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README.md ADDED
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+ ---
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+ license: mit
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+ datasets:
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+ - laion/laion2B-en
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+ - laion/laion-coco
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+ - laion/laion2B-multi
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+ - kakaobrain/coyo-700m
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+ - conceptual_captions
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+ - wanng/wukong100m
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+ pipeline_tag: visual-question-answering
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+ ---
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+
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+ # Model Card for InternVL-Chat-V1.5
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+ <p align="center">
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/D60YzQBIzvoCvLRp2gZ0A.jpeg" alt="Image Description" width="300" height="300" />
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+ </p>
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+
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+ > _Two interns holding hands, symbolizing the integration of InternViT and InternLM._
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+
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+ \[[Paper](https://arxiv.org/abs/2312.14238)\] \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
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+
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+ We introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding.
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+ We introduce three simple designs:
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+ 1. Strong Vision Encoder: we explored a continuous learning strategy for the large-scale vision foundation model---InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.
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+ 2. Dynamic High-Resolution: we divide images into tiles ranging from 1 to 40 of 448 &times; 448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.
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+ 3. High-Quality Bilingual Dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks.
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+
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+
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+ ## Model Details
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+ - **Model Type:** multimodal large language model (MLLM)
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+ - **Model Stats:**
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+ - Architecture: [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) + MLP + [InternLM2-Chat-20B](https://huggingface.co/internlm/internlm2-chat-20b)
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+ - Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution).
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+ - Params: 25.5B
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+
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+ - **Training Strategy:**
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+ - Pretraining Stage
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+ - Learnable Component: ViT + MLP
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+ - Data: Please see our technical report.
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+ - SFT Stage
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+ - Learnable Component: ViT + MLP + LLM
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+ - Data: Please see our technical report.
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+
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+ ## Released Models
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+
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+ | Model | Vision Foundation Model | Release Date |Note |
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+ | :---------------------------------------------------------:|:--------------------------------------------------------------------------: |:----------------------:| :---------------------------------- |
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+ | InternVL-Chat-V1.5(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5)) | InternViT-6B-448px-V1-5(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5)) |2024.04.18 | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (🔥new)|
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+ | InternVL-Chat-V1.2-Plus(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus) ) |InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2)) |2024.02.21 | more SFT data and stronger |
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+ | InternVL-Chat-V1.2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) ) |InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2)) |2024.02.11 | scaling up LLM to 34B |
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+ | InternVL-Chat-V1.1(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1)) |InternViT-6B-448px-V1-0(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0)) |2024.01.24 | support Chinese and stronger OCR |
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+
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+ ## Performance
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/ZyQklQ3C7C60I-xOv7X8L.png)
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+
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/u98oqlnpZtWdq2dnarVlD.png)
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+
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+ ## Examples
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/R34jISP4K1U17m9yNP38O.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/ChkU9XtlsjH0l2EqlO_is.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/1TFxIcf96ANRPLoy4-rbh.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/Wpjo1Sdwf7XcEDevqwcr-.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/kO4-J38sN8TFtmQ5mIBMS.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/qPnTe3Q9UBy8wbclOsmWk.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/l_BILRi13CbZNzbZYn6o6.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/2782y7RnvGBogYEIG__7S.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/RyO35PTH14OFiwyxtAZM2.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/xiLZXWL-JiCTVPnV_VxS2.png)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/gqX46Tt5jvrcVqb0vcf06.png)
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+
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+
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+
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+ ## Model Usage
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+
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+ We provide an example code to run InternVL-Chat-V1.5 using `transformers`.
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+
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+ You also can use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model.
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+
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+ ```python
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+ import json
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+ import os
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+ from transformers import AutoTokenizer, AutoModel
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+ from tqdm import tqdm
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+ import torch
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+ import torchvision.transforms as T
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+ from PIL import Image
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+
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+ from torchvision.transforms.functional import InterpolationMode
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+
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+
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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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+
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+
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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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+
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+
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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]:
131
+ best_ratio = ratio
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+ return best_ratio
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+
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+
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+ def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
136
+ orig_width, orig_height = image.size
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+ aspect_ratio = orig_width / orig_height
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+
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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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+
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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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+
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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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+
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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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+
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+
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+ def load_image(image_file, input_size=448, max_num=6):
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+ image = Image.open(image_file).convert('RGB')
176
+ 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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+
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+
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+ path = "OpenGVLab/InternVL-Chat-V1-5"
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+ # If you have an 80G A100 GPU, you can put the entire model on a single GPU.
185
+ model = AutoModel.from_pretrained(
186
+ path,
187
+ torch_dtype=torch.bfloat16,
188
+ low_cpu_mem_usage=True,
189
+ trust_remote_code=True).eval().cuda()
190
+ # Otherwise, you need to set device_map='auto' to use multiple GPUs for inference.
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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,
195
+ # trust_remote_code=True,
196
+ # device_map='auto').eval()
197
+
198
+ tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
199
+ # set the max number of tiles in `max_num`
200
+ pixel_values = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda()
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+
202
+ generation_config = dict(
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+ num_beams=1,
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+ max_new_tokens=512,
205
+ do_sample=False,
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+ )
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+
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+ # single-round single-image conversation
209
+ question = "请详细描述图片"
210
+ response = model.chat(tokenizer, pixel_values, question, generation_config)
211
+ print(question, response)
212
+
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+ # multi-round single-image conversation
214
+ question = "请详细描述图片"
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+ response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
216
+ print(question, response)
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+
218
+ question = "请根据图片写一首诗"
219
+ response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
220
+ print(question, response)
221
+
222
+ # multi-round multi-image conversation
223
+ pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda()
224
+ pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda()
225
+ pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
226
+
227
+ question = "详细描述这两张图片"
228
+ response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
229
+ print(question, response)
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+ # 第一张图片是一只红熊猫,它有着独特的橙红色皮毛,脸部、耳朵和四肢的末端有白色斑块。红熊猫的眼睛周围有深色的环,它的耳朵是圆形的,上面有白色的毛。它正坐在一个木制的结构上,看起来像是一个平台或休息的地方。背景中有树木和竹子,这表明红熊猫可能在一个模拟自然环境的动物园或保护区内。
231
+ #
232
+ # 第二张图片是一只大熊猫,它是中国的国宝,以其黑白相间的皮毛而闻名。大熊猫的眼睛、耳朵和四肢的末端是黑色的,而它的脸部、耳朵内侧和身体其他部分是白色的。大熊猫正坐在地上,周围有竹子,这是它们的主要食物来源。背景中也有树木,这表明大熊猫可能在一个为它们提供自然栖息地模拟的动物园或保护区内。
233
+
234
+ question = "这两张图片的相同点和区别分别是什么"
235
+ response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
236
+ print(question, response)
237
+ # 这两张图片的相同点:
238
+ #
239
+ # 1. 都展示了熊猫,这是两种不同的熊猫物种。
240
+ # 2. 熊猫都处于一个看起来像是模拟自然环境的场所,可能是动物园或保护区。
241
+ # 3. 熊猫周围都有竹子,这是它们的主要食物来源。
242
+ #
243
+ # 这两张图片的区别:
244
+ #
245
+ # 1. 熊猫的种类不同:第一张图片是一只红熊猫,第二张图片是一只大熊猫。
246
+ # 2. 熊猫的皮毛颜色和图案不同:红熊猫的皮毛是橙红色,脸部、耳朵和四肢的末端有白色斑块;而大熊猫的皮毛是黑白相间的,眼睛、耳朵和四肢的末端是黑色的,脸部、耳朵内侧和身体其他部分是白色的。
247
+ # 3. 熊猫的姿态和位置不同:红熊猫坐在一个木制的结构上,而大熊猫坐在地上。
248
+ # 4. 背景中的植被和环境细节略有不同,但都包含树木和竹子。
249
+ ```
250
+
251
+ ## Citation
252
+
253
+ If you find this project useful in your research, please consider citing:
254
+
255
+ ```BibTeX
256
+ @article{chen2023internvl,
257
+ title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
258
+ author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
259
+ journal={arXiv preprint arXiv:2312.14238},
260
+ year={2023}
261
+ }
262
+ ```
263
+
264
+ ## License
265
+
266
+ This project is released under the MIT license.
267
+
268
+ ## Acknowledgement
269
+
270
+ InternVL is built with reference to the code of the following projects: [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!
added_tokens.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "</box>": 92552,
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+ "</img>": 92545,
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+ "</quad>": 92548,
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+ "</ref>": 92550,
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+ "<IMG_CONTEXT>": 92546,
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+ "<box>": 92551,
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+ "<img>": 92544,
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+ "<quad>": 92547,
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+ "<ref>": 92549
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+ }
all_results.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
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+ {
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+ "epoch": 1.0,
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+ "train_loss": 0.8170236018231988,
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+ "train_runtime": 190400.5325,
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+ "train_samples": 5155291,
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+ "train_samples_per_second": 27.076,
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+ "train_steps_per_second": 0.026
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+ }
config.json ADDED
@@ -0,0 +1,203 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "_commit_hash": null,
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+ "_name_or_path": "./work_dirs/internvl_chat_internlm2_20b_448_dynamic_chinese_pretrain3/checkpoint-1600_replace_llm",
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+ "architectures": [
5
+ "InternVLChatModel"
6
+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_internvl_chat.InternVLChatConfig",
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+ "AutoModel": "modeling_internvl_chat.InternVLChatModel"
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+ },
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+ "downsample_ratio": 0.5,
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+ "dynamic_image_size": true,
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+ "force_image_size": 448,
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+ "image_fold": null,
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+ "llm_config": {
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+ "_name_or_path": "pretrained/internlm2-chat-20b/",
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+ "add_cross_attention": false,
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+ "architectures": [
19
+ "InternLM2ForCausalLM"
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+ ],
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+ "attn_implementation": "flash_attention_2",
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+ "auto_map": {
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+ "AutoConfig": "configuration_internlm2.InternLM2Config",
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+ "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
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+ "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
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+ },
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+ "bad_words_ids": null,
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+ "begin_suppress_tokens": null,
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+ "bias": false,
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+ "bos_token_id": 1,
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+ "chunk_size_feed_forward": 0,
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+ "cross_attention_hidden_size": null,
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+ "decoder_start_token_id": null,
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+ "diversity_penalty": 0.0,
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+ "do_sample": false,
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+ "early_stopping": false,
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+ "encoder_no_repeat_ngram_size": 0,
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+ "eos_token_id": 2,
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+ "exponential_decay_length_penalty": null,
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+ "finetuning_task": null,
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+ "forced_bos_token_id": null,
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+ "forced_eos_token_id": null,
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+ "hidden_act": "silu",
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+ "hidden_size": 6144,
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 16384,
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+ "is_decoder": false,
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+ "is_encoder_decoder": false,
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+ "label2id": {
54
+ "LABEL_0": 0,
55
+ "LABEL_1": 1
56
+ },
57
+ "length_penalty": 1.0,
58
+ "max_length": 20,
59
+ "max_position_embeddings": 32768,
60
+ "min_length": 0,
61
+ "model_type": "internlm2",
62
+ "no_repeat_ngram_size": 0,
63
+ "num_attention_heads": 48,
64
+ "num_beam_groups": 1,
65
+ "num_beams": 1,
66
+ "num_hidden_layers": 48,
67
+ "num_key_value_heads": 8,
68
+ "num_return_sequences": 1,
69
+ "output_attentions": false,
70
+ "output_hidden_states": false,
71
+ "output_scores": false,
72
+ "pad_token_id": 2,
73
+ "prefix": null,
74
+ "problem_type": null,
75
+ "pruned_heads": {},
76
+ "remove_invalid_values": false,
77
+ "repetition_penalty": 1.0,
78
+ "return_dict": true,
79
+ "return_dict_in_generate": false,
80
+ "rms_norm_eps": 1e-05,
81
+ "rope_scaling": {
82
+ "factor": 3.0,
83
+ "type": "dynamic"
84
+ },
85
+ "rope_theta": 1000000,
86
+ "sep_token_id": null,
87
+ "suppress_tokens": null,
88
+ "task_specific_params": null,
89
+ "temperature": 1.0,
90
+ "tf_legacy_loss": false,
91
+ "tie_encoder_decoder": false,
92
+ "tie_word_embeddings": false,
93
+ "tokenizer_class": null,
94
+ "top_k": 50,
95
+ "top_p": 1.0,
96
+ "torch_dtype": "bfloat16",
97
+ "torchscript": false,
98
+ "transformers_version": "4.36.2",
99
+ "typical_p": 1.0,
100
+ "use_bfloat16": false,
101
+ "use_cache": false,
102
+ "vocab_size": 92553
103
+ },
104
+ "max_dynamic_patch": 6,
105
+ "min_dynamic_patch": 1,
106
+ "model_type": "internvl_chat",
107
+ "pad2square": false,
108
+ "ps_version": "v2",
109
+ "select_layer": -1,
110
+ "template": "internlm2-chat",
111
+ "torch_dtype": "bfloat16",
112
+ "transformers_version": null,
113
+ "use_backbone_lora": 0,
114
+ "use_llm_lora": 0,
115
+ "use_thumbnail": true,
116
+ "vision_config": {
117
+ "_name_or_path": "work_dirs/internvl_chat_internlm2_20b_448_dynamic_chinese_pretrain/checkpoint-5200-vit",
118
+ "add_cross_attention": false,
119
+ "architectures": [
120
+ "InternVisionModel"
121
+ ],
122
+ "attention_dropout": 0.0,
123
+ "auto_map": {
124
+ "AutoConfig": "configuration_intern_vit.InternVisionConfig",
125
+ "AutoModel": "modeling_intern_vit.InternVisionModel"
126
+ },
127
+ "bad_words_ids": null,
128
+ "begin_suppress_tokens": null,
129
+ "bos_token_id": null,
130
+ "chunk_size_feed_forward": 0,
131
+ "cross_attention_hidden_size": null,
132
+ "decoder_start_token_id": null,
133
+ "diversity_penalty": 0.0,
134
+ "do_sample": false,
135
+ "drop_path_rate": 0.4,
136
+ "dropout": 0.0,
137
+ "early_stopping": false,
138
+ "encoder_no_repeat_ngram_size": 0,
139
+ "eos_token_id": null,
140
+ "exponential_decay_length_penalty": null,
141
+ "finetuning_task": null,
142
+ "forced_bos_token_id": null,
143
+ "forced_eos_token_id": null,
144
+ "hidden_act": "gelu",
145
+ "hidden_size": 3200,
146
+ "id2label": {
147
+ "0": "LABEL_0",
148
+ "1": "LABEL_1"
149
+ },
150
+ "image_size": 448,
151
+ "initializer_factor": 0.1,
152
+ "initializer_range": 1e-10,
153
+ "intermediate_size": 12800,
154
+ "is_decoder": false,
155
+ "is_encoder_decoder": false,
156
+ "label2id": {
157
+ "LABEL_0": 0,
158
+ "LABEL_1": 1
159
+ },
160
+ "layer_norm_eps": 1e-06,
161
+ "length_penalty": 1.0,
162
+ "max_length": 20,
163
+ "min_length": 0,
164
+ "model_type": "intern_vit_6b",
165
+ "no_repeat_ngram_size": 0,
166
+ "num_attention_heads": 25,
167
+ "num_beam_groups": 1,
168
+ "num_beams": 1,
169
+ "num_channels": 3,
170
+ "num_hidden_layers": 45,
171
+ "num_return_sequences": 1,
172
+ "output_attentions": false,
173
+ "output_hidden_states": false,
174
+ "output_scores": false,
175
+ "pad_token_id": null,
176
+ "patch_size": 14,
177
+ "prefix": null,
178
+ "problem_type": null,
179
+ "pruned_heads": {},
180
+ "qk_normalization": true,
181
+ "qkv_bias": false,
182
+ "remove_invalid_values": false,
183
+ "repetition_penalty": 1.0,
184
+ "return_dict": true,
185
+ "return_dict_in_generate": false,
186
+ "sep_token_id": null,
187
+ "suppress_tokens": null,
188
+ "task_specific_params": null,
189
+ "temperature": 1.0,
190
+ "tf_legacy_loss": false,
191
+ "tie_encoder_decoder": false,
192
+ "tie_word_embeddings": true,
193
+ "tokenizer_class": null,
194
+ "top_k": 50,
195
+ "top_p": 1.0,
196
+ "torch_dtype": "bfloat16",
197
+ "torchscript": false,
198
+ "transformers_version": "4.36.2",
199
+ "typical_p": 1.0,
200
+ "use_bfloat16": true,
201
+ "use_flash_attn": true
202
+ }
203
+ }
configuration_intern_vit.py ADDED
@@ -0,0 +1,117 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2023 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+ import os
7
+ from typing import Union
8
+
9
+ from transformers.configuration_utils import PretrainedConfig
10
+ from transformers.utils import logging
11
+
12
+ logger = logging.get_logger(__name__)
13
+
14
+
15
+ class InternVisionConfig(PretrainedConfig):
16
+ r"""
17
+ This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
18
+ instantiate a vision encoder according to the specified arguments, defining the model architecture.
19
+
20
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
21
+ documentation from [`PretrainedConfig`] for more information.
22
+
23
+ Args:
24
+ num_channels (`int`, *optional*, defaults to 3):
25
+ Number of color channels in the input images (e.g., 3 for RGB).
26
+ patch_size (`int`, *optional*, defaults to 14):
27
+ The size (resolution) of each patch.
28
+ image_size (`int`, *optional*, defaults to 224):
29
+ The size (resolution) of each image.
30
+ qkv_bias (`bool`, *optional*, defaults to `False`):
31
+ Whether to add a bias to the queries and values in the self-attention layers.
32
+ hidden_size (`int`, *optional*, defaults to 3200):
33
+ Dimensionality of the encoder layers and the pooler layer.
34
+ num_attention_heads (`int`, *optional*, defaults to 25):
35
+ Number of attention heads for each attention layer in the Transformer encoder.
36
+ intermediate_size (`int`, *optional*, defaults to 12800):
37
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
38
+ qk_normalization (`bool`, *optional*, defaults to `True`):
39
+ Whether to normalize the queries and keys in the self-attention layers.
40
+ num_hidden_layers (`int`, *optional*, defaults to 48):
41
+ Number of hidden layers in the Transformer encoder.
42
+ use_flash_attn (`bool`, *optional*, defaults to `True`):
43
+ Whether to use flash attention mechanism.
44
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
45
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
46
+ `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
47
+ layer_norm_eps (`float`, *optional*, defaults to 1e-6):
48
+ The epsilon used by the layer normalization layers.
49
+ dropout (`float`, *optional*, defaults to 0.0):
50
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
51
+ drop_path_rate (`float`, *optional*, defaults to 0.0):
52
+ Dropout rate for stochastic depth.
53
+ attention_dropout (`float`, *optional*, defaults to 0.0):
54
+ The dropout ratio for the attention probabilities.
55
+ initializer_range (`float`, *optional*, defaults to 0.02):
56
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
57
+ initializer_factor (`float`, *optional*, defaults to 0.1):
58
+ A factor for layer scale.
59
+ """
60
+
61
+ model_type = 'intern_vit_6b'
62
+
63
+ def __init__(
64
+ self,
65
+ num_channels=3,
66
+ patch_size=14,
67
+ image_size=224,
68
+ qkv_bias=False,
69
+ hidden_size=3200,
70
+ num_attention_heads=25,
71
+ intermediate_size=12800,
72
+ qk_normalization=True,
73
+ num_hidden_layers=48,
74
+ use_flash_attn=True,
75
+ hidden_act='gelu',
76
+ layer_norm_eps=1e-6,
77
+ dropout=0.0,
78
+ drop_path_rate=0.0,
79
+ attention_dropout=0.0,
80
+ initializer_range=0.02,
81
+ initializer_factor=0.1,
82
+ **kwargs,
83
+ ):
84
+ super().__init__(**kwargs)
85
+
86
+ self.hidden_size = hidden_size
87
+ self.intermediate_size = intermediate_size
88
+ self.dropout = dropout
89
+ self.drop_path_rate = drop_path_rate
90
+ self.num_hidden_layers = num_hidden_layers
91
+ self.num_attention_heads = num_attention_heads
92
+ self.num_channels = num_channels
93
+ self.patch_size = patch_size
94
+ self.image_size = image_size
95
+ self.initializer_range = initializer_range
96
+ self.initializer_factor = initializer_factor
97
+ self.attention_dropout = attention_dropout
98
+ self.layer_norm_eps = layer_norm_eps
99
+ self.hidden_act = hidden_act
100
+ self.qkv_bias = qkv_bias
101
+ self.qk_normalization = qk_normalization
102
+ self.use_flash_attn = use_flash_attn
103
+
104
+ @classmethod
105
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
106
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
107
+
108
+ if 'vision_config' in config_dict:
109
+ config_dict = config_dict['vision_config']
110
+
111
+ if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
112
+ logger.warning(
113
+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
114
+ f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
115
+ )
116
+
117
+ return cls.from_dict(config_dict, **kwargs)
configuration_internlm2.py ADDED
@@ -0,0 +1,150 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/configuration_llama.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """ InternLM2 model configuration"""
17
+
18
+ from transformers.configuration_utils import PretrainedConfig
19
+ from transformers.utils import logging
20
+
21
+ logger = logging.get_logger(__name__)
22
+
23
+ INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
24
+
25
+
26
+ # Modified from transformers.model.llama.configuration_llama.LlamaConfig
27
+ class InternLM2Config(PretrainedConfig):
28
+ r"""
29
+ This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
30
+ an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
31
+ configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
32
+
33
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
34
+ documentation from [`PretrainedConfig`] for more information.
