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README.md

@@ -1,3 +1,438 @@
----
-license: mit
----
+---
+license: llama3
+pipeline_tag: image-text-to-text
+---
+
+# InternVL2-Llama3-76B
+
+[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821)
+
+[\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) \[🌟 [魔搭社区](https://modelscope.cn/organization/OpenGVLab) | [教程](https://mp.weixin.qq.com/s/OUaVLkxlk1zhFb1cvMCFjg) \]
+
+## Introduction
+
+We are excited to announce the release of InternVL 2.0, the latest addition to the InternVL series of multimodal large language models. InternVL 2.0 features a variety of **instruction-tuned models**, ranging from 2 billion to 108 billion parameters. This repository contains the instruction-tuned InternVL2-Llama3-76B model.
+
+Compared to the state-of-the-art open-source multimodal large language models, InternVL 2.0 surpasses most open-source models. It demonstrates competitive performance on par with proprietary commercial models across various capabilities, including document and chart comprehension, infographics QA, scene text understanding and OCR tasks, scientific and mathematical problem solving, as well as cultural understanding and integrated multimodal capabilities.
+
+InternVL 2.0 is trained with an 8k context window and utilizes training data consisting of long texts, multiple images, and videos, significantly improving its ability to handle these types of inputs compared to InternVL 1.5. For more details, please refer to our blog and GitHub.
+
+## Model Details
+
+InternVL 2.0 is a multimodal large language model series, featuring models of various sizes. For each size, we release instruction-tuned models optimized for multimodal tasks. InternVL2-Llama3-76B consists of [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5), an MLP projector, and [Hermes-2-Theta-Llama-3-70B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B).
+
+## Performance
+
+### Image Benchmarks
+
+| Benchmark | GPT-4T-20240409 | Gemini-1.5-Pro | InternVL2-40B | InternVL2-Llama3-76B |
+| :--------------------------: | :-------------: | :------------: | :-----------: | :------------------: |
+| Model Size | - | - | 40B | 76B |
+| | | | | |
+| DocVQA<sub>test</sub> | 87.2 | 86.5 | 93.9 | |
+| ChartQA<sub>test</sub> | 78.1 | 81.3 | 86.2 | |
+| InfoVQA<sub>test</sub> | - | 72.7 | 78.7 | |
+| TextVQA<sub>val</sub> | - | 73.5 | 83.0 | |
+| OCRBench | 678 | 754 | 837 | |
+| MME<sub>sum</sub> | 2070.2 | 2110.6 | 2315.0 | |
+| RealWorldQA | 68.0 | 67.5 | 71.8 | |
+| AI2D<sub>test</sub> | 89.4 | 80.3 | 87.1 | |
+| MMMU<sub>val</sub> | 63.1 | 58.5 | 53.9 | |
+| MMBench-EN<sub>test</sub> | 81.0 | 73.9 | 86.8 | |
+| MMBench-CN<sub>test</sub> | 80.2 | 73.8 | 86.5 | |
+| CCBench<sub>dev</sub> | 57.3 | 28.4 | 80.6 | |
+| MMVet<sub>GPT-4-0613</sub> | - | - | 68.5 | |
+| MMVet<sub>GPT-4-Turbo</sub> | 67.5 | 64.0 | 65.5 | |
+| SEED-Image | - | - | 78.2 | |
+| HallBench<sub>avg</sub> | 43.9 | 45.6 | 56.9 | |
+| MathVista<sub>testmini</sub> | 58.1 | 57.7 | 63.7 | |
+
+- We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet, and SEED-Image were tested using the InternVL repository. MMMU, OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.
+
+- Please note that evaluating the same model using different testing toolkits like InternVL and VLMEvalKit can result in slight differences, which is normal. Updates to code versions and variations in environment and hardware can also cause minor discrepancies in results.
+
+- It is important to mention that the MMVet scores we report are evaluated using GPT-4-0613 as the judge model. Different versions of GPT-4 can lead to significant variations in the scores for this dataset. For instance, using GPT-4-Turbo would result in significantly lower scores.
+
+### Video Benchmarks
+
+| Benchmark | GPT-4V | VILA-1.5 | LLaVA-NeXT-Video | InternVL2-40B | InternVL2-Llama3-76B |
+| :------------------: | :----: | :------: | :--------------: | :-----------: | :------------------: |
+| Model Size | - | 34B | 34B | 40B | 76B |
+| | | | | | |
+| MVBench | - | - | - | 72.5 | |
+| Video-MME<br>wo subs | 59.9 | 59.0 | 52.0 | TBD | TBD |
+| Video-MME<br>w/ subs | 63.3 | 59.4 | 54.9 | TBD | TBD |
+
+- We evaluate our models on MVBench by extracting 16 frames from each video, and each frame was resized to a 448x448 image.
+
+Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
+
+## Quick Start
+
+We provide an example code to run InternVL2-Llama3-76B using `transformers`.
+
+> Please use transformers==4.37.2 to ensure the model works normally.
+
+```python
+import numpy as np
+import torch
+import torchvision.transforms as T
+from decord import VideoReader, cpu
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+def build_transform(input_size):
+ MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+ transform = T.Compose([
+ T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+ T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+ T.ToTensor(),
+ T.Normalize(mean=MEAN, std=STD)
+ ])
+ return transform
+
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+ best_ratio_diff = float('inf')
+ best_ratio = (1, 1)
+ area = width * height
+ for ratio in target_ratios:
+ target_aspect_ratio = ratio[0] / ratio[1]
+ ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+ if ratio_diff < best_ratio_diff:
+ best_ratio_diff = ratio_diff
+ best_ratio = ratio
+ elif ratio_diff == best_ratio_diff:
+ if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+ best_ratio = ratio
+ return best_ratio
+
+
+def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
+ orig_width, orig_height = image.size
+ aspect_ratio = orig_width / orig_height
+
+ # calculate the existing image aspect ratio
+ target_ratios = set(
+ (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+ i * j <= max_num and i * j >= min_num)
+ target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+ # find the closest aspect ratio to the target
+ target_aspect_ratio = find_closest_aspect_ratio(
+ aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+ # calculate the target width and height
+ target_width = image_size * target_aspect_ratio[0]
+ target_height = image_size * target_aspect_ratio[1]
+ blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+ # resize the image
+ resized_img = image.resize((target_width, target_height))
+ processed_images = []
+ for i in range(blocks):
+ box = (
+ (i % (target_width // image_size)) * image_size,
+ (i // (target_width // image_size)) * image_size,
+ ((i % (target_width // image_size)) + 1) * image_size,
+ ((i // (target_width // image_size)) + 1) * image_size
+ )
+ # split the image
+ split_img = resized_img.crop(box)
+ processed_images.append(split_img)
+ assert len(processed_images) == blocks
+ if use_thumbnail and len(processed_images) != 1:
+ thumbnail_img = image.resize((image_size, image_size))
+ processed_images.append(thumbnail_img)
+ return processed_images
+
+
+def load_image(image_file, input_size=448, max_num=6):
+ image = Image.open(image_file).convert('RGB')
+ transform = build_transform(input_size=input_size)
+ images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+ pixel_values = [transform(image) for image in images]
+ pixel_values = torch.stack(pixel_values)
+ return pixel_values
+
+
+path = 'OpenGVLab/InternVL2-Llama3-76B'
+# You need to set device_map='auto' to use multiple GPUs for inference.
+import os
+os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
+model = AutoModel.from_pretrained(
+ path,
+ torch_dtype=torch.bfloat16,
+ low_cpu_mem_usage=True,
+ trust_remote_code=True,
+ device_map='auto').eval()
+
+tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
+# set the max number of tiles in `max_num`
+pixel_values = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda()
+
+generation_config = dict(
+ num_beams=1,
+ max_new_tokens=1024,
+ do_sample=False,
+)
+
+# pure-text conversation (纯文本对话)
+question = 'Hello, who are you?'
+response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+question = 'Can you tell me a story?'
+response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+# single-image single-round conversation (单图单轮对话)
+question = '<image>\nPlease describe the image shortly.'
+response = model.chat(tokenizer, pixel_values, question, generation_config)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+# single-image multi-round conversation (单图多轮对话)
