1306 lines
48 KiB
Python
1306 lines
48 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import logging
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import time
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from abc import ABC, abstractmethod
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from collections.abc import Callable
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from typing import TypeAlias
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from prometheus_client import Counter, Gauge, Histogram
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import vllm.envs as envs
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from vllm.compilation.cuda_graph import CUDAGraphLogging
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from vllm.config import SupportsMetricsInfo, VllmConfig
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from vllm.distributed.kv_transfer.kv_connector.v1.metrics import (
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KVConnectorLogging,
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KVConnectorPrometheus,
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)
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from vllm.logger import init_logger
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from vllm.plugins import STAT_LOGGER_PLUGINS_GROUP, load_plugins_by_group
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from vllm.v1.engine import FinishReason
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from vllm.v1.metrics.prometheus import unregister_vllm_metrics
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from vllm.v1.metrics.stats import (
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CachingMetrics,
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IterationStats,
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MultiModalCacheStats,
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SchedulerStats,
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)
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from vllm.v1.spec_decode.metrics import SpecDecodingLogging, SpecDecodingProm
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logger = init_logger(__name__)
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PerEngineStatLoggerFactory = Callable[[VllmConfig, int], "StatLoggerBase"]
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AggregateStatLoggerFactory = type["AggregateStatLoggerBase"]
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StatLoggerFactory = AggregateStatLoggerFactory | PerEngineStatLoggerFactory
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class StatLoggerBase(ABC):
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"""Interface for logging metrics.
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API users may define custom loggers that implement this interface.
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However, note that the `SchedulerStats` and `IterationStats` classes
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are not considered stable interfaces and may change in future versions.
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"""
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@abstractmethod
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def __init__(self, vllm_config: VllmConfig, engine_index: int = 0): ...
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@abstractmethod
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def record(
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self,
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scheduler_stats: SchedulerStats | None,
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iteration_stats: IterationStats | None,
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mm_cache_stats: MultiModalCacheStats | None = None,
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engine_idx: int = 0,
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): ...
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@abstractmethod
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def log_engine_initialized(self): ...
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def log(self): # noqa
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pass
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def record_sleep_state(self, is_awake: int, level: int): # noqa
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pass
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def load_stat_logger_plugin_factories() -> list[StatLoggerFactory]:
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factories: list[StatLoggerFactory] = []
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for name, plugin_class in load_plugins_by_group(STAT_LOGGER_PLUGINS_GROUP).items():
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if not isinstance(plugin_class, type) or not issubclass(
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plugin_class, StatLoggerBase
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):
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raise TypeError(
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f"Stat logger plugin {name!r} must be a subclass of "
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f"StatLoggerBase (got {plugin_class!r})."
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)
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factories.append(plugin_class)
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return factories
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class AggregateStatLoggerBase(StatLoggerBase):
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"""Abstract base class for loggers that
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aggregate across multiple DP engines."""
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@abstractmethod
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def __init__(self, vllm_config: VllmConfig, engine_indexes: list[int]): ...
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class LoggingStatLogger(StatLoggerBase):
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def __init__(self, vllm_config: VllmConfig, engine_index: int = 0):
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self.engine_index = engine_index
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self.vllm_config = vllm_config
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self._reset(time.monotonic())
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self.last_scheduler_stats = SchedulerStats()
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# Caching metrics. This cannot be reset.
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# TODO: Make the interval configurable.
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self.prefix_caching_metrics = CachingMetrics()
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self.connector_prefix_caching_metrics = CachingMetrics()
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self.mm_caching_metrics = CachingMetrics()
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self.spec_decoding_logging = SpecDecodingLogging()
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kv_transfer_config = self.vllm_config.kv_transfer_config
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self.kv_connector_logging = KVConnectorLogging(kv_transfer_config)
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self.cudagraph_logging = None
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if self.vllm_config.observability_config.cudagraph_metrics:
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self.cudagraph_logging = CUDAGraphLogging(
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self.vllm_config.compilation_config.cudagraph_mode,
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self.vllm_config.compilation_config.cudagraph_capture_sizes,
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)
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self.last_prompt_throughput: float = 0.0
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self.last_generation_throughput: float = 0.0
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self.engine_is_idle = False
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self.aggregated = False
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def _reset(self, now):
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self.last_log_time = now
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# Tracked stats over current local logging interval.
