diff --git a/README.md b/README.md index b3f328c..0b6bb6b 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,8 @@ **[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。 +[两个实际2B检查点的参数球壳结论](docs/MODEL_SHELL_FINDINGS.md) · [完整数值报告](results/model-shell/REPORT.md) · [固定分组和均匀参照协议](model_shell/PROTOCOL.md)。 + 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. diff --git a/docs/FIGURE_GALLERY.md b/docs/FIGURE_GALLERY.md index 9a8077a..017aa6b 100644 --- a/docs/FIGURE_GALLERY.md +++ b/docs/FIGURE_GALLERY.md @@ -1,6 +1,6 @@ -# 研究图册：Qwen3.5-2B 头贡献集中度 +# 研究图册：Qwen3.5-2B 参数几何与头贡献 -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。 +这份图册与原始PNG/PDF/SVG一同保存在Git中。头贡献基线图片已随PR #6合并到main；其固定提交链接不依赖聊天附件或本机路径。后续实验分别列于下方，可通过图册所在的Git提交查看对应版本。 原始结果提交：`098ea8650c7d0e06339c66e3a0509e38b85b3d7d`；模型revision：`15852e8c16360a2fea060d615a32b45270f8a8fc`。 @@ -43,3 +43,15 @@ ![稀疏球壳对照](../results/sparse-shell/sparse_shell_controls.png) [条件定理、反例与数据说明](../results/sparse-shell/REPORT.md) · [PDF](../results/sparse-shell/sparse_shell_controls.pdf) · [SVG](../results/sparse-shell/sparse_shell_controls.svg) + +## 两个实际2B检查点的参数球壳检验 + +[结论与解释](MODEL_SHELL_FINDINGS.md) · [实测报告、分组数量与限制](../results/model-shell/REPORT.md) · [固定协议](../model_shell/PROTOCOL.md) · [指标源表](../results/model-shell/metrics.csv) · [校验清单](../results/model-shell/audit.json) + +![内侧质量对均匀参照的敏感性](../results/model-shell/figures/interior_reference_sensitivity.png) + +![径向分布的内外两尾](../results/model-shell/figures/radial_shape_two_tails.png) + +![固定角色的径向CDF示例](../results/model-shell/figures/fixed_role_radial_cdfs.png) + +[微调前后配对图](../results/model-shell/figures/finetune_paired_radii.png) · [有限样本参与率](../results/model-shell/figures/participation_reference.png)。每幅图同目录均保存PDF/SVG，完整CDF及参照数据保存在`profiles.json.gz`，实测范数保存在`measured_radii.npz`；均随本次成果进入Git。 diff --git a/docs/MODEL_SHELL_FINDINGS.md b/docs/MODEL_SHELL_FINDINGS.md new file mode 100644 index 0000000..6f4ba10 --- /dev/null +++ b/docs/MODEL_SHELL_FINDINGS.md @@ -0,0 +1,50 @@ +# 实际2B参数：比均匀参照更靠里吗？ + +**在明确的“校准最大半径以内”口径下，是；但数据同时显示更宽的径向分布，不能概括为整团参数向内收缩。此次GSM8K微调几乎没有改变这些几何指标。** + +这轮直接读取了保留的Qwen3.5-2B权重和同一底座的已有纯LoRA GRPO检查点，在8.211.154.134上使用CPU完成。没有训练、推理或删除权重。 + +## 一个模型有多少组高维分布？ + +按事先固定的参数矩阵行／列与语言注意力头视图规则，每个模型有 **1,558组**：554个矩阵视图、1,004个按头视图。它们有重叠，不是模型固有的唯一分布数量。 + +主要汇总使用1,521个样本充足、未抽取坐标的视图。另有36个小样本视图和1个坐标抽样视图，全部单列并保留数据。整个检查点包括语言、视觉与MTP，共632个参数张量；319个一维张量和18个低维卷积核不满足这次“高维向量云”的规则。 + +## 有更多内侧质量，但也有更长的外尾 + +每组最多512个参数向量，一半校准中心及半径，另一半测量。对照也使用同样的校准规则；没有用所有向量的最大范数事后包住全部数据。 + +以原点为中心，比较归一化半径 `r / R_cal <= 0.9`： + +|视图|组数|相对均匀球的内侧质量差中位数|相对匹配均值、逐坐标方差的均匀盒的质量差中位数| +|---|---:|---:|---:| +|矩阵行／列|517|+98.0个百分点|+97.7个百分点| +|按头|1,004|+97.3个百分点|+97.3个百分点| + +改用校准均值作为中心，结果仍有约96.9–97.7个百分点的差异。因此，这个现象并非仅来自中心放在原点。 + +但最大半径很容易受到少数大范数向量影响。换成各自的校准RMS尺度，并比较完整CDF后，**93.8%的矩阵视图和87.8%的头视图同时存在超过10个百分点的内尾、外尾差异**。去均值后分别为94.4%和86.4%。这说明两尾同时增加是普遍现象，比“所有向量整体更靠内”更符合这些数据。 + +![最大半径与RMS尺度下的固定角色示例](../results/model-shell/figures/fixed_role_radial_cdfs.png) + +这三组示例按模块角色固定：GDN的Q头、全注意力的Q头、MLP上投影行。上排用校准最大半径，下排用RMS；它们只是帮助读图，不代替全量统计。 + +## 是否说明稀疏或低维？ + +还不能如此推断。采样视图的逐组精确零值率中位数为0；点坐标能量参与率除以维度的中位数约0.31。径向内侧质量、坐标能量集中和样本间的有效维数是不同的测量对象。 + +本轮同时记录了有限样本Gram参与率，并与同样本数的均匀参照比较。它受校准样本秩上限限制，不能作为真实内在维度的直接估计。盒参照也只匹配边际范围或逐坐标矩，并未匹配相关结构。因此，本轮没有单独识别“稀疏支持”导致的效应。 + +## 微调前后改变了吗？ + +LoRA实际改变72个投影矩阵，影响972个视图，其中936个属于主要汇总。只对这些受影响的主要视图配对，以免大量未改动参数稀释差异。 + +原点中心下，逐组中位 `r/R_cal` 的配对变化中位数为 **6.30×10⁻⁷**，最大绝对变化 **4.03×10⁻⁴**。去均值后的结论相近。90%半径质量差在原点中心口径下未跨越当前经验CDF的分辨率；这不表示两套权重完全相同。 + +因此，这两个检查点不能支持“微调后、更好的模型更靠内”的结论；本轮也没有测任务质量。参数几何中的宽径向结构可以继续挖掘，但它本身还不是功能重复或可互相替代的证据。 + +## 数据与核验 + +[完整数值报告](../results/model-shell/REPORT.md) · [冻结协议](../model_shell/PROTOCOL.md) · [复现方式](../model_shell/README.md) · [指标CSV](../results/model-shell/metrics.csv) · [摘要JSON](../results/model-shell/summary.json) · [审计SHA](../results/model-shell/audit.json) + +6,232个模型×中心×视图组合全部核验；CDF从保留的校准／留出范数复算，检查有限样本秩上限，原模型和适配器SHA均与既有记录一致。远程4项测试通过，覆盖索引、LoRA读取、中心与尺度、均匀球校准和稀疏贴壳反例。图表、完整CDF及对照摘要、实测范数一同保存。结论为描述性统计：视图相关且重叠，没有将其当作独立重复实验进行显著性检验。 diff --git a/model_shell/PROTOCOL.md b/model_shell/PROTOCOL.md new file mode 100644 index 0000000..6c975ce --- /dev/null +++ b/model_shell/PROTOCOL.md @@ -0,0 +1,83 @@ +# Actual-model parameter clouds: frozen grouping and null protocol + +Assets: retained Qwen3.5-2B original15852e8c16360a2fea060d615a32b45270f8a8fc, +and pure GSM8K GRPO LoRA seed0 at HF038d8be9bd0edd3883fa019651b02bdf65609468. +Use the established read-only FP32 W+2BA wrapper. No training, GPU, new models, +weight mutation, inference requirement or deletion. Every retained asset stays. + +## What constitutes a distribution + +A model has no unique canonical number of parameter distributions. Enumerate a +fixed family, not "all possible distributions": +1. Each ndim>=2 tensor flattened as [axis0, product(other axes)]: row vectors + and column vectors, when point count>=16 and feature dimension>=32. +2. Decoder language/MTP full-attention Q and interleaved gate rows per query + head; K/V rows per KV head. GDN concatenated Q/K/V rows per corresponding head. + No claim that these groups are disjoint or iid; whole-matrix views overlap. +3. Primary sample-size stratum has >=64 available points;16..63 is small-sample + descriptive only. One-dimensional norm/bias vectors have no cloud of vectors; + GDN kernel row dimension4 is low dimensional and excluded with explicit reason. + Vision parameters are covered by matrix/flattened views, not vision-head splits. + +Include all checkpoint components (language, vision and MTP). Per view choose +up to512 point indices without replacement, split deterministically into