440 lines
16 KiB
Python
440 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import asyncio
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import tempfile
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from collections.abc import Awaitable
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from http import HTTPStatus
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from io import StringIO
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from typing import Callable, Optional
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import aiohttp
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import torch
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from prometheus_client import start_http_server
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from tqdm import tqdm
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from vllm.engine.arg_utils import AsyncEngineArgs, nullable_str
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from vllm.engine.async_llm_engine import AsyncLLMEngine
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from vllm.entrypoints.logger import RequestLogger, logger
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# yapf: disable
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from vllm.entrypoints.openai.protocol import (BatchRequestInput,
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BatchRequestOutput,
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BatchResponseData,
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ChatCompletionResponse,
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EmbeddingResponse, ErrorResponse,
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ScoreResponse)
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# yapf: enable
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from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
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from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
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from vllm.entrypoints.openai.serving_models import (BaseModelPath,
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OpenAIServingModels)
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from vllm.entrypoints.openai.serving_score import ServingScores
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from vllm.usage.usage_lib import UsageContext
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from vllm.utils import FlexibleArgumentParser, random_uuid
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from vllm.version import __version__ as VLLM_VERSION
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def parse_args():
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parser = FlexibleArgumentParser(
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description="vLLM OpenAI-Compatible batch runner.")
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parser.add_argument(
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"-i",
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"--input-file",
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required=True,
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type=str,
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help=
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"The path or url to a single input file. Currently supports local file "
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"paths, or the http protocol (http or https). If a URL is specified, "
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"the file should be available via HTTP GET.")
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parser.add_argument(
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"-o",
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"--output-file",
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required=True,
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type=str,
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help="The path or url to a single output file. Currently supports "
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"local file paths, or web (http or https) urls. If a URL is specified,"
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" the file should be available via HTTP PUT.")
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parser.add_argument(
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"--output-tmp-dir",
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type=str,
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default=None,
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help="The directory to store the output file before uploading it "
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"to the output URL.",
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)
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parser.add_argument("--response-role",
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type=nullable_str,
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default="assistant",
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help="The role name to return if "
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"`request.add_generation_prompt=True`.")
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parser = AsyncEngineArgs.add_cli_args(parser)
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parser.add_argument('--max-log-len',
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type=int,
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default=None,
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help='Max number of prompt characters or prompt '
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'ID numbers being printed in log.'
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'\n\nDefault: Unlimited')
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parser.add_argument("--enable-metrics",
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action="store_true",
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help="Enable Prometheus metrics")
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parser.add_argument(
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"--url",
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type=str,
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default="0.0.0.0",
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help="URL to the Prometheus metrics server "
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"(only needed if enable-metrics is set).",
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)
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parser.add_argument(
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"--port",
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type=int,
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default=8000,
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help="Port number for the Prometheus metrics server "
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"(only needed if enable-metrics is set).",
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)
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parser.add_argument(
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"--enable-prompt-tokens-details",
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action='store_true',
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default=False,
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help="If set to True, enable prompt_tokens_details in usage.")
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return parser.parse_args()
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# explicitly use pure text format, with a newline at the end
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# this makes it impossible to see the animation in the progress bar
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# but will avoid messing up with ray or multiprocessing, which wraps
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# each line of output with some prefix.
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_BAR_FORMAT = "{desc}: {percentage:3.0f}% Completed | {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]\n" # noqa: E501
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class BatchProgressTracker:
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def __init__(self):
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self._total = 0
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self._pbar: Optional[tqdm] = None
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def submitted(self):
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self._total += 1
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def completed(self):
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if self._pbar:
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self._pbar.update()
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def pbar(self) -> tqdm:
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enable_tqdm = not torch.distributed.is_initialized(
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) or torch.distributed.get_rank() == 0
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self._pbar = tqdm(total=self._total,
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unit="req",
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desc="Running batch",
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mininterval=5,
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disable=not enable_tqdm,
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bar_format=_BAR_FORMAT)
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return self._pbar
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async def read_file(path_or_url: str) -> str:
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if path_or_url.startswith("http://") or path_or_url.startswith("https://"):
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async with aiohttp.ClientSession() as session, \
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session.get(path_or_url) as resp:
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return await resp.text()
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else:
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with open(path_or_url, encoding="utf-8") as f:
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return f.read()
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async def write_local_file(output_path: str,
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batch_outputs: list[BatchRequestOutput]) -> None:
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"""
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Write the responses to a local file.
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output_path: The path to write the responses to.
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batch_outputs: The list of batch outputs to write.