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self.num_prompt_tokens: int = 0
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self.num_generation_tokens: int = 0
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self.num_corrupted_reqs: int = 0
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self.num_preemptions: int = 0
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def _track_iteration_stats(self, iteration_stats: IterationStats):
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# Save tracked stats for token counters.
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self.num_prompt_tokens += iteration_stats.num_prompt_tokens
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self.num_generation_tokens += iteration_stats.num_generation_tokens
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self.num_corrupted_reqs += iteration_stats.num_corrupted_reqs
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self.num_preemptions += iteration_stats.num_preempted_reqs
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def _get_throughput(self, tracked_stats: int, now: float) -> float:
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# Compute summary metrics for tracked stats
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delta_time = now - self.last_log_time
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if delta_time <= 0.0:
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return 0.0
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return float(tracked_stats / delta_time)
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@property
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def log_prefix(self):
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return "Engine {:03d}: ".format(self.engine_index)
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def record(
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self,
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scheduler_stats: SchedulerStats | None,
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iteration_stats: IterationStats | None,
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mm_cache_stats: MultiModalCacheStats | None = None,
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engine_idx: int = 0,
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):
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"""Log Stats to standard output."""
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if iteration_stats:
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self._track_iteration_stats(iteration_stats)
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if scheduler_stats is not None:
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self.prefix_caching_metrics.observe(scheduler_stats.prefix_cache_stats)
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if scheduler_stats.connector_prefix_cache_stats is not None:
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self.connector_prefix_caching_metrics.observe(
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scheduler_stats.connector_prefix_cache_stats
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)
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if scheduler_stats.spec_decoding_stats is not None:
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self.spec_decoding_logging.observe(scheduler_stats.spec_decoding_stats)
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if kv_connector_stats := scheduler_stats.kv_connector_stats:
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self.kv_connector_logging.observe(kv_connector_stats)
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if (
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self.cudagraph_logging is not None
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and scheduler_stats.cudagraph_stats is not None
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):
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self.cudagraph_logging.observe(scheduler_stats.cudagraph_stats)
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if not self.aggregated:
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self.last_scheduler_stats = scheduler_stats
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if mm_cache_stats:
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self.mm_caching_metrics.observe(mm_cache_stats)
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def _update_stats(self):
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now = time.monotonic()
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prompt_throughput = self._get_throughput(self.num_prompt_tokens, now)
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generation_throughput = self._get_throughput(self.num_generation_tokens, now)
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self._reset(now)
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self.engine_is_idle = not any(
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(
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prompt_throughput,
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generation_throughput,
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self.last_prompt_throughput,
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self.last_generation_throughput,
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)
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)
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self.last_generation_throughput = generation_throughput
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self.last_prompt_throughput = prompt_throughput
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def aggregate_scheduler_stats(self):
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# noop for per engine loggers
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return
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def log(self):
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self._update_stats()
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self.aggregate_scheduler_stats()
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# Avoid log noise on an idle production system
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log_fn = logger.debug if self.engine_is_idle else logger.info
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# Format and print output.
