Update AWQ models
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610
README.md
610
README.md
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---
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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license: mit
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pipeline_tag: image-text-to-text
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library_name: transformers
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base_model:
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- OpenGVLab/InternViT-300M-448px
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- microsoft/Phi-3-mini-128k-instruct
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new_version: OpenGVLab/InternVL2_5-4B
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base_model_relation: merge
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language:
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- multilingual
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tags:
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- internvl
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- custom_code
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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# Mini-InternVL-Chat-4B-V1-5
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[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271)
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[\[🆕 Blog\]](https://internvl.github.io/blog/) [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/)
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## Introduction
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/pvfKc16O-ej91632FHaIK.png" style="width:80%;" alt="image/png">
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</p>
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You can run multimodal large models using a 1080Ti now.
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We are delighted to introduce the Mini-InternVL-Chat series. In the era of large language models, many researchers have started to focus on smaller language models, such as Gemma-2B, Qwen-1.8B, and InternLM2-1.8B. Inspired by their efforts, we have distilled our vision foundation model [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) down to 300M and used [InternLM2-Chat-1.8B](https://huggingface.co/internlm/internlm2-chat-1_8b) or [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) as our language model. This resulted in a small multimodal model with excellent performance.
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As shown in the figure below, we adopted the same model architecture as InternVL 1.5. We simply replaced the original InternViT-6B with InternViT-300M and InternLM2-Chat-20B with InternLM2-Chat-1.8B / Phi-3-mini-128k-instruct. For training, we used the same data as InternVL 1.5 to train this smaller model. Additionally, due to the lower training costs of smaller models, we used a context length of 8K during training.
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## Model Details
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- **Model Type:** multimodal large language model (MLLM)
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- **Model Stats:**
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- Architecture: [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px) + MLP + [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
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- Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution).
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- Params: 4.2B
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- **Training Strategy:**
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- Learnable component in the pre-training stage: MLP
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- Learnable component in the fine-tuning stage: ViT + MLP + LLM
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- For more details on training hyperparameters, please see our [blog](https://internvl.github.io/blog/2024-05-25-Mini-InternVL-1.5/).
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## Performance
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- We simultaneously use [InternVL](https://github.com/OpenGVLab/InternVL) and [VLMEvalKit](https://github.com/open-compass/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. OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.
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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.
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## Quick Start
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We provide an example code to run Mini-InternVL-Chat-4B-V1-5 using `transformers`.
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> Please use transformers>=4.37.2 to ensure the model works normally.
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### Model Loading
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#### 16-bit (bf16 / fp16)
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('Shanghai_AI_Laboratory/Mini-InternVL-Chat-4B-V1-5')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/Shanghai_AI_Laboratory/Mini-InternVL-Chat-4B-V1-5.git
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import torch
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from transformers import AutoTokenizer, AutoModel
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path = "OpenGVLab/Mini-InternVL-Chat-4B-V1-5"
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model = AutoModel.from_pretrained(
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path,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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use_flash_attn=True,
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trust_remote_code=True).eval().cuda()
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```
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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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#### BNB 8-bit Quantization
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```python
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import torch
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from transformers import AutoTokenizer, AutoModel
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path = "OpenGVLab/Mini-InternVL-Chat-4B-V1-5"
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model = AutoModel.from_pretrained(
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path,
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torch_dtype=torch.bfloat16,
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load_in_8bit=True,
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low_cpu_mem_usage=True,
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use_flash_attn=True,
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trust_remote_code=True).eval()
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```
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#### BNB 4-bit Quantization
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```python
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import torch
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from transformers import AutoTokenizer, AutoModel
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path = "OpenGVLab/Mini-InternVL-Chat-4B-V1-5"
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model = AutoModel.from_pretrained(
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path,
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torch_dtype=torch.bfloat16,
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load_in_4bit=True,
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low_cpu_mem_usage=True,
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use_flash_attn=True,
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trust_remote_code=True).eval()
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```
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#### Multiple GPUs
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The reason for writing the code this way is to avoid errors that occur during multi-GPU inference due to tensors not being on the same device. By ensuring that the first and last layers of the large language model (LLM) are on the same device, we prevent such errors.
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```python
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import math
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import torch
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from transformers import AutoTokenizer, AutoModel
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def split_model(model_name):
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device_map = {}
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world_size = torch.cuda.device_count()
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num_layers = {'Mini-InternVL-2B-V1-5': 24, 'Mini-InternVL-4B-V1-5': 32, 'InternVL-Chat-V1-5': 48}[model_name]
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# Since the first GPU will be used for ViT, treat it as half a GPU.
