312 lines
9.8 KiB
Python
312 lines
9.8 KiB
Python
"""
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Benchmark the throughput of using the offline LLM engine.
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This script does not launch a server.
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It accepts server arguments (the same as launch_server.py) and benchmark arguments (the same as bench_serving.py).
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# Usage
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## Sharegpt dataset with default args
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python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3.1-8B-Instruct
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## Random dataset with default args
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python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --dataset-name random
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## Shared prefix dataset with default args
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python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --dataset-name generated-shared-prefix
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## Sharegpt dataset on runtime backend
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python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --backend runtime
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"""
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import argparse
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import dataclasses
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import json
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import logging
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import random
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import time
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from typing import List, Optional, Tuple
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import numpy as np
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from sglang.api import Engine
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from sglang.bench_serving import (
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get_dataset,
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get_tokenizer,
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sample_random_requests,
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set_ulimit,
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)
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from sglang.srt.server import Runtime
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from sglang.srt.server_args import ServerArgs
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@dataclasses.dataclass
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class BenchArgs:
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backend: str = "engine"
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result_filename: str = ""
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dataset_name: str = "sharegpt"
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dataset_path: str = ""
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num_prompts: int = 1000
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sharegpt_output_len: Optional[int] = None
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random_input_len: int = 1024
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random_output_len: int = 1024
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random_range_ratio: float = 0.0
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gen_num_groups: int = 64
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gen_prompts_per_group: int = 16
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gen_system_prompt_len: int = 2048
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gen_question_len: int = 128
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gen_output_len: int = 256
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disable_ignore_eos: bool = False
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seed: int = 1
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@staticmethod
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def add_cli_args(parser: argparse.ArgumentParser):
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parser.add_argument("--backend", type=str, default=BenchArgs.backend)
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parser.add_argument(
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"--result-filename", type=str, default=BenchArgs.result_filename
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)
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parser.add_argument(
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"--dataset-name",
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type=str,
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default="sharegpt",
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choices=["sharegpt", "random", "generated-shared-prefix"],
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help="Name of the dataset to benchmark on.",
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)
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parser.add_argument(
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"--dataset-path", type=str, default="", help="Path to the dataset."
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)
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parser.add_argument(
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"--num-prompts",
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type=int,
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default=BenchArgs.num_prompts,
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help="Number of prompts to process. Default is 1000.",
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)
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parser.add_argument(
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"--sharegpt-output-len",
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type=int,
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default=BenchArgs.sharegpt_output_len,
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help="Output length for each request. Overrides the output length from the ShareGPT dataset.",
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)
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parser.add_argument(
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"--random-input-len",
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type=int,
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default=BenchArgs.random_input_len,
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help="Number of input tokens per request, used only for random dataset.",
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)
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parser.add_argument(
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"--random-output-len",
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type=int,
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default=BenchArgs.random_output_len,
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help="Number of output tokens per request, used only for random dataset.",
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)
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parser.add_argument(
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"--random-range-ratio",
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type=float,
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default=BenchArgs.random_range_ratio,
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help="Range of sampled ratio of input/output length, "
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"used only for random dataset.",
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)
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parser.add_argument(
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"--gen-num-groups",
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type=int,
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default=BenchArgs.gen_num_groups,
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help="Number of groups with shared prefix, used"
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"only for generate-shared-prefix",
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)
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parser.add_argument(
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"--gen-prompts-per-group",
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type=int,
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default=BenchArgs.gen_prompts_per_group,
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help="Number of prompts per group of shared prefix, used"
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"only for generate-shared-prefix",
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)
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parser.add_argument(
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"--gen-system-prompt-len",
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type=int,
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default=BenchArgs.gen_system_prompt_len,
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help="System prompt length, used" "only for generate-shared-prefix",
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)
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parser.add_argument(
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"--gen-question-len",
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type=int,
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default=BenchArgs.gen_question_len,
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help="Question length, used" "only for generate-shared-prefix",
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)
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parser.add_argument(
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"--gen-output-len",
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type=int,
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default=BenchArgs.gen_output_len,
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help="Target length in tokens for outputs in generated-shared-prefix dataset",
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)
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parser.add_argument(
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"--disable-ignore-eos",
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type=bool,
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default=BenchArgs.disable_ignore_eos,
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help="Disable ignore EOS token",
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)
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parser.add_argument("--seed", type=int, default=1, help="The random seed.")
