diff --git a/.gitignore b/.gitignore index f6632b4..d0879e6 100644 --- a/.gitignore +++ b/.gitignore @@ -7,4 +7,7 @@ __pycache__/ *.db *.sqlite3 *.log -*.bak \ No newline at end of file +*.bak +logs/ +outputs/ +tests/_artifacts/ \ No newline at end of file diff --git a/README.md b/README.md index eecd032..f349deb 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,8 @@ A hackable, single-file-per-variant LoRA library built on PyTorch forward hooks. + + - ~600 LoC total - One file per variant, ~50 LoC each - No module replacement, no merge/unmerge, no PEFT config soup @@ -135,13 +137,29 @@ class ActSVD: ## Smoke test ```bash -python tests/smoke.py +just test +just smoke +just qwen-probe ``` -Verifies for each of `lora`, `pissa`, `delora`: +`just test` verifies, for each of `lora`, `pissa`, `delora`: + 1. Identity at t=0: `max|y_adapter - y_base|` within float tolerance. -2. Save/load round-trip preserves outputs. -3. 20 SGD steps reduce a random regression loss by >5%. +2. Adapter hooks are live: perturbing only `lora_*` changes outputs. +3. Save/load round-trip preserves full-path adapter keys and tensors. +4. Missing or unexpected `lora_*` checkpoint keys fail loudly. +5. Only `lora_*` parameters are trainable and base parameters get no gradients. +6. A 20-step tiny regression training probe gets finite nonzero adapter gradients and >5% loss drop. + +`just qwen-probe` is the real-model proof. It loads `Qwen/Qwen3-0.6B` fresh per variant, attaches only layer-0 `q_proj`/`v_proj`, trains one fixed LM batch, saves adapters, reloads into a fresh base model, and checks logits match. Last verified on 2026-04-26: + +| variant | targets | trainable | identity err | perturb delta | loss0 | lossN | drop % | grad norm | adapter delta | reload err | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| LoRA | 2 | 20,480 | 0 | 0.3750 | 5.250 | 3.131 | 40.36 | 1.432 | 4.262 | 0 | +| PiSSA | 2 | 20,480 | 0.3125 | 0.7500 | 5.250 | 3.629 | 30.88 | 6.124 | 4.381 | 0 | +| DeLoRA | 2 | 20,482 | 0.3750 | 0.4062 | 5.246 | 5.166 | 1.537 | 0.04778 | 8.196 | 0 | + +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. ## What's NOT in v1 diff --git a/docs/spec/20260426_code_review.md b/docs/spec/20260426_code_review.md new file mode 100644 index 0000000..5477463 --- /dev/null +++ b/docs/spec/20260426_code_review.md @@ -0,0 +1,22 @@ +# 2026-04-26 code review: testing proof + +## External review + +Reviewer: Gemini 2.5 Flash CLI, read-only prompt. + +Findings: + +- Critical: `tests/smoke.py` could silently pass if base gradients leaked because it did not check non-`lora_*` grads. +- Important: `tests/smoke.py` did not explicitly assert the expected number of attached TinyModel targets. + +Resolution: + +- Added `assert_no_base_grads(model)` to the smoke training loop. +- Added `assert n_targets == 28` immediately after smoke attach. +- Re-ran `just test` and `just smoke`; both passed. + +## Fresh-eyes subagent review + +Verdict: PASS. + +The reviewer could not name a remaining blocker for skipped targets, dead hooks, base-gradient leakage, or broken save/load producing the collected evidence. Caveat: Qwen coverage is intentionally narrow, layer-0 `q_proj`/`v_proj`, one prompt, tiny steps. This supports interface/training proof, not downstream finetuning quality. \ No newline at end of file diff --git a/docs/spec/20260426_lora_lite_plan.md b/docs/spec/20260426_lora_lite_plan.md index c6bf106..6c5e235 100644 --- a/docs/spec/20260426_lora_lite_plan.md +++ b/docs/spec/20260426_lora_lite_plan.md @@ -55,6 +55,59 @@ Last verified log: `/home/wassname/.cache/agent-tmp/lora_lite_smoke_after_review | bnb `Linear8bitLt` | identity `0.000e+00`, grad nonzero | | bnb `Linear4bit` | identity `0.000e+00`, grad nonzero | +## 2026-04-26 