facebookresearch/faiss
Jan 2025 – Jan 2025
1 Month active
Languages Used
C++Python
Technical Skills
Algorithm ImplementationDeep LearningMachine LearningPyTorchVector Quantization
Maria Lomeli
PROFILE
Maria Lomeli
Developed and integrated a PyTorch-based Product Quantization module into the facebookresearch/faiss repository, delivering a PyTorch-only implementation that supports end-to-end training, encoding, and decoding of quantized vectors. The work involved refactoring existing clustering code to improve modularity and introducing reusable vector and product quantization classes, enabling seamless adoption in PyTorch-centric machine learning workflows. Validation was performed through dedicated unit tests comparing results against the established Faiss implementation to ensure correctness and maintain parity. This contribution, implemented primarily in Python and C++, enhances Faiss interoperability with PyTorch, laying the groundwork for differentiable quantization and broader adoption in deep learning projects.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
95
Activity Months1
Your Network
3220 peopleSame Organization
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Aaditya NairMember
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Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron FarberMember
Aaron PollackMember
Aaryaman SagarMember
Work History
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for facebookresearch/faiss: Key feature delivered is a PyTorch-based Product Quantization (PQ) module integrated into Faiss. The PQ module adds a PyTorch-only implementation of Product Quantization, refactors clustering code, and introduces vector and product quantization classes with end-to-end training, encoding, and decoding capabilities. A dedicated correctness test compares PQ results against the existing Faiss implementation to ensure parity. This work expands Faiss interoperability with PyTorch-driven pipelines, enabling seamless integration into PyTorch-centric ML workflows and potentially enabling differentiable PQ use cases. Impact includes improved modularity, test coverage, and a foundation for broader adoption in PyTorch-based projects. Demonstrated technologies: PyTorch, product/vector quantization concepts, code refactoring, and testing.
1 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for facebookresearch/faiss: Key feature delivered is a PyTorch-based Product Quantization (PQ) module integrated into Faiss. The PQ module adds a PyTorch-only implementation of Product Quantization, refactors clustering code, and introduces vector and product quantization classes with end-to-end training, encoding, and decoding capabilities. A dedicated correctness test compares PQ results against the existing Faiss implementation to ensure parity. This work expands Faiss interoperability with PyTorch-driven pipelines, enabling seamless integration into PyTorch-centric ML workflows and potentially enabling differentiable PQ use cases. Impact includes improved modularity, test coverage, and a foundation for broader adoption in PyTorch-based projects. Demonstrated technologies: PyTorch, product/vector quantization concepts, code refactoring, and testing.
January 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Algorithm ImplementationDeep LearningMachine LearningPyTorchVector Quantization
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