add qwen3
This commit is contained in:
Chranos
@@ -0,0 +1,192 @@
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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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results_folder = Path("results/")
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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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"gpu_type": "GPU",
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"avg_latency": "Mean latency (ms)",
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# "P10": "P10 (s)",
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# "P25": "P25 (s)",
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"P50": "Median latency (ms)",
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# "P75": "P75 (s)",
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# "P90": "P90 (s)",
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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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"gpu_type": "GPU",
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# "num_requests": "# of req.",
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# "total_num_tokens": "Total # 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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"gpu_type": "GPU",
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# "completed": "# of req.",
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"request_throughput": "Tput (req/s)",
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# "input_throughput": "Input Tput (tok/s)",
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# "output_throughput": "Output Tput (tok/s)",
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"mean_ttft_ms": "Mean TTFT (ms)",
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"median_ttft_ms": "Median TTFT (ms)",
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"p99_ttft_ms": "P99 TTFT (ms)",
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# "mean_tpot_ms": "Mean TPOT (ms)",
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# "median_tpot_ms": "Median",
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# "p99_tpot_ms": "P99",
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"mean_itl_ms": "Mean ITL (ms)",
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"median_itl_ms": "Median ITL (ms)",
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"p99_itl_ms": "P99 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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# 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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# attach the benchmarking command to raw_result
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with open(test_file.with_suffix(".commands")) as f:
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command = json.loads(f.read())
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raw_result.update(command)
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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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# attach the benchmarking command to raw_result
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with open(test_file.with_suffix(".commands")) as f:
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command = json.loads(f.read())
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raw_result.update(command)
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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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# attach the benchmarking command to raw_result
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with open(test_file.with_suffix(".commands")) as f:
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command = json.loads(f.read())
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raw_result.update(command)
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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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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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with open(results_folder / "benchmark_results.md", "w") as f:
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results = read_markdown("../.buildkite/nightly-benchmarks/" +
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"performance-benchmarks-descriptions.md")
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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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# document benchmarking results in json
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with open(results_folder / "benchmark_results.json", "w") as f:
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results = latency_results.to_dict(
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orient='records') + throughput_results.to_dict(
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orient='records') + serving_results.to_dict(orient='records')
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f.write(json.dumps(results))
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@@ -0,0 +1,26 @@
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import argparse
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from transformers import AutoTokenizer
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def main(model, cachedir):
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# Load the tokenizer and save it to the specified directory
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tokenizer = AutoTokenizer.from_pretrained(model)
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tokenizer.save_pretrained(cachedir)
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print(f"Tokenizer saved to {cachedir}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Download and save Hugging Face tokenizer")
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parser.add_argument("--model",
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type=str,
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required=True,
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help="Name of the model")
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parser.add_argument("--cachedir",
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type=str,
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required=True,
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help="Directory to save the tokenizer")
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args = parser.parse_args()
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main(args.model, args.cachedir)
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@@ -0,0 +1,95 @@
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from tabulate import tabulate
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def parse_arguments():
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parser = argparse.ArgumentParser(
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description=
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'Parse command line arguments for summary-nightly-results script.')
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parser.add_argument('--results-folder',
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type=str,
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required=True,
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help='The folder where the results are stored.')
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parser.add_argument('--description',
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type=str,
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required=True,
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help='Description of the results.')
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args = parser.parse_args()
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return args
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def get_perf(df, method, model, metric):
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means = []
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for qps in [2, 4, 8, 16, "inf"]:
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target = df['Test name'].str.contains(model)
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target = target & df['Engine'].str.contains(method)
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target = target & df['Test name'].str.contains("qps_" + str(qps))
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filtered_df = df[target]
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if filtered_df.empty:
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means.append(0.)
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else:
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means.append(filtered_df[metric].values[0])
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return np.array(means)
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def get_perf_w_std(df, method, model, metric):
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if metric in ["TTFT", "ITL"]:
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mean = get_perf(df, method, model, "Mean " + metric + " (ms)")
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mean = mean.tolist()
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std = get_perf(df, method, model, "Std " + metric + " (ms)")
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if std.mean() == 0:
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std = None
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success = get_perf(df, method, model, "Successful req.")
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if std is not None:
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std = std / np.sqrt(success)
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std = std.tolist()
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else:
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assert metric == "Tput"
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mean = get_perf(df, method, model, "Input Tput (tok/s)") + get_perf(
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df, method, model, "Output Tput (tok/s)")
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mean = mean.tolist()
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std = None
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return mean, std
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def main(args):
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results_folder = Path(args.results_folder)
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results = []
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# collect results
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for test_file in results_folder.glob("*_nightly_results.json"):
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with open(test_file) as f:
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results = results + json.loads(f.read())
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# generate markdown table
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df = pd.DataFrame.from_dict(results)
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md_table = tabulate(df, headers='keys', tablefmt='pipe', showindex=False)
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with open(args.description) as f:
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description = f.read()
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description = description.format(
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nightly_results_benchmarking_table=md_table)
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with open("nightly_results.md", "w") as f:
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f.write(description)
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if __name__ == '__main__':
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args = parse_arguments()
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main(args)
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@@ -0,0 +1,6 @@
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from lmdeploy.serve.openai.api_client import APIClient
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api_client = APIClient("http://localhost:8000")
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model_name = api_client.available_models[0]
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print(model_name)
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@@ -0,0 +1,228 @@
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#!/bin/bash
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# Currently FP8 benchmark is NOT enabled.
