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](https://pytorch.org/get-started/locally/). 2. Install dependencies and Tevatron. ```bash 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](https://jax.readthedocs.io/en/latest/installation.html#pip-installation-google-cloud-tpu) 2. Install dependencies ```bash pip install transformers datasets pip install flax optax ``` 3. Install Magix and GradCache ```bash 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 ```bash pip install -e . ```

JAX (GPU)

To run the JAX implementation of Tevatron on GPU, we encourage using the jax-toolbox [jax container](https://github.com/NVIDIA/JAX-Toolbox/pkgs/container/jax) image from NVIDIA. Below is a Dockerfile example to set up Tevatron on top of the jax container. ```Dockerfile 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 ```json { "query_id": "", "query": "", "positive_passages": [ {"docid": "", "title": "", "text": ""}, ... ], "negative_passages": [ {"docid": "", "title": "", "text": ""}, ... ] } ``` 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 ```json { "docid": "", "title": "", "text": "" } ``` where each line represents a passage in the corpus. ### Self-Contained Dataset Tevatron self-contained several commonlly used datasets for neural retrieval. (via [HuggingFace](https://huggingface.co/Tevatron)). These datasets can downloaded automatically during training and encoding by setting `--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 ```bash 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 ```bash EMBEDDING_OUTPUT_DIR= 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 $EMBEDDING_OUTPUT_DIR/query-dev.pkl ``` #### Corpus Encoding ```bash EMBEDDING_OUTPUT_DIR= for s in 0 1 2 3 do gpuid=$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 ```bash 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 ` ` 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. ```bash 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 ```bash 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 ```bash 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 ```bash 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 ` ` 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.

texttron / tevatron

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