Merge pull request #3604 from jacquesqiao/fix-rowwise-add

Fix rowwise add

paddle/operators/rowwise_add_op.h

@@ -1,16 +1,16 @@
 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 
-   Licensed under the Apache License, Version 2.0 (the "License");
+Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
+you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
+You may obtain a copy of the License at
 
-   http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
-   Unless required by applicable law or agreed to in writing, software
+Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
+distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
+See the License for the specific language governing permissions and
-   limitations under the License. */
+limitations under the License. */
 
 #pragma once
 #include "paddle/framework/eigen.h"
@@ -63,7 +63,7 @@ class RowwiseAddGradKernel : public framework::OpKernel {
 
     // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html
     // colwise add
-    Eigen::array<int, 1> dims{{1}}; /* dimension to reduce */
+    Eigen::array<int, 1> dims{{0}}; /* dimension to reduce */
     EigenVector<T>::Flatten(*db).device(place) = OutGrad.sum(dims);
   }
 };

python/paddle/v2/framework/tests/test_rowwise_add_op.py

@@ -20,7 +20,7 @@ class RowwiseAddGradOpTest(GradientChecker):
     def test_rowwise_add(self):
         op = create_op("rowwise_add")
         inputs = {
-            "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32"),
+            "X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
             "b": np.random.uniform(0.1, 1, [10]).astype("float32")
         }
         self.check_grad(op, inputs, set(["X", "b"]), "Out")