259 lines
10 KiB
Python
259 lines
10 KiB
Python
import time
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Dict, List, Optional, Protocol
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import numpy as np
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from prometheus_client import (REGISTRY, Counter, Gauge, Histogram, Info,
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disable_created_metrics)
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from vllm.logger import init_logger
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if TYPE_CHECKING:
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from vllm.spec_decode.metrics import SpecDecodeWorkerMetrics
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logger = init_logger(__name__)
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disable_created_metrics()
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# The begin-* and end* here are used by the documentation generator
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# to extract the metrics definitions.
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# begin-metrics-definitions
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class Metrics:
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def __init__(self, labelnames: List[str]):
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# Unregister any existing vLLM collectors
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for collector in list(REGISTRY._collector_to_names):
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if hasattr(collector, "_name") and "vllm" in collector._name:
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REGISTRY.unregister(collector)
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# Config Information
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self.info_cache_config = Info(
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name='vllm:cache_config',
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documentation='information of cache_config')
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# System stats
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self.gauge_scheduler_running = Gauge(
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name="vllm:num_requests_running",
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documentation="Number of requests currently running on GPU.",
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labelnames=labelnames)
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self.gauge_scheduler_swapped = Gauge(
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name="vllm:num_requests_swapped",
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documentation="Number of requests swapped to CPU.",
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labelnames=labelnames)
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self.gauge_scheduler_waiting = Gauge(
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name="vllm:num_requests_waiting",
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documentation="Number of requests waiting to be processed.",
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labelnames=labelnames)
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self.gauge_gpu_cache_usage = Gauge(
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name="vllm:gpu_cache_usage_perc",
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documentation="GPU KV-cache usage. 1 means 100 percent usage.",
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labelnames=labelnames)
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self.gauge_cpu_cache_usage = Gauge(
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name="vllm:cpu_cache_usage_perc",
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documentation="CPU KV-cache usage. 1 means 100 percent usage.",
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labelnames=labelnames)
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# Raw stats from last model iteration
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self.counter_prompt_tokens = Counter(
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name="vllm:prompt_tokens_total",
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documentation="Number of prefill tokens processed.",
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labelnames=labelnames)
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self.counter_generation_tokens = Counter(
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name="vllm:generation_tokens_total",
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documentation="Number of generation tokens processed.",
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labelnames=labelnames)
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self.histogram_time_to_first_token = Histogram(
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name="vllm:time_to_first_token_seconds",
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documentation="Histogram of time to first token in seconds.",
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labelnames=labelnames,
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buckets=[
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0.001, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.25, 0.5,
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0.75, 1.0, 2.5, 5.0, 7.5, 10.0
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])
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self.histogram_time_per_output_token = Histogram(
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name="vllm:time_per_output_token_seconds",
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documentation="Histogram of time per output token in seconds.",
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labelnames=labelnames,
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buckets=[
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0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75,
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1.0, 2.5
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])
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self.histogram_e2e_request_latency = Histogram(
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name="vllm:e2e_request_latency_seconds",
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documentation="Histogram of end to end request latency in seconds.",
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labelnames=labelnames,
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buckets=[1.0, 2.5, 5.0, 10.0, 15.0, 20.0, 30.0, 40.0, 50.0, 60.0])
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# Legacy metrics
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self.gauge_avg_prompt_throughput = Gauge(
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name="vllm:avg_prompt_throughput_toks_per_s",
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documentation="Average prefill throughput in tokens/s.",
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labelnames=labelnames,
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)
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self.gauge_avg_generation_throughput = Gauge(
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name="vllm:avg_generation_throughput_toks_per_s",
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documentation="Average generation throughput in tokens/s.",
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labelnames=labelnames,
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)
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# end-metrics-definitions
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@dataclass
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class Stats:
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"""Created by LLMEngine for use by StatLogger."""
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now: float
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# System stats.
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num_running: int
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num_waiting: int
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num_swapped: int
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gpu_cache_usage: float
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cpu_cache_usage: float
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# Raw stats from last model iteration.
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num_prompt_tokens: int
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num_generation_tokens: int
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time_to_first_tokens: List[float]
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time_per_output_tokens: List[float]
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time_e2e_requests: List[float]
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spec_decode_metrics: Optional["SpecDecodeWorkerMetrics"] = None
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class SupportsMetricsInfo(Protocol):
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def metrics_info(self) -> Dict[str, str]:
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...
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class StatLogger:
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"""StatLogger is used LLMEngine to log to Promethus and Stdout."""
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def __init__(self, local_interval: float, labels: Dict[str, str]) -> None:
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# Metadata for logging locally.
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self.last_local_log = time.time()
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self.local_interval = local_interval
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# Tracked stats over current local logging interval.