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num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5))
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num_layers_per_gpu = [num_layers_per_gpu] * world_size
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num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
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layer_cnt = 0
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for i, num_layer in enumerate(num_layers_per_gpu):
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for j in range(num_layer):
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device_map[f'language_model.model.layers.{layer_cnt}'] = i
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layer_cnt += 1
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device_map['vision_model'] = 0
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device_map['mlp1'] = 0
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device_map['language_model.model.tok_embeddings'] = 0
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device_map['language_model.model.embed_tokens'] = 0
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device_map['language_model.output'] = 0
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device_map['language_model.model.norm'] = 0
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device_map['language_model.lm_head'] = 0
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device_map[f'language_model.model.layers.{num_layers - 1}'] = 0
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return device_map
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path = "OpenGVLab/Mini-InternVL-Chat-4B-V1-5"
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device_map = split_model('Mini-InternVL-Chat-4B-V1-5')
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model = AutoModel.from_pretrained(
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path,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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use_flash_attn=True,
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trust_remote_code=True,
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device_map=device_map).eval()
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```
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### Inference with Transformers
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```python
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import numpy as np
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import torch
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import torchvision.transforms as T
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from decord import VideoReader, cpu
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from PIL import Image
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from torchvision.transforms.functional import InterpolationMode
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from transformers import AutoModel, AutoTokenizer
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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def build_transform(input_size):
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
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transform = T.Compose([
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=MEAN, std=STD)
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])
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return transform
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def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
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best_ratio_diff = float('inf')
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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if ratio_diff < best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_ratio = ratio
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elif ratio_diff == best_ratio_diff:
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
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best_ratio = ratio
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return best_ratio
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def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
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orig_width, orig_height = image.size
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aspect_ratio = orig_width / orig_height
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# calculate the existing image aspect ratio
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target_ratios = set(
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(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
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i * j <= max_num and i * j >= min_num)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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# find the closest aspect ratio to the target
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target_aspect_ratio = find_closest_aspect_ratio(
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aspect_ratio, target_ratios, orig_width, orig_height, image_size)
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# calculate the target width and height
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target_width = image_size * target_aspect_ratio[0]
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target_height = image_size * target_aspect_ratio[1]
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
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# resize the image
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size
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)
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# split the image
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
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processed_images.append(thumbnail_img)
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return processed_images
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def load_image(image_file, input_size=448, max_num=12):
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image = Image.open(image_file).convert('RGB')
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transform = build_transform(input_size=input_size)
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images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
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pixel_values = [transform(image) for image in images]
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pixel_values = torch.stack(pixel_values)
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return pixel_values
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# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
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path = 'OpenGVLab/Mini-InternVL-Chat-4B-V1-5'
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model = AutoModel.from_pretrained(
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path,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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use_flash_attn=True,
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trust_remote_code=True).eval().cuda()
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
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# set the max number of tiles in `max_num`
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pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
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generation_config = dict(max_new_tokens=1024, do_sample=True)
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# pure-text conversation (纯文本对话)
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question = 'Hello, who are you?'
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response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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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}\nAssistant: {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}\nAssistant: {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}\nAssistant: {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}\nAssistant: {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=12).to(torch.bfloat16).cuda()
|
||||
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).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)
|
||||
print(f'User: {question}\nAssistant: {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}\nAssistant: {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=12).to(torch.bfloat16).cuda()
|
||||
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).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}\nAssistant: {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}\nAssistant: {response}')
|
||||
|
||||
# batch inference, single image per sample (单图批处理)
|
||||
pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
|
||||
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).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}\nAssistant: {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=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...\nFrame8: <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}\nAssistant: {response}')
|
||||
|
||||
question = 'Describe this video in detail.'
|
||||
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}\nAssistant: {response}')
|
||||
```
|
||||
|
||||
#### Streaming Output
|
||||
|
||||
Besides this method, you can also use the following code to get streamed output.
