Merge pull request #200 from rajamukerji/fix/bayesian-silent-skip-and-warmstart-bounds

fix(bayesian): surface skipped optimization and clamp warm start to its search space

README.md

@@ -182,6 +182,11 @@ Pick a model from the dropdown, pick a method, hit Run All. Download the result
 
 For automation, CI pipelines, or remote servers without a display:
 
+The normal installation includes Optuna for the `optimized` method; no separate
+optimizer install is required. If optimization cannot run, its reason appears
+in the pipeline log and in
+`abliteration_metadata.json` under `method_config.bayesian_optimization_skipped`.
+
 ```bash
 pip install -e .
 

ci/supply-chain-policy.json

@@ -41,6 +41,7 @@
       "BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0",
       "ISC License (ISCL)",
       "MIT",
+      "MIT AND PSF-2.0",
       "MIT License",
       "MIT-CMU",
       "MPL-2.0",

ci/test-risk-map.json

@@ -81,6 +81,7 @@
         "tests/test_qwen35_contracts.py",
         "tests/test_auto_obliterate.py",
         "tests/test_bayesian_optimizer_contracts.py",
+        "tests/test_bayesian_optimizer_optuna.py",
         "tests/test_informed_pipeline.py",
         "tests/test_informed_pipeline_contracts.py",
         "tests/test_offline_integration.py",
@@ -777,9 +778,10 @@
     {
       "path": "obliteratus/bayesian_optimizer.py",
       "risk_class": "conditional-runtime",
-      "risk": "optional optimizer trials over repeated live model mutation and evaluation",
+      "risk": "optimizer trials over repeated live model mutation and evaluation",
       "required_tests": [
         "tests/test_bayesian_optimizer_contracts.py",
+        "tests/test_bayesian_optimizer_optuna.py",
         "tests/test_module_imports.py",
         "tests/conditional/test_model_download_runtime.py"
       ],

docs/SUPPLY_CHAIN_POLICY.md

@@ -9,6 +9,35 @@ CI uses exact Python tool versions and checksum-pinned standalone binaries.
 `ci/digests.txt` records executable and GitHub Action pins; build, test, and
 license-tool transitive dependencies are covered by `uv.lock`.
 
+## Bayesian optimizer dependency
+
+Optuna is a core dependency (`>=4.9,<5`), installed by both `pip install .`
+and the documented locked development setup. The `optimized` and `heretic`
+pipeline methods therefore receive their requested Bayesian optimization
+without a separate manual installation. A partial environment that lacks
+Optuna still reports the fallback in the pipeline log and checkpoint metadata.
+
+The lock pins Optuna 4.9.0 and adds Alembic 1.19.2, colorlog 6.12.0,
+greenlet 3.5.5, Mako 1.4.1, and SQLAlchemy 2.0.52 without upgrading existing
+packages. These distributions come from PyPI, with artifact SHA-256 hashes in
+`uv.lock`; the signed repository change records the reviewed lock. The package
+registry and package publishers remain the source trust boundary. No hosted
+Optuna service, dashboard, database connection, or integration extra is used:
+the optimizer creates an in-memory study.
+
+The package license inventory identifies MIT/MIT License for Optuna, Alembic,
+colorlog, Mako, and SQLAlchemy. Optuna's bundled third-party notices cover
+SciPy-derived BSD code and fdlibm's permissive notice. Greenlet's included
+`LICENSE` and `LICENSE.PSF` identify MIT code and Stackless/Python-derived code
+under PSF-2.0. The reviewed composite expression `MIT AND PSF-2.0` is therefore
+included in the allow list; preserve both notices when redistributing it.
+No vulnerability suppression is added. Dependency updates remain subject to
+the same vulnerability and license gates as the rest of the lock.
+
+`tests/test_bayesian_optimizer_optuna.py` runs real Optuna trials with tiny
+local CPU tensors, verifies in-range warm starts and model restoration, and
+uses no model download, network, credentials, or accelerator.
+
 ## Digest-bound release evidence
 
 The release artifact is a deterministic ZIP snapshot of the tested repository

obliteratus/abliterate.py

@@ -1242,6 +1242,7 @@ class AbliterationPipeline:
         # Float layer interpolation: continuous layer weights
         self._float_layer_weights: dict[int, float] = {}
         # Bayesian optimizer component-specific scales (set by optimizer)
+        self._bayesian_skipped: str | None = None
         self._bayesian_attn_scale: float | None = None
         self._bayesian_mlp_scale: float | None = None
         # CoT-aware: identified reasoning-critical directions to preserve
@@ -1552,6 +1553,8 @@ class AbliterationPipeline:
 
     def run(self) -> Path:
         """Execute the full abliteration pipeline. Returns path to saved model."""
