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Commit 006693ed authored by zhuwenwen's avatar zhuwenwen
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Merge tag 'v0.11.2' into v0.11.2-ori

parents 4b51e6f1 275de341
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.buildkite/nightly-benchmarks/scripts/nightly-annotate.sh deleted 100644 → 0
View file @ 4b51e6f1
#!/bin/bash
set -ex
set -o pipefail
main() {
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
(which jq) || (apt-get update && apt-get -y install jq)
(which zip) || (apt-get install -y zip)
if [ ! -f /workspace/buildkite-agent ]; then
echo "buildkite-agent binary not found. Skip plotting the results."
exit 0
fi
# initial annotation
#description="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-descriptions.md"
# download results
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
mkdir -p results/
/workspace/buildkite-agent artifact download 'results/*nightly_results.json' results/
ls
ls results/
# upload benchmark results
zip -r results.zip results/
/workspace/buildkite-agent artifact upload "results.zip"
# upload benchmarking scripts
cd "$VLLM_SOURCE_CODE_LOC/"
zip -r nightly-benchmarks.zip .buildkite/ benchmarks/
/workspace/buildkite-agent artifact upload "nightly-benchmarks.zip"
cd "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
# upload benchmarking pipeline
/workspace/buildkite-agent artifact upload "nightly-pipeline.yaml"
cd "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
/workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly-annotation.md
# The figures should be generated by a separate process outside the CI/CD pipeline
# # generate figures
# python3 -m pip install tabulate pandas matplotlib
# python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py \
# --description $description \
# --results-folder results/
# python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
# --description $description \
# --results-folder results/ \
# --dataset sharegpt
# python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
# --description $description \
# --results-folder results/ \
# --dataset sonnet_2048_128
# python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
# --description $description \
# --results-folder results/ \
# --dataset sonnet_128_2048
# # upload results and figures
# /workspace/buildkite-agent artifact upload "nightly_results*.png"
# /workspace/buildkite-agent artifact upload $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-pipeline.yaml
# /workspace/buildkite-agent artifact upload $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/tests/nightly-tests.json
# /workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly_results.md
}
main "$@"
.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh deleted 100644 → 0
View file @ 4b51e6f1
#!/bin/bash
set -o pipefail
set -x
check_gpus() {
# check the number of GPUs and GPU type.
declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
if [[ $gpu_count -gt 0 ]]; then
echo "GPU found."
else
echo "Need at least 1 GPU to run benchmarking."
exit 1
fi
declare -g gpu_type="$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')"
echo "GPU type is $gpu_type"
}
check_hf_token() {
# check if HF_TOKEN is available and valid
if [[ -z "$HF_TOKEN" ]]; then
echo "Error: HF_TOKEN is not set."
exit 1
elif [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
echo "Error: HF_TOKEN does not start with 'hf_'."
exit 1
else
echo "HF_TOKEN is set and valid."
fi
}
upload_to_buildkite() {
# upload the benchmarking results to buildkite
# if the agent binary is not found, skip uploading the results, exit 0
if [ ! -f /workspace/buildkite-agent ]; then
echo "buildkite-agent binary not found. Skip uploading the results."
return 0
fi
# /workspace/buildkite-agent annotate --style "success" --context "benchmark-results" --append < $RESULTS_FOLDER/${CURRENT_LLM_SERVING_ENGINE}_nightly_results.md
/workspace/buildkite-agent artifact upload "$RESULTS_FOLDER/*"
}
get_current_llm_serving_engine() {
if which lmdeploy >/dev/null; then
echo "Container: lmdeploy"
export CURRENT_LLM_SERVING_ENGINE=lmdeploy
return
fi
if [ -e /tgi-entrypoint.sh ]; then
echo "Container: tgi"
export CURRENT_LLM_SERVING_ENGINE=tgi
return
fi
if which trtllm-build >/dev/null; then
echo "Container: tensorrt-llm"
export CURRENT_LLM_SERVING_ENGINE=trt
return
fi
if [ -e /sgl-workspace ]; then
echo "Container: sglang"
export CURRENT_LLM_SERVING_ENGINE=sglang
return
fi
if [ -e /vllm-workspace ]; then
echo "Container: vllm"
# move to a completely irrelevant directory, to avoid import vllm from current folder
export CURRENT_LLM_SERVING_ENGINE=vllm
return
fi
}
json2args() {
# transforms the JSON string to command line args, and '_' is replaced to '-'
# example:
# input: { "model": "meta-llama/Llama-2-7b-chat-hf", "tensor_parallel_size": 1 }
# output: --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel-size 1
local json_string=$1
local args=$(
echo "$json_string" | jq -r '
to_entries |
map("--" + (.key | gsub("_"; "-")) + " " + (.value | tostring)) |
join(" ")
'
)
echo "$args"
}
kill_gpu_processes() {
pkill -f '[p]ython'
pkill -f '[p]ython3'
pkill -f '[t]ritonserver'
pkill -f '[p]t_main_thread'
pkill -f '[t]ext-generation'
pkill -f '[l]mdeploy'
# vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
pkill -f '[V]LLM'
while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
sleep 1
done
}
wait_for_server() {
# wait for vllm server to start
# return 1 if vllm server crashes
timeout 1200 bash -c '
until curl -s localhost:8000/v1/completions > /dev/null; do
sleep 1
done' && return 0 || return 1
}
ensure_installed() {
# Ensure that the given command is installed by apt-get
local cmd=$1
if ! which "$cmd" >/dev/null; then
apt-get update && apt-get install -y "$cmd"
fi
}
run_serving_tests() {
# run serving tests using `vllm bench serve` command
# $1: a json file specifying serving test cases
local serving_test_file
serving_test_file=$1
# Iterate over serving tests
jq -c '.[]' "$serving_test_file" | while read -r params; do
# get the test name, and append the GPU type back to it.
test_name=$(echo "$params" | jq -r '.test_name')
# if TEST_SELECTOR is set, only run the test cases that match the selector
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
echo "Skip test case $test_name."