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set -x
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server_params=$1
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common_params=$2
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json2args() {
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# transforms the JSON string to command line args, and '_' is replaced to '-'
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# example:
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# input: { "model": "meta-llama/Llama-2-7b-chat-hf", "tensor_parallel_size": 1 }
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# output: --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel-size 1
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local json_string=$1
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local args=$(
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echo "$json_string" | jq -r '
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to_entries |
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map("--" + (.key | gsub("_"; "-")) + " " + (.value | tostring)) |
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join(" ")
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'
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)
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echo "$args"
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}
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launch_trt_server() {
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model_path=$(echo "$common_params" | jq -r '.model')
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model_name="${model_path#*/}"
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model_type=$(echo "$server_params" | jq -r '.model_type')
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model_dtype=$(echo "$server_params" | jq -r '.model_dtype')
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model_tp_size=$(echo "$common_params" | jq -r '.tp')
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max_batch_size=$(echo "$server_params" | jq -r '.max_batch_size')
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max_input_len=$(echo "$server_params" | jq -r '.max_input_len')
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max_seq_len=$(echo "$server_params" | jq -r '.max_seq_len')
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max_num_tokens=$(echo "$server_params" | jq -r '.max_num_tokens')
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trt_llm_version=$(echo "$server_params" | jq -r '.trt_llm_version')
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# create model caching directory
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cd ~
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rm -rf models
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mkdir -p models
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cd models
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models_dir=$(pwd)
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trt_model_path=${models_dir}/${model_name}-trt-ckpt
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trt_engine_path=${models_dir}/${model_name}-trt-engine
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# clone tensorrt backend
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cd /
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rm -rf tensorrtllm_backend
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git clone https://github.com/triton-inference-server/tensorrtllm_backend.git
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git lfs install
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cd tensorrtllm_backend
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git checkout "$trt_llm_version"
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git submodule update --init --recursive
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# build trtllm engine
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cd /tensorrtllm_backend
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cd "./tensorrt_llm/examples/${model_type}"
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python3 convert_checkpoint.py \
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--model_dir "${model_path}" \
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--dtype "${model_dtype}" \
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--tp_size "${model_tp_size}" \
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--output_dir "${trt_model_path}"
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trtllm-build \
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--checkpoint_dir "${trt_model_path}" \
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--use_fused_mlp \
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--reduce_fusion disable \
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--workers 8 \
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--gpt_attention_plugin "${model_dtype}" \
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--gemm_plugin "${model_dtype}" \
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--tp_size "${model_tp_size}" \
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--max_batch_size "${max_batch_size}" \
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--max_input_len "${max_input_len}" \
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--max_seq_len "${max_seq_len}" \
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--max_num_tokens "${max_num_tokens}" \
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--output_dir "${trt_engine_path}"
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# handle triton protobuf files and launch triton server
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cd /tensorrtllm_backend
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mkdir triton_model_repo
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cp -r all_models/inflight_batcher_llm/* triton_model_repo/
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cd triton_model_repo
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rm -rf ./tensorrt_llm/1/*
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cp -r "${trt_engine_path}"/* ./tensorrt_llm/1
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python3 ../tools/fill_template.py -i tensorrt_llm/config.pbtxt triton_backend:tensorrtllm,engine_dir:/tensorrtllm_backend/triton_model_repo/tensorrt_llm/1,decoupled_mode:true,batching_strategy:inflight_fused_batching,batch_scheduler_policy:guaranteed_no_evict,exclude_input_in_output:true,triton_max_batch_size:2048,max_queue_delay_microseconds:0,max_beam_width:1,max_queue_size:2048,enable_kv_cache_reuse:false
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python3 ../tools/fill_template.py -i preprocessing/config.pbtxt "triton_max_batch_size:2048,tokenizer_dir:$model_path,preprocessing_instance_count:5"
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python3 ../tools/fill_template.py -i postprocessing/config.pbtxt "triton_max_batch_size:2048,tokenizer_dir:$model_path,postprocessing_instance_count:5,skip_special_tokens:false"
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python3 ../tools/fill_template.py -i ensemble/config.pbtxt triton_max_batch_size:"$max_batch_size"
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python3 ../tools/fill_template.py -i tensorrt_llm_bls/config.pbtxt "triton_max_batch_size:$max_batch_size,decoupled_mode:true,accumulate_tokens:False,bls_instance_count:1"
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cd /tensorrtllm_backend
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python3 scripts/launch_triton_server.py \
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--world_size="${model_tp_size}" \
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--model_repo=/tensorrtllm_backend/triton_model_repo &
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}
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launch_tgi_server() {
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model=$(echo "$common_params" | jq -r '.model')
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tp=$(echo "$common_params" | jq -r '.tp')
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port=$(echo "$common_params" | jq -r '.port')
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server_args=$(json2args "$server_params")
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if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
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echo "Key 'fp8' exists in common params."
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server_command="/tgi-entrypoint.sh \
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--model-id $model \
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--num-shard $tp \
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--port $port \
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--quantize fp8 \
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$server_args"
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else
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echo "Key 'fp8' does not exist in common params."
