translation
Run and troubleshoot checkpoint translation with translate.py and transformer.Translator beam search.
- License 许可
- MIT
- 最近更新
- 2026-09-03
- Stars
- ★ 351
- Forks
- 27
01 前言 Front Matter
- name
- translation
- license
- MIT
- metadata
- { "disco-role": "operating" }
- description
- Run and troubleshoot checkpoint translation with translate.py and transformer.Translator beam search.
- disable-model-invocation
- true
02 资源文件 5
- 参考文档references/api-reference.md4.5 KB
- 参考文档references/cli-reference.md5.4 KB
- 参考文档references/troubleshooting.md4.0 KB
- 脚本scripts/inspect_checkpoint.py15.9 KB
- 脚本scripts/translation_smoke_check.py6.5 KB
03 SKILL.md 正文
Translation Sub-skill
Use this sub-skill when a future task needs to run, validate, or troubleshoot
checkpoint-based translation for this repository. It covers the translate.py
command-line flow, the load_model checkpoint contract, and programmatic beam
search with transformer.Translator.
Route here for
- Validating that a training checkpoint has the
settingsandmodelobjects required by translation before launching a full decode job. - Checking that a preprocessing pickle contains the
testsplit plus source and target vocab fields expected bytranslate.py. - Running the repository translation CLI on CPU or CUDA and interpreting its output file.
- Calling
Translator.translate_sentence(...)directly for one already-tokenized source sentence. - Debugging beam-size, max-length, token-index, batch-size-one, and device mismatches in translation.
Do not route here for
- Creating preprocessing pickles or BPE files; use the data-preparation skill.
- Producing checkpoints, choosing training flags, or optimizer settings; use the training skill.
- Explaining encoder/decoder internals, attention masks, or parameter shapes in depth; use the model-architecture skill.
Start with these references
- Read references/cli-reference.md for the
translate.pyCLI, checkpoint/data-pickle schemas, device selection, and output semantics. - Read references/api-reference.md for
programmatic
Transformerconstruction from a checkpoint andTranslator.translate_sentencebeam search usage. - Read references/troubleshooting.md when a checkpoint, pickle, vocabulary, CUDA, OOV, or BPE issue appears.
Bundled helpers
scripts/inspect_checkpoint.pysafely gates unpickling behind an explicit trust flag, then validates checkpoint and optional data-pickle schemas.scripts/translation_smoke_check.pybuilds a tiny deterministic Transformer and exercisesTranslatorbeam search without requiring a trained checkpoint or dataset.
Typical preflight sequence:
python scripts/inspect_checkpoint.py \
--checkpoint trained.chkpt --data-pkl m30k_deen_shr.pkl --trust-inputs
python scripts/translation_smoke_check.py \
--repo-root /path/to/repo --device cpu
Operating reminders
translate.pyloads the checkpoint withtorch.load(..., map_location=device)and then reconstructsTransformerfromcheckpoint['settings']; incompatible settings are found only at load time unless you preflight them.- The CLI sets
opt.cuda = not opt.no_cuda; on a CPU-only host include-no_cudaor model/device transfer will fail. Translator.translate_sentenceasserts batch size one. For multiple examples, iterate one sentence at a time or implement a separate batched decoder.- Output lines are target-vocabulary token strings with
<s>and</s>removed by simple string replacement. BPE post-decoding is marked TODO/not ready in the repository flow.
04 同仓库更多 Skills
passnet-triton-opt
Make a single PassNet Triton kernel fast AND numerically faithful: performance model (when a replacement can win at all), block/grid/warp tuning, autotune policy, launch overhead, and per-op numeric recipes to pass the dtype baseline tolerances. Use when a pass matches and is correct but speedup below expected, or when correctness fails by small numeric margins.
passnet-solve
END-TO-END playbook for solving one PassNet sample: analyze the computation graphs, decide the optimization strategy (what to fuse, what to replace, what to leave alone), drive the iteration loop, and maximize the sample score. This is the ENTRY skill — invoke it first for any PassNet optimization task; it tells you when to use passnet-pattern-fusion, passnet-triton-opt and passnet-feedback.
passnet-skill
PassNet GPU kernel optimization via compiler passes. Design and implement Triton-based optimization passes, create pass files under ./pass_dir/, self-evaluate with pass_evaluator, and iterate to maximize GPU speedup.
passnet-pattern-fusion
Author PassNet pass files: write patterns that actually MATCH the FX graph, pick fusion regions (which ops to absorb into one Triton kernel), and structure multi-pass submissions with the mandatory shared-dispatch architecture. Use when creating or fixing pattern()/replacement_args()/replacement_func() files, when a pass "failed to match", or when deciding how to fuse multiple kernels/ops.
passnet-orchestrate
Round/batch planning and decision support for PassNet graph optimization. Use for multi-sample triage, worker allocation, eval-budget policy, and post-eval keep/revert decisions. For solving one concrete sample end to end, use passnet-solve as the entry skill; consult this skill only when you need broader planning or a specific decision gate.
passnet-feedback
Fast, deterministic feedback for PassNet work: pre-flight pattern/match verification WITHOUT burning a GPU evaluation, per-node bottleneck analysis of a sample's graphs, and eval-log parsing into per-variant status + estimated score + failure classification. Use BEFORE every GPU evaluation (check_pattern), at the START of a sample (analyze_graph), and AFTER every evaluation (parse_eval_log).