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self.num_prompt_tokens: List[int] = []
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self.num_generation_tokens: List[int] = []
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# Prometheus metrics
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self.labels = labels
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self.metrics = Metrics(labelnames=list(labels.keys()))
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def info(self, type: str, obj: SupportsMetricsInfo) -> None:
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if type == "cache_config":
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self.metrics.info_cache_config.info(obj.metrics_info())
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def _get_throughput(self, tracked_stats: List[int], now: float) -> float:
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return float(np.sum(tracked_stats) / (now - self.last_local_log))
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def _local_interval_elapsed(self, now: float) -> bool:
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elapsed_time = now - self.last_local_log
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return elapsed_time > self.local_interval
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def _log_prometheus(self, stats: Stats) -> None:
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# Set system stat gauges.
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self.metrics.gauge_scheduler_running.labels(**self.labels).set(
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stats.num_running)
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self.metrics.gauge_scheduler_swapped.labels(**self.labels).set(
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stats.num_swapped)
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self.metrics.gauge_scheduler_waiting.labels(**self.labels).set(
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stats.num_waiting)
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self.metrics.gauge_gpu_cache_usage.labels(**self.labels).set(
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stats.gpu_cache_usage)
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self.metrics.gauge_cpu_cache_usage.labels(**self.labels).set(
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stats.cpu_cache_usage)
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# Add to token counters.
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self.metrics.counter_prompt_tokens.labels(**self.labels).inc(
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stats.num_prompt_tokens)
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self.metrics.counter_generation_tokens.labels(**self.labels).inc(
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stats.num_generation_tokens)
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# Observe request level latencies in histograms.
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for ttft in stats.time_to_first_tokens:
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self.metrics.histogram_time_to_first_token.labels(
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**self.labels).observe(ttft)
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for tpot in stats.time_per_output_tokens:
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self.metrics.histogram_time_per_output_token.labels(
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**self.labels).observe(tpot)
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for e2e in stats.time_e2e_requests:
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self.metrics.histogram_e2e_request_latency.labels(
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**self.labels).observe(e2e)
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def _log_prometheus_interval(self, prompt_throughput: float,
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generation_throughput: float) -> None:
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# Logs metrics to prometheus that are computed every logging_interval.
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# Support legacy gauge metrics that make throughput calculations on
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# the vLLM side. Moving forward, we should use counters like
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# counter_prompt_tokens, counter_generation_tokens
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# Which log raw data and calculate summaries using rate() on the
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# grafana/prometheus side. See
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# https://github.com/vllm-project/vllm/pull/2316#discussion_r1464204666
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self.metrics.gauge_avg_prompt_throughput.labels(
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**self.labels).set(prompt_throughput)
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self.metrics.gauge_avg_generation_throughput.labels(
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**self.labels).set(generation_throughput)
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def log(self, stats: Stats) -> None:
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"""Called by LLMEngine.
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Logs to prometheus and tracked stats every iteration.
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Logs to Stdout every self.local_interval seconds."""
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# Log to prometheus.
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self._log_prometheus(stats)
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# Save tracked stats for token counters.
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self.num_prompt_tokens.append(stats.num_prompt_tokens)
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self.num_generation_tokens.append(stats.num_generation_tokens)
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# Log locally every local_interval seconds.
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if self._local_interval_elapsed(stats.now):
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# Compute summary metrics for tracked stats (and log them
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# to promethus if applicable).
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prompt_throughput = self._get_throughput(self.num_prompt_tokens,
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now=stats.now)
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generation_throughput = self._get_throughput(
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self.num_generation_tokens, now=stats.now)
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self._log_prometheus_interval(
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prompt_throughput=prompt_throughput,
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generation_throughput=generation_throughput)
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# Log to stdout.
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logger.info(
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f"Avg prompt throughput: {prompt_throughput:.1f} tokens/s, "
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f"Avg generation throughput: "
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f"{generation_throughput:.1f} tokens/s, "
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f"Running: {stats.num_running} reqs, "
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f"Swapped: {stats.num_swapped} reqs, "
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f"Pending: {stats.num_waiting} reqs, "
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f"GPU KV cache usage: {stats.gpu_cache_usage * 100:.1f}%, "
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f"CPU KV cache usage: {stats.cpu_cache_usage * 100:.1f}%")
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# Reset tracked stats for next interval.
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self.num_prompt_tokens = []
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self.num_generation_tokens = []
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self.last_local_log = stats.now
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if stats.spec_decode_metrics is not None:
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logger.info(
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self._format_spec_decode_metrics_str(
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stats.spec_decode_metrics))
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def _format_spec_decode_metrics_str(
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self, metrics: "SpecDecodeWorkerMetrics") -> str:
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return ("Speculative metrics: "
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f"Draft acceptance rate: {metrics.draft_acceptance_rate:.3f}, "
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f"System efficiency: {metrics.system_efficiency:.3f}, "
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f"Number of speculative tokens: {metrics.num_spec_tokens}, "
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f"Number of accepted tokens: {metrics.accepted_tokens}, "
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f"Number of draft tokens tokens: {metrics.draft_tokens}, "
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f"Number of emitted tokens tokens: {metrics.emitted_tokens}.")
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