1. Add template T which indicates data type to `CopyFrom()`, `Slice()`
and `ShareData()` functions. This makes `CopyData()` code much clearer.

2. Add `set_dim()`.

3. `product(DDim)` transforms `DDim` to `vector<int>` first and then calculate
its product. That might be quite slow. For `product(dims_)` is frequently
used in Tensor, we add a mumber variable `numel_` as a cache of the
product result.
TODO: refactor `product()` to make it more efficient.

4. Unable Tensor::operator=

5. Remove the limit of POD type, because `float16` and `int8` are not POD type.

* Convert `op` --> `operators`
* Remove AddType in OpProtoMaker, because type is part of registry.
* Rename CPU_OR_GPU --> DEVICE_TYPE in registry macro.

* Refine register methods, make Op can get rid of whole-archieve
* `USE_OP` before a op is used.
* Add unittest for add_op.

Add unit test for OpKernel

1. Add `Tensor::CopyFrom`. Current version can only support CPU memory
copy. The support of GPU will be provided later by `paddle::memory`.
The current implementation of `Tensor::CopyFrom` is a little inefficient:
Every time `CopyFrom` is called, tensor will re-allocate its memory. However, if
we try to check and reuse `placeholder_`, we have to provide a template
parameter for `CopyFrom` to indicate the data type. It seems strange for
a simple copy function.

2. Add `Tensor::mutable_data(Place place)`, which directly use member
variable `dims_` as its dim parameter. This interface is required by
`Op::InferShape`.

* User can register OpKernel to its Ops. The OpKernelMap saved in
  OperatorWithKernel. Each Op which inherits OperatorWithKernel will
  use `OpKernel::Compute` instead of Run.

Paddle's data block is row-major order, while Dim::contiguous and
Dim::contiguous_strides are based on column-order. So remove them to
prevent misuse.

Add OperatorBase.

issue: https://github.com/PaddlePaddle/Paddle/issues/2790

Paddle design the Operator with Kernel. OperatorBase has no type and device information when create, One operator can have multiple kernels, Operator will choose a kernel to run according to context. The kernel should be bind to Operator before or during Operator running.

1. Add member variable 'DDim dims_' and a getter function 'dims()'.
'dims' is supposed to hold tensor's shape during Op::InferShape.
2. Remove 'mutable_data' which use default Place. User must specify a
explicit Place when call 'mutable_data'.
3. A PlaceHolder may be shared by more than one tensor, and some of them may be the others' slices. So we add a new member variable 'offset_' for Tensor, which is used to show the byte offset between PlaceHolder::ptr_ and where tensor's data really begins.
4. Add functions 'ShareDataFrom' and 'Slice' for Tensor.

TODO: Tensor needs a 'CopyFrom' function.

* Move static variable defined in .cc

We cannot define static variable in .h, because it will be multi-defined
errors.

Also fix some cpp syntax, like:

* Prefer to use algorithm not manually for-loop, to make code more
  readable.
* Remove unused `()`.
* Enforce take a bool. It is no need `xxx==true`.
* Use range-based for-loop iterator from op_desc.attrs

* Fix a protential static variable init order error

* generic.cmake can propogate dependencies through libraries. It is no
  need to specific all dependencies.

* init op_registry.h

* dev op_registry.h

* add 'attr_checker.h', which is a draft of op attribute checker.

* rename some macro parameters

* 1. Use `Attribute` and `AttributeMap` instead of `OpDesc`. `AttributeMap` is a unordered_map of <string, Attribute>, and `Attribute` is a boost::variant object to hold multiple types of attribute value.
2. Use `PADDLE_ENFORCE` to print checkers' fail message.
3. Abstract default value operations to a new function: `DefaultChecker`.

* rename DefaultChecker to DefaultValueSetter

ZZ

* Finish op_registry

1. Complete the development of interfaces between OpRegistry and
Protobuf.
2. Add unit test for op_registry.h

* Add demo and test of custome checker

* fix merge conflict

Python should be able to manipulate Protobuf message because:

1. Python's `create_op_creation_methods` take the `OpProto` array to
   generate all `op_creation_methods` in RunTime.

2. All `op_creation_methods` will create an `OpDesc` and pass it to
   Paddle C++ method `CreateOp` and return the Op handle.

Here is the list of what is added in this commit:

* Add `protobuf_generate_python` if it is not defined.
  * Before cmake 3.4, `protobuf_generate_python` is not defined. Just
    copy the implementation of that function in `protobuf.cmake`
* Add `py_proto_compile` function in `cmake/generic.cmake`.
  * It follows bazel's API interface.
    * https://github.com/pubref/rules_protobuf#rules
* Add an empty package named `paddle.v2.framework`, all python code of
  `paddle::framework` will be in that package.
* Generate protobuf's python module `__init__.py` by `touch` while
  compiling.
* Change setup.py.in, make `paddle.v2.framework.proto` uses the
  generated protobuf pythons.

* add op_desc.proto

In Operator design, we need a proto message to describe an Operator.
Third-party language such as python can build this proto message and use
AddOp(const OpDesc& op_desc) of Paddle core to construct an Op in the
Network.

It will fix #2728.

Maybe it is silly to `target_link_libraries` for static library,
because a static library do not need to link other libraries. But
it will tell cmake how to propagate dependencies.

The solution comes from
[here](http://floooh.github.io/2016/01/12/cmake-dependency-juggling.html).

* Also change op_proto_test DEPS for testing this fix works.

Missing DEPS will cause compile error when parallel is large.

* move net_design to framework

* change CreateNet result to unique_ptr

* rename "ScratchNet" -> "PlainNet"

* add three methods to NetBase

* add NetBuilder

* add InferShape to NetBuilder.Run

* rename ApplyGradient, ApplyOptimizer -> AddGradientOps, AddOptimiz

* rename PlainNet::CreateNet -> BuildNet

* add Error and other rename actions