| 1 | """Tests for :mod:`dlm_sway.probes.delta_kl`.""" |
| 2 | |
| 3 | from __future__ import annotations |
| 4 | |
| 5 | import numpy as np |
| 6 | |
| 7 | from dlm_sway.backends.dummy import DummyDifferentialBackend, DummyResponses |
| 8 | from dlm_sway.core.result import Verdict |
| 9 | from dlm_sway.core.scoring import TokenDist |
| 10 | from dlm_sway.probes.base import RunContext, build_probe |
| 11 | |
| 12 | |
| 13 | def _diverging_backend() -> DummyDifferentialBackend: |
| 14 | """Base peaks tightly on token 1; ft is broad uniform. Real divergence.""" |
| 15 | base = DummyResponses( |
| 16 | token_dists={ |
| 17 | "q1": TokenDist( |
| 18 | token_ids=np.array([1, 2, 3], dtype=np.int64), |
| 19 | logprobs=np.log(np.array([0.9, 0.05, 0.05], dtype=np.float32)), |
| 20 | vocab_size=100, |
| 21 | ), |
| 22 | "q2": TokenDist( |
| 23 | token_ids=np.array([5, 6], dtype=np.int64), |
| 24 | logprobs=np.log(np.array([0.8, 0.2], dtype=np.float32)), |
| 25 | vocab_size=100, |
| 26 | ), |
| 27 | } |
| 28 | ) |
| 29 | ft = DummyResponses( |
| 30 | token_dists={ |
| 31 | "q1": TokenDist( |
| 32 | token_ids=np.array([1, 2, 3], dtype=np.int64), |
| 33 | logprobs=np.log(np.array([0.3, 0.35, 0.35], dtype=np.float32)), |
| 34 | vocab_size=100, |
| 35 | ), |
| 36 | "q2": TokenDist( |
| 37 | token_ids=np.array([5, 6], dtype=np.int64), |
| 38 | logprobs=np.log(np.array([0.4, 0.6], dtype=np.float32)), |
| 39 | vocab_size=100, |
| 40 | ), |
| 41 | } |
| 42 | ) |
| 43 | return DummyDifferentialBackend(base=base, ft=ft) |
| 44 | |
| 45 | |
| 46 | def _identical_backend() -> DummyDifferentialBackend: |
| 47 | dist = TokenDist( |
| 48 | token_ids=np.array([1, 2, 3], dtype=np.int64), |
| 49 | logprobs=np.log(np.array([0.5, 0.3, 0.2], dtype=np.float32)), |
| 50 | vocab_size=100, |
| 51 | ) |
| 52 | base = DummyResponses(token_dists={"q1": dist}) |
| 53 | ft = DummyResponses(token_dists={"q1": dist}) |
| 54 | return DummyDifferentialBackend(base=base, ft=ft) |
| 55 | |
| 56 | |
| 57 | class TestDeltaKL: |
| 58 | def test_passes_when_distributions_diverge(self) -> None: |
| 59 | probe, spec = build_probe( |
| 60 | { |
| 61 | "name": "dk", |
| 62 | "kind": "delta_kl", |
| 63 | "prompts": ["q1", "q2"], |
| 64 | "assert_mean_gte": 0.01, |
| 65 | } |
| 66 | ) |
| 67 | ctx = RunContext(backend=_diverging_backend()) |
| 68 | result = probe.run(spec, ctx) |
| 69 | assert result.verdict == Verdict.PASS |
| 70 | assert result.raw is not None |
| 71 | assert result.raw > 0.01 |
| 72 | assert result.evidence["num_prompts"] == 2 |
| 73 | assert len(result.evidence["per_prompt"]) == 2 |
| 74 | |
| 75 | def test_fails_when_distributions_identical(self) -> None: |
| 76 | probe, spec = build_probe( |
| 77 | { |
| 78 | "name": "dk", |
| 79 | "kind": "delta_kl", |
| 80 | "prompts": ["q1"], |
| 81 | "assert_mean_gte": 0.01, |
| 82 | } |
| 83 | ) |
| 84 | ctx = RunContext(backend=_identical_backend()) |
| 85 | result = probe.run(spec, ctx) |
| 86 | assert result.verdict == Verdict.FAIL |
| 87 | assert result.raw == 0.0 |
| 88 | |
| 89 | def test_z_score_path_when_null_stats_present(self) -> None: |
| 90 | probe, spec = build_probe( |
| 91 | { |
| 92 | "name": "dk", |
| 93 | "kind": "delta_kl", |
| 94 | "prompts": ["q1"], |
| 95 | "assert_z_gte": 2.0, |
| 96 | } |
| 97 | ) |
| 98 | null_stats = {"delta_kl": {"mean": 0.01, "std": 0.01, "n": 3.0}} |
| 99 | ctx = RunContext(backend=_diverging_backend(), null_stats=null_stats) |
| 100 | result = probe.run(spec, ctx) |
| 101 | assert result.z_score is not None |
| 102 | # Our synthetic ft diverges ~0.1+, far above μ=0.01, σ=0.01 → huge z. |
| 103 | assert result.z_score > 2.0 |
| 104 | assert result.verdict == Verdict.PASS |
| 105 | |
| 106 | def test_error_on_empty_prompts(self) -> None: |
| 107 | probe, spec = build_probe({"name": "dk", "kind": "delta_kl", "prompts": []}) |
| 108 | ctx = RunContext(backend=_identical_backend()) |
| 109 | result = probe.run(spec, ctx) |
| 110 | assert result.verdict == Verdict.ERROR |
| 111 | |
| 112 | def test_kl_kind_available(self) -> None: |
