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add sdpa to ViT [follow up of #29325] (#30555)

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remove blank line (+1 squashed commit)
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[24ccd2061] [run-slow]vit_msn,vision_encoder_decoder (+24 squashed commits)
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[08bd27e7a] [run-slow]vit_msn,vision_encoder_decoder
[ec96a8db3] [run-slow]vit_msn
[ead817eca] fix vit msn multi gpu
[d12cdc8fd] [run-slow]audio_spectrogram_transformer,deit,vision_encoder_decoder,vision_text_dual_encoder,vit,vit_hybrid,vit_mae,vit_msn,videomae,yolos
[3fdbfa88f] doc
[a3ff33e4a] finish implementation
[e20b7b7fb] Update test_modeling_common.py
[e290c5810] Update test_modeling_flax_common.py
[d3af86f46] comment
[ff7dd32d8] more comments
[59b137889] suggestion
[7e2ba6d67] attn_implementation as attribute of the class
[fe66ab71f] minor
[38642b568] Apply suggestions from code review

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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
[22cde7d52] Update tests/test_modeling_common.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
[48e137cc6] Update tests/test_modeling_common.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
[99f4c679f] Update tests/test_modeling_common.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
[96cf20a6d] Update src/transformers/models/vit_msn/modeling_vit_msn.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
[c59377d23] Update src/transformers/models/vit_mae/modeling_vit_mae.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
[b70a47259] Update tests/models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
[00c84d216] [run-slow]audio_spectrogram_transformer,deit,vision_encoder_decoder,vision_text_dual_encoder,vit,vit_hybrid,vit_mae,vit_msn,videomae,yolos
[61f00ebb0] all tests are passing locally
[e9e0b82b7] vision encoder/decoder
[4d5076b56] test-vision (+20 squashed commits)
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[d1add8db9] yolo
[9fde65716] fix flax
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[3333efd7a] easy models change
[ebfc21402] [run-slow]audio_spectrogram_transformer,deit,vision_encoder_decoder,vision_text_dual_encoder,vit,vit_hybrid,vit_mae,vit_msn,videomae,yolos
[b8b8603ed] [run-slow]vision_encoder_decoder,vision_text_dual_encoder,yolos
[48ecc7e26] all tests are passing locally
[bff7fc366] minor
[62f88306f] fix yolo and text_encoder tests
[121507555] [run-slow]audio_spectrogram_transformer,deit,vit,vit_hybrid,vit_mae,vit_msn,videomae
[1064cae0a] [run-slow]vision_encoder_decoder,vision_text_dual_encoder,yolos
[b7f52ff3a] [run-slow]audio_spectrogram_transformer,deit,vit,vit_hybrid,vit_mae,vit_msn,videomae
[cffaa10dd] fix-copies
[ef6c511c4] test vit hybrid
[7d4ba8644] vit hybrid
[66f919033] [run-slow]audio_spectrogram_transformer,deit,vit,vit_hybrid,vit_mae,vit_msn,videomae
[1fcc0a031] fixes
[cfde6eb21] fixup
[e77df1ed3] all except yolo end encoder decoder (+17 squashed commits)
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[602913e22] vit + vit_mae are working
[547f6c4cc] RUN_SLOW=1 pytest tests/models/audio_spectrogram_transformer/ tests/models/deit/ tests/models/videomae/  passes
[61a97dfa9] it s the complete opposite...
[aefab37d4] fix more tests
[71802a1b9] fix all torch tests
[40b12eb58] encoder - decoder tests
[941552b69] slow decorator where appropriate
[14d055d80] has_attentions to yolo and msn
[3381fa19f] add correct name
[e261316a7] repo consistency
[31c6d0c08] fixup
[9d214276c] minor fix
[11ed2e1b7] chore
[eca6644c4] add sdpa to vit-based models
[cffbf390b] make fix-copies result
[6468319b0] fix style
[d324cd02a] add sdpa for vit

Co-authored-by: Liubov Yaronskaya <luba.yaronskaya@gmail.com>
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docs/source/en/model_doc/yolos.md
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This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/hustvl/YOLOS).
## Using Scaled Dot Product Attention (SDPA)
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
or the [GPU Inference](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention)
page for more information.
SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set
`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
```
from transformers import AutoModelForObjectDetection
model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base", attn_implementation="sdpa", torch_dtype=torch.float16)
...
```
For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
On a local benchmark (A100-40GB, PyTorch 2.3.0, OS Ubuntu 22.04) with `float32` and `hustvl/yolos-base` model, we saw the following speedups during inference.
| Batch size | Average inference time (ms), eager mode | Average inference time (ms), sdpa model | Speed up, Sdpa / Eager (x) |
|--------------|-------------------------------------------|-------------------------------------------|------------------------------|
| 1 | 106 | 76 | 1.39 |
| 2 | 154 | 90 | 1.71 |
| 4 | 222 | 116 | 1.91 |
| 8 | 368 | 168 | 2.19 |
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with YOLOS.