659 lines
23 KiB
Python
659 lines
23 KiB
Python
"""Benchmark online serving throughput.
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On the server side, run one of the following commands:
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vLLM OpenAI API server
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python -m vllm.entrypoints.openai.api_server \
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--model <your_model> --swap-space 16 \
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--disable-log-requests
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(TGI backend)
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./launch_tgi_server.sh <your_model> <max_batch_total_tokens>
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On the client side, run:
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python benchmarks/benchmark_serving.py \
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--backend <backend> \
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--model <your_model> \
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--dataset-name sharegpt \
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--dataset-path <path to dataset> \
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--request-rate <request_rate> \ # By default <request_rate> is inf
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--num-prompts <num_prompts> # By default <num_prompts> is 1000
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when using tgi backend, add
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--endpoint /generate_stream
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to the end of the command above.
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"""
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import argparse
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import asyncio
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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 dataclasses import dataclass
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from datetime import datetime
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from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple
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import numpy as np
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from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
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RequestFuncOutput)
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from tqdm.asyncio import tqdm
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from transformers import PreTrainedTokenizerBase
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try:
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from vllm.transformers_utils.tokenizer import get_tokenizer
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except ImportError:
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from backend_request_func import get_tokenizer
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@dataclass
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class BenchmarkMetrics:
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completed: int
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total_input: int
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total_output: int
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request_throughput: float
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input_throughput: float
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output_throughput: float
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mean_ttft_ms: float
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median_ttft_ms: float
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p99_ttft_ms: float
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mean_tpot_ms: float
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median_tpot_ms: float
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p99_tpot_ms: float
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mean_itl_ms: float
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median_itl_ms: float
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p99_itl_ms: float
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def sample_sharegpt_requests(
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dataset_path: str,
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num_requests: int,
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tokenizer: PreTrainedTokenizerBase,
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fixed_output_len: Optional[int] = None,
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) -> List[Tuple[str, int, int]]:
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if fixed_output_len is not None and fixed_output_len < 4:
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raise ValueError("output_len too small")
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# Load the dataset.
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with open(dataset_path) as f:
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dataset = json.load(f)
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# Filter out the conversations with less than 2 turns.
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dataset = [data for data in dataset if len(data["conversations"]) >= 2]
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# Only keep the first two turns of each conversation.
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dataset = [(data["conversations"][0]["value"],
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data["conversations"][1]["value"]) for data in dataset]
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# Shuffle the dataset.
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random.shuffle(dataset)
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# Filter out sequences that are too long or too short
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filtered_dataset: List[Tuple[str, int, int]] = []
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for i in range(len(dataset)):
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if len(filtered_dataset) == num_requests:
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break
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# Tokenize the prompts and completions.
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prompt = dataset[i][0]
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prompt_token_ids = tokenizer(prompt).input_ids
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completion = dataset[i][1]
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completion_token_ids = tokenizer(completion).input_ids
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prompt_len = len(prompt_token_ids)
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output_len = len(completion_token_ids
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) if fixed_output_len is None else fixed_output_len
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if prompt_len < 4 or output_len < 4:
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# Prune too short sequences.
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continue
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if prompt_len > 1024 or prompt_len + output_len > 2048:
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# Prune too long sequences.
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continue
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filtered_dataset.append((prompt, prompt_len, output_len))
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return filtered_dataset
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def sample_sonnet_requests(
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dataset_path: str,
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num_requests: int,
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input_len: int,
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output_len: int,
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prefix_len: int,
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tokenizer: PreTrainedTokenizerBase,
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) -> List[Tuple[str, str, int, int]]:
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assert (
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input_len > prefix_len
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), "'args.sonnet-input-len' must be greater than 'args.prefix-input-len'."
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# Load the dataset.
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with open(dataset_path) as f:
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poem_lines = f.readlines()
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# Tokenize the poem lines.
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poem_token_ids = tokenizer(poem_lines).input_ids
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average_poem_len = sum(
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len(token_ids) for token_ids in poem_token_ids) / len(poem_token_ids)
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# Base prefix for all requests.
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base_prompt = "Pick as many lines as you can from these poem lines:\n"
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base_message = [{
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"role": "user",
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"content": base_prompt,
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}]
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base_prompt_formatted = tokenizer.apply_chat_template(
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base_message, add_generation_prompt=True, tokenize=False)
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base_prompt_offset = len(tokenizer(base_prompt_formatted).input_ids)
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assert (
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input_len > base_prompt_offset
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), f"Please set 'args.sonnet-input-len' higher than {base_prompt_offset}."
