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luopingyi
2026-01-09 13:34:11 +08:00
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import time
from dataclasses import dataclass
from typing import TYPE_CHECKING
from typing import Counter as CollectionsCounter
from typing import Dict, List, Optional, Protocol, Union
import numpy as np
from prometheus_client import (REGISTRY, Counter, Gauge, Histogram, Info,
disable_created_metrics)
from vllm.logger import init_logger
if TYPE_CHECKING:
from vllm.spec_decode.metrics import SpecDecodeWorkerMetrics
logger = init_logger(__name__)
disable_created_metrics()
# The begin-* and end* here are used by the documentation generator
# to extract the metrics definitions.
# begin-metrics-definitions
class Metrics:
labelname_finish_reason = "finished_reason"
def __init__(self, labelnames: List[str], max_model_len: int):
# Unregister any existing vLLM collectors
for collector in list(REGISTRY._collector_to_names):
if hasattr(collector, "_name") and "vllm" in collector._name:
REGISTRY.unregister(collector)
# Config Information
self.info_cache_config = Info(
name='vllm:cache_config',
documentation='information of cache_config')
# System stats
# Scheduler State
self.gauge_scheduler_running = Gauge(
name="vllm:num_requests_running",
documentation="Number of requests currently running on GPU.",
labelnames=labelnames)
self.gauge_scheduler_waiting = Gauge(
name="vllm:num_requests_waiting",
documentation="Number of requests waiting to be processed.",
labelnames=labelnames)
self.gauge_scheduler_swapped = Gauge(
name="vllm:num_requests_swapped",
documentation="Number of requests swapped to CPU.",
labelnames=labelnames)
# KV Cache Usage in %
self.gauge_gpu_cache_usage = Gauge(
name="vllm:gpu_cache_usage_perc",
documentation="GPU KV-cache usage. 1 means 100 percent usage.",
labelnames=labelnames)
self.gauge_cpu_cache_usage = Gauge(
name="vllm:cpu_cache_usage_perc",
documentation="CPU KV-cache usage. 1 means 100 percent usage.",
labelnames=labelnames)
# Iteration stats
self.counter_prompt_tokens = Counter(
name="vllm:prompt_tokens_total",
documentation="Number of prefill tokens processed.",
labelnames=labelnames)
self.counter_generation_tokens = Counter(
name="vllm:generation_tokens_total",
documentation="Number of generation tokens processed.",
labelnames=labelnames)
self.histogram_time_to_first_token = Histogram(
name="vllm:time_to_first_token_seconds",
documentation="Histogram of time to first token in seconds.",
labelnames=labelnames,
buckets=[
0.001, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.25, 0.5,
0.75, 1.0, 2.5, 5.0, 7.5, 10.0
])
self.histogram_time_per_output_token = Histogram(
name="vllm:time_per_output_token_seconds",
documentation="Histogram of time per output token in seconds.",
labelnames=labelnames,
buckets=[
0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75,
1.0, 2.5
])
# Request stats
# Latency
self.histogram_e2e_time_request = Histogram(
name="vllm:e2e_request_latency_seconds",
documentation="Histogram of end to end request latency in seconds.",
labelnames=labelnames,
buckets=[1.0, 2.5, 5.0, 10.0, 15.0, 20.0, 30.0, 40.0, 50.0, 60.0])
# Metadata
self.histogram_num_prompt_tokens_request = Histogram(
name="vllm:request_prompt_tokens",
documentation="Number of prefill tokens processed.",
labelnames=labelnames,
buckets=build_1_2_5_buckets(max_model_len),
)
self.histogram_num_generation_tokens_request = Histogram(
name="vllm:request_generation_tokens",
documentation="Number of generation tokens processed.",
labelnames=labelnames,
buckets=build_1_2_5_buckets(max_model_len),
)
self.histogram_best_of_request = Histogram(
name="vllm:request_params_best_of",
documentation="Histogram of the best_of request parameter.",
labelnames=labelnames,
buckets=[1, 2, 5, 10, 20],
)
self.histogram_n_request = Histogram(
name="vllm:request_params_n",
documentation="Histogram of the n request parameter.",
labelnames=labelnames,
buckets=[1, 2, 5, 10, 20],
)
self.counter_request_success = Counter(
name="vllm:request_success_total",
documentation="Count of successfully processed requests.",
labelnames=labelnames + [Metrics.labelname_finish_reason])
# Deprecated in favor of vllm:prompt_tokens_total
self.gauge_avg_prompt_throughput = Gauge(
name="vllm:avg_prompt_throughput_toks_per_s",
documentation="Average prefill throughput in tokens/s.",
labelnames=labelnames,
)
# Deprecated in favor of vllm:generation_tokens_total
self.gauge_avg_generation_throughput = Gauge(
name="vllm:avg_generation_throughput_toks_per_s",
documentation="Average generation throughput in tokens/s.",
labelnames=labelnames,
)
# end-metrics-definitions
def build_1_2_5_buckets(max_value: int):
"""
Builds a list of buckets with increasing powers of 10 multiplied by
mantissa values (1, 2, 5) until the value exceeds the specified maximum.
