Sync from v0.13
This commit is contained in:
@@ -0,0 +1,13 @@
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local_cpu: False
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max_local_cpu_size: 0
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#local_disk:
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max_local_disk_size: 0
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remote_serde: NULL
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enable_nixl: True
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nixl_role: "receiver"
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nixl_peer_host: "localhost"
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nixl_peer_port: 55555
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nixl_buffer_size: 1073741824 # 1GB
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nixl_buffer_device: "cuda"
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nixl_enable_gc: True
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@@ -0,0 +1,13 @@
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local_cpu: False
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max_local_cpu_size: 0
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#local_disk:
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max_local_disk_size: 0
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remote_serde: NULL
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enable_nixl: True
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nixl_role: "sender"
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nixl_peer_host: "localhost"
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nixl_peer_port: 55555
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nixl_buffer_size: 1073741824 # 1GB
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nixl_buffer_device: "cuda"
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nixl_enable_gc: True
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@@ -0,0 +1,142 @@
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#!/bin/bash
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echo "Warning: LMCache disaggregated prefill support for vLLM v1 is experimental and subject to change."
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PIDS=()
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# Switch to the directory of the current script
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cd "$(dirname "${BASH_SOURCE[0]}")"
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check_hf_token() {
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if [ -z "$HF_TOKEN" ]; then
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echo "HF_TOKEN is not set. Please set it to your Hugging Face token."
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exit 1
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fi
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if [[ "$HF_TOKEN" != hf_* ]]; then
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echo "HF_TOKEN is not a valid Hugging Face token. Please set it to your Hugging Face token."
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exit 1
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fi
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echo "HF_TOKEN is set and valid."
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}
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check_num_gpus() {
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# can you check if the number of GPUs are >=2 via nvidia-smi/rocm-smi?
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which rocm-smi > /dev/null 2>&1
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if [ $? -ne 0 ]; then
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num_gpus=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
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else
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num_gpus=$(rocm-smi --showid | grep Instinct | wc -l)
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fi
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if [ "$num_gpus" -lt 2 ]; then
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echo "You need at least 2 GPUs to run disaggregated prefill."
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exit 1
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else
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echo "Found $num_gpus GPUs."
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fi
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}
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ensure_python_library_installed() {
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echo "Checking if $1 is installed..."
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python3 -c "import $1" > /dev/null 2>&1
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if [ $? -ne 0 ]; then
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if [ "$1" == "nixl" ]; then
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echo "$1 is not installed. Please refer to https://github.com/ai-dynamo/nixl for installation."
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else
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echo "$1 is not installed. Please install it via pip install $1."
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fi
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exit 1
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else
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echo "$1 is installed."
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fi
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}
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cleanup() {
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echo "Stopping everything…"
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trap - INT TERM # prevent re-entrancy
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kill -- -$$ # negative PID == “this whole process-group”
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wait # reap children so we don't leave zombies
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exit 0
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}
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wait_for_server() {
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local port=$1
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local timeout_seconds=1200
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local start_time=$(date +%s)
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echo "Waiting for server on port $port..."
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while true; do
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if curl -s "localhost:${port}/v1/completions" > /dev/null; then
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return 0
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fi
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local now=$(date +%s)
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if (( now - start_time >= timeout_seconds )); then
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echo "Timeout waiting for server"
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return 1
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fi
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sleep 1
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done
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}
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main() {
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check_hf_token
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check_num_gpus
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ensure_python_library_installed lmcache
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ensure_python_library_installed nixl
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ensure_python_library_installed pandas
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ensure_python_library_installed datasets
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ensure_python_library_installed vllm
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trap cleanup INT
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trap cleanup USR1
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trap cleanup TERM
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echo "Launching prefiller, decoder and proxy..."
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echo "Please check prefiller.log, decoder.log and proxy.log for logs."
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bash disagg_vllm_launcher.sh prefiller \
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> >(tee prefiller.log) 2>&1 &
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prefiller_pid=$!
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PIDS+=($prefiller_pid)
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bash disagg_vllm_launcher.sh decoder \
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> >(tee decoder.log) 2>&1 &
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decoder_pid=$!
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PIDS+=($decoder_pid)
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python3 disagg_proxy_server.py \
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--host localhost \
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--port 9000 \
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--prefiller-host localhost \
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--prefiller-port 8100 \
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--decoder-host localhost \
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--decoder-port 8200 \
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> >(tee proxy.log) 2>&1 &
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proxy_pid=$!