calibration +and held-out halves. Use full vector coordinates up to dimension8192; only larger +vectors use a fixed random coordinate subset, labelled projected rather than full. +Use identical indices, coordinates and null random draws for original/FT. +No overlap-group parameter savings or total distribution counts are additive. + +## Centers and radius/scaling + +Analyze origin and calibration-mean centers separately. Fit R_max using calibration +norms only; do not silently clip held-out points outside R_max. Also fit calibration +RMS radius. Store held-out maximum, outside-radius mass and radial spread. + +Null A: uniform d-dimensional ball. Simulate the SAME center-fitting and calibration +maximum/RMS fitting before held-out evaluation. For origin, also retain exact +expected max-fitted null CDF a^d*n_cal/(n_cal+1), a<=1; log probabilities remain +available and zero Monte Carlo hits are not exact zeros. +Null B: independent coordinate-uniform box bounded by calibration min/max, +including asymmetric coordinates. Fit its center/radii using its own calibration +sample by exactly the same procedure. Record energy/range mismatch and held-out +coordinate violations; this null is conditional on fitted bounds, not a statement +that all real vectors lie inside that box. +Null C: independent coordinate-uniform box with calibration mean mu and standard +deviation sigma, bounds mu +/- sqrt(3)*sigma (sample variance ddof1). This matches +first and diagonal second moments in its generating law. Use its own calibration +and held-out center/max/RMS fits too; RMS-normalized comparisons target shape +rather than a change of global scale. Matching diagonal moments is not covariance +matching and does not preserve correlations. + +Use512 validation null draws (not iid model observations), same calibration count +as each data cloud. Reuse isotropic-ball nulls only for identical dimension/count +and center-fitting rules. Monte Carlo resolution/error must be stated. Fit both +centers from the same raw draws where applicable; paired FT uses common randomness. + +## Metrics and interpretation + +Retain measured norm arrays, empirical CDF grids, max-normalized interior fractions +at0.5/0.75/0.9/0.95/0.99 and outside1, RMS-normalized CDF from0.5 to1.5, +median radial ratios and radial spread. Prefer probability-point differences over +astronomical ratios against tiny a^d. Inspect positive AND negative CDF gaps: +more inner tail plus more outer tail is not global inward stochastic dominance. + +Record exact zeros, coordinate energy participation (sum squares)^2/sum fourths, +coordinate-variance participation, empirical covariance/second-moment participation +via calibration Gram matrices. Label sample rank ceiling n_cal-1 (mean centered) +or n_cal (origin); compare to equally sampled nulls, do not interpret the raw +finite-sample participation as true intrinsic dimension. + +Report both full-radius and moment-controlled results for each center, whole-view +and head subgroups, primary versus small sample/projection strata, original and +changed72-LoRA-target groups. Unchanged parameter views can reuse identical results +with explicit references rather than re-read/recompute. Results describe parameters, +not activations, model quality, functional duplicates, or pruning gains. + +Execution budget: one CPU, 512 MiB RSS ceiling, 7200 seconds per invocation; +checkpoint every group, resume without replacing completed groups. Any incomplete +coverage is reported before interpretation. No numerical analysis runs locally. + +Illustrative radial CDFs use these fixed original/origin views, selected by module +role before the census completes, not by the resulting similarity/interior score: +language layer0 linear-attention Q head0, layer3 full-attention Q head0, and layer0 +MLP up-projection rows. Their plots are illustrations, not independent replications. diff --git a/model_shell/README.md b/model_shell/README.md new file mode 100644 index 0000000..0484c39 --- /dev/null +++ b/model_shell/README.md @@ -0,0 +1,42 @@ +# Actual-model shell census + +Read [PROTOCOL.md](PROTOCOL.md) before interpreting the results. This analyzes +retained parameter arrays, not model activations or task accuracy. + +Run only on the authorized CPU server from the repository root: + +```sh +OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m unittest model_shell.test_analysis -v +OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.analyze +OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.report +``` + +Inputs are the already retained `runs/cpu-qwen35-2b/weights` and +`runs/finetune-head-comparison/adapter.safetensors` with their existing JSON +configuration and metadata. The adapter is evaluated as FP32 W+2BA. No weights +are modified, no training or inference is run, and no files are deleted. + +`runs/model-shell/checkpoints` is a resumable group ledger. Re-running skips +completed groups; do not reuse a ledger after changing its protocol/code/inputs. +One invocation stops at 7,200 seconds, RSS above 512 MiB or available memory below +128 MiB. Resume after addressing a resource stop; do not relax guards silently. + +`results/model-shell` contains the report, group inventory, per-profile metric +CSV, compressed complete CDF/control summaries, and measured calibration/held-out +radii. The NPZ keys are `GROUPINDEX_MODEL_CENTER_cal` / `_eval`, with zero-based +group indices into `inventory.json`. Unchanged fine-tune profiles explicitly +reference the original profiles in `profiles.json.gz`; the CSV expands