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single_op_model_unittest.cc 16 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 "aicpu/common/aicpu_task_struct.h"
  27. #include "single_op/task/op_task.h"
  28. #include "framework/common/helper/model_helper.h"
  29. #include "single_op/single_op.h"
  30. #include "single_op/stream_resource.h"
  31. #include "graph/passes/graph_builder_utils.h"
  32. #include "graph/op_desc_impl.h"
  33. #undef private
  34. #undef protected
  35. using namespace std;
  36. using namespace testing;
  37. using namespace ge;
  38. namespace {
  39. constexpr char const *kAttrSupportDynamicShape = "support_dynamicshape";
  40. const char *const kEngineNameAiCore = "AIcoreEngine";
  41. const char *const kEngineNameAiCpu = "aicpu_ascend_kernel";
  42. const char *const kEngineNameAiCpuTf = "aicpu_tf_kernel";
  43. struct AicpuTaskStruct {
  44. aicpu::AicpuParamHead head;
  45. uint64_t io_addrp[6];
  46. }__attribute__((packed));
  47. } // namespace
  48. class UtestSingleOpModel : public testing::Test {
  49. protected:
  50. void SetUp() {}
  51. void TearDown() {}
  52. };
  53. //rt api stub
  54. rtError_t rtGetTaskIdAndStreamID(uint32_t *taskId, uint32_t *streamId) {
  55. return RT_ERROR_NONE;
  56. }
  57. /*
  58. TEST_F(UtestSingleOpModel, test_init_model) {
  59. string model_data_str = "123456789";
  60. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  61. ASSERT_EQ(model.InitModel(), FAILED);
  62. }
  63. void ParseOpModelParamsMock(ModelHelper &model_helper, SingleOpModelParam &param) {}
  64. TEST_F(UtestSingleOpModel, test_parse_input_node) {
  65. string model_data_str = "123456789";
  66. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  67. auto op_desc = make_shared<OpDesc>("Data", "Data");
  68. ASSERT_EQ(model.ParseInputNode(op_desc), PARAM_INVALID);
  69. vector<int64_t> shape{1, 2, 3, 4};
  70. vector<int64_t> offsets{16};
  71. GeShape ge_shape(shape);
  72. GeTensorDesc desc(ge_shape);
  73. op_desc->AddOutputDesc(desc);
  74. op_desc->SetOutputOffset(offsets);
  75. ASSERT_EQ(model.ParseInputNode(op_desc), SUCCESS);
  76. op_desc->AddOutputDesc(desc);
  77. offsets.push_back(32);
  78. op_desc->SetOutputOffset(offsets);
  79. ASSERT_EQ(model.ParseInputNode(op_desc), PARAM_INVALID);
  80. }
  81. */
  82. TEST_F(UtestSingleOpModel, test_parse_output_node) {
  83. string model_data_str = "123456789";
  84. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  85. auto op_desc = make_shared<OpDesc>("NetOutput", "NetOutput");
  86. vector<int64_t> shape{1, 2, 3, 4};
  87. vector<int64_t> offsets{16};
  88. GeShape ge_shape(shape);
  89. GeTensorDesc desc(ge_shape);
  90. op_desc->AddInputDesc(desc);
  91. op_desc->SetInputOffset(offsets);
  92. op_desc->AddOutputDesc(desc);
  93. op_desc->SetOutputOffset(offsets);
  94. ASSERT_NO_THROW(model.ParseOutputNode(op_desc));
  95. ASSERT_NO_THROW(model.ParseOutputNode(op_desc));
  96. }
  97. TEST_F(UtestSingleOpModel, test_set_inputs_and_outputs) {
  98. string model_data_str = "123456789";
  99. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  100. model.input_offset_list_.push_back(0);
  101. model.input_sizes_.push_back(16);
  102. model.output_offset_list_.push_back(0);
