[Misc] Add a script to benchmark compilation time (#29919)

Signed-off-by: Bin Bao <binbao@meta.com>

vllm/benchmarks/startup.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Benchmark the cold and warm startup time of vLLM models.
+
+This script measures total startup time (including model loading, compilation,
+and cache operations) for both cold and warm scenarios:
+- Cold startup: Fresh start with no caches (temporary cache directories)
+- Warm startup: Using cached compilation and model info
+"""
+
+import argparse
+import dataclasses
+import json
+import multiprocessing
+import os
+import shutil
+import tempfile
+import time
+from contextlib import contextmanager
+from typing import Any
+
+import numpy as np
+from tqdm import tqdm
+
+from vllm.benchmarks.lib.utils import (
+    convert_to_pytorch_benchmark_format,
+    write_to_json,
+)
+from vllm.engine.arg_utils import EngineArgs
+
+
+@contextmanager
+def cold_startup():
+    """
+    Context manager to measure cold startup time:
+    1. Uses a temporary directory for vLLM cache to avoid any pollution
+       between cold startup iterations.
+    2. Uses inductor's fresh_cache to clear torch.compile caches.
+    """
+    from torch._inductor.utils import fresh_cache
+
+    # Use temporary directory for caching to avoid any pollution between cold startups
+    original_cache_root = os.environ.get("VLLM_CACHE_ROOT")
+    temp_cache_dir = tempfile.mkdtemp(prefix="vllm_startup_bench_cold_")
+    try:
+        os.environ["VLLM_CACHE_ROOT"] = temp_cache_dir
+        with fresh_cache():
+            yield
+    finally:
+        # Clean up temporary cache directory
+        shutil.rmtree(temp_cache_dir, ignore_errors=True)
+        if original_cache_root:
+            os.environ["VLLM_CACHE_ROOT"] = original_cache_root
+        else:
+            os.environ.pop("VLLM_CACHE_ROOT", None)
+
+
+def run_startup_in_subprocess(engine_args_dict, result_queue):
+    """
+    Run LLM startup in a subprocess and return timing metrics via a queue.
+    This ensures complete isolation between iterations.
+    """
+    try:
+        # Import inside the subprocess to avoid issues with forking
+        from vllm import LLM
+        from vllm.engine.arg_utils import EngineArgs
+
+        engine_args = EngineArgs(**engine_args_dict)
+
+        # Measure total startup time
+        start_time = time.perf_counter()
+
+        llm = LLM(**dataclasses.asdict(engine_args))
+
+        total_startup_time = time.perf_counter() - start_time
+
+        # Extract compilation time if available
+        compilation_time = 0.0
+        if hasattr(llm.llm_engine, "vllm_config"):
+            vllm_config = llm.llm_engine.vllm_config
+            if (
+                hasattr(vllm_config, "compilation_config")
+                and vllm_config.compilation_config is not None
+            ):
+                compilation_time = vllm_config.compilation_config.compilation_time
+
+        result_queue.put(
+            {
+                "total_startup_time": total_startup_time,
+                "compilation_time": compilation_time,
+            }
+        )
+
+    except Exception as e:
+        result_queue.put(None)
+        result_queue.put(str(e))
+
+
+def save_to_pytorch_benchmark_format(
+    args: argparse.Namespace, results: dict[str, Any]
+) -> None:
+    base_name = os.path.splitext(args.output_json)[0]
+
+    cold_startup_records = convert_to_pytorch_benchmark_format(
+        args=args,
+        metrics={
+            "avg_cold_startup_time": results["avg_cold_startup_time"],
+        },
+        extra_info={
+            "cold_startup_times": results["cold_startup_times"],
+            "cold_startup_percentiles": results["cold_startup_percentiles"],
+        },
+    )
+    if cold_startup_records:
+        write_to_json(f"{base_name}.cold_startup.pytorch.json", cold_startup_records)
+
+    cold_compilation_records = convert_to_pytorch_benchmark_format(
+        args=args,
+        metrics={
+            "avg_cold_compilation_time": results["avg_cold_compilation_time"],
+        },
+        extra_info={
+            "cold_compilation_times": results["cold_compilation_times"],
+            "cold_compilation_percentiles": results["cold_compilation_percentiles"],
+        },
+    )
+    if cold_compilation_records:
+        write_to_json(
+            f"{base_name}.cold_compilation.pytorch.json", cold_compilation_records
+        )
+
+    warm_startup_records = convert_to_pytorch_benchmark_format(
+        args=args,
+        metrics={
+            "avg_warm_startup_time": results["avg_warm_startup_time"],
+        },
+        extra_info={
+            "warm_startup_times": results["warm_startup_times"],
+            "warm_startup_percentiles": results["warm_startup_percentiles"],
+        },
+    )
+    if warm_startup_records:
+        write_to_json(f"{base_name}.warm_startup.pytorch.json", warm_startup_records)
+
+    warm_compilation_records = convert_to_pytorch_benchmark_format(
+        args=args,
+        metrics={
+            "avg_warm_compilation_time": results["avg_warm_compilation_time"],
+        },
+        extra_info={
+            "warm_compilation_times": results["warm_compilation_times"],
+            "warm_compilation_percentiles": results["warm_compilation_percentiles"],
+        },
+    )
+    if warm_compilation_records:
+        write_to_json(
+            f"{base_name}.warm_compilation.pytorch.json", warm_compilation_records
+        )
+
+
+def add_cli_args(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "--num-iters-cold",
+        type=int,
+        default=5,
+        help="Number of cold startup iterations.",
+    )
+    parser.add_argument(
+        "--num-iters-warmup",
+        type=int,
+        default=3,
+        help="Number of warmup iterations before benchmarking warm startups.",
+    )
+    parser.add_argument(
+        "--num-iters-warm",
+        type=int,
+        default=5,
+        help="Number of warm startup iterations.",
+    )
+    parser.add_argument(
+        "--output-json",
+        type=str,
+        default=None,
+        help="Path to save the startup time results in JSON format.",
