Add histogram test

README.md

@@ -111,7 +111,9 @@ 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 /opt/tritonserver/backends/vllm/vllm_backend
+cp -r /opt/tritonserver/backends/vllm/vllm_backend/src/* /opt/tritonserver/backends/vllm
+rm -rf /opt/tritonserver/backends/vllm/vllm_backend
 ```
 
 ## Using the vLLM Backend

ci/L0_backend_vllm/metrics_test/test.sh

@@ -0,0 +1,98 @@
+#!/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=${BACKEND_DIR} --model-control-mode=explicit --load-model=vllm_opt --log-verbose=1"
+SERVER_LOG="./vllm_metrics_server.log"
+CLIENT_LOG="./vllm_metrics_client.log"
+TEST_RESULT_FILE='test_results.txt'
+CLIENT_PY="./vllm_metrics_test.py"
+SAMPLE_MODELS_REPO="../../../samples/model_repository"
+EXPECTED_NUM_TESTS=1
+
+# Helpers =======================================
+function assert_curl_success {
+  message="${1}"
+  if [ "$code" != "200" ]; then
+    cat ./curl.out
+    echo -e "\n***\n*** ${message} : line ${BASH_LINENO}\n***"
+    RET=1
+  fi
+}
+
+rm -rf models && mkdir -p models
+cp -r ${SAMPLE_MODELS_REPO}/vllm_model models/vllm_opt
+# `vllm_opt`` model will be loaded on server start and stay loaded throughout
+# unittesting. To ensure that vllm's memory profiler will not error out
+# on `vllm_load_test` load, we reduce "gpu_memory_utilization" for `vllm_opt`,
+# so that at least 60% of GPU memory was available for other models.
+sed -i 's/"gpu_memory_utilization": 0.5/"gpu_memory_utilization": 0.4/' models/vllm_opt/1/model.json
+
+RET=0
+
+run_server
+if [ "$SERVER_PID" == "0" ]; then
+    cat $SERVER_LOG
+    echo -e "\n***\n*** Failed to start $SERVER\n***"
+    exit 1
+fi
+
+set +e
+python3 $CLIENT_PY -v > $CLIENT_LOG 2>&1
+
+if [ $? -ne 0 ]; then
+    cat $CLIENT_LOG
+    echo -e "\n***\n*** Running $CLIENT_PY 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
+rm -rf "./models"
+
+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

ci/L0_backend_vllm/metrics_test/vllm_metrics_test.py

@@ -0,0 +1,141 @@
+# 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.
+
+import os
+import re
+import sys
+import unittest
+from functools import partial
+
+import requests
+import tritonclient.grpc as grpcclient
+from tritonclient.utils import *
+
+sys.path.append("../../common")
+from test_util import TestResultCollector, UserData, callback, create_vllm_request
+
+
+class VLLMTritonMetricsTest(TestResultCollector):
+    def setUp(self):
+        self.triton_client = grpcclient.InferenceServerClient(url="localhost:8001")
+        self.tritonserver_ipaddr = os.environ.get("TRITONSERVER_IPADDR", "localhost")
+        self.vllm_model_name = "vllm_opt"
+        self.prompts = [
+            "The most dangerous animal is",
+            "The capital of France is",
+            "The future of AI is",
+        ]
+        self.sampling_parameters = {"temperature": "0", "top_p": "1"}
+
+    def get_metrics(self):
+        """
+        Store vllm metrics in a dictionary.
+        """
+        r = requests.get(f"http://{self.tritonserver_ipaddr}:8002/metrics")
+        r.raise_for_status()
+
+        # Regular expression to match the pattern
+        pattern = r"^(vllm:[^ {]+)(?:{.*})? ([0-9.-]+)$"
+        vllm_dict = {}
+
+        # Find all matches in the text
+        matches = re.findall(pattern, r.text, re.MULTILINE)
+
+        for match in matches:
+            key, value = match
+            vllm_dict[key] = float(value) if "." in value else int(value)
+
+        return vllm_dict
+
+    def vllm_async_stream_infer(
+        self,
+        prompts,
+        sampling_parameters,
+        stream,
+        send_parameters_as_tensor,
+        model_name,
+    ):
+        """
+        Helper function to send async stream infer requests to vLLM.
+        """
+        user_data = UserData()
+        number_of_vllm_reqs = len(prompts)
+
+        self.triton_client.start_stream(callback=partial(callback, user_data))
+        for i in range(number_of_vllm_reqs):
+            request_data = create_vllm_request(
+                prompts[i],
+                i,
+                stream,
+                sampling_parameters,
+                model_name,
+                send_parameters_as_tensor,
+            )
+            self.triton_client.async_stream_infer(
+                model_name=model_name,
+                request_id=request_data["request_id"],
+                inputs=request_data["inputs"],
+                outputs=request_data["outputs"],
+                parameters=sampling_parameters,
+            )
+
+        for _ in range(number_of_vllm_reqs):
+            result = user_data._completed_requests.get()
+            if type(result) is InferenceServerException:
+                print(result.message())
+            self.assertIsNot(type(result), InferenceServerException, str(result))
+
+            output = result.as_numpy("text_output")
+            self.assertIsNotNone(output, "`text_output` should not be None")
+
+        self.triton_client.stop_stream()
+
+    def test_vllm_metrics(self):
+        # Test vLLM metrics
+        self.vllm_async_stream_infer(
+            prompts=self.prompts,
+            sampling_parameters=self.sampling_parameters,
+            stream=False,
+            send_parameters_as_tensor=True,
+            model_name=self.vllm_model_name,
+        )
+        metrics_dict = self.get_metrics()
+
+        self.assertEqual(metrics_dict["vllm:time_to_first_token_seconds_count"], 3)
+        self.assertTrue(
+            0.0001 < metrics_dict["vllm:time_to_first_token_seconds_sum"] < 0.0003
+        )
+        self.assertEqual(metrics_dict["vllm:time_per_output_token_seconds_count"], 45)
+        self.assertTrue(
+            0.001 <= metrics_dict["vllm:time_per_output_token_seconds_sum"] <= 0.003
+        )
+
+    def tearDown(self):
+        self.triton_client.close()
+
+
+if __name__ == "__main__":
+    unittest.main()

ci/L0_backend_vllm/test.sh

@@ -26,7 +26,7 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 RET=0
-SUBTESTS="accuracy_test request_cancellation enabled_stream vllm_backend"
+SUBTESTS="accuracy_test request_cancellation enabled_stream vllm_backend metrics_test"
 
 python3 -m pip install --upgrade pip && pip3 install tritonclient[grpc]
 

src/model.py

@@ -39,6 +39,8 @@
 from vllm.sampling_params import SamplingParams
 from vllm.utils import random_uuid
 
+from utils.metrics import VllmStatLogger
+
 _VLLM_ENGINE_ARGS_FILENAME = "model.json"
 _MULTI_LORA_ARGS_FILENAME = "multi_lora.json"
 
@@ -151,6 +153,14 @@ def init_engine(self):
             AsyncEngineArgs(**self.vllm_engine_config)
         )
 
+        # Create vLLM custom Metrics
+        labels = {
+            "model": self.args["model_name"],
+            "version": self.args["model_version"],
+        }
+        logger = VllmStatLogger(labels=labels)
+        self.llm_engine.add_logger("triton", logger)
+
     def setup_lora(self):
         self.enable_lora = False