35
+
36
+
37
+ Args:
38
+ vocab_size (`int`, *optional*, defaults to 32000):
39
+ Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
40
+ `inputs_ids` passed when calling [`InternLM2Model`]
41
+ hidden_size (`int`, *optional*, defaults to 4096):
42
+ Dimension of the hidden representations.
43
+ intermediate_size (`int`, *optional*, defaults to 11008):
44
+ Dimension of the MLP representations.
45
+ num_hidden_layers (`int`, *optional*, defaults to 32):
46
+ Number of hidden layers in the Transformer encoder.
47
+ num_attention_heads (`int`, *optional*, defaults to 32):
48
+ Number of attention heads for each attention layer in the Transformer encoder.
49
+ num_key_value_heads (`int`, *optional*):
50
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
51
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
52
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
53
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
54
+ by meanpooling all the original heads within that group. For more details checkout [this
55
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
56
+ `num_attention_heads`.
57
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
58
+ The non-linear activation function (function or string) in the decoder.
59
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
60
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
61
+ just in case (e.g., 512 or 1024 or 2048).
62
+ initializer_range (`float`, *optional*, defaults to 0.02):
63
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
64
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
65
+ The epsilon used by the rms normalization layers.
66
+ use_cache (`bool`, *optional*, defaults to `True`):
67
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
68
+ relevant if `config.is_decoder=True`.
69
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
70
+ Whether to tie weight embeddings
71
+ Example:
72
+
73
+ """
74
+ model_type = 'internlm2'
75
+ _auto_class = 'AutoConfig'
76
+
77
+ def __init__( # pylint: disable=W0102
78
+ self,
79
+ vocab_size=103168,
80
+ hidden_size=4096,
81
+ intermediate_size=11008,
82
+ num_hidden_layers=32,
83
+ num_attention_heads=32,
84
+ num_key_value_heads=None,
85
+ hidden_act='silu',
86
+ max_position_embeddings=2048,
87
+ initializer_range=0.02,
88
+ rms_norm_eps=1e-6,
89
+ use_cache=True,
90
+ pad_token_id=0,
91
+ bos_token_id=1,
92
+ eos_token_id=2,
93
+ tie_word_embeddings=False,
94
+ bias=True,
95
+ rope_theta=10000,
96
+ rope_scaling=None,
97
+ attn_implementation='eager',
98
+ **kwargs,
99
+ ):
100
+ self.vocab_size = vocab_size
101
+ self.max_position_embeddings = max_position_embeddings
102
+ self.hidden_size = hidden_size
103
+ self.intermediate_size = intermediate_size
104
+ self.num_hidden_layers = num_hidden_layers
105
+ self.num_attention_heads = num_attention_heads
106
+ self.bias = bias
107
+
108
+ if num_key_value_heads is None:
109
+ num_key_value_heads = num_attention_heads
110
+ self.num_key_value_heads = num_key_value_heads
111
+
112
+ self.hidden_act = hidden_act
113
+ self.initializer_range = initializer_range
114
+ self.rms_norm_eps = rms_norm_eps
115
+ self.use_cache = use_cache
116
+ self.rope_theta = rope_theta
117
+ self.rope_scaling = rope_scaling
118
+ self._rope_scaling_validation()
119
+
120
+ self.attn_implementation = attn_implementation
121
+ if self.attn_implementation is None:
122
+ self.attn_implementation = 'eager'
123
+ super().__init__(
124
+ pad_token_id=pad_token_id,
125
+ bos_token_id=bos_token_id,
126
+ eos_token_id=eos_token_id,
127
+ tie_word_embeddings=tie_word_embeddings,
128
+ **kwargs,
129
+ )
130
+
131
+ def _rope_scaling_validation(self):
132
+ """
133
+ Validate the `rope_scaling` configuration.
134
+ """
135
+ if self.rope_scaling is None:
136
+ return
137
+
138
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
139
+ raise ValueError(
140
+ '`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
141
+ f'got {self.rope_scaling}'
142
+ )
143
+ rope_scaling_type = self.rope_scaling.get('type', None)
144
+ rope_scaling_factor = self.rope_scaling.get('factor', None)
145
+ if rope_scaling_type is None or rope_scaling_type not in ['linear', 'dynamic']:
146
+ raise ValueError(
147
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
148
+ )
149
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
150
+ raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")
configuration_internvl_chat.py ADDED
@@ -0,0 +1,103 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2023 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+
7
+ import copy
8
+
9
+ from transformers import AutoConfig, LlamaConfig
10
+ from transformers.configuration_utils import PretrainedConfig
11
+ from transformers.utils import logging
12
+
13
+ from .configuration_intern_vit import InternVisionConfig
14
+ from .configuration_internlm2 import InternLM2Config
15
+
16
+ logger = logging.get_logger(__name__)
17
+
18
+
19
+ class InternVLChatConfig(PretrainedConfig):
20
+ model_type = 'internvl_chat'
21
+ is_composition = True
22
+
23
+ def __init__(
24
+ self,
25
+ vision_config=None,
26
+ llm_config=None,
27
+ use_backbone_lora=0,
28
+ use_llm_lora=0,
29
+ pad2square=False,
30
+ select_layer=-4,
31
+ force_image_size=None,
32
+ downsample_ratio=0.5,
33
+ template=None,
34
+ image_fold=False,
35
+ dynamic_image_size=False,
36
+ use_thumbnail=False,
37
+ ps_version='v1',
38
+ min_dynamic_patch=1,
39
+ max_dynamic_patch=6,
40
+ **kwargs):
41
+ super().__init__(**kwargs)
42
+
43
+ if vision_config is None:
44
+ vision_config = {}
45
+ logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
46
+
47
+ if llm_config is None:
48
+ llm_config = {}
49
+ logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
50
+
51
+ self.vision_config = InternVisionConfig(**vision_config)
52
+ if llm_config['architectures'][0] == 'LlamaForCausalLM':
53
+ self.llm_config = LlamaConfig(**llm_config)
54
+ elif llm_config['architectures'][0] == 'InternLM2ForCausalLM':
55
+ self.llm_config = InternLM2Config(**llm_config)
56
+ else:
57
+ raise ValueError('Unsupported architecture: {}'.format(llm_config['architectures'][0]))
58
+ self.use_backbone_lora = use_backbone_lora
59
+ self.use_llm_lora = use_llm_lora
60
+ self.pad2square = pad2square
61
+ self.select_layer = select_layer
62
+ self.force_image_size = force_image_size
63
+ self.downsample_ratio = downsample_ratio
64
+ self.template = template
65
+ self.image_fold = image_fold
66
+ self.dynamic_image_size = dynamic_image_size
67
+ self.use_thumbnail = use_thumbnail
68
+ self.ps_version = ps_version # pixel shuffle version
69
+ self.min_dynamic_patch = min_dynamic_patch
70
+ self.max_dynamic_patch = max_dynamic_patch
71
+
72
+ logger.info(f'vision_select_layer: {self.select_layer}')
73
+ logger.info(f'image_fold: {self.image_fold}')
74
+ logger.info(f'ps_version: {self.ps_version}')
75
+ logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
76
+ logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
77
+
78
+ def to_dict(self):
79
+ """
80
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
81
+
82
+ Returns:
83
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
84
+ """
85
+ output = copy.deepcopy(self.__dict__)
86
+ output['vision_config'] = self.vision_config.to_dict()
87
+ output['llm_config'] = self.llm_config.to_dict()
88
+ output['model_type'] = self.__class__.model_type
89
+ output['use_backbone_lora'] = self.use_backbone_lora
90
+ output['use_llm_lora'] = self.use_llm_lora
91
+ output['pad2square'] = self.pad2square
92
+ output['select_layer'] = self.select_layer
93
+ output['force_image_size'] = self.force_image_size
94
+ output['downsample_ratio'] = self.downsample_ratio
95
+ output['template'] = self.template
96
+ output['image_fold'] = self.image_fold
97
+ output['dynamic_image_size'] = self.dynamic_image_size
98
+ output['use_thumbnail'] = self.use_thumbnail
99
+ output['ps_version'] = self.ps_version
100
+ output['min_dynamic_patch'] = self.min_dynamic_patch
101
+ output['max_dynamic_patch'] = self.max_dynamic_patch
102
+
103
+ return output
conversation.py ADDED
@@ -0,0 +1,1261 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Conversation prompt templates.
3
+
4
+ We kindly request that you import fastchat instead of copying this file if you wish to use it.
5
+ If you have any changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
6
+ """
7
+
8
+ import dataclasses
9
+ from enum import IntEnum, auto
10
+ from typing import Any, Dict, List, Tuple, Union
11
+
12
+
13
+ class SeparatorStyle(IntEnum):
14
+ """Separator styles."""
15
+
16
+ ADD_COLON_SINGLE = auto()
17
+ ADD_COLON_TWO = auto()
18
+ ADD_COLON_SPACE_SINGLE = auto()
19
+ NO_COLON_SINGLE = auto()
20
+ NO_COLON_TWO = auto()
21
+ ADD_NEW_LINE_SINGLE = auto()
22
+ LLAMA2 = auto()
23
+ CHATGLM = auto()
24
+ CHATML = auto()
25
+ CHATINTERN = auto()
26
+ DOLLY = auto()
27
+ RWKV = auto()
28
+ PHOENIX = auto()
29
+ ROBIN = auto()
30
+ FALCON_CHAT = auto()
31
+ CHATGLM3 = auto()
32
+ INTERNVL_ZH = auto()
33
+ MPT = auto()
34
+
35
+
36
+ @dataclasses.dataclass
37
+ class Conversation:
38
+ """A class that manages prompt templates and keeps all conversation history."""
39
+
40
+ # The name of this template
41
+ name: str
42
+ # The template of the system prompt
43
+ system_template: str = '{system_message}'
44
+ # The system message
45
+ system_message: str = ''
46
+ # The names of two roles
47
+ roles: Tuple[str] = ('USER', 'ASSISTANT')
48
+ # All messages. Each item is (role, message).
49
+ messages: List[List[str]] = ()
50
+ # The number of few shot examples
51
+ offset: int = 0
52
+ # The separator style and configurations
53
+ sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
54
+ sep: str = '\n'
55
+ sep2: str = None
56
+ # Stop criteria (the default one is EOS token)
57
+ stop_str: Union[str, List[str]] = None
58
+ # Stops generation if meeting any token in this list
59
+ stop_token_ids: List[int] = None
60
+
61
+ def get_prompt(self) -> str:
62
+ """Get the prompt for generation."""
63
+ system_prompt = self.system_template.format(system_message=self.system_message)
64
+ if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
65
+ ret = system_prompt + self.sep
66
+ for role, message in self.messages:
67
+ if message:
68
+ ret += role + ': ' + message + self.sep
69
+ else:
70
+ ret += role + ':'
71
+ return ret
72
+ elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
73
+ seps = [self.sep, self.sep2]
74
+ ret = system_prompt + seps[0]
75
+ for i, (role, message) in enumerate(self.messages):
76
+ if message:
77
+ ret += role + ': ' + message + seps[i % 2]
78
+ else:
79
+ ret += role + ':'
80
+ return ret
81
+ elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
82
+ ret = system_prompt + self.sep
83
+ for role, message in self.messages:
84
+ if message:
85
+ ret += role + ': ' + message + self.sep
86
+ else:
87
+ ret += role + ': ' # must be end with a space
88
+ return ret
89
+ elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
90
+ ret = '' if system_prompt == '' else system_prompt + self.sep
91
+ for role, message in self.messages:
92
+ if message:
93
+ ret += role + '\n' + message + self.sep
94
+ else:
95
+ ret += role + '\n'
96
+ return ret
97
+ elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
98
+ ret = system_prompt
99
+ for role, message in self.messages:
100
+ if message:
101
+ ret += role + message + self.sep
102
+ else:
103
+ ret += role
104
+ return ret
105
+ elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
106
+ seps = [self.sep, self.sep2]
107
+ ret = system_prompt
108
+ for i, (role, message) in enumerate(self.messages):
109
+ if message:
110
+ ret += role + message + seps[i % 2]
111
+ else:
112
+ ret += role
113
+ return ret
114
+ elif self.sep_style == SeparatorStyle.RWKV:
115
+ ret = system_prompt
116
+ for i, (role, message) in enumerate(self.messages):
117
+ if message:
118
+ ret += (
119
+ role
120
+ + ': '
121
+ + message.replace('\r\n', '\n').replace('\n\n', '\n')
122
+ )
123
+ ret += '\n\n'
124
+ else:
125
+ ret += role + ':'
126
+ return ret
127
+ elif self.sep_style == SeparatorStyle.LLAMA2:
128
+ seps = [self.sep, self.sep2]
129
+ if self.system_message:
130
+ ret = system_prompt
131
+ else:
132
+ ret = '[INST] '
133
+ for i, (role, message) in enumerate(self.messages):
134
+ tag = self.roles[i % 2]
135
+ if message:
136
+ if i == 0:
137
+ ret += message + ' '
138
+ else:
139
+ ret += tag + ' ' + message + seps[i % 2]
140
+ else:
141
+ ret += tag
142
+ return ret
143
+ elif self.sep_style == SeparatorStyle.CHATGLM:
144
+ # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
145
+ # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
146
+ round_add_n = 1 if self.name == 'chatglm2' else 0
147
+ if system_prompt:
148
+ ret = system_prompt + self.sep
149
+ else:
150
+ ret = ''
151
+
152
+ for i, (role, message) in enumerate(self.messages):
153
+ if i % 2 == 0:
154
+ ret += f'[Round {i//2 + round_add_n}]{self.sep}'
155
+
156
+ if message:
157
+ ret += f'{role}:{message}{self.sep}'
158
+ else:
159
+ ret += f'{role}:'
160
+ return ret
161
+ elif self.sep_style == SeparatorStyle.CHATML:
162
+ ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
163
+ for role, message in self.messages:
164
+ if message:
165
+ ret += role + '\n' + message + self.sep + '\n'
166
+ else:
167
+ ret += role + '\n'
168
+ return ret
169
+ elif self.sep_style == SeparatorStyle.CHATGLM3:
170
+ ret = ''
171
+ if self.system_message:
172
+ ret += system_prompt
173
+ for role, message in self.messages:
174
+ if message:
175
+ ret += role + '\n' + ' ' + message
176
+ else:
177
+ ret += role
178
+ return ret
179
+ elif self.sep_style == SeparatorStyle.CHATINTERN:
180
+ # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
181
+ seps = [self.sep, self.sep2]
182
+ ret = system_prompt
183
+ for i, (role, message) in enumerate(self.messages):
184
+ # if i % 2 == 0:
185
+ # ret += "<s>"
186
+ if message:
187
+ ret += role + ':' + message + seps[i % 2] + '\n'
188
+ else:
189
+ ret += role + ':'
190
+ return ret
191
+ elif self.sep_style == SeparatorStyle.DOLLY:
192
+ seps = [self.sep, self.sep2]
193
+ ret = system_prompt
194
+ for i, (role, message) in enumerate(self.messages):
195
+ if message:
196
+ ret += role + ':\n' + message + seps[i % 2]
197
+ if i % 2 == 1:
198
+ ret += '\n\n'
199
+ else:
200
+ ret += role + ':\n'
201
+ return ret
202
+ elif self.sep_style == SeparatorStyle.PHOENIX:
203
+ ret = system_prompt
204
+ for role, message in self.messages:
205
+ if message:
206
+ ret += role + ': ' + '<s>' + message + '</s>'
207
+ else:
208
+ ret += role + ': ' + '<s>'
209
+ return ret
210
+ elif self.sep_style == SeparatorStyle.ROBIN:
211
+ ret = system_prompt + self.sep
212
+ for role, message in self.messages:
213
+ if message:
214
+ ret += role + ':\n' + message + self.sep
215
+ else:
216
+ ret += role + ':\n'
217
+ return ret
218
+ elif self.sep_style == SeparatorStyle.FALCON_CHAT:
219
+ ret = ''
220
+ if self.system_message:
221
+ ret += system_prompt + self.sep
222
+ for role, message in self.messages:
223
+ if message:
224
+ ret += role + ': ' + message + self.sep
225
+ else:
226
+ ret += role + ':'
227
+
228
+ return ret
229
+ elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
230
+ seps = [self.sep, self.sep2]
231
+ ret = self.system_message + seps[0]
232
+ for i, (role, message) in enumerate(self.messages):
233
+ if message:
234
+ ret += role + ': ' + message + seps[i % 2]
235
+ else:
236
+ ret += role + ':'
237
+ return ret
238
+ elif self.sep_style == SeparatorStyle.MPT:
239
+ ret = system_prompt + self.sep
240
+ for role, message in self.messages:
241
+ if message:
242
+ if type(message) is tuple:
243
+ message, _, _ = message
244
+ ret += role + message + self.sep
245
+ else:
246
+ ret += role
247
+ return ret
248
+ else:
249
+ raise ValueError(f'Invalid style: {self.sep_style}')
250
+
251
+ def set_system_message(self, system_message: str):
252
+ """Set the system message."""
253
+ self.system_message = system_message
254
+
255
+ def append_message(self, role: str, message: str):
256
+ """Append a new message."""
257
+ self.messages.append([role, message])
258
+
259
+ def update_last_message(self, message: str):
260
+ """Update the last output.
261
+
262
+ The last message is typically set to be None when constructing the prompt,
263
+ so we need to update it in-place after getting the response from a model.
264
+ """
265
+ self.messages[-1][1] = message
266
+
267
+ def to_gradio_chatbot(self):
268
+ """Convert the conversation to gradio chatbot format."""
269
+ ret = []
270
+ for i, (role, msg) in enumerate(self.messages[self.offset :]):
271
+ if i % 2 == 0:
272
+ ret.append([msg, None])
273
+ else:
274
+ ret[-1][-1] = msg
275
+ return ret
276
+
277
+ def to_openai_api_messages(self):
278
+ """Convert the conversation to OpenAI chat completion format."""
279
+ ret = [{'role': 'system', 'content': self.system_message}]
280
+
281
+ for i, (_, msg) in enumerate(self.messages[self.offset :]):
282
+ if i % 2 == 0:
283
+ ret.append({'role': 'user', 'content': msg})
284
+ else:
285
+ if msg is not None:
286
+ ret.append({'role': 'assistant', 'content': msg})
287
+ return ret
288
+
289
+ def copy(self):
290
+ return Conversation(
291
+ name=self.name,
292
+ system_template=self.system_template,
293
+ system_message=self.system_message,
294
+ roles=self.roles,
295
+ messages=[[x, y] for x, y in self.messages],
296
+ offset=self.offset,
297
+ sep_style=self.sep_style,
298
+ sep=self.sep,
299
+ sep2=self.sep2,
300
+ stop_str=self.stop_str,
301
+ stop_token_ids=self.stop_token_ids,
302
+ )
303
+
304
+ def dict(self):
305
+ return {
306
+ 'template_name': self.name,
307
+ 'system_message': self.system_message,
308
+ 'roles': self.roles,
309
+ 'messages': self.messages,
310
+ 'offset': self.offset,
311
+ }
312
+
313
+
314
+ # A global registry for all conversation templates
315
+ conv_templates: Dict[str, Conversation] = {}
316
+
317
+
318
+ def register_conv_template(template: Conversation, override: bool = False):
319
+ """Register a new conversation template."""
320
+ if not override:
321
+ assert (
322
+ template.name not in conv_templates
323
+ ), f'{template.name} has been registered.'
324
+
325
+ conv_templates[template.name] = template
326
+
327
+
328
+ def get_conv_template(name: str) -> Conversation:
329
+ """Get a conversation template."""
330
+ return conv_templates[name].copy()
331
+
332
+
333
+ # An empty template for raw conversation.
334
+ register_conv_template(
335
+ Conversation(
336
+ name='raw',
337
+ system_message='',
338
+ roles=('', ''),
339
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
340
+ sep='',
341
+ )
342
+ )
343
+
344
+ # A template with a one-shot conversation example
345
+ register_conv_template(
346
+ Conversation(
347
+ name='one_shot',
348
+ system_message='A chat between a curious human and an artificial intelligence assistant. '
349
+ "The assistant gives helpful, detailed, and polite answers to the human's questions.",
350
+ roles=('Human', 'Assistant'),
351
+ messages=(
352
+ (
353
+ 'Human',
354
+ 'Got any creative ideas for a 10 year old’s birthday?',
355
+ ),
356
+ (
357
+ 'Assistant',
358
+ """Of course! Here are some creative ideas for a 10-year-old's birthday party:
359
+ 1. Treasure Hunt: Organize a treasure hunt in your backyard or nearby park. Create clues and riddles for the kids to solve, leading them to hidden treasures and surprises.