  103. model.output_sizes_.push_back(16);
  104. std::mutex stream_mu_;
  105. rtStream_t stream_ = nullptr;
  106. // SingleOp single_op(&stream_mu_, stream_);
  107. //
  108. // ASSERT_EQ(model.SetInputsAndOutputs(single_op), SUCCESS);
  109. }
  110. /*
  111. TEST_F(UtestSingleOpModel, test_build_kernel_task) {
  112. string model_data_str = "123456789";
  113. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  114. model.input_offset_list_.push_back(0);
  115. model.input_sizes_.push_back(16);
  116. model.output_offset_list_.push_back(0);
  117. model.output_sizes_.push_back(16);
  118. auto graph = make_shared<ComputeGraph>("graph");
  119. auto op_desc = make_shared<OpDesc>("AddN", "AddN");
  120. vector<int64_t> shape{16, 16};
  121. GeShape ge_shape(shape);
  122. GeTensorDesc desc(ge_shape);
  123. op_desc->AddInputDesc(desc);
  124. op_desc->AddOutputDesc(desc);
  125. auto node = graph->AddNode(op_desc);
  126. std::mutex stream_mu_;
  127. rtStream_t stream_ = nullptr;
  128. SingleOp single_op(&stream_mu_, stream_);
  129. domi::KernelDef kernel_def;
  130. kernel_def.mutable_context()->set_kernel_type(cce::ccKernelType::TE);
  131. TbeOpTask *task = nullptr;
  132. ASSERT_EQ(model.BuildKernelTask(kernel_def, &task), UNSUPPORTED);
  133. kernel_def.mutable_context()->set_kernel_type(cce::ccKernelType::TE);
  134. ASSERT_EQ(model.BuildKernelTask(kernel_def, &task), INTERNAL_ERROR);
  135. model.op_list_[0] = node;
  136. ASSERT_EQ(model.BuildKernelTask(kernel_def, &task), PARAM_INVALID);
  137. ASSERT_EQ(task, nullptr);
  138. delete task;
  139. }
  140. TEST_F(UtestSingleOpModel, test_init) {
  141. string model_data_str = "123456789";
  142. SingleOpModel op_model("model", model_data_str.c_str(), model_data_str.size());
  143. ASSERT_EQ(op_model.Init(), FAILED);
  144. }
  145. */
  146. /*
  147. TEST_F(UtestSingleOpModel, test_parse_arg_table) {
  148. string model_data_str = "123456789";
  149. SingleOpModel op_model("model", model_data_str.c_str(), model_data_str.size());
  150. TbeOpTask task;
  151. OpDescPtr op_desc;
  152. std::mutex stream_mu_;
  153. rtStream_t stream_ = nullptr;
  154. SingleOp op(&stream_mu_, stream_);
  155. op.arg_table_.resize(2);
  156. auto args = std::unique_ptr<uint8_t[]>(new uint8_t[sizeof(uintptr_t) * 2]);
  157. auto *arg_base = (uintptr_t*)args.get();
  158. arg_base[0] = 0x100000;
  159. arg_base[1] = 0x200000;
  160. task.SetKernelArgs(std::move(args), 16, 1, op_desc);
  161. op_model.model_params_.addr_mapping_[0x100000] = 1;
  162. op_model.ParseArgTable(&task, op);
  163. ASSERT_EQ(op.arg_table_[0].size(), 0);
  164. ASSERT_EQ(op.arg_table_[1].size(), 1);
  165. ASSERT_EQ(op.arg_table_[1].front(), &arg_base[0]);
  166. }
  167. */
  168. TEST_F(UtestSingleOpModel, test_op_task_get_profiler_args) {
  169. string name = "relu";
  170. string type = "relu";
  171. auto op_desc = std::make_shared<ge::OpDesc>(name, type);
  172. op_desc->SetStreamId(0);
  173. op_desc->SetId(0);
  174. TbeOpTask task;
  175. task.op_desc_ = op_desc;
  176. task.model_name_ = "resnet_50";
  177. task.model_id_ = 1;
  178. TaskDescInfo task_desc_info;
  179. uint32_t model_id;
  180. task.GetProfilingArgs(task_desc_info, model_id);
