562 lines
20 KiB
Python
562 lines
20 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utilities for Prometheus Metrics Collection."""
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import time
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from dataclasses import dataclass
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from enum import Enum
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from typing import Dict, List, Optional, Union
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from sglang.srt.metrics.utils import 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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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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class RequestType(Enum):
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UNIFIED = "unified"
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PREFILL = "prefill"
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DECODE = "decode"
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INVALID = "invalid"
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def __str__(self) -> str:
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# if unified
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_type = self.get_type()
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if _type == self.RequestType.UNIFIED:
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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}"
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elif _type == self.RequestType.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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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}"
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# if decode
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elif _type == self.RequestType.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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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"
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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}"
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else:
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return "Invalid 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 get_type(self) -> RequestType:
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"""Determine the type of request based on timestamp values."""
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if (
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self.prefill_bootstrap_queue_entry_time == 0.0
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and self.prefill_transfer_queue_entry_time == 0.0
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and self.decode_prealloc_queue_entry_time == 0.0
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and self.decode_transfer_queue_entry_time == 0.0
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):
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return self.RequestType.UNIFIED
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elif (
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self.prefill_bootstrap_queue_entry_time > 0.0
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and self.prefill_transfer_queue_entry_time > 0.0
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):
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return self.RequestType.PREFILL
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elif (
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self.decode_prealloc_queue_entry_time > 0.0
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and self.decode_transfer_queue_entry_time > 0.0
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and self.wait_queue_entry_time > 0.0
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):
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return self.RequestType.DECODE
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else:
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return self.RequestType.INVALID
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@dataclass
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class SchedulerStats:
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num_running_reqs: int = 0
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num_used_tokens: int = 0
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token_usage: float = 0.0
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gen_throughput: float = 0.0
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num_queue_reqs: int = 0
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cache_hit_rate: float = 0.0
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num_grammar_queue_reqs: int = 0
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spec_accept_length: float = 0.0
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avg_request_queue_latency: float = 0.0
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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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total_retracted_reqs: int = 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
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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.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.cache_hit_rate = Gauge(
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name="sglang:cache_hit_rate",
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documentation="The prefix cache hit rate.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.spec_accept_length = Gauge(
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name="sglang:spec_accept_length",
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documentation="The average acceptance length of speculative decoding.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.avg_request_queue_latency = Gauge(
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name="sglang:avg_request_queue_latency",
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documentation="The average request queue latency for the last batch of requests in seconds.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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self.total_retracted_reqs = Gauge(
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name="sglang:total_retracted_reqs",
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documentation="The total number of retracted requests due to kvcache full.",
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labelnames=labels.keys(),
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multiprocess_mode="mostrecent",
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)
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# Disaggregation queue metrics
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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",
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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",
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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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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 increment_bootstrap_failed_reqs(self) -> None:
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self.num_bootstrap_failed_reqs.labels(**self.labels).inc(1)
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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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def log_stats(self, stats: SchedulerStats) -> None:
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self._log_gauge(self.num_running_reqs, stats.num_running_reqs)
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self._log_gauge(self.num_used_tokens, stats.num_used_tokens)
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self._log_gauge(self.token_usage, stats.token_usage)
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self._log_gauge(self.gen_throughput, stats.gen_throughput)
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self._log_gauge(self.num_queue_reqs, stats.num_queue_reqs)
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self._log_gauge(self.num_grammar_queue_reqs, stats.num_grammar_queue_reqs)
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self._log_gauge(self.cache_hit_rate, stats.cache_hit_rate)
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self._log_gauge(self.spec_accept_length, stats.spec_accept_length)
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self._log_gauge(self.total_retracted_reqs, stats.total_retracted_reqs)
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# Disaggregation metrics
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self._log_gauge(
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self.num_prefill_prealloc_queue_reqs, stats.num_prefill_prealloc_queue_reqs
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)
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self._log_gauge(
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self.num_prefill_inflight_queue_reqs, stats.num_prefill_inflight_queue_reqs
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)
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self._log_gauge(
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self.num_decode_prealloc_queue_reqs, stats.num_decode_prealloc_queue_reqs
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)
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self._log_gauge(
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self.num_decode_transfer_queue_reqs, stats.num_decode_transfer_queue_reqs
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)
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self.last_log_time = time.perf_counter()
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class TokenizerMetricsCollector:
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def __init__(
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self,
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server_args: ServerArgs,
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labels: Dict[str, str],
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bucket_time_to_first_token: Optional[List[float]] = None,
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bucket_inter_token_latency: Optional[List[float]] = None,
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bucket_e2e_request_latency: Optional[List[float]] = None,
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collect_tokens_histogram: bool = False,
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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, Histogram
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self.labels = labels
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self.collect_tokens_histogram = collect_tokens_histogram
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self.prompt_tokens_total = Counter(
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name="sglang:prompt_tokens_total",
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documentation="Number of prefill tokens processed.",
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labelnames=labels.keys(),
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)
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self.generation_tokens_total = Counter(
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name="sglang:generation_tokens_total",
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documentation="Number of generation tokens processed.",
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labelnames=labels.keys(),
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)
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if collect_tokens_histogram:
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default_bucket_prompt_tokens = [
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100,
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300,
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500,
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700,
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1000,
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1500,
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2000,
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3000,
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4000,
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5000,
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6000,
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7000,
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8000,
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9000,
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10000,
