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[CI/Build][Doc] Fully deprecate old bench scripts for serving / throughput / latency (#24411)
Signed-off-by: Ye (Charlotte) Qi <yeq@meta.com>
This commit is contained in:
Ye (Charlotte) Qi
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GitHub
GitHub
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commit
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@ -694,7 +694,7 @@ python -m vllm.entrypoints.openai.api_server \
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Send requests with images:
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```bash
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python benchmarks/benchmark_serving.py \
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vllm bench serve \
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--backend openai-chat \
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--model Qwen/Qwen2.5-VL-7B-Instruct \
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--dataset-name sharegpt \
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@ -721,7 +721,7 @@ python -m vllm.entrypoints.openai.api_server \
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Send requests with videos:
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```bash
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python benchmarks/benchmark_serving.py \
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vllm bench serve \
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--backend openai-chat \
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--model Qwen/Qwen2.5-VL-7B-Instruct \
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--dataset-name sharegpt \
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@ -1,191 +1,17 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Benchmark the latency of processing a single batch of requests."""
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import argparse
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import dataclasses
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import json
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import os
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import time
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from typing import Any, Optional
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import numpy as np
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from tqdm import tqdm
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from typing_extensions import deprecated
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import vllm.envs as envs
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from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
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from vllm import LLM, SamplingParams
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from vllm.engine.arg_utils import EngineArgs
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from vllm.inputs import PromptType
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from vllm.sampling_params import BeamSearchParams
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from vllm.utils import FlexibleArgumentParser
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def save_to_pytorch_benchmark_format(
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args: argparse.Namespace, results: dict[str, Any]
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) -> None:
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pt_records = convert_to_pytorch_benchmark_format(
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args=args,
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metrics={"latency": results["latencies"]},
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extra_info={k: results[k] for k in ["avg_latency", "percentiles"]},
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)
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if pt_records:
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pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
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write_to_json(pt_file, pt_records)
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@deprecated(
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"benchmark_latency.py is deprecated and will be removed in a "
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"future version. Please use 'vllm bench latency' instead.",
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)
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def main(args: argparse.Namespace):
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print(args)
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engine_args = EngineArgs.from_cli_args(args)
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# NOTE(woosuk): If the request cannot be processed in a single batch,
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# the engine will automatically process the request in multiple batches.
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llm = LLM(**dataclasses.asdict(engine_args))
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assert llm.llm_engine.model_config.max_model_len >= (
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args.input_len + args.output_len
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), (
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"Please ensure that max_model_len is greater than"
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" the sum of input_len and output_len."
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)
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sampling_params = SamplingParams(
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n=args.n,
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temperature=1.0,
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top_p=1.0,
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ignore_eos=True,
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max_tokens=args.output_len,
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detokenize=not args.disable_detokenize,
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)
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print(sampling_params)
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dummy_prompt_token_ids = np.random.randint(
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10000, size=(args.batch_size, args.input_len)
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)
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dummy_prompts: list[PromptType] = [
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{"prompt_token_ids": batch} for batch in dummy_prompt_token_ids.tolist()
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]
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def llm_generate():
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if not args.use_beam_search:
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llm.generate(dummy_prompts, sampling_params=sampling_params, use_tqdm=False)
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else:
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llm.beam_search(
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dummy_prompts,
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BeamSearchParams(
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beam_width=args.n,
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max_tokens=args.output_len,
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ignore_eos=True,
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),
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)
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def run_to_completion(profile_dir: Optional[str] = None):
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if profile_dir:
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llm.start_profile()
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llm_generate()
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llm.stop_profile()
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else:
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start_time = time.perf_counter()
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llm_generate()
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end_time = time.perf_counter()
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latency = end_time - start_time
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return latency
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print("Warming up...")
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for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
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run_to_completion(profile_dir=None)
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if args.profile:
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profile_dir = envs.VLLM_TORCH_PROFILER_DIR
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print(f"Profiling (results will be saved to '{profile_dir}')...")
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run_to_completion(profile_dir=profile_dir)
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return
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# Benchmark.
