!22070 [lite]enhance dumpgraph function and adjust examples on the basis of unified api

Merge pull request !22070 from 徐安越/master_core

mindspore/lite/examples/runtime_extend/main.cc

@@ -14,206 +14,97 @@
  * limitations under the License.
  */
 
-#include <algorithm>
-#include <random>
 #include <iostream>
-#include <fstream>
 #include <cstring>
-#include "include/errorcode.h"
-#include "include/model.h"
-#include "include/context.h"
-#include "include/lite_session.h"
+#include <memory>
+#include <string>
+#include <vector>
+#include "include/api/status.h"
+#include "include/api/context.h"
+#include "include/api/model.h"
+
 namespace mindspore {
 namespace lite {
 namespace {
 constexpr int kNumPrintOfOutData = 20;
-std::string RealPath(const char *path) {
-  const size_t max = 4096;
-  if (path == nullptr) {
-    std::cerr << "path is nullptr" << std::endl;
-    return "";
+Status FillInputData(const std::vector<mindspore::MSTensor> &inputs) {
+  for (auto tensor : inputs) {
+    auto input_data = tensor.MutableData();
+    if (input_data == nullptr) {
+      std::cerr << "MallocData for inTensor failed.\n";
+      return kLiteError;
+    }
+    std::vector<float> temp(tensor.ElementNum(), 1.0f);
+    memcpy(input_data, temp.data(), tensor.DataSize());
   }
-  if ((strlen(path)) >= max) {
-    std::cerr << "path is too long" << std::endl;
-    return "";
-  }
-  auto resolved_path = std::make_unique<char[]>(max);
-  if (resolved_path == nullptr) {
-    std::cerr << "new resolved_path failed" << std::endl;
-    return "";
-  }
-#ifdef _WIN32
-  char *real_path = _fullpath(resolved_path.get(), path, 1024);
-#else
-  char *real_path = realpath(path, resolved_path.get());
-#endif
-  if (real_path == nullptr || strlen(real_path) == 0) {
-    std::cerr << "file path is not valid : " << path << std::endl;
-    return "";
-  }
-  std::string res = resolved_path.get();
-  return res;
-}
-
-char *ReadFile(const char *file, size_t *size) {
-  if (file == nullptr) {
-    std::cerr << "file is nullptr." << std::endl;
-    return nullptr;
-  }
-
-  std::ifstream ifs(file);
-  if (!ifs.good()) {
-    std::cerr << "file: " << file << " is not exist." << std::endl;
-    return nullptr;
-  }
-
-  if (!ifs.is_open()) {
-    std::cerr << "file: " << file << " open failed." << std::endl;
-    return nullptr;
-  }
-
-  ifs.seekg(0, std::ios::end);
-  *size = ifs.tellg();
-  std::unique_ptr<char[]> buf(new (std::nothrow) char[*size]);
-  if (buf == nullptr) {
-    std::cerr << "malloc buf failed, file: " << file << std::endl;
-    ifs.close();
-    return nullptr;
-  }
-
-  ifs.seekg(0, std::ios::beg);
-  ifs.read(buf.get(), *size);
-  ifs.close();
-
-  return buf.release();
+  return kSuccess;
 }
 }  // namespace
 
-template <typename T, typename Distribution>
-void GenerateRandomData(int size, void *data, Distribution distribution) {
-  std::mt19937 random_engine;
-  int elements_num = size / sizeof(T);
-  (void)std::generate_n(static_cast<T *>(data), elements_num,
-                        [&distribution, &random_engine]() { return static_cast<T>(distribution(random_engine)); });
-}
-
-int GenerateInputDataWithRandom(std::vector<mindspore::tensor::MSTensor *> inputs) {
-  for (auto tensor : inputs) {
-    auto input_data = tensor->MutableData();
-    if (input_data == nullptr) {
-      std::cerr << "MallocData for inTensor failed." << std::endl;
-      return RET_ERROR;
-    }
-    GenerateRandomData<float>(tensor->Size(), input_data, std::uniform_real_distribution<float>(1.0f, 1.0f));
+Status CompileAndRun(int argc, const char **argv) {
+  if (argc < 2) {
+    std::cerr << "Model file must be provided.\n";
+    return kLiteError;
   }
-  return RET_OK;
-}
+  // generate context.
+  auto context = std::make_shared<mindspore::Context>();
+  if (context == nullptr) {
+    std::cerr << "New context failed while running.\n";
+    return kLiteError;
+  }
+  auto &device_list = context->MutableDeviceInfo();
+  std::shared_ptr<CPUDeviceInfo> device_info = std::make_shared<CPUDeviceInfo>();
+  device_info->SetProvider("Tutorial");
+  device_info->SetProviderDevice("Tutorial");
+  device_list.push_back(device_info);
 
