input : inputs) {
  118. args_[arg_index++] = reinterpret_cast<uintptr_t>(input.data);
  119. }
  120. for (auto &output : outputs) {
  121. args_[arg_index++] = reinterpret_cast<uintptr_t>(output.data);
  122. }
  123. return SUCCESS;
  124. }
  125. Status SingleOp::UpdateArgs(const std::vector<DataBuffer> &inputs, const std::vector<DataBuffer> &outputs) {
  126. Status ret = GetArgs(inputs, outputs);
  127. if (ret != SUCCESS) {
  128. return ret;
  129. }
  130. // update tbe task args
  131. size_t num_args = arg_table_.size();
  132. for (size_t i = 0; i < num_args; ++i) {
  133. std::vector<uintptr_t *> &ptr_to_arg_in_tasks = arg_table_[i];
  134. if (ptr_to_arg_in_tasks.empty()) {
  135. GELOGW("found NO arg address to update for arg[%lu]", i);
  136. continue;
  137. }
  138. for (uintptr_t *arg_addr : ptr_to_arg_in_tasks) {
  139. *arg_addr = args_[i];
  140. }
  141. }
  142. // update aicpu_TF or aicpu_CC args
  143. for (auto &task : tasks_) {
  144. size_t io_addr_num = args_.size();
  145. if (task->GetOpTaskType() == OP_TASK_AICPU) {
  146. GELOGD("Update aicpu_TF task args");
  147. task->SetIoAddrsForDump(args_);
  148. auto *dst_io_addr = const_cast<uintptr_t *>(reinterpret_cast<const uintptr_t *>(task->GetIOAddr()));
  149. GE_CHECK_NOTNULL(dst_io_addr);
  150. auto rt_ret = rtMemcpyAsync(dst_io_addr,
  151. sizeof(uint64_t) * args_.size(),
  152. &args_[0],
  153. sizeof(uint64_t) * args_.size(),
  154. RT_MEMCPY_HOST_TO_DEVICE_EX,
  155. stream_);
  156. if (rt_ret != RT_ERROR_NONE) {
  157. GELOGE(rt_ret, "rtMemcpyAsync addresses failed, ret = %d", rt_ret);
  158. return rt_ret;
  159. }
  160. } else if (task->GetOpTaskType() == OP_TASK_AICPUCC) {
  161. GELOGD("Update aicpu_CC task args");
  162. const uintptr_t *task_io_addr = reinterpret_cast<const uintptr_t *>(task->GetIOAddr());
  163. GE_CHECK_NOTNULL(task_io_addr);
  164. auto io_addr = reinterpret_cast<uint64_t *>(const_cast<uintptr_t *>(task_io_addr));
  165. for (size_t i = 0; i < io_addr_num; ++i) {
  166. io_addr[i] = static_cast<uintptr_t>(args_[i]);
  167. }
  168. } else {
  169. GELOGW("Only TF_kernel aicpu and aicpu_CC are supported, but got %u", task->GetOpTaskType());
  170. continue;
  171. }
  172. }
  173. return SUCCESS;
  174. }
  175. FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY Status SingleOp::ExecuteAsync(const std::vector<DataBuffer> &inputs,
  176. const std::vector<DataBuffer> &outputs) {
  177. Status ret = ValidateArgs(inputs, outputs);
  178. if (ret != SUCCESS) {
  179. return ret;
  180. }
  181. std::lock_guard<std::mutex> lk(*stream_mutex_);
  182. ret = UpdateArgs(inputs, outputs);
  183. if (ret != SUCCESS) {
  184. return ret;
  185. }
  186. for (auto &task : tasks_) {
  187. ret = task->LaunchKernel(stream_);
  188. if (ret != SUCCESS) {
  189. return ret;
  190. }
  191. GE_CHK_STATUS_RET_NOLOG(ProfilingTaskInfo(task));
  192. }
  193. return ret;
  194. }
  195. void SingleOp::SetStream(rtStream_t stream) {
  196. stream_ = stream;
  197. }
  198. DynamicSingleOp::DynamicSingleOp(uintptr_t resource_id, std::mutex *stream_mutex, rtStream_t stream)
  199. : resource_id_(resource_id), stream_mutex_(stream_mutex), stream_(stream) {
  200. }
  201. DynamicSingleOp::~DynamicSingleOp() {
  202. }
  203. Status DynamicSingleOp::ValidateParams(const vector<GeTensorDesc> &input_desc,
  204. const std::vector<DataBuffer> &inputs,
  205. std::vector<GeTensorDesc> &output_desc,
  206. std::vector<DataBuffer> &outputs) const {
  207. if (inputs.size() != input_desc.size()) {
  208. GELOGE(ACL_ERROR_GE_PARAM_INVALID,
  209. "Input number mismatches input desc number. Input num = %zu, input desc num = %zu",
  210. inputs.size(),
  211. input_desc.size());
  212. return ACL_ERROR_GE_PARAM_INVALID;
  213. }
  214. if (outputs.size() != output_desc.size()) {
  215. GELOGE(ACL_ERROR_GE_PARAM_INVALID,
  216. "Output number mismatches output desc number. Output num = %zu, output desc num = %zu",
  217. outputs.size(),
  218. output_desc.size());
  219. return ACL_ERROR_GE_PARAM_INVALID;