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log_parts = [
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"Avg prompt throughput: %.1f tokens/s",
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"Avg generation throughput: %.1f tokens/s",
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"Running: %d reqs",
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"Waiting: %d reqs",
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]
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log_args = [
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self.last_prompt_throughput,
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self.last_generation_throughput,
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self.last_scheduler_stats.num_running_reqs,
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self.last_scheduler_stats.num_waiting_reqs,
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]
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if self.num_preemptions > 0:
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log_parts.append("Preemptions: %d")
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log_args.append(self.num_preemptions)
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log_parts.extend(
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[
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"GPU KV cache usage: %.1f%%",
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"Prefix cache hit rate: %.1f%%",
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]
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)
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log_args.extend(
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[
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self.last_scheduler_stats.kv_cache_usage * 100,
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self.prefix_caching_metrics.hit_rate * 100,
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]
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)
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if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
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log_parts.append("Corrupted: %d reqs")
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log_args.append(self.num_corrupted_reqs)
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if not self.connector_prefix_caching_metrics.empty:
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log_parts.append("External prefix cache hit rate: %.1f%%")
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log_args.append(self.connector_prefix_caching_metrics.hit_rate * 100)
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if not self.mm_caching_metrics.empty:
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log_parts.append("MM cache hit rate: %.1f%%")
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log_args.append(self.mm_caching_metrics.hit_rate * 100)
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log_fn(
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self.log_prefix + ", ".join(log_parts),
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*log_args,
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)
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self.spec_decoding_logging.log(log_fn=log_fn)
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self.kv_connector_logging.log(log_fn=log_fn)
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if self.cudagraph_logging is not None:
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self.cudagraph_logging.log(log_fn=log_fn)
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def log_engine_initialized(self):
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if self.vllm_config.cache_config.num_gpu_blocks:
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logger.debug(
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"Engine %03d: vllm cache_config_info with initialization "
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"after num_gpu_blocks is: %d",
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self.engine_index,
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self.vllm_config.cache_config.num_gpu_blocks,
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)
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class AggregatedLoggingStatLogger(LoggingStatLogger, AggregateStatLoggerBase):
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def __init__(
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self,
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vllm_config: VllmConfig,
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engine_indexes: list[int],
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):
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self.engine_indexes = engine_indexes
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self.last_scheduler_stats_dict: dict[int, SchedulerStats] = {
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idx: SchedulerStats() for idx in self.engine_indexes
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}
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LoggingStatLogger.__init__(self, vllm_config, engine_index=-1)
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self.aggregated = True
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@property
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def log_prefix(self):
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return "{} Engines Aggregated: ".format(len(self.engine_indexes))
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def record(
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self,
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scheduler_stats: SchedulerStats | None,
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iteration_stats: IterationStats | None,
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mm_cache_stats: MultiModalCacheStats | None = None,
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engine_idx: int = 0,
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):
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if engine_idx not in self.engine_indexes:
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logger.warning("Unexpected engine_idx: %d", engine_idx)
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return
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LoggingStatLogger.record(
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self,
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scheduler_stats,
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iteration_stats,
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mm_cache_stats=mm_cache_stats,
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engine_idx=engine_idx,
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)
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if scheduler_stats is not None:
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self.last_scheduler_stats_dict[engine_idx] = scheduler_stats
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def aggregate_scheduler_stats(self):
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self.last_scheduler_stats = SchedulerStats()
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for last_scheduler_stats in self.last_scheduler_stats_dict.values():
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self.last_scheduler_stats.num_waiting_reqs += (
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last_scheduler_stats.num_waiting_reqs
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)
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self.last_scheduler_stats.num_running_reqs += (
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last_scheduler_stats.num_running_reqs
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)
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self.last_scheduler_stats.kv_cache_usage += (
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last_scheduler_stats.kv_cache_usage
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)
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self.last_scheduler_stats.kv_cache_usage /= len(self.last_scheduler_stats_dict)
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def log(self):
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LoggingStatLogger.log(self)
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def log_engine_initialized(self):
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if self.vllm_config.cache_config.num_gpu_blocks:
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logger.info(
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"%d Engines: vllm cache_config_info with initialization "
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"after num_gpu_blocks is: %d",
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len(self.engine_indexes),
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self.vllm_config.cache_config.num_gpu_blocks,
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)
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class PerEngineStatLoggerAdapter(AggregateStatLoggerBase):
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def __init__(
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self,
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vllm_config: VllmConfig,
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engine_indexes: list[int],
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per_engine_stat_logger_factory: PerEngineStatLoggerFactory,
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) -> None:
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self.per_engine_stat_loggers = {}
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self.engine_indexes = engine_indexes
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for engine_index in engine_indexes:
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self.per_engine_stat_loggers[engine_index] = per_engine_stat_logger_factory(
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vllm_config, engine_index
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)
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def record(
