sgl-project--sglang/benchmark/lora/lora_bench.py

# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

import argparse
import asyncio
import json
import random
import resource
import sys
import time
import traceback
from argparse import ArgumentParser
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple

import numpy as np
from tqdm.asyncio import tqdm
from transformers import PreTrainedTokenizerBase

from sglang.bench_serving import (
    RequestFuncInput,
    RequestFuncOutput,
    _create_bench_client_session,
    calculate_metrics,
    get_request,
)
from sglang.benchmark.datasets.random import sample_random_requests
from sglang.benchmark.utils import get_tokenizer, remove_prefix

DEFAULT_BASE_MODEL_PATH = "meta-llama/Llama-2-7b-hf"
DEFAULT_NUM_LORAS = 4

global args


# set ignore_eos True by default
async def async_request_openai_completions(
    request_func_input: RequestFuncInput,
    pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
    api_url = request_func_input.api_url
    # assert api_url.endswith(
    #     "completions"
    # ), "OpenAI Completions API URL must end with 'completions'."

    prompt = request_func_input.prompt

    async with _create_bench_client_session() as session:
        # payload = {
        #     "model": request_func_input.model,
        #     "prompt": prompt,
        #     "temperature": 0.0,
        #     "best_of": 1,
        #     "max_tokens": request_func_input.output_len,
        #     "stream": not args.disable_stream,
        #     "ignore_eos": not args.disable_ignore_eos,
        #     **request_func_input.extra_request_body,
        # }
        # headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
        if args.base_only:
            payload = {
                "text": prompt,
                "sampling_params": {"max_new_tokens": request_func_input.output_len},
            }
        else:
            payload = {
                "text": prompt,
                "sampling_params": {"max_new_tokens": request_func_input.output_len},
                "lora_path": f"lora{random.randint(0, args.num_loras - 1)}",
            }
        headers = {"Authorization": ""}

        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len

        generated_text = ""
        ttft = 0.0
        st = time.perf_counter()
        most_recent_timestamp = st
        try:
            async with session.post(
                url=api_url, json=payload, headers=headers
            ) as response:
                if response.status == 200:
                    async for chunk_bytes in response.content:
                        chunk_bytes = chunk_bytes.strip()
                        if not chunk_bytes:
                            continue

                        chunk = remove_prefix(chunk_bytes.decode("utf-8"), "data: ")
                        latency = time.perf_counter() - st
                        if chunk == "[DONE]":
                            pass
                        else:
                            data = json.loads(chunk)

                            # NOTE: Some completion API might have a last
                            # usage summary response without a token so we
                            # want to check a token was generated
                            if data["text"]:
                                # if data["choices"][0]["text"]:
                                timestamp = time.perf_counter()
                                # First token
                                if ttft == 0.0:
                                    ttft = time.perf_counter() - st
                                    output.ttft = ttft

                                # Decoding phase
                                else:
                                    output.itl.append(timestamp - most_recent_timestamp)

                                most_recent_timestamp = timestamp
                                # generated_text += data["choices"][0]["text"]
                                generated_text += data["text"]

                    output.generated_text = generated_text
                    output.success = True
                    output.latency = latency
                    output.output_len = request_func_input.output_len
                else:
                    output.error = response.reason or ""
                    output.success = False
        except Exception:
            output.success = False
            exc_info = sys.exc_info()
            output.error = "".join(traceback.format_exception(*exc_info))

    if pbar:
        pbar.update(1)
    return output


ASYNC_REQUEST_FUNCS = {
    "sglang": async_request_openai_completions,
}


async def benchmark(
    backend: str,
    api_url: str,
    model_id: str,
    tokenizer: PreTrainedTokenizerBase,
    input_requests: List[Tuple[str, int, int]],
    request_rate: float,
    disable_tqdm: bool,
    extra_request_body: Dict[str, Any],
):
    if backend in ASYNC_REQUEST_FUNCS:
        request_func = ASYNC_REQUEST_FUNCS[backend]
    else:
        raise ValueError(f"Unknown backend: {backend}")

