Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

doc/design/register_grad_op.md

@@ -33,22 +33,45 @@ The mapping relationship between an operator and its gradient operators is a fun
 
 ```cpp
 // (OpDesc) --> vector<OpDesc>
-using GradOpDescMaker = std::function<std::vector<OpDesc>(const OpDesc&)>;
+std::function<std::vector<OpDescBind>(const OpDescBind&)>;
 ```
 
-The function take a `OpDesc` of the forward operator and return one or many gradient operator descriptions.
+The function takes an `OpDescBind` of the forward operator and returns one or many gradient operator descriptions. `OpDescBind` is a C++ wrapper for protobuf message `OpDesc` to manipulate `OpDesc` fast.
 
 The `GradOpDescMaker` will be registered in `OpInfo`, to replace `grad_op_type_` field. The `OpInfo` should be
 
 ```cpp
 struct OpInfo {
-  GradOpDescMaker grad_op_maker_;
+  std::function<std::vector<std::unique_ptr<OpDescBind>>(const OpDescBind&)>  grad_op_maker_;
   ...
 };
 ```
 
 The `grad_op_maker_ ` is `nullptr` if the operator does not have associated gradient operators.
 
+We propose a base class called `GradOpDescMakerBase` to let operator developers generate `Gradient Operators` easily. The public interface of that class is
+
+```cpp
+class GradOpDescMakerBase {
+public:
+  GradOpDescMakerBase(const OpDescBind& );
+  virtual std::vector<std::unique_ptr<OpDescBind>> operator()()const = 0;
+};
+```
+
+We can convert `GradOpDescMakerBase` to `std::function<std::vector<std::unique_ptr<OpDescBind>>(const OpDescBind&)>` by
+
+```cpp
+using GradOpMaker = ...;
+std::function<std::vector<OpDescBind>(const OpDescBind&)> func;
+func = [] (const OpDescBind& fwd_op) {
+  GradOpMaker maker(fwd_op);
+  return maker();
+};
+```
+
+We can write many helper functions since the `GradOpDescMakerBase` is a class now. The basic helper functions get the variables of `Input`, `Output`, `InputGradient` and `OutputGradient` in the forwarding operator.
+
 We should chagne register macros at the same time. In the current solution, there is no difference between forwarding operators and backward operators. So `REGISTER_OP` just register one operator. If the `REGISTER_OPERATOR ` contains `OpProtoAndCheckerMaker` and `GradOpDescMaker`, we just list them in the same macro. It can be done by a macro contains `__VA_ARGS__`.
 
 The user interface should be

paddle/framework/backward.cc

@@ -147,7 +147,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
       for (size_t output_idx = 0; output_idx < dup_outputs.size() - 1;
            ++output_idx) {
         auto insert_add_x = dup_outputs[output_idx];
-        auto insert_add_y = dup_outputs[output_idx];
+        auto insert_add_y = dup_outputs[output_idx + 1];
         auto insert_add_out = name + "@SHARED@" + std::to_string(output_idx);
         // first add op inserted
         if (output_idx == dup_outputs.size() - 2) {
@@ -158,8 +158,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
         }
         insert_position.push_back(
             {dup_op.back(),
-             OpRegistry::CreateOp(
-                 "sum", {{"X", {insert_add_x}}, {"X", {insert_add_y}}},
+             OpRegistry::CreateOp("sum", {{"X", {insert_add_x, insert_add_y}}},
                                   {{"Out", {insert_add_out}}}, {})});
       }
     }
@@ -200,7 +199,8 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
 
     // process recurrent gradient op as a special operator.
     if (forwardOp.Type() == "recurrent") {
-      // NOTE clean up cycle call somewhere (RNN's stepnet constains itself), or
+      // NOTE clean up cycle call somewhere (RNN's stepnet constains itself),
+      // or
       // this will result in infinite loop.
       const auto& rnnop =
           *static_cast<const operators::RecurrentOp*>(&forwardOp);

paddle/framework/details/op_registry.h

@@ -14,6 +14,7 @@
 
 #pragma once
 
+#include "paddle/framework/grad_op_desc_maker.h"
 #include "paddle/framework/op_info.h"
 #include "paddle/framework/op_proto_maker.h"
 #include "paddle/framework/operator.h"
@@ -96,7 +97,10 @@ struct OpInfoFiller<T, kOpProtoAndCheckerMaker> {
 template <typename T>
 struct OpInfoFiller<T, kGradOpDescMaker> {
   void operator()(const char* op_type, OpInfo* info) const {
-    info->grad_op_maker_ = new T();
+    info->grad_op_maker_ = [](const OpDescBind& fwd_op) {
+      T maker(fwd_op);
+      return maker();
+    };
   }
 };
 }  // namespace details

