Sparsity has witnessed a resurgence of research interests over the last few years, with many ex-
citing developments and variations of standard sparsity approaches. See Hoeﬂer et al. (2021); Liu
& Wang (2023) for comparisons between various sparsity methods and a comprehensive analysis.
Despite progress made, there is a need for sparsity libraries, benchmarks, and evaluation protocols.
Gale et al. (2019) compared few popular algorithms across different domains and architectures.
Similarly (Blalock et al., 2020) provided an extensive report on benchmarks used in pruning papers.
One of the key existing libraries, OpenLTH (Frankle, 2019), is primarily focused on easing research
related to the Lottery Ticket Hypothesis (Frankle & Carbin, 2018), and facilitates implementation
of magnitude-based pruning methods as well as computation of related metrics. Other mask-based
pruning libraries in PyTorch include (Paganini, 2021). Alternatively, Ivanov et al. (2022) focuses
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