941 lines
33 KiB
Python
941 lines
33 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, field
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from typing import Dict, List, Optional, Union
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from sglang.srt.disaggregation.utils import DisaggregationMode
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from sglang.srt.metrics.utils import exponential_buckets, generate_buckets
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from sglang.srt.server_args import ServerArgs
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from sglang.srt.utils import get_bool_env_var
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SGLANG_TEST_REQUEST_TIME_STATS = get_bool_env_var("SGLANG_TEST_REQUEST_TIME_STATS")
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@dataclass
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class TimeStats:
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"""
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Store the timestamps for each stage of a request.
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Unified: wait_queue -> forward -> completion
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Prefill: bootstrap_queue -> wait_queue -> forward -> transfer_queue -> completion
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Decode: prealloc_queue -> transfer_queue -> wait_queue -> forward -> completion
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"""
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disagg_mode: DisaggregationMode = DisaggregationMode.NULL
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lb_entry_time: float = 0.0
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wait_queue_entry_time: float = 0.0
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forward_entry_time: float = 0.0
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completion_time: float = 0.0
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prefill_bootstrap_queue_entry_time: float = 0.0
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prefill_transfer_queue_entry_time: float = 0.0
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decode_prealloc_queue_entry_time: float = 0.0
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decode_transfer_queue_entry_time: float = 0.0
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def get_queueing_time(self) -> float:
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return self.forward_entry_time - self.wait_queue_entry_time
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def convert_to_duration(self) -> str:
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if self.disagg_mode == DisaggregationMode.NULL:
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queue_duration = self.forward_entry_time - self.wait_queue_entry_time
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forward_duration = self.completion_time - self.forward_entry_time
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if SGLANG_TEST_REQUEST_TIME_STATS:
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assert (
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queue_duration >= 0 and forward_duration >= 0
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), f"queue_duration={queue_duration} < 0 or forward_duration={forward_duration} < 0"
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return f"queue_duration={self.format_duration(queue_duration)}, forward_duration={self.format_duration(forward_duration)}, start_time={self.wait_queue_entry_time:.3f}"
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elif self.disagg_mode == DisaggregationMode.PREFILL:
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bootstrap_duration = (
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self.wait_queue_entry_time - self.prefill_bootstrap_queue_entry_time
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)
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queue_duration = self.forward_entry_time - self.wait_queue_entry_time
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forward_duration = self.completion_time - self.forward_entry_time
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if SGLANG_TEST_REQUEST_TIME_STATS:
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if self.wait_queue_entry_time > 0:
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assert (
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bootstrap_duration >= 0
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and queue_duration >= 0
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and forward_duration >= 0
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), f"bootstrap_duration={bootstrap_duration} < 0 or queue_duration={queue_duration} < 0 or forward_duration={forward_duration} < 0"
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return f"bootstrap_duration={self.format_duration(bootstrap_duration)}, queue_duration={self.format_duration(queue_duration)}, forward_duration={self.format_duration(forward_duration)}, start_time={self.prefill_bootstrap_queue_entry_time:.3f}"
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elif self.disagg_mode == DisaggregationMode.DECODE:
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prealloc_duration = (
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self.decode_transfer_queue_entry_time
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- self.decode_prealloc_queue_entry_time
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)
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transfer_duration = (
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self.wait_queue_entry_time - self.decode_transfer_queue_entry_time
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)
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queue_duration = self.forward_entry_time - self.wait_queue_entry_time
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forward_duration = self.completion_time - self.forward_entry_time
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if SGLANG_TEST_REQUEST_TIME_STATS:
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if self.wait_queue_entry_time > 0:
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assert (
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prealloc_duration >= 0
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and transfer_duration >= 0
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and queue_duration >= 0