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latencies = []
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for _ in tqdm(range(args.num_iters), desc="Profiling iterations"):
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latencies.append(run_to_completion(profile_dir=None))
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latencies = np.array(latencies)
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percentages = [10, 25, 50, 75, 90, 99]
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percentiles = np.percentile(latencies, percentages)
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print(f"Avg latency: {np.mean(latencies)} seconds")
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for percentage, percentile in zip(percentages, percentiles):
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print(f"{percentage}% percentile latency: {percentile} seconds")
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# Output JSON results if specified
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if args.output_json:
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results = {
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"avg_latency": np.mean(latencies),
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"latencies": latencies.tolist(),
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"percentiles": dict(zip(percentages, percentiles.tolist())),
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}
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with open(args.output_json, "w") as f:
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json.dump(results, f, indent=4)
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save_to_pytorch_benchmark_format(args, results)
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def create_argument_parser():
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parser = FlexibleArgumentParser(
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description="Benchmark the latency of processing a single batch of "
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"requests till completion."
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)
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parser.add_argument("--input-len", type=int, default=32)
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parser.add_argument("--output-len", type=int, default=128)
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parser.add_argument("--batch-size", type=int, default=8)
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parser.add_argument(
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"--n",
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type=int,
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default=1,
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help="Number of generated sequences per prompt.",
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)
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parser.add_argument("--use-beam-search", action="store_true")
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parser.add_argument(
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"--num-iters-warmup",
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type=int,
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default=10,
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help="Number of iterations to run for warmup.",
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)
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parser.add_argument(
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"--num-iters", type=int, default=30, help="Number of iterations to run."
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)
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parser.add_argument(
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"--profile",
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action="store_true",
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help="profile the generation process of a single batch",
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)
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parser.add_argument(
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"--output-json",
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type=str,
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default=None,
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help="Path to save the latency results in JSON format.",
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)
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parser.add_argument(
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"--disable-detokenize",
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action="store_true",
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help=(
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"Do not detokenize responses (i.e. do not include "
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"detokenization time in the latency measurement)"
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),
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)
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parser = EngineArgs.add_cli_args(parser)
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# V1 enables prefix caching by default which skews the latency
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# numbers. We need to disable prefix caching by default.
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parser.set_defaults(enable_prefix_caching=False)
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return parser
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import sys
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if __name__ == "__main__":
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parser = create_argument_parser()
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args = parser.parse_args()
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if args.profile and not envs.VLLM_TORCH_PROFILER_DIR:
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raise OSError(
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"The environment variable 'VLLM_TORCH_PROFILER_DIR' is not set. "
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"Please set it to a valid path to use torch profiler."
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)
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main(args)
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print("""DEPRECATED: This script has been moved to the vLLM CLI.
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Please use the following command instead:
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vllm bench latency
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For help with the new command, run:
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vllm bench latency --help
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Alternatively, you can run the new command directly with:
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python -m vllm.entrypoints.cli.main bench latency --help
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""")
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sys.exit(1)
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File diff suppressed because it is too large
Load Diff
@ -1,741 +1,17 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Benchmark offline inference throughput."""
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import argparse
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import dataclasses
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import json
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import os
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import random
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import time
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import warnings
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from typing import Any, Optional, Union
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import torch
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import uvloop
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from tqdm import tqdm
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from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase
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from typing_extensions import deprecated
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from benchmark_dataset import (
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AIMODataset,
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BurstGPTDataset,
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ConversationDataset,
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InstructCoderDataset,
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RandomDataset,
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SampleRequest,
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ShareGPTDataset,
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SonnetDataset,
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VisionArenaDataset,
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)
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from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
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from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
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from vllm.entrypoints.openai.api_server import (
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build_async_engine_client_from_engine_args,
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)
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from vllm.inputs import TextPrompt, TokensPrompt
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from vllm.lora.request import LoRARequest
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from vllm.outputs import RequestOutput
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from vllm.sampling_params import BeamSearchParams
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from vllm.utils import FlexibleArgumentParser, merge_async_iterators
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def run_vllm(
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requests: list[SampleRequest],
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n: int,
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engine_args: EngineArgs,
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disable_detokenize: bool = False,
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) -> tuple[float, Optional[list[RequestOutput]]]:
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from vllm import LLM, SamplingParams
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llm = LLM(**dataclasses.asdict(engine_args))
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assert all(
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llm.llm_engine.model_config.max_model_len
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>= (request.prompt_len + request.expected_output_len)
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for request in requests
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), (
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"Please ensure that max_model_len is greater than the sum of"
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" prompt_len and expected_output_len for all requests."