|
||||
|
||||
```python
|
||||
from transformers import TextIteratorStreamer
|
||||
from threading import Thread
|
||||
|
||||
# Initialize the streamer
|
||||
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=10)
|
||||
# Define the generation configuration
|
||||
generation_config = dict(max_new_tokens=1024, do_sample=False, streamer=streamer)
|
||||
# Start the model chat in a separate thread
|
||||
thread = Thread(target=model.chat, kwargs=dict(
|
||||
tokenizer=tokenizer, pixel_values=pixel_values, question=question,
|
||||
history=None, return_history=False, generation_config=generation_config,
|
||||
))
|
||||
thread.start()
|
||||
|
||||
# Initialize an empty string to store the generated text
|
||||
generated_text = ''
|
||||
# Loop through the streamer to get the new text as it is generated
|
||||
for new_text in streamer:
|
||||
if new_text == model.conv_template.sep:
|
||||
break
|
||||
generated_text += new_text
|
||||
print(new_text, end='', flush=True) # Print each new chunk of generated text on the same line
|
||||
```
|
||||
|
||||
## Finetune
|
||||
|
||||
Many repositories now support fine-tuning of the InternVL series models, including [InternVL](https://github.com/OpenGVLab/InternVL), [SWIFT](https://github.com/modelscope/ms-swift), [XTurner](https://github.com/InternLM/xtuner), and others. Please refer to their documentation for more details on fine-tuning.
|
||||
|
||||
## Deployment
|
||||
|
||||
### LMDeploy
|
||||
|
||||
LMDeploy is a toolkit for compressing, deploying, and serving LLMs & VLMs.
|
||||
|
||||
```sh
|
||||
pip install lmdeploy>=0.5.3
|
||||
```
|
||||
|
||||
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
|
||||
from lmdeploy.vl import load_image
|
||||
|
||||
model = 'OpenGVLab/Mini-InternVL-Chat-4B-V1-5'
|
||||
image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
|
||||
pipe = pipeline(model, 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.
|
||||
|
||||
> Warning: Due to the scarcity of multi-image conversation data, the performance on multi-image tasks may be unstable, and it may require multiple attempts to achieve satisfactory results.
|
||||
|
||||
```python
|
||||
from lmdeploy import pipeline, TurbomindEngineConfig
|
||||
from lmdeploy.vl import load_image
|
||||
from lmdeploy.vl.constants import IMAGE_TOKEN
|
||||
|
||||
model = 'OpenGVLab/Mini-InternVL-Chat-4B-V1-5'
|
||||
pipe = pipeline(model, 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
|
||||
from lmdeploy.vl import load_image
|
||||
|
||||
model = 'OpenGVLab/Mini-InternVL-Chat-4B-V1-5'
|
||||
pipe = pipeline(model, 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, GenerationConfig
|
||||
from lmdeploy.vl import load_image
|
||||
|
||||
model = 'OpenGVLab/Mini-InternVL-Chat-4B-V1-5'
|
||||
pipe = pipeline(model, 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)
|
||||
```
|
||||
|
||||
#### Service
|
||||
|
||||
LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
|
||||
|
||||
```shell
|
||||
lmdeploy serve api_server OpenGVLab/Mini-InternVL-Chat-4B-V1-5 --server-port 23333
|
||||
```
|
||||
|
||||
To use the OpenAI-style interface, you need to install OpenAI:
|
||||
|
||||
```shell
|
||||
pip install openai
|
||||
```
|
||||
|
||||
Then, use the code below to make the API call:
|
||||
|
||||
```python
|
||||
from openai import OpenAI
|
||||
|
||||
client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
|
||||
model_name = client.models.list().data[0].id
|
||||
response = client.chat.completions.create(
|
||||
model=model_name,
|
||||
messages=[{
|
||||
'role':
|
||||
'user',
|
||||
'content': [{
|
||||
'type': 'text',
|
||||
'text': 'describe this image',
|
||||
}, {
|
||||
'type': 'image_url',
|
||||
'image_url': {
|
||||
'url':
|
||||
'https://modelscope.oss-cn-beijing.aliyuncs.com/resource/tiger.jpeg',
|
||||
},
|
||||
}],
|
||||
}],
|
||||
temperature=0.8,
|
||||
top_p=0.8)
|
||||
print(response)
|
||||
```
|
||||
|
||||
## License
|
||||
|
||||
This project is released under the MIT License. This project uses the pre-trained Phi-3-mini-128k-instruct as a component, which is also licensed under the MIT License.