    print("Starting initial single prompt test run...")
    test_request = input_requests[0]
    test_input = RequestFuncInput(
        model=model_id,
        prompt=test_request.prompt,
        api_url=api_url,
        prompt_len=test_request.prompt_len,
        output_len=test_request.output_len,
        lora_name="dummy",  # the lora_name argument will not be used
        image_data=None,
        extra_request_body=extra_request_body,
    )
    test_output = await request_func(request_func_input=test_input)
    if not test_output.success:
        raise ValueError(
            "Initial test run failed - Please make sure benchmark arguments "
            f"are correctly specified. Error: {test_output.error}"
        )
    else:
        print("Initial test run completed. Starting main benchmark run...")

    pbar = None if disable_tqdm else tqdm(total=len(input_requests))

    benchmark_start_time = time.perf_counter()
    tasks: List[asyncio.Task] = []
    async for request in get_request(input_requests, request_rate):
        request_func_input = RequestFuncInput(
            model=model_id,
            prompt=request.prompt,
            api_url=api_url,
            prompt_len=request.prompt_len,
            output_len=request.output_len,
            lora_name="dummy",
            image_data=None,
            extra_request_body=extra_request_body,
        )
        tasks.append(
            asyncio.create_task(
                request_func(request_func_input=request_func_input, pbar=pbar)
            )
        )
    outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks)

    if pbar is not None:
        pbar.close()

    benchmark_duration = time.perf_counter() - benchmark_start_time

    metrics, output_lens = calculate_metrics(
        input_requests=input_requests,
        outputs=outputs,
        dur_s=benchmark_duration,
        tokenizer=tokenizer,
        backend=backend,
    )

    print("\n{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
    print("{:<40} {:<10}".format("Backend:", backend))
    print("{:<40} {:<10}".format("Traffic request rate:", request_rate))
    print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
    print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
    print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
    print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
    print(
        "{:<40} {:<10}".format(
            "Total generated tokens (retokenized):", metrics.total_output_retokenized
        )
    )
    print(
        "{:<40} {:<10.2f}".format(
            "Request throughput (req/s):", metrics.request_throughput
        )
    )
    print(
        "{:<40} {:<10.2f}".format(
            "Input token throughput (tok/s):", metrics.input_throughput
        )
    )
    print(
        "{:<40} {:<10.2f}".format(
            "Output token throughput (tok/s):", metrics.output_throughput
        )
    )
    print(
        "{:<40} {:<10.2f}".format("Total throughput (tok/s):", metrics.total_throughput)
    )
    print("{s:{c}^{n}}".format(s="End-to-End Latency", n=50, c="-"))
    print(
        "{:<40} {:<10.2f}".format("Mean E2E Latency (ms):", metrics.mean_e2e_latency_ms)
    )
    print(
        "{:<40} {:<10.2f}".format(
            "Median E2E Latency (ms):", metrics.median_e2e_latency_ms
        )
    )
    print("{s:{c}^{n}}".format(s="Time to First Token", n=50, c="-"))
    print("{:<40} {:<10.2f}".format("Mean TTFT (ms):", metrics.mean_ttft_ms))
    print("{:<40} {:<10.2f}".format("Median TTFT (ms):", metrics.median_ttft_ms))
    print("{:<40} {:<10.2f}".format("P99 TTFT (ms):", metrics.p99_ttft_ms))
    print(
        "{s:{c}^{n}}".format(s="Time per Output Token (excl. 1st token)", n=50, c="-")
    )
    print("{:<40} {:<10.2f}".format("Mean TPOT (ms):", metrics.mean_tpot_ms))
    print("{:<40} {:<10.2f}".format("Median TPOT (ms):", metrics.median_tpot_ms))
    print("{:<40} {:<10.2f}".format("P99 TPOT (ms):", metrics.p99_tpot_ms))
    print("{s:{c}^{n}}".format(s="Inter-token Latency", n=50, c="-"))
    print("{:<40} {:<10.2f}".format("Mean ITL (ms):", metrics.mean_itl_ms))
    print("{:<40} {:<10.2f}".format("Median ITL (ms):", metrics.median_itl_ms))
    print("{:<40} {:<10.2f}".format("P99 ITL (ms):", metrics.p99_itl_ms))
    print("=" * 50)

