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PyTorch ExecuTorch Quantization Overview
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        • A Brief Review of Hypernetworks in Deep Learning
        • A ConvNet for the 2020s
        • A Hierarchy of Graph Neural Networks Based on Learnable Local Features
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        • A survey of quantization methods for efficient neural network inference
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        • Adapting Vision Foundation Models for Plant Phenotyping
        • An Image is Worth More Than 16x16 Patches Exploring Transformers on Individual Pixels
        • An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
        • An image is worth 16x16 words Transformers for image recognition at scale
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        • Battle of the Backbones A Large Scale Comparison of Pretrained Models across Computer Vision Tasks
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        • Building on Efficient Foundations Effectively Training LLMs with Structured Feedforward Layers
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        • ConViT Improving Vision Transformers with Soft Convolutional Inductive Biases
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        • Deep Learning Book
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        • Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution
        • EVA 02 A Visual Representation for Neon Genesis
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        • Efficient Equivariant Transfer Learning from Pretrained Models
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        • Emergent Equivariance in Deep Ensembles
        • Emerging Properties in Self Supervised Vision Transformers
        • End to End Object Detection with Transformers
        • Equi Tuning Group Equivariant Fine Tuning of Pretrained Models
        • Equivariance with Learned Canonicalization Functions
        • Equivariance aware architectural optimization of neural networks
        • Exact Conversion of In Context Learning to Model Weights in Linearized Attention Transformers
        • Exploiting Redundancy Separable Group Convolutional Networks on Lie Groups
        • Exploring Plain Vision Transformer Backbones for Object Detection
        • Fast, Expressive SE(n) Equivariant Networks through Weight Sharing in Position Orientation Space
        • FlexiViT One Model for All Patch Sizes
        • G SGD Optimizing ReLU Neural Networks in its Positively Scale Invariant Space
        • Grokked Transformers are Implicit Reasoners A Mechanistic Journey to the Edge of Generalization
        • Harmonics of Learning Universal Fourier Features Emerge in Invariant Networks
        • How do vision transformers work?
        • Hydra Bidirectional State Space Models Through Generalized Matrix Mixers
        • Hyperspherical Variational Auto Encoders
        • Improving Convergence and Generalization Using Parameter Symmetries
        • In Search of Projectively Equivariant Networks
        • Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task specific Models
        • LRP QViT Mixed Precision Vision Transformer Quantization via Layer wise Relevance Propagation
        • Learned Gridification for Efficient Point Cloud Processing
        • Learning Partial Equivariances from Data
        • Learning both Weights and Connections for Efficient Neural Networks
        • Learning with Unmasked Tokens Drives Stronger Vision Learners
        • Llama 2 Open Foundation and Fine Tuned Chat Models
        • LoRA Low Rank Adaptation of Large Language Models
        • Mamba Linear Time Sequence Modeling with Selective State Spaces
        • Memorization Through the Lens of Curvature of Loss Function Around Samples
        • Mixture of LoRa Experts
        • MobileCLIP Fast Image Text Models through Multi Modal Reinforced Training
        • MobileViT light weight, general purpose, and mobile friendly vision transformer
        • Model Compression in Practice Lessons Learned from Practitioners Creating On device Machine Learning Experiences
        • Neural Mechanics Symmetry and Broken Conservation Laws in Deep Learning Dynamics
        • On Good Practices for Task Specific Distillation of Large Pretrained Visual Models
        • On the Relationship between Self Attention and Convolutional Layers
        • On the Symmetries of Deep Learning Models and their Internal Representations
        • OpenELM An Efficient Language Model Family with Open source Training and Inference Framework
        • Optimal Brain Damage
        • Optimization Dynamics of Equivariant and Augmented Neural Networks
        • Parameter Efficient Fine tuning of Self supervised ViTs without Catastrophic Forgetting
        • Parameter Efficient Fine Tuning for Pre Trained Vision Models A Survey
        • Progress measures for grokking via mechanistic interpretability
        • Provably Strict Generalisation Benefit for Equivariant Models
        • ProxylessNAS Direct Neural Architecture Search on Target Task and Hardware
        • R MAE Regions Meet Masked Autoencoders
        • Refusal in Language Models Is Mediated by a Single Direction
        • Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems
        • Relaxing Equivariance Constraints with Non stationary Continuous Filters
        • Retrospective EIE Efficient Inference Engine onSparse and Compressed Neural Network
        • Revealing the Utilized Rank of Subspaces of Learning in Neural Networks
