Tevatron V2

Tevatron aims to provide a flexible and efficient toolkit that enables training and inference for neural retrieval models at scale.

Some of the features in Tevatron v1 is not yet migrated to Tevatron v2. We are working on it. If you are looking for the Tevatron v1 features, please pull the v1 branch.

Features

• Training billion-scale LLM neural retriever on GPUs and TPUs.
• Parameter efficient tuning with LoRA.
• Integration with DeepSpeed, flash attention, gradient accumulation, and other efficient training techniques.
• Self-contained datasets for neural retrieval and open-domain QA tasks.
• Direct loading and finetuning SoTA pre-trained models (BGE-Embbedding, Instruct-E5) from HuggingFace.

Installation

PyTorch (GPU)

0. Clone the repository.
1. Install PyTorch based on your CUDA version from PyTorch.
2. Install dependencies and Tevatron.

pip install transformers datasets peft
pip install deepspeed accelerate
pip install faiss
pip install -e .

JAX (TPU)

0. Clone the repository.
1. Install JAX by following the official guide
2. Install dependencies

pip install transformers datasets
pip install flax optax

3. Install Magix and GradCache

git clone https://github.com/luyug/magix.git
cd magix && pip install -e . && cd ..
git clone https://github.com/luyug/GradCache.git
cd GradCache && pip install -e . && cd ..

4. Install Tevatron

pip install -e .

JAX (GPU)

To run the JAX implementation of Tevatron on GPU, we encourage using the jax-toolbox jax container image from NVIDIA.

Below is a Dockerfile example to set up Tevatron on top of the jax container.

FROM ghcr.io/nvidia/jax:jax-2024-03-08

RUN apt-get update && \
    apt-get install -y --no-install-recommends python3-pip && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/* && \
    pip install --no-cache-dir transformers sentencepiece simple_parsing datasets orbax==0.4.8 && \
    pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu

RUN git clone https://github.com/luyug/magix.git && \
    cd magix && pip install -e . && cd .. && \
    git clone https://github.com/luyug/GradCache.git \
    cd GradCache && pip install -e . && cd .. \
    git clone https://github.com/texttron/tevatron.git && \
    cd tevatron && pip install -e .

Tevatron 101

In this example, we will demonstrate how to use Tevatron to LoRA fine-tune a Mistral-7B model on the MSMARCO passage dataset. The obtained LLM Retriever is expected to have MRR@10=42.3 on the MS MARCO dev set with straightforward training.

Data Preparation

Tevatron takes training or inference data in jsonl format with each line organized as a json object as follows:

1. Training Data

{
   "query_id": "<query id>",
   "query": "<query text>",
   "positive_passages": [
     {"docid": "<passage id>", "title": "<passage title>", "text": "<passage body>"},
     ...
   ],
   "negative_passages": [
     {"docid": "<passage id>", "title": "<passage title>", "text": "<passage body>"},
     ...
   ]
}

where the passages in positive_passages are the annotated relevant passages of the query and passages in negative_passages are usually non-relevant (hard negative) passages from top results of a retrieval system (e.g. BM25, DPR). Additional fields such as answers for QA datasets can be included as well.

2. Corpus Data

{
   "docid": "<passage id>",
   "title": "<passage title>",
   "text": "<passage body>"
}

where each line represents a passage in the corpus.

Self-Contained Dataset

Tevatron self-contained several commonlly used datasets for neural retrieval. (via HuggingFace). These datasets can downloaded automatically during training and encoding by setting --dataset_name <hgf dataset name>.

In this example, we will use the self-contained dataset Tevatron/msmarco-passage-aug for training, whose hard negative passages are sampled from the mix of top200 BM25 and top200 CoCondenser results.

Run with PyTorch (GPU)

Training

deepspeed --include localhost:0,1,2,3 --master_port 60000 --module tevatron.retriever.driver.train \
  --deepspeed deepspeed/ds_zero3_config.json \
  --output_dir retriever-mistral \
  --model_name_or_path mistralai/Mistral-7B-v0.1 \
  --lora \
  --lora_target_modules q_proj,k_proj,v_proj,o_proj,down_proj,up_proj,gate_proj \
  --save_steps 50 \
  --dataset_name Tevatron/msmarco-passage-aug \
  --query_prefix "Query: " \
  --passage_prefix "Passage: " \
  --bf16 \
  --pooling eos \
  --append_eos_token \
  --normalize \
  --temperature 0.01 \
  --per_device_train_batch_size 8 \
  --gradient_checkpointing \
  --train_group_size 16 \
  --learning_rate 1e-4 \
  --query_max_len 32 \
  --passage_max_len 156 \
  --num_train_epochs 1 \
  --logging_steps 10 \
  --overwrite_output_dir \
  --gradient_accumulation_steps 4

In batch passages per query: 8x4x16 = 512

Number of queries per update: 8x4x4 = 128

The above training setting tooks about 70 hours on 4xA6000 GPU.

Equivalent training tooks about 110 hours on 1xA100 GPU.

