AdamW optimizer for bfloat16 models in pytorch.

Related tags

Deep Learning adamw_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
ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

Katherine Crowson 53 Dec 29, 2022
PyTorch implementation DRO: Deep Recurrent Optimizer for Structure-from-Motion

DRO: Deep Recurrent Optimizer for Structure-from-Motion This is the official PyTorch implementation code for DRO-sfm. For technical details, please re

Alibaba Cloud 56 Dec 12, 2022
A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer from NNAISENSE.

PGPElib A mini library for Policy Gradients with Parameter-based Exploration [1] and friends. This library serves as a clean re-implementation of the

NNAISENSE 56 Jan 1, 2023
Ranger deep learning optimizer rewrite to use newest components

Ranger21 - integrating the latest deep learning components into a single optimizer Ranger deep learning optimizer rewrite to use newest components Ran

Less Wright 266 Dec 28, 2022
auto-tuning momentum SGD optimizer

YellowFin YellowFin is an auto-tuning optimizer based on momentum SGD which requires no manual specification of learning rate and momentum. It measure

Jian Zhang 288 Nov 19, 2022
Ranger - a synergistic optimizer using RAdam (Rectified Adam), Gradient Centralization and LookAhead in one codebase

Ranger-Deep-Learning-Optimizer Ranger - a synergistic optimizer combining RAdam (Rectified Adam) and LookAhead, and now GC (gradient centralization) i

Less Wright 1.1k Dec 21, 2022
Apollo optimizer in tensorflow

Apollo Optimizer in Tensorflow 2.x Notes: Warmup is important with Apollo optimizer, so be sure to pass in a learning rate schedule vs. a constant lea

Evan Walters 1 Nov 9, 2021
This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras)

Yogi-Optimizer_Keras This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras) The NeurIPS-Paper can be found here: http://papers.nips.c

null 14 Sep 13, 2022
DeepOBS: A Deep Learning Optimizer Benchmark Suite

DeepOBS - A Deep Learning Optimizer Benchmark Suite DeepOBS is a benchmarking suite that drastically simplifies, automates and improves the evaluation

Aaron Bahde 7 May 12, 2020
An Implicit Function Theorem (IFT) optimizer for bi-level optimizations

iftopt An Implicit Function Theorem (IFT) optimizer for bi-level optimizations. Requirements Python 3.7+ PyTorch 1.x Installation $ pip install git+ht

The Money Shredder Lab 2 Dec 2, 2021