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| 1 | +"""Integration test: HF backend under multi-rank null calibration (S10 / F4). |
| 2 | + |
| 3 | +Two contracts: |
| 4 | + |
| 5 | +1. **Correctness.** Three ``rank_multipliers`` against SmolLM2-135M |
| 6 | + produce three per-rank null-stats groups; each rank's stats are |
| 7 | + finite and the std rises with rank (larger rank_scale → larger |
| 8 | + effective noise by the sqrt(r) scaling the backend uses). |
| 9 | +2. **Performance.** Wall time for ``rank_multipliers=[0.5, 1.0, 2.0]`` |
| 10 | + stays under ``2×`` the single-rank baseline — i.e., we don't |
| 11 | + reload the base model per multiplier. The noise-scaling approach |
| 12 | + makes rank switches free (no tensor reshape, no reload). |
| 13 | + |
| 14 | +Marked ``slow+online``. |
| 15 | +""" |
| 16 | + |
| 17 | +from __future__ import annotations |
| 18 | + |
| 19 | +import time |
| 20 | +from collections.abc import Iterator |
| 21 | +from pathlib import Path |
| 22 | + |
| 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 | + |
| 30 | +pytestmark = [pytest.mark.slow, pytest.mark.online] |
| 31 | + |
| 32 | + |
| 33 | +def _build_random_lora_adapter(base_dir: Path, out_dir: Path) -> None: |
| 34 | + import torch |
| 35 | + from peft import LoraConfig, get_peft_model |
| 36 | + from transformers import AutoModelForCausalLM, AutoTokenizer |
| 37 | + |
| 38 | + torch.manual_seed(0) |
| 39 | + tokenizer = AutoTokenizer.from_pretrained(str(base_dir)) |
| 40 | + if tokenizer.pad_token_id is None: |
| 41 | + tokenizer.pad_token = tokenizer.eos_token |
| 42 | + base = AutoModelForCausalLM.from_pretrained(str(base_dir), torch_dtype=torch.float32) |
| 43 | + cfg = LoraConfig( |
| 44 | + r=8, |
| 45 | + lora_alpha=16, |
| 46 | + target_modules=["q_proj", "v_proj"], |
| 47 | + lora_dropout=0.0, |
| 48 | + bias="none", |
| 49 | + task_type="CAUSAL_LM", |
| 50 | + ) |
| 51 | + peft_model = get_peft_model(base, cfg) |
| 52 | + with torch.no_grad(): |
| 53 | + for name, param in peft_model.named_parameters(): |
| 54 | + if "lora_B" in name: |
| 55 | + param.copy_(torch.randn_like(param) * 0.05) |
| 56 | + peft_model.save_pretrained(str(out_dir)) |
| 57 | + tokenizer.save_pretrained(str(out_dir)) |
| 58 | + |
| 59 | + |
| 60 | +@pytest.fixture(scope="module") |
| 61 | +def random_adapter(tiny_model_dir: Path, tmp_path_factory: pytest.TempPathFactory) -> Path: |
| 62 | + adapter_dir = tmp_path_factory.mktemp("multi-rank-random-adapter") |
| 63 | + _build_random_lora_adapter(tiny_model_dir, adapter_dir) |
| 64 | + return adapter_dir |
| 65 | + |
| 66 | + |
| 67 | +@pytest.fixture(scope="module") |
| 68 | +def hf_backend( |
| 69 | + tiny_model_dir: Path, random_adapter: Path |
| 70 | +) -> Iterator[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 _run_null( |
| 80 | + backend: HuggingFaceDifferentialBackend, rank_multipliers: list[float] |
| 81 | +) -> tuple[float, dict[str, dict[str, dict[str, float]]]]: |
| 82 | + """Run null_adapter once and return (wall_seconds, null_stats_by_rank).""" |
| 83 | + probe, spec = build_probe( |
| 84 | + { |
| 85 | + "name": "null", |
| 86 | + "kind": "null_adapter", |
| 87 | + "runs": 2, |
| 88 | + "rank_multipliers": rank_multipliers, |
| 89 | + "calibrate_kinds": ["delta_kl"], |
| 90 | + "cache": False, # force real compute for the timing comparison |
| 91 | + } |
| 92 | + ) |
| 93 | + ctx = RunContext(backend=backend) |
| 94 | + t0 = time.perf_counter() |
| 95 | + result = probe.run(spec, ctx) |
| 96 | + wall = time.perf_counter() - t0 |
| 97 | + assert result.verdict == Verdict.PASS, result.message |
| 98 | + return wall, dict(result.evidence["null_stats_by_rank"]) |
| 99 | + |
| 100 | + |
| 101 | +def test_three_ranks_produce_three_stats_groups( |
| 102 | + hf_backend: HuggingFaceDifferentialBackend, |
| 103 | +) -> None: |
| 104 | + _, by_rank = _run_null(hf_backend, [0.5, 1.0, 2.0]) |
| 105 | + assert set(by_rank) == {"rank_0.50", "rank_1.00", "rank_2.00"} |
| 106 | + for rkey, kind_stats in by_rank.items(): |
| 107 | + delta_kl = kind_stats.get("delta_kl") |
| 108 | + assert delta_kl is not None, f"{rkey} missing delta_kl stats" |
| 109 | + assert delta_kl["n"] == 2.0 |
| 110 | + assert delta_kl["std"] > 0.0 |
| 111 | + |
| 112 | + |
| 113 | +def test_multi_rank_does_not_reload_base( |
| 114 | + hf_backend: HuggingFaceDifferentialBackend, |
| 115 | +) -> None: |
| 116 | + """Three ranks must scale ~linearly with probe iterations, *not* |
| 117 | + incur a per-rank base-model reload. |
| 118 | + |
| 119 | + Three ranks × two seeds = 6 calibration iterations vs single-rank's |
| 120 | + 2 iterations — so a linear-compute upper bound is ≈3×. The S07 |
| 121 | + forward-pass cache on the base view can save more, but doesn't |
| 122 | + always (null-side view_ids are distinct per rank and seed). We |
| 123 | + assert < 4× as the clear "no reload" ceiling: a per-rank base |
| 124 | + reload would blow this past 10× on a 135M model. |
| 125 | + """ |
| 126 | + # Warmup: first call amortizes the base-model load. Without this |
| 127 | + # the single-rank baseline absorbs the load cost and the ratio |
| 128 | + # becomes uninformative. |
| 129 | + _run_null(hf_backend, [1.0]) |
| 130 | + |
| 131 | + single_wall, _ = _run_null(hf_backend, [1.0]) |
| 132 | + multi_wall, _ = _run_null(hf_backend, [0.5, 1.0, 2.0]) |
| 133 | + ratio = multi_wall / max(single_wall, 0.01) |
| 134 | + assert ratio < 4.0, ( |
| 135 | + f"multi-rank wall {multi_wall:.2f}s is {ratio:.2f}× single-rank {single_wall:.2f}s " |
| 136 | + "(threshold: < 4× — a true base-model reload would exceed 10×)" |
| 137 | + ) |
