BioNeMo core interfaces and PyTorch-related code.
Project description
bionemo-core
Common code that all BioNeMo framework packages depend on. Contains highly reusable, battle-tested abstractions and implementations that are valuable across a wide variety of domains and applications.
Crucially, the bionemo-core Python package (namespace bionemo.core) depends on PyTorch and PyTorch
Lightning. Other key BioNeMo component libraries, such as bionemo-llm and
bionemo-geometric, obtain their PyTorch dependencies via bionemo-core.
Developer Setup
After following the setup specified in the README, you may install this project's code in your environment via executing:
pip install -e .
To run unit tests with code coverage, execute:
pytest -v --cov=bionemo --cov-report=term .
Package Highlights
In bionemo.core.model.config:
ModelOutput: A Model's forward pass may produce a tensor, multiple tensors, or named tensors.LossType: A generic type parameter for a loss function.Model: An interface for any ML model that accepts and producestorch.Tensors.ModelType: A generic type parameter that is constrained to theModelinterface.BionemoModelConfig: An abstract class that enables parameterizable model instantiation that is compatible with Megatron.BionemoTrainableModelConfig: An extension that includes the loss function to use with the model during training.
In bionemo.core.utils:
- the
batching_utilsmodule'spad_token_ids, which pads token ids with padding value & returns a mask. - the
dtypemodule'sget_autocast_dtype, which converts from nemo/nvidia datatypes to their PyTorch equivalents. - the
random_utilsmodule, which includes functions for managing random seeds and performing sampling.
In the bionemo.data package, there is:
multi_epoch_dataset: contains many dataset implements that are useful for mutli-epoch training.resamplers: contains a P-RNG based Dataset implementation.
There's a constant global value, bionemo.core.BIONEMO_CACHE_DIR, which is used as a local on-disk cache for resources.
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