Study cross-layer QKV functions and conditional retrieval overlap
This commit is contained in:
@@ -8,6 +8,12 @@ weights were deleted after archival; the functional pilot executes streaming FP3
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The current CPU functional checkpoint is retained until the user explicitly requests deletion.
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No model weights are committed here.
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The latest cross-layer QKV study covers all 15 full-attention layer pairs. Its primary
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protocol had no passing candidate; an exploratory H23-conditioned self-token retrieval
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pattern is reported separately with its fresh-test failures.
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- [Cross-layer QKV findings and evidence limits](docs/CROSS_LAYER_QKV_FINDINGS.md)
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- [Cross-layer QKV fixed protocol](docs/CROSS_LAYER_QKV_PROTOCOL.md)
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- [CPU learned-function repetition findings](docs/CPU_FUNCTIONAL_FINDINGS.md)
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- [Current learned-function repetition criterion](docs/LEARNED_FUNCTION_REPETITION.md)
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- [CPU-first feasibility and validation](docs/CPU_FIRST_VALIDATION.md)
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@@ -0,0 +1,42 @@
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# Cross-layer QKV CPU study
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This phase targets learned QKV functions across different layers, independent of cost.
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The six full-attention layers (indices 3/7/11/15/19/23) form 15 unordered pairs.
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No within-layer Q/K/V substitution, training, continuous R fitting, GPU, or new model download.
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Read [fixed primary protocol](../docs/CROSS_LAYER_QKV_PROTOCOL.md) and
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[findings with evidence limits](../docs/CROSS_LAYER_QKV_FINDINGS.md).
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The original main protocol had no passing candidate. A later fixed one-source audit
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showed near-self-token retrieval and small readout errors, but failed strict fresh
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mean-JS/row-JS-P95/row-TV-P95 criteria; it is not relabeled as a primary pass.
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- `core.py`: exact discrete head assignment, actual attention parts, common-space head
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decomposition, fixed recipient V/gate/O transplants and temporal-cache controls.
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- `protocol.py`, `fresh_samples.json`: predeclared layer/unit/gate scope and untouched fresh texts.
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- `run.py`, `fresh.py`: calibration, frozen selection, old validation and gated fresh/network steps.
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- `conditional.py`: separately registered post-screen metric audit on one fixed H23 candidate;
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no fallback selection or later threshold changes.
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- `audit_case.py`: recompute selected fresh probabilities, self mass, shared top5 mass and
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actual-H3 versus same-H23 difference; no full-network intervention after failure.
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- `report.py`, `audit.py`: saved-data scientific plots, raw-count/matching/probability audits,
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SHA256 and explicit small Git delivery with external large-cache metadata.
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Use the authorized remote CPU environment with the retained source checkpoint/cache:
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```sh
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OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -B -m unittest tests.test_crossqkv -v
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OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 timeout 7200 venv/bin/python -B -m crossqkv.run --source runs/cpu-qwen35-2b --root runs/NEW_QKV_DIRECTORY
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```
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The main run requires a fresh directory and never overwrites prior protocol evidence.
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The recorded execution used `runs/cross-layer-qkv-v2` after fixing a controls-variable
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shadowing error in the first attempt; old logs and its profile cache are retained.
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The first attempt took about 10.3 seconds, then a 60-second reserve was deducted from
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the next 7140-second run limit. The main study finished in about 74.8 seconds.
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The fixed conditional audit used only remaining shared budget and took about 105.3 seconds.
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Final tests/plots/audits fit comfortably within the two-hour computation budget.
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All weights and caches stay on the server until explicit user deletion. Git contains
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all figure tables and the selected small probability matrices; large caches have exact
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paths/sizes/hashes in `external-caches.json`. Old validation texts were used in earlier
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experiments; the four fresh texts were fixed before the new cross-layer measurements.
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@@ -0,0 +1 @@
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"""Cross-layer learned QKV function comparisons, CPU only."""
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@@ -0,0 +1,95 @@
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"""Audit and package only science outputs; retain all weights/profile caches."""
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import argparse
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import csv
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import hashlib
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import json
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from pathlib import Path
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import shutil
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import time
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import numpy as np
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from functional.run import write_json
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from .protocol import LAYERS,PAIRS
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from .core import assignment
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def digest(path):
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h=hashlib.sha256()
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with path.open('rb') as f:
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while b:=f.read(4*1024*1024):h.update(b)
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return h.hexdigest()
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def main():
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p=argparse.ArgumentParser();p.add_argument('--root',type=Path,required=True);p.add_argument('--source',type=Path,required=True);p.add_argument('--output',type=Path,required=True);a=p.parse_args();root=a.root.resolve();source=a.source.resolve();out=a.output.resolve();assert root!=out
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status=json.loads((root/'status.json').read_text());assert status['state']=='completed' and status['last_source_layer']==23 and status['last_pair']==[19,23]
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expected={'full_attention':15,'qkv_head':960,'qk_map':30,'qk_head':960,'v_group':60};assert status['candidate_counts']==expected
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summary=json.loads((root/'calibration-summary.json').read_text());assert len(summary)==2025
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assert not status['selected'] and all(not x['strict_pass'] and not x['approximate_pass'] for x in summary)
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selection=json.loads((root/'selection.json').read_text());assert selection['qk_head_prequalified']==selection['v_group_prequalified']==0
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from collections import defaultdict
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rawout=defaultdict(list)
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for r in csv.DictReader((root/'calibration-output-metrics.csv').open()):rawout[r['candidate']].append(r)
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for c in summary:
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if c['output_verified']:
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rr=rawout[c['id']];assert len(rr)==c['output_observations']
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worst=max(max(float(r['error_a']),float(r['error_b'])) for r in rr)
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assert abs(worst-c['worst_output_error'])<1e-12 and worst>.20
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assert min(c['worst_mean_js'] for c in summary if c['kind']=='qk_head')>.05
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assert min(c['worst_value_error'] for c in summary if c['kind']=='v_group')>.20
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rows=list(csv.DictReader((root/'head-assignment-costs.csv').open()));assert len(rows)==960
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for i,j in PAIRS:
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r=[x for x in rows if int(x['i'])==i and int(x['j'])==j];cost=np.empty((8,8));chosen=[]
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for x in r:
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ha,hb=int(x['head_i']),int(x['head_j']);cost[ha,hb]=float(x['calibration_mean_js'])
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if x['chosen']=='True':chosen.append((ha,hb))
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assert len(chosen)==8;assert [b for _,b in sorted(chosen)]==assignment(cost)
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tests=(root/'final-tests.txt').read_text();assert 'Ran 20 tests' in tests and '\nOK\n' in tests
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cond=json.loads((root/'conditional-status.json').read_text());assert cond['state']=='completed' and cond['fresh_function_pass'] is False
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case=json.loads((root/'conditional-case-audit.json').read_text());assert case['passed_recomputation'] and case['no_success_relabeling']
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assert len(case['strict_failures'])==3 and case['network_intervention_not_run_due_to_fresh_gate_failure']
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assert cond['shared_budget_seconds_including_60s_early_reserve']+600<7200
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prob=np.load(root/'conditional-fresh-probabilities.npz');target=prob['target_layer23_head6'];donor=prob['donor_layer3_head7_on_H23']
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from .core import distribution_metrics
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from .conditional import tv_metrics
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metric={**distribution_metrics(target,donor),**tv_metrics(target,donor)}
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fresh=[r for r in csv.DictReader((root/'conditional-function-metrics.csv').open()) if r['split']=='fresh_test']
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assert len(fresh)==4
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for k,values in metric.items():
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if k in fresh[0] and values[0] is not None:np.testing.assert_allclose(values,[float(r[k]) for r in fresh],atol=1e-12,rtol=1e-12)
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for file in root.glob('*.csv'):
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with file.open() as f:
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for r in csv.DictReader(f):
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for value in r.values():
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if value in ('',None):continue
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try:x=float(value)
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except ValueError:continue
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assert np.isfinite(x),(file,value)
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weights=json.loads((source/'manifest.json').read_text());assert weights['revision']=='15852e8c16360a2fea060d615a32b45270f8a8fc'
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for entry in weights['files']:
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if entry['name'].endswith('.safetensors'):assert (source/'weights'/entry['name']).stat().st_size==entry['bytes'] and entry['sha256']==entry['upstream_sha256']
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assert json.loads((source/'retention-policy.json').read_text())['automatic_cleanup'] is False
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reference=json.loads((source/'reference-check.json').read_text());assert reference['implementation_sha256']==digest(Path('functional/model.py'))
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write_json(root/'final-source-hashes.json',{str(file):digest(file) for file in [*sorted(Path('crossqkv').glob('*.py')),Path('crossqkv/fresh_samples.json'),Path('tests/test_crossqkv.py'),Path('functional/model.py')]})
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caches=[]
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for file in sorted((root/'cache').glob('*')):
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if file.is_file():caches.append({'server_path':str(file),'bytes':file.stat().st_size,'sha256':digest(file),'retained':True})
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old_cache=root.parent/'cross-layer-qkv'/'cache'/'calibration-source-3.npz'
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if old_cache.exists():caches.append({'server_path':str(old_cache),'bytes':old_cache.stat().st_size,'sha256':digest(old_cache),'retained':True,'failed_attempt_cache':True})
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write_json(root/'external-caches.json',{'host':'8.211.154.134','source_weights_directory':str(source/'weights'),'source_baseline_cache':str(source/'baseline-activations.npz'),
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'files':caches,'automatic_deletion':False,'all_figure_source_tables_and_selected_probabilities_in_git':True})
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(root/'DELIVERY.md').write_text('# Cross-layer QKV scientific delivery\n\nAll plot CSV/JSON, the selected fresh probability matrices, protocols, reference tests and failure logs are included.\nLarge intermediate profiles remain on 8.211.154.134 with locations/sizes/SHA256 in external-caches.json.\nThe fixed checkpoint and all caches are retained until explicit user deletion. No weights are committed.\nThe primary protocol has no passing candidate. The later fixed conditional audit is exploratory and its strict fresh checks failed; observed readout overlap is reported without relabeling it a complete pass.\n')
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out.mkdir(exist_ok=True,parents=True)
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files=[f for f in root.iterdir() if f.is_file() and f.name not in ('checksums.json','audit-validation.json','archive-files.json')]
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files += list((root/'figures').glob('*'))
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for file in files:
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dest=out/file.relative_to(root);dest.parent.mkdir(exist_ok=True,parents=True);shutil.copy2(file,dest)
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checks={str(f.relative_to(out)):digest(f) for f in sorted(out.rglob('*')) if f.is_file() and f.name not in ('checksums.json','audit-validation.json','archive-files.json')}
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write_json(out/'checksums.json',checks)
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write_json(out/'archive-files.json',[*checks,'checksums.json','audit-validation.json','archive-files.json'])
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write_json(out/'audit-validation.json',{'passed':True,'validated_unix':time.time(),'checksummed_files':len(checks),'primary_counts_rechecked':expected,
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'fresh_probabilities_independently_recomputed':True,'no_unrun_network_quality_claims':True,'all_weights_and_caches_retained':True})
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print(json.dumps({'passed':True,'checksummed_files':len(checks),'external_cache_files':len(caches),'weights_retained':True}))
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if __name__=='__main__':main()
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@@ -0,0 +1,62 @@
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"""Recompute the frozen exploratory case for transparent plotted probabilities.
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No new selection, no threshold changes, no full-network intervention after failure.
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"""
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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from functional.run import save_csv,write_json
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from .core import QKVModel,distribution_metrics
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from .conditional import tv_metrics
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def ranks_and_mass(a,b):
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result=[]
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for bi in range(a.shape[0]):
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d={'target_self_mass':[],'donor_self_mass':[],'argmax_agreement':[],'shared_top5_mass_target':[],'shared_top5_mass_donor':[]}
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for t in range(7,32):
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x,y=a[bi,0,t,:t+1],b[bi,0,t,:t+1]
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common=np.intersect1d(np.argsort(x)[-5:],np.argsort(y)[-5:])
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d['target_self_mass'].append(float(x[t]));d['donor_self_mass'].append(float(y[t]))
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d['argmax_agreement'].append(float(np.argmax(x)==np.argmax(y)))
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d['shared_top5_mass_target'].append(float(x[common].sum()));d['shared_top5_mass_donor'].append(float(y[common].sum()))
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result.append({k:float(np.mean(v)) for k,v in d.items()})
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return result
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def main():
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p=argparse.ArgumentParser();p.add_argument('--source',type=Path,required=True);p.add_argument('--root',type=Path,required=True);args=p.parse_args();root=args.root;source=args.source
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status=json.loads((root/'conditional-status.json').read_text());assert status['state']=='completed'
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data=np.load(root/'cache'/'conditional-fresh-inputs.npz')
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samples=json.loads((root/'conditional-fresh-samples.json').read_text())['samples']
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lm=QKVModel(source/'weights',json.loads((source/'model_config.json').read_text()),seconds=300)
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own=lm.parts(data['attn_23'],23);same=lm.parts(data['attn_23'],3);actual=lm.parts(data['attn_3'],3)
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target=own['p'][:,6:7];donor=same['p'][:,7:8];cached=actual['p'][:,7:8]
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np.savez(root/'conditional-fresh-probabilities.npz',target_layer23_head6=target,donor_layer3_head7_on_H23=donor,donor_layer3_head7_on_H3=cached)
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rows=[]
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from .run import outrows
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for mode,profile,prob in [('same_H23',same,donor),('actual_H3_to_H23',actual,cached)]:
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stats={**distribution_metrics(target,prob),**tv_metrics(target,prob)}
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mass=ranks_and_mass(target,prob)
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changed=lm.head_transplant_output(own,profile,23,6,7)
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out=outrows(own['heads'][:,:,6],changed,samples,{},1e-6,1e-6)
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for n,s in enumerate(samples):rows.append({'mode':mode,'sample':s['id'],**mass[n],**{k:(None if v[n] is None else float(v[n])) for k,v in stats.items()},'head_output_error':max(out[n]['error_a'],out[n]['error_b'])})
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save_csv(root/'conditional-fresh-source-diagnostic.csv',rows)
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with (root/'conditional-function-metrics.csv').open() as f:import csv;allrows=list(csv.DictReader(f))
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failures=[]
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for r in allrows:
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if r['split']!='fresh_test':continue
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for field,limit in [('js',.01),('js_p95',.05),('total_variation',.10),('total_variation_p95',.20)]:
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if float(r[field])>limit:failures.append({'sample':r['sample'],'field':field,'value':float(r[field]),'limit':limit})
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same_rows=[r for r in rows if r['mode']=='same_H23']
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classification='self_token_dominated' if min(min(r['target_self_mass'],r['donor_self_mass']) for r in same_rows)>.85 else 'inspect detailed source diagnostic'
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write_json(root/'conditional-case-audit.json',{'passed_recomputation':True,'strict_failures':failures,'pattern_classification':classification,
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'minimum_same_input_target_self_mass':min(r['target_self_mass'] for r in same_rows),'minimum_same_input_donor_self_mass':min(r['donor_self_mass'] for r in same_rows),
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'minimum_same_input_argmax_agreement':min(r['argmax_agreement'] for r in same_rows),
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'minimum_shared_top5_probability_mass':min(min(r['shared_top5_mass_target'],r['shared_top5_mass_donor']) for r in same_rows),
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'maximum_same_input_head_output_error':max(r['head_output_error'] for r in same_rows),
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'no_success_relabeling':True,'network_intervention_not_run_due_to_fresh_gate_failure':True})
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if __name__=='__main__':main()
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@@ -0,0 +1,113 @@
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"""One explicitly exploratory, fixed-candidate audit of unweighted top-k rejection.
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Primary protocol remains unchanged/negative. A new confirmation uses untouched
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fresh texts; no further candidate selection, threshold tuning, weights or R fitting.
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"""
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import argparse
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import hashlib
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import json
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from pathlib import Path
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import resource
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import time
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import traceback
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import numpy as np
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from tokenizers import Tokenizer
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from functional.run import write_json,save_csv
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from .core import InterventionLM,distribution_metrics,output_metrics
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from .run import head_floor,rows_for
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CANDIDATE={'id':'conditional_qk_head_3_23_7_6','kind':'qk_head','i':3,'j':23,'a':7,'b':6,'perm':None}
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PROTOCOL={'classification':'post-screen exploratory metric audit; not a primary-protocol positive',
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'candidate':CANDIDATE,'conditioning_input_layer':23,'donor_projection_layer':3,'target_head':6,'donor_head':7,
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'selection_basis':'one minimum worst-text JS among the already measured source-conditioned pairs; no raw readout/fresh data used',
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'js_max':.01,'js_row_p95_max':.05,'mean_total_variation_max':.10,'row_total_variation_p95_max':.20,
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'target_head_output_relative_l2_max':.20,'target_head_output_cosine_min':.98,
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'primary_query_positions':list(range(7,32)),'top5_is_diagnostic_only':True,
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'active_head_required':True,'no_candidate_fallback':True,'fresh_text_file':'crossqkv/fresh_samples.json',
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'same_input_network_direction':'layer23 head6 uses QK probability of layer3 head7 recomputed on H23',
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'actual_cache_separate':'layer3 probability on H3 reused at23; different-input diagnostic, not function-equivalence evidence',
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'quality_gate':{'mean_nll_delta_max':.01,'per_text_p95_nll_delta_max':.05},'training':False,'R_fitting':False,'cost_required':False}
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def tv_metrics(a,b):
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tv=.5*np.sum(np.abs(a.astype(np.float64)-b.astype(np.float64)),axis=-1)[:,:,7:]
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return {'total_variation':tv.mean((1,2)),'total_variation_p95':np.quantile(tv.reshape(tv.shape[0],-1),.95,axis=1)}
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def evaluate(lm,h,samples,split,donor_h=None):
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target=lm.parts(h,23);donor=lm.parts(h if donor_h is None else donor_h,3)
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a,b=target['p'][:,6:7],donor['p'][:,7:8]
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pm={**distribution_metrics(a,b),**tv_metrics(a,b)}
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changed=lm.head_transplant_output(target,donor,23,6,7)
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om=output_metrics(target['heads'][:,7:,6],changed[:,7:]);floor=head_floor(target)
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uniform=np.zeros_like(a)
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for t in range(a.shape[-1]):uniform[:,:,t,:t+1]=1/(t+1)
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null=distribution_metrics(a,uniform)
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rows=rows_for({**pm,**{f'output_{k}':v for k,v in om.items()},'target_uniform_js':null['js']},samples,{'split':split,'mode':'same_H23' if donor_h is None else 'actual_H3_to_H23'})
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for n,r in enumerate(rows):r['active']=bool(om['rms_a'][n]>=floor[n] and om['rms_b'][n]>=floor[n])
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passed=all(r['js']<=.01 and r['js_p95']<=.05 and r['total_variation']<=.10 and r['total_variation_p95']<=.20 and
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max(r['output_error_a'],r['output_error_b'])<=.20 and r['output_cosine']>=.98 and r['active'] for r in rows)
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return rows,passed
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def main():
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p=argparse.ArgumentParser();p.add_argument('--source',type=Path,required=True);p.add_argument('--root',type=Path,required=True);a=p.parse_args();source=a.source.resolve();root=a.root.resolve()
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assert not (root/'conditional-protocol.json').exists(),'Fresh conditional audit protocol required'
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write_json(root/'conditional-protocol.json',{**PROTOCOL,'registered_unix':time.time(),'fresh_text_sha256':hashlib.sha256(Path(__file__).with_name('fresh_samples.json').read_bytes()).hexdigest()})
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prior=json.loads((root/'status.json').read_text());assert prior['state']=='completed' and not prior['selected']
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reserve=prior['elapsed_seconds']+60
|
||||
remaining=int(7200-reserve-180)
|
||||
if remaining<120:raise TimeoutError('Insufficient shared two-hour budget')
|
||||
started=time.monotonic();status={'state':'running','phase':'calibration_readout','primary_protocol_unchanged_and_negative':True}
|
||||
rows=[]
|
||||
try:
|
||||
lm=InterventionLM(source/'weights',json.loads((source/'model_config.json').read_text()),seconds=remaining)
|
||||
old=json.loads((source/'samples-tokenized.json').read_text())['samples'];data=np.load(source/'baseline-activations.npz')
|
||||
for split in ('calibration','heldout'):
|
||||
index=np.array([i for i,s in enumerate(old) if s['split']==split]);samples=[old[i] for i in index]
|
||||
rr,passed=evaluate(lm,data['attn_23'][index],samples,split)
|
||||
rows.extend(rr);save_csv(root/'conditional-function-metrics.csv',rows)
|
||||
status[split+'_pass']=passed
|
||||
write_json(root/'conditional-status.json',status)
|
||||
if not passed:
|
||||
status['stop_reason']=f'{split} probability-mass/readout/activity screen failed';break
|
||||
else:
|
||||
fresh=json.loads(Path(__file__).with_name('fresh_samples.json').read_text())['samples'];tokenizer=Tokenizer.from_file(str(source/'tokenizer.json'));ids=[]
|
||||
for s in fresh:
|
||||
tokens=tokenizer.encode(s['text'],add_special_tokens=False).ids;assert len(tokens)>=32
|
||||
s.update(token_ids=tokens[:32],full_token_count=len(tokens),text_sha256=hashlib.sha256(s['text'].encode()).hexdigest());ids.append(tokens[:32])
|
||||
ids=np.array(ids,dtype=np.int64);write_json(root/'conditional-fresh-samples.json',{'samples':fresh,'candidate_already_fixed':True})
|
||||
status['phase']='fresh_baseline';write_json(root/'conditional-status.json',status)
|
||||
h,inputs=lm.forward(ids,capture=[('attn',3),('attn',23)])
|
||||
np.savez(root/'cache'/'conditional-fresh-inputs.npz',**inputs,final=h)
|
||||
base=lm.nll(h,ids);np.save(root/'conditional-fresh-baseline-token-nll.npy',base)
|
||||
rr,passed=evaluate(lm,inputs['attn_23'],fresh,'fresh_test');rows.extend(rr);save_csv(root/'conditional-function-metrics.csv',rows)
|
||||
status['fresh_function_pass']=passed
|
||||
if passed:
|
||||
status['phase']='one_direction_same_input_network';write_json(root/'conditional-status.json',status)
|
||||
lm.configure(CANDIDATE,reverse=True,cached=False);changed,_=lm.forward(ids);nll=lm.nll(changed,ids);np.save(root/'conditional-same-input-network-token-nll.npy',nll)
|
||||
delta=nll.mean(1)-base.mean(1)
|
||||
network={'mode':'layer23_head6_uses_layer3_head7_on_H23','sequence_nll_deltas':delta.tolist(),'mean_nll_delta':float(delta.mean()),'sequence_p95_nll_delta':float(np.quantile(delta,.95)),
|
||||
'quality_pass':bool(delta.mean()<=.01 and np.quantile(delta,.95)<=.05),'one_direction_only':True}
|
||||
write_json(root/'conditional-network.json',network);status['same_input_network_quality_pass']=network['quality_pass']
|
||||
# Check the actual-cache probability/readout separately, then run its network experiment.
|
||||
cached_rows,cached_function_pass=evaluate(lm,inputs['attn_23'],fresh,'fresh_test_actual_cache',donor_h=inputs['attn_3'])
|
||||
save_csv(root/'conditional-actual-cache-metrics.csv',cached_rows)
|
||||
status['actual_cache_function_screen_pass']=cached_function_pass
|
||||
lm.configure(CANDIDATE,reverse=True,cached=True);changed,_=lm.forward(ids);nll=lm.nll(changed,ids);np.save(root/'conditional-actual-cache-network-token-nll.npy',nll)
|
||||
delta=nll.mean(1)-base.mean(1)
|
||||
cache={'mode':'actual_layer3_H3_probability_reused_at23_H23','sequence_nll_deltas':delta.tolist(),'mean_nll_delta':float(delta.mean()),'sequence_p95_nll_delta':float(np.quantile(delta,.95)),
|
||||
'quality_pass':bool(delta.mean()<=.01 and np.quantile(delta,.95)<=.05),'different_hidden_inputs':True}
|
||||
write_json(root/'conditional-actual-cache-network.json',cache);status['actual_cache_network_quality_pass']=cache['quality_pass']
|
||||
status.update(state='completed',phase='done',conditional_function_positive=bool(status.get('fresh_function_pass')),
|
||||
position_pattern_classification='near_uniform' if rows and max(r['target_uniform_js'] for r in rows)<=.01 else 'inspect first-key/self/recent3 masses',
|
||||
claim_limit='Single-source H23-conditioned, single-head QK probability/readout similarity; not bidirectional, not V similarity, not full-QKV-head equivalence; exploratory addendum independently fresh-tested.')
|
||||
except Exception as exc:
|
||||
status.update(state='stopped',error_type=type(exc).__name__,error=str(exc));(root/'conditional-error.txt').write_text(traceback.format_exc());raise
|
||||
finally:
|
||||
status.update(elapsed_seconds=time.monotonic()-started,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,
|
||||
shared_budget_seconds_including_60s_early_reserve=reserve+time.monotonic()-started)
|
||||
write_json(root/'conditional-status.json',status)
|
||||
|
||||
|
||||
if __name__=='__main__':main()
|
||||
@@ -0,0 +1,171 @@
|
||||
"""Attention functions and discrete diagnostics, no continuous alignment/fitting."""
|
||||
import numpy as np
|
||||
from functional.model import StreamingLM,PREFIX,attention,rope,sigmoid
|
||||
|
||||
|
||||
def assignment(cost):
|
||||
"""Exact minimum-cost bijection via O(n^2 2^n) dynamic programming."""
|
||||
cost=np.asarray(cost,dtype=np.float64);n=cost.shape[0]
|
||||
assert cost.shape==(n,n) and n<=12 and np.isfinite(cost).all()
|
||||
states={0:(0.,())}
|
||||
for i in range(n):
|
||||
nxt={}
|
||||
for mask,(value,perm) in states.items():
|
||||
for j in range(n):
|
||||
if mask&(1<<j):continue
|
||||
key=mask|(1<<j);candidate=(value+float(cost[i,j]),perm+(j,))
|
||||
if key not in nxt or candidate<nxt[key]:nxt[key]=candidate
|
||||
states=nxt
|
||||
return list(states[(1<<n)-1][1])
|
||||
|
||||
|
||||
def inverse(perm):
|
||||
out=[0]*len(perm)
|
||||
for i,j in enumerate(perm):out[j]=i
|
||||
return out
|
||||
|
||||
|
||||
def output_metrics(a,b):
|
||||
"""Per-text directional error, cosine and output RMS. No fitted scaling."""
|
||||
x=a.reshape(a.shape[0],-1).astype(np.float64)
|
||||
y=b.reshape(b.shape[0],-1).astype(np.float64)
|
||||
xx=np.sum(x*x,-1);yy=np.sum(y*y,-1);diff=np.sum((x-y)**2,-1)
|
||||
return {'error_a':np.sqrt(diff/np.maximum(xx,1e-30)),
|
||||
'error_b':np.sqrt(diff/np.maximum(yy,1e-30)),
|
||||
'cosine':np.sum(x*y,-1)/np.maximum(np.sqrt(xx*yy),1e-30),
|
||||
'rms_a':np.sqrt(xx/x.shape[1]),'rms_b':np.sqrt(yy/y.shape[1])}
|
||||
|
||||
|
||||
def distribution_metrics(a,b):
|
||||
"""Aligned heads; primary queries have at least 8 legal keys (t>=7)."""
|
||||
a,b=a.astype(np.float64),b.astype(np.float64)
|
||||
assert a.shape==b.shape and a.shape[-1]>=8
|
||||
m=(a+b)*.5
|
||||
def term(x):return np.where(x>0,x*np.log(np.maximum(x,1e-300)/np.maximum(m,1e-300)),0)
|
||||
js=np.maximum(.5*np.sum(term(a)+term(b),axis=-1),0)
|
||||
eligible=js[...,7:]
|
||||
overlaps=[]
|
||||
for t in range(7,a.shape[-2]):
|
||||
aa=np.argsort(a[:,:,t,:t+1],axis=-1)[...,-5:]
|
||||
bb=np.argsort(b[:,:,t,:t+1],axis=-1)[...,-5:]
|
||||
overlaps.append(np.mean((aa[...,None]==bb[...,None,:]).any(-1),-1))
|
||||
# First-key ablation is diagnostic, not an unreported filter of positional repetitions.
|
||||
ar,br=a[...,1:],b[...,1:];ma,mb=ar.sum(-1),br.sum(-1)
|
||||
valid=(ma>1e-8)&(mb>1e-8)
|
||||
ar=ar/np.maximum(ma[...,None],1e-300);br=br/np.maximum(mb[...,None],1e-300)
|
||||
mr=(ar+br)*.5
|
||||
cond=.5*np.sum(np.where(ar>0,ar*np.log(np.maximum(ar,1e-300)/np.maximum(mr,1e-300)),0)+np.where(br>0,br*np.log(np.maximum(br,1e-300)/np.maximum(mr,1e-300)),0),-1)
|
||||
sink=[]
|
||||
for bi in range(a.shape[0]):
|
||||
which=valid[bi,:,7:];values=cond[bi,:,7:]
|
||||
sink.append(float(np.maximum(values[which],0).mean()) if which.any() else None)
|
||||
diag=np.stack([a[:,:,t,t] for t in range(7,a.shape[-2])],-1).mean((1,2))
|
||||
local=np.stack([a[:,:,t,max(0,t-2):t+1].sum(-1) for t in range(7,a.shape[-2])],-1).mean((1,2))
|
||||
return {'js':eligible.mean((1,2)),'js_p95':np.quantile(eligible.reshape(a.shape[0],-1),.95,axis=1),
|
||||
'top5':np.stack(overlaps,-1).mean((1,2)),'js_all_queries':js.mean((1,2)),
|
||||
'first_key_mass_a':a[:,:,7:,0].mean((1,2)),'first_key_mass_b':b[:,:,7:,0].mean((1,2)),
|
||||
'self_mass_a':diag,'recent3_mass_a':local,'js_without_first_key':sink}
|
||||
|
||||
|
||||
def cross_head_output_metrics(a,b):
|
||||
"""All head pairs, per text. a/b: [B,T,H,Dhidden]."""