+        from obliteratus.bayesian_optimizer import _reset_bayesian_state
+        _reset_bayesian_state(self)
         try:
             return self._run_pipeline()
         except PipelineFailure:
@@ -4209,11 +4212,13 @@ class AbliterationPipeline:
         # When enabled, run Optuna TPE to find optimal per-layer regularization
         # before the standard projection loop.  The found values override the
         # static layer_adaptive_strength weights.
+        from obliteratus.bayesian_optimizer import _reset_bayesian_state
+        _reset_bayesian_state(self)
         bayesian_regs: dict[int, float] = {}
         bayesian_trials = getattr(self, "_bayesian_trials", 0) or (
             METHODS.get(self.method, {}).get("bayesian_trials", 0)
         )
-        if bayesian_trials > 0 and self._strong_layers and self.handle:
+        if bayesian_trials > 0:
             self.log(f"Running Bayesian optimization ({bayesian_trials} trials)...")
             from obliteratus.bayesian_optimizer import run_bayesian_optimization
             bayesian_regs = run_bayesian_optimization(
@@ -4231,6 +4236,17 @@ class AbliterationPipeline:
                     f"{idx}:{reg:.3f}" for idx, reg in sorted(bayesian_regs.items())
                 )
                 self.log(f"  Optimal regs: {regs_str}")
+            else:
+                # Reached whenever the optimizer no-ops for any reason. Without
+                # this the run logs "Running Bayesian optimization (50 trials)",
+                # prints nothing further, and completes as a success — so the
+                # only difference between an optimized and an unoptimized
+                # checkpoint is an absence of output.
+                self.log(
+                    "WARNING: Bayesian optimization returned no layer "
+                    f"regularizations (reason: {getattr(self, '_bayesian_skipped', 'unknown')}). "
+                    "Continuing with method defaults — this checkpoint is NOT optimized."
+                )
 
         # ── LoRA-based reversible ablation ──────────────────────────────
         # When enabled, compute LoRA adapters and merge them instead of
@@ -7584,6 +7600,11 @@ class AbliterationPipeline:
                 "som_diversity_penalty": self.som_diversity_penalty if self.direction_method == "som" else None,
                 "som_min_signal_to_noise": self.som_min_signal_to_noise if self.direction_method == "som" else None,
                 "layer_selection": self.layer_selection,
+                # None when the optimizer ran (or was never requested); a reason
+                # string when it no-opped. Without it a checkpoint's provenance
+                # records the method that ASKED for an optimization, with nothing
+                # to say whether one happened.
+                "bayesian_optimization_skipped": getattr(self, "_bayesian_skipped", None),
                 "min_layer_fraction": self.min_layer_fraction,
                 "max_layer_fraction": self.max_layer_fraction,
                 "harmless_pc_count": self.harmless_pc_count,

obliteratus/bayesian_optimizer.py

@@ -41,6 +41,63 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
+# Kernel search space, declared once. `objective` suggests from these bounds and
+# the warm-start trial is clamped to them; keeping two copies is what let the
+# warm start drift outside the space it was seeding (see `_clamp_to_space`).
+KERNEL_SPACE: dict[str, tuple[float, float]] = {
+    "attn_max_weight": (0.5, 1.0),
+    "attn_peak_position": (0.1, 0.9),
+    "attn_min_weight": (0.0, 0.3),
+    "attn_spread": (0.1, 0.6),
+    "mlp_max_weight": (0.3, 1.0),
+    "mlp_peak_position": (0.1, 0.9),
+    "mlp_min_weight": (0.0, 0.3),
+    "mlp_spread": (0.1, 0.6),
+}
+
+
+def _reset_bayesian_state(pipeline: AbliterationPipeline) -> None:
+    """Clear status and component overrides from an earlier attempt."""
+    pipeline._bayesian_skipped = None
+    pipeline._bayesian_attn_scale = None
+    pipeline._bayesian_mlp_scale = None
+
+
+def _skip(pipeline: AbliterationPipeline, reason: str, message: str) -> None:
+    """Record and surface a no-op so it cannot be mistaken for a completed run.
+
+    Reported three ways because each reaches a different audience: the stdlib
+    logger for anyone capturing logs, the pipeline log for the console panel the
+    operator is watching, and an attribute the caller persists into checkpoint
+    metadata so the provenance of an already-written model still shows it.
+    """
+    logger.warning(message)
+    log = getattr(pipeline, "log", None)
+    if callable(log):
+        log(f"WARNING: {message}")
+    try:
+        pipeline._bayesian_skipped = reason
+    except AttributeError:  # pragma: no cover - pipeline stubs may use __slots__
+        pass
+
+
+def _clamp_to_space(params: dict[str, float]) -> dict[str, float]:
+    """Clamp warm-start values into the declared search space.
