metrics.py

from typing import TYPE_CHECKING
from typing import Counter as CollectionsCounter
from typing import Dict, List, Optional, Type, Union, cast

import numpy as np
import prometheus_client

from vllm.engine.metrics_types import (StatLoggerBase, Stats,
                                       SupportsMetricsInfo)
from vllm.executor.ray_utils import ray
from vllm.logger import init_logger

if ray is not None:
    from ray.util import metrics as ray_metrics
else:
    ray_metrics = None

if TYPE_CHECKING:
    from vllm.spec_decode.metrics import SpecDecodeWorkerMetrics

logger = init_logger(__name__)

prometheus_client.disable_created_metrics()

# The begin-* and end* here are used by the documentation generator
# to extract the metrics definitions.


# begin-metrics-definitions
class Metrics:
    """
    vLLM uses a multiprocessing-based frontend for the OpenAI server.
    This means that we need to run prometheus_client in multiprocessing mode
    See https://prometheus.github.io/client_python/multiprocess/ for more
    details on limitations.
    """

    labelname_finish_reason = "finished_reason"
    labelname_waiting_lora_adapters = "waiting_lora_adapters"
    labelname_running_lora_adapters = "running_lora_adapters"
    labelname_max_lora = "max_lora"
    _gauge_cls = prometheus_client.Gauge
    _counter_cls = prometheus_client.Counter
    _histogram_cls = prometheus_client.Histogram

    def __init__(self, labelnames: List[str], max_model_len: int):
        # Unregister any existing vLLM collectors (for CI/CD)
        self._unregister_vllm_metrics()

        # System stats
        #   Scheduler State
        self.gauge_scheduler_running = self._gauge_cls(
            name="vllm:num_requests_running",
            documentation="Number of requests currently running on GPU.",
            labelnames=labelnames,
            multiprocess_mode="sum")
        self.gauge_scheduler_waiting = self._gauge_cls(
            name="vllm:num_requests_waiting",
            documentation="Number of requests waiting to be processed.",
            labelnames=labelnames,
            multiprocess_mode="sum")
        self.gauge_lora_info = self._gauge_cls(
            name="vllm:lora_requests_info",
            documentation="Running stats on lora requests.",
            labelnames=[
                self.labelname_running_lora_adapters,
                self.labelname_max_lora,
                self.labelname_waiting_lora_adapters,
            ],
            multiprocess_mode="livemostrecent",
        )
        self.gauge_scheduler_swapped = self._gauge_cls(
            name="vllm:num_requests_swapped",
            documentation="Number of requests swapped to CPU.",
            labelnames=labelnames,
            multiprocess_mode="sum")
        #   KV Cache Usage in %
        self.gauge_gpu_cache_usage = self._gauge_cls(
            name="vllm:gpu_cache_usage_perc",
            documentation="GPU KV-cache usage. 1 means 100 percent usage.",
            labelnames=labelnames,
            multiprocess_mode="sum")
        self.gauge_cpu_cache_usage = self._gauge_cls(
            name="vllm:cpu_cache_usage_perc",
            documentation="CPU KV-cache usage. 1 means 100 percent usage.",
            labelnames=labelnames,
            multiprocess_mode="sum")
        #   Prefix caching block hit rate
        self.gauge_cpu_prefix_cache_hit_rate = self._gauge_cls(
            name="vllm:cpu_prefix_cache_hit_rate",
            documentation="CPU prefix cache block hit rate.",
            labelnames=labelnames,
            multiprocess_mode="sum")
        self.gauge_gpu_prefix_cache_hit_rate = self._gauge_cls(
            name="vllm:gpu_prefix_cache_hit_rate",
            documentation="GPU prefix cache block hit rate.",
            labelnames=labelnames,
            multiprocess_mode="sum")

