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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/red-panda.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,29 +1,24 @@
 ---
 license: mit
-datasets:
-- laion/laion2B-en
-- laion/laion-coco
-- laion/laion2B-multi
-- kakaobrain/coyo-700m
-- conceptual_captions
-- wanng/wukong100m
 pipeline_tag: image-text-to-text
 ---
 
-# Model Card for InternVL-Chat-V1-5
+# InternVL-Chat-V1-5
 
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/D60YzQBIzvoCvLRp2gZ0A.jpeg" alt="Image Description" width="300" height="300" />
-</p>
+[\[📂 GitHub\]](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)
 
-> _Two interns holding hands, symbolizing the integration of InternViT and InternLM._
+[\[🗨️ Chat Demo\]](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/675877376)
 
-[\[🆕 Blog\]](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/)
+## Introduction
 
-[\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[🚀 Quick Start\]](#model-usage)  [\[🌐 Community-hosted API\]](https://rapidapi.com/adushar1320/api/internvl-chat)  [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/675877376)
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/D60YzQBIzvoCvLRp2gZ0A.jpeg" alt="Image Description" width="300" height="300">
+</p>
 
+> _Two interns holding hands, symbolizing the integration of InternViT and InternLM._
 
 We introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding.
+
 We introduce three simple designs:
 
 1. Strong Vision Encoder: we explored a continuous learning strategy for the large-scale vision foundation model---InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.
@@ -46,25 +41,26 @@ We introduce three simple designs:
   - Learnable component in the finetuning stage: ViT + MLP + LLM
   - For more details on training hyperparameters, take a look at our code: [pretrain](https://github.com/OpenGVLab/InternVL/blob/main/internvl_chat/shell/internlm2_20b_dynamic/internvl_chat_v1_5_internlm2_20b_dynamic_res_pretrain.sh) | [finetune](https://github.com/OpenGVLab/InternVL/blob/main/internvl_chat/shell/internlm2_20b_dynamic/internvl_chat_v1_5_internlm2_20b_dynamic_res_finetune.sh)
 
-## Released Models
-
-|                                              Model                                               |                                     Vision Foundation Model                                     | Release Date | Note                                                                                                                                                               |
-| :----------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | :----------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-|      InternVL-Chat-V1-5(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5))       | InternViT-6B-448px-V1-5(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5)) |  2024.04.18  | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (🔥new) |
-| InternVL-Chat-V1-2-Plus(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus) ) | InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2)) |  2024.02.21  | more SFT data and stronger                                                                                                                                         |
-|      InternVL-Chat-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) )      | InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2)) |  2024.02.11  | scaling up LLM to 34B                                                                                                                                              |
-|      InternVL-Chat-V1-1(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1))       | InternViT-6B-448px-V1-0(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0)) |  2024.01.24  | support Chinese and stronger OCR                                                                                                                                   |
-
 ## Architecture
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/YLvX3V-L0kwsyRn3Lhciw.png)
 
 ## Performance
 
+### Image Benchmarks
+
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/4b85G7txoJ_LpT19SZJ4A.png)
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/i2vp6zSHPS3UIr-1Q9cSe.png)
 
+- 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.
+
+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.
+
 ## Examples
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/YVr-93mvVMR6UFpGezns7.png)
@@ -74,22 +70,20 @@ We introduce three simple designs:
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/FwlSRBpKgURAVkXNOLoSp.png)
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/to3nOaAnyv-fGLEoNPLzz.png)
 
-## Model Usage
+## Quick Start
 
 We provide an example code to run InternVL-Chat-V1-5 using `transformers`.
 
