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

paddle/framework/grad_op_builder_test.cc

@@ -39,28 +39,6 @@ class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
 
 namespace f = paddle::framework;
 
-TEST(GradOpBuilder, AddTwo) {
-  std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
-      "sum", {{"X", {"x", "y"}}}, {{"Out", {"out"}}}, {}));
-  std::shared_ptr<f::OperatorBase> grad_add_op =
-      f::OpRegistry::CreateGradOp(*add_op);
-
-  EXPECT_EQ(grad_add_op->Inputs().size(), 1UL);
-  EXPECT_EQ(grad_add_op->Outputs().size(), 1UL);
-  EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out"));
-  auto &outputs = grad_add_op->Outputs(f::GradVarName("X"));
-  EXPECT_EQ(2UL, outputs.size());
-  auto in_output = [&outputs](const std::string &name) {
-    for (auto &output_name : outputs) {
-      if (output_name == name) return true;
-    }
-    return false;
-  };
-
-  EXPECT_TRUE(in_output(f::GradVarName("x")));
-  EXPECT_TRUE(in_output(f::GradVarName("y")));
-}
-
 REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
 REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
 

paddle/operators/CMakeLists.txt

@@ -103,12 +103,16 @@ set(DEPS_OPS
     recurrent_op
     cond_op
     cross_entropy_op
-    softmax_with_cross_entropy_op)
+    softmax_with_cross_entropy_op
+    sum_op)
+
+
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
   DEPS framework_proto tensor net_op)
 op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
 op_library(cross_entropy_op DEPS cross_entropy)
 op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
+op_library(sum_op DEPS net_op)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})

paddle/operators/sgd_op.cc

@@ -27,6 +27,8 @@ class SGDOp : public framework::OperatorWithKernel {
                    "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.");
 
@@ -42,9 +44,9 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
   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");
-    AddAttr<float>("learning_rate", "learning rate of sgd");
     AddComment(R"DOC(
 
 Simplest sgd algorithm.

paddle/operators/sgd_op.h

@@ -31,7 +31,7 @@ class SGDOpKernel : public framework::OpKernel<T> {
     auto param = ctx.Input<Tensor>("param");
     auto grad = ctx.Input<Tensor>("grad");
     auto param_out = ctx.Output<Tensor>("param_out");
-    float lr = ctx.Attr<float>("learning_rate");
+    float lr = *ctx.Input<float>("learning_rate");
 
     param_out->mutable_data<T>(ctx.GetPlace());
 

paddle/operators/sum_op.cc

@@ -11,6 +11,7 @@ limitations under the License. */
 
 #include "paddle/operators/sum_op.h"
 #include <vector>
+#include "paddle/operators/net_op.h"
 
 namespace paddle {
 namespace operators {
@@ -57,21 +58,23 @@ or not. But the output only shares the LoD with the first input.
   }
 };
 
-class SumGradOp : public framework::OperatorWithKernel {
+class SumGradOp : public NetOp {
  public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
+  SumGradOp(const std::string& type, const framework::VariableNameMap& inputs,
+            const framework::VariableNameMap& outputs,
+            const framework::AttributeMap& attrs)
+      : NetOp(type, inputs, outputs, attrs) {
+    auto& x_grad_names = Outputs(framework::GradVarName("X"));
+    auto out_grad_name = this->Input(framework::GradVarName("Out"));
 
- protected:
-  void InferShape(framework::InferShapeContextBase* ctx) const override {
-    auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
-    auto x_grad_names = ctx->Outputs(framework::GradVarName("X"));
-    size_t x_length = x_grad_names.size();
-    std::vector<framework::DDim> x_grad_dims;
-    x_grad_dims.reserve(x_length);
-    for (size_t i = 0; i < x_length; ++i) {
-      x_grad_dims.push_back(out_grad_dims);
+    framework::AttributeMap grad_attrs;
+    grad_attrs["scale"] = 1.0f;
+    for (auto& x_grad_name : x_grad_names) {
+      AppendOp(framework::OpRegistry::CreateOp(
+          "scale", {{"X", {out_grad_name}}}, {{"Out", {x_grad_name}}},
+          grad_attrs));
     }
-    ctx->SetOutputsDim(framework::GradVarName("X"), x_grad_dims);
+    CompleteAddOp(false);
   }
 };
 
@@ -81,5 +84,3 @@ class SumGradOp : public framework::OperatorWithKernel {
 namespace ops = paddle::operators;
 REGISTER_OP(sum, ops::SumOp, ops::SumOpMaker, sum_grad, ops::SumGradOp);
 REGISTER_OP_CPU_KERNEL(sum, ops::SumKernel<paddle::platform::CPUPlace, float>);
-REGISTER_OP_CPU_KERNEL(sum_grad,
-                       ops::SumGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/sum_op.cu

@@ -14,5 +14,3 @@ limitations under the License. */
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(sum, ops::SumKernel<paddle::platform::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(sum_grad,
-                       ops::SumGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/sum_op.h

@@ -42,24 +42,5 @@ class SumKernel : public framework::OpKernel<T> {
   }
 };
 
-template <typename Place, typename T>
-class SumGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* input = context.Input<Tensor>(framework::GradVarName("Out"));
-    auto outs = context.MultiOutput<Tensor>(framework::GradVarName("X"));
-    for (auto out : outs) {
-      out->mutable_data<T>(context.GetPlace());
-    }
-
-    auto place = context.GetEigenDevice<Place>();
-    auto in = EigenVector<T>::Flatten(*input);
-    for (auto out : outs) {
-      auto result = EigenVector<T>::Flatten(*out);
-      result.device(place) = in;
-    }
-  }
-};
-
 }  // namespace operators
 }  // namespace paddle

paddle/pybind/pybind.cc

@@ -143,6 +143,13 @@ All parameter, weight, gradient are variables in Paddle.
       .def("set_int",
            [](Variable &var, int val) -> void { *var.GetMutable<int>() = val; })
       .def("get_int", [](const Variable &var) -> int { return var.Get<int>(); })
+      .def("is_float", [](const Variable &var) { return var.IsType<float>(); })
+      .def("set_float",
+           [](Variable &var, float val) -> void {
+             *var.GetMutable<float>() = val;
+           })
+      .def("get_float",
+           [](const Variable &var) -> float { return var.Get<float>(); })
       .def("get_tensor",
            [](Variable &self) -> LoDTensor * {
              return self.GetMutable<LoDTensor>();

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

@@ -46,12 +46,17 @@ def create_op(scope, op_type, inputs, outputs, attrs):
 
 def set_input(scope, op, inputs, place):
     def __set_input__(var_name, var):
+        if isinstance(var, tuple) or isinstance(var, np.ndarray):
             tensor = scope.find_var(var_name).get_tensor()
             if isinstance(var, tuple):
                 tensor.set_lod(var[1])
                 var = var[0]
             tensor.set_dims(var.shape)
             tensor.set(var, place)
+        elif isinstance(var, float):
+            scope.find_var(var_name).set_float(var)
+        elif isinstance(var, int):
+            scope.find_var(var_name).set_int(var)
 
     for in_name, in_dup in Operator.get_op_inputs(op.type()):
         if in_name in inputs:

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

@@ -10,8 +10,7 @@ class TestSGDOp(OpTest):
         g = np.random.random((102, 105)).astype("float32")
         lr = 0.1
 
-        self.inputs = {'param': w, 'grad': g}
-        self.attrs = {'learning_rate': lr}
+        self.inputs = {'param': w, 'grad': g, 'learning_rate': lr}
         self.outputs = {'param_out': w - lr * g}
 
     def test_check_output(self):