File size: 9,971 Bytes
2d5c26a | 1 2 3 4 5 6 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | """Verify a trusted trained checkpoint, longest training inputs and real HTTP.
No optimizer step is taken and the source checkpoint is never changed. Optimizer
state is restored so the stress pass measures the memory needed during training.
"""
import argparse
from datetime import datetime, timezone
import fcntl
import json
import os
from pathlib import Path
import sys
import threading
import time
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
import torch
from experiment import (data_for, decision_backward, guard_memory, load_artifact,
predict, sha256, write_json)
from jev_harness import LocalBackend, RemoteBackend, compile_request, create_server
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--checkpoint', required=True)
parser.add_argument('--reference', required=True)
parser.add_argument('--output', required=True)
parser.add_argument('--inference-max-tokens',type=int,default=1024)
args = parser.parse_args()
lock_path = Path.home()/'ai/opensysone/runs/.smoke.lock'
lock = lock_path.open('w')
try:
fcntl.flock(lock,fcntl.LOCK_EX | fcntl.LOCK_NB)
except BlockingIOError:
raise RuntimeError('Another project run holds the model-load lock') from None
out = Path(args.output).resolve()
out.mkdir(parents=True, exist_ok=False)
before = sha256(args.checkpoint)
write_json(out/'manifest.json',{'pid':os.getpid(),'checkpoint_sha256':before,
'started_utc':datetime.now(timezone.utc).isoformat(),
'verification_source_sha256':sha256(__file__)})
guard_memory()
scorer, artifact = load_artifact(args.checkpoint)
if not 256 <= args.inference_max_tokens <= scorer.lm.config.max_position_embeddings:
raise ValueError('Verification inference limit must be 256 to the base context limit')
config = artifact['config']
data, signature = data_for(scorer, config['dataset'], out)
if signature != artifact['data_signature']:
raise ValueError('Frozen data/model signature mismatch')
reference = json.loads(Path(args.reference).read_text())
by_id = {row['id']: row for row in reference}
selected = []
for family in sorted({r['family'] for r in data['validation']}):
selected.extend([r for r in data['validation'] if r['family'] == family][:4])
fresh = predict(scorer, selected)
max_difference = max(abs(a-b) for row in fresh
for a,b in zip(row['probabilities'], by_id[row['id']]['probabilities']))
if max_difference > 1e-4:
raise RuntimeError(f'Fresh reconstruction differs: {max_difference}')
write_json(out / 'reload_predictions.json', fresh)
parameters = [p for p in scorer.parameters() if p.requires_grad]
optimizer = torch.optim.AdamW([
{'params': [p for p in scorer.lm.parameters() if p.requires_grad], 'lr': config['lr']},
{'params': scorer.head.parameters(), 'lr': config['head_lr']}], weight_decay=0.01)
optimizer.load_state_dict(artifact['optimizer'])
# Longest complete decision in each family, plus each candidate-count class.
stress_rows = {}
for key in sorted({r['family'] for r in data['train']}):
row = max((r for r in data['train'] if r['family']==key),
key=lambda r:max(map(len,r['_sequences'])))
stress_rows[row['id']] = row
for count in sorted({len(r['choices']) for r in data['train']}):
row = max((r for r in data['train'] if len(r['choices'])==count),
key=lambda r:max(map(len,r['_sequences'])))
stress_rows[row['id']] = row
stress = []
for row in stress_rows.values():
scorer.train()
optimizer.zero_grad(set_to_none=True)
loss = decision_backward(scorer,row,1,config.get('two_pass',False))
norm = torch.nn.utils.clip_grad_norm_(parameters,1.0,error_if_nonfinite=True)
stress.append({'id':row['id'],'family':row['family'],'branches':len(row['choices']),
'max_branch_tokens':max(map(len,row['_sequences'])),
'loss':loss,'gradient_norm':norm.item(),
'peak_cuda_allocated_bytes':torch.cuda.max_memory_allocated()})
write_json(out / 'stress.json', stress)
optimizer.zero_grad(set_to_none=True)
training_peak = torch.cuda.max_memory_allocated()
training_reserved = torch.cuda.max_memory_reserved()
del optimizer
torch.cuda.reset_peak_memory_stats()
scorer.eval()
backend = LocalBackend.__new__(LocalBackend)
backend.scorer, backend.temperature = scorer, artifact.get('temperature',1.0)
backend.checkpoint, backend.calibrated = str(Path(args.checkpoint).resolve()), 'temperature' in artifact