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server_command="/tgi-entrypoint.sh \
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--model-id $model \
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--num-shard $tp \
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--port $port \
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$server_args"
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fi
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echo "Server command: $server_command"
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eval "$server_command" &
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}
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launch_lmdeploy_server() {
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model=$(echo "$common_params" | jq -r '.model')
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tp=$(echo "$common_params" | jq -r '.tp')
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port=$(echo "$common_params" | jq -r '.port')
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server_args=$(json2args "$server_params")
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server_command="lmdeploy serve api_server $model \
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--tp $tp \
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--server-port $port \
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$server_args"
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# run the server
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echo "Server command: $server_command"
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bash -c "$server_command" &
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}
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launch_sglang_server() {
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|
||||
model=$(echo "$common_params" | jq -r '.model')
|
||||
tp=$(echo "$common_params" | jq -r '.tp')
|
||||
port=$(echo "$common_params" | jq -r '.port')
|
||||
server_args=$(json2args "$server_params")
|
||||
|
||||
if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
|
||||
echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
|
||||
model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
|
||||
server_command="python3 \
|
||||
-m sglang.launch_server \
|
||||
--tp $tp \
|
||||
--model-path $model \
|
||||
--port $port \
|
||||
$server_args"
|
||||
else
|
||||
echo "Key 'fp8' does not exist in common params."
|
||||
server_command="python3 \
|
||||
-m sglang.launch_server \
|
||||
--tp $tp \
|
||||
--model-path $model \
|
||||
--port $port \
|
||||
$server_args"
|
||||
fi
|
||||
|
||||
# run the server
|
||||
echo "Server command: $server_command"
|
||||
eval "$server_command" &
|
||||
}
|
||||
|
||||
launch_vllm_server() {
|
||||
|
||||
export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
|
||||
|
||||
model=$(echo "$common_params" | jq -r '.model')
|
||||
tp=$(echo "$common_params" | jq -r '.tp')
|
||||
port=$(echo "$common_params" | jq -r '.port')
|
||||
server_args=$(json2args "$server_params")
|
||||
|
||||
if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
|
||||
echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
|
||||
model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
|
||||
server_command="python3 \
|
||||
-m vllm.entrypoints.openai.api_server \
|
||||
-tp $tp \
|
||||
--model $model \
|
||||
--port $port \
|
||||
$server_args"
|
||||
else
|
||||
echo "Key 'fp8' does not exist in common params."
|
||||
server_command="python3 \
|
||||
-m vllm.entrypoints.openai.api_server \
|
||||
-tp $tp \
|
||||
--model $model \
|
||||
--port $port \
|
||||
$server_args"
|
||||
fi
|
||||
|
||||
# run the server
|
||||
echo "Server command: $server_command"
|
||||
eval "$server_command" &
|
||||
}
|
||||
|
||||
main() {
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "trt" ]]; then
|
||||
launch_trt_server
|
||||
fi
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "tgi" ]]; then
|
||||
launch_tgi_server
|
||||
fi
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "lmdeploy" ]]; then
|
||||
launch_lmdeploy_server
|
||||
fi
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "sglang" ]]; then
|
||||
launch_sglang_server
|
||||
fi
|
||||
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == *"vllm"* ]]; then
|
||||
launch_vllm_server
|
||||
fi
|
||||
}
|
||||
|
||||
main
|
||||
@@ -0,0 +1,78 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -ex
|
||||
set -o pipefail
|
||||
|
||||
|
||||
main() {
|
||||
|
||||
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
||||
(which jq) || (apt-get update && apt-get -y install jq)
|
||||
(which zip) || (apt-get install -y zip)
|
||||
|
||||
if [ ! -f /workspace/buildkite-agent ]; then
|
||||
echo "buildkite-agent binary not found. Skip plotting the results."
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# initial annotation
|
||||
#description="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-descriptions.md"
|
||||
|
||||
# download results
|
||||
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
|
||||
mkdir -p results/
|
||||
/workspace/buildkite-agent artifact download 'results/*nightly_results.json' results/
|
||||
ls
|
||||
ls results/
|
||||
|
||||
# upload benchmark results
|
||||
zip -r results.zip results/
|
||||
/workspace/buildkite-agent artifact upload "results.zip"
|
||||
|
||||
# upload benchmarking scripts
|
||||
cd "$VLLM_SOURCE_CODE_LOC/"
|
||||
zip -r nightly-benchmarks.zip .buildkite/ benchmarks/
|
||||
/workspace/buildkite-agent artifact upload "nightly-benchmarks.zip"
|
||||
|
||||
cd "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
|
||||
# upload benchmarking pipeline
|
||||
/workspace/buildkite-agent artifact upload "nightly-pipeline.yaml"
|
||||
|
||||
cd "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
|
||||
/workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly-annotation.md
|
||||
|
||||
|
||||
|
||||
# The figures should be genereated by a separate process outside the CI/CD pipeline
|
||||
|
||||
# # generate figures
|
||||
# python3 -m pip install tabulate pandas matplotlib
|
||||
|
||||
# python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py \
|
||||
# --description $description \
|
||||
# --results-folder results/
|
||||
|
||||
|
||||
# python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
|
||||
# --description $description \
|
||||
# --results-folder results/ \
|
||||
# --dataset sharegpt
|
||||
|
||||
# python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
|
||||
# --description $description \
|
||||
# --results-folder results/ \
|
||||
# --dataset sonnet_2048_128
|
||||
|
||||
# python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
|
||||
# --description $description \
|
||||
# --results-folder results/ \
|
||||
# --dataset sonnet_128_2048
|
||||
|
||||
# # upload results and figures
|
||||
# /workspace/buildkite-agent artifact upload "nightly_results*.png"
|
||||
# /workspace/buildkite-agent artifact upload $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-pipeline.yaml
|
||||
# /workspace/buildkite-agent artifact upload $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/tests/nightly-tests.json
|
||||
# /workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly_results.md
|
||||
}
|
||||
|
||||
main "$@"
|
||||
@@ -0,0 +1,355 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -o pipefail
|
||||
set -x
|
||||
|
||||
check_gpus() {
|
||||
# check the number of GPUs and GPU type.
|
||||
declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
|
||||
if [[ $gpu_count -gt 0 ]]; then
|
||||
echo "GPU found."
|
||||
else
|
||||
echo "Need at least 1 GPU to run benchmarking."
