1567 lines
57 KiB
Python
1567 lines
57 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 dataclasses
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import logging
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import os
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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.environ import envs
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from sglang.srt.metrics.utils import exponential_buckets, generate_buckets
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from sglang.srt.model_executor.forward_batch_info import ForwardMode
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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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from sglang.srt.utils.gauge_histogram import GaugeHistogram
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SGLANG_TEST_REQUEST_TIME_STATS = get_bool_env_var("SGLANG_TEST_REQUEST_TIME_STATS")
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logger = logging.getLogger(__name__)
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def get_histogram_conf_from_env(env_var_name: str) -> Optional[List[float]]:
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"""
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Get the histogram configuration from the environment variable.
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env value should be like "0.1,0.2,0.5,1,2"
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"""
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if env_var_name not in os.environ:
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return None
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# if the env var is not set or empty, return None
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env_var_value = os.environ[env_var_name]
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if not env_var_value:
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return None
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return [float(x) for x in env_var_value.split(",")]
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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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# TODO: correct set them
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bootstrap_duration: float = 0.0
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alloc_waiting_duration: float = 0.0
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prefill_start_time_host: float = 0.0
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prefill_end_time_host: float = 0.0
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transfer_speed_gb_s: float = 0.0
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transfer_total_mb: float = 0.0
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# Number of prefill retries for this request
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prefill_retry_count: int = 0
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# Timestamp when prefill phase finishes, obtained from `time.time()`.
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# Note that this differs from the other `_time` fields tracked by the
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# `TimeStats` class, which are obtained from `time.perf_counter()`.
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# We use `time.time()` instead of `time.perf_counter()` here in order to
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# maintain unit consistency with other timestamp fields tracked by the `ReqState` class.
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prefill_finished_ts: 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 get_prefill_launch_delay(self) -> Optional[float]:
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if self.prefill_start_time_host > 0.0:
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return self.prefill_start_time_host - self.forward_entry_time
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return None
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def get_prefill_launch_latency(self) -> Optional[float]:
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if self.prefill_start_time_host > 0.0 and self.prefill_end_time_host > 0.0:
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return self.prefill_end_time_host - self.prefill_start_time_host
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return None
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def get_prefill_finished_ts(self) -> Optional[float]:
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if self.prefill_finished_ts > 0.0:
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return self.prefill_finished_ts
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return None
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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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other = max(
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0.0,
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bootstrap_duration
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- (self.alloc_waiting_duration + self.bootstrap_duration),
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)
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return (
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f"bootstrap_queue_duration({self.format_duration(bootstrap_duration)}) "
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f"= alloc_wait({self.format_duration(self.alloc_waiting_duration)}) "
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f"+ bootstrap({self.format_duration(self.bootstrap_duration)}) "
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f"+ other({self.format_duration(other)}); "
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f"queue_duration={self.format_duration(queue_duration)}, "
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f"forward_duration={self.format_duration(forward_duration)}, "
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f"start={self.prefill_bootstrap_queue_entry_time:.3f}, "
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f"transfer_speed={self.transfer_speed_gb_s:.2f}GB/s, "
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f"transfer_total={self.transfer_total_mb:.2f}MB, "
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f"#retries={self.prefill_retry_count}"
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)
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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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other = max(
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0.0,
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prealloc_duration
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- (self.alloc_waiting_duration + self.bootstrap_duration),
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)
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return (
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f"prealloc_queue_duration({self.format_duration(prealloc_duration)}) "
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f"= alloc_wait({self.format_duration(self.alloc_waiting_duration)}) "
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f"+ bootstrap({self.format_duration(self.bootstrap_duration)}) "
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f"+ other({self.format_duration(other)}); "
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f"transfer_duration={self.format_duration(transfer_duration)}; "
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f"queue_duration={self.format_duration(queue_duration)}, "
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f"forward_duration={self.format_duration(forward_duration)}, "
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f"start={self.decode_prealloc_queue_entry_time:.3f}"
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)
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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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pending_prealloc_token_usage: float = 0.0
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swa_token_usage: float = 0.0
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mamba_usage: float = 0.0
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decode_sum_seq_lens: int = 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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max_total_num_tokens: int = 0
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# Speculative decoding
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spec_accept_length: float = 0.0
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spec_accept_rate: 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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kv_transfer_bootstrap_ms: float = 0.0
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kv_transfer_alloc_ms: float = 0.0
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kv_transfer_total_mb: 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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new_token_ratio: float = 0.0
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# CUDA graph
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is_cuda_graph: float = 0.0
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# LoRA pool metrics
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lora_pool_slots_used: int = 0
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lora_pool_slots_total: int = 0
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lora_pool_utilization: float = 0.0
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# Routing key metrics
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num_unique_running_routing_keys: int = 0
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routing_key_running_req_counts: List[int] = field(default_factory=list)
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routing_key_all_req_counts: List[int] = field(default_factory=list)
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ROUTING_KEY_REQ_COUNT_BUCKET_BOUNDS = [1, 2, 3, 5, 7, 10, 20, 50, 100, 200]
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def compute_routing_key_stats(routing_keys: List[Optional[str]]) -> tuple:
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"""Returns (num_unique_keys, per_key_counts)."""