|
||||
|
||||
## Citation
|
||||
|
||||
If you find this project useful in your research, please consider citing:
|
||||
|
||||
```BibTeX
|
||||
@article{chen2024expanding,
|
||||
title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
|
||||
author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
|
||||
journal={arXiv preprint arXiv:2412.05271},
|
||||
year={2024}
|
||||
}
|
||||
@article{gao2024mini,
|
||||
title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
|
||||
author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
|
||||
journal={arXiv preprint arXiv:2410.16261},
|
||||
year={2024}
|
||||
}
|
||||
@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}
|
||||
}
|
||||
@inproceedings{chen2024internvl,
|
||||
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 others},
|
||||
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
|
||||
pages={24185--24198},
|
||||
year={2024}
|
||||
}
|
||||
```
|
||||
|
||||
22
added_tokens.json
Normal file
22
added_tokens.json
Normal file
@@ -0,0 +1,22 @@
|
||||
{
|
||||
"</box>": 32019,
|
||||
"</img>": 32012,
|
||||
"</quad>": 32015,
|
||||
"</ref>": 32017,
|
||||
"<IMG_CONTEXT>": 32013,
|
||||
"<box>": 32018,
|
||||
"<img>": 32011,
|
||||
"<quad>": 32014,
|
||||
"<ref>": 32016,
|
||||
"<|assistant|>": 32001,
|
||||
"<|endoftext|>": 32000,
|
||||
"<|end|>": 32007,
|
||||
"<|placeholder1|>": 32002,
|
||||
"<|placeholder2|>": 32003,
|
||||
"<|placeholder3|>": 32004,
|
||||
"<|placeholder4|>": 32005,
|
||||
"<|placeholder5|>": 32008,
|
||||
"<|placeholder6|>": 32009,
|
||||
"<|system|>": 32006,
|
||||
"<|user|>": 32010
|
||||
}
|
||||
246
config.json
Normal file
246
config.json
Normal file
@@ -0,0 +1,246 @@
|
||||
{
|
||||
"_commit_hash": null,
|
||||
"architectures": [
|
||||
"InternVLChatModel"
|
||||
],
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_internvl_chat.InternVLChatConfig",
|
||||
"AutoModel": "modeling_internvl_chat.InternVLChatModel",
|
||||
"AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"
|
||||
},
|
||||
"system_message": "You are an AI assistant whose name is Phi-3.",
|
||||
"downsample_ratio": 0.5,
|
||||
"dynamic_image_size": true,
|
||||
"force_image_size": 448,
|
||||
"llm_config": {
|
||||
"_name_or_path": "microsoft/Phi-3-mini-128k-instruct",
|
||||
"add_cross_attention": false,
|
||||
"architectures": [
|
||||
"Phi3ForCausalLM"
|
||||
],
|
||||
"attn_implementation": "flash_attention_2",
|
||||
"attention_dropout": 0.0,
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_phi3.Phi3Config",
|
||||
"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
|
||||
},
|
||||
"bad_words_ids": null,
|
||||
"begin_suppress_tokens": null,
|
||||
"bos_token_id": 1,
|
||||
"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,
|
||||
"embd_pdrop": 0.0,
|
||||
"encoder_no_repeat_ngram_size": 0,
|
||||
"eos_token_id": 32000,
|
||||
"exponential_decay_length_penalty": null,
|
||||
"finetuning_task": null,
|
||||
"forced_bos_token_id": null,
|
||||
"forced_eos_token_id": null,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3072,
|
||||
"id2label": {
|
||||
"0": "LABEL_0",
|
||||
"1": "LABEL_1"
|
||||
},
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8192,
|
||||
"is_decoder": false,
|
||||
"is_encoder_decoder": false,
|
||||
"label2id": {
|
||||
"LABEL_0": 0,
|
||||
"LABEL_1": 1
|
||||
},
|
||||
"length_penalty": 1.0,
|
||||
"max_length": 20,
|
||||
"max_position_embeddings": 131072,
|
||||
"min_length": 0,
|
||||
"model_type": "phi3",
|
||||
"no_repeat_ngram_size": 0,
|
||||
"num_attention_heads": 32,
|
||||
"num_beam_groups": 1,
|
||||
"num_beams": 1,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 32,
|
||||
"num_return_sequences": 1,
|
||||
"original_max_position_embeddings": 4096,
|
||||
"output_attentions": false,
|
||||
"output_hidden_states": false,
|
||||
"output_scores": false,
|
||||
"pad_token_id": 32000,
|
||||
"prefix": null,
|
||||
"problem_type": null,
|
||||
"pruned_heads": {},
|
||||
"remove_invalid_values": false,
|
||||
"repetition_penalty": 1.0,
|
||||
"resid_pdrop": 0.0,
|
||||
"return_dict": true,
|
||||
"return_dict_in_generate": false,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": {
|
||||
"long_factor": [
|
||||
1.0299999713897705,
|
||||
1.0499999523162842,
|
||||
1.0499999523162842,
|
||||
1.0799999237060547,
|
||||
1.2299998998641968,
|
||||
1.2299998998641968,
|
||||
1.2999999523162842,
|
||||
1.4499999284744263,
|
||||
1.5999999046325684,
|
||||
1.6499998569488525,
|
||||
1.8999998569488525,
|
||||
2.859999895095825,
|
||||
3.68999981880188,
|
||||
