Merge remote-tracking branch 'upstream/develop' into windows/build

paddle/fluid/framework/operator.cc

@@ -262,6 +262,8 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
           if (row_size >= 0) {
             ss << "[row_size=" << row_size << "]";
           }
+          std::string dtype = GetDtype(*scope, output.second[i]);
+          ss << ":" << dtype;
           ss << "[" << GetDims(*scope, var_name, true) << "]";
           ss << "(" << GetLoD(*scope, var_name) << ")";
         }

paddle/fluid/framework/var_type_inference.h

@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <string>
+#include "paddle/fluid/framework/block_desc.h"
+#include "paddle/fluid/framework/op_desc.h"
 #include "paddle/fluid/framework/type_defs.h"
 
 namespace paddle {
@@ -24,5 +27,27 @@ class VarTypeInference {
   virtual void operator()(const OpDesc& op_desc, BlockDesc* block) const = 0;
 };
 
+class PassInDtypeAndVarTypeToOutput : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc& op_desc,
+                  framework::BlockDesc* block) const final {
+    auto in_out_var_names = this->GetInputOutputWithSameType();
+
+    for (auto& i_o_n : in_out_var_names) {
+      auto& x_name = op_desc.Input(i_o_n.first).at(0);
+      auto& out_name = op_desc.Output(i_o_n.second).at(0);
+
+      auto& x = block->FindRecursiveOrCreateVar(x_name);
+      auto& out = block->FindRecursiveOrCreateVar(out_name);
+      out.SetType(x.GetType());
+      out.SetDataType(x.GetDataType());
+    }
+  }
+
+ protected:
+  virtual std::unordered_map<std::string, std::string>
+  GetInputOutputWithSameType() const = 0;
+};
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/operators/activation_op.cc

@@ -91,16 +91,12 @@ class ActivationOp : public framework::OperatorWithKernel {
   }
 };
 
-class ActivationOpInferVarType : public framework::VarTypeInference {
- public:
-  void operator()(const framework::OpDesc& op_desc,
-                  framework::BlockDesc* block) const override {
-    auto x_name = op_desc.Input("X")[0];
-    auto out_name = op_desc.Output("Out")[0];
-    auto& x = block->FindRecursiveOrCreateVar(x_name);
-    auto& out = block->FindRecursiveOrCreateVar(out_name);
-    out.SetType(x.GetType());
-    out.SetDataType(x.GetDataType());
+class ActivationOpInferVarType
+    : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
   }
 };
 

paddle/fluid/operators/batch_norm_op.cc

@@ -170,6 +170,15 @@ The required data format for this layer is one of the following:
   }
 };
 
+class BatchNormOpInferVarType
+    : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Y"}};
+  }
+};
+
 template <typename T>
 class BatchNormKernel<platform::CPUDeviceContext, T>
     : public framework::OpKernel<T> {
@@ -525,7 +534,7 @@ class BatchNormGradMaker : public framework::SingleGradOpDescMaker {
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker,
-                  ops::BatchNormGradMaker);
+                  ops::BatchNormOpInferVarType, ops::BatchNormGradMaker);
 REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp);
 
 REGISTER_OP_CPU_KERNEL(

paddle/fluid/operators/conv_op.cc

@@ -224,6 +224,15 @@ $$
 )DOC");
 }
 
+class ConvOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{
+        {"Input", /*->*/ "Output"}};
+  }
+};
+
 void Conv3DOpMaker::Make() {
   AddInput(
       "Input",
@@ -365,6 +374,7 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(conv2d, ops::ConvOp, ops::Conv2DOpMaker,
+                  ops::ConvOpInferVarType,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(conv2d_grad, ops::ConvOpGrad);
 
@@ -372,7 +382,9 @@ REGISTER_OPERATOR(conv2d_grad, ops::ConvOpGrad);
 REGISTER_OPERATOR(depthwise_conv2d, ops::ConvOp, ops::Conv2DOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(depthwise_conv2d_grad, ops::ConvOpGrad);
+
 REGISTER_OPERATOR(conv3d, ops::ConvOp, ops::Conv3DOpMaker,
+                  ops::ConvOpInferVarType,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(conv3d_grad, ops::ConvOpGrad);
 

paddle/fluid/operators/cross_entropy_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/cross_entropy_op.h"
+#include <string>
 
 namespace paddle {
 namespace operators {
@@ -179,6 +180,15 @@ or not. But the output only shares the LoD information with input X.
 )DOC");
   }
 };
+
+class CrossEntropyOpInferVarType
+    : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Y"}};
+  }
+};
 }  // namespace operators
 }  // namespace paddle
 
@@ -186,6 +196,7 @@ namespace ops = paddle::operators;
 using CPUCtx = paddle::platform::CPUDeviceContext;
 
 REGISTER_OPERATOR(cross_entropy, ops::CrossEntropyOp, ops::CrossEntropyOpMaker,
+                  ops::CrossEntropyOpInferVarType,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(cross_entropy_grad, ops::CrossEntropyGradientOp);
 REGISTER_OP_CPU_KERNEL(cross_entropy, ops::CrossEntropyOpKernel<CPUCtx, float>,

paddle/fluid/operators/elementwise_op.h

@@ -75,16 +75,12 @@ class ElementwiseOp : public framework::OperatorWithKernel {
   }
 };
 
