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Model: OpenGVLab/Mini-InternVL2-4B-DA-BDD Source: Original Platform
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---
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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/InternVL2-4B
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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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# Mini-InternVL
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[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 1.0\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5\]](https://arxiv.org/abs/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271)
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[\[🗨️ InternVL Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#data-preparation)
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## Introduction
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We release the adaptation models for the specific domains: autonomous driving, medical images, and remote sensing.
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These models are built upon Mini-InternVL and fine-tuned using a unified adaptation framework, achieving good performance on tasks in specific domains.
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<table>
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<tr>
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<th>Model Name</th>
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<th>HF Link</th>
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<th>Note</th>
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</tr>
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<tr>
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<td>Mini-InternVL2-DA-Drivelm</td>
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<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Drivelm">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Drivelm">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Drivelm">🤗4B</a></td>
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<td> Adaptation for <a href="https://github.com/OpenDriveLab/DriveLM/tree/main/challenge"> CVPR 2024 Autonomous Driving Challenge </a></td>
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</tr>
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<tr>
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<td>Mini-InternVL2-DA-BDD</td>
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<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-BDD">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-BDD">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-BDD">🤗4B</a></td>
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<td> Fine-tuning with data constructed by <a href="https://tonyxuqaq.github.io/projects/DriveGPT4/"> DriveGPT4 </a></td>
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</tr>
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<tr>
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<td>Mini-InternVL2-DA-RS</td>
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<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-RS">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-RS">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-RS">🤗4B</a></td>
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<td> Adaptation for remote sensing domain </td>
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</tr>
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<tr>
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<td>Mini-InternVL2-DA-Medical</td>
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<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Medical">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Medical">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Medical">🤗4B</a></td>
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<td> Fine-tuning using our <a href="https://huggingface.co/datasets/OpenGVLab/InternVL-Domain-Adaptation-Data/blob/main/train_meta/internvl_1_2_finetune_medical.json">medical data</a>.</td>
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</tr>
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</table>
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The script for evaluation is in the [document](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#id3).
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## Training datasets
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- General domain dataset:
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ShareGPT4V, AllSeeingV2, LLaVA-Instruct-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, SynthDoG-EN
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- Autonomous driving dataset:
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[DriveGPT4](https://tonyxuqaq.github.io/projects/DriveGPT4/).
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## Quick Start
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We provide an example code to run `Mini-InternVL2-4B` using `transformers`.
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> Please use transformers>=4.37.2 to ensure the model works normally.
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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-InternVL2-4B-DA-BDD'
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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('path/to/image.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?'
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response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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# single-image single-round conversation (单图单轮对话)
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question = '<image>\nPlease describe the image shortly.'
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response = model.chat(tokenizer, pixel_values, question, generation_config)
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print(f'User: {question}\nAssistant: {response}')
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# single-image multi-round conversation (单图多轮对话)
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question = '<image>\nPlease describe the image in detail.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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question = 'Please write a poem according to the image.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
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pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
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question = '<image>\nDescribe the two images in detail.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config,
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history=None, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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question = 'What are the similarities and differences between these two images.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config,
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history=history, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