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"""
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# We should make this async, but as long as run_batch runs as a
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# standalone program, blocking the event loop won't effect performance.
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with open(output_path, "w", encoding="utf-8") as f:
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for o in batch_outputs:
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print(o.model_dump_json(), file=f)
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async def upload_data(output_url: str, data_or_file: str,
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from_file: bool) -> None:
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"""
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Upload a local file to a URL.
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output_url: The URL to upload the file to.
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data_or_file: Either the data to upload or the path to the file to upload.
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from_file: If True, data_or_file is the path to the file to upload.
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"""
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# Timeout is a common issue when uploading large files.
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# We retry max_retries times before giving up.
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max_retries = 5
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# Number of seconds to wait before retrying.
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delay = 5
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for attempt in range(1, max_retries + 1):
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try:
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# We increase the timeout to 1000 seconds to allow
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# for large files (default is 300).
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async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(
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total=1000)) as session:
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if from_file:
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with open(data_or_file, "rb") as file:
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async with session.put(output_url,
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data=file) as response:
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if response.status != 200:
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raise Exception(f"Failed to upload file.\n"
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f"Status: {response.status}\n"
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f"Response: {response.text()}")
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else:
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async with session.put(output_url,
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data=data_or_file) as response:
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if response.status != 200:
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raise Exception(f"Failed to upload data.\n"
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f"Status: {response.status}\n"
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f"Response: {response.text()}")
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except Exception as e:
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if attempt < max_retries:
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logger.error(
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f"Failed to upload data (attempt {attempt}). "
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f"Error message: {str(e)}.\nRetrying in {delay} seconds..."
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)
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await asyncio.sleep(delay)
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else:
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raise Exception(f"Failed to upload data (attempt {attempt}). "
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f"Error message: {str(e)}.") from e
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async def write_file(path_or_url: str, batch_outputs: list[BatchRequestOutput],
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output_tmp_dir: str) -> None:
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"""
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Write batch_outputs to a file or upload to a URL.
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path_or_url: The path or URL to write batch_outputs to.
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batch_outputs: The list of batch outputs to write.
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output_tmp_dir: The directory to store the output file before uploading it
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to the output URL.
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"""
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if path_or_url.startswith("http://") or path_or_url.startswith("https://"):
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if output_tmp_dir is None:
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logger.info("Writing outputs to memory buffer")
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output_buffer = StringIO()
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for o in batch_outputs:
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print(o.model_dump_json(), file=output_buffer)
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output_buffer.seek(0)
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logger.info("Uploading outputs to %s", path_or_url)
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await upload_data(
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path_or_url,
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output_buffer.read().strip().encode("utf-8"),
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from_file=False,
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)
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else:
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# Write responses to a temporary file and then upload it to the URL.
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with tempfile.NamedTemporaryFile(
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mode="w",
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encoding="utf-8",
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dir=output_tmp_dir,
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prefix="tmp_batch_output_",
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suffix=".jsonl",
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) as f:
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logger.info("Writing outputs to temporary local file %s",
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f.name)
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await write_local_file(f.name, batch_outputs)
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logger.info("Uploading outputs to %s", path_or_url)
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await upload_data(path_or_url, f.name, from_file=True)
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else:
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logger.info("Writing outputs to local file %s", path_or_url)
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await write_local_file(path_or_url, batch_outputs)
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def make_error_request_output(request: BatchRequestInput,
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error_msg: str) -> BatchRequestOutput:
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batch_output = BatchRequestOutput(
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id=f"vllm-{random_uuid()}",
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custom_id=request.custom_id,
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response=BatchResponseData(
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status_code=HTTPStatus.BAD_REQUEST,
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request_id=f"vllm-batch-{random_uuid()}",
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),
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error=error_msg,
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)
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return batch_output
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async def make_async_error_request_output(
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request: BatchRequestInput, error_msg: str) -> BatchRequestOutput:
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return make_error_request_output(request, error_msg)
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async def run_request(serving_engine_func: Callable,
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request: BatchRequestInput,
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tracker: BatchProgressTracker) -> BatchRequestOutput:
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response = await serving_engine_func(request.body)
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if isinstance(response,
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(ChatCompletionResponse, EmbeddingResponse, ScoreResponse)):
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batch_output = BatchRequestOutput(
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id=f"vllm-{random_uuid()}",
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custom_id=request.custom_id,
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response=BatchResponseData(
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body=response, request_id=f"vllm-batch-{random_uuid()}"),
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error=None,
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)
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elif isinstance(response, ErrorResponse):
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batch_output = BatchRequestOutput(
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id=f"vllm-{random_uuid()}",
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custom_id=request.custom_id,
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response=BatchResponseData(
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status_code=response.code,
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request_id=f"vllm-batch-{random_uuid()}"),
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error=response,
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)
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else:
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batch_output = make_error_request_output(
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request, error_msg="Request must not be sent in stream mode")
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tracker.completed()
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return batch_output
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async def main(args):
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if args.served_model_name is not None:
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served_model_names = args.served_model_name
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else:
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served_model_names = [args.model]
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engine_args = AsyncEngineArgs.from_cli_args(args)
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engine = AsyncLLMEngine.from_engine_args(
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engine_args, usage_context=UsageContext.OPENAI_BATCH_RUNNER)
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model_config = await engine.get_model_config()
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base_model_paths = [
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BaseModelPath(name=name, model_path=args.model)
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for name in served_model_names
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]
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if args.disable_log_requests:
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request_logger = None
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else:
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request_logger = RequestLogger(max_log_len=args.max_log_len)
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# Create the openai serving objects.
