support prometheus metrics (#1853)

Co-authored-by: Lianmin Zheng <lianminzheng@gmail.com>
Co-authored-by: Byron Hsu <byronhsu1230@gmail.com>

python/sglang/srt/managers/schedule_batch.py

@@ -31,6 +31,7 @@ ScheduleBatch -> ModelWorkerBatch -> ForwardBatch
 
 import dataclasses
 import logging
+import time
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -254,6 +255,16 @@ class Req:
         # For Qwen2-VL
         self.mrope_position_delta = []  # use mutable object
 
+        # Lifetime traces
+        # time when request is created and added to waitlist
+        self.created_time = None
+        # time when request is added to prefill batch
+        self.queued_time = None
+        # time when request is being processed
+        self.started_time = None
+        # time when request is finished
+        self.finished_time = None
+
     # whether request reached finished condition
     def finished(self) -> bool:
         return self.finished_reason is not None
@@ -1028,6 +1039,9 @@ class ScheduleBatch:
             f"#req={(len(self.reqs))})"
         )
 
+    def mark_reqs_started(self):
+        for req in self.reqs:
+            req.started_time = time.time()
 
 @dataclasses.dataclass
 class ModelWorkerBatch:

python/sglang/srt/managers/schedule_policy.py

@@ -17,6 +17,7 @@ limitations under the License.
 
 import os
 import random
+import time
 from collections import defaultdict
 from contextlib import contextmanager
 from enum import Enum, auto
@@ -306,6 +307,7 @@ class PrefillAdder:
             ):
                 # Non-chunked prefill
                 self.can_run_list.append(req)
+                req.queued_time = time.time()
                 self.tree_cache.inc_lock_ref(req.last_node)
                 self._prefill_one_req(
                     prefix_len,
@@ -324,6 +326,7 @@ class PrefillAdder:
                 req.extend_input_len = trunc_len
                 req.fill_ids = req.fill_ids[: len(req.prefix_indices) + trunc_len]
                 self.can_run_list.append(req)
+                req.queued_time = time.time()
                 self.new_inflight_req = req
                 self.tree_cache.inc_lock_ref(req.last_node)
                 self._prefill_one_req(prefix_len, trunc_len, 0)

python/sglang/srt/managers/scheduler.py

@@ -62,6 +62,8 @@ from sglang.srt.managers.tp_worker import TpModelWorker
 from sglang.srt.managers.tp_worker_overlap_thread import TpModelWorkerClient
 from sglang.srt.mem_cache.chunk_cache import ChunkCache
 from sglang.srt.mem_cache.radix_cache import RadixCache
+from sglang.srt.metrics.metrics_collector import PrometheusMetricsCollector
+from sglang.srt.metrics.metrics_types import Stats
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.utils import (
     broadcast_pyobj,
@@ -222,7 +224,8 @@ class Scheduler:
         self.forward_ct = 0
         self.forward_ct_decode = 0
         self.num_generated_tokens = 0
-        self.last_stats_tic = time.time()
+        self.last_stats_tic = time.time() # time of last stats for every iter
+        self.last_log_tic = time.time() # time of last log for print decode log
         self.stream_interval = server_args.stream_interval
 
         # Init chunked prefill
@@ -291,6 +294,15 @@ class Scheduler:
                 ],
                 with_stack=True,
             )
+        # Init metrics stats
+        self.stats = Stats()
+        self.metrics_collector = PrometheusMetricsCollector(
+            labels={
+                "model_name": self.server_args.served_model_name,
+                # TODO: Add lora name/path in the future,
+            },
+            max_model_len=self.max_total_num_tokens,
+        )
 
     def watchdog_thread(self):
         self.watchdog_last_forward_ct = 0
@@ -338,6 +350,11 @@ class Scheduler:
             else:
                 self.check_memory()
                 self.new_token_ratio = self.init_new_token_ratio
+            # log stats
+            if self.is_generation and self.server_args.enable_metrics:
+                stats = self.get_stats(batch)
+                self.log_stats(stats)
+            self.last_stats_tic = time.time()
 
             self.last_batch = batch
 
@@ -476,6 +493,7 @@ class Scheduler:
             self.max_req_len - len(req.origin_input_ids) - 1,
         )
 
