support qwen2-vl

docs/source/en/_toctree.yml

@@ -514,6 +514,8 @@
         title: Qwen2Audio
       - local: model_doc/qwen2_moe
         title: Qwen2MoE
+      - local: model_doc/qwen2_vl
+        title: Qwen2VL
       - local: model_doc/rag
         title: RAG
       - local: model_doc/realm

docs/source/en/index.md

@@ -260,6 +260,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                         [Qwen2](model_doc/qwen2)                         |       ✅        |         ❌         |      ❌      |
 |                   [Qwen2Audio](model_doc/qwen2_audio)                    |       ✅        |         ❌         |      ❌      |
 |                     [Qwen2MoE](model_doc/qwen2_moe)                      |       ✅        |         ❌         |      ❌      |
+|                      [Qwen2VL](model_doc/qwen2_vl)                       |       ✅        |         ❌         |      ❌      |
 |                           [RAG](model_doc/rag)                           |       ✅        |         ✅         |      ❌      |
 |                         [REALM](model_doc/realm)                         |       ✅        |         ❌         |      ❌      |
 |               [RecurrentGemma](model_doc/recurrent_gemma)                |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/qwen2_vl.md

@@ -0,0 +1,329 @@
+<!--Copyright 2024 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.
+
+-->
+
+# Qwen2_VL
+
+
+## Overview
+
+The [Qwen2_VL](https://qwenlm.github.io/blog/qwen2-vl/) is a major update to our [Qwen-VL](https://arxiv.org/pdf/2308.12966) model from the Qwen team. 
+
+The abstract from the blog is the following:
+
+*This blog introduces Qwen2-VL, an advanced version of the Qwen-VL model that has undergone significant enhancements over the past year. Key improvements include enhanced image comprehension, advanced video understanding, integrated visual agent functionality, and expanded multilingual support. The model architecture has been optimized for handling arbitrary image resolutions through Naive Dynamic Resolution support and utilizes Multimodal Rotary Position Embedding (M-ROPE) to effectively process both 1D textual and multi-dimensional visual data. This updated model demonstrates competitive performance against leading AI systems like GPT-4o and Claude 3.5 Sonnet in vision-related tasks and ranks highly among open-source models in text capabilities. These advancements make Qwen2-VL a versatile tool for various applications requiring robust multimodal processing and reasoning abilities.*
+
+
+## Usage example
+
+### Single Media inference
+
+The model can accept both images and videos as input. Here's an example code for inference.
+
+```python
+
+from PIL import Image
+import requests
+import torch
+from torchvision import io
+from typing import Dict
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+
+# Load the model in half-precision on the available device(s)
+model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", device_map="auto")
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+
+# Image
+url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+conversation = [
+    {
+        "role":"user",
+        "content":[
+            {
+                "type":"image",
+            },
+            {
+                "type":"text",
+                "text":"Describe this image."
+            }
+        ]
+    }
+]
+
+
+# Preprocess the inputs
+text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>\n<|im_start|>assistant\n'
+
+inputs = processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt")
+inputs = inputs.to('cuda')
+
+# Inference: Generation of the output
+output_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+print(output_text)
+
+
+
+# Video
+def fetch_video(ele: Dict, nframe_factor=2):
+    if isinstance(ele['video'], str):
+        def round_by_factor(number: int, factor: int) -> int:
+            return round(number / factor) * factor
+
+        video = ele["video"]
+        if video.startswith("file://"):
+            video = video[7:]
+
+        video, _, info = io.read_video(
+            video,
+            start_pts=ele.get("video_start", 0.0),
+            end_pts=ele.get("video_end", None),
+            pts_unit="sec",
+            output_format="TCHW",
+        )
+        assert not ("fps" in ele and "nframes" in ele), "Only accept either `fps` or `nframes`"
+        if "nframes" in ele:
+            nframes = round_by_factor(ele["nframes"], nframe_factor)
+        else:
+            fps = ele.get("fps", 1.0)
+            nframes = round_by_factor(video.size(0) / info["video_fps"] * fps, nframe_factor)
+        idx = torch.linspace(0, video.size(0) - 1, nframes, dtype=torch.int64)
+        return video[idx]
+
+video_info = {"type": "video", "video": "/path/to/video.mp4", "fps": 1.0}
+video = fetch_video(video_info)
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "video"},
+            {"type": "text", "text": "What happened in the video?"},
+        ],
+    }
+]
+
+# Preprocess the inputs
+text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|video_pad|><|vision_end|>What happened in the video?<|im_end|>\n<|im_start|>assistant\n'
+
+inputs = processor(text=[text_prompt], videos=[video], padding=True, return_tensors="pt")
+inputs = inputs.to('cuda')
+
+# Inference: Generation of the output
+output_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+print(output_text)
+
+```
+
+
+### Batch Mixed Media Inference
+
+The model can batch inputs composed of mixed samples of various types such as images, videos, and text. Here is an example.
+
+```python
+
+image1 = Image.open("/path/to/image1.jpg")
+image2 = Image.open("/path/to/image2.jpg")
+image3 = Image.open("/path/to/image3.jpg")
+image4 = Image.open("/path/to/image4.jpg")
+image5 = Image.open("/path/to/image5.jpg")
+video = fetch_video({
+    "type": "video",
+    "video": "/path/to/video.mp4",
+    "fps": 1.0
+})
+
+# Conversation for the first image
+conversation1 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "Describe this image."}
+        ]
+    }
+]
+
+# Conversation with two images
+conversation2 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "image"},
+            {"type": "text", "text": "What is written in the pictures?"}
+        ]
+    }
+]
+
+# Conversation with pure text
+conversation3 = [
+    {
+        "role": "user",
+        "content": "who are you?"
+    }
+]
+
+
+# Conversation with mixed midia
+conversation4 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "image"},
+            {"type": "video"},
+            {"type": "text", "text": "What are the common elements in these medias?"},
+        ],
+    }
+]
+
+conversations = [conversation1, conversation2, conversation3, conversation4]
+# Preparation for batch inference
+texts = [processor.apply_chat_template(msg, add_generation_prompt=True) for msg in conversations]
+inputs = processor(
+    text=texts,
+    images=[image1, image2, image3, image4, image5],
+    videos=[video],
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to('cuda')
+
+# Batch Inference
+output_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+print(output_text)
+```
+
+### Usage Tips
+
+#### Image Resolution for performance boost
+
+The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs.
+
+```python
+
+min_pixels = 224*224
+max_pixels = 2048*2048
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
+
+```
+
+
+
+#### Multiple Image Inputs
+
+By default, images and video content are directly included in the conversation. When handling multiple images, it's helpful to add labels to the images and videos for better reference. Users can control this behavior with the following settings:
+
+
+
+```python
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"}, 
+            {"type": "text", "text": "Hello, how are you?"}
+        ]
+    },
+    {
+        "role": "assistant",
+        "content": "I'm doing well, thank you for asking. How can I assist you today?"
+    },
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Can you describe these images and video?"}, 
+            {"type": "image"}, 
+            {"type": "image"}, 
+            {"type": "video"}, 
+            {"type": "text", "text": "These are from my vacation."}
+        ]
+    },
+    {
+        "role": "assistant",
+        "content": "I'd be happy to describe the images and video for you. Could you please provide more context about your vacation?"
+    },
+    {
+        "role": "user",
+        "content": "It was a trip to the mountains. Can you see the details in the images and video?"
+    }
+]
+
+# default:
+prompt_without_id = processor.apply_chat_template(conversation, add_generation_prompt=True)
+# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Hello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing well, thank you for asking. How can I assist you today?<|im_end|>\n<|im_start|>user\nCan you describe these images and video?<|vision_start|><|image_pad|><|vision_end|><|vision_start|><|image_pad|><|vision_end|><|vision_start|><|video_pad|><|vision_end|>These are from my vacation.<|im_end|>\n<|im_start|>assistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?<|im_end|>\n<|im_start|>user\nIt was a trip to the mountains. Can you see the details in the images and video?<|im_end|>\n<|im_start|>assistant\n'
+
+
+# add ids
+prompt_with_id = processor.apply_chat_template(conversation, add_generation_prompt=True, add_vision_id=True)
+# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nPicture 1: <|vision_start|><|image_pad|><|vision_end|>Hello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing well, thank you for asking. How can I assist you today?<|im_end|>\n<|im_start|>user\nCan you describe these images and video?Picture 2: <|vision_start|><|image_pad|><|vision_end|>Picture 3: <|vision_start|><|image_pad|><|vision_end|>Video 1: <|vision_start|><|video_pad|><|vision_end|>These are from my vacation.<|im_end|>\n<|im_start|>assistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?<|im_end|>\n<|im_start|>user\nIt was a trip to the mountains. Can you see the details in the images and video?<|im_end|>\n<|im_start|>assistant\n'
+
+```
+
+#### Flash-Attention 2 to speed up generation
+
+First, make sure to install the latest version of Flash Attention 2:
+
+```bash
+pip install -U flash-attn --no-build-isolation
+```
+
+Also, you should have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention-2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.
+
+To load and run a model using Flash Attention-2, simply add `attn_implementation="flash_attention_2"` when loading the model as follows:
+
+```python
+from transformers import Qwen2VLForConditionalGeneration
+
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2-VL-7B-Instruct", 
+    torch_dtype=torch.bfloat16, 
+    attn_implementation="flash_attention_2",
+)
+```
+
+
+## Qwen2VLConfig
+
+[[autodoc]] Qwen2VLConfig
+
+## Qwen2VLImageProcessor
+
+[[autodoc]] Qwen2VLImageProcessor
+    - preprocess
+
+## Qwen2VLProcessor
+
+[[autodoc]] Qwen2VLProcessor
+
+## Qwen2VLModel
+
+[[autodoc]] Qwen2VLModel
+    - forward
+
+## Qwen2VLForConditionalGeneration
+
+[[autodoc]] Qwen2VLForConditionalGeneration
+    - forward

