Merge pull request #16491 from sneaxiy/feature/advance_gc

Fix grad op makers

paddle/fluid/operators/bpr_loss_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/bpr_loss_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -127,6 +128,23 @@ neural networks>(https://arxiv.org/abs/1511.06939)
 )DOC");
   }
 };
+
+class BprLossGradDescMaker : 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("bpr_loss_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("Label", Input("Label"));
+    op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
 }  // namespace operators
 }  // namespace paddle
 
@@ -134,7 +152,7 @@ namespace ops = paddle::operators;
 using CPUCtx = paddle::platform::CPUDeviceContext;
 
 REGISTER_OPERATOR(bpr_loss, ops::BprLossOp, ops::BprLossOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::BprLossGradDescMaker);
 REGISTER_OPERATOR(bpr_loss_grad, ops::BprLossGradientOp);
 REGISTER_OP_CPU_KERNEL(bpr_loss, ops::BprLossOpKernel<CPUCtx, float>,
                        ops::BprLossOpKernel<CPUCtx, double>);

paddle/fluid/operators/detection/roi_perspective_transform_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <algorithm>
+#include <memory>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/math_function.h"
@@ -568,13 +569,31 @@ class ROIPerspectiveTransformOpMaker
   }
 };
 
+class ROIPerspectiveTransformGradDescMaker
+    : 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("roi_perspective_transform_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("ROIs", Input("ROIs"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(roi_perspective_transform, ops::ROIPerspectiveTransformOp,
                   ops::ROIPerspectiveTransformOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::ROIPerspectiveTransformGradDescMaker);
 REGISTER_OPERATOR(roi_perspective_transform_grad,
                   ops::ROIPerspectiveTransformGradOp);
 REGISTER_OP_CPU_KERNEL(roi_perspective_transform,

paddle/fluid/operators/gaussian_random_batch_size_like_op.cc

@@ -65,11 +65,17 @@ by input arguments.
   }
 };
 
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(
+    GaussianRandomBatchSizeLikeNoNeedBufferVarsInference, "Input");
+
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP_WITHOUT_GRADIENT(
+REGISTER_OPERATOR(
     gaussian_random_batch_size_like,
     paddle::operators::GaussianRandomBatchSizeLikeOp,
-    paddle::operators::GaussianRandomBatchSizeLikeOpMaker);
+    paddle::operators::GaussianRandomBatchSizeLikeOpMaker,
+    paddle::framework::EmptyGradOpMaker,
+    paddle::operators::GaussianRandomBatchSizeLikeNoNeedBufferVarsInference);
+
 // Kernels are registered in gaussian_random_op.cc and gaussian_random_op.cu

paddle/fluid/operators/im2sequence_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/im2sequence_op.h"
+#include <memory>
 #include <string>
 #include <vector>
 
@@ -146,12 +147,28 @@ class Im2SequenceGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class Im2SequenceGradDescMaker : 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("im2sequence_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(im2sequence, ops::Im2SequenceOp, ops::Im2SequenceOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::Im2SequenceGradDescMaker);
 REGISTER_OPERATOR(im2sequence_grad, ops::Im2SequenceGradOp);
 REGISTER_OP_CPU_KERNEL(
     im2sequence,

paddle/fluid/operators/interpolate_op.cc

@@ -10,6 +10,7 @@
    limitations under the License. */
 
 #include "paddle/fluid/operators/interpolate_op.h"
+#include <memory>
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
@@ -194,21 +195,46 @@ class InterpolateOpGrad : public framework::OperatorWithKernel {
 
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
-                                   ctx.GetPlace());
+    return framework::OpKernelType(
+        ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
+        ctx.GetPlace());
+  }
+};
+
+class InterpolateGradDescMaker : 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(ForwardOp().Type() + "_grad");
+    op->SetInput("X", Input("X"));
+    if (ForwardOp().Inputs().count("OutSize") > 0) {
+      op->SetInput("OutSize", Input("OutSize"));
+    }
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
   }
 };
 
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(InterpolateGradNoNeedBufferVarsInference,
+                                      "X");
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(bilinear_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(bilinear_interp_grad, ops::InterpolateOpGrad);
+                  ops::InterpolateGradDescMaker);
+REGISTER_OPERATOR(bilinear_interp_grad, ops::InterpolateOpGrad,
+                  ops::InterpolateGradNoNeedBufferVarsInference);
 REGISTER_OPERATOR(nearest_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(nearest_interp_grad, ops::InterpolateOpGrad);
+                  ops::InterpolateGradDescMaker);
+REGISTER_OPERATOR(nearest_interp_grad, ops::InterpolateOpGrad,
+                  ops::InterpolateGradNoNeedBufferVarsInference);
 REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::InterpolateKernel<float>,
                        ops::InterpolateKernel<double>,
                        ops::InterpolateKernel<uint8_t>);

paddle/fluid/operators/l1_norm_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/l1_norm_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -62,12 +63,28 @@ $$Out = \sum{|X|}$$
   }
 };
 
