ReadFromArray/WriteToArray op (#5407)

* Use stable_sort in lod_rank_table

It is easy to debug and test when use `stable_sort`and the time
complexity is not changed.

* Add LoDTensorArray

* Stash

* Better debug message for IsInitialized

* Stash

* Better debug message for IsInitialized

* Complete array read/write op unittests

paddle/framework/op_desc.cc

@@ -349,6 +349,9 @@ void OpDescBind::InferVarType(BlockDescBind *block) const {
     info.infer_var_type_(*this, block);
   } else {
     // all output type is LoDTensor by default
+    VLOG(10) << this->Type()
+             << " has not registered InferVarType. Set output variables to "
+                "LOD_TENSOR";
     for (auto &out_pair : this->outputs_) {
       for (auto &out_var_name : out_pair.second) {
         block->Var(out_var_name)->SetType(VarDesc::LOD_TENSOR);

paddle/operators/CMakeLists.txt

@@ -118,6 +118,11 @@ function(op_library TARGET)
         file(APPEND ${pybind_file} "USE_OP(reduce_sum);\n")
     endif()
 
+    if ("${TARGET}" STREQUAL "tensor_array_read_write_op")
+        set(pybind_flag 1)
+        file(APPEND ${pybind_file} "USE_NO_KERNEL_OP(read_from_array);\nUSE_NO_KERNEL_OP(write_to_array);\n")
+    endif()
+
     # pybind USE_NO_KERNEL_OP
     # HACK: if REGISTER_OP_CPU_KERNEL presents the operator must have kernel
     file(READ ${TARGET}.cc TARGET_CONTENT)
@@ -161,6 +166,7 @@ set(DEPS_OPS
     sequence_pool_op
     lod_rank_table_op
     lstm_op
+    tensor_array_read_write_op
     gru_op)
 
 op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
@@ -171,6 +177,7 @@ op_library(sum_op DEPS net_op selected_rows_functor)
 op_library(pool_op DEPS pooling)
 op_library(pool_with_index_op DEPS pooling)
 op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
+op_library(tensor_array_read_write_op SRCS tensor_array_read_write_op.cc)
 if(WITH_GPU)
 op_library(nccl_op DEPS nccl_common)
 endif()

paddle/operators/fill_constant_op.cc

@@ -35,7 +35,9 @@ class FillConstantOp : public framework::OperatorWithKernel {
  protected:
   framework::DataType IndicateDataType(
       const framework::ExecutionContext &ctx) const override {
-    return static_cast<framework::DataType>(ctx.Attr<int>("data_type"));
+    int data_type = ctx.Attr<int>("data_type");
+    VLOG(10) << " FillConstant data_type = " << data_type;
+    return static_cast<framework::DataType>(data_type);
   }
 };
 
@@ -71,4 +73,5 @@ REGISTER_OP_WITHOUT_GRADIENT(fill_constant, ops::FillConstantOp,
 REGISTER_OP_CPU_KERNEL(
     fill_constant, ops::FillConstantOpKernel<paddle::platform::CPUPlace, float>,
     ops::FillConstantOpKernel<paddle::platform::CPUPlace, double>,
-    ops::FillConstantOpKernel<paddle::platform::CPUPlace, int>);
+    ops::FillConstantOpKernel<paddle::platform::CPUPlace, int>,
+    ops::FillConstantOpKernel<paddle::platform::CPUPlace, int64_t>);

paddle/operators/fill_constant_op.cu

@@ -20,4 +20,5 @@ namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
     fill_constant, ops::FillConstantOpKernel<paddle::platform::GPUPlace, float>,
     ops::FillConstantOpKernel<paddle::platform::GPUPlace, double>,
-    ops::FillConstantOpKernel<paddle::platform::GPUPlace, int>);
+    ops::FillConstantOpKernel<paddle::platform::GPUPlace, int>,
+    ops::FillConstantOpKernel<paddle::platform::GPUPlace, int64_t>);

paddle/operators/increment_op.cc

@@ -31,7 +31,6 @@ class IncrementOp : public framework::OperatorWithKernel {
   }
 };
 
