Optimize the CUDA implementation of sequence_expand op by reduce the times of copying lod data from CPU to GPU. (#15493)

* Optimize the CUDA implementation of sequence_expand op by reduce the times of copying lod data from CPU to GPU.
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* Refine the op benchmark to support setting lod in config.
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* This PR improve performance of prior_box op about 1.25x faster on CPU.

* Test Env:SKX 8180 with fake data on 28 threads(bs=1).
* The below table shows the ~25% improvement which generated by [eval_tp_fake_data.py](https://github.com/PaddlePaddle/Paddle/issues/15618#issuecomment-464613976).

| Type |Event | Calls |   Total     |  Min.    |   Max.      |  Ave.      |  Ratio.|
| ---------------- | ------------------ | ---- | ------- | -------- | -------- | ------------ | -------- |
| w/ optimization  | thread0::prior_box | 6000 | 921.201 | 0.110572 | 0.383402 | **0.153533** | 0.084585 |
| w/o optimization | thread0::prior_box | 6000 | 1151.85 | 0.102276 | 0.426702 | **0.191976** | 0.103337 |

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* Fix the style issue.

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* add alloc_continuous_space_op
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* Polish code
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* follow comment
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* refile profiler
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* follow comment
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* Refine doc of uniform_random and fix dtype
* Update defaule value in the arguments

* - Implemented draft of primitive desc keeping in Tensor

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- TransposeMKLDNNHandler::AcquireSrcMemory was reimplemented

- Added nchw and nc formats setting for sake of compatiblity

Fixed unit tests

- Worakaround to problem with 5D data in conv

- Added 3D and 1D MKL-DNN formats for name handles for tensor

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- Fix to UTs

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- Conv fp32 op was updated

Cosmetic fixes

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- tensor mkldnn cosmetics

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- Moved most of mkl-dnn specific code from Tensor to mkl-dnn utils

* - Lint fixes

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* - setting prim dec in Tensor , sets also layout to kMKLDNN

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* - Moved creation of prim desc totally out of Tensor

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* - Cosmetic fixes adter review

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reason: dereferencing smart pointer is the same as the underlying pointer
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* Revert "Optimze Gelu with MKL Erf function (#15770)"

This reverts commit 676995c8.

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* Initialize the benchmark tester for operator.
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* Rearrange the codes.
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* 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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* MKL-DNN: Add test for conv bias fuse pass

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* Remove const cast from Conv Bias Pass Test

* Add conv with bias test case for conv+bias fuse ut

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