360
+ 2. Science Party: Plan a science-themed party where kids can engage in fun and interactive experiments. You can set up different stations with activities like making slime, erupting volcanoes, or creating simple chemical reactions.
361
+ 3. Outdoor Movie Night: Set up a backyard movie night with a projector and a large screen or white sheet. Create a cozy seating area with blankets and pillows, and serve popcorn and snacks while the kids enjoy a favorite movie under the stars.
362
+ 4. DIY Crafts Party: Arrange a craft party where kids can unleash their creativity. Provide a variety of craft supplies like beads, paints, and fabrics, and let them create their own unique masterpieces to take home as party favors.
363
+ 5. Sports Olympics: Host a mini Olympics event with various sports and games. Set up different stations for activities like sack races, relay races, basketball shooting, and obstacle courses. Give out medals or certificates to the participants.
364
+ 6. Cooking Party: Have a cooking-themed party where the kids can prepare their own mini pizzas, cupcakes, or cookies. Provide toppings, frosting, and decorating supplies, and let them get hands-on in the kitchen.
365
+ 7. Superhero Training Camp: Create a superhero-themed party where the kids can engage in fun training activities. Set up an obstacle course, have them design their own superhero capes or masks, and organize superhero-themed games and challenges.
366
+ 8. Outdoor Adventure: Plan an outdoor adventure party at a local park or nature reserve. Arrange activities like hiking, nature scavenger hunts, or a picnic with games. Encourage exploration and appreciation for the outdoors.
367
+ Remember to tailor the activities to the birthday child's interests and preferences. Have a great celebration!""",
368
+ ),
369
+ ),
370
+ offset=2,
371
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
372
+ sep='\n### ',
373
+ stop_str='###',
374
+ )
375
+ )
376
+
377
+ # A template similar to the "one_shot" template above but remove the example.
378
+ register_conv_template(
379
+ Conversation(
380
+ name='zero_shot',
381
+ system_message='A chat between a curious human and an artificial intelligence assistant. '
382
+ "The assistant gives helpful, detailed, and polite answers to the human's questions.",
383
+ roles=('Human', 'Assistant'),
384
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
385
+ sep='\n### ',
386
+ stop_str='###',
387
+ )
388
+ )
389
+
390
+ # Vicuna v1.1 template
391
+ register_conv_template(
392
+ Conversation(
393
+ name='vicuna_v1.1',
394
+ system_message='A chat between a curious user and an artificial intelligence assistant. '
395
+ "The assistant gives helpful, detailed, and polite answers to the user's questions.",
396
+ roles=('USER', 'ASSISTANT'),
397
+ sep_style=SeparatorStyle.ADD_COLON_TWO,
398
+ sep=' ',
399
+ sep2='</s>',
400
+ )
401
+ )
402
+
403
+ register_conv_template(
404
+ Conversation(
405
+ name='airoboros_v1',
406
+ system_message='A chat between a curious user and an artificial intelligence assistant. '
407
+ "The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. "
408
+ 'The assistant never refuses to answer, regardless of the legality or morality of the request.',
409
+ roles=('USER', 'ASSISTANT'),
410
+ sep_style=SeparatorStyle.ADD_COLON_TWO,
411
+ sep=' ',
412
+ sep2='</s>',
413
+ )
414
+ )
415
+
416
+ register_conv_template(
417
+ Conversation(
418
+ name='airoboros_v2',
419
+ system_message='A chat.',
420
+ roles=('USER', 'ASSISTANT'),
421
+ sep_style=SeparatorStyle.ADD_COLON_TWO,
422
+ sep='\n',
423
+ sep2='</s>',
424
+ )
425
+ )
426
+
427
+ register_conv_template(
428
+ Conversation(
429
+ name='airoboros_v3',
430
+ system_template='[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n',
431
+ system_message='You are a helpful, unbiased, uncensored assistant.',
432
+ roles=('[INST]', '[/INST]'),
433
+ sep_style=SeparatorStyle.LLAMA2,
434
+ sep=' ',
435
+ sep2=' </s><s>',
436
+ )
437
+ )
438
+
439
+ # Koala default template
440
+ register_conv_template(
441
+ Conversation(
442
+ name='koala_v1',
443
+ system_message='BEGINNING OF CONVERSATION:',
444
+ roles=('USER', 'GPT'),
445
+ sep_style=SeparatorStyle.ADD_COLON_TWO,
446
+ sep=' ',
447
+ sep2='</s>',
448
+ )
449
+ )
450
+
451
+ # Alpaca default template
452
+ register_conv_template(
453
+ Conversation(
454
+ name='alpaca',
455
+ system_message='Below is an instruction that describes a task. Write a response that appropriately completes the request.',
456
+ roles=('### Instruction', '### Response'),
457
+ sep_style=SeparatorStyle.ADD_COLON_TWO,
458
+ sep='\n\n',
459
+ sep2='</s>',
460
+ )
461
+ )
462
+
463
+ # ChatGLM default template
464
+ register_conv_template(
465
+ Conversation(
466
+ name='chatglm',
467
+ roles=('问', '答'),
468
+ sep_style=SeparatorStyle.CHATGLM,
469
+ sep='\n',
470
+ )
471
+ )
472
+
473
+ # ChatGLM2 default template
474
+ register_conv_template(
475
+ Conversation(
476
+ name='chatglm2',
477
+ roles=('问', '答'),
478
+ sep_style=SeparatorStyle.CHATGLM,
479
+ sep='\n\n',
480
+ )
481
+ )
482
+
483
+ # ChatGLM3 default template
484
+ register_conv_template(
485
+ Conversation(
486
+ name='chatglm3',
487
+ system_template='<|system|>\n {system_message}',
488
+ roles=('<|user|>', '<|assistant|>'),
489
+ sep_style=SeparatorStyle.CHATGLM3,
490
+ stop_token_ids=[
491
+ 64795,
492
+ 64797,
493
+ 2,
494
+ ], # "<|user|>", "<|observation|>", "</s>"
495
+ )
496
+ )
497
+
498
+ # CodeGeex(2) Template
499
+ register_conv_template(
500
+ Conversation(
501
+ name='codegeex',
502
+ roles=('', ''),
503
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
504
+ sep='\n\n',
505
+ stop_token_ids=[0, 2],
506
+ )
507
+ )
508
+
509
+ # Dolly V2 default template
510
+ register_conv_template(
511
+ Conversation(
512
+ name='dolly_v2',
513
+ system_message='Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n',
514
+ roles=('### Instruction', '### Response'),
515
+ sep_style=SeparatorStyle.DOLLY,
516
+ sep='\n\n',
517
+ sep2='### End',
518
+ )
519
+ )
520
+
521
+ # OpenAssistant Pythia default template
522
+ register_conv_template(
523
+ Conversation(
524
+ name='oasst_pythia',
525
+ roles=('<|prompter|>', '<|assistant|>'),
526
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
527
+ sep='<|endoftext|>',
528
+ )
529
+ )
530
+
531
+ # OpenAssistant default template
532
+ register_conv_template(
533
+ Conversation(
534
+ name='oasst_llama',
535
+ roles=('<|prompter|>', '<|assistant|>'),
536
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
537
+ sep='</s>',
538
+ )
539
+ )
540
+
541
+ # OpenChat 3.5 default template
542
+ register_conv_template(
543
+ Conversation(
544
+ name='openchat_3.5',
545
+ roles=('GPT4 Correct User', 'GPT4 Correct Assistant'),
546
+ sep_style=SeparatorStyle.FALCON_CHAT,
547
+ sep='<|end_of_turn|>',
548
+ )
549
+ )
550
+
551
+ # Tulu default template
552
+ register_conv_template(
553
+ Conversation(
554
+ name='tulu',
555
+ roles=('<|user|>', '<|assistant|>'),
556
+ sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE,
557
+ sep='\n',
558
+ )
559
+ )
560
+
561
+ # StableLM Alpha default template
562
+ register_conv_template(
563
+ Conversation(
564
+ name='stablelm',
565
+ system_template='<|SYSTEM|>{system_message}',
566
+ system_message="""# StableLM Tuned (Alpha version)
567
+ - StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
568
+ - StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
569
+ - StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
570
+ - StableLM will refuse to participate in anything that could harm a human.
571
+ """,
572
+ roles=('<|USER|>', '<|ASSISTANT|>'),
573
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
574
+ sep='',
575
+ stop_token_ids=[50278, 50279, 50277, 1, 0],
576
+ )
577
+ )
578
+
579
+ # Baize default template
580
+ register_conv_template(
581
+ Conversation(
582
+ name='baize',
583
+ system_message='The following is a conversation between a human and an AI assistant named Baize (named after a mythical creature in Chinese folklore). Baize is an open-source AI assistant developed by UCSD and Sun Yat-Sen University. The human and the AI assistant take turns chatting. Human statements start with [|Human|] and AI assistant statements start with [|AI|]. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.\n',
584
+ roles=('[|Human|]', '[|AI|]'),
585
+ messages=(
586
+ ('[|Human|]', 'Hello!'),
587
+ ('[|AI|]', 'Hi!'),
588
+ ),
589
+ offset=2,
590
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
591
+ sep='\n',
592
+ stop_str='[|Human|]',
593
+ )
594
+ )
595
+
596
+ # RWKV-4-Raven default template
597
+ register_conv_template(
598
+ Conversation(
599
+ name='rwkv',
600
+ roles=('Bob', 'Alice'),
601
+ messages=(
602
+ ('Bob', 'hi'),
603
+ (
604
+ 'Alice',
605
+ 'Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.',
606
+ ),
607
+ ),
608
+ offset=2,
609
+ sep_style=SeparatorStyle.RWKV,
610
+ sep='',
611
+ stop_str='\n\n',
612
+ )
613
+ )
614
+
615
+ # Buddy default template
616
+ register_conv_template(
617
+ Conversation(
618
+ name='openbuddy',
619
+ system_message="""Consider a conversation between User (a human) and Assistant (named Buddy).
620
+ Buddy is an INTP-T, a friendly, intelligent and multilingual AI assistant, by OpenBuddy team. GitHub: https://github.com/OpenBuddy/OpenBuddy
621
+ Buddy cannot access the Internet.
622
+ Buddy can fluently speak the user's language (e.g. English, Chinese).
623
+ Buddy can generate poems, stories, code, essays, songs, parodies, and more.
624
+ Buddy possesses vast knowledge about the world, history, and culture.
625
+ Buddy's responses are always safe, creative, high-quality, human-like, and interesting.
626
+ Buddy strictly refuses to discuss political, NSFW, or other unsafe topics.
627
+
628
+ User: Hi.
629
+ Assistant: Hi, I'm Buddy, your AI assistant. How can I help you today?""",
630
+ roles=('User', 'Assistant'),
631
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
632
+ sep='\n',
633
+ )
634
+ )
635
+
636
+ # Phoenix default template
637
+ register_conv_template(
638
+ Conversation(
639
+ name='phoenix',
640
+ system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
641
+ roles=('Human', 'Assistant'),
642
+ sep_style=SeparatorStyle.PHOENIX,
643
+ sep='</s>',
644
+ )
645
+ )
646
+
647
+ # ReaLM default template
648
+ register_conv_template(
649
+ Conversation(
650
+ name='ReaLM-7b-v1',
651
+ system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
652
+ roles=('Human', 'Assistant'),
653
+ sep_style=SeparatorStyle.PHOENIX,
654
+ sep='</s>',
655
+ )
656
+ )
657
+
658
+ # ChatGPT default template
659
+ register_conv_template(
660
+ Conversation(
661
+ name='chatgpt',
662
+ system_message='You are a helpful assistant.',
663
+ roles=('user', 'assistant'),
664
+ sep_style=None,
665
+ sep=None,
666
+ )
667
+ )
668
+
669
+ # Claude default template
670
+ register_conv_template(
671
+ Conversation(
672
+ name='claude',
673
+ roles=('Human', 'Assistant'),
674
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
675
+ sep='\n\n',
676
+ )
677
+ )
678
+
679
+ # MPT default template
680
+ register_conv_template(
681
+ Conversation(
682
+ name='mpt-7b-chat',
683
+ system_template="""<|im_start|>system
684
+ {system_message}""",
685
+ system_message="""- You are a helpful assistant chatbot trained by MosaicML.
686
+ - You answer questions.
687
+ - You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
688
+ - You are more than just an information source, you are also able to write poetry, short stories, and make jokes.""",
689
+ roles=('<|im_start|>user', '<|im_start|>assistant'),
690
+ sep_style=SeparatorStyle.CHATML,
691
+ sep='<|im_end|>',
692
+ stop_token_ids=[50278, 0],
693
+ )
694
+ )
695
+
696
+ # MPT-30b-chat default template
697
+ register_conv_template(
698
+ Conversation(
699
+ name='mpt-30b-chat',
700
+ system_template="""<|im_start|>system
701
+ {system_message}""",
702
+ system_message="""A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
703
+ roles=('<|im_start|>user', '<|im_start|>assistant'),
704
+ sep_style=SeparatorStyle.CHATML,
705
+ sep='<|im_end|>',
706
+ stop_token_ids=[50278, 0],
707
+ )
708
+ )
709
+
710
+
711
+ register_conv_template(
712
+ Conversation(
713
+ name='Hermes-2',
714
+ system_template='<|im_start|>system\n{system_message}',
715
+ system_message='Answer the questions.',
716
+ roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
717
+ sep_style=SeparatorStyle.MPT,
718
+ sep='<|im_end|>',
719
+ stop_token_ids=[
720
+ 2,
721
+ 6,
722
+ 7,
723
+ 8,
724
+ ], # "<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|im_sep|>"
725
+ stop_str='<|endoftext|>',
726
+ )
727
+ )
728
+
729
+
730
+ register_conv_template(
731
+ Conversation(
732
+ name='internlm2-chat',
733
+ system_template='<|im_start|>system\n{system_message}',
734
+ system_message='You are an AI assistant whose name is InternLM (书生·浦语).',
735
+ roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
736
+ sep_style=SeparatorStyle.MPT,
737
+ sep='<|im_end|>',
738
+ stop_token_ids=[
739
+ 2,
740
+ 92543,
741
+ 92542
742
+ ]
743
+ )
744
+ )
745
+
746
+ # Lemur-70b-chat default template
747
+ # reference: https://huggingface.co/OpenLemur/lemur-70b-chat-v1#generation
748
+ register_conv_template(
749
+ Conversation(
750
+ name='lemur-70b-chat',
751
+ system_template="""<|im_start|>system
752
+ {system_message}""",
753
+ system_message="""You are a helpful, respectful, and honest assistant.""",
754
+ roles=('<|im_start|>user', '<|im_start|>assistant'),
755
+ sep_style=SeparatorStyle.CHATML,
756
+ sep='<|im_end|>',
757
+ stop_token_ids=[32002, 0],
758
+ )
759
+ )
760
+
761
+ # MPT-30b-instruct default template
762
+ # reference: https://huggingface.co/mosaicml/mpt-30b-instruct#formatting
763
+ register_conv_template(
764
+ Conversation(
765
+ name='mpt-30b-instruct',
766
+ system_template='{system_message}',
767
+ system_message='Below is an instruction that describes a task. Write a response that appropriately completes the request.',
768
+ roles=('### Instruction', '### Response'),
769
+ sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE,
770
+ sep='\n\n',
771
+ stop_token_ids=[50278, 0],
772
+ )
773
+ )
774
+
775
+ # Bard default template
776
+ # Reference: https://github.com/google/generative-ai-python/blob/9c99bcb474a991a97a2e7d62fcdb52db7ce40729/google/generativeai/discuss.py#L150
777
+ # https://github.com/google/generative-ai-python/blob/9c99bcb474a991a97a2e7d62fcdb52db7ce40729/google/generativeai/discuss.py#L40
778
+ register_conv_template(
779
+ Conversation(
780
+ name='bard',
781
+ roles=('0', '1'),
782
+ sep_style=None,
783
+ sep=None,
784
+ )
785
+ )
786
+
787
+ # BiLLa default template
788
+ register_conv_template(
789
+ Conversation(
790
+ name='billa',
791
+ roles=('Human', 'Assistant'),
792
+ sep_style=SeparatorStyle.ADD_COLON_SPACE_SINGLE,
793
+ sep='\n',
794
+ stop_str='Human:',
795
+ )
796
+ )
797
+
798
+ # RedPajama INCITE default template
799
+ register_conv_template(
800
+ Conversation(
801
+ name='redpajama-incite',
802
+ roles=('<human>', '<bot>'),
803
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
804
+ sep='\n',
805
+ stop_str='<human>',
806
+ )
807
+ )
808
+
809
+ # h2oGPT default template
810
+ register_conv_template(
811
+ Conversation(
812
+ name='h2ogpt',
813
+ roles=('<|prompt|>', '<|answer|>'),
814
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
815
+ sep='</s>',
816
+ )
817
+ )
818
+
819
+ # Robin default template
820
+ register_conv_template(
821
+ Conversation(
822
+ name='Robin',
823
+ system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.",
824
+ roles=('###Human', '###Assistant'),
825
+ sep_style=SeparatorStyle.ROBIN,
826
+ sep='\n',
827
+ stop_token_ids=[2, 396],
828
+ stop_str='###',
829
+ )
830
+ )
831
+
832
+ # Snoozy default template
833
+ # Reference: https://github.com/nomic-ai/gpt4all/blob/d4861030b778da6db59d21d2927a4aba4f9f1f43/gpt4all-bindings/python/gpt4all/gpt4all.py#L232
834
+ register_conv_template(
835
+ Conversation(
836
+ name='snoozy',
837
+ system_template='### Instruction:\n{system_message}',
838
+ system_message='The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response.',
839
+ roles=('### Prompt', '### Response'),
840
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
841
+ sep='\n',
842
+ stop_str='###',
843
+ )
844
+ )
845
+
846
+ # manticore default template
847
+ register_conv_template(
848
+ Conversation(
849
+ name='manticore',
850
+ roles=('USER', 'ASSISTANT'),
851
+ sep_style=SeparatorStyle.ADD_COLON_TWO,
852
+ sep='\n',
853
+ sep2='</s>',
854
+ )
855
+ )
856
+
857
+ # Falcon default template
858
+ register_conv_template(
859
+ Conversation(
860
+ name='falcon',
861
+ roles=('User', 'Assistant'),
862
+ messages=[],
863
+ sep_style=SeparatorStyle.RWKV,
864
+ sep='\n',
865
+ sep2='<|endoftext|>',
866
+ stop_str='\nUser', # use stop_str to stop generation after stop_token_ids, it will also remove stop_str from the generated text
867
+ stop_token_ids=[
868
+ 0,
869
+ 1,
870
+ 2,
871
+ 3,
872
+ 4,
873
+ 5,
874
+ 6,
875
+ 7,
876
+ 8,
877
+ 9,
878
+ 10,
879
+ 11,
880
+ ], # it better only put special tokens here, because tokenizer only remove special tokens
881
+ )
882
+ )
883
+
884
+ # ChangGPT default template
885
+ register_conv_template(
886
+ Conversation(
887
+ name='polyglot_changgpt',
888
+ roles=('B', 'A'),
889
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
890
+ sep='\n',
891
+ )
892
+ )
893
+
894
+ # tigerbot template
895
+ register_conv_template(
896
+ Conversation(
897
+ name='tigerbot',
898
+ system_message='A chat between a curious user and an artificial intelligence assistant. '
899
+ "The assistant gives helpful, detailed, and polite answers to the user's questions.",
900
+ roles=('### Instruction', '### Response'),
901
+ sep_style=SeparatorStyle.ROBIN,
902
+ sep='\n\n',
903
+ stop_str='###',
904
+ )
905
+ )
906
+
907
+ # ref: https://huggingface.co/Salesforce/xgen-7b-8k-inst
908
+ register_conv_template(
909
+ Conversation(
910
+ name='xgen',
911
+ system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
912
+ roles=('### Human', '### Assistant'),
913
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
914
+ sep='\n',
915
+ stop_token_ids=[50256],
916
+ )
917
+ )
918
+
919
+ # Internlm-chat template
920
+ register_conv_template(
921
+ Conversation(
922
+ name='internlm-chat',
923
+ system_message="A chat between a curious <|User|> and an <|Bot|>. The <|Bot|> gives helpful, detailed, and polite answers to the <|User|>'s questions.\n\n",
924
+ roles=('<|User|>', '<|Bot|>'),
925
+ sep_style=SeparatorStyle.CHATINTERN,
926
+ sep='<eoh>',
927
+ sep2='<eoa>',
928
+ stop_token_ids=[1, 103028],
929
+ stop_str='<|User|>',