testing proof pass + +Goal: upgrade from smoke-tested sketch to evidence that the current PEFT-lite interface trains on both toy models and a real HF Qwen model. + +### Scope + +In: + +- Pytest coverage for LoRA, PiSSA, and DeLoRA correctness invariants. +- A real `Qwen/Qwen3-0.6B` probe that trains each current variant on layer-0 `q_proj` and `v_proj`. +- Repeatable `just` recipes and workspace-local logs/artifacts. + +Out: + +- Benchmark claims. +- Quantized Qwen proof for PiSSA. PiSSA remains fp-only because it mutates `weight`. +- Full default-target training over every Qwen layer. + +### Requirements and evidence + +| Requirement | Distinguishing check | Evidence | +|---|---|---| +| R1: toy tests catch skipped targets/hooks | Perturb only `lora_*`; output must change. Missing target must raise. | `just test` -> `8 passed in 2.43s` in `logs/pytest.log` | +| R2: toy tests catch base-gradient leakage | After backward, all non-`lora_*` grads are `None`; all trainable names contain `lora_`. | `just test` -> `8 passed in 2.43s` | +| R3: save/load is exact for adapters | Saved key set equals full-path `lora_*` state; reload tensors equal; missing/extra `lora_*` keys raise. | `just test` -> `8 passed in 2.43s` | +| R4: current variants train on tiny task | 28 TinyModel targets; non-`lora_*` grads stay `None`; 20-step loss drop >5%. | `just smoke` -> LoRA 6.1%, PiSSA 11.5%, DeLoRA 93.4% | +| R5: current variants train on real Qwen | Fresh Qwen per variant; exact targets are layer-0 `q_proj`/`v_proj`; perturb >0; lossN < loss0; reload err < tol. | `pueue` task 70, `logs/qwen_probe.log`, all probes pass | +| R6: cold review cannot explain evidence under silent failure | External review findings fixed, then fresh-eyes subagent says PASS. | `docs/spec/20260426_code_review.md` | + +### Qwen proof table + +Command: + +```bash +pueue add --immediate --follow --label "why: verify warning-free current Qwen probe after dtype API cleanup; resolve: same pass table proves current script" --working-directory "$PWD" --priority 1 -- just qwen-probe +``` + +Result from task 70: + +| variant | targets | trainable | id_err | perturb | loss0 | lossN | drop% | grad | dθ | reload | adapter | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---| +| lora | 2 | 20480 | 0 | 0.375 | 5.25 | 3.131 | 40.36 | 1.432 | 4.262 | 0 | `outputs/qwen_train_probe/lora_adapter.pt` | +| pissa | 2 | 20480 | 0.3125 | 0.75 | 5.25 | 3.629 | 30.88 | 6.124 | 4.381 | 0 | `outputs/qwen_train_probe/pissa_adapter.pt` | +| delora | 2 | 20482 | 0.375 | 0.4062 | 5.246 | 5.166 | 1.537 | 0.04778 | 8.196 | 0 | `outputs/qwen_train_probe/delora_adapter.pt` | + +Failure-mode interpretation: + +- If targeting silently skipped, exact target-set assertion would fail before training. +- If hooks were attached but dead, perturb delta would be 0. +- If base params trained, the non-`lora_*` gradient check would fail. +- 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. + ## Review history A cold subagent review first returned `PASS_WITH_BLOCKERS`: @@ -142,3 +195,6 @@ This repo is good enough for a first real experiment when: 2. A 4bit or 8bit loaded model can train LoRA/DeLoRA params with nonzero gradients. 3. Saved adapter tensors use full-path keys and reload without calibration data. 4. Smoke tests distinguish target-skipping, hook identity drift, and missing-key load failure. + +see interesting adapters here https://github.com/wassname/adapters_as_hypotheses +how peft handle 4bit here https://github.com/huggingface/peft/blob/6030f9160ed2fc17220f6f41382a66f1257b6a93/src/peft/tuners/lora/layer.py \ No newline at end of file diff --git a/justfile b/justfile new file mode 100644 index 0000000..132050b --- /dev/null +++ b/justfile @@ -0,0 +1,13 @@ +set shell := ["bash", "-cu"] + +default: + @just --list + +test: + uv run --extra test pytest -q + +smoke: + uv run --extra test python tests/smoke.py + +qwen-probe: + uv run --extra test --extra hf-test python scripts/qwen_train_probe.py \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 48aa034..4a74d47 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,8 @@ dependencies = [ ] [project.optional-dependencies] -test = ["pytest"] +test = ["pytest", "tabulate"] +hf-test = ["accelerate>=1.6", "safetensors>=0.5", "transformers>=4.51"] [build-system] requires = ["setuptools>=68"] @@ -18,3 +19,6 @@ build-backend = "setuptools.build_meta" [tool.setuptools.packages.find] where = ["src"] + +[tool.uv] +exclude-newer = "5 days" \ No newline at end of file diff --git a/scripts/qwen_train_probe.py b/scripts/qwen_train_probe.py new file mode 100644 index 0000000..82e32c3 --- /dev/null +++ b/scripts/qwen_train_probe.py @@ -0,0 +1,198 @@ +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: + for name, p in model.named_parameters(): + if "lora_lambda" 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 + 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"]) + 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 5% over 20 SGD steps for all variants. ELSE grad/wiring bug. """ from __future__ import annotations -import tempfile, os, sys, math +import os, sys, math +from pathlib import Path import torch from torch import nn @@ -25,6 +26,14 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) import lora_lite as ll # noqa: E402 +ARTIFACT_DIR = Path(__file__).parent / "_artifacts" + + +def assert_no_base_grads(model: 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 == [], f"base params received grads: {leaked}" + + # ---- a tiny transformer-like stack: 4 blocks of (q,k,v,o, gate,up,down) Linears ---- class TinyBlock(nn.Module): def __init__(self, d=64, ff=128): @@ -106,6 +115,7 @@ def variant_test(variant: str, dtype=torch.float32): n_targets = len(handles) n_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) print(f" attached {n_targets} targets, trainable params={n_trainable}") + assert n_targets == 28, f"expected 28 TinyModel targets, got {n_targets}" with torch.no_grad(): y_adapt = model(ids) @@ -123,25 +133,22 @@ def variant_test(variant: str, dtype=torch.float32): print(f" SHOULD: err<{tol:.1e}. PASS.") # save/load round-trip - with tempfile.TemporaryDirectory() as d: - p = os.path.join(d, "adapter.pt") - ll.save(model, p) - # detach + fresh model + load - ll.detach(model) - torch.manual_seed(0) - model2 = TinyModel().to(dtype) - # for PiSSA, base weights got mutated; we need them mutated again for the load - # path to make sense. Easiest: re-attach with same cfg first... but that's what - # load() does. The catch: load reads cfg from the file, runs attach (which - # re-runs PiSSA init -> same SVD on same weights -> same A,B -> mutates W - # to the same W_res). Then state_dict overwrites lora_A/B with saved values. - ll.load(model2, p) - with torch.no_grad(): - y_loaded = model2(ids) - err2 = (y_loaded - y_adapt).abs().max().item() - print(f" save/load: max|y_loaded - y_adapt| = {err2:.3e}") - assert err2 < tol, f" FAIL save/load: {err2} > {tol}" - print(f" SHOULD: err2<{tol:.1e}. PASS.") + ARTIFACT_DIR.mkdir(exist_ok=True) + p = ARTIFACT_DIR / f"{variant}_smoke_adapter.pt" + ll.save(model, str(p)) + # detach + fresh model + load + ll.detach(model) + torch.manual_seed(0) + model2 = TinyModel().to(dtype) + # for PiSSA, base weights got mutated; load() re-runs PiSSA init on the fresh + # same-seed base, then overwrites lora_A/B with saved values. + ll.load(model2, str(p)) + with torch.no_grad(): + y_loaded = model2(ids) + err2 = (y_loaded - y_adapt).abs().max().item() + print(f" save/load: max|y_loaded - y_adapt| = {err2:.3e}") + assert err2 < tol, f" FAIL save/load: {err2} > {tol}" + print(f" SHOULD: err2<{tol:.1e}. PASS.") ll.detach(model2) # gradient flow: 20 SGD steps on random target. @@ -167,6 +174,7 @@ def variant_test(variant: str, dtype=torch.float32): opt.zero_grad() loss = (model(ids) - target).pow(2).mean() loss.backward() + assert_no_base_grads(model) opt.step() losses.append(loss.item()) drop = (losses[0] - losses[-1]) / max(losses[0], 1e-12) diff --git a/tests/test_lora_lite.py b/tests/test_lora_lite.py new file mode 100644 index 0000000..3eed389 --- /dev/null +++ b/tests/test_lora_lite.py @@ -0,0 +1,248 @@ +from __future__ import annotations + +import math +from pathlib import Path + +import pytest +import torch +from torch import nn + +import lora_lite as ll + + +ARTIFACT_DIR = Path(__file__).parent / "_artifacts" + + +class TinyBlock(nn.Module): + def __init__(self, d: int = 64, ff: int = 128): + super().__init__() + self.q_proj = nn.Linear(d, d, bias=False) + self.k_proj = nn.Linear(d, d, bias=False) + self.v_proj = nn.Linear(d, d, bias=False) + self.o_proj = nn.Linear(d, d, bias=False) + self.gate_proj = nn.Linear(d, ff, bias=False) + self.up_proj = nn.Linear(d, ff, bias=False) + self.down_proj = nn.Linear(ff, d, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h = self.o_proj(self.q_proj(x) + self.k_proj(x) + self.v_proj(x)) + m = self.down_proj(torch.nn.functional.silu(self.gate_proj(x)) * self.up_proj(x)) + return x + h + m + + +class TinyModel(nn.Module): + def __init__(self, n_layers: int = 4, d: int = 64, ff: int = 128, vocab: int = 100): + super().__init__() + self.embed_tokens = nn.Embedding(vocab, d) + self.layers = nn.ModuleList([TinyBlock(d, ff) for _ in range(n_layers)]) + self.lm_head = nn.Linear(d, vocab, bias=False) + self.config = type("Cfg", (), {"hidden_size": d})() + + def forward(self, ids: torch.Tensor) -> torch.Tensor: + x = self.embed_tokens(ids) + for block in self.layers: + x = block(x) + return self.lm_head(x) + + +class FakeLinearLike(nn.Module): + def __init__(self, d_in: int = 8, d_out: int = 8): + super().__init__() + self.in_features = d_in + self.out_features = d_out + self.weight = nn.Parameter(torch.empty(d_out, d_in)) + nn.init.kaiming_uniform_(self.weight, a=5**0.5) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.nn.functional.linear(x, self.weight) + + +class FakeBnbModel(nn.Module): + def __init__(self): + super().__init__() + self.config = type("Cfg", (), {"hidden_size": 8})() + self.layers = nn.ModuleList([FakeLinearLike(8, 8)]) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.layers[0](x) + + +def cfg_for_variant(variant: str, *, training: bool = False) -> ll.LoraLiteConfig: + return ll.LoraLiteConfig( + 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 {}, + ) + + +def adapter_state(model: 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: nn.Module) -> None: + trainable_names = [name for name, p in model.named_parameters() if p.requires_grad] + assert trainable_names + assert all("lora_" in name for name in trainable_names) + + +def assert_no_base_grads(model: 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 == [] + + +def perturb_first_adapter(model: nn.Module) -> None: + for name, p in model.named_parameters(): + if "lora_lambda" 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 + raise AssertionError("no perturbable adapter parameter found") + + +@pytest.mark.parametrize("variant", ["lora", "pissa", "delora"]) +def test_variant_identity_hook_save_load_and_training(variant: str): + ARTIFACT_DIR.mkdir(exist_ok=True) + torch.manual_seed(0) + model = TinyModel() + ids = torch.randint(0, 100, (2, 16)) + + with torch.no_grad(): + y_base = model(ids).clone() + + cfg = cfg_for_variant(variant) + handles = ll.attach(model, cfg) + assert len(handles) == 28 + assert_only_lora_trainable(model) + + 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}[variant] + assert identity_err < identity_tol + + before_perturb = adapter_state(model) + perturb_first_adapter(model) + with torch.no_grad(): + perturb_delta = (model(ids) - y_init).abs().max().item() + assert perturb_delta > 1e-7 + for name, value in before_perturb.items(): + model.state_dict()[name].copy_(value) + + path = ARTIFACT_DIR / f"{variant}_adapter.pt" + ll.save(model, str(path)) + saved = torch.load(path, weights_only=True, map_location="cpu") + assert set(saved["state"]) == set(adapter_state(model)) + assert any(k.startswith("layers.0.q_proj.lora_") for k in saved["state"]) + + torch.manual_seed(0) + model_loaded = TinyModel() + ll.load(model_loaded, str(path)) + loaded_state = adapter_state(model_loaded) + for name, value in saved["state"].items(): + assert torch.equal(loaded_state[name].cpu(), value) + with torch.no_grad(): + y_loaded = model_loaded(ids) + assert (y_loaded - y_init).abs().max().item() < identity_tol + + torch.manual_seed(0) + train_model = TinyModel() + ll.attach(train_model, cfg_for_variant(variant, training=True)) + assert_only_lora_trainable(train_model) + target = torch.randn(2, 16, 100) * 0.1 + trainable = [p for p in train_model.parameters() if p.requires_grad] + opt = torch.optim.Adam(trainable, lr=0.1) if variant == "delora" else torch.optim.SGD(trainable, lr=1e-2) + losses = [] + first_grad_norm = math.nan + before_train = adapter_state(train_model) + for step in range(20): + opt.zero_grad() + loss = (train_model(ids) - target).pow(2).mean() + loss.backward() + assert_no_base_grads(train_model) + grad_norm = sum( + p.grad.detach().float().norm().item() + for name, p in train_model.named_parameters() + if "lora_" in name and p.grad is not None + ) + assert math.isfinite(grad_norm) + if step == 0: + first_grad_norm = grad_norm + opt.step() + losses.append(loss.item()) + after_train = adapter_state(train_model) + adapter_delta = sum((after_train[k] - before_train[k]).float().norm().item() for k in before_train) + drop = (losses[0] - losses[-1]) / losses[0] + assert first_grad_norm > 0 + assert adapter_delta > 0 + assert drop > 0.05 + + +def test_load_fails_on_missing_and_unexpected_lora_keys(): + ARTIFACT_DIR.mkdir(exist_ok=True) + torch.manual_seed(0) + model = TinyModel() + ll.attach(model, cfg_for_variant("lora")) + good_path = ARTIFACT_DIR / "lora_good.pt" + ll.save(model, str(good_path)) + blob = torch.load(good_path, weights_only=True, map_location="cpu") + + missing_blob = {"cfg": blob["cfg"], "state": dict(blob["state"])} + missing_blob["state"].pop(next(iter(missing_blob["state"]))) + missing_path = ARTIFACT_DIR / "lora_missing.pt" + torch.save(missing_blob, missing_path) + with pytest.raises(RuntimeError, match="missing lora keys"): + ll.load(TinyModel(), str(missing_path)) + + unexpected_blob = {"cfg": blob["cfg"], "state": dict(blob["state"])} + unexpected_blob["state"]["layers.0.q_proj.lora_extra"] = torch.zeros(1) + unexpected_path = ARTIFACT_DIR / "lora_unexpected.pt" + torch.save(unexpected_blob, unexpected_path) + with pytest.raises(RuntimeError, match="unexpected lora keys"): + ll.load(TinyModel(), str(unexpected_path)) + + +def test_no_target_layers_is_loud_failure(): + cfg = ll.LoraLiteConfig(variant="lora", target_names=("definitely_missing",)) + with pytest.raises(RuntimeError, match="no target layers"): + ll.attach(TinyModel(), cfg) + + +@pytest.mark.parametrize("variant", ["lora", "delora"]) +def test_structural_non_linear_target_trains_for_forward_only_variants(variant: str): + torch.manual_seed(0) + model = FakeBnbModel() + x = torch.randn(2, 3, 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