  220. }
  221. if (input_desc.size() != num_inputs_) {
  222. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Input number mismatches. expect %zu, but given %zu",
  223. num_inputs_, input_desc.size());
  224. return ACL_ERROR_GE_PARAM_INVALID;
  225. }
  226. if (output_desc.size() != num_outputs_) {
  227. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Output number mismatches. expect %zu, but given %zu",
  228. num_outputs_, output_desc.size());
  229. return ACL_ERROR_GE_PARAM_INVALID;
  230. }
  231. return SUCCESS;
  232. }
  233. Status DynamicSingleOp::AllocateWorkspaces(const std::vector<int64_t> &workspace_sizes,
  234. std::vector<void *> &workspaces) {
  235. static const std::string kPurpose("malloc workspace memory for dynamic op.");
  236. if (workspace_sizes.empty()) {
  237. GELOGD("No need to allocate workspace.");
  238. return SUCCESS;
  239. }
  240. int64_t total_size = 0;
  241. std::vector<int64_t> ws_offsets;
  242. for (auto ws_size : workspace_sizes) {
  243. // alignment and padding should be done in OpParaCalculate
  244. GE_CHK_STATUS_RET_NOLOG(CheckInt64AddOverflow(total_size, ws_size));
  245. ws_offsets.emplace_back(total_size);
  246. total_size += ws_size;
  247. }
  248. GELOGD("Total workspace size is %ld", total_size);
  249. StreamResource *stream_resource = SingleOpManager::GetInstance().GetResource(resource_id_, stream_);
  250. GE_CHECK_NOTNULL(stream_resource);
  251. auto ws_base = stream_resource->MallocMemory(kPurpose, static_cast<size_t>(total_size));
  252. if (ws_base == nullptr) {
  253. GELOGE(ACL_ERROR_GE_MEMORY_ALLOCATION, "Failed to allocate memory of size: %ld", total_size);
  254. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  255. }
  256. GELOGD("Done allocating workspace memory successfully.");
  257. for (auto ws_offset : ws_offsets) {
  258. workspaces.emplace_back(ws_base + ws_offset);
  259. }
  260. return SUCCESS;
  261. }
  262. Status DynamicSingleOp::ExecuteTbeTask(const vector<GeTensorDesc> &input_desc,
  263. const vector<void *> &inputs,
  264. vector<GeTensorDesc> &output_desc,
  265. vector<void *> &outputs) {
  266. GE_CHK_STATUS_RET_NOLOG(op_task_->UpdateRunInfo(input_desc, output_desc));
  267. std::vector<void *> workspace_buffers;
  268. GE_CHK_STATUS_RET_NOLOG(AllocateWorkspaces(op_task_->GetWorkspaceSizes(), workspace_buffers));
  269. return op_task_->LaunchKernel(inputs, outputs, workspace_buffers, stream_);
  270. }
  271. Status DynamicSingleOp::ExecuteAsync(const vector<GeTensorDesc> &input_desc,
  272. const vector<DataBuffer> &input_buffers,
  273. vector<GeTensorDesc> &output_desc,
  274. vector<DataBuffer> &output_buffers) {
  275. GE_CHECK_NOTNULL(op_task_);
  276. GE_CHK_STATUS_RET_NOLOG(ValidateParams(input_desc, input_buffers, output_desc, output_buffers));
  277. std::lock_guard<std::mutex> lk(*stream_mutex_);
  278. std::vector<void *> inputs;
  279. std::vector<void *> outputs;
  280. for (auto &buffer : input_buffers) {
  281. inputs.emplace_back(buffer.data);
  282. }
  283. for (auto &buffer : output_buffers) {
  284. outputs.emplace_back(buffer.data);
  285. }
  286. if (op_task_->GetOpTaskType() == OP_TASK_TBE) {
  287. auto ret = ExecuteTbeTask(input_desc, inputs, output_desc, outputs);
  288. if (ret == SUCCESS) {
  289. GE_CHK_STATUS_RET_NOLOG(ProfilingTaskInfo(op_task_.get()));
  290. }
  291. return ret;
  292. } else if (op_task_->GetOpTaskType() == OP_TASK_AICPU || op_task_->GetOpTaskType() == OP_TASK_AICPUCC) {
  293. auto aicpu_ret = op_task_->LaunchKernel(input_desc, input_buffers, output_desc, output_buffers, stream_);
  294. if (aicpu_ret == SUCCESS) {
  295. GE_CHK_STATUS_RET_NOLOG(ProfilingTaskInfo(op_task_.get()));
  296. }
  297. return aicpu_ret;
  298. } else {
  299. GELOGE(ACL_ERROR_GE_OP_TASK_TYPE_INVALID,
  300. "Only TBE_Task, AI_CPU_Task and AI_CPUCC_Task are supported, but got %u",
  301. op_task_->GetOpTaskType());
  302. return ACL_ERROR_GE_OP_TASK_TYPE_INVALID;
  303. }
  304. }
  305. } // 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