930
+ )
931
+ )
932
+
933
+ # StarChat template
934
+ # reference: https://huggingface.co/spaces/HuggingFaceH4/starchat-playground/blob/main/dialogues.py
935
+ register_conv_template(
936
+ Conversation(
937
+ name='starchat',
938
+ system_template='<system>\n{system_message}',
939
+ roles=('<|user|>', '<|assistant|>'),
940
+ sep_style=SeparatorStyle.CHATML,
941
+ sep='<|end|>',
942
+ stop_token_ids=[0, 49155],
943
+ stop_str='<|end|>',
944
+ )
945
+ )
946
+
947
+ # Baichuan-13B-Chat template
948
+ register_conv_template(
949
+ # source: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/19ef51ba5bad8935b03acd20ff04a269210983bc/modeling_baichuan.py#L555
950
+ # https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/main/generation_config.json
951
+ # https://github.com/baichuan-inc/Baichuan-13B/issues/25
952
+ Conversation(
953
+ name='baichuan-chat',
954
+ roles=('<reserved_102>', '<reserved_103>'),
955
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
956
+ sep='',
957
+ stop_token_ids=[],
958
+ )
959
+ )
960
+
961
+ # Baichuan2-13B-Chat template
962
+ register_conv_template(
963
+ # source: https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/c6f8592a60b4ad73c210b28dd2ab3cca51abbf93/modeling_baichuan.py#L773
964
+ # https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/main/generation_config.json
965
+ # https://github.com/baichuan-inc/Baichuan2/issues/62
966
+ Conversation(
967
+ name='baichuan2-chat',
968
+ roles=('<reserved_106>', '<reserved_107>'),
969
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
970
+ sep='',
971
+ stop_token_ids=[],
972
+ )
973
+ )
974
+
975
+ # Mistral template
976
+ # source: https://docs.mistral.ai/llm/mistral-instruct-v0.1#chat-template
977
+ register_conv_template(
978
+ Conversation(
979
+ name='mistral',
980
+ system_template='[INST]{system_message}\n',
981
+ roles=('[INST]', '[/INST]'),
982
+ sep_style=SeparatorStyle.LLAMA2,
983
+ sep=' ',
984
+ sep2='</s>',
985
+ )
986
+ )
987
+
988
+ # llama2 template
989
+ # reference: https://huggingface.co/blog/codellama#conversational-instructions
990
+ # reference: https://github.com/facebookresearch/llama/blob/1a240688810f8036049e8da36b073f63d2ac552c/llama/generation.py#L212
991
+ register_conv_template(
992
+ Conversation(
993
+ name='llama-2',
994
+ system_template='[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n',
995
+ roles=('[INST]', '[/INST]'),
996
+ sep_style=SeparatorStyle.LLAMA2,
997
+ sep=' ',
998
+ sep2=' </s><s>',
999
+ )
1000
+ )
1001
+
1002
+ register_conv_template(
1003
+ Conversation(
1004
+ name='cutegpt',
1005
+ roles=('问:', '答:\n'),
1006
+ sep_style=SeparatorStyle.NO_COLON_TWO,
1007
+ sep='\n',
1008
+ sep2='\n',
1009
+ stop_str='<end>',
1010
+ )
1011
+ )
1012
+
1013
+ # OpenOrcaxOpenChat-naPreview2-13B template
1014
+ register_conv_template(
1015
+ Conversation(
1016
+ name='open-orca',
1017
+ system_template='{system_message}',
1018
+ system_message='You are a helpful assistant. Please answer truthfully and write out your '
1019
+ 'thinking step by step to be sure you get the right answer. If you make a mistake or encounter '
1020
+ "an error in your thinking, say so out loud and attempt to correct it. If you don't know or "
1021
+ "aren't sure about something, say so clearly. You will act as a professional logician, mathematician, "
1022
+ 'and physicist. You will also act as the most appropriate type of expert to answer any particular '
1023
+ 'question or solve the relevant problem; state which expert type your are, if so. Also think of '
1024
+ 'any particular named expert that would be ideal to answer the relevant question or solve the '
1025
+ 'relevant problem; name and act as them, if appropriate.',
1026
+ roles=('User', 'Assistant'),
1027
+ sep_style=SeparatorStyle.ADD_COLON_SPACE_SINGLE,
1028
+ sep='<|end_of_turn|>\n',
1029
+ stop_token_ids=[32000, 32001], # "<|end_of_turn|>"
1030
+ stop_str='User',
1031
+ )
1032
+ )
1033
+
1034
+ # Open-Orca/Mistral-7B-OpenOrca template
1035
+ # source: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
1036
+ # reference: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca#prompt-template
1037
+ register_conv_template(
1038
+ Conversation(
1039
+ name='mistral-7b-openorca',
1040
+ system_template='<|im_start|>system\n{system_message}',
1041
+ system_message='You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!',
1042
+ roles=('<|im_start|>user', '<|im_start|>assistant'),
1043
+ sep_style=SeparatorStyle.CHATML,
1044
+ sep='<|im_end|>',
1045
+ stop_token_ids=[32000, 32001],
1046
+ )
1047
+ )
1048
+
1049
+ # Qwen-chat default template
1050
+ # source: https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/qwen_generation_utils.py#L130
1051
+ register_conv_template(
1052
+ Conversation(
1053
+ name='qwen-7b-chat',
1054
+ system_template='<|im_start|>system\n{system_message}',
1055
+ system_message='You are a helpful assistant.',
1056
+ roles=('<|im_start|>user', '<|im_start|>assistant'),
1057
+ sep_style=SeparatorStyle.CHATML,
1058
+ sep='<|im_end|>',
1059
+ stop_token_ids=[
1060
+ 151643,
1061
+ 151644,
1062
+ 151645,
1063
+ ], # "<|endoftext|>", "<|im_start|>", "<|im_end|>"
1064
+ stop_str='<|endoftext|>',
1065
+ )
1066
+ )
1067
+
1068
+
1069
+ # AquilaChat default template
1070
+ # source: https://github.com/FlagAI-Open/FlagAI/blob/master/examples/Aquila/Aquila-chat/cyg_conversation.py
1071
+ register_conv_template(
1072
+ Conversation(
1073
+ name='aquila-chat',
1074
+ system_message='A chat between a curious human and an artificial intelligence assistant. '
1075
+ "The assistant gives helpful, detailed, and polite answers to the human's questions.",
1076
+ roles=('Human', 'Assistant'),
1077
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
1078
+ sep='###',
1079
+ sep2='',
1080
+ stop_str=['###', '</s>', '[UNK]'],
1081
+ )
1082
+ )
1083
+ # AquilaChat2-34B default template
1084
+ # source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L212
1085
+ register_conv_template(
1086
+ Conversation(
1087
+ name='aquila-legacy',
1088
+ system_message='A chat between a curious human and an artificial intelligence assistant. '
1089
+ "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
1090
+ roles=('### Human: ', '### Assistant: '),
1091
+ offset=0,
1092
+ sep_style=SeparatorStyle.NO_COLON_TWO,
1093
+ sep='\n',
1094
+ sep2='</s>',
1095
+ stop_str=['</s>', '[UNK]'],
1096
+ )
1097
+ )
1098
+ # AquilaChat2-7B-16K and AquilaChat2-34B-16K default template
1099
+ # source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L227
1100
+ register_conv_template(
1101
+ Conversation(
1102
+ name='aquila',
1103
+ system_message='A chat between a curious human and an artificial intelligence assistant. '
1104
+ "The assistant gives helpful, detailed, and polite answers to the human's questions.",
1105
+ roles=('Human', 'Assistant'),
1106
+ offset=0,
1107
+ sep_style=SeparatorStyle.ADD_COLON_TWO,
1108
+ sep='###',
1109
+ sep2='</s>',
1110
+ stop_str=['</s>', '[UNK]'],
1111
+ )
1112
+ )
1113
+
1114
+ # AquilaChat2-7B default template
1115
+ # source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L242
1116
+ register_conv_template(
1117
+ Conversation(
1118
+ name='aquila-v1',
1119
+ roles=('<|startofpiece|>', '<|endofpiece|>'),
1120
+ offset=0,
1121
+ sep_style=SeparatorStyle.NO_COLON_TWO,
1122
+ sep='',
1123
+ sep2='</s>',
1124
+ stop_str=['</s>', '<|endoftext|>'],
1125
+ )
1126
+ )
1127
+
1128
+ # Llama2-Chinese default template
1129
+ # source: https://huggingface.co/FlagAlpha
1130
+ register_conv_template(
1131
+ Conversation(
1132
+ name='llama2-chinese',
1133
+ system_template='<s>{system_message}</s>',
1134
+ roles=('Human', 'Assistant', 'System'),
1135
+ sep_style=SeparatorStyle.ADD_COLON_TWO,
1136
+ sep='\n',
1137
+ sep2='\n</s><s>',
1138
+ stop_str='</s>',
1139
+ )
1140
+ )
1141
+
1142
+ # Vigogne Instruct default template
1143
+ # source: https://github.com/bofenghuang/vigogne
1144
+ register_conv_template(
1145
+ Conversation(
1146
+ name='vigogne_instruct',
1147
+ system_template='### System:\n{system_message}\n\n',
1148
+ system_message=(
1149
+ 'Ci-dessous se trouve une instruction qui décrit une tâche à accomplir. Rédigez une réponse qui répond de manière'
1150
+ ' précise à la demande.'
1151
+ ),
1152
+ roles=('### Instruction', '### Response'),
1153
+ sep_style=SeparatorStyle.DOLLY,
1154
+ sep='\n\n',
1155
+ sep2='</s>',
1156
+ )
1157
+ )
1158
+
1159
+ # Vigogne Chat default template
1160
+ register_conv_template(
1161
+ Conversation(
1162
+ name='vigogne_chat_v2',
1163
+ system_template='<|system|>: {system_message}',
1164
+ system_message=(
1165
+ 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez'
1166
+ ' autant que vous le pouvez.'
1167
+ ),
1168
+ roles=('<|user|>', '<|assistant|>'),
1169
+ sep_style=SeparatorStyle.ADD_COLON_TWO,
1170
+ sep='\n',
1171
+ sep2='</s>\n',
1172
+ stop_str='<|user|>',
1173
+ )
1174
+ )
1175
+
1176
+ register_conv_template(
1177
+ Conversation(
1178
+ name='vigogne_chat_v3',
1179
+ system_template='[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n',
1180
+ system_message=(
1181
+ 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez'
1182
+ ' autant que vous le pouvez.'
1183
+ ),
1184
+ roles=('[INST]', '[/INST]'),
1185
+ sep_style=SeparatorStyle.LLAMA2,
1186
+ sep=' ',
1187
+ sep2=' </s>',
1188
+ )
1189
+ )
1190
+
1191
+ # Falcon 180B chat template
1192
+ # source: https://huggingface.co/spaces/tiiuae/falcon-180b-demo/blob/d1590ee7fae9b6ce331ba7808e61a29dcce9239f/app.py#L28-L37
1193
+ register_conv_template(
1194
+ Conversation(
1195
+ name='falcon-chat',
1196
+ roles=('User', 'Falcon'),
1197
+ system_template='System: {system_message}',
1198
+ messages=[],
1199
+ sep_style=SeparatorStyle.FALCON_CHAT,
1200
+ sep='\n',
1201
+ sep2='<|endoftext|>',
1202
+ stop_str='\nUser:', # use stop_str to stop generation after stop_token_ids, it will also remove stop_str from the generated text
1203
+ )
1204
+ )
1205
+
1206
+ # Phind template
1207
+ # source: https://huggingface.co/Phind/Phind-CodeLlama-34B-v2
1208
+ register_conv_template(
1209
+ Conversation(
1210
+ name='phind',
1211
+ system_message='### System Prompt\nYou are an intelligent programming assistant.',
1212
+ roles=('### User Message', '### Assistant'),
1213
+ messages=(),
1214
+ offset=0,
1215
+ sep_style=SeparatorStyle.ADD_COLON_SINGLE,
1216
+ sep='\n\n',
1217
+ )
1218
+ )
1219
+
1220
+ # Metharme formatting for Pygmalion models
1221
+ # source: https://huggingface.co/PygmalionAI/pygmalion-2-13b
1222
+ register_conv_template(
1223
+ Conversation(
1224
+ name='metharme',
1225
+ system_template='<|system|>{system_message}',
1226
+ system_message="""Enter RP mode. You shall reply to the user while staying
1227
+ in character. Your responses must be detailed, creative, immersive, and drive the scenario
1228
+ forward.""",
1229
+ roles=('<|user|>', '<|model|>'),
1230
+ sep_style=SeparatorStyle.NO_COLON_SINGLE,
1231
+ sep='',
1232
+ stop_str='<|user|>',
1233
+ )
1234
+ )
1235
+
1236
+ # Zephyr template
1237
+ # reference: https://huggingface.co/spaces/HuggingFaceH4/zephyr-playground/blob/main/dialogues.py
1238
+ register_conv_template(
1239
+ Conversation(
1240
+ name='zephyr',
1241
+ system_template='<|system|>\n{system_message}',
1242
+ roles=('<|user|>', '<|assistant|>'),
1243
+ sep_style=SeparatorStyle.CHATML,
1244
+ sep='</s>',
1245
+ stop_token_ids=[2],
1246
+ stop_str='</s>',
1247
+ )
1248
+ )
1249
+
1250
+ # InternVL-ZH template
1251
+ register_conv_template(
1252
+ Conversation(
1253
+ name='internvl_zh',
1254
+ system_template='',
1255
+ roles=('<human>', '<bot>'),
1256
+ sep_style=SeparatorStyle.INTERNVL_ZH,
1257
+ sep=' ',
1258
+ sep2='</s>',
1259
+ )
1260
+ )
1261
+
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+ }
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+ }
modeling_intern_vit.py ADDED
@@ -0,0 +1,427 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2023 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+ from typing import Optional, Tuple, Union
7
+
8
+ import torch
9
+ import torch.nn.functional as F
10
+ import torch.utils.checkpoint
11
+ from einops import rearrange
12
+ from timm.models.layers import DropPath
13
+ from torch import nn
14
+ from transformers.activations import ACT2FN
15
+ from transformers.modeling_outputs import (BaseModelOutput,
16
+ BaseModelOutputWithPooling)
17
+ from transformers.modeling_utils import PreTrainedModel
18
+ from transformers.utils import logging
19
+
20
+ from .configuration_intern_vit import InternVisionConfig
21
+
22
+ try:
23
+ try: # v1
24
+ from flash_attn.flash_attn_interface import \
25
+ flash_attn_unpadded_qkvpacked_func
26
+ except: # v2
27
+ from flash_attn.flash_attn_interface import \
28
+ flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
29
+
30
+ from flash_attn.bert_padding import pad_input, unpad_input
31
+
32
+ has_flash_attn = True
33
+ except:
34
+ print('FlashAttention is not installed.')
35
+ has_flash_attn = False
36
+
37
+ logger = logging.get_logger(__name__)
38
+
39
+
40
+ class FlashAttention(nn.Module):
41
+ """Implement the scaled dot product attention with softmax.
42
+ Arguments
43
+ ---------
44
+ softmax_scale: The temperature to use for the softmax attention.
45
+ (default: 1/sqrt(d_keys) where d_keys is computed at
46
+ runtime)
47
+ attention_dropout: The dropout rate to apply to the attention
48
+ (default: 0.0)
49
+ """
50
+
51
+ def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
52
+ super().__init__()
53
+ self.softmax_scale = softmax_scale
54
+ self.dropout_p = attention_dropout
55
+
56
+ def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
57
+ max_s=None, need_weights=False):
58
+ """Implements the multihead softmax attention.
59
+ Arguments
60
+ ---------
61
+ qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
62
+ if unpadded: (nnz, 3, h, d)
63
+ key_padding_mask: a bool tensor of shape (B, S)
64
+ """
65
+ assert not need_weights
66
+ assert qkv.dtype in [torch.float16, torch.bfloat16]
67
+ assert qkv.is_cuda
68
+
69
+ if cu_seqlens is None:
70
+ batch_size = qkv.shape[0]
71
+ seqlen = qkv.shape[1]
72
+ if key_padding_mask is None:
73
+ qkv = rearrange(qkv, 'b s ... -> (b s) ...')
74
+ max_s = seqlen
75
+ cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
76
+ device=qkv.device)
77
+ output = flash_attn_unpadded_qkvpacked_func(
78
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
79
+ softmax_scale=self.softmax_scale, causal=causal
80
+ )
81
+ output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
82
+ else:
83
+ nheads = qkv.shape[-2]
84
+ x = rearrange(qkv, 'b s three h d -> b s (three h d)')
85
+ x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
86
+ x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
87
+ output_unpad = flash_attn_unpadded_qkvpacked_func(
88
+ x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
89
+ softmax_scale=self.softmax_scale, causal=causal
90
+ )
91
+ output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
92
+ indices, batch_size, seqlen),
93
+ 'b s (h d) -> b s h d', h=nheads)
94
+ else:
95
+ assert max_s is not None
96
+ output = flash_attn_unpadded_qkvpacked_func(
97
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
98
+ softmax_scale=self.softmax_scale, causal=causal
99
+ )
100
+
101
+ return output, None
102
+
103
+
104
+ class InternRMSNorm(nn.Module):
105
+ def __init__(self, hidden_size, eps=1e-6):
106
+ super().__init__()
107
+ self.weight = nn.Parameter(torch.ones(hidden_size))
108
+ self.variance_epsilon = eps
109
+
110
+ def forward(self, hidden_states):
111
+ input_dtype = hidden_states.dtype
112
+ hidden_states = hidden_states.to(torch.float32)
113
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
114
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
115
+ return self.weight * hidden_states.to(input_dtype)
116
+
117
+
118
+ try:
119
+ from apex.normalization import FusedRMSNorm
120
+
121
+ InternRMSNorm = FusedRMSNorm # noqa
122
+
123
+ logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
124
+ except ImportError:
125
+ # using the normal InternRMSNorm
126
+ pass
127
+ except Exception:
128
+ logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
129
+ pass
130
+
131
+
132
+ class InternVisionEmbeddings(nn.Module):
133
+ def __init__(self, config: InternVisionConfig):
134
+ super().__init__()
135
+ self.config = config
136
+ self.embed_dim = config.hidden_size
137
+ self.image_size = config.image_size
138
+ self.patch_size = config.patch_size
139
+
140
+ self.class_embedding = nn.Parameter(
141
+ torch.randn(1, 1, self.embed_dim),
142
+ )
143
+
144
+ self.patch_embedding = nn.Conv2d(
145
+ in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
146
+ )
147
+
148
+ self.num_patches = (self.image_size // self.patch_size) ** 2
149
+ self.num_positions = self.num_patches + 1
150
+
151
+ self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
152
+
153
+ def _get_pos_embed(self, pos_embed, H, W):
154
+ target_dtype = pos_embed.dtype
155
+ pos_embed = pos_embed.float().reshape(
156
+ 1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
157
+ pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False).\
158
+ reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)
159
+ return pos_embed
160
+
161
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
162
+ target_dtype = self.patch_embedding.weight.dtype
163
+ patch_embeds = self.patch_embedding(pixel_values) # shape = [*, channel, width, height]
164
+ batch_size, _, height, width = patch_embeds.shape
165
+ patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
166
+ class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
167
+ embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
168
+ position_embedding = torch.cat([
169
+ self.position_embedding[:, :1, :],
170
+ self._get_pos_embed(self.position_embedding[:, 1:, :], height, width)
171
+ ], dim=1)
172
+ embeddings = embeddings + position_embedding.to(target_dtype)
173
+ return embeddings
174
+
175
+
176
+ class InternAttention(nn.Module):
177
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
178
+
179
+ def __init__(self, config: InternVisionConfig):
180
+ super().__init__()
181
+ self.config = config
182
+ self.embed_dim = config.hidden_size
183
+ self.num_heads = config.num_attention_heads
184
+ self.use_flash_attn = config.use_flash_attn and has_flash_attn
185
+ if config.use_flash_attn and not has_flash_attn:
186
+ print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
187
+ self.head_dim = self.embed_dim // self.num_heads
188
+ if self.head_dim * self.num_heads != self.embed_dim:
189
+ raise ValueError(
190
+ f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
191
+ f' {self.num_heads}).'