|
||||
aa=a.transpose(0,2,1,3).reshape(a.shape[0],a.shape[2],-1).astype(np.float64)
|
||||
bb=b.transpose(0,2,1,3).reshape(b.shape[0],b.shape[2],-1).astype(np.float64)
|
||||
xx=np.sum(aa*aa,-1);yy=np.sum(bb*bb,-1)
|
||||
dot=np.einsum('bif,bjf->bij',aa,bb,optimize=True)
|
||||
diff=np.maximum(xx[:,:,None]+yy[:,None,:]-2*dot,0)
|
||||
return {'error_a':np.sqrt(diff/np.maximum(xx[:,:,None],1e-30)),
|
||||
'error_b':np.sqrt(diff/np.maximum(yy[:,None,:],1e-30)),
|
||||
'cosine':dot/np.maximum(np.sqrt(xx[:,:,None]*yy[:,None,:]),1e-30),
|
||||
'rms_a':np.broadcast_to(np.sqrt(xx/aa.shape[-1])[:,:,None],dot.shape),
|
||||
'rms_b':np.broadcast_to(np.sqrt(yy/bb.shape[-1])[:,None,:],dot.shape)}
|
||||
|
||||
|
||||
def value_gram_cka(a,b):
|
||||
"""Centered token Gram agreement, diagnostic only; not interchangeability."""
|
||||
a=a.astype(np.float64)-a.mean(axis=1,keepdims=True,dtype=np.float64)
|
||||
b=b.astype(np.float64)-b.mean(axis=1,keepdims=True,dtype=np.float64)
|
||||
ga=np.einsum('btd,bsd->bts',a,a);gb=np.einsum('btd,bsd->bts',b,b)
|
||||
return np.sum(ga*gb,(1,2))/np.maximum(np.sqrt(np.sum(ga*ga,(1,2))*np.sum(gb*gb,(1,2))),1e-30)
|
||||
|
||||
|
||||
class QKVModel(StreamingLM):
|
||||
def project(self,context,gate,layer):
|
||||
return self.linear((context*sigmoid(gate)).reshape(*context.shape[:2],-1),f'layers.{layer}.self_attn.o_proj')
|
||||
|
||||
def project_heads(self,context,gate,layer):
|
||||
"""Each head's contribution in the common residual output coordinates."""
|
||||
c=context*sigmoid(gate);b,t,h,d=c.shape
|
||||
weight=self.tensors[PREFIX+f'layers.{layer}.self_attn.o_proj.weight']
|
||||
outdim,indim=weight.shape;assert indim==h*d
|
||||
out=np.empty((b,t,h,outdim),dtype=np.float32)
|
||||
for start in range(0,outdim,256):
|
||||
self.guard();end=min(start+256,outdim)
|
||||
w=weight.read(slice(start*indim,end*indim)).reshape(end-start,indim)
|
||||
for head in range(h):out[:,:,head,start:end]=c[:,:,head,:]@w[:,head*d:(head+1)*d].T
|
||||
del w;weight.evict()
|
||||
return out
|
||||
|
||||
def parts(self,h,layer,heads=True):
|
||||
p=f'layers.{layer}.self_attn.';c=self.config;d=c['head_dim'];nh=c['num_attention_heads'];nk=c['num_key_value_heads']
|
||||
mixed=self.linear(h,p+'q_proj').reshape(*h.shape[:2],nh,2*d)
|
||||
q,gate=mixed[...,:d],mixed[...,d:].copy()
|
||||
k=self.linear(h,p+'k_proj').reshape(*h.shape[:2],nk,d)
|
||||
v=self.linear(h,p+'v_proj').reshape(*h.shape[:2],nk,d)
|
||||
q=self.normalization(q,p+'q_norm');k=self.normalization(k,p+'k_norm')
|
||||
r=c['rope_parameters'];q,k=(rope(x,r['rope_theta'],r['partial_rotary_factor']) for x in (q,k))
|
||||
context,probs=attention(q,k,v)
|
||||
out={'q':q,'k':k,'p':probs,'v':v,'gate':gate,'context':context,'output':self.project(context,gate,layer)}
|
||||
if heads:out['heads']=self.project_heads(context,gate,layer)
|
||||
return out
|
||||
|
||||
def transplant(self,own,donor,layer,kind,target_head=None,donor_head=None,perm=None):
|
||||
repeats=self.config['num_attention_heads']//self.config['num_key_value_heads']
|
||||
if kind=='full_attention':return donor['output']
|
||||
if kind=='qkv_head':return own['output']-own['heads'][:,:,target_head]+donor['heads'][:,:,donor_head]
|
||||
probs=own['p'].copy();v=own['v'].copy()
|
||||
if kind=='qk_map':probs=donor['p'][:,perm]
|
||||
elif kind=='qk_head':probs[:,target_head]=donor['p'][:,donor_head]
|
||||
elif kind=='v_group':v[:,:,target_head]=donor['v'][:,:,donor_head]
|
||||
else:raise ValueError(kind)
|
||||
context=np.einsum('bhts,bshd->bthd',probs,np.repeat(v,repeats,axis=2))
|
||||
return self.project(context,own['gate'],layer)
|
||||
|
||||
def head_transplant_output(self,own,donor,layer,target_head,donor_head):
|
||||
"""QK-only transplanted target head contribution; all other heads omitted."""
|
||||
repeats=self.config['num_attention_heads']//self.config['num_key_value_heads']
|
||||
c=own['context'].copy()
|
||||
c[:,:,target_head]=np.einsum('bts,bsd->btd',donor['p'][:,donor_head],own['v'][:,:,target_head//repeats])
|
||||
# Zero remaining head contexts before projection to isolate this learned subfunction.
|
||||
for head in range(c.shape[2]):
|
||||
if head!=target_head:c[:,:,head]=0
|
||||
return self.project(c,own['gate'],layer)
|
||||
|
||||
def value_group_output(self,own,donor,layer,target_group,donor_group):
|
||||
repeats=self.config['num_attention_heads']//self.config['num_key_value_heads'];c=np.zeros_like(own['context'])
|
||||
for head in range(target_group*repeats,(target_group+1)*repeats):
|
||||
c[:,:,head]=np.einsum('bts,bsd->btd',own['p'][:,head],donor['v'][:,:,donor_group])
|
||||
return self.project(c,own['gate'],layer)
|
||||
|
||||
|
||||
class InterventionLM(QKVModel):
|
||||
def configure(self,candidate,reverse=False,cached=False):
|
||||
self.candidate=candidate;self.reverse=reverse;self.use_cache=cached;self.donor_cache=None
|
||||
self.target_layer=candidate['j'] if reverse else candidate['i']
|
||||
self.donor_layer=candidate['i'] if reverse else candidate['j']
|
||||
self.target_head=candidate.get('b') if reverse else candidate.get('a')
|
||||
self.donor_head=candidate.get('a') if reverse else candidate.get('b')
|
||||
self.perm=inverse(candidate['perm']) if reverse and candidate.get('perm') else candidate.get('perm')
|
||||
if cached:assert self.donor_layer<self.target_layer,'Cannot reuse future layer state'
|
||||
|
||||
def full_attention(self,h,layer,shared_p=None):
|
||||
if not hasattr(self,'candidate'):return super().full_attention(h,layer,shared_p)
|
||||
if self.use_cache and layer==self.donor_layer:
|
||||
self.donor_cache=self.parts(h,layer,heads=self.candidate['kind']=='qkv_head')
|
||||
return self.donor_cache['output'],self.donor_cache['p']
|
||||
if layer!=self.target_layer:return super().full_attention(h,layer,shared_p)
|
||||
own=self.parts(h,layer,heads=self.candidate['kind']=='qkv_head')
|
||||
donor=self.donor_cache if self.use_cache else self.parts(h,self.donor_layer,heads=self.candidate['kind']=='qkv_head')
|
||||
assert donor is not None
|
||||
out=self.transplant(own,donor,layer,self.candidate['kind'],self.target_head,self.donor_head,self.perm)
|
||||
return out,own['p']
|
||||
@@ -0,0 +1,56 @@
|
||||
"""Fresh heldout confirmation and same-input versus actual-state interventions."""
|
||||
import hashlib
|
||||
import json
|
||||
from pathlib import Path
|
||||
import time
|
||||
import numpy as np
|
||||
from functional.run import write_json,save_csv
|
||||
from .protocol import LAYERS
|
||||
|
||||
|
||||
def run_fresh(source,root,c,tier,lm,status):
|
||||
from tokenizers import Tokenizer
|
||||
from .run import validate_selected,candidate_pass,summarize
|
||||
samples=json.loads(Path(__file__).with_name('fresh_samples.json').read_text())['samples']
|
||||
tokenizer=Tokenizer.from_file(str(source/'tokenizer.json'));ids=[]
|
||||
for s in samples:
|
||||
encoded=tokenizer.encode(s['text'],add_special_tokens=False).ids
|
||||
assert len(encoded)>=32,s['id']
|
||||
s.update(token_ids=encoded[:32],full_token_count=len(encoded),text_sha256=hashlib.sha256(s['text'].encode()).hexdigest())
|
||||
ids.append(encoded[:32])
|
||||
ids=np.array(ids,dtype=np.int64)
|
||||
write_json(root/'fresh-samples-tokenized.json',{'samples':samples,'selection_already_frozen':True})
|
||||
status['phase']='fresh_test_baseline';write_json(root/'status.json',status)
|
||||
h,inputs=lm.forward(ids,capture=[('attn',i) for i in LAYERS])
|
||||
np.savez(root/'cache'/'fresh-baseline-activations.npz',final=h,**inputs)
|
||||
nll=lm.nll(h,ids);np.save(root/'fresh-baseline-token-nll.npy',nll)
|
||||
checked=validate_selected(lm,c,inputs,samples,np.arange(len(samples)))
|
||||
passed=candidate_pass(checked,tier)
|
||||
write_json(root/'fresh-function-summary.json',summarize(checked))
|
||||
save_csv(root/'fresh-output-metrics.csv',checked['output_rows']);save_csv(root/'fresh-probability-metrics.csv',checked['probability_rows']);save_csv(root/'fresh-value-metrics.csv',checked['value_rows'])
|
||||
status.update(fresh_function_pass=passed,fresh_confirmation_tier=tier)
|
||||
if not passed:return
|
||||
status['phase']='same_input_network_substitution';write_json(root/'status.json',status)
|
||||
interventions=[]
|
||||
for reverse in (False,True):
|
||||
lm.configure(c,reverse=reverse,cached=False)
|
||||
t=time.monotonic();changed,_=lm.forward(ids);loss=lm.nll(changed,ids)
|
||||
label=f'{lm.target_layer}_uses_{lm.donor_layer}'
|
||||
np.save(root/f'network-{label}-token-nll.npy',loss)
|
||||
delta=loss.mean(1)-nll.mean(1)
|
||||
rec={'mode':'same_input_donor_function','target_layer':lm.target_layer,'donor_layer':lm.donor_layer,
|
||||
'target_head_or_group':lm.target_head,'donor_head_or_group':lm.donor_head,
|
||||
'sequence_nll_deltas':delta.tolist(),'mean_nll_delta':float(delta.mean()),'sequence_p95_nll_delta':float(np.quantile(delta,.95)),
|
||||
'quality_pass':bool(delta.mean()<=.01 and np.quantile(delta,.95)<=.05),'elapsed_seconds':time.monotonic()-t}
|
||||
interventions.append(rec);write_json(root/'network-interventions.json',interventions)
|
||||
# Temporal reuse is a separate intervention and only goes from earlier to later.
|
||||
status['phase']='earlier_to_later_actual_cache';write_json(root/'status.json',status)
|
||||
lm.configure(c,reverse=True,cached=True)
|
||||
changed,_=lm.forward(ids);loss=lm.nll(changed,ids);np.save(root/'network-earlier-actual-cache-token-nll.npy',loss)
|
||||
delta=loss.mean(1)-nll.mean(1)
|
||||
cache={'mode':'earlier_actual_state_reused_later','donor_layer':lm.donor_layer,'target_layer':lm.target_layer,
|
||||
'sequence_nll_deltas':delta.tolist(),'mean_nll_delta':float(delta.mean()),'sequence_p95_nll_delta':float(np.quantile(delta,.95)),
|
||||
'quality_pass':bool(delta.mean()<=.01 and np.quantile(delta,.95)<=.05),
|
||||
'not_same_input_comparison':True,'no_reverse_future_state_claim':True}
|
||||
write_json(root/'actual-cache-intervention.json',cache)
|
||||
status.update(bidirectional_same_input_network_pass=all(r['quality_pass'] for r in interventions),actual_cache_quality_pass=cache['quality_pass'])
|
||||
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"origin":"Four experiment-authored fresh plain texts, fixed for this cross-layer QKV goal before measurements; no chat template, first 32 tokenizer tokens.",
|
||||
"samples":[
|
||||
{"id":"fresh-zh-1","language":"zh","text":"植物学家在不同海拔采集了叶片样本，并为每一份标本记录温度、光照和土壤湿度。回到实验室后，她按地点整理标签，先核对缺失记录，再比较不同环境中的叶片形状。"},
|
||||
{"id":"fresh-zh-2","language":"zh","text":"如果一个盒子里有十二枚蓝色棋子和八枚白色棋子，随机取出一枚后不放回，第二次取到蓝色的概率需要根据第一次的结果分别计算。请先列出各个事件，再检查它们的概率之和。"},
|
||||
{"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."},
|
||||
{"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."}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,30 @@
|
||||
"""Fixed protocol, constants frozen before any new cross-layer measurements."""
|
||||
from itertools import combinations
|
||||
|
||||
LAYERS=[3,7,11,15,19,23]
|
||||
PAIRS=list(combinations(LAYERS,2))
|
||||
TIERS={
|
||||
'strict':{'output_error':.05,'output_cosine':.99,'js':.01,'js_p95':.05,'top5':.8,'value_error':.05,'value_cosine':.99},
|
||||
'approximate':{'output_error':.20,'output_cosine':.98,'js':.05,'js_p95':.15,'top5':.6,'value_error':.20,'value_cosine':.98}}
|
||||
PROTOCOL={'layers':LAYERS,'layer_pairs':PAIRS,'heads':8,'kv_heads':2,'head_dim':256,
|
||||
'calibration_samples':['cal-zh-1','cal-zh-2','cal-en-1','cal-en-2'],
|
||||
'validation_samples':['test-zh-1','test-zh-2','test-en-1','test-en-2'],
|
||||
'fresh_test_file':'crossqkv/fresh_samples.json','fresh_test_count':4,'tokens_per_text':32,
|
||||
'query_positions_for_primary_metrics':list(range(7,32)),
|
||||
'candidate_types':['full_attention','qkv_head','qk_map','qk_head','v_group'],
|
||||
'discrete_head_matching':'Exact 8-head minimum mean-JS bijection, two source-layer calibration input distributions; frozen before validation; P injection only',
|
||||
'continuous_alignment':False,'training':False,'scale_fitting':False,'gpu':False,'cost_required':False,
|
||||
'tiers':TIERS,'max_qk_head_output_candidates':32,'max_v_group_output_candidates':16,
|
||||
'all_qk_map_output_candidates':30,'max_final_candidate':1,
|
||||
'selection':'strict before approximate; type priority full_attention/qkv_head/qk_map/qk_head/v_group; then worst output error, JS, ID; no validation-driven fallback',
|
||||
'head_output_activity_floor':'max(1e-6, 0.1 * within-layer median head RMS) for every calibration/validation/test text',
|
||||
'fresh_test_run_only_if_frozen_candidate_passes_validation':True,
|
||||
'network_interventions':'two same-input donor-function substitution directions; plus one earlier-to-later actual cached-state reuse test reported separately',
|
||||
'network_quality':{'mean_token_nll_delta_max':.01,'per_text_p95_nll_delta_max':.05},
|
||||
'retention':'all weights and caches retained until explicit user deletion request',
|
||||
'seconds':7200,'controls':['identity','uniform causal P','seeded within-causal-row key shuffle','first-key ablation','self/recent3 mass','value Gram to input','value Gram of two seeded Gaussian projections'],
|
||||
'positional_repeat_labels':{'sink_dominated':.90,'self_dominated':.90,'recent3_dominated':.90},
|
||||
'limitations':['Old validation texts were used in prior experiments; four fresh texts are reserved for this new goal',
|
||||
'Direct V-coordinate disagreement does not disprove V/O functional equivalence; centered value Gram CKA is diagnostic only',
|
||||
'Arbitrary P-head assignment does not authorize arbitrary GQA KV-weight permutation',
|
||||
'Short text-only pilot does not establish broad task equivalence or linear-attention reuse']}
|
||||
@@ -0,0 +1,126 @@
|
||||
"""Scientific plots from saved tables/probabilities only; no model access."""
|
||||
import argparse
|
||||
import csv
|
||||
import json
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.patches import Rectangle
|
||||
from .protocol import LAYERS,PAIRS
|
||||
|
||||
|
||||
def read_csv(path):
|
||||
with path.open() as f:return list(csv.DictReader(f))
|
||||
|
||||
|
||||
def main():
|
||||
p=argparse.ArgumentParser();p.add_argument('--root',type=Path,required=True);args=p.parse_args();root=args.root;out=root/'figures';out.mkdir(exist_ok=True)
|
||||
plt.rcParams.update({'font.size':9,'pdf.fonttype':42,'svg.fonttype':'none','savefig.bbox':'tight'})
|
||||
def save(fig,name):
|
||||
for ext in ('png','pdf','svg'):fig.savefig(out/f'{name}.{ext}',dpi=180)
|
||||
plt.close(fig)
|
||||
status=json.loads((root/'status.json').read_text());summary=json.loads((root/'calibration-summary.json').read_text())
|
||||
matrices=[]
|
||||
for mode in ('qk_head','identity','matched'):
|
||||
matrix=np.full((6,6),np.nan)
|
||||
for i,j in PAIRS:
|
||||
rows=[r for r in summary if r['i']==i and r['j']==j and ((mode=='qk_head' and r['kind']=='qk_head') or (mode!='qk_head' and r['id']==f'qk_map_{i}_{j}_{mode}'))]
|
||||
value=min(r['worst_mean_js'] for r in rows)
|
||||
a,b=LAYERS.index(i),LAYERS.index(j);matrix[a,b]=matrix[b,a]=value
|
||||
matrices.append(matrix)
|
||||
fig,axes=plt.subplots(1,3,figsize=(13,4.7));vmax=max(np.nanmax(m) for m in matrices)
|
||||
for ax,m,title in zip(axes,matrices,['Best single head pair','All heads: original order','All heads: calibration bijection']):
|
||||
im=ax.imshow(m,cmap='YlOrRd',vmin=0,vmax=vmax)
|
||||
for a in range(6):
|
||||
ax.add_patch(Rectangle((a-.5,a-.5),1,1,facecolor='#eeeeee',edgecolor='#aaaaaa',hatch='///',lw=.3))
|
||||
for b in range(6):
|
||||
if a!=b:ax.text(b,a,f'{m[a,b]:.3f}',ha='center',va='center',fontsize=7,color='black' if m[a,b]<vmax*.65 else 'white')
|
||||
ax.set_xticks(range(6),LAYERS);ax.set_yticks(range(6),LAYERS);ax.set_xlabel('Layer index');ax.set_ylabel('Layer index');ax.set_title(title)
|
||||
fig.suptitle('QK retrieval divergence: worst-text mean JS over both source input sets\nCalibration only; single-head panel minimizes over 64 cross-layer head pairs; hatched diagonal unused')
|
||||
fig.subplots_adjust(top=.77,bottom=.18,right=.9,wspace=.35);cax=fig.add_axes([.92,.2,.012,.5]);fig.colorbar(im,cax=cax,label='JS (natural log); lower is closer');save(fig,'cross_layer_qk_divergence')
|
||||
order=['full_attention','qkv_head','qk_map','qk_head','v_group'];labels=['Whole attention','QKV+gate/O head','QK map transplant','QK head transplant','V group transplant']
|
||||
fig,ax=plt.subplots(figsize=(10,4.9));rng=np.random.default_rng(0)
|
||||
for idx,(kind,label) in enumerate(zip(order,labels)):
|
||||
rows=[r for r in summary if r['kind']==kind and r['output_verified']]
|
||||
if rows:
|
||||
x=idx+rng.uniform(-.15,.15,len(rows));y=[r['worst_output_error'] for r in rows]
|
||||
ax.scatter(x,y,s=12,alpha=.35,color='#606060')
|
||||
ax.text(idx,max(y)*1.1,f'n={len(rows)}',ha='center',fontsize=8)
|
||||
else:ax.text(idx,.3,'not output-probed\ninitial gate failed',ha='center',va='center',fontsize=8)
|
||||
ax.axhline(.05,color='#b2182b',ls='--',label='Strict output error = 0.05')
|
||||
ax.axhline(.2,color='#d95f02',ls=':',label='Approximate output error = 0.20')
|
||||
ax.set_yscale('log');ax.set_xlim(-.5,4.5);ax.set_xticks(range(5),labels,rotation=20,ha='right');ax.set_ylabel('Worst directional relative L2, queries 7–31')
|
||||
ax.set_title('Primary protocol: calibrated cross-layer output comparisons\nEach point is one unit; no candidate passed all predeclared gates')
|
||||
ax.legend(loc='upper left',bbox_to_anchor=(0,-.28),fontsize=8);fig.tight_layout();save(fig,'cross_layer_output_errors')
|
||||
values=read_csv(root/'calibration-value-metrics.csv');unit={}
|
||||
for r in values:unit.setdefault(r['candidate'],[]).append(r)
|
||||
xx=[np.mean([float(r['centered_value_gram_cka']) for r in rows]) for rows in unit.values()]
|
||||
yy=[max(max(float(r['error_a']),float(r['error_b'])) for r in rows) for rows in unit.values()]
|
||||
controls=read_csv(root/'controls.csv');random_cka=[float(r['gram_cka']) for r in controls if r['control']=='value_gram_random_projection_pair']
|
||||
fig,ax=plt.subplots(figsize=(7,4.5));ax.scatter(xx,yy,s=24,color='#377eb8',label='60 cross-layer KV-head pairs')
|
||||
ax.axvspan(min(random_cka),max(random_cka),color='gray',alpha=.15,label='Random-projection Gram CKA range')
|
||||
ax.axvline(np.median(random_cka),color='gray',ls=':')
|
||||
ax.axhline(.2,color='#b2182b',ls='--',label='Approximate direct V error = 0.20')
|
||||
ax.set_xlabel('Mean centered token-Gram CKA');ax.set_ylabel('Worst direct V relative L2');ax.set_title('V geometry is a diagnostic, not substitution evidence')
|
||||
ax.legend(loc='upper left',bbox_to_anchor=(0,-.19),fontsize=8);fig.tight_layout();save(fig,'value_geometry_and_direct_error')
|
||||
conditional_path=root/'conditional-status.json'
|
||||
cond=json.loads(conditional_path.read_text()) if conditional_path.exists() else {}
|
||||
if (root/'conditional-function-metrics.csv').exists():
|
||||
rows=read_csv(root/'conditional-function-metrics.csv');labels=[r['sample'] for r in rows];x=np.arange(len(rows))
|
||||
fig,axes=plt.subplots(1,2,figsize=(13,5))
|
||||
axes[0].plot(x,[float(r['top5']) for r in rows],'o-',label='Unweighted top-5 overlap')
|
||||
axes[0].plot(x,[1-float(r['total_variation']) for r in rows],'o-',label='Probability-mass overlap (1−TV)')
|
||||
axes[0].set_ylim(0,1.03);axes[0].set_ylabel('Overlap');axes[0].legend(loc='lower left',fontsize=8)
|
||||
axes[1].plot(x,[max(float(r['output_error_a']),float(r['output_error_b'])) for r in rows],'o-',color='#238b45')
|
||||
axes[1].set_ylim(0,max(.05,max(max(float(r['output_error_a']),float(r['output_error_b'])) for r in rows)*1.15))
|
||||
axes[1].set_ylabel('Head readout relative L2');axes[1].set_title('Fixed layer23 V/gate/O',fontsize=9)
|
||||
for ax in axes:
|
||||
for pos in (3.5,7.5):ax.axvline(pos,color='gray',lw=.6)
|
||||
ax.set_xticks(x,labels,rotation=50,ha='right');ax.set_xlabel('Calibration / old validation / fresh tests, 4 each')
|
||||
fig.suptitle('Exploratory fixed case: layer3 head7 vs layer23 head6, both applied to H23\nFresh JS / tail-mass limits not fully passed; network substitution not run')
|
||||
fig.tight_layout();save(fig,'conditional_probability_mass_and_readout')
|
||||
if (root/'conditional-fresh-probabilities.npz').exists():
|
||||
p=np.load(root/'conditional-fresh-probabilities.npz');a=p['target_layer23_head6'][0,0];b=p['donor_layer3_head7_on_H23'][0,0];d=p['donor_layer3_head7_on_H3'][0,0]
|
||||
fig,axes=plt.subplots(2,2,figsize=(9,8));images=[a,b,np.abs(a-b),d]
|
||||
titles=['Layer23 head6 on H23','Layer3 head7 on the same H23','Absolute same-input probability difference','Layer3 head7 on its actual H3 (different input)']
|
||||
for idx,(ax,z,title) in enumerate(zip(axes.flat,images,titles)):
|
||||
im=ax.imshow(z,cmap='magma',vmin=0,vmax=(max(float(z.max()),1e-8) if idx==2 else 1.));fig.colorbar(im,ax=ax,fraction=.045,pad=.03)
|
||||
ax.set_xticks([0,7,15,23,31]);ax.set_yticks([0,7,15,23,31]);ax.set_xlabel('Key position');ax.set_ylabel('Query position');ax.set_title(title,fontsize=9)
|
||||
fig.suptitle('One fresh Chinese text: cross-layer conditional self-token retrieval\nRows 0–6 shown for context; primary metrics use queries 7–31')
|
||||
fig.tight_layout();save(fig,'conditional_cross_layer_probability_maps')
|
||||
text=['# Cross-layer QKV CPU study','',
|
||||
'Primary protocol and exploratory addendum are reported separately; no post-hoc success relabeling.',
|
||||
f'- Primary state: {status["state"]}; selected candidate: {status.get("selected",[])}.',
|
||||
f'- Candidate counts: {status.get("candidate_counts")}.',
|
||||
f'- Passing counts (approximate tier, all gates): {status.get("passing_counts")}.',
|
||||
f'- Primary computation seconds: {status.get("elapsed_seconds")}; peak RSS KiB: {status.get("peak_rss_kib")}.','',
|
||||
'## What was measured','',
|
||||
'Six full-attention layers, indices 3/7/11/15/19/23; all 15 cross-layer pairs. No within-layer Q/K/V interchange.',
|
||||
'Same normalized hidden inputs from both source layers; frozen Q/K norm, partial RoPE, GQA, V, output gate and O.',
|
||||
'QK head probabilities, whole-map injection in original/frozen matched head order, direct V functions, complete head contributions in residual coordinates, and complete branches were separated.',
|
||||
'Primary metrics use queries 7–31. Discrete matching uses calibration only; no continuous alignment, scale fitting or training.',
|
||||
'No primary candidate passed, so primary validation/fresh/network intervention stages were not entered.','']
|
||||
for kind in order:
|
||||
group=[r for r in summary if r['kind']==kind];outrows=[r for r in group if r['worst_output_error'] is not None];p=[r for r in group if r['worst_mean_js'] is not None];v=[r for r in group if r['worst_value_error'] is not None]
|
||||
text += [f'- {kind}: n={len(group)}, best worst output error={min((r["worst_output_error"] for r in outrows),default=None)}, minimum worst-text mean JS={min((r["worst_mean_js"] for r in p),default=None)}, best worst direct V error={min((r["worst_value_error"] for r in v),default=None)}.']
|
||||
if cond:
|
||||
text+=['','## Exploratory metric audit: frozen one-source candidate','',
|
||||
'After inspecting primary calibration results, one candidate was frozen: layer3 head7 vs layer23 head6 on H23.',
|
||||
'This addendum audited the instability of unweighted top-k tail ranking; original primary results were not changed.',
|
||||
'Four old validation and four previously unused fresh texts were evaluated with frozen extra gates and no candidate fallback.',
|
||||
f'- Addendum fresh function pass: {cond.get("fresh_function_pass")}.',
|
||||
f'- Overall addendum state: {cond.get("state")}; primary unchanged/negative: {cond.get("primary_protocol_unchanged_and_negative")}.',
|
||||
'Fresh probability-mass and fixed V/gate/O readout measurements show conditional behavioral overlap; strict fresh mean-JS, row-JS-P95 and row-TV-P95 limits still failed.',
|
||||
'No full-network substitution was run after that failure. V similarity, bidirectionality and complete QKV-head equivalence are not established.',
|
||||
'The actual-H3 cache probability is a separate different-input diagnostic, not the same-input function comparison.']