both. +Raw null draws are reproducible from the recorded deterministic seeds; null CDFs, +scales and participation summaries are stored. `audit.json` verifies coverage, +recomputes empirical CDFs from the saved norms, checks finite-sample rank limits, +verifies the original asset SHA256 values, and hashes the output artifacts. +`example_radii.npz` also saves calibration/held-out radii for the three fixed role +examples and their regenerated nulls; their grid CDFs are checked against the +original ledger before plotting full empirical step curves from radius zero. + +The count of views is defined by the protocol and is not a unique number of +distributions inherent in a neural network. These overlapping views are equally +weighted for description; they are not independent replicates. The box nulls +match fitted marginal ranges or moments, not correlations. The maximum fitted +radius is not the unknown support boundary. Quantities near numerical or Monte +Carlo resolution cannot support a definitive population statement. diff --git a/model_shell/analyze.py b/model_shell/analyze.py new file mode 100644 index 0000000..5979ec0 --- /dev/null +++ b/model_shell/analyze.py @@ -0,0 +1,124 @@ +"""CPU-only, read-only parameter cloud census with paired uniform references.""" +import argparse, hashlib, json, math, os, time +from pathlib import Path +from types import SimpleNamespace +import numpy as np +from pilot.tensors import load_tensors +from finetune_compare.lora import install +from model_shell.groups import enumerate_groups + +MAX_GRID=np.array([.5,.75,.9,.95,.99,1.,1.05,1.1,1.25]) +RMS_GRID=np.linspace(.5,1.5,41) + +def seed(key):return int.from_bytes(hashlib.sha256(key.encode()).digest()[:8],'little') + +def guard(start): + mem=dict((s.split(':')[0],int(s.split()[1])) for s in Path('/proc/meminfo').read_text().splitlines() if s.startswith('MemAvailable:')) + rss=int(Path('/proc/self/statm').read_text().split()[1])*os.sysconf('SC_PAGE_SIZE') + if rss>512*1024**2 or mem['MemAvailable']<128*1024 or time.monotonic()-start>7200: + raise RuntimeError(f'Resource guard: RSS={rss}, available_kB={mem}, elapsed={time.monotonic()-start:.0f}') + +def sample(t,g): + rng=np.random.default_rng(seed(g['id']+'/indices')) + n=min(512,g['population']);d=min(8192,g['dimension']) + points=np.sort(rng.choice(g['population'],n,replace=False)) + features=np.sort(rng.choice(g['dimension'],d,replace=False)) if d0 and rms>0 + a=r/maximum if maximum else np.zeros_like(r);b=r/rms if rms else np.zeros_like(r) + coordvar=z[:ncal].var(0,ddof=1);cv=float(coordvar.sum()**2/np.square(coordvar).sum()) if coordvar.any() else 0. + e=np.square(y[ncal:]);den=np.square(e).sum(1) + pe=np.divide(e.sum(1)**2,den,out=np.zeros(len(e)),where=den>0) + out=dict(valid=bool(valid),ncal=ncal,neval=len(r),d=z.shape[1],rmax=maximum,rms=rms, + median_max=float(np.median(a)),median_rms=float(np.median(b)), + p10_rms=float(np.quantile(b,.1)),p90_rms=float(np.quantile(b,.9)), + outside=float(np.mean(a>1)),cdf_max=[float(np.mean(a<=v)) for v in MAX_GRID], + cdf_rms=[float(np.mean(b<=v)) for v in RMS_GRID], + covariance_participation=participation(y[:ncal]),rank_ceiling=ncal-(center=='mean'), + coordinate_variance_participation=cv,point_energy_participation_median=float(np.median(pe)), + zero_fraction=float(np.mean(z==0))) + if raw:out.update(cal_norm=cal.tolist(),eval_norm=r.tolist()) + return out + +BALL_CACHE={} +def references(x,ncal,key): + d=x.shape[1];cachekey=(d,ncal) + if cachekey not in BALL_CACHE: + rng=np.random.default_rng(seed(f'ball/{d}/{ncal}')) + y=rng.standard_normal((ncal+512,d),dtype=np.float32) + y*=((rng.random(len(y))**(1/d))/np.linalg.norm(y,axis=1))[:,None] + BALL_CACHE[cachekey]={c:geometry(y,ncal,c) for c in ('origin','mean')} + cal=x[:ncal].astype(np.float64);lo=cal.min(0);hi=cal.max(0);mu=cal.mean(0);sd=cal.std(0,ddof=1) + rng=np.random.default_rng(seed(key+'/uniform')) + u=rng.random((ncal+512,d),dtype=np.float32) + refs={'ball':BALL_CACHE[cachekey]} + for name,left,right in [('range_box',lo,hi),('moment_box',mu-np.sqrt(3)*sd,mu+np.sqrt(3)*sd)]: + y=(left+(right-left)*u).astype(np.float32) + refs[name]={c:geometry(y,ncal,c) for c in ('origin','mean')} + return refs,dict(heldout_outside_coordinate_range=float(np.mean((x[ncal:]hi))), + range_box_expected_energy=float(np.sum((lo**2+lo*hi+hi**2)/3)), + observed_cal_energy=float(np.mean(np.sum(cal**2,axis=1)))) + +def analyze(x,key): + ncal=len(x)//2;refs,extra=references(x,ncal,key);out={} + for center in ('origin','mean'): + p=geometry(x,ncal,center,True);p['references']=refs; p.update(extra) + p['comparisons']={} + for name,bycenter in refs.items(): + ref=bycenter[center];diff=np.array(p['cdf_rms'])-ref['cdf_rms'] + p['comparisons'][name]=dict(interior90_gap=p['cdf_max'][2]-ref['cdf_max'][2], + interior95_gap=p['cdf_max'][3]-ref['cdf_max'][3], + rms_cdf_positive_gap=float(max(0,diff.max())),rms_cdf_negative_gap=float(max(0,-diff.min())), + median_rms_delta=p['median_rms']-ref['median_rms'], + participation_ratio=p['covariance_participation']/ref['covariance_participation'] if ref['covariance_participation'] else None) + p['ball_origin_log_expected_cdf90']=float(x.shape[1]*np.log(.9)+np.log(ncal/(ncal+1))) + out[center]=p + return out + +def