|
||||
exit 1
|
||||
fi
|
||||
declare -g gpu_type="$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')"
|
||||
echo "GPU type is $gpu_type"
|
||||
}
|
||||
|
||||
check_hf_token() {
|
||||
# check if HF_TOKEN is available and valid
|
||||
if [[ -z "$HF_TOKEN" ]]; then
|
||||
echo "Error: HF_TOKEN is not set."
|
||||
exit 1
|
||||
elif [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
|
||||
echo "Error: HF_TOKEN does not start with 'hf_'."
|
||||
exit 1
|
||||
else
|
||||
echo "HF_TOKEN is set and valid."
|
||||
fi
|
||||
}
|
||||
|
||||
|
||||
upload_to_buildkite() {
|
||||
# upload the benchmarking results to buildkite
|
||||
|
||||
# if the agent binary is not found, skip uploading the results, exit 0
|
||||
if [ ! -f /workspace/buildkite-agent ]; then
|
||||
echo "buildkite-agent binary not found. Skip uploading the results."
|
||||
return 0
|
||||
fi
|
||||
# /workspace/buildkite-agent annotate --style "success" --context "benchmark-results" --append < $RESULTS_FOLDER/${CURRENT_LLM_SERVING_ENGINE}_nightly_results.md
|
||||
/workspace/buildkite-agent artifact upload "$RESULTS_FOLDER/*"
|
||||
}
|
||||
|
||||
|
||||
get_current_llm_serving_engine() {
|
||||
|
||||
if which lmdeploy >/dev/null; then
|
||||
echo "Container: lmdeploy"
|
||||
export CURRENT_LLM_SERVING_ENGINE=lmdeploy
|
||||
return
|
||||
fi
|
||||
|
||||
if [ -e /tgi-entrypoint.sh ]; then
|
||||
echo "Container: tgi"
|
||||
export CURRENT_LLM_SERVING_ENGINE=tgi
|
||||
return
|
||||
fi
|
||||
|
||||
if which trtllm-build >/dev/null; then
|
||||
echo "Container: tensorrt-llm"
|
||||
export CURRENT_LLM_SERVING_ENGINE=trt
|
||||
return
|
||||
fi
|
||||
|
||||
if [ -e /sgl-workspace ]; then
|
||||
echo "Container: sglang"
|
||||
export CURRENT_LLM_SERVING_ENGINE=sglang
|
||||
return
|
||||
fi
|
||||
|
||||
if [ -e /vllm-workspace ]; then
|
||||
echo "Container: vllm"
|
||||
# move to a completely irrelevant directory, to avoid import vllm from current folder
|
||||
export CURRENT_LLM_SERVING_ENGINE=vllm
|
||||
|
||||
return
|
||||
fi
|
||||
}
|
||||
|
||||
json2args() {
|
||||
# transforms the JSON string to command line args, and '_' is replaced to '-'
|
||||
# example:
|
||||
# input: { "model": "meta-llama/Llama-2-7b-chat-hf", "tensor_parallel_size": 1 }
|
||||
# output: --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel-size 1
|
||||
local json_string=$1
|
||||
local args=$(
|
||||
echo "$json_string" | jq -r '
|
||||
to_entries |
|
||||
map("--" + (.key | gsub("_"; "-")) + " " + (.value | tostring)) |
|
||||
join(" ")
|
||||
'
|
||||
)
|
||||
echo "$args"
|
||||
}
|
||||
|
||||
kill_gpu_processes() {
|
||||
pkill -f python
|
||||
pkill -f python3
|
||||
pkill -f tritonserver
|
||||
pkill -f pt_main_thread
|
||||
pkill -f text-generation
|
||||
pkill -f lmdeploy
|
||||
|
||||
while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
|
||||
sleep 1
|
||||
done
|
||||
}
|
||||
|
||||
wait_for_server() {
|
||||
# wait for vllm server to start
|
||||
# return 1 if vllm server crashes
|
||||
timeout 1200 bash -c '
|
||||
until curl -s localhost:8000/v1/completions > /dev/null; do
|
||||
sleep 1
|
||||
done' && return 0 || return 1
|
||||
}
|
||||
|
||||
ensure_installed() {
|
||||
# Ensure that the given command is installed by apt-get
|
||||
local cmd=$1
|
||||
if ! which "$cmd" >/dev/null; then
|
||||
apt-get update && apt-get install -y "$cmd"
|
||||
fi
|
||||
}
|
||||
|
||||
run_serving_tests() {
|
||||
# run serving tests using `benchmark_serving.py`
|
||||
# $1: a json file specifying serving test cases
|
||||
|
||||
local serving_test_file
|
||||
serving_test_file=$1
|
||||
|
||||
# Iterate over serving tests
|
||||
jq -c '.[]' "$serving_test_file" | while read -r params; do
|
||||
# get the test name, and append the GPU type back to it.
|
||||
test_name=$(echo "$params" | jq -r '.test_name')
|
||||
|
||||
# if TEST_SELECTOR is set, only run the test cases that match the selector
|
||||
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
|
||||
echo "Skip test case $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# prepend the current serving engine to the test name
|
||||
test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
|
||||
|
||||
# get common parameters
|
||||
common_params=$(echo "$params" | jq -r '.common_parameters')
|
||||
model=$(echo "$common_params" | jq -r '.model')
|
||||
tp=$(echo "$common_params" | jq -r '.tp')
|
||||
dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
|
||||
dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
|
||||
port=$(echo "$common_params" | jq -r '.port')
|
||||
num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
|
||||
reuse_server=$(echo "$common_params" | jq -r '.reuse_server')
|
||||
|
||||
# get client and server arguments
|
||||
server_params=$(echo "$params" | jq -r ".${CURRENT_LLM_SERVING_ENGINE}_server_parameters")
|
||||
client_params=$(echo "$params" | jq -r ".${CURRENT_LLM_SERVING_ENGINE}_client_parameters")
|
||||
client_args=$(json2args "$client_params")
|
||||
qps_list=$(echo "$params" | jq -r '.qps_list')
|
||||
qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
|
||||
echo "Running over qps list $qps_list"
|
||||
|
||||
# check if there is enough GPU to run the test
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required num-shard $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
if [[ $reuse_server == "true" ]]; then
|
||||
echo "Reuse previous server for test case $test_name"
|
||||
else
|
||||
kill_gpu_processes
|
||||
bash "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/launch-server.sh" \
|
||||
"$server_params" "$common_params"
|
||||
fi
|
||||
|
||||
if wait_for_server; then
|
||||
echo ""
|
||||
echo "$CURRENT_LLM_SERVING_ENGINE server is up and running."