+        req.created_time = time.time()
         self.waiting_queue.append(req)
 
     def handle_embedding_request(
@@ -504,9 +522,11 @@ class Scheduler:
         num_used = self.max_total_num_tokens - (
             self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
         )
-        throughput = self.num_generated_tokens / (time.time() - self.last_stats_tic)
+        throughput = self.num_generated_tokens / (time.time() - self.last_log_tic)
         self.num_generated_tokens = 0
-        self.last_stats_tic = time.time()
+        self.last_log_tic = time.time()
+        # set system stats
+        self.stats.token_usage = round(num_used / self.max_total_num_tokens, 2)
         num_running_reqs = len(self.running_batch.reqs) if self.running_batch else 0
         logger.info(
             f"Decode batch. "
@@ -676,6 +696,9 @@ class Scheduler:
                 self.token_to_kv_pool.available_size()
                 + self.tree_cache.evictable_size()
             )
+            # set system stats
+            self.stats.cache_hit_rate = round(100.0 * tree_cache_hit_rate, 2)
+            self.stats.token_usage = round(num_used / self.max_total_num_tokens, 2)
 
             if num_mixed_running > 0:
                 logger.info(
@@ -770,6 +793,7 @@ class Scheduler:
         if self.is_generation:
             if batch.forward_mode.is_decode() or batch.extend_num_tokens != 0:
                 model_worker_batch = batch.get_model_worker_batch()
+                batch.mark_reqs_started()
                 logits_output, next_token_ids = self.tp_worker.forward_batch_generation(
                     model_worker_batch
                 )
@@ -789,6 +813,88 @@ class Scheduler:
             embeddings = self.tp_worker.forward_batch_embedding(model_worker_batch)
             ret = embeddings, model_worker_batch.bid
         return ret
+    def get_stats(self,batch: ScheduleBatch):
+        # TODO: get stats for chunked prefill
+
+        now = time.time()
+        # system stats
+        #   Scheduler State
+        new_seq: int = 0
+        num_running_req = len(self.running_batch.reqs) if self.running_batch else 0
+        num_waiting_req = len(self.waiting_queue)
+        #   Cache State
+        cache_hit_rate: float = 0.0
+        token_usage: float = 0.0
+
+        # set stats from prefill
+        if self.stats is not None:
+            # new_seq=self.stats.new_seq
+            cache_hit_rate=self.stats.cache_hit_rate
+            token_usage=self.stats.token_usage
+        # Iteration stats
+        num_prompt_tokens_iter = 0
+        num_generation_tokens_iter = 0
+        time_to_first_tokens_iter: List[float] = []
+        time_per_output_tokens_iter: List[float] = []
+
+        # Request stats
+        #   Decode 
+        gen_throughput: float = 0.0
+        #   Latency
+        time_e2e_requests: List[float] = []
+        time_waiting_requests: List[float] = []
+        #   Metadata
+        num_prompt_tokens_requests: List[int] = []
+        num_generation_tokens_requests: List[int] = []
+        finished_reason_requests: List[str] = []
+
+        # _, next_token_ids, _ = result
+        if batch is not None:
+            num_generation_tokens_iter = len(batch.output_ids)
+            gen_throughput = round(num_generation_tokens_iter / (now - self.last_stats_tic), 2)
+
+            for i, req in enumerate(batch.reqs):
+                # NOTE: Batch forward mode is extend befor start decode,
+                if batch.forward_mode.is_extend():
+                    num_prompt_tokens_iter=len(batch.input_ids)+sum(batch.prefix_lens)
+                    time_to_first_tokens_iter.append(now - req.started_time)
+                else:
+                    time_per_output_tokens_iter.append(now-self.last_stats_tic)
+
+                if req.finished():
+                    time_e2e_requests.append(now - req.created_time)
+                    time_waiting_requests.append(req.queued_time - req.created_time)
+                    num_prompt_tokens_requests.append(len(req.origin_input_ids))
+                    num_generation_tokens_requests.append(len(req.output_ids))
+                    finished_reason_requests.append(                            
+                            req.finished_reason.to_json()
+                            if req.finished_reason is not None
+                            else None)
+    
+        return Stats(
+            new_seq=new_seq,
+            num_running_req=num_running_req,
+            num_waiting_req=num_waiting_req,
+            cache_hit_rate=cache_hit_rate,
+            token_usage=token_usage,
+            num_prompt_tokens_iter=num_prompt_tokens_iter,
+            num_generation_tokens_iter=num_generation_tokens_iter,
+            time_to_first_tokens_iter=time_to_first_tokens_iter,
+            time_per_output_tokens_iter=time_per_output_tokens_iter,
+            gen_throughput=gen_throughput,
+            time_e2e_requests=time_e2e_requests,
+            time_waiting_requests=time_waiting_requests,
+            num_prompt_tokens_requests=num_prompt_tokens_requests,
+            num_generation_tokens_requests=num_generation_tokens_requests,
+            finished_reason_requests=finished_reason_requests,
+            context_len=self.model_config.context_len,
+            max_total_num_tokens=self.max_total_num_tokens,
+            max_prefill_tokens=self.max_prefill_tokens,
+            max_running_requests=self.max_running_requests,
+        )
+
+    def log_stats(self,stats:Stats):
+        self.metrics_collector.log_stats(stats)
 
