Multimodal Foundation Models for
Scientific Data
Deployment in Astrophysics
François Lanusse
National Center for Scientific Research (CNRS)
Polymathic AI
Methodologies and practices that will
transfer across domains
Why a Dedicated Effort?
Transposing these methodologies to scientific data and problems brings
unique challenges
-
Scientific Data is Complex and Diverse
- Impacts data collection
- Requires dedicated architectures
- Adoption by Scientists Requires Flexible Models and Quantitative Methods
Data
Challenge
The Challenges of Scientific Data
- Success of recent foundation models is driven by large corpora of uniform data (e.g LAION 5B).
- Scientific data comes with many additional challenges:
- Metadata matters
- Wide variety of measurements/observations
- Accessing and formatting data requires very specific expertise
Credit: Melchior et al. 2021
Credit:DESI collaboration/DESI Legacy Imaging Surveys/LBNL/DOE & KPNO/CTIO/NOIRLab/NSF/AURA/unWISE
The Multimodal Universe
Enabling Large-Scale Machine Learning with 100TBs of Astronomical Scientific Data
Collaborative project with about 30 contributors
Presented at NeurIPS 2024 Datasets & Benchmark track
The MultiModal Universe Project
- Goal: Assemble the first large-scale multimodal dataset for machine learning in astrophysics.
-
Strategy:
- Engage with a broad community of AI+Astro experts.
- Target large astronomical surveys, varied types of instruments, many different astrophysics sub-fields.
- Adopt standardized conventions for storing and accessing data and metadata through mainstream tools (e.g. Hugging Face Datasets).
Ground-based imaging from Legacy Survey
Space-based imaging from JWST
MultiModal Universe Infrastructure
Presented at NeurIPS 2024 Datasets & Benchmark Track
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
- 55B tokens from 3M frames
=> First ImageNet scale dataset for fluids
-
18 subsets spanning problems in astro, bio, aerospace, chemistry, atmospheric science, and more.
- Simple self-documented HDF5 files, with pytorch readers provided.
Presented at NeurIPS 2024 Datasets & Benchmark Track
Architecture
Challenge
The Universal Neural Architecture Challenge
Most General
Most Specific
Single model capable of processing all types of data
Independent models for all types of data
The Universal Neural Architecture Challenge
Most General
Most Specific
Independent models for all types of data
Single model capable of processing all types of data
Bytes Are All You Need (Horton et al. 2023)
The Universal Neural Architecture Challenge
Most General
Most Specific
Independent models for all types of data
Single model capable of processing all types of data
Bytes Are All You Need (Horton et al. 2023)
AstroCLIP (Parker et al. 2024)
AstroCLIP
The Universal Neural Architecture Challenge
Most General
Most Specific
Independent models for all types of data
Single model capable of processing all types of data
Bytes Are All You Need (Horton et al. 2023)
Early Fusion Multimodal Models
AstroCLIP (Parker et al. 2024)
Early-Fusion Multimodal Models
Flamingo: a Visual Language Model for Few-Shot Learning (Alayrac et al. 2022)
Chameleon: Mixed-Modal Early-Fusion Foundation Models (Chameleon team, 2024)
AION-1
AstronomIcal Omnimodal Network
with extensive support from the rest of the team.
Project led by:
Francois
Lanusse
Liam
Parker
Jeff
Shen
Tom
Hehir
Ollie
Liu
Lucas
Meyer
Leopoldo
Sarra
Sebastian Wagner-Carena
Helen
Qu
Micah
Bowles
Diverse data modalities for diverse science cases
(Blanco Telescope and Dark Energy Camera.
Credit: Reidar Hahn/Fermi National Accelerator Laboratory)
(Subaru Telescope and Hyper Suprime Cam. Credit: NAOJ)
(Dark Energy Spectroscopic Instrument)
(Sloan Digital Sky Survey. Credit: SDSS)
(Gaia Satellite. Credit: ESA/ATG)
- Galaxy formation
- Cosmology
- Stellar physics
- Galaxy archaeology
- ...
Standardizing all modalities through tokenization
- For each modality class (e.g. image, spectrum) we build dedicated metadata-aware tokenizers
- For Aion-1, we integrate 39 different modalities (different instruments, different measurements, etc.)
