mirror of
https://github.com/wassname/lora-lite.git
synced 2026-06-27 15:15:55 +08:00
remove base_weight_fingerprint and test_lora_lite.py
- _base_weight_fingerprint was PiSSA-only defensive check that cluttered every save with per-target SHA256. If you load onto wrong base, you get wrong weights -- that's user error, not a library bug. - test_lora_lite.py deleted. All coverage lives in test_metamath_smoke.py which runs the real benchmark pipeline per variant.
This commit is contained in:
wassname
@@ -98,34 +98,12 @@ def detach(model: nn.Module) -> None:
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delattr(model, _ATTACHED_ATTR)
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def _base_weight_fingerprint(model: nn.Module) -> dict[str, str]:
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"""Per-target fingerprint of the (post-init) base weights so PiSSA-style
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variants that mutate `layer.weight` can fail loud on base mismatch.
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Uses a cheap fp32 sum-of-squares + shape signature; not cryptographic.
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"""
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state = getattr(model, _ATTACHED_ATTR, None)
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if state is None:
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return {}
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fp = {}
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for name, layer in model.named_modules():
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if not hasattr(layer, "_lora_variant"):
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continue
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if name not in state["targets"]:
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continue
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w = layer.weight.detach().to(torch.float32, copy=False)
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fp[name] = f"{tuple(w.shape)}|{float((w * w).sum()):.6e}"
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return fp
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def save(model: nn.Module, path: str) -> None:
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state = getattr(model, _ATTACHED_ATTR, None)
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if state is None:
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raise RuntimeError("no adapter attached; call attach() first")
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sd = {k: v.detach().cpu() for k, v in model.state_dict().items() if "lora_" in k}
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metadata = {
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"cfg": json.dumps(state["cfg"].to_dict()),
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"base_fp": json.dumps(_base_weight_fingerprint(model)),
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}
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metadata = {"cfg": json.dumps(state["cfg"].to_dict())}
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from safetensors.torch import save_file
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save_file(sd, path, metadata=metadata)
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@@ -145,14 +123,4 @@ def load(model: nn.Module, path: str) -> list[RemovableHandle]:
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unexpected_lora = [k for k in unexpected if "lora_" in k]
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if unexpected_lora:
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raise RuntimeError(f"unexpected lora keys in checkpoint: {unexpected_lora}")
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saved_fp = json.loads(metadata.get("base_fp", "{}"))
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if saved_fp:
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cur_fp = _base_weight_fingerprint(model)
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diffs = [k for k in saved_fp if saved_fp[k] != cur_fp.get(k)]
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if diffs:
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raise RuntimeError(
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f"base weight fingerprint mismatch on {len(diffs)} layer(s) "
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f"(e.g. {diffs[0]}). For PiSSA the saved adapter assumes the same "
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"base; reload onto the original model or re-run init."
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)
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return handles
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@@ -29,7 +29,12 @@ def _classify(m: nn.Module, d_model: int, name: str) -> str:
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if do == d_model and di != d_model:
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return "writer"
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if di == d_model and do == d_model:
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return "writer" if any(s in name for s in ("o_proj", "out_proj")) else "reader"
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if any(s in name for s in ("o_proj", "out_proj", "down_proj")):
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return "writer"
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elif any(s in name for s in ("q_proj", "k_proj", "v_proj", "kq_proj")):
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return "reader"
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else:
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raise ValueError(f"ambiguous role for {name} with in/out features {di}/{do}")
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return "inner"
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@@ -1,366 +0,0 @@
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"""Per-variant attach + train + save + load round-trip, plus surgical regressions.
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The big invariant is the parametrized train_save_load test: identity at t=0,
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gradient flow on a real loss, then save -> reload onto a fresh model and
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confirm the trained outputs survive the round-trip. Cheap on CPU.
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"""
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from __future__ import annotations
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from pathlib import Path
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import pytest
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import torch
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from torch import nn
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import lora_lite as ll
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CFG_BY_VARIANT = {
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"lora": ll.LoRAConfig,
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"pissa": ll.PiSSAConfig,
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"delora": ll.DeLoRAConfig,
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"ia3": ll.IA3Config,
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"ia3_ff": ll.IA3FFConfig,
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"dora": ll.DoRAConfig,
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"hra": ll.HRAConfig,
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"eva": ll.EVAConfig,
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"antipasto": ll.AntiPaSTOConfig,
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"road": ll.RoadConfig,
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}
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# Per-variant identity tolerance at t=0 (after attach, before any step).
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# fp32 SVD round-trip + per-row norm = looser tolerance for pissa/dora/antipasto.
