282 lines
9.1 KiB
Python
282 lines
9.1 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utilities for Prometheus Metrics Collection."""
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import time
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from dataclasses import dataclass
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from typing import Dict, Union
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@dataclass
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class SchedulerStats:
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num_running_reqs: int = 0
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num_used_tokens: int = 0
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token_usage: float = 0.0
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gen_throughput: float = 0.0
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num_queue_reqs: int = 0
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cache_hit_rate: float = 0.0
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spec_accept_length: float = 0.0
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class SchedulerMetricsCollector:
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def __init__(self, labels: Dict[str, str]) -> None:
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# We need to import prometheus_client after setting the env variable `PROMETHEUS_MULTIPROC_DIR`
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from prometheus_client import Gauge
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self.labels = labels
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self.last_log_time = time.time()
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self.num_running_reqs = Gauge(
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name="sglang:num_running_reqs",
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documentation="The number of running requests.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.num_used_tokens = Gauge(
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name="sglang:num_used_tokens",
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documentation="The number of used tokens.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.token_usage = Gauge(
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name="sglang:token_usage",
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documentation="The token usage.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.gen_throughput = Gauge(
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name="sglang:gen_throughput",
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documentation="The generation throughput (token/s).",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.num_queue_reqs = Gauge(
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name="sglang:num_queue_reqs",
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documentation="The number of requests in the waiting queue.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.cache_hit_rate = Gauge(
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name="sglang:cache_hit_rate",
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documentation="The prefix cache hit rate.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.spec_accept_length = Gauge(
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name="sglang:spec_accept_length",
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documentation="The average acceptance length of speculative decoding.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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def _log_gauge(self, gauge, data: Union[int, float]) -> None:
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# Convenience function for logging to gauge.
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gauge.labels(**self.labels).set(data)
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def log_stats(self, stats: SchedulerStats) -> None:
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self._log_gauge(self.num_running_reqs, stats.num_running_reqs)
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self._log_gauge(self.num_used_tokens, stats.num_used_tokens)
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self._log_gauge(self.token_usage, stats.token_usage)
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self._log_gauge(self.gen_throughput, stats.gen_throughput)
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self._log_gauge(self.num_queue_reqs, stats.num_queue_reqs)
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self._log_gauge(self.cache_hit_rate, stats.cache_hit_rate)
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self._log_gauge(self.spec_accept_length, stats.spec_accept_length)
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self.last_log_time = time.time()
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class TokenizerMetricsCollector:
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def __init__(self, labels: Dict[str, str]) -> None:
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# We need to import prometheus_client after setting the env variable `PROMETHEUS_MULTIPROC_DIR`
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from prometheus_client import Counter, Histogram
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self.labels = labels
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self.prompt_tokens_total = Counter(
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name="sglang:prompt_tokens_total",
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documentation="Number of prefill tokens processed.",
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labelnames=labels.keys(),
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)
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self.generation_tokens_total = Counter(
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name="sglang:generation_tokens_total",
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documentation="Number of generation tokens processed.",
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labelnames=labels.keys(),
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)
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self.cached_tokens_total = Counter(
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name="sglang:cached_tokens_total",
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documentation="Number of cached prompt tokens.",
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labelnames=labels.keys(),
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)
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self.num_requests_total = Counter(
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name="sglang:num_requests_total",
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documentation="Number of requests processed.",
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labelnames=labels.keys(),
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)
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self.histogram_time_to_first_token = Histogram(
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name="sglang:time_to_first_token_seconds",
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documentation="Histogram of time to first token in seconds.",
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labelnames=labels.keys(),
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buckets=[
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0.1,
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0.3,
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0.5,
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0.7,
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0.9,
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1,
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2,
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4,
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6,
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8,
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10,
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20,
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40,
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60,
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80,
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120,
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160,
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],
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)
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self.histogram_time_per_output_token = Histogram(
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name="sglang:time_per_output_token_seconds",
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documentation="Histogram of time per output token in seconds.",
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labelnames=labels.keys(),
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buckets=[
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0.002,
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0.005,
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0.010,
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0.020,
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0.030,
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0.040,
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0.050,
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0.060,
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0.070,
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0.080,
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0.090,
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0.100,
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0.150,
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0.200,
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0.300,
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0.400,
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0.600,
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0.800,
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1.000,
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2.000,
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],
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)
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self.histogram_inter_token_latency_seconds = Histogram(
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name="sglang:inter_token_latency_seconds",
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documentation="Histogram of inter-token latency in seconds.",
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labelnames=labels.keys(),
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buckets=[
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0.002,
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0.004,
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0.006,
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0.008,
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0.010,
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0.015,
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0.020,
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0.025,
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0.030,
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0.035,
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0.040,
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0.050,
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0.075,
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0.100,
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0.150,
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0.200,
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0.300,
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0.400,
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0.500,
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0.750,
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1.000,
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2.000,
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],
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)
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self.histogram_e2e_request_latency = Histogram(
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name="sglang:e2e_request_latency_seconds",
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documentation="Histogram of End-to-end request latency in seconds",
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labelnames=labels.keys(),
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buckets=[
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0.1,
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0.2,
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0.4,
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0.8,
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1,
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2,
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5,
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10,
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20,
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40,
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60,
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80,
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100,
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150,
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200,
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250,
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300,
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350,
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500,
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1000,
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],
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)
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def _log_histogram(self, histogram, data: Union[int, float]) -> None:
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histogram.labels(**self.labels).observe(data)
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def observe_one_finished_request(
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self,
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prompt_tokens: int,
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generation_tokens: int,
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cached_tokens: int,
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e2e_latency: float,
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):
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self.prompt_tokens_total.labels(**self.labels).inc(prompt_tokens)
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self.generation_tokens_total.labels(**self.labels).inc(generation_tokens)
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self.cached_tokens_total.labels(**self.labels).inc(cached_tokens)
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self.num_requests_total.labels(**self.labels).inc(1)
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self._log_histogram(self.histogram_e2e_request_latency, e2e_latency)
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if generation_tokens >= 1:
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self.histogram_time_per_output_token.labels(**self.labels).observe(
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e2e_latency / generation_tokens
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)
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def observe_time_to_first_token(self, value: float):
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self.histogram_time_to_first_token.labels(**self.labels).observe(value)
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def observe_inter_token_latency(self, internval: float, num_new_tokens: int):
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adjusted_interval = internval / num_new_tokens
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# A faster version of the Histogram::observe which observes multiple values at the same time.
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# reference: https://github.com/prometheus/client_python/blob/v0.21.1/prometheus_client/metrics.py#L639
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his = self.histogram_inter_token_latency_seconds.labels(**self.labels)
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his._sum.inc(internval)
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for i, bound in enumerate(his._upper_bounds):
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if adjusted_interval <= bound:
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his._buckets[i].inc(num_new_tokens)
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break
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