continue
fi
# prepend the current serving engine to the test name
test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
# get common parameters
common_params=$(echo "$params" | jq -r '.common_parameters')
model=$(echo "$common_params" | jq -r '.model')
tp=$(echo "$common_params" | jq -r '.tp')
dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
port=$(echo "$common_params" | jq -r '.port')
num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
reuse_server=$(echo "$common_params" | jq -r '.reuse_server')
# get client and server arguments
server_params=$(echo "$params" | jq -r ".${CURRENT_LLM_SERVING_ENGINE}_server_parameters")
client_params=$(echo "$params" | jq -r ".${CURRENT_LLM_SERVING_ENGINE}_client_parameters")
client_args=$(json2args "$client_params")
qps_list=$(echo "$params" | jq -r '.qps_list')
qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
echo "Running over qps list $qps_list"
# check if there is enough GPU to run the test
if [[ $gpu_count -lt $tp ]]; then
echo "Required num-shard $tp but only $gpu_count GPU found. Skip testcase $test_name."
continue
fi
if [[ $reuse_server == "true" ]]; then
echo "Reuse previous server for test case $test_name"
else
kill_gpu_processes
bash "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/launch-server.sh" \
"$server_params" "$common_params"
fi
if wait_for_server; then
echo ""
echo "$CURRENT_LLM_SERVING_ENGINE server is up and running."
else
echo ""
echo "$CURRENT_LLM_SERVING_ENGINE failed to start within the timeout period."
break
fi
# prepare tokenizer
# this is required for lmdeploy.
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
rm -rf /tokenizer_cache
mkdir /tokenizer_cache
python3 ../.buildkite/nightly-benchmarks/scripts/download-tokenizer.py \
--model "$model" \
--cachedir /tokenizer_cache
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
# change model name for lmdeploy (it will not follow standard hf name)
if [[ "$CURRENT_LLM_SERVING_ENGINE" == "lmdeploy" ]]; then
model=$(python ../.buildkite/nightly-benchmarks/scripts/get-lmdeploy-modelname.py)
fi
# iterate over different QPS
for qps in $qps_list; do
# remove the surrounding single quote from qps
if [[ "$qps" == *"inf"* ]]; then
echo "qps was $qps"
qps="inf"
echo "now qps is $qps"
fi
new_test_name=$test_name"_qps_"$qps
backend=$CURRENT_LLM_SERVING_ENGINE
if [[ $backend = "trt" ]]; then
backend="tensorrt-llm"
fi
if [[ "$backend" == *"vllm"* ]]; then
backend="vllm"
fi
if [[ "$dataset_name" = "sharegpt" ]]; then
client_command="vllm bench serve \
--backend $backend \
--tokenizer /tokenizer_cache \
--model $model \
--dataset-name $dataset_name \
--dataset-path $dataset_path \
--num-prompts $num_prompts \
--port $port \
--save-result \
--result-dir $RESULTS_FOLDER \
--result-filename ${new_test_name}.json \
--request-rate $qps \
--ignore-eos \
$client_args"
elif [[ "$dataset_name" = "sonnet" ]]; then
sonnet_input_len=$(echo "$common_params" | jq -r '.sonnet_input_len')
sonnet_output_len=$(echo "$common_params" | jq -r '.sonnet_output_len')
sonnet_prefix_len=$(echo "$common_params" | jq -r '.sonnet_prefix_len')
client_command="vllm bench serve \
--backend $backend \
--tokenizer /tokenizer_cache \
--model $model \
--dataset-name $dataset_name \
--dataset-path $dataset_path \
--num-prompts $num_prompts \
--sonnet-input-len $sonnet_input_len \
--sonnet-output-len $sonnet_output_len \
--sonnet-prefix-len $sonnet_prefix_len \
--port $port \
--save-result \
--result-dir $RESULTS_FOLDER \
--result-filename ${new_test_name}.json \
--request-rate $qps \
--ignore-eos \
$client_args"
else
echo "The dataset name must be either 'sharegpt' or 'sonnet'. Got $dataset_name."
exit 1
fi
echo "Running test case $test_name with qps $qps"
echo "Client command: $client_command"
eval "$client_command"
server_command="None"
# record the benchmarking commands
jq_output=$(jq -n \
--arg server "$server_command" \
--arg client "$client_command" \
--arg gpu "$gpu_type" \
--arg engine "$CURRENT_LLM_SERVING_ENGINE" \
'{
server_command: $server,
client_command: $client,
gpu_type: $gpu,
engine: $engine
}')
echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
done
done
kill_gpu_processes
}
run_genai_perf_tests() {
# run genai-perf tests
# $1: a json file specifying genai-perf test cases
local genai_perf_test_file
genai_perf_test_file=$1
# Iterate over genai-perf tests
jq -c '.[]' "$genai_perf_test_file" | while read -r params; do
# get the test name, and append the GPU type back to it.
test_name=$(echo "$params" | jq -r '.test_name')
# if TEST_SELECTOR is set, only run the test cases that match the selector
if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
echo "Skip test case $test_name."
continue
fi
# prepend the current serving engine to the test name
test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
# get common parameters
common_params=$(echo "$params" | jq -r '.common_parameters')
model=$(echo "$common_params" | jq -r '.model')
tp=$(echo "$common_params" | jq -r '.tp')
dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
port=$(echo "$common_params" | jq -r '.port')
num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
reuse_server=$(echo "$common_params" | jq -r '.reuse_server')
# get client and server arguments
server_params=$(echo "$params" | jq -r ".${CURRENT_LLM_SERVING_ENGINE}_server_parameters")
qps_list=$(echo "$params" | jq -r '.qps_list')
qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
echo "Running over qps list $qps_list"
# check if there is enough GPU to run the test
if [[ $gpu_count -lt $tp ]]; then
echo "Required num-shard $tp but only $gpu_count GPU found. Skip testcase $test_name."
