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single_op.cc 11 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/math/math_util.h"
  19. #include "common/profiling/profiling_manager.h"
  20. #include "framework/common/debug/ge_log.h"
  21. #include "framework/common/util.h"
  22. #include "graph/load/new_model_manager/model_utils.h"
  23. #include "runtime/mem.h"
  24. #include "single_op/single_op_manager.h"
  25. #include "graph/load/new_model_manager/model_manager.h"
  26. namespace ge {
  27. namespace {
  28. const size_t kDataMemAlignSize = 32;
  29. size_t GetAlignedSize(size_t size) {
  30. size_t aligned_size = (size + 2 * kDataMemAlignSize - 1) / kDataMemAlignSize * kDataMemAlignSize;
  31. return aligned_size;
  32. }
  33. } // namespace
  34. SingleOp::SingleOp(std::mutex *stream_mutex, rtStream_t stream) : stream_mutex_(stream_mutex), stream_(stream) {
  35. }
  36. FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY SingleOp::~SingleOp() {
  37. for (auto task : tasks_) {
  38. delete task;
  39. task = nullptr;
  40. }
  41. }
  42. Status SingleOp::ValidateArgs(const std::vector<DataBuffer> &inputs, const std::vector<DataBuffer> &outputs) {
  43. auto num_inputs = inputs.size();
  44. if (num_inputs != input_sizes_.size()) {
  45. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Input num mismatch. model expect %zu, but given %zu", input_addr_list_.size(),
  46. inputs.size());
  47. return ACL_ERROR_GE_PARAM_INVALID;
  48. }
  49. for (size_t i = 0; i < num_inputs; ++i) {
  50. // preventing from read out of bound
  51. size_t aligned_size = GetAlignedSize(inputs[i].length);
  52. GELOGI("Input [%zu], aligned_size:%zu, inputs.length:%lu, input_sizes_:%zu",
  53. i, aligned_size, inputs[i].length, input_sizes_[i]);
  54. if (aligned_size < input_sizes_[i]) {
  55. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Input size mismatch. index = %zu, model expect %zu,"
  56. " but given %zu(after align)", i, input_sizes_[i], aligned_size);
  57. return ACL_ERROR_GE_PARAM_INVALID;
  58. }
  59. }
  60. auto num_outputs = outputs.size();
  61. if (num_outputs != output_sizes_.size()) {
  62. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "output num mismatch. model expect %zu, but given %zu",
  63. output_sizes_.size(), outputs.size());
  64. return ACL_ERROR_GE_PARAM_INVALID;
  65. }
  66. for (size_t i = 0; i < num_outputs; ++i) {
  67. // preventing from write out of bound
  68. size_t aligned_size = GetAlignedSize(outputs[i].length);
  69. GELOGI("Output [%zu], aligned_size:%zu, outputs.length:%lu, output_sizes_:%zu",
  70. i, aligned_size, outputs[i].length, output_sizes_[i]);
  71. if (aligned_size < output_sizes_[i]) {
  72. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Output size mismatch. index = %zu, model expect %zu,"
  73. "but given %zu(after align)", i, output_sizes_[i], aligned_size);
  74. return ACL_ERROR_GE_PARAM_INVALID;
  75. }
  76. }
  77. return SUCCESS;
  78. }
  79. Status SingleOp::GetArgs(const std::vector<DataBuffer> &inputs, const std::vector<DataBuffer> &outputs) {
  80. size_t arg_index = 0;
  81. for (auto &input : inputs) {
  82. args_[arg_index++] = reinterpret_cast<uintptr_t>(input.data);
  83. }
  84. for (auto &output : outputs) {
  85. args_[arg_index++] = reinterpret_cast<uintptr_t>(output.data);
  86. }
  87. return SUCCESS;
  88. }
  89. Status SingleOp::UpdateArgs(const std::vector<DataBuffer> &inputs, const std::vector<DataBuffer> &outputs) {
  90. Status ret = GetArgs(inputs, outputs);
  91. if (ret != SUCCESS) {
  92. return ret;
  93. }
  94. // update tbe task args