|
||||
else
|
||||
echo ""
|
||||
echo "$CURRENT_LLM_SERVING_ENGINE failed to start within the timeout period."
|
||||
break
|
||||
fi
|
||||
|
||||
# prepare tokenizer
|
||||
# this is required for lmdeploy.
|
||||
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
|
||||
rm -rf /tokenizer_cache
|
||||
mkdir /tokenizer_cache
|
||||
python3 ../.buildkite/nightly-benchmarks/scripts/download-tokenizer.py \
|
||||
--model "$model" \
|
||||
--cachedir /tokenizer_cache
|
||||
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
|
||||
|
||||
|
||||
# change model name for lmdeploy (it will not follow standard hf name)
|
||||
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "lmdeploy" ]]; then
|
||||
model=$(python ../.buildkite/nightly-benchmarks/scripts/get-lmdeploy-modelname.py)
|
||||
fi
|
||||
|
||||
# iterate over different QPS
|
||||
for qps in $qps_list; do
|
||||
# remove the surrounding single quote from qps
|
||||
if [[ "$qps" == *"inf"* ]]; then
|
||||
echo "qps was $qps"
|
||||
qps="inf"
|
||||
echo "now qps is $qps"
|
||||
fi
|
||||
|
||||
new_test_name=$test_name"_qps_"$qps
|
||||
|
||||
backend=$CURRENT_LLM_SERVING_ENGINE
|
||||
|
||||
if [[ $backend = "trt" ]]; then
|
||||
backend="tensorrt-llm"
|
||||
fi
|
||||
|
||||
if [[ "$backend" == *"vllm"* ]]; then
|
||||
backend="vllm"
|
||||
fi
|
||||
|
||||
if [[ "$dataset_name" = "sharegpt" ]]; then
|
||||
|
||||
client_command="python3 benchmark_serving.py \
|
||||
--backend $backend \
|
||||
--tokenizer /tokenizer_cache \
|
||||
--model $model \
|
||||
--dataset-name $dataset_name \
|
||||
--dataset-path $dataset_path \
|
||||
--num-prompts $num_prompts \
|
||||
--port $port \
|
||||
--save-result \
|
||||
--result-dir $RESULTS_FOLDER \
|
||||
--result-filename ${new_test_name}.json \
|
||||
--request-rate $qps \
|
||||
--ignore-eos \
|
||||
$client_args"
|
||||
|
||||
elif [[ "$dataset_name" = "sonnet" ]]; then
|
||||
|
||||
sonnet_input_len=$(echo "$common_params" | jq -r '.sonnet_input_len')
|
||||
sonnet_output_len=$(echo "$common_params" | jq -r '.sonnet_output_len')
|
||||
sonnet_prefix_len=$(echo "$common_params" | jq -r '.sonnet_prefix_len')
|
||||
|
||||
client_command="python3 benchmark_serving.py \
|
||||
--backend $backend \
|
||||
--tokenizer /tokenizer_cache \
|
||||
--model $model \
|
||||
--dataset-name $dataset_name \
|
||||
--dataset-path $dataset_path \
|
||||
--num-prompts $num_prompts \
|
||||
--sonnet-input-len $sonnet_input_len \
|
||||
--sonnet-output-len $sonnet_output_len \
|
||||
--sonnet-prefix-len $sonnet_prefix_len \
|
||||
--port $port \
|
||||
--save-result \
|
||||
--result-dir $RESULTS_FOLDER \
|
||||
--result-filename ${new_test_name}.json \
|
||||
--request-rate $qps \
|
||||
--ignore-eos \
|
||||
$client_args"
|
||||
|
||||
else
|
||||
|
||||
echo "The dataset name must be either 'sharegpt' or 'sonnet'. Got $dataset_name."
