This reverts commit ce16400d.

removed diagnostic

- Added Unit tests for Softmax MKLDNN Forward

Added fix for div by 0 to happen in cross_entropy backward

Conflicts:
	paddle/fluid/operators/CMakeLists.txt

- Cosmetic fixes to SoftMax MKLDNN fluid operator

Added misssing softmax fluid operator file

Disabled MKLDNN softmax operator by default

Fix to softmax op unittest merge

clang_formater fixes

clang_formatter fixes

- Name changing of softmax mkldnn operator to maintin consistency
  across codebase

- updated comment

fix to comment

* Add reset and guard to unique_name

neural machine translation model support beam search with while op

* "fix decode bug"

* "follow commnet"

* "fix error"

* "fix hook bug"

* fix based comment

* fix copyright

* fix based on comment

* add copyright hook

* add copyright hook

* refine copyright hook

* "test copyright hook"

* fix check style

* fix ci

It is useful to reorder RNN memory block.

* Simplize system_allocator and fix GPU_INFO

* Remove main_program/startup_program

* Fix optimizer

* Fix CI

* Follow comments

* Add DataFeeder

A v2 API like data feeder for book demos.
We can feed data directly from reader.

* Fix CI

* Add an unittest for while/rnn op forward

* Add unittest for raw while op backward

* Fix CI

* Complete Dynamic RNN

* add crf_decoding layer

* fix some typo

* fix test_crf_decoding_op

* Make param_attr as a strong typed class

Fix #5819

* Unify fluid submodules to fluid module

Change books just use `import fluid`, not submodules

* Remove g_main_program/g_startup_program

Use default_main_program/default_startup_program instead

* Typo

* Fix CI

Change books just use `import fluid`, not submodules

* Change all `data_type` in Python to `dtype`

* Change `date_type` in C++ to `dtype`

* Refine

* init label_semantic_roles.py

* add linear_chain_crf and test

* complete test_linear_chain_crf

* correct last layer of db_lstm

* update optimizer and initializer

* update param_initializer of embedding_layer

* support load pre trained embedding

* rm unused parameter

* optimize code

* clean code

* fix test

* add todo

* Improve the initializer Interface for fc, sequence_conv and conv2d layers
* Fix some typos in python code
* Fix CI

The size of the bias parameter should be the number of filters.

* init commit

* change some dir name

* add lstm layer

* set hidden shape

* rename input parameter

* add dynamic lstm

* refine dynamic lstm layer

* change parameter using XavierInitializer by default

* refine dynamic lstm layer

* add acc layer
* memory log level change from 3 to 10
* use gaussian random to init conv parameters
* use initializer
* fix import
* batch_norm use helper to create persistable var
* refine code
* train only 2 batches for test
* use g_program and g_init_program
* use XavierInitializer to init fc parameter