benchmark_throughput.py

# SPDX-License-Identifier: Apache-2.0
"""Benchmark offline inference throughput."""
import argparse
import dataclasses
import json
import os
import random
import time
import warnings
from typing import Any, Optional, Union

import torch
import uvloop
from benchmark_dataset import (BurstGPTDataset, RandomDataset, SampleRequest,
                               ShareGPTDataset, SonnetDataset)
from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
from tqdm import tqdm
from transformers import (AutoModelForCausalLM, AutoTokenizer,
                          PreTrainedTokenizerBase)

from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
from vllm.entrypoints.openai.api_server import (
    build_async_engine_client_from_engine_args)
from vllm.inputs import TextPrompt, TokensPrompt
from vllm.lora.request import LoRARequest
from vllm.sampling_params import BeamSearchParams
from vllm.utils import FlexibleArgumentParser, merge_async_iterators


def run_vllm(
    requests: list[SampleRequest],
    n: int,
    engine_args: EngineArgs,
    disable_detokenize: bool = False,
) -> float:
    from vllm import LLM, SamplingParams
    llm = LLM(**dataclasses.asdict(engine_args))
    assert all(
        llm.llm_engine.model_config.max_model_len >= (
            request.prompt_len + request.expected_output_len)
        for request in requests), (
            "Please ensure that max_model_len is greater than the sum of"
            " prompt_len and expected_output_len for all requests.")
    # Add the requests to the engine.
    prompts: list[Union[TextPrompt, TokensPrompt]] = []
    sampling_params: list[SamplingParams] = []
    for request in requests:
        prompts.append(
            TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
                       multi_modal_data=request.multi_modal_data)
            if "prompt_token_ids" in request.prompt else \
            TextPrompt(prompt=request.prompt,
                       multi_modal_data=request.multi_modal_data))
        sampling_params.append(
            SamplingParams(
                n=n,
                temperature=1.0,
                top_p=1.0,
                ignore_eos=True,
                max_tokens=request.expected_output_len,
                detokenize=not disable_detokenize,
            ))
    lora_requests: Optional[list[LoRARequest]] = None
    if engine_args.enable_lora:
        lora_requests = [request.lora_request for request in requests]

    use_beam_search = False

    if not use_beam_search:
        start = time.perf_counter()
        llm.generate(prompts,
                     sampling_params,
                     lora_request=lora_requests,
                     use_tqdm=True)
        end = time.perf_counter()
    else:
        assert lora_requests is None, "BeamSearch API does not support LoRA"
        prompts = [request.prompt for request in requests]
        # output_len should be the same for all requests.
        output_len = requests[0][2]
        for request in requests:
            assert request.expected_output_len == output_len
        start = time.perf_counter()
        llm.beam_search(
            prompts,
            BeamSearchParams(
                beam_width=n,
                max_tokens=output_len,
                ignore_eos=True,
            ))
        end = time.perf_counter()
    return end - start


async def run_vllm_async(
    requests: list[SampleRequest],
    n: int,
    engine_args: AsyncEngineArgs,
    disable_frontend_multiprocessing: bool = False,
    disable_detokenize: bool = False,
) -> float:
    from vllm import SamplingParams

    async with build_async_engine_client_from_engine_args(
            engine_args, disable_frontend_multiprocessing) as llm:
        assert all(
            llm.model_config.max_model_len >= (request.prompt_len +
                                               request.expected_output_len)
            for request in requests), (
                "Please ensure that max_model_len is greater than the sum of"
                " prompt_len and expected_output_len for all requests.")

        # Add the requests to the engine.
        prompts: list[Union[TextPrompt, TokensPrompt]] = []
        sampling_params: list[SamplingParams] = []
        lora_requests: list[Optional[LoRARequest]] = []
        for request in requests:
            prompts.append(
                TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
                        multi_modal_data=request.multi_modal_data)
                if "prompt_token_ids" in request.prompt else \
                TextPrompt(prompt=request.prompt,
                           multi_modal_data=request.multi_modal_data))
            sampling_params.append(
                SamplingParams(
                    n=n,
                    temperature=1.0,
                    top_p=1.0,
                    ignore_eos=True,
                    max_tokens=request.expected_output_len,
                    detokenize=not disable_detokenize,
                ))
            lora_requests.append(request.lora_request)

        generators = []
        start = time.perf_counter()
        for i, (prompt, sp,
                lr) in enumerate(zip(prompts, sampling_params, lora_requests)):
            generator = llm.generate(prompt,
                                     sp,
                                     lora_request=lr,
                                     request_id=f"test{i}")
            generators.append(generator)
        all_gens = merge_async_iterators(*generators)
        async for i, res in all_gens:
            pass
        end = time.perf_counter()
        return end - start