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num_input_lines = round(
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(input_len - base_prompt_offset) / average_poem_len)
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# First approximately `prefix_len` number of tokens in the
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# prompt are fixed poem lines.
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assert (
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prefix_len > base_prompt_offset
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), f"Please set 'args.sonnet-prefix-len' higher than {base_prompt_offset}."
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num_prefix_lines = round(
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(prefix_len - base_prompt_offset) / average_poem_len)
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prefix_lines = poem_lines[:num_prefix_lines]
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# Sample the rest of lines per request.
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sampled_requests: List[Tuple[str, int, int]] = []
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for _ in range(num_requests):
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sampled_lines = "".join(
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prefix_lines +
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random.sample(poem_lines, num_input_lines - num_prefix_lines))
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prompt = f"{base_prompt}{sampled_lines}"
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message = [
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{
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"role": "user",
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"content": prompt,
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},
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]
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prompt_formatted = tokenizer.apply_chat_template(
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message, add_generation_prompt=True, tokenize=False)
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prompt_len = len(tokenizer(prompt_formatted).input_ids)
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sampled_requests.append(
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(prompt, prompt_formatted, prompt_len, output_len))
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return sampled_requests
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async def get_request(
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input_requests: List[Tuple[str, int, int]],
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request_rate: float,
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) -> AsyncGenerator[Tuple[str, int, int], None]:
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input_requests = iter(input_requests)
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for request in input_requests:
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yield request
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if request_rate == float("inf"):
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# If the request rate is infinity, then we don't need to wait.
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continue
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# Sample the request interval from the exponential distribution.
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interval = np.random.exponential(1.0 / request_rate)
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# The next request will be sent after the interval.
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await asyncio.sleep(interval)
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def calculate_metrics(
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input_requests: List[Tuple[str, int, int]],
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outputs: List[RequestFuncOutput],
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dur_s: float,
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tokenizer: PreTrainedTokenizerBase,
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) -> Tuple[BenchmarkMetrics, List[int]]:
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actual_output_lens: List[int] = []
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total_input = 0
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completed = 0
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itls: List[float] = []
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tpots: List[float] = []
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ttfts: List[float] = []
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for i in range(len(outputs)):
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if outputs[i].success:
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# We use the tokenizer to count the number of output tokens for all
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# serving backends instead of looking at len(outputs[i].itl) since
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# multiple output tokens may be bundled together
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# Note: this may inflate the output token count slightly
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output_len = len(
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tokenizer(outputs[i].generated_text,
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add_special_tokens=False).input_ids)
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actual_output_lens.append(output_len)
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total_input += input_requests[i][1]
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if output_len > 1:
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tpots.append(
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(outputs[i].latency - outputs[i].ttft) / (output_len - 1))
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itls += outputs[i].itl
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ttfts.append(outputs[i].ttft)
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completed += 1
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else:
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actual_output_lens.append(0)
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if completed == 0:
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warnings.warn(
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"All requests failed. This is likely due to a misconfiguration "
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"on the benchmark arguments.",
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stacklevel=2)
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metrics = BenchmarkMetrics(
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completed=completed,
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total_input=total_input,
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total_output=sum(actual_output_lens),
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request_throughput=completed / dur_s,
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input_throughput=total_input / dur_s,
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output_throughput=sum(actual_output_lens) / dur_s,
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mean_ttft_ms=np.mean(ttfts or 0) *
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1000, # ttfts is empty if streaming is not supported by backend
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median_ttft_ms=np.median(ttfts or 0) * 1000,
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p99_ttft_ms=np.percentile(ttfts or 0, 99) * 1000,
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mean_tpot_ms=np.mean(tpots or 0) * 1000,
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median_tpot_ms=np.median(tpots or 0) * 1000,
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p99_tpot_ms=np.percentile(tpots or 0, 99) * 1000,
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mean_itl_ms=np.mean(itls or 0) * 1000,
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median_itl_ms=np.median(itls or 0) * 1000,
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p99_itl_ms=np.percentile(itls or 0, 99) * 1000,
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)
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return metrics, actual_output_lens
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async def benchmark(
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backend: str,
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api_url: str,
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model_id: str,
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tokenizer: PreTrainedTokenizerBase,
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input_requests: List[Tuple[str, int, int]],
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best_of: int,
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use_beam_search: bool,
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request_rate: float,
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disable_tqdm: bool,
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):
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if backend in ASYNC_REQUEST_FUNCS:
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request_func = ASYNC_REQUEST_FUNCS[backend]
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else:
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raise ValueError(f"Unknown backend: {backend}")
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print("Starting initial single prompt test run...")