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IDENTITY_TOL = {
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"lora": 5e-3, # near_zero B: B@A ~ sqrt(r)*eps*kaiming
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"pissa": 5e-4, # SVD round-trip
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"delora": 1e-6, # exact-zero B, lambda0-scaled
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"ia3": 5e-3, # near_one gate
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"ia3_ff": 5e-3, # near_one gate
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"dora": 5e-3, # near_zero B + m
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"hra": 1e-2, # near_zero U + paired-symmetry init
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"eva": 5e-4, # exact-zero B, SVD A overwritten in group_init
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"antipasto": 5e-4, # SVD round-trip
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"road": 5e-3, # near_zero theta
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}
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class TinyBlock(nn.Module):
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def __init__(self, d: int = 64, ff: int = 128):
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super().__init__()
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self.q_proj = nn.Linear(d, d, bias=False)
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self.k_proj = nn.Linear(d, d, bias=False)
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self.v_proj = nn.Linear(d, d, bias=False)
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self.o_proj = nn.Linear(d, d, bias=False)
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self.gate_proj = nn.Linear(d, ff, bias=False)
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self.up_proj = nn.Linear(d, ff, bias=False)
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self.down_proj = nn.Linear(ff, d, bias=False)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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h = self.o_proj(self.q_proj(x) + self.k_proj(x) + self.v_proj(x))
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m = self.down_proj(torch.nn.functional.silu(self.gate_proj(x)) * self.up_proj(x))
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return x + h + m
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class TinyModel(nn.Module):
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def __init__(self, n_layers: int = 4, d: int = 64, ff: int = 128, vocab: int = 100):
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super().__init__()
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self.embed_tokens = nn.Embedding(vocab, d)
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self.layers = nn.ModuleList([TinyBlock(d, ff) for _ in range(n_layers)])
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self.lm_head = nn.Linear(d, vocab, bias=False)
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self.config = type("Cfg", (), {"hidden_size": d})()
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def forward(self, ids: torch.Tensor) -> torch.Tensor:
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x = self.embed_tokens(ids)
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for block in self.layers:
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x = block(x)
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return self.lm_head(x)
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class FakeLinearLike(nn.Module):
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"""linear-like, but not nn.Linear: stand-in for bnb 4/8-bit modules."""
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def __init__(self, d_in: int = 8, d_out: int = 8):
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super().__init__()
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self.in_features = d_in
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self.out_features = d_out
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self.weight = nn.Parameter(torch.empty(d_out, d_in))
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nn.init.kaiming_uniform_(self.weight, a=5 ** 0.5)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return torch.nn.functional.linear(x, self.weight)
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class FakeBnbModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.config = type("Cfg", (), {"hidden_size": 8})()
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self.layers = nn.ModuleList([FakeLinearLike(8, 8)])
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.layers[0](x)
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def cfg_for(variant: str) -> ll.AdapterConfig:
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return CFG_BY_VARIANT[variant](
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r=4,
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alpha=8,
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dtype=torch.float32,
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)
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def attach_with_calib(model: nn.Module, cfg: ll.AdapterConfig, ids: torch.Tensor) -> None:
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if cfg.variant == "eva":
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calib = [ids for _ in range(2)]
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ll.attach(model, cfg, calibration_data=calib)
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else:
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ll.attach(model, cfg)
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def trainable_grad_norm(model: nn.Module) -> float:
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return sum(
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p.grad.detach().float().norm().item()
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for n, p in model.named_parameters()
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if "lora_" in n and p.grad is not None
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)
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@pytest.mark.parametrize("variant", list(CFG_BY_VARIANT))
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def test_train_save_load(variant: str, tmp_path: Path):
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"""Identity at t=0, one SGD step, save, reload onto fresh model, outputs match."""
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torch.manual_seed(0)
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model = TinyModel()
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ids = torch.randint(0, 100, (2, 16))
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with torch.no_grad():
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y_base = model(ids).clone()
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cfg = cfg_for(variant)
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attach_with_calib(model, cfg, ids)
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trainable = [p for p in model.parameters() if p.requires_grad]
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assert trainable
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assert all("lora_" in n for n, p in model.named_parameters() if p.requires_grad)
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with torch.no_grad():
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y_init = model(ids).clone()
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assert (y_init - y_base).abs().max().item() < IDENTITY_TOL[variant]
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target = torch.randn_like(y_init) * 0.1
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opt = torch.optim.SGD(trainable, lr=1e-2)
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opt.zero_grad()
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loss = (model(ids) - target).pow(2).mean()
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loss.backward()
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leaked = [n for n, p in model.named_parameters() if "lora_" not in n and p.grad is not None]
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assert leaked == []
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assert trainable_grad_norm(model) > 0
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opt.step()
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with torch.no_grad():
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y_trained = model(ids).clone()
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path = tmp_path / "adapter.pt"
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ll.save(model, str(path))
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torch.manual_seed(0)
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model_loaded = TinyModel()
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ll.load(model_loaded, str(path)) # EVA load skips group_init; calibration_data not needed
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with torch.no_grad():
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y_loaded = model_loaded(ids)
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assert (y_loaded - y_trained).abs().max().item() < max(IDENTITY_TOL[variant], 1e-5)
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@pytest.mark.parametrize("variant", ["lora", "delora", "ia3", "hra", "road"])
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def test_hook_only_variants_attach_to_non_linear_target(variant: str):
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"""bnb-style targets are linear-like but not nn.Linear; hook-only variants must accept them."""