+
+    Optuna warns about an out-of-range enqueued value but still evaluates it.
+    Clamping keeps the informed starting point within the same bounds as later
+    sampled trials. This is reachable in normal use: `attn_peak_position` is
+    seeded from
+    `peak_layer / (n_layers - 1)`, which for an early peak layer falls below the
+    0.1 floor — a 36-layer model peaking at layer 2 seeds 0.057.
+    """
+    out = dict(params)
+    for name, (lo, hi) in KERNEL_SPACE.items():
+        if name in out:
+            out[name] = min(max(float(out[name]), lo), hi)
+    return out
+
+
 def _measure_refusal_rate(
     pipeline: AbliterationPipeline,
     n_prompts: int = 10,
@@ -261,17 +318,33 @@ def run_bayesian_optimization(
     Returns:
         Dict mapping layer_idx -> optimal regularization value.
     """
+    _reset_bayesian_state(pipeline)
+    if n_trials <= 0:
+        _skip(pipeline, "no-trials", "Bayesian optimization SKIPPED: no trials requested.")
+        return {}
+
     try:
         import optuna
         from optuna.samplers import TPESampler
     except ImportError:
-        logger.warning(
-            "Optuna not installed — skipping Bayesian optimization. "
-            "Install with: pip install optuna"
+        # Keep the operator log and saved provenance explicit even when a
+        # partial or older installation lacks the optimizer dependency.
+        _skip(
+            pipeline,
+            "optuna-not-installed",
+            "Optuna not installed — Bayesian optimization SKIPPED. Layer "
+            "weights fall back to method defaults; results are NOT optimized. "
+            "Install with: pip install optuna",
         )
         return {}
 
     if not pipeline.handle or not pipeline._strong_layers:
+        _skip(
+            pipeline,
+            "no-strong-layers",
+            "Bayesian optimization SKIPPED: no model handle or no selected "
+            "layers to optimize over.",
+        )
         return {}
 
     model = pipeline.handle.model
@@ -307,7 +380,11 @@ def run_bayesian_optimization(
     pipeline._free_gpu_memory()
 
     if not reference_logits:
-        pipeline.log("  Failed to collect reference logits — skipping optimization")
+        _skip(
+            pipeline,
+            "reference-logits-unavailable",
+            "Bayesian optimization SKIPPED: Failed to collect reference logits.",
+        )
         return {}
 
     from obliteratus.strategies.utils import (
@@ -401,16 +478,16 @@ def run_bayesian_optimization(
         _restore_all()
 
         # Attention kernel: 4 params
-        attn_max = trial.suggest_float("attn_max_weight", 0.5, 1.0)
-        attn_peak = trial.suggest_float("attn_peak_position", 0.1, 0.9)
-        attn_min = trial.suggest_float("attn_min_weight", 0.0, 0.3)
-        attn_spread = trial.suggest_float("attn_spread", 0.1, 0.6)
+        attn_max = trial.suggest_float("attn_max_weight", *KERNEL_SPACE["attn_max_weight"])
+        attn_peak = trial.suggest_float("attn_peak_position", *KERNEL_SPACE["attn_peak_position"])
+        attn_min = trial.suggest_float("attn_min_weight", *KERNEL_SPACE["attn_min_weight"])
+        attn_spread = trial.suggest_float("attn_spread", *KERNEL_SPACE["attn_spread"])
 
         # MLP kernel: 4 params (separate — can peak at a different layer)
-        mlp_max = trial.suggest_float("mlp_max_weight", 0.3, 1.0)
-        mlp_peak = trial.suggest_float("mlp_peak_position", 0.1, 0.9)
-        mlp_min = trial.suggest_float("mlp_min_weight", 0.0, 0.3)
-        mlp_spread = trial.suggest_float("mlp_spread", 0.1, 0.6)
+        mlp_max = trial.suggest_float("mlp_max_weight", *KERNEL_SPACE["mlp_max_weight"])
+        mlp_peak = trial.suggest_float("mlp_peak_position", *KERNEL_SPACE["mlp_peak_position"])
+        mlp_min = trial.suggest_float("mlp_min_weight", *KERNEL_SPACE["mlp_min_weight"])
+        mlp_spread = trial.suggest_float("mlp_spread", *KERNEL_SPACE["mlp_spread"])
 
         # Float direction index (cross-layer interpolation, Heretic-style)
         dir_idx = trial.suggest_float("dir_idx", 0.0, max(n_layers_with_dirs - 1, 0.0))
@@ -521,7 +598,7 @@ def run_bayesian_optimization(
             "mlp_spread": 0.3,
             "dir_idx": 0.0,
         }
-    study.enqueue_trial(warm_params)
+    study.enqueue_trial(_clamp_to_space(warm_params))
 
     pipeline.log(f"Bayesian optimization: running {n_trials} trials (parametric kernel)...")
