639 lines
25 KiB
Python
639 lines
25 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Benchmark offline inference throughput."""
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import argparse
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import dataclasses
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import json
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import os
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import random
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import time
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import warnings
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from typing import Any, Optional, Union
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import torch
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import uvloop
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from benchmark_dataset import (AIMODataset, BurstGPTDataset,
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ConversationDataset, InstructCoderDataset,
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RandomDataset, SampleRequest, ShareGPTDataset,
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SonnetDataset, VisionArenaDataset)
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from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
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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, TokensPrompt
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from vllm.lora.request import LoRARequest
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from vllm.outputs import RequestOutput
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from vllm.sampling_params import BeamSearchParams
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from vllm.utils import FlexibleArgumentParser, merge_async_iterators
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def run_vllm(
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requests: list[SampleRequest],
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n: int,
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engine_args: EngineArgs,
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disable_detokenize: bool = False,
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) -> tuple[float, Optional[list[RequestOutput]]]:
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from vllm import LLM, SamplingParams
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llm = LLM(**dataclasses.asdict(engine_args))
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assert all(
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llm.llm_engine.model_config.max_model_len >= (
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request.prompt_len + request.expected_output_len)
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for request in requests), (
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"Please ensure that max_model_len is greater than the sum of"
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" prompt_len and expected_output_len for all requests.")
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# Add the requests to the engine.
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prompts: list[Union[TextPrompt, TokensPrompt]] = []
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sampling_params: list[SamplingParams] = []
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for request in requests:
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prompts.append(
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TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
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multi_modal_data=request.multi_modal_data)
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if "prompt_token_ids" in request.prompt else \
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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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detokenize=not disable_detokenize,
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))
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lora_requests: Optional[list[LoRARequest]] = None
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if engine_args.enable_lora:
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lora_requests = [request.lora_request for request in requests]
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use_beam_search = False
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outputs = None
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if not use_beam_search:
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start = time.perf_counter()
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outputs = llm.generate(prompts,
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sampling_params,
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lora_request=lora_requests,
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use_tqdm=True)
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end = time.perf_counter()
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else:
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assert lora_requests is None, "BeamSearch API does not support LoRA"
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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, outputs
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def run_vllm_chat(
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requests: list[SampleRequest],
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n: int,
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engine_args: EngineArgs,
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disable_detokenize: bool = False) -> tuple[float, list[RequestOutput]]:
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"""
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Run vLLM chat benchmark. This function is recommended ONLY for benchmarking
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multimodal models as it properly handles multimodal inputs and chat
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formatting. For non-multimodal models, use run_vllm() instead.
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"""
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from vllm import LLM, SamplingParams
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llm = LLM(**dataclasses.asdict(engine_args))
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assert all(
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llm.llm_engine.model_config.max_model_len >= (
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request.prompt_len + request.expected_output_len)
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for request in requests), (
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"Please ensure that max_model_len is greater than the sum of "
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"prompt_len and expected_output_len for all requests.")
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prompts = []
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sampling_params: list[SamplingParams] = []
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for request in requests:
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prompts.append(request.prompt)
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sampling_params.append(
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SamplingParams(
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n=n,
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temperature=1.0,
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top_p=1.0,
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ignore_eos=True,
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max_tokens=request.expected_output_len,
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detokenize=not disable_detokenize,
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))
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start = time.perf_counter()
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outputs = llm.chat(prompts, sampling_params, use_tqdm=True)
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end = time.perf_counter()
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return end - start, outputs
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async def run_vllm_async(
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requests: list[SampleRequest],
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n: int,
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engine_args: AsyncEngineArgs,
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disable_frontend_multiprocessing: bool = False,
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disable_detokenize: bool = False,
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) -> float:
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from vllm import SamplingParams
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async with build_async_engine_client_from_engine_args(
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engine_args, disable_frontend_multiprocessing) as llm:
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assert all(
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llm.model_config.max_model_len >= (request.prompt_len +
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request.expected_output_len)
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for request in requests), (
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"Please ensure that max_model_len is greater than the sum of"
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" prompt_len and expected_output_len for all requests.")
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# Add the requests to the engine.
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prompts: list[Union[TextPrompt, TokensPrompt]] = []
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sampling_params: list[SamplingParams] = []
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lora_requests: list[Optional[LoRARequest]] = []
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for request in requests:
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prompts.append(
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TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
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multi_modal_data=request.multi_modal_data)
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if "prompt_token_ids" in request.prompt else \
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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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detokenize=not disable_detokenize,
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))
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lora_requests.append(request.lora_request)
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generators = []
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start = time.perf_counter()
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for i, (prompt, sp,
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lr) in enumerate(zip(prompts, sampling_params, lora_requests)):
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generator = llm.generate(prompt,
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sp,
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lora_request=lr,
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request_id=f"test{i}")
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generators.append(generator)
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all_gens = merge_async_iterators(*generators)
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async for i, res in all_gens:
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pass
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end = time.perf_counter()
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return end - start
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def run_hf(
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requests: list[SampleRequest],
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model: str,
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tokenizer: PreTrainedTokenizerBase,
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n: int,
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max_batch_size: int,
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trust_remote_code: bool,
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disable_detokenize: bool = False,
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) -> float:
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llm = AutoModelForCausalLM.from_pretrained(
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model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
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if llm.config.model_type == "llama":
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# To enable padding in the HF backend.
