1. Add option WITH_AMD_GPU.
2. Add cmake/hip.cmake for HIP toolchain.
3. Some external module such as eigen may need HIP port.
4. Add macro hip_library/hip_binary/hip_test to cmake/generic.cmake.
5. Add one HIP source concat.hip.cu as an example. Each .cu may have its corresponding .hip.cu.

* fix nccl op unit test

* fix build error

* format code

* refine nccl related unit test

* fix build error

* add setGPUData

* clean up

* follow comments

* rm test_nccl.cu

* follow comment

* rm wait

* compile and install the static library of fluid inference

* fix dynload_cuda not in CPU mode

* update shared library and adjust the deploy of openblas

* adjust the deploy of openblas

* * auto add all fluid modules for static library
* use libprotobuf.a instead of libprotobuf-lite.a for profiler

* use set_property to set the global varible instead of ENV

* add gpu depends of fluid modules, auto add inference_lib_dist depends

* change the condition of openblas_lib, and fix a typo

* Make CUPTI_LIB_PATH not passing by macro.
* Add missing header

* refine inference_lib_dist after code move, and add it to docker/build.sh

* remove is_directory in inference_lib.cmake

* It will sightly faster compile and make warpctc compile
well on CUDA 9 and GCC 5.5

Enable whole-archive flag in cc_test and use cc_test to rewrite the CMakeLists.txt of inference unittest.

* "fix decode bug"

* "follow commnet"

* "fix error"

* "fix hook bug"

* fix based comment

* fix copyright

* fix based on comment

Fix grpc bugs

* add copyright hook

* add copyright hook

* refine copyright hook

* "test copyright hook"

* fix check style

* fix ci

* 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.