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)
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# Add the requests to the engine.
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prompts: list[Union[TextPrompt, TokensPrompt]] = []
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sampling_params: list[SamplingParams] = []
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for request in requests:
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prompts.append(
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TokensPrompt(
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prompt_token_ids=request.prompt["prompt_token_ids"],
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multi_modal_data=request.multi_modal_data,
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)
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if "prompt_token_ids" in request.prompt
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else TextPrompt(
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prompt=request.prompt, multi_modal_data=request.multi_modal_data
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)
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)
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sampling_params.append(
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SamplingParams(
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n=n,
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temperature=1.0,
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top_p=1.0,
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ignore_eos=True,
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max_tokens=request.expected_output_len,
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detokenize=not disable_detokenize,
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)
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)
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lora_requests: Optional[list[LoRARequest]] = None
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if engine_args.enable_lora:
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lora_requests = [request.lora_request for request in requests]
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use_beam_search = False
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outputs = None
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if not use_beam_search:
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start = time.perf_counter()
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outputs = llm.generate(
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prompts, sampling_params, lora_request=lora_requests, use_tqdm=True
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)
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end = time.perf_counter()
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else:
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assert lora_requests is None, "BeamSearch API does not support LoRA"
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# output_len should be the same for all requests.
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output_len = requests[0].expected_output_len
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for request in requests:
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assert request.expected_output_len == output_len
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start = time.perf_counter()
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llm.beam_search(
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prompts,
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BeamSearchParams(
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beam_width=n,
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max_tokens=output_len,
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ignore_eos=True,
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),
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)
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end = time.perf_counter()
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return end - start, outputs
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def run_vllm_chat(
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requests: list[SampleRequest],
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n: int,
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engine_args: EngineArgs,
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disable_detokenize: bool = False,
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) -> tuple[float, list[RequestOutput]]:
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"""
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Run vLLM chat benchmark. This function is recommended ONLY for benchmarking
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multimodal models as it properly handles multimodal inputs and chat
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formatting. For non-multimodal models, use run_vllm() instead.
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"""
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from vllm import LLM, SamplingParams
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llm = LLM(**dataclasses.asdict(engine_args))
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assert all(
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llm.llm_engine.model_config.max_model_len
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>= (request.prompt_len + request.expected_output_len)
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for request in requests
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), (
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"Please ensure that max_model_len is greater than the sum of "
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"prompt_len and expected_output_len for all requests."
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)
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prompts = []
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sampling_params: list[SamplingParams] = []
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for request in requests:
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prompts.append(request.prompt)
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sampling_params.append(
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SamplingParams(
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n=n,
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temperature=1.0,
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top_p=1.0,
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ignore_eos=True,
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max_tokens=request.expected_output_len,
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detokenize=not disable_detokenize,
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)
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)
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start = time.perf_counter()
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outputs = llm.chat(prompts, sampling_params, use_tqdm=True)
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end = time.perf_counter()
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return end - start, outputs
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async def run_vllm_async(
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requests: list[SampleRequest],
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n: int,
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engine_args: AsyncEngineArgs,
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disable_frontend_multiprocessing: bool = False,
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disable_detokenize: bool = False,
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) -> float:
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from vllm import SamplingParams
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async with build_async_engine_client_from_engine_args(
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engine_args,
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disable_frontend_multiprocessing=disable_frontend_multiprocessing,
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) as llm:
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model_config = await llm.get_model_config()
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assert all(
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model_config.max_model_len
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>= (request.prompt_len + request.expected_output_len)
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for request in requests
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), (
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"Please ensure that max_model_len is greater than the sum of"
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" prompt_len and expected_output_len for all requests."
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)
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# Add the requests to the engine.