    if (
        metrics.median_ttft_ms is not None
        and metrics.mean_itl_ms is not None
        and metrics.output_throughput is not None
    ):
        result = {
            "backend": args.backend,
            "request_rate": request_rate,
            "base_model_path": args.base_model_path,
            "base_only": args.base_only,
            "num_loras": args.num_loras,
            "total_input_tokens": metrics.total_input,
            "total_output_tokens": metrics.total_output,
            "total_output_tokens_retokenized": metrics.total_output_retokenized,
            "mean_e2e_latency_ms": metrics.mean_e2e_latency_ms,
            "median_e2e_latency_ms": metrics.median_e2e_latency_ms,
            "median_ttft_ms": metrics.median_ttft_ms,
            "median_itl_ms": metrics.median_itl_ms,
            "output_throughput": metrics.output_throughput,
            "random_input_len": args.random_input_len,
            "random_output_len": args.random_output_len,
            "random_range_ratio": args.random_range_ratio,
            "duration": benchmark_duration,
            "completed": metrics.completed,
        }
    else:
        print(f"Error running benchmark for request rate: {request_rate}")
        print("-" * 30)

    # Determine output file name
    if args.output_file:
        output_file_name = args.output_file
    else:
        now = datetime.now().strftime("%m%d")
        output_file_name = f"{args.backend}_{now}_{args.num_prompts}_{args.random_input_len}_{args.random_output_len}.jsonl"

    # Append results to a JSONL file
    with open(output_file_name, "a") as file:
        file.write(json.dumps(result) + "\n")

    result = {
        "backend": args.backend,
        "request_rate": request_rate,
        "base_model_path": args.base_model_path,
        "base_only": args.base_only,
        "num_loras": args.num_loras,
        "duration": benchmark_duration,
        "completed": metrics.completed,
        "total_input_tokens": metrics.total_input,
        "total_output_tokens": metrics.total_output,
        "total_output_tokens_retokenized": metrics.total_output_retokenized,
        "request_throughput": metrics.request_throughput,
        "input_throughput": metrics.input_throughput,
        "output_throughput": metrics.output_throughput,
        "mean_ttft_ms": metrics.mean_ttft_ms,
        "median_ttft_ms": metrics.median_ttft_ms,
        "std_ttft_ms": metrics.std_ttft_ms,
        "p99_ttft_ms": metrics.p99_ttft_ms,
        "mean_tpot_ms": metrics.mean_tpot_ms,
        "median_tpot_ms": metrics.median_tpot_ms,
        "std_tpot_ms": metrics.std_tpot_ms,
        "p99_tpot_ms": metrics.p99_tpot_ms,
        "mean_itl_ms": metrics.mean_itl_ms,
        "median_itl_ms": metrics.median_itl_ms,
        "std_itl_ms": metrics.std_itl_ms,
        "p99_itl_ms": metrics.p99_itl_ms,
        "input_lens": [output.prompt_len for output in outputs],
        "output_lens": output_lens,
        "ttfts": [output.ttft for output in outputs],
        "itls": [output.itl for output in outputs],
        "generated_texts": [output.generated_text for output in outputs],
        "errors": [output.error for output in outputs],
        "mean_e2e_latency_ms": metrics.mean_e2e_latency_ms,
        "median_e2e_latency_ms": metrics.median_e2e_latency_ms,
    }
    return result


def run_benchmark(args_: argparse.Namespace):
    global args
    args = args_

    # Set global environments
    set_ulimit()
    random.seed(args.seed)
    np.random.seed(args.seed)
    if not args.base_only and args.num_loras <= 0:
        raise ValueError("--num-loras must be greater than 0 unless --base-only is set")

