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1b62586 | 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 | #!/usr/bin/env python3
"""Run the released SEGALE-COMET pipeline on the example or benchmark data."""
import argparse
import hashlib
import json
import os
import statistics
import subprocess
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parent
BANDS = ['4k', '8k', '16k', '32k', '64k']
def read_rows(path):
with path.open(encoding='utf-8') as stream:
return [json.loads(line) for line in stream if line.strip()]
def prepare_generations(rows):
"""Accept case_id/mt; preserve supplied metadata, mark absent values unknown."""
result = []
for row in rows:
item = dict(row)
item.setdefault('status', 'ok')
if item['status'] == 'ok' and isinstance(item.get('mt'), str):
for key in ['finish_reason', 'input_tokens', 'output_tokens', 'cap_hit',
'model_revision', 'generation_config_sha256']:
item.setdefault(key, None)
item.setdefault('output_sha256', hashlib.sha256(item['mt'].encode()).hexdigest())
result.append(item)
return result
def five_band_macro(summary):
groups = summary['groups'].get('length_band', {})
means = {band: groups.get(band, {}).get('segale_comet') for band in BANDS}
return {'value': statistics.fmean(means.values())
if all(x is not None for x in means.values()) else None,
'scale': [0, 1], 'band_means': means,
'coverage': {band: {'scored': groups.get(band, {}).get('scored_cases', 0),
'total': groups.get(band, {}).get('cases', 0)} for band in BANDS}}
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--example', action='store_true')
parser.add_argument('--data-root', type=Path)
parser.add_argument('--generations', type=Path)
parser.add_argument('--output', type=Path, required=True, help='New output directory')
parser.add_argument('--device', choices=['cpu', 'cuda'], default='cpu')
parser.add_argument('--batch-size', type=int, default=8)
parser.add_argument('--case-workers', type=int, default=1)
args = parser.parse_args()
if args.example:
if args.data_root or args.generations:
parser.error('--example supplies both data and predictions')
args.data_root = ROOT / 'example'
args.generations = ROOT / 'example/generations.jsonl'
if not args.data_root or not args.generations:
parser.error('provide --example, or --data-root and --generations')
if args.batch_size < 1 or args.case_workers < 1:
parser.error('batch size and case workers must be positive')
if args.output.exists():
parser.error('--output must be a new directory')
from huggingface_hub import hf_hub_download
models = json.loads((ROOT / 'models.json').read_text())
resolved = {}
for name, spec in models.items():
filename = 'checkpoints/model.ckpt' if name == 'comet' else 'config.json'
resolved[name] = Path(hf_hub_download(spec['repo_id'], filename,
revision=spec['revision'], local_files_only=True))
args.output.mkdir(parents=True)
generations = args.output / 'generations.jsonl'
generations.write_text(''.join(json.dumps(r, ensure_ascii=False) + '\n'
for r in prepare_generations(read_rows(args.generations))), encoding='utf-8')
cases = args.data_root / 'cases.jsonl'
units = args.data_root / 'segale/alignment-units.jsonl'
adapter = args.output / 'adapter'
comet = args.output / 'comet'
chrf = args.output / 'chrf2'
env = os.environ.copy()
env['PYTHONPATH'] = os.pathsep.join([str(ROOT / 'vendor/segale'), str(ROOT / 'scripts')])
env['HF_HUB_OFFLINE'] = '1'
env['TRANSFORMERS_OFFLINE'] = '1'
env['TOKENIZERS_PARALLELISM'] = 'false'
env.setdefault('OMP_NUM_THREADS', '4')
identity = ['--suite-id', 'nativelongbenchmark', '--system-key', 'prediction']
def run(script, *values):
subprocess.run([sys.executable, str(ROOT / 'scripts' / script),
*map(str, values)], env=env, check=True)
run('score_document_chrf.py', '--data-manifest', args.data_root / 'manifest.json',
'--cases', cases, '--alignment-units', units, '--generations', generations,
'--output-dir', chrf, '--group-by', 'length_band', *identity)
run('prepare_document_segale.py', '--cases', cases, '--alignment-units', units,
'--generations', generations, '--output-dir', adapter, '--allow-incomplete', *identity)
selected = json.loads((adapter / 'manifest.json').read_text())['cases']
if selected:
run('run_document_segale_alignment.py', '--system-file', adapter / 'system.jsonl',
'--ref-file', adapter / 'reference.jsonl', '--task-lang', 'en',
'--proc-device', args.device, '--embedding-model', resolved['bge'].parent,
'--max-size', '8', '--scratch-dir', args.output / 'scratch',
'--case-workers', args.case_workers, '--search-mode', 'online-stop')
run('evaluate_comet.py', '--input-file', adapter / 'system/aligned_spacy_system.jsonl',
'--manifest', adapter / 'manifest.json', '--output-dir', comet,
'--model-checkpoint', resolved['comet'], '--encoder-model', resolved['encoder'].parent,
'--batch-size', args.batch_size, '--gpus', int(args.device == 'cuda'),
'--spacy-model', 'en_core_web_sm',
'--target-sentences', adapter / 'system/target_sentences.jsonl')
else:
comet.mkdir()
(comet / 'summary.json').write_text(json.dumps({'cases': []}) + '\n')
run('summarize_document_segale.py', '--cases', cases, '--generations', generations,
'--comet-summary', comet / 'summary.json', '--output', args.output / 'summary.json',
'--group-by', 'work_id', '--group-by', 'length_band', '--chrf-dir', chrf, *identity)
summary = json.loads((args.output / 'summary.json').read_text())
summary['five_band_macro'] = five_band_macro(summary)
summary['evaluation_profile'] = {'max_size': 8, 'segmenter': 'en_core_web_sm-3.8.0',
'embedding_max_tokens': 512, 'search_mode': 'online-stop', 'device': args.device,
'batch_size': args.batch_size, 'case_workers': args.case_workers,
'models': {k: {'repo_id': v['repo_id'], 'revision': v['revision']} for k, v in models.items()}}
(args.output / 'summary.json').write_text(json.dumps(summary, ensure_ascii=False, indent=2) + '\n')
print(json.dumps({'case_count': summary['case_count'], 'scored': summary['scored_case_count'],
'segale_comet': summary['scored']['segale_comet'],
'five_band_macro': summary['five_band_macro']['value'],
'summary': str(args.output / 'summary.json')}, indent=2))
if __name__ == '__main__':
main()
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