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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* - 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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* refine profiler && add runtime tracer

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* fix bug && test=develop

* add thread id map && test=develop

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* testing

* bug fix

* remove cuda event && refine code && test=develop

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* fix windows temp file && test=develop

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* fix windows bug && test=develop

* fix start up issue && test=develop

* code polish &&  test=develop

* remove unused code && test=develop

* add some cupti cbid && test=develop

* add FLAGS_multiple_of_cupti_buffer_size && test=develop

* fix compile error && test=develop

* add keyword && test=develop

* fix && test=develop

* code polish && test=develop

* Enable momentum operator for a ngraph engine
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* Update tests
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* Unnecessary line of the code as intended was removed
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* fix warnings

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* fix enforce test

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* Enable cross_entropy operator for a ngraph engine
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* Update tests
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* Added PADDLE_ENFORCE for the batch_norm operator
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* Update the message about which format are supported right now
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* Correct the comment in control_flow.py.

* Correct the argument list of ops.
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* Update API.spec.
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* Skip op_callstack attr for all op apis.
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* Remove use_mkldnn and is_test from python api.
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* Remove use_mkldnn and is_test from op_proto_maker and hard-coding them in python when generating doc string.
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* Add the CRFDecoding and LayerNorm's test case

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* Fix the size checking issue

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* Remove the remnant code

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* Add TestAllImpls and double support

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* Clean Code

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* Add benchmark test for LayerNorm & CRFDecoding

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* inplace group

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