|
||||
text+=['','## Limits and retention','',
|
||||
'This is a short text-only pilot, not proof of absence of repetition elsewhere. Exact arithmetic, matrix coordinates, retrieval behavior and final residual output are distinct questions.',
|
||||
'Centered value Gram CKA can be high even for independent Gaussian projections of common inputs; it is not substitution evidence.',
|
||||
'All weights and large profile caches remain on the authorized server. Git delivery includes all plot tables, probabilities, protocols and cache location/hash metadata.',
|
||||
'No GPU, new checkpoint download, model training, continuous R fitting or automatic weight/cache deletion.']
|
||||
(root/'REPORT.md').write_text('\n'.join(text)+'\n')
|
||||
|
||||
|
||||
if __name__=='__main__':main()
|
||||
+279
@@ -0,0 +1,279 @@
|
||||
"""Bounded 15-pair CPU study, calibration selection before any heldout feedback."""
|
||||
import argparse
|
||||
from collections import defaultdict
|
||||
import hashlib
|
||||
import json
|
||||
from pathlib import Path
|
||||
import resource
|
||||
import time
|
||||
import traceback
|
||||
import numpy as np
|
||||
from functional.run import save_csv,write_json
|
||||
from .core import (QKVModel,InterventionLM,assignment,inverse,output_metrics,
|
||||
cross_head_output_metrics,distribution_metrics,value_gram_cka)
|
||||
from .protocol import LAYERS,PAIRS,PROTOCOL,TIERS
|
||||
|
||||
PRIMARY=slice(7,None)
|
||||
TYPE_ORDER={'full_attention':0,'qkv_head':1,'qk_map':2,'qk_head':3,'v_group':4}
|
||||
|
||||
|
||||
def candidate_id(kind,i,j,a=None,b=None,suffix=''):
|
||||
return f'{kind}_{i}_{j}'+(f'_{a}_{b}' if a is not None else '')+suffix
|
||||
|
||||
|
||||
def rows_for(metrics,samples,meta):
|
||||
rows=[]
|
||||
for n,s in enumerate(samples):
|
||||
r={**meta,'sample':s['id']}
|
||||
for key,value in metrics.items():
|
||||
v=value[n]
|
||||
r[key]=None if v is None else float(v)
|
||||
rows.append(r)
|
||||
return rows
|
||||
|
||||
|
||||
def outrows(a,b,samples,meta,floor_a=1e-6,floor_b=1e-6):
|
||||
m=output_metrics(a[:,PRIMARY],b[:,PRIMARY]);rows=rows_for(m,samples,meta)
|
||||
for n,r in enumerate(rows):r['active']=bool(m['rms_a'][n]>=np.broadcast_to(floor_a,m['rms_a'].shape)[n] and m['rms_b'][n]>=np.broadcast_to(floor_b,m['rms_b'].shape)[n])
|
||||
return rows
|
||||
|
||||
|
||||
def head_floor(profile):
|
||||
rms=np.sqrt(np.mean(profile['heads'][:,7:].astype(np.float64)**2,axis=(1,3)))
|
||||
return np.maximum(1e-6,.1*np.median(rms,axis=1))
|
||||
|
||||
|
||||
def candidate_pass(c,tier):
|
||||
t=TIERS[tier];out=c.get('output_rows',[])
|
||||
if not out or not all(r['active'] and max(r['error_a'],r['error_b'])<=t['output_error'] and r['cosine']>=t['output_cosine'] for r in out):return False
|
||||
if c['kind'] in ('qk_head','qk_map'):
|
||||
p=c.get('probability_rows',[])
|
||||
if not p or not all(r['js']<=t['js'] and r['js_p95']<=t['js_p95'] and r['top5']>=t['top5'] for r in p):return False
|
||||
if c['kind']=='v_group':
|
||||
v=c.get('value_rows',[])
|
||||
if not v or not all(max(r['error_a'],r['error_b'])<=t['value_error'] and r['cosine']>=t['value_cosine'] and r['active'] for r in v):return False
|
||||
return True
|
||||
|
||||
|
||||
def preliminary_probability(c):
|
||||
t=TIERS['approximate'];r=c['probability_rows']
|
||||
return bool(r) and all(x['js']<=t['js'] and x['js_p95']<=t['js_p95'] and x['top5']>=t['top5'] for x in r)
|
||||
|
||||
|
||||
def preliminary_value(c):
|
||||
t=TIERS['approximate'];r=c['value_rows']
|
||||
return bool(r) and all(x['active'] and max(x['error_a'],x['error_b'])<=t['value_error'] and x['cosine']>=t['value_cosine'] for x in r)
|
||||
|
||||
|
||||
def load_profile(root,source,layer):
|
||||
with np.load(root/'cache'/f'calibration-source-{source}.npz') as data:
|
||||
prefix=f'layer{layer}_'
|
||||
return {key[len(prefix):]:data[key] for key in data.files if key.startswith(prefix)}
|
||||
|
||||
|
||||
def map_profile_values(p):return {k:v for k,v in p.items()}
|
||||
|
||||
|
||||
def new_bank():return {}
|
||||
|
||||
|
||||
def get_candidate(bank,kind,i,j,a=None,b=None,suffix='',perm=None):
|
||||
name=candidate_id(kind,i,j,a,b,suffix)
|
||||
if name not in bank:
|
||||
bank[name]={'id':name,'kind':kind,'i':i,'j':j,'a':a,'b':b,'perm':perm,
|
||||
'output_rows':[],'probability_rows':[],'value_rows':[]}
|
||||
return bank[name]
|
||||
|
||||
|
||||
def add_output(c,a,b,samples,source,target=None,floor_a=1e-6,floor_b=1e-6):
|
||||
c['output_rows'].extend(outrows(a,b,samples,{'candidate':c['id'],'source_input_layer':source,'target':target},floor_a,floor_b))
|
||||
|
||||
|
||||
def observe_pair(lm,bank,pi,pj,i,j,source,samples,diagnostics):
|
||||
fi,fj=head_floor(pi),head_floor(pj)
|
||||
whole=get_candidate(bank,'full_attention',i,j)
|
||||
add_output(whole,pi['output'],pj['output'],samples,source)
|
||||
hm=cross_head_output_metrics(pi['heads'][:,7:],pj['heads'][:,7:])
|
||||
qm=cross_head_output_metrics(pi['q'][:,7:],pj['q'][:,7:])
|
||||
repeats=lm.config['num_attention_heads']//lm.config['num_key_value_heads']
|
||||
ki,kj=np.repeat(pi['k'],repeats,axis=2),np.repeat(pj['k'],repeats,axis=2)
|
||||
km=cross_head_output_metrics(ki[:,7:],kj[:,7:])
|
||||
for a in range(pi['p'].shape[1]):
|
||||
for b in range(pj['p'].shape[1]):
|
||||
qk=get_candidate(bank,'qk_head',i,j,a,b);qkv=get_candidate(bank,'qkv_head',i,j,a,b)
|
||||
pm=distribution_metrics(pi['p'][:,a:a+1],pj['p'][:,b:b+1])
|
||||
rr=rows_for(pm,samples,{'candidate':qk['id'],'source_input_layer':source})
|
||||
qk['probability_rows'].extend(rr);qkv['probability_rows'].extend(rr)
|
||||
for n,s in enumerate(samples):
|
||||
rec={'candidate':qkv['id'],'source_input_layer':source,'sample':s['id'],**{key:float(v[n,a,b]) for key,v in hm.items()}}
|
||||
rec['active']=bool(rec['rms_a']>=fi[n] and rec['rms_b']>=fj[n]);qkv['output_rows'].append(rec)
|
||||
diagnostics.append({'i':i,'j':j,'head_i':a,'head_j':b,'source_input_layer':source,'sample':s['id'],
|
||||
'q_error':float(max(qm['error_a'][n,a,b],qm['error_b'][n,a,b])),
|
||||
'q_cosine':float(qm['cosine'][n,a,b]),'k_error':float(max(km['error_a'][n,a,b],km['error_b'][n,a,b])),
|
||||
'k_cosine':float(km['cosine'][n,a,b]),'qkv_head_output_error':max(rec['error_a'],rec['error_b'])})
|
||||
vm=cross_head_output_metrics(pi['v'][:,7:],pj['v'][:,7:])
|
||||
vfi=np.maximum(1e-6,.1*np.median(np.sqrt(np.mean(pi['v'][:,7:].astype(np.float64)**2,axis=(1,3))),axis=1))
|
||||
vfj=np.maximum(1e-6,.1*np.median(np.sqrt(np.mean(pj['v'][:,7:].astype(np.float64)**2,axis=(1,3))),axis=1))
|
||||
for a in range(pi['v'].shape[2]):
|
||||
for b in range(pj['v'].shape[2]):
|
||||
c=get_candidate(bank,'v_group',i,j,a,b)
|
||||
gram=value_gram_cka(pi['v'][:,7:,a],pj['v'][:,7:,b])
|
||||
for n,s in enumerate(samples):
|
||||
rec={'candidate':c['id'],'source_input_layer':source,'sample':s['id'],**{key:float(v[n,a,b]) for key,v in vm.items()},'centered_value_gram_cka':float(gram[n])}
|
||||
rec['active']=bool(rec['rms_a']>=vfi[n] and rec['rms_b']>=vfj[n]);c['value_rows'].append(rec)
|
||||
|
||||
|
||||
def probe_candidate(lm,c,pi,pj,source,samples):
|
||||
i,j=c['i'],c['j'];kind=c['kind'];fi,fj=head_floor(pi),head_floor(pj)
|
||||
if kind=='qk_map':
|
||||
perm=c['perm'];p=distribution_metrics(pi['p'],pj['p'][:,perm])
|
||||
c['probability_rows'].extend(rows_for(p,samples,{'candidate':c['id'],'source_input_layer':source}))
|
||||
changed_i=lm.transplant(pi,pj,i,kind,perm=perm)
|
||||
changed_j=lm.transplant(pj,pi,j,kind,perm=inverse(perm))
|
||||
add_output(c,pi['output'],changed_i,samples,source,i)
|
||||
add_output(c,pj['output'],changed_j,samples,source,j)
|
||||
elif kind=='qk_head':
|
||||
a,b=c['a'],c['b']
|
||||
changed_i=lm.head_transplant_output(pi,pj,i,a,b)
|
||||
changed_j=lm.head_transplant_output(pj,pi,j,b,a)
|
||||
add_output(c,pi['heads'][:,:,a],changed_i,samples,source,i,fi,fi)
|
||||
add_output(c,pj['heads'][:,:,b],changed_j,samples,source,j,fj,fj)
|
||||
elif kind=='v_group':
|
||||
a,b=c['a'],c['b'];rep=lm.config['num_attention_heads']//lm.config['num_key_value_heads']
|
||||
own_i=pi['heads'][:,:,a*rep:(a+1)*rep].sum(2);own_j=pj['heads'][:,:,b*rep:(b+1)*rep].sum(2)
|
||||
changed_i=lm.value_group_output(pi,pj,i,a,b);changed_j=lm.value_group_output(pj,pi,j,b,a)
|
||||
add_output(c,own_i,changed_i,samples,source,i,fi,fi);add_output(c,own_j,changed_j,samples,source,j,fj,fj)
|
||||
else:raise ValueError(kind)
|
||||
|
||||
|
||||
def summarize(c):
|
||||
out=c['output_rows'];p=c['probability_rows'];v=c['value_rows']
|
||||
m={'id':c['id'],'kind':c['kind'],'i':c['i'],'j':c['j'],'a':c['a'],'b':c['b'],'perm':c['perm'],
|
||||
'output_verified':bool(out),'output_observations':len(out),'probability_observations':len(p),'value_observations':len(v),
|
||||
'strict_pass':candidate_pass(c,'strict'),'approximate_pass':candidate_pass(c,'approximate')}
|
||||
m['worst_output_error']=max((max(r['error_a'],r['error_b']) for r in out),default=None)
|
||||
m['minimum_output_cosine']=min((r['cosine'] for r in out),default=None)
|
||||
m['worst_mean_js']=max((r['js'] for r in p),default=None)
|
||||
m['minimum_top5']=min((r['top5'] for r in p),default=None)
|
||||
m['worst_value_error']=max((max(r['error_a'],r['error_b']) for r in v),default=None)
|
||||
return m
|
||||
|
||||
|
||||
def controls_for_profiles(profiles,source,samples,rows,h):
|
||||
rng=np.random.default_rng(20261109+source)
|
||||
ra=(h@rng.standard_normal((h.shape[-1],256),dtype=np.float32))/np.float32(np.sqrt(h.shape[-1]))
|
||||
rb=(h@rng.standard_normal((h.shape[-1],256),dtype=np.float32))/np.float32(np.sqrt(h.shape[-1]))
|
||||
random_cka=value_gram_cka(ra[:,7:],rb[:,7:])
|
||||
for n,s in enumerate(samples):rows.append({'control':'value_gram_random_projection_pair','source_input_layer':source,'sample':s['id'],'gram_cka':float(random_cka[n])})
|
||||
for layer,p in profiles.items():
|
||||
for kv in range(p['v'].shape[2]):
|
||||
for label,other in [('value_gram_to_input',h),('value_gram_to_random_projection',ra)]:
|
||||
vcka=value_gram_cka(p['v'][:,7:,kv],other[:,7:])
|
||||
for n,s in enumerate(samples):rows.append({'control':label,'source_input_layer':source,'projection_layer':layer,'kv_head':kv,'sample':s['id'],'gram_cka':float(vcka[n])})
|
||||
probs=p['p'];uniform=np.zeros_like(probs);shuffled=probs.copy()
|
||||
for t in range(probs.shape[-1]):
|
||||
uniform[:,:,t,:t+1]=1/(t+1)
|
||||
for bi in range(probs.shape[0]):
|
||||
for head in range(probs.shape[1]):shuffled[bi,head,t,:t+1]=probs[bi,head,t,rng.permutation(t+1)]
|
||||
for name,other in [('identity',probs),('uniform_causal',uniform),('shuffled_legal_keys',shuffled)]:
|
||||
for head in range(probs.shape[1]):
|
||||
m=distribution_metrics(probs[:,head:head+1],other[:,head:head+1])
|
||||
rows.extend(rows_for(m,samples,{'control':name,'source_input_layer':source,'projection_layer':layer,'head':head}))
|
||||
|
||||
|
||||
def validate_selected(lm,selected,inputs,samples,indices):
|
||||
i,j=selected['i'],selected['j'];c={k:v for k,v in selected.items() if k not in ('output_rows','probability_rows','value_rows')}
|
||||
c.update(output_rows=[],probability_rows=[],value_rows=[])
|
||||
for source in (i,j):
|
||||
h=inputs[f'attn_{source}'][indices];pi,pj=lm.parts(h,i),lm.parts(h,j)
|
||||
temporary={};diag=[];observe_pair(lm,temporary,pi,pj,i,j,source,samples,diag)
|
||||
if c['kind'] in ('full_attention','qkv_head','qk_head','v_group'):
|
||||
original=temporary[candidate_id(c['kind'],i,j,c['a'],c['b'])]
|
||||
c['probability_rows'].extend(original['probability_rows']);c['value_rows'].extend(original['value_rows'])
|
||||
if c['kind'] in ('full_attention','qkv_head'):c['output_rows'].extend(original['output_rows'])
|
||||
if c['kind'] in ('qk_map','qk_head','v_group'):probe_candidate(lm,c,pi,pj,source,samples)
|
||||
return c
|
||||
|
||||
|
||||
def main():
|
||||
p=argparse.ArgumentParser();p.add_argument('--source',required=True,type=Path);p.add_argument('--root',required=True,type=Path);p.add_argument('--seconds',type=int,default=7200)
|
||||
args=p.parse_args();source=args.source.resolve();root=args.root.resolve();root.mkdir(parents=True,exist_ok=True);(root/'cache').mkdir(exist_ok=True)
|
||||
assert not (root/'protocol.json').exists(),'Fresh experiment directory required; do not overwrite evidence'
|
||||
protocol={**PROTOCOL,'registered_unix':time.time(),'source_run':str(source),'seconds':args.seconds,
|
||||
'fresh_sample_sha256':hashlib.sha256(Path(__file__).with_name('fresh_samples.json').read_bytes()).hexdigest()}
|
||||
write_json(root/'protocol.json',protocol)
|
||||
write_json(root/'retention-policy.json',{'automatic_cleanup':False,'weights_and_caches':'retain_until_explicit_user_deletion'})
|
||||
start=time.monotonic();status={'state':'running','phase':'calibration_profiles','started_unix':time.time()}
|
||||
bank={};diagnostics=[];controls=[]
|
||||
try:
|
||||
manifest=json.loads((source/'manifest.json').read_text());assert manifest['revision']=='15852e8c16360a2fea060d615a32b45270f8a8fc'
|
||||
reference=json.loads((source/'reference-check.json').read_text());assert reference['passed']
|
||||
assert reference['implementation_sha256']==hashlib.sha256(Path('functional/model.py').read_bytes()).hexdigest()
|
||||
config=json.loads((source/'model_config.json').read_text());lm=InterventionLM(source/'weights',config,seconds=args.seconds-180)
|
||||
assert [i for i,t in enumerate(lm.config['layer_types']) if t=='full_attention']==LAYERS
|
||||
samples=json.loads((source/'samples-tokenized.json').read_text())['samples'];cal=[s for s in samples if s['split']=='calibration'];valid=[s for s in samples if s['split']=='heldout']
|
||||
ci=np.array([i for i,s in enumerate(samples) if s['split']=='calibration']);vi=np.array([i for i,s in enumerate(samples) if s['split']=='heldout'])
|
||||
write_json(root/'source-samples.json',{'samples':samples,'note':'old validation samples used in earlier studies; fresh test separately fixed'})
|
||||
inputs=np.load(source/'baseline-activations.npz')
|
||||
write_json(root/'source-provenance.json',{'revision':manifest['revision'],'weights_manifest':str(source/'manifest.json'),
|
||||
'baseline_activation_sha256':hashlib.sha256((source/'baseline-activations.npz').read_bytes()).hexdigest(),
|
||||
'source_hashes':{str(f):hashlib.sha256(f.read_bytes()).hexdigest() for f in sorted(Path('crossqkv').glob('*')) if f.is_file()}})
|
||||
for src in LAYERS:
|
||||
lm.guard();h=inputs[f'attn_{src}'][ci];profiles={layer:lm.parts(h,layer) for layer in LAYERS}
|
||||
np.savez(root/'cache'/f'calibration-source-{src}.npz',**{f'layer{layer}_{key}':value for layer,profile in profiles.items() for key,value in profile.items()})
|
||||
controls_for_profiles(profiles,src,cal,controls,h)
|
||||
for i,j in PAIRS:
|
||||
if src in (i,j):observe_pair(lm,bank,profiles[i],profiles[j],i,j,src,cal,diagnostics)
|
||||
del profiles
|
||||
status.update(last_source_layer=src,elapsed_seconds=time.monotonic()-start);write_json(root/'status.json',status)
|
||||
save_csv(root/'raw-qk-diagnostics.csv',diagnostics);save_csv(root/'controls.csv',controls)
|
||||
print(json.dumps(status),flush=True)
|
||||
status['phase']='discrete_head_matching_and_output_probes';write_json(root/'status.json',status)
|
||||
mapping_rows=[]
|
||||
qk_pre=[c for c in bank.values() if c['kind']=='qk_head' and preliminary_probability(c)]
|
||||
qk_probe=sorted(qk_pre,key=lambda c:(max(r['js'] for r in c['probability_rows']),c['id']))[:32]
|
||||
v_pre=[c for c in bank.values() if c['kind']=='v_group' and preliminary_value(c)]
|
||||
v_probe=sorted(v_pre,key=lambda c:(max(max(r['error_a'],r['error_b']) for r in c['value_rows']),c['id']))[:16]
|
||||
for i,j in PAIRS:
|
||||
cost=np.empty((8,8),dtype=np.float64)
|
||||
for a in range(8):
|
||||
for b in range(8):cost[a,b]=np.mean([r['js'] for r in bank[candidate_id('qk_head',i,j,a,b)]['probability_rows']])
|
||||
perm=assignment(cost)
|
||||
for a in range(8):
|
||||
for b in range(8):mapping_rows.append({'i':i,'j':j,'head_i':a,'head_j':b,'calibration_mean_js':float(cost[a,b]),'chosen':perm[a]==b})
|
||||
maps=[get_candidate(bank,'qk_map',i,j,suffix='_identity',perm=list(range(8))),get_candidate(bank,'qk_map',i,j,suffix='_matched',perm=perm)]
|
||||
for src in (i,j):
|
||||
pi,pj=load_profile(root,src,i),load_profile(root,src,j)
|
||||
for c in [*maps,*[c for c in qk_probe+v_probe if c['i']==i and c['j']==j]]:
|
||||
lm.guard();probe_candidate(lm,c,pi,pj,src,cal)
|
||||
status.update(last_pair=[i,j],elapsed_seconds=time.monotonic()-start);write_json(root/'status.json',status)
|
||||
save_csv(root/'head-assignment-costs.csv',mapping_rows)
|
||||
summaries=[summarize(c) for c in bank.values()];write_json(root/'calibration-summary.json',summaries)
|
||||
save_csv(root/'calibration-output-metrics.csv',[r for c in bank.values() for r in c['output_rows']])
|
||||
save_csv(root/'calibration-probability-metrics.csv',[r for c in bank.values() if c['kind']!='qkv_head' for r in c['probability_rows']])
|
||||
save_csv(root/'calibration-value-metrics.csv',[r for c in bank.values() for r in c['value_rows']])
|
||||
eligible=[c for c in summaries if c['strict_pass'] or c['approximate_pass']]
|
||||
eligible.sort(key=lambda c:(not c['strict_pass'],TYPE_ORDER[c['kind']],c['worst_output_error'],c['worst_mean_js'] or 0,c['id']))
|
||||
selected=eligible[:1]
|
||||
write_json(root/'selection.json',{'selected':selected,'qk_head_prequalified':len(qk_pre),'qk_head_output_verified':len(qk_probe),
|
||||
'v_group_prequalified':len(v_pre),'v_group_output_verified':len(v_probe),'selection_uses_validation':False})
|
||||
status['selected']=selected
|
||||
if selected:
|
||||
c=selected[0];status['phase']='old_validation';write_json(root/'status.json',status)
|
||||
checked=validate_selected(lm,c,inputs,valid,vi);tier='strict' if c['strict_pass'] else 'approximate'
|
||||
write_json(root/'validation-summary.json',summarize(checked));save_csv(root/'validation-output-metrics.csv',checked['output_rows']);save_csv(root/'validation-probability-metrics.csv',checked['probability_rows']);save_csv(root/'validation-value-metrics.csv',checked['value_rows'])
|
||||
passed=candidate_pass(checked,tier);status['validation_pass']=passed
|
||||
if passed:
|
||||
from .fresh import run_fresh
|
||||
run_fresh(source,root,c,tier,lm,status)
|
||||
status.update(state='completed',phase='done',elapsed_seconds=time.monotonic()-start,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,
|
||||
candidate_counts={kind:sum(c['kind']==kind for c in summaries) for kind in TYPE_ORDER},
|
||||
passing_counts={kind:sum(c['kind']==kind and c['approximate_pass'] for c in summaries) for kind in TYPE_ORDER},
|
||||
original_11_and_norm_positive_not_used_as_qkv_evidence=True)
|
||||
except Exception as exc:
|
||||
status.update(state='stopped',error_type=type(exc).__name__,error=str(exc),elapsed_seconds=time.monotonic()-start,peak_rss_kib=resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
|
||||
(root/'error.txt').write_text(traceback.format_exc());raise
|
||||
finally:write_json(root/'status.json',status)
|
||||
|
||||
|
||||
if __name__=='__main__':main()
|
||||
@@ -0,0 +1,90 @@
|
||||
# 跨层 QKV：主检验阴性，发现条件性的自 token 检索重合
|
||||
|
||||
全部在 CPU 上运行，冻结 Qwen3.5-2B 权重，无训练、连续 R 拟合或缩放拟合。
|
||||
层索引从0开始。成本不作为判据；归一化相似不作为本轮 QKV 正例。
|
||||
|
||||
## 结论分级
|
||||
|
||||
**主协议没有候选通过全部门槛。** 覆盖六个 full-attention 层的全部15个跨层配对，
|
||||
同时测试两个来源层的真实输入，不能将该结论扩大为模型不存在任何功能重复。
|
||||
|
||||
**探索性补充有一条具体行为观察，但严格新测试仍未全部通过：**
|
||||
把层23的同一隐藏输入 H23 分别交给层3头7、层23头6的 QK 检索函数，
|
||||
它们主要都读取当前 token；在4条新文本上，query位置7～31的 argmax 完全相同。
|
||||
这不是同层 Q/K/V 互换，不是两个完整 attention 头等价，也没有证明 V 相似。
|
||||
|
||||
| 新文本证据（逐文本平均或最坏值） | 结果 |
|
||||
|---|---:|
|
||||
| 层23头6分配给当前token的平均概率，四文本最小值 | 95.71% |
|
||||
| 层3头7在同一H23上分配给当前token的平均概率，最小值 | 92.07% |
|
||||
| 两者最高概率key一致率 | 100% |
|
||||
| 两套top5交集覆盖各自概率质量的最小值 | 97.97% |
|
||||
| 分布总变差的最大文本平均值 | 6.96% |
|
||||
| 保留层23 V/gate/O后的最坏双向头读出相对误差 | 3.82% |
|
||||
| 最坏文本平均JS | 0.018921 |
|
||||
|
||||
新英文文本 fresh-en-2 超过补充协议的三条严格线：平均 JS 0.018921>0.01、
|
||||
行 JS P95 0.072943>0.05、行总变差 P95 0.238731>0.20。
|
||||
因此补充试验的 `fresh_function_pass=false`，**没有重标为完全通过，也未运行替换后的全LM NLL**。
|
||||
已计算新文本原模型基线，不能把该基线文件当作替换实验结果。
|
||||
可支持的观察是特定输入条件下的主要检索行为和实际读出接近；严格可互换尚未确认。
|
||||
|
||||
## 主协议实际覆盖
|
||||
|
||||
| 对象 | 初筛/直接比较数量 | 后续输出核验 |
|
||||
|---|---:|---|
|
||||
| 完整 attention 分支 | 15 | 全部测量 |
|
||||
| QKV+gate/O 的完整头函数 | 960 | 全部测量，输出在共同residual坐标中 |
|
||||
| 全8头P图映射（原序/冻结匹配） | 30 | 全部保留接收层V/gate/O做替换读出 |
|
||||
| 单头联合QK检索 | 960 | 无候选通过初筛，主协议未进入单头读出 |
|
||||
| 跨层KV组的直接V函数 | 60 | 无候选通过原始V初筛，未进入V组替换 |
|
||||
|
||||
主协议最接近的若干项，指标来自不同候选，不能拼接成同一个正例：
|
||||
|
||||
- QK两来源检索：层7头0↔层11头6，最坏文本平均JS约0.08609，最低top5约70.4%。
|
||||
- 完整头输出：同一对头的最坏方向相对误差约134.4%。检索接近不意味着V/O组成的输出接近。
|
||||
- 直接V函数：层7 KV头0↔层11 KV头1，最坏方向误差约150.3%。
|
||||
- 完整分支：层7↔层11的最坏方向误差约185.3%。
|
||||
- 保留目标V/O的完整P图映射：最小最坏输出误差约70.3%（层15↔19、冻结头匹配）。
|
||||
|
||||
V token-Gram CKA另与输入Gram和两个独立高斯投影比较。共同输入几何可以造成高Gram相似，
|
||||
不能据此宣布V可替换；完整头输出检验已经包含各自的O/gate，避免仅因内部坐标差异就漏掉输出等价。
|
||||
|
||||
## 为什么增加单独的条件性审计
|
||||
|
||||
主协议要求两个来源层的输入分布均通过，并把无权重top5重合率作为硬门槛。
|
||||
复核全部已保存的单来源指标后，固定了唯一一对：层3头7与层23头6，条件为H23。
|
||||
它在校准集的JS很小，却因概率很低的尾部排名不同而top5重合低。
|
||||
|
||||
无权重top-k在近似并列处不连续：可以构造总变差趋近0而top5集合仍不同的例子，
|
||||
本轮已加入独立回归测试。补充协议在观察该候选保留接收端V/O的读出、旧验证或新测试之前固定。
|
||||
它改为单一H23输入条件，将top5作为诊断，并固定概率质量/实际读出/活跃度约束；
|
||||
不换候选、不重新拟合参数。
|
||||
它使用4条原验证文本，再使用本轮事先冻结的4条新文本，保留全部未通过项。
|
||||
这是探索性补充，不改写主协议，不是事后声称原协议成功。
|
||||
|
||||
原主检验的图与条件性图使用不同输入集合：主检验取两个来源分布的最坏情况；
|
||||
条件性图只研究H23。条件性相似不能推广到H3，也不能把早层在H3上已算好的P直接当作P_3(H23)。
|
||||
实际H3缓存与H23接收端的差异另有概率/读出诊断，未做失败后追加的全模型替换。
|
||||
|
||||
## 图表阅读
|
||||
|
||||
- `cross_layer_qk_divergence`：15对层的JS，左为各配对最佳单头，中为原头序，右为冻结双射；
|
||||
颜色越浅分布越近，斜线对角不作经验数据。全部仅为校准结果。
|
||||
- `cross_layer_output_errors`：各类输出误差。QK单头和V组未通过初筛时，不画成零误差，而标未输出核验。
|
||||
- `value_geometry_and_direct_error`：V Gram几何与直接V误差，灰区为随机投影对照，仅诊断。
|
||||
- `conditional_probability_mass_and_readout`：同一条件候选在校准、旧验证和新测试上的top5、质量重合与实际读出差异。
|
||||
- `conditional_cross_layer_probability_maps`：一条新文本的具体P图；层3在H23上的结果与层3在实际H3上的结果分开。
|
||||
|
||||
## 复现、预算与保留
|
||||
|
||||
20项完整远程测试通过。主计算约74.8秒，补充条件试验约105.3秒；峰值进程RSS分别约168/174 MiB。
|
||||
另有出图、审计和测试，仍在两小时预算内。早期对照生成器的变量遮蔽错误及10.3秒失败记录保留，
|
||||
修复后在新目录按相同配对和门槛重跑；没有删除原缓存。
|
||||
|
||||
模型revision 15852e8c16360a2fea060d615a32b45270f8a8fc，与此前官方参考校验的流式CPU实现一致。
|
||||
全部权重和大型profile缓存继续保留；Git只上传代码、全部图表源表、选定的新文本概率矩阵、
|
||||
日志、协议和缓存位置/SHA。没有新模型下载或自动清理。
|
||||
|
||||
后续若继续，应重新预注册“单向输入条件”和概率质量/读出判据，使用新文本及更长上下文；
|
||||
当前结果不能证明跨上下文稳定，也不应把归一化正例或代数重参数化当作QKV主体验证。
|
||||
@@ -0,0 +1,59 @@
|
||||
# 跨层 QKV 功能重复：CPU 预注册方案
|
||||
|
||||
目标：两个不同层中的学习部分是否实现相似检索、内容变换或完整头函数。不以成本为条件。
|
||||
冻结 Qwen3.5-2B 权重；不训练、不拟合连续 R 或缩放，不使用 GPU。权重与缓存持续保留。
|
||||
|
||||
## 配对与数据
|
||||
|
||||
仅研究完整注意力层索引 3/7/11/15/19/23（从0开始）的全部15个跨层配对。
|
||||
18个线性注意力层不套用 softmax 概率指标，不在本轮自动扩大搜索。
|
||||
复用已采集的同一隐藏输入，四条原校准文本选对，四条旧留出文本作验证；
|
||||
后者曾用于其他试验，不称完全未见的最终测试。另固定4条新文本作新测试，32 tokens，中文英文各半。
|
||||
仅对冻结候选通过验证时采集新测试输入，不根据测试结果重选。
|
||||
|
||||
## 拆开回答的问题
|
||||
|
||||
- QK：比较实际 Q/K norm、部分 RoPE 和 GQA 后产生的逐头检索概率，保留接收层 V/gate/O 时替换检索分布。
|
||||
- V：比较跨层两个 KV 头的 V(H)；保留接收层 P/gate/O，再替换一个 KV 组，覆盖该组4个Q头。
|
||||
token Gram/CKA仅用于诊断坐标表示问题，不能代替实际输出替换；另与输入Gram、固定seed的两个高斯投影Gram比较，以控制共同输入结构造成的高相似。
|
||||
- 完整头：比较 P V、gate、O 共同形成的每个头在 residual 坐标中的输出函数。
|
||||
它不要求内部 V 坐标一致，能够避免仅比较内部元素漏掉表示差异。
|
||||
- 完整分支：比较全部8个头组成的完整 attention 输出。
|
||||
|
||||
因此原始 Q/K/V 输出相似、QK 检索相似、V 可替换、完整头可替换分别报告，归一化单独相似不作主正例。
|
||||
同一层 Q、K、V 互相比较不进入主实验。
|
||||
|
||||
## 头匹配与位置对照
|
||||
|
||||
15对层各在校准集选择一次最小平均JS的8头双射（精确离散DP，不求连续R）。
|
||||
该排列只用于给接收Q头注入 donor P；接收层V/gate/O保持顺序，不宣称任意GQA权重排列合法。
|
||||
全部960个跨层Q头配对、60个跨层KV头配对和960个完整头函数配对均作初筛。
|
||||
完整P映射考察原顺序及冻结排列，共30项；完整分支共15项。
|
||||
|
||||
主指标只用 query 位置7～31，避免第一行概率必为1、早期合法key过少的平凡相似。
|
||||
记录均匀因果P、固定seed的合法key打乱、去首key后重归一化、首key/self/最近3key质量。
|
||||
首key或局部位置主导的真实重复单独标为位置型，不冒充内容语义重复。
|
||||
|
||||
## 固定门槛与预算
|
||||
|
||||
两档门槛在读取新跨层指标前固定，不依据结果放宽：
|
||||
|
||||
| 档位 | 输出双向相对L2 | 输出余弦 | 每文本平均JS | JS行P95 | top5重合 |
|
||||
|---|---:|---:|---:|---:|---:|
|
||||
| strict | ≤0.05 | ≥0.99 | ≤0.01 | ≤0.05 | ≥0.8 |
|
||||
| approximate | ≤0.20 | ≥0.98 | ≤0.05 | ≤0.15 | ≥0.6 |
|
||||
|
||||
V另需同档原始V直接误差/余弦；QK候选另需同档检索门槛。
|
||||
每个来源层、每条文本、两个替换方向分别检查，输出不含共同 residual 输入。
|
||||
头输出需达到该层头RMS中位数的10%，且绝对RMS≥1e-6，避免近零头制造假正例。
|
||||
|
||||
QK单头最多精确核验32个初筛通过者，V组最多16个；完整P的30个输出均核验。
|
||||
只按校准选定1个最终候选：先strict，再approximate；同档优先完整分支、完整头、完整P、QK单头、V组。
|
||||
之后按最坏输出误差/JS/ID排序。不按验证或新测试结果换选。
|
||||
|
||||
同输入函数验证通过后，做两个方向的真实网络干预，计算新测试token NLL（均值增量≤0.01、
|
||||
逐文本增量P95≤0.05）。另外只做一次时间顺序合法的“早层实际缓存→晚层”复用，单独报告；
|
||||
同输入函数可互换不代表来自不同实际输入的已计算激活可以直接复制。
|
||||
|
||||
CPU总计算限两小时，保留阶段覆盖率和停止原因；没有候选即停止，不自动扩模型、层数或阈值。
|
||||
较大的中间profile缓存在服务器，Git交付全部图表源表、协议、NLL和缓存位置/SHA。
|
||||
@@ -0,0 +1,6 @@
|
||||
# Cross-layer QKV scientific delivery
|
||||
|
||||
All plot CSV/JSON, the selected fresh probability matrices, protocols, reference tests and failure logs are included.