continue
fi
if [[ $reuse_server == "true" ]]; then
echo "Reuse previous server for test case $test_name"
else
kill_gpu_processes
bash "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/launch-server.sh" \
"$server_params" "$common_params"
fi
if wait_for_server; then
echo ""
echo "$CURRENT_LLM_SERVING_ENGINE server is up and running."
else
echo ""
echo "$CURRENT_LLM_SERVING_ENGINE failed to start within the timeout period."
break
fi
# iterate over different QPS
for qps in $qps_list; do
# remove the surrounding single quote from qps
if [[ "$qps" == *"inf"* ]]; then
echo "qps was $qps"
qps=$num_prompts
echo "now qps is $qps"
fi
new_test_name=$test_name"_qps_"$qps
backend=$CURRENT_LLM_SERVING_ENGINE
if [[ "$backend" == *"vllm"* ]]; then
backend="vllm"
fi
#TODO: add output dir.
client_command="genai-perf profile \
-m $model \
--service-kind openai \
--backend "$backend" \
--endpoint-type chat \
--streaming \
--url localhost:$port \
--request-rate $qps \
--num-prompts $num_prompts \
"
echo "Client command: $client_command"
eval "$client_command"
#TODO: process/record outputs
done
done
kill_gpu_processes
}
prepare_dataset() {
# download sharegpt dataset
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
# duplicate sonnet by 4x, to allow benchmarking with input length 2048
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
echo "" > sonnet_4x.txt
for _ in {1..4}
do
cat sonnet.txt >> sonnet_4x.txt
done
}
main() {
# check if the environment variable is successfully injected from yaml
check_gpus
check_hf_token
get_current_llm_serving_engine
pip install -U transformers
pip install -r requirements/dev.txt
which genai-perf
# check storage
df -h
ensure_installed wget
ensure_installed curl
ensure_installed jq
# genai-perf dependency
ensure_installed libb64-0d
prepare_dataset
cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
declare -g RESULTS_FOLDER=results/
mkdir -p $RESULTS_FOLDER
BENCHMARK_ROOT="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
# run the test
run_serving_tests "$BENCHMARK_ROOT/tests/nightly-tests.json"
# run genai-perf tests
run_genai_perf_tests "$BENCHMARK_ROOT/tests/genai-perf-tests.json"
mv artifacts/ $RESULTS_FOLDER/
# upload benchmark results to buildkite
python3 -m pip install tabulate pandas
python3 "$BENCHMARK_ROOT/scripts/summary-nightly-results.py"
upload_to_buildkite
}
main "$@"
.buildkite/nightly-benchmarks/scripts/summary-nightly-results.py deleted 100644 → 0
View file @ 4b51e6f1
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import datetime
import json
import os
from pathlib import Path
import pandas as pd
from tabulate import tabulate
results_folder = Path("results/")
# serving results and the keys that will be printed into markdown
serving_results = []
serving_column_mapping = {
"test_name": "Test name",
"gpu_type": "GPU",
"completed": "Successful req.",
"request_throughput": "Tput (req/s)",
"mean_ttft_ms": "Mean TTFT (ms)",
"std_ttft_ms": "Std TTFT (ms)",
"median_ttft_ms": "Median TTFT (ms)",
"mean_itl_ms": "Mean ITL (ms)",
"std_itl_ms": "Std ITL (ms)",
"median_itl_ms": "Median ITL (ms)",
"mean_tpot_ms": "Mean TPOT (ms)",
"std_tpot_ms": "Std TPOT (ms)",
"median_tpot_ms": "Median TPOT (ms)",
"total_token_throughput": "Total Token Tput (tok/s)",
"output_throughput": "Output Tput (tok/s)",
"total_input_tokens": "Total input tokens",
"total_output_tokens": "Total output tokens",
"engine": "Engine",
}
if __name__ == "__main__":
# collect results
for test_file in results_folder.glob("*.json"):
with open(test_file) as f:
raw_result = json.loads(f.read())
# attach the benchmarking command to raw_result
with open(test_file.with_suffix(".commands")) as f:
command = json.loads(f.read())
raw_result.update(command)
# update the test name of this result
raw_result.update({"test_name": test_file.stem})
# add the result to raw_result
serving_results.append(raw_result)
continue
serving_results = pd.DataFrame.from_dict(serving_results)
if not serving_results.empty:
serving_results = serving_results[list(serving_column_mapping.keys())].rename(
columns=serving_column_mapping
)
serving_md_table_with_headers = tabulate(
serving_results, headers="keys", tablefmt="pipe", showindex=False
)
# remove the first line of header
serving_md_table_lines = serving_md_table_with_headers.split("\n")
serving_md_table_without_header = "\n".join(serving_md_table_lines[2:])
prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
# document benchmarking results in markdown
with open(results_folder / f"{prefix}_nightly_results.md", "w") as f:
# document results with header.
# for those who wants to reproduce our benchmark.
f.write(serving_md_table_with_headers)
f.write("\n")
# document benchmarking results in json
with open(results_folder / f"{prefix}_nightly_results.json", "w") as f:
results = serving_results.to_dict(orient="records")
f.write(json.dumps(results))
.buildkite/nightly-benchmarks/scripts/wait-for-image.sh deleted 100644 → 0
View file @ 4b51e6f1
#!/bin/sh
TOKEN=$(curl -s -L "https://public.ecr.aws/token?service=public.ecr.aws&scope=repository:q9t5s3a7/vllm-ci-postmerge-repo:pull" | jq -r .token)
if [[ "$BUILDKITE_BRANCH" == "main" ]]; then
URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-postmerge-repo/manifests/$BUILDKITE_COMMIT"
else
URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-test-repo/manifests/$BUILDKITE_COMMIT"
fi
TIMEOUT_SECONDS=10
retries=0
while [ $retries -lt 1000 ]; do
if [ "$(curl -s --max-time "$TIMEOUT_SECONDS" -L -H "Authorization: Bearer $TOKEN" -o /dev/null -w "%{http_code}" "$URL")" -eq 200 ]; then
exit 0
fi
echo "Waiting for image to be available..."
