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[Feat][Doc] Add a load_balance_dp_proxy in examples and external dp doc. (#4265)

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### What this PR does / why we need it?
This PR adds a load-balance dp proxy server which can be used in
external DP scenario without Disaggregated-Prefill enabled. What's more,
add a doc of external dp and load-balance dp proxy server.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
See the new doc.

- vLLM version: v0.11.0
- vLLM main:
2918c1b49c

---------

Signed-off-by: whx-sjtu <2952154980@qq.com>
This commit is contained in:
whx
2025-11-21 16:33:23 +08:00
committed by GitHub
parent 6c157cb75a
commit a5554b6661
6 changed files with 514 additions and 18 deletions
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86
docs/source/user_guide/feature_guide/external_dp.md Normal file
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@@ -0,0 +1,86 @@
# External DP
For larger scale deployments especially, it can make sense to handle the orchestration and load balancing of data parallel ranks externally.
In this case, it's more convenient to treat each DP rank like a separate vLLM deployment, with its own endpoint, and have an external router balance HTTP requests between them, making use of appropriate real-time telemetry from each server for routing decisions.
## Getting Start
The functionality of [external DP](https://docs.vllm.ai/en/latest/serving/data_parallel_deployment/?h=external#external-load-balancing) is already natively supported by vLLM. In vllm-ascend we provide two enhanced functionalities:
1. A launch script which helps to launch multi vllm instances in one command.
2. A request-length-aware load balance proxy for external dp.
This tutorial will introduce the usage of them.
### Prerequisites:
- Python 3.10+
- Install dependencies needed by load-balance proxy server:
```
pip install fastapi httpx uvicorn
```
## Starting Exeternal DP Servers
First you need to have at least two vLLM servers running in data parallel. These can be mock servers or actual vLLM servers. Note that this proxy also works with only one vLLM server running, but will fall back to direct request forwarding which is meaningless.
You can start external vLLM dp servers one-by-one manually or using the launch script in `examples/external_online_dp`. For scenarios of large dp size across multi nodes, we recommend using our launch script for convenience.
### Manually Launch
```
# This example shows how to manually launch a vLLM service with DP size 2 in one node.
vllm serve --host 0.0.0.0 --port 8100 --data-parallel-size 2 --data-parallel-rank 0 ... # vLLM DP0
vllm serve --host 0.0.0.0 --port 8101 --data-parallel-size 2 --data-parallel-rank 1 ... # vLLM DP1
```
### Use Launch Script
Firstly, you need to modify the `examples/external_online_dp/run_dp_template.sh` according to your vLLM configuration. Then you can use `examples/external_online_dp/launch_online_dp.py` to launch multiple vLLM instances in one command each node. It will internally call `examples/external_online_dp/run_dp_template.sh` for each DP rank with proper DP-related parameters.
An example of running external DP in one single node:
```
cd examples/external_online_dp
# running DP4 TP4 in a node with 16 NPUs
python launch_online_dp.py --dp-size 4 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address x.x.x.x --dp-rpc-port 12342
```
An example of running external DP in two nodes:
```
cd examples/external_online_dp
# running DP4 TP4 in two nodes with 8 NPUs each
# Node 0 holds DP0 DP1 and node 1 holds DP2 DP3
# Here x.x.x.x:12342 is served as the common data parallel RPC address
# On node 0:
python launch_online_dp.py --dp-size 4 --tp-size 4 --dp-size-local 2 --dp-rank-start 0 --dp-address x.x.x.x --dp-rpc-port 12342
# On node 1:
python launch_online_dp.py --dp-size 4 --tp-size 4 --dp-size-local 2 --dp-rank-start 2 --dp-address x.x.x.x --dp-rpc-port 12342
```
## Starting Load-balance Proxy Server
After all vLLM DP instances are launched, you can now launch the load-balance proxy server which serves as entrypoint for coming requests and load balance them between vLLM DP instances.
The proxy server has following features:
- Load balances requests to multiple vLLM servers based on request length.
- Supports OpenAI-compatible `/v1/completions` and `/v1/chat/completions` endpoints.
