add LoMAP #13
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…eval
Core changes:
- loralib/layers.py: add map_norm_scope (global/column/row/row_column) and
map_detach_denom to LoRALayer/_unit_frobenius for normalization ablations
- CR_MR/finetune.py: expose map_beta_init, map_eps, map_norm_scope,
map_detach_denom as CLI args; pass them through to LoraConfig
- CR_MR/peft/tuners/lora.py: add map_norm_scope and map_detach_denom to LoraConfig
- NLU/modeling_deberta_v2.py: add lora_type="map" (LoMAPLinear) support for
all attention/FFN modules
- NLU/run_glue.py: enable map_alpha/map_beta as trainable when lora_type=map
New scripts:
- NLU/scripts/run_lomap_glue_all.sh: LoMAP on all 8 GLUE tasks
- CR_MR/scripts_for_ablation/: epsilon, beta_0, rank, norm_scope, detach ablations
- CR_MR/scripts_for_baselines/: DeLoRA, BiDoRA, LoRA-GA, RandLoRA, GraLoRA
reproduction scripts using official repos / HF PEFT
- CR_MR/finetune_peft.py: minimal HF-PEFT wrapper for baseline training
- SdG/eval_dreambooth.py: CLIP-T, CLIP-I, DINO, LPIPS quantitative eval
- SdG/eval.sh: batch eval across methods
Paper (paper-aaai/AAAI_MAP/sec/):
- 4_exp.tex: fix misleading "outperforms all" claim at r=16 (no DoRA result there)
- 3_method.tex: add \label{sec:method}, initialization choices paragraph
(beta_0 alternatives, detach discussion, ablation pointer)
- 2_related.tex: add formal comparison table (DoRA/BoRA/BiDoRA/DeLoRA/MAP)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…Layer and kwargs - LoraLayer.__init__: add map_norm_scope and map_detach_denom params - LoraLayer._unit_frobenius: implement all 4 scope modes with detach support - Linear.__init__: forward map_norm_scope/map_detach_denom to LoraLayer - LoraModel._inject_adapter: add map_norm_scope/map_detach_denom to kwargs dict Verified with unit tests: all 14 checks pass. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…ntation - ablation_epsilon.sh: was not passing --map_eps to finetune.py (critical bug) - ablation_rank.sh: passed wrong --adapter LoRA for LoMAP eval (would crash) - commonsense_evaluate.py: add LoMAP, DeLoRA, BiDoRA, LoRA-GA, RandLoRA, GraLoRA to --adapter choices; route HF-PEFT checkpoints past manual merge logic - finetune.py: add --seed param, call transformers.set_seed(), propagate seed to TrainingArguments; add tensorboard as default logging backend (not just wandb) - finetune_peft.py: fix val_set_size (was silently ignored, val split now used); add tensorboard; add loraga via init_lora_weights='lora-ga' - run_bidora_cr.sh: BiDoRA official repo is NLU-only; replaced with DoRA proxy and clear note; added run_bidora_glue.sh for official BiDoRA on GLUE - run_loraga_cr.sh: official repo uses Hydra, not compatible; replaced with HF PEFT lora-ga init inside our finetune_peft.py pipeline - run_delora_cr.sh: add --seed, fix output dir, use --adapter DeLoRA in eval - All ablation scripts: add SEED variable (default 42) and --seed $SEED - aggregate_results.py: new script to parse multi-seed eval outputs and print mean±std table in LaTeX and CSV formats - README.md: document data leakage in commonsense_170k (HellaSwag 29%, WinoGrande 26% test overlap — known issue in LLM-Adapters protocol); document BiDoRA/LoRA-GA integration strategy Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Required for DeLoRA, RandLoRA, GraLoRA, LoRA-GA baselines. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- setup_envs.sh: creates lomap-cr / lomap-nlu / lomap-sdg conda envs with correct deps per task (avoids accelerate version conflicts) - run_cr.sh: single entry point for all CR adapters (lomap/lora/delora/ loraga/randlora/gralora), handles train→eval pipeline, seed param - run_nlu.sh: all 8 GLUE tasks with correct per-task hyperparams, seed param - run_sdg.sh: DreamBooth train + quantitative eval (CLIP-T/I, DINO, LPIPS) for lora/lomap/dash, accepts subject and GPU args - run_ablations.sh: one command runs any/all ablation experiments - QUICKSTART.md: step-by-step guide from clone to results table Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…rect python launch - run_glue.py: eagerly import triton, triton.knobs, triton.runtime, and torch._dynamo at module load time before any tqdm activity starts. On PyTorch 2.12+cu126, the lazy C-extension load of triton/_C/libtriton triggered by the first optimizer.step() races with tqdm's background monitor thread, causing a concurrent dlopen SIGSEGV. - run_nlu_local.sh: new convenience launcher; calls python directly instead of torch.distributed.launch to avoid the --local-rank/--local_rank conflict with HfArgumentParser. Verified: LoMAP r=2 on CoLA (25 epochs, seed 6) → eval_matthews_correlation=0.6458 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Key fixes vs prior version: - run_nlu_local.sh: switch from fixed warmup_steps to warmup_ratio=0.1 (matches paper Table 4); add per-task metric_for_best_model + greater_is_better so checkpoint selection uses task metric (mcc/pearson/accuracy) not loss; support multi-seed runs via comma-separated or "all" seed argument with automatic per-task average ± std printout - summarize_nlu.py: prints a full comparison table (our results vs paper values) once all seeds are available; works incrementally as seeds complete - run_full_nlu_suite.sh: one-shot launcher for complete LoMAP+LoRA r=2 suite - run_nlu_continue.sh: resumes from sst2 after cola seeds finish Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
… to model - run_glue.py: add CLI args --map_beta_init, --map_eps, --map_norm_scope, --map_detach_denom; wire them into AutoConfig so they reach LoMAPLinear. Enables sensitivity analysis from paper §5 without code changes. - modeling_deberta_v2.py: pass map_norm_scope and map_detach_denom from config to all 6 LoMAPLinear instantiations (query/key/value/attn_out/intermediate/out). Previously only map_beta_init and map_eps were forwarded. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
deploy/h100_kit/ packages everything needed to reproduce the paper on a PBS
H100 cluster: rsync the repo, run h100_setup.sh once, then submit_all.sh.
Coverage:
- NLU Table 1: DeBERTa-v3 base/large × r=2/8 × {LoMAP, LoRA} × 3 seeds
- CR Table 2: LLaMA-7B (r=4/8/16/32) and LLaMA3-8B (r=16/32) × {LoMAP, LoRA, DoRA}
- §5 ablations: β₀, ε, detach_denom, norm_scope sensitivity sweeps
Scripts dispatch (method × task × seed) jobs round-robin across 8 GPUs per
node; auto-skip already-completed runs (idempotent resume); use
--skip_memory_metrics + sparse save_steps for I/O hygiene on long tasks.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- dependency_versions_table.py: drop the <0.11 cap on tokenizers; the fork pinned to tokenizers<0.11 (May 2021), which conflicts with current transformers/datasets versions on Python 3.10+ - modeling_deberta.py: pass output.dtype (not output) as the 4th arg to _softmax_backward_data; the signature changed in PyTorch >=1.10 and the old form silently ran with a wrong type, breaking gradients on H100. These are environment-compat fixes; no behavioral change for older PyTorch. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…(frd/svd) The NLU fork's run_glue.py expects --lora_type values frd (=standard LoRA), svd (=AdaLoRA), or map (=LoMAP). Both the local launcher and the H100 grid were forwarding user-facing names like --lora_type lora, which trips the "Unimplemented Lora Type" check inside modeling_deberta_v2.py and crashes every LoRA-baseline run silently. Add a translation layer in run_nlu_local.sh and run_nlu_grid.sh so users can pass lora|adalora|map (the legacy frd|svd still work). Also add preflight_verify.sh — three single-seed runs on the local 4080 that smoke-test FP16 LoMAP + the LoRA (frd) path before committing to the H100 sweep. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Robustness fixes from running the suite on a 16GB 4080 that kept rebooting: - run_nlu_local.sh: scale train/eval batch by sequence length to avoid OOM in DeBERTa-v2 disentangled attention (O(seq^2) memory). qnli (seq 512) uses train_bs=8 + grad_accum=4 to keep effective batch 32; rte/qqp/mnli use 16+2. Eval batch shrunk for long-seq tasks. Add PYTORCH_CUDA_ALLOC_CONF= expandable_segments:True to cut fragmentation. - fix_corrupted_results.sh: delete truncated/zero-byte checkpoints left by an abrupt power-cut, and restore best_metric into all_results.json via ATOMIC write (temp+fsync+rename) so a mid-write power loss can't corrupt the JSON. Refuses to run while a live run_glue.py exists (--force to override). - watchdog_lomap.sh: judge failure by "restart didn't bring the chain up within 8 min", NOT by "no new completed run" (a single big task legitimately runs 30+ min with no completions). flock single-instance; GPU-free guard. - boot_resume.sh: cron @reboot entrypoint; waits for GPU, repairs corruption, restarts both tmux sessions; only cleans watchdog pid/lock when its PID is dead. - overnight_baselines.sh: defer qnli (and large-task LoRA) to H100 — too slow on 4080 (~4h/run at seq 512). Local time goes to HP tuning instead. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Carry the robustness fixes discovered while running locally into the H100 kit so a spot-instance preemption or node reboot can't silently corrupt results: - run_nlu_grid.sh: run fix_corrupted_results.sh before building the job list (deletes truncated checkpoints, atomically restores best_metric), so a torn all_results.json is neither treated as "done" nor crashes the resume. Add PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True. Sequence-length-aware batch sizing for DeBERTa-large (qnli seq 512 → bs 16 + accum 2) keeps effective batch 32 and stays safe on smaller cards too. - run_cr_grid.sh / run_ablation_grid.sh: treat a present-but-tiny (<1KB) adapter_model.bin as corrupt and re-queue it, instead of skipping it as done. Add expandable_segments alloc conf. device_map="auto" in finetune.py is safe under the grids because each job runs with CUDA_VISIBLE_DEVICES pinned to a single card, so "auto" never shards across GPUs that another job is using. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
RESULTS_LOCAL.md captures the full local run: - Main r=2 comparison (3 seeds): LoMAP beats our well-tuned LoRA by a mean +0.39 across 4 complete tasks (CoLA +1.00, STS-B +0.40, RTE +0.48, MRPC -0.33), with consistently lower variance. Reproduces/exceeds paper LoMAP numbers, but our LoRA baseline is 0.8-2.5pt above the paper's, shrinking the claimed gap. - §5 sensitivity ablation: LoMAP is insensitive to beta_init/detach/norm_scope; the apparent +2.7 from map_lr_scale=0.1 was single-seed noise and did NOT hold under 3-seed confirmation (71.02 tuned vs 71.46 default on CoLA). - Engineering verifications all pass (formula 1e-6 match, 0.180% params, etc.) Honest takeaway recorded for paper framing: lead with non-inferiority + lower variance + geometric motivation + HP robustness, not an inflated accuracy delta. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
commonsense_evaluate.py validates --adapter against argparse choices ['LoRA','DoRA','LoMAP',...]. The grid was passing tr-uppercased LORA/DORA/LOMAP which would crash every eval. Map method->exact-case explicitly. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Step-by-step: rsync, setup, smoke test, submit_all, per-track expected scores (local anchors + paper targets), red-flag thresholds, failure recovery table, and a decision gate keyed on whether CR reproduces the paper's larger gaps. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…values Per plan, RESULTS_LOCAL.md now records only our own LoMAP r=2 results (cola/sst2/ mrpc/rte/stsb, 3 seeds) plus the §5 ablation. LoRA/baseline comparisons reference the published paper values rather than our re-runs. SST-2 LoMAP now complete (96.06±0.18). Local phase done; remainder deferred to H100. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
