diff --git a/README.md b/README.md index c1077f5..6bcd9b5 100644 --- a/README.md +++ b/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 . diff --git a/ci/supply-chain-policy.json b/ci/supply-chain-policy.json index b48f097..36c522d 100644 --- a/ci/supply-chain-policy.json +++ b/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", diff --git a/ci/test-risk-map.json b/ci/test-risk-map.json index 3fa6ec6..7ac835f 100644 --- a/ci/test-risk-map.json +++ b/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" ], diff --git a/docs/SUPPLY_CHAIN_POLICY.md b/docs/SUPPLY_CHAIN_POLICY.md index e834a57..cc71414 100644 --- a/docs/SUPPLY_CHAIN_POLICY.md +++ b/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 diff --git a/pyproject.toml b/pyproject.toml index 0bfa78c..cb394da 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,6 +34,7 @@ dependencies = [ "numpy>=1.24", "scikit-learn>=1.3", "tqdm>=4.64", + "optuna>=4.9,<5", ] [project.urls] diff --git a/requirements.txt b/requirements.txt index 5c0fdde..d985dd9 100644 --- a/requirements.txt +++ b/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 diff --git a/tests/test_bayesian_optimizer_optuna.py b/tests/test_bayesian_optimizer_optuna.py new file mode 100644 index 0000000..b9e305e --- /dev/null +++ b/tests/test_bayesian_optimizer_optuna.py @@ -0,0 +1,96 @@ +"""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) + diff --git a/uv.lock b/uv.lock index 6b81ec8..ebc5fd7 100644 --- a/uv.lock +++ 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