Add FA2 and sdpa support for SigLIP (#31499)

* Rebase to main

* Fix attention implementation autoset for tex and vision configs

* Fixup

* Minor fixes

* Fix copies

* Fix attention_mask for FA2

* Add eqvivalence tests for siglip

* Remove right padding test

* Uncomment flaky

* Fix import

* Add to docs

* Fix test message

* Add sdpa

* Add sdpa equivalence test

* Add siglip sdpa to docs

* Fix typing for attention output

* Add sdpa tests

* Fix signature of FA2

* Autoset attn_implementation in config

* Rename bsz -> batch_size

* Move back autoset attn method

* Mark as flaky

* Correct attention mask padding

* [run-slow] siglip

* Add FA2 and sdpa docs

* Style fix

* Remove flaky for FA2 test

* Change attention implementation set

* Change attn_implementaiton propogation

* Fix typos

* Add modality to assert message

* Add more sdpa backends in test

* [run slow] siglip

* Add math sdpa backend for all options

* [run slow] siglip

docs/source/en/model_doc/siglip.md

@@ -107,6 +107,88 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
+
+## Combining SigLIP and Flash Attention 2
+
+First, make sure to install the latest version of Flash Attention 2.
+
+```bash
+pip install -U flash-attn --no-build-isolation
+```
+
+Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. `torch.float16``)
+
+To load and run a model using Flash Attention 2, refer to the snippet below:
+
+```python
+>>> import torch
+>>> import requests
+>>> from PIL import Image
+>>> from transformers import SiglipProcessor, SiglipModel
+>>> device = "cuda" # the device to load the model onto
+
+>>> model = SiglipModel.from_pretrained(
+...     "google/siglip-so400m-patch14-384",
+...     attn_implementation="flash_attention_2",
+...     torch_dtype=torch.float16,
+...     device_map=device,
+... )
+>>> processor = SiglipProcessor.from_pretrained("google/siglip-so400m-patch14-384")
+
+>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> candidate_labels = ["2 cats", "2 dogs"]
+# follows the pipeline prompt template to get same results
+>>> candidate_labels = [f'This is a photo of {label}.' for label in candidate_labels]
+# important: we pass `padding=max_length` since the model was trained with this
+>>> inputs = processor(text=candidate_labels, images=image, padding="max_length", return_tensors="pt")
+>>> inputs.to(device)
+
+>>> with torch.no_grad():
+...     with torch.autocast(device):
+...         outputs = model(**inputs)
+
+>>> logits_per_image = outputs.logits_per_image
+>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
+>>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
+51.3% that image 0 is 'This is a photo of 2 cats.'
+```
+
+
+## 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.
+
+You may set `attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used. Make sure you have `torch>=2.1.1`.
+
+```python
+>>> from transformers import SiglipModel
+
+>>> model = SiglipModel.from_pretrained(
+...     "google/siglip-so400m-patch14-384",
+...     attn_implementation="sdpa",
+...     torch_dtype=torch.float16,
+...     device_map=device,
+... )
+```
+
+For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
+
+
+## Expected speedups
+
+Below is an expected speedup diagram that compares inference time between the native implementation in transformers using `google/siglip-so400m-patch14-384` checkpoint in `float16` precision and the Flash Attention 2 / SDPA version of the model using different batch sizes.
+
+<div style="text-align: center">
+<img src="https://i.imgur.com/cWm4rsn.png">
+</div>
+
+
 ## SiglipConfig
 
 [[autodoc]] SiglipConfig