5.419999599456787,
|
||||
5.489999771118164,
|
||||
5.489999771118164,
|
||||
9.09000015258789,
|
||||
11.579999923706055,
|
||||
15.65999984741211,
|
||||
15.769999504089355,
|
||||
15.789999961853027,
|
||||
18.360000610351562,
|
||||
21.989999771118164,
|
||||
23.079999923706055,
|
||||
30.009998321533203,
|
||||
32.35000228881836,
|
||||
32.590003967285156,
|
||||
35.56000518798828,
|
||||
39.95000457763672,
|
||||
53.840003967285156,
|
||||
56.20000457763672,
|
||||
57.95000457763672,
|
||||
59.29000473022461,
|
||||
59.77000427246094,
|
||||
59.920005798339844,
|
||||
61.190006256103516,
|
||||
61.96000671386719,
|
||||
62.50000762939453,
|
||||
63.3700065612793,
|
||||
63.48000717163086,
|
||||
63.48000717163086,
|
||||
63.66000747680664,
|
||||
63.850006103515625,
|
||||
64.08000946044922,
|
||||
64.760009765625,
|
||||
64.80001068115234,
|
||||
64.81001281738281,
|
||||
64.81001281738281
|
||||
],
|
||||
"short_factor": [
|
||||
1.05,
|
||||
1.05,
|
||||
1.05,
|
||||
1.1,
|
||||
1.1,
|
||||
1.1500000000000001,
|
||||
1.2000000000000002,
|
||||
1.2500000000000002,
|
||||
1.3000000000000003,
|
||||
1.3500000000000003,
|
||||
1.5000000000000004,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.0500000000000007,
|
||||
2.0500000000000007,
|
||||
2.0500000000000007,
|
||||
2.1000000000000005,
|
||||
2.1000000000000005,
|
||||
2.1000000000000005,
|
||||
2.1500000000000004,
|
||||
2.1500000000000004,
|
||||
2.3499999999999996,
|
||||
2.549999999999999,
|
||||
2.5999999999999988,
|
||||
2.5999999999999988,
|
||||
2.7499999999999982,
|
||||
2.849999999999998,
|
||||
2.849999999999998,
|
||||
2.9499999999999975
|
||||
],
|
||||
"type": "su"
|
||||
},
|
||||
"rope_theta": 10000.0,
|
||||
"sep_token_id": null,
|
||||
"sliding_window": 262144,
|
||||
"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": 32020
|
||||
},
|
||||
"max_dynamic_patch": 12,
|
||||
"min_dynamic_patch": 1,
|
||||
"model_type": "internvl_chat",
|
||||
"ps_version": "v2",
|
||||
"select_layer": -1,
|
||||
"template": "phi3-chat",
|
||||
"torch_dtype": "bfloat16",
|
||||
"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": 1024,
|
||||
"image_size": 448,
|
||||
"initializer_factor": 1.0,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 4096,
|
||||
"layer_norm_eps": 1e-06,
|
||||
"model_type": "intern_vit_6b",
|
||||
"norm_type": "layer_norm",
|
||||
"num_attention_heads": 16,
|
||||
"num_channels": 3,
|
||||
"num_hidden_layers": 24,
|
||||
"output_attentions": false,
|
||||
"output_hidden_states": false,
|
||||
"patch_size": 14,
|
||||
"qk_normalization": false,
|
||||
"qkv_bias": true,
|
||||
"return_dict": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.37.2",
|
||||
"use_bfloat16": true,
|
||||
"use_flash_attn": true
|
||||
}
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "image-text-to-text", "allow_remote": true}
|
||||
120
configuration_intern_vit.py
Normal file
120
configuration_intern_vit.py
Normal file
@@ -0,0 +1,120 @@
|
||||
# --------------------------------------------------------
|
||||
# InternVL
|
||||
# Copyright (c) 2024 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)
|
||||
96
configuration_internvl_chat.py
Normal file
96
configuration_internvl_chat.py
Normal file
@@ -0,0 +1,96 @@
|
||||
# --------------------------------------------------------
|
||||
# InternVL
|
||||
# Copyright (c) 2024 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
|
||||
from .configuration_phi3 import Phi3Config
|
||||
|
||||
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 = {'architectures': ['InternVisionModel']}
|
||||
logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
|
||||
|
||||
if llm_config is None:
|
||||
llm_config = {'architectures': ['Phi3ForCausalLM']}
|
||||
logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
|
||||
|
||||
self.vision_config = InternVisionConfig(**vision_config)
|
||||
if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
|
||||
self.llm_config = LlamaConfig(**llm_config)
|
||||
elif llm_config.get('architectures')[0] == 'Phi3ForCausalLM':
|
||||
self.llm_config = Phi3Config(**llm_config)
|
||||
else:
|
||||
raise ValueError('Unsupported architecture: {}'.format(llm_config.get('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
|
||||
211
configuration_phi3.py
Normal file
211
configuration_phi3.py
Normal file
@@ -0,0 +1,211 @@
|
||||
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License atd
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
""" Phi-3 model configuration"""
|
||||
|
||||
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import logging
