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https://github.com/wassname/lora-lite.git
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wassname
87 lines
3.4 KiB
Markdown
87 lines
3.4 KiB
Markdown
# Developer guide
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This is the implementation note for people adding adapter variants. The README is only for prospective users.
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## Design principles
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- Variants own adapter math.
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- The runtime owns targeting, parameter attachment, hooks, and save/load.
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- Adapter parameters live directly on target layers as `lora_*` parameters.
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- Save/load uses normal full-path `state_dict()` keys filtered by `"lora_"`.
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- Fail loudly on unsupported weight semantics. No silent quantized PiSSA or merge fallback.
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## Variant contract
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A variant is a registered class with a small static interface:
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```python
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@register
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class MyVariant:
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name = "myvariant"
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@staticmethod
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def param_specs(d_in, d_out, cfg) -> dict[str, ParamSpec]:
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return {"lora_A": ParamSpec((cfg.r, d_in), init="kaiming")}
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@staticmethod
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def init(layer, cfg) -> None:
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...
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@staticmethod
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def forward(layer, x, y):
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return y_new
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```
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Pseudocode for the runtime:
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```python
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def attach(model, cfg):
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targets ← find_linear_like_modules(model, cfg)
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freeze(model.parameters())
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for name, layer in targets:
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layer.lora_* ← variant.param_specs(layer, cfg)
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variant.init(layer, cfg)
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hook(layer, lambda x, y: variant.forward(layer, x, y))
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def save(model, path):
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torch.save({"cfg": cfg, "state": state_dict_keys_containing("lora_")}, path)
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```
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## Data-calibrated init
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LoRA, PiSSA, DeLoRA, and IA3 only use `layer.weight` or identity constants for init.
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Variants that need data, e.g. AntiPaSTO, LoRA-GA, or activation-aware SVD, should keep dataloaders out of `cfg` so adapter checkpoints stay serializable:
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```python
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ll.attach(model, cfg, calibration_data=calib)
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```
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Activation-aware variants implement `group_init(model, targets, cfg, calibration_data)`. The variant may add temporary hooks, run calibration batches, remove hooks, then write `lora_*` params. `load()` should not require calibration data.
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## Current limitations
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| Feature | Current choice |
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|---|---|
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| merge/unmerge | reload the base model if vanilla weights are needed |
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| multiple named adapters | one variant per `attach()` |
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| mixed-adapter batches | out of scope until needed |
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| quantized PiSSA | fail-fast; explicit dequantize/requantize required |
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| AdaLoRA rank scheduling | needs a future `Variant.on_step(step)` hook |
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| ReFT-style interventions | likely a sibling module or different hook site |
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## Adapter roadmap
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| Variant | Fit to current runtime | Status |
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|---|---|---|
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| LoRA | Hook-only additive low-rank. | Done. Tested. |
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| PiSSA | Mutates `layer.weight` into `W_res`; identity via SVD round-trip. | Done. fp-only. Tested. |
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| DeLoRA | Per-input-channel weight-norm scale, per-rank A/B normalization, learned `lambda`. | Done. Tested. |
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| IA3 / IA3_FF | Output gate (k/v) and input gate (down_proj) variants, init to ones. | Done. Tested. |
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| DoRA | Reads dense `weight` for `||V||_c`; bias passes through unscaled. | Done. fp-only. Tested. |
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| HRA | Householder product applied via `forward_input` pre-hook; bnb-friendly. | Done. Tested. |
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| EVA | LoRA forward; `lora_A` init from PCA on calibration activations via `group_init`. | Done. fp-only. Tested. |
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| AntiPaSTO | Top-r weight SVD, learnable singular-value deltas + block-diagonal Cayley rotation. | Done. fp-only. Tested. |
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| SSVD | Could fit the weight-SVD init path. | Planned. |
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| OFT / ROAD | Block-diagonal rotations; needs clearer hook-only formulation. | Planned. |
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