     def process_batch_result(self, batch: ScheduleBatch, result):
         if batch.forward_mode.is_decode():

python/sglang/srt/metrics/metrics_collector.py

@@ -0,0 +1,297 @@
+"""
+Copyright 2023-2024 SGLang Team
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+"""Utilities for Prometheus Metrics Collection."""
+
+import logging
+from abc import ABC, abstractmethod
+from typing import Counter as CollectionsCounter
+from typing import Dict, List, Union
+
+import numpy as np
+from prometheus_client import Counter, Gauge, Histogram
+
+from sglang.srt.metrics.metrics_types import Stats
+
+
+class Metrics:
+    """
+    SGLang Metrics
+    """
+
+    def __init__(self, labelnames: List[str], max_model_len):
+
+        # Configuration Stats
+        self.max_total_num_tokens = Gauge(
+            name="sglang:max_total_num_tokens",
+            documentation="Maximum total number of tokens",
+            labelnames=labelnames,
+            multiprocess_mode="min",
+        )  # static across processes
+
+        self.max_prefill_tokens = Gauge(
+            name="sglang:max_prefill_tokens",
+            documentation="Maximum prefill tokens",
+            labelnames=labelnames,
+            multiprocess_mode="min",
+        )  # static across processes
+
+        self.max_running_requests = Gauge(
+            name="sglang:max_running_requests",
+            documentation="Maximum running requests",
+            labelnames=labelnames,
+            multiprocess_mode="min",
+        )  # static across processes
+
+        self.context_len = Gauge(
+            name="sglang:context_len",
+            documentation="Context length",
+            labelnames=labelnames,
+            multiprocess_mode="min",
+        )  # static across processes
+        # Decode Stats
+        self.num_running_sys = Gauge(
+            name="sglang:num_requests_running",
+            documentation="Number of requests currently running on GPU",
+            labelnames=labelnames,
+            multiprocess_mode="sum",
+        )
+        self.num_waiting_sys = Gauge(
+            name="sglang:num_requests_waiting",
+            documentation="Number of requests waiting to be processed.",
+            labelnames=labelnames,
+            multiprocess_mode="sum",
+        )
+        self.gen_throughput = Gauge(
+            name="sglang:gen_throughput",
+            documentation="Gen token throughput (token/s)",
+            labelnames=labelnames,
+            multiprocess_mode="sum",
+        )
+        self.token_usage = Gauge(
+            name="sglang:token_usage",
+            documentation="Total token usage",
+            labelnames=labelnames,
+            multiprocess_mode="sum",
+        )
+        # System Stats
+        #   KV Cache Usage in %
+        # self.gpu_cache_usage_sys = Gauge(
+        #     "gpu_cache_usage_perc",
+        #     "GPU KV-cache usage. 1 means 100 percent usage.",
+        #     labelnames=labelnames,
+        #     multiprocess_mode="sum")
+
+        self.new_seq = Gauge(
+            name="sglang:new_seq",
+            documentation="Number of new sequences",
+            labelnames=labelnames,
+            multiprocess_mode="sum",
+        )
+        self.new_token = Gauge(
+            name="sglang:new_token",
+            documentation="Number of new token",
+            labelnames=labelnames,
+            multiprocess_mode="sum",
+        )
+        #   Prefix caching block hit rate
+        self.cached_token = Gauge(
+            name="sglang:cached_token",
+            documentation="Number of cached token",
+            labelnames=labelnames,
+            multiprocess_mode="sum",
+        )
+        self.cache_hit_rate = Gauge(
+            name="sglang:cache_hit_rate",
+            documentation="Cache hit rate",
+            labelnames=labelnames,
+            multiprocess_mode="sum",
+        )
+        self.queue_req = Gauge(
+            name="sglang:queue_req",
+            documentation="Number of queued requests",
+            labelnames=labelnames,
+            multiprocess_mode="sum",
+        )
+
+        # Iteration stats
+        self.counter_prompt_tokens = Counter(
+            name="sglang:prompt_tokens_total",
+            documentation="Number of prefill tokens processed.",
+            labelnames=labelnames)
+        self.counter_generation_tokens = Counter(
+            name="sglang:generation_tokens_total",
+            documentation="Number of generation tokens processed.",
+            labelnames=labelnames)
+        self.histogram_time_to_first_token = Histogram(
+            name="sglang:time_to_first_token_seconds",
+            documentation="Histogram of time to first token in seconds.",
+            labelnames=labelnames,
+            buckets=[
+                0.001, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.25, 0.5,
+                0.75, 1.0, 2.5, 5.0, 7.5, 10.0, 15.0, 20.0, 25.0, 30.0
+            ])
+        self.histogram_time_per_output_token = Histogram(
+            name="sglang:time_per_output_token_seconds",
+            documentation="Histogram of time per output token in seconds.",
+            labelnames=labelnames,
+            buckets=[
+                0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.04, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75,
+                1.0, 2.5
+            ])
+        
+        # Request Stats
+        #   Metadata
+        self.num_prompt_tokens_requests = Histogram(
+            name="sglang:request_prompt_tokens",
+            documentation="Number of prefill tokens processed",
+            labelnames=labelnames,
+            buckets=build_1_2_5_buckets(max_model_len),
+        )