-int Run(mindspore::session::LiteSession *session) {
-  auto inputs = session->GetInputs();
+  // build model.
+  std::string model_file = std::string(argv[1]);
+  mindspore::Model model;
+  auto ret = model.Build(model_file, kMindIR, context);
+  if (ret != kSuccess) {
+    std::cerr << "build model failed.\n";
+    return kLiteError;
+  }
 
-  // Generate random data as input data.
-  auto ret = GenerateInputDataWithRandom(inputs);
-  if (ret != RET_OK) {
-    std::cerr << "Generate Random Input Data failed." << std::endl;
+  // fill input data.
+  auto inputs = model.GetInputs();
+  ret = FillInputData(inputs);
+  if (ret != kSuccess) {
+    std::cerr << "Generate Random Input Data failed.\n";
     return ret;
   }
 
-  // Run Inference.
-  ret = session->RunGraph();
-  if (ret != RET_OK) {
-    std::cerr << "Inference error " << ret << std::endl;
+  // run model.
+  std::vector<MSTensor> outputs;
+  ret = model.Predict(inputs, &outputs);
+  if (ret != kSuccess) {
+    std::cerr << "run model failed.\n";
     return ret;
   }
 
-  // Get Output Tensor Data.
-  auto out_tensors = session->GetOutputs();
-  for (auto tensor : out_tensors) {
-    std::cout << "tensor name is:" << tensor.first << " tensor size is:" << tensor.second->Size()
-              << " tensor elements num is:" << tensor.second->ElementsNum() << std::endl;
-    auto out_data = reinterpret_cast<float *>(tensor.second->MutableData());
+  // display output result.
+  for (auto tensor : outputs) {
+    std::cout << "tensor name is:" << tensor.Name() << " tensor size is:" << tensor.DataSize()
+              << " tensor elements num is:" << tensor.ElementNum() << std::endl;
+    auto out_data = std::static_pointer_cast<const float>(tensor.Data());
     std::cout << "output data is:";
-    for (int i = 0; i < tensor.second->ElementsNum() && i <= kNumPrintOfOutData; i++) {
-      std::cout << out_data[i] << " ";
+    for (int i = 0; i < tensor.ElementNum() && i <= kNumPrintOfOutData; i++) {
+      std::cout << out_data.get()[i] << " ";
     }
     std::cout << std::endl;
   }
-  return RET_OK;
-}
-
-mindspore::session::LiteSession *Compile(mindspore::lite::Model *model) {
-  // Create and init context.
-  auto context = std::make_shared<Context>();
-  if (context == nullptr) {
-    std::cerr << "New context failed while." << std::endl;
-    return nullptr;
-  }
-  context->device_list_[0].provider_ = "Tutorial";
-  context->device_list_[0].provider_device_ = "Tutorial";
-  // Create the session.
-  auto *session = mindspore::session::LiteSession::CreateSession(context.get());
-  if (session == nullptr) {
-    std::cerr << "CreateSession failed while running." << std::endl;
-    return nullptr;
-  }
-
-  // Compile graph.
-  auto ret = session->CompileGraph(model);
-  if (ret != RET_OK) {
-    delete session;
-    std::cerr << "Compile failed while running." << std::endl;
-    return nullptr;
-  }
-
-  return session;
-}
-
-int CompileAndRun(int argc, const char **argv) {
-  if (argc < 2) {
-    std::cerr << "Model file must be provided.\n";
-    return RET_ERROR;
-  }
-  // Read model file.
-  auto model_path = RealPath(argv[1]);
-  if (model_path.empty()) {
-    std::cerr << "model path " << argv[1] << " is invalid.";
-    return RET_ERROR;
-  }
-  size_t size = 0;
-  char *model_buf = ReadFile(model_path.c_str(), &size);
-  if (model_buf == nullptr) {
-    std::cerr << "Read model file failed." << std::endl;
-    return RET_ERROR;
-  }
-  // Load the .ms model.
-  auto model = Model::Import(model_buf, size);
-  delete[](model_buf);
-  if (model == nullptr) {
-    std::cerr << "Import model file failed." << std::endl;
-    return RET_ERROR;
-  }
-  // Compile MindSpore Lite model.
-  auto session = Compile(model);
-  if (session == nullptr) {
-    delete model;
-    std::cerr << "Create session failed." << std::endl;
-    return RET_ERROR;
-  }
-  // Run inference.
-  auto ret = Run(session);
-  if (ret != RET_OK) {
-    delete model;
-    delete session;
-    std::cerr << "MindSpore Lite run failed." << std::endl;
-    return RET_ERROR;
-  }
-  // Delete model buffer.
-  delete model;
-  // Delete session buffer.
-  delete session;
-  return RET_OK;
+  return kSuccess;
 }
 }  // namespace lite
 }  // namespace mindspore
 
