* add _get_all_register_op_kernels api test=develop

* refine usage of check_op_register_type test=develop

* add import in core test=develop

* support SelectedRows in dygraph, test=develop

* fix bug of _grad_ivar interface, test=develop

* add optest for support seletedrows, test=develop

* fix bug for gradient_accumulator in GPU mode, test=develop

* fix error when Selectedrows addto LodTensor in sorted_gradient mdoe in dygraph, test=develop

* refine and simplify gradient accumulator code, test=develop

* add optest, test=develop

* add optest and simplify code, test=develop

* fix bug for test_imperative_selected_rows, test=develop

* add optest for Coverage, test=develop

* fix gradient interface and simplify code, test=develop

* update api for gradient, test=develop

* fix ShareDim's bug in DygraphExecutionContext class, test=develop

* add optest, test=develop

* use system allocator in unittests, test=develop

* fix op bugs, test=develop

* fix tensor copy bug when src and dst are the same, test=develop

* add numpy bridge

* fix template compile

* add unittest, add default
test=develop

* fix unittest
test=develop

* fix unittest
test=develop

* zero_copy=True for to_variable,
test=develop

* bug fix
test=develop

* disable deprecated NumPy API
test=develop

* use better design of NumpyAllocator
test=develop

* fix Py_None check
test=develop

* reset c++ tracer when jump out dygraph guard
test=develop

* refine PADDLE_ENFORCE_xx format
test=develop

* bug fix of tracer switch
test=develop

* update decref
test=develop

* print table stat
test=develop

* notes
test=develop

* notes
test=develop

* add global value getter setter, test=develop

* fix error messages, test=develop

* fix fetch handler problem and refactor
when a user define FetchHandler class, he or she should initialize a handler
with variable dict. the key of a variable dict is a user defined name,
the value of a variable dict is a Varaible generated from python API.

For each fetching, a user should implement handler function in which
fetched_result_dict will be available and the user can access the fetched value
with user defined keys.

* general table

* add sparse table
test=develop

* no cvm
test=develop

* add no_cvm
test=develop

* add note
test=develop

* code style
test=develop

* code style
test=develop

* code style
test=develop

* code style
test=develop

* code style
test=develop

* add key of optimizer
test=develop

* solve pslib stop core
test=develop

* barrier
test=develop

* add notes
test=develop

* polish jit trace codes, test=develop

* polish codes again by removing var_id, test=develop

* copy some feasigns and corresponding embeddings from one sparse table to another
* copy all feasigns and corresponding embeddings from one sparse table to another
* copy all dense params from one table to another
* copy some local vars to other local vars

* friendly trace interface, test=develop

* refine TracedLayer, test=develop

* add some docs, test=develop

* Add c++ global current tracer for dygraph, test=develop

* add tracer property in c++, test=develop

* support different place, test=develop

* add unittest for tracer, test=develop

* don't expose numerous Tensor.set(), test=develop

* fix condition, test=develop

* fix float16 bug, test=develop

* feed should be Tensor or np.array, not Variable or number, test=develop

* use forcecast to copy numpy slice to new array, test=develop

* remove float16-uint16 hacking, test=develop

* refactor dygraph,test=develop

* fix failed unittest,test=develop

* polish code,test=develop

* check windows ci error,test=develop
try to fix windows ci error by np.allclose,test=develop

* polish vlog and profiler, test=develop

* try to fix preceding ops order,test=develop

* test transformer in windows ci, test=develop

* use python c-api to speed up tracer.trace,test=develop

* test=develop, fix docker with paddle nccl problem

* test=develop, add ut for debug string and gradient_accumulator

* test=develop, add tests for layer/gradient_accumulator/prepared_op

* test=develop, fix complie error for test_prepared_op

* test=develop, add more ut for dygraph

* test=develop, create API.spec for dygraph api change

* optimize grad maker; test=develop

* optimize grad maker

* test

* grad make optim; test=develop

* fix unittest bugs; test=develop

* add dygraph grad op maker and split_op

* grad op maker refactor; test=develop

* add dygraph grad maker; test=develop

* fix op deformable_conv_v1_op bug; test=develop

* fix deformable_conv prroi pool bugs;