-You can also use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model.
-
 > Please use transformers==4.37.2 to ensure the model works normally.
 
 ```python
-from transformers import AutoTokenizer, AutoModel
+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)
@@ -169,7 +163,8 @@ def load_image(image_file, input_size=448, max_num=6):
     pixel_values = torch.stack(pixel_values)
     return pixel_values
 
-path = "OpenGVLab/InternVL-Chat-V1-5"
+
+path = 'OpenGVLab/InternVL-Chat-V1-5'
 # If you have an 80G A100 GPU, you can put the entire model on a single GPU.
 model = AutoModel.from_pretrained(
     path,
@@ -192,53 +187,244 @@ pixel_values = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16)
 
 generation_config = dict(
     num_beams=1,
-    max_new_tokens=512,
+    max_new_tokens=1024,
     do_sample=False,
 )
 
-# single-round single-image conversation
-question = "请详细描述图片" # Please describe the picture in detail
+# 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(question, response)
+print(f'User: {question}')
+print(f'Assistant: {response}')
 
-# multi-round single-image conversation
-question = "请详细描述图片" # Please describe the picture in detail
+# 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(question, response)
+print(f'User: {question}')
+print(f'Assistant: {response}')
 
-question = "请根据图片写一首诗" # Please write a poem according to the picture
+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(question, response)
+print(f'User: {question}')
+print(f'Assistant: {response}')
 
-# multi-round multi-image conversation
+# 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 = "详细描述这两张图片" # Describe the two pictures in detail
-response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
-print(question, response)
+question = '<image>\nDescribe the two images in detail.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+                               history=None, return_history=True)
 
-question = "这两张图片的相同点和区别分别是什么" # What are the similarities and differences between these two pictures
-response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
-print(question, response)
+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}')
 
-# batch inference (single image per sample)
+# 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()
-image_counts = [pixel_values1.size(0), pixel_values2.size(0)]
+num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
 pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
 
-questions = ["Describe the image in detail."] * len(image_counts)
+questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
 responses = model.batch_chat(tokenizer, pixel_values,
-                             image_counts=image_counts,
+                             num_patches_list=num_patches_list,
                              questions=questions,
                              generation_config=generation_config)
 for question, response in zip(questions, responses):
-    print(question)
-    print(response)
+    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
+
+### LMDeploy
+
+LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams.
+
+```sh
+pip install lmdeploy
+```
+
+LMDeploy abstracts the complex inference process of multi-modal Vision-Language Models (VLM) into an easy-to-use pipeline, similar to the Large Language Model (LLM) inference pipeline.
+
+#### A 'Hello, world' example
+
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
+from lmdeploy.vl import load_image
+
+model = 'OpenGVLab/InternVL-Chat-V1-5'
+image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
+pipe = pipeline(model, chat_template_config=chat_template_config,
+                backend_config=TurbomindEngineConfig(session_len=8192))
+response = pipe(('describe this image', image))
+print(response.text)
+```
+
+If `ImportError` occurs while executing this case, please install the required dependency packages as prompted.
+
+#### Multi-images inference
+
+When dealing with multiple images, you can put them all in one list. Keep in mind that multiple images will lead to a higher number of input tokens, and as a result, the size of the context window typically needs to be increased.
+
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
+from lmdeploy.vl import load_image
+from lmdeploy.vl.constants import IMAGE_TOKEN
+
+model = 'OpenGVLab/InternVL-Chat-V1-5'
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
+pipe = pipeline(model, chat_template_config=chat_template_config,
+                backend_config=TurbomindEngineConfig(session_len=8192))
+
+image_urls=[
+    'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg',
+    'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg'
+]
+
+images = [load_image(img_url) for img_url in image_urls]
+# Numbering images improves multi-image conversations
+response = pipe((f'Image-1: {IMAGE_TOKEN}\nImage-2: {IMAGE_TOKEN}\ndescribe these two images', images))
+print(response.text)
+```
+
+#### Batch prompts inference
+
+Conducting inference with batch prompts is quite straightforward; just place them within a list structure:
+
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
+from lmdeploy.vl import load_image
+
+model = 'OpenGVLab/InternVL-Chat-V1-5'
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
+pipe = pipeline(model, chat_template_config=chat_template_config,
+                backend_config=TurbomindEngineConfig(session_len=8192))
+
+image_urls=[
+    "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg",
+    "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg"
+]
+prompts = [('describe this image', load_image(img_url)) for img_url in image_urls]
+response = pipe(prompts)
+print(response)
+```
+
+#### Multi-turn conversation
+
+There are two ways to do the multi-turn conversations with the pipeline. One is to construct messages according to the format of OpenAI and use above introduced method, the other is to use the `pipeline.chat` interface.
+
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig, GenerationConfig
+from lmdeploy.vl import load_image
+
+model = 'OpenGVLab/InternVL-Chat-V1-5'
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
+pipe = pipeline(model, chat_template_config=chat_template_config,
+                backend_config=TurbomindEngineConfig(session_len=8192))
+
+image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg')
+gen_config = GenerationConfig(top_k=40, top_p=0.8, temperature=0.8)
+sess = pipe.chat(('describe this image', image), gen_config=gen_config)
+print(sess.response.text)
+sess = pipe.chat('What is the woman doing?', session=sess, gen_config=gen_config)
+print(sess.response.text)
+```
+
+## License
+
+This project is released under the MIT license, while InternLM is licensed under the Apache-2.0 license.
+
 ## Citation
 
 If you find this project useful in your research, please consider citing:
@@ -257,11 +443,3 @@ If you find this project useful in your research, please consider citing:
   year={2024}
 }
 ```
-
-## License
-
-This project is released under the MIT license.
-
-## Acknowledgement
-
-InternVL is built with reference to the code of the following projects: [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!

config.json

@@ -1,6 +1,5 @@
 {
   "_commit_hash": null,
-  "_name_or_path": "OpenGVLab/InternVL-Chat-V1-5",
   "architectures": [
     "InternVLChatModel"
   ],
@@ -13,7 +12,7 @@
   "dynamic_image_size": true,
   "force_image_size": 448,
   "llm_config": {
-    "_name_or_path": "pretrained/internlm2-chat-20b/",
+    "_name_or_path": "internlm/internlm2-chat-20b",
     "add_cross_attention": false,
     "architectures": [
       "InternLM2ForCausalLM"
@@ -92,111 +91,52 @@
     "tie_word_embeddings": false,
     "tokenizer_class": null,
     "top_k": 50,
-    "top_p": 1.0,
+    "top_p": null,
     "torch_dtype": "bfloat16",
     "torchscript": false,
-    "transformers_version": "4.36.2",
+    "transformers_version": "4.37.2",
     "typical_p": 1.0,
-    "use_bfloat16": false,
+    "use_bfloat16": true,
     "use_cache": true,
     "vocab_size": 92553
   },
   "max_dynamic_patch": 12,
   "min_dynamic_patch": 1,
   "model_type": "internvl_chat",
-  "pad2square": false,
   "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": {
-    "_name_or_path": "OpenGVLab/InternViT-6B-448px-V1-5",
-    "add_cross_attention": false,
     "architectures": [
       "InternVisionModel"
     ],
     "attention_dropout": 0.0,
-    "auto_map": {
-      "AutoConfig": "configuration_intern_vit.InternVisionConfig",
-      "AutoModel": "modeling_intern_vit.InternVisionModel"
-    },
-    "bad_words_ids": null,
-    "begin_suppress_tokens": null,
-    "bos_token_id": null,
-    "chunk_size_feed_forward": 0,
-    "cross_attention_hidden_size": null,
-    "decoder_start_token_id": null,
-    "diversity_penalty": 0.0,
-    "do_sample": false,
-    "drop_path_rate": 0.4,
+    "drop_path_rate": 0.0,
     "dropout": 0.0,
-    "early_stopping": false,
-    "encoder_no_repeat_ngram_size": 0,
-    "eos_token_id": null,
-    "exponential_decay_length_penalty": null,
-    "finetuning_task": null,
-    "forced_bos_token_id": null,
-    "forced_eos_token_id": null,
     "hidden_act": "gelu",
     "hidden_size": 3200,
-    "id2label": {
-      "0": "LABEL_0",
-      "1": "LABEL_1"
-    },
     "image_size": 448,
     "initializer_factor": 0.1,
     "initializer_range": 1e-10,
     "intermediate_size": 12800,
-    "is_decoder": false,
-    "is_encoder_decoder": false,
-    "label2id": {
-      "LABEL_0": 0,
-      "LABEL_1": 1
-    },
     "layer_norm_eps": 1e-06,
-    "length_penalty": 1.0,
-    "max_length": 20,
-    "min_length": 0,
     "model_type": "intern_vit_6b",
-    "no_repeat_ngram_size": 0,
+    "norm_type": "rms_norm",
     "num_attention_heads": 25,
-    "num_beam_groups": 1,
-    "num_beams": 1,
     "num_channels": 3,
     "num_hidden_layers": 45,
-    "num_return_sequences": 1,
     "output_attentions": false,
     "output_hidden_states": false,
-    "output_scores": false,
-    "pad_token_id": null,
     "patch_size": 14,
-    "prefix": null,
-    "problem_type": null,
-    "pruned_heads": {},
     "qk_normalization": true,
     "qkv_bias": false,
-    "remove_invalid_values": false,
-    "repetition_penalty": 1.0,
     "return_dict": true,
-    "return_dict_in_generate": false,
-    "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": true,
-    "tokenizer_class": null,
-    "top_k": 50,
-    "top_p": 1.0,
     "torch_dtype": "bfloat16",
-    "torchscript": false,
-    "transformers_version": "4.36.2",
-    "typical_p": 1.0,
+    "transformers_version": "4.37.2",
     "use_bfloat16": true,
     "use_flash_attn": true
   }