     study.optimize(objective, n_trials=n_trials, show_progress_bar=False)
@@ -585,4 +662,10 @@ def run_bayesian_optimization(
     del original_params
     pipeline._free_gpu_memory()
 
+    if not best_result:
+        _skip(
+            pipeline,
+            "no-successful-trials",
+            "Bayesian optimization SKIPPED: no usable trial results.",
+        )
     return best_result

pyproject.toml

@@ -34,6 +34,7 @@ dependencies = [
     "numpy>=1.24",
     "scikit-learn>=1.3",
     "tqdm>=4.64",
+    "optuna>=4.9,<5",
 ]
 
 [project.urls]

requirements.txt

@@ -12,4 +12,5 @@ pandas>=2.0
 numpy>=1.24
 scikit-learn>=1.3
 tqdm>=4.64
+optuna>=4.9,<5
 bitsandbytes>=0.46.1

tests/test_bayesian_optimizer_contracts.py

@@ -253,6 +253,7 @@ def test_run_bayesian_optimization_returns_empty_when_reference_logits_fail(monk
 
     assert bo.run_bayesian_optimization(pipeline, n_kl_prompts=2) == {}
     assert pipeline.freed == 1
+    assert pipeline._bayesian_skipped == "reference-logits-unavailable"
     assert "Failed to collect reference logits" in pipeline.logs[-1]
 
 
@@ -443,13 +444,17 @@ def test_run_bayesian_optimization_no_pareto_uses_objective_best_and_restores_af
         n_kl_prompts=1,
     )
 
+    # peak_position was 0.0 here before the warm start was clamped. 0.0 is
+    # outside the distribution `objective` declares for it (0.1..0.9), so Optuna
+    # warned but still evaluated a value outside the search space. Clamping
+    # keeps the initial trial within the same bounds as sampled trials.
     assert study.enqueued == [{
         "attn_max_weight": 0.9,
-        "attn_peak_position": 0.0,
+        "attn_peak_position": 0.1,
         "attn_min_weight": 0.05,
         "attn_spread": 0.3,
         "mlp_max_weight": 0.6,
-        "mlp_peak_position": 0.0,
+        "mlp_peak_position": 0.1,
         "mlp_min_weight": 0.05,
         "mlp_spread": 0.3,
         "dir_idx": 0.0,
@@ -460,3 +465,232 @@ def test_run_bayesian_optimization_no_pareto_uses_objective_best_and_restores_af
     assert pipeline.moe_calls == []
     assert torch.allclose(layers[0].self_attn.o_proj.weight, original_attn)
     assert torch.allclose(layers[0].mlp.down_proj.weight, original_mlp)
+
+
+def test_missing_optuna_is_surfaced_on_the_pipeline_log_and_recorded(monkeypatch):
+    """A skipped optimization must not be indistinguishable from a completed one.
+
+    `logger.warning` reaches stderr only through logging's last-resort handler —
+    the `obliterate` command never configures logging — while the console panel
+    the operator watches is fed by `pipeline.log`. Before this contract the
+    pipeline log received nothing at all, so a run whose headline feature never
+    executed still read as a clean success.
+    """
+    real_import = builtins.__import__
+
+    def fake_import(name, *args, **kwargs):
+        if name == "optuna" or name.startswith("optuna."):
+            raise ImportError("no optuna in this test")
+        return real_import(name, *args, **kwargs)
+
+    monkeypatch.setattr(builtins, "__import__", fake_import)
+
+    pipeline = _Pipeline()
+    pipeline.handle = object()
+    pipeline._strong_layers = [0]
+
+    assert bo.run_bayesian_optimization(pipeline) == {}
+    assert any("SKIPPED" in message for message in pipeline.logs)
+    assert any("optuna" in message.lower() for message in pipeline.logs)
+    assert pipeline._bayesian_skipped == "optuna-not-installed"
+
+
+def test_skip_without_handle_or_layers_is_also_recorded(monkeypatch):
+    _install_fake_optuna(monkeypatch)
+
+    pipeline = _Pipeline()
+    pipeline.handle = None
+    pipeline._strong_layers = [0]
+
+    assert bo.run_bayesian_optimization(pipeline) == {}
+    assert pipeline._bayesian_skipped == "no-strong-layers"
+
+
+def test_clamp_to_space_pulls_out_of_range_warm_start_into_the_distribution():
+    """Reachable in normal use, not a synthetic edge case.
+
+    `attn_peak_position` is seeded from ``peak_layer / (n_layers - 1)``; a
+    36-layer model whose peak layer is 2 seeds 0.057, below the 0.1 floor.
+    Optuna warns but evaluates an enqueued value outside its distribution;
+    the warm-start trial should obey the same bounds as sampled trials.