-int main(int argc, const char **argv) { return mindspore::lite::CompileAndRun(argc, argv); }
+int main(int argc, const char **argv) {
+  auto ret = mindspore::lite::CompileAndRun(argc, argv);
+  if (ret != mindspore::kSuccess) {
+    std::cerr << "run failed.\n";
+    return -1;
+  }
+  std::cout << "run success.\n";
+  return 0;
+}

mindspore/lite/tools/converter/export_model.cc

@@ -191,7 +191,8 @@ STATUS ExportModel(const FuncGraphPtr &graph, const converter::Flags *flags) {
     return RET_ERROR;
   }
   (void)Manage(mirror_graph, true);
-  if (!RunOptimizerPass(mirror_graph, {"InferShapePass", "DeleteRedundantTranspose", "DecreaseTransposeAlgo"})) {
+  if (!RunOptimizerPass(mirror_graph,
+                        {"ToNHWCFormat", "InferShapePass", "DeleteRedundantTranspose", "DecreaseTransposeAlgo"})) {
     MS_LOG(ERROR) << "Run transpose opt pass failed.";
     return RET_ERROR;
   }

mindspore/lite/tools/converter/parser/unify_format.cc

@@ -177,7 +177,8 @@ void UnifyFormatToNHWC::SetSensitiveOps() {
   sensitive_ops_.insert(sensitive_nchw_ops.begin(), sensitive_nchw_ops.end());
 }
 
-bool UnifyFormatToNHWC::DecideWhetherHandleGraphInput(const FuncGraphPtr &func_graph, const ShapeVector &shape) {
+bool UnifyFormatToNHWC::DecideWhetherHandleGraphInput(const FuncGraphPtr &func_graph, const ParameterPtr &input,
+                                                      const ShapeVector &shape) {
   MS_ASSERT(func_graph != nullptr);
   if (fmk_type_ == converter::kFmkTypeTf || fmk_type_ == converter::kFmkTypeTflite) {
     return false;

mindspore/lite/tools/converter/parser/unify_format.h

@@ -36,7 +36,8 @@ class UnifyFormatToNHWC : public opt::ToFormatBase {
   STATUS ConvertOnnxResizeForVariableShape(const FuncGraphPtr &func_graph, const CNodePtr &cnode);
   STATUS GetTransNodeFormatType(const CNodePtr &cnode, opt::TransTypePair *trans_info) override;
   void SetSensitiveOps() override;
-  bool DecideWhetherHandleGraphInput(const FuncGraphPtr &func_graph, const ShapeVector &shape) override;
+  bool DecideWhetherHandleGraphInput(const FuncGraphPtr &func_graph, const ParameterPtr &input,
+                                     const ShapeVector &shape) override;
   bool DecideWhetherInferShapeForNewNode() override;
   STATUS DecideConvWeightSrcAndDstFormat(const CNodePtr &cnode, schema::Format *src_format,
                                          schema::Format *dst_format) override;

mindspore/lite/tools/optimizer/format/to_format_base.cc

@@ -178,6 +178,31 @@ STATUS ToFormatBase::InsertPostTransNode(const FuncGraphPtr &func_graph, const C
   return lite::RET_OK;
 }
 
