[Llava] Add Llava to transformers (#27662)

* add model like

* logits match

* minor fixes

* fixes

* up

* up

* add todo

* llava processor

* keep the processor simple

* add conversion script

* fixup

* fix copies

* up

* add to index

* fix config + logits

* fix

* refactor

* more refactor

* more refactor

* fix copies

* add authors

* v1 tests

* add `LlavaProcessor` in init

* remove unneeded import

* up

* up

* docs

* up

* fix CI

* fix CI

* add attention  mask in test

* make fixup

* remove the vision model

* that' s the dirty way to do it

* nits

* nits

* updates

* add more tests

* add input tests

* fixup

* more styling

* nits

* updates amd cleanup

* fixup the generation expected results

* fix the testing script

* some cleanup and simplification which does not work yet but almost there!

* make correct dispatch operations

* vectorize works for batch of images and text

* last todos

* nits

* update test and modeling code

* remove useless function for now

* fix few issues

* fix generation

* some nits

* add bakllava

* nits

* remove duplicated code

* finis merge

* cleanup

* missed this line

* fill the todos

* add left padding offset

* add left and rignt padding logic

* bool to properly index

* make sure

* more cleanups

* batch is fixed 😉

* add correct device for tensor creation

* fix some dtype missmatch

* ruff

* update conversion script

* Update src/transformers/__init__.py

* fa 2 support + fix conversion script

* more

* correct reshaping

* fix test dict

* fix copies by ignoring

* fix nit

* skip clip vision model

* fixup

* fixup

* LlavaForVisionText2Text -> LlavaForCausalLM

* update

* fix

* raise correct errors

* fix

* docs

* nuke for now

* nits here and there

* fixup

* fix remaining tests

* update LlavaForConditionalGeneration instead of CausalLM

* fixups

* pipeline support

* slow and piepline tests

* supports batch

* nits

* cleanup

* fix first integration tests

* add pad token where needed

* correct etsts

* fixups

* update pipeline testr

* fix quality

* nits

* revert unneeded change

* nit

* use BatchFeature

* from ...feature_extraction_utils import BatchFeature

* nits

* nits

* properly update

* more f*** nits

* fix copies

* comment

* keep slow test slow

* Update src/transformers/models/llava/processing_llava.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* add piepline example

* add pixel values in docstrign

* update pr doctest

* fix

* fix slow tests

* remove hack

* fixup

* small note

* forward contrib credits from PR25789

* forward contrib credits from original implementation and work

* add arthur

* Update src/transformers/models/llava/processing_llava.py

Co-authored-by: Lysandre Debut <hi@lysand.re>

* update docstring

* nit

* move to not doctested because of timeout issues

* fixup

* add description

* more

* fix-copies

* fix docs

* add beam search

* add more comments

* add typehints on processor

* add speedup plot

* update slow tests and docs

* push test

* push batched test

* fix batched generation with different number of images

* remove benchmark due to a bug

* fix test

* fix copies

* add gcolab demo

---------

Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: shauray8 <shauray8@users.noreply.github.com>
Co-authored-by: haotian-liu <haotian-liu@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>

docs/source/en/model_doc/llava.md

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+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Llava
+
+## Overview
+
+Llava is an open-source chatbot trained by fine-tuning LlamA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. In other words, it is an multi-modal version of LLMs fine-tuned for chat / instructions.
+
+The Llava model was proposed in [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
+
+The abstract from the paper is the following:
+
+*Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA, namely, using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data with simple response formatting prompts, we establish stronger baselines that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint uses merely 1.2M publicly available data, and finishes full training in ∼1 day on a single 8-A100 node. We hope this can make state-of-the-art LMM research more accessible. Code and model will be publicly available*
+
+Tips:
+
+- We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simply make sure to call `processor.tokenizer.padding_side = "left"` before generating.
+
+- Note the model has not been explicitly trained to process multiple images in the same prompt, although this is technically possible, you may experience inaccurate results.
+
+- For better results, we recommend users to prompt the model with the correct prompt format: 
+
+```bash
+"USER: <image>\n<prompt>ASSISTANT:"
+```
+
+For multiple turns conversation:
+
+```bash
+"USER: <image>\n<prompt1>ASSISTANT: <answer1>USER: <prompt2>ASSISTANT: <answer2>USER: <prompt3>ASSISTANT:"
+```
+
+This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ) and [ybelkada](https://huggingface.co/ybelkada).
+The original code can be found [here](https://github.com/haotian-liu/LLaVA/tree/main/llava).
+
+Check out also this [Google Colab demo](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing) on how to run Llava on a free-tier Google colab instance.
+
+### Using Flash Attention 2
+
+Flash Attention 2 is an even faster, optimized version of the previous optimization, please refer to the [Flash Attention 2 section of performance docs](https://huggingface.co/docs/transformers/perf_infer_gpu_one).
+
+## LlavaConfig
+
+[[autodoc]] LlavaConfig
+
+## LlavaProcessor
+
+[[autodoc]] LlavaProcessor
+
+## LlavaForConditionalGeneration
+
+[[autodoc]] LlavaForConditionalGeneration
+    - forward