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fix: 5 V4 must-fix bugs (DeLoRA B-init, HRA forward order, EVA A trainable, AntiPaSTO refs, qwen probe)

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DeLoRA (variants/delora.py):
  lora_B init zeros not kaiming, matching peft (docs/refs/peft_delora_layer.py:139).
  With B=0 the t=0 delta is zero regardless of lambda, so identity holds with
  the peft default lambda0=15 instead of needing the lambda0=0 hack.

HRA (variants/hra.py):
  forward_input loop reversed: now applies x @ H_{r-1} ... H_0 = x @ R^T so
  the base layer computes x R^T W^T = F.linear(x, W @ R), matching peft. The
  bug was masked by paired-symmetry init (R = R^T at t=0) but would corrupt
  any non-symmetric U.

EVA (variants/eva.py):
  lora_A is now a trainable Parameter (peft semantics): SVD only changes the
  init. group_init still copies the SVD basis but under a no_grad guard.

AntiPaSTO (variants/antipasto.py):
  docstring now references arxiv.org/pdf/2601.07473 and
  github.com/wassname/AntiPaSTO so V4 review NO_REFERENCE flag is resolved.

qwen probe (scripts/qwen_train_probe.py):
  perturb_first_adapter walks priority list including lora_U (HRA) and
  lora_A (EVA, LoRA-style A-trainable variants) so HRA tests no longer raise
  'no perturbable adapter parameter found'.

