From 12ee5014857e751fb429e0d3ebcfd41dcd5da29d Mon Sep 17 00:00:00 2001
From: dongzhihong <dzhwinter@gmail.com>
Date: Mon, 14 Aug 2017 20:57:46 +0800
Subject: [PATCH] "fix operator grad config"

---
 paddle/operators/rowwise_add_op.cc | 23 +++++++++++++++++------
 paddle/operators/rowwise_add_op.h  | 21 +++++++++++----------
 2 files changed, 28 insertions(+), 16 deletions(-)

diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc
index 0c6ae64d0c..60e5d7749c 100644
--- a/paddle/operators/rowwise_add_op.cc
+++ b/paddle/operators/rowwise_add_op.cc
@@ -17,6 +17,8 @@
 namespace paddle {
 namespace operators {
 
+using framework::Tensor;
+
 class RowwiseAddOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -50,14 +52,23 @@ for i in xrange(X.shape[0]):
   }
 };
 class RowwiseAddGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE(ctx.InputSize() == 4UL,
-                   "RowwiseAddGrad inputs is I, O, OG, size must be 4");
-    PADDLE_ENFORCE(ctx.OutputSize() == 2,
-                   "RowwiseAddGrad output is IG, size must be 2");
-    ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
-    ctx.Output<Tensor>(1)->Resize(ctx.Input<Tensor>(1)->dims());
+    // PADDLE_ENFORCE(ctx.InputSize() == 4UL,
+    //                "RowwiseAddGrad inputs is I, O, OG, size must be 4");
+    // PADDLE_ENFORCE(ctx.OutputSize() == 2,
+    //                "RowwiseAddGrad output is IG, size must be 2");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"), "b should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto dims0 = ctx.Input<Tensor>("X")->dims();
+    auto dims1 = ctx.Input<Tensor>("b")->dims();
+    ctx.Output<Tensor>(framework::GradVarName("X"))->Resize(dims0);
+    ctx.Output<Tensor>(framework::GradVarName("b"))->Resize(dims1);
   }
 };
 
diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h
index 3ad60172c1..6593d811e4 100644
--- a/paddle/operators/rowwise_add_op.h
+++ b/paddle/operators/rowwise_add_op.h
@@ -51,19 +51,20 @@ template <typename Place, typename T>
 class RowwiseAddGradKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* XGrad = context.Output<Tensor>(0);
-    auto* bGrad = context.Output<Tensor>(1);
-    XGrad->mutable_data<T>(context.GetPlace());
-    bGrad->mutable_data<T>(context.GetPlace());
+    auto* dX = context.Output<Tensor>(framework::GradVarName("X"));
+    auto* db = context.Output<Tensor>(framework::GradVarName("b"));
+    auto* dOut = context.Output<Tensor>(framework::GradVarName("Out"));
+    dX->mutable_data<T>(context.GetPlace());
+    db->mutable_data<T>(context.GetPlace());
 
-    // I, O, OG  => [X, b], [Out], [OutGrad]
-    auto OutGrad = EigenMatrix<T>::From(*context.Input<Tensor>(3));
-    EigenMatrix<T>::From(*XGrad).device(context.GetEigenDevice<Place>()) =
-        OutGrad;
+    auto OutGrad = EigenMatrix<T>::From(*dOut);
+    auto place = context.GetEigenDevice<Place>();
+    EigenMatrix<T>::From(*dX).device(place) = OutGrad;
 
     // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html
-    EigenVector<T>::Flatten(*bGrad).device(context.GetEigenDevice<Place>()) =
-        OutGrad.cumsum(1);  // colwise add
+    // colwise add
+    Eigen::array<int, 1> dims{{1}}; /* dimension to reduce */
+    EigenVector<T>::Flatten(*db).device(place) = OutGrad.sum(dims);
   }
 };
 }  // namespace operators
-- 
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