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openai_serving_models = OpenAIServingModels(
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engine_client=engine,
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model_config=model_config,
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base_model_paths=base_model_paths,
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lora_modules=None,
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prompt_adapters=None,
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)
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openai_serving_chat = OpenAIServingChat(
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engine,
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model_config,
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openai_serving_models,
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args.response_role,
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request_logger=request_logger,
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chat_template=None,
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chat_template_content_format="auto",
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enable_prompt_tokens_details=args.enable_prompt_tokens_details,
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) if model_config.runner_type == "generate" else None
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openai_serving_embedding = OpenAIServingEmbedding(
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engine,
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model_config,
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openai_serving_models,
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request_logger=request_logger,
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chat_template=None,
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chat_template_content_format="auto",
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) if model_config.task == "embed" else None
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openai_serving_scores = (ServingScores(
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engine,
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model_config,
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openai_serving_models,
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request_logger=request_logger,
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) if model_config.task == "score" else None)
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tracker = BatchProgressTracker()
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logger.info("Reading batch from %s...", args.input_file)
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# Submit all requests in the file to the engine "concurrently".
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response_futures: list[Awaitable[BatchRequestOutput]] = []
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for request_json in (await read_file(args.input_file)).strip().split("\n"):
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# Skip empty lines.
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request_json = request_json.strip()
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if not request_json:
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continue
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request = BatchRequestInput.model_validate_json(request_json)
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# Determine the type of request and run it.
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if request.url == "/v1/chat/completions":
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chat_handler_fn = (None if openai_serving_chat is None else
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openai_serving_chat.create_chat_completion)
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if chat_handler_fn is None:
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response_futures.append(
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make_async_error_request_output(
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request,
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error_msg=
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"The model does not support Chat Completions API",
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))
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continue
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response_futures.append(
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run_request(chat_handler_fn, request, tracker))
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tracker.submitted()
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elif request.url == "/v1/embeddings":
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embed_handler_fn = (None if openai_serving_embedding is None else
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openai_serving_embedding.create_embedding)
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if embed_handler_fn is None:
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response_futures.append(
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make_async_error_request_output(
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request,
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error_msg="The model does not support Embeddings API",
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))
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continue
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response_futures.append(
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run_request(embed_handler_fn, request, tracker))
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tracker.submitted()
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elif request.url == "/v1/score":
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score_handler_fn = (None if openai_serving_scores is None else
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openai_serving_scores.create_score)
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if score_handler_fn is None:
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response_futures.append(
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make_async_error_request_output(
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request,
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error_msg="The model does not support Scores API",
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))
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continue
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response_futures.append(
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run_request(score_handler_fn, request, tracker))
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tracker.submitted()
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else:
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response_futures.append(
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make_async_error_request_output(
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request,
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error_msg=
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"Only /v1/chat/completions, /v1/embeddings, and /v1/score "
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"are supported in the batch endpoint.",
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))
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with tracker.pbar():
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responses = await asyncio.gather(*response_futures)
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await write_file(args.output_file, responses, args.output_tmp_dir)
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if __name__ == "__main__":
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args = parse_args()
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logger.info("vLLM batch processing API version %s", VLLM_VERSION)
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logger.info("args: %s", args)
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# Start the Prometheus metrics server. LLMEngine uses the Prometheus client
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# to publish metrics at the /metrics endpoint.
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if args.enable_metrics:
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logger.info("Prometheus metrics enabled")
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start_http_server(port=args.port, addr=args.url)
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else:
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logger.info("Prometheus metrics disabled")
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asyncio.run(main(args))
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