model inference

NCE does not need to set its activation.

Update annotations of layers.py.

Add ScaleSubRegion Layer.

add clip_by_norm op

update tags of MKLML and MKL-DNN

* Add LoDRankTable

LoD Rank Table stores the `level` of `lod` which is ordered by sequence
length in descending order. It is useful when implement dynamic RNN and
is shared by dynamic RNN memory, dynamic RNN slice input and dynamic
RNN slice output operators.

* Add skeleton for array_to_lod_tensor and lod_tensor_to_array

* Add VarType::LoDTensorArray

* Add PyBind of LoDTensorArray

* Add InferVarType

* Add first unittest

* Add ut

* Add unittest

* Add unittest

* Add unittests

* update

* init

* add infershape for lod_tensor_to_array_op

* compelete array_to_lod_tensor_op

* copy data

* clean code

* clean code

* Fix unittest data

* fix bugs

* fix compile error

* Refine TensorToArrayOp

* refactor array_to_lod_tensor

* Unittest

* fix bugs

* Fix unittest

* Fix unittest

* debug

* Debug

* Fix unittest

* Add grad for ops

* Debug

* Fix a bug

* fix a bug

* fix a bug

Add force_cpu for fill_constant op

CompareOp's kernel device type is decided by input tensor place

Feature/shrink memory op

and abandoned deprecated sum API interface

add doc for image.py

fix accuracy cudamemset

enable bias for mkldnn_addto

fix_pool_op_attr_name

* add fill_constant_batch_size_like_op to rnn h_boot

* first commit

* merge develop; fix conflict

* update to main_program

reduce the lr in case of nan in small batchsize

Add `op::math::set_constant` without template

uci_housing.py can download the trained model automatically.

CompareOp can run on CPU even other operators are running on GPU, since
opeatations like comparing control flags should be performed only on CPU