TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards

Related tags

Pytorch Utilitiespytorchmodel-parallelshard-training
Overview

Documents | Projects | API References

TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards. It can reduce GPU memory and scale up the training when the model has massive linear layers (e.g., ViT, BERT and GPT) or huge classes (millions). It has the same API design as PyTorch.

Installation

pip install torchshard

More options in INSTALL.md.

Usage

import torchshard as ts

ts.init_process_group(group_size=2)                       # init parallel groups

m = torch.nn.Sequential(
    torch.nn.Linear(20, 30, bias=True),               
    ts.nn.ParallelLinear(30, 30, bias=True, dim=None),    # equal to nn.Linear()
    ts.nn.ParallelLinear(30, 30, bias=True, dim=0),       # parallel in row dimension
    ts.nn.ParallelLinear(30, 30, bias=True, dim=1),       # parallel in column dimension
).cuda()

x = m(x)                                                  # forward
loss = ts.nn.functional.parallel_cross_entropy(x, y)      # parallel loss function
loss.backward()                                           # backward

torch.save(
  ts.collect_state_dict(m, m.state_dict()), 'm.pt')       # save model state

Performance

The following figure is a showcase of training ResNet-50 on 8 NVIDIA TITAN-XP (12196 MiB) GPUs with scaling up classes from 1000 → 1 Million. The input size is 224 x 224, and the batch size is 256. Parallelism is with 8-way data parallel and 8-way model parallel.

The following figure shows training minGPT on 8 NVIDIA TITAN-XP (12196 MiB) GPUs with scaling up parameters from 10 Million → 808 Million. The input size is 32 x 32, and the batch size is 16. Parallelism is with 1-way data parallel and 8-way model parallel.

Contributing

The TorchShard welcomes your expertise and enthusiasm!

If you are interested in torchshard, you are welcome to help

  • polish code and develop new features
  • develop high-quality tutorials, projects, and advanced materials

Direct pull requests are welcome. Contact: kaiyuyue [at] umd.edu.

Citing TorchShard

If you think TorchShard is helpful in your research and consider to cite it, please use the following BibTeX entry.

@misc{torchshard2021,
  author =       {Kaiyu Yue},
  title =        {TorchShard},
  howpublished = {\url{https://github.com/KaiyuYue/torchshard}},
  year =         {2021}
}
Comments
  • Future Planinig on this project.

    Future Planinig on this project.

    Hello Kaiyu, I love this awesome project. The API design is elegant and simple and the software is lightweight and user-friendly. My understanding is that this project has realized a series of PyTorch wrappers for tensor slicing.

    1. I am curious about the future planning of this project.
    2. Is there some overlap in functionality between torchshard and N-D parallelism proposed in ColossalAI.
    3. How is compatibility with ZeRO? According to am+zero example, the memory footprint has a little change after combination torchshard with ZeRO.
    opened by feifeibear 2
  • Which one is faster?

    Which one is faster?

    Thanks for contributing this great lib. I have one question. Which one is faster (in speed) between dim=0and dim=1? The documentations seem to only contain accuracy results.

    opened by NOBLES5E 2
  • 8 gpus test example raise error.

    8 gpus test example raise error.

    When I do Unit Tests, it can pass when use two gpu devices, run command below: CUDA_VISIBLE_DEVICES=0,1 python3 -m unittest discover -v -s tests

    But I do Unit Tests with eight gpu devices, it raise ncclSystemError. run command: CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m unittest discover -v -s tests raise error: RuntimeError: NCCL error in ../torch/lib/c10d/ProcessGroupNCCL.cpp:825, unhandled system error, NCCL version 2.7.8 ncclSystemError: System call (socket, malloc, munmap, etc) failed.

    Is it necessary to pass unittest in eights gpu devices?

    opened by JiaquanYe 1
  • Error?

    Error?

    Hi, thanks for the excellent job! When I install it from pip, and

    import torchshard as ts
ts.init_process_group(group_size=2)

    The AttributeError occurs:

    AttributeError: module 'torchshard' has no attribute 'init_process_group'
    opened by WangWenhao0716 1
  • Multi-node setting?

    Multi-node setting?

    https://github.com/KaiyuYue/torchshard/blob/89e21def180bf6063ceb2e312a61631173abc7e7/projects/minGPT/main.py#L150

    I have noticed that the group_size is set to world_size in examples, but in fact the group_size can be set to other numbers according to my understanding.

    https://github.com/KaiyuYue/torchshard/blob/main/torchshard/distributed/core.py#L18

    I have also found that the get_world_size() will return the number of all processes.

    The two findings make me confused in a multi-node setting, say 2 nodes with each node with 2 processes.

