Fix some grad op desc makers (#16633)

* fix some grad op desc maker
test=develop

* fix grad op desc makers
test=develop

paddle/fluid/framework/op_desc.cc

@@ -617,6 +617,25 @@ void OpDesc::Flush() {
 
 static std::once_flag init_infer_shape_funcs;
 
+/**
+ * NOTE(paddle-dev): Very tricky code here. Maybe we should find a
+ * better way to register compile-time infershape method gentlely.
+ *
+ * Normally, we can register a class derived from InferShapeBase, so that
+ * we can set the field of `infer_shape_` inside OpInfo when registering op.
+ *
+ * However, there is another way we can set the field of `infer_shape_` inside
+ * OpInfo. Usually, we overload InferShape method of OperatorWithKernel. After
+ * running the following method InitInferShapeFuncs, `infer_shape_` would be set
+ * to be the InferShape method of OperatorWithKernel. That is to say, we borrow
+ * the run-time InferShape method of OperatorWithKernel to be the compile-time
+ * InferShape method.
+ *
+ * However, during compiling time, we may not know inputs, outputs and attrs of
+ * run-time OperatorWithKernel. So the following code creates a fake
+ * OperatorWithKernel object. That is why the field info_ of OperatorBase
+ * would be null.
+ */
 static void InitInferShapeFuncs() {
   std::call_once(init_infer_shape_funcs, [] {
     auto &map = OpInfoMap::Instance();
@@ -628,11 +647,16 @@ static void InitInferShapeFuncs() {
       PADDLE_ENFORCE(it != info_map.end(), "%s has not been registered",
                      op_type);
       auto &op_info = it->second;
-      auto op = static_cast<OperatorWithKernel *>(op_info.Creator()(
-          "", VariableNameMap{}, VariableNameMap{}, AttributeMap{}));
       if (op_info.infer_shape_) {  // infer_shape has been registered.
         continue;
       }
+
+      auto op = dynamic_cast<OperatorWithKernel *>(op_info.Creator()(
+          "", VariableNameMap{}, VariableNameMap{}, AttributeMap{}));
+
+      PADDLE_ENFORCE_NOT_NULL(
+          op, "InferShapeBase is not registered to Operator %s", op_type);
+
       op_info.infer_shape_ = [op](InferShapeContext *ctx) {
         op->InferShape(ctx);
       };

paddle/fluid/op_use_default_grad_op_maker.spec

@@ -8,9 +8,6 @@ conv_shift
 cos
 cos_sim
 dequantize
-elementwise_div
-elementwise_max
-elementwise_min
 elu
 fc
 flatten
@@ -28,8 +25,6 @@ gelu
 gru
 hard_shrink
 hierarchical_sigmoid
-hinge_loss
-huber_loss
 leaky_relu
 log
 logsigmoid
@@ -57,7 +52,6 @@ requantize
 reshape
 rnn_memory_helper
 round
-row_conv
 sequence_softmax
 sin
 softplus

paddle/fluid/operators/batch_size_like.h

@@ -74,5 +74,8 @@ class BatchSizeLikeOpMaker : public framework::OpProtoAndCheckerMaker {
   virtual void Apply() = 0;
 };
 
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(BatchSizeLikeNoNeedBufferVarsInference,
+                                      "Input");
+
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/elementwise/elementwise_div_op.cc

@@ -13,10 +13,47 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/elementwise/elementwise_div_op.h"
+#include <memory>
+#include <string>
 #include "paddle/fluid/operators/elementwise/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ElementwiseDivOpMaker : public ElementwiseOpMaker {
+ protected:
+  std::string GetName() const override { return "Div"; }
+  std::string GetEquation() const override { return "Out = X / Y"; }
+};
+
+class ElementwiseDivGradOpDescMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
+    op->SetType("elementwise_div_grad");
+    op->SetInput("Y", Input("Y"));
+    op->SetInput("Out", Output("Out"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
 namespace ops = paddle::operators;
 
-REGISTER_ELEMWISE_OP(elementwise_div, "Div", "Out = X / Y");
+REGISTER_OPERATOR(elementwise_div, ops::ElementwiseOp,
+                  ops::ElementwiseDivOpMaker, ops::ElementwiseOpInferVarType,
+                  ops::ElementwiseDivGradOpDescMaker);
+
+REGISTER_OPERATOR(elementwise_div_grad, ops::ElementwiseOpGrad);
 