+    """
+    clamped = bo._clamp_to_space(
+        {
+            "attn_peak_position": 2 / 35,   # early peak layer on a 36-layer model
+            "mlp_peak_position": 34 / 35,   # late peak layer
+            "mlp_max_weight": 0.25,         # max_weight 0.5 x mlp_scale 0.5
+            "dir_idx": 3.0,                 # bounds are dynamic; must pass through
+        }
+    )
+
+    for name, value in clamped.items():
+        if name in bo.KERNEL_SPACE:
+            low, high = bo.KERNEL_SPACE[name]
+            assert low <= value <= high, f"{name}={value} outside {low}..{high}"
+
+    assert clamped["attn_peak_position"] == pytest.approx(0.1)
+    assert clamped["mlp_peak_position"] == pytest.approx(0.9)
+    assert clamped["mlp_max_weight"] == pytest.approx(0.3)
+    assert clamped["dir_idx"] == 3.0
+
+
+def test_objective_bounds_and_warm_start_share_one_declaration(monkeypatch):
+    """The two copies of the search space are why the warm start could drift."""
+    study = _FakeStudy(best_trials=[_FakeTrial()])
+    _install_fake_optuna(monkeypatch, study=study)
+
+    recorded: dict[str, tuple[float, float]] = {}
+
+    class _RecordingTrial(_FakeTrial):
+        def suggest_float(self, name, low, high):
+            recorded[name] = (low, high)
+            return self.params[name]
+
+    monkeypatch.setattr(
+        study,
+        "optimize",
+        lambda objective, n_trials, show_progress_bar: objective(_RecordingTrial()),
+    )
+    monkeypatch.setattr(bo, "_measure_refusal_rate", lambda *_a, **_k: 0.25)
+    monkeypatch.setattr(bo, "_measure_kl_divergence", lambda *_a, **_k: 0.1)
+
+    bo.run_bayesian_optimization(_optimization_pipeline([_Layer()]), n_trials=1)
+
+    for name, bounds in bo.KERNEL_SPACE.items():
+        assert recorded.get(name) == bounds, f"{name} suggested outside KERNEL_SPACE"
+
+
+def test_retry_clears_skip_reason_and_component_overrides(monkeypatch):
+    study = _FakeStudy(best_trials=[_FakeTrial()])
+    _install_fake_optuna(monkeypatch, study)
+    monkeypatch.setattr(bo, "_measure_refusal_rate", lambda *_a, **_k: 0.25)
+    monkeypatch.setattr(bo, "_measure_kl_divergence", lambda *_a, **_k: 0.1)
+    pipeline = _optimization_pipeline([_Layer()])
+    tokenizer = pipeline.handle.tokenizer
+    pipeline.handle.tokenizer = lambda *_a, **_k: (_ for _ in ()).throw(RuntimeError("tokenization"))
+    assert bo.run_bayesian_optimization(pipeline, n_trials=1) == {}
+    assert pipeline._bayesian_skipped == "reference-logits-unavailable"
+    pipeline.handle.tokenizer = tokenizer
+    assert bo.run_bayesian_optimization(pipeline, n_trials=1)
+    assert pipeline._bayesian_skipped is None
+    assert pipeline._bayesian_attn_scale == 0.8
+    pipeline._strong_layers = []
+    assert bo.run_bayesian_optimization(pipeline, n_trials=1) == {}
+    assert pipeline._bayesian_skipped == "no-strong-layers"
+    assert pipeline._bayesian_attn_scale is None
+    assert pipeline._bayesian_mlp_scale is None
+
+
+@pytest.mark.parametrize("n_trials", [0, -1])
+def test_nonpositive_trial_budget_records_skip(n_trials):
+    pipeline = _optimization_pipeline([_Layer()])
+    assert bo.run_bayesian_optimization(pipeline, n_trials=n_trials) == {}
+    assert pipeline._bayesian_skipped == "no-trials"
+
+
+def test_empty_study_records_skip(monkeypatch):
+    study = _FakeStudy(best_trials=[])
+    _install_fake_optuna(monkeypatch, study)
+    monkeypatch.setattr(study, "optimize", lambda *_a, **_k: None)
+    pipeline = _optimization_pipeline([_Layer()])
+    assert bo.run_bayesian_optimization(pipeline, n_trials=1) == {}
+    assert pipeline._bayesian_skipped == "no-successful-trials"
+
+
+@pytest.mark.parametrize("outcome,reason", [
+    ("missing", "optuna-not-installed"),
+    ("reference-failure", "reference-logits-unavailable"),
+    ("no-layers", "no-strong-layers"),
+    ("success", None),
+    ("not-requested", None),
+])
+def test_caller_warning_and_saved_checkpoint_status(monkeypatch, tmp_path, outcome, reason):
+    """Exercise EXCISE and the actual on-disk checkpoint provenance together."""
