test=develop

paddle/fluid/operators/rmsprop_op.h

@@ -131,21 +131,21 @@ template <typename DeviceContext, typename T>
 class RmspropOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
-    using Tensor = framework::LoDTensor;
+    using LoDTensor = framework::LoDTensor;
     auto *grad_var = ctx.InputVar("Grad");
-    auto *param_out = ctx.Output<Tensor>("ParamOut");
-    auto *moment_out = ctx.Output<Tensor>("MomentOut");
-    auto *mean_square_out = ctx.Output<Tensor>("MeanSquareOut");
+    auto *param_out = ctx.Output<LoDTensor>("ParamOut");
+    auto *moment_out = ctx.Output<LoDTensor>("MomentOut");
+    auto *mean_square_out = ctx.Output<LoDTensor>("MeanSquareOut");
 
     auto epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
     auto rho = static_cast<T>(ctx.Attr<float>("decay"));
     auto momentum = static_cast<T>(ctx.Attr<float>("momentum"));
     bool centered = ctx.Attr<bool>("centered");
 
-    auto &p_tensor = *ctx.Input<Tensor>("Param");
-    auto &ms_tensor = *ctx.Input<Tensor>("MeanSquare");
-    auto &lr_tensor = *ctx.Input<Tensor>("LearningRate");
-    auto &mom_tensor = *ctx.Input<Tensor>("Moment");
+    auto &p_tensor = *ctx.Input<LoDTensor>("Param");
+    auto &ms_tensor = *ctx.Input<LoDTensor>("MeanSquare");
+    auto &lr_tensor = *ctx.Input<LoDTensor>("LearningRate");
+    auto &mom_tensor = *ctx.Input<LoDTensor>("Moment");
 
     PADDLE_ENFORCE_EQ(&p_tensor, param_out,
                       "Param and ParamOut must be the same Tensor");
@@ -157,8 +157,8 @@ class RmspropOpKernel : public framework::OpKernel<T> {
     auto &dev_ctx = ctx.template device_context<DeviceContext>();
     size_t limit = static_cast<size_t>(ms_tensor.numel());
 
-    if (grad_var->IsType<Tensor>()) {
-      auto &grad_tensor = grad_var->Get<Tensor>();
+    if (grad_var->IsType<LoDTensor>()) {
+      auto &grad_tensor = grad_var->Get<LoDTensor>();
 
       if (std::is_same<DeviceContext, platform::CPUDeviceContext>::value) {
         auto &place =
@@ -176,9 +176,9 @@ class RmspropOpKernel : public framework::OpKernel<T> {
 
         ms_out.device(place) = rho * ms + (1 - rho) * g * g;
         if (centered) {
-          auto &mg_tensor = *ctx.Input<Tensor>("MeanGrad");
+          auto &mg_tensor = *ctx.Input<LoDTensor>("MeanGrad");
           auto mg = EigenVector<T>::Flatten(mg_tensor);
-          auto *mean_grad_out = ctx.Output<Tensor>("MeanGradOut");
+          auto *mean_grad_out = ctx.Output<LoDTensor>("MeanGradOut");
           PADDLE_ENFORCE(&mg_tensor, mean_grad_out,
                          "MeanGrad and MeanGradOut must be the same Tensor");
           auto mg_out = EigenVector<T>::Flatten(*mean_grad_out);
@@ -196,8 +196,8 @@ class RmspropOpKernel : public framework::OpKernel<T> {
         DenseRmspropGradFunctor<T> grad_func(grad_tensor.data<T>());
         platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
         if (centered) {
-          auto &mg_tensor = *ctx.Input<Tensor>("MeanGrad");
-          auto *mean_grad_out = ctx.Output<Tensor>("MeanGradOut");
+          auto &mg_tensor = *ctx.Input<LoDTensor>("MeanGrad");
+          auto *mean_grad_out = ctx.Output<LoDTensor>("MeanGradOut");
           PADDLE_ENFORCE(&mg_tensor, mean_grad_out,
                          "MeanGrad and MeanGradOut must be the same Tensor");
           for_range(CenteredRmspropFunctor<T, DenseRmspropGradFunctor<T>>(
@@ -241,8 +241,8 @@ class RmspropOpKernel : public framework::OpKernel<T> {
                                             row_numel, row_count);
 
       if (centered) {
-        auto &mg_tensor = *ctx.Input<Tensor>("MeanGrad");
-        auto *mean_grad_out = ctx.Output<Tensor>("MeanGradOut");
+        auto &mg_tensor = *ctx.Input<LoDTensor>("MeanGrad");
+        auto *mean_grad_out = ctx.Output<LoDTensor>("MeanGradOut");
         PADDLE_ENFORCE(&mg_tensor, mean_grad_out,
                        "MeanGrad and MeanGradOut must be the same Tensor");
         for_range(CenteredRmspropFunctor<T, SparseRmspropGradFunctor<T>>(