192
+ )
193
+
194
+ self.scale = self.head_dim ** -0.5
195
+ self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
196
+ self.attn_drop = nn.Dropout(config.attention_dropout)
197
+ self.proj_drop = nn.Dropout(config.dropout)
198
+
199
+ self.qk_normalization = config.qk_normalization
200
+
201
+ if self.qk_normalization:
202
+ self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
203
+ self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
204
+
205
+ if self.use_flash_attn:
206
+ self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
207
+ self.proj = nn.Linear(self.embed_dim, self.embed_dim)
208
+
209
+ def _naive_attn(self, x):
210
+ B, N, C = x.shape
211
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
212
+ q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
213
+
214
+ if self.qk_normalization:
215
+ B_, H_, N_, D_ = q.shape
216
+ q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
217
+ k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
218
+
219
+ attn = ((q * self.scale) @ k.transpose(-2, -1))
220
+ attn = attn.softmax(dim=-1)
221
+ attn = self.attn_drop(attn)
222
+
223
+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
224
+ x = self.proj(x)
225
+ x = self.proj_drop(x)
226
+ return x
227
+
228
+ def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
229
+ qkv = self.qkv(x)
230
+ qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
231
+
232
+ if self.qk_normalization:
233
+ q, k, v = qkv.unbind(2)
234
+ q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
235
+ k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
236
+ qkv = torch.stack([q, k, v], dim=2)
237
+
238
+ context, _ = self.inner_attn(
239
+ qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
240
+ )
241
+ outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
242
+ outs = self.proj_drop(outs)
243
+ return outs
244
+
245
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
246
+ x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
247
+ return x
248
+
249
+
250
+ class InternMLP(nn.Module):
251
+ def __init__(self, config: InternVisionConfig):
252
+ super().__init__()
253
+ self.config = config
254
+ self.act = ACT2FN[config.hidden_act]
255
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
256
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
257
+
258
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
259
+ hidden_states = self.fc1(hidden_states)
260
+ hidden_states = self.act(hidden_states)
261
+ hidden_states = self.fc2(hidden_states)
262
+ return hidden_states
263
+
264
+
265
+ class InternVisionEncoderLayer(nn.Module):
266
+ def __init__(self, config: InternVisionConfig, drop_path_rate: float):
267
+ super().__init__()
268
+ self.embed_dim = config.hidden_size
269
+ self.intermediate_size = config.intermediate_size
270
+
271
+ self.attn = InternAttention(config)
272
+ self.mlp = InternMLP(config)
273
+ self.norm1 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
274
+ self.norm2 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
275
+
276
+ self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
277
+ self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
278
+ self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
279
+ self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
280
+
281
+ def forward(
282
+ self,
283
+ hidden_states: torch.Tensor,
284
+ ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
285
+ """
286
+ Args:
287
+ hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
288
+ """
289
+ hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
290
+
291
+ hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
292
+
293
+ return hidden_states
294
+
295
+
296
+ class InternVisionEncoder(nn.Module):
297
+ """
298
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
299
+ [`InternEncoderLayer`].
300
+
301
+ Args:
302
+ config (`InternConfig`):
303
+ The corresponding vision configuration for the `InternEncoder`.
304
+ """
305
+
306
+ def __init__(self, config: InternVisionConfig):
307
+ super().__init__()
308
+ self.config = config
309
+ # stochastic depth decay rule
310
+ dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
311
+ self.layers = nn.ModuleList([
312
+ InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
313
+ self.gradient_checkpointing = True
314
+
315
+ def forward(
316
+ self,
317
+ inputs_embeds,
318
+ output_hidden_states: Optional[bool] = None,
319
+ return_dict: Optional[bool] = None,
320
+ ) -> Union[Tuple, BaseModelOutput]:
321
+ r"""
322
+ Args:
323
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
324
+ Embedded representation of the inputs. Should be float, not int tokens.
325
+ output_hidden_states (`bool`, *optional*):
326
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
327
+ for more detail.
328
+ return_dict (`bool`, *optional*):
329
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
330
+ """
331
+ output_hidden_states = (
332
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
333
+ )
334
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
335
+
336
+ encoder_states = () if output_hidden_states else None
337
+ hidden_states = inputs_embeds
338
+
339
+ for idx, encoder_layer in enumerate(self.layers):
340
+ if output_hidden_states:
341
+ encoder_states = encoder_states + (hidden_states,)
342
+ if self.gradient_checkpointing and self.training:
343
+ layer_outputs = torch.utils.checkpoint.checkpoint(
344
+ encoder_layer,
345
+ hidden_states)
346
+ else:
347
+ layer_outputs = encoder_layer(
348
+ hidden_states,
349
+ )
350
+ hidden_states = layer_outputs
351
+
352
+ if output_hidden_states:
353
+ encoder_states = encoder_states + (hidden_states,)
354
+
355
+ if not return_dict:
356
+ return tuple(v for v in [hidden_states, encoder_states] if v is not None)
357
+ return BaseModelOutput(
358
+ last_hidden_state=hidden_states, hidden_states=encoder_states
359
+ )
360
+
361
+
362
+ class InternVisionModel(PreTrainedModel):
363
+ main_input_name = 'pixel_values'
364
+ config_class = InternVisionConfig
365
+ _no_split_modules = ['InternVisionEncoderLayer']
366
+
367
+ def __init__(self, config: InternVisionConfig):
368
+ super().__init__(config)
369
+ self.config = config
370
+
371
+ self.embeddings = InternVisionEmbeddings(config)
372
+ self.encoder = InternVisionEncoder(config)
373
+
374
+ def resize_pos_embeddings(self, old_size, new_size, patch_size):
375
+ pos_emb = self.embeddings.position_embedding
376
+ _, num_positions, embed_dim = pos_emb.shape
377
+ cls_emb = pos_emb[:, :1, :]
378
+ pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
379
+ pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
380
+ pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
381
+ pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
382
+ self.embeddings.position_embedding = nn.Parameter(pos_emb)
383
+ self.embeddings.image_size = new_size
384
+ logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
385
+
386
+ def get_input_embeddings(self):
387
+ return self.embeddings
388
+
389
+ def forward(
390
+ self,
391
+ pixel_values: Optional[torch.FloatTensor] = None,
392
+ output_hidden_states: Optional[bool] = None,
393
+ return_dict: Optional[bool] = None,
394
+ pixel_embeds: Optional[torch.FloatTensor] = None,
395
+ ) -> Union[Tuple, BaseModelOutputWithPooling]:
396
+ output_hidden_states = (
397
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
398
+ )
399
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
400
+
401
+ if pixel_values is None and pixel_embeds is None:
402
+ raise ValueError('You have to specify pixel_values or pixel_embeds')
403
+
404
+ if pixel_embeds is not None:
405
+ hidden_states = pixel_embeds
406
+ else:
407
+ if len(pixel_values.shape) == 4:
408
+ hidden_states = self.embeddings(pixel_values)
409
+ else:
410
+ raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
411
+ encoder_outputs = self.encoder(
412
+ inputs_embeds=hidden_states,
413
+ output_hidden_states=output_hidden_states,
414
+ return_dict=return_dict,
415
+ )
416
+ last_hidden_state = encoder_outputs.last_hidden_state
417
+ pooled_output = last_hidden_state[:, 0, :]
418
+
419
+ if not return_dict:
420
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
421
+
422
+ return BaseModelOutputWithPooling(
423
+ last_hidden_state=last_hidden_state,
424
+ pooler_output=pooled_output,
425
+ hidden_states=encoder_outputs.hidden_states,
426
+ attentions=encoder_outputs.attentions,
427
+ )
modeling_internlm2.py ADDED
@@ -0,0 +1,1396 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/modeling_llama.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """ PyTorch InternLM2 model."""
17
+ import math
18
+ import queue
19
+ import threading
20
+ import warnings
21
+ from typing import List, Optional, Tuple, Union
22
+
23
+ import torch
24
+ import torch.nn.functional as F
25
+ import torch.utils.checkpoint
26
+ from einops import rearrange
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+ from transformers.activations import ACT2FN
30
+ from transformers.modeling_outputs import (BaseModelOutputWithPast,
31
+ CausalLMOutputWithPast,
32
+ SequenceClassifierOutputWithPast)
33
+ from transformers.modeling_utils import PreTrainedModel
34
+ from transformers.utils import (add_start_docstrings,
35
+ add_start_docstrings_to_model_forward, logging,
36
+ replace_return_docstrings)
37
+
38
+ try:
39
+ from transformers.generation.streamers import BaseStreamer
40
+ except: # noqa # pylint: disable=bare-except
41
+ BaseStreamer = None
42
+
43
+ from .configuration_internlm2 import InternLM2Config
44
+
45
+ logger = logging.get_logger(__name__)
46
+
47
+ _CONFIG_FOR_DOC = 'InternLM2Config'
48
+
49
+ flash_attn_func, flash_attn_varlen_func = None, None
50
+ pad_input, index_first_axis, unpad_input = None, None, None
51
+
52
+
53
+ def _import_flash_attn():
54
+ global flash_attn_func, flash_attn_varlen_func
55
+ global pad_input, index_first_axis, unpad_input
56
+ try:
57
+ from flash_attn import flash_attn_func as _flash_attn_func
58
+ from flash_attn import \
59
+ flash_attn_varlen_func as _flash_attn_varlen_func
60
+ from flash_attn.bert_padding import \
61
+ index_first_axis as _index_first_axis
62
+ from flash_attn.bert_padding import pad_input as _pad_input
63
+ from flash_attn.bert_padding import unpad_input as _unpad_input
64
+ flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
65
+ pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
66
+ except ImportError:
67
+ raise ImportError('flash_attn is not installed.')
68
+
69
+
70
+ # Copied from transformers.models.llama.modeling_llama._get_unpad_data
71
+ def _get_unpad_data(attention_mask):
72
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
73
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
74
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
75
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
76
+ return (
77
+ indices,
78
+ cu_seqlens,
79
+ max_seqlen_in_batch,
80
+ )
81
+
82
+
83
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
84
+ def _make_causal_mask(
85
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
86
+ ):
87
+ """
88
+ Make causal mask used for bi-directional self-attention.
89
+ """
90
+ bsz, tgt_len = input_ids_shape
91
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
92
+ mask_cond = torch.arange(mask.size(-1), device=device)
93
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
94
+ mask = mask.to(dtype)
95
+
96
+ if past_key_values_length > 0:
97
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
98
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
99
+
100
+
101
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
102
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
103
+ """
104
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
105
+ """
106
+ bsz, src_len = mask.size()
107
+ tgt_len = tgt_len if tgt_len is not None else src_len
108
+
109
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
110
+
111
+ inverted_mask = 1.0 - expanded_mask
112
+
113
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
114
+
115
+
116
+ # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
117
+ class InternLM2RMSNorm(nn.Module):
118
+ def __init__(self, hidden_size, eps=1e-6):
119
+ """
120
+ InternLM2RMSNorm is equivalent to T5LayerNorm
121
+ """
122
+ super().__init__()
123
+ self.weight = nn.Parameter(torch.ones(hidden_size))
124
+ self.variance_epsilon = eps
125
+
126
+ def forward(self, hidden_states):
127
+ input_dtype = hidden_states.dtype
128
+ hidden_states = hidden_states.to(torch.float32)
129
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
130
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
131
+ return self.weight * hidden_states.to(input_dtype)
132
+
133
+
134
+ # Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
135
+ class InternLM2RotaryEmbedding(nn.Module):
136
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
137
+ super().__init__()
138
+
139
+ self.dim = dim
140
+ self.max_position_embeddings = max_position_embeddings
141
+ self.base = base
142
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
143
+ self.register_buffer('inv_freq', inv_freq, persistent=False)
144
+
145
+ # Build here to make `torch.jit.trace` work.
146
+ self._set_cos_sin_cache(
147
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
148
+ )
149
+
150
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
151
+ self.max_seq_len_cached = seq_len
152
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
153
+
154
+ freqs = torch.einsum('i,j->ij', t, self.inv_freq)
155
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
156
+ emb = torch.cat((freqs, freqs), dim=-1)
157
+ self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
158
+ self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
159
+
160
+ def forward(self, x, seq_len=None):
161
+ # x: [bs, num_attention_heads, seq_len, head_size]
162
+ if seq_len > self.max_seq_len_cached:
163
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
164
+
165
+ return (
166
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
167
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
168
+ )
169
+
170
+
171
+ # Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
172
+ class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
173
+ """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
174
+
175
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
176
+ self.scaling_factor = scaling_factor
177
+ super().__init__(dim, max_position_embeddings, base, device)
178
+
179
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
180
+ self.max_seq_len_cached = seq_len
181
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
182
+ t = t / self.scaling_factor
183
+
184
+ freqs = torch.einsum('i,j->ij', t, self.inv_freq)
185
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
186
+ emb = torch.cat((freqs, freqs), dim=-1)
187
+ self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
188
+ self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
189
+
190
+
191
+ # Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
192
+ class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
193
+ """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
194
+ Credits to the Reddit users /u/bloc97 and /u/emozilla.
195
+ """
196
+
197
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
198
+ self.scaling_factor = scaling_factor
199
+ super().__init__(dim, max_position_embeddings, base, device)
200
+
201
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
202
+ self.max_seq_len_cached = seq_len
203
+
204
+ if seq_len > self.max_position_embeddings:
205
+ base = self.base * (
206
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
207
+ ) ** (self.dim / (self.dim - 2))
208
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
209
+ self.register_buffer('inv_freq', inv_freq, persistent=False)
210
+
211
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
212
+
213
+ freqs = torch.einsum('i,j->ij', t, self.inv_freq)
214
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
215
+ emb = torch.cat((freqs, freqs), dim=-1)
216
+ self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
217
+ self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
218
+
219
+
220
+ # Copied from transformers.model.llama.modeling_llama.rotate_half
221
+ def rotate_half(x):
222
+ """Rotates half the hidden dims of the input."""
223
+ x1 = x[..., : x.shape[-1] // 2]
224
+ x2 = x[..., x.shape[-1] // 2 :]
225
+ return torch.cat((-x2, x1), dim=-1)
226
+
227
+
228
+ # Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
229
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
230
+ """Applies Rotary Position Embedding to the query and key tensors."""
231
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
232
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
233
+ q_embed = (q * cos) + (rotate_half(q) * sin)
234
+ k_embed = (k * cos) + (rotate_half(k) * sin)
235
+ return q_embed, k_embed
236
+
237
+
238
+ class InternLM2MLP(nn.Module):
239
+ def __init__(self, config):
240
+ super().__init__()
241
+ self.config = config
242
+ self.hidden_size = config.hidden_size
243
+ self.intermediate_size = config.intermediate_size
244
+ self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
245
+ self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
246
+ self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
247
+ self.act_fn = ACT2FN[config.hidden_act]
248
+
249
+ def forward(self, x):
250
+ down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
251
+
252
+ return down_proj
253
+
254
+
255
+ # Copied from transformers.model.llama.modeling_llama.repeat_kv
256
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
257
+ """
258
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
259
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
260
+ """
261
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
262
+ if n_rep == 1:
263
+ return hidden_states
264
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
265
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
266
+
267
+
268
+ # Modified from transformers.model.llama.modeling_llama.LlamaAttention
269
+ class InternLM2Attention(nn.Module):
270
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
271
+
272
+ def __init__(self, config: InternLM2Config):
273
+ super().__init__()
274
+ self.config = config
275
+ self.hidden_size = config.hidden_size
276
+ self.num_heads = config.num_attention_heads
277
+ self.head_dim = self.hidden_size // self.num_heads
278
+ self.num_key_value_heads = config.num_key_value_heads
279
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
280
+ self.max_position_embeddings = config.max_position_embeddings
281
+ self.is_causal = True
282
+
283
+ if (self.head_dim * self.num_heads) != self.hidden_size:
284
+ raise ValueError(
285
+ f'hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}'
286
+ f' and `num_heads`: {self.num_heads}).'
287
+ )
288
+
289
+ self.wqkv = nn.Linear(
290
+ self.hidden_size,
291
+ (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
292
+ bias=config.bias,
293
+ )
294
+
295
+ self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
296
+ self._init_rope()
297
+
298
+ def _init_rope(self):
299
+ if self.config.rope_scaling is None:
300
+ self.rotary_emb = InternLM2RotaryEmbedding(
301
+ self.head_dim,
302
+ max_position_embeddings=self.max_position_embeddings,
303
+ base=self.config.rope_theta,
304
+ )
305
+ else:
306
+ scaling_type = self.config.rope_scaling['type']
307
+ scaling_factor = self.config.rope_scaling['factor']
308
+ if scaling_type == 'dynamic':
309
+ self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
310
+ self.head_dim,
311
+ max_position_embeddings=self.max_position_embeddings,
312
+ base=self.config.rope_theta,
313
+ scaling_factor=scaling_factor,
314
+ )
315
+ elif scaling_type == 'linear':
316
+ self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
317
+ self.head_dim,
318
+ max_position_embeddings=self.max_position_embeddings,
319
+ base=self.config.rope_theta,
320
+ scaling_factor=scaling_factor,
321
+ )
322
+ else:
323
+ raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
324
+ return self.rotary_emb
325
+
326
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
327
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
328
+
329
+ def forward(
330
+ self,
331
+ hidden_states: torch.Tensor,
332
+ attention_mask: Optional[torch.Tensor] = None,
333
+ position_ids: Optional[torch.LongTensor] = None,
334
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
335
+ output_attentions: bool = False,
336
+ use_cache: bool = False,
337
+ **kwargs,
338
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
339
+ if 'padding_mask' in kwargs:
340
+ warnings.warn(
341
+ 'Passing `padding_mask` is deprecated and will be removed in v4.37. '
342
+ 'Please make sure use `attention_mask` instead.`'
343
+ )
344
+
345
+ bsz, q_len, _ = hidden_states.size()
346
+
347
+ qkv_states = self.wqkv(hidden_states)
348
+
349
+ qkv_states = rearrange(
350
+ qkv_states,
351
+ 'b q (h gs d) -> b q h gs d',
352
+ gs=2 + self.num_key_value_groups,
353
+ d=self.head_dim,
354
+ )
355
+
356
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
357
+ query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
358
+ key_states = qkv_states[..., -2, :]
359
+ value_states = qkv_states[..., -1, :]
360
+
361
+ query_states = query_states.transpose(1, 2)
362
+ key_states = key_states.transpose(1, 2)
363
+ value_states = value_states.transpose(1, 2)
364
+
365
+ kv_seq_len = key_states.shape[-2]
366
+ if past_key_value is not None:
367
+ kv_seq_len += past_key_value[0].shape[-2]
368
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
369
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
370
+
371
+ if past_key_value is not None:
372
+ # reuse k, v, self_attention
373
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
374
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
375
+
376
+ past_key_value = (key_states, value_states) if use_cache else None
377
+
378
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
379
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
380
+
381
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
382
+
383
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
384
+ raise ValueError(
385
+ f'Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is'
386
+ f' {attn_weights.size()}'
387
+ )
388
+
389
+ if attention_mask is not None:
390
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
391
+ raise ValueError(
392
+ f'Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}'
393
+ )
394
+ attn_weights = attn_weights + attention_mask
395
+
396
+ # upcast attention to fp32
397
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
398
+ attn_output = torch.matmul(attn_weights, value_states)
399
+
400
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
401
+ raise ValueError(
402
+ f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
403
+ f' {attn_output.size()}'
404
+ )
405
+
406
+ attn_output = attn_output.transpose(1, 2).contiguous()
407
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
408
+
409
+ attn_output = self.wo(attn_output)
410
+
411
+ if not output_attentions:
412
+ attn_weights = None
413
+
414
+ return attn_output, attn_weights, past_key_value
415
+
416
+
417
+ # Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
418
+ class InternLM2FlashAttention2(InternLM2Attention):
419
+ """
420
+ InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
421
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
422
+ flash attention and deal with padding tokens in case the input contains any of them.
423
+ """
424
+
425
+ def forward(
426
+ self,
427
+ hidden_states: torch.Tensor,
428
+ attention_mask: Optional[torch.LongTensor] = None,
429
+ position_ids: Optional[torch.LongTensor] = None,
430
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
431
+ output_attentions: bool = False,
432
+ use_cache: bool = False,
433
+ **kwargs,
434
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
435
+ # InternLM2FlashAttention2 attention does not support output_attentions
436
+ if 'padding_mask' in kwargs:
437
+ warnings.warn(
438
+ 'Passing `padding_mask` is deprecated and will be removed in v4.37. '
439
+ 'Please make sure use `attention_mask` instead.`'
440
+ )
441
+
442
+ # overwrite attention_mask with padding_mask
443
+ attention_mask = kwargs.pop('padding_mask')
444
+
445
+ output_attentions = False
446
+
447
+ bsz, q_len, _ = hidden_states.size()
448
+
449
+ qkv_states = self.wqkv(hidden_states)
450
+
451
+ qkv_states = rearrange(
452
+ qkv_states,
453
+ 'b q (h gs d) -> b q h gs d',
454
+ gs=2 + self.num_key_value_groups,
455
+ d=self.head_dim,
456
+ )
457
+
458
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
459
+ query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
460
+ key_states = qkv_states[..., -2, :]
461
+ value_states = qkv_states[..., -1, :]
462
+
463
+ query_states = query_states.transpose(1, 2)
464
+ key_states = key_states.transpose(1, 2)
465
+ value_states = value_states.transpose(1, 2)
466
+
467
+ kv_seq_len = key_states.shape[-2]
468
+ if past_key_value is not None:
469
+ kv_seq_len += past_key_value[0].shape[-2]
470
+
471
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
472
+
473
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
474
+
475
+ if past_key_value is not None:
476
+ # reuse k, v, self_attention
477
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
478
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
479
+
480
+ past_key_value = (key_states, value_states) if use_cache else None
481
+
482
+ query_states = query_states.transpose(1, 2)
483
+ key_states = key_states.transpose(1, 2)
484
+ value_states = value_states.transpose(1, 2)
485
+
486
+ attn_output = self._flash_attention_forward(
487
+ query_states, key_states, value_states, attention_mask, q_len
488
+ )
489
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
490
+ attn_output = self.wo(attn_output)
491
+
492
+ if not output_attentions:
493
+ attn_weights = None
494
+
495
+ return attn_output, attn_weights, past_key_value
496
+
497
+ def _flash_attention_forward(
498
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
499
+ ):
500
+ """
501
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
502
+ first unpad the input, then computes the attention scores and pad the final attention scores.
