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single_op_model_unittest.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 <gtest/gtest.h>
  17. #include <vector>
  18. #define protected public
  19. #define private public
  20. #include "graph/load/model_manager/model_utils.h"
  21. #include "graph/utils/graph_utils.h"
  22. #include "runtime/rt.h"
  23. #include "single_op/single_op_model.h"
  24. #include "single_op/task/tbe_task_builder.h"
  25. #include "single_op/task/rts_kernel_task_builder.h"
  26. #include "single_op/task/op_task.h"
  27. #include "framework/common/helper/model_helper.h"
  28. #include "single_op/single_op.h"
  29. #include "single_op/stream_resource.h"
  30. #include "graph/passes/graph_builder_utils.h"
  31. #include "graph/op_desc_impl.h"
  32. #undef private
  33. #undef protected
  34. using namespace std;
  35. using namespace testing;
  36. using namespace ge;
  37. namespace {
  38. constexpr char const *kAttrSupportDynamicShape = "support_dynamicshape";
  39. const char *const kEngineNameAiCore = "AIcoreEngine";
  40. const char *const kEngineNameAiCpu = "aicpu_ascend_kernel";
  41. const char *const kEngineNameAiCpuTf = "aicpu_tf_kernel";
  42. } // namespace
  43. class UtestSingleOpModel : public testing::Test {
  44. protected:
  45. void SetUp() {}
  46. void TearDown() {}
  47. };
  48. //rt api stub
  49. rtError_t rtGetTaskIdAndStreamID(uint32_t *taskId, uint32_t *streamId) {
  50. return RT_ERROR_NONE;
  51. }
  52. /*
  53. TEST_F(UtestSingleOpModel, test_init_model) {
  54. string model_data_str = "123456789";
  55. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  56. ASSERT_EQ(model.InitModel(), FAILED);
  57. }
  58. void ParseOpModelParamsMock(ModelHelper &model_helper, SingleOpModelParam &param) {}
  59. TEST_F(UtestSingleOpModel, test_parse_input_node) {
  60. string model_data_str = "123456789";
  61. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  62. auto op_desc = make_shared<OpDesc>("Data", "Data");
  63. ASSERT_EQ(model.ParseInputNode(op_desc), PARAM_INVALID);
  64. vector<int64_t> shape{1, 2, 3, 4};
  65. vector<int64_t> offsets{16};
  66. GeShape ge_shape(shape);
  67. GeTensorDesc desc(ge_shape);
  68. op_desc->AddOutputDesc(desc);
  69. op_desc->SetOutputOffset(offsets);
  70. ASSERT_EQ(model.ParseInputNode(op_desc), SUCCESS);
  71. op_desc->AddOutputDesc(desc);
  72. offsets.push_back(32);
  73. op_desc->SetOutputOffset(offsets);
  74. ASSERT_EQ(model.ParseInputNode(op_desc), PARAM_INVALID);
  75. }
  76. */
  77. TEST_F(UtestSingleOpModel, test_parse_output_node) {
  78. string model_data_str = "123456789";
  79. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  80. auto op_desc = make_shared<OpDesc>("NetOutput", "NetOutput");
  81. vector<int64_t> shape{1, 2, 3, 4};
  82. vector<int64_t> offsets{16};
  83. GeShape ge_shape(shape);
  84. GeTensorDesc desc(ge_shape);
  85. op_desc->AddInputDesc(desc);
  86. op_desc->SetInputOffset(offsets);
  87. op_desc->AddOutputDesc(desc);
  88. op_desc->SetOutputOffset(offsets);
  89. ASSERT_NO_THROW(model.ParseOutputNode(op_desc));
  90. ASSERT_NO_THROW(model.ParseOutputNode(op_desc));
  91. }
  92. TEST_F(UtestSingleOpModel, test_set_inputs_and_outputs) {
  93. string model_data_str = "123456789";
  94. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  95. model.input_offset_list_.push_back(0);
  96. model.input_sizes_.push_back(16);
  97. model.output_offset_list_.push_back(0);
