Adds VIP-llava to transformers (#27932)

* v1

* add-new-model-like

* revert

* fix forward and conversion script

* revert

* fix copies

* fixup

* fix

* Update docs/source/en/index.md

* Apply suggestions from code review

* push

* fix

* fixes here and there

* up

* fixup and fix tests

* Apply suggestions from code review

* add docs

* fixup

* fixes

* docstring

* add docstring

* fixup

* docstring

* fixup

* nit

* docs

* more copies

* fix copies

* nit

* update test

docs/source/en/_toctree.yml

@@ -741,6 +741,8 @@
         title: TVP
       - local: model_doc/vilt
         title: ViLT
+      - local: model_doc/vipllava
+        title: VipLlava
       - local: model_doc/vision-encoder-decoder
         title: Vision Encoder Decoder Models
       - local: model_doc/vision-text-dual-encoder

docs/source/en/index.md

@@ -280,6 +280,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                           [VAN](model_doc/van)                           |       ✅        |         ❌         |      ❌      |
 |                      [VideoMAE](model_doc/videomae)                      |       ✅        |         ❌         |      ❌      |
 |                          [ViLT](model_doc/vilt)                          |       ✅        |         ❌         |      ❌      |
+|                      [VipLlava](model_doc/vipllava)                      |       ✅        |         ❌         |      ❌      |
 |        [Vision Encoder decoder](model_doc/vision-encoder-decoder)        |       ✅        |         ✅         |      ✅      |
 |       [VisionTextDualEncoder](model_doc/vision-text-dual-encoder)        |       ✅        |         ✅         |      ✅      |
 |                   [VisualBERT](model_doc/visual_bert)                    |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/vipllava.md

@@ -0,0 +1,61 @@
+<!--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.
+
+-->
+
+# VipLlava
+
+## Overview
+
+The VipLlava model was proposed in [Making Large Multimodal Models Understand Arbitrary Visual Prompts](https://arxiv.org/abs/2312.00784) by Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae Lee.
+
+VipLlava enhances the training protocol of Llava by marking images and interact with the model using natural cues like a "red bounding box" or "pointed arrow" during training.
+
+The abstract from the paper is the following:
+
+*While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge, we introduce a novel multimodal model capable of decoding arbitrary visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow". Our simple design directly overlays visual markers onto the RGB image, eliminating the need for complex region encodings, yet achieves state-of-the-art performance on region-understanding tasks like Visual7W, PointQA, and Visual Commonsense Reasoning benchmark. Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain. Code, data, and model are publicly available.*
+
+Tips:
+
+- The architecture is similar than llava architecture except that the multi-modal projector takes a set of concatenated vision hidden states and has an additional layernorm layer on that module.
+
+- 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:"
+```
+
+The original code can be found [here](https://github.com/mu-cai/ViP-LLaVA).
+
+This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada)
+
+
+## VipLlavaConfig
+
+[[autodoc]] VipLlavaConfig
+
+## VipLlavaForConditionalGeneration
+
+[[autodoc]] VipLlavaForConditionalGeneration
+    - forward

docs/source/en/perf_infer_gpu_one.md

@@ -46,6 +46,7 @@ FlashAttention-2 is currently supported for the following architectures:
 * [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
 * [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
 * [Llava](https://huggingface.co/docs/transformers/model_doc/llava)
+* [VipLlava](https://huggingface.co/docs/transformers/model_doc/vipllava)
 * [MBart](https://huggingface.co/docs/transformers/model_doc/mbart#transformers.MBartModel)
 * [Mistral](https://huggingface.co/docs/transformers/model_doc/mistral#transformers.MistralModel)
 * [Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral#transformers.MixtralModel)