Top 7 Python sparsity Projects

sparseml

12 1,974 9.6 Python

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
neural-compressor

3 1,950 9.8 Python

SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
WorkOS

workos.com sponsored

The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
model-optimization

1 1,465 6.7 Python

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
nncf

2 777 9.7 Python

Neural Network Compression Framework for enhanced OpenVINO™ inference

Project mention: FLaNK Stack Weekly 06 Nov 2023 | dev.to | 2023-11-06

sporco

1 247 4.8 Python

Sparse Optimisation Research Code
UPop

1 83 8.4 Python

[ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.

Project mention: Show HN: Compress vision-language and unimodal AI models by structured pruning | news.ycombinator.com | 2023-07-31

OWL

1 39 7.3 Python

Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity" (by luuyin)

Project mention: Outlier Weighed Layerwise Sparsity: A Missing Secret Sauce for Pruning LLMs | news.ycombinator.com | 2023-10-10

Paper abstract: Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge due to their colossal model size when it comes to practical deployment. In response to this challenge, efforts have been directed toward the application of traditional network pruning techniques to LLMs, uncovering a massive number of parameters can be pruned in one-shot without hurting performance. Building upon insights gained from pre-LLM models, particularly BERT-level language models, prevailing LLM pruning strategies have consistently adhered to the practice of uniformly pruning all layers at equivalent sparsity levels, resulting in robust performance. However, this observation stands in contrast to the prevailing trends observed in the field of vision models, where non-uniform layerwise sparsity typically yields substantially improved results. To elucidate the underlying reasons for this disparity, we conduct a comprehensive analysis of the distribution of token features within LLMs. In doing so, we discover a strong correlation with the emergence of outliers, defined as features exhibiting significantly greater magnitudes compared to their counterparts in feature dimen- sions. Inspired by this finding, we introduce a novel LLM pruning methodology that incorporates a tailored set of non-uniform layerwise sparsity ratios specif- ically designed for LLM pruning, termed as Outlier Weighed Layerwise sparsity (OWL). The sparsity ratio of OWL is directly proportional to the outlier ratio observed within each layer, facilitating a more effective alignment between layer- wise weight sparsity and outlier ratios. Our empirical evaluation, conducted across the LLaMA-V1 family and OPT, spanning various benchmarks, demonstrates the distinct advantages offered by OWL over previous methods. For instance, our approach exhibits a remarkable performance gain, surpassing the state-of-the-art Wanda and SparseGPT by 61.22 and 6.80 perplexity at a high sparsity level of 70%, respectively. Codes are available at https://github.com/luuyin/OWL.

InfluxDB

www.influxdata.com sponsored

Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).