|
||||
Large intermediate profiles remain on 8.211.154.134 with locations/sizes/SHA256 in external-caches.json.
|
||||
The fixed checkpoint and all caches are retained until explicit user deletion. No weights are committed.
|
||||
The primary protocol has no passing candidate. The later fixed conditional audit is exploratory and its strict fresh checks failed; observed readout overlap is reported without relabeling it a complete pass.
|
||||
@@ -0,0 +1,39 @@
|
||||
# Cross-layer QKV CPU study
|
||||
|
||||
Primary protocol and exploratory addendum are reported separately; no post-hoc success relabeling.
|
||||
- Primary state: completed; selected candidate: [].
|
||||
- Candidate counts: {'full_attention': 15, 'qkv_head': 960, 'qk_map': 30, 'qk_head': 960, 'v_group': 60}.
|
||||
- Passing counts (approximate tier, all gates): {'full_attention': 0, 'qkv_head': 0, 'qk_map': 0, 'qk_head': 0, 'v_group': 0}.
|
||||
- Primary computation seconds: 74.78326310915872; peak RSS KiB: 172056.
|
||||
|
||||
## What was measured
|
||||
|
||||
Six full-attention layers, indices 3/7/11/15/19/23; all 15 cross-layer pairs. No within-layer Q/K/V interchange.
|
||||
Same normalized hidden inputs from both source layers; frozen Q/K norm, partial RoPE, GQA, V, output gate and O.
|
||||
QK head probabilities, whole-map injection in original/frozen matched head order, direct V functions, complete head contributions in residual coordinates, and complete branches were separated.
|
||||
Primary metrics use queries 7–31. Discrete matching uses calibration only; no continuous alignment, scale fitting or training.
|
||||
No primary candidate passed, so primary validation/fresh/network intervention stages were not entered.
|
||||
|
||||
- full_attention: n=15, best worst output error=1.8534952718777882, minimum worst-text mean JS=None, best worst direct V error=None.
|
||||
- qkv_head: n=960, best worst output error=1.3442678606109162, minimum worst-text mean JS=0.08609102699674716, best worst direct V error=None.
|
||||
- qk_map: n=30, best worst output error=0.7028184861876081, minimum worst-text mean JS=0.1696159433927246, best worst direct V error=None.
|
||||
- qk_head: n=960, best worst output error=None, minimum worst-text mean JS=0.08609102699674716, best worst direct V error=None.
|
||||
- v_group: n=60, best worst output error=None, minimum worst-text mean JS=None, best worst direct V error=1.503141145681371.
|
||||
|
||||
## Exploratory metric audit: frozen one-source candidate
|
||||
|
||||
After inspecting primary calibration results, one candidate was frozen: layer3 head7 vs layer23 head6 on H23.
|
||||
This addendum audited the instability of unweighted top-k tail ranking; original primary results were not changed.
|
||||
Four old validation and four previously unused fresh texts were evaluated with frozen extra gates and no candidate fallback.
|
||||
- Addendum fresh function pass: False.
|
||||
- Overall addendum state: completed; primary unchanged/negative: True.
|
||||
Fresh probability-mass and fixed V/gate/O readout measurements show conditional behavioral overlap; strict fresh mean-JS, row-JS-P95 and row-TV-P95 limits still failed.
|
||||
No full-network substitution was run after that failure. V similarity, bidirectionality and complete QKV-head equivalence are not established.
|
||||
The actual-H3 cache probability is a separate different-input diagnostic, not the same-input function comparison.
|
||||
|
||||
## Limits and retention
|
||||
|
||||
This is a short text-only pilot, not proof of absence of repetition elsewhere. Exact arithmetic, matrix coordinates, retrieval behavior and final residual output are distinct questions.
|
||||
Centered value Gram CKA can be high even for independent Gaussian projections of common inputs; it is not substitution evidence.
|
||||
All weights and large profile caches remain on the authorized server. Git delivery includes all plot tables, probabilities, protocols and cache location/hash metadata.
|
||||
No GPU, new checkpoint download, model training, continuous R fitting or automatic weight/cache deletion.
|
||||
@@ -0,0 +1,55 @@
|
||||
[
|
||||
"DELIVERY.md",
|
||||
"REPORT.md",
|
||||
"calibration-output-metrics.csv",
|
||||
"calibration-probability-metrics.csv",
|
||||
"calibration-summary.json",
|
||||
"calibration-value-metrics.csv",
|
||||
"case-audit.log",
|
||||
"conditional-case-audit.json",
|
||||
"conditional-fresh-baseline-token-nll.npy",
|
||||
"conditional-fresh-probabilities.npz",
|
||||
"conditional-fresh-samples.json",
|
||||
"conditional-fresh-source-diagnostic.csv",
|
||||
"conditional-function-metrics.csv",
|
||||
"conditional-protocol.json",
|
||||
"conditional-status.json",
|
||||
"conditional.log",
|
||||
"controls.csv",
|
||||
"external-caches.json",
|
||||
"figures/conditional_cross_layer_probability_maps.pdf",
|
||||
"figures/conditional_cross_layer_probability_maps.png",
|
||||
"figures/conditional_cross_layer_probability_maps.svg",
|
||||
"figures/conditional_probability_mass_and_readout.pdf",
|
||||
"figures/conditional_probability_mass_and_readout.png",
|
||||
"figures/conditional_probability_mass_and_readout.svg",
|
||||
"figures/cross_layer_output_errors.pdf",
|
||||
"figures/cross_layer_output_errors.png",
|
||||
"figures/cross_layer_output_errors.svg",
|
||||
"figures/cross_layer_qk_divergence.pdf",
|
||||
"figures/cross_layer_qk_divergence.png",
|
||||
"figures/cross_layer_qk_divergence.svg",
|
||||
"figures/value_geometry_and_direct_error.pdf",
|
||||
"figures/value_geometry_and_direct_error.png",
|
||||
"figures/value_geometry_and_direct_error.svg",
|
||||
"final-source-hashes.json",
|
||||
"final-tests.txt",
|
||||
"first-attempt-error.txt",
|
||||
"first-attempt-protocol.json",
|
||||
"first-attempt-status.json",
|
||||
"head-assignment-costs.csv",
|
||||
"preflight-tests.txt",
|
||||
"protocol.json",
|
||||
"raw-qk-diagnostics.csv",
|
||||
"report.log",
|
||||
"retention-policy.json",
|
||||
"run.log",
|
||||
"selection.json",
|
||||
"source-conditioned-retrieval-diagnostic.csv",
|
||||
"source-provenance.json",
|
||||
"source-samples.json",
|
||||
"status.json",
|
||||
"checksums.json",
|
||||
"audit-validation.json",
|
||||
"archive-files.json"
|
||||
]
|
||||
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"passed": true,
|
||||
"validated_unix": 1794210762.196902,
|
||||
"checksummed_files": 50,
|
||||
"primary_counts_rechecked": {
|
||||
"full_attention": 15,
|
||||
"qkv_head": 960,
|
||||
"qk_map": 30,
|
||||
"qk_head": 960,
|
||||
"v_group": 60
|
||||
},
|
||||
"fresh_probabilities_independently_recomputed": true,
|
||||
"no_unrun_network_quality_claims": true,
|
||||
"all_weights_and_caches_retained": true
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,481 @@
|
||||
candidate,source_input_layer,sample,error_a,error_b,cosine,rms_a,rms_b,centered_value_gram_cka,active
|
||||
v_group_3_7_0_0,3,cal-zh-1,1.4323826812587181,1.391162916196307,0.004087494131477744,0.5761960614907249,0.5932685883731068,0.9141337138481168,True
|
||||
v_group_3_7_0_0,3,cal-zh-2,1.4315023349091076,1.3965811072928411,0.0007004356520309391,0.5608288080244765,0.5748522187354705,0.8914788855640744,True
|
||||
v_group_3_7_0_0,3,cal-en-1,1.4198510512349787,1.4089214047816607,-0.00019941139583572943,0.5824392419751171,0.5869574890354016,0.8887315205154036,True
|
||||
v_group_3_7_0_0,3,cal-en-2,1.4413498845898522,1.3809634694068311,0.005690234368731806,0.5614481775883549,0.5859990390026425,0.8964569996356869,True
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v_group_3_7_0_0,7,cal-zh-1,1.6963395602861697,1.2493476632949505,-0.01252070998313501,0.5020436862695836,0.6816649928851196,0.936213145998887,True
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v_group_3_7_0_0,7,cal-zh-2,1.783674462312341,1.2403252294359302,-0.0394450057421654,0.49616866008890836,0.7135252488597659,0.9104815341866702,True
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v_group_3_7_0_0,7,cal-en-1,1.7803870260462265,1.228675420652782,-0.02418558124608226,0.49476267035982435,0.7169257433445044,0.9494786353698025,True
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v_group_3_7_0_0,7,cal-en-2,1.7346992825861849,1.2168140830744298,0.008128163689859857,0.49586288029244224,0.7069058409737389,0.9467241512126245,True
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v_group_3_7_0_1,3,cal-zh-1,1.4122332396976491,1.3840843809636179,0.022877700786288705,0.5761960614907249,0.5879144666407892,0.8673520794273484,True
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v_group_3_7_0_1,3,cal-zh-2,1.4246220179743523,1.3606257559393165,0.03186772530944765,0.5608288080244765,0.587207073464817,0.8352609808994033,True
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v_group_3_7_0_1,3,cal-en-1,1.410127789956694,1.3760069924822325,0.03012715587770514,0.5824392419751171,0.5968819675754875,0.8504517936409285,True
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v_group_3_7_0_1,3,cal-en-2,1.4789947347079742,1.394006486926224,-0.029112440859667753,0.5614481775883549,0.5956779299467573,0.8422380725145518,True
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v_group_3_7_0_1,7,cal-zh-1,1.6416328511443525,1.249222697289008,0.012160975244733137,0.5020436862695836,0.6597473852166851,0.888194449720632,True
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v_group_3_7_0_1,7,cal-zh-2,1.7171001886812838,1.2328132715834974,-0.003035423256108088,0.49616866008890836,0.691078948851745,0.8511856446986377,True
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v_group_3_7_0_1,7,cal-en-1,1.6975117144460503,1.2202263696199713,0.019314366520048806,0.49476267035982435,0.6882865751114527,0.9055205753071466,True
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v_group_3_7_0_1,7,cal-en-2,1.677570311727246,1.2641925616392775,-0.02009846248489318,0.49586288029244224,0.658004857731096,0.9187740372129736,True
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v_group_3_7_1_0,3,cal-zh-1,1.2551919930291116,1.6125843558323454,0.019500590092200634,0.7621906845569104,0.5932685883731068,0.8935241963633521,True
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v_group_3_7_1_0,3,cal-zh-2,1.2247004018878516,1.6360862244082828,0.040373391519243074,0.7679492835095556,0.5748522187354705,0.8828610066325204,True
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v_group_3_7_1_0,3,cal-en-1,1.2465049303194524,1.6352756434922011,0.017884652113762084,0.7700228552958449,0.5869574890354016,0.8835260267355238,True
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v_group_3_7_1_0,3,cal-en-2,1.227495380625793,1.5897290332554639,0.05792801279680993,0.758927243609968,0.5859990390026425,0.8774464319384544,True
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v_group_3_7_1_0,7,cal-zh-1,1.5375855700606327,1.3192273629755458,-0.0024594416032465097,0.5848592289800942,0.6816649928851196,0.9110423917236689,True
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v_group_3_7_1_0,7,cal-zh-2,1.5400479322281633,1.2960729603050853,0.016903418811958844,0.6004883109086829,0.7135252488597659,0.8993800369452146,True
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v_group_3_7_1_0,7,cal-en-1,1.5660778384621667,1.3127127138308519,-0.012292350388541947,0.6009391838946584,0.7169257433445044,0.9147634612973805,True
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v_group_3_7_1_0,7,cal-en-2,1.5439635918426586,1.2878067551358243,0.022337016250565965,0.5896240831459476,0.7069058409737389,0.888587863431183,True
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v_group_3_7_1_1,3,cal-zh-1,1.2368296713603184,1.6034646319911048,0.04228344157009311,0.7621906845569104,0.5879144666407892,0.9077851581778318,True
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v_group_3_7_1_1,3,cal-zh-2,1.2511641008169818,1.636272140091731,0.012598930483491767,0.7679492835095556,0.587207073464817,0.9166589704106202,True
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v_group_3_7_1_1,3,cal-en-1,1.2507273581929612,1.6135328320679603,0.02356715497811741,0.7700228552958449,0.5968819675754875,0.9130804712938935,True
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v_group_3_7_1_1,3,cal-en-2,1.2381470510050232,1.5774691009402373,0.05290613543912621,0.758927243609968,0.5956779299467573,0.9240128155349371,True
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v_group_3_7_1_1,7,cal-zh-1,1.4904553388827033,1.3212732325447607,0.022617830613847,0.5848592289800942,0.6597473852166851,0.9421372064058651,True
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v_group_3_7_1_1,7,cal-zh-2,1.5578379750756746,1.353627535431233,-0.04447828136744951,0.6004883109086829,0.691078948851745,0.9432112338272337,True
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v_group_3_7_1_1,7,cal-en-1,1.5174704505380823,1.3248950177227439,0.003978434635174957,0.6009391838946584,0.6882865751114527,0.9574705009683716,True
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v_group_3_7_1_1,7,cal-en-2,1.4526010565148506,1.3016447463286376,0.06064079689841443,0.5896240831459476,0.658004857731096,0.9369539426026078,True
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v_group_3_11_0_0,3,cal-zh-1,1.545731876865113,1.3321796238592016,-0.018522845423531893,0.5761960614907249,0.6685619593776916,0.8756763978316128,True
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v_group_3_11_0_0,3,cal-zh-2,1.5470683628818829,1.3144331710388135,-0.0034522119676936793,0.5608288080244765,0.6600871957618942,0.8312111317318915,True
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v_group_3_11_0_0,3,cal-en-1,1.5359687518041154,1.337419743712005,-0.017522238343721186,0.5824392419751171,0.6689062874272159,0.8514295431853922,True
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v_group_3_11_0_0,3,cal-en-2,1.5981070785678422,1.3228033899749945,-0.03906433847739744,0.5614481775883549,0.678297556275484,0.8609931982145157,True
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v_group_3_11_0_0,11,cal-zh-1,1.8558766812669003,1.2077676491340388,-0.02703519113187431,0.47660394729021455,0.732357877452556,0.9281854918344926,True
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v_group_3_11_0_0,11,cal-zh-2,1.9070399656592678,1.1905515524068482,-0.02216214984097279,0.48152706432896847,0.7713159118271558,0.9009126686293306,True
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v_group_3_11_0_0,11,cal-en-1,1.9649433369997293,1.2149150388129406,-0.0757967985719766,0.4710019987631518,0.7617752761439864,0.9284593184301129,True
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v_group_3_11_0_0,11,cal-en-2,1.816051490946273,1.2007469846444498,-0.0034990205676567426,0.4753499305805328,0.7189357634802654,0.9322387390641272,True
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v_group_3_11_0_1,3,cal-zh-1,1.4742917545701877,1.3813506047098822,-0.016136108165473173,0.5761960614907249,0.6149641514436552,0.9147552354265243,True
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v_group_3_11_0_1,3,cal-zh-2,1.4935415622688977,1.4142969214973058,-0.054669161372513195,0.5608288080244765,0.5922526743645153,0.8968566327016371,True
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v_group_3_11_0_1,3,cal-en-1,1.4585403630663745,1.3795147011064905,-0.004487044469038821,0.5824392419751171,0.6158043424786337,0.9086724105647803,True
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v_group_3_11_0_1,3,cal-en-2,1.4704862145295863,1.3316635025920516,0.025824387211512565,0.5614481775883549,0.6199777974761795,0.9109255414905518,True
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v_group_3_11_0_1,11,cal-zh-1,1.7816520595953853,1.2684892706710083,-0.07174324135673878,0.47660394729021455,0.6694123662959482,0.945197901422434,True
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v_group_3_11_0_1,11,cal-zh-2,1.8270643318093642,1.2191427240192836,-0.030768379589467938,0.48152706432896847,0.721638990007552,0.9016352620339282,True
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v_group_3_11_0_1,11,cal-en-1,1.8343265498401313,1.237157837039188,-0.05610472386098317,0.4710019987631518,0.6983518557556945,0.9386127427210832,True
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v_group_3_11_0_1,11,cal-en-2,1.7412738894452298,1.2287105534230272,-0.008363625143822783,0.4753499305805328,0.6736447572323523,0.9461439001028096,True
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v_group_3_11_1_0,3,cal-zh-1,1.3316483086426478,1.5181389274647665,-0.002211894565651883,0.7621906845569104,0.6685619593776916,0.9203009474331887,True
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v_group_3_11_1_0,3,cal-zh-2,1.3142937232337897,1.5290569633206037,0.0066605730090914484,0.7679492835095556,0.6600871957618942,0.9237051874774409,True
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v_group_3_11_1_0,3,cal-en-1,1.323314377279545,1.5233558637430424,0.0019859900809834166,0.7700228552958449,0.6689062874272159,0.9143113886105426,True
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v_group_3_11_1_0,3,cal-en-2,1.3407756813971123,1.5001545896307469,0.0006291125584721685,0.758927243609968,0.678297556275484,0.8972913064856486,True
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v_group_3_11_1_0,11,cal-zh-1,1.6636346860510371,1.2477573491350302,0.0037531394580707636,0.5492822020665206,0.732357877452556,0.9341091965460455,True
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v_group_3_11_1_0,11,cal-zh-2,1.6441348522412103,1.2433224523988717,0.01719955045145553,0.5832820767469004,0.7713159118271558,0.9189191545858412,True
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v_group_3_11_1_0,11,cal-en-1,1.6582601217818451,1.27162769706039,-0.018899836866295677,0.5841632005475906,0.7617752761439864,0.9351038553931905,True
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v_group_3_11_1_0,11,cal-en-2,1.6036170086466743,1.2678344767242498,0.011167818385050344,0.5683972810063781,0.7189357634802654,0.9319122717011493,True
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v_group_3_11_1_1,3,cal-zh-1,1.303340018677511,1.6153683409255541,-0.029564923887991205,0.7621906845569104,0.6149641514436552,0.8607357503716281,True
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v_group_3_11_1_1,3,cal-zh-2,1.279939914772841,1.659644579131303,-0.0281869407168636,0.7679492835095556,0.5922526743645153,0.8435413320061871,True
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v_group_3_11_1_1,3,cal-en-1,1.2834990546854077,1.6049312073382578,-0.004885626324319741,0.7700228552958449,0.6158043424786337,0.8305804512179791,True
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v_group_3_11_1_1,3,cal-en-2,1.2847451786249076,1.5726823139863475,0.010268682997527577,0.758927243609968,0.6199777974761795,0.8323118858139686,True
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v_group_3_11_1_1,11,cal-zh-1,1.5692761653627612,1.2876599106325475,0.009276876950231713,0.5492822020665206,0.6694123662959482,0.8818176429875604,True
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v_group_3_11_1_1,11,cal-zh-2,1.5952386906504594,1.2893900541320096,-0.005703359226245975,0.5832820767469004,0.721638990007552,0.8635290151276411,True
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v_group_3_11_1_1,11,cal-en-1,1.5508287491010548,1.2972502014700942,0.010074674554074877,0.5841632005475906,0.6983518557556945,0.8734384746226067,True
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v_group_3_11_1_1,11,cal-en-2,1.5323961989457926,1.2929809421865215,0.02378519370331129,0.5683972810063781,0.6736447572323523,0.8873665728217106,True
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v_group_3_15_0_0,3,cal-zh-1,1.791500515853225,1.2067929758512772,-0.0019176786741098729,0.5761960614907249,0.8553708565175171,0.9254656527514737,True
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v_group_3_15_0_0,3,cal-zh-2,1.7897355860467277,1.229056917134014,-0.028387697504211384,0.5608288080244765,0.8166711088874079,0.9268476837975197,True
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v_group_3_15_0_0,3,cal-en-1,1.7829159979339628,1.2215427277434603,-0.016604804688366417,0.5824392419751171,0.8501055417523247,0.9227239437453265,True
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v_group_3_15_0_0,3,cal-en-2,1.7617479544415051,1.208288624427455,0.00759935915417888,0.5614481775883549,0.8186207818184933,0.9252514335215619,True
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v_group_3_15_0_0,15,cal-zh-1,2.036606712671732,1.1628922350827764,-0.02301514814959651,0.5639769024542763,0.987709016088177,0.9149869997360587,True
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v_group_3_15_0_0,15,cal-zh-2,2.103230661697204,1.1371071720930161,-0.0006590280321260085,0.5572350246780882,1.0306801490991593,0.9253511593610628,True
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v_group_3_15_0_0,15,cal-en-1,2.157223519886969,1.1478268698522494,-0.032317924287390665,0.5584091688430112,1.0494730733248452,0.9030997889495451,True
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v_group_3_15_0_0,15,cal-en-2,1.9635527102676857,1.167488575669259,-0.00799266960055014,0.5622671993868973,0.945654892269824,0.882472148927542,True
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v_group_3_15_0_1,3,cal-zh-1,1.8657139942608747,1.165937493864953,0.024902979154005696,0.5761960614907249,0.9220194573190053,0.8825434291676424,True
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v_group_3_15_0_1,3,cal-zh-2,1.8818420459844138,1.1379290976459144,0.05851446344968765,0.5608288080244765,0.9274666002680805,0.8443884983013036,True
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v_group_3_15_0_1,3,cal-en-1,1.8197088825049808,1.190410861611396,0.00830733458220919,0.5824392419751171,0.8903395426910831,0.8694632700969815,True
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v_group_3_15_0_1,3,cal-en-2,1.8942475924736308,1.1641654701599264,0.018245443515102686,0.5614481775883549,0.9135487058805722,0.8419003239314065,True
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v_group_3_15_0_1,15,cal-zh-1,1.9648999460529704,1.1602615717161688,0.0020967317872275107,0.5639769024542763,0.955093413607095,0.8447568414417492,True
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v_group_3_15_0_1,15,cal-zh-2,1.975736095935673,1.1240283340522317,0.05293044857254968,0.5572350246780882,0.9794676155600762,0.8406012433784209,True
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v_group_3_15_0_1,15,cal-en-1,2.0132364149779187,1.1566595507722963,-0.006770385496697074,0.5584091688430112,0.9719451781828745,0.8538226880561681,True
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v_group_3_15_0_1,15,cal-en-2,1.980150570270239,1.1626702153928752,-0.005996802162759274,0.5622671993868973,0.9576006168989177,0.8112963317363142,True
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v_group_3_15_1_0,3,cal-zh-1,1.4975473640324488,1.334411433182741,0.007486683279052872,0.7621906845569104,0.8553708565175171,0.8615743964382445,True
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v_group_3_15_1_0,3,cal-zh-2,1.4634483122649817,1.3761403711075857,-0.005062645291039263,0.7679492835095556,0.8166711088874079,0.8140618872177604,True
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v_group_3_15_1_0,3,cal-en-1,1.4953627424995743,1.3544947446278646,-0.007831901208508888,0.7700228552958449,0.8501055417523247,0.8000478915622752,True
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v_group_3_15_1_0,3,cal-en-2,1.4807732424787996,1.3727957807636038,-0.013531876798680363,0.758927243609968,0.8186207818184933,0.8302489815080406,True
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v_group_3_15_1_0,15,cal-zh-1,1.8940317712787802,1.1844914824434871,-0.009529132877864789,0.6176944518196689,0.987709016088177,0.8708819870484904,True
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v_group_3_15_1_0,15,cal-zh-2,1.8942424776113322,1.18622767727384,-0.011956184502641203,0.6454407678682691,1.0306801490991593,0.8977428514931777,True
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v_group_3_15_1_0,15,cal-en-1,1.920278540690493,1.1857698162714376,-0.020036644889284454,0.6480484299380599,1.0494730733248452,0.900411248651334,True
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v_group_3_15_1_0,15,cal-en-2,1.7998109148693886,1.231771215526687,-0.03570461050025071,0.6471960284808532,0.945654892269824,0.882664328140647,True
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v_group_3_15_1_1,3,cal-zh-1,1.5597646636994704,1.289385042078844,0.012606461714600887,0.7621906845569104,0.9220194573190053,0.8674597935434188,True
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v_group_3_15_1_1,3,cal-zh-2,1.5776827848142696,1.3063331486581578,-0.012626641045929178,0.7679492835095556,0.9274666002680805,0.868800197864626,True
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v_group_3_15_1_1,3,cal-en-1,1.526190016173755,1.3199472084841237,0.003312407542480493,0.7700228552958449,0.8903395426910831,0.8577302724792217,True
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v_group_3_15_1_1,3,cal-en-2,1.5420765556276852,1.2810744542295573,0.029484164668036616,0.758927243609968,0.9135487058805722,0.8645827731479319,True
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v_group_3_15_1_1,15,cal-zh-1,1.8379777153583352,1.1886885839337136,0.0040885788864275276,0.6176944518196689,0.955093413607095,0.9159946788235969,True
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v_group_3_15_1_1,15,cal-zh-2,1.8407190052770024,1.212980469513883,-0.028133879377482626,0.6454407678682691,0.9794676155600762,0.9406664671137216,True
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v_group_3_15_1_1,15,cal-en-2,1.7823575681318298,1.2046094364092046,0.004210730883784706,0.6471960284808532,0.9576006168989177,0.9176616988431759,True
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v_group_3_19_0_0,3,cal-zh-1,2.1387778009797,1.1292745193776113,0.003336156828672862,0.5761960614907249,1.0912805736620155,0.9066868008588593,True
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v_group_3_19_0_0,3,cal-zh-2,2.230877966766892,1.126467313053981,-0.013823998548937181,0.5608288080244765,1.110676374228702,0.9156729393850706,True
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v_group_3_19_0_0,3,cal-en-1,2.111487213716288,1.1366617340640162,-0.0020512301200578047,0.5824392419751171,1.081951626716594,0.8973187153763751,True
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v_group_3_19_0_0,3,cal-en-2,2.115781896427422,1.1380726157748013,-0.005462158435846553,0.5614481775883549,1.0437839145394814,0.8877784194607925,True
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v_group_3_19_0_0,19,cal-zh-1,2.4530344316229806,1.0919863377130128,0.006436954796709541,0.5693041821940151,1.2788829976697977,0.8458814731915995,True
