Installation

gena-lm-bigbird-base-sparse sparse ops require DeepSpeed.

DeepSpeed

DeepSpeed installation is needed to work with SparseAttention versions of language models. DeepSpeed Sparse attention supports only GPUs with compute compatibility >= 7 (V100, T4, A100).

bash

pip install triton==1.0.0
DS_BUILD_SPARSE_ATTN=1 pip install deepspeed==0.6.0 --global-option="build_ext" --global-option="-j8" --no-cache

and check installation with

ds_report

APEX for FP16

Install APEX https://github.com/NVIDIA/apex#quick-start

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Examples

How to load pre-trained model for Masked Language Modeling

python

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse')
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse', trust_remote_code=True)

How to load pre-trained model to fine-tune it on classification task

Get model class from GENA-LM repository:

git clone https://github.com/AIRI-Institute/GENA_LM.git

python

from GENA_LM.src.gena_lm.modeling_bert import BertForSequenceClassification
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse')

or you can just download modeling_bert.py and put it close to your code.

OR you can get model class from HuggingFace AutoModel:

python

from transformers import AutoTokenizer, AutoModel
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse', trust_remote_code=True)
gena_module_name = model.__class__.__module__
print(gena_module_name)
import importlib
# available class names:
# - BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction,
# - BertForSequenceClassification, BertForMultipleChoice, BertForTokenClassification,
# - BertForQuestionAnswering
# check https://huggingface.co/docs/transformers/model_doc/bert
cls = getattr(importlib.import_module(gena_module_name), 'BertForSequenceClassification')
print(cls)
model = cls.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse', num_labels=2)

Model description

GENA-LM (gena-lm-bigbird-base-sparse) model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 15% of tokens. Model config for gena-lm-bigbird-base-sparse is similar to the google/bigbird-roberta-base:

4096 Maximum sequence length
12 Layers, 12 Attention heads
768 Hidden size
sparse config:
- block size: 64
- random blocks: 3
- global blocks: 2
- sliding window blocks: 3
Rotary positional embeddings
32k Vocabulary size, tokenizer trained on DNA data.

We pre-trained gena-lm-bigbird-base-sparse using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). Pre-training was performed for 810,000 iterations with batch size 256. We modified Transformer with Pre-Layer normalization.

Evaluation

For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1

Citation

bibtex

@article{GENA_LM,
	author = {Veniamin Fishman and Yuri Kuratov and Maxim Petrov and Aleksei Shmelev and Denis Shepelin and Nikolay Chekanov and Olga Kardymon and Mikhail Burtsev},
	title = {GENA-LM: A Family of Open-Source Foundational Models for Long DNA Sequences},
	elocation-id = {2023.06.12.544594},
	year = {2023},
	doi = {10.1101/2023.06.12.544594},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594},
	eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594.full.pdf},
	journal = {bioRxiv}
}