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tokenizer.pad_token = tokenizer.eos_token
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llm = llm.cuda()
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pbar = tqdm(total=len(requests))
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start = time.perf_counter()
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batch: list[str] = []
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max_prompt_len = 0
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max_output_len = 0
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for i in range(len(requests)):
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prompt = requests[i].prompt
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prompt_len = requests[i].prompt_len
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output_len = requests[i].expected_output_len
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# Add the prompt to the batch.
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batch.append(prompt)
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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 = requests[i + 1].prompt_len
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next_output_len = requests[i + 1].expected_output_len
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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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if not disable_detokenize:
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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 save_to_pytorch_benchmark_format(args: argparse.Namespace,
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results: dict[str, Any]) -> None:
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pt_records = convert_to_pytorch_benchmark_format(
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args=args,
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metrics={
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"requests_per_second": [results["requests_per_second"]],
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"tokens_per_second": [results["tokens_per_second"]],
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},
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extra_info={
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k: results[k]
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for k in ["elapsed_time", "num_requests", "total_num_tokens"]
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})
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if pt_records:
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# Don't use json suffix here as we don't want CI to pick it up
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pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
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write_to_json(pt_file, pt_records)
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def get_requests(args, tokenizer):
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# Common parameters for all dataset types.
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common_kwargs = {
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"dataset_path": args.dataset_path,
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"random_seed": args.seed,
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}
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sample_kwargs = {
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"tokenizer": tokenizer,
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"lora_path": args.lora_path,
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"max_loras": args.max_loras,
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"num_requests": args.num_prompts,
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"input_len": args.input_len,
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"output_len": args.output_len,
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}
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if args.dataset_path is None or args.dataset_name == "random":
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sample_kwargs["range_ratio"] = args.random_range_ratio
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sample_kwargs["prefix_len"] = args.prefix_len
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dataset_cls = RandomDataset
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elif args.dataset_name == "sharegpt":
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dataset_cls = ShareGPTDataset
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if args.backend == "vllm-chat":
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sample_kwargs["enable_multimodal_chat"] = True
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elif args.dataset_name == "sonnet":
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assert 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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dataset_cls = SonnetDataset
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sample_kwargs["prefix_len"] = args.prefix_len
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sample_kwargs["return_prompt_formatted"] = True
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elif args.dataset_name == "burstgpt":
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dataset_cls = BurstGPTDataset
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elif args.dataset_name == "hf":
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if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
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dataset_cls = VisionArenaDataset
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common_kwargs['dataset_subset'] = None
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common_kwargs['dataset_split'] = "train"
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sample_kwargs["enable_multimodal_chat"] = True
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elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
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dataset_cls = InstructCoderDataset
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common_kwargs['dataset_split'] = "train"
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elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
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dataset_cls = ConversationDataset
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common_kwargs['dataset_subset'] = args.hf_subset
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common_kwargs['dataset_split'] = args.hf_split
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sample_kwargs["enable_multimodal_chat"] = True
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elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
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dataset_cls = AIMODataset
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common_kwargs['dataset_subset'] = None
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common_kwargs['dataset_split'] = "train"
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else:
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raise ValueError(f"Unknown dataset name: {args.dataset_name}")
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# Remove None values
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sample_kwargs = {k: v for k, v in sample_kwargs.items() if v is not None}
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return dataset_cls(**common_kwargs).sample(**sample_kwargs)
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def main(args: argparse.Namespace):
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if args.seed is None:
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args.seed = 0
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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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requests = get_requests(args, tokenizer)
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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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request_outputs: Optional[list[RequestOutput]] = None
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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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args.disable_detokenize,
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))
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else:
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elapsed_time, request_outputs = run_vllm(
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requests, args.n, EngineArgs.from_cli_args(args),
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args.disable_detokenize)
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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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args.disable_detokenize)
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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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elif args.backend == "vllm-chat":
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elapsed_time, request_outputs = run_vllm_chat(
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requests, args.n, EngineArgs.from_cli_args(args),
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args.disable_detokenize)
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else:
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raise ValueError(f"Unknown backend: {args.backend}")
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if request_outputs:
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# Note: with the vllm and vllm-chat backends,
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# we have request_outputs, which we use to count tokens.
