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graph_preprocess_unittest.cc 12 kB

4 years ago
4 years ago
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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 <memory>
  18. #include "common/ge_inner_error_codes.h"
  19. #include "common/types.h"
  20. #include "common/util.h"
  21. #include "graph/passes/graph_builder_utils.h"
  22. #include "graph/utils/attr_utils.h"
  23. #include "graph/debug/ge_attr_define.h"
  24. #include "graph/manager/graph_var_manager.h"
  25. #define private public
  26. #define protected public
  27. #include "graph/preprocess/graph_preprocess.h"
  28. #include "ge/ge_api.h"
  29. #undef private
  30. #undef protected
  31. using namespace std;
  32. namespace ge {
  33. class UtestGraphPreproces : public testing::Test {
  34. protected:
  35. void SetUp() {
  36. }
  37. void TearDown() {
  38. }
  39. };
  40. ComputeGraphPtr BuildGraph1(){
  41. auto builder = ut::GraphBuilder("g1");
  42. auto data1 = builder.AddNode("data1",DATA,1,1);
  43. auto data_opdesc = data1->GetOpDesc();
  44. AttrUtils::SetInt(data_opdesc, ATTR_NAME_INDEX, 0);
  45. data1->UpdateOpDesc(data_opdesc);
  46. return builder.GetGraph();
  47. }
  48. ComputeGraphPtr BuildGraph2() {
  49. auto builder = ut::GraphBuilder("g2");
  50. auto data1 = builder.AddNode("data1", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, std::vector<int64_t>({22, -1}));
  51. ge::AttrUtils::SetStr(data1->GetOpDesc(), ATTR_ATC_USER_DEFINE_DATATYPE, "DT_INT8");
  52. auto data_opdesc = data1->GetOpDesc();
  53. AttrUtils::SetInt(data_opdesc, ATTR_NAME_INDEX, 0);
  54. data1->UpdateOpDesc(data_opdesc);
  55. return builder.GetGraph();
  56. }
  57. ComputeGraphPtr BuildGraph3() {
  58. auto builder = ut::GraphBuilder("g3");
  59. auto data1 = builder.AddNode("data1", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT);
  60. ge::AttrUtils::SetStr(data1->GetOpDesc(), ATTR_ATC_USER_DEFINE_DATATYPE, "DT_INT8");
  61. auto data_opdesc = data1->GetOpDesc();
  62. AttrUtils::SetInt(data_opdesc, ATTR_NAME_INDEX, 0);
  63. data1->UpdateOpDesc(data_opdesc);
  64. return builder.GetGraph();
  65. }
  66. ComputeGraphPtr BuildGraph5() {
  67. auto builder = ut::GraphBuilder("g5");
  68. auto data1 = builder.AddNode("input1", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, {1, 2, 3});
  69. auto data2 = builder.AddNode("input2", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, {4, 10});
  70. auto add = builder.AddNode("add", ADD, 2, 1);
  71. auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0);
  72. builder.AddDataEdge(data1, 0, add, 0);
  73. builder.AddDataEdge(data2, 0, add, 1);
  74. builder.AddDataEdge(add, 0,netoutput, 0);
  75. return builder.GetGraph();
  76. }
  77. /*
  78. * MapIndex Data1 subgraph1 subgraph2
  79. * \ /
  80. * Case ===> Data2 Data3
  81. * |
  82. * Netoutput
  83. */
  84. ComputeGraphPtr BuildGraph4() {
  85. auto builder = ut::GraphBuilder("mbatch_Case");
  86. auto data1 = builder.AddNode("data1", DATA, 1, 1);
  87. auto data_desc = data1->GetOpDesc();