  98. model.output_sizes_.push_back(16);
  99. std::mutex stream_mu_;
  100. rtStream_t stream_ = nullptr;
  101. // SingleOp single_op(&stream_mu_, stream_);
  102. //
  103. // ASSERT_EQ(model.SetInputsAndOutputs(single_op), SUCCESS);
  104. }
  105. /*
  106. TEST_F(UtestSingleOpModel, test_build_kernel_task) {
  107. string model_data_str = "123456789";
  108. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  109. model.input_offset_list_.push_back(0);
  110. model.input_sizes_.push_back(16);
  111. model.output_offset_list_.push_back(0);
  112. model.output_sizes_.push_back(16);
  113. auto graph = make_shared<ComputeGraph>("graph");
  114. auto op_desc = make_shared<OpDesc>("AddN", "AddN");
  115. vector<int64_t> shape{16, 16};
  116. GeShape ge_shape(shape);
  117. GeTensorDesc desc(ge_shape);
  118. op_desc->AddInputDesc(desc);
  119. op_desc->AddOutputDesc(desc);
  120. auto node = graph->AddNode(op_desc);
  121. std::mutex stream_mu_;
  122. rtStream_t stream_ = nullptr;
  123. SingleOp single_op(&stream_mu_, stream_);
  124. domi::KernelDef kernel_def;
  125. kernel_def.mutable_context()->set_kernel_type(cce::ccKernelType::TE);
  126. TbeOpTask *task = nullptr;
  127. ASSERT_EQ(model.BuildKernelTask(kernel_def, &task), UNSUPPORTED);
  128. kernel_def.mutable_context()->set_kernel_type(cce::ccKernelType::TE);
  129. ASSERT_EQ(model.BuildKernelTask(kernel_def, &task), INTERNAL_ERROR);
  130. model.op_list_[0] = node;
  131. ASSERT_EQ(model.BuildKernelTask(kernel_def, &task), PARAM_INVALID);
  132. ASSERT_EQ(task, nullptr);
  133. delete task;
  134. }
  135. TEST_F(UtestSingleOpModel, test_init) {
  136. string model_data_str = "123456789";
  137. SingleOpModel op_model("model", model_data_str.c_str(), model_data_str.size());
  138. ASSERT_EQ(op_model.Init(), FAILED);
  139. }
  140. */
  141. /*
  142. TEST_F(UtestSingleOpModel, test_parse_arg_table) {
  143. string model_data_str = "123456789";
  144. SingleOpModel op_model("model", model_data_str.c_str(), model_data_str.size());
  145. TbeOpTask task;
  146. OpDescPtr op_desc;
  147. std::mutex stream_mu_;
  148. rtStream_t stream_ = nullptr;
  149. SingleOp op(&stream_mu_, stream_);
  150. op.arg_table_.resize(2);
  151. auto args = std::unique_ptr<uint8_t[]>(new uint8_t[sizeof(uintptr_t) * 2]);
  152. auto *arg_base = (uintptr_t*)args.get();
  153. arg_base[0] = 0x100000;
  154. arg_base[1] = 0x200000;
  155. task.SetKernelArgs(std::move(args), 16, 1, op_desc);
  156. op_model.model_params_.addr_mapping_[0x100000] = 1;
  157. op_model.ParseArgTable(&task, op);
  158. ASSERT_EQ(op.arg_table_[0].size(), 0);
  159. ASSERT_EQ(op.arg_table_[1].size(), 1);
  160. ASSERT_EQ(op.arg_table_[1].front(), &arg_base[0]);
  161. }
  162. */
  163. TEST_F(UtestSingleOpModel, test_op_task_get_profiler_args) {
  164. string name = "relu";
  165. string type = "relu";
  166. auto op_desc = std::make_shared<ge::OpDesc>(name, type);
  167. op_desc->SetStreamId(0);
  168. op_desc->SetId(0);
  169. TbeOpTask task;
  170. task.op_desc_ = op_desc;
  171. task.model_name_ = "resnet_50";
  172. task.model_id_ = 1;
  173. TaskDescInfo task_desc_info;
  174. uint32_t model_id;
  175. task.GetProfilingArgs(task_desc_info, model_id);
  176. ASSERT_EQ(task_desc_info.model_name, "resnet_50");
  177. ASSERT_EQ(model_id, 1);
  178. }
  179. TEST_F(UtestSingleOpModel, test_build_dynamic_op) {
  180. string model_data_str = "123456789";
