Cherry-pick of #31051 and #31082

* Add Support for Tuple in for Loop (#30998)

Dy2stat didn't support tuple as iteration variable in the past. This PR added there main cases:

       1). Non-enumerate case: for var1, var2 in var|var.numpy() will be re-written as:
          for FOR_ITER_TUPLE_PREFIX_x in var | var.numpy():
            var1 = FOR_ITER_TUPLE_PREFIX_x[0]
            var2 = FOR_ITER_TUPLE_PREFIX_x[1]
        2). Enumerate out tuple case: for t in enumerate(var|var.numpy) will be rewritten as:
          for FOR_ITER_TUPLE_INDEX_PREFIX_x, FOR_ITER_TUPLE_PREFIX_x in enumerate(var|var.numpy):
            t = (FOR_ITER_TUPLE_INDEX_PREFIX_x, FOR_ITER_TUPLE_PREFIX_x)
        3). Enumerate inner tuple case: for i, (var1, (var2, va3)) in enumerate(var|var.numpy()) will
        be re-written as:
          for i, FOR_ITER_TUPLE_PREFIX_x in var | var.numpy():
            var1 = FOR_ITER_TUPLE_PREFIX_x[0]
            var2 = FOR_ITER_TUPLE_PREFIX_x[1][0]
            var3 = FOR_ITER_TUPLE_PREFIX_x[1][1]

* Refine fake_interface Error Message (#30981)

Refine fake_interface Error Message

cherry-pick #29998
* Polish and Optimize the print/repr message of all layer
* fix some code format

[Dy2static]Fix paddle prefix in is_paddle_api (#30569)
cherry-pick #30569

[Cherry-pick] Fix prune input bug of jit.save

cheryy-pick of #30384

* set expected place in child thread for dataloader

* set device id when set tensor from numpy

* revert tensor_py change

* add compile guard

* fix ci

* fix bug

* Fix bug for 'save mutiple method'

* To pass coverage.

* edit code to pass coverage.

* edit code to pass coverage.

* add unittest for coverage.

* change for coverage.

* edit for coverage.

Cherry-pick of PR #30208 , this PR added clone method for static Variable so that this interface will be same as dygraph. It fixed some bugs in dy2stat where users called clone of dygraph Tensor.

cherry-pick #28769, add support for place string representation 

* fix tensor shape bug

* fix op_num

* clean code

* fix dynamic to static error

* delete paddle.nn.functional.assign

[cherry-pick] [Dy2Stat] Don't convert to paddle.shape if var_x.shape is not negetive #29965 (#30235)

* [Cherry-Pick 2.0] [Dy2Stat] Don't convert to paddle.shape if var_x.shape is not negetive (#29965)

1. When x is Variable, call nn.shape(x) only in following cases:
 1）The shape of x is used in control flow condition.
 2）The dim to be used is negetive
2. When x is Variable, but x.shape or x.shape[idx] doesn't contain negetive value, don't convert to paddle.shape()

* [Cherry-Pick 2.0] [Dy2Stat] Use Paddle2.0 api paddle.tensor.array_* (#30156)

* Improve performance of elementwise_add grad op (#29187)

* pass stop_gradient for cast op

* improve performance of elementwise_add grad

* use tensor copy async

* dygraph branch

* fix dygraph branch

* add ut

* make gelu fp16 computing more robust (#29484)

* Add fast path for dropout when p == 0  (#29553)

* add fast path for p == 0 in dropout

* add ut

* add fp16 check into max and avg pool (#29479)

* Add ReserveSpace in dygraph batch_norm. (#29221)

* Add ReserveSpace in dygraph batch_norm.

* Add unittest for reservespace

* add float16 into adaptive_avg_pool2d check list. (#29547)

* [cherry-pick 2.0] Fix unitest test_slice (#29740)

Before this commit, test_slice use old api `dygraph_to_static_func` to use Dynamic-t-Static and use Executor explicitly，which is not recommended to users.
After fixed, use recommended API `paddle.jit.to_static` to replace `dygraph_to_static_func`, which won't trigger the random exception on coverage CI.

* [cherry-pick 2.0][Dy2Stat] Support grammar: for ele in var[idx] (#29541)

Support to transformfor ele in var stms in which var is a slice of Tensor.

* [cherry-pick 2.0][Dy2Stat] Fix bug for loop: a variable is used and created in loop, but used before created (#29769)

* support deepcopy for Layer/Tensor/Paramerbase

* fix some code

* add distributed.split, test=develop

[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'