Simplify prometheus metrics (#1981)
Co-authored-by: Mohit Reddy <mohitreddy1996@users.noreply.github.com>
This commit is contained in:
Lianmin Zheng
211
python/sglang/srt/metrics/collector.py
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211
python/sglang/srt/metrics/collector.py
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"""
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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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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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.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="sum",
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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="sum",
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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 generate throughput (token/s)",
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labelnames=labels.keys(),
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multiprocess_mode="sum",
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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="sum",
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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 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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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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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.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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0.001,
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0.005,
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0.01,
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0.02,
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0.04,
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0.06,
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0.08,
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0.1,
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0.25,
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0.5,
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0.75,
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1.0,
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2.5,
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5.0,
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7.5,
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10.0,
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15.0,
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20.0,
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25.0,
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30.0,
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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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0.005,
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0.01,
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0.015,
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0.02,
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0.025,
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0.03,
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0.04,
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0.05,
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0.075,
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0.1,
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0.15,
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0.2,
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0.3,
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0.4,
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0.5,
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0.75,
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1.0,
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2.5,
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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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0.3,
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0.5,
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0.8,
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1.0,
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1.5,
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2.0,
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2.5,
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5.0,
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10.0,
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15.0,
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20.0,
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30.0,
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40.0,
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50.0,
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60.0,
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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 _log_counter(self, counter, data: Union[int, float]) -> None:
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# Convenience function for logging to counter.
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counter.labels(**self.labels).inc(data)
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def inc_prompt_tokens(self, value: int):
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self._log_counter(self.prompt_tokens_total, value)
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def inc_generation_tokens(self, value: int):
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self._log_counter(self.generation_tokens_total, value)
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def observe_time_to_first_token(self, value: Union[float, int]):
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self._log_histogram(self.histogram_time_to_first_token, value)
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def observe_time_per_output_token(self, value: Union[float, int]):
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self._log_histogram(self.histogram_time_per_output_token, value)
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def observe_e2e_request_latency(self, value: Union[float, int]):
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self._log_histogram(self.histogram_e2e_request_latency, value)
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