503
+
504
+ Args:
505
+ query_states (`torch.Tensor`):
506
+ Input query states to be passed to Flash Attention API
507
+ key_states (`torch.Tensor`):
508
+ Input key states to be passed to Flash Attention API
509
+ value_states (`torch.Tensor`):
510
+ Input value states to be passed to Flash Attention API
511
+ attention_mask (`torch.Tensor`):
512
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
513
+ position of padding tokens and 1 for the position of non-padding tokens.
514
+ dropout (`int`, *optional*):
515
+ Attention dropout
516
+ softmax_scale (`float`, *optional*):
517
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
518
+ """
519
+ # Contains at least one padding token in the sequence
520
+ causal = self.is_causal and query_length != 1
521
+ if attention_mask is not None:
522
+ batch_size = query_states.shape[0]
523
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
524
+ query_states, key_states, value_states, attention_mask, query_length
525
+ )
526
+
527
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
528
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
529
+
530
+ attn_output_unpad = flash_attn_varlen_func(
531
+ query_states,
532
+ key_states,
533
+ value_states,
534
+ cu_seqlens_q=cu_seqlens_q,
535
+ cu_seqlens_k=cu_seqlens_k,
536
+ max_seqlen_q=max_seqlen_in_batch_q,
537
+ max_seqlen_k=max_seqlen_in_batch_k,
538
+ dropout_p=dropout,
539
+ softmax_scale=softmax_scale,
540
+ causal=causal,
541
+ )
542
+
543
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
544
+ else:
545
+ attn_output = flash_attn_func(
546
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
547
+ )
548
+
549
+ return attn_output
550
+
551
+ def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
552
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
553
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
554
+
555
+ key_layer = index_first_axis(
556
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
557
+ )
558
+ value_layer = index_first_axis(
559
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
560
+ )
561
+
562
+ if query_length == kv_seq_len:
563
+ query_layer = index_first_axis(
564
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
565
+ )
566
+ cu_seqlens_q = cu_seqlens_k
567
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
568
+ indices_q = indices_k
569
+ elif query_length == 1:
570
+ max_seqlen_in_batch_q = 1
571
+ cu_seqlens_q = torch.arange(
572
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
573
+ ) # There is a memcpy here, that is very bad.
574
+ indices_q = cu_seqlens_q[:-1]
575
+ query_layer = query_layer.squeeze(1)
576
+ else:
577
+ # The -q_len: slice assumes left padding.
578
+ attention_mask = attention_mask[:, -query_length:]
579
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
580
+
581
+ return (
582
+ query_layer,
583
+ key_layer,
584
+ value_layer,
585
+ indices_q.to(torch.int64),
586
+ (cu_seqlens_q, cu_seqlens_k),
587
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
588
+ )
589
+
590
+
591
+ INTERNLM2_ATTENTION_CLASSES = {
592
+ 'eager': InternLM2Attention,
593
+ 'flash_attention_2': InternLM2FlashAttention2,
594
+ }
595
+
596
+
597
+ # Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
598
+ class InternLM2DecoderLayer(nn.Module):
599
+ def __init__(self, config: InternLM2Config):
600
+ super().__init__()
601
+ self.hidden_size = config.hidden_size
602
+
603
+ self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
604
+
605
+ self.feed_forward = InternLM2MLP(config)
606
+ self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
607
+ self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
608
+
609
+ def forward(
610
+ self,
611
+ hidden_states: torch.Tensor,
612
+ attention_mask: Optional[torch.Tensor] = None,
613
+ position_ids: Optional[torch.LongTensor] = None,
614
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
615
+ output_attentions: Optional[bool] = False,
616
+ use_cache: Optional[bool] = False,
617
+ **kwargs,
618
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
619
+ """
620
+ Args:
621
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
622
+ attention_mask (`torch.FloatTensor`, *optional*):
623
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
624
+ query_sequence_length, key_sequence_length)` if default attention is used.
625
+ output_attentions (`bool`, *optional*):
626
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
627
+ returned tensors for more detail.
628
+ use_cache (`bool`, *optional*):
629
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
630
+ (see `past_key_values`).
631
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
632
+ """
633
+ if 'padding_mask' in kwargs:
634
+ warnings.warn(
635
+ 'Passing `padding_mask` is deprecated and will be removed in v4.37. '
636
+ 'Please make sure use `attention_mask` instead.`'
637
+ )
638
+
639
+ residual = hidden_states
640
+
641
+ hidden_states = self.attention_norm(hidden_states)
642
+
643
+ # Self Attention
644
+ hidden_states, self_attn_weights, present_key_value = self.attention(
645
+ hidden_states=hidden_states,
646
+ attention_mask=attention_mask,
647
+ position_ids=position_ids,
648
+ past_key_value=past_key_value,
649
+ output_attentions=output_attentions,
650
+ use_cache=use_cache,
651
+ **kwargs,
652
+ )
653
+ hidden_states = residual + hidden_states
654
+
655
+ # Fully Connected
656
+ residual = hidden_states
657
+ hidden_states = self.ffn_norm(hidden_states)
658
+ hidden_states = self.feed_forward(hidden_states)
659
+ hidden_states = residual + hidden_states
660
+
661
+ outputs = (hidden_states,)
662
+
663
+ if output_attentions:
664
+ outputs += (self_attn_weights,)
665
+
666
+ if use_cache:
667
+ outputs += (present_key_value,)
668
+
669
+ return outputs
670
+
671
+
672
+ InternLM2_START_DOCSTRING = r"""
673
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
674
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
675
+ etc.)
676
+
677
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
678
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
679
+ and behavior.
680
+
681
+ Parameters:
682
+ config ([`InternLM2Config`]):
683
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
684
+ load the weights associated with the model, only the configuration. Check out the
685
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
686
+ """
687
+
688
+
689
+ # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
690
+ @add_start_docstrings(
691
+ 'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
692
+ InternLM2_START_DOCSTRING,
693
+ )
694
+ class InternLM2PreTrainedModel(PreTrainedModel):
695
+ config_class = InternLM2Config
696
+ base_model_prefix = 'model'
697
+ supports_gradient_checkpointing = True
698
+ _no_split_modules = ['InternLM2DecoderLayer']
699
+ _skip_keys_device_placement = 'past_key_values'
700
+
701
+ def _init_weights(self, module):
702
+ std = self.config.initializer_range
703
+ if isinstance(module, nn.Linear):
704
+ module.weight.data.normal_(mean=0.0, std=std)
705
+ if module.bias is not None:
706
+ module.bias.data.zero_()
707
+ elif isinstance(module, nn.Embedding):
708
+ module.weight.data.normal_(mean=0.0, std=std)
709
+ if module.padding_idx is not None:
710
+ module.weight.data[module.padding_idx].zero_()
711
+
712
+
713
+ InternLM2_INPUTS_DOCSTRING = r"""
714
+ Args:
715
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
716
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
717
+ it.
718
+
719
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
720
+ [`PreTrainedTokenizer.__call__`] for details.
721
+
722
+ [What are input IDs?](../glossary#input-ids)
723
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
724
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
725
+
726
+ - 1 for tokens that are **not masked**,
727
+ - 0 for tokens that are **masked**.
728
+
729
+ [What are attention masks?](../glossary#attention-mask)
730
+
731
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
732
+ [`PreTrainedTokenizer.__call__`] for details.
733
+
734
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
735
+ `past_key_values`).
736
+
737
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
738
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
739
+ information on the default strategy.
740
+
741
+ - 1 indicates the head is **not masked**,
742
+ - 0 indicates the head is **masked**.
743
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
744
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
745
+ config.n_positions - 1]`.
746
+
747
+ [What are position IDs?](../glossary#position-ids)
748
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
749
+ when `config.use_cache=True`):
750
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
751
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
752
+ `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
753
+
754
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
755
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
756
+
757
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
758
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
759
+ of shape `(batch_size, sequence_length)`.
760
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
761
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
762
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
763
+ model's internal embedding lookup matrix.
764
+ use_cache (`bool`, *optional*):
765
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
766
+ `past_key_values`).
767
+ output_attentions (`bool`, *optional*):
768
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
769
+ tensors for more detail.
770
+ output_hidden_states (`bool`, *optional*):
771
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
772
+ more detail.
773
+ return_dict (`bool`, *optional*):
774
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
775
+ """
776
+
777
+
778
+ # Modified from transformers.model.llama.modeling_llama.LlamaModel
779
+ @add_start_docstrings(
780
+ 'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
781
+ InternLM2_START_DOCSTRING,
782
+ )
783
+ class InternLM2Model(InternLM2PreTrainedModel):
784
+ """
785
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
786
+
787
+ Args:
788
+ config: InternLM2Config
789
+ """
790
+
791
+ _auto_class = 'AutoModel'
792
+
793
+ def __init__(self, config: InternLM2Config):
794
+ super().__init__(config)
795
+ self.padding_idx = config.pad_token_id
796
+ self.vocab_size = config.vocab_size
797
+ self.config = config
798
+
799
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
800
+
801
+ self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
802
+ self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
803
+
804
+ self.gradient_checkpointing = False
805
+ # Initialize weights and apply final processing
806
+ self.post_init()
807
+
808
+ def get_input_embeddings(self):
809
+ return self.tok_embeddings
810
+
811
+ def set_input_embeddings(self, value):
812
+ self.tok_embeddings = value
813
+
814
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
815
+ # create causal mask
816
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
817
+ combined_attention_mask = None
818
+ if input_shape[-1] > 1:
819
+ combined_attention_mask = _make_causal_mask(
820
+ input_shape,
821
+ inputs_embeds.dtype,
822
+ device=inputs_embeds.device,
823
+ past_key_values_length=past_key_values_length,
824
+ )
825
+
826
+ if attention_mask is not None:
827
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
828
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
829
+ inputs_embeds.device
830
+ )
831
+ combined_attention_mask = (
832
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
833
+ )
834
+
835
+ return combined_attention_mask
836
+
837
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
838
+ def forward(
839
+ self,
840
+ input_ids: torch.LongTensor = None,
841
+ attention_mask: Optional[torch.Tensor] = None,
842
+ position_ids: Optional[torch.LongTensor] = None,
843
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
844
+ inputs_embeds: Optional[torch.FloatTensor] = None,
845
+ use_cache: Optional[bool] = None,
846
+ output_attentions: Optional[bool] = None,
847
+ output_hidden_states: Optional[bool] = None,
848
+ return_dict: Optional[bool] = None,
849
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
850
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
851
+ output_hidden_states = (
852
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
853
+ )
854
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
855
+
856
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
857
+
858
+ if self.config.attn_implementation == 'flash_attention_2':
859
+ _import_flash_attn()
860
+
861
+ # retrieve input_ids and inputs_embeds
862
+ if input_ids is not None and inputs_embeds is not None:
863
+ raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
864
+ elif input_ids is not None:
865
+ batch_size, seq_length = input_ids.shape[:2]
866
+ elif inputs_embeds is not None:
867
+ batch_size, seq_length = inputs_embeds.shape[:2]
868
+ else:
869
+ raise ValueError('You have to specify either input_ids or inputs_embeds')
870
+
871
+ seq_length_with_past = seq_length
872
+ past_key_values_length = 0
873
+ if past_key_values is not None:
874
+ past_key_values_length = past_key_values[0][0].shape[2]
875
+ seq_length_with_past = seq_length_with_past + past_key_values_length
876
+
877
+ if position_ids is None:
878
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
879
+ position_ids = torch.arange(
880
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
881
+ )
882
+ position_ids = position_ids.unsqueeze(0)
883
+
884
+ if inputs_embeds is None:
885
+ inputs_embeds = self.tok_embeddings(input_ids)
886
+
887
+ if self.config.attn_implementation == 'flash_attention_2':
888
+ # 2d mask is passed through the layers
889
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
890
+ else:
891
+ if attention_mask is None:
892
+ attention_mask = torch.ones(
893
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
894
+ )
895
+ attention_mask = self._prepare_decoder_attention_mask(
896
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
897
+ )
898
+
899
+ # embed positions
900
+ hidden_states = inputs_embeds
901
+
902
+ if self.gradient_checkpointing and self.training:
903
+ if use_cache:
904
+ logger.warning_once(
905
+ '`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...'
906
+ )
907
+ use_cache = False
908
+
909
+ # decoder layers
910
+ all_hidden_states = () if output_hidden_states else None
911
+ all_self_attns = () if output_attentions else None
912
+ next_decoder_cache = () if use_cache else None
913
+
914
+ for idx, decoder_layer in enumerate(self.layers):
915
+ if output_hidden_states:
916
+ all_hidden_states += (hidden_states,)
917
+
918
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
919
+
920
+ if self.gradient_checkpointing and self.training:
921
+
922
+ def create_custom_forward(module):
923
+ def custom_forward(*inputs):
924
+ # None for past_key_value
925
+ return module(*inputs, output_attentions, None)
926
+
927
+ return custom_forward
928
+
929
+ layer_outputs = torch.utils.checkpoint.checkpoint(
930
+ create_custom_forward(decoder_layer),
931
+ hidden_states,
932
+ attention_mask,
933
+ position_ids,
934
+ None,
935
+ )
936
+ else:
937
+ layer_outputs = decoder_layer(
938
+ hidden_states,
939
+ attention_mask=attention_mask,
940
+ position_ids=position_ids,
941
+ past_key_value=past_key_value,
942
+ output_attentions=output_attentions,
943
+ use_cache=use_cache,
944
+ )
945
+
946
+ hidden_states = layer_outputs[0]
947
+
948
+ if use_cache:
949
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
950
+
951
+ if output_attentions:
952
+ all_self_attns += (layer_outputs[1],)
953
+
954
+ hidden_states = self.norm(hidden_states)
955
+
956
+ # add hidden states from the last decoder layer
957
+ if output_hidden_states:
958
+ all_hidden_states += (hidden_states,)
959
+
960
+ next_cache = next_decoder_cache if use_cache else None
961
+ if not return_dict:
962
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
963
+ return BaseModelOutputWithPast(
964
+ last_hidden_state=hidden_states,
965
+ past_key_values=next_cache,
966
+ hidden_states=all_hidden_states,
967
+ attentions=all_self_attns,
968
+ )
969
+
970
+
971
+ # Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
972
+ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
973
+ _auto_class = 'AutoModelForCausalLM'
974
+
975
+ _tied_weights_keys = ['output.weight']
976
+
977
+ def __init__(self, config):
978
+ super().__init__(config)
979
+ self.model = InternLM2Model(config)
980
+ self.vocab_size = config.vocab_size
981
+ self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
982
+
983
+ # Initialize weights and apply final processing
984
+ self.post_init()
985
+
986
+ def get_input_embeddings(self):
987
+ return self.model.tok_embeddings
988
+
989
+ def set_input_embeddings(self, value):
990
+ self.model.tok_embeddings = value
991
+
992
+ def get_output_embeddings(self):
993
+ return self.output
994
+
995
+ def set_output_embeddings(self, new_embeddings):
996
+ self.output = new_embeddings
997
+
998
+ def set_decoder(self, decoder):
999
+ self.model = decoder
1000
+
1001
+ def get_decoder(self):
1002
+ return self.model
1003
+
1004
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1005
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1006
+ def forward(
1007
+ self,
1008
+ input_ids: torch.LongTensor = None,
1009
+ attention_mask: Optional[torch.Tensor] = None,
1010
+ position_ids: Optional[torch.LongTensor] = None,
1011
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1012
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1013
+ labels: Optional[torch.LongTensor] = None,
1014
+ use_cache: Optional[bool] = None,
1015
+ output_attentions: Optional[bool] = None,
1016
+ output_hidden_states: Optional[bool] = None,
1017
+ return_dict: Optional[bool] = None,
1018
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1019
+ r"""
1020
+ Args:
1021
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1022
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1023
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1024
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1025
+
1026
+ Returns:
1027
+
1028
+ Example:
1029
+
1030
+ ```python
1031
+ >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
1032
+
1033
+ >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1034
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1035
+
1036
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1037
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1038
+
1039
+ >>> # Generate
1040
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1041
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1042
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1043
+ ```"""
1044
+
1045
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1046
+ output_hidden_states = (
1047
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1048
+ )
1049
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1050
+
1051
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1052
+ outputs = self.model(
1053
+ input_ids=input_ids,
1054
+ attention_mask=attention_mask,
1055
+ position_ids=position_ids,
1056
+ past_key_values=past_key_values,
1057
+ inputs_embeds=inputs_embeds,
1058
+ use_cache=use_cache,
1059
+ output_attentions=output_attentions,
1060
+ output_hidden_states=output_hidden_states,
1061
+ return_dict=return_dict,
1062
+ )
1063
+
1064
+ hidden_states = outputs[0]
1065
+ logits = self.output(hidden_states)
1066
+ logits = logits.float()
1067
+
1068
+ loss = None
1069
+ if labels is not None:
1070
+ # Shift so that tokens < n predict n
1071
+ shift_logits = logits[..., :-1, :].contiguous()
1072
+ shift_labels = labels[..., 1:].contiguous()
1073
+ # Flatten the tokens
1074
+ loss_fct = CrossEntropyLoss()
1075
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1076
+ shift_labels = shift_labels.view(-1)
1077
+ # Enable model parallelism
1078
+ shift_labels = shift_labels.to(shift_logits.device)
1079
+ loss = loss_fct(shift_logits, shift_labels)
1080
+
1081
+ if not return_dict:
1082
+ output = (logits,) + outputs[1:]
1083
+ return (loss,) + output if loss is not None else output
1084
+
1085
+ return CausalLMOutputWithPast(
1086
+ loss=loss,
1087
+ logits=logits,
1088
+ past_key_values=outputs.past_key_values,
1089
+ hidden_states=outputs.hidden_states,
1090
+ attentions=outputs.attentions,
1091
+ )
1092
+
1093
+ def prepare_inputs_for_generation(
1094
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1095
+ ):
1096
+ if past_key_values is not None:
1097
+ past_length = past_key_values[0][0].shape[2]
1098
+
1099
+ # Some generation methods already pass only the last input ID
1100
+ if input_ids.shape[1] > past_length:
1101
+ remove_prefix_length = past_length
1102
+ else:
1103
+ # Default to old behavior: keep only final ID
1104
+ remove_prefix_length = input_ids.shape[1] - 1
1105
+
1106
+ input_ids = input_ids[:, remove_prefix_length:]
1107
+
1108
+ position_ids = kwargs.get('position_ids', None)
1109
+ if attention_mask is not None and position_ids is None:
1110
+ # create position_ids on the fly for batch generation
1111
+ position_ids = attention_mask.long().cumsum(-1) - 1
1112
+ position_ids.masked_fill_(attention_mask == 0, 1)
1113
+ if past_key_values:
1114
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1115
+
1116
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1117
+ if inputs_embeds is not None and past_key_values is None:
1118
+ model_inputs = {'inputs_embeds': inputs_embeds}
1119
+ else:
1120
+ model_inputs = {'input_ids': input_ids}
1121
+
1122
+ model_inputs.update(
1123
+ {
1124
+ 'position_ids': position_ids,
1125
+ 'past_key_values': past_key_values,
1126
+ 'use_cache': kwargs.get('use_cache'),
1127
+ 'attention_mask': attention_mask,
1128
+ }
1129
+ )
1130
+ return model_inputs
1131
+
1132
+ @staticmethod
1133
+ def _reorder_cache(past_key_values, beam_idx):
1134
+ reordered_past = ()
1135
+ for layer_past in past_key_values:
1136
+ reordered_past += (
1137
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1138
+ )
1139
+ return reordered_past
1140
+
1141
+ def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=''):
1142
+ if tokenizer.add_bos_token:
1143
+ prompt = ''
1144
+ else:
1145
+ prompt = tokenizer.bos_token
1146
+ if meta_instruction:
1147
+ prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
1148
+ for record in history:
1149
+ prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
1150
+ prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
1151
+ return tokenizer([prompt], return_tensors='pt')
1152
+
1153
+ @torch.no_grad()
1154
+ def chat(
1155
+ self,
1156
+ tokenizer,
1157
+ query: str,
1158
+ history: List[Tuple[str, str]] = [],
1159
+ streamer: Optional[BaseStreamer] = None,
1160
+ max_new_tokens: int = 1024,
1161
+ do_sample: bool = True,
1162
+ temperature: float = 0.8,
1163
+ top_p: float = 0.8,
1164
+ meta_instruction: str = 'You are an AI assistant whose name is InternLM (书生·浦语).\n'
1165
+ '- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n'
1166
+ '- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.',
1167
+ **kwargs,
1168
+ ):
1169
+ inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
1170
+ inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
1171
+ # also add end-of-assistant token in eos token id to avoid unnecessary generation
1172
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(['<|im_end|>'])[0]]
1173
+ outputs = self.generate(
1174
+ **inputs,
1175
+ streamer=streamer,
1176
+ max_new_tokens=max_new_tokens,
1177
+ do_sample=do_sample,
1178
+ temperature=temperature,
1179
+ top_p=top_p,
1180
+ eos_token_id=eos_token_id,
1181
+ **kwargs,
1182
+ )
1183
+ outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]) :]
1184
+ response = tokenizer.decode(outputs, skip_special_tokens=True)
1185
+ response = response.split('<|im_end|>')[0]
1186
+ history = history + [(query, response)]
1187
+ return response, history
1188
+
1189
+ @torch.no_grad()
1190
+ def stream_chat(
1191
+ self,
1192
+ tokenizer,
1193
+ query: str,
1194
+ history: List[Tuple[str, str]] = [],
1195
+ max_new_tokens: int = 1024,
1196
+ do_sample: bool = True,
1197
+ temperature: float = 0.8,
1198
+ top_p: float = 0.8,
1199
+ **kwargs,
1200
+ ):
1201
+ """
1202
+ Return a generator in format: (response, history)
1203
+ Eg.
