Add Production Metrics in Prometheus format (#1890)

docs/source/index.rst

@@ -67,6 +67,7 @@ Documentation
    serving/deploying_with_triton
    serving/deploying_with_docker
    serving/serving_with_langchain
+   serving/metrics
 
 .. toctree::
    :maxdepth: 1

docs/source/serving/metrics.rst

+Production Metrics
+==================
+
+vLLM exposes a number of metrics that can be used to monitor the health of the
+system. These metrics are exposed via the `/metrics` endpoint on the vLLM
+OpenAI compatible API server.
+
+The following metrics are exposed:
+
+.. literalinclude:: ../../../vllm/engine/metrics.py
+    :language: python
+    :start-after: begin-metrics-definitions
+    :end-before: end-metrics-definitions

requirements.txt

@@ -12,3 +12,4 @@ xformers >= 0.0.22.post7  # Required for CUDA 12.1.
 fastapi
 uvicorn[standard]
 pydantic == 1.10.13  # Required for OpenAI server.
+aioprometheus[starlette]

vllm/engine/llm_engine.py

@@ -7,6 +7,7 @@ from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
                          SchedulerConfig)
 from vllm.core.scheduler import Scheduler, SchedulerOutputs
 from vllm.engine.arg_utils import EngineArgs
+from vllm.engine.metrics import record_metrics
 from vllm.engine.ray_utils import RayWorkerVllm, initialize_cluster, ray
 from vllm.logger import init_logger
 from vllm.outputs import RequestOutput
@@ -591,8 +592,8 @@ class LLMEngine:
         else:
             self.num_generation_tokens.append((now, num_batched_tokens))
 
-        elapsed_time = now - self.last_logging_time
-        if elapsed_time < _LOGGING_INTERVAL_SEC:
+        should_log = now - self.last_logging_time >= _LOGGING_INTERVAL_SEC
+        if not should_log:
             return
 
         # Discard the old stats.
@@ -631,6 +632,16 @@ class LLMEngine:
         else:
             cpu_cache_usage = 0.0
 
+        record_metrics(
+            avg_prompt_throughput=avg_prompt_throughput,
+            avg_generation_throughput=avg_generation_throughput,
+            scheduler_running=len(self.scheduler.running),
+            scheduler_swapped=len(self.scheduler.swapped),
+            scheduler_waiting=len(self.scheduler.waiting),
+            gpu_cache_usage=gpu_cache_usage,
+            cpu_cache_usage=cpu_cache_usage,
+        )
+
         logger.info("Avg prompt throughput: "
                     f"{avg_prompt_throughput:.1f} tokens/s, "
                     "Avg generation throughput: "

vllm/engine/metrics.py

+from aioprometheus import Gauge
+
+# The begin-* and end* here are used by the documentation generator
+# to extract the metrics definitions.
+
+# begin-metrics-definitions
+gauge_avg_prompt_throughput = Gauge("vllm:avg_prompt_throughput_toks_per_s",
+                                    "Average prefill throughput in tokens/s.")
+gauge_avg_generation_throughput = Gauge(
+    "vllm:avg_generation_throughput_toks_per_s",
+    "Average generation throughput in tokens/s.")
+
+gauge_scheduler_running = Gauge(
+    "vllm:num_requests_running",
+    "Number of requests that is currently running for inference.")
+gauge_scheduler_swapped = Gauge("vllm:num_requests_swapped",
+                                "Number requests swapped to CPU.")
+gauge_scheduler_waiting = Gauge("vllm:num_requests_waiting",
+                                "Number of requests waiting to be processed.")
+
+gauge_gpu_cache_usage = Gauge(
+    "vllm:gpu_cache_usage_perc",
+    "GPU KV-cache usage. 1 means 100 percent usage.")
+gauge_cpu_cache_usage = Gauge(
+    "vllm:cpu_cache_usage_perc",
+    "CPU KV-cache usage. 1 means 100 percent usage.")
+# end-metrics-definitions
+
+labels = {}
+
+
+def add_global_metrics_labels(**kwargs):
+    labels.update(kwargs)
+
+
+def record_metrics(
+    avg_prompt_throughput: float,
+    avg_generation_throughput: float,
+    scheduler_running: int,
+    scheduler_swapped: int,
+    scheduler_waiting: int,
+    gpu_cache_usage: float,
+    cpu_cache_usage: float,
+):
+    gauge_avg_prompt_throughput.set(labels, avg_prompt_throughput)
+    gauge_avg_generation_throughput.set(labels, avg_generation_throughput)
+    gauge_scheduler_running.set(labels, scheduler_running)
+    gauge_scheduler_swapped.set(labels, scheduler_swapped)
+    gauge_scheduler_waiting.set(labels, scheduler_waiting)
+    gauge_gpu_cache_usage.set(labels, gpu_cache_usage)
+    gauge_cpu_cache_usage.set(labels, cpu_cache_usage)

vllm/entrypoints/openai/api_server.py

@@ -9,6 +9,8 @@ import time
 from http import HTTPStatus
 from typing import AsyncGenerator, Dict, List, Optional, Tuple, Union
 
+from aioprometheus import MetricsMiddleware
+from aioprometheus.asgi.starlette import metrics
 import fastapi
 import uvicorn
 from fastapi import Request
@@ -18,6 +20,7 @@ from fastapi.responses import JSONResponse, StreamingResponse, Response
 
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.engine.metrics import add_global_metrics_labels
 from vllm.entrypoints.openai.protocol import (
     CompletionRequest, CompletionResponse, CompletionResponseChoice,
     CompletionResponseStreamChoice, CompletionStreamResponse,
@@ -82,6 +85,10 @@ def parse_args():
     return parser.parse_args()
 
 
+app.add_middleware(MetricsMiddleware)  # Trace HTTP server metrics
+app.add_route("/metrics", metrics)  # Exposes HTTP metrics
+
+
 def create_error_response(status_code: HTTPStatus,
                           message: str) -> JSONResponse:
     return JSONResponse(ErrorResponse(message=message,
@@ -722,6 +729,9 @@ if __name__ == "__main__":
         trust_remote_code=engine_model_config.trust_remote_code)
     load_chat_template(args, tokenizer)
 
+    # Register labels for metrics
+    add_global_metrics_labels(model_name=engine_args.model)
+
     uvicorn.run(app,
                 host=args.host,
                 port=args.port,