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nvda-mesharma authored Sep 30, 2024
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1 change: 1 addition & 0 deletions .gitignore
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Expand Up @@ -50,6 +50,7 @@ coverage.xml
.hypothesis/
.pytest_cache/
cover/
*.out

# Translations
*.mo
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114 changes: 112 additions & 2 deletions README.md
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Expand Up @@ -114,10 +114,12 @@ cd server
--upstream-container-version=${TRITON_CONTAINER_VERSION}
--backend=python:r${TRITON_CONTAINER_VERSION}
--backend=vllm:r${TRITON_CONTAINER_VERSION}
--backend=ensemble
--vllm-version=${VLLM_VERSION}
# Build Triton Server
cd build
bash -x ./docker_build
```

### Option 3. Add the vLLM Backend to the Default Triton Container
Expand All @@ -129,7 +131,8 @@ container with the following commands:

```
mkdir -p /opt/tritonserver/backends/vllm
wget -P /opt/tritonserver/backends/vllm https://raw.githubusercontent.com/triton-inference-server/vllm_backend/main/src/model.py
git clone https://github.com/triton-inference-server/vllm_backend.git /tmp/vllm_backend
cp -r /tmp/vllm_backend/src/* /opt/tritonserver/backends/vllm
```

## Using the vLLM Backend
Expand Down Expand Up @@ -212,14 +215,121 @@ starting from 23.10 release.

You can use `pip install ...` within the container to upgrade vLLM version.


## Running Multiple Instances of Triton Server

If you are running multiple instances of Triton server with a Python-based backend,
you need to specify a different `shm-region-prefix-name` for each server. See
[here](https://github.com/triton-inference-server/python_backend#running-multiple-instances-of-triton-server)
for more information.

## Triton Metrics
Starting with the 24.08 release of Triton, users can now obtain specific
vLLM metrics by querying the Triton metrics endpoint (see complete vLLM metrics
[here](https://docs.vllm.ai/en/latest/serving/metrics.html)). This can be
accomplished by launching a Triton server in any of the ways described above
(ensuring the build code / container is 24.08 or later) and querying the server.
Upon receiving a successful response, you can query the metrics endpoint by entering
the following:
```bash
curl localhost:8002/metrics
```
VLLM stats are reported by the metrics endpoint in fields that are prefixed with
`vllm:`. Triton currently supports reporting of the following metrics from vLLM.
```bash
# Number of prefill tokens processed.
counter_prompt_tokens
# Number of generation tokens processed.
counter_generation_tokens
# Histogram of time to first token in seconds.
histogram_time_to_first_token
# Histogram of time per output token in seconds.
histogram_time_per_output_token
# Histogram of end to end request latency in seconds.
histogram_e2e_time_request
# Number of prefill tokens processed.
histogram_num_prompt_tokens_request
# Number of generation tokens processed.
histogram_num_generation_tokens_request
# Histogram of the best_of request parameter.
histogram_best_of_request
# Histogram of the n request parameter.
histogram_n_request
```
Your output for these fields should look similar to the following:
```bash
# HELP vllm:prompt_tokens_total Number of prefill tokens processed.
# TYPE vllm:prompt_tokens_total counter
vllm:prompt_tokens_total{model="vllm_model",version="1"} 10
# HELP vllm:generation_tokens_total Number of generation tokens processed.
# TYPE vllm:generation_tokens_total counter
vllm:generation_tokens_total{model="vllm_model",version="1"} 16
# HELP vllm:time_to_first_token_seconds Histogram of time to first token in seconds.
# TYPE vllm:time_to_first_token_seconds histogram
vllm:time_to_first_token_seconds_count{model="vllm_model",version="1"} 1
vllm:time_to_first_token_seconds_sum{model="vllm_model",version="1"} 0.03233122825622559
vllm:time_to_first_token_seconds_bucket{model="vllm_model",version="1",le="0.001"} 0
...
vllm:time_to_first_token_seconds_bucket{model="vllm_model",version="1",le="+Inf"} 1
# HELP vllm:time_per_output_token_seconds Histogram of time per output token in seconds.
# TYPE vllm:time_per_output_token_seconds histogram
vllm:time_per_output_token_seconds_count{model="vllm_model",version="1"} 15
vllm:time_per_output_token_seconds_sum{model="vllm_model",version="1"} 0.04501533508300781
vllm:time_per_output_token_seconds_bucket{model="vllm_model",version="1",le="0.01"} 14
...