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and forward_duration >= 0
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), f"prealloc_duration={prealloc_duration} < 0 or transfer_duration={transfer_duration} < 0 or queue_duration={queue_duration} < 0 or forward_duration={forward_duration} < 0. {self=}"
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return f"prealloc_duration={self.format_duration(prealloc_duration)}, transfer_duration={self.format_duration(transfer_duration)}, queue_duration={self.format_duration(queue_duration)}, forward_duration={self.format_duration(forward_duration)}, start_time={self.decode_prealloc_queue_entry_time:.3f}"
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else:
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return "Unknown Time Stats"
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def format_duration(self, duration: float) -> str:
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return f"{duration * 1e3:.2f}ms"
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def disagg_mode_str(self) -> str:
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if self.disagg_mode == DisaggregationMode.NULL:
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return "unified"
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elif self.disagg_mode == DisaggregationMode.DECODE:
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return "decode"
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elif self.disagg_mode == DisaggregationMode.PREFILL:
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return "prefill"
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else:
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return "unknown"
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@dataclass
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class SchedulerStats:
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# Basics
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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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swa_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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num_grammar_queue_reqs: int = 0
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num_running_reqs_offline_batch: int = 0
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cache_hit_rate: float = 0.0
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# Speculative decoding
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spec_accept_length: float = 0.0
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# Retract
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num_retracted_reqs: int = 0
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num_paused_reqs: int = 0
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# PD disaggregation
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num_prefill_prealloc_queue_reqs: int = 0
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num_prefill_inflight_queue_reqs: int = 0
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num_decode_prealloc_queue_reqs: int = 0
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num_decode_transfer_queue_reqs: int = 0
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kv_transfer_speed_gb_s: float = 0.0
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kv_transfer_latency_ms: float = 0.0
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# Utilization
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utilization: float = 0.0
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max_running_requests_under_SLO: Optional[int] = None
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# Engine startup
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engine_startup_time: float = 0.0
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engine_load_weights_time: 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 Counter, Gauge, Histogram
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self.labels = labels
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self.last_log_time = time.perf_counter()
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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.swa_token_usage = Gauge(
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name="sglang:swa_token_usage",
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documentation="The token usage for SWA layers.",
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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.num_grammar_queue_reqs = Gauge(
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name="sglang:num_grammar_queue_reqs",
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documentation="The number of requests in the grammar 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.num_running_reqs_offline_batch = Gauge(
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name="sglang:num_running_reqs_offline_batch",
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documentation="The number of running low-priority offline batch requests(label is 'batch').",
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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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# Speculative decoding
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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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# Retract
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self.num_retracted_reqs = Gauge(
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name="sglang:num_retracted_reqs",
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documentation="The number of retracted requests.",
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labelnames=labels.keys(),
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)
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self.num_paused_reqs = Gauge(
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name="sglang:num_paused_reqs",
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documentation="The number of paused requests by async weight sync.",
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labelnames=labels.keys(),
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)
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# PD disaggregation
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self.num_prefill_prealloc_queue_reqs = Gauge(
