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