- Streams responses from backend servers to clients.
To run the proxy server, you need to specify the host and port for each vLLM DP Instance:
```
# For example, we have already started two DP instances in single node:
# python launch_online_dp.py --dp-size 2 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address x.x.x.x --dp-rpc-port 12342
# By default, launch_online_dp.py will launch vLLM instances from starting port 9000,
# so the vLLM ports for DP0 and DP1 are 9000 and 9001 separately.
# Then you can start the load-balance proxy server by:
cd examples/external_online_dp
python dp_load_balance_proxy_server.py \
--host 0.0.0.0 --port 8000 \
--dp-hosts 127.0.0.1 127.0.0.1 \
--dp-ports 9000 9001 \
```
After this, you can directly send requests to the proxy server and run DP with external load-balance.

1
docs/source/user_guide/feature_guide/index.md
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@@ -14,4 +14,5 @@ eplb_swift_balancer
netloader
dynamic_batch
kv_pool_mooncake
external_dp
:::

2
examples/disaggregated_prefill_v1/load_balance_proxy_server_example.py
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@@ -15,7 +15,7 @@
# - Streams responses from backend servers to clients.
#
# Prerequisites:
# - Python 3.8+
# - Python 3.10+
# - Install dependencies:
# pip install fastapi httpx uvicorn vllm
#

18
examples/external_online_dp/README.md
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@@ -1,4 +1,4 @@
Here is an example guiding how to use `launch_online_dp.py` to launch external dp server in vllm. User can easily launch external dp server following the steps below:
Here is an example guiding how to use `launch_online_dp.py` to launch external dp vllm servers. User can easily launch external dp servers following the steps below:
### Modify parameters in `run_dp_template.sh`
`run_dp_template.sh` is an template script used to launch each dp vllm instance separately. It will be called by `launch_online_dp.py` in multi threads and most of its configurations are set by `launch_online_dp.py`. Parameters you need to set manually include:
@@ -36,3 +36,19 @@ python launch_online_dp.py --dp-size 4 --tp-size 4 --dp-size-local 2 --dp-rank-s
python launch_online_dp.py --dp-size 4 --tp-size 4 --dp-size-local 2 --dp-rank-start 2 --dp-address x.x.x.x --dp-rpc-port 12342
```
### (Optional) Run `dp_load_balance_proxy_server.py` to load balance requests between external dp servers
External dp server means that you need to handle load balance between multiple dp instances out of vllm by implementing your custom proxy server. Here we provide an example of request-length-aware dp load-balance proxy server for you. The arguments of `dp_load_balance_proxy_server.py` include:
1. `--port`: port of proxy server, default 8000
2. `--host`: host address of proxy server, default localhost
3. `--dp-hosts`: host addresses of external dp servers
4. `--dp-ports`: ports of external dp servers, the number of dp ports should be the same as dp hosts.
5. `--max-retries`: Max number of retries for HTTP requests, default 3
For example, if you have two external dp servers running in x.x.x.a:10001 and x.x.x.b:10002, then you can start the proxy server by:
```(python)
python dp_load_balance_proxy_server.py --host x.x.x.c --port 8000 --dp-hosts x.x.x.a x.x.x.b --dp-ports 10001 10002
```
which will then serve as the entrypoint for inference requests at x.x.x.c:8000, and load balance coming requests between these two external dp servers according to request length.

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examples/external_online_dp/dp_load_balance_proxy_server.py Normal file