 REGISTER_OP_CPU_KERNEL(
     elementwise_div,

paddle/fluid/operators/elementwise/elementwise_div_op.h

@@ -47,7 +47,7 @@ struct DivGradDX {
 template <typename T>
 struct DivGradDY {
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
-    return -dout * x / (y * y);
+    return -dout * out / y;
   }
 };
 
@@ -58,13 +58,15 @@ class ElementwiseDivGradKernel : public ElemwiseGradKernel<T> {
     ElemwiseGradKernel<T>::Compute(ctx);
     using Tensor = framework::Tensor;
 
-    auto* x = ctx.Input<Tensor>("X");
     auto* y = ctx.Input<Tensor>("Y");
     auto* out = ctx.Input<Tensor>("Out");
     auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
     auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
     int axis = ctx.Attr<int>("axis");
+
+    auto* x = dout;  // Fake x, not used
+
     ElemwiseGradCompute<DeviceContext, T, DivGradDX<T>, DivGradDY<T>>(
         ctx, *x, *y, *out, *dout, axis, dx, dy, DivGradDX<T>(), DivGradDY<T>());
   }

paddle/fluid/operators/elementwise/elementwise_max_op.cc

@@ -13,9 +13,48 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/elementwise/elementwise_max_op.h"
+#include <memory>
+#include <string>
 #include "paddle/fluid/operators/elementwise/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ElementwiseMaxOpMaker : public ElementwiseOpMaker {
+ protected:
+  std::string GetName() const override { return "Max"; }
+  std::string GetEquation() const override { return "Out = max(X, Y)"; }
+};
+
+class ElementwiseMaxGradOpDescMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
+    op->SetType("elementwise_max_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("Y", Input("Y"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
 namespace ops = paddle::operators;
-REGISTER_ELEMWISE_OP(elementwise_max, "Max", "Out = max(X, Y)");
+
+REGISTER_OPERATOR(elementwise_max, ops::ElementwiseOp,
+                  ops::ElementwiseMaxOpMaker, ops::ElementwiseOpInferVarType,
+                  ops::ElementwiseMaxGradOpDescMaker);
+
+REGISTER_OPERATOR(elementwise_max_grad, ops::ElementwiseOpGrad);
+
 REGISTER_OP_CPU_KERNEL(
     elementwise_max,
     ops::ElementwiseMaxKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/elementwise/elementwise_max_op.h

@@ -63,10 +63,10 @@ class ElementwiseMaxGradKernel : public ElemwiseGradKernel<T> {
 
     auto* x = ctx.Input<Tensor>("X");
     auto* y = ctx.Input<Tensor>("Y");
-    auto* out = ctx.Input<Tensor>("Out");
     auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
     auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    auto* out = dout;  // Fake out, not used
     int axis = ctx.Attr<int>("axis");
     ElemwiseGradCompute<DeviceContext, T, MaxGradDx<T>, MaxGradDy<T>>(
         ctx, *x, *y, *out, *dout, axis, dx, dy, MaxGradDx<T>(), MaxGradDy<T>());

paddle/fluid/operators/elementwise/elementwise_min_op.cc

@@ -13,9 +13,48 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/elementwise/elementwise_min_op.h"
+#include <memory>
+#include <string>
 #include "paddle/fluid/operators/elementwise/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ElementwiseMinOpMaker : public ElementwiseOpMaker {
+ protected:
+  std::string GetName() const override { return "Min"; }
+  std::string GetEquation() const override { return "Out = min(X, Y)"; }
+};
+
+class ElementwiseMinGradOpDescMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
+    op->SetType("elementwise_min_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("Y", Input("Y"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
 namespace ops = paddle::operators;
-REGISTER_ELEMWISE_OP(elementwise_min, "Min", "Out = min(X, Y)");
+
+REGISTER_OPERATOR(elementwise_min, ops::ElementwiseOp,
+                  ops::ElementwiseMinOpMaker, ops::ElementwiseOpInferVarType,
+                  ops::ElementwiseMinGradOpDescMaker);
+
+REGISTER_OPERATOR(elementwise_min_grad, ops::ElementwiseOpGrad);
+
 REGISTER_OP_CPU_KERNEL(
     elementwise_min,
     ops::ElementwiseMinKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/elementwise/elementwise_min_op.h

