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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", output_sizes_.size(), outputs.size());
  99. return ACL_ERROR_GE_PARAM_INVALID;
  100. }
  101. for (size_t i = 0; i < num_outputs; ++i) {
  102. // preventing from write out of bound
  103. size_t aligned_size = GetAlignedSize(outputs[i].length);
  104. GELOGI("Output [%zu], aligned_size:%zu, outputs.length:%lu, output_sizes_:%zu",
  105. i, aligned_size, outputs[i].length, output_sizes_[i]);
  106. if (aligned_size < output_sizes_[i]) {
  107. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Output size mismatch. index = %zu, model expect %zu,"
  108. "but given %zu(after align)", i, output_sizes_[i], aligned_size);
  109. return ACL_ERROR_GE_PARAM_INVALID;
  110. }
  111. }
  112. return SUCCESS;
  113. }
  114. Status SingleOp::GetArgs(const std::vector<DataBuffer> &inputs, const std::vector<DataBuffer> &outputs) {
  115. size_t arg_index = 0;
  116. for (auto &input : inputs) {
  117. args_[arg_index++] = reinterpret_cast<uintptr_t>(input.data);
  118. }
  119. for (auto &output : outputs) {
  120. args_[arg_index++] = reinterpret_cast<uintptr_t>(output.data);
  121. }
  122. return SUCCESS;
  123. }
  124. Status SingleOp::UpdateArgs(const std::vector<DataBuffer> &inputs, const std::vector<DataBuffer> &outputs) {
  125. Status ret = GetArgs(inputs, outputs);
  126. if (ret != SUCCESS) {
  127. return ret;
  128. }
  129. // update tbe task args
  130. size_t num_args = arg_table_.size();
  131. for (size_t i = 0; i < num_args; ++i) {
  132. std::vector<uintptr_t *> &ptr_to_arg_in_tasks = arg_table_[i];
  133. if (ptr_to_arg_in_tasks.empty()) {
  134. GELOGW("found NO arg address to update for arg[%lu]", i);
  135. continue;
  136. }
  137. for (uintptr_t *arg_addr : ptr_to_arg_in_tasks) {
  138. *arg_addr = args_[i];
  139. }
  140. }
  141. // update aicpu_TF or aicpu_CC args
  142. for (auto &task : tasks_) {
  143. size_t io_addr_num = args_.size();
  144. if (task->GetOpTaskType() == OP_TASK_AICPU) {
  145. GELOGD("Update aicpu_TF task args");
  146. task->SetIoAddrsForDump(args_);
  147. auto *dst_io_addr = const_cast<uintptr_t *>(reinterpret_cast<const uintptr_t *>(task->GetIOAddr()));
  148. GE_CHECK_NOTNULL(dst_io_addr);
  149. auto rt_ret = rtMemcpyAsync(dst_io_addr,
  150. sizeof(uint64_t) * args_.size(),
  151. &args_[0],
  152. sizeof(uint64_t) * args_.size(),
  153. RT_MEMCPY_HOST_TO_DEVICE_EX,
  154. stream_);
  155. if (rt_ret != RT_ERROR_NONE) {
  156. GELOGE(rt_ret, "rtMemcpyAsync addresses failed, ret = %d", rt_ret);
  157. return rt_ret;
  158. }
  159. } else if (task->GetOpTaskType() == OP_TASK_AICPUCC) {
  160. GELOGD("Update aicpu_CC task args");
  161. const uintptr_t *task_io_addr = reinterpret_cast<const uintptr_t *>(task->GetIOAddr());
  162. GE_CHECK_NOTNULL(task_io_addr);
  163. auto io_addr = reinterpret_cast<uint64_t *>(const_cast<uintptr_t *>(task_io_addr));
  164. for (size_t i = 0; i < io_addr_num; ++i) {
  165. io_addr[i] = static_cast<uintptr_t>(args_[i]);
  166. }
  167. } else {
  168. GELOGW("Only TF_kernel aicpu and aicpu_CC are supported, but got %u", task->GetOpTaskType());
  169. continue;
  170. }
  171. }
  172. return SUCCESS;
  173. }
  174. FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY Status SingleOp::ExecuteAsync(const std::vector<DataBuffer> &inputs,
  175. const std::vector<DataBuffer> &outputs) {
  176. Status ret = ValidateArgs(inputs, outputs);
  177. if (ret != SUCCESS) {
  178. return ret;
  179. }
  180. std::lock_guard<std::mutex> lk(*stream_mutex_);
  181. ret = UpdateArgs(inputs, outputs);
  182. if (ret != SUCCESS) {
  183. return ret;
  184. }
  185. for (auto &task : tasks_) {
  186. ret = task->LaunchKernel(stream_);
  187. if (ret != SUCCESS) {
  188. return ret;
  189. }
  190. GE_CHK_STATUS_RET_NOLOG(ProfilingTaskInfo(task));
  191. }
  192. return ret;
  193. }
  194. void SingleOp::SetStream(rtStream_t stream) {
  195. stream_ = stream;
  196. }
  197. DynamicSingleOp::DynamicSingleOp(uintptr_t resource_id, std::mutex *stream_mutex, rtStream_t stream)
  198. : resource_id_(resource_id), stream_mutex_(stream_mutex), stream_(stream) {
  199. }
  200. DynamicSingleOp::~DynamicSingleOp() {
  201. }
  202. Status DynamicSingleOp::ValidateParams(const vector<GeTensorDesc> &input_desc,
  203. const std::vector<DataBuffer> &inputs,
  204. std::vector<GeTensorDesc> &output_desc,
  205. std::vector<DataBuffer> &outputs) const {
  206. if (inputs.size() != input_desc.size()) {