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from collections import Counter
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key_counts = Counter(k for k in routing_keys if k is not None)
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return len(key_counts), list(key_counts.values())
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@dataclass
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class DPCooperationInfo:
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# Users can derive that, except for cases with idle, num_decode_ranks=world_size-num_prefill_ranks
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# We do not provide `num_decode_ranks` to avoid cardinality explosion.
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num_prefill_ranks: int
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@staticmethod
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def create(forward_modes: List[int]):
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return DPCooperationInfo(
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num_prefill_ranks=sum(
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1 for mode in forward_modes if mode == ForwardMode.EXTEND.value
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),
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)
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def to_labels(self):
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return dataclasses.asdict(self)
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class SchedulerMetricsCollector:
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def __init__(
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self,
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labels: Dict[str, str],
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enable_lora: bool = False,
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server_args: Optional["ServerArgs"] = None,
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) -> 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, Summary
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self.labels = labels
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self.enable_lora = enable_lora
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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.pending_prealloc_token_usage = Gauge(
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name="sglang:pending_prealloc_token_usage",
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documentation="The token usage for pending preallocated tokens (not preallocated yet).",
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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.mamba_usage = Gauge(
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name="sglang:mamba_usage",
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documentation="The token usage for Mamba layers.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.decode_sum_seq_lens = Gauge(
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name="sglang:decode_sum_seq_lens",
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documentation="The sum of all sequence lengths in decode.",
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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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self.max_total_num_tokens = Gauge(
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name="sglang:max_total_num_tokens",
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documentation="Maximum total number of tokens in the KV cache pool.",
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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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self.spec_accept_rate = Gauge(
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name="sglang:spec_accept_rate",
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documentation="The average acceptance rate of speculative decoding (`accepted tokens / total draft tokens` in batch).",
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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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# TODO maybe remove this old gauge in favor of the new counter
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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_retracted_reqs_total = Counter(
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# The name is `requests` instead of `reqs` to avoid dup name error
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name="sglang:num_retracted_requests_total",
