From b179771cc67a9bfd65ad857a51b097350bbda4ed Mon Sep 17 00:00:00 2001 From: wassname <1103714+wassname@users.noreply.github.com> Date: Mon, 27 Apr 2026 06:23:30 +0800 Subject: [PATCH] tyro and benchmark --- README.md | 5 +- justfile | 51 +- pyproject.toml | 1 + scripts/qwen_train_probe.py | 214 ----- src/lora_lite/__init__.py | 25 +- src/lora_lite/adapter.py | 10 +- src/lora_lite/config.py | 55 +- src/lora_lite/variant.py | 6 +- src/lora_lite/variants/antipasto.py | 25 +- src/lora_lite/variants/delora.py | 13 +- src/lora_lite/variants/dora.py | 8 + src/lora_lite/variants/eva.py | 8 + src/lora_lite/variants/hra.py | 8 + src/lora_lite/variants/ia3.py | 18 +- src/lora_lite/variants/lora.py | 8 + src/lora_lite/variants/pissa.py | 8 + tests/smoke.py | 45 +- tests/test_lora_lite.py | 68 +- tests/test_metamath_gsm8k_benchmark.py | 37 + uv.lock | 1216 +++++++++++++++++++++++- 20 files changed, 1504 insertions(+), 325 deletions(-) delete mode 100644 scripts/qwen_train_probe.py create mode 100644 tests/test_metamath_gsm8k_benchmark.py diff --git a/README.md b/README.md index fdb9240..ddc17ac 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ pip install -e git+https://github.com/wassname/lora-lite.git#egg=lora-lite import torch, lora_lite as ll model = MyTransformer() -cfg = ll.LoraLiteConfig(variant="lora", r=8, alpha=16, dtype=torch.bfloat16) +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) @@ -54,7 +54,8 @@ See [docs/spec/20260426_lora_lite_plan.md](docs/spec/20260426_lora_lite_plan.md) By default, `lora-lite` targets linear-like modules with `in_features`, `out_features`, and `weight`, excluding `lm_head` and `embed_tokens`. -Useful `LoraLiteConfig` fields: +Useful `AdapterConfig` fields (shared across all variants; subclasses add +variant-specific knobs like `lambda0` on `DeLoRAConfig`): - `target_roles`: subset of `("reader", "writer", "inner")`; `()` means all. - `target_names`: regex includes. diff --git a/justfile b/justfile index 75d64e6..9793fec 100644 --- a/justfile +++ b/justfile @@ -19,13 +19,56 @@ build: uv build uv run --extra build twine check dist/* -qwen-probe variants="lora pissa delora ia3" steps="8": - uv run --extra test --extra hf-test python scripts/qwen_train_probe.py --variants {{variants}} --steps {{steps}} +qwen-probe variants="lora pissa delora ia3" steps="5": + #!/usr/bin/env bash + set -euo pipefail + for variant in {{variants}}; do + uv run --extra benchmark python scripts/metamath_gsm8k_benchmark.py \ + --mode probe \ + --model Qwen/Qwen3-0.6B-Base \ + --variant "$variant" \ + --steps {{steps}} \ + --batch-size 1 \ + --batch-size-eval 10 \ + --max-train-samples 32 \ + --max-eval-samples 10 \ + --max-new-tokens 32 \ + --max-seq-length 384 \ + --r 4 \ + --alpha 8 \ + --layers 0 \ + --lr 5e-3 \ + --target-name 'model\.layers\.0\.self_attn\.(q_proj|v_proj)$' + done qwen-queue variants="lora pissa delora ia3" 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 only if exact targets, lora-only grads, loss drop, reload identity" \ + -l "why: verify Qwen0.6B train/save-load proof for {{variants}} at {{steps}} steps via benchmark probe mode; resolve: publish only if exact layer0 q/v targets, lora-only grads, perturb>0, reloadGSM8K benchmark for {{model}} {{variant}} at {{steps}} steps; resolve: result JSON under outputs/metamath_gsm8k proves grad>0 dθ>0 base_grad_leaks=0 and reports valid/test accuracy" \ + -w "$PWD" -o 1 -- \ + uv run --extra benchmark python scripts/metamath_gsm8k_benchmark.py --model {{model}} --variant {{variant}} --steps {{steps}} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 43eca99..61cde92 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ build = ["twine>=6"] test = ["pytest", "tabulate", "beartype>=0.18"] hf-test = ["accelerate>=1.6", "safetensors>=0.5", "transformers>=4.51"] bnb-test = ["bitsandbytes>=0.46"] +benchmark = ["accelerate>=1.6", "datasets>=3.6", "safetensors>=0.5", "tabulate", "transformers>=4.51", "tyro>=0.9"] [build-system] requires = ["setuptools>=68"] diff --git a/scripts/qwen_train_probe.py b/scripts/qwen_train_probe.py deleted file mode 100644 index ec3b389..0000000 --- a/scripts/qwen_train_probe.py +++ /dev/null @@ -1,214 +0,0 @@ -from __future__ import