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lora-lite/docs/audit/variants_review_v3.md
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copilot 55757e829d fix V3 review must-fixes: DoRA bias passthrough + EVA load path 
V3 external review (docs/audit/variants_review_v3.md, 97KB) found 3
must-fix bugs.

DoRA: bias was being scaled by m/||V|| because we operated on the full
base layer output. Now subtract bias before normalization, add back
after. Matches peft DoRA exactly (docs/refs/peft_lora_dora.py:157-161).
New smoke dora_bias_smoke verifies identity at t=0 with bias=True.

EVA load: adapter.load() called attach() which called group_init() which
required calibration_data and raised. Added _skip_group_init flag to
attach(); load() passes it. EVA group_init still raises loudly when
called directly without data. New smoke verifies save+load WITHOUT
calibration data on load path.

Also tightened EVA error message.

Smoke now covers 8 variants + EVA roundtrip + DoRA-bias roundtrip + bnb
4/8-bit. ALL PASS.

V3 nice-to-haves (PiSSA scaling, AntiPaSTO init choice, stale GH refs)
deferred -- documented as intentional in module docstrings.
2026-04-26 19:50:48 +08:00

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[client] initialize (running)
[client] session/new (running)
I’ll use the `gsd` workflow because this is a multi-step audit, plus the repo’s local AGENTS instructions you pasted. I’m going to read the variant/runtime files and the relevant skill instructions first, then compare each module against its local refs and live links.
[tool] Read SKILL.md (running)
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[tool] Run git status --short (running)
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output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
---
name: gsd
description: "Get Shit Done workflow for non-trivial tasks: spec -> implement -> test -> review -> wrap. Mandatory for anything with more than one step."
---
<!--
Design rationale and sources — for human readers, not the agent.
- GSD spec-first: spec-driven development, scope fencing, verify-before-done
- Manus file-as-memory: one file is working memory, checklist is state, append-only log
- Boomerang: return concrete artifacts not summaries
- DCP: invisible dedup, superseded writes, error purging
Core principles:
- Spec file is the only persistent artifact (single-file invariant).
- Agent shouldn't know about its own resource limits.
- Review scales with input size: frontier for plans, cheap for bulk.
- Observable outputs: show evidence, not narration.
-->
# GSD (Get Shit Done)
Mandatory for all non-trivial tasks (more than one step). Skip only for single-line fixes.
## Part 1: Core workflow
### 1.1 Spec-first planning
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[tool] Run git status --short (completed)
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output:
?? docs/audit/REVIEW_PROMPT_VARIANTS_V3.md
?? docs/audit/variants_review_v3.md
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kind: search
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files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/lora_lite
output:
src/lora_lite/__init__.py
src/lora_lite/variant.py
src/lora_lite/config.py
src/lora_lite/adapter.py
tests/test_lora_lite.py
src/lora_lite/variants/ia3.py
tests/smoke.py
src/lora_lite/variants/lora.py
src/lora_lite/variants/dora.py
src/lora_lite/variants/delora.py
src/lora_lite/variants/__init__.py
src/lora_lite/variants/hra.py
src/lora_lite/variants/pissa.py
src/lora_lite/variants/eva.py
src/lora_lite/variants/antipasto.py
src/lora_lite/target.py
docs/refs/peft_lora_dora.py
docs/refs/orig_pissa_init.py
docs/refs/baukit_nethook.py
docs/refs/peft_eva.py
docs/refs/peft_lora_layer.py
docs/refs/peft_delora_layer.py
docs/refs/peft_lora_variants.py
docs/refs/orig_delora.py
docs/refs/antipasto3_svd_adapter.py
docs/refs/peft_hra_layer.py
docs/refs/orig_hra_layer.py
docs/refs/peft_ia3_layer.py
... (17 more lines)
I found two untracked audit files already present, so I’ll leave them untouched and treat the repo as read-only for this review. Next I’m pulling numbered source listings and the offline refs so the final can cite exact lines instead of hand-waving.
[tool] Read lora.py (running)
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output:
1 """Vanilla LoRA. Hu et al. 2021 https://arxiv.org/abs/2106.09685
2
3 h = W x + (alpha/r) B A x
4
5 Identity at t=0 from B=0. Faithful to the paper.
6
7 Reference implementations (for review/cross-check):
8 - peft Linear.update_layer + lora_A/B init, forward:
9 https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/layer.py
10 (see docs/refs/peft_lora_layer.py for offline copy)
11 """
12 from einops import einsum
13 from torch import nn
14 import torch
15
16 from ..variant import register, ParamSpec
17
18
19 @register
20 class LoRA:
21 name = "lora"
22
23 @staticmethod
24 def param_specs(d_in, d_out, cfg):
25 return {
26 "lora_A": ParamSpec((cfg.r, d_in), init="kaiming", trainable=True),
27 "lora_B": ParamSpec((d_out, cfg.r), init="zeros", trainable=True),
28 }
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[tool] Read pissa.py (running)
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[tool] Read delora.py (running)
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[tool] Read ia3.py (running)
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output:
1 """DeLoRA: per-input-channel weight-norm scaling, per-rank A/B normalization.
2
3 Bini et al. 2025 (ICLR'25) https://arxiv.org/abs/2503.18225
4
5 Paper Eq. 8: W' = W + (lambda * ||W||_F / r) B Xi A
6 where Xi_{i,i} = 1 / (||b_i|| ||a_i||) makes each rank-1 component unit-norm.
7
8 Implementation follows the peft upstream (which the DeLoRA authors maintain),
9 which differs from the paper notation in two ways that are equivalent at the
10 forward level but matter for gradients/numerics:
11 1. ||W|| is captured PER INPUT CHANNEL (shape (d_in,)), not as a scalar
12 Frobenius norm. Used to scale `x` element-wise on the input dim.
13 See docs/refs/peft_delora_layer.py:150 (init) and :250 (forward).
14 2. Per-rank normalization applied via division (1/||A_i||*||B^j||) inside
15 the diagonal scaling, instead of as F.normalize on A,B themselves.
16 This keeps the gradient flowing through the un-normalized parameters.
17
18 Identity at t=0: lambda0=0 -> delta is exactly zero (bit-identity).
19
20 KNOWN GRADIENT ISSUE (flagged by external review 2026-04-26):
21 With lambda0=0 the *forward* is identity but `A,B` get zero gradient on step 0
22 (delta is proportional to lambda). Only `lora_lambda` moves first step.
23 The paper's true initialization (frozen-copy trick, Eq. 9) achieves both
24 identity AND non-zero A/B gradients; we do NOT implement it here.
25
26 Reference implementations:
27 - DeLoRA paper authors (ExplainableML/DeLoRA) -- their fork of peft:
28 https://github.com/ExplainableML/DeLoRA/blob/main/peft/src/peft/tuners/delora.py
... (56 more lines)
[tool] Read ia3.py (completed)
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output:
1 """IA3-style elementwise gating. Liu et al. 2022 https://arxiv.org/abs/2205.05638
2
3 Two registered variants, matching the paper's two regimes:
4
5 * `ia3` -- OUTPUT-side gating, parameter shape (d_out,).
6 y_new = y * g. Use for attention projections (k_proj, v_proj).
7
8 * `ia3_ff` -- INPUT-side gating, parameter shape (d_in,).
9 y_new = base_layer(x * g). Use for FFN-down layers (down_proj,
10 fc2). Equivalent to the paper's "gate the FFN intermediate (post-
11 activation)" position because down_proj's input IS that
12 intermediate hidden state.
13
14 In both cases g is initialized to 1 -> identity at t=0.
15
16 To match the paper exactly on a Llama/Qwen-style block requires TWO attach
17 passes (one per variant), since each variant uses one hook type:
18
19 cfg_attn = LoraLiteConfig(variant="ia3", target_names=(r"\\.k_proj$", r"\\.v_proj$"))
20 cfg_ffn = LoraLiteConfig(variant="ia3_ff", target_names=(r"\\.down_proj$",))
21
22 Reference implementation:
23 - peft IA3 layer (is_feedforward toggles input-vs-output gating, see
24 docs/refs/peft_ia3_layer.py:177-188 forward and :214 update_layer):
25 https://github.com/huggingface/peft/blob/main/src/peft/tuners/ia3/layer.py
26 """
27 import torch
28 from torch import nn
... (36 more lines)
[tool] Read pissa.py (completed)
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files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/pissa.py
output:
1 """PiSSA: top-r SVD of W into A,B; replace W with W_res = W - B@A.
2
3 Meng et al. 2024 https://arxiv.org/abs/2404.02948
4 W_eff(t=0) = W_res + B@A = W (numerically; bf16 round-trip not bit-exact).
5
6 DEVIATION FROM PAPER (documented):
7 - Paper sets adapter scale = 1 (no alpha/r factor); we keep LoRA's alpha/r
8 pipeline so callers must pass alpha=r to get paper-faithful identity.
9 - Saved adapter does NOT include W_res (would double checkpoint size). Instead
10 `adapter.save` records a fingerprint of the post-init base weights and
11 `adapter.load` re-runs PiSSA init then verifies the fingerprint matches
12 -- so loading onto a different base weight raises loudly instead of
13 silently producing wrong outputs.
14
15 Reference implementations (for review/cross-check):
16 - PiSSA original (NeurIPS'24 spotlight) init script (SVD on dequant W):
17 https://github.com/MuLabPKU/PiSSA/blob/main/utils/init_pissa.py
18 (offline: docs/refs/orig_pissa_init.py)
19 - peft PiSSA flavor (init_lora_weights='pissa') in:
20 https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/layer.py
21 (offline: docs/refs/peft_lora_layer.py, see pissa_init / loftq_init paths)
22 """
23 import torch
24 from einops import einsum
25 from torch import nn
26
27 from ..variant import register, ParamSpec
28
... (43 more lines)
[tool] Read dora.py (running)
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[tool] Read eva.py (running)
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[tool] Read dora.py (completed)
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output:
1 """DoRA: weight-decomposed LoRA. Liu et al. 2024 https://arxiv.org/abs/2402.09353
2
3 W' = m * V / ||V||_c where V = W + (alpha/r) B A (||.||_c = per-output-row L2 norm)
4
5 At t=0: B=0 -> V=W -> y_new = (m_init / ||W||_c) (Wx + 0) = Wx when m_init = ||W||_c.
6
7 Limitation: requires materializing the dense weight to compute ||V||_c. v1 supports
8 plain nn.Linear only; bnb 4/8-bit layers raise loudly.