+question = '<image>\nPlease describe the image in detail.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+question = 'Please write a poem according to the image.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
+pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda()
+pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda()
+pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
+
+question = '<image>\nDescribe the two images in detail.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+ history=None, return_history=True)
+
+question = 'What are the similarities and differences between these two images.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+ history=history, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
+pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda()
+pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda()
+pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
+num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
+
+question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+ num_patches_list=num_patches_list,
+ history=None, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+question = 'What are the similarities and differences between these two images.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+ num_patches_list=num_patches_list,
+ history=history, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+# batch inference, single image per sample (单图批处理)
+pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda()
+pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda()
+num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
+pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
+
+questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
+responses = model.batch_chat(tokenizer, pixel_values,
+ num_patches_list=num_patches_list,
+ questions=questions,
+ generation_config=generation_config)
+for question, response in zip(questions, responses):
+ print(f'User: {question}')
+ print(f'Assistant: {response}')
+
+# video multi-round conversation (视频多轮对话)
+def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
+ if bound:
+ start, end = bound[0], bound[1]
+ else:
+ start, end = -100000, 100000
+ start_idx = max(first_idx, round(start * fps))
+ end_idx = min(round(end * fps), max_frame)
+ seg_size = float(end_idx - start_idx) / num_segments
+ frame_indices = np.array([
+ int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
+ for idx in range(num_segments)
+ ])
+ return frame_indices
+
+def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
+ vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+ max_frame = len(vr) - 1
+ fps = float(vr.get_avg_fps())
+
+ pixel_values_list, num_patches_list = [], []
+ transform = build_transform(input_size=input_size)
+ frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
+ for frame_index in frame_indices:
+ img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
+ img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
+ pixel_values = [transform(tile) for tile in img]
+ pixel_values = torch.stack(pixel_values)
+ num_patches_list.append(pixel_values.shape[0])
+ pixel_values_list.append(pixel_values)
+ pixel_values = torch.cat(pixel_values_list)
+ return pixel_values, num_patches_list
+
+
+video_path = './examples/red-panda.mp4'
+# pixel_values, num_patches_list = load_video(video_path, num_segments=32, max_num=1)
+pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=1)
+pixel_values = pixel_values.to(torch.bfloat16).cuda()
+video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
+question = video_prefix + 'What is the red panda doing?'
+# Frame1: <image>\nFrame2: <image>\n...\nFrame31: <image>\n{question}
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+ num_patches_list=num_patches_list,
+ history=None, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+question = 'Describe this video in detail. Don\'t repeat.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+ num_patches_list=num_patches_list,
+ history=history, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+```
+
+## Deployment
+
+TODO
+
+## License
+
+This project is released under the MIT license, while Llama3 is licensed under the Llama 3 Community License.
+
+## Citation
+
+If you find this project useful in your research, please consider citing:
+
+```BibTeX
+@article{chen2023internvl,
+ title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
+ author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
+ journal={arXiv preprint arXiv:2312.14238},
+ year={2023}
+}
+@article{chen2024far,
+ title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
+ author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
+ journal={arXiv preprint arXiv:2404.16821},
+ year={2024}
+}
+```
+
+## 简介
+
+我们很高兴宣布 InternVL 2.0 的发布，这是 InternVL 系列多模态大语言模型的最新版本。InternVL 2.0 提供了多种**指令微调**的模型，参数从 20 亿到 1080 亿不等。此仓库包含经过指令微调的 InternVL2-Llama3-76B 模型。
+
+与最先进的开源多模态大语言模型相比，InternVL 2.0 超越了大多数开源模型。它在各种能力上表现出与闭源商业模型相媲美的竞争力，包括文档和图表理解、信息图表问答、场景文本理解和 OCR 任务、科学和数学问题解决，以及文化理解和综合多模态能力。
+
+InternVL 2.0 使用 8k 上下文窗口进行训练，训练数据包含长文本、多图和视频数据，与 InternVL 1.5 相比，其处理这些类型输入的能力显著提高。更多详细信息，请参阅我们的博客和 GitHub。
+
+## 模型细节
+
+InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模型。对于每个规模的模型，我们都会发布针对多模态任务优化的指令微调模型。InternVL2-Llama3-76B 包含 [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5)、一个 MLP 投影器和 [Hermes-2-Theta-Llama-3-70B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B)。
+
+## 性能测试
+
+### 图像相关评测
+
+| 评测数据集 | GPT-4T-20240409 | Gemini-1.5-Pro | InternVL2-40B | InternVL2-Llama3-76B |
+| :--------------------------: | :-------------: | :------------: | :-----------: | :------------------: |
+| 模型大小 | - | - | 40B | 76B |
+| | | | | |
+| DocVQA<sub>test</sub> | 87.2 | 86.5 | 93.9 | |
+| ChartQA<sub>test</sub> | 78.1 | 81.3 | 86.2 | |
+| InfoVQA<sub>test</sub> | - | 72.7 | 78.7 | |
+| TextVQA<sub>val</sub> | - | 73.5 | 83.0 | |
+| OCRBench | 678 | 754 | 837 | |
+| MME<sub>sum</sub> | 2070.2 | 2110.6 | 2315.0 | |
+| RealWorldQA | 68.0 | 67.5 | 71.8 | |
+| AI2D<sub>test</sub> | 89.4 | 80.3 | 87.1 | |
+| MMMU<sub>val</sub> | 63.1 | 58.5 | 53.9 | |
+| MMBench-EN<sub>test</sub> | 81.0 | 73.9 | 86.8 | |
+| MMBench-CN<sub>test</sub> | 80.2 | 73.8 | 86.5 | |
+| CCBench<sub>dev</sub> | 57.3 | 28.4 | 80.6 | |
+| MMVet<sub>GPT-4-0613</sub> | - | - | 68.5 | |
+| MMVet<sub>GPT-4-Turbo</sub> | 67.5 | 64.0 | 65.5 | |
+| SEED-Image | - | - | 78.2 | |
+| HallBench<sub>avg</sub> | 43.9 | 45.6 | 56.9 | |
+| MathVista<sub>testmini</sub> | 58.1 | 57.7 | 63.7 | |
+
+- 我们同时使用 InternVL 和 VLMEvalKit 仓库进行模型评估。具体来说，DocVQA、ChartQA、InfoVQA、TextVQA、MME、AI2D、MMBench、CCBench、MMVet 和 SEED-Image 的结果是使用 InternVL 仓库测试的。MMMU、OCRBench、RealWorldQA、HallBench 和 MathVista 是使用 VLMEvalKit 进行评估的。
+
+- 请注意，使用不同的测试工具包（如 InternVL 和 VLMEvalKit）评估同一模型可能会导致细微差异，这是正常的。代码版本的更新、环境和硬件的变化也可能导致结果的微小差异。
+
+- 需要提到的是，我们报告的 MMVet 分数是使用 GPT-4-0613 作为评判模型评估的。不同版本的 GPT-4 会导致该数据集分数的显著变化。例如，使用 GPT-4-Turbo 会导致分数显著降低。
+
+### 视频相关评测
+
+| 评测数据集 | GPT-4V | VILA-1.5 | LLaVA-NeXT-Video | InternVL2-40B | InternVL2-Llama3-76B |
+| :------------------: | :----: | :------: | :--------------: | :-----------: | :------------------: |
+| 模型大小 | - | 34B | 34B | 40B | 76B |
+| | | | | | |
+| MVBench | - | - | - | 72.5 | |
+| Video-MME<br>wo subs | 59.9 | 59.0 | 52.0 | TBD | TBD |
+| Video-MME<br>w/ subs | 63.3 | 59.4 | 54.9 | TBD | TBD |
+
+- 我们通过从每个视频中提取16帧来评估我们的模型在MVBench上的性能，每个视频帧被调整为448x448的图像。
+
+限制：尽管在训练过程中我们非常注重模型的安全性，尽力促使模型输出符合伦理和法律要求的文本，但受限于模型大小以及概率生成范式，模型可能会产生各种不符合预期的输出，例如回复内容包含偏见、歧视等有害内容，请勿传播这些内容。由于传播不良信息导致的任何后果，本项目不承担责任。
+
+## 快速启动
+
+我们提供了一个示例代码，用于使用 `transformers` 运行 InternVL2-Llama3-76B。
+
+> 请使用 transformers==4.37.2 以确保模型正常运行。
+
+示例代码请[点击这里](#quick-start)。
+
+## 部署
+
+### LMDeploy
+
+TODO
+
+## 开源许可证
+
+该项目采用 MIT 许可证发布，而 LLama3 则采用 Llama 3 Community License 许可证。
+
+## 引用
+
+如果您发现此项目对您的研究有用，可以考虑引用我们的论文：
+
+```BibTeX
+@article{chen2023internvl,
+ title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
+ author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
+ journal={arXiv preprint arXiv:2312.14238},
+ year={2023}
+}
+@article{chen2024far,
+ title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
+ author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
+ journal={arXiv preprint arXiv:2404.16821},
+ year={2024}
+}
+```

