diff --git a/README.md b/README.md index 346748b..b3f328c 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # Model parameter similarity experiments +**[研究图册与固定在线副本](docs/FIGURE_GALLERY.md)**：连续分布、联合消融及原始PDF/SVG。 + Research on repeated learned functions in **Qwen3.5-2B**: a completed static weight baseline and a training-free CPU functional pilot. The CPU study found similar existing RMSNorm functions at layer indices 12/13, with a uniform analytic error bound and heldout substitution checks. @@ -51,3 +53,7 @@ removal controls. See [definitions and reproduction](dominance/README.md), [fixed protocol](dominance/PROTOCOL.md), and [measured report](results/head-dominance/REPORT.md). These are descriptive intervention effects, not additive contribution probabilities or validated final-LM pruning gains. + +## Existing GSM8K task-finetuning comparison + +[Paired study and reproduction](finetune_compare/README.md) · [Fixed protocol](finetune_compare/PROTOCOL.md) · [Results](results/finetune-comparison/REPORT.md). This compares an existing pure-LoRA GRPO checkpoint to exactly the same original release; no training is performed here. diff --git a/docs/FIGURE_GALLERY.md b/docs/FIGURE_GALLERY.md new file mode 100644 index 0000000..9e80d14 --- /dev/null +++ b/docs/FIGURE_GALLERY.md @@ -0,0 +1,39 @@ +# 研究图册：Qwen3.5-2B 头贡献集中度 + +这份图册与原始PNG/PDF/SVG一同保存在Git中。图片文件已随PR #6合并到main；以下固定提交链接不依赖聊天附件或本机路径。 + +原始结果提交：`098ea8650c7d0e06339c66e3a0509e38b85b3d7d`；模型revision：`15852e8c16360a2fea060d615a32b45270f8a8fc`。 + +| 基线指标（逐观测中位数） | QKV复合变化 | √JS检索距离 | +|---|---:|---:| +| 有效头数÷总头数 | 76.0% | 55.8% | +| 最强25%头的影响份额 | 44.6% | 56.5% | +| 最弱25%头的影响份额 | 10.6% | 6.8% | + +**√JS有效头数：8.931822/16；P10=5.784081，P90=11.672658。** 这是消融影响的集中程度，不是注意力概率质量。原基线为4条文本各32 token，普通注意力接收层的query7–31。 + +## 连续分布与尺度敏感性 + +![连续分布与尺度敏感性](../results/head-dominance/figures/retrieval_scale_sensitivity.png) + +[PNG固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/retrieval_scale_sensitivity.png) +[PDF固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/retrieval_scale_sensitivity.pdf) +[SVG固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/retrieval_scale_sensitivity.svg) + +## 联合强弱头消融 + +![联合强弱头消融](../results/head-dominance/figures/joint_removal_effects.png) + +[PNG固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/joint_removal_effects.png) +[PDF固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/joint_removal_effects.pdf) +[SVG固定提交副本](https://gitea.entroia.com/Zayden-Jung/exp-model-parameter-similarity/raw/commit/098ea8650c7d0e06339c66e3a0509e38b85b3d7d/results/head-dominance/figures/joint_removal_effects.svg) + +[完整基线报告](../results/head-dominance/REPORT.md) · [全部图表目录](../results/head-dominance/figures) · [图册SHA清单](figure-gallery-manifest.json) + +使用√JS距离进行微调前后配对比较；保留JS散度作为尺度敏感性参考，避免混淆平方尺度与距离尺度。 + +## GSM8K微调前后配对图 + +[配对报告](../results/finetune-comparison/REPORT.md) · [CDF](../results/finetune-comparison/figures/paired_effective_heads_cdf.png) · [逐层分布](../results/finetune-comparison/figures/paired_effective_heads_by_layer.png) · [配对变化](../results/finetune-comparison/figures/paired_effective_heads_delta.png) + +同目录保留PDF/SVG。图表随代码、原始数据和SHA清单一同提交，不依赖聊天附件。 diff --git a/docs/figure-gallery-manifest.json 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--git a/finetune_compare/PROTOCOL.md b/finetune_compare/PROTOCOL.md new file mode 100644 index 0000000..399f879 --- /dev/null +++ b/finetune_compare/PROTOCOL.md @@ -0,0 +1,42 @@ +# Paired task-finetuning comparison (registered before reading finetuned effects) + +Primary checkpoint: Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo, +HF revision038d8be9bd0edd3883fa019651b02bdf65609468, pure lora/seed0, +step150 after shared40-step warmup. GSM8K GRPO, not SFT. Selected by smallest +seed index before observing concentration, not by benchmark score or our metric. +No gate01/gate02 arm or architecture changes. Exact base revision matches retained +Qwen/Qwen3.5-2B15852e8c16360a2fea060d615a32b45270f8a8fc. + +Download existing18.9MB F32 LoRA tensors, verify official file SHA. All144 tensors +must form72 A/B pairs on the4 declared GDN projections of18 linear-attention +layers, rank16 alpha32. Apply FP32 effective W +2BA in streamed row chunks, also +when decomposing source output heads. Do not quantize merged weights, mutate or +save over base weights, execute foreign .pt files, train or use GPU. Validate +against separate base-output +2B(Ax) forward and check head sums/null masks. + +Primary comparison exactly matches the reported8.931822/16 statistic: all six +ordinary attention receivers3/7/11/15/19/23, previous-layer16 donor output heads, +all32 tokens computed, query7..31, original4 plain-text inputs and same token IDs. +Reuse corresponding immutable base single-ablation arrays, recompute FT baselines +and all16 single donor removals, source MLP/residual/recipient normalization exact. +Use the same sqrt(JS) distance influence, normalized shares and N_eff=1/sum(p²). +Preserve JS divergence as a sensitivity row; no change of ranking or threshold. +All4800 per-token/query-head observations are paired. No new joint-mask search: +the requested statistic depends on single removals, not the previous joint tests. + +Secondary task-conditioned comparison: first4 original GSM8K test questions with +at least32 base-tokenizer tokens (length selection only, before model effects), +question-only plain text, first32 tokens. Freeze original author repository commit, +JSONL SHA, item indices, question hashes and exact token IDs. No answers, generation, +chat-template change or accuracy claim. Compute both base and FT on these same +questions; never compare old general-text base to new GSM8K FT as if paired. + +Report overall median/P10/P90, paired differences, per receiver layer and per text, +strong/weak-quartile shares, and dominant donor identity agreement. No iid-token +p-values, no claim over training seeds (one fixed trained seed), no proof of +GSM8K accuracy improvement or long-context behavior. Positive, negative and near +zero changes all count as results. Source cohort remains4+4 short texts. + +All model work/tests/plots on authorized CPU server8.211.154.134, bounded7200s. +Preserve all base/FT weights, adapters and caches; no automatic cleanup. Save raw +arrays, source provenance, SHA manifests, fixed chart gallery and reviewable PR. diff --git a/finetune_compare/README.md b/finetune_compare/README.md new file mode 100644 index 0000000..6308539 --- /dev/null +++ b/finetune_compare/README.md @@ -0,0 +1,61 @@ +# Existing GSM8K fine-tune: paired head-concentration comparison + +First restore the original charts through [the fixed figure gallery](../docs/FIGURE_GALLERY.md). +The original 8.931822/16, P10=5.784081 and P90=11.672658 refer to sqrt(JS) +influences of all16 prior-layer heads on the six full-attention receivers. + +Selected release: [Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo](https://huggingface.co/Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo), +pinned038d8be9bd0edd3883fa019651b02bdf65609468, pure lora/seed0. The publisher +records GSM8K GRPO from a shared warmup, rank16 alpha32, exact same original +Qwen3.5-2B revision. This is extra RL fine-tuning of the post-trained release, +not pretraining-to-SFT, and not a beta-gate architecture modification. Our seed +choice is fixed