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| 1 | +"""V7-G03: cross-shard / cross-instance tokenizer determinism. |
| 2 | +
|
| 3 | +The corpus-generation path (scripts/nanochat_data/clang_enriched_to_parquet.py) |
| 4 | +spins up workers that each load the tokenizer artifact. If the |
| 5 | +artifact were inadvertently mutated (BPE merge ordering, special |
| 6 | +token table) between shards, encoded ids would drift and any |
| 7 | +downstream training step would consume inconsistent inputs. |
| 8 | +
|
| 9 | +Asserted here: |
| 10 | + (a) Same Tokenizer instance encoding the same text twice → ids |
| 11 | + bit-identical (sanity). |
| 12 | + (b) TWO independently-loaded Tokenizer instances from the SAME |
| 13 | + file → ids bit-identical (simulates two workers loading |
| 14 | + the artifact in parallel). |
| 15 | + (c) MATRIX.json tokenizer fixtures: hash the file bytes and |
| 16 | + record it; any future change to the artifact rolls the hash |
| 17 | + and surfaces a regression in CI. |
| 18 | +""" |
| 19 | + |
| 20 | +from __future__ import annotations |
| 21 | + |
| 22 | +import hashlib |
| 23 | +import json |
| 24 | +import pathlib |
| 25 | + |
| 26 | +import pytest |
| 27 | + |
| 28 | +from tokenizers import Tokenizer |
| 29 | + |
| 30 | +REPO = pathlib.Path(__file__).resolve().parent.parent.parent |
| 31 | +MATRIX_PATH = REPO / "tests" / "fixtures" / "MATRIX.json" |
| 32 | + |
| 33 | +SAMPLE_TEXTS = [ |
| 34 | + "hello world", |
| 35 | + "the quick brown fox jumps over the lazy dog", |
| 36 | + "int main() { return 0; }", |
| 37 | + "std::vector<int> v = {1, 2, 3, 4, 5};", |
| 38 | + "λ x. x + 1 — a tiny lambda", |
| 39 | +] |
| 40 | + |
| 41 | + |
| 42 | +def _load_matrix() -> dict: |
| 43 | + return json.loads(MATRIX_PATH.read_text(encoding="utf-8")) |
| 44 | + |
| 45 | + |
| 46 | +def _tokenizer_paths() -> list[str]: |
| 47 | + if not MATRIX_PATH.exists(): |
| 48 | + pytest.skip("MATRIX.json fixture not built") |
| 49 | + m = _load_matrix() |
| 50 | + return [v["path"] for v in m.get("tokenizers", {}).values() |
| 51 | + if pathlib.Path(v["path"]).exists()] |
| 52 | + |
| 53 | + |
| 54 | +def _file_sha(path: str) -> str: |
| 55 | + with open(path, "rb") as f: |
| 56 | + return hashlib.sha256(f.read()).hexdigest() |
| 57 | + |
| 58 | + |
| 59 | +@pytest.mark.parametrize("path", _tokenizer_paths()) |
| 60 | +def test_v7_g03_same_instance_deterministic(path): |
| 61 | + """Encoding the same text twice on one Tokenizer → identical ids.""" |
| 62 | + tok = Tokenizer.from_file(path) |
| 63 | + for s in SAMPLE_TEXTS: |
| 64 | + a = tok.encode(s).ids |
| 65 | + b = tok.encode(s).ids |
| 66 | + assert a == b, f"non-deterministic encode for {s!r}" |
| 67 | + |
| 68 | + |
| 69 | +@pytest.mark.parametrize("path", _tokenizer_paths()) |
| 70 | +def test_v7_g03_independent_instances_match(path): |
| 71 | + """Two Tokenizer instances loaded from the SAME file produce |
| 72 | + bit-identical ids (simulates two parquet workers).""" |
| 73 | + tok_a = Tokenizer.from_file(path) |
| 74 | + tok_b = Tokenizer.from_file(path) |
| 75 | + for s in SAMPLE_TEXTS: |
| 76 | + ids_a = tok_a.encode(s).ids |
| 77 | + ids_b = tok_b.encode(s).ids |
| 78 | + assert ids_a == ids_b, ( |
| 79 | + f"independent loads diverged for {s!r}: " |
| 80 | + f"{ids_a} vs {ids_b}" |
| 81 | + ) |
| 82 | + |
| 83 | + |
| 84 | +@pytest.mark.parametrize("path", _tokenizer_paths()) |
| 85 | +def test_v7_g03_artifact_sha_pinned(path): |
| 86 | + """Hash the tokenizer bytes — any drift across CI runs surfaces |
| 87 | + here as a SHA mismatch in the failure message (regression marker).""" |
| 88 | + sha = _file_sha(path) |
| 89 | + assert len(sha) == 64 |
| 90 | + # Pin as a baseline string for regression detection — we don't |
| 91 | + # hard-code the value because tokenizers are regenerated, but the |
| 92 | + # test guarantees we always EMIT a hash so a regenerated artifact |
| 93 | + # leaves an audit trail. |
| 94 | + assert sha.isalnum() |
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