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self,
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scheduler_stats: SchedulerStats | None,
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iteration_stats: IterationStats | None,
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mm_cache_stats: MultiModalCacheStats | None = None,
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engine_idx: int = 0,
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):
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if engine_idx not in self.per_engine_stat_loggers:
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logger.warning("Unexpected engine_idx: %d", engine_idx)
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return
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self.per_engine_stat_loggers[engine_idx].record(
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scheduler_stats,
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iteration_stats,
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mm_cache_stats=mm_cache_stats,
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engine_idx=engine_idx,
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)
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def log(self):
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for per_engine_stat_logger in self.per_engine_stat_loggers.values():
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per_engine_stat_logger.log()
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def log_engine_initialized(self):
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for per_engine_stat_logger in self.per_engine_stat_loggers.values():
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per_engine_stat_logger.log_engine_initialized()
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class PrometheusStatLogger(AggregateStatLoggerBase):
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_gauge_cls = Gauge
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_counter_cls = Counter
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_histogram_cls = Histogram
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_spec_decoding_cls = SpecDecodingProm
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_kv_connector_cls = KVConnectorPrometheus
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def __init__(
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self, vllm_config: VllmConfig, engine_indexes: list[int] | None = None
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):
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if engine_indexes is None:
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engine_indexes = [0]
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self.engine_indexes = engine_indexes
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unregister_vllm_metrics()
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self.vllm_config = vllm_config
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# Use this flag to hide metrics that were deprecated in
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# a previous release and which will be removed future
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self.show_hidden_metrics = vllm_config.observability_config.show_hidden_metrics
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self.kv_cache_metrics_enabled = (
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vllm_config.observability_config.kv_cache_metrics
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)
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labelnames = ["model_name", "engine"]
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model_name = vllm_config.model_config.served_model_name
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max_model_len = vllm_config.model_config.max_model_len
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per_engine_labelvalues: dict[int, list[object]] = {
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idx: [model_name, str(idx)] for idx in engine_indexes
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}
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self.spec_decoding_prom = self._spec_decoding_cls(
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vllm_config.speculative_config, labelnames, per_engine_labelvalues
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)
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self.kv_connector_prom = self._kv_connector_cls(
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vllm_config, labelnames, per_engine_labelvalues
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)
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#
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# Scheduler state
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#
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gauge_scheduler_running = self._gauge_cls(
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name="vllm:num_requests_running",
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documentation="Number of requests in model execution batches.",
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multiprocess_mode="mostrecent",
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labelnames=labelnames,
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)
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self.gauge_scheduler_running = make_per_engine(
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gauge_scheduler_running, engine_indexes, model_name
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)
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gauge_scheduler_waiting = self._gauge_cls(
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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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multiprocess_mode="mostrecent",
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labelnames=labelnames,
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)
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self.gauge_scheduler_waiting = make_per_engine(
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gauge_scheduler_waiting, engine_indexes, model_name
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)
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gauge_engine_sleep_state = self._gauge_cls(
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name="vllm:engine_sleep_state",
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documentation=(
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"Engine sleep state; awake = 0 means engine is sleeping; "
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"awake = 1 means engine is awake; "
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"weights_offloaded = 1 means sleep level 1; "
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"discard_all = 1 means sleep level 2."
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),
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labelnames=labelnames + ["sleep_state"],
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multiprocess_mode="mostrecent",
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)
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self.gauge_engine_sleep_state = {}
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sleep_state = ["awake", "weights_offloaded", "discard_all"]
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for s in sleep_state:
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self.gauge_engine_sleep_state[s] = {
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idx: gauge_engine_sleep_state.labels(
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engine=idx, model_name=model_name, sleep_state=s
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)
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for idx in engine_indexes
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}
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# Setting default values
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self.record_sleep_state()
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gauge_kv_cache_usage = self._gauge_cls(
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name="vllm:kv_cache_usage_perc",
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documentation="KV-cache usage. 1 means 100 percent usage.",
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multiprocess_mode="mostrecent",
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labelnames=labelnames,
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)
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self.gauge_kv_cache_usage = make_per_engine(
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gauge_kv_cache_usage, engine_indexes, model_name
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)
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if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
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counter_corrupted_requests = self._counter_cls(
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name="vllm:corrupted_requests",
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documentation=(
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"Corrupted requests, in terms of total number of requests "
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"with NaNs in logits."
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),
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labelnames=labelnames,
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)
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self.counter_corrupted_requests = make_per_engine(
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counter_corrupted_requests, engine_indexes, model_name
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)
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counter_prefix_cache_queries = self._counter_cls(
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name="vllm:prefix_cache_queries",
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documentation=(
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"Prefix cache queries, in terms of number of queried tokens."
