mirror of
https://github.com/wassname/lora-lite.git
synced 2026-06-27 18:05:16 +08:00
wassname
109 lines
5.8 KiB
Markdown
109 lines
5.8 KiB
Markdown
# lora-lite
|
|
|
|
Hackable PyTorch adapters for LoRA-family and small PEFT experiments.
|
|
|
|
## Hackable code
|
|
|
|
|
|
To keep it simple and hackable we make these choices:
|
|
|
|
- Simple forward hooks, no module replacement or custom modules.
|
|
- Simple code over fast performance
|
|
- No merge/unmerge
|
|
- Single test where we train on MetaMathQA and test on GSM8K for each variant
|
|
|
|
Take a look at [lora.py](src/lora_lite/variants/lora.py)
|
|
|
|
## Install
|
|
|
|
```bash
|
|
pip install -e git+https://github.com/wassname/lora-lite.git#egg=lora-lite
|
|
```
|
|
|
|
## Quickstart
|
|
|
|
```python
|
|
import torch, lora_lite as ll
|
|
|
|
model = MyTransformer()
|
|
cfg = ll.LoRAConfig(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.safetensors")
|
|
ll.detach(model)
|
|
ll.load(model, "adapter.safetensors")
|
|
```
|
|
|
|
## Does it work?
|
|
|
|
```bash
|
|
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
|
|
```
|
|
|
|
## Variants
|
|
|
|
Trained on a MetaMathQA subset, tested on GSM8K, all on `Qwen/Qwen3.5-0.8B-Base` targeting
|
|
`down_proj` in all 24 layers (2500 steps, effective batch 8 = 20k samples). Standard adapters
|
|
use r=32; the AntiPaSTO family uses r=256 (it tunes only S-space gain, so it needs the rank).
|
|
|
|
| Variant | test % | valid % | Params | +MACs/tok | fwd/bwd (ms) | init (s) |
|
|
| --------------------------------------------- | -----: | ------: | ------: | --------: | -----------: | -------: |
|
|
| [DoRA](https://arxiv.org/abs/2402.09353) | 60.2 | 68.0 | 3.56M | 3.54M | 161 / 556 | 0.16 |
|
|
| [LoRA](https://arxiv.org/abs/2106.09685) | 59.8 | 68.0 | 3.54M | 3.54M | 173 / 573 | 0.02 |
|
|
| [PiSSA](https://arxiv.org/abs/2404.02948) | 59.8 | 76.0 | 3.54M | 3.54M | 146 / 549 | 2.04 |
|
|
| [HRA](https://arxiv.org/abs/2405.17484) | 59.2 | 70.0 | 2.75M | 2.75M | 225 / 948 | 0.04 |
|
|
| [EVA](https://arxiv.org/abs/2410.07170) | 59.3 | 74.0 | 3.54M | 3.54M | 151 / 660 | 28.3 |
|
|
| [IA3-FF](https://arxiv.org/pdf/2205.05638) | 56.3 | 62.0 | 0.086M | 0M | 140 / 510 | 0.01 |
|
|
| [DeLoRA](https://arxiv.org/abs/2503.18225) | 56.2 | 62.0 | 3.54M | 3.54M | 169 / 593 | 0.21 |
|
|
| [AntiPaSTO](https://arxiv.org/abs/2601.07473) | 56.0 | 62.0 | 0.0061M | 28.3M | 166 / 571 | 2.5 |
|
|
| AntiPaSTO-rot | 57.2 | 60.0 | 0.0154M | 28.3M | 165 / 596 | 2.0 |
|
|
| AntiPaSTO-ablate | 56.0 | 68.0 | 0.0062M | 28.3M | 166 / 580 | 2.2 |
|
|
| AntiPaSTO-dplr | 56.0 | 64.0 | 0.1044M | 28.4M | 153 / 582 | 3.6 |
|
|
| AntiPaSTO-ASVD (diag C) | 55.6 | 64.0 | 0.0061M | 28.3M | 150 / 533 | 34 |
|
|
| AntiPaSTO-CorDA (full C) | 54.7 | 58.0 | 0.0061M | 28.3M | 146 / 576 | 120 |
|
|
| [IA3](https://arxiv.org/pdf/2205.05638) | 52.3 | 62.0 | 0.0061M | 0M | 161 / 515 | 0.01 |
|
|
|
|
test/valid % = GSM8K exact-match accuracy. Params = trainable adapter params. +MACs/tok = added
|
|
forward MACs per token (analytic, hardware-independent). fwd/bwd = median ms over one batch.
|
|
init = one-time calibration (CorDA's `d_in x d_in` covariance eigh; ~0 for the rest). Peak CUDA
|
|
memory is ~9.8 GB for every row. Empty rows fill in as the sweep lands.
|
|
|
|
We validate our adapters the same way [PEFT](https://github.com/huggingface/peft/tree/main/method_comparison) does: train on a MetaMathQA subset and check meaningful GSM8K accuracy. See [this file](scripts/metamath_gsm8k_benchmark.py) for details.
|
|
|
|
AntiPaSTO is the novel row here: instead of adding trainable directions like LoRA, it freezes W's own top-r SVD and learns only a bounded per-direction gain `S_eff = S * (1 + ELU(g))`. The singular basis stays fixed and interpretable, and the adapter is O(r) params (the 6.1K gain is ~580x smaller than LoRA's 3.54M). The variants change only the basis or core: rot learns a small block-rotation of the frozen basis, CorDA/ASVD orient it by the input second moment (full covariance vs diagonal-only, [Yang+ 2024](https://arxiv.org/abs/2406.05223) / [Yuan+ 2023](https://arxiv.org/abs/2312.05821)), ablate learns a contractive directional ablation ([Arditi+ 2024](https://arxiv.org/abs/2406.11717)), dplr adds a small low-rank core for cross-direction mixing.
|
|
|
|
CorDA (full C) and ASVD (diag C) are a metric-axis ablation against plain AntiPaSTO (C=I): does
|
|
covariance orientation earn its `d_in x d_in` eigh over the cheap diagonal or no calibration at
|
|
all? On GSM8K/down_proj the answer is no: C=I 56.0, diag C 55.6, full C 54.7 (single seed). The
|
|
off-diagonal orientation is the slowest arm (120 s init vs 2.5 s) and lands slightly *below* no
|
|
calibration, so plain top-r SVD is the right default for this bounded-gain adapter here.
|
|
|
|
AntiPaSTO-rot tunes that basis instead of the metric: a block-diagonal Cayley rotation of the
|
|
input (V), output (U), or both. The table row is V (the default); the ablation gives V 57.2 >
|
|
U 56.5 > both 55.6 (single seed). So rotating which inputs feed each frozen direction helps most,
|
|
the output-side rotation is slightly worse, and doing both is worst -- the second rotation is
|
|
redundant capacity that hurts. rot(V) is the best small-parameter arm overall (57.2 at 15K params
|
|
vs LoRA's 59.8 at 3.54M).
|
|
|
|
|
|
## Developer docs
|
|
|
|
See [docs/developer_guide.md](docs/developer_guide.md) for the variant API, data-calibrated init, and save/load format.
|
|
|
|
## Citation
|
|
|
|
```bibtex
|
|
@misc{wassname2026loralite,
|
|
title = {LoRA-Lite: A Hackable Adapter Library for Research},
|
|
author = {Michael J. Clark},
|
|
year = {2026},
|
|
url = {https://github.com/wassname/lora-lite/}
|
|
}
|
|
```
|
|
|