[CI][Benchmark] Optimize performance benchmark workflow (#1039)
### What this PR does / why we need it? This is a post patch of #1014, for some convenience optimization - Set cached dataset path for speed - Use pypi to install escli-tool - Add benchmark results convert script to have a developer-friendly result - Patch the `benchmark_dataset.py` to disable streaming load for internet - Add more trigger ways for different purpose, `pr` for debug, `schedule` for daily test, `dispatch` and `pr-labled` for manual testing of a single(current) commit - Disable latency test for `qwen-2.5-vl`, (This script does not support multi-modal yet) ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? CI passed --------- Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
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benchmarks/scripts/convert_json_to_markdown.py
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183
benchmarks/scripts/convert_json_to_markdown.py
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import argparse
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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 pandas as pd
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from tabulate import tabulate
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CUR_PATH = Path(__file__).parent.resolve()
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# latency results and the keys that will be printed into markdown
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latency_results = []
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latency_column_mapping = {
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"test_name": "Test name",
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"avg_latency": "Mean latency (ms)",
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"P50": "Median latency (ms)",
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"P99": "P99 latency (ms)",
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}
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# throughput tests and the keys that will be printed into markdown
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throughput_results = []
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throughput_results_column_mapping = {
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"test_name": "Test name",
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"num_requests": "Num of reqs",
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"total_num_tokens": "Total num of tokens",
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"elapsed_time": "Elapsed time (s)",
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"requests_per_second": "Tput (req/s)",
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"tokens_per_second": "Tput (tok/s)",
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}
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# serving results and the keys that will be printed into markdown
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serving_results = []
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serving_column_mapping = {
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"test_name": "Test name",
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"request_rate": "Request rate (req/s)",
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"request_throughput": "Tput (req/s)",
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"output_throughput": "Output Tput (tok/s)",
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"median_ttft_ms": "TTFT (ms)",
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"median_tpot_ms": "TPOT (ms)",
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"median_itl_ms": "ITL (ms)",
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}
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def read_markdown(file):
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if os.path.exists(file):
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with open(file) as f:
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return f.read() + "\n"
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else:
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return f"{file} not found.\n"
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def results_to_json(latency, throughput, serving):
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return json.dumps({
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'latency': latency.to_dict(),
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'throughput': throughput.to_dict(),
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'serving': serving.to_dict()
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})
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Process the results of the benchmark tests.")
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parser.add_argument(
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"--results_folder",
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type=str,
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default="../results/",
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help="The folder where the benchmark results are stored.")
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parser.add_argument(
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"--output_folder",
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type=str,
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default="../results/",
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help="The folder where the benchmark results are stored.")
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parser.add_argument("--markdown_template",
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type=str,
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default="./perf_result_template.md",
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help="The template file for the markdown report.")
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parser.add_argument("--tag",
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default="main",
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help="Tag to be used for release message.")
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parser.add_argument("--commit_id",
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default="",
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help="Commit ID to be used for release message.")
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args = parser.parse_args()
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results_folder = (CUR_PATH / args.results_folder).resolve()
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output_folder = (CUR_PATH / args.output_folder).resolve()
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markdown_template = (CUR_PATH / args.markdown_template).resolve()
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# collect results
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for test_file in results_folder.glob("*.json"):
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with open(test_file) as f:
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raw_result = json.loads(f.read())
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if "serving" in str(test_file):
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# this result is generated via `benchmark_serving.py`
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# update the test name of this result
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raw_result.update({"test_name": test_file.stem})
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# add the result to raw_result
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serving_results.append(raw_result)
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continue
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elif "latency" in f.name:
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# this result is generated via `benchmark_latency.py`
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# update the test name of this result
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raw_result.update({"test_name": test_file.stem})
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# get different percentiles
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for perc in [10, 25, 50, 75, 90, 99]:
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# Multiply 1000 to convert the time unit from s to ms
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raw_result.update(
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{f"P{perc}": 1000 * raw_result["percentiles"][str(perc)]})
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raw_result["avg_latency"] = raw_result["avg_latency"] * 1000
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# add the result to raw_result
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latency_results.append(raw_result)
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continue
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elif "throughput" in f.name:
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# this result is generated via `benchmark_throughput.py`
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# update the test name of this result
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raw_result.update({"test_name": test_file.stem})
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# add the result to raw_result
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throughput_results.append(raw_result)
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continue
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print(f"Skipping {test_file}")
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serving_results.sort(key=lambda x: (len(x['test_name']), x['test_name']))
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latency_results = pd.DataFrame.from_dict(latency_results)
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serving_results = pd.DataFrame.from_dict(serving_results)
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throughput_results = pd.DataFrame.from_dict(throughput_results)
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raw_results_json = results_to_json(latency_results, throughput_results,
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serving_results)
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# remapping the key, for visualization purpose
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if not latency_results.empty:
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latency_results = latency_results[list(
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latency_column_mapping.keys())].rename(
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columns=latency_column_mapping)
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if not serving_results.empty:
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serving_results = serving_results[list(
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serving_column_mapping.keys())].rename(
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columns=serving_column_mapping)
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if not throughput_results.empty:
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throughput_results = throughput_results[list(
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throughput_results_column_mapping.keys())].rename(
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columns=throughput_results_column_mapping)
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processed_results_json = results_to_json(latency_results,
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throughput_results,
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serving_results)
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# get markdown tables
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latency_md_table = tabulate(latency_results,
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headers='keys',
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tablefmt='pipe',
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showindex=False)
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serving_md_table = tabulate(serving_results,
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headers='keys',
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tablefmt='pipe',
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showindex=False)
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throughput_md_table = tabulate(throughput_results,
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headers='keys',
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tablefmt='pipe',
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showindex=False)
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# document the result
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print(output_folder)
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with open(output_folder / "benchmark_results.md", "w") as f:
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results = read_markdown(markdown_template)
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results = results.format(
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latency_tests_markdown_table=latency_md_table,
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throughput_tests_markdown_table=throughput_md_table,
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serving_tests_markdown_table=serving_md_table,
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benchmarking_results_in_json_string=processed_results_json)
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f.write(results)
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