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total_prompt_tokens = 0
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total_output_tokens = 0
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for ro in request_outputs:
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if not isinstance(ro, RequestOutput):
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continue
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total_prompt_tokens += len(
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ro.prompt_token_ids) if ro.prompt_token_ids else 0
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total_output_tokens += sum(
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len(o.token_ids) for o in ro.outputs if o)
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total_num_tokens = total_prompt_tokens + total_output_tokens
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else:
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total_num_tokens = sum(r.prompt_len + r.expected_output_len
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for r in requests)
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total_output_tokens = sum(r.expected_output_len for r in requests)
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total_prompt_tokens = total_num_tokens - total_output_tokens
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if is_multi_modal and args.backend != "vllm-chat":
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print("\033[91mWARNING\033[0m: Multi-modal request with "
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f"{args.backend} backend 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 multi-modal token length.
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# vllm-chat backend counts the image tokens now
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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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print(f"Total num prompt tokens: {total_prompt_tokens}")
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print(f"Total num output tokens: {total_output_tokens}")
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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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save_to_pytorch_benchmark_format(args, results)
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def validate_args(args):
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"""
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Validate command-line arguments.
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"""
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# === Deprecation and Defaulting ===
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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 release. "
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"Please use '--dataset-name' and '--dataset-path' instead.",
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stacklevel=2)
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args.dataset_path = args.dataset
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if not getattr(args, "tokenizer", None):
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args.tokenizer = args.model
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# === Backend Validation ===
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valid_backends = {"vllm", "hf", "mii", "vllm-chat"}
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if args.backend not in valid_backends:
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raise ValueError(f"Unsupported backend: {args.backend}")
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# === Dataset Configuration ===
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if not args.dataset and not args.dataset_path:
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print(
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"When dataset path is not set, it will default to random dataset")
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args.dataset_name = 'random'
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if args.input_len is None:
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raise ValueError("input_len must be provided for a random dataset")
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# === Dataset Name Specific Checks ===
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# --hf-subset and --hf-split: only used
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# when dataset_name is 'hf'
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if args.dataset_name != "hf" and (
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getattr(args, "hf_subset", None) is not None
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or getattr(args, "hf_split", None) is not None):
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warnings.warn("--hf-subset and --hf-split will be ignored \
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since --dataset-name is not 'hf'.",
|
|
stacklevel=2)
|
|
elif args.dataset_name == "hf":
|
|
if args.dataset_path in (
|
|
VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
|
|
| ConversationDataset.SUPPORTED_DATASET_PATHS):
|
|
assert args.backend == "vllm-chat", f"{args.dataset_path} needs to use vllm-chat as the backend." #noqa: E501
|
|
elif args.dataset_path in (InstructCoderDataset.SUPPORTED_DATASET_PATHS
|
|
| AIMODataset.SUPPORTED_DATASET_PATHS):
|
|
assert args.backend == "vllm", f"{args.dataset_path} needs to use vllm as the backend." #noqa: E501
|
|
else:
|
|
raise ValueError(
|
|
f"{args.dataset_path} is not supported by hf dataset.")
|
|
|
|
# --random-range-ratio: only used when dataset_name is 'random'
|
|
if args.dataset_name != 'random' and args.random_range_ratio is not None:
|
|
warnings.warn("--random-range-ratio will be ignored since \
|
|
--dataset-name is not 'random'.",
|
|
stacklevel=2)
|
|
|
|
# --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
|
|
# set.
|
|
if args.dataset_name not in {"random", "sonnet", None
|
|
} and args.prefix_len is not None:
|
|
warnings.warn("--prefix-len will be ignored since --dataset-name\
|
|
is not 'random', 'sonnet', or not set.",
|
|
stacklevel=2)
|
|
|
|
# === LoRA Settings ===
|
|
if getattr(args, "enable_lora", False) and args.backend != "vllm":
|
|
raise ValueError(
|
|
"LoRA benchmarking is only supported for vLLM backend")
|
|
if getattr(args, "enable_lora", False) and args.lora_path is None:
|
|
raise ValueError("LoRA path must be provided when enable_lora is True")
|
|
|
|
# === Backend-specific Validations ===
|
|
if args.backend == "hf" and args.hf_max_batch_size is None:
|
|
raise ValueError("HF max batch size is required for HF backend")
|
|
if args.backend != "hf" and args.hf_max_batch_size is not None:
|
|
raise ValueError("HF max batch size is only for HF backend.")
|
|
|
|
if args.backend in {"hf", "mii"} and getattr(args, "quantization",
|
|
None) is not None:
|
|
raise ValueError("Quantization is only for vLLM backend.")
|
|
|
|
if args.backend == "mii" and args.dtype != "auto":
|
|
raise ValueError("dtype must be auto for MII backend.")
