An implementation of decentralized data parallel based on PyTorch
Project description
Decentralized Data Parallel
The package is an PyTorch extension that faciliates multi-GPU decentralized data parallel training.
Install
pip install decent-dp
How to use
Firstly, it should have distributed environment available, which means the script should be run by torchrun
.
Before making the model distributed, it should initialize the distributed environment by
import torch.distributed as dist
dist.init_process_group()
Then wrap the model with DecentralizedDataParallel
. Since the optimizer and the learning rate scheduler are fused in the backward pass, one will need to provide two functions: (1) optim_fn
(Callable[[List[Tuple[Tensor, str]]], Optimizer]
): the function constructs the optimizer based on the list of parameters with their names. (2) lr_scheduler_fn
(Callable[[Optimizer], LRScheduler]
, optional): the function constructs the learning rate scheduler based on the provided optimizer. Examples of two functions can be found at decent_dp.optim.optim_fn_adam
and decent_dp.optim.lr_scheduler_fn_cosine_with_warmup
.
from decent_dp import DecentralizedDataParallel as DDP
model = ...
model = DDP(model,
optim_fn=...,
lr_scheduler_fn=...,
topology=...)
Supported Schema
The decentralized algorithm schema should follow $$x_{i}^{(t)}=d_i^{(t)}+\sum_{j\in\mathcal{N}(i)}W_{ij}x_i^{(t-1)}\ d_i^{(t)}=G\left(F_i,x_j^{(t-1)}\right)$$ which means that the local update doesn't depend on the neighbors' models in the same iteration, but it can
Customized Communication Topology
Currently, the provided communication topologies are complete
, ring
, one-peer-exp
and alternating-exp-ring
.
One can introduce customized by registering additional topologies.
from decent_dp.topo import Topology, TopologyReg, Edge
@TopologyReg.register('custom-topology')
class CustomTopology(Topology):
def _get_topo_edges(self) -> List[List[Edge]]:
...
One should override the _get_topo_edges
method to provide the edges in every iteration in the loop. In the current version, it performs sanity check to make sure that (1) every worker is involved in every iteraion. (2) every worker is involvede in only one edge in every iteration.
The preset topologies are good examples which can be found at decent_dp.topo.CompleteTopology
, decent_dp.topo.RingTopology
, and etc..
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for decent_dp-0.0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6774528fe0efc38f1068b59eba37c95d26ed22ab8eec6578cbf8cef9be3ef486 |
|
MD5 | 80419458f7bf9fb6923fb22d5ae2f702 |
|
BLAKE2b-256 | e2c09cfc5071f73b60b78390a8707aa5b1ecbbd38fdd08e5eb7ab2c67bbf7917 |