@@ -62,10 +62,10 @@ class ElementwiseMinGradKernel : public ElemwiseGradKernel<T> {
 
     auto* x = ctx.Input<Tensor>("X");
     auto* y = ctx.Input<Tensor>("Y");
-    auto* out = ctx.Input<Tensor>("Out");
     auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
     auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    auto* out = dout;  // Fake out, not used
     int axis = ctx.Attr<int>("axis");
     ElemwiseGradCompute<DeviceContext, T, MinGradDx<T>, MinGradDy<T>>(
         ctx, *x, *y, *out, *dout, axis, dx, dy, MinGradDx<T>(), MinGradDy<T>());

paddle/fluid/operators/elementwise/elementwise_op.h

@@ -173,12 +173,12 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel {
   using Tensor = framework::Tensor;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
+    auto out_grad_name = framework::GradVarName("Out");
     PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+    PADDLE_ENFORCE(ctx->HasInput(out_grad_name),
                    "Input(Out@GRAD) should not be null");
 
-    auto x_dims = ctx->GetInputDim("X");
+    auto x_dims = ctx->GetInputDim(out_grad_name);
     auto y_dims = ctx->GetInputDim("Y");
 
     PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
@@ -187,8 +187,8 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel {
     auto x_grad_name = framework::GradVarName("X");
     auto y_grad_name = framework::GradVarName("Y");
     if (ctx->HasOutput(x_grad_name)) {
-      ctx->ShareDim("X", /*->*/ x_grad_name);
-      ctx->ShareLoD("X", /*->*/ x_grad_name);
+      ctx->ShareDim(out_grad_name, /*->*/ x_grad_name);
+      ctx->ShareLoD(out_grad_name, /*->*/ x_grad_name);
     }
     if (ctx->HasOutput(y_grad_name)) {
       ctx->ShareDim("Y", /*->*/ y_grad_name);

paddle/fluid/operators/fill_constant_batch_size_like_op.cc

@@ -46,6 +46,7 @@ obtained from the `input` tensor.
 )DOC");
   }
 };
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -53,7 +54,8 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(fill_constant_batch_size_like,
                   ops::FillConstantBatchSizeLikeOp,
                   paddle::framework::EmptyGradOpMaker,
-                  ops::FillConstantBatchSizeLikeOpMaker);
+                  ops::FillConstantBatchSizeLikeOpMaker,
+                  ops::BatchSizeLikeNoNeedBufferVarsInference);
 REGISTER_OP_CPU_KERNEL(
     fill_constant_batch_size_like,
     ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,

paddle/fluid/operators/fill_zeros_like_op.cc

@@ -36,6 +36,7 @@ class FillZerosLikeOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X", "The input of fill-zeros-like op.");
     AddOutput("Out", "The variable will be filled up with zeros.");
+    ExtraMake();
     AddComment(R"DOC(
 FillZerosLike Operator.
 
@@ -44,13 +45,49 @@ The output will have the same size as the input.
 
 )DOC");
   }
+
+ protected:
+  virtual void ExtraMake() {}
+};
+
+class FillZerosLikeOp2 : public FillZerosLikeOp {
+ public:
+  using FillZerosLikeOp::FillZerosLikeOp;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype")),
+        ctx.GetPlace());
+  }
 };
+
+class FillZerosLikeOp2Maker : public FillZerosLikeOpMaker {
+ protected:
+  void ExtraMake() override {
+    this->AddAttr<int>("dtype",
+                       "(int, default 5(FP32)) "
+                       "Output data type.")
+        .SetDefault(framework::proto::VarType::FP32);
+  }
+};
+
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(FillZerosLikeOp2NoNeedBufferVarsInference,
+                                      "X");
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, ops::FillZerosLikeOp,
                              ops::FillZerosLikeOpMaker);
+
+REGISTER_OPERATOR(fill_zeros_like2, ops::FillZerosLikeOp2,
+                  ops::FillZerosLikeOp2Maker,
+                  ops::FillZerosLikeOp2NoNeedBufferVarsInference,
+                  paddle::framework::EmptyGradOpMaker);
+
 REGISTER_OP_CPU_KERNEL(
     fill_zeros_like,
     ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, int>,
@@ -58,3 +95,11 @@ REGISTER_OP_CPU_KERNEL(
     ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, float>,
     ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, double>,
     ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, bool>);
+
+REGISTER_OP_CPU_KERNEL(
+    fill_zeros_like2,
+    ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, int64_t>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUDeviceContext, bool>);

