Genomic BERT with ALiBi Attention

A specialized framework for pre-training BERT models on genomic sequences with support for long sequence extrapolation using ALiBi attention. This project provides a complete solution for training language models on DNA data that can effectively handle sequences much longer than those seen during training.

Overview

This project implements a transformer-based architecture for genomic sequence modeling, with special focus on:

1. DNA-specific tokenization using SentencePiece BPE
2. Long sequence handling with ALiBi (Attention with Linear Biases) attention mechanism
3. Distributed training support via PyTorch Accelerate
4. Robust safety features for handling genomic data quirks

The resulting models can be used for various genomic tasks such as:

• DNA sequence classification
• Motif discovery
• Sequence generation
• Anomaly detection in genomic sequences
• Transfer learning for genomic tasks

Key Features

ALiBi Attention

Unlike standard transformer attention which relies on positional embeddings limited to the training sequence length, ALiBi applies linear biases to attention scores based on the relative positions of tokens. This approach allows models to effectively process sequences much longer than those seen during training.

Benefits:

• Improved extrapolation to longer sequences
• No position embedding limit – handles sequences of arbitrary length
• Better performance on long-range dependencies

Genomic Tokenization

Custom SentencePiece BPE tokenizer designed specifically for genomic sequences (A, T, G, C).

Benefits:

• DNA-specific vocabulary that captures biologically relevant patterns
• Efficient representation of genomic motifs and structures
• Robust handling of invalid characters and outlier sequences

Distributed Training

Built with PyTorch Accelerate for efficient multi-GPU and multi-node training.

Benefits:

• Faster training on large genomic datasets
• Seamless scaling from single GPU to multi-node
• Efficient checkpointing and resumption

Installation

# Clone the repository
git clone https://github.com/yourusername/genomic-bert.git
cd genomic-bert

# Install dependencies
pip install -r requirements.txt

Quick Start

python train.py 
  --input_files path/to/your/genomic.fasta 
  --output_dir ./output 
  --attention_type alibi 
  --batch_size 16

Command-line Options

Input/Output

Option	Description
--input_files	Input genomic sequence files in FASTA format (required)
--output_dir	Output directory for model, tokenizer, and logs (required)
--tokenizer_path	Path to existing tokenizer (optional)
--model_path	Path to existing model checkpoint (optional)
--resume_from_checkpoint	Path to checkpoint to resume from, or ‘latest’ to find latest checkpoint
--test_sequence_length	Override sequence length for testing (useful for debugging)

GPU Options

Option	Description
--force_gpu	Force GPU usage even if distributed mode is active
--gpu_ids	Comma-separated list of GPU IDs to use (defaults to all available)

Tokenizer Options

Option	Description
--vocab_size	Vocabulary size for BPE tokenizer (default: 4096)
--tokenizer_sample_size	Number of sequences to sample for tokenizer training (default: 100000)

Model Architecture

Option	Description
--hidden_size	Hidden size of the model (default: 768)
--num_hidden_layers	Number of hidden layers (default: 12)
--num_attention_heads	Number of attention heads (default: 12)
--dropout	Dropout probability (default: 0.1)
--attention_type	Type of attention mechanism to use: "standard" or "alibi" (default: alibi)

Sequence Options

Option	Description
--pre_training_length	Sequence length during pre-training (default: 512)
--max_inference_length	Maximum sequence length for inference (default: None = unlimited)
--mlm_probability	Probability of masking a token for MLM (default: 0.15)
--chunk_size	Base size of sequence chunks (default: 2000)
--stride	Stride for overlapping chunks (default: 1000)
--sample_long_sequences	Include longer sequences during training to help with extrapolation
--max_safe_sequence_length	Maximum safe sequence length for processing (default: 50000)
--max_supported_model_length	Maximum sequence length the model should support (default: 16384)

Training Options

Option	Description
--batch_size	Batch size per GPU/TPU core/CPU (default: 16)
--gradient_accumulation_steps	Number of steps for gradient accumulation (default: 1)
--epochs	Number of training epochs (default: 3)
--learning_rate	Learning rate (default: 5e-5)
--weight_decay	Weight decay (default: 0.01)
--warmup_steps	Number of warmup steps (default: 10000)
--max_grad_norm	Maximum gradient norm (default: 1.0)
--num_workers	Number of data loader workers (default: 4)
--lr_scheduler_type	LR scheduler type: "linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup" (default: linear)

Logging and Checkpointing

Option	Description
--logging_steps	Log every X steps (default: 100)
--checkpointing_steps	Save a checkpoint every X steps (default: None)
--save_steps	Save a checkpoint every X steps (default: 1000)
--save_total_limit	Maximum number of checkpoints to keep (default: 3)
--log_with_tensorboard	Log training with TensorBoard

Other Options

Option	Description
--seed	Random seed (default: 42)
--debug	Enable debug output

Advanced Usage

Training with Multiple GPUs

python train.py 
  --input_files data/*.fasta 
  --output_dir ./multi_gpu_output 
  --attention_type alibi 
  --batch_size 8 
  --gradient_accumulation_steps 2

Fine-tuning a Pre-trained Model

python train.py 
  --input_files path/to/your/genomic.fasta 
  --output_dir ./finetune_output 
  --model_path ./pretrained_model 
  --tokenizer_path ./pretrained_model/tokenizer 
  --learning_rate 2e-5 
  --epochs 1

Testing Extrapolation to Longer Sequences

The model automatically tests its ability to handle sequences of different lengths than those seen during training. You can customize this with:

python train.py 
  --input_files path/to/your/genomic.fasta 
  --output_dir ./output 
  --pre_training_length 512 
  --max_supported_model_length 16384 
  --sample_long_sequences

Project Structure

.
├── data/
│   ├── __init__.py
│   └── data_collator.py         # Dataset and data collator implementations
├── modeling/
│   ├── __init__.py
│   └── alibi_attention.py       # ALiBi attention implementation
├── tokenization/
│   ├── __init__.py
│   └── genomic_tokenizer.py     # Genomic tokenizer implementation
├── training/
│   ├── __init__.py
│   ├── accelerate_utils.py      # Accelerate utilities
│   └── train_utils.py           # Training utilities
└── train.py                     # Main training script

Customizing the Tokenizer

The tokenizer is automatically trained on your input data unless you provide an existing tokenizer. You can customize vocabulary size and other parameters:

python train.py 
  --input_files path/to/your/genomic.fasta 
  --output_dir ./output 
  --vocab_size 8192 
  --tokenizer_sample_size 200000

Performance Considerations

• Memory Usage: ALiBi attention has slightly higher memory requirements than standard attention.
• Sequence Length: Training with longer sequences requires more memory. Use --gradient_accumulation_steps to trade speed for memory.
• Multi-GPU: For large genomic datasets, multi-GPU training can significantly reduce training time.

Citation

If you use this code in your research, please cite:

@article{tbd,
  title={TBD},
  author={TBD},
  journal={arXiv preprint},
  year={2025}
}

License

Apache 2.0

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Imported from gh:codybum/DNABERT-PLUS. Source last updated 2025-03-15. Synced 2026-07-27.