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documentation="Total number of retracted requests.",
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labelnames=labels.keys(),
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)
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self.num_retracted_input_tokens_total = Counter(
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name="sglang:num_retracted_input_tokens_total",
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documentation="Total number of retracted input tokens.",
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labelnames=labels.keys(),
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)
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self.num_retracted_output_tokens_total = Counter(
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name="sglang:num_retracted_output_tokens_total",
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documentation="Total number of retracted output tokens.",
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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.num_prefill_retries_total = Counter(
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name="sglang:num_prefill_retries_total",
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documentation="Total number of prefill retries.",
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labelnames=labels.keys(),
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)
|
|
self.kv_transfer_speed_gb_s = Gauge(
|
|
name="sglang:kv_transfer_speed_gb_s",
|
|
documentation="The transfer speed of the KV cache in GB/s.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.kv_transfer_latency_ms = Gauge(
|
|
name="sglang:kv_transfer_latency_ms",
|
|
documentation="The transfer latency of the KV cache in ms.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.kv_transfer_bootstrap_ms = Gauge(
|
|
name="sglang:kv_transfer_bootstrap_ms",
|
|
documentation="The bootstrap time of the KV transfer in ms.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.kv_transfer_alloc_ms = Gauge(
|
|
name="sglang:kv_transfer_alloc_ms",
|
|
documentation="The allocation waiting time of the KV transfer in ms.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.kv_transfer_total_mb = Gauge(
|
|
name="sglang:kv_transfer_total_mb",
|
|
documentation="The total number of tokens transferred in the KV cache.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
|
|
# Utilization
|
|
self.utilization = Gauge(
|
|
name="sglang:utilization",
|
|
documentation="The utilization.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.max_running_requests_under_SLO = Gauge(
|
|
name="sglang:max_running_requests_under_SLO",
|
|
documentation="The maximum number of running requests under SLO.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
|
|
# Engine startup
|
|
self.engine_startup_time = Gauge(
|
|
name="sglang:engine_startup_time",
|
|
documentation="The time taken for the engine to start up.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.engine_load_weights_time = Gauge(
|
|
name="sglang:engine_load_weights_time",
|
|
documentation="The time taken for the engine to load weights.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
|
|
# Additional queueing time histogram
|
|
self.queue_time = Histogram(
|
|
name="sglang:queue_time_seconds",
|
|
documentation="Histogram of queueing time in seconds.",
|
|
labelnames=labels.keys(),
|
|
buckets=[
|
|
0.0,
|
|
0.1,
|
|
0.2,
|
|
0.5,
|
|
1,
|
|
2,
|
|
3,
|
|
4,
|
|
5,
|
|
10,
|
|
15,
|
|
20,
|
|
30,
|
|
40,
|
|
50,
|
|
60,
|
|
70,
|
|
80,
|
|
90,
|
|
100,
|
|
200,
|
|
300,
|
|
400,
|
|
500,
|
|
600,
|
|
700,
|
|
800,
|
|
900,
|
|
1000,
|
|
1200,
|
|
1400,
|
|
1600,
|
|
1800,
|
|
2000,
|
|
2500,
|
|
3000,
|
|
],
|
|
)
|
|
|
|
# Grammar metrics
|
|
self.grammar_compilation_time = Histogram(
|
|
name="sglang:grammar_compilation_time_seconds",
|
|
documentation="Histogram of grammar compilation time in seconds.",
|
|
labelnames=labels.keys(),
|
|
buckets=[
|
|
0.0,
|
|
0.01,
|
|
0.02,
|
|
0.05,
|
|
0.1,
|
|
0.2,
|
|
0.5,
|
|
1,
|
|
2,
|
|
5,
|
|
10,
|
|
20,
|
|
30,
|
|
60,
|
|
90,
|
|
120,
|
|
240,
|
|
],
|
|
)
|
|
self.num_grammar_cache_hit = Counter(
|
|
name="sglang:num_grammar_cache_hit_total",
|
|
documentation="Number of grammar cache hits.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
self.num_grammar_aborted = Counter(
|
|
name="sglang:num_grammar_aborted_total",
|
|
documentation="Number of grammar aborted requests.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
self.num_grammar_timeout = Counter(
|
|
name="sglang:num_grammar_timeout_total",
|
|
documentation="Number of grammar timeouts.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
