Torch-Pruning

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Torch-Pruning is an open-source framework for structural pruning of deep neural networks. It uses the DepGraph algorithm to automatically identify dependencies and remove coupled parameters, supporting a wide range of models including LLMs and Vision Foundation Models.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Torch-Pruning?

Torch-Pruning (TP) is a comprehensive open-source framework designed for structural pruning of deep neural networks. Unlike traditional methods that zeroize parameters via masking, TP employs an innovative algorithm called DepGraph to group and remove coupled parameters, ensuring efficient model compression. It supports a diverse array of models, including Large Language Models (LLMs), Segment Anything Model (SAM), Diffusion Models, Vision Transformers, and various CNN architectures like ConvNext and Yolov7/v8. The framework offers general-purpose pruning capabilities, high-level pruners for automated channel pruning based on importance criteria, and options for global or isomorphic pruning. It also provides functionalities for sparse training, interactive pruning, and customized layer handling, making it a versatile tool for researchers and developers aiming to optimize model size and performance.

Best used for

Ideal for developers and data scientists who need to reduce the computational footprint of deep neural networks, accelerate inference times, and deploy models more efficiently. Especially valuable for optimizing large language models and vision foundation models for resource-constrained environments.

Common actions

prune neural networks

optimize AI models

reduce model size

accelerate model inference

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Capabilities

Key features

DepGraph structural pruning
Supports LLMs, Vision Models
High-level pruners
Global and isomorphic pruning
Sparse training
Customized layer handling

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the core difference between Torch-Pruning and torch.nn.utils.prune?

Torch-Pruning utilizes the DepGraph algorithm to identify and remove coupled parameters across multiple layers, enabling structural pruning. In contrast, torch.nn.utils.prune primarily focuses on zeroizing individual parameters via masking, which is a different approach to model compression.

Which types of deep learning models can be pruned using Torch-Pruning?

Torch-Pruning supports a wide array of deep learning models, including Large Language Models (LLMs), Segment Anything Model (SAM), Diffusion Models, Vision Transformers, and various convolutional neural networks like ConvNext, Yolov7, Yolov8, ResNe(X)t, and BERT.

How does Torch-Pruning handle dependencies between layers during pruning?

Torch-Pruning addresses inter-layer dependencies through its DepGraph algorithm. This algorithm automatically analyzes the model structure to identify how pruning a parameter in one layer affects others, ensuring that all dependent parameters are removed consistently to maintain model integrity.

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