def run_hf(
    requests: list[SampleRequest],
    model: str,
    tokenizer: PreTrainedTokenizerBase,
    n: int,
    max_batch_size: int,
    trust_remote_code: bool,
    disable_detokenize: bool = False,
) -> float:
    llm = AutoModelForCausalLM.from_pretrained(
        model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
    if llm.config.model_type == "llama":
        # To enable padding in the HF backend.
        tokenizer.pad_token = tokenizer.eos_token
    llm = llm.cuda()

    pbar = tqdm(total=len(requests))
    start = time.perf_counter()
    batch: list[str] = []
    max_prompt_len = 0
    max_output_len = 0
    for i in range(len(requests)):
        prompt, prompt_len, output_len = requests[i]
        # Add the prompt to the batch.
        batch.append(prompt)
        max_prompt_len = max(max_prompt_len, prompt_len)
        max_output_len = max(max_output_len, output_len)
        if len(batch) < max_batch_size and i != len(requests) - 1:
            # Check if we can add more requests to the batch.
            _, next_prompt_len, next_output_len = requests[i + 1]
            if (max(max_prompt_len, next_prompt_len) +
                    max(max_output_len, next_output_len)) <= 2048:
                # We can add more requests to the batch.
                continue

        # Generate the sequences.
        input_ids = tokenizer(batch, return_tensors="pt",
                              padding=True).input_ids
        llm_outputs = llm.generate(
            input_ids=input_ids.cuda(),
            do_sample=True,
            num_return_sequences=n,
            temperature=1.0,
            top_p=1.0,
            use_cache=True,
            max_new_tokens=max_output_len,
        )
        if not disable_detokenize:
            # Include the decoding time.
            tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
        pbar.update(len(batch))

        # Clear the batch.
        batch = []
        max_prompt_len = 0
        max_output_len = 0
    end = time.perf_counter()
    return end - start


def run_mii(
    requests: list[SampleRequest],
    model: str,
    tensor_parallel_size: int,
    output_len: int,
) -> float:
    from mii import client, serve
    llm = serve(model, tensor_parallel=tensor_parallel_size)
    prompts = [request.prompt for request in requests]

    start = time.perf_counter()
    llm.generate(prompts, max_new_tokens=output_len)
    end = time.perf_counter()
    client = client(model)
    client.terminate_server()
    return end - start


def save_to_pytorch_benchmark_format(args: argparse.Namespace,
                                     results: dict[str, Any]) -> None:
    pt_records = convert_to_pytorch_benchmark_format(
        args=args,
        metrics={
            "requests_per_second": [results["requests_per_second"]],
            "tokens_per_second": [results["tokens_per_second"]],
        },
        extra_info={
            k: results[k]
            for k in ["elapsed_time", "num_requests", "total_num_tokens"]
        })
    if pt_records:
        # Don't use json suffix here as we don't want CI to pick it up
        pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
        write_to_json(pt_file, pt_records)


def get_requests(args, tokenizer):
    # Common parameters for all dataset types.
    common_kwargs = {
        "dataset_path": args.dataset_path,
        "random_seed": args.seed,
    }
    sample_kwargs = {
        "tokenizer": tokenizer,
        "lora_path": args.lora_path,
        "max_loras": args.max_loras,
        "num_requests": args.num_prompts,
        "input_len": args.input_len,
        "output_len": args.output_len,
    }
    if args.dataset_path is None or args.dataset_name == "random":
        sample_kwargs["range_ratio"] = args.random_range_ratio
        sample_kwargs["prefix_len"] = args.prefix_len
        dataset_cls = RandomDataset
    elif args.dataset_name == "sharegpt":
        dataset_cls = ShareGPTDataset
    elif args.dataset_name == "sonnet":
        assert tokenizer.chat_template or tokenizer.default_chat_template, (
            "Tokenizer/model must have chat template for sonnet dataset.")
        dataset_cls = SonnetDataset
        sample_kwargs["prefix_len"] = args.prefix_len
        sample_kwargs["return_prompt_formatted"] = True
    elif args.dataset_name == "burstgpt":
        dataset_cls = BurstGPTDataset
    else:
        raise ValueError(f"Unknown dataset name: {args.dataset_name}")
    # Remove None values
    sample_kwargs = {k: v for k, v in sample_kwargs.items() if v is not None}
    return dataset_cls(**common_kwargs).sample(**sample_kwargs)