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name="sglang:num_prefill_prealloc_queue_reqs",
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documentation="The number of requests in the prefill prealloc queue.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.num_prefill_inflight_queue_reqs = Gauge(
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name="sglang:num_prefill_inflight_queue_reqs",
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documentation="The number of requests in the prefill inflight queue.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.num_decode_prealloc_queue_reqs = Gauge(
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name="sglang:num_decode_prealloc_queue_reqs",
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documentation="The number of requests in the decode prealloc queue.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.num_decode_transfer_queue_reqs = Gauge(
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name="sglang:num_decode_transfer_queue_reqs",
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documentation="The number of requests in the decode transfer queue.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.num_bootstrap_failed_reqs = Counter(
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name="sglang:num_bootstrap_failed_reqs_total",
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documentation="The number of bootstrap failed requests.",
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labelnames=labels.keys(),
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)
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self.num_transfer_failed_reqs = Counter(
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name="sglang:num_transfer_failed_reqs_total",
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documentation="The number of transfer failed requests.",
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labelnames=labels.keys(),
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)
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self.kv_transfer_speed_gb_s = Gauge(
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name="sglang:kv_transfer_speed_gb_s",
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documentation="The transfer speed of the KV cache in GB/s.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.kv_transfer_latency_ms = Gauge(
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name="sglang:kv_transfer_latency_ms",
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documentation="The transfer latency of the KV cache in ms.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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# Utilization
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self.utilization = Gauge(
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name="sglang:utilization",
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documentation="The utilization.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.max_running_requests_under_SLO = Gauge(
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name="sglang:max_running_requests_under_SLO",
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documentation="The maximum number of running requests under SLO.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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# Engine startup
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self.engine_startup_time = Gauge(
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name="sglang:engine_startup_time",
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documentation="The time taken for the engine to start up.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.engine_load_weights_time = Gauge(
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name="sglang:engine_load_weights_time",
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documentation="The time taken for the engine to load weights.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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# Additional queueing time histogram
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self.queue_time = Histogram(
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name="sglang:queue_time_seconds",
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documentation="Histogram of queueing time in seconds.",
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labelnames=labels.keys(),
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buckets=[
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0.0,
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0.1,
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0.2,
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0.5,
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1,
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2,
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3,
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4,
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5,
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10,
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15,
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20,
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30,
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40,
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50,
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60,
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70,
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80,
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90,
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100,
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200,
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300,
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400,
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500,
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600,
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700,
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800,
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900,
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1000,
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1200,
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1400,
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1600,
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1800,
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2000,