main(): + ap=argparse.ArgumentParser();ap.add_argument('--root',default='runs');ap.add_argument('--out',default='runs/model-shell');args=ap.parse_args() + root=Path(args.root);out=Path(args.out);out.mkdir(exist_ok=True,parents=True);(out/'checkpoints').mkdir(exist_ok=True) + cfg=json.loads((root/'cpu-qwen35-2b/model_config.json').read_text())['text_config'] + base=load_tensors(root/'cpu-qwen35-2b/weights');ft=SimpleNamespace(tensors=dict(base),config=cfg) + meta=json.loads((root/'finetune-head-comparison/adapter_meta.json').read_text()) + pairs,scale=install(ft,root/'finetune-head-comparison',meta) + groups,skips=enumerate_groups(base,cfg) + manifest=dict(groups=groups,skips=skips,total_tensors=len(base),parameters=sum(t.numel for t in base.values()),changed_tensors=sorted(pairs),adapter_meta=meta, + max_grid=MAX_GRID.tolist(),rms_grid=RMS_GRID.tolist(),null_eval=512,point_cap=512,feature_cap=8192) + (out/'inventory.json').write_text(json.dumps(manifest,indent=2));start=time.monotonic() + for i,g in enumerate(groups): + target=out/'checkpoints'/f'{i:04d}.json' + if target.exists():continue + guard(start);x,digest=sample(base[g['tensor']],g);p=analyze(x,g['id']);del x + changed=g['tensor'] in pairs + if changed: + guard(start);x,digest2=sample(ft.tensors[g['tensor']],g);assert digest==digest2 + q=analyze(x,g['id']);del x + else:q=None + record=dict(group=g,sampling_sha256=digest,changed=changed,original=p,finetuned=q,finetuned_reuses_original=not changed) + temp=target.with_suffix('.tmp');temp.write_text(json.dumps(record,allow_nan=False));temp.replace(target) + if i%10==0:print(f'{i+1}/{len(groups)} elapsed={time.monotonic()-start:.1f}s',flush=True) + print('COMPLETE',len(groups),flush=True) + +if __name__=='__main__':main() diff --git a/model_shell/groups.py b/model_shell/groups.py new file mode 100644 index 0000000..6b06990 --- /dev/null +++ b/model_shell/groups.py @@ -0,0 +1,29 @@ +"""Deterministic finite family of matrix/vector clouds; no data-dependent choice.""" +import math,re + +def enumerate_groups(tensors,cfg): + groups=[];skips=[] + for name,t in sorted(tensors.items()): + if len(t.shape)<2:skips.append({'name':name,'reason':'one vector, no vector cloud','shape':t.shape});continue + rows=t.shape[0];cols=math.prod(t.shape[1:]);count=0 + for axis,n,d in [('rows',rows,cols),('columns',cols,rows)]: + if n>=16 and d>=32: + groups.append({'id':name+'::'+axis,'tensor':name,'kind':'matrix','orientation':axis,'start':0,'stop':rows,'population':n,'dimension':d,'stratum':'primary' if n>=64 else 'small_sample'});count+=1 + if not count:skips.append({'name':name,'reason':'insufficient points or dimension <32','shape':t.shape}) + if not (name.startswith('model.language_model.layers.') or name.startswith('mtp.layers.')):continue + segments=[] + if name.endswith('.linear_attn.in_proj_qkv.weight'): + kh,kd,vh,vd=[cfg[k] for k in ('linear_num_key_heads','linear_key_head_dim','linear_num_value_heads','linear_value_head_dim')] + for role,num,dim,offset in [('Q',kh,kd,0),('K',kh,kd,kh*kd),('V',vh,vd,2*kh*kd)]: + for h in range(num):segments.append((role,h,offset+h*dim,offset+(h+1)*dim)) + elif name.endswith('.self_attn.q_proj.weight'): + dim=cfg['head_dim'];assert rows==cfg['num_attention_heads']*2*dim + for h in range(cfg['num_attention_heads']): + for role,offset in [('Q',0),('gate',dim)]:segments.append((role,h,h*2*dim+offset,h*2*dim+offset+dim)) + elif name.endswith(('.self_attn.k_proj.weight','.self_attn.v_proj.weight')): + role='K' if '.k_proj.' in name else 'V';dim=cfg['head_dim'];assert rows==cfg['num_key_value_heads']*dim + for h in range(cfg['num_key_value_heads']):segments.append((role,h,h*dim,(h+1)*dim)) + for role,h,start,stop in segments: + assert stop<=rows + groups.append({'id':name+f'::{role}_head{h}','tensor':name,'kind':'head_rows','orientation':'rows','start':start,'stop':stop,'population':stop-start,'dimension':cols,'stratum':'primary' if stop-start>=64 else 'small_sample'}) + return groups,skips diff --git a/model_shell/report.py b/model_shell/report.py new file mode 100644 index 0000000..b643cfb --- /dev/null +++ b/model_shell/report.py @@ -0,0 +1,176 @@ +"""Validate complete checkpoints; write compact scientific data, figures and report.""" +import argparse,csv,gzip,hashlib,json,platform,sys +from pathlib import Path +import numpy as np +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +from model_shell.analyze import MAX_GRID,RMS_GRID,sample,seed +from pilot.tensors import load_tensors + +def sha(p): + h=hashlib.sha256() + with p.open('rb') as f: + for b in iter(lambda:f.read(1024*1024),b''):h.update(b) + return h.hexdigest() + +def main(): + ap=argparse.ArgumentParser();ap.add_argument('--input',default='runs/model-shell');ap.add_argument('--out',default='results/model-shell');a=ap.parse_args() + source=Path(a.input);out=Path(a.out);out.mkdir(parents=True,exist_ok=True);figdir=out/'figures';figdir.mkdir(exist_ok=True) + inventory=json.loads((source/'inventory.json').read_text());groups=inventory['groups'];records=[];rows=[];radii={} + for i,g in enumerate(groups): + r=json.loads((source/'checkpoints'/f'{i:04d}.json').read_text());assert r['group']==g + assert r['changed']==(g['tensor'] in inventory['changed_tensors']) + for model in ('original','finetuned'): + profiles=r[model] if r[model] is not None else r['original'] + for center,p in profiles.items(): + if center=='origin': + p['ball_log_expected_max_cdf']=[float(p['d']*np.log(z)+np.log(p['ncal']/(p['ncal']+1))) for z in MAX_GRID if z<=1] + assert p['valid'] and p['d']==min(8192,g['dimension']) + assert p['covariance_participation']<=p['rank_ceiling']+1e-6 + v=np.asarray(p['eval_norm']);cal=np.asarray(p['cal_norm']) + assert abs(p['rmax']-cal.max())<1e-12 + assert abs(p['outside']-np.mean(v>p['rmax']))<1e-12 + np.testing.assert_allclose(p['cdf_max'],[np.mean(v<=z*p['rmax']) for z in