  181. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  182. model.netoutput_op_ = make_shared<OpDesc>("NetOutput", "NetOutput");
  183. model.model_helper_.model_ = ge::MakeShared<ge::GeModel>();
  184. // make graph
  185. ut::GraphBuilder builder = ut::GraphBuilder("graph");
  186. auto data = builder.AddNode("Data", "Data", 1, 1);
  187. auto transdata = builder.AddNode("Transdata", "Transdata", 1, 1);
  188. auto netoutput = builder.AddNode("Netoutput", "NetOutput", 1, 0);
  189. builder.AddDataEdge(data, 0, transdata, 0);
  190. builder.AddDataEdge(transdata, 0, netoutput, 0);
  191. auto compute_graph = builder.GetGraph();
  192. auto graph = GraphUtils::CreateGraphFromComputeGraph(compute_graph);
  193. model.model_helper_.model_->SetGraph(graph);
  194. model.op_list_[0] = transdata;
  195. auto op_desc = transdata->GetOpDesc();
  196. const vector<string> depend_names = { "Data" };
  197. op_desc->SetOpInferDepends(depend_names);
  198. (void)AttrUtils::SetBool(op_desc, kAttrSupportDynamicShape, true);
  199. // set task_def
  200. auto model_task_def = make_shared<domi::ModelTaskDef>();
  201. domi::TaskDef *task_def = model_task_def->add_task();
  202. task_def->set_type(RT_MODEL_TASK_KERNEL);
  203. domi::KernelDef *kernel_def = task_def->mutable_kernel();
  204. domi::KernelContext *context = kernel_def->mutable_context();
  205. context->set_kernel_type(2); // ccKernelType::TE
  206. model.model_helper_.model_->SetModelTaskDef(model_task_def);
  207. std::mutex stream_mu_;
  208. DynamicSingleOp dynamic_single_op(0, &stream_mu_, nullptr);
  209. StreamResource res((uintptr_t)1);
  210. model.BuildDynamicOp(res, dynamic_single_op);
  211. op_desc->impl_->input_name_idx_["Data"] = 0;
  212. model.BuildDynamicOp(res, dynamic_single_op);
  213. auto tensor = std::make_shared<GeTensor>();
  214. auto data_desc = data->GetOpDesc();
  215. auto tensor_desc = data_desc->MutableInputDesc(0);
  216. AttrUtils::SetTensor(tensor_desc, "_value", tensor);
  217. model.BuildDynamicOp(res, dynamic_single_op);
  218. }
  219. TEST_F(UtestSingleOpModel, test_host_mem) {
  220. string model_data_str = "123456789";
  221. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  222. // make graph
  223. ut::GraphBuilder builder = ut::GraphBuilder("graph");
  224. auto data = builder.AddNode("Data", "Data", 0, 1);
  225. auto netoutput = builder.AddNode("Netoutput", "NetOutput", 1, 0);
  226. builder.AddDataEdge(data, 0, netoutput, 0);
  227. auto graph = builder.GetGraph();
  228. model.op_with_hostmem_[0] = data;
  229. std::mutex stream_mu_;
  230. DynamicSingleOp single_op(0, &stream_mu_, nullptr);
  231. ASSERT_EQ(model.SetHostMemTensor(single_op), SUCCESS);
  232. }
  233. TEST_F(UtestSingleOpModel, BuildTaskList) {
  234. ComputeGraphPtr graph = make_shared<ComputeGraph>("single_op");
  235. GeModelPtr ge_model = make_shared<GeModel>();
  236. ge_model->SetGraph(GraphUtils::CreateGraphFromComputeGraph(graph));
  237. shared_ptr<domi::ModelTaskDef> model_task_def = make_shared<domi::ModelTaskDef>();
  238. ge_model->SetModelTaskDef(model_task_def);
  239. NodePtr node = nullptr;
  240. {
  241. auto op_desc = std::make_shared<ge::OpDesc>("memcpy", MEMCPYASYNC);
  242. GeTensorDesc tensor(GeShape(), FORMAT_NCHW, DT_FLOAT);
  243. op_desc->AddInputDesc(tensor);
  244. op_desc->AddOutputDesc(tensor);
  245. op_desc->SetInputOffset({0});
  246. op_desc->SetOutputOffset({0});
  247. node = graph->AddNode(op_desc);
  248. domi::TaskDef *task_def = model_task_def->add_task();