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v_group_3_19_0_0,19,cal-zh-2,2.562618507699084,1.0886131980634468,-0.0054370741489785605,0.5552008338472196,1.3069545131712863,0.7880326589670854,True
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v_group_3_19_0_0,19,cal-en-1,2.473107990375322,1.0922972357759582,0.0022177259102170175,0.5983192469333956,1.3546753227249124,0.7958869818601046,True
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v_group_3_19_0_0,19,cal-en-2,2.339789708122884,1.1024193573685226,0.00707287135559412,0.581786895805997,1.2347923519566644,0.7592303602956822,True
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v_group_3_19_0_1,3,cal-zh-1,2.5134890066406674,1.085272509387548,0.009978407283295085,0.5761960614907249,1.334468950147724,0.8841694901431139,True
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v_group_3_19_0_1,3,cal-zh-2,2.564902257153027,1.0891446228651234,-0.006973067658143293,0.5608288080244765,1.3207346805737825,0.8649932997128789,True
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v_group_3_19_0_1,3,cal-en-1,2.3997670525347545,1.099130993160828,0.0018443078356558523,0.5824392419751171,1.2716578021112042,0.8679497231218936,True
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v_group_3_19_0_1,3,cal-en-2,2.498665759627334,1.0880715532840664,0.006575329430980649,0.5614481775883549,1.2893190093160507,0.831343764713669,True
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v_group_3_19_0_1,19,cal-zh-1,3.2390007000979524,1.039215222302559,0.035800606611788834,0.5693041821940151,1.774393412569017,0.7766259032955134,True
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v_group_3_19_0_1,19,cal-zh-2,3.389171924784933,1.0562936484258603,-0.02987135541397067,0.5552008338472196,1.7813901290577085,0.7241372787510743,True
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v_group_3_19_0_1,19,cal-en-1,3.1645117976855226,1.0415934447391595,0.035579184049615326,0.5983192469333956,1.8177805604154877,0.71602545983148,True
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v_group_3_19_0_1,19,cal-en-2,3.1803325477918793,1.0416932020429799,0.0338264517618455,0.581786895805997,1.7762195212389158,0.6929904324288088,True
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v_group_3_19_1_0,3,cal-zh-1,1.7656749919567738,1.2332126707877762,-0.023623683019394534,0.7621906845569104,1.0912805736620155,0.7836174881235206,True
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v_group_3_19_1_0,3,cal-zh-2,1.761528739729888,1.2179648050013414,-0.003883209702188474,0.7679492835095556,1.110676374228702,0.7770512096119847,True
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v_group_3_19_1_0,3,cal-en-1,1.7267683130301756,1.2289376290037906,-0.0026511184669404967,0.7700228552958449,1.081951626716594,0.7680729586419178,True
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v_group_3_19_1_0,3,cal-en-2,1.7047670256892233,1.239522971930746,-0.005332191083900878,0.758927243609968,1.0437839145394814,0.8023287544316657,True
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v_group_3_19_1_0,19,cal-zh-1,2.361860213006372,1.1071522538550322,-0.006450524188197151,0.5994928004162863,1.2788829976697977,0.8303267596452917,True
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v_group_3_19_1_0,19,cal-zh-2,2.331299796039782,1.108567495337039,-0.0029521601677554954,0.6214761797890229,1.3069545131712863,0.861109794701705,True
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v_group_3_19_1_0,19,cal-en-1,2.361653062589342,1.1087694696109487,-0.009531730695334684,0.6360047810857993,1.3546753227249124,0.858432476308045,True
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v_group_3_19_1_0,19,cal-en-2,2.2729252880770194,1.1141519366560488,-0.0010751499844431022,0.605275632031235,1.2347923519566644,0.8203210787260378,True
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v_group_3_19_1_1,3,cal-zh-1,2.0482832151475887,1.1698903790507447,-0.037138409544712446,0.7621906845569104,1.334468950147724,0.8688700928957473,True
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v_group_3_19_1_1,3,cal-zh-2,2.023566294496786,1.1766150377135338,-0.039841104529857105,0.7679492835095556,1.3207346805737825,0.8640921980657612,True
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v_group_3_19_1_1,3,cal-en-1,1.9575132957313441,1.185326724497866,-0.03165571090655481,0.7700228552958449,1.2716578021112042,0.844734835865005,True
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v_group_3_19_1_1,3,cal-en-2,1.9899599283539846,1.1713430054220004,-0.02171442091569174,0.758927243609968,1.2893190093160507,0.8459774010056795,True
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v_group_3_19_1_1,19,cal-zh-1,3.1526667823654964,1.065153322116597,-0.030195612099080702,0.5994928004162863,1.774393412569017,0.8704590731400326,True
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v_group_3_19_1_1,19,cal-zh-2,3.041125717233326,1.0609619769275667,-0.0056310410698682725,0.6214761797890229,1.7813901290577085,0.8628703743147619,True
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v_group_3_19_1_1,19,cal-en-1,3.041429949637327,1.0641350399658507,-0.014244143157525214,0.6360047810857993,1.8177805604154877,0.8140464347842146,True
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v_group_3_19_1_1,19,cal-en-2,3.1202358899534404,1.0632710246652446,-0.021163551795847318,0.605275632031235,1.7762195212389158,0.8012907865196233,True
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v_group_3_23_0_0,3,cal-zh-1,2.5325593905374704,1.0978025886417955,-0.019919769634531376,0.5761960614907249,1.3292469533383322,0.8593249553368174,True
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v_group_3_23_0_0,3,cal-zh-2,2.689370721715856,1.0802775243275653,-0.0070321344429330225,0.5608288080244765,1.3961936097251293,0.8452572757025786,True
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v_group_3_23_0_0,3,cal-en-1,2.5428249166384735,1.0896024383272724,-0.004224686109949596,0.5824392419751171,1.3592489928675324,0.8504221043602921,True
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v_group_3_23_0_0,3,cal-en-2,2.5589301401827593,1.0935565371580773,-0.015489246600322094,0.5614481775883549,1.3137927623890586,0.7532733246972084,True
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v_group_3_23_0_0,23,cal-zh-1,6.314715927657433,1.0115790106405909,0.007397262649045514,0.5961984520028376,3.7217299106699713,0.8623622864182763,True
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v_group_3_23_0_0,23,cal-zh-2,6.582746433886306,1.002784941116688,0.05786056734912915,0.5660821398995916,3.7160262733514156,0.7966106557098843,True
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v_group_3_23_0_0,23,cal-en-1,6.222299452824851,1.0097625580115666,0.020688716885734638,0.6271790673850405,3.8647659658702014,0.7718088189333528,True
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v_group_3_23_0_0,23,cal-en-2,6.5062645665442185,1.0152263640957444,-0.020304709203748775,0.5969443799167155,3.825627667489882,0.8057381075857623,True
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v_group_3_23_0_1,3,cal-zh-1,2.4962510729862895,1.0941175817192668,-0.005683976088889132,0.5761960614907249,1.3146028002644317,0.8388182418857919,True
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v_group_3_23_0_1,3,cal-zh-2,2.5733651041895516,1.07089383893816,0.031675607144456025,0.5608288080244765,1.3476754011634102,0.8402922522338886,True
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v_group_3_23_0_1,3,cal-en-1,2.468157528955309,1.0900344020008894,0.007777767706325841,0.5824392419751171,1.3188132389226515,0.8303210401880273,True
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v_group_3_23_0_1,3,cal-en-2,2.557652136136515,1.0973166180522025,-0.02334860026349611,0.5614481775883549,1.3086370033179386,0.7900276047329294,True
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v_group_3_23_0_1,23,cal-zh-1,6.197276288864568,1.0121018312984849,0.007106978550738162,0.5961984520028376,3.6506272548827043,0.8475157551074508,True
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v_group_3_23_0_1,23,cal-zh-2,6.63914341360049,1.0053735203743175,0.04013537199003796,0.5660821398995916,3.7382131461667756,0.7783158074892113,True
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v_group_3_23_0_1,23,cal-en-1,5.975231447628363,1.0145822460901517,-0.001607142608356388,0.6271790673850405,3.693677965660293,0.812945618086598,True
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v_group_3_23_0_1,23,cal-en-2,6.313394272237381,1.0092006745969357,0.022102663839672184,0.5969443799167155,3.734386355335804,0.8385999789262057,True
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v_group_3_23_1_0,3,cal-zh-1,2.0046870126255754,1.149488259226821,0.006509166904659427,0.7621906845569104,1.3292469533383322,0.8225999244083507,True
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v_group_3_23_1_0,3,cal-zh-2,2.081869604272063,1.1450920988499615,-0.007910645571858183,0.7679492835095556,1.3961936097251293,0.8200927996560631,True
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v_group_3_23_1_0,3,cal-en-1,2.034207822141951,1.1523911540051008,-0.0062454778116254545,0.7700228552958449,1.3592489928675324,0.8647988597551656,True
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v_group_3_23_1_0,3,cal-en-2,2.0144285528807346,1.1636574297356812,-0.017662623847039125,0.758927243609968,1.3137927623890586,0.7566915654750324,True
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v_group_3_23_1_0,23,cal-zh-1,6.277763088160963,1.0130180677809972,-0.000516191036954132,0.6005609944121181,3.7217299106699713,0.9071331282851747,True
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v_group_3_23_1_0,23,cal-zh-2,6.253505726735381,1.0143504270483992,-0.008003137825984693,0.602758356993599,3.7160262733514156,0.9042951085127707,True
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v_group_3_23_1_0,23,cal-en-1,6.432206805763406,1.012590175776882,-0.0017666587124045888,0.608410793821259,3.8647659658702014,0.8716035372405754,True
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v_group_3_23_1_0,23,cal-en-2,6.672455110252242,1.0116859983730087,-0.0017133324304754378,0.5800464569991829,3.825627667489882,0.9032941845272089,True
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v_group_3_23_1_1,3,cal-zh-1,1.9433092846106501,1.1267070431047723,0.057508235976642966,0.7621906845569104,1.3146028002644317,0.8426443970281835,True
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v_group_3_23_1_1,3,cal-zh-2,1.9585684702423296,1.116056026643042,0.0694309762150568,0.7679492835095556,1.3476754011634102,0.8355688778127401,True
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v_group_3_23_1_1,3,cal-en-1,1.9416077524461186,1.1336573680625563,0.04772564156717925,0.7700228552958449,1.3188132389226515,0.8791900114489358,True
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v_group_3_23_1_1,3,cal-en-2,1.9765805563654837,1.1462925391932104,0.019261171522129524,0.758927243609968,1.3086370033179386,0.7779060649238136,True
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v_group_3_23_1_1,23,cal-zh-1,6.199142599546911,1.0198146740692198,-0.03938618359716485,0.6005609944121181,3.6506272548827043,0.9010958511164613,True
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v_group_3_23_1_1,23,cal-zh-2,6.280931223963968,1.0127522527249209,0.0010294357455041317,0.602758356993599,3.7382131461667756,0.8824740252975743,True
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v_group_3_23_1_1,23,cal-en-1,6.166803229495207,1.0157760592769955,-0.014173965326379663,0.608410793821259,3.693677965660293,0.8927807726224396,True
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v_group_3_23_1_1,23,cal-en-2,6.527125776698839,1.0138308736461075,-0.011997183921616217,0.5800464569991829,3.734386355335804,0.8855541377425988,True
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v_group_7_11_0_0,7,cal-zh-1,1.3590647431914042,1.4227457740694431,0.03424682104595654,0.6816649928851196,0.6511541804465534,0.9596573793765382,True
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v_group_7_11_0_0,7,cal-zh-2,1.385094582257532,1.4324673408348387,0.00851416330135579,0.7135252488597659,0.689928439083044,0.9602675120848562,True
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v_group_7_11_0_0,7,cal-en-1,1.3265867116383734,1.4445549109355424,0.045467358554094124,0.7169257433445044,0.6583786861631596,0.9601232911956866,True
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v_group_7_11_0_0,7,cal-en-2,1.3652521916663036,1.4759119703599899,-0.004457407033166956,0.7069058409737389,0.6539040051661819,0.960126960876628,True
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v_group_7_11_0_0,11,cal-zh-1,1.5317716094861975,1.2930853983519182,0.024025724458799087,0.61823921519185,0.732357877452556,0.9634038160729521,True
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v_group_7_11_0_0,11,cal-zh-2,1.5307959598286778,1.3119505884320646,0.007759339913623475,0.6610471878315538,0.7713159118271558,0.9590589338367865,True
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v_group_7_11_0_0,11,cal-en-1,1.5099752151915873,1.3230667734973132,0.009844299617146468,0.667480861008681,0.7617752761439864,0.9678793140349622,True
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v_group_7_11_0_0,11,cal-en-2,1.5482747562875272,1.3279089716303765,-0.01617418461798738,0.6166097112120626,0.7189357634802654,0.9600429553350653,True
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v_group_7_11_0_1,7,cal-zh-1,1.3495588166955654,1.4950089488708287,-0.003558461901091167,0.6816649928851196,0.6153454812933818,0.966927293158344,True
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v_group_7_11_0_1,7,cal-zh-2,1.3131866880031267,1.4264778229842567,0.06681002200026138,0.7135252488597659,0.6568569404020126,0.9692055018672671,True
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v_group_7_11_0_1,7,cal-en-1,1.32214260901372,1.503141145681371,0.014558701443153667,0.7169257433445044,0.6305981813470566,0.9710012396977696,True
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v_group_7_11_0_1,7,cal-en-2,1.3362440174615924,1.4759651173669088,0.0188243881968289,0.7069058409737389,0.6399871445437402,0.9800388663309468,True
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v_group_7_11_0_1,11,cal-zh-1,1.4814936185948042,1.3682410098573785,-0.010356398654907837,0.61823921519185,0.6694123662959482,0.9731104598776446,True
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v_group_7_11_0_1,11,cal-zh-2,1.4312932116993835,1.3111158981949806,0.06555244821174869,0.6610471878315538,0.721638990007552,0.9715822984840667,True
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v_group_7_11_0_1,11,cal-en-1,1.4571566687896989,1.3927423259379788,-0.013699631663316555,0.667480861008681,0.6983518557556945,0.9729418413615413,True
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v_group_7_11_0_1,11,cal-en-2,1.477597866645427,1.3524950414266217,0.004693832139406584,0.6166097112120626,0.6736447572323523,0.9747524460058449,True
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v_group_7_11_1_0,7,cal-zh-1,1.39992536646198,1.4184000160889843,0.0072588634871393765,0.6597473852166851,0.6511541804465534,0.9518998235702384,True
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v_group_7_11_1_0,7,cal-zh-2,1.4607046478146466,1.4631404873471223,-0.06860666704274519,0.691078948851745,0.689928439083044,0.9471618123375283,True
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v_group_7_11_1_0,7,cal-en-1,1.412470658158268,1.476634362838785,-0.04186440219197837,0.6882865751114527,0.6583786861631596,0.9543213987306998,True
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v_group_7_11_1_0,7,cal-en-2,1.4294009951093931,1.438365250857598,-0.02798081817389893,0.658004857731096,0.6539040051661819,0.9602664038273591,True
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v_group_7_11_1_0,11,cal-zh-1,1.5960604245786816,1.2902732748820969,-0.006974359405710902,0.5920463801837895,0.732357877452556,0.9623963079308823,True
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v_group_7_11_1_0,11,cal-zh-2,1.6397045367074465,1.302938977683933,-0.04167535264366895,0.6129016192425962,0.7713159118271558,0.9425108403681077,True
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v_group_7_11_1_0,11,cal-en-1,1.6267360126815866,1.3344855075333568,-0.06575576752947196,0.6249188916249518,0.7617752761439864,0.9667174592134935,True
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v_group_7_11_1_0,11,cal-en-2,1.6144002981959313,1.3040000986321145,-0.02970543258219023,0.5807062272820778,0.7189357634802654,0.9442531463695479,True
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v_group_7_11_1_1,7,cal-zh-1,1.3780134159071724,1.4774476705790789,-0.015543200175059484,0.6597473852166851,0.6153454812933818,0.9014862720434296,True
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v_group_7_11_1_1,7,cal-en-2,1.400382195709064,1.4398074950609028,-0.007754939829026272,0.658004857731096,0.6399871445437402,0.9116877956634163,True
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v_group_7_11_1_1,11,cal-zh-1,1.5125682103099711,1.3377561855485423,-0.004172457964358748,0.5920463801837895,0.6694123662959482,0.912043840836572,True
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v_group_7_11_1_1,11,cal-zh-2,1.5365777899748885,1.305044528647161,0.010715256500539271,0.6129016192425962,0.721638990007552,0.9006649890707459,True
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v_group_7_11_1_1,11,cal-en-1,1.5060753560422646,1.3477088010070952,-0.00869741692505149,0.6249188916249518,0.6983518557556945,0.917286294564562,True
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v_group_7_11_1_1,11,cal-en-2,1.5294486877706794,1.3184402725047752,0.0027967575833321096,0.5807062272820778,0.6736447572323523,0.9095902645598368,True
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v_group_7_15_0_0,7,cal-zh-1,1.5569048596546016,1.3028309128911548,0.0017206734675930314,0.6816649928851196,0.8146011347889529,0.9512518197767236,True
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v_group_7_15_0_0,7,cal-zh-2,1.5729222141331871,1.2900435191985529,0.0051487647633175135,0.7135252488597659,0.8699859327022424,0.9627308945779495,True
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v_group_7_15_0_0,7,cal-en-1,1.5368669705050517,1.2966178520329759,0.018117984912830207,0.7169257433445044,0.849764248983153,0.9528696164818339,True
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v_group_7_15_0_0,7,cal-en-2,1.4705351321955804,1.317075486003281,0.037676675411163214,0.7069058409737389,0.7892712948903489,0.9556875603688989,True
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v_group_7_15_0_0,15,cal-zh-1,1.8064583640807623,1.1788225179714629,0.027746151973229785,0.6445394217323496,0.987709016088177,0.9356480705673035,True
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v_group_7_15_0_0,15,cal-zh-2,1.8223942852440718,1.1955428800653456,0.0008008979647849695,0.6761557166072645,1.0306801490991593,0.9613292258259053,True
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v_group_7_15_0_0,15,cal-en-1,1.8370468334668244,1.1881579052679156,0.005102274925945226,0.6787740550324127,1.0494730733248452,0.9440885032468835,True
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v_group_7_15_0_0,15,cal-en-2,1.7457125995837404,1.179991278485705,0.04771974337577139,0.6392028823082366,0.945654892269824,0.9468722677460633,True
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v_group_7_15_0_1,7,cal-zh-1,1.529638011693102,1.3380586128169485,-0.01440655320228269,0.6816649928851196,0.7792638337138618,0.8895315825360072,True
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v_group_7_15_0_1,7,cal-zh-2,1.5110790913435765,1.3542088747763328,-0.017145538801148923,0.7135252488597659,0.7961793079193885,0.9041294861688265,True
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v_group_7_15_0_1,7,cal-en-1,1.4704264115682142,1.3901390505582425,-0.020471837872431405,0.7169257433445044,0.7583317278394569,0.8915239054486477,True
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v_group_7_15_0_1,7,cal-en-2,1.4518092147747548,1.3939722307524762,-0.011064412921566678,0.7069058409737389,0.7362359100581017,0.8625669120749795,True
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v_group_7_15_0_1,15,cal-zh-1,1.7874487290690932,1.2062497278243802,0.00027897556868457224,0.6445394217323496,0.955093413607095,0.848771024934287,True
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v_group_7_15_0_1,15,cal-zh-2,1.7507119519467151,1.2085686914360545,0.01152854564152503,0.6761557166072645,0.9794676155600762,0.8808316849429574,True
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v_group_7_15_0_1,15,cal-en-1,1.7687821482259698,1.2352583851127314,-0.027311845738046136,0.6787740550324127,0.9719451781828745,0.8642037542950373,True
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v_group_7_15_0_1,15,cal-en-2,1.797881060183342,1.2000940010232768,0.003997628321673514,0.6392028823082366,0.9576006168989177,0.8477522669009253,True
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v_group_7_15_1_0,7,cal-zh-1,1.6010026869994904,1.2966558618242732,-0.015665211659213897,0.6597473852166851,0.8146011347889529,0.9068786107264657,True
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v_group_7_15_1_0,7,cal-zh-2,1.6173907371822414,1.2847847861891883,-0.012380937601433002,0.691078948851745,0.8699859327022424,0.9232754051400207,True
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v_group_7_15_1_0,7,cal-en-1,1.5983354432198853,1.2946094512795523,-0.012319205282017758,0.6882865751114527,0.849764248983153,0.914108598750399,True
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v_group_7_15_1_0,7,cal-en-2,1.5877377947573081,1.323675634116035,-0.034235847970775835,0.658004857731096,0.7892712948903489,0.9297758441903189,True
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v_group_7_15_1_0,15,cal-zh-1,1.9337834220206909,1.184114435894833,-0.0221926262634617,0.6048042873339816,0.987709016088177,0.9228545926305083,True
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v_group_7_15_1_0,15,cal-zh-2,1.9058590848381105,1.1861089699279206,-0.015695825578067302,0.641442475836036,1.0306801490991593,0.9182130571842873,True
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v_group_7_15_1_0,15,cal-en-1,1.8939368053398529,1.186648467058825,-0.012423659975874614,0.6575486627480011,1.0494730733248452,0.9239990152221395,True
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v_group_7_15_1_0,15,cal-en-2,1.8285901520855836,1.1960557287227085,-0.002079577026431645,0.61853988987306,0.945654892269824,0.9074414909661811,True
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v_group_7_15_1_1,7,cal-zh-1,1.5535612398681848,1.3152900486760957,-0.007799870903764235,0.6597473852166851,0.7792638337138618,0.9039457824467736,True
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v_group_7_15_1_1,7,cal-zh-2,1.5296339189175412,1.3277132302972872,-0.005419765431227022,0.691078948851745,0.7961793079193885,0.9229216993817316,True
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v_group_7_15_1_1,7,cal-en-1,1.4995515443199279,1.3610418221371834,-0.01577618536519215,0.6882865751114527,0.7583317278394569,0.9263950777193685,True
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v_group_7_15_1_1,7,cal-en-2,1.5087704593441198,1.3484513291008318,-0.010935181945558963,0.658004857731096,0.7362359100581017,0.9041913578769004,True
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v_group_7_15_1_1,15,cal-zh-1,1.8811797208658982,1.1912400862744115,-0.01425903212904786,0.6048042873339816,0.955093413607095,0.9201275868350303,True
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v_group_7_15_1_1,15,cal-zh-2,1.8488656195496849,1.2108015840972295,-0.028371984657058983,0.641442475836036,0.9794676155600762,0.9455752632921178,True
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v_group_7_15_1_1,15,cal-en-1,1.7959920175394761,1.2150398766801302,-0.013769575580058773,0.6575486627480011,0.9719451781828745,0.9278224568541319,True
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v_group_7_15_1_1,15,cal-en-2,1.8585976530735657,1.2005180107061293,-0.018595048318740277,0.61853988987306,0.9576006168989177,0.9174292665346274,True
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v_group_7_19_0_0,7,cal-zh-1,1.6129409563448271,1.2486427309798793,0.025954377934053734,0.6816649928851196,0.880544416951106,0.8341748004880644,True
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v_group_7_19_0_0,7,cal-zh-2,1.6485067103725086,1.2875628181351657,-0.030588441777941112,0.7135252488597659,0.9135485618240787,0.8779361562934789,True
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v_group_7_19_0_0,7,cal-en-1,1.5851947538837512,1.3174657722322225,-0.027059064735262404,0.7169257433445044,0.8626159033705797,0.84533001186125,True
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v_group_7_19_0_0,7,cal-en-2,1.5663782956833436,1.3091660352485543,-0.009193607051191637,0.7069058409737389,0.8457918526604767,0.8410737704781521,True
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v_group_7_19_0_0,19,cal-zh-1,2.4240666153951946,1.096253770064537,0.0030364656871200726,0.5783588201590688,1.2788829976697977,0.8225181192534375,True
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v_group_7_19_0_0,19,cal-zh-2,2.4362711944008115,1.1019301348706267,-0.01069307512526254,0.5911380335155229,1.3069545131712863,0.7673432399827309,True
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v_group_7_19_0_0,19,cal-en-1,2.339862825778156,1.096966960440844,0.01754700713687364,0.6350945255346598,1.3546753227249124,0.7699142493575787,True
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v_group_7_19_0_0,19,cal-en-2,2.2694897834127725,1.119172598921317,-0.009492069215501374,0.6089235456215132,1.2347923519566644,0.7787010442954936,True
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v_group_7_19_0_1,7,cal-zh-1,1.919896330047686,1.15697404263873,0.020382362638390824,0.6816649928851196,1.1311629042059486,0.8359271541245736,True
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v_group_7_19_0_1,7,cal-zh-2,1.8730024088804516,1.2036123521799755,-0.027803375437586025,0.7135252488597659,1.1103529367165625,0.8612010535776801,True
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v_group_7_19_0_1,7,cal-en-1,1.7864113926551268,1.1925819927551087,0.0175395778039998,0.7169257433445044,1.0739088158120118,0.8539029662501013,True
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v_group_7_19_0_1,7,cal-en-2,1.8095894601534095,1.2052595026659787,-0.006786969725422758,0.7069058409737389,1.0613559621952018,0.7991244871505474,True
