initial commit

CODEOWNERS

@@ -94,3 +94,5 @@ sub-packages/bionemo-geneformer @jstjohn @malcolmgreaves @skothenhill-nv
 sub-packages/bionemo-scdl @jstjohn @malcolmgreaves @polinabinder1 @skothenhill-nv
 
 sub-packages/bionemo-noodles @skothenhill-nv @malcolmgreaves @jstjohn @edawson @cspades
+
+sub-packages/bionemo-moco @nvdreidenbach @DejunL @dorotat-nv @guoqing-zhou @jstjohn @malcolmgreaves @pstjohn

LICENSE/third_party.txt

@@ -590,3 +590,258 @@ https://github.com/aqlaboratory/openfold/blob/main/LICENSE
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+SOFTWARE.

VERSION

@@ -1 +1 @@
-2.2
+1.0

pyproject.toml

@@ -21,6 +21,7 @@ dependencies = [
     'bionemo-geneformer',
     'bionemo-geometric',
     'bionemo-llm',
+    'bionemo-moco',
     'bionemo-scdl',
     'bionemo-size-aware-batching',
     'bionemo-testing',
@@ -61,6 +62,7 @@ bionemo-scdl = { workspace = true }
 bionemo-size-aware-batching = { workspace = true }
 bionemo-testing = { workspace = true }
 bionemo-webdatamodule = { workspace = true }
+bionemo-moco = { workspace = true }
 # in internal/
 infra-bionemo = { workspace = true }
 
@@ -136,6 +138,7 @@ executionEnvironments = [
         './sub-packages/bionemo-size-aware-batching/src',
         './sub-packages/bionemo-testing/src',
         './sub-packages/bionemo-webdatamodule/src',
+        './sub-packages/bionemo-moco/src',
         # internal
         './internal/infra-bionemo/src',
     ] },

sub-packages/bionemo-moco/LICENSE

@@ -0,0 +1 @@
+../../LICENSE/license.txt

sub-packages/bionemo-moco/README.md

@@ -0,0 +1,35 @@
+# Modular Co-Design (MoCo) Interpolants
+
+## Description
+MoCo enables abstracted interpolants for building and sampling from a variety of popular generative model frameworks. Specifically, MoCo supports interpolants for both continuous and discrete data types.
+
+### Continuous Data Interpolants
+MoCo currently supports the following continuous data interpolants:
+- DDPM (Denoising Diffusion Probabilistic Models)
+- VDM (Variational Diffusion Models)
+- CFM (Conditional Flow Matching)
+
+### Discrete Data Interpolants
+MoCo also supports the following discrete data interpolants:
+- D3PM (Discrete Denoising Diffusion Probabilistic Models)
+- MDLM (Markov Diffusion Language Models)
+- DFM (Discrete Flow Matching)
+
+### Useful Abstractions
+MoCo also provides useful wrappers for customizable time distributions and infernece time schedules.
+
+### Extendible
+If the desired interpolant or sampling method is not already supported, MoCo was designed to be easily extended.
+
+## Installation
+ For Conda environment setup, please refer to the `environment` directory for specific instructions.
+
+Once your environment is set up, you can install this project by running the following command:
+
+```bash
+pip install -e .
+```
+This will install the project in editable mode, allowing you to make changes and see them reflected immediately.
+
+## Examples
+Please see examples of all interpolants in the [examples directory](./examples).

sub-packages/bionemo-moco/VERSION

@@ -0,0 +1 @@
+../../VERSION

sub-packages/bionemo-moco/documentation.md

@@ -0,0 +1,3 @@
+# Table of Contents
+
+

sub-packages/bionemo-moco/environment/Instructions.md

@@ -0,0 +1,15 @@
+Environment Setup
+===============
+
+While you can install bionemo-moco directly here is an example of specifying the specific torch and cuda versions:
+
+conda create --name moco python=3.10 pytorch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 pytorch-cuda=12.1 -c pytorch -c nvidia -y
+
+conda activate moco
+python -m pip install 'numpy<2.0'
+
+pip install -r environment/requirements.txt
+
+pre-commit install
+
+pip install -e .