V3 external review (docs/audit/variants_review_v3.md, 97KB) found 3
must-fix bugs.
DoRA: bias was being scaled by m/||V|| because we operated on the full
base layer output. Now subtract bias before normalization, add back
after. Matches peft DoRA exactly (docs/refs/peft_lora_dora.py:157-161).
New smoke dora_bias_smoke verifies identity at t=0 with bias=True.
EVA load: adapter.load() called attach() which called group_init() which
required calibration_data and raised. Added _skip_group_init flag to
attach(); load() passes it. EVA group_init still raises loudly when
called directly without data. New smoke verifies save+load WITHOUT
calibration data on load path.
Also tightened EVA error message.
Smoke now covers 8 variants + EVA roundtrip + DoRA-bias roundtrip + bnb
4/8-bit. ALL PASS.
V3 nice-to-haves (PiSSA scaling, AntiPaSTO init choice, stale GH refs)
deferred -- documented as intentional in module docstrings.
DeLoRA: per-input-channel wnorm buffer (not scalar Parameter), forward
matches peft (x*wnorm @ A.T then per-rank scale (lambda/r)/(An*Bn)).
Smoke: 89.7% loss drop (was 35.8%).
HRA: symmetric repeated-column init (PEFT-style) instead of zero gate.
Adjacent Householder pairs cancel exactly so R=I at t=0, and U receives
gradient from step 0 (no dead-grad). Even r required.
IA3: split into two variants. ia3 stays output-side (k_proj/v_proj);
new ia3_ff is input-side (down_proj/fc2), matching peft is_feedforward.
Config: dropout field removed (never honored by any variant).
PiSSA: adapter.save records base-weight fingerprint per target;
adapter.load recomputes init then verifies fingerprint -> fails loud
when reloaded onto a different base.
EVA (new): data-driven init via group_init + calibration_data. Top-r
right singular vectors of pooled layer-input activations -> lora_A
(buffer, frozen); only lora_B trains. Stress-tests group_init API.
AntiPaSTO (new): SVD steering with frozen U,S,Vh,W_res and learnable
delta_s (per-singular-value bias) + rot_T (block-diagonal Cayley
rotation on V or U). Lite port of antipasto3 SVD adapter.
ParamSpec: as_buffer field + make_tensor() for buffer registration.
adapter.attach honors as_buffer with register_buffer; detach cleans
both _parameters and _buffers.
Smoke covers all 8 variants: identity at t=0, save/load round-trip,
gradient-driven loss drop. EVA gets dedicated test for calibration
data path. ALL PASS including bnb 4/8-bit path.
- Fetch canonical reference impls for offline review:
* peft_{lora,hra,delora,ia3}_layer.py + peft_lora_{dora,variants}.py
* orig_pissa_init.py (MuLabPKU/PiSSA)
* orig_hra_layer.py (DaShenZi721/HRA)
* orig_delora.py (ExplainableML/DeLoRA author fork)
- Add reference-impl URLs to all 6 variant docstrings
- Document HRA gate=0 dead-grad issue and DoRA detach-omission in their docstrings
- Re-run external review (codex) with refs available -> docs/audit/variants_review_v2.md
Major NEW findings vs paper-only review:
* DeLoRA: scalar W.norm() should be per-input-channel norm(dim=0)
* HRA: PEFT uses symmetric repeated-column init (no dead grad), not zero gate
* IA3: FFN targets need input-side gating, not output, our up_proj advice wrong
* All LoRA-family: cfg.dropout silently ignored (no-op)
* DeLoRA: wnorm should be persistent buffer, not Parameter
HRA and DeLoRA upgraded to BUGGY (from Partial)
copilot