mirror of
https://github.com/wassname/lora-lite.git
synced 2026-06-27 15:15:55 +08:00
feat: near_zero/near_one init for trainable params (breaks bf16 dead-grad symmetry)
Trainable params that were init'd at exact 0 or 1 now use near_zero (N(0,1e-4)) or near_one (1 + N(0,1e-4)) to break bf16 symmetry without meaningfully breaking identity-at-t=0. Exact-zero init is kept where zero IS the identity constraint (DeLoRA lora_B, EVA lora_B -- both scaled by other params so any nonzero B would blow up the output). AntiPaSTO: delta_s and rot_T now near_zero. The old exact-zero could leave rotation learning dead in bf16 where step sizes round back to zero. IA3: lora_g now near_one instead of exact ones. Avoids the bf16 spacing issue around 1.0 where eps_bf16 ~ 7.8e-3 and lr=1e-3 updates were rounding away. PiSSA: lora_A and lora_B now near_zero (both overwritten by SVD in init(), so the init value is moot -- but ParamSpec now documents intent correctly). HRA: lora_U now near_zero (overwritten by symmetric init in init()). ParamSpec: added 'near_zero' and 'near_one' init modes. Default changed from 'zeros' to 'near_zero'. Tests relaxed identity tolerances accordingly.
This commit is contained in:
wassname
@@ -12,6 +12,7 @@ build/
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dist/
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*.egg-info/
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logs/
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docs/spec/
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outputs/
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tests/_artifacts/
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docs/papers/*.pdf
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@@ -47,21 +47,25 @@ just qwen-probe # Qwen/Qwen3-0.6B train/save-load probe
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## Variants
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| Variant | 4bit/8bit | GSM8K % |
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| --------------------------------------------- | --------- | ------- |
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| [LoRA](https://arxiv.org/abs/2106.09685) | yes | 63.2% |
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| [PiSSA](https://arxiv.org/abs/2404.02948) | no | — |
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| [DeLoRA](https://arxiv.org/abs/2503.18225) | yes | — |
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| [IA3](https://arxiv.org/pdf/2205.05638) | yes | — |
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| [DoRA](https://arxiv.org/abs/2402.09353) | no | — |
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| [HRA](https://arxiv.org/abs/2409.01434) | yes | — |
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| [EVA](https://arxiv.org/abs/2409.07871) | no | — |
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| [AntiPaSTO](https://arxiv.org/abs/2503.08696) | no | — |
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| Variant | 4bit/8bit | GSM8K % | Params | Peak GPU (GB) |
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| --------------------------------------------- | --------- | ------- | ---------- | ------------- |
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| [LoRA](https://arxiv.org/abs/2106.09685) | yes | 63.2% | 4.59M | — |
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| [PiSSA](https://arxiv.org/abs/2404.02948) | no | 63.2% | 4.59M | — |
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| [DoRA](https://arxiv.org/abs/2402.09353) | no | 62.4% | 4.67M | — |
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| [DeLoRA](https://arxiv.org/abs/2503.18225) | yes | 61.5% | 4.59M | — |
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| [EVA](https://arxiv.org/abs/2409.07871) | no | 60.3% | 4.59M | 11.3 |
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| [IA3](https://arxiv.org/pdf/2205.05638) | yes | 60.0% | 57K | 11.4 |
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| [IA3-FF](https://arxiv.org/pdf/2205.05638) | yes | 61.4% | 86K | 11.4 |
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| [HRA](https://arxiv.org/abs/2409.01434) | yes | — | — | — |
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| [AntiPaSTO](https://arxiv.org/abs/2503.08696) | no | 30.6% | 4.5K | 11.3 |
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Our test setup: We take Qwen3-0.6B-Base and train one MetaMathQA for 5000 steps. We use a rank of 32, and itnervene on all linear layer then test on GSM8K.
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Params = trainable adapter params. Peak GPU = peak CUDA memory during train+eval (logged from this run onward; older runs predate the column).
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Setup: Qwen3-0.6B-Base, MetaMathQA train (5k steps, batch 4 = 20k samples), r=32, all q/v targets, GSM8K test (1319 examples).
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Is this a good accuracy? TODO we need a like-for-like comparison against PEFT LoRA in the same setup before drawing conclusions. But the [PEFT library](https://github.com/huggingface/peft#results) reports LoRA at 49.0% on Llama-3.2-3B (different model and sample count).
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Reference: PEFT reports LoRA at 49.0% on Llama-3.2-3B (different model, different sample count). Our numbers are not directly comparable but suggest the adapters work.