MAX_GRID]) + np.testing.assert_allclose(p['cdf_rms'],[np.mean(v<=z*p['rms']) for z in RMS_GRID]) + key=f'{i}_{model}_{center}';radii[key+'_cal']=cal;radii[key+'_eval']=v + row=dict(group_index=i,group=g['id'],model=model,center=center,kind=g['kind'],stratum=g['stratum'],changed=r['changed'],projected=g['dimension']>8192, + dimension=g['dimension'],analyzed_dimension=p['d'],population=g['population'],ncal=p['ncal'],neval=p['neval'],sampling_sha256=r['sampling_sha256']) + for k in ('median_max','median_rms','p10_rms','p90_rms','outside','rmax','rms','covariance_participation','rank_ceiling','coordinate_variance_participation','point_energy_participation_median','zero_fraction','heldout_outside_coordinate_range'): + row[k]=p[k] + for name,c in p['comparisons'].items(): + for k,vv in c.items():row[name+'_'+k]=vv + rows.append(row) + # Compact profiles keep CDF and controls; raw measured radii live in NPZ. + for model in ('original','finetuned'): + if r[model] is not None: + for p in r[model].values():p.pop('cal_norm');p.pop('eval_norm') + records.append(r) + with gzip.open(out/'profiles.json.gz','wt',encoding='utf-8') as f:json.dump(records,f,allow_nan=False) + np.savez_compressed(out/'measured_radii.npz',**radii) + with (out/'metrics.csv').open('w',newline='') as f: + w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows) + (out/'inventory.json').write_text(json.dumps(inventory,indent=2)) + def subset(model='original',center='origin',kind=None,changed=None): + return [r for r in rows if r['model']==model and r['center']==center and r['stratum']=='primary' and not r['projected'] and (kind is None or r['kind']==kind) and (changed is None or r['changed']==changed)] + summary={} + for center in ('origin','mean'): + for kind in ('matrix','head_rows'): + rs=subset(center=center,kind=kind);s={'n':len(rs), + 'median_zero_fraction':float(np.median([r['zero_fraction'] for r in rs])), + 'median_coordinate_energy_participation_fraction':float(np.median([r['point_energy_participation_median']/r['analyzed_dimension'] for r in rs])), + 'median_coordinate_variance_participation_fraction':float(np.median([r['coordinate_variance_participation']/r['analyzed_dimension'] for r in rs]))} + for ref in ('ball','range_box','moment_box'): + gap=np.array([r[ref+'_interior90_gap'] for r in rs]);pos=np.array([r[ref+'_rms_cdf_positive_gap'] for r in rs]);neg=np.array([r[ref+'_rms_cdf_negative_gap'] for r in rs]) + s[ref]=dict(median_interior90_gap=float(np.median(gap)),fraction_gap_gt10pp=float(np.mean(gap>.1)),median_positive_rms_gap=float(np.median(pos)),median_negative_rms_gap=float(np.median(neg)), + fraction_two_sided_rms_gt10pp=float(np.mean((pos>.1)&(neg>.1))),median_participation_ratio=float(np.median([r[ref+'_participation_ratio'] for r in rs]))) + summary[center+'/'+kind]=s + paired={} + for center in ('origin','mean'): + b=subset(center=center,changed=True);t=subset(model='finetuned',center=center,changed=True);assert [r['group'] for r in b]==[r['group'] for r in t] + paired[center]={'n':len(b)} + for field in ('median_max','median_rms','covariance_participation','moment_box_interior90_gap'): + delta=np.array([q[field]-p[field] for p,q in zip(b,t)]) + paired[center][field]=dict(median=float(np.median(delta)),p10=float(np.quantile(delta,.1)),p90=float(np.quantile(delta,.9)),max_abs=float(np.max(abs(delta)))) + secondary=[] + for model in ('original','finetuned'): + for center in ('origin','mean'): + for label in ('small_sample','projected'): + ss=[r for r in rows if r['model']==model and r['center']==center and (r['stratum']=='small_sample' if label=='small_sample' else r['projected'])] + secondary.append(dict(model=model,center=center,stratum=label,n=len(ss),median_max=float(np.median([r['median_max'] for r in ss])),moment_interior90_gap=float(np.median([r['moment_box_interior90_gap'] for r in ss])))) + (out/'summary.json').write_text(json.dumps(dict(strata=summary,paired_changed=paired,secondary=secondary),indent=2)) + plt.rcParams.update({'font.size':10,'axes.spines.top':False,'axes.spines.right':False}) + def save(fig,name): + fig.tight_layout() + for ext in ('png','pdf','svg'):fig.savefig(figdir/(name+'.'+ext),dpi=180,bbox_inches='tight') + plt.close(fig) + fig,axs=plt.subplots(2,2,figsize=(11,7),sharex=True,sharey=True) + for i,center in enumerate(('origin','mean')): + for j,kind in enumerate(('matrix','head_rows')): + rs=subset(center=center,kind=kind);ax=axs[i,j] + for ref,label in [('ball','Uniform ball'),('range_box','Range-matched box'),('moment_box','Moment-matched box')]: + v=np.sort([r[ref+'_interior90_gap'] for r in rs]);ax.plot(v,np.arange(1,len(v)+1)/len(v),label=label) + ax.axvline(0,color='gray',lw=.8);ax.set_title(f'{center} center / {kind}, n={len(rs)}');ax.set_xlabel('F(data r/Rcal <= 0.9) - F(null)');ax.set_ylabel('Fraction of sampled groups') + axs[0,0].legend(fontsize=8);fig.suptitle('Actual Qwen3.5-2B: interior mass depends on the reference');save(fig,'interior_reference_sensitivity') + fig,axs=plt.subplots(1,2,figsize=(11,4.3)) + for ax,center in zip(axs,('origin','mean')): + for kind,marker in [('matrix','o'),('head_rows','.')]: + rs=subset(center=center,kind=kind);ax.scatter([r['moment_box_rms_cdf_positive_gap'] for r in rs],[r['moment_box_rms_cdf_negative_gap'] for r in rs],s=10,alpha=.25,label=kind,marker=marker) + ax.axvline(.1,color='gray',lw=.7);ax.axhline(.1,color='gray',lw=.7);ax.set(xlabel='Largest positive CDF gap (more inner tail)',ylabel='Largest negative CDF gap (more outer tail)',title=f'{center} / moment-matched box',xlim=(-.02,1),ylim=(-.02,1));ax.legend() + fig.suptitle('RMS-normalized