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rUv
acddd777a7
Record ADRs 421 to 423, actual dependency integration, bounded input and evidence authority, duration evaluation, security regressions and measured receipts. Preserve negative predictive and caching results; no model promotion.
108 lines
6.5 KiB
Python
108 lines
6.5 KiB
Python
"""Synthetic learning/lineage checks; no occupancy or public-task accuracy."""
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import copy
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import json
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import os
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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 (Predictor, PatchEncoder, make_mask, objective,
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ema_update, synthetic, train, FrozenEncoder, split_training, validate_parts, normalize, _read, _json)
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class PredictiveTests(unittest.TestCase):
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def test_mask_exclusion_gradient_and_stopped_ema(self):
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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(3,52,190);mask=make_mask(3,torch.Generator().manual_seed(7))
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self.assertTrue(torch.equal(mask.sum(1),torch.full((3,),52)))
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self.assertFalse(mask.all())
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# Perturb only masked target patch values; context predictions cannot see them.
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patch_mask=mask.reshape(3,13,10)[:,:,None,:,None].expand(3,13,4,10,19).reshape(3,52,190)
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altered=x.clone();altered[patch_mask]+=100
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np.testing.assert_array_equal(student(x,mask).detach().numpy(),student(altered,mask).detach().numpy())
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loss,diagnostics,target=objective(student,teacher,x,mask)
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self.assertFalse(target.requires_grad);self.assertTrue(torch.isfinite(loss))
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loss.backward()
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self.assertTrue(any(p.grad is not None and p.grad.abs().sum()>0 for p in student.parameters()))
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self.assertTrue(all(p.grad is None for p in teacher.parameters()))
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before=next(teacher.parameters()).clone()
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with torch.no_grad():next(student.encoder.parameters()).add_(.1)
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ema_update(teacher,student)
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torch.testing.assert_close(next(teacher.parameters()),before+.001)
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self.assertTrue(torch.isfinite(diagnostics['sample_std']))
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def test_group_leakage_duplicates_and_original_train_only(self):
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parts,_=synthetic();validate_parts(parts)
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leaking=copy.deepcopy(parts);leaking['heldout']['groups'][:,0]=1
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with self.assertRaisesRegex(ValueError,'leakage'):validate_parts(leaking)
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duplicate=copy.deepcopy(parts);duplicate['heldout']['x'][0]=duplicate['train']['x'][0]
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with self.assertRaisesRegex(ValueError,'Duplicate'):validate_parts(duplicate)
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with self.assertRaises(ValueError):validate_parts(dict(parts,test=parts['heldout']))
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x=np.concatenate([parts['train']['x'],parts['heldout']['x']])
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ids=np.column_stack([np.concatenate([parts['train']['groups'],parts['heldout']['groups']]),np.ones((len(x),2),dtype=int)])
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derived=split_training(x,ids)
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self.assertEqual(len(derived['train']['x']),24);self.assertEqual(len(derived['heldout']['x']),8)
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ids[0,0]=3
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with self.assertRaisesRegex(ValueError,'original training'):split_training(x,ids)
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def test_train_save_load_baselines_and_no_heldout_influence(self):
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parts,lineage=synthetic()
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# Keep CPU verification bounded; no outcome requirement on synthetic loss.
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with tempfile.TemporaryDirectory() as tmp:
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root=Path(tmp);a=train(parts,lineage,root/'a',epochs=1,batch_size=8,max_seconds=120)
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changed=copy.deepcopy(parts);changed['heldout']['x']*=3
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b=train(changed,lineage,root/'b',epochs=1,batch_size=8,max_seconds=120)
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self.assertEqual(a['model_sha256'],b['model_sha256'])
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self.assertEqual(a['history'],b['history'])
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self.assertEqual(set(a['heldout_latent_mse']),{'trained','untrained','constant','position_constant'})
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self.assertTrue(all(np.isfinite(v) for v in a['heldout_latent_mse'].values()))
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self.assertEqual(len(a['inference_latency']['rounds']),3)
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self.assertEqual(a['inference_latency']['calls_per_round'],100)
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self.assertIn('collapse_warning',a['anti_collapse']);self.assertFalse(a['promotionAuthorized'])
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bundle=FrozenEncoder(root/'a');second=FrozenEncoder(root/'a')
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values=parts['heldout']['x'];np.testing.assert_array_equal(bundle.encode(values),second.encode(values))
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self.assertEqual(bundle.encode(values[:1]).shape,(1,32))
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logged=np.log(np.maximum(parts['train']['x'].astype(np.float64),1e-6))
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np.testing.assert_array_equal(bundle.mean,logged.mean((0,2)))
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self.assertTrue(all(not p.requires_grad for p in bundle.encoder.parameters()))
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adapter=bundle.new_adapter(3);adapter(values).sum().backward()
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self.assertTrue(all(p.grad is None for p in bundle.encoder.parameters()))
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self.assertIsNotNone(adapter.head.weight.grad)
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path=root/'a'/'model.safetensors';path.write_bytes(path.read_bytes()+b'corrupt')
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with self.assertRaisesRegex(ValueError,'hash mismatch'):FrozenEncoder(root/'a')
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with self.assertRaises(FileExistsError):train(parts,lineage,root/'b',epochs=1)
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def test_failure_lifecycle_and_bounds(self):
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parts,lineage=synthetic()
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with tempfile.TemporaryDirectory() as tmp:
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root=Path(tmp)/'failed'
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with patch('csi_training.predictive.objective',side_effect=RuntimeError('injected')):
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with self.assertRaises(RuntimeError):train(parts,lineage,root,epochs=1)
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self.assertEqual(json.loads((root/'status.json').read_text())['status'],'failed')
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with self.assertRaises(ValueError):FrozenEncoder(root)
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for bad in [True,False,float('nan'),float('inf')]:
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with self.assertRaises(ValueError):train(parts,lineage,'unused',max_seconds=bad)
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for x in [np.ones((1,52,189)),np.full((2,52,190),np.nan),-np.ones((2,52,190))]:
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with self.assertRaises(ValueError):normalize(x,np.zeros(52),np.ones(52))
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def test_strict_json_and_opened_file_security(self):
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for text in ['{"a":1,"a":2}','{"a":NaN}','{"a":Infinity}','{"a":1e999}']:
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with self.assertRaises(ValueError):_json(text)
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with tempfile.TemporaryDirectory() as tmp:
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root=Path(tmp);regular=root/'regular';regular.write_bytes(b'123')
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self.assertEqual(_read(regular,3),b'123')
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with self.assertRaises(ValueError):_read(regular,2)
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link=root/'link';link.symlink_to(regular)
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with self.assertRaises(ValueError):_read(link,100)
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if hasattr(os,'mkfifo'):
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fifo=root/'fifo';os.mkfifo(fifo)
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with self.assertRaises(ValueError):_read(fifo,100)
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with self.assertRaises(ValueError):_read(root,100)
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if __name__=='__main__':unittest.main()
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