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extra = {"lambda0": 0.1} if variant == "delora" else {"group_size": 8} if variant == "road" else {}
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cfg = CFG_BY_VARIANT[variant](r=2, alpha=4, dtype=torch.float32, target_roles=(), **extra)
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model = FakeBnbModel()
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ll.attach(model, cfg)
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x = torch.randn(2, 3, 8)
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model(x).pow(2).mean().backward()
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assert trainable_grad_norm(model) > 0
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@pytest.mark.parametrize("variant", ["pissa", "dora", "antipasto"])
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def test_weight_reading_variants_reject_non_linear(variant: str):
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r = 4 if variant == "antipasto" else 2 # antipasto needs r % block_size==0
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cfg = CFG_BY_VARIANT[variant](r=r, alpha=r, dtype=torch.float32, target_roles=())
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with pytest.raises(TypeError, match="plain nn.Linear"):
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ll.attach(FakeBnbModel(), cfg)
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def test_save_load_strict_keys(tmp_path: Path):
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import json
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from safetensors.torch import load_file, save_file
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torch.manual_seed(0)
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model = TinyModel()
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ll.attach(model, ll.LoRAConfig(r=4, alpha=8, dtype=torch.float32))
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p = tmp_path / "lora.safetensors"
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ll.save(model, str(p))
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sd = load_file(str(p), device="cpu")
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# missing key: drop first lora key
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missing_sd = dict(sd)
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dropped_key = next(iter(missing_sd))
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del missing_sd[dropped_key]
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from safetensors import safe_open
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with safe_open(str(p), framework="pt", device="cpu") as f:
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meta = f.metadata()
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save_file(missing_sd, str(p), metadata=meta)
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with pytest.raises(RuntimeError, match="missing lora keys"):
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ll.load(TinyModel(), str(p))
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# unexpected key: add a bogus lora key
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bad_sd = dict(sd)
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bad_sd["layers.0.q_proj.lora_extra"] = torch.zeros(1)
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save_file(bad_sd, str(p), metadata=meta)
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with pytest.raises(RuntimeError, match="unexpected lora keys"):
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ll.load(TinyModel(), str(p))
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def test_no_target_layers_is_loud():
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cfg = ll.LoRAConfig(target_names=("definitely_missing",))
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with pytest.raises(RuntimeError, match="no target layers"):
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ll.attach(TinyModel(), cfg)
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def test_eva_requires_calibration():
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"""EVA's group_init must error loudly if calibration_data is missing."""
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with pytest.raises(ValueError, match="calibration_data"):
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ll.attach(TinyModel(), ll.EVAConfig(r=4, alpha=8, dtype=torch.float32))
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def test_delora_default_has_live_step0_gradient():
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"""Default lambda0 must be nonzero; B=0 preserves identity while B gets gradient."""
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torch.manual_seed(0)
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model = TinyModel(n_layers=1)
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ids = torch.randint(0, 100, (2, 8))
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ll.attach(model, ll.DeLoRAConfig(r=4, alpha=8, dtype=torch.float32))
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assert model.layers[0].q_proj.lora_lambda.item() == pytest.approx(15.0)
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loss = model(ids).pow(2).mean()
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loss.backward()
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b_grad = model.layers[0].q_proj.lora_B.grad.detach().abs().max().item()
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assert b_grad > 0
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def test_pissa_identity_with_nonunit_scale():
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"""Regression: PiSSA must pre-divide S by alpha/r, not require alpha == r."""
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torch.manual_seed(0)
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model = TinyModel(n_layers=1)
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ids = torch.randint(0, 100, (2, 8))
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with torch.no_grad():
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y_base = model(ids).clone()
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ll.attach(model, ll.PiSSAConfig(r=4, alpha=8, dtype=torch.float32))
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with torch.no_grad():
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y = model(ids)
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assert (y - y_base).abs().max().item() < IDENTITY_TOL["pissa"]
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def test_antipasto_blockwise_rotation_matches_explicit_blockdiag():
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"""The einsum/rearrange path must equal the old explicit blockdiag math."""
