* add recordio support

* disable the openblas multi-thread on windows since no support
adjust the python script

* code style

* code style
test=develop

* add create_recordio_file_reader back

* fix code style
test=develop

* fix the gtest.cmake on windows

* fix cc_test on windows

* fix the win build
test=develop

* remove fused compile support on windows
test=develop

* add the jit support
test=develop

* add the jit support, test=develop

* add the jit support, test=develop

* add the jit back
fix compile error on windows

* rollback test=develop

* test case fix

* disable DSO by default on windows

* exclude warpctc_op on windows

* exclude the dynload_warpctc out on windows
test=develop

* fix the scripts error
test=develop

* disable avx on windows by default
test=develop

* re-organize the cmake file

* disable mkl on windows by default

* add warp_ctc back

* fix the dependency

* fix the dependency

* fix the build issue on windows

* remove unsupported flag on windows

* code style

* code style
test=develop

* fix issue

* add profiler, parallel_executor back

* clean up the pre-definitions on windows

* fix build issue

* test=develop

It is used by NetOp before.

* Shrink reshape_op library size
* User can register a standard C++ functor as a op kernel

* "split into multiple .ccl"

* "refine file structure"

* "refine files"

* "remove the cmakelist"

* "fix typo"

* "fix typo"

* fix ci

* Add MKLDNN layout support in Paddle

Add MKLDNN layout in Paddle so that MKLDNN friendly memory layout
can be used in MKLDNN enabled OP kernel. Before this commit, NCHW
is hardcode to be used in all MKLDNN op kernels. As a result,
non-optimized execution path is selected in MKLDNN primitive which
bring worse performance.
Besides framework change, three MKLDNN OP kernels were updated
for using new MKLDNN layout. They are conv/pool2d/batch_norm.
Other MKLDNN OP kernels need be also updated in similar way to
achieve best performance.

* Add MKLDNN layout support in activation OP

* Don't populate layout from input to output when kMKLDNN in

* Refine pool mkldnn op kernel

* MKLDNN layout

* Remove the inferitance from tensor file

* MKLDNN layout: refactoring

* Remove additional #define to register new operator

* Prepare mkldnn tests to work with layout

* "refine kernel registrar"

* "refine registrar with multikey"

* "fix register"

* "refine multikernel register"

* "fix CI"

* "fix CI"

* "fix registry"

* "swtich GPU to CUDA"

* "add register macro test case"

* "fix CI"

For input argument with a list of variables, drop_empty_grad is not allowed because it makes the correspondence bewteen a variable and its gradient ambiguous. Use REGISTER_OP_EX to register the op or call InputGrad(?,false) in GradOpDescMaker.

* Rename XXDescBind --> XXDesc

* Fix Compile

* Move framework.proto to proto namespace

* Fix compile

* Fix compile

* Fix Compile

There are mainly following fixes:

- take `DeviceContext` as the template parameter of math functors and OpKernel instead of `Place`
- remove `eigen_device` interface in base class  `DeviceContext`
- remove `GetEigenDevice` interface in `ExecutionContext` and base class `DeviceContext`
- remove unused `platform::EigenDeviceConverter`
- rename `REGISTER_OP_GPU_KERNEL` to `REGISTER_OP_CUDA_KERNEL`
- rename `USE_GPU_ONLY_OP` to `USE_CUDA_ONLY_OP`

* Chage `IndicateDataType` to `GetKernelType`. Make it easier to
  understand.
* Change `OpKernelKey` to `OpKernelType`
* Make operator developers can customize which kernel the operator will
  use in runtime.

* Init commit

* Make executor use ProgramDescBind

* Change Attribute from BlockDesc to BlockDescBind

* Since we will get the program desc in RNN, just BlockDesc is not
  enough.

* Cast Operator

Cast input variable to other data type

* Fix compile error

* Add cast op

* Follow comments

* Shrink Operator.h

* Fix CI compile

* Add glog as dependencies of ops

* Use VLOG to logging some information is helpful when we debug Paddle

* Fix Unittests

* Change ProgramDesc not a global variable

* Polish code style

* Correct implement BlockDesc destructor

* Unify program as parameter name

* Add no_grad_vars for grad_op_maker

* Add unittest

* Fix unittest

* Fix unittest

* Follow comment