vllm:time_per_output_token_seconds_bucket{model="vllm_model",version="1",le="+Inf"} 15
# HELP vllm:e2e_request_latency_seconds Histogram of end to end request latency in seconds.
# TYPE vllm:e2e_request_latency_seconds histogram
vllm:e2e_request_latency_seconds_count{model="vllm_model",version="1"} 1
vllm:e2e_request_latency_seconds_sum{model="vllm_model",version="1"} 0.08686184883117676
vllm:e2e_request_latency_seconds_bucket{model="vllm_model",version="1",le="1"} 1
...
vllm:e2e_request_latency_seconds_bucket{model="vllm_model",version="1",le="+Inf"} 1
# HELP vllm:request_prompt_tokens Number of prefill tokens processed.
# TYPE vllm:request_prompt_tokens histogram
vllm:request_prompt_tokens_count{model="vllm_model",version="1"} 1
vllm:request_prompt_tokens_sum{model="vllm_model",version="1"} 10
vllm:request_prompt_tokens_bucket{model="vllm_model",version="1",le="1"} 0
...
vllm:request_prompt_tokens_bucket{model="vllm_model",version="1",le="+Inf"} 1
# HELP vllm:request_generation_tokens Number of generation tokens processed.
# TYPE vllm:request_generation_tokens histogram
vllm:request_generation_tokens_count{model="vllm_model",version="1"} 1
vllm:request_generation_tokens_sum{model="vllm_model",version="1"} 16
vllm:request_generation_tokens_bucket{model="vllm_model",version="1",le="1"} 0
...
vllm:request_generation_tokens_bucket{model="vllm_model",version="1",le="+Inf"} 1
# HELP vllm:request_params_best_of Histogram of the best_of request parameter.
# TYPE vllm:request_params_best_of histogram
vllm:request_params_best_of_count{model="vllm_model",version="1"} 1
vllm:request_params_best_of_sum{model="vllm_model",version="1"} 1
vllm:request_params_best_of_bucket{model="vllm_model",version="1",le="1"} 1
...
vllm:request_params_best_of_bucket{model="vllm_model",version="1",le="+Inf"} 1
# HELP vllm:request_params_n Histogram of the n request parameter.
# TYPE vllm:request_params_n histogram
vllm:request_params_n_count{model="vllm_model",version="1"} 1
vllm:request_params_n_sum{model="vllm_model",version="1"} 1
vllm:request_params_n_bucket{model="vllm_model",version="1",le="1"} 1
...
vllm:request_params_n_bucket{model="vllm_model",version="1",le="+Inf"} 1
```
To enable vLLM engine colleting metrics, "disable_log_stats" option need to be either false
or left empty (false by default) in [model.json](https://github.com/triton-inference-server/vllm_backend/blob/main/samples/model_repository/vllm_model/1/model.json).
```bash
"disable_log_stats": false
```
*Note:* vLLM metrics are not reported to Triton metrics server by default
due to potential performance slowdowns. To enable vLLM model's metrics
reporting, please add following lines to its config.pbtxt as well.
```bash
parameters: {
key: "REPORT_CUSTOM_METRICS"
value: {
string_value:"yes"
}
}
```

## Referencing the Tutorial

You can read further in the
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167 changes: 167 additions & 0 deletions ci/L0_backend_vllm/metrics_test/test.sh
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#!/bin/bash
# Copyright 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

source ../../common/util.sh

TRITON_DIR=${TRITON_DIR:="/opt/tritonserver"}
SERVER=${TRITON_DIR}/bin/tritonserver
BACKEND_DIR=${TRITON_DIR}/backends
SERVER_ARGS="--model-repository=$(pwd)/models --backend-directory=${BACKEND_DIR} --model-control-mode=explicit --load-model=vllm_opt --log-verbose=1"
SERVER_LOG="./vllm_metrics_server.log"
CLIENT_LOG="./vllm_metrics_client.log"
TEST_RESULT_FILE='test_results.txt'
CLIENT_PY="./vllm_metrics_test.py"
SAMPLE_MODELS_REPO="../../../samples/model_repository"
EXPECTED_NUM_TESTS=1