1204
+ ('你好,有什么可以帮助您的吗', [('你好', '你好,有什么可以帮助您的吗')])
1205
+ ('你好,有什么可以帮助您的吗?', [('你好', '你好,有什么可以帮助您的吗?')])
1206
+ """
1207
+ if BaseStreamer is None:
1208
+ raise ModuleNotFoundError(
1209
+ 'The version of `transformers` is too low. Please make sure '
1210
+ 'that you have installed `transformers>=4.28.0`.'
1211
+ )
1212
+
1213
+ response_queue = queue.Queue(maxsize=20)
1214
+
1215
+ class ChatStreamer(BaseStreamer):
1216
+ def __init__(self, tokenizer) -> None:
1217
+ super().__init__()
1218
+ self.tokenizer = tokenizer
1219
+ self.queue = response_queue
1220
+ self.query = query
1221
+ self.history = history
1222
+ self.response = ''
1223
+ self.cache = []
1224
+ self.received_inputs = False
1225
+ self.queue.put((self.response, history + [(self.query, self.response)]))
1226
+
1227
+ def put(self, value):
1228
+ if len(value.shape) > 1 and value.shape[0] > 1:
1229
+ raise ValueError('ChatStreamer only supports batch size 1')
1230
+ elif len(value.shape) > 1:
1231
+ value = value[0]
1232
+
1233
+ if not self.received_inputs:
1234
+ # The first received value is input_ids, ignore here
1235
+ self.received_inputs = True
1236
+ return
1237
+
1238
+ self.cache.extend(value.tolist())
1239
+ token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
1240
+ if token.strip() != '<|im_end|>':
1241
+ self.response = self.response + token
1242
+ history = self.history + [(self.query, self.response)]
1243
+ self.queue.put((self.response, history))
1244
+ self.cache = []
1245
+ else:
1246
+ self.end()
1247
+
1248
+ def end(self):
1249
+ self.queue.put(None)
1250
+
1251
+ def stream_producer():
1252
+ return self.chat(
1253
+ tokenizer=tokenizer,
1254
+ query=query,
1255
+ streamer=ChatStreamer(tokenizer=tokenizer),
1256
+ history=history,
1257
+ max_new_tokens=max_new_tokens,
1258
+ do_sample=do_sample,
1259
+ temperature=temperature,
1260
+ top_p=top_p,
1261
+ **kwargs,
1262
+ )
1263
+
1264
+ def consumer():
1265
+ producer = threading.Thread(target=stream_producer)
1266
+ producer.start()
1267
+ while True:
1268
+ res = response_queue.get()
1269
+ if res is None:
1270
+ return
1271
+ yield res
1272
+
1273
+ return consumer()
1274
+
1275
+
1276
+ # Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
1277
+ @add_start_docstrings(
1278
+ """
1279
+ The InternLM2 Model transformer with a sequence classification head on top (linear layer).
1280
+
1281
+ [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
1282
+ as other causal models (e.g. GPT-2) do.
1283
+
1284
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1285
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1286
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1287
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1288
+ each row of the batch).
1289
+ """,
1290
+ InternLM2_START_DOCSTRING,
1291
+ )
1292
+ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
1293
+ def __init__(self, config):
1294
+ super().__init__(config)
1295
+ self.num_labels = config.num_labels
1296
+ self.model = InternLM2Model(config)
1297
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1298
+
1299
+ # Initialize weights and apply final processing
1300
+ self.post_init()
1301
+
1302
+ def get_input_embeddings(self):
1303
+ return self.model.tok_embeddings
1304
+
1305
+ def set_input_embeddings(self, value):
1306
+ self.model.tok_embeddings = value
1307
+
1308
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1309
+ def forward(
1310
+ self,
1311
+ input_ids: torch.LongTensor = None,
1312
+ attention_mask: Optional[torch.Tensor] = None,
1313
+ position_ids: Optional[torch.LongTensor] = None,
1314
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1315
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1316
+ labels: Optional[torch.LongTensor] = None,
1317
+ use_cache: Optional[bool] = None,
1318
+ output_attentions: Optional[bool] = None,
1319
+ output_hidden_states: Optional[bool] = None,
1320
+ return_dict: Optional[bool] = None,
1321
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1322
+ r"""
1323
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1324
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1325
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1326
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1327
+ """
1328
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1329
+
1330
+ transformer_outputs = self.model(
1331
+ input_ids,
1332
+ attention_mask=attention_mask,
1333
+ position_ids=position_ids,
1334
+ past_key_values=past_key_values,
1335
+ inputs_embeds=inputs_embeds,
1336
+ use_cache=use_cache,
1337
+ output_attentions=output_attentions,
1338
+ output_hidden_states=output_hidden_states,
1339
+ return_dict=return_dict,
1340
+ )
1341
+ hidden_states = transformer_outputs[0]
1342
+ logits = self.score(hidden_states)
1343
+
1344
+ if input_ids is not None:
1345
+ batch_size = input_ids.shape[0]
1346
+ else:
1347
+ batch_size = inputs_embeds.shape[0]
1348
+
1349
+ if self.config.pad_token_id is None and batch_size != 1:
1350
+ raise ValueError('Cannot handle batch sizes > 1 if no padding token is defined.')
1351
+ if self.config.pad_token_id is None:
1352
+ sequence_lengths = -1
1353
+ else:
1354
+ if input_ids is not None:
1355
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
1356
+ logits.device
1357
+ )
1358
+ else:
1359
+ sequence_lengths = -1
1360
+
1361
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1362
+
1363
+ loss = None
1364
+ if labels is not None:
1365
+ labels = labels.to(logits.device)
1366
+ if self.config.problem_type is None:
1367
+ if self.num_labels == 1:
1368
+ self.config.problem_type = 'regression'
1369
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1370
+ self.config.problem_type = 'single_label_classification'
1371
+ else:
1372
+ self.config.problem_type = 'multi_label_classification'
1373
+
1374
+ if self.config.problem_type == 'regression':
1375
+ loss_fct = MSELoss()
1376
+ if self.num_labels == 1:
1377
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1378
+ else:
1379
+ loss = loss_fct(pooled_logits, labels)
1380
+ elif self.config.problem_type == 'single_label_classification':
1381
+ loss_fct = CrossEntropyLoss()
1382
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1383
+ elif self.config.problem_type == 'multi_label_classification':
1384
+ loss_fct = BCEWithLogitsLoss()
1385
+ loss = loss_fct(pooled_logits, labels)
1386
+ if not return_dict:
1387
+ output = (pooled_logits,) + transformer_outputs[1:]
1388
+ return ((loss,) + output) if loss is not None else output
1389
+
1390
+ return SequenceClassifierOutputWithPast(
1391
+ loss=loss,
1392
+ logits=pooled_logits,
1393
+ past_key_values=transformer_outputs.past_key_values,
1394
+ hidden_states=transformer_outputs.hidden_states,
1395
+ attentions=transformer_outputs.attentions,
1396
+ )
modeling_internvl_chat.py ADDED
@@ -0,0 +1,370 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2023 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+ import warnings
7
+ from typing import Any, List, Optional, Tuple, Union
8
+
9
+ import torch.utils.checkpoint
10
+ from peft import LoraConfig, get_peft_model
11
+ from torch import nn
12
+ from torch.nn import CrossEntropyLoss
13
+ from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
14
+ LlamaTokenizer)
15
+ from transformers.modeling_outputs import CausalLMOutputWithPast
16
+ from transformers.modeling_utils import PreTrainedModel
17
+ from transformers.utils import ModelOutput, logging
18
+
19
+ from .configuration_internvl_chat import InternVLChatConfig
20
+ from .modeling_intern_vit import InternVisionModel
21
+ from .modeling_internlm2 import InternLM2ForCausalLM
22
+
23
+ logger = logging.get_logger(__name__)
24
+
25
+
26
+ def window_partition(x, window_size):
27
+ """
28
+ Args:
29
+ x: (B, C, H, W)
30
+ window_size (int): window size, assuming square window
31
+
32
+ Returns:
33
+ windows: (num_windows*B, C, window_size, window_size)
34
+ """
35
+ B, C, H, W = x.shape
36
+ assert H % window_size == 0 and W % window_size == 0, 'H and W must be divisible by window_size'
37
+
38
+ x = x.view(B, C, H // window_size, window_size, W // window_size, window_size)
39
+ windows = x.permute(0, 2, 4, 1, 3, 5).contiguous().view(-1, C, window_size, window_size)
40
+ return windows
41
+
42
+
43
+ def window_reverse(windows, window_size, H, W):
44
+ """
45
+ Args:
46
+ windows: (num_windows*B, window_size, window_size, C)
47
+ window_size (int): Window size
48
+ H (int): Height of image
49
+ W (int): Width of image
50
+
51
+ Returns:
52
+ x: (B, H * W, C)
53
+ """
54
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
55
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
56
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H * W, -1)
57
+ return x
58
+
59
+
60
+ class InternVLChatModel(PreTrainedModel):
61
+ config_class = InternVLChatConfig
62
+ main_input_name = 'pixel_values'
63
+ _no_split_modules = ['InternVisionEncoderLayer', 'LlamaDecoderLayer', 'LlamaForCausalLM']
64
+
65
+ def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None):
66
+ super().__init__(config)
67
+
68
+ image_size = config.force_image_size or config.vision_config.image_size
69
+ patch_size = config.vision_config.patch_size
70
+ self.patch_size = patch_size
71
+ self.select_layer = config.select_layer
72
+ self.template = config.template
73
+ self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
74
+ self.downsample_ratio = config.downsample_ratio
75
+ self.image_fold = config.image_fold
76
+ self.ps_version = config.ps_version
77
+
78
+ logger.info(f'num_image_token: {self.num_image_token}')
79
+ logger.info(f'ps_version: {self.ps_version}')
80
+ if vision_model is not None:
81
+ self.vision_model = vision_model
82
+ else:
83
+ self.vision_model = InternVisionModel(config.vision_config)
84
+ if language_model is not None:
85
+ self.language_model = language_model
86
+ else:
87
+ if config.llm_config.architectures[0] == 'LlamaForCausalLM':
88
+ self.language_model = LlamaForCausalLM(config.llm_config)
89
+ elif config.llm_config.architectures[0] == 'InternLM2ForCausalLM':
90
+ self.language_model = InternLM2ForCausalLM(config.llm_config)
91
+ else:
92
+ raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
93
+
94
+ vit_hidden_size = config.vision_config.hidden_size
95
+ llm_hidden_size = config.llm_config.hidden_size
96
+
97
+ self.mlp1 = nn.Sequential(
98
+ nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
99
+ nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
100
+ nn.GELU(),
101
+ nn.Linear(llm_hidden_size, llm_hidden_size)
102
+ )
103
+
104
+ # if config.force_image_size != config.vision_config.image_size:
105
+ # self.vision_model.resize_pos_embeddings(
106
+ # old_size=config.vision_config.image_size,
107
+ # new_size=config.force_image_size,
108
+ # patch_size=config.vision_config.patch_size
109
+ # )
110
+
111
+ self.img_context_token_id = None
112
+ self.neftune_alpha = None
113
+
114
+ if config.use_backbone_lora:
115
+ self.wrap_backbone_lora(r=config.use_backbone_lora, lora_alpha=2 * config.use_backbone_lora)
116
+
117
+ if config.use_llm_lora:
118
+ self.wrap_llm_lora(r=config.use_llm_lora, lora_alpha=2 * config.use_llm_lora)
119
+
120
+ def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
121
+ lora_config = LoraConfig(
122
+ r=r,
123
+ target_modules=['attn.qkv', 'attn.proj', 'mlp.fc1', 'mlp.fc2'],
124
+ lora_alpha=lora_alpha,
125
+ lora_dropout=lora_dropout,
126
+ )
127
+ self.vision_model = get_peft_model(self.vision_model, lora_config)
128
+ self.vision_model.print_trainable_parameters()
129
+
130
+ def wrap_llm_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
131
+ lora_config = LoraConfig(
132
+ r=r,
133
+ target_modules=['self_attn.q_proj', 'self_attn.k_proj', 'self_attn.v_proj', 'self_attn.o_proj',
134
+ 'mlp.gate_proj', 'mlp.down_proj', 'mlp.up_proj'],
135
+ lora_alpha=lora_alpha,
136
+ lora_dropout=lora_dropout,
137
+ task_type='CAUSAL_LM'
138
+ )
139
+ self.language_model = get_peft_model(self.language_model, lora_config)
140
+ self.language_model.enable_input_require_grads()
141
+ self.language_model.print_trainable_parameters()
142
+
143
+ def forward(
144
+ self,
145
+ pixel_values: torch.FloatTensor,
146
+ input_ids: torch.LongTensor = None,
147
+ attention_mask: Optional[torch.Tensor] = None,
148
+ position_ids: Optional[torch.LongTensor] = None,
149
+ image_flags: Optional[torch.LongTensor] = None,
150
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
151
+ labels: Optional[torch.LongTensor] = None,
152
+ use_cache: Optional[bool] = None,
153
+ output_attentions: Optional[bool] = None,
154
+ output_hidden_states: Optional[bool] = None,
155
+ return_dict: Optional[bool] = None,
156
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
157
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
158
+
159
+ image_flags = image_flags.squeeze(-1)
160
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
161
+
162
+ vit_embeds = self.extract_feature(pixel_values)
163
+ vit_embeds = vit_embeds[image_flags == 1]
164
+ vit_batch_size = pixel_values.shape[0]
165
+
166
+ B, N, C = input_embeds.shape
167
+ input_embeds = input_embeds.reshape(B * N, C)
168
+
169
+ if torch.distributed.get_rank() == 0:
170
+ print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
171
+
172
+ input_ids = input_ids.reshape(B * N)
173
+ selected = (input_ids == self.img_context_token_id)
174
+ try:
175
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
176
+ except Exception as e:
177
+ vit_embeds = vit_embeds.reshape(-1, C)
178
+ print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
179
+ f'vit_embeds.shape={vit_embeds.shape}')
180
+ n_token = selected.sum()
181
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
182
+
183
+ input_embeds = input_embeds.reshape(B, N, C)
184
+
185
+ outputs = self.language_model(
186
+ inputs_embeds=input_embeds,
187
+ attention_mask=attention_mask,
188
+ position_ids=position_ids,
189
+ past_key_values=past_key_values,
190
+ use_cache=use_cache,
191
+ output_attentions=output_attentions,
192
+ output_hidden_states=output_hidden_states,
193
+ return_dict=return_dict,
194
+ )
195
+ logits = outputs.logits
196
+
197
+ loss = None
198
+ if labels is not None:
199
+ # Shift so that tokens < n predict n
200
+ shift_logits = logits[..., :-1, :].contiguous()
201
+ shift_labels = labels[..., 1:].contiguous()
202
+ # Flatten the tokens
203
+ loss_fct = CrossEntropyLoss()
204
+ shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
205
+ shift_labels = shift_labels.view(-1)
206
+ # Enable model parallelism
207
+ shift_labels = shift_labels.to(shift_logits.device)
208
+ loss = loss_fct(shift_logits, shift_labels)
209
+
210
+ if not return_dict:
211
+ output = (logits,) + outputs[1:]
212
+ return (loss,) + output if loss is not None else output
213
+
214
+ return CausalLMOutputWithPast(
215
+ loss=loss,
216
+ logits=logits,
217
+ past_key_values=outputs.past_key_values,
218
+ hidden_states=outputs.hidden_states,
219
+ attentions=outputs.attentions,
220
+ )
221
+
222
+ def pixel_shuffle(self, x, scale_factor=0.5):
223
+ n, w, h, c = x.size()
224
+ # N, W, H, C --> N, W, H * scale, C // scale
225
+ x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
226
+ # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
227
+ x = x.permute(0, 2, 1, 3).contiguous()
228
+ # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
229
+ x = x.view(n, int(h * scale_factor), int(w * scale_factor),
230
+ int(c / (scale_factor * scale_factor)))
231
+ if self.ps_version == 'v1':
232
+ warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
233
+ 'which results in a transposed image.')