before seeing effects; no new training is performed. + +`lora.py` validates all144 tensors/72 pairs, maps only declared GDN projections, +and provides read-only FP32 effective row chunks W+2BA. Source head decomposition +uses the same effective weights. `ExplicitLoRA` adds2B(Ax) independently to the +base projection as a numerical reference. No BF16 re-rounding is introduced: +the author's training code also warns that rounding merged increments can erase +small LoRA changes. Author configuration commit/SHA and public HF metadata are +saved alongside the protocol; foreign training code and pickle checkpoints are +not executed. + +`run.py` reuses the exact original single-ablation arrays for the original cohort, +then computes matched finetuned states; it computes both models on4 question-only +GSM8K test prefixes as a separate cohort. Every input has32 tokens. All16 donor +heads, six receiver layers andquery7..31 remain identical to the original metric. +The task corpus is the canonical author's test JSONL at a fixed Git commit, and +selection uses only the first4 items with at least32 tokenizer tokens. All item +indices, question hashes and token IDs are recorded; answers are not model inputs. + +`report.py` generates paired CDFs, per-layer medians/quantiles, paired change +histograms, raw observations and tables. `audit.py` independently reconstructs +effective-head counts via(sum d)^2/sum(d^2), checks pairing, exact old baselines, +head sums, null masks, adapter/dataset/base SHA and retained external caches. +`tests/test_finetune_compare.py` checks streamed row arithmetic, an independent +low-rank forward and head decomposition, plus exact zero-scale restoration. + +No iid-token p-values, training-seed generalization, GSM8K accuracy result, +long-context claim, pruning recommendation or functional-duplicate claim. +The paired delta median is distinguished from the difference of pooled medians. +All weights, adapters and caches remain on the authorized CPU server until user +explicit deletion. Git stores all raw influence arrays, not the model/adapter. + +Remote workflow on8.211.154.134: +``` +OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -B -m unittest tests.test_finetune_compare -v +OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -B -m finetune_compare.run --source runs/cpu-qwen35-2b --parent runs/head-dominance --assets runs/finetune-head-comparison --out runs/finetune-head-comparison/NEW_EXPERIMENT +venv/bin/python -B -m finetune_compare.report runs/finetune-head-comparison/NEW_EXPERIMENT +venv/bin/python -B -m finetune_compare.audit runs/finetune-head-comparison/NEW_EXPERIMENT runs/head-dominance runs/finetune-head-comparison runs/cpu-qwen35-2b +``` + +The supplemental QKV field is measured only on the same six full-attention +receivers, so its baseline must not be confused with the older76.0% pooled +QKV effective-head fraction across all23 adjacent transitions. The gallery +preserves that original table separately; the requested8.931822 metric uses +exactly the matching six receivers in both studies. + +The audit also records all72 low-rank update Frobenius norms (via small Gram +matrices) and final pre-normalization residual changes per text. These separate +a stable concentration statistic from an accidentally inactive adapter. Residual +changes are not logit distributions, accuracy differences or capability scores. diff --git a/finetune_compare/__init__.py b/finetune_compare/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/finetune_compare/audit.py b/finetune_compare/audit.py new file mode 100644 index 0000000..56c32cc --- /dev/null +++ b/finetune_compare/audit.py @@ -0,0 +1,74 @@ +"""Independent paired effective-head, source, preservation and delivery checks.""" +import json,hashlib,sys +from pathlib import Path +import numpy as np + +def sha(path): + with Path(path).open('rb') as f:return hashlib.file_digest(f,'sha256').hexdigest() +def main(root,parent,assets,source): + root,parent,assets,source=map(Path,[root,parent,assets,source]);status=json.loads((root/'status.json').read_text());assert status['state']=='completed' and len(status['links'])==96 + samples=json.loads((root/'samples.json').read_text());assert set(samples)=={'legacy','gsm8k'} and all(len(v)==4 for v in samples.values());assert all(len(s['token_ids'])==32 for ss in samples.values() for s in ss) + assert status['explicit_lora_forward_relative_error']<1e-4;rawstats={};expected=set();obs=dict(np.load(root/'observations.npz'));summary=json.loads((root/'summary.json').read_text()) + def check(x,record): + x=np.asarray(x,dtype=np.float64);x=x[np.isfinite(x)];assert record['count']==len(x) + for name,value in zip(('p10','median','p90','mean'),[*np.quantile(x,[.1,.5,.9]),x.mean()]):np.testing.assert_allclose(record[name],value,atol=1e-8,rtol=1e-6) + for cohort,ss in samples.items(): + for role in ('base','finetuned'): + for sample in ss: + for layer in (2,6,10,14,18,22): + name=f'{cohort}-{role}-{sample["id"]}-{layer:02d}.npz';expected.add(name);d=dict(np.load(root/'raw'/name)) + for key in ('Q','K','V','QKV','JS'): + assert d['single_'+key].shape==(16,32,8) and np.isfinite(d['single_'+key]).all() and np.min(d['single_'+key])>=0 + assert np.max(d['null_'+key])<2e-5 + if cohort=='legacy' and role=='base': + with np.load(parent/'raw'/f'{sample["id"]}-link-{layer:02d}.npz') as original: + for key in d:np.testing.assert_array_equal(d[key],original[key]) + for metric,impacts in [('distance',np.sqrt(d['single_JS'])),('divergence',d['single_JS']),('QKV',d['single_QKV'])]: + x=impacts[:,7:,:].transpose(1,2,0).astype(np.float64);total=x.sum(-1);den=np.sum(x*x,-1);assert np.all(den>0) + neff=total*total/den + rawstats.setdefault((cohort,role,metric),[]).append(neff.ravel()) + assert expected=={f.name for f in (root/'raw').glob('*.npz')} + for cohort in samples: + for metric in ('distance','divergence','QKV'): + for role in ('base','finetuned'): + x=np.concatenate(rawstats[(cohort,role,metric)]);assert len(x)==4800 + np.testing.assert_allclose(obs['__'.join((cohort,role,metric,'effective_heads'))],x,rtol=1e-8,atol=1e-8) + check(x,summary[cohort][metric][role]['effective_heads']) + before=np.concatenate(rawstats[(cohort,'base',metric)]);after=np.concatenate(rawstats[(cohort,'finetuned',metric)]);check(after-before,summary[cohort][metric]['paired']['effective_heads_delta']) + base=summary['legacy']['distance']['base']['effective_heads'];np.testing.assert_allclose([base[x] for x in ('median','p10','p90')],[8.93182211792415,5.7840811612328125,11.67265834734814],rtol=1e-10) + controls=json.loads((root/'head-sum-controls.json').read_text());assert len(controls)==288 and all(c['relative_l2']<2e-5 for c in controls) + refs=json.loads((root/'baseline-references.json').read_text());assert len(refs)==24 and all(sha(v['source'])==v['sha256'] for v in refs) + assert sha(assets/'adapter.safetensors')=='93a3f43a4356f84da0d13ecc622fee13e41ba15d237cdaad6094f243899c62d6' + dataset=json.loads((root/'gsm8k-source.json').read_text());assert sha(assets/'gsm8k-test.jsonl')==dataset['sha256'] + oldweights=list((source/'weights').glob('*.safetensors'));assert len(oldweights)==1 + base_sha=sha(oldweights[0]);assert base_sha=='aa33250c4fc64891ddfaba3a314fd9542ea371843c387178b425fbcc5ed680b1' + from pilot.tensors import load_tensors + import re + tensors=load_tensors(assets);pairs={} + for name,t in tensors.items(): + stem,which=name.split('.lora_');pairs.setdefault(stem,{})[which[0]]=t.read().reshape(t.shape).copy();t.evict() + updates=[] + for name,pair in pairs.items(): + a=pair['A'].astype(np.float64);b=pair['B'].astype(np.float64) + norm2=4*float(np.sum((b.T@b)*(a@a.T).T)) + updates.append({'module':name,'delta_frobenius':float(np.sqrt(max(norm2,0))),'A_nonzero':int(np.count_nonzero(a)),'B_nonzero':int(np.count_nonzero(b))}) + assert len(updates)==72 and any(x['delta_frobenius']>0 for x in updates) + (root/'weight-update-summary.json').write_text(json.dumps({'scale':2,'modules':updates,'nonzero_update_modules':sum(x['delta_frobenius']>0 for x in updates),'norm_identity':'||2BA||_F^2 = 4 trace((B^T B)(A A^T))'},indent=2)+'\n') + changes=[] + for cohort,ss in samples.items(): + for sample in ss: + basefile=parent/'cache'/sample['id']/'final.npy' if cohort=='legacy' else root/'cache'/f"{cohort}-base-{sample['id']}"/'final.npy' + ftfile=root/'cache'/f"{cohort}-finetuned-{sample['id']}"/'final.npy' + before=np.load(basefile).astype(np.float64);after=np.load(ftfile).astype(np.float64) + changes.append({'cohort':cohort,'sample':sample['id'],'final_pre_norm_residual_relative_l2':float(np.linalg.norm(after-before)/np.linalg.norm(before))}) + assert any(x['final_pre_norm_residual_relative_l2']>0 for x in changes) + (root/'hidden-state-changes.json').write_text(json.dumps({'definition':'final residual before final RMSNorm, not logits, accuracy or LM quality','changes':changes},indent=2)+'\n') + caches=[{'path':str(p.resolve()),'bytes':p.stat().st_size,'sha256':sha(p)} for p in sorted((root/'cache').rglob('*')) if p.is_file()] + assert len(caches)==300 + (root/'external-caches.json').write_text(json.dumps({'host':'8.211.154.134','base_weights':str(oldweights[0].resolve()),'base_sha256':base_sha,'adapter_path':str((assets/'adapter.safetensors').resolve()),'adapter_sha256':sha(assets/'adapter.safetensors'),'caches':caches,'automatic_deletion':False},indent=2)+'\n') + files=list(Path('finetune_compare').glob('*.py'))+[Path('finetune_compare/PROTOCOL.md'),Path('tests/test_finetune_compare.py'),Path('dominance/core.py'),Path('dominance/run.py'),Path('functional/model.py')] + (root/'source-hashes.json').write_text(json.dumps({str(f):sha(f) for f in files},indent=2)+'\n') + proof={'passed':True,'links':96,'paired_observations_per_cohort_metric':4800,'legacy_baseline_exact':True,'effective_head_formula_independently_recomputed':True,'head_sum_checks':288,'explicit_lora_forward_relative_error':status['explicit_lora_forward_relative_error'],'adapter_sha_verified':True,'base_sha_verified':True,'dataset_sha_verified':True,'retained_cache_files':len(caches),'nonzero_update_modules':sum(x['delta_frobenius']>0 for x in updates),'nonzero_hidden_state_changes_verified':True} + (root/'audit.json').write_text(json.dumps(proof,indent=2)+'\n');print(json.dumps(proof,indent=2),flush=True) + included=[p for p in root.rglob('*') if p.is_file() and 'cache' not in p.relative_to(root).parts and p.name!='checksums.json'];(root/'checksums.json').write_text(json.dumps({str(p.relative_to(root)):sha(p) for p in sorted(included)},indent=2)+'\n') +if __name__=='__main__':main(*sys.argv[1:]) diff --git a/finetune_compare/author-configuration-provenance.json 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"lora_targets": [ + "in_proj_qkv", + "in_proj_a", + "in_proj_b", + "out_proj" + ], + "gate": null, + "n_tensors": 144, + "n_params": 4727808, + "source_sha256": "de2794c085f5096fea53374ba1cea31075071bb6de3f227bb3350b3035970447" +} diff --git a/finetune_compare/lora.py b/finetune_compare/lora.py new file mode 100644 index 0000000..07be93e --- /dev/null +++ b/finetune_compare/lora.py @@ -0,0 +1,47 @@ +"""Read-only FP32 effective weights for an existing pure-LoRA checkpoint.""" +import re +import numpy as np +from functional.model import PREFIX,StreamingLM +from pilot.tensors import load_tensors +BASE_REVISION='15852e8c16360a2fea060d615a32b45270f8a8fc' +TARGETS={'in_proj_qkv','in_proj_a','in_proj_b','out_proj'} + +class EffectiveWeight: + def __init__(self,base,a,b,scale): + assert a.ndim==b.ndim==2 and (b.shape[0],a.shape[1])==base.shape and a.shape[0]==b.shape[1] + self.base,self.a,self.b,self.scale=base,a,b,scale + self.shape,self.numel,self.dtype=base.shape,base.numel,'F32' + def read(self,selector=slice(None)): + if not isinstance(selector,slice):raise ValueError('Only contiguous row chunks supported') + start,stop,step=selector.indices(self.numel);dim=self.shape[1] + if step!=1 or start%dim or stop%dim:raise ValueError('Row aligned chunks required') + w=self.base.read(slice(start,stop)).reshape(-1,dim) + return (w+self.scale*(self.b[start//dim:stop//dim]@self.a)).astype(np.float32).reshape(-1) + def evict(self):self.base.evict() + + +def install(lm,directory,meta,multiplier=1.): + assert meta['base_model']=='Qwen/Qwen3.5-2B' and meta['base_revision']==BASE_REVISION and meta['gate'] is None and meta['arm']=='lora' + tensors=load_tensors(directory);pairs={};pattern=re.compile(r'^base_model\.model\.model\.(layers\.(\d+)\.linear_attn\.(in_proj_qkv|in_proj_a|in_proj_b|out_proj))\.lora_([AB])\.default\.weight$') + for name,tensor in tensors.items(): + match=pattern.fullmatch(name) + if not match:raise ValueError('Unexpected trainable tensor: '+name) + target=PREFIX+match[1]+'.weight';assert lm.config['layer_types'][int(match[2])]=='linear_attention' + assert target in lm.tensors + value=tensor.read().reshape(tensor.shape).copy();tensor.evict();assert np.isfinite(value).all() + pairs.setdefault(target,{})[match[4]]=value + expected={PREFIX+f'layers.{layer}.linear_attn.{role}.weight' for layer,kind in enumerate(lm.config['layer_types']) if kind=='linear_attention' for role in TARGETS} + assert set(pairs)==expected and len(tensors)==meta['n_tensors'] + scale=meta['lora_alpha']/meta['lora_rank']*multiplier + for target,pair in pairs.items(): + assert set(pair)=={'A','B'} and pair['A'].shape[0]==meta['lora_rank'] + lm.tensors[target]=EffectiveWeight(lm.tensors[target],pair['A'],pair['B'],scale) + return pairs,scale + + +class ExplicitLoRA(StreamingLM): + """Independent reference: base projection + scaled low-rank output, no W merge.""" + def linear(self,x,name,rows=256): + y=super().linear(x,name,rows);pair=self.pairs.get(PREFIX+name+'.weight') + if pair is not None:y=y+self.scale*((x@pair['A'].T)@pair['B'].T) + return y.astype(np.float32) diff --git a/finetune_compare/report.py b/finetune_compare/report.py new file mode 100644 index 0000000..0b3e090 --- /dev/null +++ b/finetune_compare/report.py @@ -0,0 +1,73 @@ +"""Paired concentration changes from saved immutable intervention arrays.""" +import csv,json,sys +from pathlib import Path +import numpy as np +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +from dominance.core import concentration + +def stats(x): + x=np.asarray(x,dtype=np.float64);v=x[np.isfinite(x)];q=np.quantile(v,[.1,.5,.9]) + return {'count':int(v.size),'p10':float(q[0]),'median':float(q[1]),'p90':float(q[2]),'mean':float(v.mean())} +def csvwrite(path,rows): + with path.open('w') as f:w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows) +def main(root): + root=Path(root);status=json.loads((root/'status.json').read_text());assert status['state']=='completed';samples=json.loads((root/'samples.json').read_text());pool={};observations={};summary={};rows=[];layer_rows=[] + for cohort in ('legacy','gsm8k'): + for role in ('base','finetuned'): + for sample in samples[cohort]: + for source in (2,6,10,14,18,22): + data=np.load(root/'raw'/f'{cohort}-{role}-{sample["id"]}-{source:02d}.npz') + for metric,key,transform in [('distance','JS',np.sqrt),('divergence','JS',lambda x:x),('QKV','QKV',lambda x:x)]: + impacts=transform(data['single_'+key])[:,7:,:].transpose(1,2,0);c=concentration(impacts) + record={'cohort':cohort,'role':role,'sample':sample['id'],'recipient':source+1,'metric':metric} + for name,x in c.items(): + observations.setdefault((cohort,role,metric,name),[]).append(x.reshape(-1)) + if name!