+class L1NormGradDescMaker : 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("l1_norm_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(l1_norm, ops::L1NormOp, ops::L1NormOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::L1NormGradDescMaker);
 REGISTER_OPERATOR(l1_norm_grad, ops::L1NormGradOp);
 REGISTER_OP_CPU_KERNEL(
     l1_norm, ops::L1NormKernel<paddle::platform::CPUDeviceContext, float>);

paddle/fluid/operators/label_smooth_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/label_smooth_op.h"
+#include <memory>
 #include <string>
 
 namespace paddle {
@@ -105,10 +106,23 @@ class LabelSmoothGradOp : public framework::OperatorWithKernel {
       : OperatorWithKernel(type, inputs, outputs, attrs) {}
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) shouldn't be null.");
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+    ctx->SetOutputDim(framework::GradVarName("X"),
+                      ctx->GetInputDim(framework::GradVarName("Out")));
+  }
+};
+
+class LabelSmoothGradDescMaker : 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("label_smooth_grad");
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
   }
 };
 
@@ -117,7 +131,7 @@ class LabelSmoothGradOp : public framework::OperatorWithKernel {
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(label_smooth, ops::LabelSmoothOp, ops::LabelSmoothOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::LabelSmoothGradDescMaker);
 REGISTER_OPERATOR(label_smooth_grad, ops::LabelSmoothGradOp);
 REGISTER_OP_CPU_KERNEL(
     label_smooth,

paddle/fluid/operators/linear_chain_crf_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/linear_chain_crf_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -250,14 +251,46 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class LinearChainCRFGradDescMaker : 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("linear_chain_crf_grad");
+    op->SetAttrMap(Attrs());
+
+    op->SetInput("Emission", Input("Emission"));
+    op->SetInput("Transition", Input("Transition"));
+    op->SetInput("Label", Input("Label"));
+
+    op->SetInput("Alpha", Output("Alpha"));
+    op->SetInput("EmissionExps", Output("EmissionExps"));
+    op->SetInput("TransitionExps", Output("TransitionExps"));
+
+    op->SetInput(framework::GradVarName("LogLikelihood"),
+                 OutputGrad("LogLikelihood"));
+
+    op->SetOutput(framework::GradVarName("Emission"), InputGrad("Emission"));
+    op->SetOutput(framework::GradVarName("Transition"),
+                  InputGrad("Transition"));
+
+    return op;
+  }
+};
+
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(
+    LinearChainCRFGradNoNeedBufferVarsInference, "Transition", "Emission");
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(linear_chain_crf, ops::LinearChainCRFOp,
-                  ops::LinearChainCRFOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(linear_chain_crf_grad, ops::LinearChainCRFGradOp);
+                  ops::LinearChainCRFOpMaker, ops::LinearChainCRFGradDescMaker);
+REGISTER_OPERATOR(linear_chain_crf_grad, ops::LinearChainCRFGradOp,
+                  ops::LinearChainCRFGradNoNeedBufferVarsInference);
 REGISTER_OP_CPU_KERNEL(
     linear_chain_crf,
     ops::LinearChainCRFOpKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/log_loss_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/log_loss_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -100,12 +101,29 @@ class LogLossGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class LogLossGradDescMaker : 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("log_loss_grad");
+    op->SetInput("Predicted", Input("Predicted"));
+    op->SetInput("Labels", Input("Labels"));
+    op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
+    op->SetOutput(framework::GradVarName("Predicted"), InputGrad("Predicted"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(log_loss, ops::LogLossOp, ops::LogLossOpMaker<float>,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::LogLossGradDescMaker);
 REGISTER_OPERATOR(log_loss_grad, ops::LogLossGradOp);
 REGISTER_OP_CPU_KERNEL(
     log_loss, ops::LogLossKernel<paddle::platform::CPUDeviceContext, float>);

paddle/fluid/operators/lstm_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/lstm_op.h"
+#include <memory>
 #include <string>
 