paddle/framework/grad_op_desc_maker.h

@@ -0,0 +1,124 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#pragma once
+#include "paddle/framework/op_desc.h"
+#include "paddle/framework/operator.h"
+
+namespace paddle {
+namespace framework {
+
+class GradOpDescMakerBase {
+ public:
+  explicit GradOpDescMakerBase(const OpDescBind& fwd_op) : fwd_op_(fwd_op) {}
+
+  virtual ~GradOpDescMakerBase() = default;
+  virtual std::vector<std::unique_ptr<OpDescBind>> operator()() const = 0;
+
+ protected:
+  static std::vector<std::string> ToGradNames(
+      const std::vector<std::string>& var_names) {
+    std::vector<std::string> ret_val;
+    ret_val.reserve(var_names.size());
+    std::transform(var_names.begin(), var_names.end(),
+                   std::back_inserter(ret_val), GradVarName);
+    return ret_val;
+  }
+
+  std::vector<std::string> InputGrad(const std::string& name) const {
+    return ToGradNames(fwd_op_.Input(name));
+  }
+
+  std::vector<std::string> OutputGrad(const std::string& name) const {
+    return ToGradNames(fwd_op_.Output(name));
+  }
+
+  std::vector<std::string> InputNames() const {
+    return this->fwd_op_.InputNames();
+  }
+
+  std::vector<std::string> OutputNames() const {
+    return this->fwd_op_.OutputNames();
+  }
+
+  std::vector<std::string> Input(const std::string& name) const {
+    return fwd_op_.Input(name);
+  }
+
+  std::vector<std::string> Output(const std::string& name) const {
+    return fwd_op_.Output(name);
+  }
+
+  const std::unordered_map<std::string, Attribute>& Attrs() const {
+    return fwd_op_.GetAttrMap();
+  }
+
+  const Attribute& GetAttr(const std::string& name) const {
+    auto& map = fwd_op_.GetAttrMap();
+    auto it = map.find(name);
+    PADDLE_ENFORCE(it != map.end(), "Cannot find attribute %s", name);
+    return it->second;
+  }
+
+  std::string ForwardOpType() const { return this->fwd_op_.Type(); }
+
+ private:
+  const OpDescBind& fwd_op_;
+};
+
+class SingleGradOpDescMaker : public GradOpDescMakerBase {
+ public:
+  using GradOpDescMakerBase::GradOpDescMakerBase;
+
+  std::vector<std::unique_ptr<OpDescBind>> operator()() const {
+    std::vector<std::unique_ptr<OpDescBind>> retv;
+    retv.emplace_back(this->Apply());
+    return retv;
+  }
+
+ protected:
+  virtual std::unique_ptr<OpDescBind> Apply() const = 0;
+};
+
+class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
+ public:
+  using SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  virtual std::unique_ptr<OpDescBind> Apply() const {
+    auto* grad = new OpDescBind();
+    grad->SetType(this->GradOpType());
+
+    for (auto& input_param : this->InputNames()) {
+      grad->SetInput(input_param, this->Input(input_param));
+      grad->SetOutput(GradVarName(input_param), this->InputGrad(input_param));
+    }
+
+    for (auto& output_param : this->OutputNames()) {
+      grad->SetInput(output_param, this->Output(output_param));
+      grad->SetInput(GradVarName(output_param), this->OutputGrad(output_param));
+    }
+
+    grad->SetAttrMap(this->Attrs());
+
+    return std::unique_ptr<OpDescBind>(grad);
+  }
+
+  virtual std::string GradOpType() const {
+    return this->ForwardOpType() + "_grad";
+  }
+};
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/op_desc.cc

@@ -31,15 +31,6 @@ const std::vector<std::string> &OpDescBind::Input(
   return it->second;
 }
 
-std::vector<std::string> OpDescBind::InputNames() const {
-  std::vector<std::string> retv;
-  retv.reserve(this->inputs_.size());
-  for (auto &ipt : this->inputs_) {
-    retv.push_back(ipt.first);
-  }
-  return retv;
-}
-
 void OpDescBind::SetInput(const std::string &param_name,
                           const std::vector<std::string> &args) {
   need_update_ = true;
@@ -54,15 +45,6 @@ const std::vector<std::string> &OpDescBind::Output(
   return it->second;
 }
 
-std::vector<std::string> OpDescBind::OutputNames() const {
-  std::vector<std::string> retv;
-  retv.reserve(this->outputs_.size());
-  for (auto &ipt : this->outputs_) {
-    retv.push_back(ipt.first);
-  }
-  return retv;
-}
-
 void OpDescBind::SetOutput(const std::string &param_name,
                            const std::vector<std::string> &args) {
   need_update_ = true;

paddle/framework/op_desc.h

@@ -35,15 +35,11 @@ class OpDescBind {
 
   const std::vector<std::string> &Input(const std::string &name) const;
 
-  std::vector<std::string> InputNames() const;
-
   void SetInput(const std::string &param_name,
                 const std::vector<std::string> &args);
 
   const std::vector<std::string> &Output(const std::string &name) const;
 
-  std::vector<std::string> OutputNames() const;
-
   void SetOutput(const std::string &param_name,
                  const std::vector<std::string> &args);
 
@@ -61,9 +57,6 @@ class OpDescBind {
 
   void SetBlockAttr(const std::string &name, BlockDescBind &block);
 