='valid': + for stat,value in stats(x).items():record[name+'_'+stat]=value + pool.setdefault((cohort,role,metric,'recipient'),[]).append(np.full(200,source+1)) + pool.setdefault((cohort,role,metric,'sample'),[]).append(np.full(200,sample['id'])) + observations.setdefault((cohort,role,metric,'winner'),[]).append(np.argmax(impacts,-1).reshape(-1)) + rows.append(record) + arrays={k:np.concatenate(v) for k,v in observations.items()};np.savez_compressed(root/'observations.npz',**{'__'.join(k):v for k,v in arrays.items()}) + for cohort in ('legacy','gsm8k'): + summary[cohort]={} + for metric in ('distance','divergence','QKV'): + item={role:{name:stats(arrays[(cohort,role,metric,name)]) for name in ('effective_heads','effective_fraction','concentration','top1','top25','bottom25','heads90','total')} for role in ('base','finetuned')} + before=arrays[(cohort,'base',metric,'effective_heads')];after=arrays[(cohort,'finetuned',metric,'effective_heads')];delta=after-before + item['paired']={'effective_heads_delta':stats(delta),'increased_fraction':float(np.mean(delta>0)),'decreased_fraction':float(np.mean(delta<0)),'winner_agreement':float(np.mean(arrays[(cohort,'base',metric,'winner')]==arrays[(cohort,'finetuned',metric,'winner')]))} + summary[cohort][metric]=item + recipients=np.concatenate(pool[(cohort,'base',metric,'recipient')]);names=np.concatenate(pool[(cohort,'base',metric,'sample')]) + for layer in (3,7,11,15,19,23): + which=recipients==layer;a=stats(before[which]);b=stats(after[which]);d=stats(delta[which]);layer_rows.append({'cohort':cohort,'metric':metric,'recipient':layer,'base_median':a['median'],'base_p10':a['p10'],'base_p90':a['p90'],'finetuned_median':b['median'],'finetuned_p10':b['p10'],'finetuned_p90':b['p90'],'paired_delta_median':d['median']}) + for sample in samples[cohort]: + which=names==sample['id'];item.setdefault('per_text',{})[sample['id']]={'base':stats(before[which]),'finetuned':stats(after[which]),'paired_delta':stats(delta[which])} + expected=(8.93182211792415,5.7840811612328125,11.67265834734814);b=summary['legacy']['distance']['base']['effective_heads'];np.testing.assert_allclose([b[k] for k in ['median','p10','p90']],expected,rtol=1e-10) + (root/'summary.json').write_text(json.dumps(summary,indent=2)+'\n');csvwrite(root/'per-text-layer.csv',rows);csvwrite(root/'per-layer.csv',layer_rows) + figs=root/'figures';figs.mkdir(exist_ok=True) + def save(fig,name): + for ext in ('png','pdf','svg'):fig.savefig(figs/(name+'.'+ext),dpi=180) + plt.close(fig) + fig,axs=plt.subplots(1,2,figsize=(13,4.8),layout='constrained') + for ax,cohort in zip(axs,['legacy','gsm8k']): + for role,label,color in [('base','Original release','C0'),('finetuned','GSM8K GRPO LoRA seed0','C1')]: + x=np.sort(arrays[(cohort,role,'distance','effective_heads')]);ax.plot(x,np.arange(1,len(x)+1)/len(x),label=label,color=color) + ax.set(title='Original four texts' if cohort=='legacy' else 'Four GSM8K held-out question prefixes',xlabel='Effective donor heads: sqrt(JS) influence',ylabel='Empirical CDF',xlim=(1,16),ylim=(0,1));ax.grid(alpha=.2);ax.legend() + fig.suptitle('Same inputs before/after existing task fine-tuning; 32 tokens; one training seed; no training here');save(fig,'paired_effective_heads_cdf') + fig,axs=plt.subplots(1,2,figsize=(13,4.8),layout='constrained') + for ax,cohort in zip(axs,['legacy','gsm8k']): + for role,prefix,color in [('base','base','C0'),('finetuned','finetuned','C1')]: + rr=[r for r in layer_rows if r['cohort']==cohort and r['metric']=='distance'];x=[r['recipient'] for r in rr];med=[r[prefix+'_median'] for r in rr];lo=[r[prefix+'_p10'] for r in rr];hi=[r[prefix+'_p90'] for r in rr];ax.plot(x,med,'o-',color=color,label=role);ax.fill_between(x,lo,hi,color=color,alpha=.12) + ax.set(title=cohort,xlabel='Full-attention recipient layer index',ylabel='Effective donor heads: median / P10–P90',ylim=(1,16));ax.legend();ax.grid(alpha=.2) + fig.suptitle('All six full-attention receivers, sixteen donor heads each; descriptive dependent observations');save(fig,'paired_effective_heads_by_layer') + fig,axs=plt.subplots(1,2,figsize=(13,4.8),layout='constrained') + for ax,cohort in zip(axs,['legacy','gsm8k']): + delta=arrays[(cohort,'finetuned','distance','effective_heads')]-arrays[(cohort,'base','distance','effective_heads')];ax.hist(delta,bins=60,density=True,alpha=.7);ax.axvline(0,color='black',linestyle='--');ax.axvline(np.median(delta),color='C3',label='Paired median');ax.set(title=cohort,xlabel='Finetuned minus original effective head count',ylabel='Density');ax.legend() + fig.suptitle('Paired changes; positive = more distributed, negative = more concentrated');save(fig,'paired_effective_heads_delta') + lines=['# GSM8K任务微调前后的有效头数','', '比较原Qwen3.5-2B发布权重与同一revision上的纯LoRA seed0（额外GSM8K GRPO，非SFT）。没有训练新模型。主指标为√JS距离消融影响的N_eff；正变化表示更分散，负变化表示更集中。','', '| 输入组 | 原权重中位数 [P10,P90] | 微调后中位数 [P10,P90] | 配对变化中位数 | 增加的观测比例 |','|---|---:|---:|---:|---:|'] + for cohort,label in [('legacy','原四条文本'),('gsm8k','GSM8K测试题前缀')]: + item=summary[cohort]['distance'];a=item['base']['effective_heads'];b=item['finetuned']['effective_heads'];d=item['paired']['effective_heads_delta'];lines.append(f"| {label} | {a['median']:.6f} [{a['p10']:.6f},{a['p90']:.6f}] | {b['median']:.6f} [{b['p10']:.6f},{b['p90']:.6f}] | {d['median']:+.6f} | {item['paired']['increased_fraction']:.1%} |") + lines+=['', '每组4条输入×6个接收层×25个query位置×8个接收query头=4800个配对观测；它们不是4800个独立文本样本。前后使用同一原模型tokenizer、完全相同token IDs。原四条输入的基线8.931822/16已精确复现。','', 'GSM8K输入取原作者固定版本test split中前4道至少32token的题目，仅用题目前32token，不含答案，不套不同聊天模板。另算了同组GSM8K输入的原权重基线；没有把旧通用文本与新任务文本混作配对。','', '## 方法与限定','', '所有6个普通注意力接收层和16个上游输出头均保留；每次移除一个上游头在全序列的投影输出，重算中间MLP/残差/归一化和检索分布。微调只改变18个GDN层的指定投影，普通注意力权重不变；隐藏状态和上游贡献仍会变化。','', '采用read-only FP32有效权重W+2BA，未把增量舍入为BF16、未修改底座；另用base projection+2B(Ax)独立前向校验。头输出求和、空消融和源数组复用校验均记录。JS散度及QKV复合相对变化也保留，避免只展示有利度量。这里QKV补充指标仅覆盖相同六个普通注意力接收层，其原基线不能与旧研究全部23连接的76.0%混作同一统计量。','', '单个发布者训练seed、4条短文本/组，仅支持这些输入上的描述性变化；不推广为所有微调都会如此，不报告GSM8K准确率，不据此推断删头可用或最终LM能力。配对变化中位数与两个总体中位数之差不是同一个统计量。','', '[公开微调权重与训练说明](https://huggingface.co/Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo)；固定版本、参数SHA、dataset版本和题目索引见protocol.json/gsm8k-source.json/samples.json。