 namespace paddle {
@@ -264,12 +265,51 @@ class LSTMGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class LSTMGradOpDescMaker : 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("lstm_grad");
+    op->SetAttrMap(Attrs());
+    op->SetInput("Input", Input("Input"));
+    op->SetOutput(framework::GradVarName("Input"), InputGrad("Input"));
+
+    if (ForwardOp().Inputs().count("H0") > 0) {
+      op->SetInput("H0", Input("H0"));
+      op->SetOutput(framework::GradVarName("H0"), InputGrad("H0"));
+    }
+
+    if (ForwardOp().Inputs().count("C0") > 0) {
+      op->SetInput("C0", Input("C0"));
+      op->SetOutput(framework::GradVarName("C0"), InputGrad("C0"));
+    }
+
+    op->SetInput("Weight", Input("Weight"));
+    op->SetOutput(framework::GradVarName("Weight"), InputGrad("Weight"));
+
+    op->SetInput("Bias", Input("Bias"));
+    op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
+
+    op->SetInput("Cell", Output("Cell"));
+
+    op->SetInput("Hidden", Output("Hidden"));
+    op->SetInput(framework::GradVarName("Hidden"), OutputGrad("Hidden"));
+
+    op->SetInput("BatchGate", Output("BatchGate"));
+    op->SetInput("BatchCellPreAct", Output("BatchCellPreAct"));
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(lstm, ops::LSTMOp, ops::LSTMOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::LSTMGradOpDescMaker);
 REGISTER_OPERATOR(lstm_grad, ops::LSTMGradOp);
 REGISTER_OP_CPU_KERNEL(
     lstm, ops::LSTMKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/margin_rank_loss_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/margin_rank_loss_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -94,8 +95,6 @@ class MarginRankLossGradOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
-    PADDLE_ENFORCE(ctx->HasInput("X1"), "Input(X1) shouldn't be null.");
-    PADDLE_ENFORCE(ctx->HasInput("X2"), "Input(X2) shouldn't be null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Input(Out@GRAD) shouldn't be null.");
     PADDLE_ENFORCE(ctx->HasInput("Activated"),
@@ -106,13 +105,31 @@ class MarginRankLossGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class MarginRankLossGradDescMaker : 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("margin_rank_loss_grad");
+    op->SetInput("Activated", Output("Activated"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetInput("Label", Input("Label"));
+    op->SetOutput(framework::GradVarName("X1"), InputGrad("X1"));
+    op->SetOutput(framework::GradVarName("X2"), InputGrad("X2"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(margin_rank_loss, ops::MarginRankLossOp,
                   ops::MarginRankLossOpMaker<float>,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::MarginRankLossGradDescMaker);
 REGISTER_OPERATOR(margin_rank_loss_grad, ops::MarginRankLossGradOp);
 REGISTER_OP_CPU_KERNEL(
     margin_rank_loss,

paddle/fluid/operators/mean_op.cc

@@ -13,7 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/mean_op.h"
+#include <memory>
 #include <string>
+#include <unordered_map>
+
 namespace paddle {
 namespace operators {
 
@@ -61,7 +64,8 @@ class MeanGradOp : public framework::OperatorWithKernel {
 
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    auto input_data_type = ctx.Input<Tensor>("X")->type();
+    auto input_data_type =
+        ctx.Input<Tensor>(framework::GradVarName("Out"))->type();
     return framework::OpKernelType(input_data_type, ctx.GetPlace());
   }
 };
@@ -81,13 +85,16 @@ class MeanGradMaker : public framework::SingleGradOpDescMaker {
   }
 };
 
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(MeanGradNoNeedBufferVarsInference, "X");
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanOpInferVarType,
                   ops::MeanGradMaker);
-REGISTER_OPERATOR(mean_grad, ops::MeanGradOp);
+REGISTER_OPERATOR(mean_grad, ops::MeanGradOp,
+                  ops::MeanGradNoNeedBufferVarsInference);
 REGISTER_OP_CPU_KERNEL(
     mean, ops::MeanKernel<paddle::platform::CPUDeviceContext, float>,
     ops::MeanKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/multiplex_op.cc