paddle/fluid/operators/fill_zeros_like_op.cu.cc

@@ -26,3 +26,13 @@ REGISTER_OP_CUDA_KERNEL(
     ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext,
                              paddle::platform::float16>,
     ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, bool>);
+
+REGISTER_OP_CUDA_KERNEL(
+    fill_zeros_like2,
+    ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext,
+                             paddle::platform::float16>,
+    ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, bool>);

paddle/fluid/operators/gaussian_random_batch_size_like_op.cc

@@ -65,17 +65,13 @@ by input arguments.
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(
-    GaussianRandomBatchSizeLikeNoNeedBufferVarsInference, "Input");
-
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OPERATOR(
-    gaussian_random_batch_size_like,
+REGISTER_OPERATOR(gaussian_random_batch_size_like,
                   paddle::operators::GaussianRandomBatchSizeLikeOp,
                   paddle::operators::GaussianRandomBatchSizeLikeOpMaker,
                   paddle::framework::EmptyGradOpMaker,
-    paddle::operators::GaussianRandomBatchSizeLikeNoNeedBufferVarsInference);
+                  paddle::operators::BatchSizeLikeNoNeedBufferVarsInference);
 
 // Kernels are registered in gaussian_random_op.cc and gaussian_random_op.cu

paddle/fluid/operators/hinge_loss_op.cc

@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/hinge_loss_op.h"
+#include <memory>
+#include <string>
+#include <vector>
 
 namespace paddle {
 namespace operators {
@@ -97,12 +100,29 @@ class HingeLossGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class HingeLossGradOpDescMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
+    op->SetType("hinge_loss_grad");
+    op->SetInput("Logits", Input("Logits"));
+    op->SetInput("Labels", Input("Labels"));
+    op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
+    op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(hinge_loss, ops::HingeLossOp, ops::HingeLossOpMaker<float>,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::HingeLossGradOpDescMaker);
 REGISTER_OPERATOR(hinge_loss_grad, ops::HingeLossGradOp);
 REGISTER_OP_CPU_KERNEL(
     hinge_loss,

paddle/fluid/operators/huber_loss_op.cc

@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/huber_loss_op.h"
+#include <memory>
+#include <string>
+#include <vector>
 
 namespace paddle {
 namespace operators {
@@ -90,29 +93,36 @@ class HuberLossGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Residual"),
-                   "Input(Residual) 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 y_dims = ctx->GetInputDim("Y");
     auto residual_dims = ctx->GetInputDim("Residual");
-    auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
-
-    PADDLE_ENFORCE_EQ(residual_dims, x_dims);
-    PADDLE_ENFORCE_EQ(out_grad_dims, x_dims);
 
     auto x_grad_name = framework::GradVarName("X");
     auto y_grad_name = framework::GradVarName("Y");
     if (ctx->HasOutput(x_grad_name)) {
-      ctx->SetOutputDim(x_grad_name, x_dims);
+      ctx->SetOutputDim(x_grad_name, residual_dims);
     }
     if (ctx->HasOutput(y_grad_name)) {
-      ctx->SetOutputDim(y_grad_name, y_dims);
+      ctx->SetOutputDim(y_grad_name, residual_dims);
+    }
   }
+};
+
+class HuberLossGradOpDescMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
+    op->SetType("huber_loss_grad");
+    op->SetInput("Residual", Output("Residual"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
+    op->SetAttrMap(Attrs());
+    return op;
   }
 };
 
@@ -121,7 +131,7 @@ class HuberLossGradOp : public framework::OperatorWithKernel {
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(huber_loss, ops::HuberLossOp, ops::HuberLossOpMaker<float>,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::HuberLossGradOpDescMaker);
 REGISTER_OPERATOR(huber_loss_grad, ops::HuberLossGradOp);
 REGISTER_OP_CPU_KERNEL(
     huber_loss, ops::HuberLossKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/row_conv_op.cc

