Decorators for maximizing memory utilization with PyTorch & CUDA

Related tags

Deep Learningcudatorch
Overview

torch-max-mem

Tests Cookiecutter template from @cthoyt PyPI PyPI - Python Version PyPI - License Documentation Status Code style: black

This package provides decorators for memory utilization maximization with PyTorch and CUDA by starting with a maximum parameter size and applying successive halving until no more out-of-memory exception occurs.

💪 Getting Started

Assume you have a function for batched computation of nearest neighbors using brute-force distance calculation.

import torch

def knn(x, y, batch_size, k: int = 3):
    return torch.cat(
        [
            torch.cdist(x[start : start + batch_size], y).topk(k=k, dim=1, largest=False).indices
            for start in range(0, x.shape[0], batch_size)
        ],
        dim=0,
    )

With torch_max_mem you can decorate this function to reduce the batch size until no more out-of-memory error occurs.

import torch
from torch_max_mem import maximize_memory_utilization


@maximize_memory_utilization(parameter_name="batch_size")
def knn(x, y, batch_size, k: int = 3):
    return torch.cat(
        [
            torch.cdist(x[start : start + batch_size], y).topk(k=k, dim=0, largest=False).indices
            for start in range(0, x.shape[0], batch_size)
        ],
        dim=0,
    )

In the code, you can now always pass the largest sensible batch size, e.g.,

x = torch.rand(100, 100, device="cuda")
y = torch.rand(200, 100, device="cuda")
knn(x, y, batch_size=x.shape[0])

🚀 Installation

The most recent release can be installed from PyPI with:

$ pip install torch_max_mem

The most recent code and data can be installed directly from GitHub with:

$ pip install git+https://github.com/mberr/torch-max-mem.git

To install in development mode, use the following:

$ git clone git+https://github.com/mberr/torch-max-mem.git
$ cd torch-max-mem
$ pip install -e .

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

Parts of the logic have been developed with Laurent Vermue for PyKEEN.

⚖️ License

The code in this package is licensed under the MIT License.

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.

🛠️ For Developers

See developer instrutions

The final section of the README is for if you want to get involved by making a code contribution.

🥼 Testing

After cloning the repository and installing tox with pip install tox, the unit tests in the tests/ folder can be run reproducibly with:

$ tox

Additionally, these tests are automatically re-run with each commit in a GitHub Action.

📖 Building the Documentation

$ tox -e docs

📦 Making a Release

After installing the package in development mode and installing tox with pip install tox, the commands for making a new release are contained within the finish environment in tox.ini. Run the following from the shell:

$ tox -e finish

This script does the following:

  1. Uses Bump2Version to switch the version number in the setup.cfg and src/torch_max_mem/version.py to not have the -dev suffix
  2. Packages the code in both a tar archive and a wheel
  3. Uploads to PyPI using twine. Be sure to have a .pypirc file configured to avoid the need for manual input at this step
  4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
  5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use tox -e bumpversion minor after.
You might also like...
Picasso: A CUDA-based Library for Deep Learning over 3D Meshes

The Picasso Library is intended for complex real-world applications with large-scale surfaces, while it also performs impressively on the small-scale applications over synthetic shape manifolds. We have upgraded the point cloud modules of SPH3D-GCN from homogeneous to heterogeneous representations, and included the upgraded modules into this latest work as well. We are happy to announce that the work is accepted to IEEE CVPR2021.

null 97 Dec 1, 2022
This Repo is the official CUDA implementation of ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures

Introduction This Repo is the official CUDA implementation of ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures. @inproceedings{Wa

Jiaqi Wang 42 Jan 7, 2023
Example repository for custom C++/CUDA operators for TorchScript

Custom TorchScript Operators Example This repository contains examples for writing, compiling and using custom TorchScript operators. See here for the

null 106 Dec 14, 2022
Convert Python 3 code to CUDA code.

Py2CUDA Convert python code to CUDA. Usage To convert a python file say named py_file.py to CUDA, run python generate_cuda.py --file py_file.py --arch

Yuval Rosen 3 Jul 14, 2021
This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust.