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prompts: list[Union[TextPrompt, TokensPrompt]] = []
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sampling_params: list[SamplingParams] = []
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lora_requests: list[Optional[LoRARequest]] = []
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for request in requests:
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prompts.append(
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TokensPrompt(
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prompt_token_ids=request.prompt["prompt_token_ids"],
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multi_modal_data=request.multi_modal_data,
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)
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if "prompt_token_ids" in request.prompt
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else TextPrompt(
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prompt=request.prompt, multi_modal_data=request.multi_modal_data
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)
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)
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sampling_params.append(
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SamplingParams(
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n=n,
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temperature=1.0,
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top_p=1.0,
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ignore_eos=True,
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max_tokens=request.expected_output_len,
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detokenize=not disable_detokenize,
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)
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)
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lora_requests.append(request.lora_request)
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generators = []
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start = time.perf_counter()
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for i, (prompt, sp, lr) in enumerate(
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zip(prompts, sampling_params, lora_requests)
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):
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generator = llm.generate(prompt, sp, lora_request=lr, request_id=f"test{i}")
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generators.append(generator)
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all_gens = merge_async_iterators(*generators)
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async for i, res in all_gens:
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pass
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end = time.perf_counter()
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return end - start
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def run_hf(
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requests: list[SampleRequest],
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model: str,
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tokenizer: PreTrainedTokenizerBase,
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n: int,
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max_batch_size: int,
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trust_remote_code: bool,
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disable_detokenize: bool = False,
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) -> float:
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llm = AutoModelForCausalLM.from_pretrained(
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model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code
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)
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if llm.config.model_type == "llama":
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# To enable padding in the HF backend.
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tokenizer.pad_token = tokenizer.eos_token
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llm = llm.cuda()
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pbar = tqdm(total=len(requests))
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start = time.perf_counter()
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batch: list[str] = []
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max_prompt_len = 0
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max_output_len = 0
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for i in range(len(requests)):
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prompt = requests[i].prompt
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prompt_len = requests[i].prompt_len
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output_len = requests[i].expected_output_len
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# Add the prompt to the batch.
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batch.append(prompt)
|
||||
max_prompt_len = max(max_prompt_len, prompt_len)
|
||||
max_output_len = max(max_output_len, output_len)
|
||||
if len(batch) < max_batch_size and i != len(requests) - 1:
|
||||
# Check if we can add more requests to the batch.
|
||||
next_prompt_len = requests[i + 1].prompt_len
|
||||
next_output_len = requests[i + 1].expected_output_len
|
||||
if (
|
||||
max(max_prompt_len, next_prompt_len)
|
||||
+ max(max_output_len, next_output_len)
|
||||
) <= 2048:
|
||||
# We can add more requests to the batch.
|
||||
continue
|
||||
|
||||
# Generate the sequences.
|
||||
input_ids = tokenizer(batch, return_tensors="pt", padding=True).input_ids
|
||||
llm_outputs = llm.generate(
|
||||
input_ids=input_ids.cuda(),
|
||||
do_sample=True,
|
||||
num_return_sequences=n,
|
||||
temperature=1.0,
|
||||
top_p=1.0,
|
||||
use_cache=True,
|
||||
max_new_tokens=max_output_len,
|
||||
)
|
||||
if not disable_detokenize:
|
||||
# Include the decoding time.
|
||||
tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
|
||||
pbar.update(len(batch))
|
||||
|
||||
# Clear the batch.
|
||||
batch = []
|
||||
max_prompt_len = 0
|
||||
max_output_len = 0
|
||||
end = time.perf_counter()
|
||||
return end - start
|
||||
|
||||
|
||||
def run_mii(
|
||||
requests: list[SampleRequest],
|
||||
model: str,
|
||||
tensor_parallel_size: int,
|
||||
output_len: int,
|
||||
) -> float:
|
||||
from mii import client, serve
|
||||
|
||||
llm = serve(model, tensor_parallel=tensor_parallel_size)
|
||||
prompts = [request.prompt for request in requests]
|
||||
|
||||
start = time.perf_counter()
|
||||
llm.generate(prompts, max_new_tokens=output_len)
|
||||
end = time.perf_counter()
|
||||
client = client(model)
|
||||
client.terminate_server()
|
||||
return end - start
|
||||
|
||||
|
||||
def save_to_pytorch_benchmark_format(
|
||||
args: argparse.Namespace, results: dict[str, Any]
|
||||
) -> None:
|
||||
pt_records = convert_to_pytorch_benchmark_format(
|
||||
args=args,
|
||||
metrics={
|
||||
"requests_per_second": [results["requests_per_second"]],
|
||||
"tokens_per_second": [results["tokens_per_second"]],
|
||||
},
|
||||
extra_info={
|
||||
k: results[k] for k in ["elapsed_time", "num_requests", "total_num_tokens"]
|
||||
},
|
||||
)
|
||||
if pt_records:
|
||||
# Don't use json suffix here as we don't want CI to pick it up
|
||||
pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
|
||||
write_to_json(pt_file, pt_records)
|
||||
|
||||
|
||||
def get_requests(args, tokenizer):