@@ -13,6 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/row_conv_op.h"
+#include <memory>
+#include <string>
+#include <vector>
+
 #include "paddle/fluid/framework/eigen.h"
 
 namespace paddle {
@@ -54,7 +58,6 @@ class RowConvGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Filter"),
                    "Input(Filter) should not be null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
@@ -62,8 +65,8 @@ class RowConvGradOp : public framework::OperatorWithKernel {
 
     auto x_grad_name = framework::GradVarName("X");
     if (ctx->HasOutput(x_grad_name)) {
-      auto x_dims = ctx->GetInputDim("X");
-      ctx->SetOutputDim(x_grad_name, x_dims);
+      auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
+      ctx->SetOutputDim(x_grad_name, dout_dims);
     }
 
     auto filter_grad_name = framework::GradVarName("Filter");
@@ -259,12 +262,31 @@ class RowConvGradKernel<platform::CPUDeviceContext, T>
     }
   }
 };
+
+class RowConvGradOpDescMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
+    op->SetType("row_conv_grad");
+    op->SetAttrMap(Attrs());
+    op->SetInput("X", Input("X"));
+    op->SetInput("Filter", Input("Filter"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Filter"), InputGrad("Filter"));
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(row_conv, ops::RowConvOp, ops::RowConvOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::RowConvGradOpDescMaker);
 REGISTER_OPERATOR(row_conv_grad, ops::RowConvGradOp);
 REGISTER_OP_CPU_KERNEL(
     row_conv, ops::RowConvKernel<paddle::platform::CPUDeviceContext, float>);

paddle/fluid/operators/uniform_random_batch_size_like_op.cc

@@ -64,8 +64,9 @@ with random values sampled from a uniform distribution.
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP_WITHOUT_GRADIENT(
-    uniform_random_batch_size_like,
+REGISTER_OPERATOR(uniform_random_batch_size_like,
                   paddle::operators::UniformRandomBatchSizeLikeOp,
-    paddle::operators::UniformRandomBatchSizeLikeOpMaker);
+                  paddle::operators::UniformRandomBatchSizeLikeOpMaker,
+                  paddle::framework::EmptyGradOpMaker,
+                  paddle::operators::BatchSizeLikeNoNeedBufferVarsInference);
 // Kernels are registered in uniform_random_op.cc and uniform_random_op.cu

python/paddle/fluid/backward.py

@@ -231,9 +231,16 @@ def _remove_no_grad_branch_(op_descs, no_grad_set):
     for idx, op_desc in enumerate(op_descs):
         for arg in op_desc.input_arg_names():
             if core.grad_var_suffix() in arg and arg in no_grad_set:
-                to_insert.append((_create_op_desc_("fill_zeros_like", {
-                    "X": [_strip_grad_suffix_(arg)]
-                }, {"Out": [arg]}, {}), idx))
+                x_in = _strip_grad_suffix_(arg)
+                x_in_var_desc = op_desc.block().find_var_recursive(
+                    cpt.to_bytes(x_in))
+                assert x_in_var_desc is not None, "Variable {} not found".format(
+                    x_in)
+                dtype = x_in_var_desc.dtype()
+
+                to_insert.append(
+                    (_create_op_desc_("fill_zeros_like2", {"X": [x_in]},
+                                      {"Out": [arg]}, {"dtype": dtype}), idx))
 
     list([op_descs.insert(p[1], p[0]) for p in reversed(to_insert)])
 

python/paddle/fluid/tests/unittests/test_eager_deletion_no_need_buffer_vars_inference.py

@@ -23,6 +23,8 @@ from test_elementwise_sub_op import *
 from test_concat_op import *
 from test_gather_op import *
 from test_gaussian_random_batch_size_like_op import *
+from test_uniform_random_batch_size_like_op import *
+from test_fill_constant_batch_size_like_op import *
 from test_lod_reset_op import *
 from test_scatter_op import *
 from test_mean_op import *
@@ -40,6 +42,7 @@ from test_sequence_unpad_op import *
 from test_sequence_scatter_op import *
 from test_sequence_slice_op import *
 from test_pad2d_op import *
+from test_fill_zeros_like2_op import *
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_fill_zeros_like2_op.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from paddle.fluid.framework import convert_np_dtype_to_dtype_
+from op_test import OpTest
+
+
+class TestFillZerosLike2Op(OpTest):
+    def setUp(self):
+        self.op_type = "fill_zeros_like2"
+        self.dtype = np.float32
+        self.init_dtype()
+        self.inputs = {'X': np.random.random((219, 232)).astype(self.dtype)}
+        self.outputs = {'Out': np.zeros_like(self.inputs["X"])}
+        self.attrs = {'dtype': convert_np_dtype_to_dtype_(self.dtype)}
+
+    def init_dtype(self):
+        pass
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestFillZerosLike2OpFp16(TestFillZerosLike2Op):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class TestFillZerosLike2OpFp64(TestFillZerosLike2Op):
+    def init_dtype(self):
+        self.dtype = np.float64
+
+
+if __name__ == "__main__":
+    unittest.main()