|
||||
exit 1
|
||||
|
||||
fi
|
||||
|
||||
|
||||
|
||||
echo "Running test case $test_name with qps $qps"
|
||||
echo "Client command: $client_command"
|
||||
|
||||
eval "$client_command"
|
||||
|
||||
server_command="None"
|
||||
|
||||
# record the benchmarking commands
|
||||
jq_output=$(jq -n \
|
||||
--arg server "$server_command" \
|
||||
--arg client "$client_command" \
|
||||
--arg gpu "$gpu_type" \
|
||||
--arg engine "$CURRENT_LLM_SERVING_ENGINE" \
|
||||
'{
|
||||
server_command: $server,
|
||||
client_command: $client,
|
||||
gpu_type: $gpu,
|
||||
engine: $engine
|
||||
}')
|
||||
echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
|
||||
|
||||
done
|
||||
|
||||
done
|
||||
|
||||
kill_gpu_processes
|
||||
}
|
||||
|
||||
|
||||
prepare_dataset() {
|
||||
|
||||
# download sharegpt dataset
|
||||
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
|
||||
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
|
||||
|
||||
# duplicate sonnet by 4x, to allow benchmarking with input length 2048
|
||||
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
|
||||
echo "" > sonnet_4x.txt
|
||||
for _ in {1..4}
|
||||
do
|
||||
cat sonnet.txt >> sonnet_4x.txt
|
||||
done
|
||||
|
||||
}
|
||||
|
||||
main() {
|
||||
|
||||
# check if the environment variable is successfully injected from yaml
|
||||
|
||||
check_gpus
|
||||
check_hf_token
|
||||
get_current_llm_serving_engine
|
||||
|
||||
pip install -U transformers
|
||||
|
||||
# check storage
|
||||
df -h
|
||||
|
||||
ensure_installed wget
|
||||
ensure_installed curl
|
||||
ensure_installed jq
|
||||
|
||||
prepare_dataset
|
||||
|
||||
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
|
||||
declare -g RESULTS_FOLDER=results/
|
||||
mkdir -p $RESULTS_FOLDER
|
||||
BENCHMARK_ROOT="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
|
||||
|
||||
# run the test
|
||||
run_serving_tests "$BENCHMARK_ROOT/tests/nightly-tests.json"
|
||||
|
||||
# upload benchmark results to buildkite
|
||||
python3 -m pip install tabulate pandas
|
||||
python3 "$BENCHMARK_ROOT/scripts/summary-nightly-results.py"
|
||||
upload_to_buildkite
|
||||
|
||||
}
|
||||
|
||||
main "$@"
|
||||
@@ -0,0 +1,380 @@
|
||||
#!/bin/bash
|
||||
|
||||
# This script should be run inside the CI process
|
||||
# This script assumes that we are already inside the vllm/ directory
|
||||
# Benchmarking results will be available inside vllm/benchmarks/results/
|
||||
|
||||
# Do not set -e, as the mixtral 8x22B model tends to crash occasionally
|
||||
# and we still want to see other benchmarking results even when mixtral crashes.
|
||||
set -o pipefail
|
||||
|
||||
check_gpus() {
|
||||
# check the number of GPUs and GPU type.
|
||||
declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
|
||||
if [[ $gpu_count -gt 0 ]]; then
|
||||
echo "GPU found."
|
||||
else
|
||||
echo "Need at least 1 GPU to run benchmarking."
|
||||
exit 1
|
||||
fi
|
||||
declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
|
||||
echo "GPU type is $gpu_type"
|
||||
}
|
||||
|
||||
check_hf_token() {
|
||||
# check if HF_TOKEN is available and valid
|
||||
if [[ -z "$HF_TOKEN" ]]; then
|
||||
echo "Error: HF_TOKEN is not set."
|
||||
exit 1
|
||||
elif [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
|
||||
echo "Error: HF_TOKEN does not start with 'hf_'."
|
||||
exit 1
|
||||
else
|
||||
echo "HF_TOKEN is set and valid."
|
||||
fi
|
||||
}
|
||||
|
||||
ensure_sharegpt_downloaded() {
|
||||
local FILE=ShareGPT_V3_unfiltered_cleaned_split.json
|
||||
if [ ! -f "$FILE" ]; then
|
||||
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/$FILE
|
||||
else
|
||||
echo "$FILE already exists."
|
||||
fi
|
||||
}
|
||||
|
||||
json2args() {
|
||||
# transforms the JSON string to command line args, and '_' is replaced to '-'
|
||||
# example:
|
||||
# input: { "model": "meta-llama/Llama-2-7b-chat-hf", "tensor_parallel_size": 1 }
|
||||
# output: --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel-size 1
|
||||
local json_string=$1
|
||||
local args=$(
|
||||
echo "$json_string" | jq -r '
|
||||
to_entries |
|
||||
map("--" + (.key | gsub("_"; "-")) + " " + (.value | tostring)) |
|
||||
join(" ")
|
||||
'
|
||||
)
|
||||
echo "$args"
|
||||
}
|
||||
|
||||
wait_for_server() {
|
||||
# wait for vllm server to start
|
||||
# return 1 if vllm server crashes
|
||||
timeout 1200 bash -c '
|
||||
until curl -X POST localhost:8000/v1/completions; do
|
||||
sleep 1
|
||||
done' && return 0 || return 1
|
||||
}
|
||||
|
||||
kill_processes_launched_by_current_bash() {
|
||||
# Kill all python processes launched from current bash script
|
||||
current_shell_pid=$$
|
||||
processes=$(ps -eo pid,ppid,command | awk -v ppid="$current_shell_pid" -v proc="$1" '$2 == ppid && $3 ~ proc {print $1}')
|
||||
if [ -n "$processes" ]; then
|
||||
echo "Killing the following processes matching '$1':"
|
||||
echo "$processes"
|
||||
echo "$processes" | xargs kill -9
|
||||
else
|
||||
echo "No processes found matching '$1'."
|
||||
fi
|
||||
}
|
||||
|
||||
kill_gpu_processes() {
|
||||
|
||||
ps -aux
|
||||
lsof -t -i:8000 | xargs -r kill -9
|
||||
pkill -f pt_main_thread
|
||||
# this line doesn't work now
|
||||
# ps aux | grep python | grep openai | awk '{print $2}' | xargs -r kill -9
|
||||
pkill -f python3
|
||||
pkill -f /usr/bin/python3
|
||||
|
||||
|
||||
# wait until GPU memory usage smaller than 1GB
|
||||
while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
|
||||
sleep 1
|
||||
done
|
||||
|
||||
# remove vllm config file
|
||||
rm -rf ~/.config/vllm
|
||||
|
||||
}
|
||||
|
||||
upload_to_buildkite() {
|
||||
# upload the benchmarking results to buildkite
|
||||
|
||||
# if the agent binary is not found, skip uploading the results, exit 0
|
||||
# Check if buildkite-agent is available in the PATH or at /workspace/buildkite-agent
|
||||
if command -v buildkite-agent >/dev/null 2>&1; then
|
||||
BUILDKITE_AGENT_COMMAND="buildkite-agent"
|
||||
elif [ -f /workspace/buildkite-agent ]; then
|
||||
BUILDKITE_AGENT_COMMAND="/workspace/buildkite-agent"
|
||||
else
|
||||
echo "buildkite-agent binary not found. Skip uploading the results."