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@classmethod
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def from_cli_args(cls, args: argparse.Namespace):
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attrs = [attr.name for attr in dataclasses.fields(cls)]
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return cls(**{attr: getattr(args, attr) for attr in attrs})
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def throughput_test_once(
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backend_name: str,
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backend,
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reqs: List[Tuple[str, int, int]],
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ignore_eos: bool,
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):
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measurement_results = {
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"backend": backend_name,
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"successful_requests": len(reqs),
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"total_latency": -1,
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"total_input_tokens": sum(r[1] for r in reqs),
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"total_output_tokens": -1,
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"request_throughput": -1,
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"input_throughput": -1,
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"output_throughput": -1,
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"total_throughput": -1,
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}
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prompt = [r[0] for r in reqs]
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sampling_params = [
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{
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"temperature": 0,
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"max_new_tokens": r[2],
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"ignore_eos": ignore_eos,
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}
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for r in reqs
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]
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st = time.perf_counter()
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gen_out = backend.generate(prompt=prompt, sampling_params=sampling_params)
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latency = time.perf_counter() - st
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if backend_name == "runtime":
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gen_out = json.loads(gen_out)
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measurement_results["total_latency"] = latency
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measurement_results["total_output_tokens"] = sum(
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o["meta_info"]["completion_tokens"] for o in gen_out
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)
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measurement_results["request_throughput"] = (
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measurement_results["successful_requests"] / latency
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)
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measurement_results["input_throughput"] = (
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measurement_results["total_input_tokens"] / latency
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)
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measurement_results["output_throughput"] = (
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measurement_results["total_output_tokens"] / latency
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)
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measurement_results["total_throughput"] = (
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measurement_results["total_input_tokens"]
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+ measurement_results["total_output_tokens"]
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) / latency
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return measurement_results
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def throughput_test(
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server_args: ServerArgs,
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bench_args: BenchArgs,
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):
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if bench_args.backend == "engine":
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backend = Engine(**dataclasses.asdict(server_args))
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if not backend:
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raise ValueError("Please provide valid engine arguments")
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elif bench_args.backend == "runtime":
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backend = Runtime(**dataclasses.asdict(server_args))
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else:
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raise ValueError('Please set backend to either "engine" or "runtime"')
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tokenizer_id = server_args.model_path
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tokenizer = get_tokenizer(tokenizer_id)
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# Set global environmnets
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set_ulimit()
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random.seed(bench_args.seed)
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np.random.seed(bench_args.seed)
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# Read dataset
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input_requests = get_dataset(bench_args, tokenizer)
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warmup_requests = sample_random_requests(
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input_len=256,
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output_len=16,
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num_prompts=16,
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range_ratio=0.8,
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tokenizer=tokenizer,
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dataset_path=bench_args.dataset_path,
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)
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# Warm up
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logging.info("\nWarmup...")
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throughput_test_once(
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backend_name=bench_args.backend,
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backend=backend,
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reqs=warmup_requests,
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ignore_eos=not bench_args.disable_ignore_eos,
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)
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logging.info("\nBenchmark...")
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result = throughput_test_once(
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backend_name=bench_args.backend,
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backend=backend,
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reqs=input_requests,
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ignore_eos=not bench_args.disable_ignore_eos,
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)
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if bench_args.result_filename:
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with open(bench_args.result_filename, "a") as fout:
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fout.write(json.dumps(result) + "\n")
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print(
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"\n{s:{c}^{n}}".format(s=" Offline Throughput Benchmark Result ", n=50, c="=")
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)
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print("{:<40} {:<10}".format("Backend:", result["backend"]))
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print("{:<40} {:<10}".format("Successful requests:", result["successful_requests"]))
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print("{:<40} {:<10.2f}".format("Benchmark duration (s):", result["total_latency"]))
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print("{:<40} {:<10}".format("Total input tokens:", result["total_input_tokens"]))
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print(
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"{:<40} {:<10}".format("Total generated tokens:", result["total_output_tokens"])
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)
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print(
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"{:<40} {:<10.2f}".format(
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"Request throughput (req/s):", result["request_throughput"]
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)
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)
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print(
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"{:<40} {:<10.2f}".format(
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"Input token throughput (tok/s):", result["input_throughput"]
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)
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)
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print(
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"{:<40} {:<10.2f}".format(
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"Output token throughput (tok/s):", result["output_throughput"]
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)
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)
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print(
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"{:<40} {:<10.2f}".format(
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"Total token throughput (tok/s):", result["total_throughput"]
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)
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)
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print("=" * 50)
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return result
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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ServerArgs.add_cli_args(parser)
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BenchArgs.add_cli_args(parser)
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args = parser.parse_args()
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server_args = ServerArgs.from_cli_args(args)
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bench_args = BenchArgs.from_cli_args(args)
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logging.basicConfig(
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level=getattr(logging, server_args.log_level.upper()),
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format="%(message)s",
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)
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throughput_test(server_args, bench_args)
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