Dist-KGE: A knowledge graph embedding library for multi-GPU and multi-machine training

This is the code and configuration accompanying the paper "Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques". The code extends the knowledge graph embedding library LibKGE. For documentation on LibKGE refer to LibKGE repository. We provide the hyper-parameter settings for the experiments in their corresponding configuration files.

News
Quick start
Dataset preparation for parallel training
Single Machine Multi-GPU Training
Multi-GPU Multi-Machine Training
Folder structure of experiment results
Results and Configurations
How to cite

News

In our follow up work "Start small, think big: On hyperparameter optimization for large-scale knowledge graph embeddings", (code), we show (i) the importance of hyperparameter optimiziation (HPO) for large-scale KGEs, and (ii) how to efficently perform HPO on large graphs.

Reported results show strong improvements in terms of MRR.

Paper	Setting	MRR
This paper	Freebase Random RLB 4@1, dim=128	0.426
Follow up paper	Freebarse Random R 4@1, dim=128	0.594
Follow up paper	Freebarse Random R 4@1, dim=512	0.678

Quick start

# retrieve and install project in development mode
git clone https://github.com/uma-pi1/dist-kge.git
cd dist-kge
pip install -e .

# download and preprocess datasets
cd data
sh download_all.sh
cd ..

# train an example model on toy dataset (you can omit '--job.device cpu' when you have a gpu)
kge start examples/toy-complex-train.yaml --job.device cpu

This example will train on a toy dataset in a sequential setup on CPU.

For further documentation on possible configuration can be found in the file config-default.yaml.

Supported Models

For a list of models for sequential training using a single GPU we refer to LibKGE.

Models supporte for Multi-GPU and Multi-Machine are:

ComplEx
RotatE
TransE

Dataset preparation for parallel training

NOTE: Freebase already comes with multiple partition settings to save preprocessing time

To partition the data run the following commands (you only need to do this once)

Random Partitioning

For random partitioning no further preparation is needed.

Relation Partitioning

cd data
python partition_relation.py <dataset-name> -n <num-partitions>
cd ..

Stratification

cd data 
python partition_stratification.py <dataset-name> -n <num-partitions>
cd ..

Graph-Cut

cd data
python partition_graph_cut.py <dataset-name> -n <num-partitions>
cd ..

Single Machine Multi-GPU Training

Run following example to train on two GPUs with random partitioning (two worker per GPU):

python -m kge start examples/fb15k-complex-parallel.yaml

The most important configuration options for multi-gpu training are:

import:
  - complex
  - distributed_model
model: distributed_model
distributed_model:
  base_model: complex
job:
  distributed:
    num_partitions: 4
    num_workers: 4
    partition_type: random
    master_port: 8888  # change in case this port is used on your machine
  device_pool:
    - cuda:0
    - cuda:1
train:
  type: distributed_negative_sampling
  optimizer:
    default:
      type: dist_adagrad

Multi-GPU Multi-Machine Training

Parameter Server

For multi-machine training we use the parameter server Lapse. To install Lapse and the corresponding python bindings run the following commands:

git clone https://github.com/alexrenz/lapse-ps.git lapse-ps
cd lapse-ps
git checkout 72c7197c6d1795a8de184de84ffbe1b08151756d
make ps KEY_TYPE=int64_t CXX11_ABI=$(python bindings/lookup_torch_abi.py) DEPS_PATH=$(pwd)/deps_bindings
cd bindings 
python setup.py install --user

For further documentation on the python bindings refer to Lapse-Binding documentation.

In case you can not use Lapse, we provide a very inefficient parameter server (for debugging). To use this debugging PS use the option --job.distributed.parameter_server torch

Interface

As we use the gloo backend to communicate between master and worker nodes you need to specify the interface connecting your machines and specify it as --job.distributed.gloo_socket_ifname. You can find out the names of your interfaces with the command

ip address

Example

Run the following example to train on two machines with one GPU each (1@2) with random partitioning:

Command for machine 1

python -m kge start examples/fb15k-complex-distributed.yaml --job.distributed.machine_id 0 --job.distributed.master_ip <ip_of_machine_0>

Command for machine 2

python -m kge start examples/fb15k-complex-distributed.yaml --job.distributed.machine_id 1 --job.distributed.master_ip <ip_of_machine_0>

Important options for distributed training in addition to the options specified in the single-machine setting are:

job:
  distributed:
    master_ip: '<ip_of_machine_0>'  # ip address of the machine with machine_id 0
    num_machines: 2
    num_workers: 4  # total number of workers over all machines
    gloo_socket_ifname: bond0  # name of the interface to use. Use command 'ip address' to find names
    parameter_server: lapse

Different number of workers per machine

If you have two machines with a varying number of GPUs you might want to set a varying number of workers per machine. You can do so with the following commands

Command for machine 1

python -m kge start examples/fb15k-complex-distributed.yaml --job.distributed.machine_id 0 --job.distributed.master_ip <ip_of_machine_0> --job.distributed_num_workers_machine 3

Command for machine 2

python -m kge start examples/fb15k-complex-distributed.yaml --job.distributed.machine_id 1 --job.distributed.master_ip <ip_of_machine_0> --job.distributed.num_workers_machine 1 --job.distributed.already_init_workers 3

Note that you need to specify for each machine how many workers are initialized on the previous machines with job.distributed.already_init_workers if the number of workers varies per machine. You can also create one configuration file per machine, with corresponding settings for num_workers_machine and already_init_workers.