        • Rewrite the Stars
        • SAM CLIP Merging Vision Foundation Models towards Semantic and Spatial Understanding
        • Scaling (Down) CLIP A Comprehensive Analysis of Data, Architecture, and Training Strategies
        • Segment Anything
        • Self Supervised Detection of Perfect and Partial Input Dependent Symmetries
        • SimPLR A Simple and Plain Transformer for Scaling Efficient Object Detection and Segmentation
        • Simultaneous linear connectivity of neural networks modulo permutation
        • Stand Alone Self Attention in Vision Models
        • Surgical Fine Tuning Improves Adaptation to Distribution Shifts
        • Surgical DINO Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery
        • Symmetries in Overparametrized Neural Networks A Mean Field View
        • Talaria Interactively Optimizing Machine Learning Models for Efficient Inference
        • The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
        • The Lie derivative for measuring learned equivariance
        • The Unreasonable Ineffectiveness of the Deeper Layers
        • TiC CLIP Continual Training of CLIP models
        • Training quantized nets A deeper understanding
        • Understanding Deep Learning Chapter 10
        • Understanding Deep Learning Chapter 20
        • Understanding symmetries in deep networks
        • Using Degeneracy in the Loss Landscape for Mechanistic Interpretability
        • ViDT An Efficient and Effective Fully Transformer based Object Detector
        • Vision Mamba Efficient Visual Representation Learning with Bidirectional State Space Model
        • Vision Transformers Need Registers
        • What Do Self Supervised Vision Transformers Learn?
        • nGPT Normalized Transformer with Representation Learning on the Hypersphere
        • Albert Gu
        • Alex Flinth
        • Alexander Kirillov
        • Alexey Dosovitskiy
        • Ananya Kumar
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        • Rui Wang
        • Ruoming Pang
        • Sachin Mehta
        • Sangdoo Yun
        • Sanghyuk Chun
        • Sara Solla
        • Sergey Zagoruyko
        • Shaohan Huang
        • Simeng Sun
        • Simon J.D. Prince
        • Skander Moalla
        • Soham De
        • Song Han
        • Songkuk Kim
        • Sourya Basu
        • Stéphane d'Ascoli
        • Sukjun Hwang
        • Taekyung Kim
        • Tete Xiao
        • Thomas Kipf
        • Tim R. Davidson
        • Tom Gunter
        • Tom Lieberum
        • Vaibhav Aggarwal
        • William J. Dally
        • Wonjae Kim
        • Xiang Yue
        • Xingyu Liu
        • Xinlei Chen
        • Xiuying Wei
        • Xu Ma
        • Xun Wu
        • Yanghao Li
        • Yann LeCun
        • Yelong Shen
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        • Zeyuan Allen Zhu
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        • EPFL CS439 Optimization for Machine Learning
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        • Introducing Apple’s On Device and Server Foundation Models
        • Introduction to Quantization on PyTorch
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        • MIT 65940 TinyML and Efficient Deep Learning Computing
        • Optimizing Vision Transformer Model for Deployment
        • PyTorch ExecuTorch Export IR Specification
        • PyTorch ExecuTorch How ExecuTorch works?
        • PyTorch ExecuTorch Quantization Overview
        • PyTorch Functionalization in PyTorch Everything you need to know
        • PyTorch PyTorch 2 Export Post Training Quantization
        • PyTorch Quantization
        • PyTorch Compilers What makes PyTorch beloved makes it hard to compile
        • PyTorch Conference 2024 Fast Sparse Vision Transformers with minimal accuracy loss
        • PyTorch Conference 2024 What’s new in torch.export?
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        • PyTorch Eager Mode Quantization TensorRT Acceleration
        • PyTorch internals
        • Quantized Transfer Learning for Computer Vision Tutorial
        • Reinforcement Learning An Introduction Chapter 10
        • Reinforcement Learning An Introduction Chapter 11
        • Reinforcement Learning An Introduction Chapter 13
        • Reinforcement Learning An Introduction Chapter 16
        • Reinforcement Learning An Introduction Chapter 2
        • Reinforcement Learning An Introduction Chapter 3
        • Reinforcement Learning An Introduction Chapter 4
        • Reinforcement Learning An Introduction Chapter 5
        • Reinforcement Learning An Introduction Chapter 6
        • Reinforcement Learning An Introduction Chapter 7
        • Reinforcement Learning An Introduction Chapter 9
        • Reinforcement Learning An Introduction
        • TinyML and Efficient Deep Learning Computing Lecture 12
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        • TinyML and Efficient Deep Learning Computing
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    PyTorch ExecuTorch Quantization Overview

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    authors PyTorch Quantization for TensorRT
    year 2024
    url https://pytorch.org/executorch/main/quantization-overview.html

    Pasted image 20240925193351.png{ width="400" }

    Quantization is usually tied to execution backends that have quantized operators implemented. Thus each backend is opinionated about how the model should be quantized, expressed in a backend specific Quantizer class.

    2024-09-28
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