test=develop

* test=develop, fix space_to_depth_doc

* test=develop, refine indent

* test=develop, refine code

* test=develop, add api spec

* add layer norm to Layers, add transformer prepare encoding

* little change

* finish encoder part

* add decoder part

* finish model part

* add test case and part of data feed

* add transformer test

* add to_parameter, add remove in set_attr

* test=develop, fix pos encoding bug, create_parameter with stantard name

* test=develop, rm dropout test in imperative

* test=develop, fix cpu error

* test=develop, fix minize bug

* test=develop, fix one hot not stop gradient

* test=develop, fix one hot not stop gradient

* test=develop, refine parameter name

* test=develop, fix transformer test in imperative mode

* test=develop, fix transformer test in imperative mode

* test=develop, fix boost and mkl download error

* test=develop, fix boost and mkl download error

* test=develop, fix ci and refine code

* test=develop, fix ci and refine code

* Add fsp operator.
1 Add unitest.
2. Add python API.
3. Add layer test.

* Add quantization strategy.
1. Add API.
2. Add unitest.

* Add distillatoin strategy.

* Add unitest config file for quantization

* Fix Copyright
test=develop

* Fix setup.py

* Fix document of layers.py.
test=develop

* Fix unitest in python3.
test=develop

* Fix documents.
test=develop

* 1. refine fsp op by batched gemm
2. remove unused import
test=develop

* Fix test_dist_se_resnext.
1. disable test distillation.
2. reset framework.py
test=develop

* Enable unitest of distillation after fixing Block._clone_variable
test=develop

* Fix cdn issue.
test=develop

* Rewrite gradient ProtoMaker for affine_channel_op to remove the Output as the input.
* Add act in Python API to make the act can be in-place by layer_help.py

test=develop

test=develop

test=develop

* Support Sync Batch Norm.
* Note, do not enable it in one device.

Usage:

build_strategy = fluid.BuildStrategy()
build_strategy.sync_batch_norm = True
binary = fluid.compiler.CompiledProgram(tp).with_data_parallel(
        loss_name=loss_mean.name,
        build_strategy=build_strategy)

test=develop

test=develop

test=develop

test=develop

test=develop

test=develop