|
||||
return 0
|
||||
fi
|
||||
|
||||
# Use the determined command to annotate and upload artifacts
|
||||
$BUILDKITE_AGENT_COMMAND annotate --style "info" --context "$BUILDKITE_LABEL-benchmark-results" < "$RESULTS_FOLDER/benchmark_results.md"
|
||||
$BUILDKITE_AGENT_COMMAND artifact upload "$RESULTS_FOLDER/*"
|
||||
}
|
||||
|
||||
run_latency_tests() {
|
||||
# run latency tests using `benchmark_latency.py`
|
||||
# $1: a json file specifying latency test cases
|
||||
|
||||
local latency_test_file
|
||||
latency_test_file=$1
|
||||
|
||||
# Iterate over latency tests
|
||||
jq -c '.[]' "$latency_test_file" | while read -r params; do
|
||||
# get the test name, and append the GPU type back to it.
|
||||
test_name=$(echo "$params" | jq -r '.test_name')
|
||||
if [[ ! "$test_name" =~ ^latency_ ]]; then
|
||||
echo "In latency-test.json, test_name must start with \"latency_\"."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# if TEST_SELECTOR is set, only run the test cases that match the selector
|
||||
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
|
||||
echo "Skip test case $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# get arguments
|
||||
latency_params=$(echo "$params" | jq -r '.parameters')
|
||||
latency_args=$(json2args "$latency_params")
|
||||
|
||||
# check if there is enough GPU to run the test
|
||||
tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
latency_command="python3 benchmark_latency.py \
|
||||
--output-json $RESULTS_FOLDER/${test_name}.json \
|
||||
$latency_args"
|
||||
|
||||
echo "Running test case $test_name"
|
||||
echo "Latency command: $latency_command"
|
||||
|
||||
# recoding benchmarking command ang GPU command
|
||||
jq_output=$(jq -n \
|
||||
--arg latency "$latency_command" \
|
||||
--arg gpu "$gpu_type" \
|
||||
'{
|
||||
latency_command: $latency,
|
||||
gpu_type: $gpu
|
||||
}')
|
||||
echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
|
||||
|
||||
# run the benchmark
|
||||
eval "$latency_command"
|
||||
|
||||
kill_gpu_processes
|
||||
|
||||
done
|
||||
}
|
||||
|
||||
run_throughput_tests() {
|
||||
# run throughput tests using `benchmark_throughput.py`
|
||||
# $1: a json file specifying throughput test cases
|
||||
|
||||
local throughput_test_file
|
||||
throughput_test_file=$1
|
||||
|
||||
# Iterate over throughput tests
|
||||
jq -c '.[]' "$throughput_test_file" | while read -r params; do
|
||||
# get the test name, and append the GPU type back to it.
|
||||
test_name=$(echo "$params" | jq -r '.test_name')
|
||||
if [[ ! "$test_name" =~ ^throughput_ ]]; then
|
||||
echo "In throughput-test.json, test_name must start with \"throughput_\"."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# if TEST_SELECTOR is set, only run the test cases that match the selector
|
||||
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
|
||||
echo "Skip test case $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# get arguments
|
||||
throughput_params=$(echo "$params" | jq -r '.parameters')
|
||||
throughput_args=$(json2args "$throughput_params")
|
||||
|
||||
# check if there is enough GPU to run the test
|
||||
tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
throughput_command="python3 benchmark_throughput.py \
|
||||
--output-json $RESULTS_FOLDER/${test_name}.json \
|
||||
$throughput_args"
|
||||
|
||||
echo "Running test case $test_name"
|
||||
echo "Throughput command: $throughput_command"
|
||||
# recoding benchmarking command ang GPU command
|
||||
jq_output=$(jq -n \
|
||||
--arg command "$throughput_command" \
|
||||
--arg gpu "$gpu_type" \
|
||||
'{
|
||||
throughput_command: $command,
|
||||
gpu_type: $gpu
|
||||
}')
|
||||
echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
|
||||
|
||||
# run the benchmark
|
||||
eval "$throughput_command"
|
||||
|
||||
kill_gpu_processes
|
||||
|
||||
done
|
||||
}
|
||||
|
||||
run_serving_tests() {
|
||||
# run serving tests using `benchmark_serving.py`
|
||||
# $1: a json file specifying serving test cases
|
||||
|
||||
local serving_test_file
|
||||
serving_test_file=$1
|
||||
|
||||
# Iterate over serving tests
|
||||
jq -c '.[]' "$serving_test_file" | while read -r params; do
|
||||
# get the test name, and append the GPU type back to it.
|
||||
test_name=$(echo "$params" | jq -r '.test_name')
|
||||
if [[ ! "$test_name" =~ ^serving_ ]]; then
|
||||
echo "In serving-test.json, test_name must start with \"serving_\"."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# if TEST_SELECTOR is set, only run the test cases that match the selector
|
||||
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
|
||||
echo "Skip test case $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# get client and server arguments
|
||||
server_params=$(echo "$params" | jq -r '.server_parameters')
|
||||
client_params=$(echo "$params" | jq -r '.client_parameters')
|
||||
server_args=$(json2args "$server_params")
|
||||
client_args=$(json2args "$client_params")
|
||||
qps_list=$(echo "$params" | jq -r '.qps_list')
|
||||
qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
|
||||
echo "Running over qps list $qps_list"
|
||||
|
||||
# check if there is enough GPU to run the test
|
||||
tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
|
||||
if [[ $gpu_count -lt $tp ]]; then
|
||||
echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
# check if server model and client model is aligned
|
||||
server_model=$(echo "$server_params" | jq -r '.model')
|
||||
client_model=$(echo "$client_params" | jq -r '.model')
|
||||
if [[ $server_model != "$client_model" ]]; then
|
||||
echo "Server model and client model must be the same. Skip testcase $test_name."