9
10 DEVIATION (numerical):
11 - We differentiate through ||V||_c every forward. The paper's sec. 4.3 suggests
12 a 'cost-saving' variant that detaches ||V|| in backward (gradient only flows
13 through V); we do NOT do that. Real impact: slower step, slightly different
14 gradient direction. Faithful to the eq.5 forward, not the optimized one.
15
16 Reference implementations (for review/cross-check):
17 - peft DoRA (separate file under lora/):
18 https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/dora.py
19 (offline: docs/refs/peft_lora_dora.py)
20 """
21 import torch
22 import torch.nn.functional as F
23 from einops import einsum
24 from torch import nn
25
26 from ..variant import register, ParamSpec
27
28
... (38 more lines)
[tool] Read hra.py (completed)
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files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/hra.py
output:
1 """HRA: Householder Reflection Adaptation. Yuan et al. 2024 https://arxiv.org/abs/2405.17484
2
3 Paper formulation (Sec. 3): adapt each frozen weight as
4
5 W' = W R, R = prod_{i=1..r} H_i, H_i = I - 2 u_i u_i^T / ||u_i||^2
6
7 so the layer output becomes y' = W' x = W (R x). R is in INPUT space (d_in x d_in).
8
9 We implement this via a `forward_input` pre-hook that returns `R x`, then the
10 frozen base layer (including bnb 4/8-bit Linear) computes `W (R x)` itself.
11
12 Identity at t=0 (PEFT-style symmetric init, requires even r):
13 Rows are kaiming-init in pairs: U[0]=U[1], U[2]=U[3], ... Adjacent pairs of
14 Householder reflections with identical vectors cancel exactly
15 (H_i H_i = I), so R = I at init -> y' = y to bit-precision.
16 After the first gradient step the paired rows diverge and the chain becomes a
17 general orthogonal matrix; gradient flows into U from step 0 (no dead-grad).
18 Odd r is rejected (matches peft warning behaviour).
19
20 OMITTED: paper also adds an orthogonality regularizer (Eq. 6 / Sec. 3.3),
21 a loss-side term. Add it in your training loop if you want regularized HRA.
22
23 Reference implementations (for review/cross-check):
24 - HRA paper authors (DaShenZi721/HRA), llama variant of OFT layer with HRA:
25 https://github.com/DaShenZi721/HRA/blob/master/llama/peft/oft/layer_GS_HRA.py
26 (offline: docs/refs/orig_hra_layer.py)
27 - peft HRA layer, reset_hra_parameters (lines 100-108):
28 https://github.com/huggingface/peft/blob/main/src/peft/tuners/hra/layer.py
... (50 more lines)
[tool] Read antipasto.py (completed)
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files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/antipasto.py
output:
1 """AntiPaSTO: SVD steering with learnable singular-value deltas + block-diagonal Cayley rotation.
2
3 Lite port of wassname's AntiPaSTO3 SVD adapter (research code, not an
4 upstream peft variant). Reference:
5 https://github.com/wassname/antipasto3 (offline: docs/refs/antipasto3_svd_adapter.py)
6
7 Decomposition (PyTorch nn.Linear convention, weight (d_out, d_in)):
8
9 W = U diag(S) Vh + W_res (top-r SVD; W_res = W - U_r diag(S_r) Vh_r)
10
11 We freeze U, S, Vh, W_res and learn:
12 - delta_s : (r,) -- additive delta to singular values
13 - rot_T : (n_blocks, bs(bs-1)/2) -- upper-triangle of skew matrix per block
14
15 Forward (matches base layer convention exactly at t=0):
16
17 R = block_diag(Cayley(skew(rot_T))) # (r, r) effective
18 Vh_rot = R @ Vh # rotates input basis
19 S_eff = S + delta_s # learnable spectrum
20 delta_y = ((x @ Vh_rot.T) * S_eff) @ U.T # rank-r path
21 base_y = x @ W_res.T # frozen residual
22 y_total = base_y + delta_y # == original output at t=0
23
24 At init: rot_T = 0 -> R = I -> Vh_rot = Vh, delta_s = 0 -> S_eff = S, so
25 delta_y reconstructs the truncated SVD term and y_total == x @ W^T to numerical
26 precision (fp32 SVD round-tripped to cfg.dtype).
27
28 WHICH BASIS IS ROTATED:
... (117 more lines)
[tool] Read eva.py (completed)
kind: read
input: {"call_id":"call_aT1ZKpUsBsF0ukmeSQy5wQSa","process_id":"30592","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/eva.py"],"cwd":"/media/wassname/SGIronWol...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/eva.py
output:
1 """EVA: Explained-Variance Adaptation. Paischer et al. 2024.
2
3 Paper: https://arxiv.org/abs/2410.07170 (also referred to as ICLR'25 EVA).
4
5 Idea: instead of random A and zero B (LoRA) or SVD of W (PiSSA), initialize
6 `lora_A` to the top-r right singular vectors of the LAYER INPUT distribution
7 on a small calibration set. Forward = `y + scale * (B @ A @ x)` exactly like
8 LoRA; with `lora_B = 0` the adapter is identity at t=0. Only B trains
9 afterwards (A frozen). The result: each rank slot points along a direction
10 that actually carries information at this layer.
11
12 This is a stripped-down EVA; we do NOT implement:
13 - rank redistribution across layers via explained-variance ratios
14 (peft EVA computes an explained_variance_ratio per layer then redistributes
15 the global rank budget; we use a uniform `cfg.r` per layer).
16 - Incremental PCA over many micro-batches (we run one full SVD on the
17 pooled calibration activations per layer).
18 - Equal-input deduplication (peft hashes inputs to share SVD across QKV).
19
20 API stress-test: this variant requires data-driven init, so it implements
21 `group_init(model, targets, cfg, calibration_data)` to drive a single forward
22 pass on `calibration_data` with hooks that capture each target's input.
23
24 Identity at t=0: `lora_B = 0` -> delta = 0 -> y unchanged.
25
26 References:
27 - peft EVA (full impl, with IncrementalPCA + redistribution):
28 https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/eva.py
... (93 more lines)
[tool] Read adapter.py (running)
input: {"call_id":"call_ZASqXizFcUeYWy0BfWtn7awK","process_id":"56583","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/adapter.py"],"cwd":"/media/wassname/SGIronWolf/pro...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/adapter.py
[tool] Read variant.py (running)
input: {"call_id":"call_DTjBD9RvOp7yhpRxBMhsmmpH","process_id":"59269","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variant.py"],"cwd":"/media/wassname/SGIronWolf/pro...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variant.py
[tool] Read config.py (running)
input: {"call_id":"call_gFTpgHVNt3AjUpsu0uccR8Xj","process_id":"57355","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/config.py"],"cwd":"/media/wassname/SGIronWolf/proj...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/config.py
[tool] Read smoke.py (running)
input: {"call_id":"call_x0N2faRQBhIMJ6dP09QwZiFv","process_id":"65832","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba tests/smoke.py"],"cwd":"/media/wassname/SGIronWolf/projects5/202...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/tests/smoke.py
[tool] Read target.py (running)
input: {"call_id":"call_J7iRBknduLUEbwo0vQAyXzkS","process_id":"13496","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/target.py"],"cwd":"/media/wassname/SGIronWolf/proj...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/target.py
[tool] Read adapter.py (completed)
kind: read
input: {"call_id":"call_ZASqXizFcUeYWy0BfWtn7awK","process_id":"56583","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/adapter.py"],"cwd":"/media/wassname/SGIronWolf/pro...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/adapter.py
output:
1 """attach / detach / save / load. The whole runtime."""
2 from __future__ import annotations
3 import torch
4 from torch import nn
5 from torch.utils.hooks import RemovableHandle
6
7 from .config import LoraLiteConfig
8 from .variant import REGISTRY
9 from .target import find_targets
10
11
12 _ATTACHED_ATTR = "_lora_lite_attached"
13
14
15 def _hook(layer, args, y):
16 (x,) = args
17 cfg: LoraLiteConfig = layer._lora_cfg
18 x_cast = x.to(cfg.dtype)
19 out = layer._lora_variant.forward(layer, x_cast, y)
20 return out.to(y.dtype)
21
22
23 def _pre_hook(layer, args):
24 (x,) = args
25 cfg: LoraLiteConfig = layer._lora_cfg
26 x_cast = x.to(cfg.dtype)
27 x_new = layer._lora_variant.forward_input(layer, x_cast)
28 return (x_new.to(x.dtype),)
... (126 more lines)
[tool] Read variant.py (completed)
kind: read
input: {"call_id":"call_DTjBD9RvOp7yhpRxBMhsmmpH","process_id":"59269","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variant.py"],"cwd":"/media/wassname/SGIronWolf/pro...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variant.py
output:
1 """Variant protocol + registry. Variants own (x, layer.weight, layer.lora_*) -> y_new."""
2 from dataclasses import dataclass
3 from typing import Callable, Protocol, Any
4 import torch
5 from torch import nn
6
7 from .config import LoraLiteConfig
8
9
10 @dataclass
11 class ParamSpec:
12 shape: tuple[int, ...]
13 init: str | Callable[[torch.Tensor], None] = "zeros" # 'zeros'|'kaiming'|'ones'|callable(t)
14 trainable: bool = True
15 as_buffer: bool = False # if True, register_buffer instead of register_parameter
16
17 def _empty(self, dtype: torch.dtype, device) -> torch.Tensor:
18 t = torch.empty(self.shape, dtype=dtype, device=device)
19 if callable(self.init):
20 self.init(t)
21 elif self.init == "zeros":
22 t.zero_()
23 elif self.init == "ones":
24 t.fill_(1.0)
25 elif self.init == "kaiming":
26 # match nn.Linear default: kaiming_uniform_(a=sqrt(5))
27 nn.init.kaiming_uniform_(t, a=5 ** 0.5) if t.ndim >= 2 else t.normal_(0, 0.02)
28 else:
... (39 more lines)
[tool] Read config.py (completed)
kind: read
input: {"call_id":"call_gFTpgHVNt3AjUpsu0uccR8Xj","process_id":"57355","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/config.py"],"cwd":"/media/wassname/SGIronWolf/proj...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/config.py
output:
1 from dataclasses import dataclass, field, asdict
2 from typing import Any
3 import torch
4
5
6 @dataclass
7 class LoraLiteConfig:
8 variant: str = "lora"
9 r: int = 8
10 alpha: float = 16.0
11 dtype: torch.dtype = torch.bfloat16
12
13 # targeting
14 target_roles: tuple[str, ...] = ("reader", "writer")
15 target_names: tuple[str, ...] = ()
16 exclude_names: tuple[str, ...] = ("lm_head", "embed_tokens")
17 layers: tuple[int, ...] | None = None
18
19 # variant-specific bag (e.g. lambda0 for DeLoRA)
20 variant_kwargs: dict[str, Any] = field(default_factory=dict)
21
22 def to_dict(self) -> dict:
23 d = asdict(self)
24 d["dtype"] = str(self.dtype).removeprefix("torch.")
