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name: graphormer description: "Routes Graphormer users to fairseq training, dataset customization, pretrained evaluation, model extension, and DiG workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Graphormer

Graphormer is a fairseq user-dir graph learning package for molecular modeling, materials-style structure tasks, and a small set of research-only DiG workflows. Use this skill when the task mentions Graphormer, Graphormer3D, fairseq --user-dir, ZINC, PCQM4M, MolHIV, OC20, IS2RE, catalyst adsorption, protein conformation sampling, protein-ligand diffusion, custom Graphormer datasets, or Graphormer pretrained checkpoints.

This skill is a router, not a monolithic manual. Start with the most specific sub-skill that matches the workflow, then read the nearest bundled reference or helper script.

Start here

• Installation and environment
• Cross-cutting troubleshooting
• Source script map
• Repository provenance
• Environment check

Route map

• fairseq-training: Graphormer property-prediction and Graphormer3D training command templates for ZINC, PCQM4M, MolHIV FLAG, and OC20/IS2RE.
• datasets-and-customization: built-in dataset selection, custom dataset registration, preprocessing, and batch-schema validation.
• pretrained-and-evaluation: pretrained checkpoint selection, fine-tuning, and evaluation.
• model-extension: Graphormer fairseq registries, model/task/criterion wiring, and extension recipes.
• distributional-graphormer: optional DiG catalyst, property-guided, protein, and protein-ligand workflows.

Minimal environment check

If you only want to confirm that a Graphormer-compatible environment is ready, run the bundled checker from a shell that can see the target environment:

python scripts/check_graphormer_environment.py --user-dir <graphormer-package-dir> --format text

Add --require-complete when you want the script to confirm the expected Graphormer model, task, criterion, and architecture registries. Add --require-cuda on a CUDA host when you also want a tiny device smoke check.

What this skill assumes

• Graphormer is loaded through fairseq's --user-dir mechanism.
• The user-dir points at the directory that contains Graphormer's models/, tasks/, and criterions/ packages.
• Full native training, dataset download, checkpoint download, or DiG runs are optional and should be handled by a Researcher session only when the required data, hardware, and time budget are available.

How to choose a sub-skill

• Need a training command: start with fairseq-training.
• Need to select a dataset or validate a custom data module: start with datasets-and-customization.
• Need to load a checkpoint or compare evaluation metrics: start with pretrained-and-evaluation.
• Need to add or inspect a registered model/task/criterion/architecture: start with model-extension.
• Need the DiG catalyst, protein, or protein-ligand research code: start with distributional-graphormer.

When to read provenance

Read Repository provenance before treating this skill as current or before refreshing it against a newer Graphormer checkout.