ci: add publishable check workflow

This commit is contained in:
wassname
2026-04-26 17:09:47 +08:00
parent 69bf5f4e44
commit f2d9021511
7 changed files with 589 additions and 56 deletions
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@@ -0,0 +1,17 @@
name: ci
on:
push:
pull_request:
jobs:
check:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: astral-sh/setup-uv@v6
- uses: extractions/setup-just@v2
- uses: actions/setup-python@v5
with:
python-version: "3.11"
- run: just check
+3
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@@ -8,6 +8,9 @@ __pycache__/
*.sqlite3 *.sqlite3
*.log *.log
*.bak *.bak
build/
dist/
*.egg-info/
logs/ logs/
outputs/ outputs/
tests/_artifacts/ tests/_artifacts/
+55 -28
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@@ -2,24 +2,7 @@
A hackable, single-file-per-variant LoRA library built on PyTorch forward hooks. A hackable, single-file-per-variant LoRA library built on PyTorch forward hooks.
<!-- Human this is too long! should be into, code, procedure to get started. why, what is it, an example of a hackable adapter, then minimal features, how to install and use, and links/citation --> The goal is not to be PEFT-compatible. The goal is to make adapter ideas easy to read, edit, test, and throw away.
- ~600 LoC total
- One file per variant, ~50 LoC each
- No module replacement, no merge/unmerge, no PEFT config soup
- Save = `torch.save({cfg, state_dict_filtered_by_'lora_'})`
- LoRA/DeLoRA forward hooks work with `nn.Linear` and bnb-style `Linear{4bit,8bitLt}` modules that expose `in_features`, `out_features`, and `weight`.
- PiSSA is fp-only in v1 because it mutates `weight` into `W_res`; quantized PiSSA needs explicit dequantize/requantize.
Currently shipped variants:
| Variant | Class | File |
|---|---|---|
| LoRA | A (additive) | [src/lora_lite/variants/lora.py](src/lora_lite/variants/lora.py) |
| PiSSA ([Meng+ 2024](https://arxiv.org/abs/2404.02948)) | A + B (special init mutates W) | [src/lora_lite/variants/pissa.py](src/lora_lite/variants/pissa.py) |
| DeLoRA ([Bini+ 2025](https://arxiv.org/abs/2503.18225)) | A (additive, normalised) | [src/lora_lite/variants/delora.py](src/lora_lite/variants/delora.py) |
See [docs/spec/20260426_lora_lite_plan.md](docs/spec/20260426_lora_lite_plan.md) for goals, status, TODOs, and the current design plan. The original broader design was stress-tested against the [adapters_as_hypotheses](https://github.com/wassname/adapters_as_hypotheses) catalog (~26/27 variants covered with 3 small API tweaks).
## Install ## Install
@@ -34,25 +17,55 @@ import torch, lora_lite as ll
model = MyTransformer() # any nn.Module containing linear-like children model = MyTransformer() # any nn.Module containing linear-like children
cfg = ll.LoraLiteConfig(variant="lora", r=8, alpha=16, dtype=torch.bfloat16) cfg = ll.LoraLiteConfig(variant="lora", r=8, alpha=16, dtype=torch.bfloat16)
handles = ll.attach(model, cfg) ll.attach(model, cfg)
# train opt = torch.optim.AdamW([p for p in model.parameters() if p.requires_grad], lr=1e-4)
trainable = [p for p in model.parameters() if p.requires_grad] # train...
opt = torch.optim.AdamW(trainable, lr=1e-4)
# ... your loop ...
ll.save(model, "adapter.pt") ll.save(model, "adapter.pt")
ll.detach(model) ll.detach(model)
# later:
ll.load(model, "adapter.pt") ll.load(model, "adapter.pt")
``` ```
Inspect a tensor live: Inspect a live adapter tensor directly:
```python ```python
A = model.layers[5].self_attn.q_proj.lora_A # just an nn.Parameter A = model.layers[5].self_attn.q_proj.lora_A
``` ```
## Core idea
Each variant owns the adapter math. The runtime only finds target layers, attaches `lora_*` parameters, registers hooks, and saves full-path adapter keys.
```python
def attach(model, cfg):
targets find_linear_like_modules(model, cfg)
freeze(model.parameters())
for name, layer in targets:
layer.lora_* variant.param_specs(layer, cfg)
hook(layer, lambda x, y: variant.forward(layer, x, y))
def save(model, path):
torch.save({"cfg": cfg, "state": state_dict_keys_containing("lora_")}, path)
```
Minimal by design:
- One file per variant.
- No module replacement, merge/unmerge, mixed-adapter batches, or PEFT config soup.