self.num_grammar_total = Counter(
|
|
name="sglang:num_grammar_total",
|
|
documentation="Number of the total grammar requests.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
self.grammar_schema_count = Histogram(
|
|
name="sglang:grammar_schema_count",
|
|
documentation="Histogram of grammar schema count.",
|
|
labelnames=labels.keys(),
|
|
buckets=[
|
|
0,
|
|
1,
|
|
2,
|
|
5,
|
|
10,
|
|
20,
|
|
30,
|
|
40,
|
|
60,
|
|
80,
|
|
100,
|
|
120,
|
|
140,
|
|
160,
|
|
180,
|
|
200,
|
|
300,
|
|
400,
|
|
500,
|
|
700,
|
|
1000,
|
|
],
|
|
)
|
|
self.grammar_ebnf_size = Histogram(
|
|
name="sglang:grammar_ebnf_size",
|
|
documentation="Histogram of grammar EBNF size.",
|
|
labelnames=labels.keys(),
|
|
buckets=[
|
|
0,
|
|
50,
|
|
100,
|
|
200,
|
|
300,
|
|
500,
|
|
1000,
|
|
2000,
|
|
3000,
|
|
5000,
|
|
10000,
|
|
20000,
|
|
30000,
|
|
50000,
|
|
100000,
|
|
],
|
|
)
|
|
|
|
tree_traversal_time_buckets = [
|
|
0.0,
|
|
0.01,
|
|
0.02,
|
|
0.05,
|
|
0.1,
|
|
0.2,
|
|
0.5,
|
|
1,
|
|
2,
|
|
5,
|
|
10,
|
|
15,
|
|
30,
|
|
60,
|
|
90,
|
|
120,
|
|
240,
|
|
]
|
|
self.grammar_tree_traversal_time_avg = Histogram(
|
|
name="sglang:grammar_tree_traversal_time_avg",
|
|
documentation="Histogram of average grammar tree traversal time in seconds.",
|
|
labelnames=labels.keys(),
|
|
buckets=tree_traversal_time_buckets,
|
|
)
|
|
self.grammar_tree_traversal_time_max = Histogram(
|
|
name="sglang:grammar_tree_traversal_time_max",
|
|
documentation="Histogram of max grammar tree traversal time in seconds.",
|
|
labelnames=labels.keys(),
|
|
buckets=tree_traversal_time_buckets,
|
|
)
|
|
|
|
self.per_stage_req_latency_seconds = Histogram(
|
|
name="sglang:per_stage_req_latency_seconds",
|
|
documentation="The latency of each stage of requests.",
|
|
# captures latency in range [1ms - ~1191s]
|
|
buckets=exponential_buckets(start=0.001, width=1.62, length=30),
|
|
labelnames=list(labels.keys()) + ["stage"],
|
|
)
|
|
|
|
# TODO maybe remove this old gauge in favor of the new counter
|
|
self.is_cuda_graph = Gauge(
|
|
name="sglang:is_cuda_graph",
|
|
documentation="Whether the batch is using CUDA graph.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.cuda_graph_passes_total = Counter(
|
|
name="sglang:cuda_graph_passes_total",
|
|
documentation="Total number of forward passes categorized by CUDA graph.",
|
|
labelnames=list(labels.keys()) + ["mode"],
|
|
)
|
|
|
|
if (
|
|
labels["moe_ep_rank"] == 0
|
|
) and envs.SGLANG_ENABLE_EPLB_BALANCEDNESS_METRIC.get():
|
|
self.eplb_balancedness = Summary(
|
|
name="sglang:eplb_balancedness",
|
|
documentation="Balancedness of MoE in expert parallelism.",
|
|
labelnames=list(labels.keys()) + ["forward_mode"],
|
|
)
|
|
|
|
# LoRA pool metrics (only created when LoRA is enabled)
|
|
if self.enable_lora:
|
|
self.lora_pool_slots_used = Gauge(
|
|
name="sglang:lora_pool_slots_used",
|
|
documentation="Number of LoRA adapter slots currently occupied in GPU memory.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.lora_pool_slots_total = Gauge(
|
|
name="sglang:lora_pool_slots_total",
|
|
documentation="Total number of LoRA adapter slots available (max_loras_per_batch).",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.lora_pool_utilization = Gauge(
|
|
name="sglang:lora_pool_utilization",
|
|
documentation="LoRA pool utilization ratio (used/total). 1.0 means pool is full.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
|
|
self.num_unique_running_routing_keys = Gauge(
|
|
name="sglang:num_unique_running_routing_keys",
|
|
documentation="Number of unique routing keys in running batch.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
self.routing_key_running_req_count = GaugeHistogram(
|
|
name="sglang:routing_key_running_req_count",
|
|
documentation="Distribution of routing keys by running request count (gt < count <= le).",
|
|
labelnames=list(labels.keys()),
|
|
bucket_bounds=ROUTING_KEY_REQ_COUNT_BUCKET_BOUNDS,
|
|
)
|
|
self.routing_key_all_req_count = GaugeHistogram(
|
|
name="sglang:routing_key_all_req_count",
|
|
documentation="Distribution of routing keys by running+waiting request count (gt < count <= le).",
|
|
labelnames=list(labels.keys()),
|
|
bucket_bounds=ROUTING_KEY_REQ_COUNT_BUCKET_BOUNDS,
|
|
)
|
|
|
|
self.new_token_ratio = Gauge(
|
|
name="sglang:new_token_ratio",
|
|
documentation="The new token ratio.",
|
|
labelnames=labels.keys(),
|
|
multiprocess_mode="mostrecent",
|
|
)
|
|
|
|
self.realtime_tokens_total = Counter(
|
|
name="sglang:realtime_tokens_total",
|
|
documentation=(
|
|
"Total number of tokens processed (updated on each log interval). "
|
|
"mode: prefill_compute, prefill_cache, decode."