Encoding

Query Encoding

EMBEDDING_OUTPUT_DIR=<folder to save query embedding>
CUDA_VISIBLE_DEVICES=4 python -m tevatron.retriever.driver.encode \
  --output_dir=temp \
  --model_name_or_path mistralai/Mistral-7B-v0.1 \
  --lora_name_or_path retriever-mistral \
  --lora \
  --query_prefix "Query: " \
  --passage_prefix "Passage: " \
  --bf16 \
  --pooling eos \
  --append_eos_token \
  --normalize \
  --encode_is_query \
  --per_device_eval_batch_size 128 \
  --query_max_len 32 \
  --passage_max_len 156 \
  --dataset_name Tevatron/msmarco-passage \
  --dataset_split dev \
  --encode_output_path $EMBEDDING_OUTPUT_DIR/query-dev.pkl

Corpus Encoding

EMBEDDING_OUTPUT_DIR=<folder to save query embedding>
for s in 0 1 2 3
do
gpuid=$s
CUDA_VISIBLE_DEVICES=$gpuid python -m tevatron.retriever.driver.encode \
  --output_dir=temp \
  --model_name_or_path mistralai/Mistral-7B-v0.1 \
  --lora_name_or_path retriever-mistral \
  --lora \
  --query_prefix "Query: " \
  --passage_prefix "Passage: " \
  --bf16 \
  --pooling eos \
  --append_eos_token \
  --normalize \
  --per_device_eval_batch_size 128 \
  --query_max_len 32 \
  --passage_max_len 156 \
  --dataset_name Tevatron/msmarco-passage-corpus \
  --dataset_number_of_shards 4 \
  --dataset_shard_index ${s} \
  --encode_output_path $EMBEDDING_OUTPUT_DIR/corpus.${s}.pkl
done

add & to the end of the command to run in the background in parallel.

Retrieval

set -f && python -m tevatron.retriever.driver.search \
    --query_reps $EMBEDDING_OUTPUT_DIR/query-dev.pkl \
    --passage_reps $EMBEDDING_OUTPUT_DIR/corpus*.pkl \
    --depth 1000 \
    --batch_size 64 \
    --save_text \
    --save_ranking_to $EMBEDDING_OUTPUT_DIR/run.dev.txt

The output file is in the format of <query_id> <passage_id> <score> in each line.

Run with JAX (TPU/GPU)

Training

For GPU training, set XLA_PYTHON_CLIENT_MEM_FRACTION=.95 and make sure the query and passage length are multiples of 64 if TransformersEngine is installed.

python -m tevatron.tevax.experimental.mp.train_lora  \
   --checkpoint_dir retriever-mistral-jax \
   --train_file Tevatron/msmarco-passage-aug \
   --model_name mistralai/Mistral-7B-v0.1 \
   --model_type mistral \
   --batch_size 128 \
   --num_target_passages 16 \
   --learning_rate 1e-4 \
   --seed 12345 \
   --mesh_shape 1 -1 \
   --weight_decay 0.00001 \
   --num_epochs 1 \
   --max_query_length 64 \
   --max_passage_length 128 \
   --pooling eos \
   --scale_by_dim True \
   --grad_cache \
   --passage_num_chunks 32 \
   --query_num_chunks 4

In batch passages per query: 128x16 = 2048

Number of queries per update: 128

The above training setting tooks about 35 hours on a v4-8 TPU VM.

Equivalent training tooks about 80 hours on 1xA100 GPU.

Encoding

Query Encoding

python -m tevatron.tevax.experimental.mp.encode  \
   --model_type mistral \
   --model_name_or_path mistralai/Mistral-7B-v0.1 \
   --model_config_name_or_path mistralai/Mistral-7B-v0.1 \
   --tokenizer_name_or_path mistralai/Mistral-7B-v0.1 \
   --dataset_name_or_path Tevatron/msmarco-passage \
   --split dev \
   --output_dir $EMBEDDING_OUTPUT_DIR/query-embedding \
   --batch_size 32 \
   --input_type query \
   --max_seq_length 64 \
   --mesh_shape 1 -1 \
   --lora retriever-mistral-jax/lora \
   --scale_by_dim

Corpus Encoding

python -m tevatron.tevax.experimental.mp.encode  \
   --model_type mistral \
   --model_name_or_path mistralai/Mistral-7B-v0.1 \
   --model_config_name_or_path mistralai/Mistral-7B-v0.1 \
   --tokenizer_name_or_path mistralai/Mistral-7B-v0.1 \
   --dataset_name_or_path Tevatron/msmarco-passage-corpus \
   --output_dir $EMBEDDING_OUTPUT_DIR/corpus-embedding \
   --batch_size 32 \
   --input_type passage \
   --max_seq_length 128 \
   --mesh_shape 1 -1 \
   --lora retriever-mistral-jax/lora \
   --scale_by_dim

Retrieval

set -f && python -m tevatron.retriever.driver.search \
    --query_reps $EMBEDDING_OUTPUT_DIR/query-embedding/*.pkl \
    --passage_reps $EMBEDDING_OUTPUT_DIR/corpus-embedding/*.pkl \
    --depth 1000 \
    --batch_size 64 \
    --save_text \
    --save_ranking_to $EMBEDDING_OUTPUT_DIR/run.dev.txt

The output file is in the format of <query_id> <passage_id> <score> in each line.

Citation

If you find Tevatron helpful, please consider citing our paper.

@article{Gao2022TevatronAE,
  title={Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval},
  author={Luyu Gao and Xueguang Ma and Jimmy J. Lin and Jamie Callan},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.05765}
}

Contacts

If you have a toolkit specific question, feel free to open an issue.

You can also reach out to us for general comments/suggestions/questions through email.

• Luyu Gao luyug@cs.cmu.edu
• Xueguang Ma x93ma@uwaterloo.ca

Acknowledgement

• We thank all the contributors of dependency libraries.
• We thank Google's TPU research cloud for providing TPU resources.