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pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
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num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
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question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config,
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num_patches_list=num_patches_list,
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history=None, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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question = 'What are the similarities and differences between these two images.'
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response, history = model.chat(tokenizer, pixel_values, question, generation_config,
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num_patches_list=num_patches_list,
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history=history, return_history=True)
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print(f'User: {question}\nAssistant: {response}')
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# batch inference, single image per sample (单图批处理)
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pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
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pixel_values2 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
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num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
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questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
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responses = model.batch_chat(tokenizer, pixel_values,
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num_patches_list=num_patches_list,
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questions=questions,
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generation_config=generation_config)
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for question, response in zip(questions, responses):
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print(f'User: {question}\nAssistant: {response}')
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```
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## Citation
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If you find this project useful in your research, please consider citing:
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```BibTeX
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@article{gao2024mini,
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title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
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||||||
|
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{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{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
|
||||||
|
}
|
||||||
300
config.json
Normal file
300
config.json
Normal file
@@ -0,0 +1,300 @@
|
|||||||
|
{
|
||||||
|
"_commit_hash": null,
|
||||||
|
"architectures": [
|
||||||
|
"InternVLChatModel"
|
||||||
|
],
|
||||||
|
"auto_map": {
|
||||||
|
"AutoConfig": "configuration_internvl_chat.InternVLChatConfig",
|
||||||
|
"AutoModel": "modeling_internvl_chat.InternVLChatModel",
|
||||||
|
"AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"
|
||||||
|
},
|
||||||
|
"downsample_ratio": 0.5,
|
||||||
|
"dynamic_image_size": true,
|
||||||
|
"force_image_size": 448,
|
||||||
|
"llm_config": {
|
||||||
|
"_name_or_path": "microsoft/Phi-3-mini-128k-instruct",
|
||||||
|
"add_cross_attention": false,
|
||||||
|
"architectures": [
|
||||||
|
"Phi3ForCausalLM"
|
||||||
|
],
|
||||||
|
"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": false,
|
||||||
|
"vocab_size": 32020
|
||||||
|
},
|
||||||
|
"max_dynamic_patch": 12,
|
||||||
|
"min_dynamic_patch": 1,
|
||||||
|
"model_type": "internvl_chat",
|
||||||
|
"pad2square": false,
|
||||||
|
"ps_version": "v2",
|
||||||
|
"select_layer": -1,
|
||||||
|
"template": "phi3-chat",
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": null,
|
||||||
|
"use_backbone_lora": 0,
|
||||||
|
"use_llm_lora": 0,
|
||||||
|
"use_thumbnail": true,
|
||||||
|
"vision_config": {
|
||||||
|
"_name_or_path": "",
|
||||||
|
"add_cross_attention": false,
|
||||||
|
"architectures": [
|
||||||
|
"InternVisionModel"
|
||||||
|
],
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bad_words_ids": null,
|
||||||
|
"begin_suppress_tokens": null,
|
||||||
|
"bos_token_id": null,
|
||||||
|
"chunk_size_feed_forward": 0,
|
||||||
|
"cross_attention_hidden_size": null,
|
||||||
|
"decoder_start_token_id": null,
|
||||||
|
"diversity_penalty": 0.0,
|
||||||
|
"do_sample": false,
|
||||||
|
"drop_path_rate": 0.1,
|
||||||
|
"dropout": 0.0,
|
||||||
|
"early_stopping": false,
|
||||||
|
"encoder_no_repeat_ngram_size": 0,
|
||||||
|
"eos_token_id": null,
|
||||||
|
"exponential_decay_length_penalty": null,
|
||||||
|
"finetuning_task": null,
|
||||||
|
"forced_bos_token_id": null,
|
||||||
|
"forced_eos_token_id": null,
|
||||||
|
"hidden_act": "gelu",
|
||||||
|
"hidden_size": 1024,
|
||||||
|
"id2label": {
|
||||||
|
"0": "LABEL_0",
|
||||||
|
"1": "LABEL_1"
|
||||||
|
},
|
||||||
|
"image_size": 448,
|
||||||
|
"initializer_factor": 1.0,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 4096,
|
||||||
|
"is_decoder": false,
|
||||||
|
"is_encoder_decoder": false,
|
||||||
|
"label2id": {
|
||||||
|
"LABEL_0": 0,
|
||||||
|
"LABEL_1": 1
|
||||||
|
},
|
||||||
|
"layer_norm_eps": 1e-06,
|
||||||
|
"length_penalty": 1.0,
|
||||||
|
"max_length": 20,
|
||||||
|
"min_length": 0,
|
||||||
|
"model_type": "intern_vit_6b",
|
||||||
|
"no_repeat_ngram_size": 0,
|
||||||
|
"norm_type": "layer_norm",
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_beam_groups": 1,
|
||||||
|
"num_beams": 1,
|
||||||
|
"num_channels": 3,
|
||||||
|
"num_hidden_layers": 24,
|
||||||
|
"num_return_sequences": 1,
|
||||||
|
"output_attentions": false,
|
||||||
|
"output_hidden_states": false,
|
||||||
|
"output_scores": false,
|
||||||
|
"pad_token_id": null,
|
||||||
|
"patch_size": 14,
|
||||||
|
"prefix": null,
|
||||||
|
"problem_type": null,
|
||||||
|
"pruned_heads": {},
|
||||||
|
"qk_normalization": false,
|
||||||
|
"qkv_bias": true,
|
||||||
|
"remove_invalid_values": false,
|
||||||
|
"repetition_penalty": 1.0,
|
||||||
|
"return_dict": true,
|
||||||
|
"return_dict_in_generate": false,
|
||||||
|
"sep_token_id": null,
|
||||||
|
"suppress_tokens": null,
|
||||||
|
"task_specific_params": null,
|
||||||
|
"temperature": 1.0,
|
||||||
|
"tf_legacy_loss": false,
|
||||||
|
"tie_encoder_decoder": false,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"tokenizer_class": null,
|
||||||
|
"top_k": 50,
|
||||||
|
"top_p": 1.0,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"torchscript": false,
|
||||||
|
"transformers_version": "4.37.2",
|
||||||
|
"typical_p": 1.0,
|
||||||
|
"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)}"
|
||||||
|
)
|
||||||
391
conversation.py
Normal file
391
conversation.py
Normal file
@@ -0,0 +1,391 @@
|
|||||||
|
"""
|
||||||
|
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.