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v_group_7_19_0_1,19,cal-zh-1,3.217264894883968,1.0486589476514454,0.010056965848962773,0.5783588201590688,1.774393412569017,0.7674207683968693,True
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v_group_7_19_0_1,19,cal-zh-2,3.181915554525891,1.0558895960144594,-0.007209001009985394,0.5911380335155229,1.7813901290577085,0.7195093641204466,True
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v_group_7_19_0_1,19,cal-en-1,2.990837587885149,1.0449361271610185,0.04318266616265943,0.6350945255346598,1.8177805604154877,0.7175870885682337,True
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v_group_7_19_0_1,19,cal-en-2,3.0797389680836744,1.0557960598959124,0.004113267340204362,0.6089235456215132,1.7762195212389158,0.7096545378734751,True
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v_group_7_19_1_0,7,cal-zh-1,1.6819987190726897,1.2602365485302245,-0.017898899165194538,0.6597473852166851,0.880544416951106,0.8344809658335943,True
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v_group_7_19_1_0,7,cal-zh-2,1.6426412611839205,1.2426211846394348,0.018606519313130832,0.691078948851745,0.9135485618240787,0.8765048258262689,True
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v_group_7_19_1_0,7,cal-en-1,1.6209991744361494,1.2934052869552068,-0.022711276653805148,0.6882865751114527,0.8626159033705797,0.8667934490795195,True
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v_group_7_19_1_0,7,cal-en-2,1.6413659510313396,1.2769415615624204,-0.01628250363750126,0.658004857731096,0.8457918526604767,0.8619722436206836,True
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v_group_7_19_1_0,19,cal-zh-1,2.5276804097685552,1.080311994614643,0.018239036792248458,0.546585176176571,1.2788829976697977,0.8788447361578459,True
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v_group_7_19_1_0,19,cal-zh-2,2.4787271338145778,1.0801968149326486,0.026486642512740764,0.5695536564433646,1.3069545131712863,0.882423120910055,True
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v_group_7_19_1_0,19,cal-en-1,2.436190508204177,1.1018223617362553,-0.010460457321808263,0.6126826118253192,1.3546753227249124,0.8679871835328036,True
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v_group_7_19_1_0,19,cal-en-2,2.339283438302971,1.0896521264177794,0.031808760508683996,0.5751736151178315,1.2347923519566644,0.8475231329006441,True
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v_group_7_19_1_1,7,cal-zh-1,1.9794480010615727,1.1545071342217543,0.006249800827629816,0.6597473852166851,1.1311629042059486,0.8609718734969634,True
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v_group_7_19_1_1,7,cal-zh-2,1.8552307284539464,1.1546877207248674,0.043439192239259,0.691078948851745,1.1103529367165625,0.8870359611602545,True
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v_group_7_19_1_1,7,cal-en-1,1.8157392236880425,1.163738404198499,0.04406787757396818,0.6882865751114527,1.0739088158120118,0.8920301131031774,True
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v_group_7_19_1_1,7,cal-en-2,1.8761726207510243,1.163163672103821,0.025330735982374678,0.658004857731096,1.0613559621952018,0.8457966968987024,True
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v_group_7_19_1_1,19,cal-zh-1,3.3712129436764964,1.0384703908927988,0.0267306441178162,0.546585176176571,1.774393412569017,0.8438709884640048,True
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v_group_7_19_1_1,19,cal-zh-2,3.230279401450716,1.0327987196174349,0.05559542437085788,0.5695536564433646,1.7813901290577085,0.8436774905294028,True
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v_group_7_19_1_1,19,cal-en-1,3.0793700329658757,1.037901117251973,0.053944328354099264,0.6126826118253192,1.8177805604154877,0.7847470463614187,True
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v_group_7_19_1_1,19,cal-en-2,3.207711868110182,1.0387180240820808,0.04002792721354586,0.5751736151178315,1.7762195212389158,0.7961754084606756,True
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v_group_7_23_0_0,7,cal-zh-1,1.8928738918044918,1.1474895917278267,0.04187036485469965,0.6816649928851196,1.1244597574488375,0.8051358407379933,True
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v_group_7_23_0_0,7,cal-zh-2,1.7948113359136397,1.1850343588966081,0.02395368783967622,0.7135252488597659,1.080680231336518,0.8532838708998824,True
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v_group_7_23_0_0,7,cal-en-1,1.8035443816149181,1.1662440690248335,0.044861242640534954,0.7169257433445044,1.1086936523717892,0.8566976360482108,True
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v_group_7_23_0_0,7,cal-en-2,1.746164892139193,1.1776429673143152,0.05041028190312191,0.7069058409737389,1.0481735091337046,0.8061265524000732,True
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v_group_7_23_0_0,23,cal-zh-1,6.240759902641683,1.0015917799794847,0.07031995637412376,0.5973077227747422,3.7217299106699713,0.8650418777001297,True
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v_group_7_23_0_0,23,cal-zh-2,6.011890341287522,0.9959309596650905,0.107342697611108,0.6155976577189147,3.7160262733514156,0.8058318101455474,True
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v_group_7_23_0_0,23,cal-en-1,5.85737933977827,1.0012993762310904,0.07786732385665274,0.6606687951085425,3.8647659658702014,0.805817284362258,True
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v_group_7_23_0_0,23,cal-en-2,6.045147730397914,1.0010998505894793,0.07615682732380537,0.6335387416715794,3.825627667489882,0.8281764909770989,True
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v_group_7_23_0_1,7,cal-zh-1,1.860540799195586,1.1967761161328565,-0.01439125753705159,0.6816649928851196,1.0597349943315055,0.7961799444055976,True
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v_group_7_23_0_1,7,cal-zh-2,1.7209848147903832,1.2162219400430025,0.014313477324957373,0.7135252488597659,1.0096562788645045,0.8352455929466881,True
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v_group_7_23_0_1,7,cal-en-1,1.731292266622375,1.20713606030845,0.020777522300589636,0.7169257433445044,1.0282254304271896,0.8535440893666582,True
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v_group_7_23_0_1,7,cal-en-2,1.782926728147381,1.2343097631479818,-0.03195851470003604,0.7069058409737389,1.0211061726848514,0.7914058388744698,True
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v_group_7_23_0_1,23,cal-zh-1,6.219651840439676,1.0176459599637,-0.0269912642129351,0.5973077227747422,3.6506272548827043,0.8493049112789856,True
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v_group_7_23_0_1,23,cal-zh-2,6.146912936764169,1.0122550689637158,0.007463652848362227,0.6155976577189147,3.7382131461667756,0.7734519824352003,True
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v_group_7_23_0_1,23,cal-en-1,5.716995968347211,1.0225690688693136,-0.038171022335335335,0.6606687951085425,3.693677965660293,0.8465952077545221,True
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v_group_7_23_0_1,23,cal-en-2,6.033878663101979,1.0236476710982612,-0.05621402998281668,0.6335387416715794,3.734386355335804,0.8388501082601372,True
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v_group_7_23_1_0,7,cal-zh-1,1.9412114744957556,1.138955116861505,0.04007519409468991,0.6597473852166851,1.1244597574488375,0.8050799646749459,True
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v_group_7_23_1_0,7,cal-zh-2,1.8397651871342364,1.1765024979557046,0.019377398182039922,0.691078948851745,1.080680231336518,0.87188933111028,True
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v_group_7_23_1_0,7,cal-en-1,1.8273938257317455,1.1344618371378004,0.07925122041004046,0.6882865751114527,1.1086936523717892,0.8861303881288498,True
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v_group_7_23_1_0,7,cal-en-2,1.8478604542672803,1.160018970830487,0.03858360658448875,0.658004857731096,1.0481735091337046,0.839204430097089,True
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|
||||
mode,sample,target_self_mass,donor_self_mass,argmax_agreement,shared_top5_mass_target,shared_top5_mass_donor,js,js_p95,top5,js_all_queries,first_key_mass_a,first_key_mass_b,self_mass_a,recent3_mass_a,js_without_first_key,total_variation,total_variation_p95,head_output_error
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||||
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||||
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||||
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||||
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||||
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|
||||
actual_H3_to_H23,fresh-en-2,0.9671855711936951,0.645685727596283,0.92,0.9823226451873779,0.7631779217720032,0.11576751242382403,0.21763839763274784,0.52,0.09630240923791714,0.0003818481252528727,0.06853020414710045,0.9671855711936951,0.9737620480834448,0.09481997920349867,0.324279492621281,0.5482284413611068,0.1875597912744845
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|
@@ -0,0 +1,13 @@
|
||||
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|
||||
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||||
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||||
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||||
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||||
fresh_test,same_H23,fresh-en-2,0.018921025381545404,0.07294334072098421,0.624,0.01696863122473362,0.0003818481252528727,0.00019207453090075433,0.9671855711936951,0.9737620480834448,0.01885906674080864,0.0696232315711734,0.2387305877473409,0.03813168423426638,0.03814551024093568,0.9992727894346689,0.11903171602231612,0.11898857245734115,0.5346062977887738,True
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|
@@ -0,0 +1,65 @@
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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@@ -0,0 +1,31 @@
|
||||
test_assignment_against_exhaustive_and_inverse (test_crossqkv.CrossQKVTests.test_assignment_against_exhaustive_and_inverse) ... ok
|
||||
test_cached_mode_rejects_future_state_reuse (test_crossqkv.CrossQKVTests.test_cached_mode_rejects_future_state_reuse) ... ok
|
||||
test_controls_cover_multiple_projection_layers (test_crossqkv.CrossQKVTests.test_controls_cover_multiple_projection_layers) ... ok
|
||||
test_head_matrix_metrics_and_gram_are_diagnostic (test_crossqkv.CrossQKVTests.test_head_matrix_metrics_and_gram_are_diagnostic) ... ok
|
||||
test_probability_identity_and_nontrivial_queries (test_crossqkv.CrossQKVTests.test_probability_identity_and_nontrivial_queries) ... ok
|
||||
test_profile_matches_canonical_and_head_sum (test_crossqkv.CrossQKVTests.test_profile_matches_canonical_and_head_sum) ... ok
|
||||
test_recipient_value_gate_output_and_gqa_group_stay_fixed (test_crossqkv.CrossQKVTests.test_recipient_value_gate_output_and_gqa_group_stay_fixed) ... ok
|
||||
test_topk_tail_rank_can_change_with_tiny_probability_difference (test_crossqkv.CrossQKVTests.test_topk_tail_rank_can_change_with_tiny_probability_difference) ... ok
|
||||
test_causal_convolution_and_norm_boundary (test_functional.FunctionalTests.test_causal_convolution_and_norm_boundary) ... ok
|
||||
test_causality_and_identical_function_metrics (test_functional.FunctionalTests.test_causality_and_identical_function_metrics) ... ok
|
||||
test_neuron_function_metrics_and_targeted_replacement (test_functional.FunctionalTests.test_neuron_function_metrics_and_targeted_replacement) ... ok
|
||||
test_nonrotary_gain_identity_and_rotary_negative_control (test_functional.FunctionalTests.test_nonrotary_gain_identity_and_rotary_negative_control) ... ok
|
||||
test_streamed_projection_embedding_and_output_head (test_functional.FunctionalTests.test_streamed_projection_embedding_and_output_head) ... ok
|
||||
test_uniform_rmsnorm_function_bound_is_tight (test_functional.FunctionalTests.test_uniform_rmsnorm_function_bound_is_tight) ... ok
|
||||
test_3d_reader_and_exact_block (test_pilot.PilotTests.test_3d_reader_and_exact_block) ... ok
|
||||
test_bfloat16 (test_pilot.PilotTests.test_bfloat16) ... ok
|
||||
test_directional_replacement_error_and_scaling (test_pilot.PilotTests.test_directional_replacement_error_and_scaling) ... ok
|
||||
test_end_to_end_synthetic_groups_and_savings (test_pilot.PilotTests.test_end_to_end_synthetic_groups_and_savings) ... Inventory: 4 tensors, 33792 bytes
|
||||
tensor: sampling 4 objects
|
||||
block: sampling 22 objects
|
||||
block: verified 100/109
|
||||
{"model": "synthetic", "revision": "test", "tensor_count": 4, "parameter_count": 8448, "model_weight_bytes": 33792, "ndim_counts": {"2": 2, "3": 2}, "elapsed_seconds": 0.11448054760694504, "analysis_budget_seconds": 60, "peak_rss_kib": 57628, "python": "3.14.4", "numpy": "2.5.4", "seed": 20261109, "limitations": ["No inference or functional equivalence measured", "No exhaustive pair search", "Block search uses per-role/shape/scale reservoirs of 256", "Different block scales overlap; savings must not be added", "Block savings count only verified disjoint sampled blocks, no extrapolation", "Spectral probes use aligned bounded subvolumes, not whole tensors", "Shared encoding is theoretical, not a runnable compressed checkpoint", "No cross-role grouping, neuron permutation alignment, or training"], "completed": true}
|
||||
ok
|
||||
test_no_transitive_chaining (test_pilot.PilotTests.test_no_transitive_chaining) ... ok
|
||||
test_ranged_download_preserves_prefix (test_pilot.PilotTests.test_ranged_download_preserves_prefix) ... Weight download 36/36 bytes (100.0%)
|
||||
ok
|
||||
|
||||
----------------------------------------------------------------------
|
||||
Ran 20 tests in 28.244s
|
||||
|
||||
OK
|
||||
@@ -0,0 +1,8 @@
|
||||
Traceback (most recent call last):
|
||||
File "/opt/exp-model-parameter-similarity/crossqkv/run.py", line 224, in main
|
||||
controls_for_profiles(profiles,src,cal,controls,h)
|
||||
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
File "/opt/exp-model-parameter-similarity/crossqkv/run.py", line 171, in controls_for_profiles
|
||||
vcka=value_gram_cka(p['v'][:,7:,kv],other[:,7:])
|
||||
~~~~~^^^^^^
|
||||
TypeError: 'int' object is not subscriptable
|
||||
@@ -0,0 +1,187 @@
|
||||
{
|
||||
"layers": [
|
||||
3,
|
||||
7,
|
||||
11,
|
||||
15,
|
||||
19,
|
||||
23
|
||||
],
|
||||
"layer_pairs": [
|
||||
[
|
||||
3,
|
||||
7
|
||||
],
|
||||
[
|
||||
3,
|
||||
11
|
||||
],
|
||||
[
|
||||
3,
|
||||
15
|
||||
],
|
||||
[
|
||||
3,
|
||||
19
|
||||
],
|
||||
[
|
||||
3,
|
||||
23
|
||||
],
|
||||
[
|
||||
7,
|
||||
11
|
||||
],
|
||||
[
|
||||
7,
|
||||
15
|
||||
],
|
||||
[
|
||||
7,
|
||||
19
|
||||
],
|
||||
[
|
||||
7,
|
||||
23
|
||||
],
|
||||
[
|
||||
11,
|
||||
15
|
||||
],
|
||||
[
|
||||
11,
|
||||
19
|
||||
],
|
||||
[
|
||||
11,
|
||||
23
|
||||
],
|
||||
[
|
||||
15,
|
||||
19
|
||||
],
|
||||
[
|
||||
15,
|
||||
23
|
||||
],
|
||||
[
|
||||
19,
|
||||
23
|
||||
]
|
||||
],
|
||||
"heads": 8,
|
||||
"kv_heads": 2,
|
||||
"head_dim": 256,
|
||||
"calibration_samples": [
|
||||
"cal-zh-1",
|
||||
"cal-zh-2",
|
||||
"cal-en-1",
|
||||
"cal-en-2"
|
||||
],
|
||||
"validation_samples": [
|
||||
"test-zh-1",
|
||||
"test-zh-2",
|
||||
"test-en-1",
|
||||
"test-en-2"
|
||||
],
|
||||
"fresh_test_file": "crossqkv/fresh_samples.json",
|
||||
"fresh_test_count": 4,
|
||||
"tokens_per_text": 32,
|
||||
"query_positions_for_primary_metrics": [
|
||||
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
|
||||
],
|
||||
"candidate_types": [
|
||||
"full_attention",
|
||||
"qkv_head",
|
||||
"qk_map",
|
||||
"qk_head",
|
||||
"v_group"
|
||||
],
|
||||
"discrete_head_matching": "Exact 8-head minimum mean-JS bijection, two source-layer calibration input distributions; frozen before validation; P injection only",
|
||||
"continuous_alignment": false,
|
||||
"training": false,
|
||||
"scale_fitting": false,
|
||||
"gpu": false,
|
||||
"cost_required": false,
|
||||
"tiers": {
|
||||
"strict": {
|
||||
"output_error": 0.05,
|
||||
"output_cosine": 0.99,
|
||||
"js": 0.01,
|
||||
"js_p95": 0.05,
|
||||
"top5": 0.8,
|
||||
"value_error": 0.05,
|
||||
"value_cosine": 0.99
|
||||
},
|
||||
"approximate": {
|
||||
"output_error": 0.2,
|
||||
"output_cosine": 0.98,
|
||||
"js": 0.05,
|
||||
"js_p95": 0.15,
|
||||
"top5": 0.6,
|
||||
"value_error": 0.2,
|
||||
"value_cosine": 0.98
|
||||
}
|
||||
},
|
||||
"max_qk_head_output_candidates": 32,
|
||||
"max_v_group_output_candidates": 16,
|
||||
"all_qk_map_output_candidates": 30,
|
||||
"max_final_candidate": 1,
|
||||
"selection": "strict before approximate; type priority full_attention/qkv_head/qk_map/qk_head/v_group; then worst output error, JS, ID; no validation-driven fallback",
|
||||
"head_output_activity_floor": "max(1e-6, 0.1 * within-layer median head RMS) for every calibration/validation/test text",
|
||||
"fresh_test_run_only_if_frozen_candidate_passes_validation": true,
|
||||
"network_interventions": "two same-input donor-function substitution directions; plus one earlier-to-later actual cached-state reuse test reported separately",
|
||||
"network_quality": {
|
||||
"mean_token_nll_delta_max": 0.01,
|
||||
"per_text_p95_nll_delta_max": 0.05
|
||||
},
|
||||
"retention": "all weights and caches retained until explicit user deletion request",
|
||||
"seconds": 7200,
|
||||
"controls": [
|
||||
"identity",
|
||||
"uniform causal P",
|
||||
"seeded within-causal-row key shuffle",
|
||||
"first-key ablation",
|
||||
"self/recent3 mass",
|
||||
"value Gram to input",
|
||||
"value Gram of two seeded Gaussian projections"
|
||||
],
|
||||
"positional_repeat_labels": {
|
||||
"sink_dominated": 0.9,
|
||||
"self_dominated": 0.9,
|
||||
"recent3_dominated": 0.9
|
||||
},
|
||||
"limitations": [
|
||||
"Old validation texts were used in prior experiments; four fresh texts are reserved for this new goal",
|
||||
"Direct V-coordinate disagreement does not disprove V/O functional equivalence; centered value Gram CKA is diagnostic only",
|
||||
"Arbitrary P-head assignment does not authorize arbitrary GQA KV-weight permutation",
|
||||
"Short text-only pilot does not establish broad task equivalence or linear-attention reuse"
|
||||
],
|
||||
"registered_unix": 1794208209.7811735,
|
||||
"source_run": "/opt/exp-model-parameter-similarity/runs/cpu-qwen35-2b",
|
||||
"fresh_sample_sha256": "d3f53fc98fa9d061a3df42f24fe307b693b41a9ddf74c8377dd52096663c40a9"
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"state": "stopped",
|
||||
"phase": "calibration_profiles",
|
||||
"started_unix": 1794208209.7829785,
|
||||
"error_type": "TypeError",
|
||||
"error": "'int' object is not subscriptable",
|
||||
"elapsed_seconds": 10.294649122282863,
|
||||
"peak_rss_kib": 128500
|
||||
}
|
||||
@@ -0,0 +1,961 @@
|
||||
i,j,head_i,head_j,calibration_mean_js,chosen
|
||||
3,7,0,0,0.312123754428338,False
|
||||
3,7,0,1,0.23101214444046472,True
|
||||
3,7,0,2,0.2991337886850626,False
|
||||
3,7,0,3,0.22615584642661302,False
|
||||
3,7,0,4,0.2818651669472114,False
|
||||
3,7,0,5,0.2497583077156788,False
|
||||
3,7,0,6,0.3384427762180371,False
|
||||
3,7,0,7,0.3848192745975429,False
|
||||
3,7,1,0,0.31640179352711506,False
|
||||
3,7,1,1,0.2167101709648335,False
|
||||
3,7,1,2,0.3147847390643972,False
|
||||
3,7,1,3,0.18512230525898943,True
|
||||
3,7,1,4,0.23535510030356316,False
|
||||
3,7,1,5,0.19728000289647862,False
|
||||
3,7,1,6,0.30722579536152117,False
|
||||
3,7,1,7,0.3970879983177512,False
|
||||
3,7,2,0,0.33603265237173974,False
|
||||
3,7,2,1,0.28400556963610685,False
|
||||
3,7,2,2,0.3373578933507262,False
|
||||
3,7,2,3,0.22848184791241835,False
|
||||
3,7,2,4,0.27987765196107695,False
|
||||
3,7,2,5,0.22084762688269347,True
|
||||
3,7,2,6,0.3355458229568511,False
|
||||
3,7,2,7,0.4192193458829923,False
|
||||
3,7,3,0,0.2999550751297716,False
|
||||
3,7,3,1,0.28929268227291316,False
|
||||
3,7,3,2,0.3050348818824162,False
|
||||
3,7,3,3,0.24835496061442244,False
|
||||
3,7,3,4,0.29838665278403204,False
|
||||
3,7,3,5,0.25837852273364836,False
|
||||
3,7,3,6,0.35088984898199493,True
|
||||
3,7,3,7,0.3720651345569457,False
|
||||
3,7,4,0,0.20736564293956455,False
|
||||
3,7,4,1,0.23023160338784038,False
|
||||
3,7,4,2,0.20890471741321018,True
|
||||
3,7,4,3,0.1945980714620341,False
|
||||
3,7,4,4,0.23854771512987213,False
|
||||
3,7,4,5,0.16528273623423276,False
|
||||
3,7,4,6,0.2722533807498106,False
|
||||
3,7,4,7,0.2734895969878596,False
|
||||
3,7,5,0,0.22237352773945968,True
|
||||
3,7,5,1,0.27130089691862486,False
|
||||
3,7,5,2,0.22723093548562653,False
|
||||
3,7,5,3,0.23530279169722612,False
|
||||
3,7,5,4,0.25589522999959263,False
|
||||
3,7,5,5,0.19454439081084646,False
|
||||
3,7,5,6,0.30530812496588416,False
|
||||
3,7,5,7,0.2887304494944471,False
|
||||
3,7,6,0,0.3116041634983429,False
|
||||
3,7,6,1,0.27190340396141244,False
|
||||
3,7,6,2,0.30252750839017806,False
|
||||
3,7,6,3,0.23723157003533005,False
|
||||
3,7,6,4,0.27226641755744996,True
|
||||
3,7,6,5,0.2280832086158187,False
|
||||
3,7,6,6,0.3428831457038405,False
|
||||
3,7,6,7,0.3853064609813004,False
|
||||
3,7,7,0,0.17292948778546857,False
|
||||
3,7,7,1,0.3737594407308512,False
|
||||
3,7,7,2,0.19369368715455437,False
|
||||
3,7,7,3,0.3995120469259691,False
|
||||
3,7,7,4,0.42662551707840946,False
|
||||
3,7,7,5,0.34158461865812956,False
|
||||
3,7,7,6,0.35594359733476466,False
|
||||
3,7,7,7,0.14821628065280026,True
|
||||
3,11,0,0,0.2926504979497134,False
|
||||
3,11,0,1,0.3412096443755786,False
|
||||
3,11,0,2,0.29803296152516157,False
|
||||
3,11,0,3,0.30324188160867876,False
|
||||
3,11,0,4,0.28322105167621814,True
|
||||
3,11,0,5,0.30187743544193785,False
|
||||
3,11,0,6,0.22474176291196507,False
|
||||
3,11,0,7,0.3046303380043726,False
|
||||
3,11,1,0,0.27440010702924367,False
|
||||
3,11,1,1,0.3323359006202896,False
|
||||
3,11,1,2,0.27680128141872506,False
|
||||
3,11,1,3,0.2975719539915451,False
|
||||
3,11,1,4,0.2860131757768321,False
|
||||
3,11,1,5,0.2850932441774947,True
|
||||
3,11,1,6,0.20338532409020396,False
|
||||
3,11,1,7,0.28461606170736703,False
|
||||
3,11,2,0,0.2817666258264434,True
|
||||
3,11,2,1,0.33526105115037985,False
|
||||
3,11,2,2,0.2621316284624621,False
|
||||
3,11,2,3,0.3167833694612486,False
|
||||
3,11,2,4,0.3215040135975552,False
|
||||
3,11,2,5,0.30062997463322905,False
|
||||
3,11,2,6,0.22143065968757802,False
|
||||
3,11,2,7,0.28725253449157384,False
|
||||
3,11,3,0,0.310181536424385,False
|
||||
3,11,3,1,0.3388078224980817,False
|
||||
3,11,3,2,0.2860931024574278,False
|
||||
3,11,3,3,0.308074980244903,False
|
||||
3,11,3,4,0.31502822709418565,False
|
||||
3,11,3,5,0.32505192375078673,False
|
||||
3,11,3,6,0.22837485455539513,False
|
||||
3,11,3,7,0.2839628617093932,True
|
||||
3,11,4,0,0.26028568396301244,False
|
||||
3,11,4,1,0.31113768497680705,False
|
||||
3,11,4,2,0.2093370589526447,False
|
||||
3,11,4,3,0.2067670458575716,False
|
||||
3,11,4,4,0.23746438969191033,False
|
||||
3,11,4,5,0.2937489908263684,False
|
||||
3,11,4,6,0.15545688325003304,True
|
||||
3,11,4,7,0.23711905454807908,False
|
||||
3,11,5,0,0.29276136509361694,False
|
||||
3,11,5,1,0.3229101260953592,False
|
||||
3,11,5,2,0.2219506367372262,True
|
||||
3,11,5,3,0.23297026185344166,False
|
||||
3,11,5,4,0.26312750782493255,False
|
||||
3,11,5,5,0.32395281539820253,False
|
||||
3,11,5,6,0.1830081685878141,False
|
||||
3,11,5,7,0.2620700534452647,False
|
||||
3,11,6,0,0.3024048435100306,False
|
||||
3,11,6,1,0.34837849843628865,True
|
||||
3,11,6,2,0.28054781093520564,False
|
||||
3,11,6,3,0.2966844489933922,False
|
||||
3,11,6,4,0.29603952105919085,False
|
||||
3,11,6,5,0.32171462605097473,False
|
||||
3,11,6,6,0.2199594641549743,False
|
||||
3,11,6,7,0.30668599605308366,False
|
||||
3,11,7,0,0.38277726821561986,False
|
||||
3,11,7,1,0.3726244425460968,False
|
||||
3,11,7,2,0.3123366061547641,False
|
||||
3,11,7,3,0.2259765543377135,True
|
||||
3,11,7,4,0.3037539840173821,False
|
||||
3,11,7,5,0.42088070134670164,False
|
||||
3,11,7,6,0.24178125597497363,False
|
||||
3,11,7,7,0.30375555484880984,False
|
||||
3,15,0,0,0.3109421927012263,False
|
||||
3,15,0,1,0.22451966572875198,True
|
||||
3,15,0,2,0.3506995339548294,False
|
||||
3,15,0,3,0.246127360196778,False
|
||||
3,15,0,4,0.2555080236197189,False
|
||||
3,15,0,5,0.2914262782203356,False
|
||||
3,15,0,6,0.2884939657082678,False
|
||||
3,15,0,7,0.31264718687775916,False
|
||||
3,15,1,0,0.2860597642263054,True
|
||||
3,15,1,1,0.20703191037527066,False
|
||||
3,15,1,2,0.3409922841653509,False
|
||||
3,15,1,3,0.22943902107852288,False
|
||||
3,15,1,4,0.24072131739104807,False
|
||||
3,15,1,5,0.2779484527200282,False
|
||||
3,15,1,6,0.24320674570017803,False
|
||||
3,15,1,7,0.3042870963644443,False
|
||||
3,15,2,0,0.31894246867655424,False
|
||||
3,15,2,1,0.27021255900435254,False
|
||||
3,15,2,2,0.34061043132204105,False
|
||||
3,15,2,3,0.2650292503498818,False
|
||||
3,15,2,4,0.27104574472537735,False
|
||||
3,15,2,5,0.3064758507141351,False
|
||||
3,15,2,6,0.21497985957834942,True
|
||||
3,15,2,7,0.3243042399829519,False
|
||||
3,15,3,0,0.35789648719943085,False
|
||||
3,15,3,1,0.2853732319696145,False
|
||||
3,15,3,2,0.3499422330672337,False
|
||||
3,15,3,3,0.2791919704471885,False
|
||||
3,15,3,4,0.2814136925353162,True
|
||||
3,15,3,5,0.31028880926373387,False
|
||||
3,15,3,6,0.2604751329025521,False
|
||||
3,15,3,7,0.32540314020860905,False
|
||||
3,15,4,0,0.3368402022336826,False
|
||||
3,15,4,1,0.23928367348160967,False
|
||||
3,15,4,2,0.3014226885978929,False
|
||||
3,15,4,3,0.23292898067366918,False
|
||||
3,15,4,4,0.2623244164293419,False
|
||||
3,15,4,5,0.25313820987502494,True
|
||||
3,15,4,6,0.23009227244131739,False
|
||||
3,15,4,7,0.28438663561148286,False
|
||||
3,15,5,0,0.35421924849443076,False
|
||||
3,15,5,1,0.26800007530741127,False
|
||||
3,15,5,2,0.32164950678129656,False
|
||||
3,15,5,3,0.25469050463445464,False
|
||||
3,15,5,4,0.27241075315861113,False
|
||||
3,15,5,5,0.2748686956712231,False
|
||||
3,15,5,6,0.2277151708796879,False
|
||||
3,15,5,7,0.2789360180522283,True
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||||
3,15,6,0,0.33261076557070796,False
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||||
3,15,6,1,0.25060028152778446,False
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||||
3,15,6,2,0.3586983512004761,False
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||||
3,15,6,3,0.25528020314618893,True
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3,15,6,4,0.25986933468661805,False
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||||
3,15,6,5,0.2915984341004238,False
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||||
3,15,6,6,0.25509878199781294,False
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||||