  88. AttrUtils::SetStr(data_desc, ATTR_ATC_USER_DEFINE_DATATYPE, "DT_FLOAT16");
  89. AttrUtils::SetStr(data_desc, "mbatch-switch-name", "case1");
  90. AttrUtils::SetInt(data_desc, ATTR_NAME_INDEX, 0);
  91. auto mapindex1 = builder.AddNode("mapindex1", "MapIndex", 0, 1);
  92. auto case1 = builder.AddNode("case1", CASE, 2, 1);
  93. auto netoutput1 = builder.AddNode("netoutput1", NETOUTPUT, 1, 0);
  94. builder.AddDataEdge(mapindex1, 0, case1, 0);
  95. builder.AddDataEdge(data1, 0, case1, 1);
  96. builder.AddDataEdge(case1, 0, netoutput1, 0);
  97. return builder.GetGraph();
  98. }
  99. ComputeGraphPtr BuildGraph4_Subgraph(string graph_name) {
  100. auto builder = ut::GraphBuilder(graph_name);
  101. auto data1 = builder.AddNode(graph_name + "_data1", DATA, 1, 1);
  102. auto data_desc = data1->GetOpDesc();
  103. AttrUtils::SetInt(data_desc, ATTR_NAME_PARENT_NODE_INDEX, 1);
  104. return builder.GetGraph();
  105. }
  106. ComputeGraphPtr BuildGraph6() {
  107. auto builder = ut::GraphBuilder("g6");
  108. auto data1 = builder.AddNode("input1", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, {3, -1, -1, 5});
  109. auto data2 = builder.AddNode("input2", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, {});
  110. AttrUtils::SetInt(data1->GetOpDesc(), ATTR_NAME_INDEX, 0);
  111. AttrUtils::SetInt(data2->GetOpDesc(), ATTR_NAME_INDEX, 1);
  112. auto add = builder.AddNode("add", ADD, 2, 1);
  113. auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0);
  114. builder.AddDataEdge(data1, 0, add, 0);
  115. builder.AddDataEdge(data2, 0, add, 1);
  116. builder.AddDataEdge(add, 0,netoutput, 0);
  117. return builder.GetGraph();
  118. }
  119. TEST_F(UtestGraphPreproces, test_dynamic_input_shape_parse) {
  120. ge::GraphPrepare graph_prepare;
  121. graph_prepare.compute_graph_ = BuildGraph6();
  122. // prepare user_input & graph option
  123. ge::GeTensorDesc tensor1;
  124. tensor1.SetFormat(ge::FORMAT_NCHW);
  125. tensor1.SetShape(ge::GeShape({3, 12, 5, 5}));
  126. tensor1.SetDataType(ge::DT_FLOAT);
  127. GeTensor input1(tensor1);
  128. ge::GeTensorDesc tensor2;
  129. tensor2.SetFormat(ge::FORMAT_NCHW);
  130. tensor2.SetShape(ge::GeShape());
  131. tensor2.SetDataType(ge::DT_FLOAT);
  132. GeTensor input2(tensor2);
  133. std::vector<GeTensor> user_input = {input1, input2};
  134. std::map<string,string> graph_option = {{"ge.exec.dynamicGraphExecuteMode","dynamic_execute"},
  135. {"ge.exec.dataInputsShapeRange","[3,1~20,2~10,5],[]"}};
  136. auto ret = graph_prepare.UpdateInput(user_input, graph_option);
  137. EXPECT_EQ(ret, ge::SUCCESS);
  138. // check data1 node output shape_range and shape
  139. auto data_node = graph_prepare.compute_graph_->FindNode("input1");
  140. auto data_output_desc = data_node->GetOpDesc()->GetOutputDescPtr(0);
  141. vector<int64_t> input1_expect_shape = {3,-1,-1,5};
  142. vector<std::pair<int64_t, int64_t>> intpu1_expect_shape_range = {{3,3},{1,20},{2,10},{5,5}};
  143. auto input1_result_shape = data_output_desc->GetShape();
  144. vector<std::pair<int64_t, int64_t>> input1_result_shape_range;
  145. data_output_desc->GetShapeRange(input1_result_shape_range);
