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| 1 | +"""Integration test: ``HF.as_scaled_adapter`` and the response-curve invariants. |
| 2 | + |
| 3 | +The adapter-ablation probe (the sway signature primitive) leans on |
| 4 | +``as_scaled_adapter(lam)`` to walk a λ sweep. Two things must hold: |
| 5 | + |
| 6 | +1. **Monotonicity of the *signal* across λ**: divergence at λ=1.25 |
| 7 | + should be strictly larger than divergence at λ=0 (which is base). |
| 8 | + We don't claim a smooth curve here — the unit tests on the probe |
| 9 | + itself cover curve shape — only that the scaling actually scales. |
| 10 | +2. **State restoration on exit**: every ``LoraLayer.scaling[adapter_name]`` |
| 11 | + value the context manager touched must be back to its original |
| 12 | + number after the ``with`` block. Anything else corrupts subsequent |
| 13 | + probes. |
| 14 | + |
| 15 | +Marked ``slow+online`` to share the tiny-model fixture with the rest |
| 16 | +of the integration suite. |
| 17 | +""" |
| 18 | + |
| 19 | +from __future__ import annotations |
| 20 | + |
| 21 | +from pathlib import Path |
| 22 | +from typing import Any |
| 23 | + |
| 24 | +import numpy as np |
| 25 | +import pytest |
| 26 | + |
| 27 | +from dlm_sway.backends.hf import HuggingFaceDifferentialBackend |
| 28 | +from dlm_sway.core.model import ModelSpec |
| 29 | +from dlm_sway.probes._divergence import divergence |
| 30 | + |
| 31 | +pytestmark = [pytest.mark.slow, pytest.mark.online] |
| 32 | + |
| 33 | + |
| 34 | +def _build_random_lora_adapter(base_dir: Path, out_dir: Path) -> None: |
| 35 | + """Same shape as the toggle-test adapter — a small but non-zero LoRA.""" |
| 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("scaled-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(tiny_model_dir: Path, random_adapter: Path) -> HuggingFaceDifferentialBackend: |
| 71 | + backend = HuggingFaceDifferentialBackend( |
| 72 | + base_spec=ModelSpec(base=str(tiny_model_dir), kind="hf", dtype="fp32", device="cpu"), |
| 73 | + adapter_path=random_adapter, |
| 74 | + ) |
| 75 | + yield backend |
| 76 | + backend.close() |
| 77 | + |
| 78 | + |
| 79 | +def _captured_scalings(backend: HuggingFaceDifferentialBackend) -> dict[tuple[int, str], float]: |
| 80 | + """Snapshot every ``LoraLayer.scaling[key]`` keyed by (id, key).""" |
| 81 | + import peft |
| 82 | + |
| 83 | + lora_cls: Any = peft.tuners.lora.LoraLayer |
| 84 | + out: dict[tuple[int, str], float] = {} |
| 85 | + for module in backend._peft_model.modules(): # type: ignore[attr-defined] |
| 86 | + if not isinstance(module, lora_cls): |
| 87 | + continue |
| 88 | + scaling = getattr(module, "scaling", None) |
| 89 | + if not isinstance(scaling, dict): |
| 90 | + continue |
| 91 | + for key, value in scaling.items(): |
| 92 | + out[(id(module), key)] = float(value) |
| 93 | + return out |
| 94 | + |
| 95 | + |
| 96 | +def test_lambda_sweep_monotonic_in_signal(hf_backend: HuggingFaceDifferentialBackend) -> None: |
| 97 | + """Divergence(@λ=0, @λ=1.25) > divergence(@λ=0, @λ=0) (which is 0).""" |
| 98 | + prompt = "The quick brown fox" |
| 99 | + with hf_backend.as_scaled_adapter(0.0) as v0: |
| 100 | + d0 = v0.next_token_dist(prompt, top_k=64) |
| 101 | + with hf_backend.as_scaled_adapter(1.25) as v_over: |
| 102 | + d_over = v_over.next_token_dist(prompt, top_k=64) |
| 103 | + |
| 104 | + div_at_zero = divergence(d0, d0, kind="js") |
| 105 | + div_at_overshoot = divergence(d0, d_over, kind="js") |
| 106 | + assert div_at_zero == pytest.approx(0.0, abs=1e-9), ( |
| 107 | + f"self-divergence at λ=0 should be ~0; got {div_at_zero}" |
| 108 | + ) |
| 109 | + assert div_at_overshoot > 1e-6, f"λ=1.25 should drift far from λ=0; got {div_at_overshoot}" |
| 110 | + |
| 111 | + |
| 112 | +def test_lambda_one_matches_finetuned_within_tolerance( |
| 113 | + hf_backend: HuggingFaceDifferentialBackend, |
| 114 | +) -> None: |
| 115 | + """``as_scaled_adapter(1.0)`` should be functionally identical to ``as_finetuned()``.""" |
| 116 | + prompt = "hello" |
| 117 | + with hf_backend.as_finetuned() as ft: |
| 118 | + d_ft = ft.next_token_dist(prompt, top_k=32) |
| 119 | + with hf_backend.as_scaled_adapter(1.0) as v1: |
| 120 | + d1 = v1.next_token_dist(prompt, top_k=32) |
| 121 | + np.testing.assert_allclose(d_ft.logprobs, d1.logprobs, rtol=1e-5, atol=1e-6) |
| 122 | + |
| 123 | + |
| 124 | +def test_scaling_restored_on_clean_exit(hf_backend: HuggingFaceDifferentialBackend) -> None: |
| 125 | + """Every LoraLayer.scaling[key] is back to its original value after exit.""" |
| 126 | + before = _captured_scalings(hf_backend) |
| 127 | + with hf_backend.as_scaled_adapter(0.42) as v: |
| 128 | + v.next_token_dist("anything", top_k=8) |
| 129 | + after = _captured_scalings(hf_backend) |
| 130 | + assert before == after, "scaling table not restored after as_scaled_adapter context" |
| 131 | + |
| 132 | + |
| 133 | +def test_scaling_restored_on_exception(hf_backend: HuggingFaceDifferentialBackend) -> None: |
| 134 | + """Same restoration invariant when the body raises.""" |
| 135 | + before = _captured_scalings(hf_backend) |
| 136 | + with pytest.raises(RuntimeError, match="boom"): |
| 137 | + with hf_backend.as_scaled_adapter(0.7): |
| 138 | + raise RuntimeError("boom") |
| 139 | + after = _captured_scalings(hf_backend) |
| 140 | + assert before == after, "scaling table not restored after exception inside the context" |