|
||||
# Common parameters for all dataset types.
|
||||
common_kwargs = {
|
||||
"dataset_path": args.dataset_path,
|
||||
"random_seed": args.seed,
|
||||
}
|
||||
sample_kwargs = {
|
||||
"tokenizer": tokenizer,
|
||||
"lora_path": args.lora_path,
|
||||
"max_loras": args.max_loras,
|
||||
"num_requests": args.num_prompts,
|
||||
"input_len": args.input_len,
|
||||
"output_len": args.output_len,
|
||||
}
|
||||
|
||||
if args.dataset_path is None or args.dataset_name == "random":
|
||||
sample_kwargs["range_ratio"] = args.random_range_ratio
|
||||
sample_kwargs["prefix_len"] = args.prefix_len
|
||||
dataset_cls = RandomDataset
|
||||
elif args.dataset_name == "sharegpt":
|
||||
dataset_cls = ShareGPTDataset
|
||||
if args.backend == "vllm-chat":
|
||||
sample_kwargs["enable_multimodal_chat"] = True
|
||||
elif args.dataset_name == "sonnet":
|
||||
assert tokenizer.chat_template or tokenizer.default_chat_template, (
|
||||
"Tokenizer/model must have chat template for sonnet dataset."
|
||||
)
|
||||
dataset_cls = SonnetDataset
|
||||
sample_kwargs["prefix_len"] = args.prefix_len
|
||||
sample_kwargs["return_prompt_formatted"] = True
|
||||
elif args.dataset_name == "burstgpt":
|
||||
dataset_cls = BurstGPTDataset
|
||||
elif args.dataset_name == "hf":
|
||||
common_kwargs["no_stream"] = args.no_stream
|
||||
if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
|
||||
dataset_cls = VisionArenaDataset
|
||||
common_kwargs["dataset_subset"] = None
|
||||
common_kwargs["dataset_split"] = "train"
|
||||
sample_kwargs["enable_multimodal_chat"] = True
|
||||
elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
|
||||
dataset_cls = InstructCoderDataset
|
||||
common_kwargs["dataset_split"] = "train"
|
||||
elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
|
||||
dataset_cls = ConversationDataset
|
||||
common_kwargs["dataset_subset"] = args.hf_subset
|
||||
common_kwargs["dataset_split"] = args.hf_split
|
||||
sample_kwargs["enable_multimodal_chat"] = True
|
||||
elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
|
||||
dataset_cls = AIMODataset
|
||||
common_kwargs["dataset_subset"] = None
|
||||
common_kwargs["dataset_split"] = "train"
|
||||
else:
|
||||
raise ValueError(f"Unknown dataset name: {args.dataset_name}")
|
||||
# Remove None values
|
||||
sample_kwargs = {k: v for k, v in sample_kwargs.items() if v is not None}
|
||||
return dataset_cls(**common_kwargs).sample(**sample_kwargs)
|
||||
|
||||
|
||||
@deprecated(
|
||||
"benchmark_throughput.py is deprecated and will be removed in a "
|
||||
"future version. Please use 'vllm bench throughput' instead.",
|
||||
)
|
||||
def main(args: argparse.Namespace):
|
||||
if args.seed is None:
|
||||
args.seed = 0
|
||||
print(args)
|
||||
random.seed(args.seed)