config.json

@@ -0,0 +1,138 @@
+{
+ "_commit_hash": null,
+ "architectures": [
+ "InternVLChatModel"
+ ],
+ "auto_map": {
+ "AutoConfig": "configuration_internvl_chat.InternVLChatConfig",
+ "AutoModel": "modeling_internvl_chat.InternVLChatModel",
+ "AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"
+ },
+ "downsample_ratio": 0.5,
+ "dynamic_image_size": true,
+ "force_image_size": 448,
+ "llm_config": {
+ "_name_or_path": "NousResearch/Hermes-2-Theta-Llama-3-70B",
+ "add_cross_attention": false,
+ "architectures": [
+ "LlamaForCausalLM"
+ ],
+ "attention_bias": false,
+ "attention_dropout": 0.0,
+ "bad_words_ids": null,
+ "begin_suppress_tokens": null,
+ "bos_token_id": 128000,
+ "chunk_size_feed_forward": 0,
+ "cross_attention_hidden_size": null,
+ "decoder_start_token_id": null,
+ "diversity_penalty": 0.0,
+ "do_sample": false,
+ "early_stopping": false,
+ "encoder_no_repeat_ngram_size": 0,
+ "eos_token_id": 128003,
+ "exponential_decay_length_penalty": null,
+ "finetuning_task": null,
+ "forced_bos_token_id": null,
+ "forced_eos_token_id": null,
+ "hidden_act": "silu",
+ "hidden_size": 8192,
+ "id2label": {
+ "0": "LABEL_0",
+ "1": "LABEL_1"
+ },
+ "initializer_range": 0.02,
+ "intermediate_size": 28672,
+ "is_decoder": false,
+ "is_encoder_decoder": false,
+ "label2id": {
+ "LABEL_0": 0,
+ "LABEL_1": 1
+ },
+ "length_penalty": 1.0,
+ "max_length": 20,
+ "max_position_embeddings": 8192,
+ "min_length": 0,
+ "mlp_bias": false,
+ "model_type": "llama",
+ "no_repeat_ngram_size": 0,
+ "num_attention_heads": 64,
+ "num_beam_groups": 1,
+ "num_beams": 1,
+ "num_hidden_layers": 80,
+ "num_key_value_heads": 8,
+ "num_return_sequences": 1,
+ "output_attentions": false,
+ "output_hidden_states": false,
+ "output_scores": false,
+ "pad_token_id": null,
+ "prefix": null,
+ "pretraining_tp": 1,
+ "problem_type": null,
+ "pruned_heads": {},
+ "remove_invalid_values": false,
+ "repetition_penalty": 1.0,
+ "return_dict": true,
+ "return_dict_in_generate": false,
+ "rms_norm_eps": 1e-05,
+ "rope_scaling": null,
+ "rope_theta": 500000.0,
+ "sep_token_id": null,
+ "suppress_tokens": null,
+ "task_specific_params": null,
+ "temperature": 1.0,
+ "tf_legacy_loss": false,
+ "tie_encoder_decoder": false,
+ "tie_word_embeddings": false,
+ "tokenizer_class": null,
+ "top_k": 50,
+ "top_p": 1.0,
+ "torch_dtype": "bfloat16",
+ "torchscript": false,
+ "transformers_version": "4.37.2",
+ "typical_p": 1.0,
+ "use_bfloat16": true,
+ "use_cache": true,
+ "vocab_size": 128265
+ },
+ "max_dynamic_patch": 12,
+ "min_dynamic_patch": 1,
+ "model_type": "internvl_chat",
+ "ps_version": "v2",
+ "select_layer": -1,
+ "template": "internlm2-chat",
+ "torch_dtype": "bfloat16",
+ "transformers_version": null,
+ "use_backbone_lora": 0,
+ "use_llm_lora": 0,
+ "use_thumbnail": true,
+ "vision_config": {
+ "architectures": [
+ "InternVisionModel"
+ ],
+ "attention_dropout": 0.0,
+ "drop_path_rate": 0.0,
+ "dropout": 0.0,
+ "hidden_act": "gelu",
+ "hidden_size": 3200,
+ "image_size": 448,
+ "initializer_factor": 0.1,
+ "initializer_range": 1e-10,
+ "intermediate_size": 12800,
+ "layer_norm_eps": 1e-06,
+ "model_type": "intern_vit_6b",
+ "norm_type": "rms_norm",
+ "num_attention_heads": 25,
+ "num_channels": 3,
+ "num_hidden_layers": 45,
+ "output_attentions": false,
+ "output_hidden_states": false,
+ "patch_size": 14,
+ "qk_normalization": true,
+ "qkv_bias": false,
+ "return_dict": true,
+ "torch_dtype": "bfloat16",
+ "transformers_version": "4.37.2",
+ "use_bfloat16": true,
+ "use_flash_attn": true
+ }
+}