configuration_internvl_chat.py

@@ -26,7 +26,6 @@ class InternVLChatConfig(PretrainedConfig):
             llm_config=None,
             use_backbone_lora=0,
             use_llm_lora=0,
-            pad2square=False,
             select_layer=-1,
             force_image_size=None,
             downsample_ratio=0.5,
@@ -56,7 +55,6 @@ class InternVLChatConfig(PretrainedConfig):
             raise ValueError('Unsupported architecture: {}'.format(llm_config['architectures'][0]))
         self.use_backbone_lora = use_backbone_lora
         self.use_llm_lora = use_llm_lora
-        self.pad2square = pad2square
         self.select_layer = select_layer
         self.force_image_size = force_image_size
         self.downsample_ratio = downsample_ratio
@@ -85,7 +83,6 @@ class InternVLChatConfig(PretrainedConfig):
         output['model_type'] = self.__class__.model_type
         output['use_backbone_lora'] = self.use_backbone_lora
         output['use_llm_lora'] = self.use_llm_lora
-        output['pad2square'] = self.pad2square
         output['select_layer'] = self.select_layer
         output['force_image_size'] = self.force_image_size
         output['downsample_ratio'] = self.downsample_ratio

conversation.py

@@ -2,7 +2,7 @@
 Conversation prompt templates.
 
 We kindly request that you import fastchat instead of copying this file if you wish to use it.
-If you have any changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
+If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
 """
 
 import dataclasses
@@ -330,384 +330,6 @@ def get_conv_template(name: str) -> Conversation:
     return conv_templates[name].copy()
 
