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| 1 | +"""Integration test: ``external_perplexity`` end-to-end on a real tiny model. |
| 2 | + |
| 3 | +Runs the probe against SmolLM2-135M with a small random LoRA so both |
| 4 | +sides produce real rolling-logprob values. The test asserts three |
| 5 | +contracts: |
| 6 | + |
| 7 | +1. The probe terminates in a non-ERROR verdict (the real backend's |
| 8 | + ``rolling_logprob`` returns finite logprobs on natural English prose). |
| 9 | +2. The per-chunk delta array has the requested length and no NaNs. |
| 10 | +3. The null-calibration path lights up the ``z_score`` field in a |
| 11 | + two-probe suite (``null_adapter`` first, then ``external_perplexity``). |
| 12 | + |
| 13 | +Marked ``slow+online``. |
| 14 | +""" |
| 15 | + |
| 16 | +from __future__ import annotations |
| 17 | + |
| 18 | +import math |
| 19 | +from collections.abc import Iterator |
| 20 | +from pathlib import Path |
| 21 | + |
| 22 | +import numpy as np |
| 23 | +import pytest |
| 24 | + |
| 25 | +from dlm_sway.backends.hf import HuggingFaceDifferentialBackend |
| 26 | +from dlm_sway.core.model import ModelSpec |
| 27 | +from dlm_sway.core.result import Verdict |
| 28 | +from dlm_sway.probes.base import RunContext, build_probe |
| 29 | +from dlm_sway.suite.runner import run as run_suite |
| 30 | +from dlm_sway.suite.spec import SwaySpec |
| 31 | + |
| 32 | +pytestmark = [pytest.mark.slow, pytest.mark.online] |
| 33 | + |
| 34 | + |
| 35 | +def _build_random_lora_adapter(base_dir: Path, out_dir: Path) -> None: |
| 36 | + import torch |
| 37 | + from peft import LoraConfig, get_peft_model |
| 38 | + from transformers import AutoModelForCausalLM, AutoTokenizer |
| 39 | + |
| 40 | + torch.manual_seed(0) |
| 41 | + tokenizer = AutoTokenizer.from_pretrained(str(base_dir)) |
| 42 | + if tokenizer.pad_token_id is None: |
| 43 | + tokenizer.pad_token = tokenizer.eos_token |
| 44 | + base = AutoModelForCausalLM.from_pretrained(str(base_dir), torch_dtype=torch.float32) |
| 45 | + cfg = LoraConfig( |
| 46 | + r=8, |
| 47 | + lora_alpha=16, |
| 48 | + target_modules=["q_proj", "v_proj"], |
| 49 | + lora_dropout=0.0, |
| 50 | + bias="none", |
| 51 | + task_type="CAUSAL_LM", |
| 52 | + ) |
| 53 | + peft_model = get_peft_model(base, cfg) |
| 54 | + with torch.no_grad(): |
| 55 | + for name, param in peft_model.named_parameters(): |
| 56 | + if "lora_B" in name: |
| 57 | + param.copy_(torch.randn_like(param) * 0.05) |
| 58 | + peft_model.save_pretrained(str(out_dir)) |
| 59 | + tokenizer.save_pretrained(str(out_dir)) |
| 60 | + |
| 61 | + |
| 62 | +@pytest.fixture(scope="module") |
| 63 | +def random_adapter(tiny_model_dir: Path, tmp_path_factory: pytest.TempPathFactory) -> Path: |
| 64 | + adapter_dir = tmp_path_factory.mktemp("ext-ppl-random-adapter") |
| 65 | + _build_random_lora_adapter(tiny_model_dir, adapter_dir) |
| 66 | + return adapter_dir |
| 67 | + |
| 68 | + |
| 69 | +@pytest.fixture(scope="module") |
| 70 | +def hf_backend( |
| 71 | + tiny_model_dir: Path, random_adapter: Path |
| 72 | +) -> Iterator[HuggingFaceDifferentialBackend]: |
| 73 | + backend = HuggingFaceDifferentialBackend( |
| 74 | + base_spec=ModelSpec(base=str(tiny_model_dir), kind="hf", dtype="fp32", device="cpu"), |
| 75 | + adapter_path=random_adapter, |
| 76 | + ) |
| 77 | + yield backend |
| 78 | + backend.close() |
| 79 | + |
| 80 | + |
| 81 | +def test_probe_runs_on_real_backend(hf_backend: HuggingFaceDifferentialBackend) -> None: |
| 82 | + probe, spec = build_probe( |
| 83 | + { |
| 84 | + "name": "ext_ppl", |
| 85 | + "kind": "external_perplexity", |
| 86 | + "max_chunks": 2, |
| 87 | + "chunk_chars": 512, |
| 88 | + } |
| 89 | + ) |
| 90 | + ctx = RunContext(backend=hf_backend) |
| 91 | + result = probe.run(spec, ctx) |
| 92 | + assert result.verdict != Verdict.ERROR, f"probe errored: {result.message}" |
| 93 | + assert result.raw is not None |
| 94 | + assert math.isfinite(result.raw) |
| 95 | + per_chunk = result.evidence["per_chunk_delta"] |
| 96 | + assert len(per_chunk) == 2 |
| 97 | + assert all(math.isfinite(d) for d in per_chunk) |
| 98 | + assert np.all(np.isfinite(np.asarray(per_chunk, dtype=np.float64))) |
| 99 | + |
| 100 | + |
| 101 | +def test_null_calibration_lights_up_zscore(hf_backend: HuggingFaceDifferentialBackend) -> None: |
| 102 | + """null_adapter → external_perplexity produces a z_score end-to-end.""" |
| 103 | + raw_spec = SwaySpec.model_validate( |
| 104 | + { |
| 105 | + "version": 1, |
| 106 | + "models": { |
| 107 | + "base": {"base": "placeholder"}, |
| 108 | + "ft": {"base": "placeholder", "adapter": "/tmp/placeholder"}, |
| 109 | + }, |
| 110 | + "suite": [ |
| 111 | + # Two null seeds keep runtime bounded; std just has to be |
| 112 | + # non-zero for the z-score path to engage. |
| 113 | + {"name": "null", "kind": "null_adapter", "runs": 2, "cache": False}, |
| 114 | + { |
| 115 | + "name": "ext", |
| 116 | + "kind": "external_perplexity", |
| 117 | + "max_chunks": 2, |
| 118 | + "chunk_chars": 512, |
| 119 | + "assert_z_gte": -100.0, # permissive — sign/magnitude is adapter-specific |
| 120 | + }, |
| 121 | + ], |
| 122 | + } |
| 123 | + ) |
| 124 | + result = run_suite(raw_spec, hf_backend) |
| 125 | + assert len(result.probes) == 2 |
| 126 | + null_result = result.probes[0] |
| 127 | + ext_result = result.probes[1] |
| 128 | + assert null_result.verdict == Verdict.PASS |
| 129 | + assert ext_result.verdict != Verdict.ERROR |
| 130 | + assert ext_result.z_score is not None, ( |
| 131 | + f"external_perplexity should have z-scored against null baseline; " |
| 132 | + f"evidence={ext_result.evidence}, message={ext_result.message}" |
| 133 | + ) |
| 134 | + assert math.isfinite(ext_result.z_score) |