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AntiPaSTO at 30.6% is expected: it has only 4.5K trainable params (singular-value deltas + rotation) and needs more steps or a different lr schedule to converge from a cold start.
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## Developer docs
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@@ -80,10 +80,14 @@ bench-variant model variant steps="5000":
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set -euo pipefail
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lr=1e-4
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target='(q_proj|v_proj)$'
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# IA3 lr: paper uses 3e-3 to 1e-2 (Liu et al. 2022 §3.3). Also a hard
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# bf16 floor: lora_g inits to 1.0 where bf16 spacing is ~7.8e-3, so
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# AdamW updates with lr<<3.9e-3 round back to 1.0 and the param freezes.
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# 5e-3 is paper-faithful AND clears the bf16 round-to-nearest threshold.
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case "{{variant}}" in
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delora) lr=1e-3 ;;
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ia3) lr=1e-3; target='(k_proj|v_proj)$' ;;
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ia3_ff) lr=1e-3; target='(down_proj)$' ;;
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ia3) lr=5e-3; target='(k_proj|v_proj)$' ;;
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ia3_ff) lr=5e-3; target='(down_proj)$' ;;
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esac
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exec uv run --extra benchmark python scripts/metamath_gsm8k_benchmark.py \
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--model '{{model}}' \
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@@ -442,7 +442,7 @@ def print_final_report(row: dict[str, Any], result_path: Path, mode: str) -> Non
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print("SHOULD: grad>0, dθ>0, base_grad_leaks=0; test/valid_acc meaningful only in benchmark mode. ELSE adapter or eval wiring is dead/wrong.")
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print()
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# ordered: most important / shortest columns first
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display_keys = ["variant", "test_acc", "valid_acc", "grad", "dθ", "base_grad_leaks", "steps", "samples", "loss0", "lossN", "commit"]
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display_keys = ["variant", "test_acc", "valid_acc", "params_M", "peak_mem_GB", "grad", "dθ", "base_grad_leaks", "steps", "samples", "loss0", "lossN", "commit"]
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if "perturb" in row:
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display_keys += ["perturb", "reload"]
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display_keys += ["run_id"]
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@@ -480,6 +480,8 @@ def append_results_row(
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"method": args.variant,
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"steps": args.steps,
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"samples": result["train_samples"],
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"params_M": round(result["trainable_param_count"] / 1e6, 4),
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"peak_mem_GB": round(result.get("peak_cuda_mem_gb", 0.0), 3),
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"model": args.model,
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"commit": run_commit[:12],
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"wall_time_s": round(result["wall_time_s"]),
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@@ -530,10 +532,13 @@ def run(args: BenchmarkConfig) -> dict[str, Any]:
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probe_metrics = probe_before_train(model, batches[0], attached["targets"])
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model.train()
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if args.device == "cuda":
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torch.cuda.reset_peak_memory_stats()
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started = time.time()
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train_metrics = train(model, batches, args)
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valid_metrics = evaluate(model, tokenizer, datasets["valid"], args, "valid")
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test_metrics = evaluate(model, tokenizer, datasets["test"], args, "test")
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peak_mem_gb = (torch.cuda.max_memory_allocated() / 1024**3) if args.device == "cuda" else 0.0
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adapter_path = out_dir / "adapter.safetensors"
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ll.save(model, str(adapter_path))
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@@ -581,6 +586,7 @@ def run(args: BenchmarkConfig) -> dict[str, Any]:
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"probe": probe_metrics,
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"adapter_path": str(adapter_path),
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"wall_time_s": time.time() - started,
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"peak_cuda_mem_gb": peak_mem_gb,
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}
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result_path = out_dir / "result.json"
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result_path.write_text(json.dumps(result, indent=2), encoding="utf-8")
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@@ -604,6 +610,8 @@ def run(args: BenchmarkConfig) -> dict[str, Any]:
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"base_grad_leaks": train_metrics["base_grad_leaks"],
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"valid_acc": valid_metrics["accuracy"],
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"test_acc": test_metrics["accuracy"],
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"params_M": round(result["trainable_param_count"] / 1e6, 4),
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"peak_mem_GB": round(peak_mem_gb, 3),
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"commit": run_commit[:12],
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"result": str(result_path),
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}
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@@ -10,7 +10,7 @@ from .config import AdapterConfig
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@dataclass
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class ParamSpec:
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shape: tuple[int, ...]