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2500,
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3000,
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],
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)
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# Grammar metrics
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self.grammar_compilation_time = Histogram(
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name="sglang:grammar_compilation_time_seconds",
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documentation="Histogram of grammar compilation time in seconds.",
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labelnames=labels.keys(),
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buckets=[
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0.0,
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0.01,
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0.02,
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0.05,
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0.1,
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0.2,
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0.5,
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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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30,
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60,
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90,
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120,
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240,
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],
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)
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self.num_grammar_cache_hit = Counter(
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name="sglang:num_grammar_cache_hit_total",
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documentation="Number of grammar cache hits.",
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labelnames=labels.keys(),
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)
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self.num_grammar_aborted = Counter(
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name="sglang:num_grammar_aborted_total",
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documentation="Number of grammar aborted requests.",
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labelnames=labels.keys(),
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)
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self.num_grammar_total = Counter(
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name="sglang:num_grammar_total",
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documentation="Number of the total grammar requests.",
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labelnames=labels.keys(),
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)
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self.grammar_schema_count = Histogram(
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name="sglang:grammar_schema_count",
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documentation="Histogram of grammar schema count.",
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labelnames=labels.keys(),
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buckets=[
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0,
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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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30,
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40,
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60,
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|
80,
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100,
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120,
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140,
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160,
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180,
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200,
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300,
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400,
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500,
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700,
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1000,
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],
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)
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self.grammar_ebnf_size = Histogram(
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name="sglang:grammar_ebnf_size",
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documentation="Histogram of grammar EBNF size.",
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labelnames=labels.keys(),
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buckets=[
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0,
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50,
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|
100,
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|
200,
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|
300,
|
|
500,
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|
1000,
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2000,
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3000,
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5000,
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10000,
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20000,
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30000,
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50000,
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100000,
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],
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)
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|
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tree_traversal_time_buckets = [
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0.0,
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0.01,
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0.02,
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0.05,
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0.1,
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|
0.2,
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|
0.5,
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1,