        # Iteration stats
        self.counter_num_preemption = self._counter_cls(
            name="vllm:num_preemptions_total",
            documentation="Cumulative number of preemption from the engine.",
            labelnames=labelnames)
        self.counter_prompt_tokens = self._counter_cls(
            name="vllm:prompt_tokens_total",
            documentation="Number of prefill tokens processed.",
            labelnames=labelnames)
        self.counter_generation_tokens = self._counter_cls(
            name="vllm:generation_tokens_total",
            documentation="Number of generation tokens processed.",
            labelnames=labelnames)
        self.histogram_time_to_first_token = self._histogram_cls(
            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 = self._histogram_cls(
            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 = self._histogram_cls(
            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 = self._histogram_cls(
            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 = \
            self._histogram_cls(
                name="vllm:request_generation_tokens",
                documentation="Number of generation tokens processed.",
                labelnames=labelnames,
                buckets=build_1_2_5_buckets(max_model_len),
            )
        self.histogram_n_request = self._histogram_cls(
            name="vllm:request_params_n",
            documentation="Histogram of the n request parameter.",
            labelnames=labelnames,
            buckets=[1, 2, 5, 10, 20],
        )
        self.counter_request_success = self._counter_cls(
            name="vllm:request_success_total",
            documentation="Count of successfully processed requests.",
            labelnames=labelnames + [Metrics.labelname_finish_reason])

        # Speculatie decoding stats
        self.gauge_spec_decode_draft_acceptance_rate = self._gauge_cls(
            name="vllm:spec_decode_draft_acceptance_rate",
            documentation="Speulative token acceptance rate.",
            labelnames=labelnames,
            multiprocess_mode="sum")
        self.gauge_spec_decode_efficiency = self._gauge_cls(
            name="vllm:spec_decode_efficiency",
            documentation="Speculative decoding system efficiency.",
            labelnames=labelnames,
            multiprocess_mode="sum")
        self.counter_spec_decode_num_accepted_tokens = (self._counter_cls(
            name="vllm:spec_decode_num_accepted_tokens_total",
            documentation="Number of accepted tokens.",
            labelnames=labelnames))
        self.counter_spec_decode_num_draft_tokens = self._counter_cls(
            name="vllm:spec_decode_num_draft_tokens_total",
            documentation="Number of draft tokens.",
            labelnames=labelnames)
        self.counter_spec_decode_num_emitted_tokens = (self._counter_cls(
            name="vllm:spec_decode_num_emitted_tokens_total",
            documentation="Number of emitted tokens.",
            labelnames=labelnames))

        # Deprecated in favor of vllm:prompt_tokens_total
        self.gauge_avg_prompt_throughput = self._gauge_cls(
            name="vllm:avg_prompt_throughput_toks_per_s",
            documentation="Average prefill throughput in tokens/s.",
            labelnames=labelnames,
            multiprocess_mode="sum",
        )
        # Deprecated in favor of vllm:generation_tokens_total
        self.gauge_avg_generation_throughput = self._gauge_cls(
            name="vllm:avg_generation_throughput_toks_per_s",
            documentation="Average generation throughput in tokens/s.",
            labelnames=labelnames,
            multiprocess_mode="sum",
        )


# end-metrics-definitions

    def _unregister_vllm_metrics(self) -> None:
        for collector in list(prometheus_client.REGISTRY._collector_to_names):
            if hasattr(collector, "_name") and "vllm" in collector._name:
                prometheus_client.REGISTRY.unregister(collector)


class _RayGaugeWrapper:
    """Wraps around ray.util.metrics.Gauge to provide same API as
    prometheus_client.Gauge"""

    def __init__(self,
                 name: str,
                 documentation: str = "",
                 labelnames: Optional[List[str]] = None,
                 multiprocess_mode: str = ""):
        del multiprocess_mode
        labelnames_tuple = tuple(labelnames) if labelnames else None
        self._gauge = ray_metrics.Gauge(name=name,
                                        description=documentation,
                                        tag_keys=labelnames_tuple)

    def labels(self, **labels):
        self._gauge.set_default_tags(labels)
        return self

    def set(self, value: Union[int, float]):
        return self._gauge.set(value)


class _RayCounterWrapper:
    """Wraps around ray.util.metrics.Counter to provide same API as
    prometheus_client.Counter"""

    def __init__(self,
                 name: str,
                 documentation: str = "",
                 labelnames: Optional[List[str]] = None):
        labelnames_tuple = tuple(labelnames) if labelnames else None
        self._counter = ray_metrics.Counter(name=name,
                                            description=documentation,
                                            tag_keys=labelnames_tuple)

    def labels(self, **labels):
        self._counter.set_default_tags(labels)
        return self

    def inc(self, value: Union[int, float] = 1.0):
        if value == 0:
            return
        return self._counter.inc(value)