|
|
),
|
|
labelnames=list(labels.keys()) + ["mode"],
|
|
)
|
|
self.gpu_execution_seconds_total = Counter(
|
|
name="sglang:gpu_execution_seconds_total",
|
|
documentation=(
|
|
"Total time that GPU is busy executing a workload. "
|
|
"Refer to ForwardMode for category labels."
|
|
),
|
|
labelnames=list(labels.keys()) + ["category"],
|
|
)
|
|
|
|
self.dp_cooperation_realtime_tokens_total = Counter(
|
|
name="sglang:dp_cooperation_realtime_tokens_total",
|
|
documentation=(
|
|
"Total number of tokens processed with labels about DP cooperation. "
|
|
"mode: prefill_compute, prefill_cache, decode."
|
|
),
|
|
labelnames=list(labels.keys()) + ["mode", "num_prefill_ranks"],
|
|
)
|
|
self.dp_cooperation_gpu_execution_seconds_total = Counter(
|
|
name="sglang:dp_cooperation_gpu_execution_seconds_total",
|
|
documentation=(
|
|
"Total time that GPU is busy executing a workload with labels about DP cooperation. "
|
|
"Refer to ForwardMode for category labels."
|
|
),
|
|
labelnames=list(labels.keys()) + ["category", "num_prefill_ranks"],
|
|
)
|
|
|
|
max_delay = server_args.prefill_delayer_max_delay_passes
|
|
self.prefill_delayer_wait_forward_passes = Histogram(
|
|
name="sglang:prefill_delayer_wait_forward_passes",
|
|
documentation="Histogram of forward passes waited by prefill delayer.",
|
|
labelnames=labels.keys(),
|
|
buckets=sorted(
|
|
set(
|
|
x
|
|
for x in (
|
|
server_args.prefill_delayer_forward_passes_buckets
|
|
or [5, 20, 50, 100, 200]
|
|
)
|
|
if x < max_delay
|
|
)
|
|
# Need bucket "<=0" for zero-delay cases, and "max_delay-1" to distinguish "max_delay" timeout passes
|
|
| {0, max_delay - 1}
|
|
),
|
|
)
|
|
self.prefill_delayer_wait_seconds = Histogram(
|
|
name="sglang:prefill_delayer_wait_seconds",
|
|
documentation="Histogram of wait time in seconds by prefill delayer.",
|
|
labelnames=labels.keys(),
|
|
buckets=sorted(
|
|
set(
|
|
server_args.prefill_delayer_wait_seconds_buckets
|
|
or [1, 2, 5, 10, 20, 50, 100, 200, 500]
|
|
)
|
|
# Need bucket "<=0" for zero-delay cases
|
|
| {0}
|
|
),
|
|
)
|
|
self.prefill_delayer_outcomes_total = Counter(
|
|
name="sglang:prefill_delayer_outcomes_total",
|
|
documentation="Prefill delayer outcome counts.",
|
|
labelnames=[
|
|
*labels.keys(),
|
|
"input_estimation",
|
|
"output_allow",
|
|
"output_reason",
|
|
"actual_execution",
|
|
],
|
|
)
|
|
|
|
def _log_gauge(self, gauge, data: Union[int, float]) -> None:
|
|
# Convenience function for logging to gauge.
|
|
gauge.labels(**self.labels).set(data)
|
|
|
|
def _log_histogram(self, histogram, data: Union[int, float]) -> None:
|
|
histogram.labels(**self.labels).observe(data)
|
|
|
|
def increment_bootstrap_failed_reqs(self) -> None:
|
|
self.num_bootstrap_failed_reqs.labels(**self.labels).inc(1)
|
|
|
|
def increment_transfer_failed_reqs(self) -> None:
|
|
self.num_transfer_failed_reqs.labels(**self.labels).inc(1)
|
|
|
|
def increment_prefill_retries(self, count: int) -> None:
|
|
if count > 0:
|
|
self.num_prefill_retries_total.labels(**self.labels).inc(count)
|
|
|
|
def observe_per_stage_req_latency(self, stage: str, latency: float) -> None:
|
|
labels_with_stage = {**self.labels, "stage": stage}
|
|
self.per_stage_req_latency_seconds.labels(**labels_with_stage).observe(latency)
|
|
|
|
def observe_queue_time(self, latency: float) -> None:
|
|
self._log_histogram(self.queue_time, latency)
|
|
|
|
def observe_prefill_delayer_outcome(
|
|
self,
|
|
forward_passes: int,
|
|
wait_seconds: float,
|
|
input_estimation: str,
|
|
output_allow: bool,
|
|
output_reason: str,
|
|
actual_execution: bool,
|
|
) -> None:
|
|
if output_allow and actual_execution:
|
|
self._log_histogram(
|
|
self.prefill_delayer_wait_forward_passes, forward_passes
|
|
)
|
|
self._log_histogram(self.prefill_delayer_wait_seconds, wait_seconds)
|
|
|
|
self.prefill_delayer_outcomes_total.labels(
|
|
**self.labels,
|
|
input_estimation=input_estimation,
|
|
output_allow=str(output_allow).lower(),
|
|
output_reason=output_reason,
|
|
actual_execution=str(actual_execution).lower(),
|
|
).inc(1)
|
|
|
|
def increment_retracted_reqs(
|
|
self,
|
|
num_retracted_reqs: int,
|
|
num_retracted_input_tokens: int,
|
|
num_retracted_output_tokens: int,
|
|
) -> None:
|
|
self.num_retracted_reqs_total.labels(**self.labels).inc(num_retracted_reqs)
|
|
self.num_retracted_input_tokens_total.labels(**self.labels).inc(
|
|
num_retracted_input_tokens
|
|
)
|
|
self.num_retracted_output_tokens_total.labels(**self.labels).inc(
|
|
num_retracted_output_tokens
|
|
)
|
|
|
|
def increment_cuda_graph_pass(self, value: bool) -> None:
|
|
# leave room for piecewise cuda graph, etc
|
|
mode = "decode_cuda_graph" if value else "decode_none"
|
|
self.cuda_graph_passes_total.labels(**self.labels, mode=mode).inc(1)
|
|
|
|
def increment_eplb_balancedness(
|
|
self, forward_mode: str, balancedness: float
|
|
) -> None:
|
|
self.eplb_balancedness.labels(**self.labels, forward_mode=forward_mode).observe(
|
|
balancedness
|
|
)
|
|
|
|
def increment_realtime_tokens(
|
|
self,
|
|
dp_cooperation_info: Optional[DPCooperationInfo],
|
|
prefill_compute_tokens=0,
|
|
prefill_cache_tokens=0,
|
|
decode_tokens=0,
|
|
):
|
|
for mode, delta in [
|
|
("prefill_compute", prefill_compute_tokens),
|
|
("prefill_cache", prefill_cache_tokens),
|
|
("decode", decode_tokens),
|
|
]:
|
|
self.realtime_tokens_total.labels(**self.labels, mode=mode).inc(delta)
|
|
if dp_cooperation_info is not None:
|
|
self.dp_cooperation_realtime_tokens_total.labels(
|
|
**self.labels,
|
|
mode=mode,
|
|
**dp_cooperation_info.to_labels(),
|
|
).inc(delta)
|
|
|
|
def increment_gpu_execution_seconds(
|
|
self,
|
|
category: str,
|
|
t: float,
|
|
dp_cooperation_info: Optional[DPCooperationInfo],
|
|
):
|
|
logger.debug(f"GPU execution seconds: {category=} {t=:.3f}")
|
|
self.gpu_execution_seconds_total.labels(**self.labels, category=category).inc(t)
|
|
if dp_cooperation_info is not None:
|
|