25 return d
26
27 @classmethod
28 def from_dict(cls, d: dict) -> "LoraLiteConfig":
... (9 more lines)
[tool] Read smoke.py (completed)
kind: read
input: {"call_id":"call_x0N2faRQBhIMJ6dP09QwZiFv","process_id":"65832","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba tests/smoke.py"],"cwd":"/media/wassname/SGIronWolf/projects5/202...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/tests/smoke.py
output:
1 """Smoke test: current variants on a tiny synthetic transformer-like model.
2
3 Verifies:
4 1. Identity at t=0 (delta ~ 0, output close to base).
5 2. Save/load round-trip preserves outputs.
6 3. A few SGD steps reduce a random loss (gradients flow).
7
8 Run:
9 cd lora-lite
10 python -m pip install -e .
11 python tests/smoke.py
12
13 BLUF format:
14 SHOULD: max|y_adapter - y_base| < tol_init for all variants. ELSE init or hook bug.
15 SHOULD: loss decreases > 5% over 20 SGD steps for all variants. ELSE grad/wiring bug.
16 """
17 from __future__ import annotations
18 import argparse
19 import os, sys, math
20 from pathlib import Path
21 import torch
22 from torch import nn
23
24 # allow running as `python tests/smoke.py` without install
25 sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
26
27 import lora_lite as ll # noqa: E402
28
... (317 more lines)
[tool] Read target.py (completed)
kind: read
input: {"call_id":"call_J7iRBknduLUEbwo0vQAyXzkS","process_id":"13496","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/target.py"],"cwd":"/media/wassname/SGIronWolf/proj...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/target.py
output:
1 """Find linear-like targets by shape (reader/writer/inner) + name regex.
2
3 Structural matching is deliberate: bnb Linear4bit/8bitLt are not nn.Linear, but
4 they expose in_features/out_features/weight and their forward already handles
5 dequantization.
6 """
7 import re
8 from torch import nn
9
10
11 def is_linear_like(m: nn.Module) -> bool:
12 return (
13 hasattr(m, "in_features")
14 and hasattr(m, "out_features")
15 and hasattr(m, "weight")
16 and callable(m)
17 )
18
19
20 def _layer_idx(name: str) -> int | None:
21 m = re.search(r"\.layers?\.(\d+)\.", name)
22 return int(m.group(1)) if m else None
23
24
25 def _classify(m: nn.Module, d_model: int, name: str) -> str:
26 di, do = m.in_features, m.out_features
27 if di == d_model and do != d_model:
28 return "reader"
... (31 more lines)
A few early runtime facts matter for every variant: adapter buffers are persistent (`register_buffer(..., persistent=True)`), all adapter tensors default to `cfg.dtype`, and hooks cast inputs into that dtype before variant forward. I’m now checking the reference snapshots around their init/forward code paths, then I’ll sanity-check the live docstring URLs.
[tool] Search lora_A|lora_B|scaling|kaiming|reset_lora|pissa|PiSSA|svd|dora|weight_norm|lora_magnitude_vector|magnitude in peft_lora_layer.py (running)
input: {"call_id":"call_CXL2dLkBuKVYv7swdUx31r7B","process_id":"21601","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"lora_A|lora_B|scaling|kaiming|reset_lora|pissa|PiSSA|svd|dora|w...
[tool] Search ia3|ia3_l|is_feedforward|forward|update_layer|ones|Parameter in peft_ia3_layer.py (running)
input: {"call_id":"call_QNRan5JW29Lt2z32ejlnQSiE","process_id":"83887","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"ia3|ia3_l|is_feedforward|forward|update_layer|ones|Parameter\" ...
[tool] Search hra|reflection|householder|reset_hra|hra_u|Parameter|forward|apply in peft_hra_layer.py (running)
input: {"call_id":"call_l6wFam8sUpDhQje5Kw1ddbP9","process_id":"88970","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"hra|reflection|householder|reset_hra|hra_u|Parameter|forward|ap...
[tool] Search lora_A|lora_B|scaling|kaiming|reset_lora|pissa|PiSSA|svd|dora|weight_norm|lora_magnitude_vector|magnitude in peft_lora_layer.py (completed)
kind: search
input: {"call_id":"call_CXL2dLkBuKVYv7swdUx31r7B","process_id":"21601","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"lora_A|lora_B|scaling|kaiming|reset_lora|pissa|PiSSA|svd|dora|w...
output:
docs/refs/peft_lora_dora.py:66: self._dora_cache: dict[str, Any] = {} # small ad hoc cache; values are not part of the state_dict
docs/refs/peft_lora_dora.py:70: self._dora_cache[key] = value
docs/refs/peft_lora_dora.py:74: return self._dora_cache.get(key, default)
docs/refs/peft_lora_dora.py:77: self._dora_cache.clear()
docs/refs/peft_lora_dora.py:86: def get_weight_norm(self, weight, lora_weight, scaling, adapter_name: Optional[str] = None) -> torch.Tensor:
docs/refs/peft_lora_dora.py:89: weight = weight + scaling * lora_weight
docs/refs/peft_lora_dora.py:90: weight_norm = torch.linalg.norm(weight, dim=1).to(weight.dtype)
docs/refs/peft_lora_dora.py:91: return weight_norm
docs/refs/peft_lora_dora.py:94: def get_lora_weight(self, lora_A, lora_B, adapter_name: Optional[str] = None):
docs/refs/peft_lora_dora.py:95: # Don't use `lora_weight = lora_B.weight @ lora_A.weight` because this causes errors with FSDP. Instead,
docs/refs/peft_lora_dora.py:97: x_eye = torch.eye(lora_A.weight.shape[1], device=lora_A.weight.device, dtype=lora_A.weight.dtype)
docs/refs/peft_lora_dora.py:98: lora_weight = lora_B(lora_A(x_eye)).T
docs/refs/peft_lora_dora.py:101: def update_layer(self, *, base_layer, lora_A, lora_B, scaling, place_on_cpu=False) -> None:
docs/refs/peft_lora_dora.py:103: dtype_is_fp16 = lora_A.dtype == torch.float16
docs/refs/peft_lora_dora.py:105: lora_A = lora_A.float()
docs/refs/peft_lora_dora.py:106: lora_B = lora_B.float()
docs/refs/peft_lora_dora.py:116: r = lora_A.shape[0]
docs/refs/peft_lora_dora.py:117: lora_weight = torch.mm(lora_B.view([-1, r]), lora_A.view([r, -1]))
docs/refs/peft_lora_dora.py:120: lora_weight = lora_B @ lora_A
docs/refs/peft_lora_dora.py:124: weight_norm = self.get_weight_norm(
docs/refs/peft_lora_dora.py:125: weight=weight.to(lora_A.device), lora_weight=lora_...
[tool] Search delora|lora_lambda|wnorm|weight_norm|norm|scaling|forward|reset|kaiming in peft_delora_layer.py (running)
input: {"call_id":"call_eZ4HOEgS7NuaCwjmwX8tSURH","process_id":"24489","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"delora|lora_lambda|wnorm|weight_norm|norm|scaling|forward|reset...
[tool] Search ia3|ia3_l|is_feedforward|forward|update_layer|ones|Parameter in peft_ia3_layer.py (completed)
kind: search
input: {"call_id":"call_QNRan5JW29Lt2z32ejlnQSiE","process_id":"83887","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"ia3|ia3_l|is_feedforward|forward|update_layer|ones|Parameter\" ...
output:
30: adapter_layer_names = ("ia3_l",)
32: def __init__(self, base_layer: nn.Module, is_feedforward: bool, **kwargs) -> None:
34: self.ia3_l = nn.ParameterDict({})
38: self.is_feedforward = is_feedforward
56: def update_layer(self, adapter_name: str, config: IA3Config, **kwargs):
57: init_ia3_weights = config.init_ia3_weights
62: if self.is_feedforward:
66: self.ia3_l[adapter_name] = nn.Parameter(weight)
67: if init_ia3_weights:
68: self.reset_ia3_parameters(adapter_name)
72: def reset_ia3_parameters(self, adapter_name):
73: if adapter_name in self.ia3_l.keys():
74: # initialize learned vector with torch.ones
75: nn.init.constant_(self.ia3_l[adapter_name], 1.0)
85: is_feedforward: bool = False, # Set to True if the layer is treated as a feedforward layer
90: IA3Layer.__init__(self, base_layer, is_feedforward=is_feedforward)
94: self.update_layer(adapter_name, config=config)
115: if active_adapter in self.ia3_l.keys():
117: ia3_l = transpose(self.ia3_l[active_adapter].data, self.fan_in_fan_out)
121: orig_weights = torch.mul(orig_weights, ia3_l)
129: base_layer.weight.data = torch.mul(base_layer.weight.data, ia3_l).to(orig_dtype)
131: if not self.is_feedforward and (base_layer.bias is not None):
132: scaling = self.ia3_l[active_adapter].reshape(base_layer.bias.shape)
149: if active_adapter in self.ia3_l.keys():
152: ia3_l = transpose(self.ia3_l[active_adapter].data, self.fan_in_fan_out) + 1e-8
154: base_layer.weight.data = torch.div(base_layer.weight.data, ia3_l).to(orig_dtype)
156: if not self.is_feedforward and (base_layer.bias is not None):
157: scaling = self.ia3_l[active_adapter].reshape(base_layer.bias.shape)
... (42 more lines)
[tool] Search hra|reflection|householder|reset_hra|hra_u|Parameter|forward|apply in peft_hra_layer.py (completed)
kind: search
input: {"call_id":"call_l6wFam8sUpDhQje5Kw1ddbP9","process_id":"88970","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"hra|reflection|householder|reset_hra|hra_u|Parameter|forward|ap...
