Phase 5 (ADR-0067 follow-up):
teaching/cross_pack_supersede.py — supersede_cross_pack_chain()
CLI: core teaching supersede ... --cross-pack
--subject-pack-id ... --object-pack-id ...
Strict per-chain residency, anti-leakage, byte-identical rollback
on any post-append re-load failure. 9 new tests.
Articulation benchmark suite (Phase 4 capability proof):
benchmarks/articulation.py — 5 sub-benches
[1] breadth — every intent shape (9 + OOV + cross-pack)
[2] determinism — N reruns / unique-surface count
[3] footprint — psutil RSS profile across T turns
[4] cross-topic — thread context across mixed subjects
[5] ollama-compare — opt-in side-by-side with local Ollama
CLI: core bench --suite articulation
--runs N (det rerun count)
--turns N (footprint sample window)
--ollama-model MODEL --ollama-reruns N
Full operator preamble + JSON report path.
10 new tests cover the bench shape (psutil import-skipped).
Documentation:
benchmarks/README.md — full operator manual: catalogue of every
bench suite, how to read good/neutral/bad results for each sub-
bench, why CORE vs Ollama comparisons are valid on the
determinism axis and not on linguistic quality, workflow guide.
README.md — articulation bench listed in the live-demo grid and
quick-start examples.
Reference run (llama3:8b, 100 turns, 5 reruns):
determinism_all_identical=True
per-turn ΔRSS ≈ 23 KiB
CORE byte_identical_on_every_prompt=True
Ollama unique_surfaces≥2 on every prompt
Verification:
18 new tests pass
Full lane: 2116 passed, 2 skipped, 0 failed in 2:38
149 lines
5.1 KiB
Python
149 lines
5.1 KiB
Python
"""Smoke + contract tests for the articulation benchmark suite.
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These are tests for the **bench itself** — not the underlying runtime
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behaviour, which is exercised by the cognition lane. The bench is
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load-bearing for the post-Phase-4 capability claims, so each sub-
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bench gets a focused test that pins the shape of its report.
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"""
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from __future__ import annotations
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import pytest
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from benchmarks.articulation import (
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INTENT_PROBE_PROMPTS,
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CROSS_TOPIC_PROMPTS,
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bench_breadth,
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bench_cross_topic,
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bench_determinism,
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bench_footprint,
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bench_ollama_compare,
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run_articulation_suite,
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)
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# ---------------------------------------------------------------------------
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# Breadth
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# ---------------------------------------------------------------------------
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@pytest.fixture(scope="module")
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def breadth_report():
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return bench_breadth()
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def test_breadth_covers_every_supported_intent_shape(breadth_report) -> None:
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labels = [p.label for p in breadth_report]
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expected = [label for label, _ in INTENT_PROBE_PROMPTS]
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assert labels == expected
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def test_breadth_emits_per_prompt_grounding_tag(breadth_report) -> None:
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for p in breadth_report:
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assert p.grounding_source in {
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"vault", "teaching", "pack", "partial", "oov", "none",
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}
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def test_breadth_oov_prompt_routes_oov(breadth_report) -> None:
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oov = next(p for p in breadth_report if p.label == "OOV_FALLBACK")
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assert oov.grounding_source == "oov"
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# The OOV invitation always names the unfamiliar token; the
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# ``PackMutationProposal`` callout follows but may be truncated
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# by the snippet limit.
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assert "photosynthesis" in oov.surface_snippet
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assert "haven't learned" in oov.surface_snippet
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def test_breadth_cross_pack_verification_routes_teaching(breadth_report) -> None:
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cp = next(
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p for p in breadth_report
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if p.label == "CROSS_PACK_VERIFICATION"
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)
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assert cp.grounding_source == "teaching"
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assert "cross-pack-grounded" in cp.surface_snippet
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# ---------------------------------------------------------------------------
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# Determinism
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# ---------------------------------------------------------------------------
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def test_determinism_byte_identical_across_runs() -> None:
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cases, all_identical = bench_determinism(runs=5)
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assert all_identical is True
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for c in cases:
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assert c.unique_surfaces == 1, (
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f"prompt {c.prompt!r} produced {c.unique_surfaces} unique "
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f"surfaces across {c.runs} runs"
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)
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# ---------------------------------------------------------------------------
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# Footprint
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# ---------------------------------------------------------------------------
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def test_footprint_emits_samples_and_bounds() -> None:
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pytest.importorskip("psutil")
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samples, start, peak, end, per_turn = bench_footprint(
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turns=20, sample_every=10,
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)
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assert len(samples) >= 2 # start + at least one mid/end sample
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assert peak >= start
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assert end >= 0
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# Per-turn ΔRSS must be a small number; if it's huge we have a leak.
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# 1 MiB / turn is a hard ceiling for the smoke test.
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assert abs(per_turn) < 1_048_576, (
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f"per-turn ΔRSS too large ({per_turn} bytes); possible leak"
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)
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# ---------------------------------------------------------------------------
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# Cross-topic
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# ---------------------------------------------------------------------------
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def test_cross_topic_visits_every_prompt() -> None:
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turns, _fires = bench_cross_topic()
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assert len(turns) == len(CROSS_TOPIC_PROMPTS)
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for i, t in enumerate(turns):
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assert t.turn == i
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assert t.prompt == CROSS_TOPIC_PROMPTS[i]
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# Every cross-topic turn either grounds via a recognised tier
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# or returns ``none`` — never a raw exception escape.
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assert t.grounding_source in {
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"vault", "teaching", "pack", "partial", "oov", "none",
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}
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# ---------------------------------------------------------------------------
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# Ollama (skipped when binary absent)
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# ---------------------------------------------------------------------------
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def test_ollama_compare_skips_cleanly_when_no_model_specified() -> None:
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"""Calling without ``model`` argument is the documented opt-out."""
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result = bench_ollama_compare(model=None)
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assert result["status"] == "skipped"
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# ---------------------------------------------------------------------------
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# Orchestrator
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# ---------------------------------------------------------------------------
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def test_run_articulation_suite_emits_shaped_report() -> None:
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pytest.importorskip("psutil")
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report = run_articulation_suite(
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determinism_runs=3, footprint_turns=10,
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footprint_sample_every=5, ollama_model=None,
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)
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d = report.as_dict()
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assert isinstance(d["breadth"], list) and len(d["breadth"]) > 0
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assert isinstance(d["determinism"], list)
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assert d["determinism_all_identical"] is True
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assert isinstance(d["footprint_samples"], list)
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assert d["ollama"]["status"] == "skipped"
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# Cross-topic walk runs every entry.
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assert len(d["cross_topic"]) == len(CROSS_TOPIC_PROMPTS)
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