+bool ToFormatBase::DecideWhetherHandleGraphInput(const FuncGraphPtr &func_graph, const ParameterPtr &input,
+                                                 const ShapeVector &shape) {
+  MS_ASSERT(func_graph != nullptr && input != nullptr);
+  auto manager = func_graph->manager();
+  MS_ASSERT(anager != nullptr);
+  auto node_users = manager->node_users()[input];
+  for (auto &node_user : node_users) {
+    auto post_node = node_user.first;
+    if (!utils::isa<CNode>(post_node)) {
+      continue;
+    }
+    auto post_cnode = post_node->cast<CNodePtr>();
+    auto prim = GetValueNode<PrimitivePtr>(post_cnode->input(0));
+    MS_ASSERT(prim != nullptr);
+    if (prim->GetAttr(ops::kFormat) != nullptr) {
+      auto node_format = GetValue<int64_t>(prim->GetAttr(ops::kFormat));
+      if (node_format == format_) {
+        MS_LOG(DEBUG) << "this graph input don't need to change.";
+        return false;
+      }
+    }
+  }
+  return true;
+}
+
 STATUS ToFormatBase::HandleGraphInput(const FuncGraphPtr &func_graph) {
   MS_ASSERT(func_graph != nullptr);
   auto graph_input = func_graph->get_inputs();
@@ -191,7 +216,7 @@ STATUS ToFormatBase::HandleGraphInput(const FuncGraphPtr &func_graph) {
       MS_LOG(ERROR) << "fetch shape failed." << input->fullname_with_scope();
       return lite::RET_ERROR;
     }
-    if (shape.size() != kDimNumber || !DecideWhetherHandleGraphInput(func_graph, shape)) {
+    if (shape.size() != kDimNumber || !DecideWhetherHandleGraphInput(func_graph, input_param, shape)) {
       continue;
     }
     ShapeVector transfer_shape;
@@ -298,8 +323,6 @@ bool ToFormatBase::BasicProcess(const FuncGraphPtr &func_graph, bool main_graph)
 STATUS ToFormatBase::ConvWeightFormatTrans(const FuncGraphPtr &graph, std::set<AnfNodePtr> *has_visited) {
   MS_ASSERT(graph != nullptr && has_visited != nullptr);
   auto node_list = TopoSort(graph->get_return());
-  schema::Format src_format = schema::Format_NUM_OF_FORMAT;
-  schema::Format dst_format = schema::Format_NUM_OF_FORMAT;
   for (auto &node : node_list) {
     if (!utils::isa<CNodePtr>(node)) {
       continue;
@@ -335,6 +358,8 @@ STATUS ToFormatBase::ConvWeightFormatTrans(const FuncGraphPtr &graph, std::set<A
       continue;
     }
     has_visited->insert(node);
+    schema::Format src_format = schema::Format_NUM_OF_FORMAT;
+    schema::Format dst_format = schema::Format_NUM_OF_FORMAT;
     if (DecideConvWeightSrcAndDstFormat(cnode, &src_format, &dst_format) != lite::RET_OK) {
       MS_LOG(ERROR) << "weight's src format and dst format get failed.";
       return lite::RET_ERROR;