output:
docs/refs/peft_hra_layer.py:30: adapter_layer_names = ("hra_u",)
docs/refs/peft_hra_layer.py:32: other_param_names = ("hra_r", "hra_apply_GS")
docs/refs/peft_hra_layer.py:36: self.hra_r = {}
docs/refs/peft_hra_layer.py:37: self.hra_apply_GS = {}
docs/refs/peft_hra_layer.py:38: self.hra_u = nn.ParameterDict({})
docs/refs/peft_hra_layer.py:42: # flag to enable/disable casting of input to weight dtype during forward call
docs/refs/peft_hra_layer.py:61: """Internal function to create hra adapter
docs/refs/peft_hra_layer.py:68: apply_GS = config.apply_GS
docs/refs/peft_hra_layer.py:75: self.hra_r[adapter_name] = r
docs/refs/peft_hra_layer.py:76: self.hra_apply_GS[adapter_name] = apply_GS
docs/refs/peft_hra_layer.py:81: self.hra_u[adapter_name] = nn.Parameter(torch.empty(self.in_features, r), requires_grad=True)
docs/refs/peft_hra_layer.py:83: self.hra_u[adapter_name] = nn.Parameter(
docs/refs/peft_hra_layer.py:92: self.reset_hra_parameters(adapter_name)
docs/refs/peft_hra_layer.py:94: self.reset_hra_parameters_random(adapter_name)
docs/refs/peft_hra_layer.py:100: def reset_hra_parameters(self, adapter_name: str):
docs/refs/peft_hra_layer.py:101: if self.hra_r[adapter_name] % 2 != 0:
docs/refs/peft_hra_layer.py:103: nn.init.kaiming_uniform_(self.hra_u[adapter_name], a=math.sqrt(5))
docs/refs/peft_hra_layer.py:105: shape = self.hra_u[adapter_name].shape
docs/refs/peft_hra_layer.py:108: self.hra_u[adapter_name] = nn.Parameter(torch.repeat_interleave(half_u, 2, dim=1))
docs/refs/peft_hra_layer.py:110: def reset_hra_parameters_random(self, adapter_name: str):
docs/refs/peft_hra_layer.py:111: nn.init.kaiming_uniform_(self.hra_u[adapter_name], a=math.sqrt(5))
docs/refs/peft_hra_layer.py:118: if active_adapter not in self.hra_u.keys():
docs/refs/peft_hra_layer.py:125: if active_adapter not in self....
[tool] Search delora|lora_lambda|wnorm|weight_norm|norm|scaling|forward|reset|kaiming in peft_delora_layer.py (completed)
kind: search
input: {"call_id":"call_eZ4HOEgS7NuaCwjmwX8tSURH","process_id":"24489","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"delora|lora_lambda|wnorm|weight_norm|norm|scaling|forward|reset...
output:
docs/refs/peft_delora_layer.py:32: "delora_A",
docs/refs/peft_delora_layer.py:33: "delora_B",
docs/refs/peft_delora_layer.py:34: "delora_lambda",
docs/refs/peft_delora_layer.py:39: "delora_dropout",
docs/refs/peft_delora_layer.py:40: "delora_w_norm",
docs/refs/peft_delora_layer.py:46: self.delora_dropout = nn.ModuleDict({})
docs/refs/peft_delora_layer.py:47: self.delora_A = nn.ParameterDict({})
docs/refs/peft_delora_layer.py:48: self.delora_B = nn.ParameterDict({})
docs/refs/peft_delora_layer.py:49: self.delora_lambda = nn.ParameterDict({})
docs/refs/peft_delora_layer.py:51: self.delora_w_norm = BufferDict({}, persistent=True)
docs/refs/peft_delora_layer.py:65: A: torch.Tensor, B: torch.Tensor, delora_lambda: torch.Tensor, r: int, w_norm: torch.Tensor
docs/refs/peft_delora_layer.py:67: """Compute delta = B @ diag(delora_lambda/r / (||A_i||*||B^j||)) @ A, scaled by provided w_norm (per-input channel)"""
docs/refs/peft_delora_layer.py:68: An = torch.clamp(A.norm(dim=1), min=1e-4)
docs/refs/peft_delora_layer.py:69: Bn = torch.clamp(B.norm(dim=0), min=1e-4)
docs/refs/peft_delora_layer.py:70: diag = torch.diag_embed(delora_lambda / r / (An * Bn))
docs/refs/peft_delora_layer.py:72: delta = delta * w_norm.unsqueeze(0)
docs/refs/peft_delora_layer.py:76: if adapter not in self.delora_A or adapter not in self.delora_B:
docs/refs/peft_delora_layer.py:80: self.delora_A[adapter],
docs/refs/peft_delora_layer.py:81: self.delora_B[adapter],
docs/refs/peft_delora_layer.py:82: self.delora_lambda[adapter],
docs/refs/peft_delora_layer.py:84: self.delora_w_norm[adapter],
docs/refs/peft_delora_layer.py:92: delora_lambda: float,
docs/refs/peft_delora_layer.py:96: """Internal function to create delora adapter
docs/refs/peft_delora_layer.py:101: delora_lambda (`float`): Boundary for the adapter's norm.
...
[tool] Read peft_lora_layer.py (running)
input: {"call_id":"call_JRbF1Xn6kmbu1aeGenPoTbn3","process_id":"19417","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '360,395p' docs/refs/peft_lora_layer.py"],"cwd":"/media/wassname...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py
[tool] Search SVD|svd|delta_s|rot|Cayley|rotate|U|Vh|V in antipasto3_svd_adapter.py (running)
input: {"call_id":"call_eDElMn5nrdWzS4IgBNYQZ4gS","process_id":"39796","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"SVD|svd|delta_s|rot|Cayley|rotate|U|Vh|V\" docs/refs/antipasto3...
[tool] Search initialize_lora_eva_weights|eva|SVD|svd|IncrementalPCA|lora_A|lora_B|rank|explained|dropout in peft_eva.py (running)
input: {"call_id":"call_XGlVYcHZnz3nyKUx46IAmL8z","process_id":"62921","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"initialize_lora_eva_weights|eva|SVD|svd|IncrementalPCA|lora_A|l...
[tool] Read peft_lora_layer.py (running)
input: {"call_id":"call_ppwxQIwOtJyElrbeCwjGwIvU","process_id":"22962","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '958,972p' docs/refs/peft_lora_layer.py"],"cwd":"/media/wassname...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py
[tool] Search SVD|svd|delta_s|rot|Cayley|rotate|U|Vh|V in antipasto3_svd_adapter.py (completed)
kind: search
input: {"call_id":"call_eDElMn5nrdWzS4IgBNYQZ4gS","process_id":"39796","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"SVD|svd|delta_s|rot|Cayley|rotate|U|Vh|V\" docs/refs/antipasto3...
output:
1:"""SVD adapter for bidirectional steering via block-diagonal Cayley rotations.
5:kernel = U @ diag(S) @ Vh + W_res (kernel is (in, out), standard Flax convention)
6:Learnable: delta_s (additive S scaling), rotation_params (block-diagonal V rotation).
7:alpha scales both: S + alpha*delta_s, U @ R(alpha).
9:Why Cayley (not Givens or matrix exponential):
10:Cayley gives exact analytical reversibility: R(-alpha) = R(alpha)^{-1}.
14:At alpha=0: U_rot = U and S_scaled = S, so the layer is identical to frozen weights.
30:class SVDParam(nnx.Param):
31: """Trainable SVD adapter parameter (base class -- use subclasses for per-group LR)."""
35:class DeltaSParam(SVDParam):
36: """Trainable delta_s scaling parameters (full LR)."""
40:class RotationParam(SVDParam):
41: """Block-diagonal rotation parameters (lower LR via rotation_lr_scale config)."""
45:class SVDFrozen(nnx.Variable):
46: """Frozen SVD component. Not differentiated."""
50:# -- SVD Steering Linear (replaces nnx.Linear) ------------------------------
52:class SVDSteeringLinear(nnx.Module):
53: """SVD steering adapter replacing a linear layer.
55: y = ((x @ U_rot) * S_scaled) @ Vh + x @ W_res
57: where U_rot, S_scaled depend on alpha (the steering coefficient).
58: Frozen: U, S, Vh, W_res. Learnable: delta_s, rotation_params.
63: U: jax.Array, # (in_dim, r) - input singular vectors
65: Vh: jax.Array, # (r, out_dim) - output singular vectors
67: rotation_block_size: int,
68: max_rotation_angle: float,
69: rotate_U: bool = True,
70: rotate_V: bool = False,
71: use_delta_s: bool = True,
... (90 more lines)
[tool] Read peft_lora_layer.py (completed)
kind: read
input: {"call_id":"call_JRbF1Xn6kmbu1aeGenPoTbn3","process_id":"19417","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '360,395p' docs/refs/peft_lora_layer.py"],"cwd":"/media/wassname...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
def pissa_init(self, adapter_name, init_lora_weights):
weight = self.get_base_layer().weight
dtype = weight.dtype
if dtype not in [torch.float32, torch.float16, torch.bfloat16]:
raise TypeError(
"Please initialize PiSSA under float32, float16, or bfloat16. "
"Subsequently, re-quantize the residual model to help minimize quantization errors."
)
weight = transpose(weight.to(torch.float32), self.fan_in_fan_out)
if init_lora_weights == "pissa":
# USV^T = W <-> VSU^T = W^T, where W^T = weight.data in R^{out_channel, in_channel},
V, S, Uh = torch.linalg.svd(weight.data, full_matrices=False)
Vr = V[:, : self.r[adapter_name]]
Sr = S[: self.r[adapter_name]]
Sr /= self.scaling[adapter_name]
Uhr = Uh[: self.r[adapter_name]]
elif len(init_lora_weights.split("_niter_")) == 2:
Vr, Sr, Ur = svd_lowrank(
weight.data, self.r[adapter_name], niter=int(init_lora_weights.split("_niter_")[-1])
)
Sr /= self.scaling[adapter_name]
Uhr = Ur.t()
else:
raise ValueError(
f"init_lora_weights should be 'pissa' or 'pissa_niter_[number of iters]', got {init_lora_weights} instead."