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test_prompt, test_prompt_len, test_output_len = input_requests[0]
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test_input = RequestFuncInput(
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model=model_id,
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prompt=test_prompt,
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api_url=api_url,
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prompt_len=test_prompt_len,
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output_len=test_output_len,
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best_of=best_of,
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use_beam_search=use_beam_search,
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)
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test_output = await request_func(request_func_input=test_input)
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if not test_output.success:
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raise ValueError(
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"Initial test run failed - Please make sure benchmark arguments "
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f"are correctly specified. Error: {test_output.error}")
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else:
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print("Initial test run completed. Starting main benchmark run...")
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print(f"Traffic request rate: {request_rate}")
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pbar = None if disable_tqdm else tqdm(total=len(input_requests))
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benchmark_start_time = time.perf_counter()
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tasks: List[asyncio.Task] = []
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async for request in get_request(input_requests, request_rate):
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prompt, prompt_len, output_len = request
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request_func_input = RequestFuncInput(
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model=model_id,
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prompt=prompt,
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api_url=api_url,
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prompt_len=prompt_len,
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output_len=output_len,
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best_of=best_of,
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use_beam_search=use_beam_search,
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)
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tasks.append(
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asyncio.create_task(
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request_func(request_func_input=request_func_input,
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pbar=pbar)))
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outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks)
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if pbar is not None:
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pbar.close()
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benchmark_duration = time.perf_counter() - benchmark_start_time
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metrics, actual_output_lens = calculate_metrics(
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input_requests=input_requests,
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outputs=outputs,
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dur_s=benchmark_duration,
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tokenizer=tokenizer,
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)
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print("{s:{c}^{n}}".format(s=' Serving Benchmark Result ', n=50, c='='))
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print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
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print("{:<40} {:<10.2f}".format("Benchmark duration (s):",
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benchmark_duration))
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print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
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print("{:<40} {:<10}".format("Total generated tokens:",
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metrics.total_output))
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print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
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metrics.request_throughput))
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print("{:<40} {:<10.2f}".format("Input token throughput (tok/s):",
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metrics.input_throughput))
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print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):",
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metrics.output_throughput))
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print("{s:{c}^{n}}".format(s='Time to First Token', n=50, c='-'))
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print("{:<40} {:<10.2f}".format("Mean TTFT (ms):", metrics.mean_ttft_ms))
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print("{:<40} {:<10.2f}".format("Median TTFT (ms):",
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metrics.median_ttft_ms))
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print("{:<40} {:<10.2f}".format("P99 TTFT (ms):", metrics.p99_ttft_ms))
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print("{s:{c}^{n}}".format(s='Time per Output Token (excl. 1st token)',
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n=50,
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c='-'))
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print("{:<40} {:<10.2f}".format("Mean TPOT (ms):", metrics.mean_tpot_ms))
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print("{:<40} {:<10.2f}".format("Median TPOT (ms):",
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metrics.median_tpot_ms))
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print("{:<40} {:<10.2f}".format("P99 TPOT (ms):", metrics.p99_tpot_ms))
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print("{s:{c}^{n}}".format(s='Inter-token Latency', n=50, c='-'))
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print("{:<40} {:<10.2f}".format("Mean ITL (ms):", metrics.mean_itl_ms))
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print("{:<40} {:<10.2f}".format("Median ITL (ms):", metrics.median_itl_ms))
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print("{:<40} {:<10.2f}".format("P99 ITL (ms):", metrics.p99_itl_ms))
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print("=" * 50)
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result = {
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"duration": benchmark_duration,
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"completed": metrics.completed,
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"total_input_tokens": metrics.total_input,
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"total_output_tokens": metrics.total_output,
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"request_throughput": metrics.request_throughput,
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"input_throughput": metrics.input_throughput,
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"output_throughput": metrics.output_throughput,
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"mean_ttft_ms": metrics.mean_ttft_ms,
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"median_ttft_ms": metrics.median_ttft_ms,
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"p99_ttft_ms": metrics.p99_ttft_ms,
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"mean_tpot_ms": metrics.mean_tpot_ms,
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"median_tpot_ms": metrics.median_tpot_ms,
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"p99_tpot_ms": metrics.p99_tpot_ms,
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"mean_itl_ms": metrics.mean_itl_ms,
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"median_itl_ms": metrics.median_itl_ms,
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"p99_itl_ms": metrics.p99_itl_ms,
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"input_lens": [output.prompt_len for output in outputs],