+    import json
+    from pathlib import Path
+    from unittest.mock import MagicMock
+
+    from transformers import LlamaConfig, LlamaForCausalLM
+
+    from obliteratus.abliterate import AbliterationPipeline
+    from obliteratus.models.loader import ModelHandle
+
+    config = LlamaConfig(
+        vocab_size=16, hidden_size=8, intermediate_size=16, num_hidden_layers=1,
+        num_attention_heads=1, num_key_value_heads=1, max_position_embeddings=16,
+    )
+    model = LlamaForCausalLM(config).eval()
+    tokenizer = MagicMock()
+    tokenizer.return_value = {"input_ids": torch.tensor([[1, 2]])}
+    tokenizer.save_pretrained.side_effect = lambda path: (
+        Path(path) / "tokenizer_config.json"
+    ).write_text("{}", encoding="utf-8")
+    pipeline = AbliterationPipeline(
+        model_name="test-model", output_dir=str(tmp_path / "checkpoint"), method="basic",
+    )
+    pipeline.handle = ModelHandle(
+        model=model, tokenizer=tokenizer, config=config, model_name="test-model", task="causal_lm",
+    )
+    logs = []
+    pipeline._on_log = logs.append
+    pipeline._on_stage = lambda _record: None
+    pipeline._strong_layers = [] if outcome == "no-layers" else [0]
+    pipeline.refusal_directions = {0: torch.ones(8) / 8**0.5}
+    pipeline.refusal_subspaces = {0: pipeline.refusal_directions[0].unsqueeze(0)}
+    pipeline._bayesian_trials = 0 if outcome == "not-requested" else 1
+    # Both a previous skip and previous successful component overrides must be reset.
+    pipeline._bayesian_skipped = "previous-attempt"
+    pipeline._bayesian_attn_scale = 0.2
+    pipeline._bayesian_mlp_scale = 0.3
+
+    study = _FakeStudy(best_trials=[_FakeTrial(params={**_FakeTrial.params, "dir_idx": 0.0})])
+    _install_fake_optuna(monkeypatch, study)
+    monkeypatch.setattr(bo, "_measure_refusal_rate", lambda *_a, **_k: 0.25)
+    monkeypatch.setattr(bo, "_measure_kl_divergence", lambda *_a, **_k: 0.1)
+    if outcome == "missing":
+        real_import = builtins.__import__
+
+        def import_without_optuna(name, *args, **kwargs):
+            if name == "optuna" or name.startswith("optuna."):
+                raise ImportError("missing optuna")
+            return real_import(name, *args, **kwargs)
+
+        monkeypatch.setattr(builtins, "__import__", import_without_optuna)
+    elif outcome == "reference-failure":
+        tokenizer.side_effect = RuntimeError("tokenization failed")
+
+    pipeline._excise()
+    result = pipeline._rebirth()
+    metadata = json.loads((result / "abliteration_metadata.json").read_text())
+    assert metadata["method_config"]["bayesian_optimization_skipped"] == reason
+    fallback = [message for message in logs if "this checkpoint is NOT optimized" in message]
+    if reason:
+        assert len(fallback) == 1
+        assert reason in fallback[0]
+    else:
+        assert not fallback
+    if outcome != "success":
+        assert pipeline._bayesian_attn_scale is None
+        assert pipeline._bayesian_mlp_scale is None
+    if outcome == "success":
+        assert any("Bayesian optimization complete" in message for message in logs)
+    if outcome == "not-requested":
+        assert not study.enqueued
+
+
+def test_run_resets_bayesian_status_before_any_stage(monkeypatch, tmp_path):
+    from obliteratus.abliterate import AbliterationPipeline
+
+    pipeline = AbliterationPipeline(model_name="test-model", output_dir=str(tmp_path), method="basic")
+    pipeline._bayesian_skipped = "previous-attempt"
+    pipeline._bayesian_attn_scale = 0.2
+    pipeline._bayesian_mlp_scale = 0.3
+
+    def stages():
+        assert pipeline._bayesian_skipped is None
+        assert pipeline._bayesian_attn_scale is None
+        assert pipeline._bayesian_mlp_scale is None
+        return tmp_path
+
+    monkeypatch.setattr(pipeline, "_run_pipeline", stages)
+    assert pipeline.run() == tmp_path

tests/test_bayesian_optimizer_optuna.py

@@ -0,0 +1,95 @@
+"""Real, in-memory Optuna contracts with a tiny deterministic CPU model."""
