hasktorch/hasktorch
Jun 2025 – Feb 2026
6 Months active
Languages Used
HaskellNixC++ShellYAML
Technical Skills
Build SystemsCI/CDDependency ManagementDevOpsNixC++ Development
Collin Arnett
PROFILE
Collin Arnett
Collin Arnett contributed to the hasktorch/hasktorch repository by delivering core features and infrastructure improvements over six months. He implemented advanced transformer attention mechanisms and expanded tensor operations, focusing on deep learning workflows in Haskell and C++. Collin modernized the build system using Nix and streamlined CI/CD pipelines with GitHub Actions, enhancing reproducibility and cross-platform stability. He improved C++ and Haskell interop, introduced BFloat16 support for mixed-precision computation, and standardized CUDA error handling for better developer experience. His work demonstrated depth in dependency management, environment configuration, and backend development, resulting in more robust, maintainable, and production-ready machine learning tooling.
Overall Statistics
Feature vs Bugs
77%Features
Repository Contributions
26Total
Bugs
3
Commits
26
Features
10
Lines of code
827
Activity Months6
Your Network
7 peopleWork History
February 2026
4 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) – hasktorch/hasktorch: Delivered two major improvements targeting CI/CD reliability and dependency stability. The CUDA-enabled CI pipeline was reactivated and optimized with a 'nothing but nix' GitHub Action, reducing CI runtime and resource usage. Dependency management was stabilized by updating flake.lock and extending flake.nix checks to validate builds in CI, reducing flaky failures and ensuring reproducible environments. Together, these changes shortened feedback loops, improved release confidence, and lowered maintenance costs.
4 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) – hasktorch/hasktorch: Delivered two major improvements targeting CI/CD reliability and dependency stability. The CUDA-enabled CI pipeline was reactivated and optimized with a 'nothing but nix' GitHub Action, reducing CI runtime and resource usage. Dependency management was stabilized by updating flake.lock and extending flake.nix checks to validate builds in CI, reducing flaky failures and ensuring reproducible environments. Together, these changes shortened feedback loops, improved release confidence, and lowered maintenance costs.
February 2026
December 2025
1 Commits
Dec 1, 2025December 2025 monthly summary for hasktorch/hasktorch with a focus on reliability and developer experience in memory error handling. Delivered standardized CUDA out-of-memory (OOM) error messages across the CUDA path to improve clarity and consistency in error handling, enabling faster triage and reducing support overhead. Linked a targeted commit (Fix GC trigger) to memory-management improvements, contributing to greater stability under memory pressure and better user outcomes.
December 2025
1 Commits
Dec 1, 2025December 2025 monthly summary for hasktorch/hasktorch with a focus on reliability and developer experience in memory error handling. Delivered standardized CUDA out-of-memory (OOM) error messages across the CUDA path to improve clarity and consistency in error handling, enabling faster triage and reducing support overhead. Linked a targeted commit (Fix GC trigger) to memory-management improvements, contributing to greater stability under memory pressure and better user outcomes.
November 2025
7 Commits • 2 Features
Nov 1, 2025November 2025: Delivered essential feature expansion and CI/build improvements for the hasktorch project. Implemented BFloat16 support across core dtype utilities, enabling efficient mixed-precision workflows on modern hardware. Completed a major CI/build upgrade cycle, including libtorch upgrade to 2.9.0, a Nix Flake refactor, updated GitHub Actions, CUDA build deprecation where appropriate, and macOS caching adjustments to address upstream issues. These efforts enhance performance portability, stability, and reproducibility across platforms, reducing build friction and accelerating downstream ML workflows.
7 Commits • 2 Features
Nov 1, 2025November 2025: Delivered essential feature expansion and CI/build improvements for the hasktorch project. Implemented BFloat16 support across core dtype utilities, enabling efficient mixed-precision workflows on modern hardware. Completed a major CI/build upgrade cycle, including libtorch upgrade to 2.9.0, a Nix Flake refactor, updated GitHub Actions, CUDA build deprecation where appropriate, and macOS caching adjustments to address upstream issues. These efforts enhance performance portability, stability, and reproducibility across platforms, reducing build friction and accelerating downstream ML workflows.
November 2025
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.
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.
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.
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
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.
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.
Activity
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Quality Metrics
Correctness93.0%
Maintainability94.6%
Architecture92.4%
Performance90.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++HaskellNixShellYAML
Technical Skills
Build System ConfigurationBuild SystemsC++C++ DevelopmentCI/CDCUDAConfiguration ManagementContinuous IntegrationDeep LearningDependency ManagementDevOpsEnvironment ManagementFFIFFI (Foreign Function Interface)Functional Programming
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