annotations - -import argparse -import gc -import math -from pathlib import Path - -import torch -from tabulate import tabulate -from transformers import AutoModelForCausalLM, AutoTokenizer - -import lora_lite as ll - - -PROMPT = "LoRA-lite probe: Paris is the capital of France. The answer is" -EXPECTED_TARGETS = { - "model.layers.0.self_attn.q_proj", - "model.layers.0.self_attn.v_proj", -} - - -def cfg_for_variant(variant: str, dtype: torch.dtype, r: int, alpha: float) -> ll.LoraLiteConfig: - return ll.LoraLiteConfig( - variant=variant, - r=r, - alpha=r if variant == "pissa" else alpha, - dtype=dtype, - target_roles=(), - target_names=(r"model\.layers\.0\.self_attn\.(q_proj|v_proj)$",), - layers=(0,), - variant_kwargs={"lambda0": 0.1} if variant == "delora" else {}, - ) - - -def adapter_state(model: torch.nn.Module) -> dict[str, torch.Tensor]: - return {k: v.detach().clone() for k, v in model.state_dict().items() if "lora_" in k} - - -def assert_only_lora_trainable(model: torch.nn.Module) -> None: - trainable = [name for name, p in model.named_parameters() if p.requires_grad] - assert trainable - assert all("lora_" in name for name in trainable), trainable[:20] - - -def assert_no_base_grads(model: torch.nn.Module) -> None: - leaked = [name for name, p in model.named_parameters() if "lora_" not in name and p.grad is not None] - assert leaked == [], leaked[:20] - - -def perturb_first_adapter(model: torch.nn.Module) -> None: - """Nudge one trainable adapter parameter so forward output changes. - - Walks through trainable lora_* params in a priority order designed to keep - the perturbation small and well-defined per variant: - - identity-breakers first (lora_lambda, lora_gate) where adding to a scalar - directly scales the delta; - - then "outer" matrices set to zero at init (lora_B, lora_g) where bumping - one entry creates a rank-1 perturbation; - - lora_U for HRA (Householder vectors -- bumping breaks the paired - cancellation and tilts the rotation away from identity); - - lora_A for EVA / LoRA-style variants where A is trainable and B starts - at zero, so we still need a way to break identity once any perturbation - propagates. - """ - priority = ("lora_lambda", "lora_gate", "lora_B", "lora_g", "lora_U", "lora_A") - for key in priority: - for name, p in model.named_parameters(): - if not p.requires_grad: - continue - if key in name: - with torch.no_grad(): - if p.ndim == 0: - p.add_(0.25) - else: - p.flatten()[0].add_(0.25) - return - raise AssertionError("no perturbable adapter parameter found") - - -def load_model(model_id: str, dtype: torch.dtype, device: str): - model = AutoModelForCausalLM.from_pretrained(model_id, dtype=dtype).to(device) - model.config.use_cache = False - return model - - -def run_variant(args, variant: str, input_ids: torch.Tensor, labels: torch.Tensor, dtype: torch.dtype): - model = load_model(args.model, dtype, args.device) - model.train() - cfg = cfg_for_variant(variant, dtype, args.r, args.alpha) - - with torch.no_grad(): - logits_base = model(input_ids=input_ids).logits.detach().clone() - - ll.attach(model, cfg) - attached_targets = set(getattr(model, "_lora_lite_attached")["targets"]) - assert attached_targets == EXPECTED_TARGETS, attached_targets - assert_only_lora_trainable(model) - - with torch.no_grad(): - logits_init = model(input_ids=input_ids).logits.detach().clone() - identity_err = (logits_init - logits_base).abs().max().item() - - clean_adapter = adapter_state(model) - perturb_first_adapter(model) - with torch.no_grad(): - perturb_delta = (model(input_ids=input_ids).logits - logits_init).abs().max().item() - assert perturb_delta > 1e-7, perturb_delta - for name, value in clean_adapter.items(): - model.state_dict()[name].copy_(value) - - opt = torch.optim.AdamW([p for p in model.parameters() if p.requires_grad], lr=args.lr) - with torch.no_grad(): - loss0 = model(input_ids=input_ids, labels=labels).loss.item() - - before_train = adapter_state(model) - first_grad_norm = math.nan - loss_last = math.nan - for step in range(args.steps): - opt.zero_grad() - loss = model(input_ids=input_ids, labels=labels).loss - loss.backward() - assert_no_base_grads(model) - grad_norm = sum( - p.grad.detach().float().norm().item() - for