Added RunInferShape

test=develop

paddle/fluid/framework/ngraph_operator.cc

@@ -278,43 +278,26 @@ std::shared_ptr<ngraph::runtime::Backend> NgraphOperator::backend_ =
     ngraph::runtime::Backend::create("CPU");
 
 void NgraphOperator::GetNgInputShape(std::shared_ptr<OperatorBase> op) {
-  RuntimeInferShapeContext infer_shape_ctx(*op, scope_);
-  std::shared_ptr<OperatorWithKernel> op_k =
-      std::dynamic_pointer_cast<OperatorWithKernel>(op);
-  op_k->InferShape(&infer_shape_ctx);
-
+  op->RunInferShape(scope_, place_);
   for (auto& var_name_item : op->Inputs()) {
-    std::vector<ngraph::Shape> vshape;
-    auto& var_prm_name = var_name_item.first;
-    auto var_name_size = var_name_item.second.size();
-    if (var_name_size == 1) {
-      auto dim = infer_shape_ctx.GetInputDim(var_prm_name);
-      vshape.push_back(Ddim2Shape(dim));
-    } else if (var_name_item.second.size() > 1) {
-      auto vdim = infer_shape_ctx.GetInputsDim(var_prm_name);
-      PADDLE_ENFORCE_EQ(vdim.size(), var_name_item.second.size(),
-                        "Need dim info for each var");
-      for (auto& dim : vdim) {
-        vshape.push_back(Ddim2Shape(dim));
-      }
-    } else {
-      // 0 size : conv2d Bias
-    }
-
-    for (size_t i = 0; i < var_name_item.second.size(); ++i) {
-      auto var_name = var_name_item.second.at(i);
+    for (auto& var_name : var_name_item.second) {
+      auto* var = scope_.FindVar(var_name);
+      if (var && VarIsTensor(*var)) {
+        auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
+        auto sp = Ddim2Shape(tensor_pd->dims());
         if (std::find(var_in_.begin(), var_in_.end(), var_name) !=
             var_in_.end()) {
           if (var_node_map_->find(var_name) == var_node_map_->end()) {
             auto ng_type = var_type_map_.at(var_name);
-          auto prm = std::make_shared<ngraph::op::Parameter>(
-              ng_type, vshape.at(i), true);
+            auto prm =
+                std::make_shared<ngraph::op::Parameter>(ng_type, sp, true);
             (*var_node_map_)[var_name] = prm;
             (*var_in_node_map_)[var_name] = prm;
           }
         }
       }
     }
+  }
 }
 
 void NgraphOperator::BuildNgNode() {

paddle/fluid/framework/operator.cc

@@ -355,7 +355,7 @@ void OperatorBase::GenerateTemporaryNames() {
   }
 }
 
-static bool VarIsTensor(const Variable& var) {
+bool VarIsTensor(const Variable& var) {
   return var.IsType<LoDTensor>() || var.IsType<SelectedRows>();
 }
 
@@ -695,6 +695,12 @@ static void CheckTensorNANOrInf(const std::string& name,
                  "Tensor %s contains NAN", name);
 }
 
+void OperatorWithKernel::RunInferShape(const Scope& scope,
+                                       const platform::Place& place) const {
+  RuntimeInferShapeContext infer_shape_ctx(*this, scope);
+  this->InferShape(&infer_shape_ctx);
+}
+
 void OperatorWithKernel::RunImpl(const Scope& scope,
                                  const platform::Place& place) const {
   RuntimeInferShapeContext infer_shape_ctx(*this, scope);

paddle/fluid/framework/operator.h

@@ -64,6 +64,7 @@ inline std::string GradVarName(const std::string& var_name) {
 }
 
 proto::VarType::Type GetDataTypeOfVar(const Variable* var);
+bool VarIsTensor(const Variable& var);
 const Tensor* GetLoDTensorOrSelectedRowsValueFromVar(const Variable& var);
 Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var);
 
@@ -128,6 +129,8 @@ class OperatorBase {
   virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
 
   void SetIsCalledByExecutor(bool x) { run_by_executor_ = x; }
+  virtual void RunInferShape(const Scope& scope,
+                             const platform::Place& place) const {}
 
  protected:
   std::string type_;
@@ -348,6 +351,9 @@ class OperatorWithKernel : public OperatorBase {
     OpInfoMap::Instance().Get(Type()).infer_shape_(ctx);
   }
 
+  void RunInferShape(const Scope& scope,
+                     const platform::Place& place) const override;
+
  protected:
   virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const;
   virtual OpKernelType GetKernelTypeForVar(