torch-sla: New Open-Source Library for Differentiable Sparse Linear Algebra in PyTorch
Researchers have released torch-sla, an open-source library that enables differentiable sparse linear algebra operations in PyTorch, addressing a gap in the framework's capabilities. The library supports multiple solver types (direct, iterative, nonlinear, eigenvalue) across CPU and GPU backends with automatic dispatch and distributed multi-GPU execution. This development is significant for scientific machine learning applications that require both sparse matrix operations and automatic differentiation.
torch-sla is a new open-source library designed to fill a gap in PyTorch's sparse linear algebra capabilities. The library provides a unified, autograd-aware API for direct, iterative, nonlinear, and eigenvalue solvers, with support for five interchangeable backends: SciPy and Eigen on CPU, and cuDSS, CuPy, and a PyTorch-native iterative solver on GPU. A key innovation is its O(1)-graph adjoint differentiation framework that enables efficient backpropagation through sparse solves. The library supports batched operations over shared or distinct sparsity patterns and distributed multi-GPU execution via domain decomposition with halo exchange, with automatic device and problem-size-based dispatch. The implementation includes an autograd-compatible distributed halo-exchange layer to make these capabilities scalable for large-scale scientific machine learning workflows.
What different sources said
- arXiv cs.AICenter
torch-sla: Differentiable Sparse Linear Algebra with Adjoint Solvers and Sparse Tensor Parallelism for PyTorch
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