Demo BERT ONNX pipeline written in rust This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust. R

Xavier Tao 14 Dec 17, 2022
LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA
LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA

LightSeq: A High Performance Library for Sequence Processing and Generation

Bytedance Inc. 2.5k Jan 6, 2023
CUDA Python Low-level Bindings

CUDA Python Low-level Bindings

NVIDIA Corporation 529 Jan 3, 2023
A dead simple python wrapper for darknet that works with OpenCV 4.1, CUDA 10.1

What Dead simple python wrapper for Yolo V3 using AlexyAB's darknet fork. Works with CUDA 10.1 and OpenCV 4.1 or later (I use OpenCV master as of Jun

Pliable Pixels 6 Jan 12, 2022
An addernet CUDA version

Training addernet accelerated by CUDA Usage cd adder_cuda python setup.py install cd .. python main.py Environment pytorch 1.10.0 CUDA 11.3 benchmark

LingXY 4 Jun 20, 2022
Comments
  • Import error

    Import error

    When trying to run the example from the README, I currently get the following error

    Traceback (most recent call last):
  File ".../torch_max_mem/tmp.py", line 2, in <module>
    from torch_max_mem import maximize_memory_utilization
ModuleNotFoundError: No module named 'torch_max_mem'

    When I check pip list, the package name appears to be the stylized name

    $ pip list | grep max
torch-max-mem     0.0.1.dev0 .../torch_max_mem/src
    opened by mberr 2
  • Add simplified key hasher

    Add simplified key hasher

    This PR adds a simplification for creating hashers based on the values associated to a subse of keys without having to define a lambda or named function.

    opened by mberr 1
  • Code fails for KEYWORD_ONLY params

    Code fails for KEYWORD_ONLY params

    The following snippet

    from torch_max_mem import maximize_memory_utilization


@maximize_memory_utilization()
def func(a, *bs, batch_size: int):
    pass

    raises an error

    Traceback (most recent call last):
  File ".../tmp.py", line 5, in <module>
    def func(a, *bs, batch_size: int):
  File ".../venv/venv-cpu/lib/python3.8/site-packages/torch_max_mem/api.py", line 274, in __call__
    wrapped = maximize_memory_utilization_decorator(
  File ".../venv/venv-cpu/lib/python3.8/site-packages/torch_max_mem/api.py", line 150, in decorator_maximize_memory_utilization
    raise ValueError(f"{parameter_name} must be a keyword based parameter, but is {_parameter.kind}.")
ValueError: batch_size must be a keyword based parameter, but is KEYWORD_ONLY.

    since _parameter.kind is KEYWORD_ONLY.

    This is overly restrictive, since we only need keyword-based parameters.

    opened by mberr 0
  • stateful decorator

    stateful decorator

    Add a decorator which remembers to maximum parameter value for next time. Since this is handled internally, we do not need to expose the found parameter value to the outside, leaving the method signature unchanged.

    opened by mberr 0
Releases(v0.0.4)

  • v0.0.4(Aug 18, 2022)

    What's Changed

    • Fix ad hoc key hashing by @mberr in https://github.com/mberr/torch-max-mem/pull/7
    • Fix default value handling by @mberr in https://github.com/mberr/torch-max-mem/pull/8

    Full Changelog: https://github.com/mberr/torch-max-mem/compare/v0.0.3...v0.0.4

    Source code(tar.gz)
    Source code(zip)

  • v0.0.3(Aug 18, 2022)

    What's Changed

    • Fix keyword only params by @mberr in https://github.com/mberr/torch-max-mem/pull/6

    Full Changelog: https://github.com/mberr/torch-max-mem/compare/v0.0.2...v0.0.3

    Source code(tar.gz)
    Source code(zip)

  • v0.0.2(May 6, 2022)

    What's Changed

    • Add simplified key hasher by @mberr in https://github.com/mberr/torch-max-mem/pull/3
    • Update README & doc by @mberr in https://github.com/mberr/torch-max-mem/pull/4

    Full Changelog: https://github.com/mberr/torch-max-mem/compare/v0.0.1...v0.0.2

    Source code(tar.gz)
    Source code(zip)

  • v0.0.1(Feb 1, 2022)