* fix new op grad op maker bug; test=develop

* fix split by ref bug; test=develop

* fix dygraph auto prune bug; test=develop

* fix test_trace bug; test=develop

* fix fused emb seq pool bug; test=develop

* remove useless code in op_desc file; test=develop

* remove useless code, StrVarBaseNode; test=develop

* fix review issues; test=develop

* fix rank_loss grad maker; test=develop

* remove flag in VarBase; test=develop

* fix distributed_notify_op compile bug ; test=develop

* fix reshape op double grad; test=develop

* fix expand as op; test=develop

* add impertive type_defs.h for demo_train; test=develop

* fix inference lib cmake; test=develop

* fix inference lib; test=develop

* fix infernce_lib; test=develop

* fix inference cmake; test=develop

* fix inference lib; test=develop

* fix inference lib; test=develop

* remove condition dygraph grad maker, modify local name; test=develop

* fix split grad maker bug; test=develop

* fix pyramid_op bug; test=develop

* change travis time out limit; test=develop

* restore travis; test=develop

* change timeout limit; test=develop

* Refine the cache of program, context and scope in executor.
test=develop

* Refine the unittest test_executor_and_use_program_cache.

* Add the test the PaddingRNN with use_program_cache=True.
test=develop

* Remove a check.
test=develop

* Refine the unittest to check whether it is correct when setting use_program_cache=True.
test=develop

* fix persistable;

* fix save load bugs; test=develop

* fix bug; test=develop

* add example for new io api; test=develop

* addd example; test=develop

* jit/trace 1st version, test=develop

* add more unittests, test=develop

* support dlpack to tensor and implement python interface test=develop

* add unittest for _to_dlpack and from_dlpack test=develop

* refine py_reader exit, test=develop

* fix multiprocess_reader exception unittest, test=develop

* increase code coverage for legacy fluid.layers.py_reader, test=develop

* update the api en doc of BuildStrategy and its setting, test=develop, test=document_fix

* update api.spec, test=develop, test=document_fix

* update the en doc of fuse_relu_depthwise_conv, test=develop, test=document_fix

* doc fix, test=develop, test=document_fix

* polish en APIs of LodTensor, test=develop, test=document_dix

* polish en APIs of LoDTensor, test=develop, test=document_fix

* follow comments, test=develop, test=document_dix

* test=develop, fix docker with paddle nccl problem

* test=develop, refine en_doc for Variable and Program

* test=document_fix, fix English doc for Variable and Program

* test=document_fix, refine astype code block style

* test=document_fix, add example code for Variable properties

* test=document_fix, fix BackwardStrategy English Doc

* test=document_fix, fix syntax

* test=document_fix, refresh API.spec

* test=document_fix, refine api spec

* test=document_fix, refine api spec

* add new save load interface; test=develop

* add new save interface; test=develop

* add save load interface ;

* fix save load error;

* fix dygraph set dict bug;

* add save load unit test; test=develop

* fix test_imperative_optimizer bug; test=develop

* fix unitest optimizer bug; test=develop

* fix code coverage; test=develop

* fix converage; test=develop

* add document for apis; test=develop

* fix unitest error; test=develop

* fix save load unit test error; test=develop

* fix error message; test=develop

* change set_parameter set_optimizer to save_dygraph; test=develop

* add load_graph check; test=develop

* fix api spec; test=develop

* Polish en doc of LoDTensorArray, test=develop, test=document_fix

* follow comments, test=develop, test=document_dix

* fix CUDA CPU places, test=document_fix, test=develop

* fix CUDAPlace param doc, test=document_fix, test=develop

* fix CUDAPlace param doc, test=document_fix, test=develop

add executor.FetchHandler for train/infer from the dataset

* refector geo sgd & communicator

* How to write custom op needs to follow framework OP spec.
* Package fluid_framework.so and headers into whl.
* Add paddle.sysconfig.get_include() and paddle.sysconfig.get_lib() to get include dir and lib dir.
* Export some C-APIs to merge OpInfo between core.so and custom_op.so.
* Add unit testing.
* Update API.spec.

* update operator compatible info, test=develop

* revert cmake/version.cmake, test=develop

* add unit_tests and fix bugs, test=develop

* update ../paddle/fluid/framework/framework.proto, test=develop

* fix bug of paddle/fluid/inference/api/analysis_predictor.cc, test=develop

* update paddle/fluid/framework/version_test.cc, test=develop

* add comments and rename interfaces, test=develop

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

* Expose `mutable_data` as python binding

test=develop

* Add test for device pointer binding

test=develop

* Make test compatible with python 2

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?