\mathcal{L} = \parallel \Sigma^{- \frac{1}{2}} \left( x - d_\theta( \lfloor e_\theta(x) \rfloor_{\text{FSQ}} \right) \parallel_2^2
Field Embedding Strategy Developed for
Multiple Physics Pretraining (McCabe et al. 2023)
DES g
DES r
DES i
DES z
HSC g
HSC r
HSC i
HSC z
HSC y
Any-to-Any Modeling with Generative Masked Modeling
- Training is done by pairing observations of the same objects from different instruments.
- Each input token is tagged with a modality embedding that specifies provenance metadata.
- Model is trained by cross-modal generative masked modeling (Mizrahi et al. 2023)
=> Learns the joint and all conditional distributions of provided modalities:
\forall m,n \quad p(x_m | x_n)
AION-1 family of models
- Models trained as part of the 2024 Jean Zay Grand Challenge, following an extension to a new partition of 1400 H100s
- AION-1 Base: 300 M parameters
- 64 H100s - 1.5 days
- AION-1 Large: 800 M parameters
- 100 H100s - 2.5 days
- AION-1 XLarge: 3B parameters
- 288 H100s - 3.5 days
Example of out-of-the-box capabilities
Survey translation
p(\bm{x}_{HSC} | \bm{x}_{DES} )
Spectrum super-resolution
p(\bm{x}_{DESI} | \bm{x}_{GAIA} )
Example of emergent multimodal understanding
p(\bm{x}_{DESI} | \bm{x}_{HSC} )
- Direct association between DESI and HSC was excluded during pretraining
=> This task is out of distribution!
Accelerating Downstream Science
The Deep Learning Boom in Astrophysics
astro-ph abstracts mentioning Deep Learning, CNN, or Neural Networks
The vast majority of these results has relied on supervised learning and networks trained from scratch.
Francois' first Deep Learning Paper in Astro (with Barnabas Poczos)
Rethinking the way we use Deep Learning
Conventional scientific workflow with deep learning
- Build a large training set of realistic data
- Design a neural network architecture for your data
- Deal with data preprocessing/normalization issues
- Train your network on some GPUs for a day or so
- Apply your network to your problem
-
Throw the network away...
=> Because it's completely specific to your data, and to the one task it's trained for.
Conventional researchers @ CMU
Circa 2016
CMU DeepLens (Lanusse et al 2017)
Rethinking the way we use Deep Learning
Foundation Model-based Scientific Workflow
- Build a small training set of realistic data
- Design a neural network architecture for your data
- Deal with data preprocessing/normalization issues
- Adapt a model in a matter of minutes
- Apply your model to your problem
- Throw the network away...
=> Because it's completely specific to your data, and to the one task it's trained for.
Already taken care of
=> Let's discuss embedding-based adaptation
\mathbf{z} = f_\theta(\mathbf{x})
Adaptation of AION-1 embeddings
Adaptation at low cost
with simple strategies:
- Mean pooling + linear probing
- Attentive pooling
y = \mathbf{M} \sum_i z_i
y = \operatorname{softmax} \left(\frac{\mathbf{Q} \mathbf{K}^\top(z)}{\sqrt{d}} \right) \mathbf{V}(z)
- Can be used trivially on any input data AION-1 was trained for
- Flexible to varying number/types of inputs
=> Allows for trivial data fusion
Morphology classification by Linear Probing
Trained on ->
Eval on ->
Physical parameter estimation and data fusion
Inputs:
measured fluxes
Inputs:
measured fluxes + image
Semantic segmentation
Segmenting central bar and spiral arms in galaxy images based on Galaxy Zoo 3D
Example-based retrieval from mean pooling embeddings
Why are such Embedding Models useful for Scientists?
-
Never have to retrain my own neural networks from scratch
- Existing pre-trained models would already be near optimal, no matter the task at hand
-
Saves a lot of time and energy
- Practical large scale Deep Learning even in very few example regime
-
Searching for very rare objects in large astronomical surveys becomes possible
-
Searching for very rare objects in large astronomical surveys becomes possible
- If the information is embedded in a space where it becomes linearly accessible, very simple analysis tools are enough for downstream analysis
Polymathic's recipe for developing Multimodal Scientific Models
Takeaways
Engagement with Scientific Communities
Data Curation And Aggregation
Dedicated ML R&D
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Multimodal Foundation Models for Scientific Data
By eiffl
Multimodal Foundation Models for Scientific Data
Polymathic AI talk at FM4Science Conference