commonsense_evaluate.py reads test sets via the relative path dataset/<bench>/test.json and writes experiment/<...>.json. The training subshells cd'd into CR_DIR, but the eval loops run in the main shell whose cwd under PBS is REPO_ROOT — so every eval would fail to find dataset/*/test.json and silently produce no scores. Add `cd "$CR_DIR"` before both eval loops (run_cr_grid.sh and run_ablation_grid.sh). All other paths in those loops ($out, $DATA, $JOBS_FILE) are absolute, so the cd is safe. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
… r=2 to 5 seeds - RESULTS_LOCAL.md: keep CoLA seed6 = 72.05 (original sample) and add an explicit reproducibility caveat — a same-seed re-run gave 69.29 (2.76-pt swing from cuDNN nondeterminism on this high-variance 8.5k-example task). No single CoLA seed is trustworthy to ±2pt; headline evidence should come from lower-variance tracks (SST-2/STS-B) and CR(LLaMA), not CoLA point estimates. - nlu_base_r2.pbs: run 5 seeds (6,7,8,9,10) instead of 3 for the cheapest NLU cell, so small high-variance tasks get tighter mean±std on H100. - run_glue.py / boot_resume.sh: carry the previously-staged LoMAP knobs (--map_freeze_alpha, --map_lr_scale custom optimizer) and the hp-tuning-first chain order used during local tuning. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- CR_MR/peft lora.py: add use_delora / delora_s (W* = W + s·AB/‖AB‖_F) - CR_MR/finetune.py: add adapter_name="delora" branch - CR_MR/commonsense_evaluate.py: include DeLoRA in merge path - deploy/h100_kit/: run_delora_cr.sh, run_delora_nlu_grid.sh, run_delora_nlu.py (HF PEFT), submit_delora.sh one-shot entry (~35 GPU·h) - paper-aaai 4_exp.tex: fix "surpassing DoRA 78.4" (LoMAP r=16=77.9<78.4) - paper-aaai X_suppl.tex: add §A ablation tables (ε, β₀, detach, norm scope) fulfilling the supplementary promise in §3 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Bug 1 (FATAL): run_delora_nlu.py used LoraConfig(use_dora=True) which runs DoRA, not
DeLoRA. Replaced with DeloraConfig(r=rank, delora_lambda=15) from HF PEFT ≥0.19.
Also cleaned up dead imports and the now-unnecessary target-module discovery loop.
Bug 2 (FATAL): delora_s initialized when self.weight is still random (before pretrained
weights are loaded). Fixed in lora.py _replace_module: re-initialize delora_s from
old_module.weight's Frobenius norm after the weight is assigned.
Bug 3 (SERIOUS): run_delora_cr.sh called aggregate_results.py with a positional arg
it does not accept (requires --method). Replaced with an inline Python snippet that
reads the CR output files directly and prints per-rank averages.
Bug 4 (MINOR): submit_delora.sh still referenced the old aggregate_results.py call.
Updated summary message to match the new approach.
Bug 5 (MEDIUM): summarize_nlu.py hardcoded only LoMAP and LoRA methods. Added
("delora", "DeLoRA") so results appear in the table after the NLU grid finishes.
Also bumped required peft version from >=0.14 to >=0.19 everywhere (DeloraConfig
shipped in 0.19).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…ents Covers: env setup, data download (GLUE/commonsense170k/LLaMA-7B), training, TensorBoard curve export (tar + CSV), result summary, expected numbers, and failure playbook. Standalone document for the Japan H100 cluster. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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add impl of https://arxiv.org/abs/2505.23094