vllm/.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py

# SPDX-License-Identifier: Apache-2.0

import json
import os
from pathlib import Path

import pandas as pd
from tabulate import tabulate

results_folder = Path("results/")

# latency results and the keys that will be printed into markdown
latency_results = []
latency_column_mapping = {
    "test_name": "Test name",
    "gpu_type": "GPU",
    "avg_latency": "Mean latency (ms)",
    # "P10": "P10 (s)",
    # "P25": "P25 (s)",
    "P50": "Median latency (ms)",
    # "P75": "P75 (s)",
    # "P90": "P90 (s)",
    "P99": "P99 latency (ms)",
}

# throughput tests and the keys that will be printed into markdown
throughput_results = []
throughput_results_column_mapping = {
    "test_name": "Test name",
    "gpu_type": "GPU",
    # "num_requests": "# of req.",
    # "total_num_tokens": "Total # of tokens",
    # "elapsed_time": "Elapsed time (s)",
    "requests_per_second": "Tput (req/s)",
    # "tokens_per_second": "Tput (tok/s)",
}

# serving results and the keys that will be printed into markdown
serving_results = []
serving_column_mapping = {
    "test_name": "Test name",
    "gpu_type": "GPU",
    # "completed": "# of req.",
    "request_throughput": "Tput (req/s)",
    # "input_throughput": "Input Tput (tok/s)",
    # "output_throughput": "Output Tput (tok/s)",
    "mean_ttft_ms": "Mean TTFT (ms)",
    "median_ttft_ms": "Median TTFT (ms)",
    "p99_ttft_ms": "P99 TTFT (ms)",
    # "mean_tpot_ms": "Mean TPOT (ms)",
    # "median_tpot_ms": "Median",
    # "p99_tpot_ms": "P99",
    "mean_itl_ms": "Mean ITL (ms)",
    "median_itl_ms": "Median ITL (ms)",
    "p99_itl_ms": "P99 ITL (ms)",
}


def read_markdown(file):
    if os.path.exists(file):
        with open(file) as f:
            return f.read() + "\n"
    else:
        return f"{file} not found.\n"


def results_to_json(latency, throughput, serving):
    return json.dumps({
        'latency': latency.to_dict(),
        'throughput': throughput.to_dict(),
        'serving': serving.to_dict()
    })


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())

        if "serving" in str(test_file):
            # this result is generated via `benchmark_serving.py`

            # 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

        elif "latency" in f.name:
            # this result is generated via `benchmark_latency.py`

            # 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})

            # get different percentiles
            for perc in [10, 25, 50, 75, 90, 99]:
                # Multiply 1000 to convert the time unit from s to ms
                raw_result.update(
                    {f"P{perc}": 1000 * raw_result["percentiles"][str(perc)]})
            raw_result["avg_latency"] = raw_result["avg_latency"] * 1000

            # add the result to raw_result
            latency_results.append(raw_result)
            continue

        elif "throughput" in f.name:
            # this result is generated via `benchmark_throughput.py`

            # 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
            throughput_results.append(raw_result)
            continue

        print(f"Skipping {test_file}")

    latency_results = pd.DataFrame.from_dict(latency_results)
    serving_results = pd.DataFrame.from_dict(serving_results)
    throughput_results = pd.DataFrame.from_dict(throughput_results)

    raw_results_json = results_to_json(latency_results, throughput_results,
                                       serving_results)

    # remapping the key, for visualization purpose
    if not latency_results.empty:
        latency_results = latency_results[list(
            latency_column_mapping.keys())].rename(
                columns=latency_column_mapping)
    if not serving_results.empty:
        serving_results = serving_results[list(
            serving_column_mapping.keys())].rename(
                columns=serving_column_mapping)
    if not throughput_results.empty:
        throughput_results = throughput_results[list(
            throughput_results_column_mapping.keys())].rename(
                columns=throughput_results_column_mapping)

    processed_results_json = results_to_json(latency_results,
                                             throughput_results,
                                             serving_results)

    for df in [latency_results, serving_results, throughput_results]:
        if df.empty:
            continue

        # Sort all dataframes by their respective "Test name" columns
        df.sort_values(by="Test name", inplace=True)

        # The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
        # we want to turn it into "8xGPUTYPE"
        df["GPU"] = df["GPU"].apply(
            lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}")

    # get markdown tables
    latency_md_table = tabulate(latency_results,
                                headers='keys',
                                tablefmt='pipe',
                                showindex=False)
    serving_md_table = tabulate(serving_results,
                                headers='keys',
                                tablefmt='pipe',
                                showindex=False)
    throughput_md_table = tabulate(throughput_results,
                                   headers='keys',
                                   tablefmt='pipe',
                                   showindex=False)

    # document the result
    with open(results_folder / "benchmark_results.md", "w") as f:

        results = read_markdown("../.buildkite/nightly-benchmarks/" +
                                "performance-benchmarks-descriptions.md")
        results = results.format(
            latency_tests_markdown_table=latency_md_table,
            throughput_tests_markdown_table=throughput_md_table,
            serving_tests_markdown_table=serving_md_table,
            benchmarking_results_in_json_string=processed_results_json)
        f.write(results)

    # document benchmarking results in json
    with open(results_folder / "benchmark_results.json", "w") as f:

        results = latency_results.to_dict(
            orient='records') + throughput_results.to_dict(
                orient='records') + serving_results.to_dict(orient='records')
        f.write(json.dumps(results))