class _RayHistogramWrapper:
    """Wraps around ray.util.metrics.Histogram to provide same API as
    prometheus_client.Histogram"""

    def __init__(self,
                 name: str,
                 documentation: str = "",
                 labelnames: Optional[List[str]] = None,
                 buckets: Optional[List[float]] = None):
        labelnames_tuple = tuple(labelnames) if labelnames else None
        boundaries = buckets if buckets else []
        self._histogram = ray_metrics.Histogram(name=name,
                                                description=documentation,
                                                tag_keys=labelnames_tuple,
                                                boundaries=boundaries)

    def labels(self, **labels):
        self._histogram.set_default_tags(labels)
        return self

    def observe(self, value: Union[int, float]):
        return self._histogram.observe(value)


class RayMetrics(Metrics):
    """
    RayMetrics is used by RayPrometheusStatLogger to log to Ray metrics.
    Provides the same metrics as Metrics but uses Ray's util.metrics library.
    """
    _gauge_cls: Type[prometheus_client.Gauge] = cast(
        Type[prometheus_client.Gauge], _RayGaugeWrapper)
    _counter_cls: Type[prometheus_client.Counter] = cast(
        Type[prometheus_client.Counter], _RayCounterWrapper)
    _histogram_cls: Type[prometheus_client.Histogram] = cast(
        Type[prometheus_client.Histogram], _RayHistogramWrapper)

    def __init__(self, labelnames: List[str], max_model_len: int):
        if ray_metrics is None:
            raise ImportError("RayMetrics requires Ray to be installed.")
        super().__init__(labelnames, max_model_len)

    def _unregister_vllm_metrics(self) -> None:
        # No-op on purpose
        pass


def build_1_2_5_buckets(max_value: int) -> List[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: List[int] = []
    while True:
        for m in mantissa_lst:
            value = m * 10**exponent
            if value <= max_value:
                buckets.append(value)
            else:
                return buckets
        exponent += 1


def local_interval_elapsed(now: float, last_log: float,
                           local_interval: float) -> bool:
    elapsed_time = now - last_log
    return elapsed_time > local_interval


def get_throughput(tracked_stats: List[int], now: float,
                   last_log: float) -> float:
    return float(np.sum(tracked_stats) / (now - last_log))


class LoggingStatLogger(StatLoggerBase):
    """LoggingStatLogger is used in LLMEngine to log to Stdout."""

    def log(self, stats: Stats) -> None:
        """Called by LLMEngine.
           Logs to Stdout every self.local_interval seconds."""

        # 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)

        # Update spec decode metrics
        self.maybe_update_spec_decode_metrics(stats)

        # Log locally every local_interval seconds.
        if local_interval_elapsed(stats.now, self.last_local_log,
                                  self.local_interval):
            # Compute summary metrics for tracked stats (and log them
            # to promethus if applicable).
            prompt_throughput = get_throughput(self.num_prompt_tokens,
                                               now=stats.now,
                                               last_log=self.last_local_log)
            generation_throughput = get_throughput(
                self.num_generation_tokens,
                now=stats.now,
                last_log=self.last_local_log)

            # 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,
            )
            if (stats.cpu_prefix_cache_hit_rate >= 0
                    or stats.gpu_prefix_cache_hit_rate >= 0):
                logger.info(
                    "Prefix cache hit rate: GPU: %.2f%%, CPU: %.2f%%",
                    stats.gpu_prefix_cache_hit_rate * 100,
                    stats.cpu_prefix_cache_hit_rate * 100,
                )
            if self.spec_decode_metrics is not None:
                logger.info(
                    self._format_spec_decode_metrics_str(
                        self.spec_decode_metrics))

            # Reset tracked stats for next interval.
            self.num_prompt_tokens = []
            self.num_generation_tokens = []
            self.last_local_log = stats.now
            self.spec_decode_metrics = None

    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: {metrics.draft_tokens}, "
                f"Number of emitted tokens: {metrics.emitted_tokens}.")

