* 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

* Add range op.
test=develop

* Add more unitests.
test=develop

* Fix API.spec
test=develop

* Fix API.spec
test=develop

* Fix API.spec
test=develop

test=develop

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* fix default create_parameter dtype maching initializers test=develop

* update type check test=develop

* update test=develop

* Add psroi_pool operator.

* Make pad2d support for variable paddings.
test=develop

* Rename get_paddings and add inline modifier.
test=develop

* Fix comments.

* Enable BatchNorm to use global mean and variane during training
* Update doc and follow comments.

* Support softmax return in softmax_with_cross_entropy

* Add test for return_softmax=False

test=develop