[CI] Organizing performance benchmark files (#7616)
This commit is contained in:
Kuntai Du
@@ -174,8 +174,8 @@ if __name__ == "__main__":
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# document the result
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with open(results_folder / "benchmark_results.md", "w") as f:
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results = read_markdown(
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"../.buildkite/nightly-benchmarks/tests/descriptions.md")
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results = read_markdown("../.buildkite/nightly-benchmarks/" +
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"performance-benchmarks-descriptions.md")
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results = results.format(
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latency_tests_markdown_table=latency_md_table,
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throughput_tests_markdown_table=throughput_md_table,
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@@ -0,0 +1,381 @@
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#!/bin/bash
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# This script should be run inside the CI process
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# This script assumes that we are already inside the vllm/ directory
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# Benchmarking results will be available inside vllm/benchmarks/results/
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# Do not set -e, as the mixtral 8x22B model tends to crash occasionally
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# and we still want to see other benchmarking results even when mixtral crashes.
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set -o pipefail
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check_gpus() {
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# check the number of GPUs and GPU type.
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declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
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if [[ $gpu_count -gt 0 ]]; then
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echo "GPU found."
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else
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echo "Need at least 1 GPU to run benchmarking."
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exit 1
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fi
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declare -g gpu_type=$(echo $(nvidia-smi --query-gpu=name --format=csv,noheader) | awk '{print $2}')
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echo "GPU type is $gpu_type"
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}
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check_hf_token() {
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# check if HF_TOKEN is available and valid
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if [[ -z "$HF_TOKEN" ]]; then
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echo "Error: HF_TOKEN is not set."
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exit 1
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elif [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
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echo "Error: HF_TOKEN does not start with 'hf_'."
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exit 1
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else
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echo "HF_TOKEN is set and valid."
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fi
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}
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ensure_sharegpt_downloaded() {
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local FILE=ShareGPT_V3_unfiltered_cleaned_split.json
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if [ ! -f "$FILE" ]; then
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wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/$FILE
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else
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echo "$FILE already exists."
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fi
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}
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json2args() {
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# transforms the JSON string to command line args, and '_' is replaced to '-'
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# example:
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# input: { "model": "meta-llama/Llama-2-7b-chat-hf", "tensor_parallel_size": 1 }
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# output: --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel-size 1
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local json_string=$1
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local args=$(
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echo "$json_string" | jq -r '
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to_entries |
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map("--" + (.key | gsub("_"; "-")) + " " + (.value | tostring)) |
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join(" ")
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'
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)
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echo "$args"
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}
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wait_for_server() {
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# wait for vllm server to start
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# return 1 if vllm server crashes
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timeout 1200 bash -c '
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until curl -X POST localhost:8000/v1/completions; do
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sleep 1
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done' && return 0 || return 1
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}
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kill_processes_launched_by_current_bash() {
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# Kill all python processes launched from current bash script
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current_shell_pid=$$
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processes=$(ps -eo pid,ppid,command | awk -v ppid="$current_shell_pid" -v proc="$1" '$2 == ppid && $3 ~ proc {print $1}')
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if [ -n "$processes" ]; then
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echo "Killing the following processes matching '$1':"
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echo "$processes"
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echo "$processes" | xargs kill -9
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else
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echo "No processes found matching '$1'."
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fi
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}
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kill_gpu_processes() {
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ps -aux
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lsof -t -i:8000 | xargs -r kill -9
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pkill -f pt_main_thread
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# this line doesn't work now
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# ps aux | grep python | grep openai | awk '{print $2}' | xargs -r kill -9
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pkill -f python3
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pkill -f /usr/bin/python3
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# wait until GPU memory usage smaller than 1GB
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while [ $(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1) -ge 1000 ]; do
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sleep 1
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done
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# remove vllm config file
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rm -rf ~/.config/vllm
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}
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upload_to_buildkite() {
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# upload the benchmarking results to buildkite
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# if the agent binary is not found, skip uploading the results, exit 0
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# Check if buildkite-agent is available in the PATH or at /workspace/buildkite-agent
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if command -v buildkite-agent >/dev/null 2>&1; then
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BUILDKITE_AGENT_COMMAND="buildkite-agent"
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elif [ -f /workspace/buildkite-agent ]; then
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BUILDKITE_AGENT_COMMAND="/workspace/buildkite-agent"
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else
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echo "buildkite-agent binary not found. Skip uploading the results."