)
... (9 more lines)
[tool] Read peft_lora_dora.py (running)
input: {"call_id":"call_76bagUsTDjh2isFAy7PqqYus","process_id":"47762","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '132,166p' docs/refs/peft_lora_dora.py"],"cwd":"/media/wassname/...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_dora.py
[tool] Search initialize_lora_eva_weights|eva|SVD|svd|IncrementalPCA|lora_A|lora_B|rank|explained|dropout in peft_eva.py (completed)
kind: search
input: {"call_id":"call_XGlVYcHZnz3nyKUx46IAmL8z","process_id":"62921","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","rg -n \"initialize_lora_eva_weights|eva|SVD|svd|IncrementalPCA|lora_A|l...
output:
docs/refs/peft_eva_finetuning.py:21:from peft import EvaConfig, LoraConfig, get_peft_model, initialize_lora_eva_weights
docs/refs/peft_eva_finetuning.py:29:rank = 16
docs/refs/peft_eva_finetuning.py:33:svd_batch_size = 4 # can be different from the batch size used in finetuning
docs/refs/peft_eva_finetuning.py:61: batch_size=svd_batch_size,
docs/refs/peft_eva_finetuning.py:66:eva_config = EvaConfig(rho=rho)
docs/refs/peft_eva_finetuning.py:68: r=rank, lora_alpha=alpha, target_modules=target_modules, init_lora_weights="eva", eva_config=eva_config
docs/refs/peft_eva_finetuning.py:74:# to optimize memory usage during eva initialization, set low_cpu_mem_usage=True
docs/refs/peft_eva_finetuning.py:76:initialize_lora_eva_weights(peft_model, dataloader)
docs/refs/peft_eva.py:31:from peft.utils.incremental_pca import IncrementalPCA
docs/refs/peft_eva.py:106:class SVDHook(_Hook):
docs/refs/peft_eva.py:108: A forward hook for calculating incremental SVD on layer inputs. The hook is designed to be registered to a PyTorch
docs/refs/peft_eva.py:111: This hook performs a step of incremental Singular Value Decomposition (SVD) on the inputs of a specified layer
docs/refs/peft_eva.py:119: prepare_layer_inputs_fn (Optional[callable]): Function to prepare layer inputs for SVD.
docs/refs/peft_eva.py:138: self.svd = IncrementalPCA(
docs/refs/peft_eva.py:141: lowrank=True,
docs/refs/peft_eva.py:142: lowrank_seed=42,
docs/refs/peft_eva.py:150: if hasattr(self.svd, "components_"):
docs/refs/peft_eva.py:151: previous_components = self.svd.components_.clone().detach()
docs/refs/peft_eva.py:156: print(f"skipping SVD for {self.name} because there are less than {self.n_components} examples")
docs/refs/peft_eva.py:158: self.svd.partial_fit(states.to(torch.float32))
docs/refs/peft_eva.py:162: components = self.svd.components_
docs/refs/peft_eva.py:171:# This is used to determine if inputs of two differe...
[tool] Read peft_lora_layer.py (completed)
kind: read
input: {"call_id":"call_ppwxQIwOtJyElrbeCwjGwIvU","process_id":"22962","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '958,972p' docs/refs/peft_lora_layer.py"],"cwd":"/media/wassname...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
lora_A_keys = self.lora_A.keys()
for active_adapter in self.active_adapters:
if active_adapter not in lora_A_keys:
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
x = self._cast_input_dtype(x, lora_A.weight.dtype)
if active_adapter not in self.lora_variant: # vanilla LoRA
result = result + lora_B(lora_A(dropout(x))) * scaling
else:
result = self.lora_variant[active_adapter].forward(
self,
[tool] Read peft_lora_dora.py (completed)
kind: read
input: {"call_id":"call_76bagUsTDjh2isFAy7PqqYus","process_id":"47762","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '132,166p' docs/refs/peft_lora_dora.py"],"cwd":"/media/wassname/...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_dora.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
def forward(self, x, *, lora_A, lora_B, scaling, base_layer, base_result=None, adapter_name="default"):
"""
For DoRA, calculate the extra output from LoRA with DoRA applied. This should be added on top of the base layer
output.
"""
lora_weight = self.get_lora_weight(lora_A=lora_A, lora_B=lora_B, adapter_name=adapter_name)
lora_weight = lora_weight.to(x.dtype)
magnitude = self.weight
weight = dequantize_module_weight(base_layer)
weight = weight.to(x.dtype)
weight_norm = self.get_weight_norm(
weight=weight, lora_weight=lora_weight.detach(), scaling=scaling, adapter_name=adapter_name
)
# see section 4.3 of DoRA (https://huggingface.co/papers/2402.09353)
# "[...] we suggest treating ||V +∆V ||_c in
# Eq. (5) as a constant, thereby detaching it from the gradient
# graph. This means that while ||V + ∆V ||_c dynamically
# reflects the updates of ∆V , it won’t receive any gradient
# during backpropagation"
weight_norm = weight_norm.detach()
mag_norm_scale = (magnitude / weight_norm).view(1, -1)
lora_result = lora_B(lora_A(x))
bias = None
if base_result is not None:
... (8 more lines)
[tool] Read peft_delora_layer.py (running)
input: {"call_id":"call_STRTnAVOq9WZ4NOQ1m9F24J2","process_id":"78221","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '246,261p' docs/refs/peft_delora_layer.py"],"cwd":"/media/wassna...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_delora_layer.py
[tool] Read peft_delora_layer.py (running)
input: {"call_id":"call_mcVVrgkceSeX6A8QIU5qL1OQ","process_id":"75092","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '128,161p' docs/refs/peft_delora_layer.py"],"cwd":"/media/wassna...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_delora_layer.py
[tool] Read peft_ia3_layer.py (running)
input: {"call_id":"call_mVR5vVoBRVvdD7i08awFEP5E","process_id":"7411","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '291,317p' docs/refs/peft_ia3_layer.py"],"cwd":"/media/wassname/S...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_ia3_layer.py
[tool] Read peft_hra_layer.py (running)
input: {"call_id":"call_L8erIsjTuP4GFpaT5V50afZP","process_id":"81187","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '75,108p' docs/refs/peft_hra_layer.py"],"cwd":"/media/wassname/S...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_hra_layer.py
[tool] Read peft_delora_layer.py (completed)
kind: read
input: {"call_id":"call_mcVVrgkceSeX6A8QIU5qL1OQ","process_id":"75092","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '128,161p' docs/refs/peft_delora_layer.py"],"cwd":"/media/wassna...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_delora_layer.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
def reset_delora_parameters(
self,
adapter_name: str,
init_weights: bool = True,
delora_lambda: float = 15.0,
) -> None:
if adapter_name not in self.delora_A.keys():
return
if init_weights is True:
nn.init.kaiming_uniform_(self.delora_A[adapter_name], a=math.sqrt(5))
nn.init.zeros_(self.delora_B[adapter_name])
else:
nn.init.kaiming_uniform_(self.delora_A[adapter_name], a=math.sqrt(5))
nn.init.kaiming_uniform_(self.delora_B[adapter_name], a=math.sqrt(5))
self.delora_lambda[adapter_name].data.fill_(float(delora_lambda))
# capture a fixed norm for this adapter to use for future delta computations
with torch.no_grad():
w = self.get_base_layer().weight
if w.device.type != "meta":
w_norm = torch.norm(w.data, dim=0).detach()
else:
# For meta tensors, we can't compute the norm, so use a default value
w_norm = torch.ones(w.shape[1], device=w.device)
self.delora_w_norm[adapter_name] = w_norm
... (7 more lines)
[tool] Read peft_delora_layer.py (completed)
kind: read
input: {"call_id":"call_STRTnAVOq9WZ4NOQ1m9F24J2","process_id":"78221","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '246,261p' docs/refs/peft_delora_layer.py"],"cwd":"/media/wassna...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_delora_layer.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
x_d = self.delora_dropout[adapter](x)
# Decomposed delta calculation
# 1. (x * w_norm) @ A.T
h = nn.functional.linear(x_d * self.delora_w_norm[adapter], self.delora_A[adapter])
# 2. h @ diag
An = torch.clamp(self.delora_A[adapter].norm(dim=1), min=1e-4)
Bn = torch.clamp(self.delora_B[adapter].norm(dim=0), min=1e-4)
scaling = (self.delora_lambda[adapter] / self.r[adapter]) / (An * Bn)
h = h * scaling
# 3. h @ B.T
h = nn.functional.linear(h, self.delora_B[adapter])
[tool] Read peft_ia3_layer.py (running)
input: {"call_id":"call_T5LuGO9J3PslVssywEOuH7uX","process_id":"64921","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '208,218p' docs/refs/peft_ia3_layer.py"],"cwd":"/media/wassname/...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_ia3_layer.py
[tool] Read peft_hra_layer.py (running)
input: {"call_id":"call_cK0RZeM4kle9U9I3sben8qMP","process_id":"80820","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '209,265p' docs/refs/peft_hra_layer.py"],"cwd":"/media/wassname/...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_hra_layer.py
[tool] Read peft_ia3_layer.py (completed)
kind: read
input: {"call_id":"call_mVR5vVoBRVvdD7i08awFEP5E","process_id":"7411","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '291,317p' docs/refs/peft_ia3_layer.py"],"cwd":"/media/wassname/S...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_ia3_layer.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
dtype = previous_dtype = x.dtype
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
ia3_scaling = 1
for active_adapter in self.active_adapters:
if active_adapter not in self.ia3_l.keys():
continue
dtype = self.ia3_l[active_adapter].dtype
ia3_scaling *= self.ia3_l[active_adapter]
if self.is_feedforward:
x = x.to(dtype)
