* fix pool2d pool3d:
1. support asymmetric padding;
2. support padding algorithm:"SAME" and "VALID";
3. support channel_last: data_format NHWC and NDHWC;
4. support inferring shape when input with negative dims in compile time;
5. change doc of python API and c++;
6. fix bug in cuda kernel when Attr(adaptive) is true.

test=develop,test=document_preview

* fix 'tensors' to 'Tensors'. test=develop,test=document_preview

* add test for converage ValueError.test=develop,test=document_preview

* resolve conflict in test_pool2d. test=develop

test=develop

* opt FC

* opt rest of dygraph.nn

* new param shape check and unittest

* add kwargs for Layer

* add new set_value api

* use property decorator

* update API.spec, test=develop

* use UserList, separate gettersetters, test=develop

* update test_custom_layer_with_kwargs, test=develop

* fix UserList compatibility, test=develop

* fix UserList compatibility, test=develop

* keep FC._w, test=develop

* add unittests, Conv3D bug fix, test=develop

* clean code, test=develop

* fix dygraph guard in unittest, test=develop

* add property setters, remove unused param in tracer, test=develop

* tracer none check, test=develop

* merge, test=develop

* refine, test=develop

* bug fix in  prelu and conv3d_transpose, test=develop

* rm __set__, test=develop

* set tensor value instead of assign op

* fix property setter call, test=develop

* fix api.spec, test=develop

* fix doc sample, test=develop

* Follow Wangzhen's comment in PR 18970, test=develop

* Review comments, test=develop

* Leave fake quantization around mul

test=develop

* Replace Fake with Real Quantized Mul

test=develop

* Fix bug in quantize placement pass

Nodes in the graph now have checked type instead of node name when they are to be marked for quantization test=develop

* 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

* fix en docs of apis, test=develop, test=document_fix

* follow chunwei's comments, test=develop

* 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 conv2d+dequantize squash for residual fusion

test=develop

* Disable conv-requant squash

test=develop

* add train demo to paddle_build.sh

test=develop
test=document_preview

* code cleaning

* test=develop

test=develop

* remove program desc files

test=develop

* add test_train to CI

test=develop
test=document_preview

* make run.sh excutable

test=develop
test=document_preview

* switch with_mkl to off

test=develop
test=document_preview

* switch with_mkl to off

test=develop

* switch with_mkl to on

test=develop

* move test_fluid_lib_train to ci_py35

test=develop

* fix bugs of MKLDNN building

* rm build_train_lib(), test=develop

* restore tensor.h, test=develop

* fix pool infershape. test=develop

* fix unittest converage. test=develop

* fix format. test=develop

* make pad and split support fp16
test=develop

* codegen code for reconstruction test=develop

* fix the cmake test=develop

* fix review advice test=develop

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

Refine document of DGCMomentumOptimizer

Polish English docs of elementwise_add/sub/mul/div

* shape and optimize paddle error message stack, test=develop

* limit exception type & add unittest, test=develop

* fix multi-platform problem, test=develop

* fix related unnitest failed, test=develop

* add doc & fix unittest errors, test=develop

* fix function name error, test=develop

* update tensor test exception msg compare, test=develop

* remove unittest on win32, the dir format is different, test=develop

* remove useless package, test=develop

* add paddle enforce handler unittest, test=develop

* add exception checkout, test=develop

* fix coverage failed, test=develop

* fix op registry test failed, test=develop

* refactor whole pr, test=develop

* remove test in CMakelist, test=develop

* fix coverage, test=develop

Use AllocateTmpTensor() for creating temporary tensors in warpctc.

Set output type LoDTensor only

After code experiment, I found data doens't support other type

* fix DistributeTranspilerConfig document, test=develop

* Make PaddleSlim support PyReader.
* Fix unittest of sensitive pruning.
* Add some assert.

test=develop

* 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

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

test=develop
test=document_fix

Polish elementwise max min pow document to add more examples

test=develop

* fix doc of apply_optimize
test=document_fix
test=document_preview

* modify doc of backward
test=develop
test=document_fix

* modify document hash
test=develop
test=document_preview

* add lod check for sequence op, test=develop

* delete unnecessary check in expend op, test=develop

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?

fix memory leak in HogwildWorker,  whose ops are  explicitly deleted in destructor