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|
2,
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|
5,
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|
10,
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|
15,
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|
30,
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|
60,
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|
90,
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|
120,
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240,
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]
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self.grammar_tree_traversal_time_avg = Histogram(
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name="sglang:grammar_tree_traversal_time_avg",
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documentation="Histogram of average grammar tree traversal time in seconds.",
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labelnames=labels.keys(),
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buckets=tree_traversal_time_buckets,
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)
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self.grammar_tree_traversal_time_max = Histogram(
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name="sglang:grammar_tree_traversal_time_max",
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|
documentation="Histogram of max grammar tree traversal time in seconds.",
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|
labelnames=labels.keys(),
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buckets=tree_traversal_time_buckets,
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)
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|
|
|
self.per_stage_req_latency_seconds = Histogram(
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name="sglang:per_stage_req_latency_seconds",
|
|
documentation="The latency of each stage of requests.",
|
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# captures latency in range [1ms - ~1191s]
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buckets=exponential_buckets(start=0.001, width=1.62, length=30),
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labelnames=list(labels.keys()) + ["stage"],
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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_histogram(self, histogram, data: Union[int, float]) -> None:
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histogram.labels(**self.labels).observe(data)
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|
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def increment_bootstrap_failed_reqs(self) -> None:
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self.num_bootstrap_failed_reqs.labels(**self.labels).inc(1)
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|
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def increment_transfer_failed_reqs(self) -> None:
|
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self.num_transfer_failed_reqs.labels(**self.labels).inc(1)
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|
|
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def observe_per_stage_req_latency(self, stage: str, latency: float) -> None:
|
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labels_with_stage = {**self.labels, "stage": stage}
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self.per_stage_req_latency_seconds.labels(**labels_with_stage).observe(latency)
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|
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def observe_queue_time(self, latency: float) -> None:
|
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self._log_histogram(self.queue_time, latency)
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|
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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)
|
|
self._log_gauge(self.token_usage, stats.token_usage)
|
|
self._log_gauge(self.swa_token_usage, stats.swa_token_usage)
|
|
self._log_gauge(self.gen_throughput, stats.gen_throughput)
|
|
self._log_gauge(self.num_queue_reqs, stats.num_queue_reqs)
|
|
self._log_gauge(self.num_grammar_queue_reqs, stats.num_grammar_queue_reqs)
|
|
self._log_gauge(
|
|
self.num_running_reqs_offline_batch, stats.num_running_reqs_offline_batch
|
|
)
|
|
self._log_gauge(self.cache_hit_rate, stats.cache_hit_rate)
|
|
|
|
# Speculative decoding
|
|
self._log_gauge(self.spec_accept_length, stats.spec_accept_length)
|
|
|
|
# PD disaggregation
|
|
self._log_gauge(
|
|
self.num_prefill_prealloc_queue_reqs, stats.num_prefill_prealloc_queue_reqs
|
|
)
|
|
self._log_gauge(
|
|
self.num_prefill_inflight_queue_reqs, stats.num_prefill_inflight_queue_reqs
|
|
)
|
|
self._log_gauge(
|
|
self.num_decode_prealloc_queue_reqs, stats.num_decode_prealloc_queue_reqs
|
|
)
|
|
self._log_gauge(
|
|
self.num_decode_transfer_queue_reqs, stats.num_decode_transfer_queue_reqs
|
|
)
|
|
self._log_gauge(self.kv_transfer_speed_gb_s, stats.kv_transfer_speed_gb_s)
|
|
self._log_gauge(self.kv_transfer_latency_ms, stats.kv_transfer_latency_ms)
|
|
|
|
# Retract
|
|
self._log_gauge(self.num_retracted_reqs, stats.num_retracted_reqs)
|
|
self._log_gauge(self.num_paused_reqs, stats.num_paused_reqs)
|
|
|
|
# Utilization
|
|
self._log_gauge(self.utilization, stats.utilization)
|
|
if stats.max_running_requests_under_SLO is not None:
|
|
self._log_gauge(
|
|
self.max_running_requests_under_SLO,
|
|
stats.max_running_requests_under_SLO,
|
|
)
|
|
|
|
# Engine startup time
|
|
self._log_gauge(self.engine_startup_time, stats.engine_startup_time)
|
|
if stats.engine_load_weights_time is not None:
|
|
self._log_gauge(
|
|
self.engine_load_weights_time, stats.engine_load_weights_time
|
|
)
|
|
|
|
self.last_log_time = time.perf_counter()
|
|
|
|
def log_grammar_stats(self, grammar_stats) -> None:
|
|
# Duck-typed GrammarStats to avoid cross-package dependency
|
|
if getattr(grammar_stats, "compilation_time", None) is not None:
|
|
self._log_histogram(
|
|
self.grammar_compilation_time, grammar_stats.compilation_time
|
|
)
|
|
if getattr(grammar_stats, "schema_count", None) is not None:
|
|
self._log_histogram(self.grammar_schema_count, grammar_stats.schema_count)
|
|
if getattr(grammar_stats, "ebnf_size", None) is not None:
|
|
self._log_histogram(self.grammar_ebnf_size, grammar_stats.ebnf_size)
|
|
tree_times = getattr(grammar_stats, "tree_traversal_time", None)
|
|
if tree_times:
|
|
