* test=develop, fix docker with paddle nccl problem

* test=develop, Add Variable api and refine dygraph related API

* test=develop, Add Variable api and refine dygraph related API

* test=develop, refine test for new api and error info

* test=develop, refine error info and test_layers

* test=develop, add API.spec

* test=devleop, fix to_string python2 and python3 compat error and refien doc

* test=devleop, add API spec

* test=devleop, update API spec

* test=devleop, update API spec

* test=develop, invoke ci

* test=develop, fix example code

* test=develop, update API spec

* test=develop, add compat test and fix inplace campat dict error

* fix pool infershape. test=develop

* fix unittest converage. test=develop

* fix format. test=develop

* make pad and split support fp16
test=develop

* add a base class for the Communicator
* add AsyncCommunicator Impl for async distributed training

test=develop
Signed-off-by: Nzhaoyuchen <zhaoyuchen01@baidu.com>

* impove error message when passing ndarray with object dtype

* imporve message format

* change assert to raise TypeError

* remind user how to locate the irregular data instead of printing

* add unittest for input array type check

* Fix conv2d+dequantize squash for residual fusion

test=develop

* Correct int8 input

test=develop

* Add if exclude or include padding in pool2d mkldnn

test=develop

* x.dims == y.dims test=develop

* refine comment

* Expose `mutable_data` as python binding

test=develop

* Add test for device pointer binding

test=develop

* Make test compatible with python 2

* fix input shape check test=develop

* move PADDLE_ENFORCE test=develop

Add dtype for coalesce_tensor_op

The new "fluid.data" changes old "fluid.layers.data":

1. Add shape and dtype check.
2. Remove "append_batch_size" parameter. We won't offer this in the new data layer because other deep learning platforms don't have this kind of data layer pre-processing. It may confuse users.
3. Remove "stop gradient" parameter because the data layer doesn't do back-propagation

TODO：
Now data layer feeded by executor is checked, will we want to check the feed data of readers in the future?

* add kernel for fill_op, test=develop

* modify PADDLE_ENFORCE to PADDLE_ENFORCE_EQ, test=develop

* add op test for fill_op, test=develop

* REGISTER COP CUDA KERNEL, test=develop

* update test_fill_op.py, test=develop

* change FillConstantOpVarTypeInference to FillOpVarTypeInference, test=develop

* fix op test, test=develop

* add head file, test=develop

* add support tensor and tensorlist for strided_slice OP test=develop

* fix the commnet test=develop

* fix test=develop

* fix the bug test=develop

* delete log test=develop

* fix API.spec test=develop

* fix test=develop

* fix bn

* treat broadcast as non-initial, test=develop

* rename the class name

* rename the class name, test=develop

* add support of matmul with multiple head even different width and height

Original matmul with multiple head supports only the mat_a.width == mat_b.height,
in that case, mat_b will be horizontally split. In this patch, we extend the
support when mat_a.width != mat_b.height but mat_a.width/head_number == mat_b.height,
in this case, mab_b will be vertically split.

One example is A is [3, 8], B is [2, 16], head_number is 4. In this
case, A will be split as [3, 2], B will be (vertically) split as
[2, 4]. The final result will be 4 matrix of 4 matrix of [3,4], i.e. [3, 16]

test=develop

* add support of matmul with multiple head even different width and height

Original matmul with multiple head supports only the mat_a.width == mat_b.height,
in that case, mat_b will be horizontally split. In this patch, we extend the
support when mat_a.width != mat_b.height but mat_a.width/head_number == mat_b.height,
in this case, mab_b will be vertically split.