3,15,6,7,0.3058398724418534,False
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||||
3,15,7,0,0.48318868347233224,False
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||||
3,15,7,1,0.36695646938564686,False
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||||
3,15,7,2,0.3982619762626106,True
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3,15,7,3,0.37067969717273036,False
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||||
3,15,7,4,0.4210580822402814,False
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||||
3,15,7,5,0.3560875723380794,False
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||||
3,15,7,6,0.37187017422923574,False
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||||
3,15,7,7,0.36373359582778564,False
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||||
3,19,0,0,0.21426188499751653,False
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||||
3,19,0,1,0.1966727871296454,True
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3,19,0,2,0.2129336801740035,False
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||||
3,19,0,3,0.19420505740999278,False
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||||
3,19,0,4,0.17463724225908328,False
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3,19,0,5,0.2215301431386797,False
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3,19,0,6,0.21578506305077533,False
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3,19,0,7,0.2106189740449793,False
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3,19,1,0,0.18683809819382635,False
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3,19,1,1,0.17055004050365508,False
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3,19,1,2,0.18178955368705108,False
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3,19,1,3,0.1658157663155539,True
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3,19,1,4,0.17085858090885409,False
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3,19,1,5,0.2026325693785994,False
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3,19,1,6,0.1842674801243316,False
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3,19,1,7,0.17965436948825247,False
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3,19,2,0,0.19791939235802106,False
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3,19,2,1,0.23405303250374707,False
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3,19,2,2,0.21476539083715038,False
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3,19,2,3,0.2123758268684245,False
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3,19,2,4,0.22191915162533826,False
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3,19,2,5,0.22413652954745972,False
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3,19,2,6,0.21586913956970832,False
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3,19,2,7,0.20230871849967297,True
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3,19,3,0,0.2316119635108843,False
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3,19,3,1,0.25549943694871313,False
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3,19,3,2,0.245371452849443,False
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3,19,3,3,0.23079418101687998,False
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3,19,3,4,0.18562884898626253,True
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3,19,3,5,0.2399624470853706,False
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||||
3,19,3,6,0.25108757896217326,False
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||||
3,19,3,7,0.23473900567649933,False
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||||
3,19,4,0,0.16153754870952408,False
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||||
3,19,4,1,0.19259932534900404,False
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||||
3,19,4,2,0.17181285895733678,False
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||||
3,19,4,3,0.1735530790669499,False
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||||
3,19,4,4,0.1810469436921917,False
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3,19,4,5,0.1636638586123268,False
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||||
3,19,4,6,0.1853282889717977,True
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3,19,4,7,0.18309552535747717,False
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||||
3,19,5,0,0.17830432196023555,True
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3,19,5,1,0.2146836208728133,False
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||||
3,19,5,2,0.18918156541647668,False
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||||
3,19,5,3,0.194082121570994,False
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||||
3,19,5,4,0.2051403518762611,False
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||||
3,19,5,5,0.19714066174079004,False
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||||
3,19,5,6,0.20276131444401024,False
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||||
3,19,5,7,0.19824125507591836,False
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||||
3,19,6,0,0.1839170811070815,False
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||||
3,19,6,1,0.18630095763934484,False
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||||
3,19,6,2,0.18048694764948534,True
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3,19,6,3,0.179335020919573,False
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||||
3,19,6,4,0.17435501207033394,False
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||||
3,19,6,5,0.20206066106084236,False
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||||
3,19,6,6,0.19971922791696725,False
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||||
3,19,6,7,0.18489568312349297,False
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||||
3,19,7,0,0.34285582410471044,False
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||||
3,19,7,1,0.3791225906322273,False
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||||
3,19,7,2,0.35193651364799294,False
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||||
3,19,7,3,0.35707267397377995,False
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||||
3,19,7,4,0.33587156971993615,False
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||||
3,19,7,5,0.2881098176844992,True
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3,19,7,6,0.36783074590747156,False
|
||||
3,19,7,7,0.3732425031308868,False
|
||||
3,23,0,0,0.23442966308326435,False
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||||
3,23,0,1,0.22730713485376478,True
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3,23,0,2,0.49497491036869734,False
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||||
3,23,0,3,0.22049444634026547,False
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||||
3,23,0,4,0.2486870493658699,False
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3,23,0,5,0.20584664220897128,False
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||||
3,23,0,6,0.4977785023149498,False
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3,23,0,7,0.22407689508597747,False
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3,23,1,0,0.20667463083363782,False
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3,23,1,1,0.1968744459176308,False
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||||
3,23,1,2,0.47167826738520274,False
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3,23,1,3,0.1849163672677863,True
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3,23,1,4,0.2006075906448494,False
|
||||
3,23,1,5,0.18056383425207895,False
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||||
3,23,1,6,0.4750635286450254,False
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||||
3,23,1,7,0.19383560187811436,False
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||||
3,23,2,0,0.20788965397141435,False
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||||
3,23,2,1,0.1886570399877726,False
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||||
3,23,2,2,0.45881343031720145,False
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||||
3,23,2,3,0.18777599638478237,False
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||||
3,23,2,4,0.1697926176555646,True
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3,23,2,5,0.19242973167579153,False
|
||||
3,23,2,6,0.4629929811822591,False
|
||||
3,23,2,7,0.1850077815938518,False
|
||||
3,23,3,0,0.24302125785469036,False
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||||
3,23,3,1,0.22629091713589378,False
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||||
3,23,3,2,0.4564642116500991,False
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||||
3,23,3,3,0.24412317184444451,False
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||||
3,23,3,4,0.2546906610054597,False
|
||||
3,23,3,5,0.20818489186318162,False
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||||
3,23,3,6,0.45882696593080513,False
|
||||
3,23,3,7,0.21755144893088219,True
|
||||
3,23,4,0,0.16416656297406443,True
|
||||
3,23,4,1,0.16200751699425409,False
|
||||
3,23,4,2,0.3528726636667838,False
|
||||
3,23,4,3,0.15842769007678004,False
|
||||
3,23,4,4,0.16005310874562062,False
|
||||
3,23,4,5,0.131282578739339,False
|
||||
3,23,4,6,0.35382478747366775,False
|
||||
3,23,4,7,0.16275443480575363,False
|
||||
3,23,5,0,0.1874349658286515,False
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||||
3,23,5,1,0.18648374719488922,False
|
||||
3,23,5,2,0.33723904485466516,True
|
||||
3,23,5,3,0.1845852786822949,False
|
||||
3,23,5,4,0.18927199087478014,False
|
||||
3,23,5,5,0.15377564472749494,False
|
||||
3,23,5,6,0.3379657548552957,False
|
||||
3,23,5,7,0.18340296970211267,False
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||||
3,23,6,0,0.1998107998500239,False
|
||||
3,23,6,1,0.20080772713566572,False
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||||
3,23,6,2,0.4589955345996658,False
|
||||
3,23,6,3,0.19334960712060312,False
|
||||
3,23,6,4,0.2059366056883351,False
|
||||
3,23,6,5,0.16676293039907333,True
|
||||
3,23,6,6,0.4602187416509731,False
|
||||
3,23,6,7,0.19185826168533027,False
|
||||
3,23,7,0,0.3023327940309889,False
|
||||
3,23,7,1,0.31777614097565643,False
|
||||
3,23,7,2,0.06806392022951314,False
|
||||
3,23,7,3,0.3391338564431989,False
|
||||
3,23,7,4,0.3515235124979729,False
|
||||
3,23,7,5,0.25508738621991,False
|
||||
3,23,7,6,0.06598393672234332,True
|
||||
3,23,7,7,0.3168303675175165,False
|
||||
7,11,0,0,0.2141927804602527,False
|
||||
7,11,0,1,0.24170520509286592,False
|
||||
7,11,0,2,0.142318075509597,False
|
||||
7,11,0,3,0.06918399632205535,False
|
||||
7,11,0,4,0.09787787642705709,False
|
||||
7,11,0,5,0.2532576764174823,False
|
||||
7,11,0,6,0.07126877338402317,False
|
||||
7,11,0,7,0.08835096181461174,True
|
||||
7,11,1,0,0.18589733496940158,False
|
||||
7,11,1,1,0.28975172400658916,False
|
||||
7,11,1,2,0.2263623083358462,False
|
||||
7,11,1,3,0.23031575021934592,False
|
||||
7,11,1,4,0.18524381944904356,False
|
||||
7,11,1,5,0.20644506335647275,False
|
||||
7,11,1,6,0.15604257604790361,True
|
||||
7,11,1,7,0.18420235276574043,False
|
||||
7,11,2,0,0.21475706417648632,False
|
||||
7,11,2,1,0.23725686892959436,False
|
||||
7,11,2,2,0.15876778303242067,False
|
||||
7,11,2,3,0.08605561878690872,False
|
||||
7,11,2,4,0.10395301183470676,True
|
||||
7,11,2,5,0.25492918996075636,False
|
||||
7,11,2,6,0.08620021042896552,False
|
||||
7,11,2,7,0.10175347935233797,False
|
||||
7,11,3,0,0.12680855458392293,True
|
||||
7,11,3,1,0.25632206495222287,False
|
||||
7,11,3,2,0.15653630689190412,False
|
||||
7,11,3,3,0.21910303232927955,False
|
||||
7,11,3,4,0.18173919961485213,False
|
||||
7,11,3,5,0.1415288535884337,False
|
||||
7,11,3,6,0.11083295431506782,False
|
||||
7,11,3,7,0.15820004133961124,False
|
||||
7,11,4,0,0.17413617971009734,False
|
||||
7,11,4,1,0.2775722304957876,False
|
||||
7,11,4,2,0.1657765821827974,False
|
||||
7,11,4,3,0.2576772712930575,False
|
||||
7,11,4,4,0.23326224691296638,False
|
||||
7,11,4,5,0.14815353118515695,True
|
||||
7,11,4,6,0.1588522847388153,False
|
||||
7,11,4,7,0.22147490438914125,False
|
||||
7,11,5,0,0.194514491869073,False
|
||||
7,11,5,1,0.29726850622874157,False
|
||||
7,11,5,2,0.1880498574399584,True
|
||||
7,11,5,3,0.2201168204795746,False
|
||||
7,11,5,4,0.2186582167525955,False
|
||||
7,11,5,5,0.2092860979819588,False
|
||||
7,11,5,6,0.15239086274519834,False
|
||||
7,11,5,7,0.17621268177890528,False
|
||||
7,11,6,0,0.1357838416637931,False
|
||||
7,11,6,1,0.20682616483479987,True
|
||||
7,11,6,2,0.14787541980305097,False
|
||||
7,11,6,3,0.12922112589870424,False
|
||||
7,11,6,4,0.1408704306642687,False
|
||||
7,11,6,5,0.17771243767745715,False
|
||||
7,11,6,6,0.09698302043279229,False
|
||||
7,11,6,7,0.10884548171911936,False
|
||||
7,11,7,0,0.28084737754695016,False
|
||||
7,11,7,1,0.27608478103645556,False
|
||||
7,11,7,2,0.2060511607593719,False
|
||||
7,11,7,3,0.09551389570426909,True
|
||||
7,11,7,4,0.14608841459805005,False
|
||||
7,11,7,5,0.32318294478808174,False
|
||||
7,11,7,6,0.13853532748965544,False
|
||||
7,11,7,7,0.1371874325869274,False
|
||||
7,15,0,0,0.3140143772439896,False
|
||||
7,15,0,1,0.22491905255744188,False
|
||||
7,15,0,2,0.22901615740079934,False
|
||||
7,15,0,3,0.19421215220406096,True
|
||||
7,15,0,4,0.31670216304114973,False
|
||||
7,15,0,5,0.24352649265594817,False
|
||||
7,15,0,6,0.3138226447389396,False
|
||||
7,15,0,7,0.29481888678224416,False
|
||||
7,15,1,0,0.21189977697067597,False
|
||||
7,15,1,1,0.1456236581205395,True
|
||||
7,15,1,2,0.2738196031241211,False
|
||||
7,15,1,3,0.17543647084357816,False
|
||||
7,15,1,4,0.2538548296348474,False
|
||||
7,15,1,5,0.24321154101806372,False
|
||||
7,15,1,6,0.2755540767316541,False
|
||||
7,15,1,7,0.2921724244736888,False
|
||||
7,15,2,0,0.3055138675599003,False
|
||||
7,15,2,1,0.21790847647667294,False
|
||||
7,15,2,2,0.23288608487691392,False
|
||||
7,15,2,3,0.1901392837186983,False
|
||||
7,15,2,4,0.30505872023224356,False
|
||||
7,15,2,5,0.23235978098679003,True
|
||||
7,15,2,6,0.3132541737226323,False
|
||||
7,15,2,7,0.2907677781814891,False
|
||||
7,15,3,0,0.18188447693721274,True
|
||||
7,15,3,1,0.1510860972579611,False
|
||||
7,15,3,2,0.2380335744249642,False
|
||||
7,15,3,3,0.14569701501347193,False
|
||||
7,15,3,4,0.20085275151902499,False
|
||||
7,15,3,5,0.20784816752658025,False
|
||||
7,15,3,6,0.21272089996692428,False
|
||||
7,15,3,7,0.23881234724072337,False
|
||||
7,15,4,0,0.24910682780608445,False
|
||||
7,15,4,1,0.24512856486045456,False
|
||||
7,15,4,2,0.28652410591575617,False
|
||||
7,15,4,3,0.21333264442897887,False
|
||||
7,15,4,4,0.22707114882085438,True
|
||||
7,15,4,5,0.2594821452769555,False
|
||||
7,15,4,6,0.24325961936505142,False
|
||||
7,15,4,7,0.26524385686151475,False
|
||||
7,15,5,0,0.2815611372705399,False
|
||||
7,15,5,1,0.20555663692699044,False
|
||||
7,15,5,2,0.2610882914246292,False
|
||||
7,15,5,3,0.19485902116830245,False
|
||||
7,15,5,4,0.28396835485843147,False
|
||||
7,15,5,5,0.25822581133123884,False
|
||||
7,15,5,6,0.2753288076764828,True
|
||||
7,15,5,7,0.29479563582636426,False
|
||||
7,15,6,0,0.29820269584700143,False
|
||||
7,15,6,1,0.23597934330272488,False
|
||||
7,15,6,2,0.21985536090055102,False
|
||||
7,15,6,3,0.20547054404951365,False
|
||||
7,15,6,4,0.2970290687083381,False
|
||||
7,15,6,5,0.23701434623046685,False
|
||||
7,15,6,6,0.28216601172443356,False
|
||||
7,15,6,7,0.2791874545923064,True
|
||||
7,15,7,0,0.41687312939786325,False
|
||||
7,15,7,1,0.32964986945508434,False
|
||||
7,15,7,2,0.31044371282364813,True
|
||||
7,15,7,3,0.3009272082299296,False
|
||||
7,15,7,4,0.42019957974000777,False
|
||||
7,15,7,5,0.3335863611061849,False
|
||||
7,15,7,6,0.40650162577424787,False
|
||||
7,15,7,7,0.37247876645148215,False
|
||||
7,19,0,0,0.244647239085375,False
|
||||
7,19,0,1,0.27959274772245934,False
|
||||
7,19,0,2,0.2548720078609493,False
|
||||
7,19,0,3,0.2581849231484324,False
|
||||
7,19,0,4,0.21041731499864763,False
|
||||
7,19,0,5,0.19819370573361644,False
|
||||
7,19,0,6,0.24705752846684126,True
|
||||
7,19,0,7,0.2706909579245046,False
|
||||
7,19,1,0,0.19917964704326477,False
|
||||
7,19,1,1,0.18654513295405928,False
|
||||
7,19,1,2,0.19437501916307648,False
|
||||
7,19,1,3,0.1905826891262411,True
|
||||
7,19,1,4,0.1833222208447697,False
|
||||
7,19,1,5,0.2176772843111801,False
|
||||
7,19,1,6,0.18378068140573922,False
|
||||
7,19,1,7,0.20164453008136626,False
|
||||
7,19,2,0,0.2463512443061293,False
|
||||
7,19,2,1,0.2780586102577256,False
|
||||
7,19,2,2,0.255234461575609,False
|
||||
7,19,2,3,0.2599625096041693,False
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7,19,2,4,0.21189243212604555,True
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7,19,2,5,0.2017044181939504,False
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7,19,2,6,0.25173654696434505,False
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7,19,2,7,0.27253701527216456,False
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7,19,3,0,0.17082835219178302,False
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7,19,3,1,0.16887173292427626,True
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7,19,3,3,0.1803778424210184,False
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7,19,3,4,0.16638734181533732,False
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7,19,3,5,0.21292429759266784,False
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7,19,3,6,0.1758932147852237,False
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7,19,3,7,0.1630554867386128,False
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7,19,4,0,0.21555027299557056,False
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7,19,4,1,0.2303232829275763,False
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7,19,4,2,0.2291590792972072,False
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7,19,4,3,0.2358036541911908,False
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7,19,4,4,0.22850146312134298,False
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7,19,4,5,0.25588474707688913,False
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7,19,4,7,0.20647075558948608,True
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7,19,5,0,0.23352354701176398,False
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7,19,5,1,0.25643647977255213,False
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7,19,5,2,0.2475366236593053,True
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7,19,5,3,0.2481359850587487,False
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7,19,5,4,0.21522783067669607,False
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7,19,5,5,0.20206207034036885,False
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7,19,5,6,0.2422710677138025,False
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7,19,5,7,0.24227201048830066,False
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7,19,6,0,0.28652543928985097,True
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7,19,6,1,0.31505554288561033,False
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7,19,6,2,0.3023422915341232,False
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7,19,6,3,0.30363296966032605,False
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7,19,6,4,0.2773050995542926,False
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7,19,6,5,0.24990556755480264,False
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7,19,6,6,0.29658003483488365,False
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7,19,6,7,0.30391755915515284,False
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7,19,7,0,0.3265084895567179,False
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7,19,7,1,0.36575329938600254,False
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7,19,7,2,0.3384257566026309,False
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7,19,7,3,0.34269794185528407,False
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7,19,7,4,0.31014891570873276,False
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7,19,7,5,0.2782579344425756,True
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7,19,7,6,0.33524333388452876,False
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7,19,7,7,0.3607332980768643,False
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7,23,0,0,0.19402308669301452,False
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7,23,0,1,0.21471947506307554,False
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7,23,0,3,0.22179107192929667,False
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7,23,0,4,0.25456393891253387,False
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7,23,0,5,0.1655048623108171,True
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7,23,0,6,0.11291796765415321,False
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7,23,0,7,0.20953119617831034,False
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7,23,1,0,0.24354161450578138,False
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7,23,1,1,0.23620847679722817,False
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7,23,1,2,0.41043641564027905,False
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7,23,1,3,0.20848862415690747,True
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7,23,1,4,0.24886444602168434,False
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7,23,1,5,0.2180606704835969,False
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7,23,1,6,0.4156223453297234,False
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7,23,1,7,0.22817186504371306,False
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7,23,2,0,0.18666704135766032,True
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7,23,2,1,0.20787783529585196,False
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7,23,2,2,0.1582675747704663,False
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7,23,2,3,0.21261287154684413,False
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7,23,2,4,0.23888270621906055,False
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7,23,2,5,0.16199079908889802,False
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7,23,2,6,0.16073061617211132,False
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7,23,2,7,0.20271952519275457,False
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7,23,3,0,0.21376343962021732,False
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7,23,3,1,0.19446184388312873,False
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7,23,3,2,0.4955256337676104,False
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7,23,3,3,0.1755190250189539,False
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7,23,3,4,0.18825658968283973,True
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7,23,3,5,0.19391564133676326,False
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7,23,3,6,0.5026048136542478,False
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7,23,3,7,0.19732288805044895,False
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7,23,4,0,0.26573702559832624,False
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7,23,4,1,0.2516924465387276,True
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7,23,4,2,0.4961832722160361,False
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||||
7,23,4,3,0.24056785070537845,False
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7,23,4,4,0.2609745059624338,False
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7,23,4,5,0.24511367975888781,False
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7,23,4,6,0.5009843332165835,False
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7,23,4,7,0.25411209842613014,False
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7,23,5,0,0.23484988615904767,False
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7,23,5,1,0.23275290367221307,False
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7,23,5,2,0.29533691630832704,False
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7,23,5,3,0.21805603719036182,False
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7,23,5,4,0.24732410921206055,False
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||||
7,23,5,5,0.20565889369485885,False
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||||
7,23,5,6,0.2990453345898386,False
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||||
7,23,5,7,0.2323863674864195,True
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7,23,6,0,0.2873400581399875,False
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7,23,6,1,0.29047506788884936,False
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||||
7,23,6,2,0.1907952467483687,True
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7,23,6,3,0.2871699885834167,False
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||||
7,23,6,4,0.3182920204558606,False
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||||
7,23,6,5,0.258368245729381,False
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||||
7,23,6,6,0.19212265636757608,False
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7,23,6,7,0.29490926759091196,False
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7,23,7,0,0.29634731629973976,False
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7,23,7,1,0.32041680255009064,False
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7,23,7,2,0.05128682885767789,False
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||||
7,23,7,3,0.3349565007075778,False
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||||
7,23,7,4,0.36005918251307945,False
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7,23,7,5,0.2657193547971203,False
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||||
7,23,7,6,0.049544018024649944,True
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||||
7,23,7,7,0.314855806706577,False
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||||
11,15,0,0,0.118812052910545,True
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11,15,0,1,0.13616982852847548,False
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||||
11,15,0,2,0.10642606491882259,False
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||||
11,15,0,3,0.07198315188715142,False
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||||
11,15,0,4,0.11993166207227803,False
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||||
11,15,0,5,0.11514314585421065,False
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||||
11,15,0,6,0.12065408801976714,False
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||||
11,15,0,7,0.1623569617868216,False
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||||
11,15,1,0,0.19911407650589905,False
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||||
11,15,1,1,0.27410098795991544,False