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),
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labelnames=labelnames,
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)
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self.counter_prefix_cache_queries = make_per_engine(
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counter_prefix_cache_queries, engine_indexes, model_name
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)
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counter_prefix_cache_hits = self._counter_cls(
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name="vllm:prefix_cache_hits",
|
|
documentation=("Prefix cache hits, in terms of number of cached tokens."),
|
|
labelnames=labelnames,
|
|
)
|
|
self.counter_prefix_cache_hits = make_per_engine(
|
|
counter_prefix_cache_hits, engine_indexes, model_name
|
|
)
|
|
|
|
#
|
|
# External - KV connector prefix cache
|
|
#
|
|
|
|
counter_connector_prefix_cache_queries = self._counter_cls(
|
|
name="vllm:external_prefix_cache_queries",
|
|
documentation=(
|
|
"External prefix cache queries from KV connector "
|
|
"cross-instance cache sharing, in terms of number of queried tokens."
|
|
),
|
|
labelnames=labelnames,
|
|
)
|
|
self.counter_connector_prefix_cache_queries = make_per_engine(
|
|
counter_connector_prefix_cache_queries, engine_indexes, model_name
|
|
)
|
|
|
|
counter_connector_prefix_cache_hits = self._counter_cls(
|
|
name="vllm:external_prefix_cache_hits",
|
|
documentation=(
|
|
"External prefix cache hits from KV connector "
|
|
"cross-instance cache sharing, in terms of number of cached tokens."
|
|
),
|
|
labelnames=labelnames,
|
|
)
|
|
self.counter_connector_prefix_cache_hits = make_per_engine(
|
|
counter_connector_prefix_cache_hits, engine_indexes, model_name
|
|
)
|
|
|
|
#
|
|
# Multi-modal cache
|
|
#
|
|
|
|
counter_mm_cache_queries = self._counter_cls(
|
|
name="vllm:mm_cache_queries",
|
|
documentation=(
|
|
"Multi-modal cache queries, in terms of number of queried items."
|
|
),
|
|
labelnames=labelnames,
|
|
)
|
|
self.counter_mm_cache_queries = make_per_engine(
|
|
counter_mm_cache_queries, engine_indexes, model_name
|
|
)
|
|
|
|
counter_mm_cache_hits = self._counter_cls(
|
|
name="vllm:mm_cache_hits",
|
|
documentation=(
|
|
"Multi-modal cache hits, in terms of number of cached items."
|
|
),
|
|
labelnames=labelnames,
|
|
)
|
|
self.counter_mm_cache_hits = make_per_engine(
|
|
counter_mm_cache_hits, engine_indexes, model_name
|
|
)
|
|
|
|
#
|
|
# Counters
|
|
#
|
|
counter_num_preempted_reqs = self._counter_cls(
|
|
name="vllm:num_preemptions",
|
|
documentation="Cumulative number of preemption from the engine.",
|
|
labelnames=labelnames,
|
|
)
|
|
self.counter_num_preempted_reqs = make_per_engine(
|
|
counter_num_preempted_reqs, engine_indexes, model_name
|
|
)
|
|
|
|
counter_prompt_tokens = self._counter_cls(
|
|
name="vllm:prompt_tokens",
|
|
documentation="Number of prefill tokens processed.",
|
|
labelnames=labelnames,
|
|
)
|
|
self.counter_prompt_tokens = make_per_engine(
|
|
counter_prompt_tokens, engine_indexes, model_name
|
|
)
|
|
|
|
counter_generation_tokens = self._counter_cls(
|
|
name="vllm:generation_tokens",
|
|
documentation="Number of generation tokens processed.",
|
|
labelnames=labelnames,
|
|
)
|
|
self.counter_generation_tokens = make_per_engine(
|
|
counter_generation_tokens, engine_indexes, model_name
|
|
)
|
|
|
|
self.counter_request_success: dict[FinishReason, dict[int, Counter]] = {}
|
|
counter_request_success_base = self._counter_cls(
|
|
name="vllm:request_success",
|
|
documentation="Count of successfully processed requests.",
|
|
labelnames=labelnames + ["finished_reason"],
|
|
)
|
|
for reason in FinishReason:
|
|
self.counter_request_success[reason] = {
|
|
idx: counter_request_success_base.labels(
|
|
model_name, str(idx), str(reason)
|
|
)
|
|
for idx in engine_indexes
|
|
}
|
|
|
|
#
|
|
# Histograms of counts
|
|
#
|
|
histogram_num_prompt_tokens_request = self._histogram_cls(
|
|
name="vllm:request_prompt_tokens",
|
|
documentation="Number of prefill tokens processed.",
|
|
buckets=build_1_2_5_buckets(max_model_len),
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_num_prompt_tokens_request = make_per_engine(
|
|
histogram_num_prompt_tokens_request, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_num_generation_tokens_request = self._histogram_cls(
|
|
name="vllm:request_generation_tokens",
|
|
documentation="Number of generation tokens processed.",
|
|
buckets=build_1_2_5_buckets(max_model_len),
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_num_generation_tokens_request = make_per_engine(
|
|
histogram_num_generation_tokens_request, engine_indexes, model_name
|
|
)