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@@ -0,0 +1,405 @@
# Adapted from https://github.com/vllm-project/vllm/tests/v1/kv_connector/nixl_integration/toy_proxy_server.py
# SPDX-License-Identifier: Apache-2.0
#
# Tutorial: Using the Load Balance Proxy Server For External DP
#
# This proxy server is designed to distribute requests between multiple
# vLLM servers running in data parallel for large language model inference.
# It is useful for scaling out inference workloads and balancing load across
# multiple vLLM instances.
#
# Features:
# - Load balances requests to multiple vLLM servers.
# - Supports OpenAI-compatible /v1/completions and /v1/chat/completions endpoints.
# - Streams responses from backend servers to clients.
#
# Prerequisites:
# - Python 3.10+
# - Install dependencies:
# pip install fastapi httpx uvicorn
#
# Step 1: Start Your Backend Servers
# ----------------------------------
# You need to have at least two vLLM servers running in data parallel.
# These can be mock servers or actual vLLM servers.
# Note that this proxy also works with only one vLLM server running, but
# will fall back to direct request forwarding which is meaningless.
#
# For testing, you can use the provided mock server:
#
# vllm serve --host 0.0.0.0 --port 8100 --data-parallel-rank 0 ... # vLLM DP0
# vllm serve --host 0.0.0.0 --port 8101 --data-parallel-rank 1 ... # vLLM DP1
#
# Step 2: Start the Proxy Server
# ------------------------------
# Run the proxy server, specifying the host/port for each vLLM DP Instance:
#
# python dp_load_balance_proxy_server.py \
# --host 0.0.0.0 --port 9000 \
# --dp-hosts 127.0.0.1 127.0.0.1 \
# --dp-ports 8100 8101 \
#
# This will start the proxy on port 9000, load balancing between two vLLM DP servers.
#
# Step 3: Send a Request to the Proxy
# -----------------------------------
# You can now send OpenAI-compatible requests to the proxy. For example:
#
# curl -X POST http://localhost:9000/v1/completions \
# -H "Content-Type: application/json" \
# -d '{
# "model": "your-model",
# "prompt": "The quick brown fox jumps over the lazy dog",
# "max_tokens": 16
# }'
#
# Or for chat completions:
#
# curl -X POST http://localhost:9000/v1/chat/completions \
# -H "Content-Type: application/json" \
# -d '{
# "model": "your-model",
# "messages": [{"role": "user", "content": "Hello!"}],
# "max_tokens": 16
# }'
#
# Step 4: Health Check
# --------------------
# To check if the proxy is running and see how many backend instances are
# connected, use:
#
# curl http://localhost:9000/healthcheck
#
# This will return a JSON object with the status and the number of vLLM DP servers.
#
# Notes:
# - You can scale the number of vLLM data parallel size as needed.
# - The proxy will consider the length of requests to balance load.
# - For production, ensure your backend servers are robust and secure.
#
# For more details, see the code and comments in this file.
import argparse
import asyncio
import functools
import heapq
import json
import os
import sys
import uuid
from contextlib import asynccontextmanager
from dataclasses import dataclass
from typing import Any, List
import httpx
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
from vllm.logger import init_logger
logger = init_logger(__name__)
# Add uvloop for faster event loop if available
try:
import uvloop
asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
except ImportError:
pass
class ServerState:
def __init__(self, host, port):
self.host = host
self.port = port
self.url = f'http://{host}:{port}/v1'
self.client = httpx.AsyncClient(timeout=None,
base_url=self.url,
limits=httpx.Limits(
max_connections=100000,
max_keepalive_connections=100000))
self.active_tokens = 0
self.aborted_requests = set() # Track aborted requests
class ProxyState:
def __init__(self, server_instances):
self.dp_servers: List[ServerState] = [
ServerState(h, p) for h, p in server_instances
]
self.req_id_lock = asyncio.Lock()
# Removed selection locks - no longer needed for synchronous methods
# Initialize priority queues for efficient server selection
# Each entry is (priority_score, server_index, server_reference)
# Lower priority score = higher priority (less loaded)
self.lb_heap = [(0, i, server)
for i, server in enumerate(self.dp_servers)]
heapq.heapify(self.lb_heap)
def _update_server_priority(self, server_idx: int):
"""Update the priority of a decoder server in the heap."""