-  // Only be used in C++
-  void SetAttrMap(const AttributeMap &attr_map);
-
   Attribute GetAttr(const std::string &name) const;
 
   int GetBlockAttr(const std::string &name) const;
@@ -71,7 +64,23 @@ class OpDescBind {
   // Only be used in C++
   const AttributeMap &GetAttrMap() const;
 
+  // Only be used in C++
+  void SetAttrMap(const AttributeMap &attr_map);
+
+  std::vector<std::string> InputNames() const { return MapKeys(inputs_); }
+  std::vector<std::string> OutputNames() const { return MapKeys(outputs_); }
+
  private:
+  template <typename MapType>
+  static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
+    std::vector<typename MapType::key_type> ret_val;
+    ret_val.reserve(map.size());
+    std::transform(
+        map.begin(), map.end(), std::back_inserter(ret_val),
+        [](const typename MapType::value_type &pair) { return pair.first; });
+    return ret_val;
+  }
+
   void Sync();
 
   OpDesc op_desc_;

paddle/framework/op_info.h

@@ -25,16 +25,10 @@
 namespace paddle {
 namespace framework {
 
-class GradOpDescMakerBase {
- public:
-  virtual ~GradOpDescMakerBase() = default;
-  virtual std::vector<OpDescBind> operator()(const OpDescBind&) const = 0;
-};
-
 struct OpInfo {
   OpCreator creator_;
   std::string grad_op_type_;
-  GradOpDescMakerBase* grad_op_maker_{nullptr};
+  GradOpMakerFN grad_op_maker_;
   OpProto* proto_{nullptr};
   OpAttrChecker* checker_{nullptr};
 

paddle/framework/type_defs.h

@@ -20,6 +20,7 @@
 namespace paddle {
 namespace framework {
 class OperatorBase;
+class OpDescBind;
 using VariableNameMap = std::map<std::string, std::vector<std::string>>;
 
 // The order should be as same as framework.proto
@@ -34,5 +35,8 @@ using OpCreator = std::function<OperatorBase*(
     const std::string& /*type*/, const VariableNameMap& /*inputs*/,
     const VariableNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
 
+using GradOpMakerFN =
+    std::function<std::vector<std::unique_ptr<OpDescBind>>(const OpDescBind&)>;
+
 }  // namespace framework
 }  // namespace paddle

paddle/operators/sgd_op.cc

@@ -23,19 +23,22 @@ class SGDOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContextBase *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("param"),
-                   "Input(param) of SGDOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("grad"),
-                   "Input(grad) of SGDOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("learning_rate"),
-                   "Input(learning_rate) of SGDOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("param_out"),
-                   "Output(param_out) of SGDOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Param"),
+                   "Input(Param) of SGDOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Grad"),
+                   "Input(Grad) of SGDOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
+                   "Input(LearningRate) of SGDOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
+                   "Output(ParamOut) of SGDOp should not be null.");
 
-    auto param_dim = ctx->GetInputDim("param");
-    PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("grad"),
+    auto lr_dims = ctx->GetInputDim("LearningRate");
+    PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
+                      "Learning rate should have 1 element");
+    auto param_dim = ctx->GetInputDim("Param");
+    PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("Grad"),
                       "Two input of SGD Op's dimension must be same.");
-    ctx->SetOutputDim("param_out", param_dim);
+    ctx->SetOutputDim("ParamOut", param_dim);
   }
 };
 
@@ -43,10 +46,10 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SGDOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("param", "input parameter");
-    AddInput("learning_rate", "learning rate of sgd");
-    AddInput("grad", "input gradient");
-    AddOutput("param_out", "output parameter");
+    AddInput("Param", "Input parameter");
+    AddInput("LearningRate", "Learning rate of SGD");
+    AddInput("Grad", "Input gradient");
+    AddOutput("ParamOut", "output parameter");
     AddComment(R"DOC(
 
 Simplest sgd algorithm.

paddle/operators/sgd_op.h

@@ -28,10 +28,10 @@ template <typename Place, typename T>
 class SGDOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto param = ctx.Input<Tensor>("param");
-    auto grad = ctx.Input<Tensor>("grad");
-    auto param_out = ctx.Output<Tensor>("param_out");
-    float lr = *ctx.Input<float>("learning_rate");
+    auto param = ctx.Input<Tensor>("Param");
+    auto grad = ctx.Input<Tensor>("Grad");
+    auto param_out = ctx.Output<Tensor>("ParamOut");
+    float lr = ctx.Input<Tensor>("LearningRate")->data<float>()[0];
 
     param_out->mutable_data<T>(ctx.GetPlace());
 

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

@@ -8,10 +8,10 @@ class TestSGDOp(OpTest):
         self.op_type = "sgd"
         w = np.random.random((102, 105)).astype("float32")
         g = np.random.random((102, 105)).astype("float32")
-        lr = 0.1
+        lr = np.array([0.1]).astype("float32")
 
-        self.inputs = {'param': w, 'grad': g, 'learning_rate': lr}
-        self.outputs = {'param_out': w - lr * g}
+        self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr}
+        self.outputs = {'ParamOut': w - lr * g}
 
     def test_check_output(self):
         self.check_output()