Command-line Tools

Graphormer reuses the fairseq-train command-line tools of fairseq for training, and here we mainly document the additional parameters in Graphormer and parameters of fairseq-train used by Graphormer.

Model

• --arch, type=enum, options: graphormer_base, graphormer_slim, graphormer_large

  • Predefined graphormer architectures

• --encoder-ffn-embed-dim, type = float

  • encoder embedding dimension for FFN

• --encoder-layers, type = int

  • number of graphormer encoder layers

• --encoder-embed-dim, type = int

  • encoder embedding dimension

• --share-encoder-input-output-embed, type = bool

  • if set, share encoder input and output embeddings

• --share-encoder-input-output-embed, type = bool

  • if set, share encoder input and output embeddings

• --encoder-learned-pos, type = bool

  • if set, use learned positional embeddings in the encoder

• --no-token-positional-embeddings, type = bool

  • if set, disables positional embeddings” ” (outside self attention)

• --max-positions, type = int

  • number of positional embeddings to learn

• --activation-fn, type = enum, options: gelu, relu

  • activation function to use

• --encoder-normalize-before

  • if set, apply layernorm before each encoder block

Training

• --apply-graphormer-init, type = bool

  • if set, use custom param initialization for Graphormer

• --dropout, type = float

  • dropout probability

• --attention-dropout, type = float

  • dropout probability for attention weights

• --act-dropout, type = float

  • dropout probability after activation in FFN

• --seed, type = int

  • random seed

• --pretrained-model-name, type = enum, default= none, options: pcqm4mv1_graphormer_base, pcqm4mv2_graphormer_base

  • name of used pretrained model

    • pcqm4mv1_graphormer_base: Pretrained Graphormer base model with PCQM4M v1 dataset.

    • pcqm4mv2_graphormer_base: Pretrained Graphormer base model with PCQM4M v2 dataset.

• --load-pretrained-model-output-layer, type = bool

  • if set, the weights of the final fully connected layer in the pre-trained model is loaded

• --optimizer, type = enum

  • optimizers from fairseq

• --lr, type = float

  • learning rate

• --lr-scheduler, type=enum

  • learning rate scheduler from fairseq

• --fp16, type=bool

  • if set, use mixed precision training

• --data-buffer-size, type=int, default=10

  • number of batches to preload

• --batch-size, type=int

  • number of examples in a batch

• --max-epoch, type=int, default=0

  • force stop training at specified epoch

• --save-dir, type=str, default=``checkpoints``

  • path to save checkpoints

Dataset

• --dataset-name, type = str, default= pcqm4m

  • name of the dataset

• --dataset-source, type = str, default= ogb

  • source of graph dataset, can be: pyg, dgl, ogb

• --num-classes, type = int, default=-1

  • number of classes or regression targets

• --num-atoms, type = int, default=512 * 9

  • number of atom types in the graph

• --num-edges, type = int, default=512 * 3

  • number of edge types in the graph

• --num-in-degree, type = int, default=512

  • number of in degree types in the graph

• --num-out-degree, type = int, default=512

  • number of out degree types in the graph

• --num-spatial, type = int, default=512

  • number of spatial types in the graph

• --num-edge-dis, type = int, default=128

  • number of edge dis types in the graph

• --multi-hop-max-dist, type = int, default=5

  • max number of edges considered in the edge encoding

• --spatial-pos-max, type = int, default=1024

  • max distance of attention in graph

• --edge-type, type = str, default=”multi_hop”

  • edge type in the graph

• --edge-type, type = str, default=”multi_hop”

  • edge type in the graph

• --user-data-dir, type = str, default=””

  • path to the module of user-defined dataset