Refactor DDim's product() and add slice_ddim()

* add NDEBUG switch to PADDLE_ENFORCE

Fix fetch record from master

Change to meet cpplint style

"fix dense vector shape in mq2007"

* using etcd as fault tolerant training

* update

* workable version, ft not tested

* small fix

* update

* remove TODO

"fix unrar in docker"

* OperatorBase should not store OpDesc because not All op contains an
  OpDesc and not all ops create from OpDesc.
  * Networks do not contain OpDesc and are not created by OpDesc
* Do not register Network to OpRegistry.
  * The network is directly created by the user in Python. Not from
    registry.
* Correctly handle the `inputs` and `outputs` of a Network.
  * Add CompleteAddOp() methods
* Remove `AddOp(OpDesc&)` in net-op. All op are added by OperatorPtr.
* Rewrite unit test for truly tested what networks do.
* optimise operator_test

1. Refactor DDim's product() to make it more efficiently.

2. Add slice_ddim().

* add ADD_OP_CPU to enable add op with only cpu kernel

Get OpProtos in Python

FIX: cpplint code style

cmake: do not run glide install every time.

Turn on race detector for all go tests

[Done] Memory Management: Buddy Allocator

Add Tensor::CopyFrom and Tensor::mutable_data(Place place)

Remove useless empty pointer check.

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.