Release of v1.0 of BioNeMo Modular Co-Design (MoCo) (#575)

Release of v1.0 of BioNeMo Modular Co-Design (MoCo)

Introduces modular interpolants for various popular generative model
frameworks including continuous and discrete diffusion and flow
matching.
---

## Summary
Introduces MoCo.

## Details
See documentation.md for details.

## Usage
pip install bionemo-moco
```python
from bionemo.moco.interpolants import ContinuousFlowMatcher
from bionemo.moco.distributions.time import UniformTimeDistribution
from bionemo.moco.distributions.prior import GaussianPrior

uniform_time = UniformTimeDistribution()
moon_prior = GaussianPrior()
sigma = 0.1
cfm = ContinuousFlowMatcher(time_distribution=uniform_time, 
                            prior_distribution=moon_prior, 
                            sigma=sigma, 
                            prediction_type="velocity")
```

see examples directory for notebook tutorials

## Testing
Unit tests for all key functions.

Tests for these changes can be run via:
```shell
pytest -v tests
```

---------

Signed-off-by: Danny <[email protected]>
Signed-off-by: Peter St. John <[email protected]>
Co-authored-by: Peter St. John <[email protected]>

.secrets.baseline

@@ -139,9 +139,9 @@
         "filename": "pyproject.toml",
         "hashed_secret": "79670e9c9d1c7ea5b81a96a2053d81437712c78e",
         "is_verified": false,
-        "line_number": 43
+        "line_number": 44
       }
     ]
   },
-  "generated_at": "2025-01-14T17:26:27Z"
+  "generated_at": "2025-01-15T19:06:19Z"
 }

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 +590,283 @@ https://github.com/aqlaboratory/openfold/blob/main/LICENSE
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
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+Copyright TorchCFM Codebase
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+
+MIT License
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+Copyright (c) 2023 Alexander Tong
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+Copyright Discrete Flow Models Codebase
+https://github.com/andrew-cr/discrete_flow_models/blob/main/LICENSE
+
+MIT License
+
+Copyright (c) 2022 Andrej Karpathy
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
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+copies or substantial portions of the Software.
+
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+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

pyproject.toml

@@ -21,6 +21,7 @@ dependencies = [
     'bionemo-geneformer',
     'bionemo-geometric',
     'bionemo-llm',
+    'bionemo-moco',
     'bionemo-scdl',
     'bionemo-size-aware-batching',
     'bionemo-testing',
@@ -52,6 +53,7 @@ bionemo-fw = { workspace = true }
 bionemo-geneformer = { workspace = true }
 bionemo-geometric = { workspace = true }
 bionemo-llm = { workspace = true }
+bionemo-moco = { workspace = true }
 bionemo-noodles = { workspace = true }
 bionemo-scdl = { workspace = true }
 bionemo-size-aware-batching = { workspace = true }
@@ -128,6 +130,7 @@ executionEnvironments = [
         './sub-packages/bionemo-geneformer/src',
         './sub-packages/bionemo-geometric/src',
         './sub-packages/bionemo-llm/src',
+        './sub-packages/bionemo-moco/src',
         './sub-packages/bionemo-noodles/src',
         './sub-packages/bionemo-scdl/src',
         './sub-packages/bionemo-size-aware-batching/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 inference 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