configuration_intern_vit.py

@@ -0,0 +1,119 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class InternVisionConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
+ instantiate a vision encoder according to the specified arguments, defining the model architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ num_channels (`int`, *optional*, defaults to 3):
+ Number of color channels in the input images (e.g., 3 for RGB).
+ patch_size (`int`, *optional*, defaults to 14):
+ The size (resolution) of each patch.
+ image_size (`int`, *optional*, defaults to 224):
+ The size (resolution) of each image.
+ qkv_bias (`bool`, *optional*, defaults to `False`):
+ Whether to add a bias to the queries and values in the self-attention layers.
+ hidden_size (`int`, *optional*, defaults to 3200):
+ Dimensionality of the encoder layers and the pooler layer.
+ num_attention_heads (`int`, *optional*, defaults to 25):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 12800):
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ qk_normalization (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the queries and keys in the self-attention layers.
+ num_hidden_layers (`int`, *optional*, defaults to 48):
+ Number of hidden layers in the Transformer encoder.
+ use_flash_attn (`bool`, *optional*, defaults to `True`):
+ Whether to use flash attention mechanism.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+ The epsilon used by the layer normalization layers.
+ dropout (`float`, *optional*, defaults to 0.0):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ drop_path_rate (`float`, *optional*, defaults to 0.0):
+ Dropout rate for stochastic depth.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ initializer_factor (`float`, *optional*, defaults to 0.1):
+ A factor for layer scale.
+ """
+
+ model_type = 'intern_vit_6b'
+
+ def __init__(
+ self,
+ num_channels=3,
+ patch_size=14,
+ image_size=224,
+ qkv_bias=False,
+ hidden_size=3200,
+ num_attention_heads=25,
+ intermediate_size=12800,
+ qk_normalization=True,
+ num_hidden_layers=48,
+ use_flash_attn=True,
+ hidden_act='gelu',
+ norm_type='rms_norm',
+ layer_norm_eps=1e-6,
+ dropout=0.0,
+ drop_path_rate=0.0,
+ attention_dropout=0.0,
+ initializer_range=0.02,
+ initializer_factor=0.1,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.dropout = dropout
+ self.drop_path_rate = drop_path_rate
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_channels = num_channels
+ self.patch_size = patch_size
+ self.image_size = image_size
+ self.initializer_range = initializer_range
+ self.initializer_factor = initializer_factor
+ self.attention_dropout = attention_dropout
+ self.layer_norm_eps = layer_norm_eps
+ self.hidden_act = hidden_act
+ self.norm_type = norm_type
+ self.qkv_bias = qkv_bias
+ self.qk_normalization = qk_normalization
+ self.use_flash_attn = use_flash_attn
+
+ @classmethod
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+ if 'vision_config' in config_dict:
+ config_dict = config_dict['vision_config']
+
+ if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
+ logger.warning(
+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+ f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
+ )
+
+ return cls.from_dict(config_dict, **kwargs)

configuration_internvl_chat.py

@@ -0,0 +1,93 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import copy
+
+from transformers import AutoConfig, LlamaConfig
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+
+logger = logging.get_logger(__name__)
+
+
+class InternVLChatConfig(PretrainedConfig):
+ model_type = 'internvl_chat'
+ is_composition = True
+
+ def __init__(
+ self,
+ vision_config=None,
+ llm_config=None,
+ use_backbone_lora=0,
+ use_llm_lora=0,
+ select_layer=-1,
+ force_image_size=None,
+ downsample_ratio=0.5,
+ template=None,
+ dynamic_image_size=False,
+ use_thumbnail=False,
+ ps_version='v1',
+ min_dynamic_patch=1,
+ max_dynamic_patch=6,
+ **kwargs):
+ super().__init__(**kwargs)
+
+ if vision_config is None:
+ vision_config = {}
+ logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
+
+ if llm_config is None:
+ llm_config = {}
+ logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
+
+ self.vision_config = InternVisionConfig(**vision_config)
+ if llm_config['architectures'][0] == 'LlamaForCausalLM':
+ self.llm_config = LlamaConfig(**llm_config)
+ else:
+ raise ValueError('Unsupported architecture: {}'.format(llm_config['architectures'][0]))
+ self.use_backbone_lora = use_backbone_lora
+ self.use_llm_lora = use_llm_lora
+ self.select_layer = select_layer
+ self.force_image_size = force_image_size
+ self.downsample_ratio = downsample_ratio
+ self.template = template
+ self.dynamic_image_size = dynamic_image_size
+ self.use_thumbnail = use_thumbnail
+ self.ps_version = ps_version # pixel shuffle version
+ self.min_dynamic_patch = min_dynamic_patch
+ self.max_dynamic_patch = max_dynamic_patch
+
+ logger.info(f'vision_select_layer: {self.select_layer}')
+ logger.info(f'ps_version: {self.ps_version}')
+ logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
+ logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
+
+ def to_dict(self):
+ """
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+ Returns:
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+ """
+ output = copy.deepcopy(self.__dict__)
+ output['vision_config'] = self.vision_config.to_dict()
+ output['llm_config'] = self.llm_config.to_dict()
+ output['model_type'] = self.__class__.model_type
+ output['use_backbone_lora'] = self.use_backbone_lora
+ output['use_llm_lora'] = self.use_llm_lora
+ output['select_layer'] = self.select_layer
+ output['force_image_size'] = self.force_image_size
+ output['downsample_ratio'] = self.downsample_ratio
+ output['template'] = self.template
+ output['dynamic_image_size'] = self.dynamic_image_size
+ output['use_thumbnail'] = self.use_thumbnail
+ output['ps_version'] = self.ps_version
+ output['min_dynamic_patch'] = self.min_dynamic_patch
+ output['max_dynamic_patch'] = self.max_dynamic_patch
+
+ return output