@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/multiplex_op.h"
+#include <memory>
+#include <vector>
 
 namespace paddle {
 namespace operators {
@@ -111,28 +113,47 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(!ctx->Inputs("X").empty(), "Input(X) should not be null.");
-    PADDLE_ENFORCE(!ctx->Outputs(framework::GradVarName("X")).empty(),
-                   "Output(X@Grad) should not be null.");
+    auto& dxs = ctx->Outputs(framework::GradVarName("X"));
+    PADDLE_ENFORCE(!dxs.empty(), "Output(X@Grad) should not be null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Input(Out@GRAD) should not be null.");
-    ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
+    auto dout_dim = ctx->GetInputDim(framework::GradVarName("Out"));
+    ctx->SetOutputsDim(framework::GradVarName("X"),
+                       std::vector<framework::DDim>(dxs.size(), dout_dim));
   }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(ctx.MultiInput<Tensor>("X")[0]->type(),
-                                   ctx.device_context());
+    return framework::OpKernelType(
+        ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
+        ctx.device_context());
+  }
+};
+
+class MultiplexGradDescMaker : 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("multiplex_grad");
+    op->SetInput("Ids", Input("Ids"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X", false));
+    op->SetAttrMap(Attrs());
+    return op;
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
+
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<false>);
+                  ops::MultiplexGradDescMaker);
 REGISTER_OPERATOR(multiplex_grad, ops::MultiplexGradOp);
 REGISTER_OP_CPU_KERNEL(
     multiplex,

paddle/fluid/operators/multiplex_op.cu

@@ -53,20 +53,25 @@ class MultiplexGradGPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const {
     auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto ins = ctx.MultiInput<Tensor>("X");
     auto* ids = ctx.Input<Tensor>("Ids");
     auto d_ins = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
+
+    size_t idx = -1UL;
     for (size_t i = 0; i < d_ins.size(); i++) {
       if (d_ins[i]) {
         d_ins[i]->mutable_data<T>(ctx.GetPlace());
         auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
         t.device(*ctx.template device_context<Place>().eigen_device()) =
             t.constant(static_cast<T>(0));
+
+        idx = i;
       }
     }
 
-    auto rows = ins[0]->dims()[0];
-    auto cols = ins[0]->numel() / rows;
+    if (idx == -1UL) return;
+
+    auto rows = d_ins[idx]->dims()[0];
+    auto cols = d_ins[idx]->numel() / rows;
     // copy index to cpu
     Tensor index_t_cpu;
     TensorCopySync(*ids, platform::CPUPlace(), &index_t_cpu);

paddle/fluid/operators/multiplex_op.h

@@ -52,20 +52,25 @@ class MultiplexGradCPUKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const {
     auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
     auto* ids = ctx.Input<framework::Tensor>("Ids");
-    auto ins = ctx.MultiInput<framework::Tensor>("X");
     auto d_ins =
         ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
+
+    size_t idx = -1UL;
     for (size_t i = 0; i < d_ins.size(); i++) {
       if (d_ins[i]) {
         d_ins[i]->mutable_data<T>(ctx.GetPlace());
         auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
         t.device(*ctx.template device_context<DeviceContext>().eigen_device()) =
             t.constant(static_cast<T>(0));
+
+        idx = i;
       }
     }
 
-    auto rows = ins[0]->dims()[0];
-    auto cols = ins[0]->numel() / rows;
+    if (idx == -1UL) return;
+
+    auto rows = d_ins[idx]->dims()[0];
+    auto cols = d_ins[idx]->numel() / rows;
     auto* index = ids->data<int32_t>();
     platform::CPUPlace place = boost::get<platform::CPUPlace>(ctx.GetPlace());
     for (auto i = 0; i < rows; i++) {

paddle/fluid/operators/pad_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/pad_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -29,7 +30,7 @@ class PadOp : public framework::OperatorWithKernel {
                    "Output(Out) of PadOp should not be null.");
 
     auto x_dim = ctx->GetInputDim("X");
-    auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+    auto& paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
     PADDLE_ENFORCE_EQ(x_dim.size() * 2, int64_t(paddings.size()),
                       "Size of paddings should be equal to 2 * dimension size "
                       "of input tensor.");
@@ -99,13 +100,20 @@ class PadOpGrad : 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(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) should not be null");
-    auto x_dims = ctx->GetInputDim("X");
+    auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
+    auto& paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+    for (int i = 0; i < dout_dims.size(); ++i) {
+      dout_dims[i] -= (paddings[i * 2] + paddings[i * 2 + 1]);
+    }
+
     auto x_grad_name = framework::GradVarName("X");
     if (ctx->HasOutput(x_grad_name)) {
-      ctx->SetOutputDim(x_grad_name, x_dims);
+      auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
+      auto& paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+      for (int i = 0; i < dout_dims.size(); ++i) {
+        dout_dims[i] -= (paddings[i * 2] + paddings[i * 2 + 1]);
+      }
+      ctx->SetOutputDim(x_grad_name, dout_dims);
     }
   }
 };
@@ -117,7 +125,6 @@ class PadOpGradMaker : public framework::SingleGradOpDescMaker {
  protected:
   std::unique_ptr<framework::OpDesc> Apply() const override {
     auto* bind = new framework::OpDesc();
-    bind->SetInput("X", Input("X"));
     bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
     bind->SetOutput(framework::GradVarName("X"), InputGrad("X"));
     bind->SetAttrMap(Attrs());