  181. ASSERT_EQ(task_desc_info.model_name, "resnet_50");
  182. ASSERT_EQ(model_id, 1);
  183. }
  184. TEST_F(UtestSingleOpModel, test_build_dynamic_op) {
  185. string model_data_str = "123456789";
  186. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  187. model.netoutput_op_ = make_shared<OpDesc>("NetOutput", "NetOutput");
  188. model.model_helper_.model_ = ge::MakeShared<ge::GeModel>();
  189. // make graph
  190. ut::GraphBuilder builder = ut::GraphBuilder("graph");
  191. auto data = builder.AddNode("Data", "Data", 1, 1);
  192. auto transdata = builder.AddNode("Transdata", "Transdata", 1, 1);
  193. auto netoutput = builder.AddNode("Netoutput", "NetOutput", 1, 0);
  194. builder.AddDataEdge(data, 0, transdata, 0);
  195. builder.AddDataEdge(transdata, 0, netoutput, 0);
  196. auto compute_graph = builder.GetGraph();
  197. auto graph = GraphUtils::CreateGraphFromComputeGraph(compute_graph);
  198. model.model_helper_.model_->SetGraph(graph);
  199. model.op_list_[0] = transdata;
  200. auto op_desc = transdata->GetOpDesc();
  201. const vector<string> depend_names = { "Data" };
  202. op_desc->SetOpInferDepends(depend_names);
  203. (void)AttrUtils::SetBool(op_desc, kAttrSupportDynamicShape, true);
  204. // set task_def
  205. auto model_task_def = make_shared<domi::ModelTaskDef>();
  206. domi::TaskDef *task_def = model_task_def->add_task();
  207. task_def->set_type(RT_MODEL_TASK_KERNEL);
  208. domi::KernelDef *kernel_def = task_def->mutable_kernel();
  209. domi::KernelContext *context = kernel_def->mutable_context();
  210. context->set_kernel_type(2); // ccKernelType::TE
  211. model.model_helper_.model_->SetModelTaskDef(model_task_def);
  212. std::mutex stream_mu_;
  213. DynamicSingleOp dynamic_single_op(0, &stream_mu_, nullptr);
  214. StreamResource res((uintptr_t)1);
  215. model.BuildDynamicOp(res, dynamic_single_op);
  216. op_desc->impl_->input_name_idx_["Data"] = 0;
  217. model.BuildDynamicOp(res, dynamic_single_op);
  218. auto tensor = std::make_shared<GeTensor>();
  219. auto data_desc = data->GetOpDesc();
  220. auto tensor_desc = data_desc->MutableInputDesc(0);
  221. AttrUtils::SetTensor(tensor_desc, "_value", tensor);
  222. model.BuildDynamicOp(res, dynamic_single_op);
  223. }
  224. TEST_F(UtestSingleOpModel, test_host_mem) {
  225. string model_data_str = "123456789";
  226. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  227. // make graph
  228. ut::GraphBuilder builder = ut::GraphBuilder("graph");
  229. auto data = builder.AddNode("Data", "Data", 0, 1);
  230. auto netoutput = builder.AddNode("Netoutput", "NetOutput", 1, 0);
  231. builder.AddDataEdge(data, 0, netoutput, 0);
  232. auto graph = builder.GetGraph();
  233. model.op_with_hostmem_[0] = data;
  234. std::mutex stream_mu_;
  235. DynamicSingleOp single_op(0, &stream_mu_, nullptr);
  236. ASSERT_EQ(model.SetHostMemTensor(single_op), SUCCESS);
  237. }
  238. TEST_F(UtestSingleOpModel, BuildTaskList) {
  239. ComputeGraphPtr graph = make_shared<ComputeGraph>("single_op");
  240. GeModelPtr ge_model = make_shared<GeModel>();
  241. ge_model->SetGraph(GraphUtils::CreateGraphFromComputeGraph(graph));
  242. shared_ptr<domi::ModelTaskDef> model_task_def = make_shared<domi::ModelTaskDef>();