- LoRA/DeLoRA hooks work with `nn.Linear` and bnb-style `Linear{4bit,8bitLt}` modules that expose `in_features`, `out_features`, and `weight`.
- PiSSA is fp-only because it mutates `weight` into `W_res`; quantized PiSSA should fail loudly until dequantize/requantize is explicit.
Currently shipped variants:
| Variant | Class | File |
|---|---|---|
| LoRA | A (additive) | [src/lora_lite/variants/lora.py](src/lora_lite/variants/lora.py) |
| PiSSA ([Meng+ 2024](https://arxiv.org/abs/2404.02948)) | A + B (special init mutates W) | [src/lora_lite/variants/pissa.py](src/lora_lite/variants/pissa.py) |
| DeLoRA ([Bini+ 2025](https://arxiv.org/abs/2503.18225)) | A (additive, normalised) | [src/lora_lite/variants/delora.py](src/lora_lite/variants/delora.py) |
See [docs/spec/20260426_lora_lite_plan.md](docs/spec/20260426_lora_lite_plan.md) for goals, status, and the current design plan. The original broader design was stress-tested against the [adapters_as_hypotheses](https://github.com/wassname/adapters_as_hypotheses) catalog (~26/27 variants covered with 3 small API tweaks).
## Targeting ## Targeting
By default we target linear-like modules (`in_features`, `out_features`, `weight`) whose shape matches a "reader" (`d_in == d_model`) or "writer" (`d_out == d_model`) role, excluding `lm_head` and `embed_tokens`. This structural test is what lets bnb Linear4bit/8bitLt modules be targeted without a backend-specific class. Knobs on `LoraLiteConfig`: By default we target linear-like modules (`in_features`, `out_features`, `weight`) whose shape matches a "reader" (`d_in == d_model`) or "writer" (`d_out == d_model`) role, excluding `lm_head` and `embed_tokens`. This structural test is what lets bnb Linear4bit/8bitLt modules be targeted without a backend-specific class. Knobs on `LoraLiteConfig`:
@@ -137,9 +150,10 @@ class ActSVD:
## Smoke test ## Smoke test
```bash ```bash
just check # pytest + smoke + package build
just test just test
just smoke just smoke
just qwen-probe just qwen-queue # queued Qwen/Qwen3-0.6B proof via pueue
``` ```
`just test` verifies, for each of `lora`, `pissa`, `delora`: `just test` verifies, for each of `lora`, `pissa`, `delora`:
@@ -161,6 +175,8 @@ just qwen-probe
This is an interface/training proof, not a benchmark: exact Qwen target names, hook activity, lora-only gradients, loss decrease, adapter tensor save/load, and reload equivalence on a 0.6B HF model. This is an interface/training proof, not a benchmark: exact Qwen target names, hook activity, lora-only gradients, loss decrease, adapter tensor save/load, and reload equivalence on a 0.6B HF model.
CI runs `just check` on GitHub. The larger Qwen proof stays in `pueue` because it needs the shared GPU.
## What's NOT in v1 ## What's NOT in v1
| Feature | Why dropped | | Feature | Why dropped |
@@ -176,4 +192,15 @@ This is an interface/training proof, not a benchmark: exact Qwen target names, h
## Status ## Status
v0.0.1: lora + pissa + delora + smoke test. See spec for next variants (DoRA, VeRA, SSVD). v0.0.1: LoRA + PiSSA + DeLoRA + minimal functional tests + Qwen proof. Next likely variants are IA3 or DoRA because they fit the current hook contract with little new machinery. OFT/ROAD/AntiPaSTO-style methods are more interesting, but should wait until the simple hook family is boring.
## 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/}
}
```
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@@ -108,6 +108,45 @@ Failure-mode interpretation:
- If adapter grads were absent, `grad` or `dθ` would be 0/non-finite. - If adapter grads were absent, `grad` or `dθ` would be 0/non-finite.
- If save/load were broken, adapter tensor equality or reload logit error would fail. - If save/load were broken, adapter tensor equality or reload logit error would fail.
## 2026-04-26 publishable workflow pass
Goal: make the repo behave like a small buildable library without adding framework surface area.
### Scope
In:
- Keep minimal functional integration tests as the default proof path.
- Add a GitHub CI hook for fast tests and package build.
- Add a `pueue` recipe for the larger Qwen proof.
- Tighten README structure so install, quickstart, core idea, tests, and status are easy to find.
Out:
- PyPI publishing token/workflow. Publishing should wait until the license decision is explicit.
- Implementing every named adapter variant in one pass. That would add complexity faster than tests can explain it.