|
||||||
|
|
||||||
|
Modified from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
|
||||||
|
"""
|
||||||
|
|
||||||
|
import dataclasses
|
||||||
|
from enum import IntEnum, auto
|
||||||
|
from typing import 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()
|
||||||
|
|
||||||
|
|
||||||
|
# Both Hermes-2 and internlm2-chat are chatml-format conversation templates. The difference
|
||||||
|
# is that during training, the preprocessing function for the Hermes-2 template doesn't add
|
||||||
|
# <s> at the beginning of the tokenized sequence, while the internlm2-chat template does.
|
||||||
|
# Therefore, they are completely equivalent during inference.
|
||||||
|
register_conv_template(
|
||||||
|
Conversation(
|
||||||
|
name='Hermes-2',
|
||||||
|
system_template='<|im_start|>system\n{system_message}',
|
||||||
|
# note: The new system prompt was not used here to avoid changes in benchmark performance.
|
||||||
|
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
||||||
|
system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
|
||||||
|
roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
|
||||||
|
sep_style=SeparatorStyle.MPT,
|
||||||
|
sep='<|im_end|>',
|
||||||
|
stop_str='<|endoftext|>',
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
register_conv_template(
|
||||||
|
Conversation(
|
||||||
|
name='internlm2-chat',
|
||||||
|
system_template='<|im_start|>system\n{system_message}',
|
||||||
|
# note: The new system prompt was not used here to avoid changes in benchmark performance.
|
||||||
|
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
||||||
|
system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
|
||||||
|
roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
|
||||||
|
sep_style=SeparatorStyle.MPT,
|
||||||
|
sep='<|im_end|>',
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
register_conv_template(
|
||||||
|
Conversation(
|
||||||
|
name='phi3-chat',
|
||||||
|
system_template='<|system|>\n{system_message}',
|
||||||
|
# note: The new system prompt was not used here to avoid changes in benchmark performance.
|
||||||
|
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
||||||
|
system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
|
||||||
|
roles=('<|user|>\n', '<|assistant|>\n'),
|
||||||
|
sep_style=SeparatorStyle.MPT,
|
||||||
|
sep='<|end|>',
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
register_conv_template(
|
||||||
|
Conversation(
|
||||||
|
name='internvl2_5',
|
||||||
|
system_template='<|im_start|>system\n{system_message}',
|
||||||
|
system_message='你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
||||||
|
roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
|
||||||
|
sep_style=SeparatorStyle.MPT,
|
||||||
|
sep='<|im_end|>\n',
|
||||||
|
)
|
||||||
|
)
|
||||||
9
generation_config.json
Normal file
9
generation_config.json
Normal file
@@ -0,0 +1,9 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
2,
|
||||||
|
32000,
|
||||||
|
32007
|
||||||
|
],
|
||||||
|
"transformers_version": "4.37.2"
|
||||||
|
}
|
||||||
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
|
||||||
|
oid sha256:bf696d10087b276296423946487faaa3074bdd678832ac7a1c38c0d9b77880c7
|
||||||
|
size 4957392176
|
||||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9641fa11652684938612544fb24849c7f7fe03b90ffe58a0c4d25aaabc95996f
|
||||||
|
size 3336385864
|
||||||
548
model.safetensors.index.json
Normal file
548
model.safetensors.index.json
Normal file
@@ -0,0 +1,548 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_size": 8293711872
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
"language_model.lm_head.weight": "model-00002-of-00002.safetensors",
|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
||||||
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||||||
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||||||
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|
||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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|
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|
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|
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|
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"vision_model.encoder.layers.4.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.ls1": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.ls2": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.norm1.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.norm2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.4.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.ls1": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.ls2": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.norm1.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.norm2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.5.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.ls1": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.ls2": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.norm1.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.norm2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.6.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.7.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.7.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.7.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.7.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.7.ls1": "model-00001-of-00002.safetensors",
|
||||||
|
"vision_model.encoder.layers.7.ls2": "model-00001-of-00002.safetensors",
|
||||||
|
"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 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__, '4.36.2', 'ge')
|
||||||
|
image_size = config.force_image_size or config.vision_config.image_size
|
||||||
|
patch_size = config.vision_config.patch_size
|
||||||
|
self.patch_size = patch_size
|
||||||
|
self.select_layer = config.select_layer
|
||||||
|
self.template = config.template
|
||||||
|
self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
|
||||||
|
self.downsample_ratio = config.downsample_ratio
|
||||||
|
self.ps_version = config.ps_version
|
||||||
|
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.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
|
||||||
|
}
|
||||||
|
}
|
||||||
93571
tokenizer.json
Normal file
93571
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
213
tokenizer_config.json
Normal file
213
tokenizer_config.json
Normal file
@@ -0,0 +1,213 @@
|
|||||||
|
{
|
||||||
|
"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>",
|
||||||
|
"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