server = self.dp_servers[server_idx]
priority = server.active_tokens
# Remove old entry and add new one
self.lb_heap = [(p, i, s) for p, i, s in self.lb_heap
if i != server_idx]
heapq.heappush(self.lb_heap,
(priority, server_idx, server)) # type: ignore
async def next_req_id(self):
async with self.req_id_lock:
return str(uuid.uuid4())
def select_server(self, token_count): # Changed to synchronous
# No lock needed - entire function is atomic
if not self.lb_heap:
raise RuntimeError("No decoder servers available")
priority, chosen, server = heapq.heappop(self.lb_heap)
# Update the chosen server atomically
self.dp_servers[chosen].active_tokens += token_count
# Update priority and re-add to heap
self._update_server_priority(chosen)
return chosen
def release_server(self, idx: int, token_count): # Changed to synchronous
# No lock needed - atomic operation
self.dp_servers[idx].active_tokens -= token_count
# Update priority queue after releasing
self._update_server_priority(idx)
def calculate_request_score(self, request_length: int, max_tokens: int = 16, ignore_eos: bool = False) -> float:
if ignore_eos:
return request_length + max_tokens
else:
# Note that 0.5 is an empirical value here because we don't know
# the actual number of tokens generated before EOS.
return request_length + 0.5 * max_tokens
proxy_state = None
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--port", type=int, default=8000)
parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--dp-hosts",
type=str,
nargs="+",
default=["localhost"])
parser.add_argument("--dp-ports",
type=int,
nargs="+",
default=[8001])
parser.add_argument("--max-retries",
type=int,
default=3,
help="Maximum number of retries for HTTP requests")
parser.add_argument(
"--retry-delay",
type=float,
default=0.001,
help="Base delay (seconds) for exponential backoff retries")
args = parser.parse_args()
if len(args.dp_hosts) != len(args.dp_ports):
raise ValueError(
"Number of dp hosts must match number of dp ports")
args.server_instances = list(zip(args.dp_hosts, args.dp_ports))
return args
@asynccontextmanager
async def lifespan(app: FastAPI):
global proxy_state
proxy_state = ProxyState(global_args.server_instances)
print(
f"Initialized {len(proxy_state.dp_servers)} dp server clients."
)
yield
for p in proxy_state.dp_servers:
await p.client.aclose()
async def listen_for_disconnect(request: Request) -> None:
"""Return if a disconnect message is received"""
while True:
message = await request.receive()
if message["type"] == "http.disconnect":
break
def with_cancellation(handler_func):
@functools.wraps(handler_func)
async def wrapper(*args, **kwargs):
request = kwargs["request"]
handler_task = asyncio.create_task(handler_func(*args, **kwargs))
cancellation_task = asyncio.create_task(listen_for_disconnect(request))
done, pending = await asyncio.wait([handler_task, cancellation_task],
return_when=asyncio.FIRST_COMPLETED)
for task in pending:
task.cancel()
if handler_task in done:
return handler_task.result()
return None
return wrapper
app = FastAPI(lifespan=lifespan)
async def stream_service_response_with_retry(client: httpx.AsyncClient,
endpoint: str,
req_data: dict,
request_id: str,
max_retries: int = 3,
base_delay: float = 0.2):
headers = {
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
"X-Request-Id": request_id
}
for attempt in range(1, max_retries + 1):
try:
async with client.stream("POST",
endpoint,
json=req_data,
headers=headers) as response:
response.raise_for_status()
first_chunk_sent = False
async for chunk in response.aiter_bytes():
first_chunk_sent = True
yield chunk
return # Success, exit after streaming
except (httpx.RequestError, httpx.HTTPStatusError) as e:
if attempt < max_retries:
logger.warning(
f"Attempt {attempt} failed for streaming {endpoint}: {str(e)}"
)
await asyncio.sleep(base_delay * (2**(attempt - 1)))
else:
logger.error(
f"All {max_retries} attempts failed for streaming {endpoint}."
)
raise e
except Exception as e:
# If any chunk has been sent, do not retry, just log and drop
if 'first_chunk_sent' in locals() and first_chunk_sent:
logger.error(
f"Streaming to client interrupted after response started: {str(e)}"
)
return
else:
if attempt < max_retries:
logger.warning(
f"Attempt {attempt} failed for streaming {endpoint}: {str(e)}"
)
await asyncio.sleep(base_delay * (2**(attempt - 1)))
else:
logger.error(
f"All {max_retries} attempts failed for streaming {endpoint}."