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PIDS+=($proxy_pid)
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wait_for_server 8100
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wait_for_server 8200
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wait_for_server 9000
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echo "All servers are up. Starting benchmark..."
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# begin benchmark
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cd ../../../../benchmarks/
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vllm bench serve --port 9000 --seed $(date +%s) \
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--model meta-llama/Llama-3.1-8B-Instruct \
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--dataset-name random --random-input-len 7500 --random-output-len 200 \
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--num-prompts 200 --burstiness 100 --request-rate 3.6 | tee benchmark.log
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echo "Benchmarking done. Cleaning up..."
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cleanup
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}
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main
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@@ -0,0 +1,225 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import argparse
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import os
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import time
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from contextlib import asynccontextmanager
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import httpx
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import numpy as np
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from fastapi import FastAPI, Request
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from fastapi.responses import StreamingResponse
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""
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Lifespan context manager to handle startup and shutdown events.
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"""
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# Startup: Initialize clients
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prefiller_base_url = (
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f"http://{global_args.prefiller_host}:{global_args.prefiller_port}/v1"
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)
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decoder_base_url = (
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f"http://{global_args.decoder_host}:{global_args.decoder_port}/v1"
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)
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app.state.prefill_client = httpx.AsyncClient(
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timeout=None,
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base_url=prefiller_base_url,
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limits=httpx.Limits(
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max_connections=None,
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max_keepalive_connections=None,
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),
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)
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app.state.decode_client = httpx.AsyncClient(
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timeout=None,
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base_url=decoder_base_url,
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limits=httpx.Limits(
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max_connections=None,
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max_keepalive_connections=None,
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),
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)
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yield
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# Shutdown: Close clients
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await app.state.prefill_client.aclose()
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await app.state.decode_client.aclose()
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# Update FastAPI app initialization to use lifespan
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app = FastAPI(lifespan=lifespan)
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class StatsCalculator:
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def __init__(self):
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self._stats = []
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self._last_log_time = time.time()
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def add(self, value):
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self._stats.append(value)
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if time.time() - self._last_log_time > 5:
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self._log_stats()
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self._last_log_time = time.time()
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def _log_stats(self):
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# Print average, median, and 99th percentile
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np_arr = np.array(self._stats)
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output_str = (
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f"\nNum requests: {len(self._stats)}"
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+ "\nPrefill node TTFT stats:"
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+ f"\n - Average (ms): {np.mean(np_arr)}"
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+ f"\n - Median (ms): {np.median(np_arr)}"
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+ f"\n - 99th Percentile (ms): {np.percentile(np_arr, 99)}\n"
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)
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print(
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"===============================",
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output_str,
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"===============================",
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)
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stats_calculator = StatsCalculator()
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counter = 0
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--port", type=int, default=8000)
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parser.add_argument("--host", type=str, default="localhost")
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parser.add_argument("--prefiller-host", type=str, default="localhost")
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parser.add_argument("--prefiller-port", type=int, default=8100)
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parser.add_argument("--decoder-host", type=str, default="localhost")
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parser.add_argument("--decoder-port", type=int, default=8200)
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args = parser.parse_args()
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return args
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# Initialize variables to hold the persistent clients
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app.state.prefill_client = None
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app.state.decode_client = None
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async def send_request_to_service(
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client: httpx.AsyncClient, endpoint: str, req_data: dict
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):
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"""
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Send a request to a service using a persistent client.
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"""
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req_data = req_data.copy()
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req_data["max_tokens"] = 1
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if "max_completion_tokens" in req_data:
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req_data["max_completion_tokens"] = 1
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headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
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response = await client.post(endpoint, json=req_data, headers=headers)
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response.raise_for_status()
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# read/consume the response body to release the connection
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# otherwise, it would http.ReadError
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await response.aread()
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return response
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async def stream_service_response(
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client: httpx.AsyncClient, endpoint: str, req_data: dict
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):
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"""
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Asynchronously stream the response from a service using a persistent client.