+        self.num_generation_tokens_requests = Histogram(
+            name="sglang:request_generation_tokens",
+            documentation="Number of generation tokens processed.",
+            labelnames=labelnames,
+            buckets=build_1_2_5_buckets(max_model_len),
+        )
+        self.finished_reason_requests = Counter(
+            name="sglang:request_success_total",
+            documentation="Count of successfully processed requests.",
+            labelnames=labelnames + ["finished_reason"],
+        )
+        self.histogram_time_e2e_requests = Histogram(
+            name="sglang:e2e_request_latency_seconds",
+            documentation="Histogram of End-to-end request latency in seconds",
+            labelnames=labelnames,
+            buckets=build_1_2_5_buckets(max_model_len),
+        )
+        self.histogram_time_waiting_requests = Histogram(
+            name="sglang:waiting_request_latency_seconds",
+            documentation="Histogram of request waiting time in seconds",
+            labelnames=labelnames,
+            buckets=build_1_2_5_buckets(max_model_len),
+        )
+        self.histogram_time_decode_requests = Histogram(
+            name="sglang:decode_request_latency_seconds",
+            documentation="Histogram of request decoding time in seconds",
+            labelnames=labelnames,
+            buckets=build_1_2_5_buckets(max_model_len),
+        )
+
+
+class MetricsCollector(ABC):
+    """
+    SGLang Metrics Collector
+    """
+
+    @abstractmethod
+    def log_stats(self, stats: Stats) -> None:
+        pass
+
+
+class PrometheusMetricsCollector(MetricsCollector):
+    """
+    SGLang Metrics Collector
+    """
+
+    def __init__(self, labels: Dict[str, str], max_model_len: int) -> None:
+        self.labels = labels
+        self.metrics = Metrics(
+            labelnames=list(labels.keys()), max_model_len=max_model_len
+        )
+
+    def _log_gauge(self, gauge, data: Union[int, float]) -> None:
+        # Convenience function for logging to gauge.
+        gauge.labels(**self.labels).set(data)
+
+    def _log_counter(self, counter, data: Union[int, float]) -> None:
+        # Convenience function for logging to counter.
+        counter.labels(**self.labels).inc(data)
+
+    def _log_counter_labels(
+        self, counter, data: CollectionsCounter, label_key: str
+    ) -> None:
+        # Convenience function for collection counter of labels.
+        for label, count in data.items():
+            counter.labels(**{**self.labels, label_key: label}).inc(count)
+
+    def _log_histogram(self, histogram, data: Union[List[int], List[float]]) -> None:
+        # Convenience function for logging list to histogram.
+        for datum in data:
+            histogram.labels(**self.labels).observe(datum)
+
+    def log_stats(self, stats: Stats) -> None:
+        self._log_gauge(self.metrics.max_total_num_tokens, stats.max_total_num_tokens)
+        self._log_gauge(self.metrics.max_prefill_tokens, stats.max_prefill_tokens)
+        self._log_gauge(self.metrics.max_running_requests, stats.max_running_requests)
+        self._log_gauge(self.metrics.context_len, stats.context_len)
+        self._log_histogram(
+            self.metrics.num_prompt_tokens_requests, stats.num_prompt_tokens_requests
+        )
+        self._log_histogram(
+            self.metrics.num_generation_tokens_requests,
+            stats.num_generation_tokens_requests,
+        )
+
+        self._log_counter(self.metrics.counter_prompt_tokens,
+                          stats.num_prompt_tokens_iter)
+        self._log_counter(self.metrics.counter_generation_tokens,
+                          stats.num_generation_tokens_iter)
+        self._log_histogram(self.metrics.histogram_time_to_first_token,
+                            stats.time_to_first_tokens_iter)
+        self._log_histogram(self.metrics.histogram_time_per_output_token,
+                            stats.time_per_output_tokens_iter)
+        
+        # self._log_gauge(self.metrics.gpu_cache_usage_sys, stats.gpu_cache_usage_sys)
+        self._log_gauge(self.metrics.num_running_sys, stats.num_running_req)
+        self._log_gauge(self.metrics.num_waiting_sys, stats.num_waiting_req)
+        self._log_gauge(self.metrics.gen_throughput, stats.gen_throughput)
+        self._log_gauge(self.metrics.token_usage, stats.token_usage)
+        self._log_histogram(
+            self.metrics.histogram_time_e2e_requests, stats.time_e2e_requests
+        )
+        self._log_histogram(
+            self.metrics.histogram_time_waiting_requests, stats.time_waiting_requests
+        )
+        self._log_histogram(
+            self.metrics.histogram_time_decode_requests, stats.time_decode_requests
+        )
+        self._log_gauge(self.metrics.new_seq, stats.new_seq)
+        self._log_gauge(self.metrics.new_token, stats.new_token)
+        self._log_gauge(self.metrics.cached_token, stats.cached_token)
+        self._log_gauge(self.metrics.cache_hit_rate, stats.cache_hit_rate)
+        self._log_gauge(self.metrics.queue_req, stats.queue_req)
+
+
+def build_1_2_5_buckets(max_value: int) -> List[int]:
+    """
+    Builds a list of buckets with increasing powers of 10 multiplied by
+    mantissa values (1, 2, 5) until the value exceeds the specified maximum.
+
+    Example:
+    >>> build_1_2_5_buckets(100)
+    [1, 2, 5, 10, 20, 50, 100]
+    """
+    mantissa_lst = [1, 2, 5]
+    exponent = 0
+    buckets: List[int] = []
+    while True:
+        for m in mantissa_lst:
+            value = m * 10**exponent
+            if value <= max_value:
+                buckets.append(value)
+            else:
+                return buckets
+        exponent += 1