radial shape: both tails can grow together');save(fig,'radial_shape_two_tails') + fig,axs=plt.subplots(1,2,figsize=(11,4.3)) + for ax,center in zip(axs,('origin','mean')): + b=subset(center=center,changed=True);t=subset(model='finetuned',center=center,changed=True) + x=[r['median_max'] for r in b];y=[r['median_max'] for r in t];ax.scatter(x,y,s=10,alpha=.3);low=min(x+y);high=max(x+y);ax.plot([low,high],[low,high],color='gray',lw=1) + ax.set(xlabel='Original median r/Rcal',ylabel='GRPO-LoRA median r/Rcal',title=f'{center}, changed primary groups n={len(b)}') + fig.suptitle('Paired same-index comparison; unchanged groups excluded');save(fig,'finetune_paired_radii') + fig,axs=plt.subplots(1,2,figsize=(11,4.3)) + for ax,center in zip(axs,('origin','mean')): + for kind in ('matrix','head_rows'): + rs=subset(center=center,kind=kind);v=np.sort([r['moment_box_participation_ratio'] for r in rs]);ax.plot(v,np.arange(1,len(v)+1)/len(v),label=kind) + ax.set(xlabel='Empirical participation / equal-sample moment-box reference',ylabel='Fraction of groups',title=center);ax.axvline(1,color='gray',lw=.8);ax.legend() + fig.suptitle('Finite-sample participation is not the true intrinsic dimension');save(fig,'participation_reference') + examples=[('model.language_model.layers.0.linear_attn.in_proj_qkv.weight::Q_head0','L0 GDN Q head0'), + ('model.language_model.layers.3.self_attn.q_proj.weight::Q_head0','L3 full-attention Q head0'), + ('model.language_model.layers.0.mlp.up_proj.weight::rows','L0 MLP up rows')] + fig,axs=plt.subplots(2,3,figsize=(13,7),sharey=True) + byid={r['group']['id']:r for r in records} + indices={g['id']:i for i,g in enumerate(groups)} + base=load_tensors(Path('runs/cpu-qwen35-2b/weights'));example_radii={} + for j,(gid,label) in enumerate(examples): + p=byid[gid]['original']['origin'] + g=byid[gid]['group'];x,digest=sample(base[g['tensor']],g);assert digest==byid[gid]['sampling_sha256'] + ncal=len(x)//2;d=x.shape[1];cal=x[:ncal].astype(np.float64) + rng=np.random.default_rng(seed(gid+'/uniform'));u=rng.random((ncal+512,d),dtype=np.float32) + mu=cal.mean(0);sd=cal.std(0,ddof=1);lo=mu-np.sqrt(3)*sd;hi=mu+np.sqrt(3)*sd + moment=(lo+(hi-lo)*u).astype(np.float32) + rng=np.random.default_rng(seed(f'ball/{d}/{ncal}'));ball=rng.standard_normal((ncal+512,d),dtype=np.float32) + ball*=((rng.random(len(ball))**(1/d))/np.linalg.norm(ball,axis=1))[:,None] + arrays={'Actual weights':(radii[f'{indices[gid]}_original_origin_cal'],radii[f'{indices[gid]}_original_origin_eval'])} + for name,ref,raw in [('Uniform ball','ball',ball),('Moment box','moment_box',moment)]: + nr=np.linalg.norm(raw.astype(np.float64),axis=1);arrays[name]=(nr[:ncal],nr[ncal:]) + np.testing.assert_allclose([np.mean(nr[ncal:]/nr[:ncal].max()<=z) for z in MAX_GRID],p['references'][ref]['origin']['cdf_max']) + np.testing.assert_allclose([np.mean(nr[ncal:]/np.sqrt(np.mean(nr[:ncal]**2))<=z) for z in RMS_GRID],p['references'][ref]['origin']['cdf_rms']) + for name,(nc,ne) in arrays.items():example_radii[f'example{j}_{name.replace(" ","_")}_cal']=nc;example_radii[f'example{j}_{name.replace(" ","_")}_eval']=ne + for i,(field,grid) in enumerate([('cdf_max',MAX_GRID),('cdf_rms',RMS_GRID)]): + ax=axs[i,j] + for name,(nc,ne) in arrays.items(): + scale=nc.max() if i==0 else np.sqrt(np.mean(nc**2));v=np.sort(ne/scale) + ax.step(np.r_[0,v, max(1.5,float(v[-1]))],np.r_[0,np.arange(1,len(v)+1)/len(v),1],where='post',label=name,lw=1.8,ls='-' if name=='Actual weights' else '--') + ax.set(title=label,xlabel='r / calibration '+('maximum' if i==0 else 'RMS'),ylabel='Held-out CDF',ylim=(-.03,1.03),xlim=(0,1.5));ax.legend(fontsize=8) + np.savez_compressed(out/'example_radii.npz',**example_radii) + fig.suptitle('Fixed role examples: maximum-radius and RMS normalization');save(fig,'fixed_role_radial_cdfs') + lines=['# 两个实际2B检查点：参数云的球壳检验','',f'完整覆盖：{len(groups)}组/模型，{len(rows)}个模型×中心组合。632个参数张量，2,274,069,824个参数（包含语言、视觉和MTP）。', + '','## 分布计数与范围','', '固定规则给出554个矩阵行／列视图、1,004个语言/MTP头视图；视图互相重叠，不能相加成参数节省量。1,522组有至少64个可用点，36组只有16个点；1组高维嵌入列视图使用8,192个抽样坐标。主表只汇总样本充足且没有投影的1,521组。319个一维张量和18个低维卷积核不构成此规则下的高维样本云。', + '','## 原模型结果（逐组等权，非独立观测）','', '|中心|视图|组数|相对均匀球：90%半径内质量差中位数|相对矩匹配均匀盒：质量差中位数|矩匹配且RMS归一后，内外两尾差均>10个百分点的组占比|','|---|---|---:|---:|---:|---:|'] + for key,s in summary.items(): + c,k=key.split('/');lines.append(f'|{c}|{k}|{s["n"]}|{s["ball"]["median_interior90_gap"]:.3f}|{s["moment_box"]["median_interior90_gap"]:.3f}|{s["moment_box"]["fraction_two_sided_rms_gt10pp"]:.1%}|') + lines+=['','这些是描述性质量差，不是显著性检验。正差表示所指定半径以内有更多样本；不能将其自动解释为整条径向分布向内平移。若RMS归一后内外尾均变多，更准确的解释是径向分布更宽。', + '', '|中心/视图|逐组零值率中位数|点坐标能量参与率÷维度，中位数|坐标方差参与率÷维度，中位数|','|---|---:|---:|---:|'] + for key,s in summary.items():lines.append(f'|{key}|{s["median_zero_fraction"]:.6g}|{s["median_coordinate_energy_participation_fraction"]:.4f}|{s["median_coordinate_variance_participation_fraction"]:.4f}|') + lines+=['','能量参与率衡量坐标能量集中程度，依赖坐标基；有限样本Gram参与率反映样本间几何，另见图。两者均不是精确非零坐标计数，也不是模型功能的有效维度。', + '','## 微调前后','', '原模型是官方Qwen3.5-2B；第二个模型是在同一revision上进行GSM8K GRPO的已有纯LoRA检查点，不是另一个完整模型。72个投影矩阵使用只读W+2BA展开，其余参数共享。972个视图受影响；其中936个属于主分析。未改变视图复用完全相同的分析结果。','', '|中心|指标|配对变化中位数|P10|P90|最大绝对变化|','|---|---|---:|---:|---:|---:|'] + for c,p in paired.items(): + for k,v in p.items(): + if k!='n':lines.append(f'|{c}|{k}|{v["median"]:.6g}|{v["p10"]:.6g}|{v["p90"]:.6g}|{v["max_abs"]:.6g}|') + lines+=['','## 单独描述的小样本与坐标抽样组','', '|模型|中心|类别|组数|中位r/Rcal的组间中位数|相对矩匹配盒的90%半径质量差中位数|','|---|---|---|---:|---:|---:|'] + for s in secondary:lines.append(f'|{s["model"]}|{s["center"]}|{s["stratum"]}|{s["n"]}|{s["median_max"]:.4f}|{s["moment_interior90_gap"]:.4f}|') + lines+=['','## 如何读图','', '1. `interior_reference_sensitivity`：不同均匀参照下，90%校准半径内质量差的组间CDF。