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||
'microsoft/Phi-3-mini-4k-instruct': 'https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json',
|
||||
'microsoft/Phi-3-mini-128k-instruct': 'https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json',
|
||||
}
|
||||
|
||||
|
||||
class Phi3Config(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
|
||||
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||
defaults will yield a similar configuration to that of the
|
||||
[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 32064):
|
||||
Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`Phi3Model`].
|
||||
hidden_size (`int`, *optional*, defaults to 3072):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 8192):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||
Number of hidden layers in the Transformer decoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer decoder.
|
||||
num_key_value_heads (`int`, *optional*):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details checkout [this
|
||||
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
||||
`num_attention_heads`.
|
||||
resid_pdrop (`float`, *optional*, defaults to 0.0):
|
||||
Dropout probability for mlp outputs.
|
||||
embd_pdrop (`int`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the embeddings.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio after computing the attention scores.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||
The maximum sequence length that this model might ever be used with.
|
||||
original_max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||
The maximum sequence length that this model was trained with. This is used to determine the size of the
|
||||
original RoPE embeddings when using long scaling.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||||
The epsilon value used for the RMSNorm.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether to tie weight embeddings
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
rope_scaling (`dict`, *optional*):
|
||||
The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
|
||||
contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
|
||||
the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
|
||||
divided by the number of attention heads divided by 2.
|
||||
bos_token_id (`int`, *optional*, defaults to 1):
|
||||
The id of the "beginning-of-sequence" token.
|
||||
eos_token_id (`int`, *optional*, defaults to 32000):
|
||||
The id of the "end-of-sequence" token.
|
||||
pad_token_id (`int`, *optional*, defaults to 32000):
|
||||
The id of the padding token.
|
||||
sliding_window (`int`, *optional*):
|
||||
Sliding window attention window size. If `None`, no sliding window is applied.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import Phi3Model, Phi3Config
|
||||
|
||||
>>> # Initializing a Phi-3 style configuration
|
||||
>>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
|
||||
|
||||
>>> # Initializing a model from the configuration
|
||||
>>> model = Phi3Model(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = 'phi3'
|
||||
keys_to_ignore_at_inference = ['past_key_values']
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=32064,
|
||||
hidden_size=3072,
|
||||
intermediate_size=8192,
|
||||
num_hidden_layers=32,
|
||||
num_attention_heads=32,
|
||||
num_key_value_heads=None,
|
||||
resid_pdrop=0.0,
|
||||
embd_pdrop=0.0,
|
||||
attention_dropout=0.0,
|
||||
hidden_act='silu',
|
||||
max_position_embeddings=4096,
|
||||
original_max_position_embeddings=4096,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=1e-5,
|
||||
use_cache=True,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000.0,
|
||||
rope_scaling=None,
|
||||
bos_token_id=1,
|
||||
eos_token_id=32000,
|
||||
pad_token_id=32000,
|
||||
sliding_window=None,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.resid_pdrop = resid_pdrop
|
||||
self.embd_pdrop = embd_pdrop
|
||||
self.attention_dropout = attention_dropout
|
||||
self.hidden_act = hidden_act
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.original_max_position_embeddings = original_max_position_embeddings
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
self._rope_scaling_validation()
|
||||
self.sliding_window = sliding_window
|
||||
|
||||
super().__init__(
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
pad_token_id=pad_token_id,
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def _rope_scaling_validation(self):
|
||||
"""
|
||||
Validate the `rope_scaling` configuration.