    # Set url
    if args.port is None:
        args.port = {
            "sglang": 30000,
        }.get(args.backend, 30000)

    # api_url = (
    #     f"{args.base_url}/v1/completions"
    #     if args.base_url
    #     else f"http://{args.host}:{args.port}/v1/completions"
    # )
    api_url = (
        f"{args.base_url}/generate"
        if args.base_url
        else f"http://{args.host}:{args.port}/generate"
    )

    print(f"{args}\n")

    # Read dataset
    backend = args.backend
    model_id = args.base_model_path
    tokenizer_id = args.base_model_path

    tokenizer = get_tokenizer(tokenizer_id)

    input_requests = sample_random_requests(
        input_len=args.random_input_len,
        output_len=args.random_output_len,
        num_prompts=args.num_prompts,
        range_ratio=args.random_range_ratio,
        tokenizer=tokenizer,
        dataset_path="",
    )

    return asyncio.run(
        benchmark(
            backend=backend,
            api_url=api_url,
            model_id=model_id,
            tokenizer=tokenizer,
            input_requests=input_requests,
            request_rate=args.request_rate,
            disable_tqdm=False,
            extra_request_body={},
        )
    )


def set_ulimit(target_soft_limit=65535):
    resource_type = resource.RLIMIT_NOFILE
    current_soft, current_hard = resource.getrlimit(resource_type)

    if current_soft < target_soft_limit:
        try:
            resource.setrlimit(resource_type, (target_soft_limit, current_hard))
        except ValueError as e:
            print(f"Fail to set RLIMIT_NOFILE: {e}")


if __name__ == "__main__":
    parser = ArgumentParser(description="Benchmark the online lora serving throughput.")
    parser.add_argument(
        "--base-model-path",
        type=str,
        default=DEFAULT_BASE_MODEL_PATH,
        help="Base model path or Hugging Face model ID.",
    )
    parser.add_argument(
        "--num-loras",
        type=int,
        default=DEFAULT_NUM_LORAS,
        help=(
            "Number of LoRA adapters used by the benchmark. Must match the "
            "server launcher."
        ),
    )
    parser.add_argument(
        "--backend",
        type=str,
        choices=list(ASYNC_REQUEST_FUNCS.keys()),
        default="sglang",
        help="Must specify a backend, depending on the LLM Inference Engine.",
    )
    parser.add_argument(
        "--base-url",
        type=str,
        default=None,
        help="Server or API base url if not using http host and port.",
    )
    parser.add_argument(
        "--host", type=str, default="0.0.0.0", help="Default host is 0.0.0.0."
    )
    parser.add_argument(
        "--port",
        type=int,
        help="If not set, the default port is configured according to its default value for different LLM Inference Engines.",
    )
    parser.add_argument(
        "--num-prompts",
        type=int,
        default=50,
        help="Number of prompts to process. Default is 1000.",
    )
    parser.add_argument(
        "--random-input-len",
        type=int,
        default=1024,
        help="Number of input tokens per request, used only for random dataset.",
    )
    parser.add_argument(
        "--random-output-len",
        type=int,
        default=128,
        help="Number of output tokens per request, used only for random dataset.",
    )
    parser.add_argument(
        "--random-range-ratio",
        type=float,
        default=0.0,
        help="Range of sampled ratio of input/output length, "
        "used only for random dataset.",
    )
    parser.add_argument(
        "--request-rate",
        type=float,
        default=float("inf"),
        help="Number of requests per second. If this is inf, then all the requests are sent at time 0. "
        "Otherwise, we use Poisson process to synthesize the request arrival times. Default is inf.",
    )
    parser.add_argument(
        "--base-only",
        action="store_true",
    )
    parser.add_argument("--output-file", type=str, help="Output JSONL file name.")
    parser.add_argument("--seed", type=int, default=1, help="The random seed.")
    args = parser.parse_args()
    run_benchmark(args)