### Requirements and evidence
| Requirement | Distinguishing check | Evidence |
|---|---|---|
| R7: fast CI catches broken tests/builds | `just check` must run pytest, smoke, `uv build`, and `twine check`; a broken test, wheel, sdist, or README metadata fails the hook. | `just check` -> pytest `8 passed in 9.53s`, smoke all pass, wheel/sdist built, `twine check dist/*` passed |
| R8: large proof is queued, not hidden in CI | `just qwen-queue` must create a pueue task in the repo cwd with why/resolve label and intended Qwen command. | `just qwen-queue && pueue status` -> task 74 queued at `/media/wassname/SGIronWolf/projects5/2026/lora-lite` with `just qwen-probe lora pissa delora 16` |
| R9: README is publishable enough to judge | Reader sees install, quickstart, pseudocode core, testing commands, proof caveat before variant wishlist. | `README.md` reordered and human note removed |
| R10: variant roadmap buys simplicity | Next variant is ranked by fit to current hook contract; non-hook variants are deferred rather than half-supported. | this section |
Fresh review first blocked on weak `qwen-queue` evidence and README citation/comment junk. Fixes: queued real pueue task 74, added `twine check`, fenced citation, removed the stray README note. Final fresh review verdict: PASS.
### Adapter roadmap, ranked by simplicity
| Variant | Why it fits or waits | Next check |
|---|---|---|
| IA3 | Multiplicative vector on activations. Probably the smallest new file and no base-weight mutation. | Identity with ones, perturb changes output, loss drops, save/load exact. |
| DoRA | Fits additive hook for fp layers; bnb norm handling must be explicit or fail-fast. | fp smoke first; quantized proof only after norm semantics are obvious. |
| SSVD / PiSSA-family | Fits current `weight`-SVD pattern and teaches the SVD adapter path. | Reconstruction/identity invariant plus train proof. |
| HRA / OFT / ROAD | Interesting, but likely wants orthogonal or weight-transform semantics. Keep until hook-only formulation is clear. | Pseudocode first, then one invariant that distinguishes real rotation from dead code. |
| S-steer / AntiPaSTO | Research adapters. Should use `group_init` and activation evidence, not be squeezed into plain LoRA tests. | Calibration is consumed, hooks removed, load does not need calibration data. |
## Review history ## Review history
A cold subagent review first returned `PASS_WITH_BLOCKERS`: A cold subagent review first returned `PASS_WITH_BLOCKERS`:
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@@ -3,11 +3,26 @@ set shell := ["bash", "-cu"]
default: default:
@just --list @just --list
check: test smoke build
test: test:
uv run --extra test pytest -q uv run --extra test pytest -q
smoke: smoke:
uv run --extra test python tests/smoke.py uv run --extra test python tests/smoke.py
qwen-probe: build:
uv run --extra test --extra hf-test python scripts/qwen_train_probe.py rm -rf dist
uv build
uv run --extra build twine check dist/*
qwen-probe variants="lora pissa delora" steps="8":
uv run --extra test --extra hf-test python scripts/qwen_train_probe.py --variants {{variants}} --steps {{steps}}
qwen-queue variants="lora pissa delora" steps="16":
#!/usr/bin/env bash
set -euo pipefail
pueue add \
-l "why: verify Qwen0.6B train/save-load proof for {{variants}} at {{steps}} steps; resolve: publish docs only if exact targets, lora-only grads, loss drop, reload pass" \
-w "$PWD" -o 1 -- \
just qwen-probe "{{variants}}" "{{steps}}"
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@@ -2,14 +2,28 @@
name = "lora-lite" name = "lora-lite"
version = "0.0.1" version = "0.0.1"
description = "Hackable forward-hook LoRA. ~600 LoC. One file per variant." description = "Hackable forward-hook LoRA. ~600 LoC. One file per variant."
readme = "README.md"
requires-python = ">=3.10" requires-python = ">=3.10"
dependencies = [ dependencies = [
"torch>=2.1", "torch>=2.1",
"einops>=0.7", "einops>=0.7",
"jaxtyping>=0.2.25", ]
keywords = ["lora", "pytorch", "peft", "adapters", "llm"]
classifiers = [
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
] ]
[project.urls]
Homepage = "https://github.com/wassname/lora-lite"
Repository = "https://github.com/wassname/lora-lite"
Issues = "https://github.com/wassname/lora-lite/issues"
[project.optional-dependencies] [project.optional-dependencies]
build = ["twine>=6"]
test = ["pytest", "tabulate"] test = ["pytest", "tabulate"]
hf-test = ["accelerate>=1.6", "safetensors>=0.5", "transformers>=4.51"] hf-test = ["accelerate>=1.6", "safetensors>=0.5", "transformers>=4.51"]
Generated
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exclude-newer-span = "P5D" exclude-newer-span = "P5D"
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