  95. size_t num_args = arg_table_.size();
  96. for (size_t i = 0; i < num_args; ++i) {
  97. std::vector<uintptr_t *> &ptr_to_arg_in_tasks = arg_table_[i];
  98. if (ptr_to_arg_in_tasks.empty()) {
  99. GELOGW("found NO arg address to update for arg[%lu]", i);
  100. continue;
  101. }
  102. for (uintptr_t *arg_addr : ptr_to_arg_in_tasks) {
  103. *arg_addr = args_[i];
  104. }
  105. }
  106. // update aicpu_TF or aicpu_CC args
  107. for (auto &task : tasks_) {
  108. size_t io_addr_num = args_.size();
  109. if (task->GetOpTaskType() == OP_TASK_AICPU) {
  110. GELOGD("Update aicpu_TF task args");
  111. task->SetIoAddrsForDump(args_);
  112. auto *dst_io_addr = const_cast<uintptr_t *>(reinterpret_cast<const uintptr_t *>(task->GetIOAddr()));
  113. GE_CHECK_NOTNULL(dst_io_addr);
  114. auto rt_ret = rtMemcpyAsync(dst_io_addr,
  115. sizeof(uint64_t) * args_.size(),
  116. &args_[0],
  117. sizeof(uint64_t) * args_.size(),
  118. RT_MEMCPY_HOST_TO_DEVICE_EX,
  119. stream_);
  120. if (rt_ret != RT_ERROR_NONE) {
  121. GELOGE(rt_ret, "rtMemcpyAsync addresses failed, ret = %d", rt_ret);
  122. return rt_ret;
  123. }
  124. } else if (task->GetOpTaskType() == OP_TASK_AICPUCC) {
  125. GELOGD("Update aicpu_CC task args");
  126. const uintptr_t *task_io_addr = reinterpret_cast<const uintptr_t *>(task->GetIOAddr());
  127. GE_CHECK_NOTNULL(task_io_addr);
  128. auto io_addr = reinterpret_cast<uint64_t *>(const_cast<uintptr_t *>(task_io_addr));
  129. for (size_t i = 0; i < io_addr_num; ++i) {
  130. io_addr[i] = static_cast<uintptr_t>(args_[i]);
  131. }
  132. } else {
  133. GELOGW("Only TF_kernel aicpu and aicpu_CC are supported, but got %u", task->GetOpTaskType());
  134. continue;
  135. }
  136. }
  137. return SUCCESS;
  138. }
  139. FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY Status SingleOp::ExecuteAsync(const std::vector<DataBuffer> &inputs,
  140. const std::vector<DataBuffer> &outputs) {
  141. Status ret = ValidateArgs(inputs, outputs);
  142. if (ret != SUCCESS) {
  143. return ret;
  144. }
  145. std::lock_guard<std::mutex> lk(*stream_mutex_);
  146. ret = UpdateArgs(inputs, outputs);
  147. if (ret != SUCCESS) {
  148. return ret;
  149. }
  150. for (auto &task : tasks_) {
  151. ret = task->LaunchKernel(stream_);
  152. if (ret != SUCCESS) {
  153. return ret;
  154. }
  155. }
  156. return ret;
  157. }
  158. void SingleOp::SetStream(rtStream_t stream) {
  159. stream_ = stream;
  160. }
  161. DynamicSingleOp::DynamicSingleOp(uintptr_t resource_id, std::mutex *stream_mutex, rtStream_t stream)
  162. : resource_id_(resource_id), stream_mutex_(stream_mutex), stream_(stream) {
  163. }
  164. DynamicSingleOp::~DynamicSingleOp() {
  165. }
  166. Status DynamicSingleOp::ValidateParams(const vector<GeTensorDesc> &input_desc,
  167. const std::vector<DataBuffer> &inputs,
  168. std::vector<GeTensorDesc> &output_desc,
  169. std::vector<DataBuffer> &outputs) const {
  170. if (inputs.size() != input_desc.size()) {
  171. GELOGE(ACL_ERROR_GE_PARAM_INVALID,
  172. "Input number mismatches input desc number. Input num = %zu, input desc num = %zu",
  173. inputs.size(),
  174. input_desc.size());
  175. return ACL_ERROR_GE_PARAM_INVALID;
  176. }
  177. if (outputs.size() != output_desc.size()) {
  178. GELOGE(ACL_ERROR_GE_PARAM_INVALID,
  179. "Output number mismatches output desc number. Output num = %zu, output desc num = %zu",
  180. outputs.size(),
  181. output_desc.size());
  182. return ACL_ERROR_GE_PARAM_INVALID;
  183. }
  184. if (input_desc.size() != num_inputs_) {
  185. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Input number mismatches. expect %zu, but given %zu", num_inputs_,