  243. ge_model->SetModelTaskDef(model_task_def);
  244. NodePtr node = nullptr;
  245. {
  246. auto op_desc = std::make_shared<ge::OpDesc>("memcpy", MEMCPYASYNC);
  247. GeTensorDesc tensor(GeShape(), FORMAT_NCHW, DT_FLOAT);
  248. op_desc->AddInputDesc(tensor);
  249. op_desc->AddOutputDesc(tensor);
  250. op_desc->SetInputOffset({0});
  251. op_desc->SetOutputOffset({0});
  252. node = graph->AddNode(op_desc);
  253. domi::TaskDef *task_def = model_task_def->add_task();
  254. task_def->set_stream_id(0);
  255. task_def->set_type(RT_MODEL_TASK_MEMCPY_ASYNC);
  256. domi::MemcpyAsyncDef *memcpy_async = task_def->mutable_memcpy_async();
  257. memcpy_async->set_src(0);
  258. memcpy_async->set_dst(0);
  259. memcpy_async->set_dst_max(512);
  260. memcpy_async->set_count(1);
  261. memcpy_async->set_kind(RT_MEMCPY_DEVICE_TO_DEVICE);
  262. memcpy_async->set_op_index(0);
  263. }
  264. string model_data_str = "123456789";
  265. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  266. StreamResource *res = new (std::nothrow) StreamResource(1);
  267. std::mutex stream_mu;
  268. rtStream_t stream = nullptr;
  269. rtStreamCreate(&stream, 0);
  270. SingleOp single_op(res, &stream_mu, stream);
  271. model.model_helper_.model_ = ge_model;
  272. model.op_list_.emplace(0, node);
  273. ASSERT_EQ(model.BuildTaskList(res, single_op), SUCCESS);
  274. MemcpyAsyncTask mem_task;
  275. ASSERT_EQ(mem_task.LaunchKernel(0), SUCCESS);
  276. }
  277. TEST_F(UtestSingleOpModel, build_dynamic_task01) {
  278. ComputeGraphPtr graph = make_shared<ComputeGraph>("single_op");
  279. GeModelPtr ge_model = make_shared<GeModel>();
  280. ge_model->SetGraph(GraphUtils::CreateGraphFromComputeGraph(graph));
  281. shared_ptr<domi::ModelTaskDef> model_task_def = make_shared<domi::ModelTaskDef>();
  282. ge_model->SetModelTaskDef(model_task_def);
  283. domi::TaskDef *task_def = model_task_def->add_task();
  284. task_def->set_type(RT_MODEL_TASK_KERNEL_EX);
  285. domi::TaskDef *task_def2 = model_task_def->add_task();
  286. task_def2->set_type(RT_MODEL_TASK_KERNEL);
  287. domi::KernelDef *kernel_def = task_def2->mutable_kernel();
  288. domi::KernelContext *context = kernel_def->mutable_context();
  289. context->set_kernel_type(6); // ccKernelType::AI_CPU
  290. domi::TaskDef *task_def3 = model_task_def->add_task();
  291. task_def3->set_type(RT_MODEL_TASK_ALL_KERNEL);
  292. domi::TaskDef *task_def4 = model_task_def->add_task();
  293. task_def4->set_type(RT_MODEL_TASK_KERNEL);
  294. string model_data_str = "dynamic_model";
  295. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  296. std::mutex stream_mu;
  297. rtStream_t stream = nullptr;
  298. rtStreamCreate(&stream, 0);
  299. DynamicSingleOp single_op(0, &stream_mu, stream);
  300. model.model_helper_.model_ = ge_model;
  301. auto op_desc = std::make_shared<ge::OpDesc>("add", "Add");
  302. AttrUtils::SetStr(op_desc, TVM_ATTR_NAME_MAGIC, "RT_DEV_BINARY_MAGIC_ELF");
  303. std::vector<char> kernelBin;
  304. TBEKernelPtr tbe_kernel = std::make_shared<ge::OpKernelBin>("name/Add", std::move(kernelBin));
  305. op_desc->SetExtAttr(ge::OP_EXTATTR_NAME_TBE_KERNEL, tbe_kernel);
  306. NodePtr node = graph->AddNode(op_desc);
  307. model.op_list_[0] = node;