|
||||
"""
|
||||
if self.rope_scaling is None:
|
||||
return
|
||||
|
||||
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
|
||||
raise ValueError(
|
||||
'`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, '
|
||||
f'got {self.rope_scaling}'
|
||||
)
|
||||
rope_scaling_type = self.rope_scaling.get('type', None)
|
||||
rope_scaling_short_factor = self.rope_scaling.get('short_factor', None)
|
||||
rope_scaling_long_factor = self.rope_scaling.get('long_factor', None)
|
||||
if rope_scaling_type is None or rope_scaling_type not in ['su', 'yarn']:
|
||||
raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
|
||||
if not (
|
||||
isinstance(rope_scaling_short_factor, list)
|
||||
and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
|
||||
):
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
|
||||
)
|
||||
if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
|
||||
)
|
||||
if not (
|
||||
isinstance(rope_scaling_long_factor, list)
|
||||
and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
|
||||
):
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
|
||||
)
|
||||
if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
|
||||
)
|
||||
383
conversation.py
Normal file
383
conversation.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""
|
||||
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()
|
||||
|
||||
|
||||
register_conv_template(
|
||||
Conversation(
|
||||
name='Hermes-2',
|
||||
system_template='<|im_start|>system\n{system_message}',
|
||||
system_message='Answer the questions.',
|
||||
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}',
|
||||
system_message='You are an AI assistant whose name is InternLM (书生·浦语).',
|
||||
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}',
|
||||
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=[
|
||||
2,
|
||||
32000,
|
||||
32007
|
||||
]
|
||||
)
|
||||
)
|
||||
BIN
examples/image1.jpg
Normal file
BIN
examples/image1.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 76 KiB |
BIN
examples/image2.jpg
Normal file
BIN
examples/image2.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 123 KiB |
3
examples/red-panda.mp4
Normal file
3
examples/red-panda.mp4
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:d921c07bb97224d65a37801541d246067f0d506f08723ffa1ad85c217907ccb8
|
||||
size 1867237
|
||||
9
generation_config.json
Normal file
9
generation_config.json
Normal file
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"transformers_version": "4.37.2",
|
||||
"eos_token_id": [
|
||||
2,
|
||||
32000,
|
||||
32007
|
||||
]
|
||||
}
|
||||
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:ceea2c6af60bead02f2d1f7c54fd0195906e59bc496589de8ca603562e586ca7
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size 4957392176
|
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3
model-00002-of-00002.safetensors
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3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:0191d413ddd6d67b2f2a5c626c5cf957ce6760d4a7a4848b06609072a600a20e
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||||
size 3336385864
|
||||
548
model.safetensors.index.json
Normal file
548
model.safetensors.index.json
Normal file
@@ -0,0 +1,548 @@
|
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{
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"vision_model.encoder.layers.7.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.7.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.7.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.7.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.7.norm1.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.7.norm1.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.7.norm2.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.7.norm2.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.ls1": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.ls2": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.norm1.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.norm1.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.norm2.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.8.norm2.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.ls1": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.ls2": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.norm1.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.norm1.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.norm2.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_model.encoder.layers.9.norm2.weight": "model-00001-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
430
modeling_intern_vit.py
Normal file
430
modeling_intern_vit.py
Normal file
@@ -0,0 +1,430 @@
|
||||
# --------------------------------------------------------
|
||||
# InternVL
|
||||
# Copyright (c) 2024 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:
|
||||
from flash_attn.bert_padding import pad_input, unpad_input
|
||||
from flash_attn.flash_attn_interface import \
|
||||
flash_attn_varlen_qkvpacked_func
|
||||
has_flash_attn = True
|
||||
except:
|
||||
print('FlashAttention2 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_varlen_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_varlen_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_varlen_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).to(hidden_states.dtype)) * self.ls1)
|
||||
|
||||
hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * 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'
|
||||
_supports_flash_attn_2 = True
|
||||
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,