-template <typename AttrType>
 class IncrementOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   IncrementOpMaker(framework::OpProto *proto,
@@ -39,7 +38,7 @@ class IncrementOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "(Tensor) The input tensor of increment operator");
     AddOutput("Out", "(Tensor) The output tensor of increment operator.");
-    AddAttr<AttrType>("step",
+    AddAttr<float>("step",
                    "(float, default 1.0) "
                    "The step size by which the "
                    "input tensor will be incremented.")
@@ -73,7 +72,10 @@ class IncrementGradOpMaker : public framework::SingleGradOpDescMaker {
 
 namespace ops = paddle::operators;
 
-REGISTER_OPERATOR(increment, ops::IncrementOp, ops::IncrementOpMaker<float>,
+REGISTER_OPERATOR(increment, ops::IncrementOp, ops::IncrementOpMaker,
                   ops::IncrementGradOpMaker);
-REGISTER_OP_CPU_KERNEL(increment,
-                       ops::IncrementKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    increment, ops::IncrementKernel<paddle::platform::CPUPlace, float>,
+    ops::IncrementKernel<paddle::platform::CPUPlace, double>,
+    ops::IncrementKernel<paddle::platform::CPUPlace, int>,
+    ops::IncrementKernel<paddle::platform::CPUPlace, int64_t>);

paddle/operators/increment_op.cu

@@ -16,4 +16,7 @@
 
 REGISTER_OP_GPU_KERNEL(
     increment,
-    paddle::operators::IncrementKernel<paddle::platform::GPUPlace, float>);
+    paddle::operators::IncrementKernel<paddle::platform::GPUPlace, float>,
+    paddle::operators::IncrementKernel<paddle::platform::GPUPlace, double>,
+    paddle::operators::IncrementKernel<paddle::platform::GPUPlace, int>,
+    paddle::operators::IncrementKernel<paddle::platform::GPUPlace, int64_t>);

paddle/operators/increment_op.h

@@ -19,7 +19,7 @@
 
 namespace paddle {
 namespace operators {
-template <typename Place, typename T, typename AttrType = T>
+template <typename Place, typename T>
 class IncrementKernel : public framework::OpKernel<T> {
  public:
   virtual void Compute(const framework::ExecutionContext& context) const {
@@ -27,7 +27,7 @@ class IncrementKernel : public framework::OpKernel<T> {
     auto* in = context.Input<framework::Tensor>("X");
     tensor->mutable_data<T>(in->place());
 
-    auto step = static_cast<T>(context.Attr<AttrType>("step"));
+    auto step = static_cast<T>(context.Attr<float>("step"));
 
     auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
     auto eigen_in = framework::EigenVector<T>::Flatten(*in);