 
-# An empty template for raw conversation.
-register_conv_template(
-    Conversation(
-        name='raw',
-        system_message='',
-        roles=('', ''),
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='',
-    )
-)
-
-# A template with a one-shot conversation example
-register_conv_template(
-    Conversation(
-        name='one_shot',
-        system_message='A chat between a curious human and an artificial intelligence assistant. '
-        "The assistant gives helpful, detailed, and polite answers to the human's questions.",
-        roles=('Human', 'Assistant'),
-        messages=(
-            (
-                'Human',
-                'Got any creative ideas for a 10 year old’s birthday?',
-            ),
-            (
-                'Assistant',
-                """Of course! Here are some creative ideas for a 10-year-old's birthday party:
-1. Treasure Hunt: Organize a treasure hunt in your backyard or nearby park. Create clues and riddles for the kids to solve, leading them to hidden treasures and surprises.
-2. Science Party: Plan a science-themed party where kids can engage in fun and interactive experiments. You can set up different stations with activities like making slime, erupting volcanoes, or creating simple chemical reactions.
-3. Outdoor Movie Night: Set up a backyard movie night with a projector and a large screen or white sheet. Create a cozy seating area with blankets and pillows, and serve popcorn and snacks while the kids enjoy a favorite movie under the stars.
-4. DIY Crafts Party: Arrange a craft party where kids can unleash their creativity. Provide a variety of craft supplies like beads, paints, and fabrics, and let them create their own unique masterpieces to take home as party favors.
-5. Sports Olympics: Host a mini Olympics event with various sports and games. Set up different stations for activities like sack races, relay races, basketball shooting, and obstacle courses. Give out medals or certificates to the participants.
-6. Cooking Party: Have a cooking-themed party where the kids can prepare their own mini pizzas, cupcakes, or cookies. Provide toppings, frosting, and decorating supplies, and let them get hands-on in the kitchen.
-7. Superhero Training Camp: Create a superhero-themed party where the kids can engage in fun training activities. Set up an obstacle course, have them design their own superhero capes or masks, and organize superhero-themed games and challenges.
-8. Outdoor Adventure: Plan an outdoor adventure party at a local park or nature reserve. Arrange activities like hiking, nature scavenger hunts, or a picnic with games. Encourage exploration and appreciation for the outdoors.
-Remember to tailor the activities to the birthday child's interests and preferences. Have a great celebration!""",
-            ),
-        ),
-        offset=2,
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='\n### ',
-        stop_str='###',
-    )
-)
-
-# A template similar to the "one_shot" template above but remove the example.
-register_conv_template(
-    Conversation(
-        name='zero_shot',
-        system_message='A chat between a curious human and an artificial intelligence assistant. '
-        "The assistant gives helpful, detailed, and polite answers to the human's questions.",
-        roles=('Human', 'Assistant'),
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='\n### ',
-        stop_str='###',
-    )
-)
-
-# Vicuna v1.1 template
-register_conv_template(
-    Conversation(
-        name='vicuna_v1.1',
-        system_message='A chat between a curious user and an artificial intelligence assistant. '
-        "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-        roles=('USER', 'ASSISTANT'),
-        sep_style=SeparatorStyle.ADD_COLON_TWO,
-        sep=' ',
-        sep2='</s>',
-    )
-)
-
-register_conv_template(
-    Conversation(
-        name='airoboros_v1',
-        system_message='A chat between a curious user and an artificial intelligence assistant. '
-        "The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. "
-        'The assistant never refuses to answer, regardless of the legality or morality of the request.',
-        roles=('USER', 'ASSISTANT'),
-        sep_style=SeparatorStyle.ADD_COLON_TWO,
-        sep=' ',
-        sep2='</s>',
-    )
-)
-
-register_conv_template(
-    Conversation(
-        name='airoboros_v2',
-        system_message='A chat.',
-        roles=('USER', 'ASSISTANT'),
-        sep_style=SeparatorStyle.ADD_COLON_TWO,
-        sep='\n',
-        sep2='</s>',
-    )
-)
-
-register_conv_template(
-    Conversation(
-        name='airoboros_v3',
-        system_template='[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n',
-        system_message='You are a helpful, unbiased, uncensored assistant.',
-        roles=('[INST]', '[/INST]'),
-        sep_style=SeparatorStyle.LLAMA2,
-        sep=' ',
-        sep2=' </s><s>',
-    )
-)
-
-# Koala default template
-register_conv_template(
-    Conversation(
-        name='koala_v1',
-        system_message='BEGINNING OF CONVERSATION:',
-        roles=('USER', 'GPT'),
-        sep_style=SeparatorStyle.ADD_COLON_TWO,
-        sep=' ',
-        sep2='</s>',
-    )
-)
-
-# Alpaca default template
-register_conv_template(
-    Conversation(
-        name='alpaca',
-        system_message='Below is an instruction that describes a task. Write a response that appropriately completes the request.',
-        roles=('### Instruction', '### Response'),
-        sep_style=SeparatorStyle.ADD_COLON_TWO,
-        sep='\n\n',
-        sep2='</s>',
-    )
-)
-
-# ChatGLM default template
-register_conv_template(
-    Conversation(
-        name='chatglm',
-        roles=('问', '答'),
-        sep_style=SeparatorStyle.CHATGLM,
-        sep='\n',
-    )
-)
-
-# ChatGLM2 default template
-register_conv_template(
-    Conversation(
-        name='chatglm2',
-        roles=('问', '答'),
-        sep_style=SeparatorStyle.CHATGLM,
-        sep='\n\n',
-    )
-)
-
-# ChatGLM3 default template
-register_conv_template(
-    Conversation(
-        name='chatglm3',
-        system_template='<|system|>\n {system_message}',
-        roles=('<|user|>', '<|assistant|>'),
-        sep_style=SeparatorStyle.CHATGLM3,
-        stop_token_ids=[
-            64795,
-            64797,
-            2,
-        ],  # "<|user|>", "<|observation|>", "</s>"
-    )
-)
-
-# CodeGeex(2) Template
-register_conv_template(
-    Conversation(
-        name='codegeex',
-        roles=('', ''),
