* Rearranged Eager AutoCodeGen directory structure

* Removed USE_OP in Eager AutoCodeGen

* Enabled generation for Operators without Grad/Inputs/Outputs

* Resolved operators without input

* Fixed merge conflicts

* Enabled Eager AutoCodeGen for 10+ more operators

* Refactored Eager AutoCodeGen with more organized helper objects

* Enabled Eager AutoCodeGen for operators with multiple OpBases

* Adjusted Eager AutoCodeGen to Enable Passing Output Tensor as Input Argument

* Handled Dispensable Inputs/Outputs in Eager AutoCodeGen

* Adjusted function generation/call between Python-C API & Dygraph API

* Synchronized auto-generated Python-C API with Dygraph Forward Functions

* support more eager tensor api

* fix merge compile error

* fix compile error and fit develop code

* support pure CPU

* fix some logic error in eager_mode

* support _varbase_creator in eager mode

* Added safe_initialized interface to EagerTensor for use in processing dispensable inputs

* for eager mode

* refine

* support multiple constructor for eager tensor

* add place related code

* polish code

* specific randint with dtype of int64

* Support pure cpu test

* eager logic

* refine test in pure cpu

* eager logic

* eager logic

* eager logic, test=develop

* skip core.eager when in inference, test=develop

* refine, test=develop

* refine, test=develop

* call RetainGrad after run forward kernel, test=develop

* refine, test=develop

* support dygraph util, meta, guard test

* support inference test

* refine test and fix initializer failed
Co-authored-by: Njim19930609 <jim19930609@gmail.com>
Co-authored-by: NWang Huan <wanghuan29@baidu.com>

* add inplace_map for trace_op in pybind

* fix inplace problem of setitem

* refactor the param format  of trace_op
Co-authored-by: Npangyoki <pangyoki@126.com>

* fix bugs in Translated layer when change train/eval

* fix python converage

* fix error when tensor_shape_transformer. Before in stmt like `if len(paddle.shape(x)[0]) > 0`, `paddle` will be used as a variable

* handle other call like `fluid.layers.mean` and `fluid.layers.shape`

* add unit test

* layer.to api support numpy.dtype and paddle.dtype

* skip the layer to eaxmple code executing for env not support

* allow ifelse return `int` in paddle cond

* add test and refine code

* polish code, add test

* code format

* remove outer comment when dy2stat

* remove all comment

* add unit test

* polish for zip in dy2stat

* polish comment

* polish is_builtin_len

* fix comment

* optimize dygraph probl

* refine code

* fix convert dtype error

* fix import datafeeder error

* optimizer __call__ to make dygraph faster

* fix return type

* remove cpp Layer

* optimizer __call__ to make dygraph faster

* fix return type

* amp.decorate optimizers set to None is ok

* refine unittest

* add unittest and refine example code

* refine unittest

* fix data parallel when VOCAB var in program

* run dy2stat pure fp16 in Linear model

* no use self._pure_fp16_inputs

* add test and fix Adam error in dy2stat pure fp16 training

* use paddle.optimizer.Adam

* run test in gpu

* change test time for CI

* enlarge atol for test_resnet_pure_fp16

* refine code and enlarge atol

* make custom_white_list and custom_black_list take effect for AMP and pure fp16

* check tracer is not None

* use default atol

* change filter_size

* change atol and add some NOTE

* Added HCCL backend support in dynamic graph mode

* fix segmentation fault

* add ut

[Dy2stat]Allow users to switch eval/train mode when using @to_static to decorate a function (#37383)

* Allow users to switch eval/train mode when using @to_static to decorate a function

* refine code for train() and eval()

* support `for i in [1,2,3]` statements in dy2stat

* add test case

* fix ci

* remove wrong code

* Expose func for varbase

* Expose func for varbase and enhance varbase init func

* Change func name and add test case for _CopyGradientWith

* Rename func

* Add test cases to increase coverage

* Refine the logic of _to func

* Replace numel() with _numel(), Add test code

* refine layer to

* delete comment

* refine logic

* refine code

* refine pure_fp16_init

* refine comment

* Support Py3 type annotations in @to_static

* support type hint for args in func

* support type hint assign

* if annotation and value(Constant) are diffent type, we use value type

* polish type_from_annotation()

* code format

* code format

* remove useless commentary

* fix review
Co-authored-by: NAurelius84 <zhangliujie@baidu.com>

* Layer.to reutrn self

* add device required

* remove additional warnning in layer.to

* remove additional warnning in layer.to

* remove additional warnning in layer.to

* remove additional warnning in layer.to

* remove additional warnning in layer.to

* [hapi] support dygrapg amp O2

* fix problem of static pure fp16 in hapi

* fix bug

* fix format

* fix ut

* follow comments

* update ut

* update amp save/load

* fix ut

* refine code format

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.

* remove no_value using var.name

* fix unit test for CI

* fix unit test

* add test case

* fix test case

* add more test case

* catch the generatorfunction and intercept it.

* add test generator

* add test case

* refine the testcase

* fix no_grad context error in train mode when using save/load

* change net to train mode in test case

* add no_sync for parameters sync

* add pipeline for moe

* add attr is_distributed

* refine code

* refine black/white list for pure fp16

* refine amp level

* fix typo

* update tracer._amp_level

* modify format

* modify format

* change time to remove static tempfile

* delete remove_static_file() function