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"""
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headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
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async with client.stream(
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"POST", endpoint, json=req_data, headers=headers
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) as response:
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response.raise_for_status()
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async for chunk in response.aiter_bytes():
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yield chunk
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@app.post("/v1/completions")
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async def handle_completions(request: Request):
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global counter, stats_calculator
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counter += 1
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st = time.time()
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try:
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req_data = await request.json()
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# Send request to prefill service, ignore the response
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await send_request_to_service(
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app.state.prefill_client, "/completions", req_data
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)
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et = time.time()
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stats_calculator.add(et - st)
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# Stream response from decode service
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async def generate_stream():
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async for chunk in stream_service_response(
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app.state.decode_client, "/completions", req_data
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):
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yield chunk
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return StreamingResponse(generate_stream(), media_type="text/event-stream")
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except Exception as e:
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import sys
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import traceback
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exc_info = sys.exc_info()
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print("Error occurred in disagg prefill proxy server - completions endpoint")
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print(e)
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print("".join(traceback.format_exception(*exc_info)))
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raise
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@app.post("/v1/chat/completions")
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async def handle_chat_completions(request: Request):
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global counter, stats_calculator
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counter += 1
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st = time.time()
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try:
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req_data = await request.json()
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# Send request to prefill service, ignore the response
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await send_request_to_service(
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app.state.prefill_client, "/chat/completions", req_data
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)
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et = time.time()
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stats_calculator.add(et - st)
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# Stream response from decode service
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async def generate_stream():
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async for chunk in stream_service_response(
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app.state.decode_client, "/chat/completions", req_data
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):
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yield chunk
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return StreamingResponse(generate_stream(), media_type="text/event-stream")
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except Exception as e:
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import sys
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import traceback
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exc_info = sys.exc_info()
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print(
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"Error occurred in disagg prefill proxy server - chat completions endpoint"
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)
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print(e)
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print("".join(traceback.format_exception(*exc_info)))
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raise
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if __name__ == "__main__":
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global global_args
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global_args = parse_args()
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import uvicorn
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uvicorn.run(app, host=global_args.host, port=global_args.port)
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@@ -0,0 +1,65 @@
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#!/bin/bash
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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if [[ $# -lt 1 ]]; then
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echo "Usage: $0 <prefiller | decoder> [model]"
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exit 1
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fi
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if [[ $# -eq 1 ]]; then
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echo "Using default model: meta-llama/Llama-3.1-8B-Instruct"
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MODEL="meta-llama/Llama-3.1-8B-Instruct"
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else
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echo "Using model: $2"
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MODEL=$2
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fi
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# The prefillers and decoders in LMCache use the same hash seed for all chunk keys.
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# This seed must be aligned so that decoders can identify and retrieve KV cache
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# entries stored by prefillers.
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#
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# WARNING: Using a fixed hash seed is insecure and makes the application vulnerable to
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# denial-of-service attacks. In a production environment, this should be set to a
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# secure random value. This is set to a fixed value for demonstration purposes only.
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export PYTHONHASHSEED=${VLLM_PYTHON_HASH_SEED:-123}
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if [[ $1 == "prefiller" ]]; then
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# Prefiller listens on port 8100
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prefill_config_file=$SCRIPT_DIR/configs/lmcache-prefiller-config.yaml
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UCX_TLS=cuda_ipc,cuda_copy,tcp \
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LMCACHE_CONFIG_FILE=$prefill_config_file \
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LMCACHE_USE_EXPERIMENTAL=True \
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VLLM_ENABLE_V1_MULTIPROCESSING=1 \
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VLLM_WORKER_MULTIPROC_METHOD=spawn \
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CUDA_VISIBLE_DEVICES=0 \
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vllm serve $MODEL \
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--port 8100 \
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--enforce-eager \
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--kv-transfer-config \
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'{"kv_connector":"LMCacheConnectorV1","kv_role":"kv_producer","kv_connector_extra_config": {"discard_partial_chunks": false, "lmcache_rpc_port": "producer1"}}'
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elif [[ $1 == "decoder" ]]; then
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# Decoder listens on port 8200
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decode_config_file=$SCRIPT_DIR/configs/lmcache-decoder-config.yaml
|
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UCX_TLS=cuda_ipc,cuda_copy,tcp \
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LMCACHE_CONFIG_FILE=$decode_config_file \
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LMCACHE_USE_EXPERIMENTAL=True \
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VLLM_ENABLE_V1_MULTIPROCESSING=1 \
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VLLM_WORKER_MULTIPROC_METHOD=spawn \
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CUDA_VISIBLE_DEVICES=1 \
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vllm serve $MODEL \
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--port 8200 \
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--enforce-eager \
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--kv-transfer-config \
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'{"kv_connector":"LMCacheConnectorV1","kv_role":"kv_consumer","kv_connector_extra_config": {"discard_partial_chunks": false, "lmcache_rpc_port": "consumer1"}}'
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||||
|
||||
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||||
else
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echo "Invalid role: $1"
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echo "Should be either prefiller, decoder"
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||||
exit 1
|
||||
fi
|
||||
Reference in New Issue
Block a user