  186. input_desc.size());
  187. return ACL_ERROR_GE_PARAM_INVALID;
  188. }
  189. if (output_desc.size() != num_outputs_) {
  190. GELOGE(ACL_ERROR_GE_PARAM_INVALID, "Output number mismatches. expect %zu, but given %zu", num_outputs_,
  191. output_desc.size());
  192. return ACL_ERROR_GE_PARAM_INVALID;
  193. }
  194. return SUCCESS;
  195. }
  196. Status DynamicSingleOp::AllocateWorkspaces(const std::vector<int64_t> &workspace_sizes,
  197. std::vector<void *> &workspaces) {
  198. static const std::string kPurpose("malloc workspace memory for dynamic op.");
  199. if (workspace_sizes.empty()) {
  200. GELOGD("No need to allocate workspace.");
  201. return SUCCESS;
  202. }
  203. int64_t total_size = 0;
  204. std::vector<int64_t> ws_offsets;
  205. for (auto ws_size : workspace_sizes) {
  206. // alignment and padding should be done in OpParaCalculate
  207. GE_CHK_STATUS_RET_NOLOG(CheckInt64AddOverflow(total_size, ws_size));
  208. ws_offsets.emplace_back(total_size);
  209. total_size += ws_size;
  210. }
  211. GELOGD("Total workspace size is %ld", total_size);
  212. StreamResource *stream_resource = SingleOpManager::GetInstance().GetResource(resource_id_, stream_);
  213. GE_CHECK_NOTNULL(stream_resource);
  214. auto ws_base = stream_resource->MallocMemory(kPurpose, static_cast<size_t>(total_size));
  215. if (ws_base == nullptr) {
  216. GELOGE(ACL_ERROR_GE_MEMORY_ALLOCATION, "Failed to allocate memory of size: %ld", total_size);
  217. return ACL_ERROR_GE_MEMORY_ALLOCATION;
  218. }
  219. GELOGD("Done allocating workspace memory successfully.");
  220. for (auto ws_offset : ws_offsets) {
  221. workspaces.emplace_back(ws_base + ws_offset);
  222. }
  223. return SUCCESS;
  224. }
  225. Status DynamicSingleOp::ExecuteTbeTask(const vector<GeTensorDesc> &input_desc,
  226. const vector<void *> &inputs,
  227. vector<GeTensorDesc> &output_desc,
  228. vector<void *> &outputs) {
  229. GE_CHK_STATUS_RET_NOLOG(op_task_->UpdateRunInfo(input_desc, output_desc));
  230. std::vector<void *> workspace_buffers;
  231. GE_CHK_STATUS_RET_NOLOG(AllocateWorkspaces(op_task_->GetWorkspaceSizes(), workspace_buffers));
  232. return op_task_->LaunchKernel(inputs, outputs, workspace_buffers, stream_);
  233. }
  234. Status DynamicSingleOp::ExecuteAsync(const vector<GeTensorDesc> &input_desc,
  235. const vector<DataBuffer> &input_buffers,
  236. vector<GeTensorDesc> &output_desc,
  237. vector<DataBuffer> &output_buffers) {
  238. GE_CHECK_NOTNULL(op_task_);
  239. GE_CHK_STATUS_RET_NOLOG(ValidateParams(input_desc, input_buffers, output_desc, output_buffers));
  240. std::lock_guard<std::mutex> lk(*stream_mutex_);
  241. std::vector<void *> inputs;
  242. std::vector<void *> outputs;
  243. for (auto &buffer : input_buffers) {
  244. inputs.emplace_back(buffer.data);
  245. }
  246. for (auto &buffer : output_buffers) {
  247. outputs.emplace_back(buffer.data);
  248. }
  249. if (op_task_->GetOpTaskType() == OP_TASK_TBE) {
  250. return ExecuteTbeTask(input_desc, inputs, output_desc, outputs);
  251. } else if (op_task_->GetOpTaskType() == OP_TASK_AICPU || op_task_->GetOpTaskType() == OP_TASK_AICPUCC) {
  252. return op_task_->LaunchKernel(input_desc, input_buffers, output_desc, output_buffers, stream_);
  253. } else {
  254. GELOGE(ACL_ERROR_GE_OP_TASK_TYPE_INVALID,
  255. "Only TBE_Task, AI_CPU_Task and AI_CPUCC_Task are supported, but got %u",
  256. op_task_->GetOpTaskType());
  257. return ACL_ERROR_GE_OP_TASK_TYPE_INVALID;
  258. }
  259. }
  260. } // 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