+
+from types import SimpleNamespace
+
+import optuna
+import pytest
+import torch
+from torch import nn
+
+from obliteratus import bayesian_optimizer as bo
+from obliteratus.abliterate import AbliterationPipeline
+
+
+class _TinyModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        layer = nn.Module()
+        layer.self_attn = nn.Module()
+        layer.self_attn.o_proj = nn.Linear(2, 2, bias=False)
+        layer.mlp = nn.Module()
+        layer.mlp.down_proj = nn.Linear(2, 2, bias=False)
+        with torch.no_grad():
+            layer.self_attn.o_proj.weight.copy_(torch.tensor([[1., .2], [.3, 1.]]))
+            layer.mlp.down_proj.weight.copy_(torch.tensor([[.7, .1], [.4, .8]]))
+        self.model = nn.Module()
+        self.model.layers = nn.ModuleList([layer])
+        self.forward_calls = 0
+        self.generation_calls = 0
+
+    def forward(self, input_ids, **_kwargs):
+        self.forward_calls += 1
+        hidden = torch.nn.functional.one_hot(input_ids, num_classes=2).float()
+        for layer in self.model.layers:
+            hidden = layer.mlp.down_proj(layer.self_attn.o_proj(hidden))
+        return SimpleNamespace(logits=hidden)
+
+    def generate(self, input_ids, **_kwargs):
+        self.generation_calls += 1
+        token = self(input_ids).logits[:, -1, :].argmax(dim=-1, keepdim=True)
+        return torch.cat([input_ids, token], dim=1)
+
+
+class _TinyTokenizer:
+    chat_template = None
+
+    def __call__(self, *_args, **_kwargs):
+        return {"input_ids": torch.tensor([[0, 1]])}
+
+    def decode(self, tokens, **_kwargs):
+        return "I cannot assist with that request." if tokens[-1] == 0 else "Here is an explanation."
+
+
+@pytest.mark.parametrize("peak", [2 / 35, 34 / 35])
+def test_real_optuna_runs_bounded_warm_start_and_restores_model(monkeypatch, peak):
+    studies = []
+    create_study = optuna.create_study
+
+    def capture_study(**kwargs):
+        study = create_study(**kwargs)
+        studies.append(study)
+        return study
+
+    monkeypatch.setattr(optuna, "create_study", capture_study)
+    pipeline = AbliterationPipeline(model_name="tiny-local", method="optimized")
+    model = _TinyModel()
+    pipeline.handle = SimpleNamespace(model=model, tokenizer=_TinyTokenizer(), architecture="llama")
+    pipeline._strong_layers = [0]
+    pipeline.refusal_directions = {0: torch.tensor([1., 0.])}
+    pipeline.harmful_prompts = ["A local test prompt"]
+    pipeline._informed_warm_start = {"peak_position": peak, "max_weight": .5, "mlp_scale": .5}
+    # The fixture owns no accelerators or large allocations.
+    monkeypatch.setattr(pipeline, "_free_gpu_memory", lambda: None)
+    original = {name: tensor.clone() for name, tensor in model.state_dict().items()}
+
+    result = bo.run_bayesian_optimization(pipeline, n_trials=2, n_refusal_prompts=1, n_kl_prompts=1)
+
+    assert set(result) == {0}
+    assert 0 <= result[0] <= 1
+    assert len(studies) == 1
+    trials = studies[0].trials
+    assert len(trials) == 2
+    assert all(trial.state == optuna.trial.TrialState.COMPLETE for trial in trials)
+    expected_peak = .1 if peak < .1 else .9
+    assert trials[0].params["attn_peak_position"] == pytest.approx(expected_peak)
+    assert trials[0].params["mlp_peak_position"] == pytest.approx(expected_peak)
+    assert trials[0].params["mlp_max_weight"] == pytest.approx(.3)
+    for trial in trials:
+        assert all(bo.KERNEL_SPACE[name][0] <= trial.params[name] <= bo.KERNEL_SPACE[name][1]
+                   for name in bo.KERNEL_SPACE)
+        assert trial.values is not None and len(trial.values) == 2
+        assert all(torch.isfinite(torch.tensor(value)) for value in trial.values)
+    assert model.generation_calls == 2
+    assert model.forward_calls >= 5  # reference, generation and KL for each trial
+    for name, tensor in model.state_dict().items():
+        torch.testing.assert_close(tensor, original[name], rtol=0, atol=0)

uv.lock

@@ -3,8 +3,8 @@ revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "(python_full_version >= '3.14' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform == 'win32')",
-    "(python_full_version == '3.13.*' and sys_platform == 'linux') or (python_full_version == '3.13.*' and sys_platform == 'win32')",
     "python_full_version >= '3.14' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "(python_full_version == '3.13.*' and sys_platform == 'linux') or (python_full_version == '3.13.*' and sys_platform == 'win32')",
     "python_full_version == '3.13.*' and sys_platform != 'linux' and sys_platform != 'win32'",
     "(python_full_version == '3.12.*' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform == 'win32')",
     "python_full_version == '3.12.*' and sys_platform != 'linux' and sys_platform != 'win32'",
@@ -15,7 +15,7 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "2026-08-24T05:10:12.868014219Z"
+exclude-newer = "0001-01-01T00:00:00Z" # This has no effect and is included for backwards compatibility when using relative exclude-newer values.