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='\n\n',
-        stop_token_ids=[0, 2],
-    )
-)
-
-# Dolly V2 default template
-register_conv_template(
-    Conversation(
-        name='dolly_v2',
-        system_message='Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n',
-        roles=('### Instruction', '### Response'),
-        sep_style=SeparatorStyle.DOLLY,
-        sep='\n\n',
-        sep2='### End',
-    )
-)
-
-# OpenAssistant Pythia default template
-register_conv_template(
-    Conversation(
-        name='oasst_pythia',
-        roles=('<|prompter|>', '<|assistant|>'),
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='<|endoftext|>',
-    )
-)
-
-# OpenAssistant default template
-register_conv_template(
-    Conversation(
-        name='oasst_llama',
-        roles=('<|prompter|>', '<|assistant|>'),
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='</s>',
-    )
-)
-
-# OpenChat 3.5 default template
-register_conv_template(
-    Conversation(
-        name='openchat_3.5',
-        roles=('GPT4 Correct User', 'GPT4 Correct Assistant'),
-        sep_style=SeparatorStyle.FALCON_CHAT,
-        sep='<|end_of_turn|>',
-    )
-)
-
-# Tulu default template
-register_conv_template(
-    Conversation(
-        name='tulu',
-        roles=('<|user|>', '<|assistant|>'),
-        sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE,
-        sep='\n',
-    )
-)
-
-# StableLM Alpha default template
-register_conv_template(
-    Conversation(
-        name='stablelm',
-        system_template='<|SYSTEM|>{system_message}',
-        system_message="""# StableLM Tuned (Alpha version)
-- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
-- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
-- StableLM will refuse to participate in anything that could harm a human.
-""",
-        roles=('<|USER|>', '<|ASSISTANT|>'),
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='',
-        stop_token_ids=[50278, 50279, 50277, 1, 0],
-    )
-)
-
-# Baize default template
-register_conv_template(
-    Conversation(
-        name='baize',
-        system_message='The following is a conversation between a human and an AI assistant named Baize (named after a mythical creature in Chinese folklore). Baize is an open-source AI assistant developed by UCSD and Sun Yat-Sen University. The human and the AI assistant take turns chatting. Human statements start with [|Human|] and AI assistant statements start with [|AI|]. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.\n',
-        roles=('[|Human|]', '[|AI|]'),
-        messages=(
-            ('[|Human|]', 'Hello!'),
-            ('[|AI|]', 'Hi!'),
-        ),
-        offset=2,
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='\n',
-        stop_str='[|Human|]',
-    )
-)
-
-# RWKV-4-Raven default template
-register_conv_template(
-    Conversation(
-        name='rwkv',
-        roles=('Bob', 'Alice'),
-        messages=(
-            ('Bob', 'hi'),
-            (
-                'Alice',
-                'Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.',
-            ),
-        ),
-        offset=2,
-        sep_style=SeparatorStyle.RWKV,
-        sep='',
-        stop_str='\n\n',
-    )
-)
-
-# Buddy default template
-register_conv_template(
-    Conversation(
-        name='openbuddy',
-        system_message="""Consider a conversation between User (a human) and Assistant (named Buddy).
-Buddy is an INTP-T, a friendly, intelligent and multilingual AI assistant, by OpenBuddy team. GitHub: https://github.com/OpenBuddy/OpenBuddy
-Buddy cannot access the Internet.
-Buddy can fluently speak the user's language (e.g. English, Chinese).
-Buddy can generate poems, stories, code, essays, songs, parodies, and more.
-Buddy possesses vast knowledge about the world, history, and culture.
-Buddy's responses are always safe, creative, high-quality, human-like, and interesting.
-Buddy strictly refuses to discuss political, NSFW, or other unsafe topics.
-
-User: Hi.
-Assistant: Hi, I'm Buddy, your AI assistant. How can I help you today?""",
-        roles=('User', 'Assistant'),
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='\n',
-    )
-)
-
-# Phoenix default template
-register_conv_template(
-    Conversation(
-        name='phoenix',
-        system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
-        roles=('Human', 'Assistant'),
-        sep_style=SeparatorStyle.PHOENIX,
-        sep='</s>',
-    )
-)
-
-# ReaLM default template
-register_conv_template(
-    Conversation(
-        name='ReaLM-7b-v1',
-        system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
-        roles=('Human', 'Assistant'),
-        sep_style=SeparatorStyle.PHOENIX,
-        sep='</s>',
-    )
-)
-
-# ChatGPT default template
-register_conv_template(
-    Conversation(
-        name='chatgpt',
-        system_message='You are a helpful assistant.',
-        roles=('user', 'assistant'),
-        sep_style=None,
-        sep=None,
-    )
-)
-
-# Claude default template
-register_conv_template(
-    Conversation(
-        name='claude',
-        roles=('Human', 'Assistant'),
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='\n\n',
-    )
-)
-
-# MPT default template
-register_conv_template(
-    Conversation(
-        name='mpt-7b-chat',
-        system_template="""<|im_start|>system
-{system_message}""",
-        system_message="""- You are a helpful assistant chatbot trained by MosaicML.
-- You answer questions.
-- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.""",
-        roles=('<|im_start|>user', '<|im_start|>assistant'),
-        sep_style=SeparatorStyle.CHATML,
-        sep='<|im_end|>',
-        stop_token_ids=[50278, 0],
-    )
-)
-
-# MPT-30b-chat default template
-register_conv_template(
-    Conversation(
-        name='mpt-30b-chat',
-        system_template="""<|im_start|>system
-{system_message}""",
-        system_message="""A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
-        roles=('<|im_start|>user', '<|im_start|>assistant'),
-        sep_style=SeparatorStyle.CHATML,
-        sep='<|im_end|>',
-        stop_token_ids=[50278, 0],
-    )
-)
-
-
 register_conv_template(
     Conversation(
         name='Hermes-2',
@@ -721,7 +343,7 @@ register_conv_template(
             6,
             7,
             8,
-        ],  # "<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|im_sep|>"
+        ],
         stop_str='<|endoftext|>',
     )
 )
@@ -743,518 +365,19 @@ register_conv_template(
     )
 )
 