self.dp_cooperation_gpu_execution_seconds_total.labels(
|
|
**self.labels,
|
|
category=category,
|
|
**dp_cooperation_info.to_labels(),
|
|
).inc(t)
|
|
|
|
def log_stats(self, stats: SchedulerStats) -> None:
|
|
self._log_gauge(self.num_running_reqs, stats.num_running_reqs)
|
|
self._log_gauge(self.num_used_tokens, stats.num_used_tokens)
|
|
self._log_gauge(self.token_usage, stats.token_usage)
|
|
self._log_gauge(
|
|
self.pending_prealloc_token_usage, stats.pending_prealloc_token_usage
|
|
)
|
|
self._log_gauge(self.swa_token_usage, stats.swa_token_usage)
|
|
self._log_gauge(self.mamba_usage, stats.mamba_usage)
|
|
self._log_gauge(self.decode_sum_seq_lens, stats.decode_sum_seq_lens)
|
|
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)
|
|
|
|
self._log_gauge(self.max_total_num_tokens, stats.max_total_num_tokens)
|
|
|
|
# Speculative decoding
|
|
self._log_gauge(self.spec_accept_length, stats.spec_accept_length)
|
|
self._log_gauge(self.spec_accept_rate, stats.spec_accept_rate)
|
|
|
|
# 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)
|
|
self._log_gauge(self.kv_transfer_bootstrap_ms, stats.kv_transfer_bootstrap_ms)
|
|
self._log_gauge(self.kv_transfer_alloc_ms, stats.kv_transfer_alloc_ms)
|
|
self._log_gauge(self.kv_transfer_total_mb, stats.kv_transfer_total_mb)
|
|
|
|
# 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._log_gauge(self.new_token_ratio, stats.new_token_ratio)
|
|
|
|
# CUDA graph
|
|
self._log_gauge(self.is_cuda_graph, stats.is_cuda_graph)
|
|
|
|
# LoRA pool metrics (only logged if LoRA is enabled)
|
|
if self.enable_lora:
|
|
self._log_gauge(self.lora_pool_slots_used, stats.lora_pool_slots_used)
|
|
self._log_gauge(self.lora_pool_slots_total, stats.lora_pool_slots_total)
|
|
self._log_gauge(self.lora_pool_utilization, stats.lora_pool_utilization)
|
|
|
|
self._log_gauge(
|
|
self.num_unique_running_routing_keys, stats.num_unique_running_routing_keys
|
|
)
|
|
self.routing_key_running_req_count.set_by_current_observations(
|
|
self.labels, stats.routing_key_running_req_counts
|
|
)
|
|
self.routing_key_all_req_count.set_by_current_observations(
|
|
self.labels, stats.routing_key_all_req_counts
|
|
)
|
|
|
|
self.last_log_time = time.perf_counter()
|
|
|
|
def log_grammar_stats(self, grammar_stats) -> None:
|
|
if grammar_stats.compilation_time is not None:
|
|
self._log_histogram(
|
|
self.grammar_compilation_time, grammar_stats.compilation_time
|
|
)
|
|
if grammar_stats.schema_count is not None:
|
|
self._log_histogram(self.grammar_schema_count, grammar_stats.schema_count)
|
|
if grammar_stats.ebnf_size is not None:
|
|
self._log_histogram(self.grammar_ebnf_size, grammar_stats.ebnf_size)
|
|
tree_times = grammar_stats.tree_traversal_time
|
|
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 grammar_stats.is_cache_hit:
|
|
self.num_grammar_cache_hit.labels(**self.labels).inc(1)
|
|
if grammar_stats.is_grammar_aborted:
|
|
self.num_grammar_aborted.labels(**self.labels).inc(1)
|
|
if grammar_stats.num_timeout > 0:
|
|
self.num_grammar_timeout.labels(**self.labels).inc(
|
|
grammar_stats.num_timeout
|
|
)
|
|
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,
|
|
)
|
|
|
|
# Retraction count histogram
|
|
self.num_retractions = Histogram(
|
|
name="sglang:num_retractions",
|
|
documentation="Histogram of retraction counts per request.",
|
|
labelnames=labels.keys(),
|
|
buckets=[
|
|
0,
|
|
1,
|
|
2,
|
|
3,
|
|
4,
|
|
5,
|
|
6,
|
|
7,
|
|
8,
|
|
9,
|
|
10,
|
|
15,
|
|
20,
|
|
25,
|
|
30,
|
|
40,
|
|
50,
|
|
75,
|
|
100,
|
|
],