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return 0
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fi
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# Use the determined command to annotate and upload artifacts
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$BUILDKITE_AGENT_COMMAND annotate --style "info" --context "$BUILDKITE_LABEL-benchmark-results" <$RESULTS_FOLDER/benchmark_results.md
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$BUILDKITE_AGENT_COMMAND artifact upload "$RESULTS_FOLDER/*"
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}
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run_latency_tests() {
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# run latency tests using `benchmark_latency.py`
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# $1: a json file specifying latency test cases
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local latency_test_file
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latency_test_file=$1
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# Iterate over latency tests
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jq -c '.[]' "$latency_test_file" | while read -r params; do
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# get the test name, and append the GPU type back to it.
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test_name=$(echo "$params" | jq -r '.test_name')
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if [[ ! "$test_name" =~ ^latency_ ]]; then
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echo "In latency-test.json, test_name must start with \"latency_\"."
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exit 1
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fi
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# if TEST_SELECTOR is set, only run the test cases that match the selector
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if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
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echo "Skip test case $test_name."
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continue
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fi
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# get arguments
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latency_params=$(echo "$params" | jq -r '.parameters')
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latency_args=$(json2args "$latency_params")
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# check if there is enough GPU to run the test
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tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
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if [[ $gpu_count -lt $tp ]]; then
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echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $testname."
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continue
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fi
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latency_command="python3 benchmark_latency.py \
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--output-json $RESULTS_FOLDER/${test_name}.json \
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$latency_args"
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echo "Running test case $test_name"
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echo "Latency command: $latency_command"
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# recoding benchmarking command ang GPU command
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jq_output=$(jq -n \
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--arg latency "$latency_command" \
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--arg gpu "$gpu_type" \
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'{
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latency_command: $latency,
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gpu_type: $gpu
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}')
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echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
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# run the benchmark
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eval "$latency_command"
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kill_gpu_processes
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done
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}
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run_throughput_tests() {
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# run throughput tests using `benchmark_throughput.py`
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# $1: a json file specifying throughput test cases
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local throughput_test_file
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throughput_test_file=$1
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# Iterate over throughput tests
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jq -c '.[]' "$throughput_test_file" | while read -r params; do
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# get the test name, and append the GPU type back to it.
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test_name=$(echo "$params" | jq -r '.test_name')
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if [[ ! "$test_name" =~ ^throughput_ ]]; then
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echo "In throughput-test.json, test_name must start with \"throughput_\"."
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exit 1
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fi
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# if TEST_SELECTOR is set, only run the test cases that match the selector
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if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
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echo "Skip test case $test_name."
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continue
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fi
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# get arguments
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throughput_params=$(echo "$params" | jq -r '.parameters')
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throughput_args=$(json2args "$throughput_params")
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# check if there is enough GPU to run the test
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tp=$(echo $throughput_params | jq -r '.tensor_parallel_size')
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if [[ $gpu_count -lt $tp ]]; then
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echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $testname."
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continue
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fi
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throughput_command="python3 benchmark_throughput.py \
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--output-json $RESULTS_FOLDER/${test_name}.json \
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$throughput_args"
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echo "Running test case $test_name"
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echo "Throughput command: $throughput_command"
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# recoding benchmarking command ang GPU command
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jq_output=$(jq -n \
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--arg command "$throughput_command" \
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--arg gpu "$gpu_type" \
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'{
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throughput_command: $command,
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gpu_type: $gpu
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}')
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echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
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# run the benchmark
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eval "$throughput_command"
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kill_gpu_processes
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done
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}
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run_serving_tests() {
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# run serving tests using `benchmark_serving.py`
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# $1: a json file specifying serving test cases
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local serving_test_file
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serving_test_file=$1
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# Iterate over serving tests
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jq -c '.[]' "$serving_test_file" | while read -r params; do
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# get the test name, and append the GPU type back to it.