# Helpers =======================================
function copy_model_repository {
rm -rf models && mkdir -p models
cp -r ${SAMPLE_MODELS_REPO}/vllm_model models/vllm_opt
# `vllm_opt` model will be loaded on server start and stay loaded throughout
# unittesting. To ensure that vllm's memory profiler will not error out
# on `vllm_load_test` load, we reduce "gpu_memory_utilization" for `vllm_opt`,
# so that at least 60% of GPU memory was available for other models.
sed -i 's/"gpu_memory_utilization": 0.5/"gpu_memory_utilization": 0.4/' models/vllm_opt/1/model.json
}

run_test() {
local TEST_CASE=$1

run_server
if [ "$SERVER_PID" == "0" ]; then
cat $SERVER_LOG
echo -e "\n***\n*** Failed to start $SERVER\n***"
exit 1
fi

set +e
python3 $CLIENT_PY $TEST_CASE -v > $CLIENT_LOG 2>&1

if [ $? -ne 0 ]; then
cat $CLIENT_LOG
echo -e "\n***\n*** Running $CLIENT_PY $TEST_CASE FAILED. \n***"
RET=1
else
check_test_results $TEST_RESULT_FILE $EXPECTED_NUM_TESTS
if [ $? -ne 0 ]; then
cat $CLIENT_LOG
echo -e "\n***\n*** Test Result Verification FAILED.\n***"
RET=1
fi
fi
set -e

kill $SERVER_PID
wait $SERVER_PID
}

RET=0

# Test disabling vLLM metrics reporting without parameter "REPORT_CUSTOM_METRICS" in config.pbtxt
copy_model_repository
run_test VLLMTritonMetricsTest.test_vllm_metrics_disabled

# Test disabling vLLM metrics reporting with parameter "REPORT_CUSTOM_METRICS" set to "no" in config.pbtxt
copy_model_repository
echo -e "
parameters: {
key: \"REPORT_CUSTOM_METRICS\"
value: {
string_value:\"no\"
}
}
" >> models/vllm_opt/config.pbtxt
run_test VLLMTritonMetricsTest.test_vllm_metrics_disabled

# Test vLLM metrics reporting with parameter "REPORT_CUSTOM_METRICS" set to "yes" in config.pbtxt
copy_model_repository
cp ${SAMPLE_MODELS_REPO}/vllm_model/config.pbtxt models/vllm_opt
echo -e "
parameters: {
key: \"REPORT_CUSTOM_METRICS\"
value: {
string_value:\"yes\"
}
}
" >> models/vllm_opt/config.pbtxt
run_test VLLMTritonMetricsTest.test_vllm_metrics

# Test vLLM metrics custom sampling parameters
# Custom sampling parameters may result in different vLLM output depending
# on the platform. Therefore, these metrics are tests separately.
copy_model_repository
cp ${SAMPLE_MODELS_REPO}/vllm_model/config.pbtxt models/vllm_opt
echo -e "
parameters: {
key: \"REPORT_CUSTOM_METRICS\"
value: {
string_value:\"yes\"
}
}
" >> models/vllm_opt/config.pbtxt
run_test VLLMTritonMetricsTest.test_custom_sampling_params

# Test enabling vLLM metrics reporting in config.pbtxt but disabling in model.json
copy_model_repository
jq '. += {"disable_log_stats" : true}' models/vllm_opt/1/model.json > "temp.json"
mv temp.json models/vllm_opt/1/model.json
echo -e "
parameters: {
key: \"REPORT_CUSTOM_METRICS\"
value: {
string_value:\"yes\"
}
}
" >> models/vllm_opt/config.pbtxt
run_test VLLMTritonMetricsTest.test_vllm_metrics_disabled

# Test enabling vLLM metrics reporting in config.pbtxt while disabling in server option
copy_model_repository
echo -e "
parameters: {
key: \"REPORT_CUSTOM_METRICS\"
value: {
string_value:\"yes\"
}
}
" >> models/vllm_opt/config.pbtxt
SERVER_ARGS="${SERVER_ARGS} --allow-metrics=false"
run_test VLLMTritonMetricsTest.test_vllm_metrics_refused

rm -rf "./models" "temp.json"

if [ $RET -eq 1 ]; then
cat $CLIENT_LOG
cat $SERVER_LOG
echo -e "\n***\n*** vLLM test FAILED. \n***"
else
echo -e "\n***\n*** vLLM test PASSED. \n***"
fi

collect_artifacts_from_subdir
exit $RET
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