* fix A100 CUDA12 ut

* fix ci uts

* fix test_sync_batch_norm_op

* fix sync bn op ut again by separating 2 files

* fix codestyle ci

* combine other PRs

* fix codestyle

* fix codestyle ci

* bump ruff to 0.0.271 and update config

* exclude third_party

* bump ruff to 0.0.272

* refine config

* static graph autogen code for check_finite_and_unscale_ op

* bug fixed

* Copy QAT files from PaddleSlim

* Integrate QAT API into Paddle

* Replace eval function

* Reduce test_quant_aware run time

* Apply new formatter on modified files

* Remove code check for Paddle version check

* Copy quant_post_quant_aware UT from PaddleSlim

* Integrate test_quant_post_quant_aware UT into PaddlePaddle

* Apply new formatter on modified files

* Remove redundant code and add unittests

* Add new unittests

* Update the time limit of new unittests

* [dygraph]unify _non_static_mode() in_dygraph_mode() and in_dynamic_mode()

* [dygraph]unify _non_static_mode() in_dygraph_mode() and in_dynamic_mode()

* [dygraph]unify _non_static_mode() in_dygraph_mode() and in_dynamic_mode()

* [dygraph]unify _non_static_mode() in_dygraph_mode() and in_dynamic_mode()

* [dygraph]unify _non_static_mode() in_dygraph_mode() and in_dynamic_mode()

* [dygraph]unify _non_static_mode() in_dygraph_mode() and in_dynamic_mode()

* fixed cyclic reference that caused patial import

* fixed bad change

* fix bad import

* fix bad import

* fix bad import

* fix ut failed caused by change in_dynamic_mode

* fix ut failed caused by change in_dynamic_mode

* fixed usage of in_dynamic_mode() or in_dygraph_mode()

* revert python3 to python in .pre-commit-config.yaml

* fix merge conflicts

* add master gradients on static graph

* add unit test for bf16 master grad static graph

* use float16 as v100 test dtype

* only skip GPU which do not support bf16

* use linear layer to test master grad

* 1.push master grad creation before all optimizer ops; 2.remove useless unittest; 3.use a function to create master grad states

* fix embedding model weight type mismatch error

* Update fp16_utils.py

---------
Co-authored-by: NZhang Ting <zhangting_2017@163.com>

* move DataLoader to paddle.io. test=develop

* [Perf]Removed usless assign op in while_loop

* refine assign

* fluid clean: remove print/switch from fluid to static

* remove Switch in static.__init__

* fix conflicts.

* replace Switch by case.

* fix piecewise_lr decay.

* fix typo

* fix conflicts.

* fix lr dtype

* keep Switch in paddle.static.nn.control_flow and fix piecewise_lr.

* fix conflicts.

* keep Switch in the fluid.

* fix Switch doc

* fix example in Switch doc

* fix Switch doc.

* fix static/__init__.

[AMP] Allow to enable multi_precision through paddle.static.amp.decorate and add documents for some apis. (#53012)

* Add document for some apis. test=docs_preview

* Allow to set master_weight in paddle.static.amp.decorate.

* Polish codes and add unittest.

* Refine docs.

* Remove the repetitive function.

* support promote dtype for static amp training

* unify o1 and o2

* update for unittest

* fix op_role

* add use_promote arg

* fix doc

* add promote unittest

* polish unittests

* fix controflow and test

* relocate metri_op.py

* reloacte nn.py

* fix bug

* fix bug

* fix bug

* fix bug

* fix bug

* fix bug

* fix variable->tensor and fix __all__

* fix ctr_metric_bundle and sparse_embedding

* fix bug of function init

* fix bug of importing sparse_embedding and ctr_metric_bundle

* fix bug

* Update __init__.py

* [AMP OP&Test] Add check_dtype for some API (part 2)

* fix ci

* fix ci

* rename _varbase_creator as create_tensor

* rename _varbase_creator as create_tensor

* Implement a common AmpTestBase.

* Support overload of decorate.

* Change the ignore list of flake and fix an error.

1. modify set_value op, use Scalars to represent attr `values`, instead of a bunch of attributs of various types; (#52408)

2. add program converter and set_value op as an example, which provides the functionality to convert `paddle::framework::ProgramDesc` between old and new formats(the differences are mainly some operators with incompatible updates in the definition);
3. program version and operator version map now are always saved when serializing `paddle::framework::ProgramDesc` to identify the version;
3. provide an option `legacy_format=false` in  serialization of `paddle::framework::ProgramDesc`, it decided whether to convert ProgramDesc back to a legacy format, which is compatible for paddle 2.4.2 or earlier versions to load and execute;
4. deserialization of `paddle::framework::ProgramDesc` is now automatically detecting whether the bytes it receives is in legacy format(contains any of the operators that has been incompatibly updated and have any attribute of type `Scalar`) and convert it to new format. But if you want a faithful deserialization without the automatic conversion, you can use protobuf's deserialization instead. Though it is not recommended, it can be used for the purpose of testing.

* Unify the static amp codes of fp16 and bf16.

* Polish apis and add unittest.

* Add operator stats collecting tools for program.

* Add the check of number of bloat16 operators in unittest.

* Add warning for operator not supported for amp.

* Add testing of BF16 O1 and O2.

* fix dtype cast in amp.

* add test case and update docs.

* remove set_prim.

* add fp16 and bf16 support for instance_norm

* fix /= operator which not support bf16

* fix instance_norm_grad kernel and unittests.

* fix fp32 unittests.

* fix instance_norm_kernel and unittests.

* fix instance_norm_grad_kernel and unittest threshold.

* add fp16/bf16 for instance_norm_grad_grad op.

* add bf16 dtype check.

* fix conflicts.

* fix cpu support for fp32 op and fix type in instance_norm_grad_kernel.

* fix type in instance_norm_kernel.

* fix bf16 outputs in unittests and refine codes.

* fix dx computation.

* delete unuseful params and head including.

* add fp16/bf16 for static graph.

* fix device condiction for instance_norm op.

* fix instance_norm_grad_grad and bf16 op tests.

* fix op_test to support grad of bf16 can be compared with fp32.

* remove updates.

* add self-defined grad.

* rem is_compiled_with_npu

* rem nup related code

* make lint happy

* rem test

* remove some tests

* Update grad_scaler.py

* fix an error