forked from EngineX-Cambricon/enginex-mlu370-vllm
add qwen3
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Chranos
309
vllm-v0.6.2/vllm/entrypoints/openai/run_batch.py
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309
vllm-v0.6.2/vllm/entrypoints/openai/run_batch.py
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import asyncio
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from http import HTTPStatus
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from io import StringIO
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from typing import Awaitable, Callable, List, 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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# 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_engine import BaseModelPath
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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("--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_file(path_or_url: str, data: str) -> None:
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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.put(path_or_url, data=data.encode("utf-8")):
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pass
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else:
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# We should make this async, but as long as this is always run as a
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# standalone program, blocking the event loop won't effect performance
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# in this particular case.
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with open(path_or_url, "w", encoding="utf-8") as f:
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f.write(data)
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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, (ChatCompletionResponse, EmbeddingResponse)):
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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_chat = OpenAIServingChat(
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engine,
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model_config,
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base_model_paths,
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args.response_role,
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lora_modules=None,
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prompt_adapters=None,
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request_logger=request_logger,
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chat_template=None,
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enable_prompt_tokens_details=args.enable_prompt_tokens_details,
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) if model_config.task == "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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base_model_paths,
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request_logger=request_logger,
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chat_template=None,
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) if model_config.task == "embedding" 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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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 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(run_request(handler_fn, request, tracker))
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tracker.submitted()
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elif request.url == "/v1/embeddings":
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handler_fn = (None if openai_serving_embedding is None else
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openai_serving_embedding.create_embedding)
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if 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(run_request(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="Only /v1/chat/completions and "
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"/v1/embeddings 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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output_buffer = StringIO()
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for response in responses:
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print(response.model_dump_json(), file=output_buffer)
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output_buffer.seek(0)
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await write_file(args.output_file, output_buffer.read().strip())
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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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