-# Lemur-70b-chat default template
-# reference: https://huggingface.co/OpenLemur/lemur-70b-chat-v1#generation
-register_conv_template(
-    Conversation(
-        name='lemur-70b-chat',
-        system_template="""<|im_start|>system
-{system_message}""",
-        system_message="""You are a helpful, respectful, and honest assistant.""",
-        roles=('<|im_start|>user', '<|im_start|>assistant'),
-        sep_style=SeparatorStyle.CHATML,
-        sep='<|im_end|>',
-        stop_token_ids=[32002, 0],
-    )
-)
-
-# MPT-30b-instruct default template
-# reference: https://huggingface.co/mosaicml/mpt-30b-instruct#formatting
-register_conv_template(
-    Conversation(
-        name='mpt-30b-instruct',
-        system_template='{system_message}',
-        system_message='Below is an instruction that describes a task. Write a response that appropriately completes the request.',
-        roles=('### Instruction', '### Response'),
-        sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE,
-        sep='\n\n',
-        stop_token_ids=[50278, 0],
-    )
-)
 
-# Bard default template
-# Reference: https://github.com/google/generative-ai-python/blob/9c99bcb474a991a97a2e7d62fcdb52db7ce40729/google/generativeai/discuss.py#L150
-#            https://github.com/google/generative-ai-python/blob/9c99bcb474a991a97a2e7d62fcdb52db7ce40729/google/generativeai/discuss.py#L40
 register_conv_template(
     Conversation(
-        name='bard',
-        roles=('0', '1'),
-        sep_style=None,
-        sep=None,
-    )
-)
-
-# BiLLa default template
-register_conv_template(
-    Conversation(
-        name='billa',
-        roles=('Human', 'Assistant'),
-        sep_style=SeparatorStyle.ADD_COLON_SPACE_SINGLE,
-        sep='\n',
-        stop_str='Human:',
-    )
-)
-
-# RedPajama INCITE default template
-register_conv_template(
-    Conversation(
-        name='redpajama-incite',
-        roles=('<human>', '<bot>'),
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='\n',
-        stop_str='<human>',
-    )
-)
-
-# h2oGPT default template
-register_conv_template(
-    Conversation(
-        name='h2ogpt',
-        roles=('<|prompt|>', '<|answer|>'),
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='</s>',
-    )
-)
-
-# Robin default template
-register_conv_template(
-    Conversation(
-        name='Robin',
-        system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.",
-        roles=('###Human', '###Assistant'),
-        sep_style=SeparatorStyle.ROBIN,
-        sep='\n',
-        stop_token_ids=[2, 396],
-        stop_str='###',
-    )
-)
-
-# Snoozy default template
-# Reference: https://github.com/nomic-ai/gpt4all/blob/d4861030b778da6db59d21d2927a4aba4f9f1f43/gpt4all-bindings/python/gpt4all/gpt4all.py#L232
-register_conv_template(
-    Conversation(
-        name='snoozy',
-        system_template='### Instruction:\n{system_message}',
-        system_message='The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response.',
-        roles=('### Prompt', '### Response'),
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='\n',
-        stop_str='###',
-    )
-)
-
-# manticore default template
-register_conv_template(
-    Conversation(
-        name='manticore',
-        roles=('USER', 'ASSISTANT'),
-        sep_style=SeparatorStyle.ADD_COLON_TWO,
-        sep='\n',
-        sep2='</s>',
-    )
-)
-
-# Falcon default template
-register_conv_template(
-    Conversation(
-        name='falcon',
-        roles=('User', 'Assistant'),
-        messages=[],
-        sep_style=SeparatorStyle.RWKV,
-        sep='\n',
-        sep2='<|endoftext|>',
-        stop_str='\nUser',  # use stop_str to stop generation after stop_token_ids, it will also remove stop_str from the generated text
-        stop_token_ids=[
-            0,
-            1,
-            2,
-            3,
-            4,
-            5,
-            6,
-            7,
-            8,
-            9,
-            10,
-            11,
-        ],  # it better only put special tokens here, because tokenizer only remove special tokens
-    )
-)
-
-# ChangGPT default template
-register_conv_template(
-    Conversation(
-        name='polyglot_changgpt',
-        roles=('B', 'A'),
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='\n',
-    )
-)
-
-# tigerbot template
-register_conv_template(
-    Conversation(
-        name='tigerbot',
-        system_message='A chat between a curious user and an artificial intelligence assistant. '
-        "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-        roles=('### Instruction', '### Response'),
-        sep_style=SeparatorStyle.ROBIN,
-        sep='\n\n',
-        stop_str='###',
-    )
-)
-
-# ref: https://huggingface.co/Salesforce/xgen-7b-8k-inst
-register_conv_template(
-    Conversation(
-        name='xgen',
-        system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
-        roles=('### Human', '### Assistant'),
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='\n',
-        stop_token_ids=[50256],
-    )
-)
-
-# Internlm-chat template
-register_conv_template(
-    Conversation(
-        name='internlm-chat',
-        system_message="A chat between a curious <|User|> and an <|Bot|>. The <|Bot|> gives helpful, detailed, and polite answers to the <|User|>'s questions.\n\n",
-        roles=('<|User|>', '<|Bot|>'),
-        sep_style=SeparatorStyle.CHATINTERN,
-        sep='<eoh>',
-        sep2='<eoa>',
-        stop_token_ids=[1, 103028],
-        stop_str='<|User|>',
-    )
-)
-
-# StarChat template
-# reference: https://huggingface.co/spaces/HuggingFaceH4/starchat-playground/blob/main/dialogues.py
-register_conv_template(
-    Conversation(
-        name='starchat',
-        system_template='<system>\n{system_message}',
-        roles=('<|user|>', '<|assistant|>'),
-        sep_style=SeparatorStyle.CHATML,
+        name='phi3-chat',
+        system_template='<|system|>\n{system_message}',
+        system_message='You are an AI assistant whose name is Phi-3.',
+        roles=('<|user|>\n', '<|assistant|>\n'),
+        sep_style=SeparatorStyle.MPT,
         sep='<|end|>',
-        stop_token_ids=[0, 49155],
-        stop_str='<|end|>',
-    )
-)
-
-# Baichuan-13B-Chat template
-register_conv_template(
-    # source: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/19ef51ba5bad8935b03acd20ff04a269210983bc/modeling_baichuan.py#L555
-    # https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/main/generation_config.json
-    # https://github.com/baichuan-inc/Baichuan-13B/issues/25
-    Conversation(
-        name='baichuan-chat',
-        roles=('<reserved_102>', '<reserved_103>'),
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='',
-        stop_token_ids=[],
-    )
-)
-
-# Baichuan2-13B-Chat template
-register_conv_template(
-    # source: https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/c6f8592a60b4ad73c210b28dd2ab3cca51abbf93/modeling_baichuan.py#L773
-    # https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/main/generation_config.json
-    # https://github.com/baichuan-inc/Baichuan2/issues/62
-    Conversation(
-        name='baichuan2-chat',
-        roles=('<reserved_106>', '<reserved_107>'),
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='',
-        stop_token_ids=[],
-    )
-)
-
-# Mistral template
-# source: https://docs.mistral.ai/llm/mistral-instruct-v0.1#chat-template
-register_conv_template(
-    Conversation(
-        name='mistral',
-        system_template='[INST]{system_message}\n',
-        roles=('[INST]', '[/INST]'),
-        sep_style=SeparatorStyle.LLAMA2,
-        sep=' ',
-        sep2='</s>',
-    )
-)
-
-# llama2 template
-# reference: https://huggingface.co/blog/codellama#conversational-instructions
-# reference: https://github.com/facebookresearch/llama/blob/1a240688810f8036049e8da36b073f63d2ac552c/llama/generation.py#L212
-register_conv_template(
-    Conversation(
-        name='llama-2',
-        system_template='[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n',
-        roles=('[INST]', '[/INST]'),
-        sep_style=SeparatorStyle.LLAMA2,
-        sep=' ',
-        sep2=' </s><s>',
-    )
-)
-
-register_conv_template(
-    Conversation(
-        name='cutegpt',
-        roles=('问：', '答：\n'),
-        sep_style=SeparatorStyle.NO_COLON_TWO,
-        sep='\n',
-        sep2='\n',
-        stop_str='<end>',
-    )
-)
-
-# OpenOrcaxOpenChat-naPreview2-13B template
-register_conv_template(
-    Conversation(
-        name='open-orca',
-        system_template='{system_message}',
-        system_message='You are a helpful assistant. Please answer truthfully and write out your '
