genobench 1.0.0

A joint biological and hardware benchmarking framework for Genomic Foundation Models (GFMs)

GenoBench

GenoBench is a benchmarking framework designed to jointly measure the biological performance and hardware resource usage of Genomic Foundation Models (GFMs).

Unlike existing GFM benchmarks that focus solely on biological capabilities or pure hardware constraints, GenoBench provides a unified evaluation framework. It measures biological classification capabilities alongside runtime physical hardware costs (peak VRAM usage, inference latency) under a standardized linear probing protocol on frozen sequence embeddings.

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Key Features

• Joint Profiling: Combines biological capability metrics (MCC, AUPRC) with physical GPU metrics (peak VRAM allocation, CUDA event-based inference latency).
• Standardized Probing: Evaluates representations by fitting a Logistic Regression linear probe on frozen mean-pooled sequence embeddings.
• Built-in Models: Support for HyenaDNA-tiny and DNABERT-2 representation extractors out of the box.
• Built-in Datasets: Wraps classification tasks from the Genomic Benchmarks registry (specifically demo_human_or_worm and human_enhancers_cohn).
• Reproducibility: Standardized seeds and shuffling processes to ensure stable classification and probing metrics across evaluation runs.
• Structured Outputs: Saves detailed run metadata, hyperparameters, metrics, hardware profiles, and environment configurations in a standardized JSONL format.

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Installation

Ensure you have a Python 3.10+ environment with PyTorch (CUDA supported) installed. Clone the repository and install the dependencies:

git clone https://github.com/<your-username>/GenoBench.git
cd GenoBench
pip install -r requirements.txt

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Quick Start

1. Run Evaluation via the CLI

You can evaluate any registered GFM on a benchmark task using the command-line interface:

python3 -m genobench.cli --model hyenadna --task human_vs_worm --batch_size 16 --max_length 1024

To run the patched DNABERT-2 implementation (which falls back to PyTorch eager attention to avoid Triton compiler compatibility warnings):

python3 -m genobench.cli --model dnabert2 --task human_vs_worm --batch_size 16 --max_length 1024

2. Supported CLI Parameters

You can pass configuration overrides dynamically:

• --model: Name of the registered model (hyenadna, dnabert2, dummy_gfm).
• --task: Name of the registered task (human_vs_worm, human_enhancers_cohn, dummy_task).
• --batch_size: Batch size for embedding extraction (default: 16).
• --max_length: Maximum sequence length (padded or truncated, default: 1024, supports up to 8192).
• --max_train_samples: Slice train dataset size for fast benchmarking.
• --max_test_samples: Slice test dataset size for fast benchmarking.

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Output Schema

Results are logged as structured JSON lines appended to results/evaluation_runs.jsonl:

{
  "timestamp": "2026-06-26T04:34:44.488418+00:00",
  "model": "dnabert2",
  "task": "human_vs_worm",
  "evaluation_protocol": "linear_probing",
  "hyperparameters": {
    "max_sequence_length": 1024,
    "batch_size": 16,
    "max_train_samples": 100,
    "max_test_samples": 50
  },
  "metrics": {
    "mcc": 0.7968,
    "auprc": 0.9515,
    "sample_count": 50
  },
  "hardware": {
    "gpu": "NVIDIA GeForce RTX 4070 Laptop GPU",
    "peak_memory_mib": 508.6,
    "avg_batch_inference_latency_ms": 25.73,
    "total_batches_measured": 4
  },
  "env": {
    "python": "3.10.20",
    "torch": "2.12.0+cu130",
    "transformers": "4.57.6"
  }
}

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Project Documentation

Detailed reference manuals are available in the docs/ directory:

• Architecture Overview: Deep-dive into registries, factory patterns, and the orchestrator flow.
• Hardware Profiling Methodology: Technical details of CUDA Event timing and peak memory metrics.
• Extensibility Guide: Step-by-step instructions for adding custom models and biological tasks.

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Directory Structure

• genobench/
  • cli.py: CLI interface entry point.
  • evaluator.py: Evaluator orchestrator that runs the linear probing and hardware profiling pipeline.
  • models/:
    • base.py: Base class BaseGFM interface.
    • hyenadna.py: Wrapper for HyenaDNA representations.
    • dnabert2.py: Wrapper for DNABERT-2 representations with eager fallback patch.
  • tasks/:
    • human_vs_worm.py: Task loader wrapper for the demo dataset demo_human_or_worm.
    • human_enhancers_cohn.py: Task loader wrapper for enhancers identification.
  • hardware/:
    • probe.py: Context-driven latency and memory profiling hooks.
  • metrics/:
    • classification.py: biological metric tracker (MCC & AUPRC).
• tests/
  • test_metrics.py: Unit test suite.

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Run Unit Tests

To run the unit test suite:

python3 -m unittest discover -s tests -p "test_*.py"

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License

This project is licensed under the MIT License - see the LICENSE file for details.