横轴向右表示更多内侧质量。','2. `radial_shape_two_tails`：匹配矩且按各自校准RMS归一后的最大正／负CDF差。右上角同时增加两尾，并非单纯向内。','3. `finetune_paired_radii`：同一视图的微调前后中位半径；对角线表示不变。','4. `participation_reference`：Gram矩阵参与率相对同样本量参照的比值。受样本秩上限影响，不能当作真实内在维度。', + '5. `fixed_role_radial_cdfs`：预先按角色固定的GDN Q、全注意力Q和MLP上投影示例，上排按校准最大半径、下排按RMS归一化。不是按结果挑选最明显的组。', + '','## 方法与限制','', '每组最多512个不同参数向量，一半校准、一半留出；模型固定，留出指未用于拟合中心/半径的参数向量，不是独立训练模型。对照每组512个验证向量，最坏单项二项标准误约2.21个百分点；模型留出样本的误差还取决于样本数，且参数向量不保证独立。10个百分点仅是描述性筛选尺度，没有多重检验或总体显著性结论。', + '','均匀球、坐标范围均匀盒、匹配均值和逐坐标方差的均匀盒都按同一规则拟合自身中心、最大半径及RMS。均匀球原点中心的精确拟合半径CDF为a^d*ncal/(ncal+1)，保留其对数以避免把模拟零命中误报成零概率。盒对照不是协方差匹配。校准最大半径不是已知总体支撑边界；留出越界率已记录。', + '','`origin`保留坐标均值贡献；`mean`使用校准向量均值。没有使用所有数据来选边界、训练或推理；没有删除模型。坐标稀疏性、有限样本参与率与径向内侧质量分别测量，不能相互代替。该实验不能证明更好的模型更靠里，也不能证明功能重复、可剪枝或可复用。', + '','复现：`OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.analyze`，完成后 `... python -m model_shell.report`。只在已授权CPU服务器运行。分组协议见 `model_shell/PROTOCOL.md`；全量指标在metrics.csv，完整CDF/参照在profiles.json.gz，实测范数在measured_radii.npz。'] + (out/'REPORT.md').write_text('\n'.join(lines)+'\n') + sources={'base':Path('runs/cpu-qwen35-2b/weights/model.safetensors-00001-of-00001.safetensors'),'adapter':Path('runs/finetune-head-comparison/adapter.safetensors'), + 'config':Path('runs/cpu-qwen35-2b/model_config.json'),'adapter_meta':Path('runs/finetune-head-comparison/adapter_meta.json')} + sourcehash={k:dict(path=str(p),bytes=p.stat().st_size,sha256=sha(p)) for k,p in sources.items()} + (out/'environment.json').write_text(json.dumps(dict(python=sys.version,platform=platform.platform(),numpy=np.__version__,matplotlib=matplotlib.__version__, + source_code_sha256={str(p):sha(p) for p in sorted(Path('model_shell').glob('*.py'))},protocol_sha256=sha(Path('model_shell/PROTOCOL.md')),seed='first 8 SHA256 bytes, little endian; group id plus purpose suffix', + thread_limit=1,server='8.211.154.134',training=False,inference=False,gpu=False),indent=2)) + assert sourcehash['base']['sha256']=='aa33250c4fc64891ddfaba3a314fd9542ea371843c387178b425fbcc5ed680b1' + assert sourcehash['adapter']['sha256']=='93a3f43a4356f84da0d13ecc622fee13e41ba15d237cdaad6094f243899c62d6' + audit=dict(complete_groups=len(groups),profile_rows=len(rows),all_finite=True,cdf_recomputed=True,sample_rank_checked=True,sources=sourcehash,files={str(p.relative_to(out)):sha(p) for p in sorted(out.rglob('*')) if p.is_file() and p.name!='audit.json'}) + (out/'audit.json').write_text(json.dumps(audit,indent=2));print(json.dumps(dict(summary=summary,paired=paired),indent=2)) + +if __name__=='__main__':main() diff --git a/model_shell/test_analysis.py b/model_shell/test_analysis.py new file mode 100644 index 0000000..58b78e1 --- /dev/null +++ b/model_shell/test_analysis.py @@ -0,0 +1,49 @@ +"""Run on the authorized CPU server: python -m unittest model_shell.test_analysis.""" +import unittest +import numpy as np +from model_shell.analyze import sample,geometry,analyze,seed +from finetune_compare.lora import EffectiveWeight + +class ArrayTensor: + def __init__(self,x):self.x=x;self.shape=x.shape;self.numel=x.size;self.dtype='F32' + def read(self,s=slice(None)):return self.x.reshape(-1)[s].copy() + def evict(self):pass + +class Checks(unittest.TestCase): + def test_sampling_and_lora(self): + rng=np.random.default_rng(17);w=rng.normal(size=(80,64)).astype('f');a=rng.normal(size=(2,64)).astype('f');b=rng.normal(size=(80,2)).astype('f') + effective=EffectiveWeight(ArrayTensor(w),a,b,2);explicit=ArrayTensor(w+2*b@a) + for axis,n,d,start,stop in [('rows',80,64,0,80),('columns',64,80,0,80),('rows',32,64,16,48)]: + g=dict(id=axis+str(start),orientation=axis,population=n,dimension=d,start=start,stop=stop) + x,h=sample(effective,g);y,k=sample(explicit,g);np.testing.assert_allclose(x,y,atol=2e-6,rtol=1e-5);self.assertEqual(h,k) + self.assertEqual(x.shape,(n,d)) + rr=np.random.default_rng(seed(g['id']+'/indices'));indices=np.sort(rr.choice(n,n,replace=False));order=rr.permutation(n) + cloud=explicit.x[start:stop] if axis=='rows' else explicit.x.T + np.testing.assert_allclose(y,cloud[indices][order],atol=0,rtol=0) + def test_center_scale_and_rank(self): + rng=np.random.default_rng(5);x=rng.normal(size=(128,96)).astype('f') + for center in ('origin','mean'): + p=geometry(x,64,center);q=geometry(x*3,64,center) + np.testing.assert_allclose(p['cdf_rms'],q['cdf_rms']);self.assertLessEqual(p['covariance_participation'],p['rank_ceiling']+1e-6) + p=geometry(x,64,'mean');q=geometry(x+4,64,'mean');np.testing.assert_allclose(p['median_rms'],q['median_rms'],rtol=1e-6) + self.assertFalse(geometry(np.zeros((32,64)),16,'mean')['valid']) + def test_ball_fit_and_sparse_counterexample(self): + rng=np.random.default_rng(33);d=32;ncal=256 + # Analytic expectation over independent calibration maxima. + c=rng.random((20000,ncal))**(1/d);v=rng.random(20000)**(1/d) + observed=np.mean(v<=.99*c.max(1));expected=.99**d*ncal/(ncal+1) + self.assertLess(abs(observed-expected),.015) + radii=rng.random(512)**(1/d);x=np.zeros((512,d),dtype='f');x[:,0]=radii + p=geometry(x,256,'origin');self.assertGreater(p['median_max'],.9) + # Sparse support alone retains the full-dimensional radial law. + self.assertLess(p['cdf_max'][2],.08) + x[:,0]=rng.random(512)**.5 + self.assertGreater(geometry(x,256,'origin')['cdf_max'][2],.65) + sphere=rng.normal(size=(512,d));sphere/=np.linalg.norm(sphere,axis=1)[:,None] + self.assertEqual(geometry(sphere,256,'origin')['cdf_max'][2],0.) + def test_reproducible_controls(self): + x=np.random.default_rng(7).normal(size=(32,40)).astype('f');a=analyze(x,'test');b=analyze(x,'test') + self.assertEqual(a,b) + self.assertTrue(a['mean']['valid']) + +if __name__=='__main__':unittest.main() diff --git a/results/model-shell/REPORT.md b/results/model-shell/REPORT.md new file mode 100644 index 0000000..28a05f0 --- /dev/null +++ b/results/model-shell/REPORT.md @@ -0,0 +1,73 @@ +# 两个实际2B检查点：参数云的球壳检验 + +完整覆盖：1558组/模型，6232个模型×中心组合。