Stochastic Gradient Push
Gossip-based distributed optimization algorithms implemented in PyTorch
Code to reproduce the experiments reported in the paper:
If you use this code for your research, please cite the paper.
It implements the following algorithms:
- Synchronous Stochastic Gradient Push (SGP), described in the paper
- Overlap Stochastic Gradient Push (OSGP), described in the paper
- AllReduce SGD (AR), standard baseline, also known as Parallel SGD, implemented using PyTorch's
- Distributed Parallel SGD (D-PSGD), described in Lian et al., NeurIPS 2017
- Asynchronous Distributed Parallel SGD (AD-PSGD), described in Lian et al., ICML 2018
Dependencies and Setup
All code runs on Python 3.6.7 using PyTorch version 1.0.0.
Our implementations build on the
torch.distributed package in PyTorch, which provides an interface for exchanging tensors between multiple machines. The
torch.distributed package in PyTorch v.1.0.0 can use different backends. We recommmend using NCCL for all algorithms (this is the default).
To install the Stochastic Gradient Push library, via pip:
git clone https://github.com/facebookresearch/stochastic_gradient_push.git cd stochastic_gradient_push pip install .
If you want to use the parsing scripts to parse results, you can instead do:
git clone https://github.com/facebookresearch/stochastic_gradient_push.git cd stochastic_gradient_push pip install -e .[parse]
Training ResNet-50 on ImageNet
There are two main scripts:
gossip_sgd.pyfor training using AR, SGP, OSGP, or D-PSGD
gossip_sgd_adpsgd.pyfor training using AD-PSGD
In order to facilitate launching experiments, we also provide example scripts for submitting jobs using the SLURM workload manager. Note that these will only be directly usable if your cluster also uses SLURM, but hopefully they will be useful, regardless, as examples of how to launch distributed jobs.
job_scripts/ directory contains the following files:
submit_ADPSGD_ETH.shruns the AD-PSGD algorithm over Ethernet
submit_AR_ETH.shruns the AR algorithm over Ethernet
submit_AR_IB.shruns the AR algorithm over InfiniBand
submit_DPSGD_ETH.shruns the D-PSGD algorithm over Ethernet
submit_DPSGD_IB.shruns the D-PSGD algorithm over InfiniBand
submit_SGP_ETH.shruns the SGP algortihm over Ethernet
submit_SGP_IB.shruns the SGP algorithm over InfiniBand
In all cases, the scripts will need to be editied/modified in order to run on your cluster/setup. They also contain instructions on how to modify the script, e.g., to vary the number of nodes or other parameters.
The SGP scripts currently implement Synchronous SGP. To run experiments for Overlap SGP (overlapping communication and computation), change the
--overlap flag to
Reproducing figures in the paper
Note that the current version in the master branch of this repo uses features introduced in PyTorch 1.0. The version of the code used to produce the results in the paper was based on PyTorch 0.5. That version of our code is available under the
sgp_pytorch0.5 tag of this repo.
Figures similar to those in the paper can be reproduced, after running the experiments to generate log files, using the script
visualization/plotting.py. This script will also need to be modified to use the same paths to log files you used when running the experiments.
Overview of the implementation, code organization
Training neural networks
The algorithms SGP, D-PSGD, and AD-PSGD are all implemented as instances of PyTorch's
nn.Module class to facilitate training neural network models. SGP and D-PSGD are implemented in the
GossipDataParallel class in
push_sum argument determines whether to use SGP (if
push_sum=True) or D-PSGD (if
push_sum=False). Overlap SGP is obtained by using the
GossipDataParallel class with
overlap=True. AD-PSGD is implemented in the
BilatGossipDataParallel class in
Gossip-based distributed averaging
The neural network modules use implementations of PushSum and gossip algorithms for distributed averaging under the hood. These are availble in
gossip/gossiper.py and could be used independently of neural network training for approximate distributed averaging. In addition:
gossip/graph_manager.pycontains code to generate different communication topologies, and
gossip/mixing_manager.pycontains code to produce weights of the mixing matrices, given a topology.
See the LICENSE file for details about the license under which this code is made available.