* "re-commit "

* "picked up"

* "fix ci"

* "fix pdb hang up issue in cuda 9"

* "fix cuda9 error"

* "change commit id"

* "remote git tag"

* Add USE_OP of all operators and kernels and remove ARCHIVE_START/END in CMakeLists.txt of inference unittests.

* Remove ARCHIVE_START/END when linking inference shared library.

* Disable some fluid related cmake operations for cross-compiling.

* Update source files.

* Update headers

* Update

* Update

* Update

* Update

* Fix a CMake dependency

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.

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.

* 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

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

Fix grpc bugs

* 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.