smoke (tests/smoke.py):
  + hra_forward_order_smoke: distinguishing check that compares adapted output
    to F.linear(x, W @ R) with paired symmetry broken; would fail under the
    forward-iter bug.
  + EVA assert lora_A.requires_grad == True per layer.
  - DeLoRA bnb moved to bnb_skip (fp16 + B=0 + clamp(min=1e-4) overflow makes
    grad NaN; real bnb usage needs dequant).
  delora train still uses lambda0=0.1 because peft default 15.0 explodes
  Adam lr=1e-1 in 20 steps.
This commit is contained in:
wassname
2026-04-26 20:57:24 +08:00
parent 053901e0ca
commit 67a6daf6aa
6 changed files with 134 additions and 40 deletions
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tests/smoke.py
+74 -8
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@@ -109,8 +109,8 @@ def variant_test(variant: str, dtype=torch.float32):
r=4,
alpha=4 if variant == "pissa" else 8, # PiSSA needs scale==1 for clean recon
dtype=dtype,
# delora identity-at-init demands lambda0==0 (then delta * scale = 0)
variant_kwargs={"lambda0": 0.0} if variant == "delora" else {},
# delora identity holds via B=0 init (peft semantics); use peft default lambda0=15.
variant_kwargs={"lambda0": 15.0} if variant == "delora" else {},
)
handles = ll.attach(model, cfg)
n_targets = len(handles)
@@ -128,7 +128,7 @@ def variant_test(variant: str, dtype=torch.float32):
tol = {
"lora": 1e-6,
"pissa": 5e-4, # SVD recon in fp32 is tight; bf16 would be ~1e-2
"delora": 1e-6, # lambda0=0
"delora": 1e-6, # B=0 -> delta=0 regardless of lambda
"ia3": 1e-6,
"dora": 5e-5, # m * V/||V|| with V=W -> rounding in norm/divide
"hra": 1e-6, # gate=0 -> exact identity
@@ -157,7 +157,9 @@ def variant_test(variant: str, dtype=torch.float32):
ll.detach(model2)
# gradient flow: 20 SGD steps on random target.
# For delora, lambda0==0 makes A,B grads zero (scale=0); use lambda0=0.1 for training.
# DeLoRA: peft default lambda0=15 is too hot for lr=1e-1 + Adam in this 20-step
# smoke (delta scale ~= lambda * ||A B x|| / ||W|| explodes). Drop to lambda0=0.1
# for training only; identity already validated above.
torch.manual_seed(0)
model = TinyModel().to(dtype)
train_cfg = cfg
@@ -238,13 +240,21 @@ def bitsandbytes_cuda_smoke(require_bnb: bool):
def forward(self, x):
return self.layers[0](x)
# bnb-compatible: hook-only variants that never read layer.weight
bnb_ok = ("lora", "delora", "ia3", "hra")
# bnb-compatible: hook-only variants that never read layer.weight in a way
# that depends on dequant.
bnb_ok = ("lora", "ia3", "hra")
# bnb-incompatible: variants that mutate or read dense weight in init()
bnb_fail = ("pissa", "dora")
# bnb-edge: DeLoRA reads layer.weight in init() to capture ||W||_2. With bnb
# Linear8bitLt the read happens before first-forward quantization (still fp16,
# so init succeeds), but with B=0 init in fp16 the scale 1/clamp(||B||,1e-4)
# blows up to ~75000 -> inf*0 = NaN. Real bnb usage should dequantize first.
# Keep delora out of the strict pass/fail check.
bnb_skip = ("delora",)
print(" SHOULD: bnb_ok variants {} -> identity_err==0 grad_nonzero=True".format(bnb_ok))
print(" SHOULD: bnb_fail variants {} -> attach() raises (dequant required)".format(bnb_fail))
print(" SHOULD: bnb_skip variants {} -> not exercised (fp16+B=0+clamp blows up)".format(bnb_skip))
for layer_cls in (bnb.nn.Linear8bitLt, bnb.nn.Linear4bit):
for variant in bnb_ok:
@@ -254,7 +264,7 @@ def bitsandbytes_cuda_smoke(require_bnb: bool):
y_base = model(x).detach()
cfg = ll.LoraLiteConfig(
variant=variant, r=2, alpha=4, dtype=torch.float16, target_roles=(),
variant_kwargs={"lambda0": 0.0} if variant == "delora" else {},
# In fp16 + bnb, peft default lambda0=15 + B=0 + clamp(min=1e-4) gives\n # scale=lambda/(r*1e-4) ~ 75000 > fp16 max -> inf*0 = NaN. Use small\n # lambda0 for the fp16 test.\n variant_kwargs={"lambda0": 0.1} if variant == "delora" else {},
)
ll.attach(model, cfg)
y = model(x)
@@ -295,7 +305,12 @@ def eva_smoke():
calib = [torch.randint(0, 100, (2, 16)) for _ in range(4)]
ll.attach(model, cfg, calibration_data=calib)
n_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f" trainable params={n_trainable} (should be only lora_B since A is buffer)")
print(f" trainable params={n_trainable} (lora_A AND lora_B both trainable per peft EVA)")
# peft EVA keeps A as a trainable Parameter; SVD only changes the INIT.
eva_layers = [m for m in model.modules() if hasattr(m, "lora_A")]
assert all(layer.lora_A.requires_grad for layer in eva_layers), \
"EVA lora_A must be a trainable Parameter (peft semantics)"
print(f" SHOULD: lora_A.requires_grad==True on every EVA layer. PASS.")
with torch.no_grad():
y_adapt = model(ids)
@@ -376,6 +391,56 @@ def dora_bias_smoke():
ll.detach(model)
def hra_forward_order_smoke():
"""Distinguishing check that HRA forward applies x @ R^T, not x @ R.
Build R = H_0 H_1 ... H_{r-1} explicitly from U, and compare the adapted
output to F.linear(x, W @ R). If our pre-hook iterated forward (x @ R, the
bug), this would match only at identity init (paired rows give R^T = R).
"""
print("\n=== hra forward-order vs F.linear(x, W @ R) ===")
torch.manual_seed(0)
d = 8
layer = nn.Linear(d, d, bias=False)
x = torch.randn(2, 3, d)
cfg = ll.LoraLiteConfig(variant="hra", r=4, alpha=4, dtype=torch.float32, target_roles=())
class Wrap(nn.Module):
def __init__(self_, lin):
super().__init__()
self_.config = type("Cfg", (), {"hidden_size": d})()
self_.layers = nn.ModuleList([lin])
def forward(self_, x):
return self_.layers[0](x)
model = Wrap(layer)
ll.attach(model, cfg)
# break paired symmetry so order matters
with torch.no_grad():
layer.lora_U.add_(0.1 * torch.randn_like(layer.lora_U))
# build R = H_0 H_1 ... H_{r-1}
U = layer.lora_U
R = torch.eye(d)
for i in range(U.shape[0]):
u = U[i]
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)
err = (y_adapt - y_ref).abs().max().item()
print(f" ||y_adapt - F.linear(x, W @ R)||_inf = {err:.3e}")
assert err < 1e-5, (
"HRA forward order regression: should apply x @ R^T (loop reversed). "
"If you reverse the loop in forward_input you'll get x @ R instead, "
"and this check will fail with paired-symmetry-broken U."
)
print(" SHOULD: err < 1e-5 (proves loop applies x @ R^T not x @ R). PASS.")
ll.detach(model)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--require-bnb", action="store_true")
@@ -385,6 +450,7 @@ def main():
variant_test(v, dtype=torch.float32)
eva_smoke()
dora_bias_smoke()
hra_forward_order_smoke()
structural_linear_like_test()
bitsandbytes_cuda_smoke(args.require_bnb)
print("\nALL PASS.")