retries=$((retries + 1))
sleep 5
done
exit 1
.buildkite/nightly-benchmarks/README.md → .buildkite/performance-benchmarks/README.md
View file @ 006693ed
......@@ -2,40 +2,23 @@
## Introduction
This directory contains two sets of benchmark for vllm.
- Performance benchmark: benchmark vllm's performance under various workload, for **developers** to gain clarity on whether their PR improves/degrades vllm's performance
- Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for **the public** to know when to choose vllm.
See [vLLM performance dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
This directory contains a benchmarking suite for **developers** to run locally and gain clarity on whether their PR improves/degrades vllm's performance.
vLLM also maintains a continuous performance benchmark under [perf.vllm.ai](https://perf.vllm.ai/), hosted under PyTorch CI HUD.
## Performance benchmark quick overview
**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) and Intel® Xeon® Processors, with different models.
**Benchmarking Coverage**: latency, throughput and fix-qps serving on B200, A100, H100, Intel® Xeon® Processors and Intel® Gaudi® 3 Accelerators with different models.
**Benchmarking Duration**: about 1hr.
**For benchmarking developers**: please try your best to constraint the duration of benchmarking to about 1 hr so that it won't take forever to run.
## Nightly benchmark quick overview
**Benchmarking Coverage**: Fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) on Llama-3 8B, 70B and Mixtral 8x7B.
**Benchmarking engines**: vllm, TGI, trt-llm and lmdeploy.
**Benchmarking Duration**: about 3.5hrs.
## Trigger the benchmark
Performance benchmark will be triggered when:
- A PR being merged into vllm.
- Every commit for those PRs with `perf-benchmarks` label AND `ready` label.
Manually Trigger the benchmark
The benchmark needs to be triggered manually:
```bash
bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
```
Runtime environment variables:
......@@ -47,14 +30,11 @@ Runtime environment variables:
- `REMOTE_HOST`: IP for the remote vLLM service to benchmark. Default value is empty string.
- `REMOTE_PORT`: Port for the remote vLLM service to benchmark. Default value is empty string.
Nightly benchmark will be triggered when:
- Every commit for those PRs with `perf-benchmarks` label and `nightly-benchmarks` label.
## Performance benchmark details
See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
> NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
For Intel® Gaudi® 3 Accelerators, use `tests/latency-tests-hpu.json`, `tests/throughput-tests-hpu.json`, `tests/serving-tests-hpu.json` instead.
>
### Latency test
......@@ -152,26 +132,3 @@ Here is an example using the script to compare result_a and result_b with Model,
A comparison diagram will be generated below the table.
Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
<img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />
## Nightly test details
See [nightly-descriptions.md](nightly-descriptions.md) for the detailed description on test workload, models and docker containers of benchmarking other llm engines.
### Workflow
- The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
- Inside each container, we run [scripts/run-nightly-benchmarks.sh](scripts/run-nightly-benchmarks.sh), which will probe the serving engine of the current container.
- The `scripts/run-nightly-benchmarks.sh` will parse the workload described in [nightly-tests.json](tests/nightly-tests.json) and launch the right benchmark for the specified serving engine via `scripts/launch-server.sh`.
- At last, we run [scripts/summary-nightly-results.py](scripts/summary-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
### Nightly tests
In [nightly-tests.json](tests/nightly-tests.json), we include the command line arguments for benchmarking commands, together with the benchmarking test cases. The format is highly similar to performance benchmark.
### Docker containers
The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `scripts/run-nightly-benchmarks.sh` and `scripts/launch-server.sh`.
WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).
.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md → .buildkite/performance-benchmarks/performance-benchmarks-descriptions.md
View file @ 006693ed
......@@ -5,7 +5,7 @@
- Input length: 32 tokens.
- Output length: 128 tokens.
- Batch size: fixed (8).
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
- GPU/HPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
- CPU Models: llama-3.1 8B.
- Evaluation metrics: end-to-end latency (mean, median, p99).
......@@ -16,7 +16,7 @@
- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
- Output length: the corresponding output length of these 200 prompts.
- Batch size: dynamically determined by vllm to achieve maximum throughput.
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
- GPU/HPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
- CPU Models: llama-3.1 8B.
- Evaluation metrics: throughput.
......@@ -28,7 +28,7 @@
- Output length: the corresponding output length of these 200 prompts.
- Batch size: dynamically determined by vllm and the arrival pattern of the requests.
- **Average QPS (query per second)**: 1, 4, 16 and inf. QPS = inf means all requests come at once. For other QPS values, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
- GPU/HPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
- We also added a speculative decoding test for llama-3 70B on GPU, under QPS 2
- CPU Models: llama-3.1 8B.
- Evaluation metrics: throughput, TTFT (time to the first token, with mean, median and p99), ITL (inter-token latency, with mean, median and p99).
......