 exclude-newer-span = "P3D"
 
 [manifest]
@@ -201,6 +201,21 @@ wheels = [
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 ]
 
+[[package]]
+name = "alembic"
+version = "1.19.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mako" },
+    { name = "sqlalchemy" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/34/10/181eecdd552217d0342492bd6f3b8a96e973083379aace3d3402830ddc03/alembic-1.19.2.tar.gz", hash = "sha256:297950a8a91f6770eb82bfbce9bea55c728b90a5386c6e81430191a319d138b0", size = 2082643, upload-time = "2026-09-04T17:10:11.212Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9c/cb/9014784dcb0585977ae23b6f43331d0a33c51ac0d692506d12b4f5ee9f3b/alembic-1.19.2-py3-none-any.whl", hash = "sha256:32d553dcd577e6fe5c3c63e91468526d35e4dcecafe865d7db4e9b328fa93cb2", size = 267399, upload-time = "2026-09-04T17:10:12.796Z" },
+]
+
 [[package]]
 name = "annotated-doc"
 version = "0.0.5"
@@ -436,7 +451,7 @@ name = "cffi"
 version = "2.1.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pycparser", marker = "(implementation_name != 'PyPy' and sys_platform == 'linux') or (implementation_name != 'PyPy' and sys_platform == 'win32')" },
+    { name = "pycparser", marker = "implementation_name != 'PyPy'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/9e/ef/008a1939e372c06329a3fce4279c02f328488f3526744906eeec3da7ad5f/cffi-2.1.1.tar.gz", hash = "sha256:dd31f52ea1086513bb9df30f8fcee9b8918323ae067a3d5b78bc826a000712be", size = 530807, upload-time = "2026-08-03T21:21:18.939Z" }
 wheels = [
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 ]
 
+[[package]]
+name = "colorlog"
+version = "6.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8c/55/ba79756cb90c8d69d599d57785398ac87bba7b19c80e87f4e8a562197c93/colorlog-6.12.0.tar.gz", hash = "sha256:2a7924c1dadf18b22a0eb8b06d1c7b01d5341707ec1641eb6fcc4fde0c3e8e5f", size = 18151, upload-time = "2026-07-23T13:40:40.71Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d4/19/0b6647bf5e331521e55d2b63bfbdc210bd9cd605189273f03614a05f702d/colorlog-6.12.0-py3-none-any.whl", hash = "sha256:30d392604e9110045a2c2aeefc27d7a017abbab63f3a8aee594eac0801df784e", size = 12239, upload-time = "2026-07-23T13:40:39.562Z" },
+]
+
 [[package]]
 name = "contourpy"
 version = "1.3.2"
@@ -603,7 +630,7 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" } },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" }
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 resolution-markers = [
     "(python_full_version >= '3.14' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform == 'win32')",
-    "(python_full_version == '3.13.*' and sys_platform == 'linux') or (python_full_version == '3.13.*' and sys_platform == 'win32')",
     "python_full_version >= '3.14' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "(python_full_version == '3.13.*' and sys_platform == 'linux') or (python_full_version == '3.13.*' and sys_platform == 'win32')",
     "python_full_version == '3.13.*' and sys_platform != 'linux' and sys_platform != 'win32'",
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     "python_full_version == '3.12.*' and sys_platform != 'linux' and sys_platform != 'win32'",
@@ -680,7 +707,7 @@ resolution-markers = [
     "python_full_version == '3.11.*' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.11.*'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
     { name = "numpy", version = "2.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
@@ -896,8 +923,8 @@ name = "cryptography"
 version = "50.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cffi", marker = "(platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (platform_python_implementation != 'PyPy' and sys_platform == 'win32')" },
-    { name = "typing-extensions", marker = "(python_full_version < '3.11' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'win32')" },
+    { name = "cffi", marker = "platform_python_implementation != 'PyPy'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/de/41/6cbdcf9142d00fe82836fbb51e503e58088575cf7a0fe1dbff6695bf0840/cryptography-50.0.0.tar.gz", hash = "sha256:eeac2acb5a20ed25e0ad6d1df9891a520b78b404266b6d11778f25d5d691a6c9", size = 880201, upload-time = "2026-07-31T14:25:10.11Z" }
 wheels = [
@@ -1002,7 +1029,7 @@ name = "exceptiongroup"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
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+[[package]]
+name = "greenlet"
+version = "3.5.5"
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