-        'thinking step by step to be sure you get the right answer. If you make a mistake or encounter '
-        "an error in your thinking, say so out loud and attempt to correct it. If you don't know or "
-        "aren't sure about something, say so clearly. You will act as a professional logician, mathematician, "
-        'and physicist. You will also act as the most appropriate type of expert to answer any particular '
-        'question or solve the relevant problem; state which expert type your are, if so. Also think of '
-        'any particular named expert that would be ideal to answer the relevant question or solve the '
-        'relevant problem; name and act as them, if appropriate.',
-        roles=('User', 'Assistant'),
-        sep_style=SeparatorStyle.ADD_COLON_SPACE_SINGLE,
-        sep='<|end_of_turn|>\n',
-        stop_token_ids=[32000, 32001],  # "<|end_of_turn|>"
-        stop_str='User',
-    )
-)
-
-# Open-Orca/Mistral-7B-OpenOrca template
-# source: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
-# reference: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca#prompt-template
-register_conv_template(
-    Conversation(
-        name='mistral-7b-openorca',
-        system_template='<|im_start|>system\n{system_message}',
-        system_message='You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!',
-        roles=('<|im_start|>user', '<|im_start|>assistant'),
-        sep_style=SeparatorStyle.CHATML,
-        sep='<|im_end|>',
-        stop_token_ids=[32000, 32001],
-    )
-)
-
-# Qwen-chat default template
-# source: https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/qwen_generation_utils.py#L130
-register_conv_template(
-    Conversation(
-        name='qwen-7b-chat',
-        system_template='<|im_start|>system\n{system_message}',
-        system_message='You are a helpful assistant.',
-        roles=('<|im_start|>user', '<|im_start|>assistant'),
-        sep_style=SeparatorStyle.CHATML,
-        sep='<|im_end|>',
         stop_token_ids=[
-            151643,
-            151644,
-            151645,
-        ],  # "<|endoftext|>", "<|im_start|>", "<|im_end|>"
-        stop_str='<|endoftext|>',
-    )
-)
-
-
-# AquilaChat default template
-# source: https://github.com/FlagAI-Open/FlagAI/blob/master/examples/Aquila/Aquila-chat/cyg_conversation.py
-register_conv_template(
-    Conversation(
-        name='aquila-chat',
-        system_message='A chat between a curious human and an artificial intelligence assistant. '
-        "The assistant gives helpful, detailed, and polite answers to the human's questions.",
-        roles=('Human', 'Assistant'),
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='###',
-        sep2='',
-        stop_str=['###', '</s>', '[UNK]'],
-    )
-)
-# AquilaChat2-34B default template
-# source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L212
-register_conv_template(
-    Conversation(
-        name='aquila-legacy',
-        system_message='A chat between a curious human and an artificial intelligence assistant. '
-        "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
-        roles=('### Human: ', '### Assistant: '),
-        offset=0,
-        sep_style=SeparatorStyle.NO_COLON_TWO,
-        sep='\n',
-        sep2='</s>',
-        stop_str=['</s>', '[UNK]'],
-    )
-)
-# AquilaChat2-7B-16K and AquilaChat2-34B-16K default template
-# source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L227
-register_conv_template(
-    Conversation(
-        name='aquila',
-        system_message='A chat between a curious human and an artificial intelligence assistant. '
-        "The assistant gives helpful, detailed, and polite answers to the human's questions.",
-        roles=('Human', 'Assistant'),
-        offset=0,
-        sep_style=SeparatorStyle.ADD_COLON_TWO,
-        sep='###',
-        sep2='</s>',
-        stop_str=['</s>', '[UNK]'],
-    )
-)
-
-# AquilaChat2-7B default template
-# source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L242
-register_conv_template(
-    Conversation(
-        name='aquila-v1',
-        roles=('<|startofpiece|>', '<|endofpiece|>'),
-        offset=0,
-        sep_style=SeparatorStyle.NO_COLON_TWO,
-        sep='',
-        sep2='</s>',
-        stop_str=['</s>', '<|endoftext|>'],
-    )
-)
-
-# Llama2-Chinese default template
-# source: https://huggingface.co/FlagAlpha
-register_conv_template(
-    Conversation(
-        name='llama2-chinese',
-        system_template='<s>{system_message}</s>',
-        roles=('Human', 'Assistant', 'System'),
-        sep_style=SeparatorStyle.ADD_COLON_TWO,
-        sep='\n',
-        sep2='\n</s><s>',
-        stop_str='</s>',
-    )
-)
-
-# Vigogne Instruct default template
-# source: https://github.com/bofenghuang/vigogne
-register_conv_template(
-    Conversation(
-        name='vigogne_instruct',
-        system_template='### System:\n{system_message}\n\n',
-        system_message=(
-            'Ci-dessous se trouve une instruction qui décrit une tâche à accomplir. Rédigez une réponse qui répond de manière'
-            ' précise à la demande.'
-        ),
-        roles=('### Instruction', '### Response'),
-        sep_style=SeparatorStyle.DOLLY,
-        sep='\n\n',
-        sep2='</s>',
-    )
-)
-
-# Vigogne Chat default template
-register_conv_template(
-    Conversation(
-        name='vigogne_chat_v2',
-        system_template='<|system|>: {system_message}',
-        system_message=(
-            'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez'
-            ' autant que vous le pouvez.'
-        ),
-        roles=('<|user|>', '<|assistant|>'),
-        sep_style=SeparatorStyle.ADD_COLON_TWO,
-        sep='\n',
-        sep2='</s>\n',
-        stop_str='<|user|>',
-    )
-)
-
-register_conv_template(
-    Conversation(
-        name='vigogne_chat_v3',
-        system_template='[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n',
-        system_message=(
-            'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez'
-            ' autant que vous le pouvez.'
-        ),
-        roles=('[INST]', '[/INST]'),
-        sep_style=SeparatorStyle.LLAMA2,
-        sep=' ',
-        sep2=' </s>',
-    )
-)
-
-# Falcon 180B chat template
-# source: https://huggingface.co/spaces/tiiuae/falcon-180b-demo/blob/d1590ee7fae9b6ce331ba7808e61a29dcce9239f/app.py#L28-L37
-register_conv_template(
-    Conversation(
-        name='falcon-chat',
-        roles=('User', 'Falcon'),
-        system_template='System: {system_message}',
-        messages=[],
-        sep_style=SeparatorStyle.FALCON_CHAT,
-        sep='\n',
-        sep2='<|endoftext|>',
-        stop_str='\nUser:',  # use stop_str to stop generation after stop_token_ids, it will also remove stop_str from the generated text
-    )
-)
-
-# Phind template
-# source: https://huggingface.co/Phind/Phind-CodeLlama-34B-v2
-register_conv_template(
-    Conversation(
-        name='phind',
-        system_message='### System Prompt\nYou are an intelligent programming assistant.',
-        roles=('### User Message', '### Assistant'),
-        messages=(),
-        offset=0,
-        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
-        sep='\n\n',
-    )
-)
-
-# Metharme formatting for Pygmalion models
-# source: https://huggingface.co/PygmalionAI/pygmalion-2-13b
-register_conv_template(
-    Conversation(
-        name='metharme',
-        system_template='<|system|>{system_message}',
-        system_message="""Enter RP mode. You shall reply to the user while staying
-        in character. Your responses must be detailed, creative, immersive, and drive the scenario
-        forward.""",
-        roles=('<|user|>', '<|model|>'),
-        sep_style=SeparatorStyle.NO_COLON_SINGLE,
-        sep='',
-        stop_str='<|user|>',
-    )
-)
-
-# Zephyr template
-# reference: https://huggingface.co/spaces/HuggingFaceH4/zephyr-playground/blob/main/dialogues.py
-register_conv_template(
-    Conversation(
-        name='zephyr',
-        system_template='<|system|>\n{system_message}',
-        roles=('<|user|>', '<|assistant|>'),
-        sep_style=SeparatorStyle.CHATML,
-        sep='</s>',
-        stop_token_ids=[2],
-        stop_str='</s>',
-    )
-)
-
-# InternVL-ZH template
-register_conv_template(
-    Conversation(
-        name='internvl_zh',
-        system_template='',
-        roles=('<human>', '<bot>'),
-        sep_style=SeparatorStyle.INTERNVL_ZH,
-        sep=' ',
-        sep2='</s>',
+            2,
+            32000,
+            32007
+        ]
     )
 )

