PyTorch-BigGraph (PBG) is a distributed system for learning graph embeddings for large graphs, particularly big web interaction graphs with up to billions of entities and trillions of edges.

PBG was introduced in the PyTorch-BigGraph: A Large-scale Graph Embedding Framework paper, presented at the SysML conference in 2019.

PBG trains on an input graph by ingesting its list of edges, each identified by its source and target entities and, possibly, a relation type. It outputs a feature vector (embedding) for each entity, trying to place adjacent entities close to each other in the vector space, while pushing unconnected entities apart. Therefore, entities that have a similar distribution of neighbors will end up being nearby.

It is possible to configure each relation type to calculate this "proximity score" in a different way, with the parameters (if any) learned during training. This allows the same underlying entity embeddings to be shared among multiple relation types.

The generality and extensibility of its model allows PBG to train a number of models from the knowledge graph embedding literature, including TransE, RESCAL, DistMult and ComplEx.

PBG is designed with scale in mind, and achieves it through:

• graph partitioning, so that the model does not have to be fully loaded into memory
• multi-threaded computation on each machine
• distributed execution across multiple machines (optional), all simultaneously operating on disjoint parts of the graph
• batched negative sampling, allowing for processing >1 million edges/sec/machine with 100 negatives per edge

PBG is not for model exploration with exotic models on small graphs, e.g. graph convnets, deep networks, etc.

Requirements

PBG is written in Python (version 3.6 or later) and relies on PyTorch (at least version 1.0) and a few other libraries.

All computations are performed on the CPU, therefore a large number of cores is advisable. No GPU is necessary.

When running on multiple machines, they need to be able to communicate to each other at high bandwidth (10 Gbps or higher recommended) and have access to a shared filesystem (for checkpointing). PBG uses torch.distributed, which uses the Gloo package which runs on top of TCP or MPI.

Installation

To install the latest version of PBG run:

pip install torchbiggraph

As an alternative, one can instead install the development version from the repository. This may have newer features but could be more unstable. To do so, clone the repository (or download it as an archive) and, inside the top-level directory, run:

pip install -r requirements.txt
./setup.py install

Getting started

The results of the paper can easily be reproduced by running the sample scripts located in the examples directory, for instance:

examples/fb15k.py

This will download the Freebase 15k knowledge base dataset, put it into the right format, train on it using the ComplEx model and finally perform an evaluation of the learned embeddings that calculates the MRR and other metrics that should match the paper. Another script, examples/livejournal.py, does the same for the LiveJournal interaction graph dataset.

To learn how to use PBG, let us walk through what the FB15k script does.

Downloading the data

First, it retrieves the dataset and unpacks it, obtaining a directory with three edge sets as TSV files, for training, validation and testing.

wget https://dl.fbaipublicfiles.com/starspace/fb15k.tgz -P data
tar xf data/fb15k.tgz -C data

Each line of these files contains information about one edge. Using tabs as separators, the lines are divided into columns which contain the identifiers of the source entities, the relation types and the target entities. For example:

/m/027rn	/location/country/form_of_government	/m/06cx9
/m/017dcd	/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor	/m/06v8s0
/m/07s9rl0	/media_common/netflix_genre/titles	/m/0170z3
/m/01sl1q	/award/award_winner/awards_won./award/award_honor/award_winner	/m/044mz_
/m/0cnk2q	/soccer/football_team/current_roster./sports/sports_team_roster/position	/m/02nzb8

Preparing the data

Then, the script converts the edge lists to PBG's input format. This amounts to assigning a numerical identifier to all entities and relation types, shuffling and partitioning the entities and edges and writing all down in the right format.

Luckily, there is a command that does all of this:

torchbiggraph_import_from_tsv \
  --lhs-col=0 --rel-col=1 --rhs-col=2 \
  examples/configs/fb15k_config.py \
  data/FB15k/freebase_mtr100_mte100-*.txt

The outputs will be stored next to the inputs in the data/FB15k directory.

This simple utility is only suitable for small graphs that fit entirely in memory. To handle larger data one will have to implement their own custom preprocessor.