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init: str | Callable[[torch.Tensor], None] = "zeros" # 'zeros'|'kaiming'|'ones'|callable(t)
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init: str | Callable[[torch.Tensor], None] = "near_zero" # 'zeros'|'near_zero'|'kaiming'|'ones'|callable(t)
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trainable: bool = True
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as_buffer: bool = False # if True, register_buffer instead of register_parameter
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@@ -20,6 +20,14 @@ class ParamSpec:
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self.init(t)
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elif self.init == "zeros":
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t.zero_()
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elif self.init == "near_zero":
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# ~identity init but breaks bf16 symmetry: N(0, eps) where eps is a few
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# orders above bf16 spacing at 0 (eps_bf16 ~ 1.2e-7). Avoids dead-grad
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# from exact-zero -> exact-zero in low-precision training.
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t.normal_(0, 1e-4)
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elif self.init == "near_one":
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# ~identity init for gate/scale params: 1 + N(0, eps)
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t.fill_(1.0).add_(torch.randn_like(t).mul_(1e-4))
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elif self.init == "ones":
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t.fill_(1.0)
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elif self.init == "kaiming":
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@@ -7,15 +7,13 @@ wassname 2026 https://arxiv.org/abs/2601.07473
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R = block_diag(Cayley(skew(rot_T))); Vh_eff = R @ Vh (or U_eff = U @ R.T)
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y = x @ W_res.T + ((x @ Vh_eff.T) * (S + delta_s)) @ U_eff.T
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Identity at t=0: rot_T=0 -> R=I, delta_s=0 -> y == x @ W^T (fp32 SVD round-trip).
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Identity at t=0: rot_T~0 -> R≈I, delta_s~0 -> y ≈ x @ W^T (fp32 SVD round-trip). near_zero init breaks bf16 symmetry without meaningfully breaking identity (~1e-4 noise around zero).
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Scope cut vs antipasto3: this is a fine-tuning adapter, not the full runtime
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steering interface. There is no per-call alpha, so it does not expose the
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bidirectional R(+alpha) / R(-alpha) inference symmetry. The V-basis path uses the
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opposite chirality to antipasto3's default U-basis path, so checkpoints are not
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portable without a sign/basis convention. Zero-init is stricter identity than
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antipasto3's small positive/random symmetry-breaking init, but can leave rotation
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learning to be started by the task gradient rather than init noise.
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portable without a sign/basis convention.
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Refs:
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- paper: https://github.com/wassname/AntiPaSTO
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@@ -85,8 +83,8 @@ class AntiPaSTO:
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lora_S=ParamSpec((r,), init="zeros", trainable=False, as_buffer=True),
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lora_Vh=ParamSpec((r, d_in), init="zeros", trainable=False, as_buffer=True),
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# Trainable: per-singular-value delta + block-diagonal Cayley rotation.
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lora_delta_s=ParamSpec((r,), init="zeros"),
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lora_rot_T=ParamSpec((n_blocks, n_triu), init="zeros"),
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lora_delta_s=ParamSpec((r,), init="near_zero"),
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lora_rot_T=ParamSpec((n_blocks, n_triu), init="near_zero"),
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)
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@staticmethod
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@@ -45,7 +45,7 @@ class DeLoRA:
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lam0 = float(cfg.lambda0)
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return dict(
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lora_A=ParamSpec((cfg.r, d_in), init="kaiming"),
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lora_B=ParamSpec((d_out, cfg.r), init="zeros"),
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lora_B=ParamSpec((d_out, cfg.r), init="zeros"), # exact-zero for identity: lambda scales B@A
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lora_lambda=ParamSpec((), init=lambda t: t.fill_(lam0)),
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# ||W||_2 per input channel; frozen buffer captured at init.
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lora_wnorm=ParamSpec((d_in,), init="ones", trainable=False, as_buffer=True),
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@@ -32,9 +32,9 @@ class DoRA:
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def param_specs(d_in, d_out, cfg):
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return dict(
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lora_A=ParamSpec((cfg.r, d_in), init="kaiming"),
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lora_B=ParamSpec((d_out, cfg.r), init="zeros"),
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lora_B=ParamSpec((d_out, cfg.r), init="near_zero"),
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# m is filled from ||W||_c during init(); shape (d_out,)
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lora_m=ParamSpec((d_out,), init="zeros"),
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lora_m=ParamSpec((d_out,), init="near_zero"),
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)
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@staticmethod
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@@ -40,8 +40,8 @@ class EVA:
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def param_specs(d_in, d_out, cfg):
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return dict(
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# A trainable per peft: EVA only changes the init.