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||||
11,15,1,2,0.1386876211517493,False
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||||
11,15,1,3,0.15279503205493203,False
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||||
11,15,1,4,0.2056138712768839,False
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||||
11,15,1,5,0.15503453065991973,False
|
||||
11,15,1,6,0.16157702280894531,False
|
||||
11,15,1,7,0.13159031622388617,True
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||||
11,15,2,0,0.21033343332544743,False
|
||||
11,15,2,1,0.1949472628217423,False
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||||
11,15,2,2,0.15370002731782,False
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||||
11,15,2,3,0.1257499009773405,False
|
||||
11,15,2,4,0.1651874967140587,False
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||||
11,15,2,5,0.17599985742540303,False
|
||||
11,15,2,6,0.15015723744416393,True
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||||
11,15,2,7,0.20934446409284962,False
|
||||
11,15,3,0,0.3081024579041289,False
|
||||
11,15,3,1,0.21323840255361534,True
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||||
11,15,3,2,0.19863283224963235,False
|
||||
11,15,3,3,0.19670652991711143,False
|
||||
11,15,3,4,0.2751132643441939,False
|
||||
11,15,3,5,0.2291191697588642,False
|
||||
11,15,3,6,0.30323689378306123,False
|
||||
11,15,3,7,0.31037043601903697,False
|
||||
11,15,4,0,0.18999707396569665,False
|
||||
11,15,4,1,0.1278142203364465,False
|
||||
11,15,4,2,0.12615186439005288,False
|
||||
11,15,4,3,0.11342216478333841,False
|
||||
11,15,4,4,0.18136346543301923,False
|
||||
11,15,4,5,0.14227598041168246,True
|
||||
11,15,4,6,0.23150248922766486,False
|
||||
11,15,4,7,0.2631835911304479,False
|
||||
11,15,5,0,0.18242833623925486,False
|
||||
11,15,5,1,0.19114087483534253,False
|
||||
11,15,5,2,0.19667420915420653,False
|
||||
11,15,5,3,0.14556204226926023,False
|
||||
11,15,5,4,0.16023229402335276,True
|
||||
11,15,5,5,0.20255074804623044,False
|
||||
11,15,5,6,0.1980498170289662,False
|
||||
11,15,5,7,0.23933736709544245,False
|
||||
11,15,6,0,0.18120374681082968,False
|
||||
11,15,6,1,0.12158148662344714,False
|
||||
11,15,6,2,0.11587245564036125,False
|
||||
11,15,6,3,0.08384622376602638,True
|
||||
11,15,6,4,0.15818298693126298,False
|
||||
11,15,6,5,0.14259631825652125,False
|
||||
11,15,6,6,0.17143006805795458,False
|
||||
11,15,6,7,0.22163623594516027,False
|
||||
11,15,7,0,0.22003871545800846,False
|
||||
11,15,7,1,0.16315561079519564,False
|
||||
11,15,7,2,0.14166041115515177,True
|
||||
11,15,7,3,0.12800669676033316,False
|
||||
11,15,7,4,0.2092563999853173,False
|
||||
11,15,7,5,0.18063616488087475,False
|
||||
11,15,7,6,0.19071544085091013,False
|
||||
11,15,7,7,0.24781157039507165,False
|
||||
11,19,0,0,0.16022305135512144,False
|
||||
11,19,0,1,0.1661523191968102,False
|
||||
11,19,0,2,0.18012693716851108,False
|
||||
11,19,0,3,0.18265647629118836,False
|
||||
11,19,0,4,0.1640600759147697,False
|
||||
11,19,0,5,0.17837529921911965,False
|
||||
11,19,0,6,0.16593764806885997,False
|
||||
11,19,0,7,0.16716492754301676,True
|
||||
11,19,1,0,0.27622515177362983,True
|
||||
11,19,1,1,0.29865879598837747,False
|
||||
11,19,1,2,0.29414504136760833,False
|
||||
11,19,1,3,0.301169159353729,False
|
||||
11,19,1,4,0.2693261067260207,False
|
||||
11,19,1,5,0.2709474432413109,False
|
||||
11,19,1,6,0.2925512615685039,False
|
||||
11,19,1,7,0.2942406696087957,False
|
||||
11,19,2,0,0.14163171719022832,False
|
||||
11,19,2,1,0.16527985663118774,False
|
||||
11,19,2,2,0.15370116407974987,False
|
||||
11,19,2,3,0.16570364792864903,False
|
||||
11,19,2,4,0.13102723203915184,False
|
||||
11,19,2,5,0.14636311591580375,False
|
||||
11,19,2,6,0.13610802566943953,True
|
||||
11,19,2,7,0.14144403464902455,False
|
||||
11,19,3,0,0.2635026728638399,False
|
||||
11,19,3,1,0.2821408211535371,False
|
||||
11,19,3,2,0.272371433616313,False
|
||||
11,19,3,3,0.27180607308393473,False
|
||||
11,19,3,4,0.24589960799622568,False
|
||||
11,19,3,5,0.20574303573767766,True
|
||||
11,19,3,6,0.27294725478793136,False
|
||||
11,19,3,7,0.29031850170707163,False
|
||||
11,19,4,0,0.19462538180748457,False
|
||||
11,19,4,1,0.20241162117976397,False
|
||||
11,19,4,2,0.20694902829133294,False
|
||||
11,19,4,3,0.2010527639854236,True
|
||||
11,19,4,4,0.18880995341549178,False
|
||||
11,19,4,5,0.17799408416275403,False
|
||||
11,19,4,6,0.20415393614009641,False
|
||||
11,19,4,7,0.22354207559168218,False
|
||||
11,19,5,0,0.2075538827145918,False
|
||||
11,19,5,1,0.20219837619390452,True
|
||||
11,19,5,2,0.22476852840778339,False
|
||||
11,19,5,3,0.22561013379127226,False
|
||||
11,19,5,4,0.2058728417200687,False
|
||||
11,19,5,5,0.22855705087254552,False
|
||||
11,19,5,6,0.2141180580827835,False
|
||||
11,19,5,7,0.21417233314775588,False
|
||||
11,19,6,0,0.1482017133113852,False
|
||||
11,19,6,1,0.16339852453528259,False
|
||||
11,19,6,2,0.1615545538123231,False
|
||||
11,19,6,3,0.15918021687573503,False
|
||||
11,19,6,4,0.12370161310133367,True
|
||||
11,19,6,5,0.12763996950170312,False
|
||||
11,19,6,6,0.15615250065826436,False
|
||||
11,19,6,7,0.16664586622669136,False
|
||||
11,19,7,0,0.17552830668691202,False
|
||||
11,19,7,1,0.19369317550887336,False
|
||||
11,19,7,2,0.18709926463923168,True
|
||||
11,19,7,3,0.18877553753644583,False
|
||||
11,19,7,4,0.15022614475099597,False
|
||||
11,19,7,5,0.16189557751385183,False
|
||||
11,19,7,6,0.17961318223646094,False
|
||||
11,19,7,7,0.19029874682011078,False
|
||||
11,23,0,0,0.2043593844510353,False
|
||||
11,23,0,1,0.19951834624975298,False
|
||||
11,23,0,2,0.43131412607762065,False
|
||||
11,23,0,3,0.17472206556045428,True
|
||||
11,23,0,4,0.19092749946414864,False
|
||||
11,23,0,5,0.18568183478782946,False
|
||||
11,23,0,6,0.43463639388189834,False
|
||||
11,23,0,7,0.20071606471122894,False
|
||||
11,23,1,0,0.3166853759686903,False
|
||||
11,23,1,1,0.3130061786581019,False
|
||||
11,23,1,2,0.32950636091567137,True
|
||||
11,23,1,3,0.30907220890511355,False
|
||||
11,23,1,4,0.32690647463283784,False
|
||||
11,23,1,5,0.29397721643052377,False
|
||||
11,23,1,6,0.33177253964038056,False
|
||||
11,23,1,7,0.3165112652601144,False
|
||||
11,23,2,0,0.16366466657967538,False
|
||||
11,23,2,1,0.1593141276645446,True
|
||||
11,23,2,2,0.3183189897316516,False
|
||||
11,23,2,3,0.15158803950054395,False
|
||||
11,23,2,4,0.17249766707229894,False
|
||||
11,23,2,5,0.1454463062979207,False
|
||||
11,23,2,6,0.3208100244700852,False
|
||||
11,23,2,7,0.16422241762228532,False
|
||||
11,23,3,0,0.22875773170667263,False
|
||||
11,23,3,1,0.24654993265601616,False
|
||||
11,23,3,2,0.17387926771274803,False
|
||||
11,23,3,3,0.2406365147372899,False
|
||||
11,23,3,4,0.2788444205763735,False
|
||||
11,23,3,5,0.20162187297886017,False
|
||||
11,23,3,6,0.17462494015614533,True
|
||||
11,23,3,7,0.23945787222579154,False
|
||||
11,23,4,0,0.2072969627534016,False
|
||||
11,23,4,1,0.21467967402170085,False
|
||||
11,23,4,2,0.25950246987156284,False
|
||||
11,23,4,3,0.1960372642656299,False
|
||||
11,23,4,4,0.23275169378385002,False
|
||||
11,23,4,5,0.1735346010058511,True
|
||||
11,23,4,6,0.2606881892731189,False
|
||||
11,23,4,7,0.2043406287151746,False
|
||||
11,23,5,0,0.25989994008693146,False
|
||||
11,23,5,1,0.24518249135382952,False
|
||||
11,23,5,2,0.46612210477584004,False
|
||||
11,23,5,3,0.22395846390971538,False
|
||||
11,23,5,4,0.23688862055045784,True
|
||||
11,23,5,5,0.23266935627972185,False
|
||||
11,23,5,6,0.46983690011779977,False
|
||||
11,23,5,7,0.2411066940139092,False
|
||||
11,23,6,0,0.16339386424295393,False
|
||||
11,23,6,1,0.16485004733930295,False
|
||||
11,23,6,2,0.2525172081647198,False
|
||||
11,23,6,3,0.14795532441020745,False
|
||||
11,23,6,4,0.17836020145902562,False
|
||||
11,23,6,5,0.12699499919868745,False
|
||||
11,23,6,6,0.25379378490218424,False
|
||||
11,23,6,7,0.15308840316542832,True
|
||||
11,23,7,0,0.1831755454406076,True
|
||||
11,23,7,1,0.18156626157242955,False
|
||||
11,23,7,2,0.27466423062864564,False
|
||||
11,23,7,3,0.17472305438390062,False
|
||||
11,23,7,4,0.1984385006218771,False
|
||||
11,23,7,5,0.15581198015427472,False
|
||||
11,23,7,6,0.27573201946367454,False
|
||||
11,23,7,7,0.18023973496304782,False
|
||||
15,19,0,0,0.1982293375060561,False
|
||||
15,19,0,1,0.1710170194178272,True
|
||||
15,19,0,2,0.20156696446447703,False
|
||||
15,19,0,3,0.19431584194503404,False
|
||||
15,19,0,4,0.18528664587573243,False
|
||||
15,19,0,5,0.20250030881498063,False
|
||||
15,19,0,6,0.18473292839834685,False
|
||||
15,19,0,7,0.19192174013223084,False
|
||||
15,19,1,0,0.16178694960244466,False
|
||||
15,19,1,1,0.13957087828544887,False
|
||||
15,19,1,2,0.16813384657801453,False
|
||||
15,19,1,3,0.15083223468307058,True
|
||||
15,19,1,4,0.14431396122706996,False
|
||||
15,19,1,5,0.14473308175909,False
|
||||
15,19,1,6,0.15696765531420193,False
|
||||
15,19,1,7,0.16262035667767444,False
|
||||
15,19,2,0,0.21165103517832023,False
|
||||
15,19,2,1,0.22759354936774323,False
|
||||
15,19,2,2,0.22561826083407854,False
|
||||
15,19,2,3,0.21446075722558233,False
|
||||
15,19,2,4,0.17212013298667245,False
|
||||
15,19,2,5,0.18100784401569808,True
|
||||
15,19,2,6,0.2067976535176983,False
|
||||
15,19,2,7,0.2124767772372611,False
|
||||
15,19,3,0,0.1417270857165419,False
|
||||
15,19,3,1,0.14304800777799137,False
|
||||
15,19,3,2,0.1544386547607156,False
|
||||
15,19,3,3,0.14087371813392227,False
|
||||
15,19,3,4,0.10829997676951798,False
|
||||
15,19,3,5,0.12374050952279185,False
|
||||
15,19,3,6,0.13890205753177046,True
|
||||
15,19,3,7,0.1417071702675079,False
|
||||
15,19,4,0,0.2062844650551886,False
|
||||
15,19,4,1,0.19611703582113998,False
|
||||
15,19,4,2,0.22743612429953278,False
|
||||
15,19,4,3,0.2124720594315494,False
|
||||
15,19,4,4,0.16684400049653123,True
|
||||
15,19,4,5,0.19945444955567684,False
|
||||
15,19,4,6,0.2201591526709624,False
|
||||
15,19,4,7,0.20928687044428546,False
|
||||
15,19,5,0,0.20909936728793496,False
|
||||
15,19,5,1,0.21504864119257844,False
|
||||
15,19,5,2,0.22534831614303308,True
|
||||
15,19,5,3,0.2162856059510101,False
|
||||
15,19,5,4,0.1902107261275519,False
|
||||
15,19,5,5,0.19896226505178974,False
|
||||
15,19,5,6,0.23730107382203391,False
|
||||
15,19,5,7,0.23629594034784515,False
|
||||
15,19,6,0,0.16657035461411468,True
|
||||
15,19,6,1,0.18786383555748382,False
|
||||
15,19,6,2,0.1850733847714278,False
|
||||
15,19,6,3,0.18335968305655248,False
|
||||
15,19,6,4,0.14714937888737434,False
|
||||
15,19,6,5,0.18357451033094233,False
|
||||
15,19,6,6,0.18467921758170736,False
|
||||
15,19,6,7,0.16900789105225006,False
|
||||
15,19,7,0,0.2520625594878395,False
|
||||
15,19,7,1,0.2655798370270646,False
|
||||
15,19,7,2,0.26829798079888856,False
|
||||
15,19,7,3,0.2626930863838302,False
|
||||
15,19,7,4,0.21547911774508427,False
|
||||
15,19,7,5,0.23966059911687043,False
|
||||
15,19,7,6,0.2684571106069467,False
|
||||
15,19,7,7,0.252789117914112,True
|
||||
15,23,0,0,0.23744983168926653,False
|
||||
15,23,0,1,0.2343377460033085,False
|
||||
15,23,0,2,0.48279539598947274,False
|
||||
15,23,0,3,0.2101711506432809,False
|
||||
15,23,0,4,0.2384970876209922,False
|
||||
15,23,0,5,0.2172744522371264,False
|
||||
15,23,0,6,0.48731718390065754,False
|
||||
15,23,0,7,0.2131915518142315,True
|
||||
15,23,1,0,0.19854865695882967,False
|
||||
15,23,1,1,0.20282979875213009,False
|
||||
15,23,1,2,0.42231221274114006,False
|
||||
15,23,1,3,0.16992167489499066,True
|
||||
15,23,1,4,0.20657300895530703,False
|
||||
15,23,1,5,0.17378039115441102,False
|
||||
15,23,1,6,0.4269463195680686,False
|
||||
15,23,1,7,0.18609376538639533,False
|
||||
15,23,2,0,0.21411342842535824,False
|
||||
15,23,2,1,0.21964630792386003,False
|
||||
15,23,2,2,0.3623042446349838,False
|
||||
15,23,2,3,0.21527706117360562,False
|
||||
15,23,2,4,0.23511869257584983,False
|
||||
15,23,2,5,0.1822401774579353,True
|
||||
15,23,2,6,0.36666847700262406,False
|
||||
15,23,2,7,0.2071858769438572,False
|
||||
15,23,3,0,0.16156028230965133,True
|
||||
15,23,3,1,0.16619018375786168,False
|
||||
15,23,3,2,0.3690387557272894,False
|
||||
15,23,3,3,0.1434447613819234,False
|
||||
15,23,3,4,0.1799310740440525,False
|
||||
15,23,3,5,0.13475789536719457,False
|
||||
15,23,3,6,0.37398659867039674,False
|
||||
15,23,3,7,0.14867452118947472,False
|
||||
15,23,4,0,0.2639308162286842,False
|
||||
15,23,4,1,0.2575848227044336,True
|
||||
15,23,4,2,0.4574935627796157,False
|
||||
15,23,4,3,0.24127772102334888,False
|
||||
15,23,4,4,0.26659453034489095,False
|
||||
15,23,4,5,0.23425746569163286,False
|
||||
15,23,4,6,0.4623568600183657,False
|
||||
15,23,4,7,0.24066699195901808,False
|
||||
15,23,5,0,0.23563847158113632,False
|
||||
15,23,5,1,0.24083960982352204,False
|
||||
15,23,5,2,0.3601696910153557,True
|
||||
15,23,5,3,0.23301096844563376,False
|
||||
15,23,5,4,0.25579823322932316,False
|
||||
15,23,5,5,0.20537892993549847,False
|
||||
15,23,5,6,0.3646526916921621,False
|
||||
15,23,5,7,0.22920299852744141,False
|
||||
15,23,6,0,0.20485336030406348,False
|
||||
15,23,6,1,0.20222210798052917,False
|
||||
15,23,6,2,0.42006916021538765,False
|
||||
15,23,6,3,0.20239265086403996,False
|
||||
15,23,6,4,0.19616089797653896,True
|
||||
15,23,6,5,0.18674626078717166,False
|
||||
15,23,6,6,0.42422569644697594,False
|
||||
15,23,6,7,0.20220045197570685,False
|
||||
15,23,7,0,0.2677880797467449,False
|
||||
15,23,7,1,0.2707898904913189,False
|
||||
15,23,7,2,0.371631188744307,False
|
||||
15,23,7,3,0.26792703502688353,False
|
||||
15,23,7,4,0.28069514099920845,False
|
||||
15,23,7,5,0.2427703239342724,False
|
||||
15,23,7,6,0.3747955776482904,True
|
||||
15,23,7,7,0.26594750157478575,False
|
||||
19,23,0,0,0.11755258858198206,False
|
||||
19,23,0,1,0.11083837580556702,True
|
||||
19,23,0,2,0.43393605274892993,False
|
||||
19,23,0,3,0.11822253465137464,False
|
||||
19,23,0,4,0.10483874999623656,False
|
||||
19,23,0,5,0.11119820052467494,False
|
||||
19,23,0,6,0.44192544019099944,False
|
||||
19,23,0,7,0.11903812854186457,False
|
||||
19,23,1,0,0.1398898916617487,True
|
||||
19,23,1,1,0.15475862681780783,False
|
||||
19,23,1,2,0.462743244716198,False
|
||||
19,23,1,3,0.12259279407665831,False
|
||||
19,23,1,4,0.12023217314181218,False
|
||||
19,23,1,5,0.13246961529166298,False
|
||||
19,23,1,6,0.4702609763302361,False
|
||||
19,23,1,7,0.13767199005061292,False
|
||||
19,23,2,0,0.1164865466548205,False
|
||||
19,23,2,1,0.1197714137741968,False
|
||||
19,23,2,2,0.4384727444266964,False
|
||||
19,23,2,3,0.11367398259346462,False
|
||||
19,23,2,4,0.10430720535103019,False
|
||||
19,23,2,5,0.10878629629471567,False
|
||||
19,23,2,6,0.44622147350409236,True
|
||||
19,23,2,7,0.11189953835461333,False
|
||||
19,23,3,0,0.12913887627608228,False
|
||||
19,23,3,1,0.1428627484969812,False
|
||||
19,23,3,2,0.4481426688926389,False
|
||||
19,23,3,3,0.1089341150209886,False
|
||||
19,23,3,4,0.11864774753534488,False
|
||||
19,23,3,5,0.10975688859516716,True
|
||||
19,23,3,6,0.4541146498101592,False
|
||||
19,23,3,7,0.11642371682652042,False
|
||||
19,23,4,0,0.13323127112014832,False
|
||||
19,23,4,1,0.11585898527871832,False
|
||||
19,23,4,2,0.44569299633051157,False
|
||||
19,23,4,3,0.08215476470270991,False
|
||||
19,23,4,4,0.1291280676056819,False
|
||||
19,23,4,5,0.10546555878334786,False
|
||||
19,23,4,6,0.4542515297373043,False
|
||||
19,23,4,7,0.09112206223685893,True
|
||||
19,23,5,0,0.11046532750111226,False
|
||||
19,23,5,1,0.12188155993923745,False
|
||||
19,23,5,2,0.407131669407236,True
|
||||
19,23,5,3,0.08658690398198869,False
|
||||
19,23,5,4,0.11462959096429762,False
|
||||
19,23,5,5,0.09773870055394686,False
|
||||
19,23,5,6,0.41512619080005625,False
|
||||
19,23,5,7,0.09963203771427342,False
|
||||
19,23,6,0,0.11666691805295151,False
|
||||
19,23,6,1,0.11856255628375452,False
|
||||
19,23,6,2,0.4726336294328968,False
|
||||
19,23,6,3,0.08281978438804452,True
|
||||
19,23,6,4,0.10612966349320337,False
|
||||
19,23,6,5,0.11401815939234225,False
|
||||
19,23,6,6,0.4805784556640488,False
|
||||
19,23,6,7,0.09753217012390888,False
|
||||
19,23,7,0,0.11997512590776621,False
|
||||
19,23,7,1,0.12174553446232719,False
|
||||
19,23,7,2,0.48858976873618887,False
|
||||
19,23,7,3,0.09693770620118121,False
|
||||
19,23,7,4,0.09875830476085201,True
|
||||
19,23,7,5,0.11836287352290106,False
|
||||
19,23,7,6,0.4966142081959978,False
|
||||
19,23,7,7,0.10691505301163796,False
|
||||
|
@@ -0,0 +1,12 @@
|
||||
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
|
||||
|
||||
----------------------------------------------------------------------
|
||||
Ran 7 tests in 0.210s
|
||||
|
||||
OK
|
||||
@@ -0,0 +1,187 @@
|
||||
{
|
||||
"layers": [
|
||||
3,
|
||||
7,
|
||||
11,
|
||||
15,
|
||||
19,
|
||||
23
|
||||
],
|
||||
"layer_pairs": [
|
||||
[
|
||||
3,
|
||||
7
|
||||
],
|
||||
[
|
||||
3,
|
||||
11
|
||||
],
|
||||
[
|
||||
3,
|
||||
15
|
||||
],
|
||||
[
|
||||
3,
|
||||
19
|
||||
],
|
||||
[
|
||||
3,
|
||||
23
|
||||
],
|
||||
[
|
||||
7,
|
||||
11
|
||||
],
|
||||
[
|
||||
7,
|
||||
15
|
||||
],
|
||||
[
|
||||
7,
|
||||
19
|
||||
],
|
||||
[
|
||||
7,
|
||||
23
|
||||
],
|
||||
[
|
||||
11,
|
||||
15
|
||||
],
|
||||
[
|
||||
11,
|
||||
19
|
||||
],
|
||||
[
|
||||
11,
|
||||
23
|
||||
],
|
||||
[
|
||||
15,
|
||||
19
|
||||
],
|
||||
[
|
||||
15,
|
||||
23
|
||||
],
|
||||
[
|
||||
19,
|
||||
23
|
||||
]
|
||||
],
|
||||
"heads": 8,
|
||||
"kv_heads": 2,
|
||||
"head_dim": 256,
|
||||
"calibration_samples": [
|
||||
"cal-zh-1",
|
||||
"cal-zh-2",
|
||||
"cal-en-1",
|
||||
"cal-en-2"
|
||||
],
|
||||
"validation_samples": [
|
||||
"test-zh-1",
|
||||
"test-zh-2",
|
||||
"test-en-1",
|
||||
"test-en-2"
|
||||
],
|
||||
"fresh_test_file": "crossqkv/fresh_samples.json",
|
||||
"fresh_test_count": 4,
|
||||
"tokens_per_text": 32,
|
||||
"query_positions_for_primary_metrics": [
|
||||
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
|
||||
],
|
||||
"candidate_types": [
|
||||
"full_attention",
|
||||
"qkv_head",
|
||||
"qk_map",
|
||||
"qk_head",
|
||||
"v_group"
|
||||
],
|
||||
"discrete_head_matching": "Exact 8-head minimum mean-JS bijection, two source-layer calibration input distributions; frozen before validation; P injection only",
|
||||
"continuous_alignment": false,
|
||||
"training": false,
|
||||
"scale_fitting": false,
|
||||
"gpu": false,
|
||||
"cost_required": false,
|
||||
"tiers": {
|
||||
"strict": {
|
||||
"output_error": 0.05,
|
||||
"output_cosine": 0.99,
|
||||
"js": 0.01,
|
||||
"js_p95": 0.05,
|
||||
"top5": 0.8,
|
||||
"value_error": 0.05,
|
||||
"value_cosine": 0.99
|
||||
},
|
||||
"approximate": {
|
||||
"output_error": 0.2,
|
||||
"output_cosine": 0.98,
|
||||
"js": 0.05,
|
||||
"js_p95": 0.15,
|
||||
"top5": 0.6,
|
||||
"value_error": 0.2,
|
||||
"value_cosine": 0.98
|
||||
}
|
||||
},
|
||||
"max_qk_head_output_candidates": 32,
|
||||
"max_v_group_output_candidates": 16,
|
||||
"all_qk_map_output_candidates": 30,
|
||||
"max_final_candidate": 1,
|
||||
"selection": "strict before approximate; type priority full_attention/qkv_head/qk_map/qk_head/v_group; then worst output error, JS, ID; no validation-driven fallback",
|
||||
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|
||||
"phase": "done",
|
||||
"started_unix": 1794208453.0788658,
|
||||
"last_source_layer": 23,
|
||||
"elapsed_seconds": 74.78326310915872,
|
||||
"last_pair": [
|
||||
19,
|
||||
23
|
||||
],
|
||||
"selected": [],
|
||||
"peak_rss_kib": 172056,
|
||||
"candidate_counts": {
|
||||
"full_attention": 15,
|
||||
"qkv_head": 960,
|
||||
"qk_map": 30,
|
||||
"qk_head": 960,
|
||||
"v_group": 60
|
||||
},
|
||||
"passing_counts": {
|
||||
"full_attention": 0,
|
||||
"qkv_head": 0,
|
||||
"qk_map": 0,
|
||||
"qk_head": 0,
|
||||
"v_group": 0
|
||||
},
|
||||
"original_11_and_norm_positive_not_used_as_qkv_evidence": true
|
||||
}
|
||||
@@ -0,0 +1,121 @@
|
||||
"""Remote-only independent checks for matching, head decomposition and interventions."""
|
||||
import itertools
|
||||
import json
|
||||
from pathlib import Path
|
||||
import struct
|
||||
import tempfile
|
||||
import unittest
|
||||
import numpy as np
|
||||
from crossqkv.core import (QKVModel,InterventionLM,assignment,inverse,distribution_metrics,
|
||||
output_metrics,cross_head_output_metrics,value_gram_cka)
|
||||
from functional.model import StreamingLM
|
||||
|
||||
|
||||
def fixture(directory):
|
||||
rng=np.random.default_rng(31);arrays={}
|
||||
cfg={'hidden_size':16,'head_dim':4,'num_attention_heads':4,'num_key_value_heads':2,'rms_norm_eps':1e-6,
|
||||
'rope_parameters':{'rope_theta':10000000.,'partial_rotary_factor':.5}}
|
||||
for layer in (0,1):
|
||||
p=f'model.language_model.layers.{layer}.self_attn.'
|
||||
for role,shape in [('q_proj',(32,16)),('k_proj',(8,16)),('v_proj',(8,16)),('o_proj',(16,16)),('q_norm',(4,)),('k_norm',(4,))]:
|
||||
arrays[p+role+'.weight']=(rng.standard_normal(shape)*.15).astype(np.float32)
|
||||
header={};offset=0;body=b''
|
||||
for name,arr in arrays.items():
|
||||
raw=arr.tobytes();header[name]={'shape':list(arr.shape),'dtype':'F32','data_offsets':[offset,offset+len(raw)]};offset+=len(raw);body+=raw
|
||||
h=json.dumps(header).encode();Path(directory,'model.safetensors').write_bytes(struct.pack('<Q',len(h))+h+body)
|
||||
return cfg,rng.standard_normal((2,9,16),dtype=np.float32)
|
||||
|
||||
|
||||
class CrossQKVTests(unittest.TestCase):
|
||||
def test_assignment_against_exhaustive_and_inverse(self):
|
||||
rng=np.random.default_rng(11);cost=rng.uniform(size=(4,4))
|
||||
p=assignment(cost)
|
||||
best=min((sum(cost[i,j] for i,j in enumerate(perm)),perm) for perm in itertools.permutations(range(4)))
|
||||
self.assertEqual(tuple(p),best[1]);self.assertEqual([p[inverse(p)[i]] for i in range(4)],list(range(4)))
|
||||
|
||||
def test_probability_identity_and_nontrivial_queries(self):
|
||||
rng=np.random.default_rng(12);p=np.zeros((2,3,9,9),dtype=np.float32)
|
||||
for t in range(9):
|
||||
x=rng.uniform(size=(2,3,t+1));p[:,:,t,:t+1]=x/x.sum(-1,keepdims=True)
|
||||
metrics=distribution_metrics(p,p)
|
||||
np.testing.assert_allclose(metrics['js'],0,atol=1e-14);np.testing.assert_array_equal(metrics['top5'],np.ones(2))
|
||||
changed=p.copy();changed[:,:,:7]=0
|
||||
# Primary query rows do not include the inevitable first row or t<7.
|
||||
np.testing.assert_allclose(distribution_metrics(p,changed)['js'],0,atol=1e-14)
|
||||
|
||||
def test_profile_matches_canonical_and_head_sum(self):
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
cfg,x=fixture(d);lm=QKVModel(d,cfg,enforce_memory=False)
|
||||
for layer in (0,1):
|
||||
parts=lm.parts(x,layer);expected,p=StreamingLM.full_attention(lm,x,layer)
|
||||
np.testing.assert_allclose(parts['output'],expected,atol=2e-6,rtol=2e-6)
|
||||
np.testing.assert_allclose(parts['p'],p,atol=2e-7)
|
||||
np.testing.assert_allclose(parts['heads'].sum(2),expected,atol=2e-6,rtol=2e-6)
|
||||
np.testing.assert_allclose(lm.transplant(parts,parts,layer,'qk_map',perm=list(range(4))),expected,atol=2e-6)
|
||||
np.testing.assert_allclose(lm.transplant(parts,parts,layer,'qk_head',1,1),expected,atol=2e-6)
|
||||
np.testing.assert_allclose(lm.transplant(parts,parts,layer,'v_group',1,1),expected,atol=2e-6)
|
||||
np.testing.assert_allclose(lm.transplant(parts,parts,layer,'qkv_head',1,1),expected,atol=2e-6)
|
||||
|
||||
def test_recipient_value_gate_output_and_gqa_group_stay_fixed(self):
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
cfg,x=fixture(d);lm=QKVModel(d,cfg,enforce_memory=False);a,b=lm.parts(x,0),lm.parts(x,1)
|
||||
v=a['v'].copy();v[:,:,1]=b['v'][:,:,0]
|
||||
context=np.einsum('bhts,bshd->bthd',a['p'],np.repeat(v,2,axis=2))
|
||||
expected=lm.project(context,a['gate'],0)
|
||||
np.testing.assert_allclose(lm.transplant(a,b,0,'v_group',1,0),expected,atol=2e-6)
|
||||
p=a['p'].copy();p[:,2]=b['p'][:,1]
|
||||
context=np.einsum('bhts,bshd->bthd',p,np.repeat(a['v'],2,axis=2))
|
||||
np.testing.assert_allclose(lm.transplant(a,b,0,'qk_head',2,1),lm.project(context,a['gate'],0),atol=2e-6)
|
||||
|
||||
def test_head_matrix_metrics_and_gram_are_diagnostic(self):
|
||||
rng=np.random.default_rng(13);x=rng.standard_normal((2,9,3,6),dtype=np.float32)
|
||||
y=rng.standard_normal((2,9,2,6),dtype=np.float32);m=cross_head_output_metrics(x,y)
|
||||
for a in range(3):
|
||||
for b in range(2):
|
||||
ref=output_metrics(x[:,:,a],y[:,:,b])
|
||||
for key in ref:np.testing.assert_allclose(m[key][:,a,b],ref[key],atol=1e-12)
|
||||
v=rng.standard_normal((2,9,6),dtype=np.float32);r=np.eye(6,dtype=np.float32)[::-1]
|
||||
np.testing.assert_allclose(value_gram_cka(v,v@r),np.ones(2),atol=1e-12)
|
||||
self.assertGreater(float(np.linalg.norm(v-v@r)),1.)
|
||||
|
||||
def test_cached_mode_rejects_future_state_reuse(self):
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
cfg,x=fixture(d);lm=InterventionLM(d,cfg,enforce_memory=False)
|
||||
c={'i':0,'j':1,'kind':'qk_head','a':2,'b':1,'perm':None}
|
||||
with self.assertRaises(AssertionError):lm.configure(c,reverse=False,cached=True)
|
||||
lm.configure(c,reverse=True,cached=True)
|
||||
lm.full_attention(x,0);out,_=lm.full_attention(x,1)
|
||||
own=lm.parts(x,1);expected=lm.transplant(own,lm.donor_cache,1,'qk_head',1,2)
|
||||
np.testing.assert_allclose(out,expected,atol=2e-6)
|
||||
|
||||
|
||||
def test_controls_cover_multiple_projection_layers(self):
|
||||
from crossqkv.run import controls_for_profiles
|
||||
rng=np.random.default_rng(20)
|
||||
h=rng.standard_normal((2,9,16),dtype=np.float32)
|
||||
probs=np.zeros((2,4,9,9),dtype=np.float32)
|
||||
for t in range(9):probs[:,:,t,:t+1]=1/(t+1)
|
||||
profiles={layer:{'p':probs.copy(),'v':rng.standard_normal((2,9,2,4),dtype=np.float32)} for layer in (0,1)}
|
||||
rows=[];controls_for_profiles(profiles,0,[{'id':'one'},{'id':'two'}],rows,h)
|
||||
self.assertEqual({r['projection_layer'] for r in rows if 'projection_layer' in r},{0,1})
|
||||
self.assertTrue(any(r['control']=='value_gram_random_projection_pair' for r in rows))
|
||||
for r in rows:
|
||||
for value in r.values():
|
||||
if isinstance(value,float):self.assertTrue(np.isfinite(value))
|
||||
|
||||
|
||||
def test_topk_tail_rank_can_change_with_tiny_probability_difference(self):
|
||||
from crossqkv.conditional import tv_metrics
|
||||
a=np.zeros((1,1,9,9),dtype=np.float64);b=a.copy()
|
||||
a[0,0,0,0]=b[0,0,0,0]=1.
|
||||
for t in range(1,9):
|
||||
delta=np.linspace(-1e-6,1e-6,t)
|
||||
a[0,0,t,:t]=.04/t+delta;b[0,0,t,:t]=.04/t-delta
|
||||
a[0,0,t,t]=b[0,0,t,t]=.96
|
||||
metric=distribution_metrics(a,b);tv=tv_metrics(a,b)
|
||||
self.assertLess(float(metric['js'][0]),1e-6)
|
||||
self.assertLess(float(tv['total_variation'][0]),1e-4)
|
||||
self.assertLess(float(metric['top5'][0]),.8)
|
||||
|
||||
|
||||
if __name__=='__main__':unittest.main()
|
||||
Reference in New Issue
Block a user