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graph_preprocess_unittest.cc 11 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. #define private public
  25. #define protected public
  26. #include "graph/preprocess/graph_preprocess.h"
  27. #include "ge/ge_api.h"
  28. #undef private
  29. #undef protected
  30. using namespace std;
  31. namespace ge {
  32. class UtestGraphPreproces : public testing::Test {
  33. protected:
  34. void SetUp() {
  35. }
  36. void TearDown() {
  37. }
  38. };
  39. ComputeGraphPtr BuildGraph1(){
  40. auto builder = ut::GraphBuilder("g1");
  41. auto data1 = builder.AddNode("data1",DATA,1,1);
  42. auto data_opdesc = data1->GetOpDesc();
  43. AttrUtils::SetInt(data_opdesc, ATTR_NAME_INDEX, 0);
  44. data1->UpdateOpDesc(data_opdesc);
  45. return builder.GetGraph();
  46. }
  47. ComputeGraphPtr BuildGraph2() {
  48. auto builder = ut::GraphBuilder("g2");
  49. auto data1 = builder.AddNode("data1", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, std::vector<int64_t>({22, -1}));
  50. ge::AttrUtils::SetStr(data1->GetOpDesc(), ATTR_ATC_USER_DEFINE_DATATYPE, "DT_INT8");
  51. auto data_opdesc = data1->GetOpDesc();
  52. AttrUtils::SetInt(data_opdesc, ATTR_NAME_INDEX, 0);
  53. data1->UpdateOpDesc(data_opdesc);
  54. return builder.GetGraph();
  55. }
  56. ComputeGraphPtr BuildGraph3() {
  57. auto builder = ut::GraphBuilder("g3");
  58. auto data1 = builder.AddNode("data1", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT);
  59. ge::AttrUtils::SetStr(data1->GetOpDesc(), ATTR_ATC_USER_DEFINE_DATATYPE, "DT_INT8");
  60. auto data_opdesc = data1->GetOpDesc();
  61. AttrUtils::SetInt(data_opdesc, ATTR_NAME_INDEX, 0);
  62. data1->UpdateOpDesc(data_opdesc);
  63. return builder.GetGraph();
  64. }
  65. ComputeGraphPtr BuildGraph5() {
  66. auto builder = ut::GraphBuilder("g5");
  67. auto data1 = builder.AddNode("input1", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, {1, 2, 3});
  68. auto data2 = builder.AddNode("input2", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, {4, 10});
  69. auto add = builder.AddNode("add", ADD, 2, 1);
  70. auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0);
  71. builder.AddDataEdge(data1, 0, add, 0);
  72. builder.AddDataEdge(data2, 0, add, 1);
  73. builder.AddDataEdge(add, 0,netoutput, 0);
  74. return builder.GetGraph();
  75. }
  76. /*
  77. * MapIndex Data1 subgraph1 subgraph2
  78. * \ /
  79. * Case ===> Data2 Data3
  80. * |
  81. * Netoutput
  82. */
  83. ComputeGraphPtr BuildGraph4() {
  84. auto builder = ut::GraphBuilder("mbatch_Case");
  85. auto data1 = builder.AddNode("data1", DATA, 1, 1);
  86. auto data_desc = data1->GetOpDesc();
  87. AttrUtils::SetStr(data_desc, ATTR_ATC_USER_DEFINE_DATATYPE, "DT_FLOAT16");
  88. AttrUtils::SetStr(data_desc, "mbatch-switch-name", "case1");
  89. AttrUtils::SetInt(data_desc, ATTR_NAME_INDEX, 0);
  90. auto mapindex1 = builder.AddNode("mapindex1", "MapIndex", 0, 1);
  91. auto case1 = builder.AddNode("case1", CASE, 2, 1);
  92. auto netoutput1 = builder.AddNode("netoutput1", NETOUTPUT, 1, 0);
  93. builder.AddDataEdge(mapindex1, 0, case1, 0);
  94. builder.AddDataEdge(data1, 0, case1, 1);
  95. builder.AddDataEdge(case1, 0, netoutput1, 0);
  96. return builder.GetGraph();
  97. }
  98. ComputeGraphPtr BuildGraph4_Subgraph(string graph_name) {
  99. auto builder = ut::GraphBuilder(graph_name);
  100. auto data1 = builder.AddNode(graph_name + "_data1", DATA, 1, 1);
  101. auto data_desc = data1->GetOpDesc();
  102. AttrUtils::SetInt(data_desc, ATTR_NAME_PARENT_NODE_INDEX, 1);
  103. return builder.GetGraph();
  104. }
  105. ComputeGraphPtr BuildGraph6() {
  106. auto builder = ut::GraphBuilder("g6");