  308. StreamResource *res = new (std::nothrow) StreamResource(1);
  309. ASSERT_EQ(model.ParseTasks(), SUCCESS);
  310. model.node_tasks_[node] = { *task_def3, *task_def4 };
  311. op_desc->SetOpKernelLibName(kEngineNameAiCore);
  312. model.BuildTaskListForDynamicOp(res, single_op);
  313. model.node_tasks_[node] = { *task_def };
  314. op_desc->SetOpKernelLibName(kEngineNameAiCpuTf);
  315. ASSERT_EQ(model.BuildTaskListForDynamicOp(res, single_op), SUCCESS);
  316. model.node_tasks_[node] = { *task_def2 };
  317. op_desc->SetOpKernelLibName(kEngineNameAiCpu);
  318. model.BuildTaskListForDynamicOp(res, single_op);
  319. }
  320. TEST_F(UtestSingleOpModel, build_dynamic_task02) {
  321. ComputeGraphPtr graph = make_shared<ComputeGraph>("single_op");
  322. GeModelPtr ge_model = make_shared<GeModel>();
  323. ge_model->SetGraph(GraphUtils::CreateGraphFromComputeGraph(graph));
  324. shared_ptr<domi::ModelTaskDef> model_task_def = make_shared<domi::ModelTaskDef>();
  325. ge_model->SetModelTaskDef(model_task_def);
  326. AicpuTaskStruct args;
  327. args.head.length = sizeof(args);
  328. args.head.ioAddrNum = 6;
  329. domi::TaskDef *task_def = model_task_def->add_task();
  330. task_def->set_type(RT_MODEL_TASK_KERNEL);
  331. domi::KernelDef *kernel_def = task_def->mutable_kernel();
  332. kernel_def->set_args(reinterpret_cast<const char *>(&args), args.head.length);
  333. kernel_def->set_args_size(args.head.length);
  334. ge::hybrid::AicpuExtInfo aicpu_ext_info;
  335. aicpu_ext_info.infoType = aicpu::FWKAdapter::FWK_ADPT_EXT_SHAPE_TYPE;
  336. aicpu_ext_info.infoLen = sizeof(int32_t);
  337. int32_t type = ge::DEPEND_COMPUTE;
  338. memcpy_s(aicpu_ext_info.infoMsg, sizeof(int32_t), &type, sizeof(int32_t));
  339. char *ext_mem = (char*)malloc(sizeof(ge::hybrid::AicpuExtInfo) + sizeof(int32_t));
  340. memcpy_s(ext_mem, sizeof(ge::hybrid::AicpuExtInfo) + sizeof(int32_t), &aicpu_ext_info,
  341. sizeof(ge::hybrid::AicpuExtInfo) + sizeof(int32_t));
  342. kernel_def->set_kernel_ext_info(ext_mem, sizeof(ge::hybrid::AicpuExtInfo) + sizeof(int32_t));
  343. kernel_def->set_kernel_ext_info_size(sizeof(ge::hybrid::AicpuExtInfo) + sizeof(int32_t));
  344. domi::KernelContext *context = kernel_def->mutable_context();
  345. context->set_kernel_type(6); // ccKernelType::AI_CPU
  346. string model_data_str = "dynamic_model";
  347. SingleOpModel model("model", model_data_str.c_str(), model_data_str.size());
  348. std::mutex stream_mu;
  349. rtStream_t stream = nullptr;
  350. rtStreamCreate(&stream, 0);
  351. DynamicSingleOp single_op(0, &stream_mu, stream);
  352. model.model_helper_.model_ = ge_model;
  353. auto op_desc = std::make_shared<ge::OpDesc>("add", "Add");
  354. AttrUtils::SetInt(op_desc, ge::ATTR_NAME_UNKNOWN_SHAPE_TYPE, ge::DEPEND_COMPUTE);
  355. NodePtr node = graph->AddNode(op_desc);
  356. model.op_list_[0] = node;
  357. StreamResource *res = new (std::nothrow) StreamResource(1);
  358. ASSERT_EQ(model.ParseTasks(), SUCCESS);
  359. model.node_tasks_[node] = { *task_def, *task_def };
  360. op_desc->SetOpKernelLibName(kEngineNameAiCpu);
  361. model.BuildTaskListForDynamicOp(res, single_op);
  362. }

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