|
|
if args.backend == "mii" and args.n != 1:
|
|
raise ValueError("n must be 1 for MII backend.")
|
|
if args.backend == "mii" and args.tokenizer != args.model:
|
|
raise ValueError(
|
|
"Tokenizer must be the same as the model for MII backend.")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = FlexibleArgumentParser(description="Benchmark the throughput.")
|
|
parser.add_argument("--backend",
|
|
type=str,
|
|
choices=["vllm", "hf", "mii", "vllm-chat"],
|
|
default="vllm")
|
|
parser.add_argument(
|
|
"--dataset-name",
|
|
type=str,
|
|
choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
|
|
help="Name of the dataset to benchmark on.",
|
|
default="sharegpt")
|
|
parser.add_argument(
|
|
"--dataset",
|
|
type=str,
|
|
default=None,
|
|
help="Path to the ShareGPT dataset, will be deprecated in\
|
|
the next release. The dataset is expected to "
|
|
"be a json in form of list[dict[..., conversations: "
|
|
"list[dict[..., value: <prompt_or_response>]]]]")
|
|
parser.add_argument("--dataset-path",
|
|
type=str,
|
|
default=None,
|
|
help="Path to the dataset")
|
|
parser.add_argument("--input-len",
|
|
type=int,
|
|
default=None,
|
|
help="Input prompt length for each request")
|
|
parser.add_argument("--output-len",
|
|
type=int,
|
|
default=None,
|
|
help="Output length for each request. Overrides the "
|
|
"output length from the dataset.")
|
|
parser.add_argument("--n",
|
|
type=int,
|
|
default=1,
|
|
help="Number of generated sequences per prompt.")
|
|
parser.add_argument("--num-prompts",
|
|
type=int,
|
|
default=1000,
|
|
help="Number of prompts to process.")
|
|
parser.add_argument("--hf-max-batch-size",
|
|
type=int,
|
|
default=None,
|
|
help="Maximum batch size for HF backend.")
|
|
parser.add_argument(
|
|
'--output-json',
|
|
type=str,
|
|
default=None,
|
|
help='Path to save the throughput results in JSON format.')
|
|
parser.add_argument("--async-engine",
|
|
action='store_true',
|
|
default=False,
|
|
help="Use vLLM async engine rather than LLM class.")
|
|
parser.add_argument("--disable-frontend-multiprocessing",
|
|
action='store_true',
|
|
default=False,
|
|
help="Disable decoupled async engine frontend.")
|
|
parser.add_argument(
|
|
"--disable-detokenize",
|
|
action="store_true",
|
|
help=("Do not detokenize the response (i.e. do not include "
|
|
"detokenization time in the measurement)"))
|
|
# LoRA
|
|
parser.add_argument(
|
|
"--lora-path",
|
|
type=str,
|
|
default=None,
|
|
help="Path to the lora adapters to use. This can be an absolute path, "
|
|
"a relative path, or a Hugging Face model identifier.")
|
|
parser.add_argument(
|
|
"--prefix-len",
|
|
type=int,
|
|
default=None,
|
|
help=f"Number of prefix tokens to be used in RandomDataset "
|
|
"and SonnetDataset. For RandomDataset, the total input "
|
|
"length is the sum of prefix-len (default: "
|
|
f"{RandomDataset.DEFAULT_PREFIX_LEN}) and a random context length "
|
|
"sampled from [input_len * (1 - range_ratio), "
|
|
"input_len * (1 + range_ratio)]. For SonnetDataset, "
|
|
f"prefix_len (default: {SonnetDataset.DEFAULT_PREFIX_LEN}) "
|
|
"controls how much of the input is fixed lines versus "
|
|
"random lines, but the total input length remains approximately "
|
|
"input_len tokens.")
|
|
# random dataset
|
|
parser.add_argument(
|
|
"--random-range-ratio",
|
|
type=float,
|
|
default=None,
|
|
help=f"Range ratio (default : {RandomDataset.DEFAULT_RANGE_RATIO}) "
|
|
"for sampling input/output length, "
|
|
"used only for RandomDataset. Must be in the range [0, 1) to "
|
|
"define a symmetric sampling range "
|
|
"[length * (1 - range_ratio), length * (1 + range_ratio)].",
|
|
)
|
|
|
|
# hf dtaset
|
|
parser.add_argument("--hf-subset",
|
|
type=str,
|
|
default=None,
|
|
help="Subset of the HF dataset.")
|
|
parser.add_argument("--hf-split",
|
|
type=str,
|
|
default=None,
|
|
help="Split of the HF dataset.")
|
|
|
|
parser = AsyncEngineArgs.add_cli_args(parser)
|
|
args = parser.parse_args()
|
|
if args.tokenizer is None:
|
|
args.tokenizer = args.model
|
|
validate_args(args)
|
|
main(args)
|