name, p in model.named_parameters() - if "lora_" in name and p.grad is not None - ) - assert math.isfinite(grad_norm), grad_norm - if step == 0: - first_grad_norm = grad_norm - opt.step() - loss_last = loss.item() - - after_train = adapter_state(model) - adapter_delta = sum((after_train[k] - before_train[k]).float().norm().item() for k in before_train) - assert first_grad_norm > 0, first_grad_norm - assert adapter_delta > 0, adapter_delta - assert loss_last < loss0, (loss0, loss_last) - - model.eval() - with torch.no_grad(): - logits_trained = model(input_ids=input_ids).logits.detach().clone() - - out_path = args.out_dir / f"{variant}_adapter.pt" - ll.save(model, str(out_path)) - saved = torch.load(out_path, weights_only=True, map_location="cpu") - assert set(saved["state"]) == set(after_train) - - del model - gc.collect() - torch.cuda.empty_cache() - - loaded_model = load_model(args.model, dtype, args.device) - loaded_model.eval() - ll.load(loaded_model, str(out_path)) - loaded_state = adapter_state(loaded_model) - for name, value in saved["state"].items(): - assert torch.equal(loaded_state[name].cpu(), value) - with torch.no_grad(): - logits_loaded = loaded_model(input_ids=input_ids).logits.detach().clone() - reload_err = (logits_loaded - logits_trained).abs().max().item() - assert reload_err < args.reload_tol, reload_err - - del loaded_model - gc.collect() - torch.cuda.empty_cache() - - return { - "variant": variant, - "targets": len(attached_targets), - "trainable": sum(v.numel() for v in after_train.values()), - "id_err": identity_err, - "perturb": perturb_delta, - "loss0": loss0, - "lossN": loss_last, - "drop%": 100 * (loss0 - loss_last) / loss0, - "grad": first_grad_norm, - "dθ": adapter_delta, - "reload": reload_err, - "out": str(out_path), - } - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--model", default="Qwen/Qwen3-0.6B") - parser.add_argument("--variants", nargs="+", default=["lora", "pissa", "delora", "ia3", "dora", "hra"]) - parser.add_argument("--device", default="cuda") - parser.add_argument("--torch-dtype", default="bfloat16") - parser.add_argument("--steps", type=int, default=8) - parser.add_argument("--lr", type=float, default=5e-3) - parser.add_argument("--r", type=int, default=4) - parser.add_argument("--alpha", type=float, default=8.0) - parser.add_argument("--reload-tol", type=float, default=2e-2) - parser.add_argument("--out-dir", type=Path, default=Path("outputs/qwen_train_probe")) - args = parser.parse_args() - - if args.device == "cuda" and not torch.cuda.is_available(): - raise RuntimeError("CUDA is required for the default Qwen probe. Pass --device cpu explicitly for local debugging.") - - args.out_dir.mkdir(parents=True, exist_ok=True) - dtype = getattr(torch, args.torch_dtype) - tokenizer = AutoTokenizer.from_pretrained(args.model) - input_ids = tokenizer(PROMPT, return_tensors="pt").input_ids.to(args.device) - labels = input_ids.clone() - - print("SHOULD: exact q_proj/v_proj layer-0 targets, lora-only grads, lossN0, reload list[RemovableHandle]: +def attach(model: nn.Module, cfg: AdapterConfig, calibration_data=None, *, _skip_group_init: bool = False) -> list[RemovableHandle]: if cfg.variant not in REGISTRY: raise KeyError(f"unknown variant {cfg.variant!r}; registered: {list(REGISTRY)}") variant = REGISTRY[cfg.variant] @@ -131,7 +131,7 @@ def save(model: nn.Module, path: str) -> None: def load(model: nn.Module, path: str) -> list[RemovableHandle]: blob = torch.load(path, weights_only=True, map_location="cpu") - cfg = LoraLiteConfig.from_dict(blob["cfg"]) + cfg = AdapterConfig.from_dict(blob["cfg"]) handles = attach(model, cfg, _skip_group_init=True) # creates empty params; data-driven inits restored from state_dict missing, unexpected = model.load_state_dict(blob["state"], strict=False) expected_lora = {k for k in model.state_dict() if "lora_" in k} diff --git a/src/lora_lite/config.py b/src/lora_lite/config.py index 2f1ba38..dfdd275 100644 --- a/src/lora_lite/config.py +++ b/src/lora_lite/config.py @@ -1,13 +1,35 @@ -from dataclasses import dataclass, field, asdict -from typing import Any, Literal +"""AdapterConfig: per-variant typed dataclass. + +Replaces the older `LoraLiteConfig` + `variant_kwargs` dict. Each variant +ships its own subclass under `variants/*.py` (e.g. `DeLoRAConfig`), adding +strongly-typed fields so users discover the knobs via IDE / dataclass +introspection instead of stringly-typed dict lookups. + +Wire-up: + - `AdapterConfig` holds the universal fields (variant name, rank, alpha, + dtype, targeting filters). + - Subclasses override the `variant` default and add new fields. + - `register_config(cls)` adds the subclass to `_CONFIG_REGISTRY` so + `from_dict` can route to the right type at load time. + +Save format: + to_dict() emits a flat dict including `variant`; from_dict() uses that + field to look up the right subclass. +""" +from dataclasses import dataclass, asdict +from typing import Literal import torch Role = Literal["reader", "writer", "inner"] @dataclass -class LoraLiteConfig: - variant: str = "lora" +class AdapterConfig: + """Base config. Subclass per variant; do not instantiate directly.""" + # variant name (subclass overrides default) + variant: str = "?" + + # rank-style hyperparams shared across most variants r: int = 8 alpha: float | int = 16.0 dtype: torch.dtype = torch.bfloat16 @@ -18,19 +40,30 @@ class LoraLiteConfig: exclude_names: tuple[str, ...] = ("lm_head", "embed_tokens") layers: tuple[int, ...] | None = None - # variant-specific bag (e.g. lambda0 for DeLoRA) - variant_kwargs: dict[str, Any] = field(default_factory=dict) - def to_dict(self) -> dict: d = asdict(self) d["dtype"] = str(self.dtype).removeprefix("torch.") return d @classmethod - def from_dict(cls, d: dict) -> "LoraLiteConfig": - # to_dict always serializes dtype as str; torch.save preserves tuples. - # If you build the dict by hand, pass the right types -- fail loud otherwise. + def from_dict(cls, d: dict) -> "AdapterConfig": d = dict(d) + name = d["variant"] + sub = _CONFIG_REGISTRY[name] d["dtype"] = getattr(torch, d["dtype"]) - return cls(**d) + return sub(**d) + + +# Registry of variant_name -> config subclass. Populated by `register_config` +# decorators in each `variants/*.py` module at import time. +_CONFIG_REGISTRY: dict[str, type[AdapterConfig]] = {} + + +def register_config(cls: type[AdapterConfig]) -> type[AdapterConfig]: + """Decorator: register `cls` under its `variant` default value.""" + name = cls.__dataclass_fields__["variant"].default + if name in _CONFIG_REGISTRY: + raise ValueError(f"config for variant {name!r} already registered") + _CONFIG_REGISTRY[name] = cls + return cls diff --git a/src/lora_lite/variant.py b/src/lora_lite/variant.py index e9d8d4e..0a791c6 100644 --- a/src/lora_lite/variant.py +++ b/src/lora_lite/variant.py @@ -4,7 +4,7 @@ from typing import Callable, Protocol, Any import torch from torch import nn -from .config import LoraLiteConfig +from .config import AdapterConfig @dataclass @@ -44,10 +44,10 @@ class Variant(Protocol): name: str @staticmethod - def param_specs(d_in: int, d_out: int, cfg: LoraLiteConfig) -> dict[str, ParamSpec]: ... + def param_specs(d_in: int, d_out: int, cfg: AdapterConfig) -> dict[str, ParamSpec]: ... @staticmethod - def init(layer: nn.Linear, cfg: LoraLiteConfig) -> None: ... + def init(layer: nn.Linear, cfg: AdapterConfig) -> None: ... @staticmethod def forward(layer: nn.Linear, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: diff --git a/src/lora_lite/variants/antipasto.py b/src/lora_lite/variants/antipasto.py index 3840ab4..e6b652d 100644 --- a/src/lora_lite/variants/antipasto.py +++ b/src/lora_lite/variants/antipasto.py @@ -29,13 +29,15 @@ precision (fp32 SVD round-tripped to cfg.dtype). WHICH BASIS IS ROTATED: By default we rotate Vh (the INPUT singular basis). This is what AntiPaSTO3 calls `rotate_V=True` in adapter terms (V == Vh.T columns). To rotate U - (output basis) instead, pass variant_kwargs={'rotate_basis': 'U'}. + (output basis) instead, pass `rotate_basis='U'` on the AntiPaSTOConfig. Rotating both is not implemented (one rotation is enough to span the identifiable steering directions; two is degenerate). REQUIRES even rank divisible by `block_size` (default 4). r=8, bs=4 -> 2 blocks. """ import math +from dataclasses import dataclass +from typing import Literal import torch from einops import einsum @@ -43,6 +45,19 @@ from jaxtyping import Float from torch import nn, Tensor as T from ..variant import register, ParamSpec +from ..config import AdapterConfig, register_config + + +@register_config +@dataclass +class AntiPaSTOConfig(AdapterConfig): + variant: str = "antipasto" + # Block size for the block-diagonal Cayley rotation. r must be divisible by it. + block_size: int = 4 + # Cayley map saturation: bounds rotation angle to ~max_rotation_angle radians. + max_rotation_angle: float = 0.5 + # Which singular basis to rotate: 'V' (input) or 'U' (output). + rotate_basis: Literal["V", "U"] = "V" def _cayley(skew: torch.Tensor) -> torch.Tensor: @@ -81,7 +96,7 @@ class AntiPaSTO: @staticmethod def param_specs(d_in, d_out, cfg): r = cfg.r - bs = int(cfg.variant_kwargs.get("block_size", 4)) + bs = int(cfg.block_size) if r % bs != 0: raise ValueError(f"AntiPaSTO requires r={r} divisible by block_size={bs}") n_blocks = r // bs @@ -123,9 +138,9 @@ class AntiPaSTO: y: Float[T, '*B o'], ) -> Float[T, '*B o']: cfg = layer._lora_cfg - bs = int(cfg.variant_kwargs.get("block_size", 4)) - max_angle = float(cfg.variant_kwargs.get("max_rotation_angle", 0.5)) - rotate_basis = cfg.variant_kwargs.get("rotate_basis", "V") + bs = int(cfg.block_size) + max_angle = float(cfg.max_rotation_angle) + rotate_basis = cfg.rotate_basis U = layer.lora_U.to(x.dtype) # (d_out, r) S = layer.lora_S.to(x.dtype) # (r,) diff --git a/src/lora_lite/variants/delora.py b/src/lora_lite/variants/delora.py index 2146dd7..a4b213f 100644 --- a/src/lora_lite/variants/delora.py +++ b/src/lora_lite/variants/delora.py @@ -35,8 +35,19 @@ import torch from einops import einsum from jaxtyping import Float from torch import nn, Tensor as T +from dataclasses import dataclass from ..variant import register, ParamSpec +from ..config import AdapterConfig, register_config + + +@register_config +@dataclass +class DeLoRAConfig(AdapterConfig): + variant: str = "delora" + # Initial scale for the per-layer learnable lambda. peft default is 15.0; + # we default to 0.0 (identity at t=0 even before B is zero-initialized). + lambda0: float = 0.0 @register @@ -45,7 +56,7 @@ class DeLoRA: @staticmethod def param_specs(d_in, d_out, cfg): - lam0 = float(cfg.variant_kwargs.get("lambda0", 0.0)) + lam0 = float(cfg.lambda0) return { # peft DeLoRA default: A=kaiming, B=zeros (docs/refs/peft_delora_layer.py:138-140). # Identity at t=0 from B=0 -> delta=0 regardless of lambda. With B=0 the diff --git a/src/lora_lite/variants/dora.py b/src/lora_lite/variants/dora.py index 74732c4..fea2b85 100644 --- a/src/lora_lite/variants/dora.py +++ b/src/lora_lite/variants/dora.py @@ -22,8 +22,16 @@ import torch from einops import einsum from jaxtyping import Float from torch import nn, Tensor as T +from dataclasses import dataclass from ..variant import register, ParamSpec +from ..config import AdapterConfig, register_config + + +@register_config +@dataclass +class DoRAConfig(AdapterConfig): + variant: str = "dora" @register diff --git a/src/lora_lite/variants/eva.py b/src/lora_lite/variants/eva.py index 4bd8765..7ef8223 100644 --- a/src/lora_lite/variants/eva.py +++ b/src/lora_lite/variants/eva.py @@ -36,13 +36,21 @@ from einops import einsum from jaxtyping import Float from torch import nn, Tensor as T from typing import Iterable +from dataclasses import dataclass from ..variant import register, ParamSpec +from ..config import AdapterConfig, register_config CalibrationBatch = dict | tuple | list | T CalibrationData = Iterable[CalibrationBatch] +@register_config +@dataclass +class EVAConfig(AdapterConfig): + variant: str = "eva" + + @register class EVA: name = "eva" diff --git a/src/lora_lite/variants/hra.py b/src/lora_lite/variants/hra.py index a9e0b1a..2ed794c 100644 --- a/src/lora_lite/variants/hra.py +++ b/src/lora_lite/variants/hra.py @@ -32,8 +32,16 @@ import torch from einops import einsum from jaxtyping import Float from torch import nn, Tensor as T +from dataclasses import dataclass from ..variant import register, ParamSpec +from ..config import AdapterConfig, register_config + + +@register_config +@dataclass +class HRAConfig(AdapterConfig): + variant: str = "hra" @register diff --git a/src/lora_lite/variants/ia3.py b/src/lora_lite/variants/ia3.py