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from lora_lite.variants.antipasto import _build_rotation
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torch.manual_seed(0)
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n_blocks, bs, d_in, d_out = 3, 4, 7, 5
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r = n_blocks * bs
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rot_T = torch.randn(n_blocks, bs * (bs - 1) // 2) * 0.1
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Vh = torch.randn(r, d_in)
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U = torch.randn(d_out, r)
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R_blocks = _build_rotation(rot_T, bs, 0.5)
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R = torch.block_diag(*list(R_blocks))
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Vh_blocks = torch.reshape(Vh, (n_blocks, bs, d_in))
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Vh_rot = torch.einsum("nab,nbi->nai", R_blocks, Vh_blocks).reshape(r, d_in)
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U_blocks = torch.reshape(U, (d_out, n_blocks, bs))
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U_rot = torch.einsum("dnb,ncb->dnc", U_blocks, R_blocks).reshape(d_out, r)
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assert (Vh_rot - R @ Vh).abs().max().item() < 1e-6
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assert (U_rot - U @ R.T).abs().max().item() < 1e-6
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def test_dora_bias_passthrough():
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"""Regression: DoRA must NOT scale bias; identity holds with bias=True at t=0."""
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torch.manual_seed(0)
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d = 16
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layer = nn.Linear(d, d, bias=True)
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x = torch.randn(2, d)
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y_base = layer(x).detach()
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class Wrap(nn.Module):
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def __init__(self, lin):
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super().__init__()
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self.config = type("Cfg", (), {"hidden_size": d})()
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self.layers = nn.ModuleList([lin])
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def forward(self, x):
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return self.layers[0](x)
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model = Wrap(layer)
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ll.attach(model, ll.DoRAConfig(r=2, alpha=4, dtype=torch.float32, target_roles=()))
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with torch.no_grad():
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y = model(x)
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assert (y - y_base).abs().max().item() < 5e-3 # near_zero B + m init
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def test_hra_forward_is_x_R_T():
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"""HRA must apply x @ R^T (loop i = r-1 down to 0). Asymmetric U makes order observable."""
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torch.manual_seed(0)
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d = 8
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layer = nn.Linear(d, d, bias=False)
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x = torch.randn(2, 3, d)
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class Wrap(nn.Module):
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def __init__(self, lin):
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super().__init__()
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self.config = type("Cfg", (), {"hidden_size": d})()
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self.layers = nn.ModuleList([lin])
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def forward(self, x):
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return self.layers[0](x)
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model = Wrap(layer)
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ll.attach(model, ll.HRAConfig(r=4, alpha=4, dtype=torch.float32, target_roles=()))
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# break paired symmetry so order matters
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with torch.no_grad():
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layer.lora_U.add_(0.1 * torch.randn_like(layer.lora_U))
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U = layer.lora_U
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R = torch.eye(d)
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for i in range(U.shape[0]):
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u = U[i]
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sq = (u * u).sum().clamp_min(1e-12)
|
||||
R = R - (2.0 / sq) * torch.outer(R @ u, u)
|
||||
with torch.no_grad():
|
||||
y_adapt = model(x)
|
||||
y_ref = torch.nn.functional.linear(x, layer.weight @ R)
|
||||
assert (y_adapt - y_ref).abs().max().item() < 1e-5
|
||||
|
||||
|
||||
@pytest.mark.parametrize("road_variant", ["road_1", "road_2", "road_4"])
|
||||
def test_road_apply_matches_explicit_matrix(road_variant: str):
|
||||
"""Fast elementwise ROAD path must match PEFT's explicit R @ y matrix construction."""
|
||||
from lora_lite.variants.road import _apply_road, _road_matrix, _road_param_size
|
||||
|
||||
torch.manual_seed(0)
|
||||
d_out = 16
|
||||
group_size = 8
|
||||
size = _road_param_size(d_out, road_variant)
|
||||
theta = torch.randn(size) * 0.2
|
||||
alpha = torch.randn(size) * 0.1 + 1.0
|
||||
y = torch.randn(2, 3, d_out)
|
||||
|
||||
y_fast = _apply_road(road_variant, group_size, theta, alpha, y)
|
||||
R = _road_matrix(road_variant, group_size, theta, alpha)
|
||||
y_ref = torch.einsum("oi,...i->...o", R, y)
|
||||
|
||||
assert (y_fast - y_ref).abs().max().item() < 1e-6
|
||||
|
||||
|
||||
def test_road_invalid_group_size_is_loud():
|
||||
with pytest.raises(ValueError, match="positive and even"):
|
||||
ll.attach(TinyModel(), ll.RoadConfig(group_size=7))
|
||||
with pytest.raises(ValueError, match="divisible"):
|
||||
ll.attach(TinyModel(), ll.RoadConfig(group_size=48))
|
||||
Reference in New Issue
Block a user