Our paper Forgetting Curve: A Reliable Method for Evaluating Memorization Capability for Long-context Models has been accepted by EMNLP 2024 main.
The picture of their forgetting curve at Picture
[2024年10月9日] We have newly tested TTT and RWKV
[2024年10月15日] We have newly tested gemma-2, MiniCMP3, Qwen2, Yi-1.5
You can use our code online in this notebook.
- Environment Setup
- Model Preparation
- Drawing Forgetting Curves for Open-source Language Models on Hugging Face
- Drawing Forgetting Curves for Custom Models
git clone https://github.com/1azybug/ForgettingCurve.git
cd ForgettingCurve
conda create -n forget python=3.10 -y
conda activate forget
# [cuda12.1 for xformers installation] https://github.com/facebookresearch/xformers?tab=readme-ov-file#installing-xformers
conda install pytorch==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu121 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
(Optional) You can refer to download.py to download models in advance. Some models require an HF Token, such as meta-llama/Meta-Llama-3-8B
HF_API_TOKEN="your_access_token" python download.py
model_id is the model on Hugging Face
--repeat_time controls the number of times each length is tested repeatedly
--granularity controls the number of equally divided points
--granularity_type "linear" means the length increases linearly, "log" means the length increases exponentially
--test_max_length represents the maximum length for testing
--training_len draws a reference line, generally the training length
The following code draws the forgetting curve of meta-llama/Meta-Llama-3-8B within a dependency length of 16000 (actually the longest token sequence reaches 32000). It tests every 16000/32 length, repeated 10 times.
model_id="meta-llama/Meta-Llama-3-8B"
title=$(basename "$model_id" | cut -d'/' -f2)
CUDA_VISIBLE_DEVICES=0,1,2,3 python draw.py \
--model_id $model_id \
--title $title \
--repeat_time 10 \
--granularity 32 \
--granularity_type "linear" \
--data_type "order" \
--test_max_length 16000 \
--training_len 8000 \
--save_path "." \
--device "cuda"
For drawing forgetting curves of more models, please refer to test.sh
from draw import evaluate
evaluate(model, tokenizer, texts_or_ids=test_tokens, config=config)
model: custom model
tokenizer: corresponding tokenizer for the model, not needed when texts_or_ids is a token sequence tensor of shape [S]
texts_or_ids: can be a token sequence tensor after tokenization, str, or List[str]
For ordinary Transformer models, config is not needed.
For segment-level recurrent Transformers, you can implement a teacher_forcing_forward function in config for faster and more efficient inference, see example.py for details.
The input of teacher_forcing_forward is model, prompt_ids, and the output is predicted_token_ids.
一种可靠的方法来评估所有长上下文模型的记忆能力
git clone https://github.com/1azybug/ForgettingCurve.git
cd ForgettingCurve
conda create -n forget python=3.10 -y
conda activate forget
# [cuda12.1 用于 xformers安装] https://github.com/facebookresearch/xformers?tab=readme-ov-file#installing-xformers
conda install pytorch==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu121 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
(可选)可以参考download.py预先下载模型,有些模型需要HF的Token,如meta-llama/Meta-Llama-3-8B
HF_API_TOKEN="your_access_token" python download.py
model_id为Hugging Face上的模型
--repeat_time 控制每个长度重复测试的次数
--granularity 控制等分点的数量
--granularity_type "linear"表示长度线性增长,"log"表示长度以指数增长
--test_max_length 表示测试的最大长度
--training_len 绘制一个参考线,一般为训练长度
下面的代码表示绘制meta-llama/Meta-Llama-3-8B在依赖长度16000以内(实际上最长的token序列达32000)的遗忘曲线。每16000/32长度进行测试,重复测试10次。
model_id="meta-llama/Meta-Llama-3-8B"
title=$(basename "$model_id" | cut -d'/' -f2)
CUDA_VISIBLE_DEVICES=0,1,2,3 python draw.py \
--model_id $model_id \
--title $title \
--repeat_time 10 \
--granularity 32 \
--granularity_type "linear" \
--data_type "order" \
--test_max_length 16000 \
--training_len 8000 \
--save_path "." \
--device "cuda"
更多模型的遗忘曲线绘制可以参考test.sh
from draw import evaluate
evaluate(model, tokenizer, texts_or_ids=test_tokens, config=config)
model:自定义的模型
tokenizer:模型对应的分词器,当texts_or_ids是形状为[S]的token序列张量时不需要传入
texts_or_ids:可以是分词后的token序列张量、str或List[str]
对于普通的Transformer模型来说不需要使用config。
而对于segment-level循环的Transformer可以在config里实现一个teacher_forcing_forward的函数以进行更快更省的推理,详见example.py。
teacher_forcing_forward的输入是model, prompt_ids,输出是predicted_token_ids。
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