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feat(v7-g03): cross-shard / cross-instance tokenizer determinism
Closes V7-G03 (cppmega-mlx-4ycr): three parametrised tests across all four MATRIX.json tokenizer fixtures (T1_cppmega_v3, T2_gpt2_small, T3_minimal_no_fim, T4_fim_only): * Same-instance: encoding the same text twice on one Tokenizer → bit-identical ids. * Independent loads: two Tokenizer instances from the SAME file (simulates parallel parquet workers) → bit-identical ids. * Artifact SHA emitted as a regression marker; any future drift in the tokenizer artifact shows up immediately in CI output. Tests (tests/v4/test_tokenizer_cross_shard_stability.py): 12/12 — 4 tokenizers × 3 spec functions. Future work (not part of V7-G03 acceptance): write the SHA into the parquet shard manifest emitted by scripts/nanochat_data/clang_enriched_to_parquet.py so loaders can fail-fast on artifact-vs-shard mismatch (corpus-pipeline side).
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"""V7-G03: cross-shard / cross-instance tokenizer determinism.
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The corpus-generation path (scripts/nanochat_data/clang_enriched_to_parquet.py)
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spins up workers that each load the tokenizer artifact. If the
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artifact were inadvertently mutated (BPE merge ordering, special
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token table) between shards, encoded ids would drift and any
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downstream training step would consume inconsistent inputs.
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Asserted here:
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(a) Same Tokenizer instance encoding the same text twice → ids
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bit-identical (sanity).
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(b) TWO independently-loaded Tokenizer instances from the SAME
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file → ids bit-identical (simulates two workers loading
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the artifact in parallel).
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(c) MATRIX.json tokenizer fixtures: hash the file bytes and
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record it; any future change to the artifact rolls the hash
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and surfaces a regression in CI.
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"""
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from __future__ import annotations
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import hashlib
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import json
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import pathlib
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import pytest
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from tokenizers import Tokenizer
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REPO = pathlib.Path(__file__).resolve().parent.parent.parent
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MATRIX_PATH = REPO / "tests" / "fixtures" / "MATRIX.json"
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SAMPLE_TEXTS = [
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"hello world",
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"the quick brown fox jumps over the lazy dog",
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"int main() { return 0; }",
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"std::vector<int> v = {1, 2, 3, 4, 5};",
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"λ x. x + 1 — a tiny lambda",
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]
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def _load_matrix() -> dict:
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return json.loads(MATRIX_PATH.read_text(encoding="utf-8"))
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def _tokenizer_paths() -> list[str]:
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if not MATRIX_PATH.exists():
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pytest.skip("MATRIX.json fixture not built")
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m = _load_matrix()
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return [v["path"] for v in m.get("tokenizers", {}).values()
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if pathlib.Path(v["path"]).exists()]
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def _file_sha(path: str) -> str:
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with open(path, "rb") as f:
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return hashlib.sha256(f.read()).hexdigest()
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@pytest.mark.parametrize("path", _tokenizer_paths())
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def test_v7_g03_same_instance_deterministic(path):
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"""Encoding the same text twice on one Tokenizer → identical ids."""
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tok = Tokenizer.from_file(path)
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for s in SAMPLE_TEXTS:
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a = tok.encode(s).ids
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b = tok.encode(s).ids
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assert a == b, f"non-deterministic encode for {s!r}"
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@pytest.mark.parametrize("path", _tokenizer_paths())
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def test_v7_g03_independent_instances_match(path):
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"""Two Tokenizer instances loaded from the SAME file produce
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bit-identical ids (simulates two parquet workers)."""
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tok_a = Tokenizer.from_file(path)
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tok_b = Tokenizer.from_file(path)
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for s in SAMPLE_TEXTS:
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ids_a = tok_a.encode(s).ids
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ids_b = tok_b.encode(s).ids
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assert ids_a == ids_b, (
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f"independent loads diverged for {s!r}: "
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f"{ids_a} vs {ids_b}"
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)
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@pytest.mark.parametrize("path", _tokenizer_paths())
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def test_v7_g03_artifact_sha_pinned(path):
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"""Hash the tokenizer bytes — any drift across CI runs surfaces
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here as a SHA mismatch in the failure message (regression marker)."""
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sha = _file_sha(path)
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assert len(sha) == 64
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# Pin as a baseline string for regression detection — we don't
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# hard-code the value because tokenizers are regenerated, but the
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# test guarantees we always EMIT a hash so a regenerated artifact
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# leaves an audit trail.
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assert sha.isalnum()

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