Fix grpc bugs

* add copyright hook

* add copyright hook

* refine copyright hook

* "test copyright hook"

* fix check style

* fix ci

1. Fix kernel
2. Add more test case

* fix while_grad_op first step loss lod problem

* optimize code

* "unified operators"

* "add CUDNN register"

* "add use cudnn attribute"

* "add attribute"

* "test conv tranpose op"

* "remove duplicated attr"

* "fix op test"

* "add attribute to set cudnn"

* "add more log"

* "need layout op register support"

* "add more log"

* "change GetExpectedKernelType "

* "fix Get attr in conv_op"

* "fix CI"

* "fix tests"

* "removed kernel priority fallback"

* "fix CI"

* "fix stack pointer bug"

* "refine buggy interface"

* "add const cast to save life"

* "fix get_output_with_grad"

* "fix op test with dataformat"

* ""fix pooling

* "fix pooling test"

* "fix CI"

* "fix with_gpu error"

* "add transform needed functional check"

* "fix unpack list error"

* "comment out parallel.do temporary"

* "fix CI"

* "fix compile doc error"

* "make threshold larger"

2. Remove num_seq arguments.
3. Refine CUDA kernel of ScaleLoDTensorFunctor.
4. Change max_relative_error of gradient unitest to 0.007

* update code comments.

* update the comments.

* follow comments.

* add GetLoD for debug

* add LoDToString

* optimize if

* typo

* add lod_tensor to operator's dependency

We need this operator to assign value to a tensor and the values are stored in the program so that they can be used independent of python.

Async GRPC sendrecv

2. Add check grad test

* init data_type_transform

* split data_layout_transform

* tmp rm data_transform_test

* change device_data_transform to data_device_transform

* clean code

* clean code

glog will not hold ARGV[0] inside.

feature/parallel_gpu

* "init use all default devices"

* "fix init test"

* add lod tensor ToAbsOffset test

* add share lod to topk op and softmax op

Added backward.calc_gradient to backpropagate gradient from given targets to inputs.

* test dist word2vec

* multiple trainers work

* Add Seq2BatchFunctor, which will be used in WarpCTCOp.

* Implement WrapCTCFunctor and WrapCTCKernel.

* Add unittest of warpctc_op.

* Modify the check_output inferface in python unittest framework to allow check a subset of outputs.

* Use absolute offset lod in warpctc_op and related functors.

* Refine the comments of warpctc_op.

* The new python unittest supports checking a subset of the outputs, so revoke the previous change.

* Rename the transform from LoDTensor to Tensor with shape [max_sequence_length, num_sequences, sequence_width] to PaddingSequenceFunctor.

* Update to the newest codes.

* Rename the PaddingSequenceFunctor to PaddingLoDTensorFunctor and remove the computation of dimensions out of the functos.