.buildkite/nightly-benchmarks/scripts/compare-json-results.py → .buildkite/performance-benchmarks/scripts/compare-json-results.py
View file @ 006693ed
......@@ -7,6 +7,7 @@ from importlib import util
import pandas as pd
pd.options.display.float_format = "{:.2f}".format
plotly_found = util.find_spec("plotly.express") is not None
......@@ -109,7 +110,10 @@ def compare_data_columns(
if len(compare_frames) >= 2:
base = compare_frames[0]
current = compare_frames[-1]
ratio = current / base
if "P99" in data_column or "Median" in data_column:
ratio = base / current # for latency
else:
ratio = current / base
ratio = ratio.mask(base == 0) # avoid inf when baseline is 0
ratio.name = f"Ratio 1 vs {len(compare_frames)}"
frames.append(ratio)
......@@ -199,6 +203,71 @@ def split_json_by_tp_pp(
return saved_paths
def _add_limit_line(fig, y_value, label):
# Visible dashed line + annotation
fig.add_hline(
y=y_value,
line_dash="dash",
line_color="red" if "ttft" in label.lower() else "blue",
annotation_text=f"{label}: {y_value} ms",
annotation_position="top left",
)
# Optional: add a legend item (as a transparent helper trace)
if plot and plotly_found:
import plotly.graph_objects as go
fig.add_trace(
go.Scatter(
x=[None],
y=[None],
mode="lines",
line=dict(
dash="dash", color="red" if "ttft" in label.lower() else "blue"
),
name=f"{label}",
)
)
def _find_concurrency_col(df: pd.DataFrame) -> str:
for c in [
"# of max concurrency.",
"# of max concurrency",
"Max Concurrency",
"max_concurrency",
"Concurrency",
]:
if c in df.columns:
return c
# Fallback: guess an integer-like column (harmless if unused)
for c in df.columns:
if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
return c
return "# of max concurrency."
def _highlight_threshold(
df: pd.DataFrame, threshold: float
) -> "pd.io.formats.style.Styler":
"""Highlight numeric per-configuration columns with value <= threshold."""
conc_col = _find_concurrency_col(df)
key_cols = [
c
for c in ["Model", "Dataset Name", "Input Len", "Output Len", conc_col]
if c in df.columns
]
conf_cols = [
c for c in df.columns if c not in key_cols and not str(c).startswith("Ratio")
]
conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
return df.style.map(
lambda v: "background-color:#e6ffe6;font-weight:bold;"
if pd.notna(v) and v <= threshold
else "",
subset=conf_cols,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
......@@ -220,6 +289,26 @@ if __name__ == "__main__":
default="# of max concurrency.",
help="column name to use as X Axis in comparison graph",
)
parser.add_argument(
"-l",
"--latency",
type=str,
default="p99",
help="take median|p99 for latency like TTFT/TPOT",
)
parser.add_argument(
"--ttft-max-ms",
type=float,
default=3000.0,
help="Reference limit for TTFT plots (ms)",
)
parser.add_argument(
"--tpot-max-ms",
type=float,
default=100.0,
help="Reference limit for TPOT plots (ms)",
)
args = parser.parse_args()
drop_column = "P99"
......@@ -234,12 +323,22 @@ if __name__ == "__main__":
"# of max concurrency.",
"qps",
]
data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
html_msgs_for_data_cols = [
"Compare Output Tokens /n",
"Median TTFT /n",
"Median TPOT /n",
]
if "median" in args.latency:
data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
html_msgs_for_data_cols = [
"Compare Output Tokens /n",
"Median TTFT /n",
"Median TPOT /n",
]
drop_column = "P99"
elif "p99" in args.latency:
data_cols_to_compare = ["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"]
html_msgs_for_data_cols = [
"Compare Output Tokens /n",
"P99 TTFT /n",
"P99 TPOT /n",
]
if len(args.file) == 1:
files = split_json_by_tp_pp(args.file[0], output_root="splits")
......@@ -275,33 +374,83 @@ if __name__ == "__main__":
f"Expected subset: {filtered_info_cols}, "
f"but DataFrame has: {list(output_df.columns)}"
)
output_df_sorted = output_df.sort_values(by=existing_group_cols)
# output_df_sorted = output_df.sort_values(by=existing_group_cols)
output_df_sorted = output_df.sort_values(by=args.xaxis)
output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
for name, group in output_groups:
html = group.to_html()
text_file.write(html_msgs_for_data_cols[i])
text_file.write(html)
if plot and plotly_found:
import plotly.express as px
df = group[raw_data_cols]
df_sorted = df.sort_values(by=info_cols[y_axis_index])
# Melt DataFrame for plotting
df_melted = df_sorted.melt(
id_vars=info_cols[y_axis_index],
var_name="Configuration",
value_name=data_cols_to_compare[i],
group_name = (
",".join(map(str, name)).replace(",", "_").replace("/", "-")
)
group_html_name = "perf_comparison_" + group_name + ".html"
metric_name = str(data_cols_to_compare[i]).lower()
if "tok/s" in metric_name:
html = group.to_html()
elif "ttft" in metric_name:
styler = _highlight_threshold(group, args.ttft_max_ms).format(
{c: "{:.2f}" for c in group.select_dtypes("number").columns},
na_rep="—",
)
html = styler.to_html(
table_attributes='border="1" class="dataframe"'
)
elif (
"tpot" in metric_name
or "median" in metric_name
or "p99" in metric_name
):
styler = _highlight_threshold(group, args.tpot_max_ms).format(
{c: "{:.2f}" for c in group.select_dtypes("number").columns},
na_rep="—",
)
title = data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
# Create Plotly line chart
fig = px.line(
df_melted,
x=info_cols[y_axis_index],
y=data_cols_to_compare[i],
color="Configuration",
title=title,
markers=True,