def main(args: argparse.Namespace):
    if args.seed is None:
        args.seed = 0
    print(args)
    random.seed(args.seed)
    # Sample the requests.
    tokenizer = AutoTokenizer.from_pretrained(
        args.tokenizer, trust_remote_code=args.trust_remote_code)
    requests = get_requests(args, tokenizer)
    is_multi_modal = any(request.multi_modal_data is not None
                         for request in requests)
    if args.backend == "vllm":
        if args.async_engine:
            elapsed_time = uvloop.run(
                run_vllm_async(
                    requests,
                    args.n,
                    AsyncEngineArgs.from_cli_args(args),
                    args.disable_frontend_multiprocessing,
                    args.disable_detokenize,
                ))
        else:
            elapsed_time = run_vllm(requests, args.n,
                                    EngineArgs.from_cli_args(args),
                                    args.disable_detokenize)
    elif args.backend == "hf":
        assert args.tensor_parallel_size == 1
        elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
                              args.hf_max_batch_size, args.trust_remote_code,
                              args.disable_detokenize)
    elif args.backend == "mii":
        elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
                               args.output_len)
    else:
        raise ValueError(f"Unknown backend: {args.backend}")
    total_num_tokens = sum(request.prompt_len + request.expected_output_len
                           for request in requests)
    total_output_tokens = sum(request.expected_output_len
                              for request in requests)
    if is_multi_modal:
        print("\033[91mWARNING\033[0m: Multi-modal request detected. The "
              "following metrics are not accurate because image tokens are not"
              " counted. See vllm-project/vllm/issues/9778 for details.")
        # TODO(vllm-project/vllm/issues/9778): Count multi-modal token length.
    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
          f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
          f"{total_output_tokens / elapsed_time:.2f} output tokens/s")

    # Output JSON results if specified
    if args.output_json:
        results = {
            "elapsed_time": elapsed_time,
            "num_requests": len(requests),
            "total_num_tokens": total_num_tokens,
            "requests_per_second": len(requests) / elapsed_time,
            "tokens_per_second": total_num_tokens / elapsed_time,
        }
        with open(args.output_json, "w") as f:
            json.dump(results, f, indent=4)
        save_to_pytorch_benchmark_format(args, results)


if __name__ == "__main__":
    parser = FlexibleArgumentParser(description="Benchmark the throughput.")
    parser.add_argument("--backend",
                        type=str,
                        choices=["vllm", "hf", "mii"],
                        default="vllm")
    parser.add_argument("--dataset-name",
                        type=str,
                        choices=["sharegpt", "random", "sonnet", "burstgpt"],
                        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="Number of prefix tokens per request."
                        "This is for the RandomDataset and SonnetDataset")
    # random dataset
    parser.add_argument(
        "--random-range-ratio",
        type=float,
        default=1.0,
        help="Range of sampled ratio of input/output length, "
        "used only for RandomDataSet.",
    )

    parser = AsyncEngineArgs.add_cli_args(parser)
    args = parser.parse_args()
    if args.tokenizer is None:
        args.tokenizer = args.model
    if args.dataset is not None:
        warnings.warn(
            "The '--dataset' argument will be deprecated in the next "
            "release. Please use '--dataset-name' and "
            "'--dataset-path' in the future runs.",
            stacklevel=2)
        args.dataset_path = args.dataset
    if args.dataset is None and args.dataset_path is None:
        # for random dataset, the default sampling setting is in
        # benchmark_dataset.RandomDataset
        print("When dataset is not set, it will default to random dataset")
    else:
        assert args.input_len is None
    if args.enable_lora:
        assert args.lora_path is not None

    if args.backend == "vllm":
        if args.hf_max_batch_size is not None:
            raise ValueError("HF max batch size is only for HF backend.")
    elif args.backend == "hf":
        if args.hf_max_batch_size is None:
            raise ValueError("HF max batch size is required for HF backend.")
        if args.quantization is not None:
            raise ValueError("Quantization is only for vLLM backend.")
        if args.enable_lora is not None:
            raise ValueError("LoRA benchmarking is only supported for vLLM"
                             " backend")
    elif args.backend == "mii":
        if args.dtype != "auto":
            raise ValueError("dtype must be auto for MII backend.")
        if args.n != 1:
            raise ValueError("n must be 1 for MII backend.")
        if args.quantization is not None:
            raise ValueError("Quantization is only for vLLM backend.")
        if args.hf_max_batch_size is not None:
            raise ValueError("HF max batch size is only for HF backend.")
        if args.tokenizer != args.model:
            raise ValueError("Tokenizer must be the same as the model for MII "
                             "backend.")
        if args.enable_lora is not None:
            raise ValueError("LoRA benchmarking is only supported for vLLM"
                             " backend")
    main(args)