vllm/.buildkite/nightly-benchmarks/scripts/summary-nightly-results.py

84 lines
2.8 KiB
Python

import datetime
import json
import os
from pathlib import Path
import pandas as pd
from tabulate import tabulate
results_folder = Path("results/")
# serving results and the keys that will be printed into markdown
serving_results = []
serving_column_mapping = {
"test_name": "Test name",
"gpu_type": "GPU",
"completed": "Successful req.",
"request_throughput": "Tput (req/s)",
"mean_ttft_ms": "Mean TTFT (ms)",
"std_ttft_ms": "Std TTFT (ms)",
"median_ttft_ms": "Median TTFT (ms)",
"mean_itl_ms": "Mean ITL (ms)",
"std_itl_ms": "Std ITL (ms)",
"median_itl_ms": "Median ITL (ms)",
"mean_tpot_ms": "Mean TPOT (ms)",
"std_tpot_ms": "Std TPOT (ms)",
"median_tpot_ms": "Median TPOT (ms)",
"total_token_throughput": "Total Token Tput (tok/s)",
"output_throughput": "Output Tput (tok/s)",
"total_input_tokens": "Total input tokens",
"total_output_tokens": "Total output tokens",
"engine": "Engine",
}
if __name__ == "__main__":
# collect results
for test_file in results_folder.glob("*.json"):
with open(test_file, "r") as f:
raw_result = json.loads(f.read())
# attach the benchmarking command to raw_result
with open(test_file.with_suffix(".commands"), "r") as f:
command = json.loads(f.read())
raw_result.update(command)
# update the test name of this result
raw_result.update({"test_name": test_file.stem})
# add the result to raw_result
serving_results.append(raw_result)
continue
serving_results = pd.DataFrame.from_dict(serving_results)
if not serving_results.empty:
serving_results = serving_results[list(
serving_column_mapping.keys())].rename(
columns=serving_column_mapping)
serving_md_table_with_headers = tabulate(serving_results,
headers='keys',
tablefmt='pipe',
showindex=False)
# remove the first line of header
serving_md_table_lines = serving_md_table_with_headers.split('\n')
serving_md_table_without_header = '\n'.join(serving_md_table_lines[2:])
prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
# document benchmarking results in markdown
with open(results_folder / f"{prefix}_nightly_results.md", "w") as f:
# document results with header.
# for those who wants to reproduce our benchmark.
f.write(serving_md_table_with_headers)
f.write('\n')
# document benchmarking results in json
with open(results_folder / f"{prefix}_nightly_results.json", "w") as f:
results = serving_results.to_dict(orient='records')
f.write(json.dumps(results))