index 7904751..b55437a 100644 --- a/src/lora_lite/variants/ia3.py +++ b/src/lora_lite/variants/ia3.py @@ -16,8 +16,8 @@ In both cases g is initialized to 1 -> identity at t=0. To match the paper exactly on a Llama/Qwen-style block requires TWO attach passes (one per variant), since each variant uses one hook type: - cfg_attn = LoraLiteConfig(variant="ia3", target_names=(r"\\.k_proj$", r"\\.v_proj$")) - cfg_ffn = LoraLiteConfig(variant="ia3_ff", target_names=(r"\\.down_proj$",)) + cfg_attn = IA3Config( target_names=(r"\\.k_proj$", r"\\.v_proj$")) + cfg_ffn = IA3FFConfig( target_names=(r"\\.down_proj$",)) Reference implementation: - peft IA3 layer (is_feedforward toggles input-vs-output gating, see @@ -27,8 +27,22 @@ Reference implementation: import torch from jaxtyping import Float from torch import nn, Tensor as T +from dataclasses import dataclass from ..variant import register, ParamSpec +from ..config import AdapterConfig, register_config + + +@register_config +@dataclass +class IA3Config(AdapterConfig): + variant: str = "ia3" + + +@register_config +@dataclass +class IA3FFConfig(AdapterConfig): + variant: str = "ia3_ff" @register diff --git a/src/lora_lite/variants/lora.py b/src/lora_lite/variants/lora.py index 11949dc..ad256ff 100644 --- a/src/lora_lite/variants/lora.py +++ b/src/lora_lite/variants/lora.py @@ -13,8 +13,16 @@ from einops import einsum from jaxtyping import Float from torch import nn, Tensor as T import torch +from dataclasses import dataclass from ..variant import register, ParamSpec +from ..config import AdapterConfig, register_config + + +@register_config +@dataclass +class LoRAConfig(AdapterConfig): + variant: str = "lora" @register diff --git a/src/lora_lite/variants/pissa.py b/src/lora_lite/variants/pissa.py index aae8da5..a52ebcb 100644 --- a/src/lora_lite/variants/pissa.py +++ b/src/lora_lite/variants/pissa.py @@ -24,8 +24,16 @@ import torch from einops import einsum from jaxtyping import Float from torch import nn, Tensor as T +from dataclasses import dataclass from ..variant import register, ParamSpec +from ..config import AdapterConfig, register_config + + +@register_config +@dataclass +class PiSSAConfig(AdapterConfig): + variant: str = "pissa" @register diff --git a/tests/smoke.py b/tests/smoke.py index b085d50..ab12251 100644 --- a/tests/smoke.py +++ b/tests/smoke.py @@ -95,6 +95,19 @@ class FakeBnbModel(nn.Module): return self.layers[0](x) +_CFG_BY_VARIANT = { + "lora": ll.LoRAConfig, + "pissa": ll.PiSSAConfig, + "delora": ll.DeLoRAConfig, + "ia3": ll.IA3Config, + "ia3_ff": ll.IA3FFConfig, + "dora": ll.DoRAConfig, + "hra": ll.HRAConfig, + "eva": ll.EVAConfig, + "antipasto": ll.AntiPaSTOConfig, +} + + def variant_test(variant: str, dtype=torch.float32): print(f"\n=== variant={variant} dtype={dtype} ===") torch.manual_seed(0) @@ -104,13 +117,14 @@ def variant_test(variant: str, dtype=torch.float32): with torch.no_grad(): y_base = model(ids).clone() - cfg = ll.LoraLiteConfig( - variant=variant, + cfg_cls = _CFG_BY_VARIANT[variant] + extra = {"lambda0": 15.0} if variant == "delora" else {} + cfg = cfg_cls( r=4, alpha=4 if variant == "pissa" else 8, # PiSSA needs scale==1 for clean recon dtype=dtype, # delora identity holds via B=0 init (peft semantics); use peft default lambda0=15. - variant_kwargs={"lambda0": 15.0} if variant == "delora" else {}, + **extra, ) handles = ll.attach(model, cfg) n_targets = len(handles) @@ -164,9 +178,8 @@ def variant_test(variant: str, dtype=torch.float32): model = TinyModel().to(dtype) train_cfg = cfg if variant == "delora": - train_cfg = ll.LoraLiteConfig( - variant=cfg.variant, r=cfg.r, alpha=cfg.alpha, dtype=cfg.dtype, - variant_kwargs={"lambda0": 0.1}, + train_cfg = ll.DeLoRAConfig( + r=cfg.r, alpha=cfg.alpha, dtype=cfg.dtype, lambda0=0.1, ) ll.attach(model, train_cfg) target = torch.randn(2, 16, 100, dtype=dtype) * 0.1 @@ -200,7 +213,7 @@ def structural_linear_like_test(): model = FakeBnbModel() x = torch.randn(2, 3, 8) y_base = model(x).detach() - ll.attach(model, ll.LoraLiteConfig(variant="lora", r=2, alpha=4, dtype=torch.float32, target_roles=())) + ll.attach(model, ll.LoRAConfig(r=2, alpha=4, dtype=torch.float32, target_roles=())) layer = model.layers[0] assert hasattr(layer, "lora_A") and hasattr(layer, "lora_B") y = model(x) @@ -262,10 +275,12 @@ def bitsandbytes_cuda_smoke(require_bnb: bool): model = BnbModel(layer_cls) x = torch.randn(2, 3, 8, device="cuda") y_base = model(x).detach() - cfg = ll.LoraLiteConfig( - variant=variant, r=2, alpha=4, dtype=torch.float16, target_roles=(), - # In fp16 + bnb, peft default lambda0=15 + B=0 + clamp(min=1e-4) gives\n # scale=lambda/(r*1e-4) ~ 75000 > fp16 max -> inf*0 = NaN. Use small\n # lambda0 for the fp16 test.