所有权重、适配器和缓存继续保留。'] + (root/'REPORT.md').write_text('\n'.join(lines)+'\n');print((root/'REPORT.md').read_text(),end='') +if __name__=='__main__':main(sys.argv[1]) diff --git a/finetune_compare/run.py b/finetune_compare/run.py new file mode 100644 index 0000000..653dda4 --- /dev/null +++ b/finetune_compare/run.py @@ -0,0 +1,72 @@ +"""Paired CPU-only existing-adapter comparison for the published sqrt(JS) metric.""" +import argparse,json,hashlib,time,resource,gc +from pathlib import Path +import numpy as np +from tokenizers import Tokenizer +from dominance.core import InfluenceLM,concentration +from dominance.run import distances +from .lora import install,ExplicitLoRA,BASE_REVISION +SOURCES=(2,6,10,14,18,22) + +def dump(p,d):p.write_text(json.dumps(d,indent=2,allow_nan=False)+'\n') +def single(lm,cache,layer,path): + base=lm.recipient(cache['next_raw'],layer+1);hs=cache['heads'].shape[2];t=cache['raw'].shape[1];assert hs==16 + scores={k:[] for k in ('Q','K','V','QKV','JS')} + for start in range(0,hs,4): + masks=np.zeros((4,t,hs),dtype=np.float32) + for n in range(4):masks[n,:,start+n]=1 + effects=distances(base,lm.intervene(cache,layer,masks)) + for k,v in effects.items():scores[k].extend(v) + null=distances(base,lm.intervene(cache,layer,np.zeros((1,t,hs),dtype=np.float32))) + assert max(float(np.max(v)) for v in null.values())<2e-5 + payload={'single_'+k:np.asarray(v,dtype=np.float32) for k,v in scores.items()};payload.update({'null_'+k:v for k,v in null.items()});np.savez_compressed(path,**payload) + +def main(): + p=argparse.ArgumentParser();p.add_argument('--source',type=Path,required=True);p.add_argument('--parent',type=Path,required=True);p.add_argument('--assets',type=Path,required=True);p.add_argument('--out',type=Path,required=True);a=p.parse_args();a.out.mkdir(parents=True,exist_ok=False);(a.out/'cache').mkdir();(a.out/'raw').mkdir() + started=time.monotonic();meta=json.loads((a.assets/'adapter_meta.json').read_text());assert meta['base_revision']==BASE_REVISION + adapter_sha=hashlib.sha256((a.assets/'adapter.safetensors').read_bytes()).hexdigest();assert adapter_sha=='93a3f43a4356f84da0d13ecc622fee13e41ba15d237cdaad6094f243899c62d6' + tok=Tokenizer.from_file(str(a.source/'tokenizer.json'));legacy=json.loads((a.parent/'samples.json').read_text());gsm=[] + for s in legacy:assert s['token_ids']==tok.encode(s['text']).ids[:32] + for index,line in enumerate((a.assets/'gsm8k-test.jsonl').read_text().splitlines()): + q=json.loads(line)['question'];ids=tok.encode(q).ids + if len(ids)<32:continue + gsm.append({'id':f'gsm8k-test-{index:04d}','row_index':index,'text':q,'question_sha256':hashlib.sha256(q.encode()).hexdigest(),'token_ids':ids[:32]}) + if len(gsm)==4:break + assert len(gsm)==4 + cohorts={'legacy':legacy,'gsm8k':gsm};dump(a.out/'samples.json',cohorts);dump(a.out/'adapter_meta.json',meta);dump(a.out/'gsm8k-source.json',json.loads((a.assets/'gsm8k-source.json').read_text())) + protocol={'primary':'same six full-attention receivers and original4x32 token cohort as published8.931822/16','adapter_repo':'Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo','adapter_revision':'038d8be9bd0edd3883fa019651b02bdf65609468','adapter_file':'lora/seed0/adapter.safetensors','adapter_sha256':adapter_sha,'base_revision':BASE_REVISION,'scale':2,'rank':16,'arm':'pure LoRA','training':'publisher-reported GSM8K GRPO; we do no training','sample_choice':'four original texts; first4 canonical GSM8K test questions with >=32tokens, length selection only, first32tokens; question only','source_layers':SOURCES,'primary_queries':list(range(7,32)),'main_metric':'concentration(sqrt(single_JS)); same FP32 tensors and sequence-wide donor-head deletion as prior run','cohort_pairing':'each comparison uses same question/tokenIDs before and after; base legacy arrays reused with elementwise checks','runtime':'read-only FP32 effective W+2BA; no BF16 re-rounding or quantization','controls':'head sum, null mask, explicit low-rank versus effective-weight full forward relL2<1e-4','budget_seconds':7200,'keep_all_weights_and_caches':True,'limitations':['one existing training seed','4 texts per cohort, 32 tokens each','dependent query/head observations, not iid textual samples','no generation or accuracy measurement','GDN targets only; ordinary attention weights stay unchanged','no pruning or functional interchangeability claim']} + dump(a.out/'protocol.json',protocol);status={'state':'running','links':[]};dump(a.out/'status.json',status);cfg=json.loads((a.source/'model_config.json').read_text());controls=[];references=[] + try: + for cohort,samples in cohorts.items(): + for role in ('base','finetuned'): + reuse=cohort=='legacy' and role=='base';lm=None + if not reuse: + lm=InfluenceLM(a.source/'weights',cfg,seconds=max(1,7200-(time.monotonic()-started))) + if role=='finetuned':pairs,scale=install(lm,a.assets,meta) + for si,s in enumerate(samples): + if time.monotonic()-started>7200:raise TimeoutError('Paired CPU budget') + if not reuse: + cache=a.out/'cache'/f'{cohort}-{role}-{s["id"]}';cache.mkdir();ids=np.array([s['token_ids']],dtype=np.int64);final=lm.baseline(ids,cache);np.save(cache/'final.npy',final) + controls.extend({'cohort':cohort,'role':role,'sample':s['id'],**v} for v in lm.head_sum_errors) + if cohort=='legacy' and role=='finetuned' and si==0: + ref=ExplicitLoRA(a.source/'weights',cfg,seconds=max(1,7200-(time.monotonic()-started)));ref.pairs,ref.scale=pairs,scale;expected,_=ref.forward(ids);actual=lm.normalization(final,'norm');error=float(np.linalg.norm(actual.astype(np.float64)-expected)/np.linalg.norm(expected.astype(np.float64)));assert error<1e-4 + status['explicit_lora_forward_relative_error']=error;del ref;gc.collect() + for layer in SOURCES: + out=a.out/'raw'/f'{cohort}-{role}-{s["id"]}-{layer:02d}.npz' + if reuse: + old=a.parent/'raw'/f'{s["id"]}-link-{layer:02d}.npz' + with np.load(old) as d:payload={prefix+k:d[prefix+k] for prefix in ('single_','null_') for k in ('Q','K','V','QKV','JS')} + np.savez_compressed(out,**payload) + with np.load(out) as d: + for k,v in payload.items():np.testing.assert_array_equal(d[k],v) + references.append({'source':str(old.resolve()),'sha256':hashlib.sha256(old.read_bytes()).hexdigest(),'derived':out.name}) + else: + with np.load(cache/f'layer-{layer}.npz') as d:loaded={k:d[k] for k in d.files} + single(lm,loaded,layer,out);del loaded + status['links'].append({'cohort':cohort,'role':role,'sample':s['id'],'source':layer,'path':str(out.relative_to(a.out))});status['elapsed_seconds']=time.monotonic()-started;dump(a.out/'status.json',status);print('completed',cohort,role,s['id'],layer,flush=True) + dump(a.out/'head-sum-controls.json',controls);dump(a.out/'baseline-references.json',references) + if lm is not None:del lm;gc.collect() + status.update(state='completed',elapsed_seconds=time.monotonic()-started,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss);dump(a.out/'status.json',status) + except BaseException as e: + status.update(state='failed',error=repr(e),elapsed_seconds=time.monotonic()-started);dump(a.out/'status.json',status);raise +if __name__=='__main__':main() diff --git a/results/finetune-comparison/REPORT.md b/results/finetune-comparison/REPORT.md new file mode 100644 index 0000000..6c10f9d --- /dev/null +++ b/results/finetune-comparison/REPORT.md @@ -0,0 +1,22 @@ +# GSM8K任务微调前后的有效头数 + +比较原Qwen3.5-2B发布权重与同一revision上的纯LoRA seed0（额外GSM8K GRPO，非SFT）。没有训练新模型。主指标为√JS距离消融影响的N_eff；正变化表示更分散，负变化表示更集中。 + +| 输入组 | 原权重中位数 [P10,P90] | 微调后中位数 [P10,P90] | 配对变化中位数 | 增加的观测比例 | +|---|---:|---:|---:|---:| +| 原四条文本 | 8.931822 [5.784081,11.672658] | 8.918301 [5.776576,11.683940] | -0.007827 | 45.6% | +| GSM8K测试题前缀 | 8.906046 [6.013504,11.801882] | 8.904117 [5.982593,11.818115] | -0.006747 | 47.2% | + +每组4条输入×6个接收层×25个query位置×8个接收query头=4800个配对观测；它们不是4800个独立文本样本。