python/sglang/srt/metrics/metrics_types.py

@@ -0,0 +1,57 @@
+"""
+Copyright 2023-2024 SGLang Team
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+"""Metrics Types"""
+
+from dataclasses import dataclass, field
+from typing import List
+
+
+@dataclass
+class Stats:
+    # config
+    max_total_num_tokens: int = 0
+    max_prefill_tokens: int = 0
+    max_running_requests: int = 0
+    context_len: int = 0
+    # request stats
+    num_prompt_tokens_requests: List[int] = field(default_factory=list)
+    num_generation_tokens_requests: List[int] = field(default_factory=list)
+    finished_reason_requests: List[str] = field(default_factory=list)
+    # decode stats
+    num_running_req: int = 0
+    num_waiting_req: int = 0
+    gen_throughput: float = 0.0
+    num_token: int = 0
+    token_usage: float = 0.0
+    waiting_queue: int = 0
+    time_e2e_requests: List[float] = field(default_factory=list)
+    time_waiting_requests: List[float] = field(default_factory=list)
+    time_decode_requests: List[float] = field(default_factory=list)
+    # system stats
+    token_usage: float = 0.0
+    is_mixed_chunk: bool = False
+    new_seq: int = 0
+    new_token: int = 0
+    cached_token: int = 0
+    cache_hit_rate: float = 0.0
+    running_req: int = 0
+    queue_req: int = 0
+
+    # Iteration stats (should have _iter suffix)
+    num_prompt_tokens_iter: int = 0
+    num_generation_tokens_iter: int = 0
+    time_to_first_tokens_iter: List[float] = field(default_factory=list)
+    time_per_output_tokens_iter: List[float] = field(default_factory=list)