html = styler.to_html(
table_attributes='border="1" class="dataframe"'
)
# Export to HTML
text_file.write(fig.to_html(full_html=True, include_plotlyjs="cdn"))
text_file.write(html_msgs_for_data_cols[i])
text_file.write(html)
with open(group_html_name, "a+") as sub_text_file:
sub_text_file.write(html_msgs_for_data_cols[i])
sub_text_file.write(html)
if plot and plotly_found:
import plotly.express as px
df = group[raw_data_cols]
df_sorted = df.sort_values(by=info_cols[y_axis_index])
# Melt DataFrame for plotting
df_melted = df_sorted.melt(
id_vars=info_cols[y_axis_index],
var_name="Configuration",
value_name=data_cols_to_compare[i],
)
title = (
data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
)
# Create Plotly line chart
fig = px.line(
df_melted,
x=info_cols[y_axis_index],
y=data_cols_to_compare[i],
color="Configuration",
title=title,
markers=True,
)
# ---- Add threshold lines based on metric name ----
if "ttft" in metric_name:
_add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
elif (
"tpot" in metric_name
or "median" in metric_name
or "p99" in metric_name
):
_add_limit_line(fig, args.tpot_max_ms, "TPOT limit")
# Export to HTML
text_file.write(
fig.to_html(full_html=True, include_plotlyjs="cdn")
)
sub_text_file.write(
fig.to_html(full_html=True, include_plotlyjs="cdn")
)
.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py → .buildkite/performance-benchmarks/scripts/convert-results-json-to-markdown.py
View file @ 006693ed
......@@ -63,9 +63,11 @@ serving_column_mapping = {
"mean_ttft_ms": "Mean TTFT (ms)",
"median_ttft_ms": "Median TTFT (ms)",
"p99_ttft_ms": "P99 TTFT (ms)",
"std_ttft_ms": "STD TTFT (ms)",
"mean_tpot_ms": "Mean TPOT (ms)",
"median_tpot_ms": "Median",
"p99_tpot_ms": "P99",
"std_tpot_ms": "STD TPOT (ms)",
"mean_itl_ms": "Mean ITL (ms)",
"median_itl_ms": "Median ITL (ms)",
"p99_itl_ms": "P99 ITL (ms)",
......@@ -368,7 +370,7 @@ if __name__ == "__main__":
# The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
# we want to turn it into "8xGPUTYPE"
df["GPU"] = df["GPU"].apply(
lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}"
lambda x: "{}x{}".format(len(x.split("\n")), x.split("\n")[0])
)
# get markdown tables
......@@ -390,7 +392,7 @@ if __name__ == "__main__":
json_file = "benchmark_results.json"
with open(results_folder / md_file, "w") as f:
results = read_markdown(
"../.buildkite/nightly-benchmarks/"
"../.buildkite/performance-benchmarks/"
+ "performance-benchmarks-descriptions.md"
)
results = results.format(
......
.buildkite/nightly-benchmarks/scripts/launch-server.sh → .buildkite/performance-benchmarks/scripts/launch-server.sh
View file @ 006693ed
......@@ -181,18 +181,14 @@ launch_vllm_server() {
if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
server_command="python3 \
-m vllm.entrypoints.openai.api_server \
server_command="vllm serve $model \
-tp $tp \
--model $model \
--port $port \
$server_args"
else
echo "Key 'fp8' does not exist in common params."
server_command="python3 \
-m vllm.entrypoints.openai.api_server \
server_command="vllm serve $model \
-tp $tp \
--model $model \
--port $port \
$server_args"
fi
......
.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh → .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
View file @ 006693ed
......@@ -15,6 +15,8 @@ check_gpus() {
declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
elif command -v amd-smi; then
declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
elif command -v hl-smi; then
declare -g gpu_count=$(hl-smi --list | grep -i "Module ID" | wc -l)
fi
if [[ $gpu_count -gt 0 ]]; then
......@@ -23,10 +25,16 @@ check_gpus() {
echo "Need at least 1 GPU to run benchmarking."
exit 1
fi
declare -g arch_suffix=''
if command -v nvidia-smi; then
declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
elif command -v amd-smi; then
declare -g gpu_type=$(amd-smi static -g 0 -a | grep 'MARKET_NAME' | awk '{print $2}')
elif command -v hl-smi; then
declare -g gpu_type=$(hl-smi -q | grep "Product Name" | head -n 1 | awk -F ':' '{print $2}' | sed 's/^ *//')
arch_suffix='-hpu'
fi
echo "GPU type is $gpu_type"
}
......@@ -138,6 +146,10 @@ kill_gpu_processes() {
while [ "$(amd-smi metric -g 0 | grep 'USED_VRAM' | awk '{print $2}')" -ge 1000 ]; do
sleep 1
done
elif command -v hl-smi; then
while [ "$(hl-smi -q | grep "Used" | head -n 1 | awk '{print $3}')" -ge 1000 ]; do
sleep 1
done
fi
# remove vllm config file
......@@ -365,8 +377,7 @@ run_serving_tests() {
continue
fi
server_command="$server_envs python3 \
-m vllm.entrypoints.openai.api_server \
server_command="$server_envs vllm serve \
$server_args"
# run the server
......@@ -452,14 +463,10 @@ main() {
ARCH='-cpu'
else
check_gpus
ARCH="$arch_suffix"
fi
check_hf_token
# Set to v1 to run v1 benchmark
if [[ "${ENGINE_VERSION:-v0}" == "v1" ]]; then
export VLLM_USE_V1=1
fi
# dependencies
(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
(which jq) || (apt-get update && apt-get -y install jq)
......@@ -475,7 +482,12 @@ main() {
ensure_sharegpt_downloaded
declare -g RESULTS_FOLDER=results/
mkdir -p $RESULTS_FOLDER
QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
QUICK_BENCHMARK_ROOT=../.buildkite/performance-benchmarks/
# dump vllm info via vllm collect-env
env_output=$(vllm collect-env)
echo "$env_output" >"$RESULTS_FOLDER/vllm_env.txt"
# benchmarking
run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
......
