475 lines
18 KiB
Python
475 lines
18 KiB
Python
"""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 math
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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 os
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os.environ['CN_NOTIFIER_POOL_MAX'] = "1000"
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import torch
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import uvloop
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from PIL import Image
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from tqdm import tqdm
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from transformers import (AutoModelForCausalLM, AutoTokenizer,
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PreTrainedTokenizerBase)
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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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from vllm.inputs import TextPrompt
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from vllm.multimodal import MultiModalDataDict
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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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from common import init_logger
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logger = init_logger(__name__)
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@dataclasses.dataclass
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class SampleRequest:
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"""A class representing a single inference request for benchmarking.
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Attributes:
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prompt: The input text prompt for the model.
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multi_modal_data: Optional dictionary containing multi-modal data (e.g.
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images).
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prompt_len: The length of the prompt in tokens.
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expected_output_len: The expected length of the output in tokens.
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"""
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prompt: str
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prompt_len: int
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expected_output_len: int
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multi_modal_data: Optional[MultiModalDataDict] = None
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def _get_prompt_for_image_model(question: str, *, model: str) -> str:
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"""Prepend and append special tokens around the question to form a prompt.
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Args:
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question: The input question text to wrap with special tokens
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model: The name of the model being used, to determine which special
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tokens to add
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Returns:
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The formatted prompt string with appropriate special tokens for the
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model
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Raises:
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ValueError: If an unsupported model name is provided
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"""
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model = model.lower()
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if "pixtral" in model:
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return f"<s>[INST]{question}\n[IMG][/INST]"
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raise ValueError(f"Unsupported model {model}")
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def sample_requests(tokenizer: PreTrainedTokenizerBase,
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args: argparse.Namespace) -> List[SampleRequest]:
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dataset_path: str = args.dataset
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num_requests: int = args.num_prompts
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fixed_output_len: Optional[int] = args.output_len
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model: str = args.model
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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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# 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[SampleRequest] = []
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for data in dataset:
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if len(filtered_dataset) == num_requests:
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break
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# Only keep the first two turns of each conversation.
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prompt = data["conversations"][0]["value"]
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completion = data["conversations"][1]["value"]
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multi_modal_data: Optional[MultiModalDataDict] = None
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if "image" in data:
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multi_modal_data = multi_modal_data or {}
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image_path = data["image"]
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# TODO(vllm-project/vllm/issues/9778): Support multiple images.
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assert isinstance(image_path,
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str), "Only support single image input"
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try:
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multi_modal_data["image"] = Image.open(image_path).convert(
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"RGB")
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except FileNotFoundError:
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# Ignore datapoint where asset is missing
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continue
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prompt = _get_prompt_for_image_model(question=prompt, model=model)
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# Tokenize the prompts and completions.
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prompt_token_ids = tokenizer(prompt).input_ids
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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(
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SampleRequest(prompt=prompt,
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prompt_len=prompt_len,
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expected_output_len=output_len,
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multi_modal_data=multi_modal_data))
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return filtered_dataset
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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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) -> float:
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enable_context_mlugraph = False
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context_batch_size_to_capture = None
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context_seq_len_to_capture = None
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if engine_args.max_num_batched_tokens is not None:
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input_len = requests[0][1]
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is_all_reqs_same_length = all(req[1] == input_len for req in requests)
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if is_all_reqs_same_length:
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logger.info(f"Prefill MLUGraph enable !")
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enable_context_mlugraph = True
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context_batch_size_to_capture = min(
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math.floor(engine_args.max_num_batched_tokens / input_len), len(requests))
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context_seq_len_to_capture = input_len
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from vllm import LLM, SamplingParams
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llm = LLM(**dataclasses.asdict(engine_args),
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enable_context_mlugraph=enable_context_mlugraph,
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context_batch_size_to_capture=context_batch_size_to_capture,
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context_seq_len_to_capture=context_seq_len_to_capture)
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# Generate a warning if the maximum sum of the input length and output
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# length is less than the maximum model length, as only the first
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# `max_model_len` will be processed.