  146. EXPECT_EQ(input1_result_shape.GetDimNum(), input1_expect_shape.size());
  147. EXPECT_EQ(input1_result_shape_range.size(), input1_expect_shape.size());
  148. for(size_t i =0; i< input1_expect_shape.size(); ++i){
  149. EXPECT_EQ(input1_result_shape.GetDim(i), input1_expect_shape.at(i));
  150. }
  151. for(size_t i =0; i< intpu1_expect_shape_range.size(); ++i){
  152. EXPECT_EQ(input1_result_shape_range.at(i).first, intpu1_expect_shape_range.at(i).first);
  153. EXPECT_EQ(input1_result_shape_range.at(i).second, intpu1_expect_shape_range.at(i).second);
  154. }
  155. // check data2 node output shape_range and shape
  156. auto data_node_2 = graph_prepare.compute_graph_->FindNode("input2");
  157. auto data_output_desc_2 = data_node_2->GetOpDesc()->GetOutputDescPtr(0);
  158. vector<std::pair<int64_t, int64_t>> intput2_result_shape_range;
  159. data_output_desc_2->GetShapeRange(intput2_result_shape_range);
  160. EXPECT_EQ(intput2_result_shape_range.size(), 0);
  161. }
  162. TEST_F(UtestGraphPreproces, test_update_input_fail) {
  163. ge::GraphPrepare graph_prepare;
  164. graph_prepare.compute_graph_ = BuildGraph1();
  165. ge::GeTensorDesc tensor1;
  166. tensor1.SetFormat(ge::FORMAT_NCHW);
  167. tensor1.SetShape(ge::GeShape({3, 12, 5, 5}));
  168. tensor1.SetDataType(ge::DT_UNDEFINED);
  169. GeTensor input1(tensor1);
  170. std::vector<GeTensor> user_input = {input1};
  171. std::map<string,string> graph_option;
  172. auto ret = graph_prepare.UpdateInput(user_input, graph_option);
  173. EXPECT_EQ(ret, ge::FAILED);
  174. }
  175. TEST_F(UtestGraphPreproces, test_check_user_input) {
  176. ge::GraphPrepare graph_prepare;
  177. graph_prepare.compute_graph_ = BuildGraph1();
  178. vector<int64_t> dim = {2, -3};
  179. GeTensor tensor;
  180. tensor.SetTensorDesc(GeTensorDesc(GeShape(dim)));
  181. std::vector<GeTensor> user_input;
  182. user_input.emplace_back(tensor);
  183. Status ret = graph_prepare.CheckUserInput(user_input);
  184. EXPECT_EQ(ret, GE_GRAPH_INIT_FAILED);
  185. }
  186. TEST_F(UtestGraphPreproces, test_update_input_output1) {
  187. ge::GraphPrepare graph_prepare;
  188. graph_prepare.compute_graph_ = BuildGraph3();
  189. Status ret = graph_prepare.UpdateInputOutputByOptions();
  190. EXPECT_EQ(ret, SUCCESS);
  191. }
  192. TEST_F(UtestGraphPreproces, check_ref_op_data_succ) {
  193. GraphPrepare graph_preparer;
  194. ComputeGraphPtr graph_test = BuildGraph5();
  195. NodePtr add_node = nullptr;
  196. for (auto &node : graph_test->GetAllNodes()) {
  197. if (node->GetName() == "add") {
  198. add_node = node;
  199. }
  200. }
  201. EXPECT_NE(add_node, nullptr);
  202. string input_name = "__input0";
  203. std::set<NodePtr> ref_nodes;
  204. auto ret = graph_preparer.CheckRefInputNode(add_node, input_name, ref_nodes);
  205. EXPECT_EQ(ret, SUCCESS);
  206. }
  207. TEST_F(UtestGraphPreproces, test_update_dtype_mbatch_case) {
  208. ge::GraphPrepare graph_prepare;
  209. graph_prepare.compute_graph_ = BuildGraph4();
  210. auto parent_graph = graph_prepare.compute_graph_;
  211. auto subgraph1 = BuildGraph4_Subgraph("subgraph1");