前后使用同一原模型tokenizer、完全相同token IDs。原四条输入的基线8.931822/16已精确复现。 + +GSM8K输入取原作者固定版本test split中前4道至少32token的题目，仅用题目前32token，不含答案，不套不同聊天模板。另算了同组GSM8K输入的原权重基线；没有把旧通用文本与新任务文本混作配对。 + +## 方法与限定 + +所有6个普通注意力接收层和16个上游输出头均保留；每次移除一个上游头在全序列的投影输出，重算中间MLP/残差/归一化和检索分布。微调只改变18个GDN层的指定投影，普通注意力权重不变；隐藏状态和上游贡献仍会变化。 + +采用read-only FP32有效权重W+2BA，未把增量舍入为BF16、未修改底座；另用base projection+2B(Ax)独立前向校验。头输出求和、空消融和源数组复用校验均记录。JS散度及QKV复合相对变化也保留，避免只展示有利度量。这里QKV补充指标仅覆盖相同六个普通注意力接收层，其原基线不能与旧研究全部23连接的76.0%混作同一统计量。 + +单个发布者训练seed、4条短文本/组，仅支持这些输入上的描述性变化；不推广为所有微调都会如此，不报告GSM8K准确率，不据此推断删头可用或最终LM能力。配对变化中位数与两个总体中位数之差不是同一个统计量。 + +[公开微调权重与训练说明](https://huggingface.co/Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo)；固定版本、参数SHA、dataset版本和题目索引见protocol.json/gsm8k-source.json/samples.json。所有权重、适配器和缓存继续保留。 diff --git a/results/finetune-comparison/adapter_meta.json b/results/finetune-comparison/adapter_meta.json new file mode 100644 index 0000000..d708f12 --- /dev/null +++ b/results/finetune-comparison/adapter_meta.json @@ -0,0 +1,22 @@ +{ + "record": "qwen3.5-2b-grpo", + "arm": "lora", + "receipt_arm": "ctrl", + "seed": 0, + "step": 150, + "base_model": "Qwen/Qwen3.5-2B", + "base_revision": "15852e8c16360a2fea060d615a32b45270f8a8fc", + "recipe": "GRPO, correctness-gated length penalty alpha 0.2, 150 steps from the shared warm-up (paper Section 4)", + "lora_rank": 16, + "lora_alpha": 32, + "lora_targets": [ + "in_proj_qkv", + "in_proj_a", + "in_proj_b", + "out_proj" + ], + "gate": null, + "n_tensors": 144, + "n_params": 4727808, + "source_sha256": "de2794c085f5096fea53374ba1cea31075071bb6de3f227bb3350b3035970447" +} diff --git a/results/finetune-comparison/audit-output.txt b/results/finetune-comparison/audit-output.txt new file mode 100644 index 0000000..caa2b7c --- /dev/null +++ b/results/finetune-comparison/audit-output.txt @@ -0,0 +1,15 @@ +{ + "passed": true, + "links": 96, + "paired_observations_per_cohort_metric": 4800, + "legacy_baseline_exact": true, + "effective_head_formula_independently_recomputed": true, + "head_sum_checks": 288, + "explicit_lora_forward_relative_error": 8.33777860933606e-07, + "adapter_sha_verified": true, + "base_sha_verified": true, + "dataset_sha_verified": true, + "retained_cache_files": 300, + "nonzero_update_modules": 72, + "nonzero_hidden_state_changes_verified": true +} diff --git a/results/finetune-comparison/audit.json 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Weight download 36/36 bytes (100.0%) +ok +test_causal_convolution_and_norm_boundary (tests.test_functional.FunctionalTests.test_causal_convolution_and_norm_boundary) ... ok +test_causality_and_identical_function_metrics (tests.test_functional.FunctionalTests.test_causality_and_identical_function_metrics) ... ok +test_neuron_function_metrics_and_targeted_replacement (tests.test_functional.FunctionalTests.test_neuron_function_metrics_and_targeted_replacement) ... ok +test_nonrotary_gain_identity_and_rotary_negative_control (tests.test_functional.FunctionalTests.test_nonrotary_gain_identity_and_rotary_negative_control) ... ok +test_streamed_projection_embedding_and_output_head (tests.test_functional.FunctionalTests.test_streamed_projection_embedding_and_output_head) ... ok +test_uniform_rmsnorm_function_bound_is_tight (tests.test_functional.FunctionalTests.test_uniform_rmsnorm_function_bound_is_tight) ... ok +test_assignment_against_exhaustive_and_inverse (tests.test_crossqkv.CrossQKVTests.test_assignment_against_exhaustive_and_inverse) ... ok +test_cached_mode_rejects_future_state_reuse (tests.test_crossqkv.CrossQKVTests.test_cached_mode_rejects_future_state_reuse) ... ok +test_controls_cover_multiple_projection_layers (tests.test_crossqkv.CrossQKVTests.test_controls_cover_multiple_projection_layers) ... ok +test_head_matrix_metrics_and_gram_are_diagnostic (tests.test_crossqkv.CrossQKVTests.test_head_matrix_metrics_and_gram_are_diagnostic) ... ok +test_probability_identity_and_nontrivial_queries (tests.test_crossqkv.CrossQKVTests.test_probability_identity_and_nontrivial_queries) ... ok +test_profile_matches_canonical_and_head_sum (tests.test_crossqkv.CrossQKVTests.test_profile_matches_canonical_and_head_sum) ... ok +test_recipient_value_gate_output_and_gqa_group_stay_fixed (tests.test_crossqkv.CrossQKVTests.test_recipient_value_gate_output_and_gqa_group_stay_fixed) ... ok +test_topk_tail_rank_can_change_with_tiny_probability_difference (tests.test_crossqkv.CrossQKVTests.test_topk_tail_rank_can_change_with_tiny_probability_difference) ... ok +test_known_activity (tests.test_activation.ActivationTests.test_known_activity) ... ok +test_zero_and_scale (tests.test_activation.ActivationTests.test_zero_and_scale) ... ok +test_continuous_limits_and_undefined_zero (tests.test_dominance.DominanceTests.test_continuous_limits_and_undefined_zero) ... ok +test_joint_recomputation_null_and_saved_raw (tests.test_dominance.DominanceTests.test_joint_recomputation_null_and_saved_raw) ... baseline layer 0 +baseline layer 1 +ok +test_js_identity_and_disjoint (tests.test_dominance.DominanceTests.test_js_identity_and_disjoint) ... ok +test_mask_ranks_vary_by_token_and_recipient (tests.test_dominance.DominanceTests.test_mask_ranks_vary_by_token_and_recipient) ... ok +test_mixed_layer_forward_and_effective_qkv (tests.test_dominance.DominanceTests.test_mixed_layer_forward_and_effective_qkv) ... baseline layer 0 +baseline layer 1 +baseline layer 0 +baseline layer 1 +ok +test_effective_rows_equal_explicit_low_rank_output (tests.test_finetune_compare.FineTuneTests.test_effective_rows_equal_explicit_low_rank_output) ... ok +test_forward_and_head_decomposition_match_independent_overlay (tests.test_finetune_compare.FineTuneTests.test_forward_and_head_decomposition_match_independent_overlay) ... baseline layer 0 +baseline layer 1 +ok +test_zero_scale_preserves_original_and_bad_metadata_rejected (tests.test_finetune_compare.FineTuneTests.test_zero_scale_preserves_original_and_bad_metadata_rejected) ... ok + +---------------------------------------------------------------------- +Ran 30 tests in 23.124s + +OK diff --git a/results/finetune-comparison/gsm8k-source.json b/results/finetune-comparison/gsm8k-source.json new file mode 100644 index 0000000..a29d47b --- /dev/null +++ b/results/finetune-comparison/gsm8k-source.json @@ -0,0 +1,9 @@ +{ + "repository": "https://github.com/openai/grade-school-math", + "revision": 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-0,0 +1,62 @@ +{ + "primary": "same six full-attention receivers and original4x32 token cohort as published8.931822/16", + "adapter_repo": "Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo", + "adapter_revision": "038d8be9bd0edd3883fa019651b02bdf65609468", + "adapter_file": "lora/seed0/adapter.safetensors", + "adapter_sha256": "93a3f43a4356f84da0d13ecc622fee13e41ba15d237cdaad6094f243899c62d6", + "base_revision": "15852e8c16360a2fea060d615a32b45270f8a8fc", + "scale": 2, + "rank": 16, + "arm": "pure LoRA", + "training": "publisher-reported GSM8K GRPO; we do no training", + "sample_choice": "four original texts; first4 canonical GSM8K test questions with >=32tokens, length selection only, first32tokens; question only", + "source_layers": [ + 2, + 6, + 10, + 14, + 18, + 22 + ], + "primary_queries": [ + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31 + ], + "main_metric": "concentration(sqrt(single_JS)); same FP32 tensors and sequence-wide donor-head deletion as prior run", + "cohort_pairing": "each comparison uses same question/tokenIDs before and after; base legacy arrays reused with elementwise checks", + "runtime": "read-only FP32 effective W+2BA; no BF16 re-rounding or quantization", + "controls": "head sum, null mask, explicit low-rank versus effective-weight full forward relL2<1e-4", + "budget_seconds": 7200, + "keep_all_weights_and_caches": true, + "limitations": [ + "one existing training seed", + "4 texts per cohort, 32 tokens each", + "dependent query/head observations, not iid textual samples", + "no generation or accuracy measurement", + "GDN targets only; ordinary attention weights stay unchanged", + "no pruning or functional interchangeability claim" + ] +} diff --git a/results/finetune-comparison/raw/gsm8k-base-gsm8k-test-0000-02.npz b/results/finetune-comparison/raw/gsm8k-base-gsm8k-test-0000-02.npz new file mode 100644 index 0000000..87c2e94 Binary files /dev/null and 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--git a/results/finetune-comparison/raw/legacy-finetuned-fresh-zh-2-18.npz b/results/finetune-comparison/raw/legacy-finetuned-fresh-zh-2-18.npz new file mode 100644 index 0000000..2f8871f Binary files /dev/null and b/results/finetune-comparison/raw/legacy-finetuned-fresh-zh-2-18.npz differ diff --git a/results/finetune-comparison/raw/legacy-finetuned-fresh-zh-2-22.npz b/results/finetune-comparison/raw/legacy-finetuned-fresh-zh-2-22.npz new file mode 100644 index 0000000..c453320 Binary files /dev/null and b/results/finetune-comparison/raw/legacy-finetuned-fresh-zh-2-22.npz differ diff --git a/results/finetune-comparison/report-output.txt b/results/finetune-comparison/report-output.txt new file mode 100644 index 0000000..6c10f9d --- /dev/null +++ b/results/finetune-comparison/report-output.txt @@ -0,0 +1,22 @@ +# GSM8K任务微调前后的有效头数 + +比较原Qwen3.5-2B发布权重与同一revision上的纯LoRA seed0（额外GSM8K GRPO，非SFT）。没有训练新模型。主指标为√JS距离消融影响的N_eff；正变化表示更分散，负变化表示更集中。 + +| 输入组 | 原权重中位数 [P10,P90] | 微调后中位数 [P10,P90] | 配对变化中位数 | 增加的观测比例 | +|---|---:|---:|---:|---:| +| 原四条文本 | 8.931822 [5.784081,11.672658] | 8.918301 [5.776576,11.683940] | -0.007827 | 45.6% | +| GSM8K测试题前缀 | 8.906046 [6.013504,11.801882] | 8.904117 [5.982593,11.818115] | -0.006747 | 47.2% | + +每组4条输入×6个接收层×25个query位置×8个接收query头=4800个配对观测；它们不是4800个独立文本样本。前后使用同一原模型tokenizer、完全相同token IDs。原四条输入的基线8.931822/16已精确复现。 + +GSM8K输入取原作者固定版本test split中前4道至少32token的题目，仅用题目前32token，不含答案，不套不同聊天模板。另算了同组GSM8K输入的原权重基线；没有把旧通用文本与新任务文本混作配对。 + +## 方法与限定 + +所有6个普通注意力接收层和16个上游输出头均保留；每次移除一个上游头在全序列的投影输出，重算中间MLP/残差/归一化和检索分布。微调只改变18个GDN层的指定投影，普通注意力权重不变；隐藏状态和上游贡献仍会变化。 + +采用read-only FP32有效权重W+2BA，未把增量舍入为BF16、未修改底座；另用base projection+2B(Ax)独立前向校验。头输出求和、空消融和源数组复用校验均记录。JS散度及QKV复合相对变化也保留，避免只展示有利度量。这里QKV补充指标仅覆盖相同六个普通注意力接收层，其原基线不能与旧研究全部23连接的76.0%混作同一统计量。 + +单个发布者训练seed、4条短文本/组，仅支持这些输入上的描述性变化；不推广为所有微调都会如此，不报告GSM8K准确率，不据此推断删头可用或最终LM能力。配对变化中位数与两个总体中位数之差不是同一个统计量。 + +[公开微调权重与训练说明](https://huggingface.co/Avifenesh/gdn-beta-gate-qwen3.5-2b-grpo)；固定版本、参数SHA、dataset版本和题目索引见protocol.json/gsm8k-source.json/samples.json。所有权重、适配器和缓存继续保留。 diff --git a/results/finetune-comparison/run-log.txt b/results/finetune-comparison/run-log.txt new file mode 100644 index 0000000..3c1e5ac --- /dev/null +++ b/results/finetune-comparison/run-log.txt @@ -0,0 +1,384 @@ +completed legacy base fresh-zh-1 2 +completed legacy base fresh-zh-1 6 +completed legacy base fresh-zh-1 10 +completed legacy base fresh-zh-1 14 +completed legacy base fresh-zh-1 18 +completed legacy base fresh-zh-1 22 +completed legacy base fresh-zh-2 2 +completed legacy base fresh-zh-2 6 +completed legacy base fresh-zh-2 10 +completed legacy base fresh-zh-2 14 +completed legacy base fresh-zh-2 18 +completed legacy base fresh-zh-2 22 +completed legacy base fresh-en-1 2 +completed legacy base fresh-en-1 6 +completed legacy base fresh-en-1 10 +completed legacy base fresh-en-1 14 +completed legacy base fresh-en-1 18 +completed legacy base fresh-en-1 22 +completed legacy base fresh-en-2 2 +completed legacy base fresh-en-2 6 +completed legacy base fresh-en-2 10 +completed legacy base fresh-en-2 14 +completed legacy base fresh-en-2 18 +completed legacy base fresh-en-2 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed legacy finetuned fresh-zh-1 2 +completed legacy finetuned fresh-zh-1 6 +completed legacy finetuned fresh-zh-1 10 +completed legacy finetuned fresh-zh-1 14 +completed legacy finetuned fresh-zh-1 18 +completed legacy finetuned fresh-zh-1 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed legacy finetuned fresh-zh-2 2 +completed legacy finetuned fresh-zh-2 6 +completed legacy finetuned fresh-zh-2 10 +completed legacy finetuned fresh-zh-2 14 +completed legacy finetuned fresh-zh-2 18 +completed legacy finetuned fresh-zh-2 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed legacy finetuned fresh-en-1 2 +completed legacy finetuned fresh-en-1 6 +completed legacy finetuned fresh-en-1 10 +completed legacy finetuned fresh-en-1 14 +completed legacy finetuned fresh-en-1 18 +completed legacy finetuned fresh-en-1 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed legacy finetuned fresh-en-2 2 +completed legacy finetuned fresh-en-2 6 +completed legacy finetuned fresh-en-2 10 +completed legacy finetuned fresh-en-2 14 +completed legacy finetuned fresh-en-2 18 +completed legacy finetuned fresh-en-2 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed gsm8k base gsm8k-test-0000 2 +completed gsm8k base gsm8k-test-0000 6 +completed gsm8k base gsm8k-test-0000 10 +completed gsm8k base gsm8k-test-0000 14 +completed gsm8k base gsm8k-test-0000 18 +completed gsm8k base gsm8k-test-0000 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed gsm8k base gsm8k-test-0002 2 +completed gsm8k base gsm8k-test-0002 6 +completed gsm8k base gsm8k-test-0002 10 +completed gsm8k base gsm8k-test-0002 14 +completed gsm8k base gsm8k-test-0002 18 +completed gsm8k base gsm8k-test-0002 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed gsm8k base gsm8k-test-0003 2 +completed gsm8k base gsm8k-test-0003 6 +completed gsm8k base gsm8k-test-0003 10 +completed gsm8k base gsm8k-test-0003 14 +completed gsm8k base gsm8k-test-0003 18 +completed gsm8k base gsm8k-test-0003 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed gsm8k base gsm8k-test-0004 2 +completed gsm8k base gsm8k-test-0004 6 +completed gsm8k base gsm8k-test-0004 10 +completed gsm8k base gsm8k-test-0004 14 +completed gsm8k base gsm8k-test-0004 18 +completed gsm8k base gsm8k-test-0004 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed gsm8k finetuned gsm8k-test-0000 2 +completed gsm8k finetuned gsm8k-test-0000 6 +completed gsm8k finetuned gsm8k-test-0000 10 +completed gsm8k finetuned gsm8k-test-0000 14 +completed gsm8k finetuned gsm8k-test-0000 18 +completed gsm8k finetuned gsm8k-test-0000 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed gsm8k finetuned gsm8k-test-0002 2 +completed gsm8k finetuned gsm8k-test-0002 6 +completed gsm8k finetuned gsm8k-test-0002 10 +completed gsm8k finetuned gsm8k-test-0002 14 +completed gsm8k finetuned gsm8k-test-0002 18 +completed gsm8k finetuned gsm8k-test-0002 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed gsm8k finetuned gsm8k-test-0003 2 +completed gsm8k finetuned gsm8k-test-0003 6 +completed gsm8k finetuned