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max_length = max((req.prompt_len + req.expected_output_len for req in requests), default=0)
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max_model_len = llm.llm_engine.model_config.max_model_len
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if max_length > max_model_len:
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logger.warning(
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f"The sum of input and output length({max_length}) is larger than"
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f" max model length({max_model_len})")
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# Add the requests to the engine.
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prompts: List[TextPrompt] = []
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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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TextPrompt(prompt=request.prompt,
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multi_modal_data=request.multi_modal_data))
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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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))
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use_beam_search = False
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if not use_beam_search:
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start = time.perf_counter()
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llm.generate(prompts, sampling_params, use_tqdm=True)
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end = time.perf_counter()
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else:
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prompts = [request.prompt for request in requests]
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# output_len should be the same for all requests.
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output_len = requests[0][2]
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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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end = time.perf_counter()
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return end - start
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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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) -> 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, disable_frontend_multiprocessing) as llm:
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# Add the requests to the engine.
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prompts: List[TextPrompt] = []
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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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TextPrompt(prompt=request.prompt,
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multi_modal_data=request.multi_modal_data))
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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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))
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generators = []
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start = time.perf_counter()
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for i, (prompt, sp) in enumerate(zip(prompts, sampling_params)):
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generator = llm.generate(prompt, sp, 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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) -> 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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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, prompt_len, output_len = requests[i]
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# Add the prompt to the batch.
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batch.append(prompt)
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max_prompt_len = max(max_prompt_len, prompt_len)
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max_output_len = max(max_output_len, output_len)
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if len(batch) < max_batch_size and i != len(requests) - 1:
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# Check if we can add more requests to the batch.
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_, next_prompt_len, next_output_len = requests[i + 1]
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if (max(max_prompt_len, next_prompt_len) +
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max(max_output_len, next_output_len)) <= 2048:
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# We can add more requests to the batch.
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continue
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# Generate the sequences.
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input_ids = tokenizer(batch, return_tensors="pt",
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padding=True).input_ids
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llm_outputs = llm.generate(
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input_ids=input_ids.cuda(),
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do_sample=True,
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num_return_sequences=n,
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temperature=1.0,
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top_p=1.0,
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use_cache=True,
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max_new_tokens=max_output_len,
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)
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# Include the decoding time.
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tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
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pbar.update(len(batch))
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# Clear the batch.
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batch = []
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max_prompt_len = 0
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max_output_len = 0
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end = time.perf_counter()
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return end - start
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def run_mii(
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requests: List[SampleRequest],
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model: str,
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tensor_parallel_size: int,
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output_len: int,
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) -> float:
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from mii import client, serve
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llm = serve(model, tensor_parallel=tensor_parallel_size)
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prompts = [request.prompt for request in requests]
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start = time.perf_counter()
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llm.generate(prompts, max_new_tokens=output_len)
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end = time.perf_counter()
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client = client(model)
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client.terminate_server()
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return end - start
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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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# Sample the requests.
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tokenizer = AutoTokenizer.from_pretrained(
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args.tokenizer, trust_remote_code=args.trust_remote_code)
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if args.dataset is None:
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# Synthesize a prompt with the given input length.
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# As tokenizer may add additional tokens like BOS, we need to try
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# different lengths to get the desired input length.
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for i in range(-10, 10):
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prompt = "hi " * (args.input_len + i)
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tokenized_prompt = tokenizer(prompt).input_ids
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if len(tokenized_prompt) == args.input_len:
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break
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else:
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raise ValueError(
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f"Failed to synthesize a prompt with {args.input_len} tokens.")