One example is A is [3, 8], B is [2, 16], head_number is 4. In this
case, A will be split as [3, 2], B will be (vertically) split as
[2, 4]. The final result will be 4 matrix of 4 matrix of [3,4], i.e. [3, 16]

test=develop

* refactor the code of matmul with multiple head even different width and height

test=develop

* refine ctc align op with padding 
* refine api sample code

* add input type and dtype check for softmax_op

test=develop

* refine error message

test=develop

* Removing last dims constraints of seq_pad and seq_unpad test=develop

* fix test_layer api code test=develop

* fix sequence_pad_op.cc conflict test=develop

* remove test_analyzer_mm_dnn test=develop

* fix vectorize bug test=develop

* fix vectorize<int> test=develop

* Add float16 support to `sync_batch_norm_op`

test=develop

* Add test for sync_bn with FP16 input

test=develop

* Remove constraint that last dimension is forced to be 1 by add
lookup_table_v2 test=develop

* modify into PADDLE_ENFORCE_CUDA_SUCCESS test=develop

* Revert "modify into PADDLE_ENFORCE_CUDA_SUCCESS test=develop"

This reverts commit 8a960bfc61e51aa27c3c529df8fb90b93ebd19f9.

* move api into fluid.embedding test=develop

* fix example code test=develop

* move one_hot into fluid.one_hot

* modify api.spec test=develop

* fix loss shape test=develop

* support change shuffle thread num
* support change train thread num
* fix receive shuffle data of each channel
* data norm stop gradient
* add check thread_tensor type and root_tensor type when merge metric
* remove sleep in shuffle, add config
* add config of pslib client to client communication
* fix xbox str
* add data norm op testcase
* add flush in trainer finalize

* give warnings when save a model without any parameters test=develop

* delete one line comment test=develop

* add py_reader combination unittest,test=develop

* follow huihuang's comments, test=develop

* make OpTest check grad inplace even if forward has no inplace, test=develop

* do not run PE when enable_inplace is False, test=develop

* add conv3d cuda kernel for float16 type, test=develop

* refactor OpTest for inplace, test=develop

* add comments, test=develop

* add recompute based checkpoints methods for large batch training
test=develop

* add append_backward_with_forward_recomputation
test=develop

* refine optimizer
test=develop

* update backward and optimizer
test=develop

* make Variable usable
test=develop

* add recompute code

* refine optimizer
test=develop

* refine addup _append_backward_ops_with_checkpoints_
1) for recompute part, just cache the grad_op_desc without appending to block
2) before appending grad_op_desc to backward part, addup_repetitive_vars, remove unused branch
test=develop

* make method private

* add recompute strategy into DistributedStrategy
test=develop

* checkpoint version3
test=develop

* remove some print information
test=develop

* remove unused sumop
test=develop

* try to fix recompute with graph building modules

* add input names to vars should be held

* add memory debug tool

* backup backward

* Fix bugs

* add backward desc for op not in any segments

* add exception info for sub_block

test=develop

* modify code style

test=develop

* modify code style

test=develop

* remove print functions

test=develop

* add API spec

test=develop
test=document_preview

* make Recompute a child class of Optimizer

test=develop
test=document_preview

* add API spec

test=develop
test=document_preview

* modify API spec

test=develop
test=document_preview

* add document for Recompute

test=develop
test=document_preview

* change API doc of Rcompute

test=develop
test=document_preview

* code cleaning

test=develop
test=document_preview

* modify API spec

* fix bugs when segments hold no element

* add testcase for Recompute Optimizer

test=develop
test=document_preview

* add test for apply_gradient, and code cleaning

test=develop
test=document_preview

* add test case for load function

* enable CI

test=develop
test=document

* add test case

test=develop
test=document_preview

* add sample code for 4 function of recompute optimizer

test=develop
test=document_preview

* optimize the error information when the input for while op has a wrong shape test=develop

* add mse_loss op

* move tree_conv to fluid.contrib.layers

test=develop

* update API.spec for tree_conv

test=develop

* update tree_conv api to increase unit coverage

test=develop

* unify DataLoader APIs, test=develop

* integrate iterable CPU Dataset, test=develop
add GPU dataset supporting, test=develop

* add unittests for dataset, test=develop

* add more docs to dataloader apis, test=develop, test=document_preview

* refine doc, test=develop

* refine doc again, test=develop

* increase coverage, test=develop

* optimize cloud rolemaker, test=develop