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https://huggingface.co/datasets/Tiiny/Speed-Bench/resolve/main/report.py
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hf download hf://datasets/Tiiny/Speed-Bench/report.py
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curl -L -o report.py https://huggingface.co/datasets/Tiiny/Speed-Bench/resolve/main/report.py
3.42 kB
| #!/usr/bin/env python3 | |
| """Turn bench.py result files into the standard speed report table. | |
| Dataset | Context | Input Tokens | Output Tokens | Prefill (tok/s) | TTFT (s) | Decode (tok/s) | E2E Latency (s) | |
| One row per (dataset, context tier); every value is the mean over that tier's samples. | |
| Failed requests (error != null) are excluded from the means and counted in a trailing note. | |
| Usage: | |
| python report.py results/bench_32_165_*.json [--out report.md] | |
| python report.py # default: every results/*.json | |
| """ | |
| import argparse | |
| import glob | |
| import json | |
| import os | |
| import statistics as st | |
| HERE = os.path.dirname(os.path.abspath(__file__)) | |
| TIER_ORDER = {'1k': 0, '4k': 1, '64k': 2, '128k': 3} | |
| COLUMNS = ('Dataset', 'Context', 'Input Tokens', 'Output Tokens', 'Prefill (tok/s)', | |
| 'TTFT (s)', 'Decode (tok/s)', 'E2E Latency (s)') | |
| def load_rows(paths: list) -> list: | |
| rows = [] | |
| for path in paths: | |
| with open(path) as fh: | |
| payload = json.load(fh) | |
| for r in payload.get('results', []): | |
| r.setdefault('tier', r.get('scale') or f"{r.get('length', 0) // 1024}k") | |
| r.setdefault('task_family', payload.get('dataset') or 'all') | |
| rows.append(r) | |
| return rows | |
| def mean(values: list) -> float: | |
| values = [v for v in values if v is not None] | |
| return st.mean(values) if values else float('nan') | |
| def build_table(rows: list) -> list: | |
| groups: dict = {} | |
| errors: dict = {} | |
| for r in rows: | |
| key = (r.get('task_family') or 'all', r.get('tier') or '?') | |
| (groups if not r.get('error') else errors).setdefault(key, []).append(r) | |
| table = [] | |
| for key in sorted(groups, key=lambda k: (k[0], TIER_ORDER.get(k[1], 9), k[1])): | |
| g = groups[key] | |
| table.append({ | |
| 'Dataset': key[0], | |
| 'Context': key[1], | |
| 'Input Tokens': round(mean([r.get('prompt_tokens') for r in g])), | |
| 'Output Tokens': round(mean([r.get('completion_tokens') for r in g])), | |
| 'Prefill (tok/s)': round(mean([r.get('prefill_tok_s') for r in g]), 1), | |
| 'TTFT (s)': round(mean([r.get('ttft_s') for r in g]), 2), | |
| 'Decode (tok/s)': round(mean([r.get('decode_tok_s') for r in g]), 1), | |
| 'E2E Latency (s)': round(mean([r.get('wall_s') for r in g]), 2), | |
| 'n': len(g), | |
| 'errors': len(errors.get(key, [])), | |
| }) | |
| return table | |
| def render(table: list) -> str: | |
| lines = ['| ' + ' | '.join(COLUMNS) + ' |', '|' + '|'.join(['---'] * len(COLUMNS)) + '|'] | |
| for row in table: | |
| lines.append('| ' + ' | '.join(str(row[c]) for c in COLUMNS) + ' |') | |
| return '\n'.join(lines) | |
| def main() -> None: | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument('results', nargs='*') | |
| ap.add_argument('--out') | |
| a = ap.parse_args() | |
| paths = a.results or sorted(glob.glob(os.path.join(HERE, 'results', '*.json'))) | |
| paths = [p for p in paths if os.path.basename(p) not in ('config.json',)] | |
| if not paths: | |
| raise SystemExit('no result files found') | |
| table = build_table(load_rows(paths)) | |
| text = render(table) | |
| print(text) | |
| bad = sum(row['errors'] for row in table) | |
| if bad: | |
| print(f'\n注:{bad} 条请求失败,未计入均值。') | |
| if a.out: | |
| with open(a.out, 'w') as fh: | |
| fh.write(text + '\n') | |
| print(f'\n-> {a.out}') | |
| if __name__ == '__main__': | |
| main() | |