max_time = max(tree_times)
|
|
avg_time = sum(tree_times) / len(tree_times)
|
|
self._log_histogram(self.grammar_tree_traversal_time_max, max_time)
|
|
self._log_histogram(self.grammar_tree_traversal_time_avg, avg_time)
|
|
if getattr(grammar_stats, "is_cache_hit", False):
|
|
self.num_grammar_cache_hit.labels(**self.labels).inc(1)
|
|
if getattr(grammar_stats, "is_grammar_aborted", False):
|
|
self.num_grammar_aborted.labels(**self.labels).inc(1)
|
|
self.num_grammar_total.labels(**self.labels).inc(1)
|
|
|
|
|
|
class TokenizerMetricsCollector:
|
|
def __init__(
|
|
self,
|
|
server_args: Optional[ServerArgs] = None,
|
|
labels: Dict[str, str] = None,
|
|
bucket_time_to_first_token: Optional[List[float]] = None,
|
|
bucket_inter_token_latency: Optional[List[float]] = None,
|
|
bucket_e2e_request_latency: Optional[List[float]] = None,
|
|
collect_tokens_histogram: bool = False,
|
|
) -> None:
|
|
# We need to import prometheus_client after setting the env variable `PROMETHEUS_MULTIPROC_DIR`
|
|
from prometheus_client import Counter, Histogram
|
|
|
|
self.labels = labels or {}
|
|
self.collect_tokens_histogram = collect_tokens_histogram
|
|
|
|
self.prompt_tokens_total = Counter(
|
|
name="sglang:prompt_tokens_total",
|
|
documentation="Number of prefill tokens processed.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
self.generation_tokens_total = Counter(
|
|
name="sglang:generation_tokens_total",
|
|
documentation="Number of generation tokens processed.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
if collect_tokens_histogram:
|
|
default_bucket_prompt_tokens = [
|
|
100,
|
|
300,
|
|
500,
|
|
700,
|
|
1000,
|
|
1500,
|
|
2000,
|
|
3000,
|
|
4000,
|
|
5000,
|
|
6000,
|
|
7000,
|
|
8000,
|
|
9000,
|
|
10000,
|
|
12000,
|
|
15000,
|
|
20000,
|
|
22000,
|
|
25000,
|
|
30000,
|
|
35000,
|
|
40000,
|
|
66000,
|
|
99000,
|
|
132000,
|
|
300000,
|
|
600000,
|
|
900000,
|
|
1100000,
|
|
]
|
|
self.prompt_tokens_histogram = Histogram(
|
|
name="sglang:prompt_tokens_histogram",
|
|
documentation="Histogram of prompt token length.",
|
|
labelnames=labels.keys(),
|
|
buckets=generate_buckets(
|
|
server_args.prompt_tokens_buckets, default_bucket_prompt_tokens
|
|
),
|
|
)
|
|
self.generation_tokens_histogram = Histogram(
|
|
name="sglang:generation_tokens_histogram",
|
|
documentation="Histogram of generation token length.",
|
|
labelnames=labels.keys(),
|
|
buckets=generate_buckets(
|
|
server_args.generation_tokens_buckets,
|
|
default_bucket_prompt_tokens,
|
|
),
|
|
)
|
|
|
|
self.cached_tokens_total = Counter(
|
|
name="sglang:cached_tokens_total",
|
|
documentation="Number of cached prompt tokens.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
self.num_requests_total = Counter(
|
|
name="sglang:num_requests_total",
|
|
documentation="Number of requests processed.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
self.num_so_requests_total = Counter(
|
|
name="sglang:num_so_requests_total",
|
|
documentation="Number of structured output requests processed.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
self.num_aborted_requests_total = Counter(
|
|
name="sglang:num_aborted_requests_total",
|
|
documentation="Number of requests aborted.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
if bucket_time_to_first_token is None:
|
|
bucket_time_to_first_token = [
|
|
0.1,
|
|
0.2,
|
|
0.4,
|
|
0.6,
|
|
0.8,
|
|
1,
|
|
2,
|
|
4,
|
|
6,
|
|
8,
|
|
10,
|
|
20,
|
|
40,
|
|
60,
|
|
80,
|
|
100,
|
|
200,
|
|
400,
|
|
]
|
|
|
|
if bucket_e2e_request_latency is None:
|
|
bucket_e2e_request_latency = [
|
|
0.1,
|
|
0.2,
|
|
0.4,
|
|
0.6,
|
|
0.8,
|
|
1,
|
|
2,
|
|
4,
|
|
6,
|
|
8,
|
|
10,
|
|
20,
|
|
40,
|
|
60,
|
|
80,
|
|
100,
|
|
200,
|
|
400,
|
|
600,
|
|
1200,
|
|
1800,
|
|
2400,
|
|
]
|
|
|
|
if bucket_inter_token_latency is None:
|
|
bucket_inter_token_latency = [
|
|
0.002,
|
|
0.004,
|
|
0.006,
|
|
0.008,
|
|
0.010,
|
|
0.015,
|
|
0.020,
|
|
0.025,
|
|
0.030,
|
|
0.035,
|
|
0.040,
|
|
0.060,
|
|
0.080,
|
|
0.100,
|
|
0.200,
|
|
0.400,
|
|
0.600,
|
|
0.800,
|
|
1.000,
|
|
2.000,
|
|
4.000,
|
|
6.000,
|
|
8.000,
|
|
]
|
|
|
|
self.histogram_time_to_first_token = Histogram(
|
|
name="sglang:time_to_first_token_seconds",
|
|
documentation="Histogram of time to first token in seconds.",
|
|
labelnames=labels.keys(),
|
|
buckets=bucket_time_to_first_token,
|
|
)
|
|
|
|
self.histogram_inter_token_latency = Histogram(
|
|
name="sglang:inter_token_latency_seconds",
|
|
documentation="Histogram of inter-token latency in seconds.",
|
|
labelnames=labels.keys(),
|
|
buckets=bucket_inter_token_latency,
|
|
)
|
|
|
|
self.histogram_e2e_request_latency = Histogram(
|
|
name="sglang:e2e_request_latency_seconds",
|
|
documentation="Histogram of End-to-end request latency in seconds",
|
|
labelnames=labels.keys(),
|
|
buckets=bucket_e2e_request_latency,
|
|
)
|
|
|
|
def observe_one_finished_request(
|
|
self,
|
|
labels: Dict[str, str],
|
|
prompt_tokens: int,
|
|
generation_tokens: int,
|
|
cached_tokens: int,
|
|
e2e_latency: float,
|
|
has_grammar: bool,
|
|
):
|
|
self.prompt_tokens_total.labels(**labels).inc(prompt_tokens)
|
|
self.generation_tokens_total.labels(**labels).inc(generation_tokens)
|
|
if cached_tokens > 0:
|
|
self.cached_tokens_total.labels(**labels).inc(cached_tokens)
|
|
self.num_requests_total.labels(**labels).inc(1)
|
|
if has_grammar:
|
|
self.num_so_requests_total.labels(**labels).inc(1)
|
|
self.histogram_e2e_request_latency.labels(**labels).observe(float(e2e_latency))
|
|
if self.collect_tokens_histogram:
|
|
self.prompt_tokens_histogram.labels(**labels).observe(float(prompt_tokens))
|
|
self.generation_tokens_histogram.labels(**labels).observe(
|
|
float(generation_tokens)
|
|
)
|
|
|
|
def observe_time_to_first_token(self, labels: Dict[str, str], value: float):
|
|
self.histogram_time_to_first_token.labels(**labels).observe(value)
|
|
|
|
def check_time_to_first_token_straggler(self, value: float) -> bool:
|
|
his = self.histogram_time_to_first_token.labels(**self.labels)
|
|
total_observations = sum(bucket._value for bucket in his._buckets)
|
|
if total_observations < 1000:
|
|
return False
|
|
p999_threshold = total_observations * 0.999
|
|
cumulative_count = 0
|
|
for i, bucket in enumerate(his._buckets):
|
|
cumulative_count += bucket._value
|
|
if cumulative_count > p999_threshold:
|
|
return value >= his._upper_bounds[i]
|
|
return False
|
|
|
|
def observe_inter_token_latency(
|
|
self, labels: Dict[str, str], internval: float, num_new_tokens: int
|
|
):