|
||||
# Sample the requests.
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
args.tokenizer, trust_remote_code=args.trust_remote_code
|
||||
)
|
||||
requests = get_requests(args, tokenizer)
|
||||
is_multi_modal = any(request.multi_modal_data is not None for request in requests)
|
||||
request_outputs: Optional[list[RequestOutput]] = None
|
||||
if args.backend == "vllm":
|
||||
if args.async_engine:
|
||||
elapsed_time = uvloop.run(
|
||||
run_vllm_async(
|
||||
requests,
|
||||
args.n,
|
||||
AsyncEngineArgs.from_cli_args(args),
|
||||
args.disable_frontend_multiprocessing,
|
||||
args.disable_detokenize,
|
||||
)
|
||||
)
|
||||
else:
|
||||
elapsed_time, request_outputs = run_vllm(
|
||||
requests,
|
||||
args.n,
|
||||
EngineArgs.from_cli_args(args),
|
||||
args.disable_detokenize,
|
||||
)
|
||||
elif args.backend == "hf":
|
||||
assert args.tensor_parallel_size == 1
|
||||
elapsed_time = run_hf(
|
||||
requests,
|
||||
args.model,
|
||||
tokenizer,
|
||||
args.n,
|
||||
args.hf_max_batch_size,
|
||||
args.trust_remote_code,
|
||||
args.disable_detokenize,
|
||||
)
|
||||
elif args.backend == "mii":
|
||||
elapsed_time = run_mii(
|
||||
requests, args.model, args.tensor_parallel_size, args.output_len
|
||||
)
|
||||
elif args.backend == "vllm-chat":
|
||||
elapsed_time, request_outputs = run_vllm_chat(
|
||||
requests, args.n, EngineArgs.from_cli_args(args), args.disable_detokenize
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unknown backend: {args.backend}")
|
||||
|
||||
if request_outputs:
|
||||
# Note: with the vllm and vllm-chat backends,
|
||||
# we have request_outputs, which we use to count tokens.
|
||||
total_prompt_tokens = 0
|
||||
total_output_tokens = 0
|
||||
for ro in request_outputs:
|
||||
if not isinstance(ro, RequestOutput):
|
||||
continue
|
||||
total_prompt_tokens += (
|
||||
len(ro.prompt_token_ids) if ro.prompt_token_ids else 0
|
||||
)
|
||||
total_output_tokens += sum(len(o.token_ids) for o in ro.outputs if o)
|
||||
total_num_tokens = total_prompt_tokens + total_output_tokens
|
||||
else:
|
||||
total_num_tokens = sum(r.prompt_len + r.expected_output_len for r in requests)
|
||||
total_output_tokens = sum(r.expected_output_len for r in requests)
|
||||
total_prompt_tokens = total_num_tokens - total_output_tokens
|
||||
|
||||
if is_multi_modal and args.backend != "vllm-chat":
|
||||
print(
|
||||
"\033[91mWARNING\033[0m: Multi-modal request with "
|
||||
f"{args.backend} backend detected. The "
|
||||
"following metrics are not accurate because image tokens are not"
|
||||
" counted. See vllm-project/vllm/issues/9778 for details."
|
||||
)
|
||||
# TODO(vllm-project/vllm/issues/9778): Count multi-modal token length.
|
||||
# vllm-chat backend counts the image tokens now
|
||||
|
||||
print(
|
||||
f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
|
||||
f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
|
||||
f"{total_output_tokens / elapsed_time:.2f} output tokens/s"
|
||||
)
|
||||
print(f"Total num prompt tokens: {total_prompt_tokens}")
|
||||
print(f"Total num output tokens: {total_output_tokens}")
|
||||
|
||||
# Output JSON results if specified
|
||||
if args.output_json:
|
||||
results = {
|
||||
"elapsed_time": elapsed_time,
|
||||
"num_requests": len(requests),
|
||||
"total_num_tokens": total_num_tokens,
|
||||
"requests_per_second": len(requests) / elapsed_time,
|
||||
"tokens_per_second": total_num_tokens / elapsed_time,
|
||||
}
|
||||
with open(args.output_json, "w") as f:
|
||||
json.dump(results, f, indent=4)
|
||||
save_to_pytorch_benchmark_format(args, results)
|
||||
|
||||
|
||||
def validate_args(args):
|
||||
"""
|
||||
Validate command-line arguments.