paddle/fluid/operators/psroi_pool_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/psroi_pool_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -154,12 +155,29 @@ class PSROIPoolGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class PSROIPoolGradDescMaker : 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("psroi_pool_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("ROIs", Input("ROIs"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(psroi_pool, ops::PSROIPoolOp, ops::PSROIPoolOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::PSROIPoolGradDescMaker);
 REGISTER_OPERATOR(psroi_pool_grad, ops::PSROIPoolGradOp);
 REGISTER_OP_CPU_KERNEL(
     psroi_pool,

paddle/fluid/operators/rank_loss_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/rank_loss_op.h"
+#include <memory>
 #include <string>
 
 namespace paddle {
@@ -116,6 +117,25 @@ class RankLossGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class RankLossGradDescMaker : 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("rank_loss_grad");
+    op->SetInput("Label", Input("Label"));
+    op->SetInput("Left", Input("Left"));
+    op->SetInput("Right", Input("Right"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("Left"), InputGrad("Left"));
+    op->SetOutput(framework::GradVarName("Right"), InputGrad("Right"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 namespace ops = paddle::operators;

paddle/fluid/operators/roi_align_op.cc

@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/roi_align_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -147,12 +148,29 @@ Thus avoid the misaligned problem.
   }
 };
 
+class ROIAlignGradDescMaker : 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("roi_align_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("ROIs", Input("ROIs"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(roi_align, ops::ROIAlignOp, ops::ROIAlignOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::ROIAlignGradDescMaker);
 REGISTER_OPERATOR(roi_align_grad, ops::ROIAlignGradOp);
 REGISTER_OP_CPU_KERNEL(
     roi_align,

paddle/fluid/operators/roi_pool_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/roi_pool_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -158,12 +159,30 @@ https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn
   }
 };
 
+class ROIPoolGradDescMaker : 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("roi_pool_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("ROIs", Input("ROIs"));
+    op->SetInput("Argmax", Output("Argmax"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(roi_pool, ops::ROIPoolOp, ops::ROIPoolOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::ROIPoolGradDescMaker);
 REGISTER_OPERATOR(roi_pool_grad, ops::ROIPoolGradOp);
 REGISTER_OP_CPU_KERNEL(
     roi_pool,

paddle/fluid/operators/scatter_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/scatter_op.h"
+#include <memory>
 #include "paddle/fluid/framework/ddim.h"
 
 namespace paddle {
@@ -63,14 +64,16 @@ class ScatterGradOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext* ctx) const override {
     ctx->SetOutputDim(framework::GradVarName("Updates"),
                       ctx->GetInputDim("Updates"));
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+    ctx->SetOutputDim(framework::GradVarName("X"),
+                      ctx->GetInputDim(framework::GradVarName("Out")));
   }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
-                                   ctx.device_context());
+    return framework::OpKernelType(
+        ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
+        ctx.device_context());
   }
 };
 
@@ -95,12 +98,34 @@ $$
   }
 };
 
+class ScatterGradDescMaker : 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("scatter_grad");
+    op->SetInput("Ids", Input("Ids"));
+    op->SetInput("Updates", Input("Updates"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("Updates"), InputGrad("Updates"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
+DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(ScatterGradNoNeedBufferVarsInference,
+                                      "Updates");
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(scatter, ops::ScatterOp, ops::ScatterOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(scatter_grad, ops::ScatterGradOp);
+                  ops::ScatterGradDescMaker);
+REGISTER_OPERATOR(scatter_grad, ops::ScatterGradOp,
+                  ops::ScatterGradNoNeedBufferVarsInference);
 REGISTER_OP_CPU_KERNEL(scatter, ops::ScatterOpKernel<float>);
 REGISTER_OP_CPU_KERNEL(scatter_grad, ops::ScatterGradientOpKernel<float>);

paddle/fluid/operators/shuffle_channel_op.cc

@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/shuffle_channel_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -91,13 +92,28 @@ class ShuffleChannelGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class ShuffleChannelGradDescMaker : 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("shuffle_channel_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(shuffle_channel, ops::ShuffleChannelOp,
-                  ops::ShuffleChannelOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+                  ops::ShuffleChannelOpMaker, ops::ShuffleChannelGradDescMaker);
 
 REGISTER_OPERATOR(shuffle_channel_grad, ops::ShuffleChannelGradOp);