Example:
>>> build_1_2_5_buckets(100)
[1, 2, 5, 10, 20, 50, 100]
"""
mantissa_lst = [1, 2, 5]
exponent = 0
buckets = []
while True:
for m in mantissa_lst:
value = m * 10**exponent
if value <= max_value:
buckets.append(value)
else:
return buckets
exponent += 1
@dataclass
class Stats:
"""Created by LLMEngine for use by StatLogger."""
now: float
# System stats (should have _sys suffix)
# Scheduler State
num_running_sys: int
num_waiting_sys: int
num_swapped_sys: int
# KV Cache Usage in %
gpu_cache_usage_sys: float
cpu_cache_usage_sys: float
# Iteration stats (should have _iter suffix)
num_prompt_tokens_iter: int
num_generation_tokens_iter: int
time_to_first_tokens_iter: List[float]
time_per_output_tokens_iter: List[float]
# Request stats (should have _requests suffix)
# Latency
time_e2e_requests: List[float]
# Metadata
num_prompt_tokens_requests: List[int]
num_generation_tokens_requests: List[int]
best_of_requests: List[int]
n_requests: List[int]
finished_reason_requests: List[str]
spec_decode_metrics: Optional["SpecDecodeWorkerMetrics"] = None
class SupportsMetricsInfo(Protocol):
def metrics_info(self) -> Dict[str, str]:
...
class StatLogger:
"""StatLogger is used LLMEngine to log to Promethus and Stdout."""
def __init__(self, local_interval: float, labels: Dict[str, str],
max_model_len: int) -> None:
# Metadata for logging locally.
self.last_local_log = time.time()
self.local_interval = local_interval
# Tracked stats over current local logging interval.
self.num_prompt_tokens: List[int] = []
self.num_generation_tokens: List[int] = []
# Prometheus metrics
self.labels = labels
self.metrics = Metrics(labelnames=list(labels.keys()),
max_model_len=max_model_len)
def info(self, type: str, obj: SupportsMetricsInfo) -> None:
if type == "cache_config":
self.metrics.info_cache_config.info(obj.metrics_info())
def _get_throughput(self, tracked_stats: List[int], now: float) -> float:
return float(np.sum(tracked_stats) / (now - self.last_local_log))
def _local_interval_elapsed(self, now: float) -> bool:
elapsed_time = now - self.last_local_log
return elapsed_time > self.local_interval
def _log_prometheus(self, stats: Stats) -> None:
# System state data
self._log_gauge(self.metrics.gauge_scheduler_running,
stats.num_running_sys)
self._log_gauge(self.metrics.gauge_scheduler_swapped,
stats.num_swapped_sys)
self._log_gauge(self.metrics.gauge_scheduler_waiting,
stats.num_waiting_sys)
self._log_gauge(self.metrics.gauge_gpu_cache_usage,
stats.gpu_cache_usage_sys)
self._log_gauge(self.metrics.gauge_cpu_cache_usage,
stats.cpu_cache_usage_sys)
# Iteration level data
self._log_counter(self.metrics.counter_prompt_tokens,
stats.num_prompt_tokens_iter)
self._log_counter(self.metrics.counter_generation_tokens,
stats.num_generation_tokens_iter)
self._log_histogram(self.metrics.histogram_time_to_first_token,
stats.time_to_first_tokens_iter)
self._log_histogram(self.metrics.histogram_time_per_output_token,
stats.time_per_output_tokens_iter)
# Request level data
# Latency
self._log_histogram(self.metrics.histogram_e2e_time_request,
stats.time_e2e_requests)
# Metadata
finished_reason_counter = CollectionsCounter(
stats.finished_reason_requests)
self._log_counter_labels(self.metrics.counter_request_success,
finished_reason_counter,
Metrics.labelname_finish_reason)
self._log_histogram(self.metrics.histogram_num_prompt_tokens_request,
stats.num_prompt_tokens_requests)
self._log_histogram(
self.metrics.histogram_num_generation_tokens_request,
stats.num_generation_tokens_requests)
self._log_histogram(self.metrics.histogram_n_request, stats.n_requests)
self._log_histogram(self.metrics.histogram_best_of_request,
stats.best_of_requests)
def _log_gauge(self, gauge: Gauge, data: Union[int, float]) -> None:
# Convenience function for logging to gauge.
gauge.labels(**self.labels).set(data)
def _log_counter(self, counter: Counter, data: Union[int, float]) -> None:
# Convenience function for logging to counter.
counter.labels(**self.labels).inc(data)
def _log_counter_labels(self, counter: Counter, data: CollectionsCounter,
label_key: str) -> None:
# Convenience function for collection counter of labels.
for label, count in data.items():
counter.labels(**{**self.labels, label_key: label}).inc(count)
def _log_histogram(self, histogram: Histogram,
data: Union[List[int], List[float]]) -> None:
# Convenience function for logging list to histogram.
for datum in data:
histogram.labels(**self.labels).observe(datum)
def _log_prometheus_interval(self, prompt_throughput: float,
generation_throughput: float) -> None:
# Logs metrics to prometheus that are computed every logging_interval.
# Support legacy gauge metrics that make throughput calculations on
# the vLLM side. Moving forward, we should use counters like
# counter_prompt_tokens, counter_generation_tokens
# Which log raw data and calculate summaries using rate() on the
# grafana/prometheus side. See
# https://github.com/vllm-project/vllm/pull/2316#discussion_r1464204666
self.metrics.gauge_avg_prompt_throughput.labels(
**self.labels).set(prompt_throughput)
self.metrics.gauge_avg_generation_throughput.labels(
**self.labels).set(generation_throughput)
def log(self, stats: Stats) -> None:
"""Called by LLMEngine.
Logs to prometheus and tracked stats every iteration.
Logs to Stdout every self.local_interval seconds."""
# Log to prometheus.
self._log_prometheus(stats)
# Save tracked stats for token counters.
self.num_prompt_tokens.append(stats.num_prompt_tokens_iter)
self.num_generation_tokens.append(stats.num_generation_tokens_iter)
# Log locally every local_interval seconds.
if self._local_interval_elapsed(stats.now):
# Compute summary metrics for tracked stats (and log them
# to promethus if applicable).
prompt_throughput = self._get_throughput(self.num_prompt_tokens,
now=stats.now)
generation_throughput = self._get_throughput(
self.num_generation_tokens, now=stats.now)
self._log_prometheus_interval(
prompt_throughput=prompt_throughput,
generation_throughput=generation_throughput)
# Log to stdout.
logger.info(
"Avg prompt throughput: %.1f tokens/s, "
"Avg generation throughput: %.1f tokens/s, "
"Running: %d reqs, Swapped: %d reqs, "
"Pending: %d reqs, GPU KV cache usage: %.1f%%, "
"CPU KV cache usage: %.1f%%",
prompt_throughput,
generation_throughput,
stats.num_running_sys,
stats.num_swapped_sys,
stats.num_waiting_sys,
stats.gpu_cache_usage_sys * 100,
stats.cpu_cache_usage_sys * 100,
)
# Reset tracked stats for next interval.
self.num_prompt_tokens = []
self.num_generation_tokens = []
self.last_local_log = stats.now
if stats.spec_decode_metrics is not None:
logger.info(
self._format_spec_decode_metrics_str(
stats.spec_decode_metrics))
def _format_spec_decode_metrics_str(
self, metrics: "SpecDecodeWorkerMetrics") -> str:
return ("Speculative metrics: "
f"Draft acceptance rate: {metrics.draft_acceptance_rate:.3f}, "
f"System efficiency: {metrics.system_efficiency:.3f}, "
f"Number of speculative tokens: {metrics.num_spec_tokens}, "
f"Number of accepted tokens: {metrics.accepted_tokens}, "
f"Number of draft tokens tokens: {metrics.draft_tokens}, "
f"Number of emitted tokens tokens: {metrics.emitted_tokens}.")