|
|
adjusted_interval = internval / num_new_tokens
|
|
|
|
# A faster version of the Histogram::observe which observes multiple values at the same time.
|
|
# reference: https://github.com/prometheus/client_python/blob/v0.21.1/prometheus_client/metrics.py#L639
|
|
his = self.histogram_inter_token_latency.labels(**labels)
|
|
his._sum.inc(internval)
|
|
|
|
for i, bound in enumerate(his._upper_bounds):
|
|
if adjusted_interval <= bound:
|
|
his._buckets[i].inc(num_new_tokens)
|
|
break
|
|
|
|
def observe_one_aborted_request(self, labels: Dict[str, str]):
|
|
self.num_aborted_requests_total.labels(**labels).inc(1)
|
|
|
|
|
|
@dataclass
|
|
class StorageMetrics:
|
|
prefetch_pgs: List[int] = field(default_factory=list)
|
|
backup_pgs: List[int] = field(default_factory=list)
|
|
prefetch_bandwidth: List[float] = field(default_factory=list)
|
|
backup_bandwidth: List[float] = field(default_factory=list)
|
|
|
|
|
|
class StorageMetricsCollector:
|
|
def __init__(
|
|
self,
|
|
labels: Dict[str, str],
|
|
):
|
|
from prometheus_client import Counter, Histogram
|
|
|
|
self.labels = labels
|
|
|
|
self.prefetched_tokens_total = Counter(
|
|
name="sglang:prefetched_tokens_total",
|
|
documentation="Number of prefetched prompt tokens.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
self.backuped_tokens_total = Counter(
|
|
name="sglang:backuped_tokens_total",
|
|
documentation="Number of backuped tokens.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
bucket_io = [
|
|
1,
|
|
5,
|
|
10,
|
|
50,
|
|
100,
|
|
]
|
|
|
|
bucket_bandwidth = [
|
|
0.1,
|
|
0.5,
|
|
1,
|
|
5,
|
|
10,
|
|
50,
|
|
100,
|
|
]
|
|
|
|
self.histogram_prefetch_pgs = Histogram(
|
|
name="sglang:prefetch_pgs",
|
|
documentation="Histogram of prefetch pages of batches.",
|
|
labelnames=labels.keys(),
|
|
buckets=bucket_io,
|
|
)
|
|
|
|
self.histogram_backup_pgs = Histogram(
|
|
name="sglang:backup_pgs",
|
|
documentation="Histogram of backup pages of batches.",
|
|
labelnames=labels.keys(),
|
|
buckets=bucket_io,
|
|
)
|
|
|
|
self.histogram_prefetch_bandwidth = Histogram(
|
|
name="sglang:prefetch_bandwidth",
|
|
documentation="Histogram of prefetch bandwidth in GB/s.",
|
|
labelnames=labels.keys(),
|
|
buckets=bucket_bandwidth,
|
|
)
|
|
|
|
self.histogram_backup_bandwidth = Histogram(
|
|
name="sglang:backup_bandwidth",
|
|
documentation="Histogram of backup bandwidth in GB/s.",
|
|
labelnames=labels.keys(),
|
|
buckets=bucket_bandwidth,
|
|
)
|
|
|
|
def log_prefetched_tokens(self, prefetched_tokens: int):
|
|
if prefetched_tokens > 0:
|
|
self.prefetched_tokens_total.labels(**self.labels).inc(prefetched_tokens)
|
|
|
|
def log_backuped_tokens(self, backuped_tokens: int):
|
|
if backuped_tokens > 0:
|
|
self.backuped_tokens_total.labels(**self.labels).inc(backuped_tokens)
|
|
|
|
def _log_histogram(self, histogram, data: Union[int, float]):
|
|
histogram.labels(**self.labels).observe(data)
|
|
|
|
def log_storage_metrics(self, storage_metrics: Optional[StorageMetrics] = None):
|
|
if storage_metrics is None:
|
|
return
|
|
|
|
assert isinstance(storage_metrics, StorageMetrics)
|
|
|
|
for v in storage_metrics.prefetch_pgs:
|
|
self._log_histogram(self.histogram_prefetch_pgs, v)
|
|
for v in storage_metrics.backup_pgs:
|
|
self._log_histogram(self.histogram_backup_pgs, v)
|
|
for v in storage_metrics.prefetch_bandwidth:
|
|
self._log_histogram(self.histogram_prefetch_bandwidth, v)
|
|
for v in storage_metrics.backup_bandwidth:
|
|
self._log_histogram(self.histogram_backup_bandwidth, v)
|