wassname
fdb4c77d6c
- 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)
lora-lite
Hackable PyTorch adapters for LoRA-family and small PEFT experiments.
lora-lite uses forward hooks instead of module replacement. Adapter parameters are plain nn.Parameters on the target layer, e.g. model.layers[5].self_attn.q_proj.lora_A.
Install
pip install -e git+https://github.com/wassname/lora-lite.git#egg=lora-lite
Quickstart
import torch, lora_lite as ll
model = MyTransformer()
cfg = ll.LoraLiteConfig(variant="lora", r=8, alpha=16, dtype=torch.bfloat16)
ll.attach(model, cfg)
opt = torch.optim.AdamW([p for p in model.parameters() if p.requires_grad], lr=1e-4)
# train...
ll.save(model, "adapter.pt")
ll.detach(model)
ll.load(model, "adapter.pt")
Does it work?
just check # pytest + smoke + package build + metadata check
just bnb-smoke # required CUDA bitsandbytes 4bit/8bit smoke
just qwen-probe # Qwen/Qwen3-0.6B train/save-load probe
See docs/spec/20260426_lora_lite_plan.md for verification history and exact results.
Variants
| Variant | Support | Notes |
|---|---|---|
| LoRA | yes | additive low-rank adapter |
| PiSSA | yes, fp only | mutates weight into W_res; quantized PiSSA intentionally fails |
| DeLoRA | yes | normalized additive adapter with learned scalar |
| IA3 | yes | output gate initialized to ones |
| DoRA | yes, fp only | reads dense weight for column-norm; quantized DoRA fails loudly |
| HRA | yes | output-side Householder reflection with identity gate; works on bnb |
| SSVD / OFT / ROAD | no | planned |
| S-steer / AntiPaSTO | no | should use data-calibrated group_init, not plain LoRA tests |
Targeting
By default, lora-lite targets linear-like modules with in_features, out_features, and weight, excluding lm_head and embed_tokens.
Useful LoraLiteConfig fields:
target_roles: subset of("reader", "writer", "inner");()means all.target_names: regex includes.exclude_names: regex excludes.layers: layer indices, matching.layers.<idx>.in module names.
This structural targeting is why LoRA, DeLoRA, and IA3 can run on bnb-style Linear4bit/Linear8bitLt modules. PiSSA is different because it edits the base weight.
Save format
Adapters are just:
torch.save({"cfg": cfg.to_dict(), "state": lora_state_dict}, "adapter.pt")
lora_state_dict contains full-path keys with "lora_" in the name. Missing or unexpected adapter keys fail on load.
Developer docs
See docs/developer_guide.md for the variant API, data-calibrated init, and adapter roadmap.
Citation
@misc{wassname2026loralite,
title = {LoRA-Lite: A Hackable Adapter Library for Research},
author = {Michael J. Clark},
year = {2026},
url = {https://github.com/wassname/lora-lite/}
}