fix doc and code style

paddle/operators/clip_by_norm_op.cc

@@ -39,15 +39,14 @@ template <typename AttrType>
 class ClipByNormOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   ClipByNormOpMaker(framework::OpProto* proto,
-    framework::OpAttrChecker* op_checker)
+                    framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X",
-             "(Tensor)The input of clip_by_norm op."
+             "(Tensor) The input of clip_by_norm op."
              "The number of dimensions must be between [1, 9].");
     AddOutput("Out",
-              "(Tensor)The output of clip_by_norm op with shape as input(X)");
-    AddAttr<AttrType>(
-        "max_norm", "(float)The maximum norm value.");
+              "(Tensor) The output of clip_by_norm op with shape as input(X)");
+    AddAttr<AttrType>("max_norm", "(float)The maximum norm value.");
     AddComment(R"DOC(
 ClipByNorm operator limits the L2 norm of the input 'X' within 'max_norm'. 
 If the L2 norm of 'X' is less than or equal to 'max_norm', 'Out' will be 
@@ -62,29 +61,11 @@ where norm('X') represents the L2 norm of 'X'.
   }
 };
 
-class ClipByNormOpGrad : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContext* 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");
-    if (ctx->HasOutput(framework::GradVarName("X"))) {
-      ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
-    }
-  }
-};
-
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(clip_by_norm,
-                             ops::ClipByNormOp,
+REGISTER_OP_WITHOUT_GRADIENT(clip_by_norm, ops::ClipByNormOp,
                              ops::ClipByNormOpMaker<float>);
-REGISTER_OP_CPU_KERNEL(clip_by_norm,
-                       ops::ClipByNormKernel
-                       <paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    clip_by_norm, ops::ClipByNormKernel<paddle::platform::CPUPlace, float>);

paddle/operators/clip_by_norm_op.cu

@@ -15,6 +15,5 @@
 #include "paddle/operators/clip_by_norm_op.h"
 
 namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(clip_by_norm,
-                       ops::ClipByNormKernel
-                       <paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    clip_by_norm, ops::ClipByNormKernel<paddle::platform::GPUPlace, float>);

paddle/operators/clip_by_norm_op.h

@@ -25,9 +25,6 @@ using Tensor = framework::Tensor;
 template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
 
 template <typename Place, typename T>
 class ClipByNormKernel : public framework::OpKernel<T> {

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

@@ -18,21 +18,19 @@ class TestClipByNormOp(OpTest):
             output = self.max_norm * input / norm
         else:
             output = input
-        self.outputs = {
-            'Out': output
-        }
+        self.outputs = {'Out': output}
 
     def test_check_output(self):
         self.check_output()
 
     def initTestCase(self):
-        self.shape = (100,)
+        self.shape = (100, )
         self.max_norm = 1.0
 
 
 class TestCase1(TestClipByNormOp):
     def initTestCase(self):
-        self.shape = (100,)
+        self.shape = (100, )
         self.max_norm = 1e20