device agnostic models testing
Author: ji-huazhong
Pull request: yes

# What does this PR do?

Part of https://github.com/huggingface/transformers/issues/25654#issuecomment-1783704306


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

Documentation: @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Comments:
Thanks for opening this PR @statelesshz ! Let us know when you'd like a review

Due to network issue， it's not easy to download the model weights on huggingface hub from my area, so I take `test_modeling_bert.py` as an example to give the execution reaults on NPU.

```
(hf) [root@localhost /home/hf/transformers]# RUN_SLOW=1 TRANSFORMERS_TEST_BACKEND="torch_npu" TRANSFORMERS_TEST_DEVICE="npu:0" TRANSFORMEs/bert/test_modeling_bert.py
========================================================================================= test session starts ===========================
platform linux -- Python 3.8.17, pytest-7.4.2, pluggy-1.3.0 -- /root/anaconda3/envs/hf/bin/python
cachedir: .pytest_cache
rootdir: /home/hf/transformers
configfile: setup.cfg
plugins: dash-2.13.0, hydra-core-1.3.2, odl-0.7.0, anyio-4.0.0
collected 135 items                                                                                                                      

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=========================================================== warnings summary ===========================================================
../../../root/anaconda3/envs/hf/lib/python3.8/site-packages/_pytest/config/__init__.py:1373
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/_pytest/config/__init__.py:1373: PytestConfigWarning: Unknown config option: doctest_glob
  
    self._warn_or_fail_if_strict(f"Unknown config option: {key}\n")

tests/test_modeling_common.py:2790
  /home/hf/transformers/tests/test_modeling_common.py:2790: PytestUnknownMarkWarning: Unknown pytest.mark.flash_attn_test - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
    @mark.flash_attn_test

tests/test_modeling_common.py:2817
  /home/hf/transformers/tests/test_modeling_common.py:2817: PytestUnknownMarkWarning: Unknown pytest.mark.flash_attn_test - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
    @mark.flash_attn_test

tests/test_modeling_common.py:2863
  /home/hf/transformers/tests/test_modeling_common.py:2863: PytestUnknownMarkWarning: Unknown pytest.mark.flash_attn_test - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
    @mark.flash_attn_test

tests/test_modeling_common.py:2905
  /home/hf/transformers/tests/test_modeling_common.py:2905: PytestUnknownMarkWarning: Unknown pytest.mark.flash_attn_test - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
    @mark.flash_attn_test

tests/test_modeling_common.py:2942
  /home/hf/transformers/tests/test_modeling_common.py:2942: PytestUnknownMarkWarning: Unknown pytest.mark.flash_attn_test - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
    @mark.flash_attn_test

tests/test_modeling_common.py:2979
  /home/hf/transformers/tests/test_modeling_common.py:2979: PytestUnknownMarkWarning: Unknown pytest.mark.flash_attn_test - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
    @mark.flash_attn_test

tests/test_modeling_common.py:3009
  /home/hf/transformers/tests/test_modeling_common.py:3009: PytestUnknownMarkWarning: Unknown pytest.mark.flash_attn_test - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
    @mark.flash_attn_test

src/transformers/deepspeed.py:23
  /home/hf/transformers/src/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
    warnings.warn(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_assisted_decoding_matches_greedy_search
  /home/hf/transformers/tests/test_modeling_common.py:3069: UserWarning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (Triggered internally at /opt/_internal/cpython-3.8.17/lib/python3.8/site-packages/torch/include/ATen/core/LegacyTypeDispatch.h:74.)
    attn_mask[:, 0] = 1

tests/models/bert/test_modeling_bert.py::BertModelTest::test_attention_outputs
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch_npu/utils/storage.py:36: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
    if self.device.type != 'cpu':

tests/models/bert/test_modeling_bert.py::BertModelTest::test_beam_sample_generate
tests/models/bert/test_modeling_bert.py::BertModelTest::test_beam_sample_generate_dict_output
  /home/hf/transformers/src/transformers/generation/utils.py:3341: UserWarning: `max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.
    warnings.warn(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_beam_search_generate
tests/models/bert/test_modeling_bert.py::BertModelTest::test_beam_search_generate_dict_output
tests/models/bert/test_modeling_bert.py::BertModelTest::test_beam_search_generate_dict_outputs_use_cache
  /home/hf/transformers/src/transformers/generation/utils.py:3005: UserWarning: `max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.
    warnings.warn(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_greedy_generate
tests/models/bert/test_modeling_bert.py::BertModelTest::test_greedy_generate_dict_outputs
tests/models/bert/test_modeling_bert.py::BertModelTest::test_greedy_generate_dict_outputs_use_cache
  /home/hf/transformers/src/transformers/generation/utils.py:2450: UserWarning: `max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])` instead.
    warnings.warn(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_group_beam_search_generate
tests/models/bert/test_modeling_bert.py::BertModelTest::test_group_beam_search_generate_dict_output
  /home/hf/transformers/src/transformers/generation/utils.py:3663: UserWarning: `max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.
    warnings.warn(

tests/models/bert/test_modeling_bert.py: 54 warnings
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/urllib3/connectionpool.py:1056: InsecureRequestWarning: Unverified HTTPS request is being made to host '90.253.31.68'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
    warnings.warn(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_pipeline_text_generation
  /home/hf/transformers/src/transformers/generation/utils.py:1273: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