# TODO: weight.dtype can be != self.ia3_l[self.active_adapters].dtype
# e.g. bf16 vs fp32. Is that okay?
interm = (x * ia3_scaling).to(self.get_base_layer().weight.dtype)
result = self.base_layer(interm, *args, **kwargs)
else:
result = self.base_layer(x, *args, **kwargs)
result = result.to(dtype) * ia3_scaling
[tool] Read peft_hra_layer.py (completed)
kind: read
input: {"call_id":"call_L8erIsjTuP4GFpaT5V50afZP","process_id":"81187","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '75,108p' docs/refs/peft_hra_layer.py"],"cwd":"/media/wassname/S...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_hra_layer.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
self.hra_r[adapter_name] = r
self.hra_apply_GS[adapter_name] = apply_GS
# Determine shape of HRA weights
base_layer = self.get_base_layer()
if isinstance(base_layer, nn.Linear):
self.hra_u[adapter_name] = nn.Parameter(torch.empty(self.in_features, r), requires_grad=True)
elif isinstance(base_layer, nn.Conv2d):
self.hra_u[adapter_name] = nn.Parameter(
torch.empty(self.in_features * base_layer.kernel_size[0] * base_layer.kernel_size[0], r),
requires_grad=True,
)
else:
raise TypeError(f"HRA is not implemented for base layers of type {type(base_layer).__name__}")
# Initialize weights
if init_weights:
self.reset_hra_parameters(adapter_name)
else:
self.reset_hra_parameters_random(adapter_name)
# Move new weights to device
self._move_adapter_to_device_of_base_layer(adapter_name)
self.set_adapter(self.active_adapters, inference_mode=inference_mode)
def reset_hra_parameters(self, adapter_name: str):
if self.hra_r[adapter_name] % 2 != 0:
... (7 more lines)
[tool] Read peft_ia3_layer.py (completed)
kind: read
input: {"call_id":"call_T5LuGO9J3PslVssywEOuH7uX","process_id":"64921","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '208,218p' docs/refs/peft_ia3_layer.py"],"cwd":"/media/wassname/...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_ia3_layer.py
output:
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def update_layer(self, adapter_name: str, config: IA3Config, **kwargs):
init_ia3_weights = config.init_ia3_weights
inference_mode = config.inference_mode
# Actual trainable parameters
num_features = self.in_features if self.is_feedforward else self.out_features
weights_size = (1, num_features) + (1,) * (self._kernel_dim - 2)
weight = torch.randn(weights_size)
self.ia3_l[adapter_name] = nn.Parameter(weight)
if init_ia3_weights:
self.reset_ia3_parameters(adapter_name)
[tool] Read peft_hra_layer.py (completed)
kind: read
input: {"call_id":"call_cK0RZeM4kle9U9I3sben8qMP","process_id":"80820","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '209,265p' docs/refs/peft_hra_layer.py"],"cwd":"/media/wassname/...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_hra_layer.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
rank = self.hra_r[adapter_name]
apply_GS = self.hra_apply_GS[adapter_name]
opt_u = self.hra_u[adapter_name]
shape = opt_u.shape
if apply_GS:
weight = [(opt_u[:, 0] / opt_u[:, 0].norm()).view(-1, 1)]
for i in range(1, rank):
ui = opt_u[:, i].view(-1, 1)
for j in range(i):
ui = ui - (weight[j].t() @ ui) * weight[j]
weight.append((ui / ui.norm()).view(-1, 1))
weight = torch.cat(weight, dim=1)
weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype) - 2 * weight @ weight.t()
else:
opt_u = opt_u / opt_u.norm(dim=0)
weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype)
if reverse:
indices = range(rank - 1, -1, -1)
else:
indices = range(rank)
for i in indices:
ui = opt_u[:, i].view(-1, 1)
weight = weight - 2 * weight @ ui @ ui.t()
... (30 more lines)
[tool] Read antipasto3_svd_adapter.py (running)
input: {"call_id":"call_3DbrzJWMQFTTW4ljfMAkk6He","process_id":"80755","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '76,104p' docs/refs/antipasto3_svd_adapter.py"],"cwd":"/media/wa...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py
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input: {"call_id":"call_AlPlAWgDyADHefwo8wb8BEUN","process_id":"30191","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '117,169p' docs/refs/antipasto3_svd_adapter.py"],"cwd":"/media/w...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py
[tool] Read peft_lora_layer.py (running)
input: {"call_id":"call_mCvMQv5zxOdAqhUiNiSkW8xv","process_id":"99551","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '197,215p' docs/refs/peft_lora_layer.py"],"cwd":"/media/wassname...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py
[tool] Read antipasto3_svd_adapter.py (running)
input: {"call_id":"call_Vo9wquYekZsvuw8ec5SyrNMu","process_id":"25940","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '180,212p' docs/refs/antipasto3_svd_adapter.py"],"cwd":"/media/w...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py
[tool] Read peft_eva.py (running)
input: {"call_id":"call_ATOXB8ZOgMjTzLGjtkSiskTI","process_id":"71854","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '466,532p' docs/refs/peft_eva.py"],"cwd":"/media/wassname/SGIron...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva.py
[tool] Read antipasto3_svd_adapter.py (completed)
kind: read
input: {"call_id":"call_AlPlAWgDyADHefwo8wb8BEUN","process_id":"30191","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '117,169p' docs/refs/antipasto3_svd_adapter.py"],"cwd":"/media/w...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
U = self.svd_U.value
S = self.svd_S.value
Vh = self.svd_Vh.value
W_res = self.svd_W_res.value
params = self.rotation_params.value # (n_blocks, n_triu)
bs = self.block_size
n_blocks = params.shape[0]
# Reconstruct skew-symmetric from upper-triangle params (like OFT/PSOFT).
# 0.5 factor matches BOFT convention: cancels the 2x gradient from A - A^T.
A = jnp.zeros((n_blocks, bs, bs), dtype=jnp.float32)
A = A.at[:, self._triu_rows, self._triu_cols].set(params.astype(jnp.float32))
A = 0.5 * (A - jnp.swapaxes(A, -1, -2))
# Angle clamping (element-wise tanh, bounds bidirectional symmetry error)
a_limit = 2 * math.tan(self.max_angle / 2)
A = a_limit * jnp.tanh(A / a_limit)
# Cayley transform in float32: R = (I - X)^{-1}(I + X)
eye = jnp.eye(bs, dtype=jnp.float32)
X = alpha * A / 2
R_blocks = jnp.linalg.solve(
eye[None] - X,
eye[None] + X,
)
# Apply rotation to U (input singular vectors)
... (26 more lines)
[tool] Read antipasto3_svd_adapter.py (completed)
kind: read
input: {"call_id":"call_3DbrzJWMQFTTW4ljfMAkk6He","process_id":"80755","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '76,104p' docs/refs/antipasto3_svd_adapter.py"],"cwd":"/media/wa...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
bs = min(rotation_block_size, r)
assert r % bs == 0, f"r={r} must be divisible by block_size={bs}"
# Frozen SVD components
self.svd_U = SVDFrozen(U.astype(jnp.float32))
self.svd_S = SVDFrozen(S.astype(jnp.float32))
self.svd_Vh = SVDFrozen(Vh.astype(jnp.float32))
self.svd_W_res = SVDFrozen(W_res.astype(jnp.bfloat16))
# Trainable: delta_s with small positive bias for symmetry breaking.
# The +4e-4 nudges the optimizer to scale up selected dims rather than just
# rotating them. Rotation alone cannot break sign symmetry at init.
key_s, key_r = jax.random.split(rngs.params())
self.delta_s = DeltaSParam(
jax.random.truncated_normal(key_s, -2.0, 2.0, (r,)) * 4e-4 + 4e-4
)
# Block-diagonal skew-symmetric rotation params (lower LR via RotationParam type)
# Upper-triangle parameterization: store only bs*(bs-1)/2 elements per block,
# like OFT/PSOFT. Avoids dead diagonal gradients and redundant (i,j)/(j,i) states.
n_blocks = r // bs
n_triu = bs * (bs - 1) // 2
self.rotation_params = RotationParam(
jax.random.truncated_normal(key_r, -2.0, 2.0, (n_blocks, n_triu)) * 1e-4
)
# Pre-compute upper-triangle indices for skew-symmetric reconstruction
rows, cols = jnp.triu_indices(bs, k=1)
... (2 more lines)
[tool] Read antipasto3_svd_adapter.py (completed)
kind: read
input: {"call_id":"call_Vo9wquYekZsvuw8ec5SyrNMu","process_id":"25940","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '180,212p' docs/refs/antipasto3_svd_adapter.py"],"cwd":"/media/w...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
"""Create SVD adapter from a kernel matrix (in_dim, out_dim).
If selected_indices is provided, uses those SVD dimensions.
Otherwise uses top-r by singular value.
"""
kernel_f32 = kernel.astype(jnp.float32)
U_full, S_full, Vh_full = jnp.linalg.svd(kernel_f32, full_matrices=False)
r_actual = min(r, S_full.shape[0])
# Ensure divisible by block size
bs = min(rotation_block_size, r_actual)
r_actual = (r_actual // bs) * bs
if r_actual == 0:
r_actual = bs
if selected_indices is not None:
indices = selected_indices[:r_actual]
else:
indices = jnp.arange(r_actual)
U = U_full[:, indices]
S = S_full[indices]
Vh = Vh_full[indices, :]
W_res = kernel_f32 - U @ jnp.diag(S) @ Vh
return SVDSteeringLinear(
... (6 more lines)
[tool] Read peft_lora_layer.py (completed)
kind: read
input: {"call_id":"call_mCvMQv5zxOdAqhUiNiSkW8xv","process_id":"99551","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '197,215p' docs/refs/peft_lora_layer.py"],"cwd":"/media/wassname...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
self.lora_A[adapter_name] = nn.Linear(self.in_features, r, bias=False)
self.lora_B[adapter_name] = nn.Linear(r, self.out_features, bias=lora_bias)
# Tying adapters is only implemented for Linear layers
# where the source is the embedding layer.
# Currently, this is the most prevelant way of tying layers (weight tying)
if tied_adapter:
lora_A_params = tied_adapter["lora_A"]
lora_B_params = tied_adapter["lora_B"]
self.lora_A[adapter_name].weight = torch.nn.Parameter(lora_A_params)
self.lora_B[adapter_name].weight = torch.nn.Parameter(lora_B_params)
self.lora_bias[adapter_name] = lora_bias
if use_rslora:
self.scaling[adapter_name] = lora_alpha / math.sqrt(r)
else:
self.scaling[adapter_name] = lora_alpha / r
[tool] Read peft_eva.py (completed)
kind: read
input: {"call_id":"call_ATOXB8ZOgMjTzLGjtkSiskTI","process_id":"71854","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '466,532p' docs/refs/peft_eva.py"],"cwd":"/media/wassname/SGIron...
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva.py
output:
sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed)
eva_state_dict = {}
for name, rank in rank_dist.items():
hook = hooks[layer_hook_map[name]][0]
if not torch.all(hook.converged[:rank]):
raise ValueError(
f"Layer {name} has not converged but was assigned rank {rank}. "
"Please report this issue at https://github.com/huggingface/peft/issues"
)
u = hook.svd.components_[:rank]
if peft_config.eva_config.whiten:
u /= hook.svd.singular_values_[:rank].sqrt().reshape(-1, 1)
eva_state_dict[name] = u
# restore model state
model.train(training)
# move tensors to device
if device is not None:
eva_state_dict = {k: v.to(device) for k, v in eva_state_dict.items()}
return eva_state_dict
def _load_eva_state_dict(
model: torch.nn.Module,
eva_state_dict: dict,
adapter_name: str,
... (40 more lines)
[tool] Searching the Web (running)
[tool] Opening: https://arxiv.org/abs/2106.09685 (completed)
kind: fetch
input: https://arxiv.org/abs/2106.09685
[tool] Searching the Web (running)
[tool] Opening: https://arxiv.org/abs/2402.09353 (completed)
kind: fetch
input: https://arxiv.org/abs/2402.09353
[tool] Searching the Web (running)
[tool] Opening: https://github.com/MuLabPKU/PiSSA/blob/main/utils/init_pissa.py (completed)
kind: fetch
input: https://github.com/MuLabPKU/PiSSA/blob/main/utils/init_pissa.py
[tool] Searching the Web (running)
[tool] Opening: https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/dora.py (completed)
kind: fetch
input: https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/dora.py
[tool] Searching the Web (running)
[tool] Opening: https://github.com/huggingface/peft/blob/main/examples/eva_finetuning/eva_finetuning.py (completed)
kind: fetch
input: https://github.com/huggingface/peft/blob/main/examples/eva_finetuning/eva_finetuning.py
[tool] Searching the Web (running)
The live GitHub pages mostly resolve, though a couple of `web.open` calls return GitHub-side/internal fetch errors for specific pages. Since this is explicitly a GitHub-link task, I’m also checking the `gh` skill instructions so I can use the local GitHub CLI path for raw-file sanity where possible.
