[Cherry-Pick 2.0][Dy2Stat] 1. Fix bug of for-range stmts. 2. Support that step value is negative in for-range stmts (#29519) (#29874)

1. Fix error in _build_cond_stmt of for-range stmts.

2. Support that step value is negative in for-range stmts

3. Fix code because of the diff between Py2 and Py3

* [Dy2stat] Enable jit.save to Save Without Running (#29579)

Enable jit.save to Save Without Running.

* Modify CublasHandleHolder to Fix Random Unittest Failure. test=develop (#29617)

Modify CublasHandleHolder from using PADDLE_ENFORCE_CUDA_SUCCESS to PADDLE_RETRY_CUDA_SUCCESS to fix random unittest failure. We checked that the unittest log showed CUDA allocation error at this file, which may due to GPU not enough. We fixed similar failure in the past, so we applied PADDLE_RETRY_CUDA_SUCCESS here.

* fix fleet for multi-stream

* fix memcpy for ncclid

* use sync to solve move operation

* Fix the dowanload bug in the case of multiple machines (#29551)

* fix the dowanload bug
* add sort for ips

* Fix bug of matmul_v2 for broadcast case (#29599)

* fix bug of matmul_v2 for broadcast

* Rebuild group automatically in dynamic graph distributed (#29255)

* add tensor_indices in AssignGroupBySize

* add rebuild group in reducer

* fix error message of gather nd (#29521)

Fix Layer train eval setting failed in static mode, more details please see #29540

* fix the bug of reducer in embedding

* rm complexvariable

* modify test_var_base unittest

* remove duplicated codes

Reduce exception type so that if covert_to_static failed, it reports right error message.

[cherry-pick 2.0rc1][Dy2Stat] Fix bug: Do not use gast.Subscript to replace gast.Name in when transforming for_enumerate_loop (#29310) (#29361)

* basic impl of type promote

* add comment & another testcase

* fix complex bugs & support python op promote type

* fix failed unittests & polish code

* add unittest for coverage

* change to only promote complex type

* polish code details

* polish several comments

[Cheery-Pick 2.0.0-rc1][Dy2stat] Add a decorator paddle.jit.not_to_static to support that not to convert a function in Dynamic-to-Static. (#29253) (#29340)

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.

* use has_grad instead of train_mode

* add vlog for debug

* fix ut

* fix ut

* Change the api of DataParallel and Fleet (#29224)

Cherry-pick of PR #29226

* The leaf tensor concept is exposed and the gradient accumulation of leaf tensor

* The leaf tensor concept is exposed and the gradient accumulation of leaf tensor

* fix coverage

* fix api doc

* fix CI unittest

* fix CI unittest

* fix unitest

* empty tensor does’t need inner_var_

* fix some error message

* 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.

* Changed a variable name error

* Add comments

* Move member functions of TranslatedLayer out of function

* edit code according to review

* Edit input argument of '_run_static_graph'

* reset due to Segmentation fault

* rename variables when stitching graph

* modify code according CI

* Add comments to '__i_m_p_l__'

* remove blanks befor 'Get...'

* edit code according to review

* Add a comment to '_execution_method_creator'

* Edit a comment to '_execution_method_creator'

[Dy2Stat] Fix bug: the return statement should be transformed to an equivalent Paddle/Python if statement, which depends on if conditions of the return stmt. (#29165)

* add reducer

* refine envent for memorycopy

* add concat&split for allreduce

* apply concat & split for fuse tensor

* fix nccl dep

* fix the untest, compile problem and ddp initialize problem

* fix untest for mac & add some comments & solve the repeated param in sublayers

* fix untest for windows & fix document

[Dynamic-to-Static] Support **kwargs as input of the function which is decorated by `jit.save.to_static` (#29098)

* split train_mode and has_grad

* fix format

* fix ci problems

* fix sample code

* Rename variables when use 'jit.load'

* Check whether the original graph contains the variable with the same name

* add comment

* rename output/input of op and edit unittest

* modify the code according to CI

* edit code according to CI

* edit code according to CI

* edit code according to CI

* edit code according to CI

* edit code according to CI

* edit code according to CI

* Support pop for dict in dy2stat

* Move convert_pop to convert_operators.py and polish convert_pop

* polish parallel api impl & doc details

* add unittest for coverage

* remove spawn test in py2.7

* add parallel api into white list

* fix some docs test=develop;test=document_fix

* add code example test=develop;test=document_fix

* fix code example test=develop;test=document_fix

* fix code example test=develop;test=document_fix

* fix code example test=develop;test=document_fix

Add support for using tuple as tensor.shape (For example: a, b, c, d = x.shape)

* upgrade comment string to raw string

* fix string in

* fix string with ' '

* revert update on comments

* upgrade only necessary

* fix sample code checker

* fix comments with '''

[Dynamic-to-Static] Fix bug of convert_logical_and/convert_logical_or: the operands are executed sequentially(#28993)

  
  1) The operands are executed sequentially according to the running logic of Python.
  
  2) If the left hand operand is True(for convert_logical_or)/False(for convert_logical_and), the right hand operand should be executed.

The PR description is long. See details in the PR link.

* fix eng doc, test=develop

* add import deprecated for layers, test=develop

* add block line for doc generate, test=develop

* remove todo for create_variable, test=develop

* add blank line for doc generate, test=develop

* add blank line for doc generate, test=develop