    def info(self, type: str, obj: SupportsMetricsInfo) -> None:
        raise NotImplementedError


class PrometheusStatLogger(StatLoggerBase):
    """PrometheusStatLogger is used LLMEngine to log to Promethus."""
    _metrics_cls = Metrics
    _gauge_cls = prometheus_client.Gauge

    def __init__(self, local_interval: float, labels: Dict[str, str],
                 max_model_len: int) -> None:
        super().__init__(local_interval)
        # Prometheus metrics
        self.labels = labels
        self.metrics = self._metrics_cls(labelnames=list(labels.keys()),
                                         max_model_len=max_model_len)

    def _log_gauge(self, gauge, data: Union[int, float]) -> None:
        # Convenience function for logging to gauge.
        gauge.labels(**self.labels).set(data)

    def _log_counter(self, counter, data: Union[int, float]) -> None:
        # Convenience function for logging to counter.
        counter.labels(**self.labels).inc(data)

    def _log_counter_labels(self, 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, 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_gauge_string(self, gauge, data: Dict[str, str]) -> None:
        gauge.labels(**data).set(1)

    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)
        self._log_gauge(self.metrics.gauge_cpu_prefix_cache_hit_rate,
                        stats.cpu_prefix_cache_hit_rate)
        self._log_gauge(self.metrics.gauge_gpu_prefix_cache_hit_rate,
                        stats.gpu_prefix_cache_hit_rate)
        # Including max-lora in metric, in future this property of lora
        # config maybe extended to be dynamic.
        lora_info = {
            self.metrics.labelname_running_lora_adapters:
            ",".join(stats.running_lora_adapters),
            self.metrics.labelname_waiting_lora_adapters:
            ",".join(stats.waiting_lora_adapters),
            self.metrics.labelname_max_lora:
            stats.max_lora,
        }
        self._log_gauge_string(self.metrics.gauge_lora_info, lora_info)
        # Iteration level data
        self._log_counter(self.metrics.counter_num_preemption,
                          stats.num_preemption_iter)
        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)

    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):
        """Logs to prometheus and tracked stats every iteration."""
        # 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)

        # Update spec decode metrics
        self.maybe_update_spec_decode_metrics(stats)

        # Log locally every local_interval seconds.
        if local_interval_elapsed(stats.now, self.last_local_log,
                                  self.local_interval):
            # Compute summary metrics for tracked stats (and log them
            # to promethus if applicable).
            prompt_throughput = get_throughput(self.num_prompt_tokens,
                                               now=stats.now,
                                               last_log=self.last_local_log)
            generation_throughput = get_throughput(
                self.num_generation_tokens,
                now=stats.now,
                last_log=self.last_local_log)

            self._log_prometheus_interval(
                prompt_throughput=prompt_throughput,
                generation_throughput=generation_throughput)

            if self.spec_decode_metrics is not None:
                self._log_gauge(
                    self.metrics.gauge_spec_decode_draft_acceptance_rate,
                    self.spec_decode_metrics.draft_acceptance_rate)
                self._log_gauge(self.metrics.gauge_spec_decode_efficiency,
                                self.spec_decode_metrics.system_efficiency)
                self._log_counter(
                    self.metrics.counter_spec_decode_num_accepted_tokens,
                    self.spec_decode_metrics.accepted_tokens)
                self._log_counter(
                    self.metrics.counter_spec_decode_num_draft_tokens,
                    self.spec_decode_metrics.draft_tokens)
                self._log_counter(
                    self.metrics.counter_spec_decode_num_emitted_tokens,
                    self.spec_decode_metrics.emitted_tokens)

            # Reset tracked stats for next interval.
            self.num_prompt_tokens = []
            self.num_generation_tokens = []
            self.last_local_log = stats.now
            self.spec_decode_metrics = None

    def info(self, type: str, obj: SupportsMetricsInfo) -> None:
        # Info type metrics are syntactic sugar for a gauge permanently set to 1
        # Since prometheus multiprocessing mode does not support Info, emulate
        # info here with a gauge.
        if type == "cache_config":
            metrics_info = obj.metrics_info()
            info_gauge = self._gauge_cls(
                name="vllm:cache_config_info",
                documentation="Information of the LLMEngine CacheConfig",
                labelnames=metrics_info.keys(),
                multiprocess_mode="mostrecent")
            info_gauge.labels(**metrics_info).set(1)


class RayPrometheusStatLogger(PrometheusStatLogger):
    """RayPrometheusStatLogger uses Ray metrics instead."""
    _metrics_cls = RayMetrics

    def info(self, type: str, obj: SupportsMetricsInfo) -> None:
        return None