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refine code

fuse cublas  calling and kernels into one cuda kernel.

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Signed-off-by: Nzhaoyuchen <zhaoyuchen01@baidu.com>

* add attr axis infershape. test=develop

* add CUDA kernel. test=develop

* fix unittest. test=develop

* fix unittest for soft_label. test=develop

* fix fp16 unittest. test=develop

* remove comment code. test=develop

* refine test for axis. test=develop

* add python api. test=develop

* fix doc. test=develop

* fix fp16 unittest. test=develop

* fix ngraph test. test=develop

* fix ENFORCE for test_imperative_transformer. test=develop

* fit for ngraph test. test=develop

* fix after rebase develop. test=develop

* fix doc. test=develop

* fix API.spec. test=develop

* fix test_layers. test=develop

* fix format. test=develop

* Support seq len equal to 0 in sequence ops

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* Add more test cases

* Fix some comments

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* Fix py3 error

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* fix overflow by int32 mul test=develop

* fix reference nullptr

* fix codestyle test=develop

* modify to point in ContextProjectFunctor test=develop

* modify to point in ContextProjectFunctor test=develop

* modify . to -> test=develop

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Make parent_idx a dispensable output for beam_search op to support models saved by older paddle version. (#16106)

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Make parent_idx a dispensable output for beam_search op to support models saved by older paddle version. (#16106)

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* Revert "Optimze Gelu with MKL Erf function (#15770)"

This reverts commit 676995c8.

* test=develop

* Optimize for gelu operator

* Set up the low accuracy mode of MKL ERF function.

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* Only enable MKLML ERF when OS is linux

* Use the speical mklml version included vmsErf function to verify gelu mkl kernel.

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* Add the CUDA macro to avoid NVCC's compile issue.

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* Add the TODO comments for mklml library modification.

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* Clean Code

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* Add the comment of marco for NVCC compiler.

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* Refine beam_search_op to output an extra parent_idx tensor.
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* Fix the unittest test_beam_search_op.
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* Fix the merging mistake.
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* Refine the beam_search op and test.

* A basic CUDA implementation of beam_search for small batch_size.

* Implement CUDA kernel for beam_search_op.

* Use multiple CUDA threads in the same block to select the top beam.

* Update the python api of beam_search op.

* Enable extend function in CPU kernel of beam_search op.

* Unify the CUDA codes.
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* Unify the CPU kernel of beam_search op.

* Ensure the seletced items of beam_search_op's CPU kernel sorted by scores.

* Update the description of beam_search in API.spec.

* Enable the use of CUDA kernel in beam_search op.

* Exclude the beam_search's CUDA unittest when there is no CUDA gpu, and delete some debuging statements.
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* Follow comments.
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* Call the CPU kernel for beam_search op when batch_size > 4.
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* Remove the except of is_empty op in PrepareData.
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* nce add check sample lables, test=develop