* full api fix

* when out is None, go old dygraph mode

* fix

* add name for buffer

* fix by code review

* fix

* by static check

* Use `self`as a parameter of _hash_with_id function to avoid error caused by hash_id reuse (#41200)

* Add fill_constant_batch_size YAML and UT (#41474)

* Switch some dy2st UT to eager mode (#41382)

* Sitch some dy2st UT to eager mode

* Fix test_lstm and remove test_transformer

* Run test_resnet_v2 in old dy mode

* add concat_grad kernel

* fix error

* remove comment code

* fix outs nullptr error

* change to phi header

* add concat_grad declare for standalone_executor_test

* add concat_grad yaml

* add concat api

* fix test concat op error

* fix test concat op error

* add randperm and range yaml

* add eager test for randperm

* support C_ops assign

* open unittest

* fix clone

* update docs dtype(core.VarDesc.VarType)

* fix code style, test=document_fix

fix code style, test=document_fix
Co-authored-by: NChen Long <1300851984@qq.com>

* 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

* fix some slice bug, test=develop

* eager slice, test=develop

* eager slice, test=develop

* refine, test=develop

* refine, test=develop

* fix bug, test=develop

* refine, test=develop

* rename function name, test=develop

* Add stft_op.

* Add stft_grad_op.

* Add stft_op unittest.

* [DLTP-45176] Add complex compatibility in static mode for stft api.

* [DLTP-45176] Add complex compatibility in static mode for stft api.

* Add doc.

* Update unitests of stft op.

* Update spectral helper.

* fix coding style.

* 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>

* add more op int16 support

* fix xpu ci

* Enhance error msg in paddle.assign in static mode

* fix unittest

This PR supports gradient clip (ClipGradByGlobalNorm) when training with AMP(auto mixed precision).

* Enable BF16 for creating global tensor and reduce_mean.

* Functional test with small model.

* cache core.ops, test=develop

* refine, test=develop

* opt dygraph python code, test=develop

* refine, test=develop

* del py2 code2

* fix test timeout

* add private api for memcpy_op

* change place dtype

* add private python api for memcpy op

* fix uint8 check in assign

* fix assign unittests

* fix xpu test_assign

* Add param_guard in ParameterList to support @to_static

* Refactor param_guard of @to_static

* fix unittest failed

* add more unittest

* Add casting initializers for bf16 training

* Changes after review

* Correct test and add comment

* fixed docs.

* Fixed docs. test=document_fix

code bak.

fixed docs. test=document_fix

* Revert to previous version of python/paddle/fluid/backward.py

* fixed bugs.

* test=document_fix. Fixed examples.

paddle.to_tensor will be translated to paddle.assign in Dy2stat, however paddle.assign doesn't support int, float, bool. This PR added the supports.

`range` API set its output shape in dygraph but not in static graph, which can cause Dy2stat error. This PR set the shape of `range` API when possible.

* delete paddle.nn.functional.assign

* fix dynamic to static error

* add dispenable input 'shape' for core.ops.reshape2

* add dispenable inputs for core.ops.reshape2/expand/slice

* add ut

* fix DLTP-15151, paddle.ParamAttr API

* fix DLTP-15083/DLTP-15274, paddle.nn.functionl.assign paddle.cast API

* fix DLTP-15431/DLTP-15432, paddle.static.nn.conv2d paddle.static.nn.conv2d_transpose API

* fix DLTP-15083, paddle.nn.functionl.assign API

* fix DLTP-15431/DLTP-15432, paddle.static.nn.conv2d paddle.static.nn.conv2d_transpose API

* support in_dygraph_mode for cast op, test=develop

* fix bug,test=develop

* fix doc

* fix DLTP-15431/DLTP-15432, paddle.static.nn.conv2d paddle.static.nn.conv2d_transpose API

* pass stop_gradient for cast op

* improve performance of elementwise_add grad

* use tensor copy async

* dygraph branch

* fix dygraph branch

* add ut

* Add a class TensorInplaceVersion to count the inplace version and put it in framework::Tensor instead of Allocation or Variable.

* Add a new attribute `_inplace_version` for VarBase.

* Raise exception if an inplace operation can result in incorrect gradient computation.

* Add a new interface _bump_inplace_version() for VarBase to bump the version whenever the Tensor is modified through an inplace operation.

* For api assign, call _bump_inplace_version() when it's an inplace operation inn dynamic mode.

* Use original var_wrapper if the inplace_version is not changed.

* Replace SnapshotVarWrapperList with SnapshotVarWrapper to optimize performane.