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"output_lens": actual_output_lens,
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"ttfts": [output.ttft for output in outputs],
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"itls": [output.itl for output in outputs],
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"generated_texts": [output.generated_text for output in outputs],
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"errors": [output.error for output in outputs],
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}
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return result
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def main(args: argparse.Namespace):
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print(args)
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random.seed(args.seed)
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np.random.seed(args.seed)
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backend = args.backend
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model_id = args.model
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tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model
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if args.base_url is not None:
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api_url = f"{args.base_url}{args.endpoint}"
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else:
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api_url = f"http://{args.host}:{args.port}{args.endpoint}"
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tokenizer = get_tokenizer(tokenizer_id,
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trust_remote_code=args.trust_remote_code)
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if args.dataset is not None:
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warnings.warn(
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"The '--dataset' argument will be deprecated in the next "
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"release. Please use '--dataset-name' and "
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"'--dataset-path' in the future runs.",
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stacklevel=2)
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input_requests = sample_sharegpt_requests(
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dataset_path=args.dataset,
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num_requests=args.num_prompts,
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tokenizer=tokenizer,
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fixed_output_len=args.sharegpt_output_len,
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)
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elif args.dataset_name == "sharegpt":
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input_requests = sample_sharegpt_requests(
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dataset_path=args.dataset_path,
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num_requests=args.num_prompts,
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tokenizer=tokenizer,
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fixed_output_len=args.sharegpt_output_len,
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)
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elif args.dataset_name == "sonnet":
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# Do not format the prompt, pass to message directly
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if args.backend == "openai-chat":
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input_requests = sample_sonnet_requests(
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dataset_path=args.dataset_path,
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num_requests=args.num_prompts,
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input_len=args.sonnet_input_len,
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output_len=args.sonnet_output_len,
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prefix_len=args.sonnet_prefix_len,
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tokenizer=tokenizer,
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)
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input_requests = [(prompt, prompt_len, output_len)
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for prompt, prompt_formatted, prompt_len,
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output_len in input_requests]
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else:
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assert (
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tokenizer.chat_template or tokenizer.default_chat_template
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), "Tokenizer/model must have chat template for sonnet dataset."
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input_requests = sample_sonnet_requests(
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dataset_path=args.dataset_path,
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num_requests=args.num_prompts,
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input_len=args.sonnet_input_len,
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output_len=args.sonnet_output_len,
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prefix_len=args.sonnet_prefix_len,
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tokenizer=tokenizer,
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)
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input_requests = [(prompt_formatted, prompt_len, output_len)
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for prompt, prompt_formatted, prompt_len,
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output_len in input_requests]
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else:
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raise ValueError(f"Unknown dataset: {args.dataset_name}")
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benchmark_result = asyncio.run(
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benchmark(
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backend=backend,
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api_url=api_url,
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model_id=model_id,
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tokenizer=tokenizer,
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input_requests=input_requests,
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best_of=args.best_of,
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use_beam_search=args.use_beam_search,
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request_rate=args.request_rate,
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disable_tqdm=args.disable_tqdm,
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))
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# Save config and results to json
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if args.save_result:
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result_json: Dict[str, Any] = {}
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# Setup
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current_dt = datetime.now().strftime("%Y%m%d-%H%M%S")
|
|
result_json["date"] = current_dt
|
|
result_json["backend"] = backend
|
|
result_json["model_id"] = model_id
|
|
result_json["tokenizer_id"] = tokenizer_id
|
|
result_json["best_of"] = args.best_of
|
|
result_json["use_beam_search"] = args.use_beam_search
|
|
result_json["num_prompts"] = args.num_prompts
|
|
|
|
# Metadata
|
|
if args.metadata:
|
|
for item in args.metadata:
|
|
if "=" in item:
|
|
kvstring = item.split("=")
|
|
result_json[kvstring[0].strip()] = kvstring[1].strip()
|
|
else:
|
|
raise ValueError(
|
|
"Invalid metadata format. Please use KEY=VALUE format."
|
|
)
|
|
|
|
# Traffic
|
|
result_json["request_rate"] = (
|
|
args.request_rate if args.request_rate < float("inf") else "inf")
|
|
|
|
# Merge with benchmark result
|
|
result_json = {**result_json, **benchmark_result}
|
|
|
|
# Save to file
|
|
base_model_id = model_id.split("/")[-1]
|
|
file_name = f"{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json" #noqa
|
|
if args.result_filename:
|
|
file_name = args.result_filename
|
|
if args.result_dir:
|
|
file_name = os.path.join(args.result_dir, file_name)
|
|
with open(file_name, "w") as outfile:
|
|
json.dump(result_json, outfile)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(
|
|
description="Benchmark the online serving throughput.")