| 113 | probe, spec = build_probe( |
| 114 | { |
| 115 | "name": "dk", |
| 116 | "kind": "delta_kl", |
| 117 | "prompts": ["q1"], |
| 118 | "divergence": "kl", |
| 119 | "assert_mean_gte": 0.0, |
| 120 | } |
| 121 | ) |
| 122 | ctx = RunContext(backend=_diverging_backend()) |
| 123 | result = probe.run(spec, ctx) |
| 124 | assert result.evidence["divergence_kind"] == "kl" |
| 125 | |
| 126 | |
| 127 | class TestB1NanLogprobsRouteToError: |
| 128 | """S01 regression: NaN logprobs must NEVER produce a passing z-score. |
| 129 | |
| 130 | The historical bug made this pass at +11639σ. Two pins here: |
| 131 | |
| 132 | 1. ``probe.run()`` raises ``ProbeError`` when ``_divergence`` sees NaN |
| 133 | (unit-level: the probe surfaces the failure). |
| 134 | 2. When routed through the suite runner, the ProbeError turns into |
| 135 | ``Verdict.ERROR`` (integration-level: the product contract — no |
| 136 | silent PASS on broken models). |
| 137 | """ |
| 138 | |
| 139 | @staticmethod |
| 140 | def _nan_backend() -> DummyDifferentialBackend: |
| 141 | """Backend whose ft view has NaN-laden TokenDist.""" |
| 142 | import math |
| 143 | |
| 144 | base = DummyResponses( |
| 145 | token_dists={ |
| 146 | "q1": TokenDist( |
| 147 | token_ids=np.array([1, 2], dtype=np.int64), |
| 148 | logprobs=np.log(np.array([0.9, 0.1], dtype=np.float32)), |
| 149 | vocab_size=100, |
| 150 | ) |
| 151 | } |
| 152 | ) |
| 153 | ft = DummyResponses( |
| 154 | token_dists={ |
| 155 | "q1": TokenDist( |
| 156 | token_ids=np.array([1, 2], dtype=np.int64), |
| 157 | logprobs=np.array([math.nan, math.nan], dtype=np.float32), |
| 158 | vocab_size=100, |
| 159 | ) |
| 160 | } |
| 161 | ) |
| 162 | return DummyDifferentialBackend(base=base, ft=ft) |
| 163 | |
| 164 | def test_probe_raises_probe_error_on_nan_logprobs(self) -> None: |
| 165 | import pytest |
| 166 | |
| 167 | from dlm_sway.core.errors import ProbeError |
| 168 | |
| 169 | probe, spec = build_probe( |
| 170 | { |
| 171 | "name": "dk", |
| 172 | "kind": "delta_kl", |
| 173 | "prompts": ["q1"], |
| 174 | "assert_mean_gte": 0.001, |
| 175 | } |
| 176 | ) |
| 177 | ctx = RunContext(backend=self._nan_backend()) |
| 178 | with pytest.raises(ProbeError, match="non-finite"): |
| 179 | probe.run(spec, ctx) |
| 180 | |
| 181 | def test_runner_converts_nan_probe_error_to_verdict_error(self) -> None: |
| 182 | """Integration: the suite runner catches the ProbeError and emits |
| 183 | ERROR, not a bogus PASS. This is the product-level invariant.""" |
| 184 | from dlm_sway.suite.runner import run as run_suite |
| 185 | from dlm_sway.suite.spec import SwaySpec |
| 186 | |
| 187 | spec = SwaySpec.model_validate( |
| 188 | { |
| 189 | "version": 1, |
| 190 | "models": { |
| 191 | "base": {"base": "b"}, |
| 192 | "ft": {"base": "b", "adapter": "/tmp/a"}, |
| 193 | }, |
| 194 | "suite": [ |
| 195 | { |
| 196 | "name": "dk", |
| 197 | "kind": "delta_kl", |
| 198 | "prompts": ["q1"], |
| 199 | "assert_mean_gte": 0.001, |
| 200 | } |
| 201 | ], |
| 202 | } |
| 203 | ) |
| 204 | # Pre-seed the preflight prompt so the backend preflight doesn't |
| 205 | # short-circuit before the real delta_kl probe runs. |
| 206 | backend = self._nan_backend() |
| 207 | backend._base_r.token_dists["preflight"] = TokenDist( |
| 208 | token_ids=np.array([1, 2], dtype=np.int64), |
| 209 | logprobs=np.log(np.array([0.5, 0.5], dtype=np.float32)), |
| 210 | vocab_size=100, |
| 211 | ) |
| 212 | backend._ft_r.token_dists["preflight"] = TokenDist( |
| 213 | token_ids=np.array([1, 2], dtype=np.int64), |
| 214 | logprobs=np.log(np.array([0.5, 0.5], dtype=np.float32)), |
| 215 | vocab_size=100, |
| 216 | ) |
| 217 | result = run_suite(spec, backend) |
| 218 | # Exactly one probe (delta_kl), verdict ERROR. |
| 219 | delta_kl_probe = next(r for r in result.probes if r.kind == "delta_kl") |
| 220 | assert delta_kl_probe.verdict == Verdict.ERROR |
| 221 | assert "non-finite" in delta_kl_probe.message.lower() |
| 222 | # No PASS in the entire suite. |
| 223 | assert not any(r.verdict == Verdict.PASS for r in result.probes) |