181 lines
5.7 KiB
Python
181 lines
5.7 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# A very basic example of vllm worker handling pre-processed requests.
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#
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# Dynamo does the HTTP handling, prompt templating and tokenization, then forwards the
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# request via NATS to this python script, which runs vllm.
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#
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# Setup a virtualenv with dynamo.llm, dynamo.runtime and vllm installed
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# in lib/bindings/python `maturin develop` and `pip install -e .` should do it
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# Start nats and etcd:
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# - nats-server -js
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#
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# Window 1: `python server_vllm.py`. Wait for log "Starting endpoint".
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# Window 2: `dynamo-run out=dyn
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import argparse
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import asyncio
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import os
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import sys
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import uvloop
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from vllm import SamplingParams
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from vllm.engine.arg_utils import AsyncEngineArgs
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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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)
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from vllm.inputs import TokensPrompt
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from dynamo.llm import ModelInput, ModelType, register_llm
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from dynamo.runtime import DistributedRuntime, dynamo_worker
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DYN_NAMESPACE = os.environ.get("DYN_NAMESPACE", "dynamo")
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DEFAULT_ENDPOINT = f"dyn://{DYN_NAMESPACE}.backend.generate"
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DEFAULT_MODEL = "Qwen/Qwen3-0.6B"
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DEFAULT_TEMPERATURE = 0.7
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class Config:
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"""Command line parameters or defaults"""
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namespace: str
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component: str
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endpoint: str
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model: str
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class RequestHandler:
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"""
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Request handler for the generate endpoint
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"""
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def __init__(self, engine):
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self.engine_client = engine
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async def generate(self, request):
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request_id = "1" # hello_world example only
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# print(f"Received request: {request}")
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prompt = TokensPrompt(prompt_token_ids=request["token_ids"])
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sampling_params = SamplingParams(
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temperature=request["sampling_options"]["temperature"]
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or DEFAULT_TEMPERATURE,
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# vllm defaults this to 16
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max_tokens=request["stop_conditions"]["max_tokens"],
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)
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num_output_tokens_so_far = 0
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gen = self.engine_client.generate(prompt, sampling_params, request_id)
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async for res in gen:
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# res is vllm's RequestOutput
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# This is the expected way for a request to end.
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# The new token ID will be eos, don't forward it.
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if res.finished:
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yield {"finish_reason": "stop", "token_ids": []}
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break
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if not res.outputs:
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yield {"finish_reason": "error", "token_ids": []}
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break
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output = res.outputs[0]
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next_total_toks = len(output.token_ids)
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out = {"token_ids": output.token_ids[num_output_tokens_so_far:]}
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if output.finish_reason:
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out["finish_reason"] = output.finish_reason
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if output.stop_reason:
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out["stop_reason"] = output.stop_reason
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yield out
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num_output_tokens_so_far = next_total_toks
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@dynamo_worker(static=False)
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async def worker(runtime: DistributedRuntime):
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await init(runtime, cmd_line_args())
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async def init(runtime: DistributedRuntime, config: Config):
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"""
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Instantiate and serve
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"""
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component = runtime.namespace(config.namespace).component(config.component)
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await component.create_service()
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endpoint = component.endpoint(config.endpoint)
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await register_llm(
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ModelInput.Tokens,
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ModelType.Chat | ModelType.Completions,
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endpoint,
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config.model,
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)
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engine_args = AsyncEngineArgs(
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model=config.model,
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task="generate",
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skip_tokenizer_init=True,
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)
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engine_context = build_async_engine_client_from_engine_args(engine_args)
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engine_client = await engine_context.__aenter__()
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# the server will gracefully shutdown (i.e., keep opened TCP streams finishes)
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# after the lease is revoked
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await endpoint.serve_endpoint(RequestHandler(engine_client).generate)
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def cmd_line_args():
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parser = argparse.ArgumentParser(
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description="vLLM server integrated with Dynamo runtime."
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)
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parser.add_argument(
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"--endpoint",
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type=str,
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default=DEFAULT_ENDPOINT,
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help=f"Dynamo endpoint string in 'dyn://namespace.component.endpoint' format. Default: {DEFAULT_ENDPOINT}",
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)
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parser.add_argument(
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"--model",
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type=str,
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default=DEFAULT_MODEL,
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help=f"Path to disk model or HuggingFace model identifier to load. Default: {DEFAULT_MODEL}",
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)
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args = parser.parse_args()
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config = Config()
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config.model = args.model
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endpoint_str = args.endpoint.replace("dyn://", "", 1)
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endpoint_parts = endpoint_str.split(".")
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if len(endpoint_parts) != 3:
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print(
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f"Invalid endpoint format: '{args.endpoint}'. Expected 'dyn://namespace.component.endpoint' or 'namespace.component.endpoint'."
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)
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sys.exit(1)
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parsed_namespace, parsed_component_name, parsed_endpoint_name = endpoint_parts
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config.namespace = parsed_namespace
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config.component = parsed_component_name
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config.endpoint = parsed_endpoint_name
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return config
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if __name__ == "__main__":
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uvloop.install()
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asyncio.run(worker())
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