* move standard apis to api.yaml

* revert erfinv

* delete dot_op.h

* fix dot

* rerun ci

* save multi program into one param and seperate model

* export class property

* add code

* add unit test

* support scope_guard

* fix test

* merge and add base support for non-local for

* for and while non-local support

* fix ci errors: v1

* fix bug

* fix

* fix code

* fix

* fix

* fix

* Add new attr of fused_multi_transformer

* fix format

* add note

* add in layer

* fixfixfixfix

* add relu6 kernel and yaml

* format files

* format code and fix bug

* fix build failed

* support complexd selected_rows kernel in yaml

* support configuring optimizer api in yaml

* fix data transform bug

* fixed bug for pass & engine

* fixed bug for benchmark GPT-3

* add comparisons trt converter

* move cross form legacy_api.yaml to api.yaml

* move diagonal to api.yaml

* skip xpu conv2d fp16 unitest
*test=kunlun

* minor
*test=kunlun

* [Dy2Stat]Polish all API name of _jst

* add unittest for PR43688

* [UPDATE FLUID API] only reference in paddlerec

* change lr

* [UPDATE FLUID API] only reference in paddlerec

* update by reviews

1. test_parallel_executor_seresnext_base_gpu failed on 2 P100 GPUs with `470.82` driver.
```
======================================================================
FAIL: test_seresnext_with_learning_rate_decay (test_parallel_executor_seresnext_base_gpu.TestResnetGPU)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/opt/paddle/paddle/build/python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext_base_gpu.py", line 32, in test_seresnext_with_learning_rate_decay
    self._compare_result_with_origin_model(
  File "/opt/paddle/paddle/build/python/paddle/fluid/tests/unittests/seresnext_test_base.py", line 56, in _compare_result_with_origin_model
    self.assertAlmostEquals(
AssertionError: 6.8825445 != 6.882531 within 1e-05 delta (1.335144e-05 difference)
----------------------------------------------------------------------
```
2. To be more accuracte on evaluating loss convergence, we proposed to apply IOU as metric, instead of comparing first and last loss values.
3. As offline discussion, we also evaluated convergence on P100 and A100 in 1000 interations to make sure this UT have the same convergence property on both devices. The curves are showed below.
![A100-Single, P100-Single and Diff (1)](https://user-images.githubusercontent.com/13541238/175461920-25df6101-6dd8-4387-862c-d1c8e9299c57.png)

* [Sparse]add SparseTensor mv kernel(csr*dense_vec->dence_vec, coo*dense_vec->dense_vec)

* fix CI

* inplace op without backward supports input of multiple inplace vars

* support inplace without backward in eager mode and fix python_c in eager_fluid

* fix output in yaml

* fix map to const&

* solve conflict v2

* enable Jacobian,Hessian supporting new autograd

* fix prim mode failed in PR-CI-Windows

* add forward_gradients api

* add forward_gradients api

* skip test_autograd_functional_prim in windows ci

* fix test_autograd_funciton_prim timeouot

* remove the block parameter in prim2orig method

* remove duplicate to_tensors code snippet # test=allcases

* [Dy2Stat]Refactor convert_shape transformer logic

* clean usless unittest

* rename eagerpylayer

* move apis from legacy_api.yaml to api.yaml

* remove comment

* revert argsort

* fix merge conflict

* [CustomDevice]add custom place supports

* sync format

* Update kaiming.py

* Update initializer.py

* fix doc bug;test=document_fix

* fix doc;test=document_fix

* Update initializer.py

* Update kaiming.py

* for ci;test=document_fix
Co-authored-by: NLigoml <39876205+Ligoml@users.noreply.github.com>

use jinja 2.11.3 in python lower than 3.6.2