|
||||
"""
|
||||
|
||||
# === Deprecation and Defaulting ===
|
||||
if args.dataset is not None:
|
||||
warnings.warn(
|
||||
"The '--dataset' argument will be deprecated in the next release. "
|
||||
"Please use '--dataset-name' and '--dataset-path' instead.",
|
||||
stacklevel=2,
|
||||
)
|
||||
args.dataset_path = args.dataset
|
||||
|
||||
if not getattr(args, "tokenizer", None):
|
||||
args.tokenizer = args.model
|
||||
|
||||
# === Backend Validation ===
|
||||
valid_backends = {"vllm", "hf", "mii", "vllm-chat"}
|
||||
if args.backend not in valid_backends:
|
||||
raise ValueError(f"Unsupported backend: {args.backend}")
|
||||
|
||||
# === Dataset Configuration ===
|
||||
if not args.dataset and not args.dataset_path:
|
||||
print("When dataset path is not set, it will default to random dataset")
|
||||
args.dataset_name = "random"
|
||||
if args.input_len is None:
|
||||
raise ValueError("input_len must be provided for a random dataset")
|
||||
|
||||
# === Dataset Name Specific Checks ===
|
||||
# --hf-subset and --hf-split: only used
|
||||
# when dataset_name is 'hf'
|
||||
if args.dataset_name != "hf" and (
|
||||
getattr(args, "hf_subset", None) is not None
|
||||
or getattr(args, "hf_split", None) is not None
|
||||
):
|
||||
warnings.warn(
|
||||
"--hf-subset and --hf-split will be ignored \
|
||||
since --dataset-name is not 'hf'.",
|
||||
stacklevel=2,
|
||||
)
|
||||
elif args.dataset_name == "hf":
|
||||
if args.dataset_path in (
|
||||
VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
|
||||
| ConversationDataset.SUPPORTED_DATASET_PATHS
|
||||
):
|
||||
assert args.backend == "vllm-chat", (
|
||||
f"{args.dataset_path} needs to use vllm-chat as the backend."
|
||||
) # noqa: E501
|
||||
elif args.dataset_path in (
|
||||
InstructCoderDataset.SUPPORTED_DATASET_PATHS
|
||||
| AIMODataset.SUPPORTED_DATASET_PATHS
|
||||
):
|
||||
assert args.backend == "vllm", (
|
||||
f"{args.dataset_path} needs to use vllm as the backend."
|
||||
) # noqa: E501
|
||||
else:
|
||||
raise ValueError(f"{args.dataset_path} is not supported by hf dataset.")
|
||||
|
||||
# --random-range-ratio: only used when dataset_name is 'random'
|
||||
if args.dataset_name != "random" and args.random_range_ratio is not None:
|
||||
warnings.warn(
|
||||
"--random-range-ratio will be ignored since \
|
||||
--dataset-name is not 'random'.",
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
# --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
|
||||
# set.
|
||||
if (
|
||||
args.dataset_name not in {"random", "sonnet", None}
|
||||
and args.prefix_len is not None
|
||||
):
|
||||
warnings.warn(
|
||||
"--prefix-len will be ignored since --dataset-name\
|
||||
is not 'random', 'sonnet', or not set.",
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
# === LoRA Settings ===
|
||||
if getattr(args, "enable_lora", False) and args.backend != "vllm":
|
||||
raise ValueError("LoRA benchmarking is only supported for vLLM backend")
|
||||
if getattr(args, "enable_lora", False) and args.lora_path is None:
|
||||
raise ValueError("LoRA path must be provided when enable_lora is True")
|
||||
|
||||
# === Backend-specific Validations ===
|
||||
if args.backend == "hf" and args.hf_max_batch_size is None:
|
||||
raise ValueError("HF max batch size is required for HF backend")
|
||||
if args.backend != "hf" and args.hf_max_batch_size is not None:
|
||||
raise ValueError("HF max batch size is only for HF backend.")
|
||||
|
||||
if (
|
||||
args.backend in {"hf", "mii"}
|
||||
and getattr(args, "quantization", None) is not None
|
||||
):
|
||||
raise ValueError("Quantization is only for vLLM backend.")
|
||||
|
||||
if args.backend == "mii" and args.dtype != "auto":
|
||||
raise ValueError("dtype must be auto for MII backend.")
|
||||
if args.backend == "mii" and args.n != 1:
|
||||
raise ValueError("n must be 1 for MII backend.")
|
||||
if args.backend == "mii" and args.tokenizer != args.model:
|
||||
raise ValueError("Tokenizer must be the same as the model for MII backend.")
|
||||
|
||||
# --data-parallel is not supported currently.
|
||||
# https://github.com/vllm-project/vllm/issues/16222
|
||||
if args.data_parallel_size > 1:
|
||||
raise ValueError(
|
||||
"Data parallel is not supported in offline benchmark, "
|
||||
"please use benchmark serving instead"
|
||||
)
|
||||
|
||||
|
||||
def create_argument_parser():
|
||||
parser = FlexibleArgumentParser(description="Benchmark the throughput.")
