run-tests

name	run-tests
description	Run the nano pytest suite correctly. Use this skill when the user wants to run the tests, run a specific test file, run or skip the slow benchmark tests, or is writing or debugging a test and needs the fixtures and conventions.
allowed-tools	Read, Bash

Run the nano tests

pytest is preconfigured in pyproject.toml: testpaths = ["tests"], addopts = "-q --tb=short", and a benchmark marker for slow perf tests.

Commands

pytest                          # everything (from the repo root)
pytest -m "not benchmark"       # skip slow perf tests — the usual dev loop / pre-flight
pytest tests/test_train_loss.py # one file
pytest tests/test_train_loss.py::test_name   # one test
pytest -m benchmark             # only the perf benchmarks
pytest -v                       # verbose (override the quiet default)

Run from the repo root so the flat top-level packages (model, diskrot, server) import correctly. Use the project venv (.venv/bin/python -m pytest or uv run pytest). No GPU or real corpus is required — tests use synthetic tokens.

Environment gotchas

• Unset NANO_CODEC (or set =dac) before running the suite. A leftover NANO_CODEC=spectrostream export changes the codec constants module-wide (frame rate 25 vs 86, K=32 vs 9) and fails DAC-assuming tests falsely — the code is fine, the env is wrong.
• g2p_en is not a project dep, so the lyric/structure tests — including the load-bearing train==inference marker guard in test_structure_markers.py — silently skip without it. Install it (uv pip install g2p_en) when touching anything on the lyric path, then check the skip count.

What's covered

~49 test files (see ls tests/), spanning the model core (delay pattern, RoPE, GPTConfig, qk-norm, cross-KV cache, CFG), the data pipeline (dataset/mmap, pack + sidecars, tokenize streaming, phonemize, filter/align lyrics, audio quality/dedup/io), training (loss, param groups, init stability), the conditioning streams and their train==inference equivalence guards (test_structure_markers.py, test_melody.py, test_stem.py, test_fim.py, test_chunked_tags.py), LoRA (test_lora.py), MLX backend parity (test_mlx_parity.py), and the server endpoints (generate batch/stream, sweetener). When you change a conditioning stream, its equivalence guard is the test that matters.

Fixtures

tests/conftest.py provides synth_tokens_dir — a factory fixture that writes synthetic int16 token .pt files into a tmp dir (params include n_files for the too-short-skip path). Use it instead of a real corpus.

Writing a new test

• Put it in tests/, named test_*.py.
• Reuse synth_tokens_dir rather than touching nano-tokens or real MP3s.
• Mark anything slow/perf with @pytest.mark.benchmark so the default dev loop (pytest -m "not benchmark") stays fast. (No benchmark tests exist yet — this is the convention for adding one.)

Tip

pytest -m "not benchmark" is a good fast pre-flight before launching a long add-songs or train-model run — it exercises the data/pipeline code on synthetic tokens without spending GPU time.