.buildkite/nightly-benchmarks/tests/latency-tests-cpu.json → .buildkite/performance-benchmarks/tests/latency-tests-cpu.json
View file @ 006693ed
[
{
"test_name": "latency_llama8B_tp1",
"test_name": "latency_llama8B_tp2",
"environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"tensor_parallel_size": 1,
"load_format": "dummy",
"num_iters_warmup": 5,
"num_iters": 15
}
},
{
"test_name": "latency_llama8B_tp4",
"environment_variables": {
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"tensor_parallel_size": 4,
"load_format": "dummy",
"tensor_parallel_size": 2,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"num_iters_warmup": 5,
"num_iters": 15
}
......
.buildkite/performance-benchmarks/tests/latency-tests-hpu.json 0 → 100644
View file @ 006693ed
[
{
"test_name": "latency_llama8B_tp1",
"environment_variables": {
"PT_HPU_LAZY_MODE": 1,
"VLLM_CONTIGUOUS_PA": 1,
"VLLM_DEFRAG": 1
},
"parameters": {
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"tensor_parallel_size": 1,
"load_format": "dummy",
"num-iters-warmup": 5,
"num-iters": 15,
"max-model-len": 256,
"async-scheduling": ""
}
},
{
"test_name": "latency_llama70B_tp4",
"environment_variables": {
"PT_HPU_LAZY_MODE": 1,
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
"VLLM_CONTIGUOUS_PA": 1,
"VLLM_DEFRAG": 1
},
"parameters": {
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
"tensor_parallel_size": 4,
"load_format": "dummy",
"num-iters-warmup": 5,
"num-iters": 15,
"max-model-len": 256,
"async-scheduling": ""
}
},
{
"test_name": "latency_mixtral8x7B_tp2",
"environment_variables": {
"PT_HPU_LAZY_MODE": 1,
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
"VLLM_CONTIGUOUS_PA": 1,
"VLLM_DEFRAG": 1
},
"parameters": {
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"tensor_parallel_size": 2,
"load_format": "dummy",
"num-iters-warmup": 5,
"num-iters": 15,
"max-model-len": 256,
"async-scheduling": ""
}
}
]
.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc3.json → .buildkite/performance-benchmarks/tests/serving-tests-cpu-snc3.json
View file @ 006693ed
......@@ -95,6 +95,38 @@
"num_prompts": 200
}
},
{
"test_name": "serving_llama8B_bf16_tp4_sharegpt",
"qps_list": ["inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"server_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"tensor_parallel_size": 4,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
},
"client_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"backend": "vllm",
"dataset_name": "sharegpt",
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
"num_prompts": 200
}
},
{
"test_name": "serving_llama8B_bf16_tp2pp3_sharegpt",
"qps_list": ["inf"],
......@@ -233,6 +265,41 @@
"num_prompts": 1000
}
},
{
"test_name": "serving_llama8B_bf16_tp4_random_128_128",
"qps_list": ["inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"server_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"tensor_parallel_size": 4,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"enable_chunked_prefill": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
},
"client_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"backend": "vllm",
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 128,
"ignore-eos": "",
"num_prompts": 1000
}
},
{
"test_name": "serving_llama8B_bf16_tp2pp3_random_128_128",
"qps_list": ["inf"],
......@@ -365,6 +432,38 @@
"num_prompts": 200
}
},
{
"test_name": "serving_llama8B_int8_tp4_sharegpt",
"qps_list": ["inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"server_parameters": {
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
"tensor_parallel_size": 4,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
},
"client_parameters": {
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
"backend": "vllm",
"dataset_name": "sharegpt",
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
"num_prompts": 200
}
},
{
"test_name": "serving_llama8B_int8_tp2pp3_sharegpt",
"qps_list": ["inf"],
......@@ -503,6 +602,41 @@
"num_prompts": 1000
}
},
{
"test_name": "serving_llama8B_int8_tp4_random_128_128",
"qps_list": ["inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"server_parameters": {
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
"tensor_parallel_size": 4,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"enable_chunked_prefill": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
},
"client_parameters": {
"model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
"backend": "vllm",
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 128,
"ignore-eos": "",
"num_prompts": 1000
}
},
{
"test_name": "serving_llama8B_int8_tp2pp3_random_128_128",
"qps_list": ["inf"],
......@@ -638,6 +772,39 @@
"num_prompts": 200
}
},
{
"test_name": "serving_llama8B_int4_tp4_sharegpt",
"qps_list": ["inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"server_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"quantization": "awq",
"tensor_parallel_size": 4,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
},
"client_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"backend": "vllm",
"dataset_name": "sharegpt",
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
"num_prompts": 200
}
},
{
"test_name": "serving_llama8B_int4_tp2pp3_sharegpt",
"qps_list": ["inf"],
......@@ -780,6 +947,42 @@
"num_prompts": 1000
}
},
{
"test_name": "serving_llama8B_int4_tp4_random_128_128",
"qps_list": ["inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"server_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"quantization": "awq",
"tensor_parallel_size": 4,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"enable_chunked_prefill": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
},
"client_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"backend": "vllm",
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 128,
"ignore-eos": "",
"num_prompts": 1000
}
},
{
"test_name": "serving_llama8B_int4_tp2pp3_random_128_128",
"qps_list": ["inf"],
......