  107. auto data1 = builder.AddNode("input1", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, {3, -1, -1, 5});
  108. auto data2 = builder.AddNode("input2", DATA, 1, 1, FORMAT_NCHW, DT_FLOAT, {});
  109. AttrUtils::SetInt(data1->GetOpDesc(), ATTR_NAME_INDEX, 0);
  110. AttrUtils::SetInt(data2->GetOpDesc(), ATTR_NAME_INDEX, 1);
  111. auto add = builder.AddNode("add", ADD, 2, 1);
  112. auto netoutput = builder.AddNode("netoutput", NETOUTPUT, 1, 0);
  113. builder.AddDataEdge(data1, 0, add, 0);
  114. builder.AddDataEdge(data2, 0, add, 1);
  115. builder.AddDataEdge(add, 0,netoutput, 0);
  116. return builder.GetGraph();
  117. }
  118. TEST_F(UtestGraphPreproces, test_dynamic_input_shape_parse) {
  119. ge::GraphPrepare graph_prepare;
  120. graph_prepare.compute_graph_ = BuildGraph6();
  121. // prepare user_input & graph option
  122. ge::GeTensorDesc tensor1;
  123. tensor1.SetFormat(ge::FORMAT_NCHW);
  124. tensor1.SetShape(ge::GeShape({3, 12, 5, 5}));
  125. tensor1.SetDataType(ge::DT_FLOAT);
  126. GeTensor input1(tensor1);
  127. ge::GeTensorDesc tensor2;
  128. tensor2.SetFormat(ge::FORMAT_NCHW);
  129. tensor2.SetShape(ge::GeShape());
  130. tensor2.SetDataType(ge::DT_FLOAT);
  131. GeTensor input2(tensor2);
  132. std::vector<GeTensor> user_input = {input1, input2};
  133. std::map<string,string> graph_option = {{"ge.exec.dynamicGraphExecuteMode","dynamic_execute"},
  134. {"ge.exec.dataInputsShapeRange","[3,1~20,2~10,5],[]"}};
  135. auto ret = graph_prepare.UpdateInput(user_input, graph_option);
  136. EXPECT_EQ(ret, ge::SUCCESS);
  137. // check data1 node output shape_range and shape
  138. auto data_node = graph_prepare.compute_graph_->FindNode("input1");
  139. auto data_output_desc = data_node->GetOpDesc()->GetOutputDescPtr(0);
  140. vector<int64_t> input1_expect_shape = {3,-1,-1,5};
  141. vector<std::pair<int64_t, int64_t>> intpu1_expect_shape_range = {{3,3},{1,20},{2,10},{5,5}};
  142. auto input1_result_shape = data_output_desc->GetShape();
  143. vector<std::pair<int64_t, int64_t>> input1_result_shape_range;
  144. data_output_desc->GetShapeRange(input1_result_shape_range);
  145. EXPECT_EQ(input1_result_shape.GetDimNum(), input1_expect_shape.size());
  146. EXPECT_EQ(input1_result_shape_range.size(), input1_expect_shape.size());
  147. for(size_t i =0; i< input1_expect_shape.size(); ++i){
  148. EXPECT_EQ(input1_result_shape.GetDim(i), input1_expect_shape.at(i));
  149. }
  150. for(size_t i =0; i< intpu1_expect_shape_range.size(); ++i){
  151. EXPECT_EQ(input1_result_shape_range.at(i).first, intpu1_expect_shape_range.at(i).first);
  152. EXPECT_EQ(input1_result_shape_range.at(i).second, intpu1_expect_shape_range.at(i).second);
  153. }
  154. // check data2 node output shape_range and shape
  155. auto data_node_2 = graph_prepare.compute_graph_->FindNode("input2");
  156. auto data_output_desc_2 = data_node_2->GetOpDesc()->GetOutputDescPtr(0);
  157. vector<std::pair<int64_t, int64_t>> intput2_result_shape_range;
  158. data_output_desc_2->GetShapeRange(intput2_result_shape_range);
  159. EXPECT_EQ(intput2_result_shape_range.size(), 0);
  160. }
  161. TEST_F(UtestGraphPreproces, test_update_input_fail) {
  162. ge::GraphPrepare graph_prepare;
  163. graph_prepare.compute_graph_ = BuildGraph1();
  164. ge::GeTensorDesc tensor1;
  165. tensor1.SetFormat(ge::FORMAT_NCHW);
  166. tensor1.SetShape(ge::GeShape({3, 12, 5, 5}));
  167. tensor1.SetDataType(ge::DT_UNDEFINED);
  168. GeTensor input1(tensor1);
  169. std::vector<GeTensor> user_input = {input1};
  170. std::map<string,string> graph_option;
  171. auto ret = graph_prepare.UpdateInput(user_input, graph_option);
  172. EXPECT_EQ(ret, ge::FAILED);