    warnings.warn(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_pipeline_zero_shot
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py:642: UserWarning: Length of IterableDataset <transformers.pipelines.pt_utils.PipelineChunkIterator object at 0xffff395d15e0> was reported to be 1 (when accessing len(dataloader)), but 2 samples have been fetched. 
    warnings.warn(warn_msg)

tests/models/bert/test_modeling_bert.py::BertModelTest::test_pipeline_zero_shot
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py:642: UserWarning: Length of IterableDataset <transformers.pipelines.pt_utils.PipelineChunkIterator object at 0xffff395d1790> was reported to be 2 (when accessing len(dataloader)), but 3 samples have been fetched. 
    warnings.warn(warn_msg)

tests/models/bert/test_modeling_bert.py::BertModelTest::test_pipeline_zero_shot
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py:642: UserWarning: Length of IterableDataset <transformers.pipelines.pt_utils.PipelineChunkIterator object at 0xffff395d1790> was reported to be 2 (when accessing len(dataloader)), but 4 samples have been fetched. 
    warnings.warn(warn_msg)

tests/models/bert/test_modeling_bert.py::BertModelTest::test_pipeline_zero_shot
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py:642: UserWarning: Length of IterableDataset <transformers.pipelines.pt_utils.PipelineChunkIterator object at 0xffff395be6a0> was reported to be 1 (when accessing len(dataloader)), but 2 samples have been fetched. 
    warnings.warn(warn_msg)

tests/models/bert/test_modeling_bert.py::BertModelTest::test_pipeline_zero_shot
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py:642: UserWarning: Length of IterableDataset <transformers.pipelines.pt_utils.PipelineChunkIterator object at 0xffff395d10a0> was reported to be 2 (when accessing len(dataloader)), but 3 samples have been fetched. 
    warnings.warn(warn_msg)

tests/models/bert/test_modeling_bert.py::BertModelTest::test_pipeline_zero_shot
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py:642: UserWarning: Length of IterableDataset <transformers.pipelines.pt_utils.PipelineChunkIterator object at 0xffff395d10a0> was reported to be 2 (when accessing len(dataloader)), but 4 samples have been fetched. 
    warnings.warn(warn_msg)

tests/models/bert/test_modeling_bert.py::BertModelTest::test_sample_generate
tests/models/bert/test_modeling_bert.py::BertModelTest::test_sample_generate_dict_output
  /home/hf/transformers/src/transformers/generation/utils.py:2728: UserWarning: `max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.
    warnings.warn(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_torch_fx
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/overrides.py:110: UserWarning: 'has_cuda' is deprecated, please use 'torch.backends.cuda.is_built()'
    torch.has_cuda,

tests/models/bert/test_modeling_bert.py::BertModelTest::test_torch_fx
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/overrides.py:111: UserWarning: 'has_cudnn' is deprecated, please use 'torch.backends.cudnn.is_available()'
    torch.has_cudnn,

tests/models/bert/test_modeling_bert.py::BertModelTest::test_torch_fx
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/overrides.py:117: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'
    torch.has_mps,

tests/models/bert/test_modeling_bert.py::BertModelTest::test_torch_fx
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/overrides.py:118: UserWarning: 'has_mkldnn' is deprecated, please use 'torch.backends.mkldnn.is_available()'
    torch.has_mkldnn,

tests/models/bert/test_modeling_bert.py::BertModelTest::test_torchscript_device_change
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/jit/_trace.py:1093: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
  Tensor-likes are not close!
  
  Mismatched elements: 2911 / 2912 (100.0%)
  Greatest absolute difference: 2.786480948328972 at index (10, 5, 26) (up to 1e-05 allowed)
  Greatest relative difference: 945.6015975578661 at index (6, 3, 1) (up to 1e-05 allowed)
    _check_trace(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_torchscript_device_change
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/jit/_trace.py:1093: TracerWarning: Output nr 2. of the traced function does not match the corresponding output of the Python function. Detailed error:
  Tensor-likes are not close!
  
  Mismatched elements: 416 / 416 (100.0%)
  Greatest absolute difference: 0.20477154292166233 at index (4, 21) (up to 1e-05 allowed)
  Greatest relative difference: 46.22814215551245 at index (11, 11) (up to 1e-05 allowed)
    _check_trace(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_torchscript_device_change
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/jit/_trace.py:1093: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
  Tensor-likes are not close!
  
  Mismatched elements: 8917 / 9009 (99.0%)
  Greatest absolute difference: 0.27886080741882324 at index (3, 4, 13) (up to 1e-05 allowed)
  Greatest relative difference: 5961.7773065868605 at index (5, 1, 61) (up to 1e-05 allowed)
    _check_trace(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_torchscript_device_change
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/jit/_trace.py:1093: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
  Tensor-likes are not close!
  
  Mismatched elements: 8916 / 9009 (99.0%)
  Greatest absolute difference: 0.33901818469166756 at index (5, 4, 33) (up to 1e-05 allowed)
  Greatest relative difference: 8042.04489700118 at index (11, 5, 78) (up to 1e-05 allowed)
    _check_trace(

tests/models/bert/test_modeling_bert.py::BertModelTest::test_training_gradient_checkpointing
  /root/anaconda3/envs/hf/lib/python3.8/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
    warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
======================================= 97 passed, 38 skipped, 93 warnings in 1067.07s (0:17:47) ======================================
```

Marking this ready for review :-) @ydshieh and @amyeroberts 

Nice to know bert works well on NPU!

Thank you @statelesshz the king of NPU!

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_27146). All of your documentation changes will be reflected on that endpoint.