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test_name=$(echo "$params" | jq -r '.test_name')
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if [[ ! "$test_name" =~ ^serving_ ]]; then
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echo "In serving-test.json, test_name must start with \"serving_\"."
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exit 1
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fi
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# if TEST_SELECTOR is set, only run the test cases that match the selector
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if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
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echo "Skip test case $test_name."
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continue
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fi
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# get client and server arguments
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server_params=$(echo "$params" | jq -r '.server_parameters')
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client_params=$(echo "$params" | jq -r '.client_parameters')
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server_args=$(json2args "$server_params")
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client_args=$(json2args "$client_params")
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qps_list=$(echo "$params" | jq -r '.qps_list')
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qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
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echo "Running over qps list $qps_list"
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# check if there is enough GPU to run the test
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tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
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if [[ $gpu_count -lt $tp ]]; then
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echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $testname."
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continue
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fi
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# check if server model and client model is aligned
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server_model=$(echo "$server_params" | jq -r '.model')
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client_model=$(echo "$client_params" | jq -r '.model')
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if [[ $server_model != "$client_model" ]]; then
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echo "Server model and client model must be the same. Skip testcase $testname."
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continue
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fi
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server_command="python3 \
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-m vllm.entrypoints.openai.api_server \
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$server_args"
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# run the server
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echo "Running test case $test_name"
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echo "Server command: $server_command"
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eval "$server_command" &
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server_pid=$!
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# wait until the server is alive
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wait_for_server
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if [ $? -eq 0 ]; then
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echo ""
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echo "vllm server is up and running."
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else
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echo ""
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echo "vllm failed to start within the timeout period."
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fi
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# iterate over different QPS
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for qps in $qps_list; do
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# remove the surrounding single quote from qps
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if [[ "$qps" == *"inf"* ]]; then
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echo "qps was $qps"
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qps="inf"
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echo "now qps is $qps"
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fi
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new_test_name=$test_name"_qps_"$qps
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client_command="python3 benchmark_serving.py \
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--save-result \
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--result-dir $RESULTS_FOLDER \
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--result-filename ${new_test_name}.json \
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--request-rate $qps \
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$client_args"
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echo "Running test case $test_name with qps $qps"
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echo "Client command: $client_command"
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eval "$client_command"
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# record the benchmarking commands
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jq_output=$(jq -n \
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--arg server "$server_command" \
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--arg client "$client_command" \
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--arg gpu "$gpu_type" \
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'{
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server_command: $server,
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client_command: $client,
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gpu_type: $gpu
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}')
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echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
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done
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# clean up
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kill -9 $server_pid
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kill_gpu_processes
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done
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}
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main() {
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check_gpus
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check_hf_token
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# dependencies
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(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
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(which jq) || (apt-get update && apt-get -y install jq)
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(which lsof) || (apt-get update && apt-get install -y lsof)
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# get the current IP address, required by benchmark_serving.py
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export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
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# turn of the reporting of the status of each request, to clean up the terminal output
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export VLLM_LOG_LEVEL="WARNING"
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# prepare for benchmarking
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cd benchmarks || exit 1
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ensure_sharegpt_downloaded
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declare -g RESULTS_FOLDER=results/
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mkdir -p $RESULTS_FOLDER
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QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
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# benchmarking
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run_serving_tests $QUICK_BENCHMARK_ROOT/tests/serving-tests.json
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run_latency_tests $QUICK_BENCHMARK_ROOT/tests/latency-tests.json
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run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/throughput-tests.json
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# postprocess benchmarking results
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pip install tabulate pandas
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python3 $QUICK_BENCHMARK_ROOT/scripts/convert-results-json-to-markdown.py
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upload_to_buildkite
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}
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main "$@"
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Block a user