  249. task_def->set_stream_id(0);
  250. task_def->set_type(RT_MODEL_TASK_MEMCPY_ASYNC);
  251. domi::MemcpyAsyncDef *memcpy_async = task_def->mutable_memcpy_async();
  252. memcpy_async->set_src(0);
  253. memcpy_async->set_dst(0);
  254. memcpy_async->set_dst_max(512);
  255. memcpy_async->set_count(1);
  256. memcpy_async->set_kind(RT_MEMCPY_DEVICE_TO_DEVICE);
  257. memcpy_async->set_op_index(0);
  258. }
  259. string model_data_str = "123456789";
  260. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  261. StreamResource *res = new (std::nothrow) StreamResource(1);
  262. std::mutex stream_mu;
  263. rtStream_t stream = nullptr;
  264. rtStreamCreate(&stream, 0);
  265. SingleOp single_op(res, &stream_mu, stream);
  266. model.model_helper_.model_ = ge_model;
  267. model.op_list_.emplace(0, node);
  268. ASSERT_EQ(model.BuildTaskList(res, single_op), SUCCESS);
  269. MemcpyAsyncTask mem_task;
  270. ASSERT_EQ(mem_task.LaunchKernel(0), SUCCESS);
  271. }
  272. TEST_F(UtestSingleOpModel, build_dynamic_task) {
  273. ComputeGraphPtr graph = make_shared<ComputeGraph>("single_op");
  274. GeModelPtr ge_model = make_shared<GeModel>();
  275. ge_model->SetGraph(GraphUtils::CreateGraphFromComputeGraph(graph));
  276. shared_ptr<domi::ModelTaskDef> model_task_def = make_shared<domi::ModelTaskDef>();
  277. ge_model->SetModelTaskDef(model_task_def);
  278. domi::TaskDef *task_def = model_task_def->add_task();
  279. task_def->set_type(RT_MODEL_TASK_KERNEL_EX);
  280. domi::TaskDef *task_def2 = model_task_def->add_task();
  281. task_def2->set_type(RT_MODEL_TASK_KERNEL);
  282. domi::KernelDef *kernel_def = task_def2->mutable_kernel();
  283. domi::KernelContext *context = kernel_def->mutable_context();
  284. context->set_kernel_type(6); // ccKernelType::AI_CPU
  285. domi::TaskDef *task_def3 = model_task_def->add_task();
  286. task_def3->set_type(RT_MODEL_TASK_ALL_KERNEL);
  287. domi::TaskDef *task_def4 = model_task_def->add_task();
  288. task_def4->set_type(RT_MODEL_TASK_KERNEL);
  289. string model_data_str = "dynamic_model";
  290. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  291. std::mutex stream_mu;
  292. rtStream_t stream = nullptr;
  293. rtStreamCreate(&stream, 0);
  294. DynamicSingleOp single_op(0, &stream_mu, stream);
  295. model.model_helper_.model_ = ge_model;
  296. auto op_desc = std::make_shared<ge::OpDesc>("add", "Add");
  297. AttrUtils::SetStr(op_desc, TVM_ATTR_NAME_MAGIC, "RT_DEV_BINARY_MAGIC_ELF");
  298. std::vector<char> kernelBin;
  299. TBEKernelPtr tbe_kernel = std::make_shared<ge::OpKernelBin>("name/Add", std::move(kernelBin));
  300. op_desc->SetExtAttr(ge::OP_EXTATTR_NAME_TBE_KERNEL, tbe_kernel);
  301. NodePtr node = graph->AddNode(op_desc);
  302. model.op_list_[0] = node;
  303. StreamResource *res = new (std::nothrow) StreamResource(1);
  304. ASSERT_EQ(model.ParseTasks(), SUCCESS);
  305. model.node_tasks_[node] = { *task_def3, *task_def4 };
  306. op_desc->SetOpKernelLibName(kEngineNameAiCore);
  307. model.BuildTaskListForDynamicOp(res, single_op);
  308. model.node_tasks_[node] = { *task_def };
  309. op_desc->SetOpKernelLibName(kEngineNameAiCpuTf);
  310. ASSERT_EQ(model.BuildTaskListForDynamicOp(res, single_op), SUCCESS);
  311. model.node_tasks_[node] = { *task_def2 };
  312. op_desc->SetOpKernelLibName(kEngineNameAiCpu);
  313. model.BuildTaskListForDynamicOp(res, single_op);
  314. }

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