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Generator(DataGenerator). Intended as the
launch/genesis king for the cascade subnet.| file | purpose |
|---|---|
generator.py | class Generator(DataGenerator) — the entrypoint |
config.json | length band, per-family mixing weights, sanitisation knobs |
requirements.txt | hash-locked, allowlisted deps (numpy, scipy, pandas, torch, scikit-learn, gpytorch, networkx) |
tempo_gen/ | vendored, import-rewritten TempoPFN subset (Apache-2.0) |
NOTICE, LICENSE | TempoPFN attribution + Apache-2.0 text |
tests/ | contract tests + a diversity sanity check |
generate_length (2048) and deterministically random-cropped into the
[min_length, max_length] band for length diversity.chain.toml [dependencies]). The TempoPFN ablation
shows this family carries a large share of the downstream signal. CauKer is
multivariate (an SCM DAG of GP-prior nodes); each channel is flattened into its
own univariate series so the emitted corpus stays 1-D. Its upstream GPU
(cupy) draw was replaced with NumPy's seeded multivariate_normal to keep the
generate path CPU-only and reproducible.hash(), both of which break the cross-process
determinism contract below (and pyo is not on the allowlist).(seed, n_series). We seed NumPy / torch /
Python random from seed, run torch on CPU with deterministic algorithms,
derive every per-family and per-series sub-seed from the master seed, and use a
separate seeded RNG for cropping. The upstream hash()-based per-generator seed
offset — salted per process by PYTHONHASHSEED — was replaced with a stable
zlib.crc32 in tempo_gen/.../abstract_classes.py, so two audit runs in
separate processes produce byte-identical corpora.1# from the cascade repo root, with deps installed and cascade importable
2cascade verify ./base_generator # full trainer-side checks
3PYTHONPATH=$PWD pytest base_generator/tests -q # contract tests
4PYTHONPATH=$PWD python base_generator/tests/diversity_check.pyconfig.json)min_length / max_length — per-series length band (defaults 64 / 2048, matching chain.toml).generate_length — length generators are drawn at before cropping (>= max_length).batch_size — internal generation batch size (memory/throughput knob).weights — per-family mixing weights (normalised; families with weight 0 or absent are skipped).standardize — z-normalise each series (default false; off preserves scale diversity).clip_sigma — if > 0, clip each series to mean ± clip_sigma·std (default 0, disabled).max_abs_value — hard magnitude clip to keep output trainer-safe finite.