+    )
+
+    parser = EngineArgs.add_cli_args(parser)
+    return parser
+
+
+def main(args: argparse.Namespace):
+    # Set multiprocessing start method to 'spawn' for clean process isolation
+    # This ensures each subprocess starts fresh without inheriting state
+    multiprocessing.set_start_method("spawn", force=True)
+
+    engine_args = EngineArgs.from_cli_args(args)
+
+    def create_llm_and_measure_startup():
+        """
+        Create LLM instance in a subprocess and measure startup time.
+        Returns timing metrics, using subprocess for complete isolation.
+        """
+        # Convert engine_args to dictionary for pickling
+        engine_args_dict = dataclasses.asdict(engine_args)
+
+        # Create a queue for inter-process communication
+        result_queue = multiprocessing.Queue()
+        process = multiprocessing.Process(
+            target=run_startup_in_subprocess,
+            args=(
+                engine_args_dict,
+                result_queue,
+            ),
+        )
+        process.start()
+        process.join()
+
+        if not result_queue.empty():
+            result = result_queue.get()
+            if result is None:
+                if not result_queue.empty():
+                    error_msg = result_queue.get()
+                    raise RuntimeError(f"Subprocess failed: {error_msg}")
+                else:
+                    raise RuntimeError("Subprocess failed with unknown error")
+            return result
+        else:
+            raise RuntimeError("Subprocess did not return a result")
+
+    os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
+    print("Setting VLLM_ENABLE_V1_MULTIPROCESSING=0 to collect startup metrics.\n")
+
+    print("Measuring cold startup time...\n")
+    cold_startup_times = []
+    cold_compilation_times = []
+    for i in tqdm(range(args.num_iters_cold), desc="Cold startup iterations"):
+        with cold_startup():
+            metrics = create_llm_and_measure_startup()
+            cold_startup_times.append(metrics["total_startup_time"])
+            cold_compilation_times.append(metrics["compilation_time"])
+
+    # Warmup for warm startup
+    print("\nWarming up for warm startup measurement...\n")
+    for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
+        create_llm_and_measure_startup()
+
+    print("\nMeasuring warm startup time...\n")
+    warm_startup_times = []
+    warm_compilation_times = []
+    for i in tqdm(range(args.num_iters_warm), desc="Warm startup iterations"):
+        metrics = create_llm_and_measure_startup()
+        warm_startup_times.append(metrics["total_startup_time"])
+        warm_compilation_times.append(metrics["compilation_time"])
+
+    # Calculate statistics
+    cold_startup_array = np.array(cold_startup_times)
+    cold_compilation_array = np.array(cold_compilation_times)
+    warm_startup_array = np.array(warm_startup_times)
+    warm_compilation_array = np.array(warm_compilation_times)
+
+    avg_cold_startup = np.mean(cold_startup_array)
+    avg_cold_compilation = np.mean(cold_compilation_array)
+    avg_warm_startup = np.mean(warm_startup_array)
+    avg_warm_compilation = np.mean(warm_compilation_array)
+
+    percentages = [10, 25, 50, 75, 90, 99]
+    cold_startup_percentiles = np.percentile(cold_startup_array, percentages)
+    cold_compilation_percentiles = np.percentile(cold_compilation_array, percentages)
+    warm_startup_percentiles = np.percentile(warm_startup_array, percentages)
+    warm_compilation_percentiles = np.percentile(warm_compilation_array, percentages)
+
+    print("\n" + "=" * 60)
+    print("STARTUP TIME BENCHMARK RESULTS")
+    print("=" * 60)
+
+    # Cold startup statistics
+    print("\nCOLD STARTUP:")
+    print(f"Avg total startup time: {avg_cold_startup:.2f} seconds")
+    print(f"Avg compilation time:   {avg_cold_compilation:.2f} seconds")
+    print("Startup time percentiles:")
+    for percentage, percentile in zip(percentages, cold_startup_percentiles):
+        print(f"  {percentage}%: {percentile:.2f} seconds")
+    print("Compilation time percentiles:")
+    for percentage, percentile in zip(percentages, cold_compilation_percentiles):
+        print(f"  {percentage}%: {percentile:.2f} seconds")
+
+    # Warm startup statistics
+    print("\nWARM STARTUP:")
+    print(f"Avg total startup time: {avg_warm_startup:.2f} seconds")
+    print(f"Avg compilation time:   {avg_warm_compilation:.2f} seconds")
+    print("Startup time percentiles:")
+    for percentage, percentile in zip(percentages, warm_startup_percentiles):
+        print(f"  {percentage}%: {percentile:.2f} seconds")
+    print("Compilation time percentiles:")
+    for percentage, percentile in zip(percentages, warm_compilation_percentiles):
+        print(f"  {percentage}%: {percentile:.2f} seconds")
+
+    print("=" * 60)
+
+    # Output JSON results if specified
+    if args.output_json:
+        results = {
+            "avg_cold_startup_time": float(avg_cold_startup),
+            "avg_cold_compilation_time": float(avg_cold_compilation),
+            "cold_startup_times": cold_startup_times,
+            "cold_compilation_times": cold_compilation_times,
+            "cold_startup_percentiles": dict(
+                zip(percentages, cold_startup_percentiles.tolist())
+            ),
+            "cold_compilation_percentiles": dict(
+                zip(percentages, cold_compilation_percentiles.tolist())
+            ),
+            "avg_warm_startup_time": float(avg_warm_startup),
+            "avg_warm_compilation_time": float(avg_warm_compilation),
+            "warm_startup_times": warm_startup_times,
+            "warm_compilation_times": warm_compilation_times,
+            "warm_startup_percentiles": dict(
+                zip(percentages, warm_startup_percentiles.tolist())
+            ),
+            "warm_compilation_percentiles": dict(
+                zip(percentages, warm_compilation_percentiles.tolist())
+            ),
+        }
+        with open(args.output_json, "w") as f:
+            json.dump(results, f, indent=4)
+        save_to_pytorch_benchmark_format(args, results)

