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LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models

🤗 LongRecipe-Llama3-8B-128k • 🤗 LongRecipe-Qwen2-7B-128k • 📃 Paper

Project Directory Structure

LongRecipe/
├── accelerate_configs/
│   ├── config_files
├── utils/
│   └── preprocess_token_PI/
│     ├── dataprocessor.py
│     └── FSProcessor.py
│   └── easy_context/
│     ├── dist_flash_attn/
│     ├── ulysses_attn/
│     └── zigzag_ring_attn/
│   ├── loader.py
│   ├── logger.py
│   └── preprocess_data.py
├── README.md
├── train_LR_llama3_target80k_use24k.sh
├── requirements.txt
└── train.py

Reproduction:

Before starting with the data preprocessing and model training, ensure that all necessary dependencies are installed. Use the following command to install the required packages:

pip install -r requirements.txt

Data Preprocessing (Example: Llama3)

To begin, download the dataset represented by the Llama3 tokenizer from this link. After downloading, execute the following command to generate the position index files for different training approaches:

# Command to load dataset and generate position index files
python preprocess_token_PI/dataprocessor.py

Model Training：

The model training process is divided into three distinct stages to effectively extend the context window of the LLM while maintaining its original capabilities.

Context Window Extension

In the first stage, we extend the context window using a dataset containing 1.7B tokens. The following command initiates this training stage:

accelerate launch \
--config_file accelerate_configs/single_node.yaml \
train.py \
--batch-size 1 \
--gradient-accumulate-every 96 \
--learning-rate 5e-5 \
--epoch 1 \
--data_path $DATA_PATH_CONTEXT_EXTENSION \
--output-dir  ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL \
--seed 2027 \
--model $MODEL \
--seq-length $SEQ_LENGTH \
--target-length $TARGET_LENGTH \
--log-path $SETTING-$SEQ_LENGTH-$MODEL_NAME-$SUB_LABEL.log \
--setting $SETTING \
--right_points-path $Right_Points_PATH \
--fs_PI-path $FS_PI_PATH \
--parallel_mode ulysses_attn \
--num_proc 5 \
--stage 0

Arguments Explanation:

  • --data_path: Path to the dataset with Llama3-tokenized samples.
  • --model: The base model used for training.
  • --seq-length: The sequence length for training.
  • --target-length: The target context window length.
  • --setting: The training method, which could include FLT, RPES, PoSE, LongRecipe.
  • --right_points-path: Path to the PoSE right point set file.
  • --fs_PI-path: Path to the LongRecipe’s position index file.

Post-training, copy the tokenizer files to the output directory and remove any unnecessary files:

cp $MODEL/special_tokens_map.json ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL/stage_0
cp $MODEL/tokenizer_config.json ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL/stage_0
cp $MODEL/tokenizer.json ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL/stage_0
rm ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL/stage_0/model.safetensors

Stage 2: Training Annealing

In the second stage, we perform training annealing using both general and domain-specific data, gradually reducing the learning rate to zero. Approximately 100M tokens of data are used in this phase.

accelerate launch \
--config_file accelerate_configs/single_node_2.yaml \
train.py \
--data_path $DATA_PATH_ANNEALING \
--batch-size 1 \
--gradient-accumulate-every 96 \
--learning-rate 5e-6 \
--epoch 1 \
--output-dir  ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL \
--seed 2027 \
--model $STAGE_1_MODEL \
--seq-length $SEQ_LENGTH \
--target-length $TARGET_LENGTH \
--log-path $SETTING-$SEQ_LENGTH-$MODEL_NAME-$SUB_LABEL.log \
--setting $SETTING \
--right_points-path $Right_Points_PATH \
--fs_PI-path $FS_PI_PATH \
--parallel_mode ulysses_attn \
--num_proc 10 \
--stage 1

Copy the updated tokenizer files to the output directory:

cp $MODEL/special_tokens_map.json ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL/stage_1
cp $MODEL/tokenizer_config.json ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL/stage_1
cp $MODEL/tokenizer.json ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL/stage_1
rm ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL/stage_1/model.safetensors

In our experiment, we merge the two datasets mentioned together in out paper, and format each sample as follows:

{
  "prompt": ,
  "response": 
}

Stage 3: Model Merge

The final stage involves merging the original model with the fine-tuned model using an average weight strategy to enhance the model's foundational capabilities.

accelerate launch \
--config_file accelerate_configs/single_node.yaml \
train.py \
accelerate launch \
--config_file accelerate_configs/single_node.yaml \
train.py \
--output-dir  ./output/$MODEL_NAME-$SETTING-$SEQ_LENGTH-$SUB_LABEL \
--seed 2027 \
--model $MODEL \
--log-path $SETTING-$SEQ_LENGTH-$MODEL_NAME-$SUB_LABEL.log \
--stage 2

You can also run

bash ./train_scirpts/train_LR_llama3_target80k_use24k.sh

after preprocess your data to do the three stage in one command.

Citation

If you find this repo helpful, please cite our paper as follows:

@article{hu2024longrecipe,
  title={LongRecipe: Recipe for Efficient Long Context Generalization in Large Languge Models},
  author={Zhiyuan Hu, Yuliang Liu, Jinman Zhao, Suyuchen Wang, Yan Wang, Wei Shen, Qing Gu, Anh Tuan Luu, See-Kiong Ng, Zhiwei Jiang, Bryan Hooi},
  journal={arXiv preprint arXiv:2409.00509},
  year={2024}
}