Assign Operator. (#5531)

* Assign Operator.

Out=X, when type in [LoDTensor/SelectedRows/LoDTensorArray]

* Follow comments

paddle/framework/var_type.h

@@ -27,10 +27,32 @@ inline VarDesc::VarType ToVarType(std::type_index type) {
     return VarDesc_VarType_LOD_RANK_TABLE;
   } else if (type.hash_code() == typeid(LoDTensorArray).hash_code()) {
     return VarDesc_VarType_LOD_TENSOR_ARRAY;
+  } else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
+    return VarDesc_VarType_SELECTED_ROWS;
   } else {
     PADDLE_THROW("ToVarType:Unsupported type %s", type.name());
   }
 }
 
+template <typename Visitor>
+inline void VisitVarType(const Variable& var, Visitor visitor) {
+  switch (ToVarType(var.Type())) {
+    case VarDesc_VarType_LOD_TENSOR:
+      visitor(var.Get<framework::LoDTensor>());
+      return;
+    case VarDesc_VarType_LOD_RANK_TABLE:
+      visitor(var.Get<LoDRankTable>());
+      return;
+    case VarDesc_VarType_LOD_TENSOR_ARRAY:
+      visitor(var.Get<LoDTensorArray>());
+      return;
+    case VarDesc_VarType_SELECTED_ROWS:
+      visitor(var.Get<SelectedRows>());
+      return;
+    default:
+      PADDLE_THROW("Not supported visit type, %d", ToVarType(var.Type()));
+  }
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/operators/assign_op.cc

+/* Copyright (c) 2016 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. */
+
+#include "paddle/framework/data_type.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/var_type.h"
+
+namespace paddle {
+namespace operators {
+class AssignFunctor {
+ public:
+  AssignFunctor(framework::Variable *out,
+                const platform::DeviceContext &dev_ctx)
+      : out_(out), dev_ctx_(dev_ctx) {}
+
+  void operator()(const framework::LoDTensor &lod_tensor) const {
+    auto &out_tensor = *out_->GetMutable<framework::LoDTensor>();
+    copy_tensor(lod_tensor, &out_tensor);
+  }
+
+  void operator()(const framework::LoDTensorArray &array) const {
+    auto &out_array = *out_->GetMutable<framework::LoDTensorArray>();
+    out_array.resize(array.size());
+    for (size_t i = 0; i < array.size(); ++i) {
+      copy_tensor(array[i], &out_array[i]);
+    }
+  }
+
+  void operator()(const framework::SelectedRows &rows) const {
+    framework::SelectedRows &out_rows =
+        *out_->GetMutable<framework::SelectedRows>();
+    out_rows.set_rows(rows.rows());
+    out_rows.set_height(rows.height());
+    auto &t = rows.value();
+    out_rows.mutable_value()->CopyFrom(t, t.place(), dev_ctx_);
+  }
+
+  template <typename T>
+  void operator()(const T &v) const {
+    PADDLE_THROW("Not support type for assign op %s", typeid(T).name());
+  }
+
+ private:
+  void copy_tensor(const framework::LoDTensor &lod_tensor,
+                   framework::LoDTensor *out) const {
+    auto &out_tensor = *out;
+    out_tensor.CopyFrom(lod_tensor, lod_tensor.place(), dev_ctx_);
+    out_tensor.set_lod(lod_tensor.lod());
+  }
+
+  framework::Variable *out_;
+  const platform::DeviceContext &dev_ctx_;
+};
+
+class AssignOp : public framework::OperatorBase {
+ public:
+  AssignOp(const std::string &type, const framework::VariableNameMap &inputs,
+           const framework::VariableNameMap &outputs,
+           const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto *x = scope.FindVar(Input("X"));
+    if (x == nullptr) {
+      return;
+    }
+    auto *out = scope.FindVar(Output("Out"));
+    PADDLE_ENFORCE(
+        out != nullptr,
+        "The Output(Out) should not be null if the Input(X) is set.");
+    framework::VisitVarType(*x, AssignFunctor(out, dev_ctx));
+  }
+};
+
+class AssignOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  AssignOpProtoMaker(framework::OpProto *proto,
+                     framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(LoDTensor, SelectedRows or LoDTensorArray) The input variable "
+             "could be LoDTensor, SelectedRows or LoDTensorArray.")
+        .AsDispensable();
+    AddOutput("Out",
+              "(LoDTensor, SelectedRows or LoDTensorArray) The type of output "
+              "is the same as input X.");
+    AddComment(R"DOC(Assign Operator
+
+Out = X,  when type in [LoDTensor/SelectedRows/LoDTensorArray]
+raise error if the type is not listed above.
+)DOC");
+  }
+};
+
+class AssignInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    if (context->HasInput("X")) {
+      auto type = context->GetInputsVarType("X")[0];
+      if (type == framework::VarDesc_VarType_SELECTED_ROWS ||
+          type == framework::VarDesc_VarType_LOD_TENSOR) {
+        context->SetOutputDim("Out", context->GetInputDim("X"));
+      }
+    }
+  }
+};
+
+class AssignGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto *op = new framework::OpDescBind();
+    op->SetType("assign");
+    op->SetInput("X", OutputGrad("Out"));
+    op->SetOutput("Out", InputGrad("X"));
+    return std::unique_ptr<framework::OpDescBind>(op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(assign, ops::AssignOp, ops::AssignGradMaker,
+                  ops::AssignInferShape, ops::AssignOpProtoMaker);

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

+import op_test
+import numpy
+import unittest
+
+
+class TestAssignOp(op_test.OpTest):
+    def setUp(self):
+        self.op_type = "assign"
+        x = numpy.random.random(size=(100, 10))
+        self.inputs = {'X': x}
+        self.outputs = {'Out': x}
+
+    def test_forward(self):
+        self.check_output()
+
+    def test_backward(self):
+        self.check_grad(['X'], 'Out')
+
+
+if __name__ == '__main__':
+    unittest.main()