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12000,
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15000,
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20000,
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22000,
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25000,
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30000,
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35000,
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40000,
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]
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self.prompt_tokens_histogram = Histogram(
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name="sglang:prompt_tokens_histogram",
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documentation="Histogram of prompt token length.",
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labelnames=labels.keys(),
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buckets=generate_buckets(
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server_args.prompt_tokens_buckets, default_bucket_prompt_tokens
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),
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)
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default_bucket_generation_tokens = [
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100,
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300,
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500,
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1000,
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1200,
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1500,
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1700,
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2000,
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2500,
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3000,
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3500,
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4000,
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4500,
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5000,
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6000,
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7000,
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8000,
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9000,
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10000,
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]
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self.generation_tokens_histogram = Histogram(
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name="sglang:generation_tokens_histogram",
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documentation="Histogram of generation token length.",
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labelnames=labels.keys(),
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buckets=generate_buckets(
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server_args.generation_tokens_buckets,
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default_bucket_generation_tokens,
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),
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)
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self.cached_tokens_total = Counter(
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name="sglang:cached_tokens_total",
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documentation="Number of cached prompt tokens.",
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labelnames=labels.keys(),
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)
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self.num_requests_total = Counter(
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name="sglang:num_requests_total",
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documentation="Number of requests processed.",
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labelnames=labels.keys(),
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)
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self.num_so_requests_total = Counter(
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name="sglang:num_so_requests_total",
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documentation="Number of structured output requests processed.",
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labelnames=labels.keys(),
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)
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self.num_aborted_requests_total = Counter(
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name="sglang:num_aborted_requests",
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documentation="Number of requests aborted.",
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labelnames=labels.keys(),
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)
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if bucket_time_to_first_token is None:
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bucket_time_to_first_token = [
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0.1,
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0.2,
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0.4,
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0.6,
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0.8,
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1,
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2,
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4,
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6,
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8,
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10,
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20,
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40,
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60,
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80,
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100,
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200,
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400,
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]
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if bucket_e2e_request_latency is None:
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bucket_e2e_request_latency = [
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0.1,
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0.2,
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0.4,
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0.6,
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0.8,
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1,
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2,
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4,
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6,
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8,
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10,
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20,
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40,
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60,
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80,
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100,
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200,
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400,
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800,
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]
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if bucket_inter_token_latency is None:
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bucket_inter_token_latency = [
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0.002,
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0.004,
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0.006,
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0.008,
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0.010,
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0.015,
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0.020,
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0.025,
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0.030,
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0.035,
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0.040,
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0.060,
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0.080,
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0.100,
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0.200,
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0.400,
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0.600,
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0.800,
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1.000,
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2.000,
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4.000,
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6.000,
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8.000,
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]
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self.histogram_time_to_first_token = Histogram(
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name="sglang:time_to_first_token_seconds",
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documentation="Histogram of time to first token in seconds.",
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labelnames=labels.keys(),
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buckets=bucket_time_to_first_token,
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)
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self.histogram_inter_token_latency_seconds = Histogram(
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name="sglang:inter_token_latency_seconds",
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documentation="Histogram of inter-token latency in seconds.",
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labelnames=labels.keys(),
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buckets=bucket_inter_token_latency,
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)
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self.histogram_e2e_request_latency = Histogram(
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name="sglang:e2e_request_latency_seconds",
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documentation="Histogram of End-to-end request latency in seconds",
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labelnames=labels.keys(),
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buckets=bucket_e2e_request_latency,
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)
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def _log_histogram(self, histogram, data: Union[int, float]) -> None:
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|
histogram.labels(**self.labels).observe(data)
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|
|
|
def observe_one_finished_request(
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self,
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prompt_tokens: int,
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|
generation_tokens: int,
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|
cached_tokens: int,
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|
e2e_latency: float,
|
|
has_grammar: bool,
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|
):
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self.prompt_tokens_total.labels(**self.labels).inc(prompt_tokens)
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self.generation_tokens_total.labels(**self.labels).inc(generation_tokens)
|
|
if cached_tokens > 0:
|
|
self.cached_tokens_total.labels(**self.labels).inc(cached_tokens)
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|
self.num_requests_total.labels(**self.labels).inc(1)
|
|
if has_grammar:
|
|
self.num_so_requests_total.labels(**self.labels).inc(1)
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|
self._log_histogram(self.histogram_e2e_request_latency, e2e_latency)
|
|
if self.collect_tokens_histogram:
|
|
self._log_histogram(self.prompt_tokens_histogram, prompt_tokens)
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|
self._log_histogram(self.generation_tokens_histogram, generation_tokens)
|
|
|
|
def observe_time_to_first_token(self, value: float):
|
|
self.histogram_time_to_first_token.labels(**self.labels).observe(value)
|
|
|
|
def observe_inter_token_latency(self, 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.
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|
# reference: https://github.com/prometheus/client_python/blob/v0.21.1/prometheus_client/metrics.py#L639
|
|
his = self.histogram_inter_token_latency_seconds.labels(**self.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):
|
|
self.num_aborted_requests_total.labels(**self.labels).inc(1)
|