|
||||
parser.add_argument(
|
||||
"--backend",
|
||||
type=str,
|
||||
choices=["vllm", "hf", "mii", "vllm-chat"],
|
||||
default="vllm",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dataset-name",
|
||||
type=str,
|
||||
choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
|
||||
help="Name of the dataset to benchmark on.",
|
||||
default="sharegpt",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no-stream",
|
||||
action="store_true",
|
||||
help="Do not load the dataset in streaming mode.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Path to the ShareGPT dataset, will be deprecated in\
|
||||
the next release. The dataset is expected to "
|
||||
"be a json in form of list[dict[..., conversations: "
|
||||
"list[dict[..., value: <prompt_or_response>]]]]",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dataset-path", type=str, default=None, help="Path to the dataset"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--input-len",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Input prompt length for each request",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output-len",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Output length for each request. Overrides the "
|
||||
"output length from the dataset.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--n", type=int, default=1, help="Number of generated sequences per prompt."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num-prompts", type=int, default=1000, help="Number of prompts to process."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--hf-max-batch-size",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Maximum batch size for HF backend.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output-json",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Path to save the throughput results in JSON format.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--async-engine",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Use vLLM async engine rather than LLM class.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--disable-frontend-multiprocessing",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Disable decoupled async engine frontend.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--disable-detokenize",
|
||||
action="store_true",
|
||||
help=(
|
||||
"Do not detokenize the response (i.e. do not include "
|
||||
"detokenization time in the measurement)"
|
||||
),
|
||||
)
|
||||
# LoRA
|
||||
parser.add_argument(
|
||||
"--lora-path",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Path to the LoRA adapters to use. This can be an absolute path, "
|
||||
"a relative path, or a Hugging Face model identifier.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--prefix-len",
|
||||
type=int,
|
||||
default=None,
|
||||
help=f"Number of prefix tokens to be used in RandomDataset "
|
||||
"and SonnetDataset. For RandomDataset, the total input "
|
||||
"length is the sum of prefix-len (default: "
|
||||
f"{RandomDataset.DEFAULT_PREFIX_LEN}) and a random context length "
|
||||
"sampled from [input_len * (1 - range_ratio), "
|
||||
"input_len * (1 + range_ratio)]. For SonnetDataset, "
|
||||
f"prefix_len (default: {SonnetDataset.DEFAULT_PREFIX_LEN}) "
|
||||
"controls how much of the input is fixed lines versus "
|
||||
"random lines, but the total input length remains approximately "
|
||||
"input_len tokens.",
|
||||
)
|
||||
# random dataset
|
||||
parser.add_argument(
|
||||
"--random-range-ratio",
|
||||
type=float,
|
||||
default=None,
|
||||
help=f"Range ratio (default : {RandomDataset.DEFAULT_RANGE_RATIO}) "
|
||||
"for sampling input/output length, "
|
||||
"used only for RandomDataset. Must be in the range [0, 1) to "
|
||||
"define a symmetric sampling range "
|
||||
"[length * (1 - range_ratio), length * (1 + range_ratio)].",
|
||||
)
|
||||
|
||||
# hf dataset
|
||||
parser.add_argument(
|
||||
"--hf-subset", type=str, default=None, help="Subset of the HF dataset."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--hf-split", type=str, default=None, help="Split of the HF dataset."
|
||||
)
|
||||
|
||||
parser = AsyncEngineArgs.add_cli_args(parser)
|
||||
|
||||
return parser
|
||||
|
||||
import sys
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = create_argument_parser()
|
||||
args = parser.parse_args()
|
||||
if args.tokenizer is None:
|
||||
args.tokenizer = args.model
|
||||
validate_args(args)
|
||||
main(args)
|
||||
print("""DEPRECATED: This script has been moved to the vLLM CLI.
|
||||
|
||||
Please use the following command instead:
|
||||
vllm bench throughput
|
||||
|
||||
For help with the new command, run:
|
||||
vllm bench throughput --help
|
||||
|
||||
Alternatively, you can run the new command directly with:
|
||||
python -m vllm.entrypoints.cli.main bench throughput --help
|
||||
""")
|
||||
sys.exit(1)
|
||||
|
||||
Reference in New Issue
Block a user