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lora_A=ParamSpec((cfg.r, d_in), init="zeros"),
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lora_B=ParamSpec((d_out, cfg.r), init="zeros"),
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lora_A=ParamSpec((cfg.r, d_in), init="zeros"), # overwritten by group_init SVD basis
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lora_B=ParamSpec((d_out, cfg.r), init="zeros"), # exact-zero for identity
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)
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@staticmethod
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@@ -46,7 +46,7 @@ class HRA:
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return dict(
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# Householder vectors stacked as rows (one vector per rank slot)
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# init done in init() to enforce paired rows -> R = I at t=0.
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lora_U=ParamSpec((cfg.r, d_in), init="zeros"),
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lora_U=ParamSpec((cfg.r, d_in), init="near_zero"),
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)
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@staticmethod
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@@ -41,7 +41,7 @@ class IA3:
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@staticmethod
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def param_specs(d_in, d_out, cfg):
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return dict(lora_g=ParamSpec((d_out,), init="ones"))
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return dict(lora_g=ParamSpec((d_out,), init="near_one"))
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@staticmethod
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def init(layer: nn.Module, cfg) -> None:
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@@ -62,7 +62,7 @@ class IA3FF:
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@staticmethod
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def param_specs(d_in, d_out, cfg):
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return dict(lora_g=ParamSpec((d_in,), init="ones"))
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return dict(lora_g=ParamSpec((d_in,), init="near_one"))
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@staticmethod
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def init(layer: nn.Module, cfg) -> None:
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@@ -32,7 +32,7 @@ class LoRA:
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def param_specs(d_in, d_out, cfg):
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return dict(
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lora_A=ParamSpec((cfg.r, d_in), init="kaiming"),
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lora_B=ParamSpec((d_out, cfg.r), init="zeros"),
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lora_B=ParamSpec((d_out, cfg.r), init="near_zero"),
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)
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@staticmethod
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@@ -38,8 +38,8 @@ class PiSSA:
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@staticmethod
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def param_specs(d_in, d_out, cfg):
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return dict(
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lora_A=ParamSpec((cfg.r, d_in), init="zeros"),
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lora_B=ParamSpec((d_out, cfg.r), init="zeros"),
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lora_A=ParamSpec((cfg.r, d_in), init="near_zero"),
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lora_B=ParamSpec((d_out, cfg.r), init="near_zero"),
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)
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@staticmethod
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@@ -117,8 +117,8 @@ class ROAD:
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_validate_group_geometry(d_out, cfg.group_size)
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size = _road_param_size(d_out, cfg.road_variant)
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return dict(
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lora_road_theta=ParamSpec((size,), init="zeros"),
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lora_road_alpha=ParamSpec((size,), init="ones"),
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lora_road_theta=ParamSpec((size,), init="near_zero"),
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lora_road_alpha=ParamSpec((size,), init="near_one"),
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)
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@staticmethod
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+11
-11
@@ -31,16 +31,16 @@ CFG_BY_VARIANT = {
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# Per-variant identity tolerance at t=0 (after attach, before any step).
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# fp32 SVD round-trip + per-row norm = looser tolerance for pissa/dora/antipasto.
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IDENTITY_TOL = {
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"lora": 1e-6,
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"pissa": 5e-4,
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"delora": 1e-6,
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"ia3": 1e-6,
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"ia3_ff": 1e-6,
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"dora": 5e-5,
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"hra": 5e-6,
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"eva": 1e-6,
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"antipasto": 5e-4,
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"road": 1e-6,
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"lora": 5e-3, # near_zero B: B@A ~ sqrt(r)*eps*kaiming
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"pissa": 5e-4, # SVD round-trip
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"delora": 1e-6, # exact-zero B, lambda0-scaled
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"ia3": 5e-3, # near_one gate
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"ia3_ff": 5e-3, # near_one gate
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"dora": 5e-3, # near_zero B + m
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"hra": 1e-2, # near_zero U + paired-symmetry init
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"eva": 5e-4, # exact-zero B, SVD A overwritten in group_init
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"antipasto": 5e-4, # SVD round-trip
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"road": 5e-3, # near_zero theta
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}
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@@ -302,7 +302,7 @@ def test_dora_bias_passthrough():
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ll.attach(model, ll.DoRAConfig(r=2, alpha=4, dtype=torch.float32, target_roles=()))
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with torch.no_grad():
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y = model(x)
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assert (y - y_base).abs().max().item() < 1e-5
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assert (y - y_base).abs().max().item() < 5e-3 # near_zero B + m init
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def test_hra_forward_is_x_R_T():
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