test=develop

* "add gradient register"

* "make some enhance"

* "better format"

* "fix typo"

* "fix reuse"

* "fix get expected kernel"

* "change the mkldnn code"

* "fix mkldnn"

* "fix mkldnn failed test"

* "add comment"

* elementwise_add with bcast: Brian's implementation by Brian added, with default bcasts

* elementwise_add with bcast: GetExpectedKernelType added to elementwise_op

* elementwise_add with bcast: use_mkldnn attribute added

* elementwise_add with bcast: changes after review and some formatting

* elementwise_add with bcast: changes after style check

* elementwise_add with bcast: changes after style check cont.

* elementwise_add with bcast: MKLDNN unittests added

* elementwise_add with bcast: original unittests with use_mkldnn flag

* elementwise_add with bcast: handling of MKLDNN format corrected

* elementwise_add with bcast: setting MKLDNN format turned into lambda

* elementwise_add with bcast: MKDNN format setting turned into separate function

* elementwise_add with bcast: condition for choosing MKLDNN simplified

* elementwise_add with bcast: fix for MKLDNN format set incorrectly in bcasts

* elementwise_add with bcast: changes in unittests for broadcasts

* elementwise_add with bcast: fixes in unittests regarding dimensions

* elementwise_add with bcast: bring back correct format setting in mklml grad path

* elementwise_add with bcast: fixed compilation error

* "add inplace attribute"

* "register inplace attribute"

* "change se-next model for memory-reuse"

* "fix typo"

* repick

* fix merge conflict

* "fix stupid error"

Fix ElementwiseOpInferVarType in elementwise_op to use the default InferVarType to find var recursively

Remove ElementwiseOpInferVarType in elementwise_op to use the default InferVarType to find var recursively

Do not use ctor

* Reduce line of codes.
* We can use virtual function for Maker now.
* The implementation does not care what maker holds, it is easier to
refactor later.

* Fix cpplint errors, round2

* Fix pointer issue

When the second argument contains batch dimension, the axis should be 0.

Also makes elementwise ops more tolerant at handling tensors with trailing
singular dimensions.

* Move framework.proto to proto namespace

* Fix compile

* Fix compile

* Fix Compile

* Fix Proformance problem of enforce

* Fix missing `;` in code

* Fix CI

They are public now

* init Infershape

* add static InferShape interface

* refactor add-op infershape

* add AttrReader

* add all maker's infershape

* add all InferShape

* add python infer api

* add VarDesc interface

* add python VarDesc and OpDesc interface

* update python code

* use infershape function to do shape inference

* clean code

* do not use pointer

* refine code of op_proto_maker

* add get_dims to VarDesc

* refine the code

* remove the dependency from operator to op registry

* remove OpProtoAndCheckerMaker from operator

* restore complete_add_op

* add shape_infer_impl.h

* code optimization

* remove const return value

* add fake BlockDesc class

* optimize code

* remove infer function in op_info

* move InferShapeContextImpl to operator.h

* optimize the interface of InferShapeContextBase

* add temperary interface of new infershape

* change add_op, clip_op, conv2d_op and activation_op

* change all operators InferShape

* fix SetDim

* update cos_sim_op

* update crop_op

* update lookup_table_op

* allocate tensor when call GetDim in InferShapeContext

* update modified_huber_loss_op

* update rowwise_add_op

* update mean_op

* update sequence_avg_pool_op

* typo

* remove old InferShape interface

* can compile

* fix or unit test

* clean code

* clean code

* remove const before InferShapeContext

* change InferenceContextBase to pointer

* rename RunTime to Runtime, code clean

Elementwise operator add/sub/mul/div