)
raise e
async def _select_instance(api: str, req_data: Any,
request_length: int):
# refer to vLLM sampling_params: max_token default value
max_tokens = req_data.get("max_tokens", 16)
ignore_eos = req_data.get("ignore_eos", False)
priority_score = proxy_state.calculate_request_score(request_length,max_tokens=max_tokens, ignore_eos=ignore_eos)
logger.debug(
f"Request length: {request_length}, max tokens: {max_tokens}, ignore_eos: {ignore_eos}, Priority score: {priority_score}"
)
request_id = await proxy_state.next_req_id()
# Select dp server based on priority score
server_idx = proxy_state.select_server(priority_score)
choosen_server = proxy_state.dp_servers[server_idx]
logger.debug(f"Choose server {choosen_server.url} to process request {request_id}")
return InstanceInfo(request_id=request_id,
server_idx=server_idx,
priority_score=priority_score,
server_state=choosen_server)
@dataclass
class InstanceInfo:
request_id: str
server_idx: int
priority_score: float
server_state: ServerState
async def _handle_completions(api: str, request: Request):
try:
req_data = await request.json()
req_body = await request.body()
request_length = len(req_body)
instance_info = await _select_instance(api, req_data,
request_length)
async def generate_stream():
nonlocal instance_info
# Only one await per chunk, minimal logic in loop
try:
async for chunk in stream_service_response_with_retry(
instance_info.server_state.client,
api,
req_data,
request_id=instance_info.request_id,
max_retries=global_args.max_retries,
base_delay=global_args.retry_delay):
yield chunk
except Exception as e:
logger.error(
f"Error during streaming from server {instance_info.server_state.url}: {str(e)}, the aborted request is: {instance_info.request_id}."
)
# After streaming done, release tokens
proxy_state.release_server(instance_info.server_idx,
instance_info.priority_score)
return StreamingResponse(generate_stream(),
media_type="application/json")
except Exception as e:
import traceback
exc_info = sys.exc_info()
print("Error occurred in external dp proxy server"
f" - {api} endpoint")
print(e)
print("".join(traceback.format_exception(*exc_info)))
raise
@app.post("/v1/completions")
@with_cancellation
async def handle_completions(request: Request):
return await _handle_completions("/completions", request)
@app.post("/v1/chat/completions")
@with_cancellation
async def handle_chat_completions(request: Request):
return await _handle_completions("/chat/completions", request)
@app.get("/healthcheck")
async def healthcheck():
return {
"status": "ok",
"dp_instances": len(proxy_state.dp_servers),
}
if __name__ == '__main__':
global global_args
global_args = parse_args()
import uvicorn
uvicorn.run(app, host=global_args.host, port=global_args.port)

20
examples/external_online_dp/run_dp_template.sh
View File

@@ -2,7 +2,6 @@ export HCCL_IF_IP=your_ip_here
export GLOO_SOCKET_IFNAME=your_socket_ifname_here
export TP_SOCKET_IFNAME=your_socket_ifname_here
export HCCL_SOCKET_IFNAME=your_socket_ifname_here
export DISAGGREGATED_PREFILL_RANK_TABLE_PATH=your_rank_table_path_here
export VLLM_LOGGING_LEVEL="info"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
@@ -24,21 +23,10 @@ vllm serve model_path \
--enable-expert-parallel \
--seed 1024 \
--served-model-name dsv3 \
--max-model-len 3500 \
--max-num-batched-tokens 3500 \
--max-num-seqs 28 \
--max-model-len 8192 \
--max-num-batched-tokens 2048 \
--max-num-seqs 16 \
--trust-remote-code \
--gpu-memory-utilization 0.9 \
--quantization ascend \
--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}' \
--kv-transfer-config \
'{"kv_connector": "LLMDataDistCMgrConnector",
"kv_buffer_device": "npu",
"kv_role": "kv_consumer",
"kv_parallel_size": "1",
"kv_port": "20001",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
}' \
--additional-config \
'{"ascend_scheduler_config": {"enabled": true}, "torchair_graph_config":{"enabled":true,"enable_kv_nz":false, "graph_batch_size":[28]}, "enable_weight_nz_layout":true, "enable_multistream_moe":false}'
--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}' \