remove unused accuracy-diff warpctc-cudnn implementation

test=develop

* support tensor input with padding for warpctc op

* merge with develop

* test=develop

* modified python API examples test=develop

* nn.py is modified for code coverage test=develop

* update documents info about warpctc op in API.spec test=develop

* add test_warpctc_with_padding in test_layers test=develop

* add warning log for cuda_version back to warpctc_op.cc

* modify API.spec for warpctc op test=develop

* modify API.spec

* update warpctc test to new CompiledProgram API test=develop

* modify code examples for warpctc op test=develop

* modify API.spec for warpctc op test=develop

* modify API.spec for warpctc op test=develop

* Cherry-pick fix random Python3 CI failure.

In some tests, SWEs used "print('xxx').format('xxx')". The syntax
is only supported in Python2, not python3. However, since those
lines are related to data download, if the CI machines already have
the data, it passes CI tests. That causes random failure.

* Cherry-pick: disable CUDNN case of test_warpctc_op

Also temporary disable a unit test. The test will be fixed under high priority.

TODO: it is a temporary fix for Paddle release 1.5. We have to fix
this failed unit test soon.

test=develop

* add cudnn ctc loss

* wip add test test=develop

* wip

* wip

* done test=develop

* move include cudnn test=develop

* test test=develop

* fix build test=develop

* fix build test=develop

* fix build on cudnn5 test=develop

* fix cudnn5 build test=develop

* fix cudnn5 build test=develop

* merge develop softmax functor change test=develop

Fix str and bytes problem
Fix sorted problem
Fix math problem
Fix CI problem

* add lod_tensor util and modify pybind

* refind pybind LoDTensor API and modify LoDTensor and DataFeeder test

* fix test error

* fix detection map op test

* fix reorder_lod_tensor test

* fix seq_concat_op

* fix chunk evel op test

* fix target assign op

* fix warp ctc op

* address comments step 1: reverse reset_lod op

* step 2: modify op test

* add warning message

* remove has_valid_lod

* add back has_valid_lod

* address comments

* add exception catching trial

Currently, our tests run with 2 GPUs, the init time is absurdly long:
about 4s for each process.  Currently, we run each OP test on
different processes. This PR:

1. create cmake function py_test_modules which will generate the
Makefile that runs a list of Python unittest module in a single Python
process.

2. move all "python unittest compatible" (e.g., used the unittest
package, not just a regular python file). from fluid/tests to
fluid/tests/unittests.

3. cmake now will run all OP tests in fluid/tests/unittests in a
single process, except the time-consuming tests, they are separated
into different processes to utilize parallelism. Please make sure to
use the unittest package if you put the python test file in
fluid/tests/unittests

4. remove all exit(0) from fluid/tests/unittests/*.py, exit(0) is used
to disable unittest, we can not do it when running all tests in a
single process since it will terminate the process without running the
other tests. Instead, the test is disabled in
fluid/tests/unittests/CMakeLists.txt. FIXME is added for each disabled
item. Please disable the unittest from
fluid/tests/unittests/CMakeLists.txt, instead of adding exit(0) to the
Python file, for all Python file in fluid/tests/unittests/.

5. add an option WITH_FAST_BUNDLE_TEST. When OFF, will run the unit
tests in separate process so that they can be tested individually.

* "fix decode bug"

* "follow commnet"

* "fix error"

* "fix hook bug"

* fix based comment

* fix copyright

* fix based on comment

* 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

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

2. Add check grad test

* 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.