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requests = [
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SampleRequest(prompt=prompt,
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prompt_len=args.input_len,
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expected_output_len=args.output_len)
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for _ in range(args.num_prompts)
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]
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else:
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requests = sample_requests(tokenizer, args)
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is_multi_modal = any(request.multi_modal_data is not None
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for request in requests)
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if args.backend == "vllm":
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if args.async_engine:
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elapsed_time = uvloop.run(
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run_vllm_async(
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requests,
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args.n,
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AsyncEngineArgs.from_cli_args(args),
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args.disable_frontend_multiprocessing,
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))
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else:
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elapsed_time = run_vllm(requests, args.n,
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EngineArgs.from_cli_args(args))
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elif args.backend == "hf":
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assert args.tensor_parallel_size == 1
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elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
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args.hf_max_batch_size, args.trust_remote_code)
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elif args.backend == "mii":
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elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
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args.output_len)
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else:
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raise ValueError(f"Unknown backend: {args.backend}")
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total_num_tokens = sum(request.prompt_len + request.expected_output_len
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for request in requests)
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total_output_tokens = sum(request.expected_output_len
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for request in requests)
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if is_multi_modal:
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print("\033[91mWARNING\033[0m: Multi-modal request detected. The "
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"following metrics are not accurate because image tokens are not"
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" counted. See vllm-project/vllm/issues/9778 for details.")
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# TODO(vllm-project/vllm/issues/9778): Count molti-modal token length.
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print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
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f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
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f"{total_output_tokens / elapsed_time:.2f} output tokens/s")
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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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"elapsed_time": elapsed_time,
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"num_requests": len(requests),
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"total_num_tokens": total_num_tokens,
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"requests_per_second": len(requests) / elapsed_time,
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"tokens_per_second": total_num_tokens / elapsed_time,
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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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if __name__ == "__main__":
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parser = FlexibleArgumentParser(description="Benchmark the throughput.")
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parser.add_argument("--backend",
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type=str,
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choices=["vllm", "hf", "mii"],
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default="vllm")
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parser.add_argument("--dataset",
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type=str,
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default=None,
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help="Path to the dataset. The dataset is expected to "
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"be a json in form of List[Dict[..., conversations: "
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"List[Dict[..., value: <prompt_or_response>]]]]")
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parser.add_argument("--input-len",
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type=int,
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default=None,
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help="Input prompt length for each request")
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parser.add_argument("--output-len",
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type=int,
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default=None,
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help="Output length for each request. Overrides the "
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"output length from the dataset.")
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parser.add_argument("--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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parser.add_argument("--num-prompts",
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type=int,
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default=1000,
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help="Number of prompts to process.")
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parser.add_argument("--hf-max-batch-size",
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type=int,
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default=None,
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help="Maximum batch size for HF backend.")
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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 throughput results in JSON format.')
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parser.add_argument("--async-engine",
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action='store_true',
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default=False,
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help="Use vLLM async engine rather than LLM class.")
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parser.add_argument("--disable-frontend-multiprocessing",
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action='store_true',
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default=False,
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help="Disable decoupled async engine frontend.")
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parser = AsyncEngineArgs.add_cli_args(parser)
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args = parser.parse_args()
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if args.tokenizer is None:
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args.tokenizer = args.model
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if args.dataset is None:
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assert args.input_len is not None
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assert args.output_len is not None
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else:
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assert args.input_len is None
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if args.backend == "vllm":
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if args.hf_max_batch_size is not None:
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raise ValueError("HF max batch size is only for HF backend.")
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elif args.backend == "hf":
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if args.hf_max_batch_size is None:
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raise ValueError("HF max batch size is required for HF backend.")
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if args.quantization is not None:
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raise ValueError("Quantization is only for vLLM backend.")
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elif args.backend == "mii":
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if args.dtype != "auto":
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raise ValueError("dtype must be auto for MII backend.")
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if args.n != 1:
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raise ValueError("n must be 1 for MII backend.")
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if args.quantization is not None:
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raise ValueError("Quantization is only for vLLM backend.")
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if args.hf_max_batch_size is not None:
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raise ValueError("HF max batch size is only for HF backend.")
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if args.tokenizer != args.model:
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raise ValueError("Tokenizer must be the same as the model for MII "
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"backend.")
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main(args)
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