{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"Optimizes PyTorch einsum operations and functions containing them by applying opt_einsum strategies through PyTorch FX compute graphs.","skillfed_tags":["tensor-optimization","pytorch-fx","performance"],"use_cases":["Accelerate batched matrix operations in neural networks by reordering einsum contraction sequences.","Optimize custom tensor algebra kernels expressed via einsum without rewriting the contraction logic.","Profile and improve performance of multi-tensor operations in research code before deployment.","Automatically find better contraction orders for complex tensor expressions in scientific computing.","Reduce computational cost of attention mechanisms or other einsum-heavy layers in transformer models."],"what_it_does":"opt_einsum_fx wraps PyTorch's FX graph tracing to automatically optimize einsum operations within traced functions. It uses opt_einsum to reorder tensor contractions for better computational efficiency, then rewrites the traced graph with the optimized contraction sequence. The package is designed for developers working with batched tensor operations, matrix products, and other multi-tensor contractions expressed via einsum notation.\n\nThe typical workflow is to define a function containing einsum calls, trace it with torch.fx.symbolic_trace, pass the traced module to optimize_einsums_full with example inputs, and receive an optimized module with reordered contractions. The fact sheet shows a concrete example achieving roughly 2x speedup on CPU for a batched matrix-vector product, though gains depend on tensor shapes and hardware.","worth_installing":"No. The package is abandoned (last commit 2022-03-17, no releases since 2021-11-07) and likely incompatible with current PyTorch and opt_einsum versions. While the idea is sound and the MIT license is permissive, the lack of maintenance means you would need to fork and patch it yourself to use it with modern libraries. For new projects, consider using opt_einsum directly or PyTorch's built-in optimization passes instead."},"id":"opt-einsum-fx","links":{"html":"https://skillfed.io/packages/opt-einsum-fx","md":"https://skillfed.io/packages/opt-einsum-fx.md","pypi":"https://pypi.org/project/opt-einsum-fx/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2021-11-07","license_spdx":null,"license_treatment":"permissive","name":"opt-einsum-fx","python_support":"supports_current","summary":"Einsum optimization using opt_einsum and PyTorch FX"},"popularity":{"monthly_downloads":504342,"position":6298,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.4"}