218 lines
8.9 KiB
Python
218 lines
8.9 KiB
Python
from vllm.logger import init_logger
|
|
from prometheus_client import Counter, Gauge, Histogram, REGISTRY, disable_created_metrics
|
|
|
|
import time
|
|
import numpy as np
|
|
from typing import Dict, List
|
|
from dataclasses import dataclass
|
|
|
|
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:
|
|
|
|
def __init__(self, labelnames: List[str]):
|
|
# 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)
|
|
|
|
# System stats
|
|
self.gauge_scheduler_running = Gauge(
|
|
name="vllm:num_requests_running",
|
|
documentation="Number of requests currently running on GPU.",
|
|
labelnames=labelnames)
|
|
self.gauge_scheduler_swapped = Gauge(
|
|
name="vllm:num_requests_swapped",
|
|
documentation="Number of requests swapped to CPU.",
|
|
labelnames=labelnames)
|
|
self.gauge_scheduler_waiting = Gauge(
|
|
name="vllm:num_requests_waiting",
|
|
documentation="Number of requests waiting to be processed.",
|
|
labelnames=labelnames)
|
|
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)
|
|
|
|
# Raw stats from last model iteration
|
|
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
|
|
])
|
|
self.histogram_e2e_request_latency = 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])
|
|
|
|
# Legacy metrics
|
|
self.gauge_avg_prompt_throughput = Gauge(
|
|
name="vllm:avg_prompt_throughput_toks_per_s",
|
|
documentation="Average prefill throughput in tokens/s.",
|
|
labelnames=labelnames,
|
|
)
|
|
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
|
|
|
|
|
|
@dataclass
|
|
class Stats:
|
|
"""Created by LLMEngine for use by StatLogger."""
|
|
now: float
|
|
|
|
# System stats.
|
|
num_running: int
|
|
num_waiting: int
|
|
num_swapped: int
|
|
gpu_cache_usage: float
|
|
cpu_cache_usage: float
|
|
|
|
# Raw stats from last model iteration.
|
|
num_prompt_tokens: int
|
|
num_generation_tokens: int
|
|
time_to_first_tokens: List[float]
|
|
time_per_output_tokens: List[float]
|
|
time_e2e_requests: List[float]
|
|
|
|
|
|
class StatLogger:
|
|
"""StatLogger is used LLMEngine to log to Promethus and Stdout."""
|
|
|
|
def __init__(self, local_interval: float, labels: Dict[str, str]) -> None:
|
|
# Metadata for logging locally.
|
|
self.last_local_log = time.monotonic()
|
|
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()))
|
|
|
|
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:
|
|
# Set system stat gauges.
|
|
self.metrics.gauge_scheduler_running.labels(**self.labels).set(
|
|
stats.num_running)
|
|
self.metrics.gauge_scheduler_swapped.labels(**self.labels).set(
|
|
stats.num_swapped)
|
|
self.metrics.gauge_scheduler_waiting.labels(**self.labels).set(
|
|
stats.num_waiting)
|
|
self.metrics.gauge_gpu_cache_usage.labels(**self.labels).set(
|
|
stats.gpu_cache_usage)
|
|
self.metrics.gauge_cpu_cache_usage.labels(**self.labels).set(
|
|
stats.cpu_cache_usage)
|
|
|
|
# Add to token counters.
|
|
self.metrics.counter_prompt_tokens.labels(**self.labels).inc(
|
|
stats.num_prompt_tokens)
|
|
self.metrics.counter_generation_tokens.labels(**self.labels).inc(
|
|
stats.num_generation_tokens)
|
|
|
|
# Observe request level latencies in histograms.
|
|
for ttft in stats.time_to_first_tokens:
|
|
self.metrics.histogram_time_to_first_token.labels(
|
|
**self.labels).observe(ttft)
|
|
for tpot in stats.time_per_output_tokens:
|
|
self.metrics.histogram_time_per_output_token.labels(
|
|
**self.labels).observe(tpot)
|
|
for e2e in stats.time_e2e_requests:
|
|
self.metrics.histogram_e2e_request_latency.labels(
|
|
**self.labels).observe(e2e)
|
|
|
|
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)
|
|
self.num_generation_tokens.append(stats.num_generation_tokens)
|
|
|
|
# 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(
|
|
f"Avg prompt throughput: {prompt_throughput:.1f} tokens/s, "
|
|
f"Avg generation throughput: {generation_throughput:.1f} tokens/s, "
|
|
f"Running: {stats.num_running} reqs, "
|
|
f"Swapped: {stats.num_swapped} reqs, "
|
|
f"Pending: {stats.num_waiting} reqs, "
|
|
f"GPU KV cache usage: {stats.gpu_cache_usage * 100:.1f}%, "
|
|
f"CPU KV cache usage: {stats.cpu_cache_usage * 100:.1f}%")
|
|
|
|
# Reset tracked stats for next interval.
|
|
self.num_prompt_tokens = []
|
|
self.num_generation_tokens = []
|
|
self.last_local_log = stats.now
|