paddle/operators/tensor_array_read_write_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/lod_tensor_array.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+class ArrayOpBase : public framework::OperatorBase {
+ public:
+  ArrayOpBase(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 {}
+
+ protected:
+  size_t GetOffset(const framework::Scope &scope,
+                   const platform::DeviceContext &dev_ctx) const {
+    auto *i = scope.FindVar(Input("I"));
+    PADDLE_ENFORCE(i != nullptr, "I must be set");
+    auto &i_tensor = i->Get<framework::LoDTensor>();
+    PADDLE_ENFORCE_EQ(i_tensor.numel(), 1);
+    size_t offset;
+    if (platform::is_gpu_place(i_tensor.place())) {
+      // FIXME: Avoid copy from GPU to CPU
+      framework::Tensor t;
+      t.CopyFrom(i_tensor, platform::CPUPlace(), dev_ctx);
+      dev_ctx.Wait();
+      offset = static_cast<size_t>(*t.data<int64_t>());
+    } else {
+      offset = static_cast<size_t>(*i_tensor.data<int64_t>());
+    }
+    return offset;
+  }
+};
+
+class WriteToArrayOp : public ArrayOpBase {
+ public:
+  WriteToArrayOp(const std::string &type,
+                 const framework::VariableNameMap &inputs,
+                 const framework::VariableNameMap &outputs,
+                 const framework::AttributeMap &attrs)
+      : ArrayOpBase(type, inputs, outputs, attrs) {}
+
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto *x = scope.FindVar(Input("X"));
+    PADDLE_ENFORCE(x != nullptr, "X must be set");
+    auto &x_tensor = x->Get<framework::LoDTensor>();
+    size_t offset = GetOffset(scope, dev_ctx);
+    auto *out =
+        scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensorArray>();
+    if (offset >= out->size()) {
+      out->resize(offset + 1);
+    }
+    auto *out_tensor = &out->at(offset);
+    out_tensor->CopyFrom(x_tensor, dev_ctx.GetPlace(), dev_ctx);
+    out_tensor->set_lod(x_tensor.lod());
+  }
+};
+
+class WriteToArrayOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  WriteToArrayOpProtoMaker(framework::OpProto *proto,
+                           framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "(LoDTensor) the tensor will be written to tensor array");
+    AddInput(
+        "I",
+        "(Tensor) the subscript index in tensor array. The number of element "
+        "should be 1");
+    AddOutput("Out", "(TensorArray) the tensor array will be written");
+    AddComment(R"DOC(Write a LoDTensor to a LoDTensor array.
+
+Assume T is LoDTensor, i is the subscript of the array, and A is the array. The
+equation is
+
+A[i] = T
+)DOC");
+  }
+};
+
+class WriteToArrayInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE(context->HasInput("I"), "Must set the subscript index");
+    PADDLE_ENFORCE_EQ(framework::product(context->GetInputDim("I")), 1,
+                      "The number of element of subscript index must be 1");
+    PADDLE_ENFORCE(context->HasInput("X"), NotHasXError());
+    PADDLE_ENFORCE(context->HasOutput("Out"), NotHasOutError());
+    context->SetOutputDim("Out", context->GetInputDim("X"));
+  }
+
+ protected:
+  virtual const char *NotHasXError() const { return "Must set the lod tensor"; }
+
+  virtual const char *NotHasOutError() const {
+    return "Must set the lod tensor array";
+  }
+};
+
+class WriteToArrayInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDescBind &op_desc,
+                  framework::BlockDescBind *block) const override {
+    VLOG(10) << "I am here?";
+    for (auto &out_var : op_desc.OutputArgumentNames()) {
+      VLOG(10) << "Set Variable " << out_var << " as LOD_TENSOR_ARRAY";
+      block->Var(out_var)->SetType(framework::VarDesc::LOD_TENSOR_ARRAY);
+    }
+  }
+};
+
+class ReadFromArrayOp : public ArrayOpBase {
+ public:
+  ReadFromArrayOp(const std::string &type,
+                  const framework::VariableNameMap &inputs,
+                  const framework::VariableNameMap &outputs,
+                  const framework::AttributeMap &attrs)
+      : ArrayOpBase(type, inputs, outputs, attrs) {}
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto *x = scope.FindVar(Input("X"));
+    PADDLE_ENFORCE(x != nullptr, "X must be set");
+    auto &x_array = x->Get<framework::LoDTensorArray>();
+    auto *out = scope.FindVar(Output("Out"));
+    PADDLE_ENFORCE(out != nullptr, "Out must be set");
+    auto *out_tesnor = out->GetMutable<framework::LoDTensor>();
+    size_t offset = GetOffset(scope, dev_ctx);
+    PADDLE_ENFORCE_LT(offset, x_array.size());
+    out_tesnor->CopyFrom(x_array[offset], dev_ctx.GetPlace(), dev_ctx);
+    out_tesnor->set_lod(x_array[offset].lod());
+  }
+};
+
+class ReadFromArrayProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ReadFromArrayProtoMaker(framework::OpProto *proto,
+                          framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "(TensorArray) the array will be read from.");
+    AddInput("I",
+             "(Tensor) the subscript index in tensor array. The number of "
+             "element should be 1");
+    AddOutput("Out", "(LoDTensor) the tensor will be read from.");
+    AddComment(R"DOC(Read a LoDTensor from a LoDTensor Array
+
+Assume T is LoDTensor, i is th e subscript of the array, and A is the array. The
+equation is
+
+T = A[i]
+)DOC");
+  }
+};
+
+class ReadFromArrayInferShape : public WriteToArrayInferShape {
+ protected:
+  const char *NotHasXError() const override {
+    return "The input array X must be set";
+  }
+  const char *NotHasOutError() const override {
+    return "The output tensor out must be set";
+  }
+};
+
+class WriteToArrayGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto *grad_op = new framework::OpDescBind();
+    grad_op->SetType("read_from_array");
+    grad_op->SetInput("I", Input("I"));
+    grad_op->SetInput("X", OutputGrad("Out"));
+    grad_op->SetOutput("Out", InputGrad("X"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDescBind>(grad_op);
+  }
+};
+
+class ReadFromArrayGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDescBind> Apply() const override {
+    auto *grad_op = new framework::OpDescBind();
+    grad_op->SetType("write_to_array");
+    grad_op->SetInput("I", Input("I"));
+    grad_op->SetInput("X", OutputGrad("Out"));
+    grad_op->SetOutput("Out", InputGrad("X"));
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDescBind>(grad_op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(write_to_array, ops::WriteToArrayOp,
+                  ops::WriteToArrayInferShape, ops::WriteToArrayOpProtoMaker,
+                  ops::WriteToArrayGradMaker, ops::WriteToArrayInferVarType);
+REGISTER_OPERATOR(read_from_array, ops::ReadFromArrayOp,
+                  ops::ReadFromArrayInferShape, ops::ReadFromArrayProtoMaker,
+                  ops::ReadFromArrayGradMaker);