Folder structure of experiment results

by default, each experiment will create a new folder in local/experiments/<timestamp>-<config-name>
this folder can be changed with command line argument --folder path/to/folder
for multi-machine training a folder is created for each machine. Therefore, specify a separate folder name for each machine if you work on a shared filesystem.
each worker will have its own subfolder logging partition-processing times
the complete epoch time over all partitions is logged in the main kge.log file
hardware information is logged into hardware_monitor.log and gpu_monitor.log
evaluation is performed on machine-0 by worker-0. Therefore, evaluation results are logged into folder <experiment-folder-machine-0>/worker-0/ in the files kge.log and trace.yaml

Results and Configurations

all ranking metrics are filtered with train, valid and test if not mentioned otherwise
configuration files for the experiments can be found here

Partitioning techniques (best-performing variant)

best performing variant in terms of time to 0.95 MRR reached in the sequential setting

FB15k

ComplEx

Setup	Partitioning Technique	Epoch Time	Time to 0.95 MRR	MRR	MRR unfiltered	Hits@1	Hits@10	Hits@100	config
.	Sequential (GPU memory)	5.9s	3.9min	0.778	0.245	0.729	0.862	0.932	config
.	Sequential (main memory)	7.7s	5.1min	0.778	0.245	0.729	0.862	0.932	config
2@1	Random (R)	2.6s	2.0min	0.775	0.243	0.726	0.859	0.931	config
1@2	Random (R)	2.9s	2.2min	0.775	0.243	0.726	0.859	0.931	config
4@2	Random (R)	1.3s	1.3min	0.766	0.241	0.712	0.858	0.929	config

RotatE

Setup	Partitioning Technique	Epoch Time	Time to 0.95 MRR	MRR	MRR unfiltered	Hits@1	Hits@10	Hits@100	config
.	Sequential (GPU memory)	9.5s	11.9min	0.705	0.232	0.630	0.834	0.928	config
.	Sequential (main memory)	11.4s	14.3min	0.705	0.232	0.630	0.834	0.928	config
2@1	Stratification (CARL)	4.6s	5.8min	0.725	0.239	0.664	0.835	0.926	config
1@2	Stratification (CARL)	5.9s	7.4min	0.725	0.239	0.664	0.835	0.926	config

Yago3-10

ComplEx

Setup	Partitioning Technique	Epoch Time	Time to 0.95 MRR	MRR	MRR unfiltered	Hits@1	Hits@10	Hits@100	config
.	Sequential (GPU memory)	24.3s	35.5min	0.542	0.111	0.468	0.675	0.791	config
.	Sequential (main memory)	42.6s	67.5min	0.542	0.111	0.468	0.675	0.791	config
2@1	Relation	19.0s	33.2min	0.538	0.107	0.465	0.669	0.787	config
1@2	Random (RL)	19.5s	35.8min	0.547	0.109	0.473	0.679	0.791	config
4@2	Random (RL)	5.6s	n.r.	0.503	0.106	0.423	0.653	0.778	config

RotatE

Setup	Partitioning Technique	Epoch Time	Time to 0.95 MRR	MRR	MRR unfiltered	Hits@1	Hits@10	Hits@100	config
.	Sequential (GPU memory)	74.1s	259.3min	0.451	0.104	0.343	0.637	0.773	config
.	Sequential (main memory)	88.0s	307.8min	0.451	0.104	0.343	0.637	0.773	config
2@1	Stratification (CARL)	40.8s	166.6min	0.438	0.115	0.350	0.607	0.764	config
1@2	Stratification (CARL)	43.3s	175.8min	0.438	0.115	0.350	0.607	0.764	config

Wikidata5m

ComplEx

Setup	Partitioning Technique	Epoch Time	Time to 0.95 MRR	MRR	MRR unfiltered	Hits@1	Hits@10	Hits@100	config
.	Sequential (GPU memory)	438.4s	219.0min	0.297	0.255	0.246	0.385	0.516	config
.	Sequential (GPU memory)	774.3s	387.0min	0.297	0.255	0.246	0.386	0.516	config
2@1	Stratification (CARL)	232.8s	77.6min	0.308	0.264	0.255	0.398	0.513	config
1@2	Stratification (CARL)	228.0s	76.0min	0.308	0.264	0.255	0.398	0.513	config