    If the group_size is 2, then there are 2 distinct groups besides the default group (w/ overlap). However, get_world_size() is used without specifying a group can make a layer be splitted to 4 parts, which is expected to be 2 in our case.

    Correct me if I am wrong.

    Good Issue 
    opened by GeneZC 1
  • Is it possible to collect state dict in cpu?

    Is it possible to collect state dict in cpu?

    When I finish one epoch in trianing, the main_worker function will call ts.collect_state_dict(model, state_dict). But because the limit of GPU resource, it will raise Out of Memory in my machine, when call ts.collect_state_dict(model, state_dict). I found that will gather the state_dict in GPU, is it anyway to gather in CPU?

    Good Issue 
    opened by JiaquanYe 2
Releases(v0.1)
Owner
Kaiyu Yue
Kaiyu Yue
GitHub Repository
PyTorch implementations of normalizing flow and its variants.

PyTorch implementations of normalizing flow and its variants.

Tatsuya Yatagawa 55 Dec 01, 2022
An optimizer that trains as fast as Adam and as good as SGD.

AdaBound An optimizer that trains as fast as Adam and as good as SGD, for developing state-of-the-art deep learning models on a wide variety of popula

LoLo 2.9k Dec 27, 2022
A lightweight wrapper for PyTorch that provides a simple declarative API for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.

A lightweight wrapper for PyTorch that provides a simple declarative API for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.

Fidelity Investments 56 Sep 13, 2022
Bunch of optimizer implementations in PyTorch

Bunch of optimizer implementations in PyTorch

Hyeongchan Kim 76 Jan 03, 2023
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

Pretrained models for Pytorch (Work in progress) The goal of this repo is: to help to reproduce research papers results (transfer learning setups for

Remi 8.7k Dec 31, 2022
PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

glow-pytorch PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

Kim Seonghyeon 433 Dec 27, 2022
A code copied from google-research which named motion-imitation was rewrited with PyTorch

motor-system Introduction A code copied from google-research which named motion-imitation was rewrited with PyTorch. More details can get from this pr

NewEra 6 Jan 08, 2022
High-fidelity performance metrics for generative models in PyTorch

High-fidelity performance metrics for generative models in PyTorch

Vikram Voleti 5 Oct 24, 2021
PyGCL: Graph Contrastive Learning Library for PyTorch

PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL components from published papers, standardized evaluation, and experiment management.

GCL: Graph Contrastive Learning Library for PyTorch 592 Jan 07, 2023
Pytorch implementation of Distributed Proximal Policy Optimization

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

Alexis David Jacq 164 Jan 05, 2023
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

Torchmeta A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch. Torchmeta contains popular meta-learning bench

Tristan Deleu 1.7k Jan 06, 2023
Riemannian Adaptive Optimization Methods with pytorch optim

geoopt Manifold aware pytorch.optim. Unofficial implementation for “Riemannian Adaptive Optimization Methods” ICLR2019 and more. Installation Make sur

642 Jan 03, 2023
The goal of this library is to generate more helpful exception messages for numpy/pytorch matrix algebra expressions.

Tensor Sensor See article Clarifying exceptions and visualizing tensor operations in deep learning code. One of the biggest challenges when writing co

Terence Parr 704 Dec 14, 2022
A Pytorch Implementation for Compact Bilinear Pooling.

CompactBilinearPooling-Pytorch A Pytorch Implementation for Compact Bilinear Pooling. Adapted from tensorflow_compact_bilinear_pooling Prerequisites I

169 Dec 23, 2022
PyTorch wrappers for using your model in audacity!

PyTorch wrappers for using your model in audacity!

130 Dec 14, 2022
This is an differentiable pytorch implementation of SIFT patch descriptor.

This is an differentiable pytorch implementation of SIFT patch descriptor. It is very slow for describing one patch, but quite fast for batch. It can

Dmytro Mishkin 150 Dec 24, 2022
TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards

TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards. It can reduce GPU memory and scale up the training when the model has massive linear layers (e.g., ViT, BERT and

Kaiyu Yue 275 Nov 22, 2022
Over9000 optimizer

Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC

Mikhail Grankin 405 Nov 27, 2022
A Closer Look at Structured Pruning for Neural Network Compression

A Closer Look at Structured Pruning for Neural Network Compression Code used to reproduce experiments in https://arxiv.org/abs/1810.04622. To prune, w

Bayesian and Neural Systems Group 140 Dec 05, 2022
An implementation of Performer, a linear attention-based transformer, in Pytorch

Performer - Pytorch An implementation of Performer, a linear attention-based transformer variant with a Fast Attention Via positive Orthogonal Random

Phil Wang 900 Dec 22, 2022