paddle/pybind/protobuf.cc

@@ -97,6 +97,15 @@ namespace pybind {
 
 using namespace paddle::framework;  // NOLINT
 
+template <typename T>
+static py::bytes SerializeMessage(T &self) {
+  // Check IsInitialized in Python
+  std::string retv;
+  PADDLE_ENFORCE(self.Proto()->SerializePartialToString(&retv),
+                 "Cannot serialize message");
+  return retv;
+}
+
 // Bind Methods
 void BindProgramDesc(py::module &m) {
   py::class_<ProgramDescBind>(m, "ProgramDesc", "")
@@ -132,17 +141,7 @@ void BindProgramDesc(py::module &m) {
       .def("block", &ProgramDescBind::MutableBlock,
            py::return_value_policy::reference)
       .def("num_blocks", &ProgramDescBind::Size)
-      .def("serialize_to_string",
-           [](ProgramDescBind &program_desc) -> py::bytes {
-             const ProgramDesc *desc = program_desc.Proto();
-             PADDLE_ENFORCE(desc->IsInitialized(),
-                            "ProgramDesc has not been initialized.");
-             std::string res;
-             PADDLE_ENFORCE(
-                 desc->SerializeToString(&res),
-                 "Serialize ProgramDesc Error. This could be a bug of Paddle.");
-             return res;
-           })
+      .def("serialize_to_string", SerializeMessage<ProgramDescBind>)
       .def("parse_from_string",
            [](ProgramDescBind &program_desc, const std::string &data) {
              ProgramDesc *desc = program_desc.Proto();
@@ -181,16 +180,7 @@ void BindBlockDesc(py::module &m) {
            py::return_value_policy::reference)
       .def("op_size", &BlockDescBind::OpSize)
       .def("op", &BlockDescBind::Op, py::return_value_policy::reference)
-      .def("serialize_to_string", [](BlockDescBind &block_desc) -> py::bytes {
-        const BlockDesc *desc = block_desc.Proto();
-        PADDLE_ENFORCE(desc->IsInitialized(),
-                       "BlockDesc has not been initialized.");
-        std::string res;
-        PADDLE_ENFORCE(
-            desc->SerializeToString(&res),
-            "Serialize BlockDesc Error. This could be a bug of Paddle.");
-        return res;
-      });
+      .def("serialize_to_string", SerializeMessage<BlockDescBind>);
 }
 
 void BindVarDsec(py::module &m) {
@@ -219,17 +209,7 @@ void BindVarDsec(py::module &m) {
       .def("set_lod_level", &VarDescBind::SetLoDLevel)
       .def("type", &VarDescBind::GetType)
       .def("set_type", &VarDescBind::SetType)
-      .def("serialize_to_string",
-           [](VarDescBind &var_desc) -> py::bytes {
-             const VarDesc *desc = var_desc.Proto();
-             PADDLE_ENFORCE(desc->IsInitialized(),
-                            "VarDesc has not been initialized.");
-             std::string res;
-             PADDLE_ENFORCE(
-                 desc->SerializeToString(&res),
-                 "Serialize VarDesc Error. This could be a bug of Paddle.");
-             return res;
-           })
+      .def("serialize_to_string", SerializeMessage<VarDescBind>)
       .def("persistable", &VarDescBind::Persistable)
       .def("set_persistable", &VarDescBind::SetPersistable);
 