|
||||
)
|
||||
349
modeling_internvl_chat.py
Normal file
349
modeling_internvl_chat.py
Normal file
@@ -0,0 +1,349 @@
|
||||
# --------------------------------------------------------
|
||||
# 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,
|
||||
LlamaTokenizer)
|
||||
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, has_flash_attn
|
||||
from .modeling_phi3 import Phi3ForCausalLM
|
||||
|
||||
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'
|
||||
base_model_prefix = 'language_model'
|
||||
_supports_flash_attn_2 = True
|
||||
_no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'Phi3DecoderLayer']
|
||||
|
||||
def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
|
||||
super().__init__(config)
|
||||
|
||||
assert version_cmp(transformers.__version__, '4.37.0', '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
|
||||
use_flash_attn = use_flash_attn if has_flash_attn else False
|
||||
config.vision_config.use_flash_attn = True if use_flash_attn else False
|
||||
config.llm_config._attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
|
||||
|
||||
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)
|
||||
elif config.llm_config.architectures[0] == 'Phi3ForCausalLM':
|
||||
self.language_model = Phi3ForCausalLM(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).clone()
|
||||
|
||||
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.is_initialized() and 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.system_message = self.system_message
|
||||
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'].to(self.device)
|
||||
attention_mask = model_inputs['attention_mask'].to(self.device)
|
||||
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
|
||||
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.strip())[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.strip())
|
||||
|
||||
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'].to(self.device)
|
||||
attention_mask = model_inputs['attention_mask'].to(self.device)
|
||||
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.strip())[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,
|
||||
**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,
|
||||
use_cache=True,
|
||||
**generate_kwargs,
|
||||
)
|
||||
|
||||
return outputs
|
||||
1610
modeling_phi3.py
Normal file
1610
modeling_phi3.py
Normal file
File diff suppressed because it is too large
Load Diff
19
preprocessor_config.json
Normal file
19
preprocessor_config.json
Normal file
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"crop_size": 448,
|
||||
"do_center_crop": true,
|
||||
"do_normalize": true,
|
||||
"do_resize": true,
|
||||
"feature_extractor_type": "CLIPFeatureExtractor",
|
||||
"image_mean": [
|
||||
0.485,
|
||||
0.456,
|
||||
0.406
|
||||
],
|
||||
"image_std": [
|
||||
0.229,
|
||||
0.224,
|
||||
0.225
|
||||
],
|
||||
"resample": 3,
|
||||
"size": 448
|
||||
}
|
||||
41
special_tokens_map.json
Normal file
41
special_tokens_map.json
Normal file
@@ -0,0 +1,41 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<img>",
|
||||
"</img>",
|
||||
"<IMG_CONTEXT>",
|
||||
"<quad>",
|
||||
"</quad>",
|
||||
"<ref>",
|
||||
"</ref>",
|
||||
"<box>",
|
||||
"</box>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
||||
size 499723
|
||||
214
tokenizer_config.json
Normal file
214
tokenizer_config.json
Normal file
@@ -0,0 +1,214 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32000": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32001": {
|
||||
"content": "<|assistant|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32002": {
|
||||
"content": "<|placeholder1|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32003": {
|
||||
"content": "<|placeholder2|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32004": {
|
||||
"content": "<|placeholder3|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32005": {
|
||||
"content": "<|placeholder4|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32006": {
|
||||
"content": "<|system|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32007": {
|
||||
"content": "<|end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32008": {
|
||||
"content": "<|placeholder5|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32009": {
|
||||
"content": "<|placeholder6|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32010": {
|
||||
"content": "<|user|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32011": {
|
||||
"content": "<img>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32012": {
|
||||
"content": "</img>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32013": {
|
||||
"content": "<IMG_CONTEXT>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32014": {
|
||||
"content": "<quad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32015": {
|
||||
"content": "</quad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32016": {
|
||||
"content": "<ref>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32017": {
|
||||
"content": "</ref>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32018": {
|
||||
"content": "<box>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32019": {
|
||||
"content": "</box>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<img>",
|
||||
"</img>",
|
||||
"<IMG_CONTEXT>",
|
||||
"<quad>",
|
||||
"</quad>",
|
||||
"<ref>",
|
||||
"</ref>",
|
||||
"<box>",
|
||||
"</box>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": false,
|
||||
"model_max_length": 8192,
|
||||
"pad_token": "</s>",
|
||||
"padding_side": "right",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user