* skip forward save

* fix bug

* more ci for jit skip forward

* jit.save support peropty serilization

* extract set property function

* fix property test file name

* fix typing error

* fix typing error

* fix test coverage

* Fix jit.save error when save params combined

* Change dict_value to list

* change fluid.mean to paddle.mean

* reverse some old code examples

* save multi program into one param and seperate model

* export class property

* use yapf to format all python file

* yapf exclude two unittests file for they rely on writing and reading file, and format will break them

* disable diff_py_file because too many diff files cause command following failed

* [Dy2Stat]Fix losting pre/post hook from outermost layer while jit.save

* fix kwargs

* fix unittest

* refactor eager flags

* fix flags error when we switch from eager to dygraph

* fix ci problem

* fix ci

* fix ci

* merge develop and fix code style

* merge develop and fix code style

* fix op test error

* fix op test error

* fix op test error

* fix op test error

* fix op test error

* merge develop

* for test_mnist.py

* remove comments

* using type() replace isinstance()

* valid vars for run program OP in io.py

* open test_mnist in eager_gurad for coverage

* eager, test=develop

* fix bug, test=develop

* eager, test=develop

* merge legacy to fluid

* eager, test=develop

* eager, test=develop

* Refactor TensorAdd func by template and remove gradient_accumulation in eager

* Remove needless target name

* eager, test=develop

* eager, test=develop

* Use overload instead of template

* Remove legacy code

* Remove legacy code

* selectedrows, test=develop

* Remove DataType test

* eager, test=develop

* eager, test=develop

* support gan, test=develop

* Using Tensor directly instead of using EagerTensor

* support gradient_accumulation

* make test_imperative_lod_tensor_to_selected_rows longer

* make test_imperative_lod_tensor_to_selected_rows longer

* refine code

* ptb, test=develop

* Rename all EagerTensor to Tensor

* Rename some EagerTensor to Tensor

* rename EagerTensor to EagerVariable

* eager, test=develop

* eager, test=develop

* eager, test=develop

* eager, test=develop

* add more test

* eager, test=develop

* Support copiable selected rows and merge develop

* save load, eager, test=develop

* save load, eager, test=develop

* refine, test=develop

* remove useless _set_value method

* refine, test=develop

* refine, test=develop

* revert static_runner, test=develop

* EagerTensor to Tensor, test=develop

* refine, test=develop

* refine, test=develop

* clear grad, test=develop

* merge, develop

* merge, develop

* merge, test=develop

* merge, test=develop

* Support quant and part of slice

* support legacy static save

* extend slim tests time

* remove imperative on inference

* remove imperative on inference

* merge develop

* fix typo

* fix typo

* split slice related code into 2 part for imperative and eager

* split slice from inference

* split slice from inference

* fix test_tensor_register_hook
Co-authored-by: NWang Huan <wanghuan29@baidu.com>
Co-authored-by: NWeilong Wu <veyron_wu@163.com>
Co-authored-by: Nwanghuancoder <wanghuancoder@163.com>

* Pre-save hooks of jit.save

1. Added pre_save_hooks features to jit.save.
2. Added related unittests

* Added jit pre_save_hooks functions's alias to paddle.jit and copyright.

* Make jit.save_pre_hook style be consisent with Paddle's rule.

* Fixed arguments passing bug in run_save_pre_hooks

* Added API Documents

* Move clear and run_pre_save_hooks as internal methonds only.

* Made register_save_pre_hook as an internal function.

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.

* add pure fp16 major function in auto_cast & tracer

* support master weight in dygraph for pure fp16

* check mix dtype of fp16&fp32 for check_finite_and_unscale op

* change pure fp16 funtion name

* refine some bug in auto_cast

* refine auto_cast interface logic

* add param _casted_by_pure_fp16 for class Layer

* support state_dict hook for save model by user appointed dtype in pure_fp16_decorator

* refine pure_fp16_decorator as decorator

* add unittest

* add comment

* add comment

* support recompute

* add comment for auto_cast and decorator

* support to_static_state_dict for paddle.jit.save

* unlimite models num and optimizers num

* add lookup_table in black_list

* fix momentum and layer state_dict

* fix bug in layer state_dict

* fix bug in layer state_dict_helper

* refine unittest

* refine test_momentun_op

* refine interface and some code

* refine amp_decorator interface

* refine pure fp16 interface

* refine master weight interface

* clip op extra information when export model,test=ocr

* rename clip_extra parameter to kwargs in save_inference_model, test=ocr

* Add build_strategy in @to_static to support open pass

* fix os.environ

* add timeout

* disable test_build_strategy on openblas

* jit.save/load support method with parameters.

* add unittest and warning

* polish warning message.

* support non-tensor type

* fix unittest failed

* add unittest with prune

* rm unused code

* coverage

* fix two or

* jit.save/load support function.

* delete unnittest test_jit_load_model_incomplete.

* edit code according to CI

* Modify the documentation.

* add note to doc.

* Modified params of some APIs to support tuple and list.

* fixed bug.

As the title

* fix bug with jit.save

* refine code

Enable jit.save to Save Without Running.

[Dy2stat] Add a decorator paddle.jit.not_to_static to support that not to convert a function in Dynamic-to-Static. (#29253)

Usage scenarios：A function could have run successfully in static mode,  you can use it to decorate a function in the following cases:
  1. An unknown error occurs in the dynamic-to-static conversion process of the function;
  2. In the internal implementation of the function, it has two branches: dynamic branch and static branch;
  3. Users don't want to convert the function in the process of dynamic to static.

Modify ProgramTranslator and TracedLayer Doc for API 2.0

* refine jit.save/load to add support for other method, not only forward

* refine the code based on unit tests

* Add unit test for the code

* Add unit test for the code

* Modify the code according to the unit test

* Delete useless comments, save only one info file, etc.

* remove static_mode_white_list.pyc

* edit the code that generate 'extra_var_info'

* polish save load en doc details, test=document_fix

* polish details, test=document_fix

* remove blank, test=document_fix

* fix ascii error, test=document_fix

* update fc's input to x, test=document_fix

* update fc argument activation, test=document_fix

* replace config by kwargs

* change save path form dir to prefix

* fix failed unittests

* revert unittest name change

* polish en docs

* add more tests for coverage

* rename StaticLayer

* rename

* init commit of new save/load

* fix failed unittests

* fix save_load_v2 unittest failed

* fix failed unittest & polish doc

* add tests for coverage

* add more tests & move static apis

* fix example code error

* polish emample code

* fix detail example code problem

* Add env value to  log to stdout; 2.Add logger name

* Optimize log messages in dygraph-to-static

* Replace logging.warn and warnings.warn with logging_utils.warn

Change ProgramTranslator.enable_declarative to ProgramTranslator.enable_to_static to meet API 2.0