* 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

* add doc config for fluid

* fix typo

* follow comments

Also, change the `labels` to `label` for api consistency

* Make param_attr as a strong typed class

Fix #5819

* Add Python wrap of conv2d_transpose and its unittest

* Follow comments

* Fix format

* 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

* Stash

* Stash

* Polish Evaluator

* Merge code

* Revert

* 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

* Forward of raw if-else op

* add backward part of mnist if-else unittest

* refine fill_constant_batch_size_like layer

* add draft ifelse operator

* Complete IfElse Op

* add unittest of ifelse api

* merge baidu/develop

* Stash

* Merge develop branch

* Support int/int64 for fill_constant_batch_size_like

* 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

* fix lod_tensor_array

* init test beam search decode op

* add test_beam_search_decode_op

* Conditional Block Forward

* Assign Operator.

Out=X, when type in [LoDTensor/SelectedRows/LoDTensorArray]

* Stash

* Add Scope::Rename

it is useful in gradient phase of an operator with block

* ConditionalBlock Grad Done

* Add comments

* yapf format code

* add split lod tensor operator

* add more test cast

* clean code

* add merge lod tensor operator

* fix bug

* clean code

* add grad operator

* make mask support GPU

* add comments

* 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

* first commit

* Python API for while op

* Python Unittest for simple while_op forward

* fix out to be list

* Fix UT

* VarType

* Fix several bugs

* Fix bug

* Fix bug

* Fix Bug

* Fix bug

* Fix unittest

* Remove debug log

* Add comments

* add PADDLE_ENFORCE

* while_grad_op first commit

* Add `BlockDescBind::FindRecursiveOrCreateVar()` and fix bugs

* refine code

* fix unittest bug

* Adding operator assignment

* Adding documentation to layers.py

* Removing file from another PR

* increament is default inplace

* Add LoDRankTable

LoD Rank Table stores the `level` of `lod` which is ordered by sequence
length in descending order. It is useful when implement dynamic RNN and
is shared by dynamic RNN memory, dynamic RNN slice input and dynamic
RNN slice output operators.

* Add skeleton for array_to_lod_tensor and lod_tensor_to_array

* Add VarType::LoDTensorArray

* Add PyBind of LoDTensorArray

* Add InferVarType

* Add first unittest

* Add ut

* Add unittest

* Add unittest

* Add unittests

* update

* init

* add infershape for lod_tensor_to_array_op

* compelete array_to_lod_tensor_op

* copy data

* clean code

* clean code

* Fix unittest data

* fix bugs

* fix compile error

* Refine TensorToArrayOp

* refactor array_to_lod_tensor

* Unittest

* fix bugs

* Fix unittest

* Fix unittest

* debug

* Debug

* Fix unittest

* Add grad for ops

* Debug

* Fix a bug

* fix a bug

* fix a bug

* add fill_constant_batch_size_like_op to rnn h_boot

* first commit

* merge develop; fix conflict

* update to main_program

Follow comments

* Add LoDRankTable

LoD Rank Table stores the `level` of `lod` which is ordered by sequence
length in descending order. It is useful when implement dynamic RNN and
is shared by dynamic RNN memory, dynamic RNN slice input and dynamic
RNN slice output operators.

* Add skeleton for array_to_lod_tensor and lod_tensor_to_array

* Add VarType::LoDTensorArray

* Add PyBind of LoDTensorArray

* Add InferVarType

* Add first unittest

* Add ut

* Add unittest

* Add unittest

* Add unittests

* update

* init

* add infershape for lod_tensor_to_array_op

* compelete array_to_lod_tensor_op

* copy data

* clean code

* clean code

* Fix unittest data

* fix bugs

* fix compile error

* Refine TensorToArrayOp

* refactor array_to_lod_tensor

* Unittest

* fix bugs

* Fix unittest

* Fix unittest

* debug

* Debug

* Fix unittest

* clean code

* refactor

* use ostream

* update test

* fix gpu build error

* make gpu test pass

Used for shrink memories state in DyRNN. The height of state could
be shrinked after running a step block.

* Use stable_sort in lod_rank_table

It is easy to debug and test when use `stable_sort`and the time
complexity is not changed.

* Add LoDTensorArray

* Stash

* Better debug message for IsInitialized

* Stash

* Better debug message for IsInitialized

* Complete array read/write op unittests

* Add unittest, Gradient of array read/write

* Follow comments