632个参数张量，2,274,069,824个参数（包含语言、视觉和MTP）。 + +## 分布计数与范围 + +固定规则给出554个矩阵行／列视图、1,004个语言/MTP头视图；视图互相重叠，不能相加成参数节省量。1,522组有至少64个可用点，36组只有16个点；1组高维嵌入列视图使用8,192个抽样坐标。主表只汇总样本充足且没有投影的1,521组。319个一维张量和18个低维卷积核不构成此规则下的高维样本云。 + +## 原模型结果（逐组等权，非独立观测） + +|中心|视图|组数|相对均匀球：90%半径内质量差中位数|相对矩匹配均匀盒：质量差中位数|矩匹配且RMS归一后，内外两尾差均>10个百分点的组占比| +|---|---|---:|---:|---:|---:| +|origin|matrix|517|0.980|0.977|93.8%| +|origin|head_rows|1004|0.973|0.973|87.8%| +|mean|matrix|517|0.977|0.977|94.4%| +|mean|head_rows|1004|0.969|0.969|86.4%| + +这些是描述性质量差，不是显著性检验。正差表示所指定半径以内有更多样本；不能将其自动解释为整条径向分布向内平移。若RMS归一后内外尾均变多，更准确的解释是径向分布更宽。 + +|中心/视图|逐组零值率中位数|点坐标能量参与率÷维度，中位数|坐标方差参与率÷维度，中位数| +|---|---:|---:|---:| +|origin/matrix|0|0.3067|0.9346| +|origin/head_rows|0|0.3117|0.9055| +|mean/matrix|0|0.3072|0.9346| +|mean/head_rows|0|0.3118|0.9055| + +能量参与率衡量坐标能量集中程度，依赖坐标基；有限样本Gram参与率反映样本间几何，另见图。两者均不是精确非零坐标计数，也不是模型功能的有效维度。 + +## 微调前后 + +原模型是官方Qwen3.5-2B；第二个模型是在同一revision上进行GSM8K GRPO的已有纯LoRA检查点，不是另一个完整模型。72个投影矩阵使用只读W+2BA展开，其余参数共享。972个视图受影响；其中936个属于主分析。未改变视图复用完全相同的分析结果。 + +|中心|指标|配对变化中位数|P10|P90|最大绝对变化| +|---|---|---:|---:|---:|---:| +|origin|median_max|6.30233e-07|-3.20939e-05|3.49557e-05|0.000402869| +|origin|median_rms|-2.02235e-06|-4.00888e-05|3.91646e-05|0.000121968| +|origin|covariance_participation|0.00011293|-0.0007975|0.00116302|0.0136976| +|origin|moment_box_interior90_gap|0|0|0|0| +|mean|median_max|3.08748e-07|-3.29473e-05|3.37828e-05|0.000395056| +|mean|median_rms|-1.97695e-06|-4.08665e-05|3.90134e-05|0.000306282| +|mean|covariance_participation|0.000126928|-0.000792364|0.00112596|0.0127131| +|mean|moment_box_interior90_gap|0|0|0|0.00390625| + +## 单独描述的小样本与坐标抽样组 + +|模型|中心|类别|组数|中位r/Rcal的组间中位数|相对矩匹配盒的90%半径质量差中位数| +|---|---|---|---:|---:|---:| +|original|origin|small_sample|36|0.7490|0.7500| +|original|origin|projected|1|0.4199|0.9961| +|original|mean|small_sample|36|0.8109|0.6875| +|original|mean|projected|1|0.4245|0.9961| +|finetuned|origin|small_sample|36|0.7490|0.7500| +|finetuned|origin|projected|1|0.4199|0.9961| +|finetuned|mean|small_sample|36|0.8110|0.6875| +|finetuned|mean|projected|1|0.4245|0.9961| + +## 如何读图 + +1. `interior_reference_sensitivity`：不同均匀参照下，90%校准半径内质量差的组间CDF。横轴向右表示更多内侧质量。 +2. `radial_shape_two_tails`：匹配矩且按各自校准RMS归一后的最大正／负CDF差。右上角同时增加两尾，并非单纯向内。 +3. `finetune_paired_radii`：同一视图的微调前后中位半径；对角线表示不变。 +4. `participation_reference`：Gram矩阵参与率相对同样本量参照的比值。受样本秩上限影响，不能当作真实内在维度。 +5. `fixed_role_radial_cdfs`：预先按角色固定的GDN Q、全注意力Q和MLP上投影示例，上排按校准最大半径、下排按RMS归一化。不是按结果挑选最明显的组。 + +## 方法与限制 + +每组最多512个不同参数向量，一半校准、一半留出；模型固定，留出指未用于拟合中心/半径的参数向量，不是独立训练模型。对照每组512个验证向量，最坏单项二项标准误约2.21个百分点；模型留出样本的误差还取决于样本数，且参数向量不保证独立。10个百分点仅是描述性筛选尺度，没有多重检验或总体显著性结论。 + +均匀球、坐标范围均匀盒、匹配均值和逐坐标方差的均匀盒都按同一规则拟合自身中心、最大半径及RMS。均匀球原点中心的精确拟合半径CDF为a^d*ncal/(ncal+1)，保留其对数以避免把模拟零命中误报成零概率。盒对照不是协方差匹配。校准最大半径不是已知总体支撑边界；留出越界率已记录。 + +`origin`保留坐标均值贡献；`mean`使用校准向量均值。没有使用所有数据来选边界、训练或推理；没有删除模型。坐标稀疏性、有限样本参与率与径向内侧质量分别测量，不能相互代替。该实验不能证明更好的模型更靠里，也不能证明功能重复、可剪枝或可复用。 + +复现：`OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.analyze`，完成后 `... python -m model_shell.report`。只在已授权CPU服务器运行。分组协议见 `model_shell/PROTOCOL.md`；全量指标在metrics.csv，完整CDF/参照在profiles.json.gz，实测范数在measured_radii.npz。 diff --git a/results/model-shell/analysis-output.txt b/results/model-shell/analysis-output.txt new file mode 100644 index 0000000..6199d9e --- /dev/null +++ b/results/model-shell/analysis-output.txt @@ -0,0 +1,157 @@ +1/1558 elapsed=25.4s +11/1558 elapsed=65.2s +21/1558 elapsed=75.1s +31/1558 elapsed=84.5s +41/1558 elapsed=93.3s +51/1558 elapsed=102.0s +61/1558 elapsed=115.8s +71/1558 elapsed=137.8s +81/1558 elapsed=149.6s +91/1558 elapsed=159.8s +101/1558 elapsed=169.0s +111/1558 elapsed=178.5s +121/1558 elapsed=193.0s +131/1558 elapsed=215.0s +141/1558 elapsed=225.0s +151/1558 elapsed=234.5s +161/1558 elapsed=243.4s +171/1558 elapsed=252.9s +181/1558 elapsed=262.7s +191/1558 elapsed=283.8s +201/1558 elapsed=297.3s +211/1558 elapsed=305.0s +221/1558 elapsed=311.2s +231/1558 elapsed=328.1s +241/1558 elapsed=337.7s +251/1558 elapsed=348.0s +261/1558 elapsed=359.5s +271/1558 elapsed=368.0s +281/1558 elapsed=382.3s +291/1558 elapsed=405.5s +301/1558 elapsed=414.4s +311/1558 elapsed=424.6s +321/1558 elapsed=433.5s +331/1558 elapsed=442.5s +341/1558 elapsed=455.7s +351/1558 elapsed=476.6s +361/1558 elapsed=485.6s +371/1558 elapsed=494.2s 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\ No newline at end of file diff --git a/results/model-shell/test-output.txt b/results/model-shell/test-output.txt new file mode 100644 index 0000000..0d44841 --- /dev/null +++ b/results/model-shell/test-output.txt @@ -0,0 +1,9 @@ +test_ball_fit_and_sparse_counterexample (model_shell.test_analysis.Checks.test_ball_fit_and_sparse_counterexample) ... ok +test_center_scale_and_rank (model_shell.test_analysis.Checks.test_center_scale_and_rank) ... ok +test_reproducible_controls (model_shell.test_analysis.Checks.test_reproducible_controls) ... ok +test_sampling_and_lora (model_shell.test_analysis.Checks.test_sampling_and_lora) ... ok + +---------------------------------------------------------------------- +Ran 4 tests in 0.394s + +OK