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.

* use OperatorPtr = std::shared_ptr<OperatorBase>;
* use ScopePtr = std::share_ptr<Scope>;

* Let OpProto support multiple and temporary

* Each input/output of Paddle's Op could be a list. Add multiple mark to
  OpProto. Also add a `input_format`/`output_format` attribute if that
  Op has multiple input or output. The format of that attribute please
  reference the comments in `op_proto.proto`
* Add temporary mark, because some output of an Op is not used by user
  but used by other op for faster computation. Explicitly mark which
  output is temporary could let future memory/computation optimization.
* Add generated field to AttrProto.

* Add `AddInputs`/`AddOutputs` function

* It is more readable to invoke `AddInputs` not
  `AddInput(multiple=true)`.

The buffer allocation size should be number of bytes, not number of
floats.

* 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`.