  212. auto subgraph2 = BuildGraph4_Subgraph("subgraph2");
  213. auto data1 = parent_graph->FindNode("data1");
  214. auto data_desc = data1->GetOpDesc();
  215. auto case_node = parent_graph->FindNode("case1");
  216. EXPECT_NE(case_node, nullptr);
  217. case_node->GetOpDesc()->AddSubgraphName("subgraph1");
  218. case_node->GetOpDesc()->SetSubgraphInstanceName(0, "subgraph1");
  219. subgraph1->SetParentNode(case_node);
  220. subgraph1->SetParentGraph(parent_graph);
  221. EXPECT_EQ(parent_graph->AddSubgraph("subgraph1", subgraph1), GRAPH_SUCCESS);
  222. case_node->GetOpDesc()->AddSubgraphName("subgraph2");
  223. case_node->GetOpDesc()->SetSubgraphInstanceName(1, "subgraph2");
  224. subgraph2->SetParentNode(case_node);
  225. subgraph2->SetParentGraph(parent_graph);
  226. EXPECT_EQ(parent_graph->AddSubgraph("subgraph2", subgraph2), GRAPH_SUCCESS);
  227. Status ret = graph_prepare.UpdateInputOutputByOptions();
  228. EXPECT_EQ(ret, SUCCESS);
  229. auto case_desc = case_node->GetOpDesc();
  230. auto case_input = case_desc->MutableInputDesc(1);
  231. EXPECT_EQ(case_input->GetDataType(), 1);
  232. auto sub1_data1 = subgraph1->FindNode("subgraph1_data1");
  233. EXPECT_NE(sub1_data1, nullptr);
  234. auto data1_desc = sub1_data1->GetOpDesc();
  235. auto data1_input = data1_desc->MutableInputDesc(0);
  236. EXPECT_EQ(data1_input->GetDataType(), 1);
  237. auto data1_output = data1_desc->MutableOutputDesc(0);
  238. EXPECT_EQ(data1_output->GetDataType(), 1);
  239. }
  240. TEST_F(UtestGraphPreproces, test_prepare_dyn_shape) {
  241. ComputeGraphPtr compute_graph = BuildGraph5();
  242. GraphPtr graph_ptr = std::make_shared<Graph>(GraphUtils::CreateGraphFromComputeGraph(compute_graph));
  243. GraphNodePtr graph_node = make_shared<GraphNode>(0);
  244. graph_node->SetComputeGraph(compute_graph);
  245. graph_node->SetGraph(graph_ptr);
  246. std::vector<GeTensor> user_input;
  247. GraphPrepare graph_prepare;
  248. EXPECT_EQ(graph_prepare.PrepareDynShape(graph_node, user_input, compute_graph, 0), SUCCESS);
  249. }
  250. TEST_F(UtestGraphPreproces, test_updar_variable_formats) {
  251. auto builder = ut::GraphBuilder("g1");
  252. auto var = builder.AddNode("var", VARIABLE, 1, 1);
  253. auto g1 = builder.GetGraph();
  254. g1->SetSessionID(0);
  255. TransNodeInfo trans_node_info;
  256. VarTransRoad fusion_road;
  257. fusion_road.emplace_back(trans_node_info);
  258. VarManager::Instance(g1->GetSessionID())->SetTransRoad(var->GetName(), fusion_road);
  259. GraphPrepare graph_prepare;
  260. EXPECT_EQ(graph_prepare.UpdateVariableFormats(g1), INTERNAL_ERROR);
  261. auto builder1 = ut::GraphBuilder("g2");
  262. auto var1 = builder1.AddNode("var1", VARIABLE, 1, 1);
  263. auto g2 = builder1.GetGraph();
  264. g2->SetSessionID(0);
  265. VarTransRoad fusion_road1;
  266. VarManager::Instance(g2->GetSessionID())->SetTransRoad(var1->GetName(), fusion_road1);
  267. AttrUtils::SetStr(var1->GetOpDesc(), REF_VAR_SRC_VAR_NAME, "var1");
  268. EXPECT_EQ(graph_prepare.UpdateVariableFormats(g2), SUCCESS);
  269. }
  270. }

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