Training

The torchbiggraph_train command is used to launch training. The training parameters are tucked away in a configuration file, whose path is given to the command. They can however be overridden from the command line with the --param flag. The sample config is used for both training and evaluation, so we will have to use the override to specify the edge set to use.

torchbiggraph_train \
  examples/configs/fb15k_config.py \
  -p edge_paths=data/FB15k/freebase_mtr100_mte100-train_partitioned

This will read data from the entity_path directory specified in the configuration and the edge_paths directory given on the command line. It will write checkpoints (which also double as the output data) to the checkpoint_path directory also defined in the configuration, which in this case is model/fb15k.

Training will proceed for 50 epochs in total, with the progress and some statistics logged to the console, for example:

Starting epoch 1 / 50 edge path 1 / 1 edge chunk 1 / 1
edge_path= data/FB15k/freebase_mtr100_mte100-train_partitioned
Swapping partitioned embeddings None ( 0 , 0 )
Loading entities
( 0 , 0 ): bucket 1 / 1 : Processed 483142 edges in 20.58 s ( 0.023 M/sec ); io: 0.02 s ( 296.64 MB/sec )
( 0 , 0 ): loss:  6663.96 , violators_lhs:  0 , violators_rhs:  0 , count:  483142
Swapping partitioned embeddings ( 0 , 0 ) None
Writing partitioned embeddings
Finished epoch 1 path 1 pass 1; checkpointing global state.
My rank: 0
Writing metadata...
Writing the checkpoint...
Switching to new checkpoint version...

Evaluation

Once training is complete, the entity embeddings it produced can be evaluated against a held-out edge set, as follows:

torchbiggraph_eval \
  examples/configs/fb15k_config.py \
  -p edge_paths=data/FB15k/freebase_mtr100_mte100-test_partitioned \
  -p relations.0.all_negs=true

This computes a set of metrics on the quality on the embeddings and prints them out. The last line should look like:

Stats: pos_rank:  65.4821 , mrr:  0.789921 , r1:  0.738501 , r10:  0.876894 , r50:  0.92647 , auc:  0.989868 , count:  59071

The values of mrr (Mean Reciprocal Rank, MRR) and r10 (Hits@10) should match the ones reported in the paper.

The evaluation performed by the examples/fb15k.py script differs from the above torchbiggraph_eval command, in order to match the literature. It calculates the ranks of the edges in the evaluation set by comparing them against all other edges except the ones that are true positives in any of the training, validation or test set. This setup, called filtered MRR, is only used to evaluate small graphs because it scales very poorly.

Converting the output

During preprocessing, the entities and relation types had their identifiers converted from strings to ordinals. In order to map the output embeddings back onto the original names, one can do:

torchbiggraph_export_to_tsv \
  --dict data/FB15k/dictionary.json \
  --checkpoint model/fb15k \
  --out joined_embeddings.tsv

This will create the joined_embeddings.tsv file, which is a text file where each line contains the identifier of an entity or the name of a relation type followed respectively by its embedding or its parameters, each in a different column, all separated by tabs. For example, with each line shortened for brevity:

/m/0fphf3v	-0.524391472	-0.016430536	-0.461346656	-0.394277513	0.125605106	...
/m/01bns_	-0.122734159	-0.091636233	0.506501377	-0.503864646	0.215775326	...
/m/02ryvsw	-0.107151665	0.002058491	-0.094485454	-0.129078045	-0.123694092	...
/m/04y6_qr	-0.577532947	-0.215747222	-0.022358289	-0.352154016	-0.051905245	...
/m/02wrhj	-0.593656778	-0.557167351	0.042525314	-0.104738958	-0.265990764	...

Documentation

More information can be found in the full documentation.

Pre-trained embeddings

We trained a PBG model on the full Wikidata graph, using a translation operator to represent relations. It can be downloaded here. We used the truthy version of data from here to train our model. The model file is in TSV format as described in the above section. Note that the first line of the file contains the number of entities, the number of relations and the dimension of the embeddings, separated by tabs. The model contains 78 milion entities, 4,131 relations and the dimension of the embeddings is 200.

Citation

To cite this work please use:

@inproceedings{pbg,
  title={{PyTorch-BigGraph: A Large-scale Graph Embedding System}},
  author={Lerer, Adam and Wu, Ledell and Shen, Jiajun and Lacroix, Timothee and Wehrstedt, Luca and Bose, Abhijit and Peysakhovich, Alex},
  booktitle={Proceedings of the 2nd SysML Conference},
  year={2019},
  address={Palo Alto, CA, USA}
}

License

PyTorch-BigGraph is BSD licensed, as found in the LICENSE file.