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name: attention-is-all-you-need-pytorch description: "Use jadore801120 attention-is-all-you-need-pytorch for Transformer architecture inspection, preprocessing, training, and checkpoint translation workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

attention-is-all-you-need-pytorch

Use this repo skill when a task involves the PyTorch implementation of the Transformer paper in jadore801120/attention-is-all-you-need-pytorch: model internals, German-English preprocessing, training commands, or checkpoint translation. The repository is script/source based rather than a packaged Python distribution, so future users normally work from a checkout that contains transformer/, preprocess.py, train.py, and translate.py.

Quick Setup And Smoke Check

Use a legacy-compatible Python environment with PyTorch, legacy torchtext APIs, spaCy, dill, tqdm, and NumPy. The source expects torchtext.data.Field; modern torchtext releases that removed this API will not work unchanged.

Minimal import check from a user's checkout:

python - <<'PY'
from transformer.Models import Transformer
from transformer.Translator import Translator
print(Transformer)
print(Translator)
PY

Run the bundled environment checker when diagnosing an existing checkout:

python scripts/check_environment.py --repo-root /path/to/attention-is-all-you-need-pytorch --device cpu

Route Map

• model-architecture: instantiate or debug Transformer, masks, positional encoding, attention layers, feed-forward blocks, weight sharing, tensor shapes, and ScheduledOptim.
• data-preparation: prepare or inspect Multi30k/spaCy and WMT+BPE data artifacts, pickle schemas, torchtext Fields, vocabularies, and BPE helper behavior.
• training: build and troubleshoot train.py commands, hyperparameters, scheduler/loss behavior, logs, checkpoints, TensorBoard, CPU/CUDA selection, and safe training preflights.
• translation: validate checkpoints and data pickles for translate.py, run checkpoint translation, or use transformer.Translator beam search programmatically.

Repo-Level References

• Read references/package-overview.md for the source layout, dependency expectations, workflow map, and known caveats.
• Read references/troubleshooting.md for cross-cutting install/import, torchtext, spaCy, CUDA, pickle, and stale README issues.
• Read references/repo-provenance.md before deciding whether this skill is current for a checkout or should be refreshed.

Decision Hints

• If the user asks about tensor dimensions, masks, weight sharing, or custom model construction, route to model-architecture before training or translation.
• If the user asks why training cannot read a pickle, route to data-preparation first, then training after the schema is identified.
• If the user asks about a .chkpt file, route to translation for checkpoint inspection unless they are asking how the checkpoint was produced.
• If the prompt mentions BPE, keep the repository caveat visible: BPE is marked not fully tested and translate.py leaves BPE decoding as TODO.
• If a README command fails with an unrecognized -log flag, prefer the inspected train.py parser over the README example for this commit.