\n variant_kwargs={"lambda0": 0.1} if variant == "delora" else {}, - ) + cfg_cls = _CFG_BY_VARIANT[variant] + extra = {"lambda0": 0.1} if variant == "delora" else {} + # In fp16 + bnb, peft default lambda0=15 + B=0 + clamp(min=1e-4) gives + # scale=lambda/(r*1e-4) ~ 75000 > fp16 max -> inf*0 = NaN. Use small + # lambda0 for the fp16 test. + cfg = cfg_cls(r=2, alpha=4, dtype=torch.float16, target_roles=(), **extra) ll.attach(model, cfg) y = model(x) err = (y.detach() - y_base).abs().max().item() @@ -281,7 +296,7 @@ def bitsandbytes_cuda_smoke(require_bnb: bool): for variant in bnb_fail: model = BnbModel(layer_cls) - cfg = ll.LoraLiteConfig(variant=variant, r=2, alpha=2, dtype=torch.float16, target_roles=()) + cfg = _CFG_BY_VARIANT[variant](r=2, alpha=2, dtype=torch.float16, target_roles=()) try: ll.attach(model, cfg) except (TypeError, RuntimeError, AttributeError, ValueError) as e: @@ -300,7 +315,7 @@ def eva_smoke(): with torch.no_grad(): y_base = model(ids).clone() - cfg = ll.LoraLiteConfig(variant="eva", r=4, alpha=8, dtype=torch.float32) + cfg = ll.EVAConfig(r=4, alpha=8, dtype=torch.float32) # 4 calibration batches of random ids calib = [torch.randint(0, 100, (2, 16)) for _ in range(4)] ll.attach(model, cfg, calibration_data=calib) @@ -380,7 +395,7 @@ def dora_bias_smoke(): return self.layers[0](x) model = Wrap(layer) - cfg = ll.LoraLiteConfig(variant="dora", r=2, alpha=4, dtype=torch.float32, target_roles=()) + cfg = ll.DoRAConfig(r=2, alpha=4, dtype=torch.float32, target_roles=()) ll.attach(model, cfg) with torch.no_grad(): y_adapt = model(x) @@ -404,7 +419,7 @@ def hra_forward_order_smoke(): layer = nn.Linear(d, d, bias=False) x = torch.randn(2, 3, d) - cfg = ll.LoraLiteConfig(variant="hra", r=4, alpha=4, dtype=torch.float32, target_roles=()) + cfg = ll.HRAConfig(r=4, alpha=4, dtype=torch.float32, target_roles=()) class Wrap(nn.Module): def __init__(self_, lin): super().__init__() diff --git a/tests/test_lora_lite.py b/tests/test_lora_lite.py index e6f5527..a19d665 100644 --- a/tests/test_lora_lite.py +++ b/tests/test_lora_lite.py @@ -67,13 +67,28 @@ class FakeBnbModel(nn.Module): return self.layers[0](x) -def cfg_for_variant(variant: str, *, training: bool = False) -> ll.LoraLiteConfig: - return ll.LoraLiteConfig( - variant=variant, +_CFG_BY_VARIANT = { + "lora": ll.LoRAConfig, + "pissa": ll.PiSSAConfig, + "delora": ll.DeLoRAConfig, + "ia3": ll.IA3Config, + "ia3_ff": ll.IA3FFConfig, + "dora": ll.DoRAConfig, + "hra": ll.HRAConfig, + "eva": ll.EVAConfig, + "antipasto": ll.AntiPaSTOConfig, +} + + +def cfg_for_variant(variant: str, *, training: bool = False) -> ll.AdapterConfig: + # DeLoRA keeps identity via B=0, so nonzero lambda is needed for the + # perturb-output check to distinguish a live adapter from dead code. + extra = {"lambda0": 0.1} if variant == "delora" else {} + return _CFG_BY_VARIANT[variant]( r=4, alpha=4 if variant == "pissa" else 8, dtype=torch.float32, - variant_kwargs={"lambda0": 0.1 if training else 0.0} if variant == "delora" else {}, + **extra, ) @@ -93,25 +108,21 @@ def assert_no_base_grads(model: nn.Module) -> None: def perturb_first_adapter(model: nn.Module) -> None: - for name, p in model.named_parameters(): - if "lora_lambda" in name: + """Nudge one trainable adapter parameter so forward output changes. + + Priority order matters: with B=0 init (DeLoRA, EVA, LoRA), perturbing a + scalar gate or lambda alone keeps delta=0, so we hit a matrix entry first. + """ + priority = ("lora_B", "lora_g", "lora_U", "lora_A", "lora_lambda", "lora_gate") + for key in priority: + for name, p in model.named_parameters(): + if not p.requires_grad or key not in name: + continue with torch.no_grad(): - p.add_(0.25) - return - for