feat: Report histogram metrics to Triton metrics server (#58)

README.md

@@ -202,6 +202,71 @@ 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
+```
+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="0.005"} 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="0.025"} 15
+...
+vllm:time_per_output_token_seconds_bucket{model="vllm_model",version="1",le="+Inf"} 15
+```
+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

ci/L0_backend_vllm/metrics_test/vllm_metrics_test.py

@@ -0,0 +1,164 @@
+# 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_vllm_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_infer(
+        self,
+        prompts,
+        sampling_parameters,
+        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,
+                False,
+                sampling_parameters,
+                model_name,
+                True,
+            )
+            self.triton_client.async_stream_infer(
+                model_name=model_name,
+                inputs=request_data["inputs"],
+                request_id=request_data["request_id"],
+                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_infer(
+            prompts=self.prompts,
+            sampling_parameters=self.sampling_parameters,
+            model_name=self.vllm_model_name,
+        )
+        metrics_dict = self.get_vllm_metrics()
+
+        # vllm:prompt_tokens_total
+        self.assertEqual(metrics_dict["vllm:prompt_tokens_total"], 18)
+        # vllm:generation_tokens_total
+        self.assertEqual(metrics_dict["vllm:generation_tokens_total"], 48)
+
+        # vllm:time_to_first_token_seconds
+        self.assertEqual(metrics_dict["vllm:time_to_first_token_seconds_count"], 3)
+        self.assertGreater(metrics_dict["vllm:time_to_first_token_seconds_sum"], 0)
+        self.assertEqual(metrics_dict["vllm:time_to_first_token_seconds_bucket"], 3)
+        # vllm:time_per_output_token_seconds
+        self.assertEqual(metrics_dict["vllm:time_per_output_token_seconds_count"], 45)
+        self.assertGreater(metrics_dict["vllm:time_per_output_token_seconds_sum"], 0)
+        self.assertEqual(metrics_dict["vllm:time_per_output_token_seconds_bucket"], 45)
+
+    def test_vllm_metrics_disabled(self):
+        # Test vLLM metrics
+        self.vllm_infer(
+            prompts=self.prompts,
+            sampling_parameters=self.sampling_parameters,
+            model_name=self.vllm_model_name,
+        )
+        metrics_dict = self.get_vllm_metrics()
+
+        # No vLLM metric found
+        self.assertEqual(len(metrics_dict), 0)
+
+    def test_vllm_metrics_refused(self):
+        # Test vLLM metrics
+        self.vllm_infer(
+            prompts=self.prompts,
+            sampling_parameters=self.sampling_parameters,
+            model_name=self.vllm_model_name,
+        )
+        with self.assertRaises(requests.exceptions.ConnectionError):
+            self.get_vllm_metrics()
+
+    def tearDown(self):
+        self.triton_client.close()
+
+
+if __name__ == "__main__":
+    unittest.main()