| 1 | """Integration test: PEFT ``disable_adapter`` actually changes logits. |
| 2 | |
| 3 | This is the load-bearing sanity check for the whole differential design. |
| 4 | If a future ``peft`` release subtly breaks the disable-context semantics, |
| 5 | sway's KL / SIS / ablation probes would all silently report zero signal. |
| 6 | We catch that here, before the rest of the test battery runs. |
| 7 | |
| 8 | The test builds a random-init LoRA adapter on a tiny model so no network |
| 9 | dependency beyond the base model snapshot itself. |
| 10 | """ |
| 11 | |
| 12 | from __future__ import annotations |
| 13 | |
| 14 | from pathlib import Path |
| 15 | |
| 16 | import pytest |
| 17 | |
| 18 | from dlm_sway.backends.hf import HuggingFaceDifferentialBackend |
| 19 | from dlm_sway.core.model import ModelSpec |
| 20 | |
| 21 | pytestmark = [pytest.mark.slow, pytest.mark.online] |
| 22 | |
| 23 | |
| 24 | def _build_random_lora_adapter(base_dir: Path, out_dir: Path) -> None: |
| 25 | """Construct a LoRA adapter with random-init weights on ``base_dir``. |
| 26 | |
| 27 | The weights are kept small so the toggle-delta is clear but the |
| 28 | adapter is structurally valid (correct ``adapter_config.json``, |
| 29 | tokenizer files, safetensors layout). |
| 30 | """ |
| 31 | import torch |
| 32 | from peft import LoraConfig, get_peft_model |
| 33 | from transformers import AutoModelForCausalLM, AutoTokenizer |
| 34 | |
| 35 | torch.manual_seed(0) |
| 36 | |
| 37 | tokenizer = AutoTokenizer.from_pretrained(str(base_dir)) |
| 38 | if tokenizer.pad_token_id is None: |
| 39 | tokenizer.pad_token = tokenizer.eos_token |
| 40 | base = AutoModelForCausalLM.from_pretrained(str(base_dir), torch_dtype=torch.float32) |
| 41 | |
| 42 | cfg = LoraConfig( |
| 43 | r=8, |
| 44 | lora_alpha=16, |
| 45 | target_modules=["q_proj", "v_proj"], |
| 46 | lora_dropout=0.0, |
| 47 | bias="none", |
| 48 | task_type="CAUSAL_LM", |
| 49 | ) |
| 50 | peft_model = get_peft_model(base, cfg) |
| 51 | |
| 52 | # Explicitly scale lora_B out of its PEFT-default zero-init so the |
| 53 | # adapter actually changes outputs. Real training does this via |
| 54 | # gradients; we do it with a scaled normal. |
| 55 | with torch.no_grad(): |
| 56 | for name, param in peft_model.named_parameters(): |
| 57 | if "lora_B" in name: |
| 58 | param.copy_(torch.randn_like(param) * 0.05) |
| 59 | |
| 60 | peft_model.save_pretrained(str(out_dir)) |
| 61 | tokenizer.save_pretrained(str(out_dir)) |
| 62 | |
| 63 | |
| 64 | @pytest.fixture(scope="module") |
| 65 | def random_adapter(tiny_model_dir: Path, tmp_path_factory: pytest.TempPathFactory) -> Path: |
| 66 | adapter_dir = tmp_path_factory.mktemp("random-adapter") |
| 67 | _build_random_lora_adapter(tiny_model_dir, adapter_dir) |
| 68 | return adapter_dir |
| 69 | |
| 70 | |
| 71 | def test_disable_adapter_changes_logits( |
| 72 | tiny_model_dir: Path, random_adapter: Path |
| 73 | ) -> None: |
| 74 | """The keystone invariant: base view ≠ ft view on the same prompt.""" |
| 75 | import numpy as np |
| 76 | |
| 77 | backend = HuggingFaceDifferentialBackend( |
| 78 | base_spec=ModelSpec(base=str(tiny_model_dir), kind="hf", dtype="fp32", device="cpu"), |
| 79 | adapter_path=random_adapter, |
| 80 | ) |
| 81 | try: |
| 82 | prompt = "The quick brown fox" |
| 83 | with backend.as_base() as b: |
| 84 | base_dist = b.next_token_dist(prompt, top_k=32) |
| 85 | with backend.as_finetuned() as f: |
| 86 | ft_dist = f.next_token_dist(prompt, top_k=32) |
| 87 | |
| 88 | # Top-k indices may shift under the adapter; take a safe shared |
| 89 | # subset instead of asserting identical ordering. |
| 90 | assert not np.array_equal(base_dist.token_ids, ft_dist.token_ids) or not np.allclose( |
| 91 | base_dist.logprobs, ft_dist.logprobs, atol=1e-5 |
| 92 | ), "adapter toggle did not change next-token distribution" |
| 93 | finally: |
| 94 | backend.close() |
| 95 | |
| 96 | |
| 97 | def test_roundtrip_toggle_restores_base( |
| 98 | tiny_model_dir: Path, random_adapter: Path |
| 99 | ) -> None: |
| 100 | """as_base → as_finetuned → as_base yields a stable base view.""" |
| 101 | import numpy as np |
| 102 | |
| 103 | backend = HuggingFaceDifferentialBackend( |
| 104 | base_spec=ModelSpec(base=str(tiny_model_dir), kind="hf", dtype="fp32", device="cpu"), |
| 105 | adapter_path=random_adapter, |
| 106 | ) |
| 107 | try: |
| 108 | prompt = "hello" |
| 109 | with backend.as_base() as b: |
| 110 | first = b.next_token_dist(prompt, top_k=16).logprobs |
| 111 | with backend.as_finetuned() as f: |
| 112 | f.next_token_dist(prompt, top_k=16) # toggle |
| 113 | with backend.as_base() as b: |
| 114 | second = b.next_token_dist(prompt, top_k=16).logprobs |
| 115 | np.testing.assert_allclose(first, second, rtol=1e-5, atol=1e-6) |
| 116 | finally: |
| 117 | backend.close() |