-class ElementwiseOpInferVarType : public framework::VarTypeInference {
- public:
-  void operator()(const framework::OpDesc &op_desc,
-                  framework::BlockDesc *block) const override {
-    auto x_name = op_desc.Input("X")[0];
-    auto out_name = op_desc.Output("Out")[0];
-    auto &x = block->FindRecursiveOrCreateVar(x_name);
-    auto &out = block->FindRecursiveOrCreateVar(out_name);
-    out.SetType(x.GetType());
-    out.SetDataType(x.GetDataType());
+class ElementwiseOpInferVarType
+    : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
   }
 };
 

paddle/fluid/operators/mean_op.cc

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/mean_op.h"
-
+#include <string>
 namespace paddle {
 namespace operators {
 
@@ -42,6 +42,14 @@ Mean Operator calculates the mean of all elements in X.
   }
 };
 
+class MeanOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
+  }
+};
+
 class MeanGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -50,6 +58,14 @@ class MeanGradOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
     ctx->ShareLoD("X", framework::GradVarName("X"));
   }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto input_data_type =
+        framework::ToDataType(ctx.Input<Tensor>("X")->type());
+
+    return framework::OpKernelType(input_data_type, ctx.GetPlace());
+  }
 };
 
 class MeanGradMaker : public framework::SingleGradOpDescMaker {
@@ -71,7 +87,8 @@ class MeanGradMaker : public framework::SingleGradOpDescMaker {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanGradMaker);
+REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanOpInferVarType,
+                  ops::MeanGradMaker);
 REGISTER_OPERATOR(mean_grad, ops::MeanGradOp);
 REGISTER_OP_CPU_KERNEL(
     mean, ops::MeanKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/mul_op.cc

@@ -126,6 +126,14 @@ or not. But the output only shares the LoD information with input $X$.
   }
 };
 
+class MulOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
+  }
+};
+
 class MulGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -178,7 +186,8 @@ class MulOpGradMaker : public framework::SingleGradOpDescMaker {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpGradMaker);
+REGISTER_OPERATOR(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpInferVarType,
+                  ops::MulOpGradMaker);
 REGISTER_OPERATOR(mul_grad, ops::MulGradOp);
 REGISTER_OP_CPU_KERNEL(
     mul, ops::MulKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/pool_op.cc

@@ -40,7 +40,7 @@ int PoolOutputSize(int input_size, int filter_size, int padding, int stride,
   return output_size;
 }
 
-void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
+void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
   PADDLE_ENFORCE(ctx->HasInput("X"), "X(Input) of Pooling should not be null.");
   PADDLE_ENFORCE(ctx->HasOutput("Out"),
                  "Out(Output) of Pooling should not be null.");
@@ -81,7 +81,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
 }
 
 framework::OpKernelType PoolOp::GetExpectedKernelType(
-    const framework::ExecutionContext &ctx) const {
+    const framework::ExecutionContext& ctx) const {
   framework::LibraryType library_{framework::LibraryType::kPlain};
   std::string data_format = ctx.Attr<std::string>("data_format");
   framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
@@ -104,7 +104,7 @@ framework::OpKernelType PoolOp::GetExpectedKernelType(
       layout_, library_);
 }
 
-void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
+void PoolOpGrad::InferShape(framework::InferShapeContext* ctx) const {
   PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
   PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
                  "Input(X@GRAD) should not be null.");
@@ -112,7 +112,7 @@ void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
 }
 
 framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
-    const framework::ExecutionContext &ctx) const {
+    const framework::ExecutionContext& ctx) const {
   framework::LibraryType library_{framework::LibraryType::kPlain};
   std::string data_format = ctx.Attr<std::string>("data_format");
   framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
@@ -262,6 +262,14 @@ Example:
 )DOC");
 }
 
+class PoolOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
+  }
+};
+
 void Pool3dOpMaker::Make() {
   AddInput("X",
            "(Tensor) The input tensor of pooling operator. "
@@ -372,6 +380,7 @@ Example:
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(pool2d, ops::PoolOp, ops::Pool2dOpMaker,
+                  ops::PoolOpInferVarType,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(pool2d_grad, ops::PoolOpGrad);
 
@@ -383,6 +392,7 @@ REGISTER_OP_CPU_KERNEL(
     ops::PoolGradKernel<paddle::platform::CPUDeviceContext, double>);
 
 REGISTER_OPERATOR(pool3d, ops::PoolOp, ops::Pool3dOpMaker,
+                  ops::PoolOpInferVarType,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(pool3d_grad, ops::PoolOpGrad);
 

paddle/fluid/operators/softmax_op.cc

@@ -124,6 +124,14 @@ For each row $i$ and each column $j$ in the matrix, we have:
   }
 };
 
+class SoftmaxOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
+  }
+};
+
 class SoftmaxOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -196,7 +204,7 @@ class SoftmaxOpGradMaker : public framework::SingleGradOpDescMaker {
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker,
-                  ops::SoftmaxOpGradMaker);
+                  ops::SoftmaxOpInferVarType, ops::SoftmaxOpGradMaker);
 REGISTER_OPERATOR(softmax_grad, ops::SoftmaxOpGrad);
 REGISTER_OP_CPU_KERNEL(
     softmax, ops::SoftmaxKernel<paddle::platform::CPUDeviceContext, float>,