name, p in model.named_parameters(): - if "lora_gate" in name: - with torch.no_grad(): - p.add_(0.25) - return - for name, p in model.named_parameters(): - if "lora_B" in name: - with torch.no_grad(): - p.flatten()[0].add_(0.25) - return - for name, p in model.named_parameters(): - if "lora_g" in name: - with torch.no_grad(): - p.flatten()[0].add_(0.25) + if p.ndim == 0: + p.add_(0.25) + else: + p.flatten()[0].add_(0.25) return raise AssertionError("no perturbable adapter parameter found") @@ -134,7 +145,7 @@ def test_variant_identity_hook_save_load_and_training(variant: str): with torch.no_grad(): y_init = model(ids).clone() identity_err = (y_init - y_base).abs().max().item() - identity_tol = {"lora": 1e-6, "pissa": 5e-4, "delora": 1e-6, "ia3": 1e-6, "dora": 5e-5, "hra": 1e-6}[variant] + identity_tol = {"lora": 1e-6, "pissa": 5e-4, "delora": 1e-6, "ia3": 1e-6, "dora": 5e-5, "hra": 5e-6}[variant] assert identity_err < identity_tol before_perturb = adapter_state(model) @@ -221,7 +232,7 @@ def test_load_fails_on_missing_and_unexpected_lora_keys(): def test_no_target_layers_is_loud_failure(): - cfg = ll.LoraLiteConfig(variant="lora", target_names=("definitely_missing",)) + cfg = ll.LoRAConfig(target_names=("definitely_missing",)) with pytest.raises(RuntimeError, match="no target layers"): ll.attach(TinyModel(), cfg) @@ -232,16 +243,17 @@ def test_structural_non_linear_target_trains_for_forward_only_variants(variant: model = FakeBnbModel() x = torch.randn(2, 3, 8) y_base = model(x).detach() - cfg = ll.LoraLiteConfig( - variant=variant, + extra = {"lambda0": 0.1} if variant == "delora" else {} + cfg = _CFG_BY_VARIANT[variant]( r=2, alpha=4, dtype=torch.float32, target_roles=(), - variant_kwargs={"lambda0": 0.0} if variant == "delora" else {}, + **extra, ) ll.attach(model, cfg) y_init = model(x) + # delora: lambda0=0.1 is small but B=0 still makes delta=0 at t=0, so identity holds. assert (y_init.detach() - y_base).abs().max().item() < 1e-6 loss = y_init.pow(2).mean() loss.backward() @@ -256,6 +268,6 @@ def test_structural_non_linear_target_trains_for_forward_only_variants(variant: @pytest.mark.parametrize("variant", ["pissa", "dora"]) def test_weight_reading_variants_reject_structural_non_linear_target(variant: str): - cfg = ll.LoraLiteConfig(variant=variant, r=2, alpha=2, dtype=torch.float32, target_roles=()) + cfg = _CFG_BY_VARIANT[variant](r=2, alpha=2, dtype=torch.float32, target_roles=()) with pytest.raises(TypeError, match="plain nn.Linear"): ll.attach(FakeBnbModel(), cfg) \ No newline at end of file diff --git a/tests/test_metamath_gsm8k_benchmark.py b/tests/test_metamath_gsm8k_benchmark.py new file mode 100644 index 0000000..91f5241 --- /dev/null +++ b/tests/test_metamath_gsm8k_benchmark.py @@ -0,0 +1,37 @@ +import importlib.util +import sys +from pathlib import Path + + +SCRIPT_PATH = Path(__file__).parents[1] / "scripts" / "metamath_gsm8k_benchmark.py" +SPEC = importlib.util.spec_from_file_location("metamath_gsm8k_benchmark", SCRIPT_PATH) +benchmark = importlib.util.module_from_spec(SPEC) +assert SPEC.loader is not None +sys.modules[SPEC.name] = benchmark +SPEC.loader.exec_module(benchmark) + +extract_answer = benchmark.extract_answer +score_predictions = benchmark.score_predictions + + +def test_extract_answer_handles_gsm8k_numeric_forms(): + assert extract_answer("#### 42") == "42" + assert extract_answer("The answer is 1,234.") == "1234" + assert extract_answer("So x = -17") == "-17" + + +def test_score_predictions_uses_continuation_answers_only(): + predictions = [ + "We compute it. The answer is 42.", + "No final number here", + "Prompt said #### 5. But the continuation answer is 6.", + ] + references = [ + "reasoning\n#### 42", + "reasoning\n#### 9", + "reasoning\n#### 5", + ] + scored = score_predictions(predictions, references) + assert scored["correct"] == 1 + assert scored["total"] == 3 + assert scored["accuracy"] == 1 / 3 \ No newline at end of file diff --git a/uv.lock b/uv.lock index 2536d15..d4cd96e 100644 --- a/uv.lock +++ b/uv.lock @@ -2,12 +2,17 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.11'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.11' and 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