.buildkite/nightly-benchmarks/tests/serving-tests-cpu.json → .buildkite/performance-benchmarks/tests/serving-tests-cpu.json
View file @ 006693ed
......@@ -2,7 +2,7 @@
{
"test_name": "serving_llama8B_tp1_sharegpt",
"qps_list": [1, 4, 16, "inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
"max_concurrency_list": [32],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
......@@ -28,13 +28,13 @@
"backend": "vllm",
"dataset_name": "sharegpt",
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
"num_prompts": 200
"num_prompts": 32
}
},
{
"test_name": "serving_llama8B_tp2_sharegpt",
"qps_list": [1, 4, 16, "inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
"max_concurrency_list": [32],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
......@@ -60,13 +60,13 @@
"backend": "vllm",
"dataset_name": "sharegpt",
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
"num_prompts": 200
"num_prompts": 32
}
},
{
"test_name": "serving_llama8B_tp4_sharegpt",
"test_name": "serving_llama8B_tp1_random_128_128",
"qps_list": [1, 4, 16, "inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
"max_concurrency_list": [32],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
......@@ -76,11 +76,12 @@
},
"server_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"tensor_parallel_size": 4,
"tensor_parallel_size": 1,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"enable_chunked_prefill": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
......@@ -90,15 +91,122 @@
"client_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"backend": "vllm",
"dataset_name": "sharegpt",
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
"num_prompts": 200
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 128,
"ignore-eos": "",
"num_prompts": 32
}
},
{
"test_name": "serving_llama8B_tp2_random_128_128",
"qps_list": [1, 4, 16, "inf"],
"max_concurrency_list": [32],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"server_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"tensor_parallel_size": 2,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"enable_chunked_prefill": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
},
"client_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"backend": "vllm",
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 128,
"ignore-eos": "",
"num_prompts": 32
}
},
{
"test_name": "serving_llama8B_tp1_random_128_2048",
"qps_list": [1, 4, 16, "inf"],
"max_concurrency_list": [32],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"server_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"tensor_parallel_size": 1,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"enable_chunked_prefill": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
},
"client_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"backend": "vllm",
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 2048,
"ignore-eos": "",
"num_prompts": 32
}
},
{
"test_name": "serving_llama8B_tp2_random_128_2048",
"qps_list": [1, 4, 16, "inf"],
"max_concurrency_list": [32],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
"VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
"VLLM_CPU_SGL_KERNEL": 1,
"VLLM_CPU_KVCACHE_SPACE": 40
},
"server_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"tensor_parallel_size": 2,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
"trust_remote_code": "",
"enable_chunked_prefill": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
},
"client_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"backend": "vllm",
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 2048,
"ignore-eos": "",
"num_prompts": 32
}
},
{
"test_name": "serving_llama8B_tp4_random_1024_128",
"test_name": "serving_llama8B_tp1_random_2048_128",
"qps_list": [1, 4, 16, "inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
"max_concurrency_list": [32],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
......@@ -108,7 +216,7 @@
},
"server_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"tensor_parallel_size": 4,
"tensor_parallel_size": 1,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
......@@ -124,16 +232,16 @@
"model": "meta-llama/Llama-3.1-8B-Instruct",
"backend": "vllm",
"dataset_name": "random",
"random-input-len": 1024,
"random-input-len": 2048,
"random-output-len": 128,
"ignore-eos": "",
"num_prompts": 100
"num_prompts": 32
}
},
{
"test_name": "serving_llama8B_pp6_random_1024_128",
"test_name": "serving_llama8B_tp2_random_2048_128",
"qps_list": [1, 4, 16, "inf"],
"max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
"max_concurrency_list": [32],
"server_environment_variables": {
"VLLM_RPC_TIMEOUT": 100000,
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
......@@ -143,7 +251,7 @@
},
"server_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
"pipeline_parallel_size": 6,
"tensor_parallel_size": 2,
"dtype": "bfloat16",
"distributed_executor_backend": "mp",
"block_size": 128,
......@@ -159,10 +267,10 @@
"model": "meta-llama/Llama-3.1-8B-Instruct",
"backend": "vllm",
"dataset_name": "random",
"random-input-len": 1024,
"random-input-len": 2048,
"random-output-len": 128,
"ignore-eos": "",
"num_prompts": 100
"num_prompts": 32
}
}
]
.buildkite/performance-benchmarks/tests/serving-tests-hpu.json 0 → 100644
View file @ 006693ed
[
{
"test_name": "serving_llama8B_tp1_sharegpt",
"qps_list": [1, 4, 16, "inf"],
"server_environment_variables": {
"PT_HPU_LAZY_MODE": 1,
"VLLM_CONTIGUOUS_PA": 1,
"VLLM_DEFRAG": 1
},
"server_parameters": {
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"tensor_parallel_size": 1,
"swap_space": 16,
"disable_log_stats": "",
"load_format": "dummy",
"max-model-len": 2048,
"max-num-seqs": 256,
"async-scheduling": ""
},
"client_parameters": {
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"backend": "vllm",
"dataset_name": "sharegpt",
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
"num_prompts": 200
}
},
{
"test_name": "serving_llama70B_tp4_sharegpt",
"qps_list": [1, 4, 16, "inf"],
"server_environment_variables": {
"PT_HPU_LAZY_MODE": 1,
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
"VLLM_CONTIGUOUS_PA": 1,
"VLLM_DEFRAG": 1
},
"server_parameters": {
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
"tensor_parallel_size": 4,
"swap_space": 16,
"disable_log_stats": "",
"load_format": "dummy",
"max-model-len": 2048,
"max-num-seqs": 256,
"async-scheduling": ""
},
"client_parameters": {
"model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
"backend": "vllm",
"dataset_name": "sharegpt",
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
"num_prompts": 200
}
},
{
"test_name": "serving_mixtral8x7B_tp2_sharegpt",
"qps_list": [1, 4, 16, "inf"],
"server_environment_variables": {
"PT_HPU_LAZY_MODE": 1,
"PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
"VLLM_CONTIGUOUS_PA": 1,
"VLLM_DEFRAG": 1
},
"server_parameters": {
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"tensor_parallel_size": 2,
"swap_space": 16,
"disable_log_stats": "",
"load_format": "dummy",
"max-model-len": 2048,
"max-num-seqs": 256,
"async-scheduling": ""
},
"client_parameters": {
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"backend": "vllm",
"dataset_name": "sharegpt",
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
"num_prompts": 200
}
}
]
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