backend.max_tokens = scorer.max_tokens
backend.model_name = 'opensysone-'+artifact['model_provenance']['model_id'].split('/')[-1].lower()
payload = json.loads((Path(__file__).resolve().parents[1]/'examples/jev_request.json').read_text())
direct = backend(payload)
server = create_server(backend,port=0,api_key='local-verification-only')
worker = threading.Thread(target=server.serve_forever,daemon=True)
worker.start()
try:
remote = RemoteBackend(f'http://127.0.0.1:{server.server_address[1]}',
api_key='local-verification-only',attempts=1)
tick = time.perf_counter()
response = remote(payload)
elapsed = time.perf_counter()-tick
if response != direct:
raise RuntimeError('HTTP response differs from direct trained-model inference')
try:
RemoteBackend(remote.base_url,api_key='wrong-key',attempts=1)(payload)
except RuntimeError as error:
if str(error) != 'Jev HTTP 401; request failed':
raise
else:
raise RuntimeError('HTTP authentication did not reject an invalid key')
try:
remote({**payload,'state':'oversized '* (config['max_tokens']*2)})
except RuntimeError as error:
if str(error) != 'Jev HTTP 422; request failed':
raise
else:
raise RuntimeError('HTTP did not reject an oversized complete candidate')
# Inference has no gradient graphs or optimizer-step temporaries. Verify
# a useful larger API context separately from the training token limit.
backend.scorer.max_tokens = backend.max_tokens = args.inference_max_tokens
for words in range(args.inference_max_tokens,32,-8):
long_payload = {'model':'opensysone','state':'background '*words+'Please help today.',
'questions':{'urgent':{'type':'noul','instructions':'Does the customer express urgency?'}}}
try:
long_sequences = scorer.sequences(compile_request(long_payload)[0][4])
break
except ValueError as error:
if 'no truncation' not in str(error):
raise
else:
raise RuntimeError('Could not construct a bounded long-context request')
if max(map(len,long_sequences)) < args.inference_max_tokens-24:
raise RuntimeError('Long-context probe did not reach its intended token length')
long_direct = backend(long_payload)
long_response = remote(long_payload)
if long_response != long_direct:
raise RuntimeError('Long-context HTTP output differs from direct inference')
write_json(out/'http_long_context_response.json',long_response)
large_payload = {'model':'opensysone','state':'A customer needs help with a failed payment.',
'questions':{'routing':{'type':'choice',
'instructions':'Which candidate routing category best fits the request?',
'criteria':{f'category_{i}':f'Routing category {i}' for i in range(255)}}}}
remote.timeout = 300
tick = time.perf_counter()
large_response = remote(large_payload)
large_elapsed = time.perf_counter()-tick
if len(large_response['answers']['routing']['probabilities']) != 255:
raise RuntimeError('HTTP maximum-choice request lost alternatives')
write_json(out/'http_255_choices_response.json',large_response)
finally:
server.shutdown()
server.server_close()
worker.join(timeout=5)
write_json(out / 'http_response.json', response)
if sha256(args.checkpoint) != before:
raise RuntimeError('Verification changed the source checkpoint')
result = {'status':'passed','checkpoint':backend.checkpoint,'checkpoint_sha256':before,
'step':artifact['step'],'fresh_reload_decisions':len(fresh),
'fresh_reload_probability_max_abs':max_difference,
'optimizer_state_restored':True,'optimizer_steps_taken':0,
'longest_input_stress':stress,'http_model':backend.model_name,
'http_matches_direct':True,'http_invalid_key_status':401,
'http_oversized_input_status':422,'http_255_choices':True,
'inference_max_tokens':args.inference_max_tokens,'http_long_context_branch_tokens':list(map(len,long_sequences)),
'http_long_context_matches_direct':True,
'http_255_choices_seconds':large_elapsed,
'http_end_to_end_seconds':elapsed,'temperature_fitted':backend.calibrated,
'training_peak_cuda_allocated_bytes':training_peak,
'inference_peak_cuda_allocated_bytes':torch.cuda.max_memory_allocated(),
'peak_cuda_allocated_bytes':max(training_peak,torch.cuda.max_memory_allocated()),
'peak_cuda_reserved_bytes':max(training_reserved,torch.cuda.max_memory_reserved())}
write_json(out / 'verification.json', result)
print(json.dumps(result),flush=True)
if __name__ == '__main__':
main()
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