* add double yaml

* add inline func

* fix windows compile test case error

* test windows ci

* cmake add onnxruntime

* cmake add onnxruntime

* test windows ci

* auto_code_generator add ort lib copy

* fallback modify windows ci bat

* ci notest;test=document_fix;test=windows_ci_inference;test=windows_ci;test=windows_op

* merge all phi lib to several big static lib

* merge all phi lib to several big static lib

* fix memory issue for eager

* fix bug

[Phi]Separate AddKernel/DivideKernel/SubtractKernel/MultiplyKernel from ElementwiseKernel（Part1） (#41806)

* seperate add/div/sub/mul from elementwise

* delete code

* fix compile bugs

* deal with conflict

* fix bugs when compile

* fix windows unit test bug

* fix ci converage bugs

* create tensor by empty in custom op

* fix some bug

* [DoubleGrad] Enabled double grad test cases in eager_mode for test_imperative_double_grad

* Fixed elementwise issue

* Addressed CI failures

* [DoubleGrad] Enabled test_imperative_triple_grad test cases under eager_mode

* [DoubleGrad] Enabled test_autograd_functional_dynamic.py under eager mode

* Enabled more test cases

* [DoubleGrad] Enabled test_imperative_star_gan_with_gradient_penalty.py under eager mode

* Adjusted test_imperative_star_gan_with_gradient_penalty.py

* support no_need_buffer in eager_fluid state

* change no_need_buffer info from fwd_info to bwd_info

* fix CI fail, gru_unit donnot use no_need_buffer

* fix conflict between no_need_buffer and dispensable

* use tensor.define in dispensable

* solve conflict

* solve conflict

* change cudnn helper for auto-tune

* Add FLAGS_use_autotune to set the global status of autotune and change the order of choosing algorithm.

* Fix the bug in calculating and printing current step cache hit rate.

* Improve the autotune cache and fix unittest.

* Change the key from AlgorithmType to int64_t.

* Fix unittest for cpu-only env.

* change ChooseAlgoByWorkspace for heuristic mode
Co-authored-by: NLiu Yiqun <liuyiqun01@baidu.com>

* [DoubleGrad] Enabled double grad test cases in eager_mode for test_imperative_double_grad

* Fixed elementwise issue

* Addressed CI failures

* [DoubleGrad] Enabled test_imperative_triple_grad test cases under eager_mode

* [DoubleGrad] Enabled test_autograd_functional_dynamic.py under eager mode

* Enabled more test cases

* Fixed performance issues

* Fixed minor issue

* fix new dygraph record event

* refine name

* fix

* fix

* fix according to review

* [DoubleGrad] Enabled double grad test cases in eager_mode for test_imperative_double_grad

* Fixed elementwise issue

* Addressed CI failures

* [DoubleGrad] Enabled test_imperative_triple_grad test cases under eager_mode

* Fixed minor issues

* [DoubleGrad] Enabled double grad test cases in eager_mode for test_imperative_double_grad

* Fixed elementwise issue

* Addressed CI failures

* support setting vector out size in yaml

* support setting size of vector<tensor> for out in yaml

* fix_ci_problem3

* support windows no default error

* support final_state in multiprocess

* fix no place.device

* set device_id in eager_gen

* [Refactor] refactored eager_gen.py PR #2

* [DoubleGrad PR #1] Decoupled code generation logics for Dygraph ForwardFunctions and GradNodes

* Fixed minor issue

* Adjusted logics of GenerateNodeCreationCodes and GenerateForwardDefinition

* Fixed issues

* Supported higher-order grad node generation

* [DoubleGrad PR #4] Supported higher-order GradNode generation

* [DoubleGrad #4] Bug Fixes to Double Grad Node Generation

* Fixed yaml typo

* Fixed yaml typo

* fixed minor issues

* [DoubleGrad PR #5] Enabled gradient computations for grad_tensors passed to paddle.grad()

* Fixed minor issue

* Fixed CI-Inference issue

* Fixed CI-inference issues

* [DoubleGrad PR #7] paddle.grad() to copy backward graph before backward run

* Fixed minor issues

* Fixed issue with backward graph construction logic

* Fixed implementation issues with backward graph reconstruction

* Fixed unittest issue

* Fixed issues

* [DoubleGrad PR #8] Enabled triple grads for sigmoid and matmul

* Fixed issues with phi kernel

* Added triple grad test case

* Fixed minor issue

* eager math op, test=develop

* eager support lookahead, test=develop

* refine,test=develop

* refine doc, test=develop

* refine,test =develop

* refie, test=develop

* refie, test=develop

* refie, test=develop

* test_paddle_multiprocessing

* refine, test=develop

* refine, test=develop

* fix bug, test=develop

* refine, test=develop

* dataloader, test=develop

* refine, test=develop

* refine, test=develop

* refine, test=develop

* test_datasets timeout, test=develop

* refine, test=develop

* support inplace no_need_buffer

* fix

* use padle.add

* [Eager]Fix tile API final_state bug

* fix backward bug

* [Refactor] refactored eager_gen.py PR #2

* [DoubleGrad PR #1] Decoupled code generation logics for Dygraph ForwardFunctions and GradNodes

* Fixed minor issue

* Adjusted logics of GenerateNodeCreationCodes and GenerateForwardDefinition

* Fixed issues

* Supported higher-order grad node generation

* [DoubleGrad PR #4] Supported higher-order GradNode generation

* [DoubleGrad #4] Bug Fixes to Double Grad Node Generation

* Fixed yaml typo

* Fixed yaml typo

* fixed minor issues

* [DoubleGrad PR #5] Enabled gradient computations for grad_tensors passed to paddle.grad()

* Fixed minor issue

* Fixed CI-Inference issue

* Fixed CI-inference issues

* [DoubleGrad PR #7] paddle.grad() to copy backward graph before backward run

* Fixed minor issues

* Fixed issue with backward graph construction logic

* Fixed implementation issues with backward graph reconstruction

* Fixed unittest issue

* Fixed issues

* fix eager gen opti bug

* polish code

* fix some bug

* fix some bugs;

* support scale final state

* fix inplace error

* pass arg directly

* pass arg directly for inplace api

* fix type

* [Eager]Enhance eager_trace_op logic to support Optimizer Op

* fix AsDispensable

* [Eager]Fix 17 unittest and open check_eager=True

* remove print

* fix unittests

* fix op_testa

* fix coverage CI failed

* fix ci

* [Refactor] refactored eager_gen.py PR #2

* [DoubleGrad PR #1] Decoupled code generation logics for Dygraph ForwardFunctions and GradNodes

* Fixed minor issue

* Adjusted logics of GenerateNodeCreationCodes and GenerateForwardDefinition

* Fixed issues

* Supported higher-order grad node generation

* [DoubleGrad PR #4] Supported higher-order GradNode generation

* [DoubleGrad #4] Bug Fixes to Double Grad Node Generation

* Fixed yaml typo

* Fixed yaml typo

* fixed minor issues

* [DoubleGrad PR #5] Enabled gradient computations for grad_tensors passed to paddle.grad()

* Fixed minor issue

* Fixed CI-Inference issue

* Fixed CI-inference issues

* fix inplace fill_ and zero_ API

* add eager unittest