docs/source/en/perf_infer_gpu_one.md

@@ -79,6 +79,7 @@ FlashAttention-2 is currently supported for the following architectures:
 * [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
 * [Qwen2Audio](https://huggingface.co/docs/transformers/model_doc/qwen2_audio#transformers.Qwen2AudioEncoder)
 * [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel)
+* [Qwen2VL](https://huggingface.co/docs/transformers/model_doc/qwen2_vl#transformers.Qwen2VLModel)
 * [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperModel)
 * [Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2Model)
 * [Hubert](https://huggingface.co/docs/transformers/model_doc/hubert#transformers.HubertModel)
@@ -230,6 +231,7 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
 * [Qwen2Audio](https://huggingface.co/docs/transformers/model_doc/qwen2_audio#transformers.Qwen2AudioEncoder)
 * [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel)
+* [Qwen2VL](https://huggingface.co/docs/transformers/model_doc/qwen2_vl#transformers.Qwen2VLModel)
 * [Musicgen](https://huggingface.co/docs/transformers/model_doc/musicgen#transformers.MusicgenModel)
 * [MusicGen Melody](https://huggingface.co/docs/transformers/model_doc/musicgen_melody#transformers.MusicgenMelodyModel)
 * [Nemotron](https://huggingface.co/docs/transformers/model_doc/nemotron)