* fix bugs of ClipGradByGlobalNorm

* add unittests

* add unittests

* fix global gather and global scatter operators

Add Tokenizer related functionalities for Transformer model in order that the process of training and predicting is consistent.

* support the text string as an input Tensor
* support the "VOCAB"unordered_map<wstring, int> as an input Tensor to lookup tokens
* Tokenizer used for BERT. This tokenizer applies an end-to-end, text string to wordpiece tokenization.
* It first applies basic tokenization, followed by wordpiece tokenization.

* bug fix for  DeserializeSelectedRows. test=develop

* fix bug for SerializeSelectedRows. test=develop

* update. test=develop

Add CINN compile option in CMake.

Now you can use CINN in Paddle by `-DWITH_CINN=ON` when `cmake`

To test it, you can run `make cinn_lib_test -j` and `ctest -R cinn_lib_test`. 

Note:
1. You should set
```
export runtime_include_dir=${CINN_SOURCE_DIR}/cinn/runtime/cuda 
```
When run test, the `${CINN_SOURCE_DIR}` should be set based on your CINN directory.

2. CINN is under developing now, you may have to change `CINN_GIT_TAG` to the git commit you need.

* fix

* remove const

* native commit for triple grad of sigmod

* Updated unittests files

* init functional jacobian api

* Updated trible_test func

* Updated gradient_checker & test_script

* finish test with dtype float32

* add float64 test case

* polish code

* use atol=1e-5 with dtype float64

* fix for ci

* set timeout for test_jacobian

* fix dygraph grad to support high differential

* polish API docstring

* Updated gradient checker and some related files

* fix double grad strip error for high differential

* fix double grad strip error for high differential

* Add Sigmoid triple grad tests

* fix dygraph double grad dtype error when calling for high differential senario

* Updated triple grad teses func

* Use np.random to initialize ddx

* Updated triple_grad_check func

* add todo for gradient checker and refine some comments

* remove additional code

* add test for warnging in backward.py

* add tanh triple grad

* format python code

* refine code

* make test_activation_nn_grad test time to 150s
Co-authored-by: Nveyron95 <veyron_wu@163.com>
Co-authored-by: Nlevi131 <limaolin01@baidu.com>

* default dist op

* add dist_attr for dist op

* add unitest

* update inputname

* update function name

* add unitest

* update CMakeLists.txt for CI

* fix dis_matmul

* fix compile error

* update matmul to matmul_v2

* unify api

* unify api

* todo

* update distop forward func

* update distop forward func

* auto parallel backward

* update dist op

* autoparallel backward

* add backward for embedding

* temp1

* temp2

* temp3

* temp4

* backward done1

* backward done2

* backward done3

* dist embedding remove mp mode

* dist matmul remove mp mode

* update dist embedding
『

* dist op init1

* dist op init 2

* update unitest

* context remove parallel mode

* partitioner remove parallel mode

* update unitest

* a more general method to support varying mesh in pipeline parallel

* support varying mesh in pipeline parallel

* embedding support varying mesh in pipeline parallel

* matmul support varying mesh in pipeline parallel

* default dist op support varying mesh in pipeline parallel

* dist attribute for startup program

* default dist op support varying mesh in pipeline parallel 2

* partitoner support varying mesh in pipeline parallel

* revise logic for auto compeletion

* revise framework.py

* revise reshard unitest

* revise unitest for parallelize

* chmod

* fixed bug for dist embedding name mapping
Co-authored-by: Nzhaoyingli <zhaoyingli@baidu.com>

* remove no_value using var.name

* fix unit test for CI

* fix unit test

* add test case

* fix test case

* add more test case

* Support elementwise_add triple grad Kernel

* Change code-format to follow CI std

* [NPU] Add iou_similarity op

* [NPU] Add iou_similarity op

* [NPU] Add iou_similarity op

* [NPU] update inference cmake, test=develop

* address review comments, test=develop

* fix compile error when WITH_ASCEND_CXX11 ON, test=develop

* update

* fix ut error

* update ut

* add feed op and new var for the generated subgraph

* perfect the test script of build_cinn_pass 

* remove useless clear and perfect some annotation

* add nearest_interp_v2 trt plugin

* fix replicate pad when input size is 0
* add unit test

* catch the generatorfunction and intercept it.

* add test generator

* add test case

* refine the testcase

* Add QR decomposition op

* Change codes to adapt to new svd_helper

* Update linalg.py

Restore the deleted comma

* Restore the deleted line

* Update linalg.py

* Update linalg.py

* Improve the qr code by reviews

* Update QR based on CI results

* Update qr doc, test=document_fix

* Change unsafe and ill-formed codes

* Add auto parallel cost model and unittests

* Fixed code styles.

* Fixed bugs and codes style

* fixed typo

* Improved code style: object encapsulation.

* Fixed codes.

* Refractored estimate_cost

* Fixed typo

* add pow2_warmup op

* remove contrib __all__

* add AttrT

* rename

* follow comments

* fix duplicate PADDLE_RESTRICT

* [HybridParallel]Support fp16 in dygraph hybrid parallel

* update

* update

* update for recompute

* add unittest of pp+fp16

* add unittest of recompute+fp16

* update

* modify ut

* added softplus

* refactored softplus op

* deleted unnecessary file

* added missing file

* added formatting

* disabled tests if GPU is used

* added reviewer suggestion

* unified softplus kernel

* init functional jacobian api

* finish test with dtype float32

* add float64 test case

* polish code

* use atol=1e-5 with dtype float64

* fix for ci

* set timeout for test_jacobian

* init hessian API

* save status

* polish API docstring

* modify docstring

* add utils.py

* save status

* fix dygraph double grad dtype error when calling for high differential senario

* reinvoke ci

* test_hessian.py is ok

* polish hessian API

* init vhp

* Revert "init vhp"

This reverts commit cbd4d3b66abe82b0ac10721b9eddeb7d82e0a1c8.

* init vhp

* finish vhp API logically

* add test for partial_engine.cc

* modify numerical_delta with dtype float32

* merge fix for dtype float64

* spell fix

* save status

* polish code

* rm _stop_gradient_pre_process

* save status

* add example for vhp interface

* add _compute_numerical_vjp and _compute_numerical_vhp

* test is ok

* vhp is ok

* add testVHPFloat64

* modify for comments

* modify format

* modify format

* save status

* test_vhp is ok

* finish code polish

* small modify for v is None
Co-authored-by: NJiabinYang <360788950@qq.com>

* add_quant_axis

* add_quant_axis

* --amend

* Update quant_conv2d_dequant_fuse_pass.cc

* fix async_read bug

* change index place to cpu

* add tensor size judge

* add async_read & async_write test

* fix bug in async_write

* fix mac py3 ci

* fix bug for cpu version paddle

* fix windows ci bug

* change input argument error type

* change const_cast to mutable_data

* add async_write out-of-bound check and consumate error hint

* fix a small bug for dst_tensor

* add docs and refine codes

* refine docs

* notest,test=windows_ci

* fix windows ci

* fix require

* fix code-block

* add core.is_compiled_with_cuda()