|
|
)
|
|
|
|
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,
|
|
retraction_count: int,
|
|
):
|
|
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)
|
|
)
|
|
self.num_retractions.labels(**labels).observe(retraction_count)
|
|
|
|
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 < 100:
|
|
return False
|
|
p99_threshold = total_observations * 0.99
|
|
cumulative_count = 0
|
|
for i, bucket in enumerate(his._buckets):
|
|
cumulative_count += bucket._value
|
|
if cumulative_count > p99_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)
|
|
|
|
|
|
class ExpertDispatchCollector:
|
|
def __init__(self, ep_size: int) -> None:
|
|
from prometheus_client import Histogram
|
|
|
|
ep_size_buckets = [i for i in range(ep_size)]
|
|
self.eplb_gpu_physical_count = Histogram(
|
|
name="sglang:eplb_gpu_physical_count",
|
|
documentation="The selected count of physical experts on each layer and GPU rank.",
|
|
labelnames={"layer"},
|
|
buckets=ep_size_buckets,
|
|
)
|
|
|
|
|
|
class RadixCacheMetricsCollector:
|
|
def __init__(
|
|
self,
|
|
labels: Dict[str, str],
|
|
) -> None:
|
|
# We need to import prometheus_client after setting the env variable `PROMETHEUS_MULTIPROC_DIR`
|
|
from prometheus_client import Counter, Histogram
|
|
|
|
self.labels = labels
|
|
|
|
bucket_eviction_duration = get_histogram_conf_from_env(
|
|
"SGLANG_BUCKET_EVICTION_DURATION"
|
|
)
|
|
if bucket_eviction_duration is None:
|
|
bucket_eviction_duration = [
|
|
0.001,
|
|
0.002,
|
|
0.003,
|
|
0.004,
|
|
0.005,
|
|
0.006,
|
|
0.007,
|
|
0.008,
|
|
0.009,
|
|
0.01,
|
|
0.02,
|
|
0.03,
|
|
0.04,
|
|
0.05,
|
|
0.1,
|
|
0.2,
|
|
0.5,
|
|
1.0,
|
|
]
|
|
bucket_load_back_duration = get_histogram_conf_from_env(
|
|
"SGLANG_BUCKET_LOAD_BACK_DURATION"
|
|
)
|
|
if bucket_load_back_duration is None:
|
|
bucket_load_back_duration = [
|
|
0.001,
|
|
0.002,
|
|
0.003,
|
|
0.004,
|
|
0.005,
|
|
0.006,
|
|
0.007,
|
|
0.008,
|
|
0.009,
|
|
0.01,
|
|
0.02,
|
|
0.03,
|
|
0.04,
|
|
0.05,
|
|
0.1,
|
|
0.2,
|
|
0.5,
|
|
1.0,
|
|
]
|
|
self.eviction_duration_seconds = Histogram(
|
|
name="sglang:eviction_duration_seconds",
|
|
documentation="Time taken to evict memory from GPU to CPU in seconds.",
|
|
labelnames=labels.keys(),
|
|
buckets=bucket_eviction_duration,
|
|
)
|
|
|
|
self.eviction_num_tokens = Counter(
|
|
name="sglang:evicted_tokens_total",
|
|
documentation="The number of tokens evicted from GPU to CPU.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
self.load_back_duration_seconds = Histogram(
|
|
name="sglang:load_back_duration_seconds",
|
|
documentation="Time taken to load memory from CPU to GPU in seconds.",
|
|
labelnames=labels.keys(),
|
|
buckets=bucket_load_back_duration,
|
|
)
|
|
|
|
self.load_back_num_tokens = Counter(
|
|
name="sglang:load_back_tokens_total",
|
|
documentation="The number of tokens loaded from CPU to GPU.",
|
|
labelnames=labels.keys(),
|
|
)
|
|
|
|
def increment_eviction_num_tokens(self, num_tokens: int) -> None:
|
|
self.eviction_num_tokens.labels(**self.labels).inc(num_tokens)
|
|
|
|
def increment_load_back_num_tokens(self, num_tokens: int) -> None:
|
|
self.load_back_num_tokens.labels(**self.labels).inc(num_tokens)
|
|
|
|
def observe_eviction_duration(self, duration_seconds: float) -> None:
|
|
self.eviction_duration_seconds.labels(**self.labels).observe(duration_seconds)
|
|
|
|
def observe_load_back_duration(self, duration_seconds: float) -> None:
|
|
self.load_back_duration_seconds.labels(**self.labels).observe(duration_seconds)
|