[tool] Opening: https://raw.githubusercontent.com/huggingface/peft/main/src/peft/tuners/lora/layer.py (completed)
kind: fetch
input: https://raw.githubusercontent.com/huggingface/peft/main/src/peft/tuners/lora/layer.py
[tool] Read SKILL.md (running)
input: {"call_id":"call_hKiKdMrKAYVBO6nRKIdT6Ulh","process_id":"5086","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /home/wassname/.claude/skills/gh/SKILL.md"],"cwd":"/medi...
files: /home/wassname/.claude/skills/gh/SKILL.md
[tool] Read SKILL.md (completed)
kind: read
input: {"call_id":"call_hKiKdMrKAYVBO6nRKIdT6Ulh","process_id":"5086","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /home/wassname/.claude/skills/gh/SKILL.md"],"cwd":"/medi...
files: /home/wassname/.claude/skills/gh/SKILL.md
output:
---
name: gh
description: "Search GitHub, fetch files, and explore repos using the `gh` CLI. Use when a user provides a GitHub URL, asks to find repos/code, or wants to read files from GitHub."
---
# GitHub CLI (gh)
Requires authentication: run `gh auth status` to check, `gh auth login` if needed.
## 1. Search
Delegate search to a sub-agent (Task tool) to scan many results (50-100+) without filling
your context. The sub-agent runs the search, filters/ranks results, and returns a short summary.
### Search repos (start here)
Include `--match name,description,readme` for best recall (default only matches name+description).
```bash
GH_PAGER="" gh search repos "query" \
--match name,description,readme \
--sort stars \
--limit 50 \
--json fullName,description,stargazersCount,url
```
Additional filters: `--owner OWNER`, `--language python`, `--topic ML`, `--stars ">=100"`,
`--created ">2024-01-01"`, `--include-forks true` (off by default, adds noise but improves recall).
... (106 more lines)
[tool] Run wc -l docs/refs/peft_lora_layer.py docs/refs/peft_lora_dora.py docs/refs/peft_delora_layer.py docs/refs/peft_ia3_layer.py docs/refs/peft_hra_layer.py docs/refs/peft_eva.py docs/refs/antipasto3_svd_adapter.py (running)
input: {"call_id":"call_WBalwg2A0Sgj102Nrdm80gLZ","process_id":"79817","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","wc -l docs/refs/peft_lora_layer.py docs/refs/peft_lora_dora.py docs/ref...
[tool] Run gh auth status (running)
input: {"call_id":"call_MDZ9JcfpcHiA7VSnDD4Hj27Q","process_id":"22378","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","gh auth status"],"cwd":"/media/wassname/SGIronWolf/projects5/2026/lora-...
[tool] Run wc -l docs/refs/peft_lora_layer.py docs/refs/peft_lora_dora.py docs/refs/peft_delora_layer.py docs/refs/peft_ia3_layer.py docs/refs/peft_hra_layer.py docs/refs/peft_eva.py docs/refs/antipasto3_svd_adapter.py (completed)
kind: execute
input: {"call_id":"call_WBalwg2A0Sgj102Nrdm80gLZ","process_id":"79817","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","wc -l docs/refs/peft_lora_layer.py docs/refs/peft_lora_dora.py docs/ref...
output:
2510 docs/refs/peft_lora_layer.py
287 docs/refs/peft_lora_dora.py
274 docs/refs/peft_delora_layer.py
336 docs/refs/peft_ia3_layer.py
462 docs/refs/peft_hra_layer.py
739 docs/refs/peft_eva.py
375 docs/refs/antipasto3_svd_adapter.py
4983 total
[tool] Run gh auth status (failed)
kind: execute
input: {"call_id":"call_MDZ9JcfpcHiA7VSnDD4Hj27Q","process_id":"22378","turn_id":"019dc999-8ad5-7161-9386-95542608fab7","command":["/usr/bin/zsh","-lc","gh auth status"],"cwd":"/media/wassname/SGIronWolf/projects5/2026/lora-...
output:
github.com
X Failed to log in to github.com account wassname (/home/wassname/.config/gh/hosts.yml)
- Active account: true
- The token in /home/wassname/.config/gh/hosts.yml is invalid.
- To re-authenticate, run: gh auth login -h github.com
- To forget about this account, run: gh auth logout -h github.com -u wassname
X Failed to log in to github.com account ShouldHaveSeenThat (default)
- Active account: false
- The token in default is invalid.
- To re-authenticate, run: gh auth login -h github.com
- To forget about this account, run: gh auth logout -h github.com -u ShouldHaveSeenThat
[tool] Searching the Web (running)
[tool] Open page (completed)
kind: fetch
input: {"query":"","action":{"type":"open_page"}}
[tool] Searching the Web (running)
## lora
### params
- `lora_A`: `(r, d_in)`, `cfg.dtype`, trainable Parameter, not buffer at `src/lora_lite/variants/lora.py:26`; matches PEFT LoRA shape.
- `lora_B`: `(d_out, r)`, `cfg.dtype`, trainable Parameter, not buffer at `src/lora_lite/variants/lora.py:27`; matches PEFT. Buffers would persist if used via `persistent=True` at `src/lora_lite/adapter.py:51-53`.
### init / group_init
- `init()` no-op at `src/lora_lite/variants/lora.py:31-33`; ParamSpec does Kaiming `A`, zero `B`.
- GRADIENT FLOW at t=0: `lora_B` gets non-zero grad; `lora_A` gets zero grad because `B=0`.
### dtype
- Params allocated in `cfg.dtype` at `src/lora_lite/adapter.py:51-56`; hook casts `x` to `cfg.dtype` then returns `y.dtype` at `src/lora_lite/adapter.py:18-20`.
- No fp32-sensitive init. bf16 identity survives because `B=0`.
### forward
Math: `y + (alpha/r) x A^T B^T` at `src/lora_lite/variants/lora.py:37-41`.
Ref math: `result + scaling * B(A(dropout(x)))`; PEFT has dropout, this library has no dropout field in `src/lora_lite/config.py:6-21`.
Match? YES, modulo intentionally absent dropout.
### links
- paper: OK
- peft ref: OK
- author ref: n/a
- offline snapshot drift: AMBIGUOUS; live URL resolves, but `gh auth status` token is invalid so I could not hash raw snapshot.
### verdict
CORRECT -- standard LoRA mechanism matches PEFT/paper.
## pissa
### params
- `lora_A`: `(r, d_in)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/pissa.py:37`.
- `lora_B`: `(d_out, r)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/pissa.py:38`.
- Shapes match PEFT PiSSA; no buffers needed.
### init / group_init
- Requires exact `nn.Linear`, then fp32 SVD of `W`, copies `B=U_r sqrt(S_r)`, `A=sqrt(S_r) Vh_r`, and mutates base to `W - scale*BA` at `src/lora_lite/variants/pissa.py:42-63`.
- GRADIENT FLOW at t=0: both `A` and `B` get non-zero grad because both are non-zero.
### dtype
- SVD is correctly fp32 at `src/lora_lite/variants/pissa.py:48-55`; subtraction uses fp32 `BA` at `src/lora_lite/variants/pissa.py:58-63`.
- Precision loss: stored `A/B` are cast to `cfg.dtype` before residual subtraction at `src/lora_lite/variants/pissa.py:54-59`; this mirrors the doc’s “bf16 round-trip not bit-exact” caveat at `src/lora_lite/variants/pissa.py:4`.
### forward
Math: `y + (alpha/r) x A^T B^T` at `src/lora_lite/variants/pissa.py:66-71`.
Ref math: PEFT divides singular values by scaling before forming `A/B`, then subtracts `scaling*BA`; equivalent identity, but different adapter parameter scale when `alpha/r != 1`.
Match? PARTIAL; documented deviation says pass `alpha=r` for paper-faithful identity at `src/lora_lite/variants/pissa.py:6-13`.
### links
- paper: OK
- peft ref: OK
- author ref: OK
- offline snapshot drift: AMBIGUOUS; live URLs resolve, raw hash not checked due invalid `gh` auth.
### verdict
PARTIAL -- mechanism is correct for `alpha=r`; non-unit scale does not match PEFT’s PiSSA parameterization.
## delora
### params
- `lora_A`: `(r, d_in)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/delora.py:49`.
- `lora_B`: `(d_out, r)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/delora.py:50`.
- `lora_lambda`: scalar `()`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/delora.py:51-53`.
- `lora_wnorm`: `(d_in,)`, `cfg.dtype`, frozen persistent buffer at `src/lora_lite/variants/delora.py:54-56` and `src/lora_lite/adapter.py:51-53`; matches PEFT’s persistent `delora_w_norm`.
### init / group_init
- Captures per-input-column norm of `W` into `lora_wnorm` at `src/lora_lite/variants/delora.py:60-66`.
- Default `lambda0=0` at `src/lora_lite/variants/delora.py:47`; this is intentionally different from PEFT/paper defaults.
- GRADIENT FLOW at t=0: with default `lambda0=0`, only `lora_lambda` gets non-zero grad; `A/B` get zero first-step grad. This is already documented at `src/lora_lite/variants/delora.py:20-24`.
### dtype
- `wnorm` is computed fp32 then stored as adapter dtype at `src/lora_lite/variants/delora.py:64-66`.
- Norms in forward use adapter dtype at `src/lora_lite/variants/delora.py:79-81`; fp16/bf16 can perturb normalization, but clamp prevents division blow-up.
### forward
Math: `y + ((x*||W||_col)A^T) * ((lambda/r)/(||A_i||||B_i||)) B^T` at `src/lora_lite/variants/delora.py:77-84`.
