* test=develop

* test=develop

* test=develop

* test=develop, double backward reduce_mean

* add comment. test=develop

* fix format. test=develop

* rename GradGrad -> DoubleGrad. test=develop

* fix op_use_default_grad_op_maker.spec. test=develop

test=develop

* add elementwise_add_grad_grad op. test=develop

* use defined GradMaker. test=develop

* add collect fpn proposals op,test=develop

* add double grad for mul_op. test=develop

* fix format. test=develop

* fix format. test=develop

* fix format. test=develop

* refine code. test=develop

* remove setzero. test=develop

* fix dx/dy init bug. test=develop

* fix format. test=develop

Fix some api example codes' bugs and these APIs include load_inference_model, load_vars, save_vars, L1DecayRegularizer and L2DecayRegularizer. (#17324)

* fix some api example codes' bugs.

* update API.spec. test=develop test=document_preview

* add import fluid. test=develop test=document_preview

* make parallel_executor support FLAGS_use_mkldnn

test=develop

* add warning when set mkldnn_enabled_op_types_ in non-mkldnn env

test=develop

test=develop

* test=develop
add sign op

* Update nn.py

test=develop
delete stop_gradient assignment

* Optimize the computing kernel of sequence_reverse operator.

test=develop

* Clean code

test=develop

* Fix for cpplint syntax checking.

test=develop

* Fix the compile warning issue.

test=develop

* Optimize the elementwise op with CUDA kernels.
test=develop

* Support setting of attr in op config file.
test=develop

* Add the support the setting dtype and initializer in config.
test=develop

* Save workspace.

* Add initializer "zeros".
test=develop

* Fix compiling error.

* Support the use of existed file to initailize tensor in op_tester.

* Use eigen to optimize the elementwise_add/mul for the case that x and y have the same dims.
test=develop

* test=develop, add gradient sort backward strategy

* test=develop, fix test by add FLAGS_cudnn_deterministic on new tests

* Fix the example code in some Python API
* Update paddle/fluid/API.spec
* Fix some examples format

This reverts commit f456c8be.

* add double grad for elementwise_mul. test=develop

* remove comment. test=develop

* fix grad sum. test=develop

* fix for axis expand. test=develop

* add test for axis expand. test=develop

* add double grad for square. test=develop

* formax code. test=develop

* fix for grad sum. test=develop

* refine shape. test=develop

* refine extract. test=develop

* test=develop, test=document_preview, fix all 13 api doc and code

* test=develop, fix rst

* test=develop, refresh API.spec

* reset drop local scope counter
test=develop

refine code

fuse cublas  calling and kernels into one cuda kernel.

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

* Add conv2d_grad_grad_op
* Extracte the cuDNN conv algo searching code in conv_cudnn_helper.h.
    - Now use it in conv2d_grad_grad.
    - Will simply the searching code in conv2d and conv2d_grad in next PR.
* Enhance and fix bug in unit testing of gradient_checker.
* Support to fetch empty variables，return None in Python.

* rename the default version from '0.0.0' to 'latest'

test=develop

1. infernce multi card occupy
2. facebox model inference occupy too much
test=develop

* test=develop
add elementwise_mod and elementwise_floordiv, fix equation problem in elementwise_mod

* fix some logic in distributed transpiler, test=develop
* reformat fleet API, test=develop

The parameter dirpath will be passed directly to c++ operater. The file address format will be different under win and UNIX.

* fix train_from_dataset and infer_from_dataset example

* add inductive dim for data_reader, example: shape=[-1, 1], then -1 will be inducted through run-time reading of number of elements

test=develop

* Fix API example code of save_inference_model

test=develop

* Add "import" in exmaple of save_inference_model

* Fix typo "exsample" -> "example"

test=develop

* modified formula for lrn

test=develop

* modified api.spec

test=develop

* Refine elementwise kernel.

Add a simple cuda kernel if grad x and y both exist
Use 2D block cuda kernel to do broadcast.

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

* refine code.

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

* refine code.

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