234
+ else:
235
+ x = x.permute(0, 2, 1, 3).contiguous()
236
+ return x
237
+
238
+ def noised_embed(self, vit_embeds, noise_alpha=5):
239
+ dims = torch.tensor(vit_embeds.size(1) * vit_embeds.size(2))
240
+ mag_norm = noise_alpha / torch.sqrt(dims)
241
+ noise = torch.zeros_like(vit_embeds).uniform_(-mag_norm, mag_norm)
242
+ return vit_embeds + noise
243
+
244
+ def extract_feature(self, pixel_values):
245
+ if self.image_fold:
246
+ image_size = pixel_values.size(-1) # B, C, H, W
247
+ pixel_values = window_partition(pixel_values, window_size=image_size // self.image_fold) # 4B, C, H/2, W/2
248
+
249
+ if self.select_layer == -1:
250
+ vit_embeds = self.vision_model(
251
+ pixel_values=pixel_values,
252
+ output_hidden_states=False,
253
+ return_dict=True).last_hidden_state
254
+ else:
255
+ vit_embeds = self.vision_model(
256
+ pixel_values=pixel_values,
257
+ output_hidden_states=True,
258
+ return_dict=True).hidden_states[self.select_layer]
259
+ vit_embeds = vit_embeds[:, 1:, :]
260
+
261
+ if self.training and self.neftune_alpha is not None:
262
+ vit_embeds = self.noised_embed(vit_embeds, self.neftune_alpha)
263
+
264
+ if self.image_fold:
265
+ vit_embeds = window_reverse(vit_embeds, window_size=image_size // (self.image_fold * self.patch_size),
266
+ H=image_size // self.patch_size, W=image_size // self.patch_size)
267
+
268
+ # if torch.distributed.get_rank() == 0:
269
+ # print("before pixel shuffle:", vit_embeds.shape)
270
+ h = w = int(vit_embeds.shape[1] ** 0.5)
271
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
272
+ vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
273
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
274
+ # if torch.distributed.get_rank() == 0:
275
+ # print("after pixel shuffle:", vit_embeds.shape)
276
+ vit_embeds = self.mlp1(vit_embeds)
277
+ return vit_embeds
278
+
279
+ def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
280
+ IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>'):
281
+
282
+ img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
283
+ self.img_context_token_id = img_context_token_id
284
+ if tokenizer.convert_tokens_to_ids('<|im_end|>') != 0:
285
+ eos_token_id = tokenizer.convert_tokens_to_ids('<|im_end|>') # 92542, InternLM2
286
+ else:
287
+ eos_token_id = tokenizer.eos_token_id
288
+
289
+ from .conversation import get_conv_template
290
+
291
+ template = get_conv_template(self.template)
292
+ image_bs = pixel_values.shape[0]
293
+ print(f'dynamic ViT batch size: {image_bs}')
294
+ if history is None:
295
+ history = []
296
+ image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * image_bs + IMG_END_TOKEN
297
+ question = image_tokens + '\n' + question
298
+ else:
299
+ for (old_question, old_answer) in history:
300
+ template.append_message(template.roles[0], old_question)
301
+ template.append_message(template.roles[1], old_answer)
302
+ template.append_message(template.roles[0], question)
303
+ template.append_message(template.roles[1], None)
304
+ query = template.get_prompt()
305
+ model_inputs = tokenizer(query, return_tensors='pt')
306
+ input_ids = model_inputs['input_ids'].cuda()
307
+ attention_mask = model_inputs['attention_mask'].cuda()
308
+ generation_config['eos_token_id'] = eos_token_id
309
+ generation_output = self.generate(
310
+ pixel_values=pixel_values,
311
+ input_ids=input_ids,
312
+ attention_mask=attention_mask,
313
+ **generation_config
314
+ )
315
+ response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
316
+ response = response.split('<|im_end|>')[0].strip() # for InternLM2
317
+ history.append((question, response))
318
+ if return_history:
319
+ return response, history
320
+ else:
321
+ query_to_print = query.replace(image_tokens, '<image>')
322
+ print(query_to_print, response)
323
+ return response
324
+ return response
325
+
326
+ @torch.no_grad()
327
+ def generate(
328
+ self,
329
+ pixel_values: Optional[torch.FloatTensor] = None,
330
+ input_ids: Optional[torch.FloatTensor] = None,
331
+ attention_mask: Optional[torch.LongTensor] = None,
332
+ visual_features: Optional[torch.FloatTensor] = None,
333
+ generation_config: Optional[GenerationConfig] = None,
334
+ output_hidden_states: Optional[bool] = None,
335
+ return_dict: Optional[bool] = None,
336
+ **generate_kwargs,
337
+ ) -> torch.LongTensor:
338
+
339
+ assert self.img_context_token_id is not None
340
+ if pixel_values is not None:
341
+ if visual_features is not None:
342
+ vit_embeds = visual_features
343
+ else:
344
+ vit_embeds = self.extract_feature(pixel_values)
345
+
346
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
347
+ B, N, C = input_embeds.shape
348
+ input_embeds = input_embeds.reshape(B * N, C)
349
+
350
+ input_ids = input_ids.reshape(B * N)
351
+ selected = (input_ids == self.img_context_token_id)
352
+ assert selected.sum() != 0
353
+ device = input_embeds.device
354
+ input_embeds[selected] = vit_embeds.reshape(-1, C).to(device)
355
+
356
+ input_embeds = input_embeds.reshape(B, N, C)
357
+ else:
358
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
359
+
360
+ outputs = self.language_model.generate(
361
+ inputs_embeds=input_embeds,
362
+ attention_mask=attention_mask,
363
+ generation_config=generation_config,
364
+ output_hidden_states=output_hidden_states,
365
+ return_dict=return_dict,
366
+ use_cache=True,
367
+ **generate_kwargs,
368
+ )
369
+
370
+ return outputs
runs/Apr15_16-44-40_SH-IDC1-10-140-37-13/events.out.tfevents.1713171220.SH-IDC1-10-140-37-13.204150.0 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:294d5bf755e6dea5c005c57af52e958a38bb42a7d17d801a25a6543bfe6ddca2
3
+ size 16662
runs/Apr15_17-33-22_SH-IDC1-10-140-37-13/events.out.tfevents.1713174123.SH-IDC1-10-140-37-13.259480.0 ADDED
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1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:57d61c0e776bfb521e58febdbd99525e011f82137ceaaa655ffa6e2b3a9b02a9
3
+ size 72471
runs/Apr15_22-00-14_SH-IDC1-10-140-37-13/events.out.tfevents.1713190241.SH-IDC1-10-140-37-13.10620.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:f4af0170833e3dadcfb87565da240daaa939c7d320917b411bd917eb6a65650d
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+ size 799892
special_tokens_map.json ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<img>",
4
+ "</img>",
5
+ "<IMG_CONTEXT>",
6
+ "<quad>",
7
+ "</quad>",
8
+ "<ref>",
9
+ "</ref>",
10
+ "<box>",
11
+ "</box>"
12
+ ],
13
+ "bos_token": {
14
+ "content": "<s>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ },
20
+ "eos_token": {
21
+ "content": "</s>",
22
+ "lstrip": false,
23
+ "normalized": false,
24
+ "rstrip": false,
25
+ "single_word": false
26
+ },
27
+ "pad_token": {
28
+ "content": "</s>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false
33
+ },
34
+ "unk_token": {
35
+ "content": "<unk>",
36
+ "lstrip": false,
37
+ "normalized": false,
38
+ "rstrip": false,
39
+ "single_word": false
40
+ }
41
+ }
tokenization_internlm2.py ADDED
@@ -0,0 +1,235 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+
17
+ """Tokenization classes for InternLM."""
18
+ import os
19
+ from shutil import copyfile
20
+ from typing import Any, Dict, List, Optional, Tuple
21
+
22
+ import sentencepiece as spm
23
+ from transformers.tokenization_utils import PreTrainedTokenizer
24
+ from transformers.utils import logging
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+ VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
29
+
30
+ PRETRAINED_VOCAB_FILES_MAP = {}
31
+
32
+
33
+ # Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
34
+ class InternLM2Tokenizer(PreTrainedTokenizer):
35
+ """
36
+ Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
37
+
38
+ Args:
39
+ vocab_file (`str`):
40
+ Path to the vocabulary file.
41
+ """
42
+
43
+ vocab_files_names = VOCAB_FILES_NAMES
44
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
45
+ model_input_names = ['input_ids', 'attention_mask']
46
+ _auto_class = 'AutoTokenizer'
47
+
48
+ def __init__(
49
+ self,
50
+ vocab_file,
51
+ unk_token='<unk>',
52
+ bos_token='<s>',
53
+ eos_token='</s>',
54
+ pad_token='</s>',
55
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
56
+ add_bos_token=True,
57
+ add_eos_token=False,
58
+ decode_with_prefix_space=False,
59
+ clean_up_tokenization_spaces=False,
60
+ **kwargs,
61
+ ):
62
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
63
+ self.vocab_file = vocab_file
64
+ self.add_bos_token = add_bos_token
65
+ self.add_eos_token = add_eos_token
66
+ self.decode_with_prefix_space = decode_with_prefix_space
67
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
68
+ self.sp_model.Load(vocab_file)
69
+ self._no_prefix_space_tokens = None
70
+ super().__init__(
71
+ bos_token=bos_token,
72
+ eos_token=eos_token,
73
+ unk_token=unk_token,
74
+ pad_token=pad_token,
75
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
76
+ **kwargs,
77
+ )
78
+
79
+ @property
80
+ def no_prefix_space_tokens(self):
81
+ if self._no_prefix_space_tokens is None:
82
+ vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
83
+ self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith('▁')}
84
+ return self._no_prefix_space_tokens
85
+
86
+ @property
87
+ def vocab_size(self):
88
+ """Returns vocab size"""
89
+ return self.sp_model.get_piece_size()
90
+
91
+ @property
92
+ def bos_token_id(self) -> Optional[int]:
93
+ return self.sp_model.bos_id()
94
+
95
+ @property
96
+ def eos_token_id(self) -> Optional[int]:
97
+ return self.sp_model.eos_id()
98
+
99
+ def get_vocab(self):
100
+ """Returns vocab as a dict"""
101
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
102
+ vocab.update(self.added_tokens_encoder)
103
+ return vocab
104
+
105
+ def _tokenize(self, text):
106
+ """Returns a tokenized string."""
107
+ return self.sp_model.encode(text, out_type=str)
108
+
109
+ def _convert_token_to_id(self, token):
110
+ """Converts a token (str) in an id using the vocab."""
111
+ return self.sp_model.piece_to_id(token)
112
+
113
+ def _convert_id_to_token(self, index):
114
+ """Converts an index (integer) in a token (str) using the vocab."""
115
+ token = self.sp_model.IdToPiece(index)
116
+ return token
117
+
118
+ def _maybe_add_prefix_space(self, tokens, decoded):
119
+ if tokens and tokens[0] not in self.no_prefix_space_tokens:
120
+ return ' ' + decoded
121
+ else:
122
+ return decoded
123
+
124
+ def convert_tokens_to_string(self, tokens):
125
+ """Converts a sequence of tokens (string) in a single string."""
126
+ current_sub_tokens = []
127
+ out_string = ''
128
+ prev_is_special = False
129
+ for token in tokens:
130
+ # make sure that special tokens are not decoded using sentencepiece model
131
+ if token in self.all_special_tokens:
132
+ if not prev_is_special:
133
+ out_string += ' '
134
+ out_string += self.sp_model.decode(current_sub_tokens) + token
135
+ prev_is_special = True
136
+ current_sub_tokens = []
137
+ else:
138
+ current_sub_tokens.append(token)
139
+ prev_is_special = False
140
+ out_string += self.sp_model.decode(current_sub_tokens)
141
+ out_string = self.clean_up_tokenization(out_string)
142
+ out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
143
+ return out_string[1:]
144
+
145
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
146
+ """
147
+ Save the vocabulary and special tokens file to a directory.
148
+
149
+ Args:
150
+ save_directory (`str`):
151
+ The directory in which to save the vocabulary.
152
+
153
+ Returns:
154
+ `Tuple(str)`: Paths to the files saved.
155
+ """
156
+ if not os.path.isdir(save_directory):
157
+ logger.error(f'Vocabulary path ({save_directory}) should be a directory')
158
+ return
159
+ out_vocab_file = os.path.join(
160
+ save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
161
+ )
162
+
163
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
164
+ copyfile(self.vocab_file, out_vocab_file)
165
+ elif not os.path.isfile(self.vocab_file):
166
+ with open(out_vocab_file, 'wb') as fi:
167
+ content_spiece_model = self.sp_model.serialized_model_proto()
168
+ fi.write(content_spiece_model)
169
+
170
+ return (out_vocab_file,)
171
+
172
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
173
+ if self.add_bos_token:
174
+ bos_token_ids = [self.bos_token_id]
175
+ else:
176
+ bos_token_ids = []
177
+
178
+ output = bos_token_ids + token_ids_0
179
+
180
+ if token_ids_1 is not None:
181
+ output = output + token_ids_1
182
+
183
+ if self.add_eos_token:
184
+ output = output + [self.eos_token_id]
185
+
186
+ return output
187
+
188
+ def get_special_tokens_mask(
189
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
190
+ ) -> List[int]:
191
+ """
192
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
193
+ special tokens using the tokenizer `prepare_for_model` method.
194
+
195
+ Args:
196
+ token_ids_0 (`List[int]`):
197
+ List of IDs.
198
+ token_ids_1 (`List[int]`, *optional*):
199
+ Optional second list of IDs for sequence pairs.
200
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
201
+ Whether or not the token list is already formatted with special tokens for the model.
202
+
203
+ Returns:
204
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
205
+ """
206
+ if already_has_special_tokens:
207
+ return super().get_special_tokens_mask(
208
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
209
+ )
210
+
211
+ if token_ids_1 is None:
212
+ return [1] + ([0] * len(token_ids_0)) + [1]
213
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
214
+
215
+ def create_token_type_ids_from_sequences(
216
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
217
+ ) -> List[int]:
218
+ """
219
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
220
+ use of token type ids, therefore a list of zeros is returned.
221
+
222
+ Args:
223
+ token_ids_0 (`List[int]`):
224
+ List of IDs.
225
+ token_ids_1 (`List[int]`, *optional*):
226
+ Optional second list of IDs for sequence pairs.
227
+
228
+ Returns:
229
+ `List[int]`: List of zeros.
230
+ """
231
+ eos = [self.eos_token_id]
232
+
233
+ if token_ids_1 is None:
234
+ return len(token_ids_0 + eos) * [0]
235
+ return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
tokenization_internlm2_fast.py ADDED
@@ -0,0 +1,211 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+
17
+ """Tokenization Fast class for InternLM."""
18
+ import os
19
+ from shutil import copyfile
20
+ from typing import Any, Dict, Optional, Tuple
21
+
22
+ from tokenizers import Tokenizer, decoders, normalizers, processors
23
+ from tokenizers.models import BPE
24
+ from transformers.convert_slow_tokenizer import (SLOW_TO_FAST_CONVERTERS,
25
+ SentencePieceExtractor,
26
+ SpmConverter)
27
+ from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
28
+ from transformers.utils import logging
29
+
30
+ from .tokenization_internlm2 import InternLM2Tokenizer
31
+
32
+ logger = logging.get_logger(__name__)
33
+
34
+ VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
35
+
36
+
37
+ # Modified from transformers.convert_slow_tokenizer.LlamaConverter
38
+ class InternLM2Converter(SpmConverter):
39
+ handle_byte_fallback = True
40
+
41
+ def vocab(self, proto):
42
+ vocab = [
43
+ ('<unk>', 0.0),
44
+ ('<s>', 0.0),
45
+ ('</s>', 0.0),
46
+ ]
47
+ vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
48
+ return vocab
49
+
50
+ def unk_id(self, proto):
51
+ unk_id = 0
52
+ return unk_id
53
+
54
+ def decoder(self, replacement, add_prefix_space):
55
+ return decoders.Sequence(
56
+ [
57
+ decoders.Replace('▁', ' '),
58
+ decoders.ByteFallback(),
59
+ decoders.Fuse(),
60
+ decoders.Strip(content=' ', left=1),
61
+ ]
62
+ )
63
+
64
+ def tokenizer(self, proto):
65
+ model_type = proto.trainer_spec.model_type
66
+ vocab_scores = self.vocab(proto)
67
+ # special tokens
68
+ added_tokens = self.original_tokenizer.added_tokens_decoder
69
+ for i in range(len(vocab_scores)):
70
+ piece, score = vocab_scores[i]
71
+ if i in added_tokens:
72
+ vocab_scores[i] = (added_tokens[i].content, score)
73
+ if model_type == 1:
74
+ raise RuntimeError('InternLM2 is supposed to be a BPE model!')
75
+
76
+ elif model_type == 2:
77
+ _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
78
+ bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
79
+ tokenizer = Tokenizer(
80
+ BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
81
+ )
82
+ tokenizer.add_special_tokens(
83
+ [ added_token for index, added_token in added_tokens.items()]
84
+ )
85
+ else:
86
+ raise Exception(
87
+ "You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
88
+ )
89
+
90
+ return tokenizer
91
+
92
+ def normalizer(self, proto):
93
+ normalizers_list = []
94
+ if proto.normalizer_spec.add_dummy_prefix:
95
+ normalizers_list.append(normalizers.Prepend(prepend='▁'))
96
+ normalizers_list.append(normalizers.Replace(pattern=' ', content='▁'))
97
+ return normalizers.Sequence(normalizers_list)
98
+
99
+ def pre_tokenizer(self, replacement, add_prefix_space):
100
+ return None
101
+
102
+
103
+ SLOW_TO_FAST_CONVERTERS['InternLM2Tokenizer'] = InternLM2Converter
104
+
105
+
106
+ # Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
107
+ class InternLM2TokenizerFast(PreTrainedTokenizerFast):
108
+ vocab_files_names = VOCAB_FILES_NAMES
109
+ slow_tokenizer_class = InternLM2Tokenizer
110
+ padding_side = 'left'
111
+ model_input_names = ['input_ids', 'attention_mask']
112
+ _auto_class = 'AutoTokenizer'
113
+
114
+ def __init__(
115
+ self,
116
+ vocab_file,
117
+ unk_token='<unk>',
118
+ bos_token='<s>',
119
+ eos_token='</s>',
120
+ pad_token='</s>',
121
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
122
+ add_bos_token=True,
123
+ add_eos_token=False,
124
+ decode_with_prefix_space=False,
125
+ clean_up_tokenization_spaces=False,
126
+ **kwargs,
127
+ ):
128
+ super().__init__(
129
+ vocab_file=vocab_file,
130
+ unk_token=unk_token,
131
+ bos_token=bos_token,
132
+ eos_token=eos_token,
133
+ pad_token=pad_token,
134
+ sp_model_kwargs=sp_model_kwargs,
135
+ add_bos_token=add_bos_token,
136
+ add_eos_token=add_eos_token,
137
+ decode_with_prefix_space=decode_with_prefix_space,
138
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
139
+ **kwargs,
140
+ )
141
+ self._add_bos_token = add_bos_token
142
+ self._add_eos_token = add_eos_token
143
+ self.update_post_processor()
144
+ self.vocab_file = vocab_file
145
+
146
+ @property
147
+ def can_save_slow_tokenizer(self) -> bool:
148
+ return os.path.isfile(self.vocab_file) if self.vocab_file else False
149
+
150
+ def update_post_processor(self):
151
+ """
152
+ Updates the underlying post processor with the current `bos_token` and `eos_token`.
153
+ """
154
+ bos = self.bos_token
155
+ bos_token_id = self.bos_token_id
156
+ if bos is None and self.add_bos_token:
157
+ raise ValueError('add_bos_token = True but bos_token = None')
158
+
159
+ eos = self.eos_token
160
+ eos_token_id = self.eos_token_id
161
+ if eos is None and self.add_eos_token:
162
+ raise ValueError('add_eos_token = True but eos_token = None')
163
+
164
+ single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
165
+ pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
166
+
167
+ special_tokens = []
168
+ if self.add_bos_token:
169
+ special_tokens.append((bos, bos_token_id))
170
+ if self.add_eos_token:
171
+ special_tokens.append((eos, eos_token_id))
172
+ self._tokenizer.post_processor = processors.TemplateProcessing(
173
+ single=single, pair=pair, special_tokens=special_tokens
174
+ )
175
+
176
+ @property
177
+ def add_eos_token(self):
178
+ return self._add_eos_token
179
+
180
+ @property
181
+ def add_bos_token(self):
182
+ return self._add_bos_token
183
+
184
+ @add_eos_token.setter
185
+ def add_eos_token(self, value):
186
+ self._add_eos_token = value
187
+ self.update_post_processor()
188
+
189
+ @add_bos_token.setter
190
+ def add_bos_token(self, value):
191
+ self._add_bos_token = value
192
+ self.update_post_processor()
193
+
194
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
195
+ if not self.can_save_slow_tokenizer:
196
+ raise ValueError(
197
+ 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
198
+ 'tokenizer.'
199
+ )
200
+
201
+ if not os.path.isdir(save_directory):
202
+ logger.error(f'Vocabulary path ({save_directory}) should be a directory')
203
+ return
204
+ out_vocab_file = os.path.join(
205
+ save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
206
+ )
207
+
208
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
209
+ copyfile(self.vocab_file, out_vocab_file)
210
+
211
+ return (out_vocab_file,)
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
3
+ size 1477754
tokenizer_config.json ADDED
@@ -0,0 +1,173 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<unk>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<s>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "92538": {
28
+ "content": "<|plugin|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "92539": {
36
+ "content": "<|interpreter|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "92540": {
44
+ "content": "<|action_end|>",
45
+ "lstrip": false,
46
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
50
+ },
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+ "92541": {
52
+ "content": "<|action_start|>",
53
+ "lstrip": false,
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+ "rstrip": false,
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+ "single_word": false,
57
+ "special": true
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+ },
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+ "92542": {
60
+ "content": "<|im_end|>",
61
+ "lstrip": false,
62
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+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "92543": {
68
+ "content": "<|im_start|>",
69
+ "lstrip": false,
70
+ "normalized": false,
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+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "92544": {
76
+ "content": "<img>",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "92545": {
84
+ "content": "</img>",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "92546": {
92
+ "content": "<IMG_CONTEXT>",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
+ "92547": {
100
+ "content": "<quad>",
101
+ "lstrip": false,
102
+ "normalized": false,
103
+ "rstrip": false,
104
+ "single_word": false,
105
+ "special": true
106
+ },
107
+ "92548": {
108
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109
+ "lstrip": false,
110
+ "normalized": false,
111
+ "rstrip": false,
112
+ "single_word": false,
113
+ "special": true
114
+ },
115
+ "92549": {
116
+ "content": "<ref>",
117
+ "lstrip": false,
118
+ "normalized": false,
119
+ "rstrip": false,
120
+ "single_word": false,
121
+ "special": true
122
+ },
123
+ "92550": {
124
+ "content": "</ref>",
125
+ "lstrip": false,
126
+ "normalized": false,
127
+ "rstrip": false,
128
+ "single_word": false,
129
+ "special": true
130
+ },
131
+ "92551": {
132
+ "content": "<box>",
133
+ "lstrip": false,
134
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135
+ "rstrip": false,
136
+ "single_word": false,
137
+ "special": true
138
+ },
139
+ "92552": {
140
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141
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142
+ "normalized": false,
143
+ "rstrip": false,
144
+ "single_word": false,
145
+ "special": true
146
+ }
147
+ },
148
+ "additional_special_tokens": [
149
+ "<img>",
150
+ "</img>",
151
+ "<IMG_CONTEXT>",
152
+ "<quad>",
153
+ "</quad>",
154
+ "<ref>",
155
+ "</ref>",
156
+ "<box>",
157
+ "</box>"
158
+ ],
159
+ "auto_map": {
160
+ "AutoTokenizer": [
161
+ "tokenization_internlm2.InternLM2Tokenizer",
162
+ null
163
+ ]
164
+ },
165
+ "bos_token": "<s>",
166
+ "chat_template": "{{ bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
167
+ "clean_up_tokenization_spaces": false,
168
+ "eos_token": "</s>",
169
+ "model_max_length": 4096,
170
+ "pad_token": "</s>",
171
+ "tokenizer_class": "InternLM2Tokenizer",
172
+ "unk_token": "<unk>"
173
+ }