vllm/entrypoints/cli/__init__.py

@@ -2,12 +2,14 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from vllm.entrypoints.cli.benchmark.latency import BenchmarkLatencySubcommand
 from vllm.entrypoints.cli.benchmark.serve import BenchmarkServingSubcommand
+from vllm.entrypoints.cli.benchmark.startup import BenchmarkStartupSubcommand
 from vllm.entrypoints.cli.benchmark.sweep import BenchmarkSweepSubcommand
 from vllm.entrypoints.cli.benchmark.throughput import BenchmarkThroughputSubcommand
 
 __all__: list[str] = [
     "BenchmarkLatencySubcommand",
     "BenchmarkServingSubcommand",
+    "BenchmarkStartupSubcommand",
     "BenchmarkSweepSubcommand",
     "BenchmarkThroughputSubcommand",
 ]

vllm/entrypoints/cli/benchmark/startup.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import argparse
+
+from vllm.benchmarks.startup import add_cli_args, main
+from vllm.entrypoints.cli.benchmark.base import BenchmarkSubcommandBase
+
+
+class BenchmarkStartupSubcommand(BenchmarkSubcommandBase):
+    """The `startup` subcommand for `vllm bench`."""
+
+    name = "startup"
+    help = "Benchmark the startup time of vLLM models."
+
+    @classmethod
+    def add_cli_args(cls, parser: argparse.ArgumentParser) -> None:
+        add_cli_args(parser)
+
+    @staticmethod
+    def cmd(args: argparse.Namespace) -> None:
+        main(args)