RotatE

Setup	Partitioning Technique	Epoch Time	Time to 0.95 MRR	MRR	MRR unfiltered	Hits@1	Hits@10	Hits@100	config
.	Sequential (GPU memory)	798.4s	199.6min	0.258	0.225	0.202	0.348	0.453	config
.	Sequential (GPU memory)	985.7s	246.4min	0.258	0.225	0.202	0.348	0.453	config
2@1	Stratification (ARL)	466.7s	77.8min	0.264	0.230	0.213	0.344	0.410	config
1@2	Stratification (ARL)	477.7s	79.6min	0.264	0.230	0.213	0.344	0.410	config

Freebase

ComplEx

Setup	Partitioning Technique	Epoch Time	Data sent per epoch	sMRR	sMRR unfiltered	MRR	MRR unfiltered	Hits@1	Hits@10	Hits@100	config
.	Sequential (main memory)	3929.0s	-	0.811	0.776	0.364	0.311	0.298	0.487	0.618	config
.	Sequential (B) (main memory)	3925.2s	-	0.815	0.782	0.426	0.345	0.370	0.528	0.642	config
2@2	Random (RLB)	966.7s	232.8GB	0.816	0.782	0.426	0.352	0.371	0.529	0.639	config
2@2	Relation (rLB)	823.8s	205.9GB	0.801	0.770	0.397	0.326	0.339	0.507	0.631	config
2@2	Stratification (CARLB)	803.9s	123.2GB	0.793	0.761	0.325	0.285	0.272	0.424	0.563	config
2@2	Graph-cut (LB)	1170.6s	42.5GB	0.789	0.761	0.407	0.335	0.351	0.512	0.624	config
4@2	Random (RLB)	591.6s	251.9GB	0.819	0.784	0.421	0.346	0.364	0.523	0.638	config

RotatE

Setup	Partitioning Technique	Epoch Time	Data sent per epoch	sMRR	sMRR unfiltered	MRR	MRR unfiltered	Hits@1	Hits@10	Hits@100	config
.	Sequential (main memory)	6495.7s	-	0.774	0.748	0.566	0.426	0.529	0.627	0.677	config
.	Sequential (B) (main memory)	6184.0s	-	0.812	0.774	0.560	0.422	0.521	0.623	0.674	config
2@2	Random (RLB)	1541.4s	232.2GB	0.812	0.773	0.567	0.425	0.529	0.630	0.678	config
2@2	Relation (rLB)	1498.1s	207.2GB	0.791	0.758	0.551	0.407	0.515	0.608	0.656	config
2@2	Stratification (CARLB)	1416.1s	123.3GB	0.734	0.720	0.529	0.395	0.491	0.592	0.641	config
2@2	Graph-cut (LB)	1867.9s	44.5GB	0.775	0.749	0.560	0.410	0.526	0.616	0.654	config

Row-Adagrad

Row-wise optimizers treat each embedding as a single parameter instead of each dimension of an embedding and therefore reduce storage and communication overhead by about 50%. We observed no negative influence on the resulting embedding quality for all partitioning methods but graph-cut partitioning, where the drop was small but noticeable. Overall, we found Row-Adagrad to be a suitable approach to reduce storage and communication costs. We report on ComplEx, 1@2.

Yago3-10

Partition Technique	Data sent (Adagrad)	MRR (Adagrad)	Data sent (Row-Adagrad)	MRR (Row-Adagrad)
Sequential	-	0.542	-	0.542
Random (R)	7.2GB	0.538	5.0GB	0.534
Relation	7.1GB	0.538	4.9GB	0.542
Stratification	0.4GB	0.531	0.2GB	0.539
Graph-cut	0.2GB	0.211	0.1GB	0.180

Wikidata5m

Partition Technique	Data sent (Adagrad)	MRR (Adagrad)	Data sent (Row-Adagrad)	MRR (Row-Adagrad)
Sequential	-	0.297	-	0.291
Random (R)	125.2GB	0.296	65.7GB	0.298
Relation	123.8GB	0.296	63.5GB	0.300
Stratification	15.0GB	0.308	7.4GB	0.306
Graph-cut	6.1GB	0.192	3.7GB	0.181

How to cite

@article{kochsiek2021parallel,
  title={Parallel training of knowledge graph embedding models: a comparison of techniques},
  author={Kochsiek, Adrian and Gemulla, Rainer},
  journal={Proceedings of the VLDB Endowment},
  volume={15},
  number={3},
  pages={633--645},
  year={2021},
  publisher={VLDB Endowment}
}

uma-pi1/dist-kge

Dist-KGE: A knowledge graph embedding library for multi-GPU and multi-machine training

Table of contents

News

Quick start

Supported Models

Dataset preparation for parallel training

Single Machine Multi-GPU Training

Multi-GPU Multi-Machine Training

Parameter Server

Interface

Example

Different number of workers per machine

Folder structure of experiment results

Results and Configurations

Partitioning techniques (best-performing variant)

FB15k

Yago3-10

Wikidata5m

Freebase

Row-Adagrad

Yago3-10

Wikidata5m

How to cite