|
||||
continue
|
||||
fi
|
||||
|
||||
server_command="python3 \
|
||||
-m vllm.entrypoints.openai.api_server \
|
||||
$server_args"
|
||||
|
||||
# run the server
|
||||
echo "Running test case $test_name"
|
||||
echo "Server command: $server_command"
|
||||
eval "$server_command" &
|
||||
server_pid=$!
|
||||
|
||||
# wait until the server is alive
|
||||
if wait_for_server; then
|
||||
echo ""
|
||||
echo "vllm server is up and running."
|
||||
else
|
||||
echo ""
|
||||
echo "vllm failed to start within the timeout period."
|
||||
fi
|
||||
|
||||
# iterate over different QPS
|
||||
for qps in $qps_list; do
|
||||
# remove the surrounding single quote from qps
|
||||
if [[ "$qps" == *"inf"* ]]; then
|
||||
echo "qps was $qps"
|
||||
qps="inf"
|
||||
echo "now qps is $qps"
|
||||
fi
|
||||
|
||||
new_test_name=$test_name"_qps_"$qps
|
||||
|
||||
client_command="python3 benchmark_serving.py \
|
||||
--save-result \
|
||||
--result-dir $RESULTS_FOLDER \
|
||||
--result-filename ${new_test_name}.json \
|
||||
--request-rate $qps \
|
||||
$client_args"
|
||||
|
||||
echo "Running test case $test_name with qps $qps"
|
||||
echo "Client command: $client_command"
|
||||
|
||||
eval "$client_command"
|
||||
|
||||
# record the benchmarking commands
|
||||
jq_output=$(jq -n \
|
||||
--arg server "$server_command" \
|
||||
--arg client "$client_command" \
|
||||
--arg gpu "$gpu_type" \
|
||||
'{
|
||||
server_command: $server,
|
||||
client_command: $client,
|
||||
gpu_type: $gpu
|
||||
}')
|
||||
echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
|
||||
|
||||
done
|
||||
|
||||
# clean up
|
||||
kill -9 $server_pid
|
||||
kill_gpu_processes
|
||||
done
|
||||
}
|
||||
|
||||
main() {
|
||||
check_gpus
|
||||
check_hf_token
|
||||
|
||||
# dependencies
|
||||
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
|
||||
(which jq) || (apt-get update && apt-get -y install jq)
|
||||
(which lsof) || (apt-get update && apt-get install -y lsof)
|
||||
|
||||
# get the current IP address, required by benchmark_serving.py
|
||||
export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
|
||||
# turn of the reporting of the status of each request, to clean up the terminal output
|
||||
export VLLM_LOG_LEVEL="WARNING"
|
||||
|
||||
# prepare for benchmarking
|
||||
cd benchmarks || exit 1
|
||||
ensure_sharegpt_downloaded
|
||||
declare -g RESULTS_FOLDER=results/
|
||||
mkdir -p $RESULTS_FOLDER
|
||||
QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
|
||||
|
||||
# benchmarking
|
||||
run_serving_tests $QUICK_BENCHMARK_ROOT/tests/serving-tests.json
|
||||
run_latency_tests $QUICK_BENCHMARK_ROOT/tests/latency-tests.json
|
||||
run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/throughput-tests.json
|
||||
|
||||
# postprocess benchmarking results
|
||||
pip install tabulate pandas
|
||||
python3 $QUICK_BENCHMARK_ROOT/scripts/convert-results-json-to-markdown.py
|
||||
|
||||
upload_to_buildkite
|
||||
}
|
||||
|
||||
main "$@"
|
||||
@@ -0,0 +1,83 @@
|
||||
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) as f:
|
||||
raw_result = json.loads(f.read())
|
||||
|
||||
# attach the benchmarking command to raw_result
|
||||
with open(test_file.with_suffix(".commands")) 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))
|
||||
@@ -0,0 +1,19 @@
|
||||
#!/bin/sh
|
||||
TOKEN=$(curl -s -L "https://public.ecr.aws/token?service=public.ecr.aws&scope=repository:q9t5s3a7/vllm-ci-test-repo:pull" | jq -r .token)
|
||||
URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-test-repo/manifests/$BUILDKITE_COMMIT"
|
||||
|
||||
TIMEOUT_SECONDS=10
|
||||
|
||||
retries=0
|
||||
while [ $retries -lt 1000 ]; do
|
||||
if [ "$(curl -s --max-time "$TIMEOUT_SECONDS" -L -H "Authorization: Bearer $TOKEN" -o /dev/null -w "%{http_code}" "$URL")" -eq 200 ]; then
|
||||
exit 0
|
||||
fi
|
||||
|
||||
echo "Waiting for image to be available..."
|
||||
|
||||
retries=$((retries + 1))
|
||||
sleep 5
|
||||
done
|
||||
|
||||
exit 1
|
||||
Reference in New Issue
Block a user