Owner
Max Berrendorf
Max Berrendorf
GitHub Repository https://torch-max-mem.readthedocs.io/en/latest/
A python/pytorch utility library

A python/pytorch utility library

Jiaqi Gu 5 Dec 02, 2022
Gradient Step Denoiser for convergent Plug-and-Play

Source code for the paper "Gradient Step Denoiser for convergent Plug-and-Play"

Samuel Hurault 11 Sep 17, 2022
A certifiable defense against adversarial examples by training neural networks to be provably robust

DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the

SRI Lab, ETH Zurich 202 Dec 13, 2022
This is the dataset and code release of the OpenRooms Dataset.

This is the dataset and code release of the OpenRooms Dataset.

Visual Intelligence Lab of UCSD 95 Jan 08, 2023
Best practices for segmentation of the corporate network of any company

Best-practice-for-network-segmentation What is this? This project was created to publish the best practices for segmentation of the corporate network

2k Jan 07, 2023
Read number plates with https://platerecognizer.com/

HASS-plate-recognizer Read vehicle license plates with https://platerecognizer.com/ which offers free processing of 2500 images per month. You will ne

Robin 69 Dec 30, 2022
chainladder - Property and Casualty Loss Reserving in Python

chainladder (python) chainladder - Property and Casualty Loss Reserving in Python This package gets inspiration from the popular R ChainLadder package

Casualty Actuarial Society 130 Dec 07, 2022
A PyTorch re-implementation of the paper 'Exploring Simple Siamese Representation Learning'. Reproduced the 67.8% Top1 Acc on ImageNet.

Exploring simple siamese representation learning This is a PyTorch re-implementation of the SimSiam paper on ImageNet dataset. The results match that

Taojiannan Yang 72 Nov 09, 2022
paper list in the area of reinforcenment learning for recommendation systems

paper list in the area of reinforcenment learning for recommendation systems

HenryZhao 23 Jun 09, 2022
Attention-based Transformation from Latent Features to Point Clouds (AAAI 2022)

Attention-based Transformation from Latent Features to Point Clouds This repository contains a PyTorch implementation of the paper: Attention-based Tr

12 Nov 11, 2022
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)

MTTS-CAN: Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement Paper Xin Liu, Josh Fromm, Shwetak Patel, Daniel M

Xin Liu 106 Dec 30, 2022
SmartSim Infrastructure Library.

Home Install Documentation Slack Invite Cray Labs SmartSim SmartSim makes it easier to use common Machine Learning (ML) libraries like PyTorch and Ten

Cray Labs 139 Jan 01, 2023
Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. Not an official Google product. Me

Google Research 27 Dec 12, 2022
This project provides the code and datasets for 'CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection', CVPR 2019.

Code-and-Dataset-for-CapSal This project provides the code and datasets for 'CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detec

lu zhang 48 Aug 19, 2022
Speech Enhancement Generative Adversarial Network Based on Asymmetric AutoEncoder

ASEGAN: Speech Enhancement Generative Adversarial Network Based on Asymmetric AutoEncoder 中文版简介 Readme with English Version 介绍 基于SEGAN模型的改进版本，使用自主设计的非

Nitin 53 Nov 17, 2022
Official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation

SegPC-2021 This is the official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation by

Datascience IIT-ISM 13 Dec 14, 2022
Pose Detection and Machine Learning for real-time body posture analysis during exercise to provide audiovisual feedback on improvement of form.

Posture: Pose Tracking and Machine Learning for prescribing corrective suggestions to improve posture and form while exercising. This repository conta

Pratham Mehta 10 Nov 11, 2022
DimReductionClustering - Dimensionality Reduction + Clustering + Unsupervised Score Metrics

Dimensionality Reduction + Clustering + Unsupervised Score Metrics Introduction

11 Nov 15, 2022
Interpretation of T cell states using reference single-cell atlases

Interpretation of T cell states using reference single-cell atlases ProjecTILs is a computational method to project scRNA-seq data into reference sing

Cancer Systems Immunology Lab 139 Jan 03, 2023
DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction This is the implementation of DeepSTD in

5 Sep 26, 2022