|
|
parser.add_argument(
|
|
"--backend",
|
|
type=str,
|
|
default="vllm",
|
|
choices=list(ASYNC_REQUEST_FUNCS.keys()),
|
|
)
|
|
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="localhost")
|
|
parser.add_argument("--port", type=int, default=8000)
|
|
parser.add_argument(
|
|
"--endpoint",
|
|
type=str,
|
|
default="/v1/completions",
|
|
help="API endpoint.",
|
|
)
|
|
parser.add_argument(
|
|
"--dataset",
|
|
type=str,
|
|
default=None,
|
|
help="Path to the ShareGPT dataset, will be deprecated in the "
|
|
"next release.",
|
|
)
|
|
parser.add_argument(
|
|
"--dataset-name",
|
|
type=str,
|
|
default="sharegpt",
|
|
choices=["sharegpt", "sonnet"],
|
|
help="Name of the dataset to benchmark on.",
|
|
)
|
|
parser.add_argument("--dataset-path",
|
|
type=str,
|
|
default=None,
|
|
help="Path to the dataset.")
|
|
parser.add_argument(
|
|
"--model",
|
|
type=str,
|
|
required=True,
|
|
help="Name of the model.",
|
|
)
|
|
parser.add_argument(
|
|
"--tokenizer",
|
|
type=str,
|
|
help=
|
|
"Name or path of the tokenizer, if not using the default tokenizer.",
|
|
)
|
|
parser.add_argument(
|
|
"--best-of",
|
|
type=int,
|
|
default=1,
|
|
help="Generates `best_of` sequences per prompt and "
|
|
"returns the best one.",
|
|
)
|
|
parser.add_argument("--use-beam-search", action="store_true")
|
|
parser.add_argument(
|
|
"--num-prompts",
|
|
type=int,
|
|
default=1000,
|
|
help="Number of prompts to process.",
|
|
)
|
|
parser.add_argument(
|
|
"--sharegpt-output-len",
|
|
type=int,
|
|
default=None,
|
|
help="Output length for each request. Overrides the output length "
|
|
"from the ShareGPT dataset.")
|
|
parser.add_argument(
|
|
"--sonnet-input-len",
|
|
type=int,
|
|
default=550,
|
|
help=
|
|
"Number of input tokens per request, used only for sonnet dataset.",
|
|
)
|
|
parser.add_argument(
|
|
"--sonnet-output-len",
|
|
type=int,
|
|
default=150,
|
|
help=
|
|
"Number of output tokens per request, used only for sonnet dataset.",
|
|
)
|
|
parser.add_argument(
|
|
"--sonnet-prefix-len",
|
|
type=int,
|
|
default=200,
|
|
help=
|
|
"Number of prefix tokens per request, used only for sonnet 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.",
|
|
)
|
|
parser.add_argument("--seed", type=int, default=0)
|
|
parser.add_argument(
|
|
"--trust-remote-code",
|
|
action="store_true",
|
|
help="Trust remote code from huggingface",
|
|
)
|
|
parser.add_argument(
|
|
"--disable-tqdm",
|
|
action="store_true",
|
|
help="Specify to disable tqdm progress bar.",
|
|
)
|
|
parser.add_argument(
|
|
"--save-result",
|
|
action="store_true",
|
|
help="Specify to save benchmark results to a json file",
|
|
)
|
|
parser.add_argument(
|
|
"--metadata",
|
|
metavar="KEY=VALUE",
|
|
nargs="*",
|
|
help="Key-value pairs (e.g, --metadata version=0.3.3 tp=1) "
|
|
"for metadata of this run to be saved in the result JSON file "
|
|
"for record keeping purposes.",
|
|
)
|
|
parser.add_argument(
|
|
"--result-dir",
|
|
type=str,
|
|
default=None,
|
|
help="Specify directory to save benchmark json results."
|
|
"If not specified, results are saved in the current directory.",
|
|
)
|
|
parser.add_argument(
|
|
"--result-filename",
|
|
type=str,
|
|
default=None,
|
|
help="Specify the filename to save benchmark json results."
|
|
"If not specified, results will be saved in "
|
|
"{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"
|
|
" format.",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
main(args)
|