gsm8k-test-0003 10 +completed gsm8k finetuned gsm8k-test-0003 14 +completed gsm8k finetuned gsm8k-test-0003 18 +completed gsm8k finetuned gsm8k-test-0003 22 +baseline layer 0 +baseline layer 1 +baseline layer 2 +baseline layer 3 +baseline layer 4 +baseline layer 5 +baseline layer 6 +baseline layer 7 +baseline layer 8 +baseline layer 9 +baseline layer 10 +baseline layer 11 +baseline layer 12 +baseline layer 13 +baseline layer 14 +baseline layer 15 +baseline layer 16 +baseline layer 17 +baseline layer 18 +baseline layer 19 +baseline layer 20 +baseline layer 21 +baseline layer 22 +baseline layer 23 +completed gsm8k finetuned gsm8k-test-0004 2 +completed gsm8k finetuned gsm8k-test-0004 6 +completed gsm8k finetuned gsm8k-test-0004 10 +completed gsm8k finetuned gsm8k-test-0004 14 +completed gsm8k finetuned gsm8k-test-0004 18 +completed gsm8k finetuned gsm8k-test-0004 22 diff --git a/results/finetune-comparison/samples.json b/results/finetune-comparison/samples.json new file mode 100644 index 0000000..1edcba6 --- /dev/null +++ b/results/finetune-comparison/samples.json @@ -0,0 +1,322 @@ +{ + "legacy": [ + { + "id": "fresh-zh-1", + "language": "zh", + "text": "\u690d\u7269\u5b66\u5bb6\u5728\u4e0d\u540c\u6d77\u62d4\u91c7\u96c6\u4e86\u53f6\u7247\u6837\u672c\uff0c\u5e76\u4e3a\u6bcf\u4e00\u4efd\u6807\u672c\u8bb0\u5f55\u6e29\u5ea6\u3001\u5149\u7167\u548c\u571f\u58e4\u6e7f\u5ea6\u3002\u56de\u5230\u5b9e\u9a8c\u5ba4\u540e\uff0c\u5979\u6309\u5730\u70b9\u6574\u7406\u6807\u7b7e\uff0c\u5148\u6838\u5bf9\u7f3a\u5931\u8bb0\u5f55\uff0c\u518d\u6bd4\u8f83\u4e0d\u540c\u73af\u5883\u4e2d\u7684\u53f6\u7247\u5f62\u72b6\u3002", + "token_ids": [ + 99951, + 101071, + 115802, + 111685, + 102099, + 95759, + 111583, + 109928, + 3709, + 115026, + 134176, + 116599, + 98151, + 99472, + 5205, + 114180, + 95793, + 104074, + 102454, + 1710, + 103744, + 103911, + 95946, + 3709, + 96343, + 96656, + 100560, + 99946, + 101659, + 3709, + 96218, + 113089 + ] + }, + { + "id": "fresh-zh-2", + "language": "zh", + "text": "\u5982\u679c\u4e00\u4e2a\u76d2\u5b50\u91cc\u6709\u5341\u4e8c\u679a\u84dd\u8272\u68cb\u5b50\u548c\u516b\u679a\u767d\u8272\u68cb\u5b50\uff0c\u968f\u673a\u53d6\u51fa\u4e00\u679a\u540e\u4e0d\u653e\u56de\uff0c\u7b2c\u4e8c\u6b21\u53d6\u5230\u84dd\u8272\u7684\u6982\u7387\u9700\u8981\u6839\u636e\u7b2c\u4e00\u6b21\u7684\u7ed3\u679c\u5206\u522b\u8ba1\u7b97\u3002\u8bf7\u5148\u5217\u51fa\u5404\u4e2a\u4e8b\u4ef6\uff0c\u518d\u68c0\u67e5\u5b83\u4eec\u7684\u6982\u7387\u4e4b\u548c\u3002", + "token_ids": [ + 127356, + 98996, + 98970, + 95762, + 98175, + 101184, + 104145, + 99357, + 111324, + 96702, + 101184, + 103828, + 129730, + 3709, + 102038, + 96284, + 98938, + 101184, + 95946, + 122271, + 96120, + 3709, + 109387, + 96284, + 95839, + 116981, + 104514, + 122501, + 103720, + 109146, + 99446, + 97792 + ] + }, + { + "id": "fresh-en-1", + "language": "en", + "text": "A technician recorded the vibration of a small motor before and after replacing its bearings. She used the same mounting position and sampling interval for both measurements. The final notebook included the raw traces, instrument settings, and a brief explanation of unusual observations.", + "token_ids": [ + 32, + 51715, + 12076, + 279, + 46219, + 314, + 264, + 2526, + 8604, + 1518, + 321, + 1238, + 24101, + 1141, + 33309, + 13, + 2838, + 1429, + 279, + 1788, + 32525, + 2234, + 321, + 24102, + 9581, + 364, + 2107, + 20934, + 13, + 561, + 1534, + 35935 + ] + }, + { + "id": "fresh-en-2", + "language": "en", + "text": "The function walks through a list of integers and stores each running total in a new list. An empty input should return an empty result. Before changing the implementation, compare a short example by hand and make sure negative numbers preserve the intended behavior.", + "token_ids": [ + 760, + 709, + 21780, + 1472, + 264, + 1103, + 314, + 24959, + 321, + 10220, + 1754, + 4162, + 2702, + 303, + 264, + 491, + 1103, + 13, + 1473, + 4147, + 1879, + 1220, + 460, + 449, + 4147, + 1067, + 13, + 12851, + 9722, + 279, + 7873, + 11 + ] + } + ], + "gsm8k": [ + { + "id": "gsm8k-test-0000", + "row_index": 0, + "text": "Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?", + "question_sha256": "2b2e3f9639f6fa282a0b0c1d622e0c75cc03797b43268945f32b134da4fee344", + "token_ids": [ + 17770, + 294, + 725, + 75119, + 10641, + 220, + 16, + 21, + 18237, + 791, + 1834, + 13, + 2838, + 48005, + 2250, + 364, + 16976, + 1396, + 6353, + 321, + 292, + 1982, + 52480, + 1283, + 364, + 1024, + 4627, + 1396, + 1834, + 440, + 2943, + 13 + ] + }, + { + "id": "gsm8k-test-0002", + "row_index": 2, + "text": "Josh decides to try flipping a house. He buys a house for $80,000 and then puts in $50,000 in repairs. This increased the value of the house by 150%. How much profit did he make?", + "question_sha256": "d3c6224db7dd6691e29bc2962559f2e3a24bdfacba0eb526357d59462b7ea046", + "token_ids": [ + 49037, + 26731, + 310, + 1380, + 62475, + 264, + 3626, + 13, + 220, + 1216, + 47866, + 264, + 3626, + 364, + 393, + 23, + 15, + 11, + 15, + 15, + 15, + 321, + 1179, + 9240, + 303, + 393, + 20, + 15, + 11, + 15, + 15, + 15 + ] + }, + { + "id": "gsm8k-test-0003", + "row_index": 3, + "text": "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week?", + "question_sha256": "94ff3611e184b3ba921a04a94b9310b4a3ada972c4966ff11054d227cc6d7c3c", + "token_ids": [ + 27174, + 26731, + 310, + 1542, + 220, + 18, + 274, + 24918, + 220, + 18, + 2942, + 264, + 1936, + 13, + 220, + 1216, + 8213, + 220, + 21, + 15, + 19434, + 1754, + 36599, + 13, + 220, + 2500, + 1599, + 2702, + 19434, + 1503, + 551, + 1542 + ] + }, + { + "id": "gsm8k-test-0004", + "row_index": 4, + "text": "Every day, Wendi feeds each of her chickens three cups of mixed chicken feed, containing seeds, mealworms and vegetables to help keep them healthy. She gives the chickens their feed in three separate meals. In the morning, she gives her flock of chickens 15 cups of feed. In the afternoon, she gives her chickens another 25 cups of feed. 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0000000..bc1f888 --- /dev/null +++ b/tests/test_finetune_compare.py @@ -0,0 +1,45 @@ +"""Independent existing-LoRA arithmetic, head decomposition and runtime checks.""" +import json,struct,tempfile,unittest +from pathlib import Path +import numpy as np +from tests.test_dominance import fixture +from dominance.core import InfluenceLM +from finetune_compare.lora import install,EffectiveWeight,ExplicitLoRA,BASE_REVISION +from functional.model import StreamingLM + +def adapter(directory,cfg,lm): + rng=np.random.default_rng(772);arrays={} + for layer,kind in enumerate(cfg['layer_types']): + if kind!='linear_attention':continue + for role in ('in_proj_qkv','in_proj_a','in_proj_b','out_proj'): + shape=lm.tensors[f'model.language_model.layers.{layer}.linear_attn.{role}.weight'].shape + prefix=f'base_model.model.model.layers.{layer}.linear_attn.{role}.' + arrays[prefix+'lora_A.default.weight']=rng.standard_normal((2,shape[1]),dtype=np.float32)*.1 + arrays[prefix+'lora_B.default.weight']=rng.standard_normal((shape[0],2),dtype=np.float32)*.1 + header={};body=b'' + for name,x in arrays.items(): + raw=x.tobytes();header[name]={'shape':list(x.shape),'dtype':'F32','data_offsets':[len(body),len(body)+len(raw)]};body+=raw + h=json.dumps(header).encode();Path(directory,'adapter.safetensors').write_bytes(struct.pack('