AdamW optimizer for bfloat16 models in pytorch.

Related tags

Deep Learningadamw_bfloat16
Overview

catchy image of op

Image source

AdamW optimizer for bfloat16 models in pytorch.

  • Bfloat16 is currently an optimal tradeoff between range and relative error for deep networks.
  • Bfloat16 can be used quite efficiently on Nvidia GPUs with Ampere architecture (A100, A10, A30, RTX3090...)

However, neither AMP in pytorch is ready for bfloat16, nor optimizers.

If you just convert all weights and inputs to bfloat16, you're likely to run into an issue of stale weights: updates are too small to modify bfloat16 weight (see gopher paper, section C2 for a large-scale example).

There are two possible remedies:

  • keep weights in float32 (precise) and bfloat16 (approximate)
  • keep weights in bfloat16, and keep correction term in bfloat16

As recent study has shown, both options are completely competitive in quality to float32 training.

Usage

Install:

pip install git+https://github.com/arogozhnikov/adamw_bfloat16.git

Use as a drop-in replacement for pytorch's AdamW:

import torch
from adamw_bfloat16 import LR, AdamW_BF16
model = model.to(torch.bfloat16)

# default preheat and decay
optimizer = AdamW_BF16(model.parameters())

# configure LR schedule. Use built-in scheduling opportunity
optimizer = AdamW_BF16(model.parameters(), lr_function=LR(lr=1e-4, preheat_steps=5000, decay_power=-0.25))
You might also like...
Storage-optimizer - Identify potintial optimizations on the cloud storage accounts

Storage Optimizer Identify potintial optimizations on the cloud storage accounts

Zaher Mousa 1 Feb 13, 2022
Ever felt tired after preprocessing the dataset, and not wanting to write any code further to train your model? Ever encountered a situation where you wanted to record the hyperparameters of the trained model and able to retrieve it afterward? Models Playground is here to help you do that. Models playground allows you to train your models right from the browser.
Ever felt tired after preprocessing the dataset, and not wanting to write any code further to train your model? Ever encountered a situation where you wanted to record the hyperparameters of the trained model and able to retrieve it afterward? Models Playground is here to help you do that. Models playground allows you to train your models right from the browser.

Models Playground 🗂️ Upload a Preprocessed Dataset 🌠 Choose whether to perform Classification or Regression 🦹 Enter the Dependent Variable ?

null 19 Dec 10, 2022
PyTorch implementation and pretrained models for XCiT models. See XCiT: Cross-Covariance Image Transformer
PyTorch implementation and pretrained models for XCiT models. See XCiT: Cross-Covariance Image Transformer

Official code Cross-Covariance Image Transformer (XCiT)

Facebook Research 605 Jan 2, 2023
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

Mayur 119 Nov 24, 2022
Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch
Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Pytorch Lightning 1.4k Jan 1, 2023
A bunch of random PyTorch models using PyTorch's C++ frontend
A bunch of random PyTorch models using PyTorch's C++ frontend

PyTorch Deep Learning Models using the C++ frontend Gettting started Clone the repo 1. https://github.com/mrdvince/pytorchcpp 2. cd fashionmnist or

Vince 0 Jul 13, 2021
PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

PyTorch-LIT PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices. With

Amin Rezaei 157 Dec 11, 2022
Pytorch-diffusion - A basic PyTorch implementation of 'Denoising Diffusion Probabilistic Models'
Pytorch-diffusion - A basic PyTorch implementation of 'Denoising Diffusion Probabilistic Models'

PyTorch implementation of 'Denoising Diffusion Probabilistic Models' This reposi

Arthur Juliani 76 Jan 7, 2023
pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.

Bayesian inference in HSMMs and HMMs This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and expli

Matthew Johnson 527 Dec 4, 2022
Releases(v0.1.0)

  • v0.1.0(Dec 14, 2021)

    Initial implementation of AdamW for pytorch supports cuda graphs and has a built-in mechanism for control of learning rate, because external are unlikely to make a friendship with cuda graphs

    Source code(tar.gz)
    Source code(zip)
Owner
Alex Rogozhnikov
ML + Science at scale
Alex Rogozhnikov
GitHub Repository
Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

ImageProcessingTransformer Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

61 Jan 01, 2023
This repository contains the code used for the implementation of the paper "Probabilistic Regression with HuberDistributions"

Public_prob_regression_with_huber_distributions This repository contains the code used for the implementation of the paper "Probabilistic Regression w

David Mohlin 1 Dec 04, 2021
Statistical and Algorithmic Investing Strategies for Everyone

Eiten - Algorithmic Investing Strategies for Everyone Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic

Tradytics 2.5k Jan 02, 2023
OpenMMLab Detection Toolbox and Benchmark

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

OpenMMLab 22.5k Jan 05, 2023
Code for Multinomial Diffusion

Code for Multinomial Diffusion Abstract Generative flows and diffusion models have been predominantly trained on ordinal data, for example natural ima

104 Jan 04, 2023
Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN)

Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN) This code implements the skeleton-based action segmentation MS-GCN model from Autom

Benjamin Filtjens 8 Nov 29, 2022
TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning

TransZero++ This repository contains the testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" submitted

Shiming Chen 6 Aug 16, 2022
Graph WaveNet apdapted for brain connectivity analysis.

Graph WaveNet for brain network analysis This is the implementation of the Graph WaveNet model used in our manuscript: S. Wein , A. Schüller, A. M. To

4 Dec 17, 2022
IDA file loader for UF2, created for the DEFCON 29 hardware badge

UF2 Loader for IDA The DEFCON 29 badge uses the UF2 bootloader, which conveniently allows you to dump and flash the firmware over USB as a mass storag

Kevin Colley 6 Feb 08, 2022
Official PyTorch implementation of MX-Font (Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts)

Introduction Pytorch implementation of Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Expert. | paper Song Park1

Clova AI Research 97 Dec 23, 2022
Official Pytorch implementation of "Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021)

Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021) Official Pytorch implementation of Unbiased Classification

Youngkyu 17 Jan 01, 2023
Crosslingual Segmental Language Model

Crosslingual Segmental Language Model This repository contains the code from Multilingual unsupervised sequence segmentation transfers to extremely lo

C.M. Downey 1 Jun 13, 2022
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022)

CMUA-Watermark The official code for CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022) arxiv. It is bas

50 Nov 26, 2022
U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

Dennis Bappert 104 Nov 25, 2022
A Blender python script for getting asset browser custom preview images for objects and collections.

asset_snapshot A Blender python script for getting asset browser custom preview images for objects and collections. Installation: Click the code butto

Johnny Matthews 44 Nov 29, 2022
PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration (NeurIPS 2021) PyTorch implementation of the paper: CoFiNet: Reli

76 Jan 03, 2023
Header-only library for using Keras models in C++.

frugally-deep Use Keras models in C++ with ease Table of contents Introduction Usage Performance Requirements and Installation FAQ Introduction Would

Tobias Hermann 927 Jan 05, 2023
Cross-Document Coreference Resolution

Cross-Document Coreference Resolution This repository contains code and models for end-to-end cross-document coreference resolution, as decribed in ou

Arie Cattan 29 Nov 28, 2022
Concept drift monitoring for HA model servers.

{Fast, Correct, Simple} - pick three Easily compare training and production ML data & model distributions Goals Boxkite is an instrumentation library

98 Dec 15, 2022
Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

DSPoint Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion" Coming soon, as soon as I finish a

Ziyao Zeng 14 Feb 26, 2022