mindspore/lite/tools/optimizer/format/to_format_base.h

@@ -52,7 +52,8 @@ class ToFormatBase : public Pass {
  protected:
   virtual STATUS GetTransNodeFormatType(const CNodePtr &cnode, opt::TransTypePair *trans_info) = 0;
   virtual void SetSensitiveOps() { sensitive_ops_ = opt::GetNHWCOpMap(); }
-  virtual bool DecideWhetherHandleGraphInput(const FuncGraphPtr &func_graph, const ShapeVector &shape) { return true; }
+  virtual bool DecideWhetherHandleGraphInput(const FuncGraphPtr &func_graph, const ParameterPtr &input,
+                                             const ShapeVector &shape);
   virtual bool DecideWhetherInferShapeForNewNode() { return true; }
   virtual STATUS DecideConvWeightSrcAndDstFormat(const CNodePtr &cnode, schema::Format *src_format,
                                                  schema::Format *dst_format) = 0;

mindspore/lite/tools/optimizer/format/to_nchw_format.cc

@@ -45,6 +45,20 @@ STATUS ToNCHWFormat::GetTransNodeFormatType(const CNodePtr &cnode, opt::TransTyp
 STATUS ToNCHWFormat::DecideConvWeightSrcAndDstFormat(const CNodePtr &cnode, schema::Format *src_format,
                                                      schema::Format *dst_format) {
   MS_ASSERT(cnode != nullptr && src_format != nullptr && dst_format != nullptr);
+  auto prim = GetValueNode<PrimitivePtr>(cnode->input(0));
+  MS_ASSERT(prim != nullptr);
+  if (prim->GetAttr(ops::kFormat) != nullptr) {
+    auto node_format = GetValue<int64_t>(prim->GetAttr(ops::kFormat));
+    if (node_format == mindspore::NCHW) {
+      MS_LOG(DEBUG) << "node's format has been nchw, no need to transfer, " << cnode->fullname_with_scope();
+      return lite::RET_OK;
+    }
+    if (node_format != mindspore::NHWC) {
+      MS_LOG(ERROR) << "node's format is invalid, which must be nhwc or nchw, now is " << node_format
+                    << ", node name is " << cnode->fullname_with_scope();
+      return lite::RET_ERROR;
+    }
+  }
   *src_format = schema::Format_KHWC;
   *dst_format = schema::Format_KCHW;
   return lite::RET_OK;

mindspore/lite/tools/optimizer/format/to_nhwc_format.cc

@@ -45,6 +45,19 @@ STATUS ToNHWCFormat::GetTransNodeFormatType(const CNodePtr &cnode, opt::TransTyp
 STATUS ToNHWCFormat::DecideConvWeightSrcAndDstFormat(const CNodePtr &cnode, schema::Format *src_format,
                                                      schema::Format *dst_format) {
   MS_ASSERT(cnode != nullptr && src_format != nullptr && dst_format != nullptr);
+  auto prim = GetValueNode<PrimitivePtr>(cnode->input(0));
+  if (prim->GetAttr(ops::kFormat) != nullptr) {
+    auto node_format = GetValue<int64_t>(prim->GetAttr(ops::kFormat));
+    if (node_format == mindspore::NHWC) {
+      MS_LOG(DEBUG) << "node's format has been nhwc, no need to transfer, " << cnode->fullname_with_scope();
+      return lite::RET_OK;
+    }
+    if (node_format != mindspore::NCHW) {
+      MS_LOG(ERROR) << "node's format is invalid, which must be nhwc or nchw, now is " << node_format
+                    << ", node name is " << cnode->fullname_with_scope();
+      return lite::RET_ERROR;
+    }
+  }
   *src_format = schema::Format_KCHW;
   *dst_format = schema::Format_KHWC;
   return lite::RET_OK;

mindspore/lite/tools/optimizer/graph/infershape_pass.cc

@@ -136,7 +136,7 @@ bool InferShapePass::JudgeAllOpsCanInfer(const FuncGraphPtr &func_graph) {
       } else {
         all_op_can_infer = all_op_can_infer && JudgeAllOpsCanInfer(sub_func_graph);
       }
-      sub_func_graph = GetValueNode<FuncGraphPtr>(cnode->input(1));
+      sub_func_graph = GetValueNode<FuncGraphPtr>(cnode->input(kInputIndexTwo));
       if (sub_func_graph == nullptr) {
         lite::ReturnCode::GetSingleReturnCode()->UpdateReturnCode(lite::RET_NULL_PTR);
         all_op_can_infer = false;