hasktorch/hasktorch
Jun 2025 – Aug 2025
3 Months active
Languages Used
HaskellNixC++
Technical Skills
Build SystemsCI/CDDependency ManagementDevOpsNixC++ Development
Collin Arnett
PROFILE
Collin Arnett
Collin Arnett contributed to the hasktorch/hasktorch repository by delivering core transformer attention features and modernizing the build system over a three-month period. He implemented scaled dot-product attention with Grouped-Query Attention using native PyTorch operations, enabling efficient and scalable transformer models. Collin enhanced C++ and Haskell interop by introducing an OptionalTensor type, supporting optional attention masks. He improved developer productivity by refining the Nix-based build environment, updating to GHC 9.8.4, and streamlining CI workflows. His work in C++, Haskell, and Nix focused on dependency management, reproducible builds, and robust type-safe tensor operations, resulting in more reliable and maintainable code.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
14Total
Bugs
2
Commits
14
Features
6
Lines of code
542
Activity Months3
Your Network
7 peopleShared Repositories
7
Work History
August 2025
10 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for hasktorch/hasktorch focusing on feature delivery, interop improvements, developer experience, and build stabilization. Key outcomes include: (1) Feature delivery for Torch.Typed.Tensor enabling where (conditional selection) and scalar operations (gt, lt, ge, le, eq, neq) against scalars, plus divScalar for scalar division via reciprocal; (2) C++ interop: Added OptionalTensor type to represent std::optional<at::Tensor>, enabling interop with Haskell Maybe Tensor in scenarios like optional attention masks; (3) Dev environment improvements to streamline contributions with cabal-install and haskell-language-server in dev shells, organized devShells, and added examples; (4) GHC 9.8 compatibility fixes to align with dynamic ghc variable usage and cabal.project updates; (5) Dependency and build stabilization by removing dontCheck flag and updating the flake.lock to latest dependencies. Overall impact: expanded feature capabilities, smoother interop with C++/Haskell layers, improved developer productivity, and more reliable builds, translating to faster delivery cycles and more robust production-grade code.
10 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for hasktorch/hasktorch focusing on feature delivery, interop improvements, developer experience, and build stabilization. Key outcomes include: (1) Feature delivery for Torch.Typed.Tensor enabling where (conditional selection) and scalar operations (gt, lt, ge, le, eq, neq) against scalars, plus divScalar for scalar division via reciprocal; (2) C++ interop: Added OptionalTensor type to represent std::optional<at::Tensor>, enabling interop with Haskell Maybe Tensor in scenarios like optional attention masks; (3) Dev environment improvements to streamline contributions with cabal-install and haskell-language-server in dev shells, organized devShells, and added examples; (4) GHC 9.8 compatibility fixes to align with dynamic ghc variable usage and cabal.project updates; (5) Dependency and build stabilization by removing dontCheck flag and updating the flake.lock to latest dependencies. Overall impact: expanded feature capabilities, smoother interop with C++/Haskell layers, improved developer productivity, and more reliable builds, translating to faster delivery cycles and more robust production-grade code.
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 hasktorch/hasktorch: Implemented scaled_dot_product_attention with Grouped-Query Attention (GQA) as a core transformer attention component. The implementation uses native PyTorch operations for efficiency and correctness, with support for scaling and causal masking, enabling scalable, autoregressive attention for transformer models. This work establishes a foundation for improved model performance and broader applicability in downstream tasks.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 hasktorch/hasktorch: Implemented scaled_dot_product_attention with Grouped-Query Attention (GQA) as a core transformer attention component. The implementation uses native PyTorch operations for efficiency and correctness, with support for scaling and causal masking, enabling scalable, autoregressive attention for transformer models. This work establishes a foundation for improved model performance and broader applicability in downstream tasks.
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for hasktorch/hasktorch focusing on key feature deliveries, build-system modernization, and overall impact. Emphasizes compatibility improvements, reproducible builds, and workflow enhancements to enable downstream adoption and CI reliability.
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for hasktorch/hasktorch focusing on key feature deliveries, build-system modernization, and overall impact. Emphasizes compatibility improvements, reproducible builds, and workflow enhancements to enable downstream adoption and CI reliability.
June 2025
Activity
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Quality Metrics
Correctness91.4%
Maintainability95.8%
Architecture91.4%
Performance87.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++HaskellNix
Technical Skills
Build System ConfigurationBuild SystemsC++C++ DevelopmentCI/CDConfiguration ManagementDeep LearningDependency ManagementDevOpsEnvironment ManagementFFIFFI (Foreign Function Interface)Functional ProgrammingHaskellHaskell Development
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