Ref math: PEFT DeLoRA same per-input norm and per-rank normalization; PEFT also has dropout.
Match? YES for upstream PEFT forward, modulo no dropout and documented init deviation.
### links
- paper: OK
- peft ref: DEAD/UNVERIFIED via browser cache miss for exact URL
- author ref: DEAD/UNVERIFIED via browser cache miss for exact URL
- offline snapshot drift: AMBIGUOUS
### verdict
PARTIAL -- forward matches PEFT, but identity/default init intentionally sacrifices first-step `A/B` gradients.
## ia3
### params
- `ia3`: `lora_g` `(d_out,)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/ia3.py:38-39`.
- `ia3_ff`: `lora_g` `(d_in,)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/ia3.py:55-56`.
- Shapes match PEFT’s output vs feedforward feature choice.
### init / group_init
- Both init no-op; ParamSpec initializes `g=1` at `src/lora_lite/variants/ia3.py:41-43` and `src/lora_lite/variants/ia3.py:58-60`.
- GRADIENT FLOW at t=0: `lora_g` gets non-zero grad immediately for both variants.
### dtype
- Gate stored in `cfg.dtype`; output hook casts `x`, not `y`, but multiplies `y * lora_g` at `src/lora_lite/variants/ia3.py:46-47`; result then returns to `y.dtype` at `src/lora_lite/adapter.py:19-20`.
- Identity survives bf16 because `1.0` is exact.
### forward
Math: `ia3: y*g`; `ia3_ff: W(x*g)` at `src/lora_lite/variants/ia3.py:46-47` and `src/lora_lite/variants/ia3.py:63-64`.
Ref math: PEFT uses output gating for non-feedforward and input gating for feedforward.
Match? YES.
### links
- paper: OK
- peft ref: OK
- author ref: n/a
- offline snapshot drift: AMBIGUOUS
### verdict
CORRECT -- both IA3 regimes match PEFT’s mechanism.
## dora
### params
- `lora_A`: `(r, d_in)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/dora.py:36`.
- `lora_B`: `(d_out, r)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/dora.py:37`.
- `lora_m`: `(d_out,)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/dora.py:38-39`; matches PEFT magnitude vector.
### init / group_init
- Requires exact `nn.Linear`; initializes `m=||W||_row` in fp32 then casts to `lora_m.dtype` at `src/lora_lite/variants/dora.py:43-52`.
- GRADIENT FLOW at t=0: `lora_m` and `lora_B` get non-zero grad; `lora_A` gets zero grad because `B=0`.
### dtype
- `V = layer.weight + scale*BA` is computed in current tensor dtype at `src/lora_lite/variants/dora.py:59-61`; not fp32.
- PEFT detaches norm; this implementation intentionally differentiates through norm at `src/lora_lite/variants/dora.py:10-14`.
### forward
Math: `(m/||W+sBA||) * (y + s*xA^T B^T)` at `src/lora_lite/variants/dora.py:57-66`.
Ref math: `bias + (m/||V||)*(Wx + s*BAx)`; PEFT subtracts bias before scaling.
Match? NO for biased `nn.Linear`: this code scales `bias` because `y` includes it at `src/lora_lite/variants/dora.py:65-66`.
### links
- paper: OK
- peft ref: OK
- author ref: n/a
- offline snapshot drift: AMBIGUOUS
### verdict
BUGGY -- DoRA is wrong for linear layers with bias; bias is multiplied by `m/||V||`.
## hra
### params
- `lora_U`: `(r, d_in)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/hra.py:49-52`; transpose of PEFT’s `(d_in, r)` but mathematically equivalent.
- Rejects odd rank at `src/lora_lite/variants/hra.py:44-48`; stricter than PEFT warning, but documented.
### init / group_init
- Kaiming initializes half rows and repeats adjacent pairs at `src/lora_lite/variants/hra.py:56-65`.
- GRADIENT FLOW at t=0: repeated Householder pairs cancel to identity, but each row participates in the chain and gets non-zero gradient.
### dtype
- Householder math runs in adapter/input cast dtype via pre-hook at `src/lora_lite/adapter.py:23-28`.
- bf16 identity is near-exact for paired reflections, but norm/division in bf16 can introduce small non-bit-exactness at `src/lora_lite/variants/hra.py:73-77`.
### forward
Math: pre-hook applies `R x`, `R=Π_i(I-2u_i u_i^T/||u_i||^2)` at `src/lora_lite/variants/hra.py:69-78`.
Ref math: PEFT computes `W R` then `F.linear(x, WR)`.
Match? YES for linear layers.
### links
- paper: OK
- peft ref: DEAD/WRONG; current search finds `hra/model.py`, exact `hra/layer.py` URL did not fetch.
- author ref: OK
- offline snapshot drift: MAJOR/AMBIGUOUS for PEFT HRA URL path.
### verdict
CORRECT -- local mechanism matches the HRA reflection chain; link is stale.
## eva
### params
- `lora_A`: `(r, d_in)`, `cfg.dtype`, frozen persistent buffer at `src/lora_lite/variants/eva.py:50-52`; BUG vs PEFT LoRA `lora_A`, which remains a trainable Parameter after EVA initialization.
- `lora_B`: `(d_out, r)`, `cfg.dtype`, trainable Parameter at `src/lora_lite/variants/eva.py:53-54`.
### init / group_init
- `init()` no-op at `src/lora_lite/variants/eva.py:57-60`.
- `group_init()` requires calibration data, captures layer inputs, runs fp32 SVD, copies top right singular vectors into `lora_A` at `src/lora_lite/variants/eva.py:63-113`.
- GRADIENT FLOW at t=0: only `lora_B` gets non-zero grad; `lora_A` never trains because it is a buffer.
### dtype
- Captured activations are fp32 CPU at `src/lora_lite/variants/eva.py:78-79`; SVD is fp32 at `src/lora_lite/variants/eva.py:111`.
- `A` is cast to buffer dtype/device at `src/lora_lite/variants/eva.py:112`; bf16 loses PCA-vector precision.
### forward
Math: `y + (alpha/r) x A^T B^T` at `src/lora_lite/variants/eva.py:116-121`.
Ref math: LoRA forward after EVA copies PCA components into trainable `lora_A`; PEFT also supports rank redistribution/whitening.
Match? PARTIAL; forward equation matches, trainability does not.
### links
- paper: OK
- peft ref: OK
- author ref: n/a
- offline snapshot drift: AMBIGUOUS
### verdict
BUGGY -- `lora_A` is frozen as a buffer, and `adapter.load()` cannot load EVA because it calls `attach(model, cfg)` without calibration data at `src/lora_lite/adapter.py:132-135` while EVA requires it at `src/lora_lite/variants/eva.py:63-70`.
## antipasto
### params
- Buffers: `lora_U (d_out,r)`, `lora_S (r,)`, `lora_Vh (r,d_in)`, all `cfg.dtype`, frozen persistent buffers at `src/lora_lite/variants/antipasto.py:88-92`.
- Trainable: `lora_delta_s (r,)`, `lora_rot_T (n_blocks, bs(bs-1)/2)`, `cfg.dtype`, Parameters at `src/lora_lite/variants/antipasto.py:93-95`.
- Requires `r % block_size == 0` at `src/lora_lite/variants/antipasto.py:81-86`.
### init / group_init
- Requires exact `nn.Linear`, fp32 SVD of `W`, stores top-r factors, and mutates base to residual `W_res` at `src/lora_lite/variants/antipasto.py:99-116`.
- GRADIENT FLOW at t=0: `delta_s` gets non-zero grad; `rot_T` should get non-zero grad unless symmetry/data cancels.
### dtype
- SVD is fp32, but frozen factors are stored in `cfg.dtype` at `src/lora_lite/variants/antipasto.py:106-115`.
- Rotation is built in fp32 from `rot_T.float()` at `src/lora_lite/variants/antipasto.py:129`, then cast to `x.dtype` at `src/lora_lite/variants/antipasto.py:130`.
### forward
Math: `y_res + ((x Vh_eff^T) * (S+delta_s)) U_eff^T` at `src/lora_lite/variants/antipasto.py:132-145`.
Ref math: AntiPaSTO3 Flax convention `xW_res + ((xU_rot)*(S+alpha delta_s))Vh_rot`.
Match? PARTIAL; PyTorch basis mapping is coherent, but reference initializes `delta_s` and rotation params with small random non-zero values, while local uses exact zeros at `src/lora_lite/variants/antipasto.py:94-95`.
### links
- paper: n/a
- peft ref: n/a
- author ref: OK repo URL
- offline snapshot drift: AMBIGUOUS
### verdict
PARTIAL -- mechanism maps correctly, but initialization differs from the referenced AntiPaSTO3 code.
## summary
| variant | verdict | main issue |
|---|---:|---|
| lora | CORRECT | no mechanism bug |
| pissa | PARTIAL | scale parameterization differs unless `alpha=r` |
| delora | PARTIAL | documented init/gradient deviation |
| ia3 / ia3_ff | CORRECT | no mechanism bug |
| dora | BUGGY | bias is scaled incorrectly |
| hra | CORRECT | code OK; PEFT link stale |
| eva | BUGGY | `A` frozen; load path broken |
| antipasto | PARTIAL | zero init differs from reference random init |
MUST-FIX:
1. DoRA: handle bias like PEFT; do not multiply `base_layer.bias` by `m/||V||` (`src/lora_lite/variants/dora.py:65-66`).
2. EVA: make `lora_A` a trainable Parameter if matching PEFT, not a buffer (`src/lora_lite/variants/eva.py:50-52`).
3. EVA: fix `load()`/`group_init` interaction; current `load()` cannot attach EVA checkpoints without calibration data (`src/lora_lite/adapter.py:132-135`, `src/lora_lite/variants/eva.py:63-70`).
Nice-to-haves:
1. PiSSA: match PEFT’s `Sr /= scaling` parameterization for arbitrary `alpha/r`, or keep documenting `alpha=r`.
2. AntiPaSTO: decide whether exact identity zero init is intentional vs reference random `delta_s/rot` init.
3. Refresh stale GitHub refs, especially HRA and DeLoRA.
[tool] Searching for: site:github.com/huggingface/peft src/peft/tuners/hra/layer.py, site:github.com/huggingface/peft src/peft/tuners/delora/layer.py, site:github.com ExplainableML DeLoRA peft tuners delora.py (completed)
kind: fetch
input: site:github.com/huggingface/peft src/peft/tuners/hra/layer.py
[done] end_turn
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