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https://github.com/ruvnet/RuView.git
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85 lines
4.9 KiB
Python
85 lines
4.9 KiB
Python
"""Synthetic fixed-arm, gradient, baseline reproduction and artifact contracts."""
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import copy
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import json
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from pathlib import Path
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import sys
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import tempfile
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import unittest
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from unittest.mock import patch
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import numpy as np
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import torch
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sys.path.insert(0,str(Path(__file__).resolve().parents[1]))
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from csi_training.predictive import synthetic, train, Predictor, make_mask
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from csi_training.predictive_transfer import (SPEC, record_penalties, transfer_objective,
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TransferEncoder, run)
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def fixture():
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parts,lineage=synthetic()
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for part in parts.values():part['y']=np.arange(len(part['x']),dtype=np.int64)%7
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return parts,lineage
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class TransferTests(unittest.TestCase):
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def test_record_regularization_gradient_and_exact_baseline_objective(self):
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from csi_training.predictive import objective
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torch.manual_seed(42);torch.set_num_threads(1)
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student=Predictor();teacher=copy.deepcopy(student.encoder).eval().requires_grad_(False)
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x=torch.randn(4,52,190);mask=make_mask(4,torch.Generator().manual_seed(42))
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baseline,_,_=transfer_objective(student,teacher,x,mask,'baseline')
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expected,_,_=objective(student,teacher,x,mask)
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torch.testing.assert_close(baseline,expected,rtol=0,atol=0)
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candidate,diagnostic,target=transfer_objective(student,teacher,x,mask,'candidate')
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variance,covariance=record_penalties(student.encoder(x).mean(1))
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torch.testing.assert_close(candidate,expected+variance+.1*covariance)
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candidate.backward()
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self.assertFalse(target.requires_grad)
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self.assertTrue(all(p.grad is None for p in teacher.parameters()))
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self.assertTrue(any(p.grad is not None and p.grad.abs().sum()>0 for p in student.encoder.parameters()))
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collapsed=torch.zeros(4,32);v,c=record_penalties(collapsed)
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self.assertAlmostEqual(float(v),.99,places=6);self.assertEqual(float(c),0.)
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self.assertIn('record_covariance',diagnostic)
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def test_fixed_campaign_reproduces_original_and_ignores_dev_updates(self):
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parts,lineage=fixture()
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with tempfile.TemporaryDirectory() as tmp:
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root=Path(tmp)
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original=train(parts,lineage,root/'original',epochs=5,batch_size=8,max_seconds=120)
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reference=root/'original'/'model.safetensors'
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first=run(parts,lineage,root/'first',max_seconds=240,baseline_artifact=reference)
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changed=copy.deepcopy(parts);changed['heldout']['x']*=1.2
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changed['heldout']['y']=(changed['heldout']['y']+1)%7
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second=run(changed,lineage,root/'second',max_seconds=240,baseline_artifact=reference)
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self.assertTrue(first['original_baseline_exact'])
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self.assertEqual(first['models']['baseline']['model_sha256'],original['model_sha256'])
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self.assertEqual(len(set(first['mask_order_sha256'].values())),1)
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for name in ('baseline','candidate','untrained'):
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self.assertEqual(first['models'][name]['model_sha256'],second['models'][name]['model_sha256'])
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loaded=TransferEncoder(root/'first'/name)
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self.assertEqual(loaded.encode(parts['heldout']['x']).shape,(8,32))
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self.assertTrue(all(not p.requires_grad for p in loaded.encoder.parameters()))
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self.assertEqual(len(first['inference_latency'][name]),3)
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self.assertEqual(first['histories'],second['histories'])
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self.assertEqual(first['spec'],SPEC);self.assertFalse(first['promotionAuthorized'])
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self.assertEqual(set(first['probes']['probes']),{'baseline','candidate','untrained','statistics'})
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frozen=json.loads((root/'first'/'representation_frozen.json').read_text())
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self.assertNotIn('probes',frozen)
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model=root/'first'/'candidate'/'model.safetensors';model.write_bytes(model.read_bytes()+b'bad')
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with self.assertRaisesRegex(ValueError,'hash mismatch'):TransferEncoder(model.parent)
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def test_failure_provenance_budget_and_no_overwrite(self):
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parts,lineage=fixture()
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with tempfile.TemporaryDirectory() as tmp:
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root=Path(tmp)
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for budget in [True,float('nan'),float('inf'),0]:
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with self.assertRaises(ValueError):run(parts,lineage,root/'invalid',max_seconds=budget)
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with self.assertRaisesRegex(ValueError,'required'):
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run(parts,dict(lineage,evidence='PUBLIC_TRAIN_ONLY'),root/'missing')
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with patch('csi_training.predictive_transfer.transfer_objective',side_effect=RuntimeError('injected')):
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with self.assertRaises(RuntimeError):run(parts,lineage,root/'failed')
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self.assertEqual(json.loads((root/'failed'/'status.json').read_text())['status'],'failed')
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with self.assertRaises(FileExistsError):run(parts,lineage,root/'failed')
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with self.assertRaises(ValueError):run(dict(parts,test=parts['heldout']),lineage,root/'test')
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if __name__=='__main__':unittest.main()
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