  173. }
  174. TEST_F(UtestGraphPreproces, test_check_user_input) {
  175. ge::GraphPrepare graph_prepare;
  176. graph_prepare.compute_graph_ = BuildGraph1();
  177. vector<int64_t> dim = {2, -3};
  178. GeTensor tensor;
  179. tensor.SetTensorDesc(GeTensorDesc(GeShape(dim)));
  180. std::vector<GeTensor> user_input;
  181. user_input.emplace_back(tensor);
  182. Status ret = graph_prepare.CheckUserInput(user_input);
  183. EXPECT_EQ(ret, GE_GRAPH_INIT_FAILED);
  184. }
  185. TEST_F(UtestGraphPreproces, test_update_input_output1) {
  186. ge::GraphPrepare graph_prepare;
  187. graph_prepare.compute_graph_ = BuildGraph3();
  188. Status ret = graph_prepare.UpdateInputOutputByOptions();
  189. EXPECT_EQ(ret, SUCCESS);
  190. }
  191. TEST_F(UtestGraphPreproces, check_ref_op_data_succ) {
  192. GraphPrepare graph_preparer;
  193. ComputeGraphPtr graph_test = BuildGraph5();
  194. NodePtr add_node = nullptr;
  195. for (auto &node : graph_test->GetAllNodes()) {
  196. if (node->GetName() == "add") {
  197. add_node = node;
  198. }
  199. }
  200. EXPECT_NE(add_node, nullptr);
  201. string input_name = "__input0";
  202. std::set<NodePtr> ref_nodes;
  203. auto ret = graph_preparer.CheckRefInputNode(add_node, input_name, ref_nodes);
  204. EXPECT_EQ(ret, SUCCESS);
  205. }
  206. TEST_F(UtestGraphPreproces, test_update_dtype_mbatch_case) {
  207. ge::GraphPrepare graph_prepare;
  208. graph_prepare.compute_graph_ = BuildGraph4();
  209. auto parent_graph = graph_prepare.compute_graph_;
  210. auto subgraph1 = BuildGraph4_Subgraph("subgraph1");
  211. auto subgraph2 = BuildGraph4_Subgraph("subgraph2");
  212. auto data1 = parent_graph->FindNode("data1");
  213. auto data_desc = data1->GetOpDesc();
  214. auto case_node = parent_graph->FindNode("case1");
  215. EXPECT_NE(case_node, nullptr);
  216. case_node->GetOpDesc()->AddSubgraphName("subgraph1");
  217. case_node->GetOpDesc()->SetSubgraphInstanceName(0, "subgraph1");
  218. subgraph1->SetParentNode(case_node);
  219. subgraph1->SetParentGraph(parent_graph);
  220. EXPECT_EQ(parent_graph->AddSubgraph("subgraph1", subgraph1), GRAPH_SUCCESS);
  221. case_node->GetOpDesc()->AddSubgraphName("subgraph2");
  222. case_node->GetOpDesc()->SetSubgraphInstanceName(1, "subgraph2");
  223. subgraph2->SetParentNode(case_node);
  224. subgraph2->SetParentGraph(parent_graph);
  225. EXPECT_EQ(parent_graph->AddSubgraph("subgraph2", subgraph2), GRAPH_SUCCESS);
  226. Status ret = graph_prepare.UpdateInputOutputByOptions();
  227. EXPECT_EQ(ret, SUCCESS);
  228. auto case_desc = case_node->GetOpDesc();
  229. auto case_input = case_desc->MutableInputDesc(1);
  230. EXPECT_EQ(case_input->GetDataType(), 1);
  231. auto sub1_data1 = subgraph1->FindNode("subgraph1_data1");
  232. EXPECT_NE(sub1_data1, nullptr);
  233. auto data1_desc = sub1_data1->GetOpDesc();
  234. auto data1_input = data1_desc->MutableInputDesc(0);
  235. EXPECT_EQ(data1_input->GetDataType(), 1);
  236. auto data1_output = data1_desc->MutableOutputDesc(0);
  237. EXPECT_EQ(data1_output->GetDataType(), 1);
  238. }
  239. TEST_F(UtestGraphPreproces, test_prepare_dyn_shape) {
  240. ComputeGraphPtr compute_graph = BuildGraph5();
  241. GraphPtr graph_ptr = std::make_shared<Graph>(GraphUtils::CreateGraphFromComputeGraph(compute_graph));
  242. GraphNodePtr graph_node = make_shared<GraphNode>(0);
  243. graph_node->SetComputeGraph(compute_graph);
  244. graph_node->SetGraph(graph_ptr);
  245. std::vector<GeTensor> user_input;
  246. GraphPrepare graph_prepare;
  247. EXPECT_EQ(graph_prepare.PrepareDynShape(graph_node, user_input, compute_graph, 0), SUCCESS);
  248. }
  249. }

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