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vllm/benchmarks/auto_tune.sh

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#!/bin/bash
# This script aims to tune the best server parameter combinations to maximize throughput for given requirement.
# The current server parameter combination is max_num_seqs and max_num_batched_tokens
# It also supports additional requirement: e2e latency and prefix cache.
# Pre-requisite:
# 1. Checkout to your branch, install/ update the correct running env. For TPU, activate conda env and install the corresponding torch, xla version.
# 2. If the model is customized, replace the MODEL's config with the customized config.
# 3. Set variables (ALL REQUIRED)
# BASE: your directory for vllm repo
# MODEL: the model served by vllm
# DOWNLOAD_DIR: directory to download and load model weights.
# INPUT_LEN: request input len
# OUTPUT_LEN: request output len
# MIN_CACHE_HIT_PCT: prefix cache rate
# MAX_LATENCY_ALLOWED_MS: (e2e) latency requirement. If there's no latency requirement, set it to a large number like 1000000000
# 4. Run the script, it might take a long time, you can use tmux to avoid the script stop if disconnection happens.
# 5. The final result will be saved in RESULT file.
# Example use cases
# 1. Given input_len=1800, output_len=20, what's the best max_num_seqs and max_num_batched_tokens to get highest throughput?
# Use INPUT_LEN=1800, OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=0, MAX_LATENCY_ALLOWED_MS=100000000000
# 2. If we have latency requirement to be lower than 500ms, what's the best server parameter?
# Use INPUT_LEN=1800, OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=0, MAX_LATENCY_ALLOWED_MS=500
# 3. If we want to reach 60% prefix cache, what's the best server parameter?
# Use INPUT_LEN=1800, OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=60, MAX_LATENCY_ALLOWED_MS=500
TAG=$(date +"%Y_%m_%d_%H_%M")
BASE=""
MODEL="meta-llama/Llama-3.1-8B-Instruct"
DOWNLOAD_DIR=""
INPUT_LEN=4000
OUTPUT_LEN=16
MIN_CACHE_HIT_PCT_PCT=0
MAX_LATENCY_ALLOWED_MS=100000000000
LOG_FOLDER="$BASE/auto-benchmark/$TAG"
RESULT="$LOG_FOLDER/result.txt"
echo "result file$ $RESULT"
echo "model: $MODEL"
echo
rm -rf $LOG_FOLDER
mkdir -p $LOG_FOLDER
cd "$BASE/vllm"
# create sonnet-4x.txt so that we can sample 2048 tokens for input
echo "" > benchmarks/sonnet_4x.txt
for _ in {1..4}
do
cat benchmarks/sonnet.txt >> benchmarks/sonnet_4x.txt
done
pip install datasets
current_hash=$(git rev-parse HEAD)
echo "hash:$current_hash" >> "$RESULT"
echo "current_hash: $current_hash"
best_throughput=0
best_max_num_seqs=0
best_num_batched_tokens=0
best_goodput=0
run_benchmark() {
local max_num_seqs=$1
local max_num_batched_tokens=$2
echo "max_num_seq: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
local vllm_log="$LOG_FOLDER/vllm_log_${max_num_seqs}_${max_num_batched_tokens}.txt"
echo "vllm_log: $vllm_log"
echo
rm -f $vllm_log
# start the server
VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 vllm serve $MODEL \
--disable-log-requests \
--port 8004 \
--gpu-memory-utilization 0.98 \
--max-num-seqs $max_num_seqs \
--max-num-batched-tokens $max_num_batched_tokens \
--tensor-parallel-size 1 \
--enable-prefix-caching \
--load-format dummy \
--download-dir $DOWNLOAD_DIR \
--max-model-len $(( INPUT_LEN+OUTPUT_LEN )) > "$vllm_log" 2>&1 &
echo "wait for 10 minutes.."
echo
# wait for 10 minutes...
server_started=0
for i in {1..60}; do
if grep -Fq "Application startup complete" "$vllm_log"; then
echo "Application started"
server_started=1
break
else
# echo "wait for 10 seconds..."
sleep 10
fi
done
if (( ! server_started )); then
echo "server did not start within 10 minutes, terminate the benchmarking. Please check server log at $vllm_log"
echo "pkill -f vllm"
echo
pkill vllm
sleep 10
return 1
fi
echo "run benchmark test..."
echo
meet_latency_requirement=0
# get a basic qps by using request-rate inf
bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_inf.txt"
prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
python benchmarks/benchmark_serving.py \
--backend vllm \
--model $MODEL \
--dataset-name sonnet \
--dataset-path benchmarks/sonnet_4x.txt \
--sonnet-input-len $INPUT_LEN \
--sonnet-output-len $OUTPUT_LEN \
--ignore-eos \
--disable-tqdm \
--request-rate inf \
--percentile-metrics ttft,tpot,itl,e2el \
--goodput e2el:$MAX_LATENCY_ALLOWED_MS \
--num-prompts 100 \
--sonnet-prefix-len $prefix_len \
--port 8004 > "$bm_log"
through_put=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
if (( $(echo "$e2el <= $MAX_LATENCY_ALLOWED_MS" | bc -l) )); then
meet_latency_requirement=1
fi
if (( ! meet_latency_requirement )); then
# start from request-rate as int(through_put) + 1
request_rate=$((${through_put%.*} + 1))
while ((request_rate > 0)); do
# clear prefix cache
curl -X POST http://0.0.0.0:8004/reset_prefix_cache
sleep 5
bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_${request_rate}.txt"
python benchmarks/benchmark_serving.py \
--backend vllm \
--model $MODEL \
--dataset-name sonnet \
--dataset-path benchmarks/sonnet_4x.txt \
--sonnet-input-len $INPUT_LEN \
--sonnet-output-len $OUTPUT_LEN \
--ignore_eos \
--disable-tqdm \
--request-rate $request_rate \
--percentile-metrics ttft,tpot,itl,e2el \
--goodput e2el:$MAX_LATENCY_ALLOWED_MS \
--num-prompts 100 \
--sonnet-prefix-len $prefix_len \
--port 8004 > "$bm_log"
through_put=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
if (( $(echo "$e2el <= $MAX_LATENCY_ALLOWED_MS" | bc -l) )); then
meet_latency_requirement=1
break
fi
request_rate=$((request_rate-1))
done
fi
# write the results and update the best result.
if ((meet_latency_requirement)); then
echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, through put: $through_put, goodput: $goodput"
echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, through put: $through_put, goodput: $goodput" >> "$RESULT"
if (( $(echo "$through_put > $best_throughput" | bc -l) )); then
best_throughput=$through_put
best_max_num_seqs=$max_num_seqs
best_num_batched_tokens=$max_num_batched_tokens
best_goodput=$goodput
fi
else
echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens does not meet latency requirement ${MAX_LATENCY_ALLOWED_MS}"
echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens does not meet latency requirement ${MAX_LATENCY_ALLOWED_MS}" >> "$RESULT"
fi
echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
echo "pkill -f vllm"
echo
pkill vllm
sleep 10
rm -f $vllm_log
printf '=%.0s' $(seq 1 20)
return 0
}
num_seqs_list="128 256"
num_batched_tokens_list="512 1024 2048 4096"
for num_seqs in $num_seqs_list; do
for num_batched_tokens in $num_batched_tokens_list; do
run_benchmark $num_seqs $num_batched_tokens
exit 0
done
done
echo "finish permutations"
echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput" >> "$RESULT"
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