@@ -274,16 +254,7 @@ void BindOpDesc(py::module &m) {
       .def("check_attrs", &OpDescBind::CheckAttrs)
       .def("infer_shape", &OpDescBind::InferShape)
       .def("infer_var_type", &OpDescBind::InferVarType)
-      .def("serialize_to_string", [](OpDescBind &op_desc) -> py::bytes {
-        const OpDesc *desc = op_desc.Proto();
-        PADDLE_ENFORCE(desc->IsInitialized(),
-                       "OpDesc has not been initialized.");
-        std::string res;
-        PADDLE_ENFORCE(
-            desc->SerializeToString(&res),
-            "Serialize OpDesc Error. This could be a bug of Paddle.");
-        return res;
-      });
+      .def("serialize_to_string", SerializeMessage<OpDescBind>);
 }
 
 }  // namespace pybind

python/paddle/v2/framework/executor.py

 import paddle.v2.framework.core as core
-from paddle.v2.framework.framework import Block, Program
+from paddle.v2.framework.framework import Block, Program, g_main_program
 
 g_scope = core.Scope()
 
@@ -18,7 +18,7 @@ class Executor(object):
         self.executor = core.Executor(act_places)
 
     def run(self,
-            program,
+            program=None,
             feed=None,
             fetch_list=None,
             feed_var_name='feed',
@@ -29,6 +29,9 @@ class Executor(object):
         if fetch_list is None:
             fetch_list = []
 
+        if program is None:
+            program = g_main_program
+
         if not isinstance(program, Program):
             raise TypeError()
 

python/paddle/v2/framework/framework.py

@@ -12,6 +12,14 @@ def unique_name(prefix):
     return "_".join([prefix, str(uid)])
 
 
+def _debug_string_(proto):
+    error_fields = list()
+    if not proto.IsInitialized(error_fields):
+        raise ValueError("{0} are not initialized\nThe message is {1}".format(
+            error_fields, proto))
+    return proto.__str__()
+
+
 class Variable(object):
     def __init__(self,
                  block,
@@ -95,7 +103,7 @@ class Variable(object):
     def __str__(self):
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.VarDesc.FromString(str(protostr))
-        return proto.__str__()
+        return _debug_string_(proto)
 
     __repr__ = __str__
 
@@ -286,7 +294,7 @@ class Operator(object):
     def __str__(self):
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.OpDesc.FromString(str(protostr))
-        return proto.__str__()
+        return _debug_string_(proto)
 
     __repr__ = __str__
 
@@ -343,7 +351,7 @@ class Block(object):
     def __str__(self):
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.BlockDesc.FromString(str(protostr))
-        return proto.__str__()
+        return _debug_string_(proto)
 
     __repr__ = __str__
 
@@ -448,7 +456,7 @@ class Program(object):
     def __str__(self):
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.ProgramDesc.FromString(str(protostr))
-        return proto.__str__()
+        return _debug_string_(proto)
 
     def clone(self):
         p = Program()

python/paddle/v2/framework/layers.py

 import paddle.v2.framework.core as core
-from paddle.v2.framework.framework import OpProtoHolder, Variable, Program, Operator
-from paddle.v2.framework.initializer import ConstantInitializer, NormalInitializer
+from paddle.v2.framework.framework import OpProtoHolder, Variable, Program, \
+    Operator
+from paddle.v2.framework.initializer import ConstantInitializer, \
+    NormalInitializer
 from paddle.v2.framework.layer_helper import LayerHelper, unique_name
 import re
 