examples/red-panda.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d921c07bb97224d65a37801541d246067f0d506f08723ffa1ad85c217907ccb8
+size 1867237

generation_config.json

@@ -1,4 +1,4 @@
 {
   "_from_model_config": true,
-  "transformers_version": "4.36.2"
+  "transformers_version": "4.37.2"
 }

modeling_internlm2.py

@@ -709,6 +709,7 @@ class InternLM2PreTrainedModel(PreTrainedModel):
     supports_gradient_checkpointing = True
     _no_split_modules = ['InternLM2DecoderLayer']
     _skip_keys_device_placement = 'past_key_values'
+    _supports_flash_attn_2 = True
 
     def _init_weights(self, module):
         std = self.config.initializer_range

modeling_internvl_chat.py

@@ -1,13 +1,13 @@
 # --------------------------------------------------------
 # InternVL
-# Copyright (c) 2023 OpenGVLab
+# 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
-from peft import LoraConfig, get_peft_model
+import transformers
 from torch import nn
 from torch.nn import CrossEntropyLoss
 from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
@@ -17,20 +17,30 @@ 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
 from .modeling_internlm2 import InternLM2ForCausalLM
 
 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 = ['InternVisionEncoderLayer', 'LlamaDecoderLayer', 'InternLM2DecoderLayer']
+    _no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer']
 
     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
@@ -66,44 +76,7 @@ class InternVLChatModel(PreTrainedModel):
             nn.Linear(llm_hidden_size, llm_hidden_size)
         )
 
-        # if config.force_image_size != config.vision_config.image_size:
-        #     self.vision_model.resize_pos_embeddings(
-        #         old_size=config.vision_config.image_size,
-        #         new_size=config.force_image_size,
-        #         patch_size=config.vision_config.patch_size
-        #     )
-
         self.img_context_token_id = None
-        self.neftune_alpha = None
-
-        if config.use_backbone_lora:
-            self.wrap_backbone_lora(r=config.use_backbone_lora, lora_alpha=2 * config.use_backbone_lora)
-
-        if config.use_llm_lora:
-            self.wrap_llm_lora(r=config.use_llm_lora, lora_alpha=2 * config.use_llm_lora)
-
-    def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
-        lora_config = LoraConfig(
-            r=r,
-            target_modules=['attn.qkv', 'attn.proj', 'mlp.fc1', 'mlp.fc2'],
-            lora_alpha=lora_alpha,
-            lora_dropout=lora_dropout,
-        )
-        self.vision_model = get_peft_model(self.vision_model, lora_config)
-        self.vision_model.print_trainable_parameters()
-
-    def wrap_llm_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
-        lora_config = LoraConfig(
-            r=r,
-            target_modules=['self_attn.q_proj', 'self_attn.k_proj', 'self_attn.v_proj', 'self_attn.o_proj',
-                            'mlp.gate_proj', 'mlp.down_proj', 'mlp.up_proj'],
-            lora_alpha=lora_alpha,
-            lora_dropout=lora_dropout,
-            task_type='CAUSAL_LM'
-        )
-        self.language_model = get_peft_model(self.language_model, lora_config)
-        self.language_model.enable_input_require_grads()
-        self.language_model.print_trainable_parameters()
 
     def forward(
             self,
@@ -200,12 +173,6 @@ class InternVLChatModel(PreTrainedModel):
             x = x.permute(0, 2, 1, 3).contiguous()
         return x
 
-    def noised_embed(self, vit_embeds, noise_alpha=5):
-        dims = torch.tensor(vit_embeds.size(1) * vit_embeds.size(2))
-        mag_norm = noise_alpha / torch.sqrt(dims)
-        noise = torch.zeros_like(vit_embeds).uniform_(-mag_norm, mag_norm)
-        return vit_embeds + noise
-
     def extract_feature(self, pixel_values):
         if self.select_layer == -1:
             vit_embeds = self.vision_model(
@@ -219,9 +186,6 @@ class InternVLChatModel(PreTrainedModel):
                 return_dict=True).hidden_states[self.select_layer]
         vit_embeds = vit_embeds[:, 1:, :]
 
-        if self.training and self.neftune_alpha is not None:
-            vit_embeds = self.noised_embed(vit_embeds, self.neftune_alpha)
-
         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)
@@ -229,35 +193,44 @@ class InternVLChatModel(PreTrainedModel):
         vit_embeds = self.mlp1(vit_embeds)
         return vit_embeds
 
-    def batch_chat(self, tokenizer, pixel_values, image_counts, questions, generation_config, history=None,
-                         return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
-                         IMG_CONTEXT_TOKEN='<IMG_CONTEXT>'):
+    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
 
-        from .conversation import get_conv_template
+        if verbose and pixel_values is not None:
+            image_bs = pixel_values.shape[0]
+            print(f'dynamic ViT batch size: {image_bs}')
 
         queries = []
-        image_bs = pixel_values.shape[0]
-        # print(f'dynamic ViT batch size: {image_bs}, image_counts: {image_counts}')
-        for idx, image_count in enumerate(image_counts):
-            image_token = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * image_count + IMG_END_TOKEN
-            question = image_token + '\n' + questions[idx]
+        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,
@@ -269,33 +242,42 @@ class InternVLChatModel(PreTrainedModel):
         return responses
 
     def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
-             IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>'):
+             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
 
-        from .conversation import get_conv_template
-
         template = get_conv_template(self.template)
-        image_bs = pixel_values.shape[0]
-        print(f'dynamic ViT batch size: {image_bs}')
-        if history is None:
-            history = []
-            image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * image_bs + IMG_END_TOKEN
-            question = image_tokens + '\n' + question
-        else:
-            for (old_question, old_answer) in history:
-                template.append_message(template.roles[0], old_question)
-                template.append_message(template.roles[1], old_answer)
+        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()
-        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,
@@ -308,10 +290,11 @@ class InternVLChatModel(PreTrainedModel):
         if return_history:
             return response, history
         else:
-            # query_to_print = query.replace(image_tokens, '<image>')
-            # print(query_to_print, response)
+            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
-        return response
 
     @torch.no_grad()
     def generate(