conversation.py

@@ -0,0 +1,390 @@
+"""
+Conversation prompt templates.
+
+We kindly request that you import fastchat instead of copying this file if you wish to use it.
+If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
+"""
+
+import dataclasses
+from enum import IntEnum, auto
+from typing import Any, Dict, List, Tuple, Union
+
+
+class SeparatorStyle(IntEnum):
+ """Separator styles."""
+
+ ADD_COLON_SINGLE = auto()
+ ADD_COLON_TWO = auto()
+ ADD_COLON_SPACE_SINGLE = auto()
+ NO_COLON_SINGLE = auto()
+ NO_COLON_TWO = auto()
+ ADD_NEW_LINE_SINGLE = auto()
+ LLAMA2 = auto()
+ CHATGLM = auto()
+ CHATML = auto()
+ CHATINTERN = auto()
+ DOLLY = auto()
+ RWKV = auto()
+ PHOENIX = auto()
+ ROBIN = auto()
+ FALCON_CHAT = auto()
+ CHATGLM3 = auto()
+ INTERNVL_ZH = auto()
+ MPT = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+ """A class that manages prompt templates and keeps all conversation history."""
+
+ # The name of this template
+ name: str
+ # The template of the system prompt
+ system_template: str = '{system_message}'
+ # The system message
+ system_message: str = ''
+ # The names of two roles
+ roles: Tuple[str] = ('USER', 'ASSISTANT')
+ # All messages. Each item is (role, message).
+ messages: List[List[str]] = ()
+ # The number of few shot examples
+ offset: int = 0
+ # The separator style and configurations
+ sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
+ sep: str = '\n'
+ sep2: str = None
+ # Stop criteria (the default one is EOS token)
+ stop_str: Union[str, List[str]] = None
+ # Stops generation if meeting any token in this list
+ stop_token_ids: List[int] = None
+
+ def get_prompt(self) -> str:
+ """Get the prompt for generation."""
+ system_prompt = self.system_template.format(system_message=self.system_message)
+ if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
+ ret = system_prompt + self.sep
+ for role, message in self.messages:
+ if message:
+ ret += role + ': ' + message + self.sep
+ else:
+ ret += role + ':'
+ return ret
+ elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
+ seps = [self.sep, self.sep2]
+ ret = system_prompt + seps[0]
+ for i, (role, message) in enumerate(self.messages):
+ if message:
+ ret += role + ': ' + message + seps[i % 2]
+ else:
+ ret += role + ':'
+ return ret
+ elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
+ ret = system_prompt + self.sep
+ for role, message in self.messages:
+ if message:
+ ret += role + ': ' + message + self.sep
+ else:
+ ret += role + ': ' # must be end with a space
+ return ret
+ elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
+ ret = '' if system_prompt == '' else system_prompt + self.sep
+ for role, message in self.messages:
+ if message:
+ ret += role + '\n' + message + self.sep
+ else:
+ ret += role + '\n'
+ return ret
+ elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
+ ret = system_prompt
+ for role, message in self.messages:
+ if message:
+ ret += role + message + self.sep
+ else:
+ ret += role
+ return ret
+ elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
+ seps = [self.sep, self.sep2]
+ ret = system_prompt
+ for i, (role, message) in enumerate(self.messages):
+ if message:
+ ret += role + message + seps[i % 2]
+ else:
+ ret += role
+ return ret
+ elif self.sep_style == SeparatorStyle.RWKV:
+ ret = system_prompt
+ for i, (role, message) in enumerate(self.messages):
+ if message:
+ ret += (
+ role
+ + ': '
+ + message.replace('\r\n', '\n').replace('\n\n', '\n')
+ )
+ ret += '\n\n'
+ else:
+ ret += role + ':'
+ return ret
+ elif self.sep_style == SeparatorStyle.LLAMA2:
+ seps = [self.sep, self.sep2]
+ if self.system_message:
+ ret = system_prompt
+ else:
+ ret = '[INST] '
+ for i, (role, message) in enumerate(self.messages):
+ tag = self.roles[i % 2]
+ if message:
+ if i == 0:
+ ret += message + ' '
+ else:
+ ret += tag + ' ' + message + seps[i % 2]
+ else:
+ ret += tag
+ return ret
+ elif self.sep_style == SeparatorStyle.CHATGLM:
+ # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
+ # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
+ round_add_n = 1 if self.name == 'chatglm2' else 0
+ if system_prompt:
+ ret = system_prompt + self.sep
+ else:
+ ret = ''
+
+ for i, (role, message) in enumerate(self.messages):
+ if i % 2 == 0:
+ ret += f'[Round {i//2 + round_add_n}]{self.sep}'
+
+ if message:
+ ret += f'{role}：{message}{self.sep}'
+ else:
+ ret += f'{role}：'
+ return ret
+ elif self.sep_style == SeparatorStyle.CHATML:
+ ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
+ for role, message in self.messages:
+ if message:
+ ret += role + '\n' + message + self.sep + '\n'
+ else:
+ ret += role + '\n'
+ return ret
+ elif self.sep_style == SeparatorStyle.CHATGLM3:
+ ret = ''
+ if self.system_message:
+ ret += system_prompt
+ for role, message in self.messages:
+ if message:
+ ret += role + '\n' + ' ' + message
+ else:
+ ret += role
+ return ret
+ elif self.sep_style == SeparatorStyle.CHATINTERN:
+ # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
+ seps = [self.sep, self.sep2]
+ ret = system_prompt
+ for i, (role, message) in enumerate(self.messages):
+ # if i % 2 == 0:
+ # ret += "<s>"
+ if message:
+ ret += role + ':' + message + seps[i % 2] + '\n'
+ else:
+ ret += role + ':'
+ return ret
+ elif self.sep_style == SeparatorStyle.DOLLY:
+ seps = [self.sep, self.sep2]
+ ret = system_prompt
+ for i, (role, message) in enumerate(self.messages):
+ if message:
+ ret += role + ':\n' + message + seps[i % 2]
+ if i % 2 == 1:
+ ret += '\n\n'
+ else:
+ ret += role + ':\n'
+ return ret
+ elif self.sep_style == SeparatorStyle.PHOENIX:
+ ret = system_prompt
+ for role, message in self.messages:
+ if message:
+ ret += role + ': ' + '<s>' + message + '</s>'
+ else:
+ ret += role + ': ' + '<s>'
+ return ret
+ elif self.sep_style == SeparatorStyle.ROBIN:
+ ret = system_prompt + self.sep
+ for role, message in self.messages:
+ if message:
+ ret += role + ':\n' + message + self.sep
+ else:
+ ret += role + ':\n'
+ return ret
+ elif self.sep_style == SeparatorStyle.FALCON_CHAT:
+ ret = ''
+ if self.system_message:
+ ret += system_prompt + self.sep
+ for role, message in self.messages:
+ if message:
+ ret += role + ': ' + message + self.sep
+ else:
+ ret += role + ':'
+
+ return ret
+ elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
+ seps = [self.sep, self.sep2]
+ ret = self.system_message + seps[0]
+ for i, (role, message) in enumerate(self.messages):
+ if message:
+ ret += role + ': ' + message + seps[i % 2]
+ else:
+ ret += role + ':'
+ return ret
+ elif self.sep_style == SeparatorStyle.MPT:
+ ret = system_prompt + self.sep
+ for role, message in self.messages:
+ if message:
+ if type(message) is tuple:
+ message, _, _ = message
+ ret += role + message + self.sep
+ else:
+ ret += role
+ return ret
+ else:
+ raise ValueError(f'Invalid style: {self.sep_style}')
+
+ def set_system_message(self, system_message: str):
+ """Set the system message."""
+ self.system_message = system_message
+
+ def append_message(self, role: str, message: str):
+ """Append a new message."""
+ self.messages.append([role, message])
+
+ def update_last_message(self, message: str):
+ """Update the last output.
+
+ The last message is typically set to be None when constructing the prompt,
+ so we need to update it in-place after getting the response from a model.
+ """
+ self.messages[-1][1] = message
+
+ def to_gradio_chatbot(self):
+ """Convert the conversation to gradio chatbot format."""
+ ret = []
+ for i, (role, msg) in enumerate(self.messages[self.offset :]):
+ if i % 2 == 0:
+ ret.append([msg, None])
+ else:
+ ret[-1][-1] = msg
+ return ret
+
+ def to_openai_api_messages(self):
+ """Convert the conversation to OpenAI chat completion format."""
+ ret = [{'role': 'system', 'content': self.system_message}]
+
+ for i, (_, msg) in enumerate(self.messages[self.offset :]):
+ if i % 2 == 0:
+ ret.append({'role': 'user', 'content': msg})
+ else:
+ if msg is not None:
+ ret.append({'role': 'assistant', 'content': msg})
+ return ret
+
+ def copy(self):
+ return Conversation(
+ name=self.name,
+ system_template=self.system_template,
+ system_message=self.system_message,
+ roles=self.roles,
+ messages=[[x, y] for x, y in self.messages],
+ offset=self.offset,
+ sep_style=self.sep_style,
+ sep=self.sep,
+ sep2=self.sep2,
+ stop_str=self.stop_str,
+ stop_token_ids=self.stop_token_ids,
+ )
+
+ def dict(self):
+ return {
+ 'template_name': self.name,
+ 'system_message': self.system_message,
+ 'roles': self.roles,
+ 'messages': self.messages,
+ 'offset': self.offset,
+ }
+
+
+# A global registry for all conversation templates
+conv_templates: Dict[str, Conversation] = {}
+
+
+def register_conv_template(template: Conversation, override: bool = False):
+ """Register a new conversation template."""
+ if not override:
+ assert (
+ template.name not in conv_templates
+ ), f'{template.name} has been registered.'
+
+ conv_templates[template.name] = template
+
+
+def get_conv_template(name: str) -> Conversation:
+ """Get a conversation template."""
+ return conv_templates[name].copy()
+
+
+# Note that for inference, using the Hermes-2 and internlm2-chat templates is equivalent.
+register_conv_template(
+ Conversation(
+ name='Hermes-2',
+ system_template='<|im_start|>system\n{system_message}',
+ # note: The new system prompt was not used here to avoid changes in benchmark performance.
+ # system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态大语言模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。',
+ system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
+ roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+ sep_style=SeparatorStyle.MPT,
+ sep='<|im_end|>',
+ stop_token_ids=[
+ 2,
+ 6,
+ 7,
+ 8,
+ ],
+ stop_str='<|endoftext|>',
+ )
+)
+
+
+register_conv_template(
+ Conversation(
+ name='internlm2-chat',
+ system_template='<|im_start|>system\n{system_message}',
+ # note: The new system prompt was not used here to avoid changes in benchmark performance.
+ # system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态大语言模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。',
+ system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
+ roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+ sep_style=SeparatorStyle.MPT,
+ sep='<|im_end|>',
+ stop_token_ids=[
+ 2,
+ 92543,
+ 92542
+ ]
+ )
+)
+
+
+register_conv_template(
+ Conversation(
+ name='phi3-chat',
+ system_template='<|system|>\n{system_message}',
+ # note: The new system prompt was not used here to avoid changes in benchmark performance.
+ # system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态大语言模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。',
+ system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
+ roles=('<|user|>\n', '<|assistant|>\n'),
+ sep_style=SeparatorStyle.MPT,
+ sep='<|end|>',
+ stop_token_ids=[
+ 2,
+ 32000,
+ 32007
+ ]
+ )
+)