@@ -751,3 +753,67 @@ def lod_rank_table(x, level=0, main_program=None):
         outputs={'Out': table},
         attrs={'level': level})
     return table
+
+
+def fill_constant(shape, dtype, value, main_program=None):
+    helper = LayerHelper("ones", **locals())
+    out = helper.create_tmp_variable(dtype=dtype)
+    helper.append_op(
+        type='fill_constant',
+        inputs={},
+        outputs={'Out': [out]},
+        attrs={
+            'shape': shape,
+            'data_type': out.data_type,
+            'value': float(value)
+        })
+    out.stop_gradient = True
+    return out
+
+
+def ones(shape, dtype, main_program=None):
+    return fill_constant(value=1.0, **locals())
+
+
+def zeros(shape, dtype, main_program=None):
+    return fill_constant(value=0.0, **locals())
+
+
+def increment(x, value=1.0, main_program=None):
+    helper = LayerHelper("increment", **locals())
+    helper.append_op(
+        type='increment',
+        inputs={'X': [x]},
+        outputs={'Out': [x]},
+        attrs={'step': value})
+    return x
+
+
+def array_write(x, i, array=None, main_program=None):
+    helper = LayerHelper('array_write', **locals())
+    if array is None:
+        array = helper.create_variable(
+            name="{0}.out".format(helper.name),
+            type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
+            dtype=x.data_type)
+    helper.append_op(
+        type='write_to_array',
+        inputs={'X': [x],
+                'I': [i]},
+        outputs={'Out': [array]})
+    return array
+
+
+def array_read(array, i, main_program=None):
+    helper = LayerHelper('array_read', **locals())
+    if not isinstance(
+            array,
+            Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
+        raise TypeError("array should be tensor array vairable")
+    out = helper.create_tmp_variable(dtype=array.data_type)
+    helper.append_op(
+        type='read_from_array',
+        inputs={'X': [array],
+                'I': [i]},
+        outputs={'Out': [out]})
+    return out

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

+import unittest
+
+import numpy
+import paddle.v2.framework.core as core
+
+import paddle.v2.framework.layers as layers
+from paddle.v2.framework.executor import Executor
+
+
+class TestArrayReadWrite(unittest.TestCase):
+    def test_read_write(self):
+        x = [
+            layers.data(
+                name='x0', shape=[100]), layers.data(
+                    name='x1', shape=[100]), layers.data(
+                        name='x2', shape=[100])
+        ]
+
+        for each_x in x:
+            each_x.stop_gradient = False
+
+        i = layers.zeros(shape=[1], dtype='int64')
+        arr = layers.array_write(x=x[0], i=i)
+        layers.increment(x=i)
+        arr = layers.array_write(x=x[1], i=i, array=arr)
+        layers.increment(x=i)
+        arr = layers.array_write(x=x[2], i=i, array=arr)
+
+        i = layers.zeros(shape=[1], dtype='int64')
+        a0 = layers.array_read(array=arr, i=i)
+        layers.increment(x=i)
+        a1 = layers.array_read(array=arr, i=i)
+        layers.increment(x=i)
+        a2 = layers.array_read(array=arr, i=i)
+
+        mean_a0 = layers.mean(x=a0)
+        mean_a1 = layers.mean(x=a1)
+        mean_a2 = layers.mean(x=a2)
+
+        a_sum = layers.sums(input=[mean_a0, mean_a1, mean_a2])
+
+        mean_x0 = layers.mean(x=x[0])
+        mean_x1 = layers.mean(x=x[1])
+        mean_x2 = layers.mean(x=x[2])
+
+        x_sum = layers.sums(input=[mean_x0, mean_x1, mean_x2])
+
+        scope = core.Scope()
+        cpu = core.CPUPlace()
+
+        exe = Executor(cpu)
+
+        tensor = core.LoDTensor()
+        tensor.set(numpy.random.random(size=(100, 100)).astype('float32'), cpu)
+
+        outs = map(numpy.array,
+                   exe.run(feed={'x0': tensor,
+                                 'x1': tensor,
+                                 'x2': tensor},
+                           fetch_list=[a_sum, x_sum],
+                           scope=scope))
+        self.assertEqual(outs[0], outs[1])
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+import unittest
+from paddle.v2.framework.framework import Program
+
+
+class TestDebugStringFramework(unittest.TestCase):
+    def test_debug_str(self):
+        p = Program()
+        p.current_block().create_var(name='t', shape=[0, 1])
+        self.assertRaises(ValueError, callableObj=p.__str__)
+
+
+if __name__ == '__main__':
+    unittest.main()