python/sglang/srt/server.py

@@ -25,12 +25,15 @@ import json
 import logging
 import multiprocessing as mp
 import os
+import re
+import tempfile
 import threading
 import time
 from http import HTTPStatus
 from typing import AsyncIterator, Dict, List, Optional, Union
 
 import orjson
+from starlette.routing import Mount
 
 # Fix a bug of Python threading
 setattr(threading, "_register_atexit", lambda *args, **kwargs: None)
@@ -86,6 +89,10 @@ from sglang.utils import get_exception_traceback
 
 logger = logging.getLogger(__name__)
 
+# Temporary directory for prometheus multiprocess mode
+# Cleaned up automatically when this object is garbage collected
+prometheus_multiproc_dir: tempfile.TemporaryDirectory
+
 asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
 
 
@@ -412,6 +419,18 @@ def launch_engine(
     for i in range(len(scheduler_pipe_readers)):
         scheduler_pipe_readers[i].recv()
 
+def add_prometheus_middleware(app: FastAPI):
+    # Adapted from https://github.com/vllm-project/vllm/blob/v0.6.1/vllm/entrypoints/openai/api_server.py#L216
+    from prometheus_client import CollectorRegistry, make_asgi_app, multiprocess
+
+    registry = CollectorRegistry()
+    multiprocess.MultiProcessCollector(registry)
+    metrics_route = Mount("/metrics", make_asgi_app(registry=registry))
+
+    # Workaround for 307 Redirect for /metrics
+    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
+    app.routes.append(metrics_route)
+
 
 def launch_server(
     server_args: ServerArgs,
@@ -439,6 +458,11 @@ def launch_server(
     if server_args.api_key:
         add_api_key_middleware(app, server_args.api_key)
 
+    # add prometheus middleware
+    if server_args.enable_metrics:
+        _set_prometheus_env()
+        add_prometheus_middleware(app)
+
     # Send a warmup request
     t = threading.Thread(
         target=_wait_and_warmup, args=(server_args, pipe_finish_writer)
@@ -466,6 +490,21 @@ def launch_server(
     finally:
         t.join()
 
+def _set_prometheus_env():
+    # Set prometheus multiprocess directory
+    # sglang uses prometheus multiprocess mode
+    # we need to set this before importing prometheus_client
+    # https://prometheus.github.io/client_python/multiprocess/
+    global prometheus_multiproc_dir
+    if "PROMETHEUS_MULTIPROC_DIR" in os.environ:
+        logger.debug(f"User set PROMETHEUS_MULTIPROC_DIR detected.")
+        prometheus_multiproc_dir = tempfile.TemporaryDirectory(
+            dir=os.environ["PROMETHEUS_MULTIPROC_DIR"]
+        )
+    else:
+        prometheus_multiproc_dir = tempfile.TemporaryDirectory()
+        os.environ["PROMETHEUS_MULTIPROC_DIR"] = prometheus_multiproc_dir.name
+    logger.debug(f"PROMETHEUS_MULTIPROC_DIR: {os.environ['PROMETHEUS_MULTIPROC_DIR']}")
 
 def _set_envs_and_config(server_args: ServerArgs):
     # Set global environments

python/sglang/srt/server_args.py

@@ -70,6 +70,7 @@ class ServerArgs:
     log_level_http: Optional[str] = None
     log_requests: bool = False
     show_time_cost: bool = False
+    enable_metrics: bool = False
 
     # Other
     api_key: Optional[str] = None
@@ -414,6 +415,12 @@ class ServerArgs:
             action="store_true",
             help="Show time cost of custom marks.",
         )
+        parser.add_argument(
+            "--enable-metrics",
+            action="store_true",
+            help="Enable log prometheus metrics.",
+        )
+
         parser.add_argument(
             "--api-key",
             type=str,