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+ "transformers_version": "4.37.2"
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modeling_intern_vit.py

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+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from timm.models.layers import DropPath
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutput,
+ BaseModelOutputWithPooling)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+
+try:
+ try: # v1
+ from flash_attn.flash_attn_interface import \
+ flash_attn_unpadded_qkvpacked_func
+ except: # v2
+ from flash_attn.flash_attn_interface import \
+ flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
+
+ from flash_attn.bert_padding import pad_input, unpad_input
+
+ has_flash_attn = True
+except:
+ print('FlashAttention is not installed.')
+ has_flash_attn = False
+
+logger = logging.get_logger(__name__)
+
+
+class FlashAttention(nn.Module):
+ """Implement the scaled dot product attention with softmax.
+ Arguments
+ ---------
+ softmax_scale: The temperature to use for the softmax attention.
+ (default: 1/sqrt(d_keys) where d_keys is computed at
+ runtime)
+ attention_dropout: The dropout rate to apply to the attention
+ (default: 0.0)
+ """
+
+ def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
+ super().__init__()
+ self.softmax_scale = softmax_scale
+ self.dropout_p = attention_dropout
+
+ def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
+ max_s=None, need_weights=False):
+ """Implements the multihead softmax attention.
+ Arguments
+ ---------
+ qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
+ if unpadded: (nnz, 3, h, d)
+ key_padding_mask: a bool tensor of shape (B, S)
+ """
+ assert not need_weights
+ assert qkv.dtype in [torch.float16, torch.bfloat16]
+ assert qkv.is_cuda
+
+ if cu_seqlens is None:
+ batch_size = qkv.shape[0]
+ seqlen = qkv.shape[1]
+ if key_padding_mask is None:
+ qkv = rearrange(qkv, 'b s ... -> (b s) ...')
+ max_s = seqlen
+ cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
+ device=qkv.device)
+ output = flash_attn_unpadded_qkvpacked_func(
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+ softmax_scale=self.softmax_scale, causal=causal
+ )
+ output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+ else:
+ nheads = qkv.shape[-2]
+ x = rearrange(qkv, 'b s three h d -> b s (three h d)')
+ x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
+ x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
+ output_unpad = flash_attn_unpadded_qkvpacked_func(
+ x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+ softmax_scale=self.softmax_scale, causal=causal
+ )
+ output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
+ indices, batch_size, seqlen),
+ 'b s (h d) -> b s h d', h=nheads)
+ else:
+ assert max_s is not None
+ output = flash_attn_unpadded_qkvpacked_func(
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+ softmax_scale=self.softmax_scale, causal=causal
+ )
+
+ return output, None
+
+
+class InternRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+try:
+ from apex.normalization import FusedRMSNorm
+
+ InternRMSNorm = FusedRMSNorm # noqa
+
+ logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
+except ImportError:
+ # using the normal InternRMSNorm
+ pass
+except Exception:
+ logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
+ pass
+
+
+NORM2FN = {
+ 'rms_norm': InternRMSNorm,
+ 'layer_norm': nn.LayerNorm,
+}
+
+
+class InternVisionEmbeddings(nn.Module):
+ def __init__(self, config: InternVisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.image_size = config.image_size
+ self.patch_size = config.patch_size
+
+ self.class_embedding = nn.Parameter(
+ torch.randn(1, 1, self.embed_dim),
+ )
+
+ self.patch_embedding = nn.Conv2d(
+ in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
+ )
+
+ self.num_patches = (self.image_size // self.patch_size) ** 2
+ self.num_positions = self.num_patches + 1
+
+ self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
+
+ def _get_pos_embed(self, pos_embed, H, W):
+ target_dtype = pos_embed.dtype
+ pos_embed = pos_embed.float().reshape(
+ 1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
+ pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False). \
+ reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)
+ return pos_embed
+
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+ target_dtype = self.patch_embedding.weight.dtype
+ patch_embeds = self.patch_embedding(pixel_values) # shape = [*, channel, width, height]
+ batch_size, _, height, width = patch_embeds.shape
+ patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+ class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
+ embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+ position_embedding = torch.cat([
+ self.position_embedding[:, :1, :],
+ self._get_pos_embed(self.position_embedding[:, 1:, :], height, width)
+ ], dim=1)
+ embeddings = embeddings + position_embedding.to(target_dtype)
+ return embeddings
+
+
+class InternAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: InternVisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.use_flash_attn = config.use_flash_attn and has_flash_attn
+ if config.use_flash_attn and not has_flash_attn:
+ print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
+ f' {self.num_heads}).'
+ )
+
+ self.scale = self.head_dim ** -0.5
+ self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
+ self.attn_drop = nn.Dropout(config.attention_dropout)
+ self.proj_drop = nn.Dropout(config.dropout)
+
+ self.qk_normalization = config.qk_normalization
+
+ if self.qk_normalization:
+ self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+ self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+ if self.use_flash_attn:
+ self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
+ self.proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+ def _naive_attn(self, x):
+ B, N, C = x.shape
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
+
+ if self.qk_normalization:
+ B_, H_, N_, D_ = q.shape
+ q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+ k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+
+ attn = ((q * self.scale) @ k.transpose(-2, -1))
+ attn = attn.softmax(dim=-1)
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+ def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
+ qkv = self.qkv(x)
+ qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
+
+ if self.qk_normalization:
+ q, k, v = qkv.unbind(2)
+ q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
+ k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
+ qkv = torch.stack([q, k, v], dim=2)
+
+ context, _ = self.inner_attn(
+ qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
+ )
+ outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
+ outs = self.proj_drop(outs)
+ return outs
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
+ return x
+
+
+class InternMLP(nn.Module):
+ def __init__(self, config: InternVisionConfig):
+ super().__init__()
+ self.config = config
+ self.act = ACT2FN[config.hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class InternVisionEncoderLayer(nn.Module):
+ def __init__(self, config: InternVisionConfig, drop_path_rate: float):
+ super().__init__()
+ self.embed_dim = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.norm_type = config.norm_type
+
+ self.attn = InternAttention(config)
+ self.mlp = InternMLP(config)
+ self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+ self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+
+ self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+ self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+ self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+ self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ """
+ hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
+
+ hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
+
+ return hidden_states
+
+
+class InternVisionEncoder(nn.Module):
+ """
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+ [`InternEncoderLayer`].
+
+ Args:
+ config (`InternConfig`):
+ The corresponding vision configuration for the `InternEncoder`.
+ """
+
+ def __init__(self, config: InternVisionConfig):
+ super().__init__()
+ self.config = config
+ # stochastic depth decay rule
+ dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+ self.layers = nn.ModuleList([
+ InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = True
+
+ def forward(
+ self,
+ inputs_embeds,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutput]:
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Embedded representation of the inputs. Should be float, not int tokens.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ encoder_states = () if output_hidden_states else None
+ hidden_states = inputs_embeds
+
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ if self.gradient_checkpointing and self.training:
+ layer_outputs = torch.utils.checkpoint.checkpoint(
+ encoder_layer,
+ hidden_states)
+ else:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ )
+ hidden_states = layer_outputs
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states
+ )
+
+
+class InternVisionModel(PreTrainedModel):
+ main_input_name = 'pixel_values'
+ config_class = InternVisionConfig
+ _no_split_modules = ['InternVisionEncoderLayer']
+
+ def __init__(self, config: InternVisionConfig):
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = InternVisionEmbeddings(config)
+ self.encoder = InternVisionEncoder(config)
+
+ def resize_pos_embeddings(self, old_size, new_size, patch_size):
+ pos_emb = self.embeddings.position_embedding
+ _, num_positions, embed_dim = pos_emb.shape
+ cls_emb = pos_emb[:, :1, :]
+ pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
+ pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
+ pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
+ pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
+ self.embeddings.position_embedding = nn.Parameter(pos_emb)
+ self.embeddings.image_size = new_size
+ logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
+
+ def get_input_embeddings(self):
+ return self.embeddings
+
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ pixel_embeds: Optional[torch.FloatTensor] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPooling]:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None and pixel_embeds is None:
+ raise ValueError('You have to specify pixel_values or pixel_embeds')
+
+ if pixel_embeds is not None:
+ hidden_states = pixel_embeds
+ else:
+ if len(pixel_values.shape) == 4:
+ hidden_states = self.embeddings(pixel_values)
+ else:
+ raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
+ encoder_outputs = self.encoder(
+ inputs_embeds=hidden_states,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = encoder_outputs.last_hidden_state
+ pooled_output = last_hidden_state[:, 0, :]
+
+ if not return_dict:
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )

modeling_internvl_chat.py

@@ -0,0 +1,340 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+import warnings
+from typing import Any, List, Optional, Tuple, Union
+
+import torch.utils.checkpoint
+import transformers
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import AutoModel, GenerationConfig, LlamaForCausalLM
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import ModelOutput, logging
+
+from .configuration_internvl_chat import InternVLChatConfig
+from .conversation import get_conv_template
+from .modeling_intern_vit import InternVisionModel
+
+logger = logging.get_logger(__name__)
+
+
+def version_cmp(v1, v2, op='eq'):
+ import operator
+
+ from packaging import version
+ op_func = getattr(operator, op)
+ return op_func(version.parse(v1), version.parse(v2))
+
+
+class InternVLChatModel(PreTrainedModel):
+ config_class = InternVLChatConfig
+ main_input_name = 'pixel_values'
+ _no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer']
+
+ def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None):
+ super().__init__(config)
+
+ assert version_cmp(transformers.__version__, '4.36.2', 'ge')
+ image_size = config.force_image_size or config.vision_config.image_size
+ patch_size = config.vision_config.patch_size
+ self.patch_size = patch_size
+ self.select_layer = config.select_layer
+ self.template = config.template
+ self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
+ self.downsample_ratio = config.downsample_ratio
+ self.ps_version = config.ps_version
+
+ logger.info(f'num_image_token: {self.num_image_token}')
+ logger.info(f'ps_version: {self.ps_version}')
+ if vision_model is not None:
+ self.vision_model = vision_model
+ else:
+ self.vision_model = InternVisionModel(config.vision_config)
+ if language_model is not None:
+ self.language_model = language_model
+ else:
+ if config.llm_config.architectures[0] == 'LlamaForCausalLM':
+ self.language_model = LlamaForCausalLM(config.llm_config)
+ else:
+ raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
+
+ vit_hidden_size = config.vision_config.hidden_size
+ llm_hidden_size = config.llm_config.hidden_size
+
+ self.mlp1 = nn.Sequential(
+ nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
+ nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
+ nn.GELU(),
+ nn.Linear(llm_hidden_size, llm_hidden_size)
+ )
+
+ self.img_context_token_id = None
+ self.conv_template = get_conv_template(self.template)
+ self.system_message = self.conv_template.system_message
+
+ def forward(
+ self,
+ pixel_values: torch.FloatTensor,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ image_flags: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ image_flags = image_flags.squeeze(-1)
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
+
+ vit_embeds = self.extract_feature(pixel_values)
+ vit_embeds = vit_embeds[image_flags == 1]
+ vit_batch_size = pixel_values.shape[0]
+
+ B, N, C = input_embeds.shape
+ input_embeds = input_embeds.reshape(B * N, C)
+
+ if torch.distributed.get_rank() == 0:
+ print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
+
+ input_ids = input_ids.reshape(B * N)
+ selected = (input_ids == self.img_context_token_id)
+ try:
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
+ except Exception as e:
+ vit_embeds = vit_embeds.reshape(-1, C)
+ print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
+ f'vit_embeds.shape={vit_embeds.shape}')
+ n_token = selected.sum()
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
+
+ input_embeds = input_embeds.reshape(B, N, C)
+
+ outputs = self.language_model(
+ inputs_embeds=input_embeds,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ logits = outputs.logits
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def pixel_shuffle(self, x, scale_factor=0.5):
+ n, w, h, c = x.size()
+ # N, W, H, C --> N, W, H * scale, C // scale
+ x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+ # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+ x = x.permute(0, 2, 1, 3).contiguous()
+ # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
+ x = x.view(n, int(h * scale_factor), int(w * scale_factor),
+ int(c / (scale_factor * scale_factor)))
+ if self.ps_version == 'v1':
+ warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
+ 'which results in a transposed image.')
+ else:
+ x = x.permute(0, 2, 1, 3).contiguous()
+ return x
+
+ def extract_feature(self, pixel_values):
+ if self.select_layer == -1:
+ vit_embeds = self.vision_model(
+ pixel_values=pixel_values,
+ output_hidden_states=False,
+ return_dict=True).last_hidden_state
+ else:
+ vit_embeds = self.vision_model(
+ pixel_values=pixel_values,
+ output_hidden_states=True,
+ return_dict=True).hidden_states[self.select_layer]
+ vit_embeds = vit_embeds[:, 1:, :]
+
+ h = w = int(vit_embeds.shape[1] ** 0.5)
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+ vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
+ vit_embeds = self.mlp1(vit_embeds)
+ return vit_embeds
+
+ def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
+ history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
+ IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
+ if history is not None or return_history:
+ print('Now multi-turn chat is not supported in batch_chat.')
+ raise NotImplementedError
+
+ if image_counts is not None:
+ num_patches_list = image_counts
+ print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
+
+ img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
+ self.img_context_token_id = img_context_token_id
+
+ if verbose and pixel_values is not None:
+ image_bs = pixel_values.shape[0]
+ print(f'dynamic ViT batch size: {image_bs}')
+
+ queries = []
+ for idx, num_patches in enumerate(num_patches_list):
+ question = questions[idx]
+ if pixel_values is not None and '<image>' not in question:
+ question = '<image>\n' + question
+ template = get_conv_template(self.template)
+ template.append_message(template.roles[0], question)
+ template.append_message(template.roles[1], None)
+ query = template.get_prompt()
+
+ image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
+ query = query.replace('<image>', image_tokens, 1)
+ queries.append(query)
+
+ tokenizer.padding_side = 'left'
+ model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
+ input_ids = model_inputs['input_ids'].cuda()
+ attention_mask = model_inputs['attention_mask'].cuda()
+ eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
+ generation_config['eos_token_id'] = eos_token_id
+ generation_output = self.generate(
+ pixel_values=pixel_values,
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ **generation_config
+ )
+ responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
+ responses = [response.split(template.sep)[0].strip() for response in responses]
+ return responses
+
+ def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
+ num_patches_list=None, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>',
+ verbose=False):
+
+ if history is None and pixel_values is not None and '<image>' not in question:
+ question = '<image>\n' + question
+
+ if num_patches_list is None:
+ num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
+ assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
+
+ img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
+ self.img_context_token_id = img_context_token_id
+
+ template = get_conv_template(self.template)
+ template.system_message = self.system_message
+ eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
+
+ history = [] if history is None else history
+ for (old_question, old_answer) in history:
+ template.append_message(template.roles[0], old_question)
+ template.append_message(template.roles[1], old_answer)
+ template.append_message(template.roles[0], question)
+ template.append_message(template.roles[1], None)
+ query = template.get_prompt()
+
+ if verbose and pixel_values is not None:
+ image_bs = pixel_values.shape[0]
+ print(f'dynamic ViT batch size: {image_bs}')
+
+ for num_patches in num_patches_list:
+ image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
+ query = query.replace('<image>', image_tokens, 1)
+
+ model_inputs = tokenizer(query, return_tensors='pt')
+ input_ids = model_inputs['input_ids'].cuda()
+ attention_mask = model_inputs['attention_mask'].cuda()
+ generation_config['eos_token_id'] = eos_token_id
+ generation_output = self.generate(
+ pixel_values=pixel_values,
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ **generation_config
+ )
+ response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
+ response = response.split(template.sep)[0].strip()
+ history.append((question, response))
+ if return_history:
+ return response, history
+ else:
+ query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
+ query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
+ if verbose:
+ print(query_to_print, response)
+ return response
+
+ @torch.no_grad()
+ def generate(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ input_ids: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ visual_features: Optional[torch.FloatTensor] = None,
+ generation_config: Optional[GenerationConfig] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **generate_kwargs,
+ ) -> torch.LongTensor:
+
+ assert self.img_context_token_id is not None
+ if pixel_values is not None:
+ if visual_features is not None:
+ vit_embeds = visual_features
+ else:
+ vit_embeds = self.extract_feature(pixel_values)
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
+ B, N, C = input_embeds.shape
+ input_embeds = input_embeds.reshape(B * N, C)
+
+ input_ids = input_ids.reshape(B * N)
+ selected = (input_ids == self.img_context_token_id)
+ assert selected.sum() != 0
+ input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
+
+ input_embeds = input_embeds.reshape(B, N, C)
+ else:
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
+
+ outputs = self.language_model.generate(
+ inputs_embeds=input_embeds,
+ attention_mask=attention_mask,
+ generation_config=generation_config,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ use_cache=True,
+ **generate_kwargs,
+ )
+
+ return outputs

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+ "bos_token": {
+ "content": "<|begin_of_text|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "<|im_end|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "<|end_of_text|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}

tokenizer_config.json

@@ -0,0 +1,2145 @@
+{
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+ },
+ "bos_token": "<|begin_of_text|>",
+ "chat_template": [
+ {
+ "name": "default",
+ "template": "{{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
+ },
+ {
+ "name": "tool_use",
+ "template": "{%- macro json_to_python_type(json_spec) %}\n{%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n\n{%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n{%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\"}}\n{%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']'}}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n{%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }} \n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n{%- else %}\n {{- \"Any\" }}\n{%- endif %}\n{%- endmacro %}\n\n\n{{- bos_token }}\n{{- \"You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> \" }}\n{%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\\n\\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\\n Returns:\\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\\n\" }}\n {%- endif %}\n{%- endfor %}\n{{- \" </tools>\" }}\n{{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n{{- \"For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:\n\" }}\n{{- \"<tool_call>\n\" }}\n{{- '{\"arguments\": <args-dict>, \"name\": <function-name>}\n' }}\n{{- '</tool_call><|im_end|>' }}\n{%- for message in messages %}\n {%- if message.role == \"user\" or message.role == \"system\" or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\\n<tool_call>\\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{ ' }}\n {%- if tool_call.arguments is defined %}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {{- ', '}}\n {%- endif %}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\"}' }}\n {{- '\\n</tool_call> ' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>' + message.role + '\\n<tool_response>\\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\\n</tool_response> <|im_end|>\\n' }} \n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
+ }
+ ],
+ "clean_up_tokenization_spaces": true,
+ "eos_token": "<|im_end|>",
+ "model_input_names": [
+ "input_ids",
+ "attention_mask"
+ ],
+ "model_max_length": 8192,
+ "pad_token": "<|end_of_text|>",
+ "tokenizer_class": "PreTrainedTokenizerFast"
+}