  207. GELOGE(ACL_ERROR_GE_PARAM_INVALID,
  208. "Input number mismatches input desc number. Input num = %zu, input desc num = %zu",
  209. inputs.size(),
  210. input_desc.size());
  211. return ACL_ERROR_GE_PARAM_INVALID;
  212. }
  213. if (outputs.size() != output_desc.size()) {
  214. GELOGE(ACL_ERROR_GE_PARAM_INVALID,
  215. "Output number mismatches output desc number. Output num = %zu, output desc num = %zu",
  216. outputs.size(),
  217. output_desc.size());
  218. return ACL_ERROR_GE_PARAM_INVALID;
  219. }
  220. if (input_desc.size() != num_inputs_) {
  221. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Input number mismatches. expect %zu, but given %zu", num_inputs_, input_desc.size());
  222. return ACL_ERROR_GE_PARAM_INVALID;
  223. }
  224. if (output_desc.size() != num_outputs_) {
  225. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Output number mismatches. expect %zu, but given %zu", num_outputs_, output_desc.size());
  226. return ACL_ERROR_GE_PARAM_INVALID;
  227. }
  228. return SUCCESS;
  229. }
  230. Status DynamicSingleOp::AllocateWorkspaces(const std::vector<int64_t> &workspace_sizes,
  231. std::vector<void *> &workspaces) {
  232. static const std::string kPurpose("malloc workspace memory for dynamic op.");
  233. if (workspace_sizes.empty()) {
  234. GELOGD("No need to allocate workspace.");
  235. return SUCCESS;
  236. }
  237. int64_t total_size = 0;
  238. std::vector<int64_t> ws_offsets;
  239. for (auto ws_size : workspace_sizes) {
  240. // alignment and padding should be done in OpParaCalculate
  241. GE_CHK_STATUS_RET_NOLOG(CheckInt64AddOverflow(total_size, ws_size));
  242. ws_offsets.emplace_back(total_size);
  243. total_size += ws_size;
  244. }
  245. GELOGD("Total workspace size is %ld", total_size);
  246. StreamResource *stream_resource = SingleOpManager::GetInstance().GetResource(resource_id_, stream_);
  247. GE_CHECK_NOTNULL(stream_resource);
  248. auto ws_base = stream_resource->MallocMemory(kPurpose, static_cast<size_t>(total_size));
  249. if (ws_base == nullptr) {
  250. GELOGE(ACL_ERROR_GE_MEMORY_ALLOCATION, "Failed to allocate memory of size: %ld", total_size);
  251. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  252. }
  253. GELOGD("Done allocating workspace memory successfully.");
  254. for (auto ws_offset : ws_offsets) {
  255. workspaces.emplace_back(ws_base + ws_offset);
  256. }
  257. return SUCCESS;
  258. }
  259. Status DynamicSingleOp::ExecuteTbeTask(const vector<GeTensorDesc> &input_desc,
  260. const vector<void *> &inputs,
  261. vector<GeTensorDesc> &output_desc,
  262. vector<void *> &outputs) {
  263. GE_CHK_STATUS_RET_NOLOG(op_task_->UpdateRunInfo(input_desc, output_desc));
  264. std::vector<void *> workspace_buffers;
  265. GE_CHK_STATUS_RET_NOLOG(AllocateWorkspaces(op_task_->GetWorkspaceSizes(), workspace_buffers));
  266. return op_task_->LaunchKernel(inputs, outputs, workspace_buffers, stream_);
  267. }
  268. Status DynamicSingleOp::ExecuteAsync(const vector<GeTensorDesc> &input_desc,
  269. const vector<DataBuffer> &input_buffers,
  270. vector<GeTensorDesc> &output_desc,
  271. vector<DataBuffer> &output_buffers) {
  272. GE_CHECK_NOTNULL(op_task_);
  273. GE_CHK_STATUS_RET_NOLOG(ValidateParams(input_desc, input_buffers, output_desc, output_buffers));
  274. std::lock_guard<std::mutex> lk(*stream_mutex_);
  275. std::vector<void *> inputs;
  276. std::vector<void *> outputs;
  277. for (auto &buffer : input_buffers) {
  278. inputs.emplace_back(buffer.data);
  279. }
  280. for (auto &buffer : output_buffers) {
  281. outputs.emplace_back(buffer.data);
  282. }
  283. if (op_task_->GetOpTaskType() == OP_TASK_TBE) {
  284. auto ret = ExecuteTbeTask(input_desc, inputs, output_desc, outputs);
  285. if (ret == SUCCESS) {
  286. GE_CHK_STATUS_RET_NOLOG(ProfilingTaskInfo(op_task_.get()));
  287. }
  288. return ret;
  289. } else if (op_task_->GetOpTaskType() == OP_TASK_AICPU || op_task_->GetOpTaskType() == OP_TASK_AICPUCC) {
  290. auto aicpu_ret = op_task_->LaunchKernel(input_desc, input_buffers, output_desc, output_buffers, stream_);
  291. if (aicpu_ret == SUCCESS) {
  292. GE_CHK_STATUS_RET_NOLOG(ProfilingTaskInfo(op_task_.get()));
  293. }
  294. return aicpu_ret;
  295. } else {
  296. GELOGE(ACL_ERROR_GE_OP_TASK_TYPE_INVALID,
  297. "Only TBE_Task, AI_CPU_Task and AI_CPUCC_Task are supported, but got %u",
  298. op_task_->GetOpTaskType());
  299. return ACL_ERROR_GE_OP_TASK_TYPE_INVALID;
  300. }
  301. }
  302. } // 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