Adapt reduce_op according to up-to-date dev

paddle/operators/reduce_op.cc

@@ -24,20 +24,20 @@ class ReduceOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
  protected:
-  void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
-                            "Input(X) of ReduceOp should not be null.");
-    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
-                            "Output(Out) of ReduceOp should not be null.");
-    auto x_dims = ctx.Input<Tensor>("X")->dims();
+  void InferShape(framework::InferShapeContextBase *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of ReduceOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of ReduceOp should not be null.");
+    auto x_dims = ctx->GetInputDim("X");
     auto x_rank = x_dims.size();
     PADDLE_ENFORCE_LE(x_rank, 6, "Tensors with rank at most 6 are supported.");
-    int dim = ctx.Attr<int>("dim");
+    int dim = ctx->Attrs().Get<int>("dim");
     if (dim < 0) dim = x_rank + dim;
     PADDLE_ENFORCE_LT(
         dim, x_rank,
         "The dim should be in the range [-rank(input), rank(input)).");
-    bool keep_dim = ctx.Attr<bool>("keep_dim");
+    bool keep_dim = ctx->Attrs().Get<bool>("keep_dim");
     auto dims_vector = vectorize(x_dims);
     if (keep_dim || x_rank == 1) {
       dims_vector[dim] = 1;
@@ -45,10 +45,10 @@ class ReduceOp : public framework::OperatorWithKernel {
       dims_vector.erase(dims_vector.begin() + dim);
     }
     auto out_dims = framework::make_ddim(dims_vector);
-    ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
+    ctx->SetOutputDim("Out", out_dims);
     if (dim != 0) {
-      // Only pass LoD when not reducing on the first dim
-      ctx.ShareLoD("X", /*->*/ "Out");
+      // Only pass LoD when not reducing on the first dim.
+      ctx->ShareLoD("X", /*->*/ "Out");
     }
   }
 };
@@ -58,21 +58,22 @@ class ReduceGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
  protected:
-  void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null.");
-    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
-                            "Input(Out@GRAD) should not be null.");
-    auto x_dims = ctx.Input<Tensor>("X")->dims();
+  void InferShape(framework::InferShapeContextBase *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) should not be null.");
+    auto x_dims = ctx->GetInputDim("X");
     auto x_rank = x_dims.size();
     PADDLE_ENFORCE_LE(x_rank, 6, "Tensors with rank at most 6 are supported.");
-    int dim = ctx.Attr<int>("dim");
+    int dim = ctx->Attrs().Get<int>("dim");
     if (dim < 0) dim = x_rank + dim;
     PADDLE_ENFORCE_LT(
         dim, x_rank,
         "The dim should be in the range [-rank(input), rank(input)).");
-    auto *x_grad =
-        ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
-    if (x_grad) x_grad->Resize(x_dims);
+    auto x_grad_name = framework::GradVarName("X");
+    if (ctx->HasOutput(x_grad_name)) {
+      ctx->SetOutputDim(x_grad_name, x_dims);
+    }
   }
 };