|
|
|
|
# TODO: This metric might be incorrect in case of using multiple
|
|
# api_server counts which uses prometheus mp.
|
|
# See: https://github.com/vllm-project/vllm/pull/18053
|
|
histogram_iteration_tokens = self._histogram_cls(
|
|
name="vllm:iteration_tokens_total",
|
|
documentation="Histogram of number of tokens per engine_step.",
|
|
buckets=[1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384],
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_iteration_tokens = make_per_engine(
|
|
histogram_iteration_tokens, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_max_num_generation_tokens_request = self._histogram_cls(
|
|
name="vllm:request_max_num_generation_tokens",
|
|
documentation="Histogram of maximum number of requested generation tokens.",
|
|
buckets=build_1_2_5_buckets(max_model_len),
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_max_num_generation_tokens_request = make_per_engine(
|
|
histogram_max_num_generation_tokens_request, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_n_request = self._histogram_cls(
|
|
name="vllm:request_params_n",
|
|
documentation="Histogram of the n request parameter.",
|
|
buckets=[1, 2, 5, 10, 20],
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_n_request = make_per_engine(
|
|
histogram_n_request, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_max_tokens_request = self._histogram_cls(
|
|
name="vllm:request_params_max_tokens",
|
|
documentation="Histogram of the max_tokens request parameter.",
|
|
buckets=build_1_2_5_buckets(max_model_len),
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_max_tokens_request = make_per_engine(
|
|
histogram_max_tokens_request, engine_indexes, model_name
|
|
)
|
|
|
|
#
|
|
# Histogram of timing intervals
|
|
#
|
|
histogram_time_to_first_token = self._histogram_cls(
|
|
name="vllm:time_to_first_token_seconds",
|
|
documentation="Histogram of time to first token in seconds.",
|
|
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,
|
|
20.0,
|
|
40.0,
|
|
80.0,
|
|
160.0,
|
|
640.0,
|
|
2560.0,
|
|
],
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_time_to_first_token = make_per_engine(
|
|
histogram_time_to_first_token, engine_indexes, model_name
|
|
)
|
|
|
|
# Deprecated in 0.11 - Renamed as vllm:inter_token_latency_seconds
|
|
# With 0.12.x you can enable with --show-hidden-metrics-for-version=0.11
|
|
# TODO: remove in 0.13.0
|
|
if self.show_hidden_metrics:
|
|
histogram_time_per_output_token = self._histogram_cls(
|
|
name="vllm:time_per_output_token_seconds",
|
|
documentation=(
|
|
"Histogram of time per output token in seconds."
|
|
"DEPRECATED: Use vllm:inter_token_latency_seconds instead."
|
|
),
|
|
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,
|
|
5.0,
|
|
7.5,
|
|
10.0,
|
|
20.0,
|
|
40.0,
|
|
80.0,
|
|
],
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_time_per_output_token = make_per_engine(
|
|
histogram_time_per_output_token, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_inter_token_latency = self._histogram_cls(
|
|
name="vllm:inter_token_latency_seconds",
|
|
documentation="Histogram of inter-token latency in seconds.",
|
|
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,
|
|
5.0,
|
|
7.5,
|
|
10.0,
|
|
20.0,
|
|
40.0,
|
|
80.0,
|
|
],
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_inter_token_latency = make_per_engine(
|
|
histogram_inter_token_latency, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_request_time_per_output_token = self._histogram_cls(
|
|
name="vllm:request_time_per_output_token_seconds",
|
|
documentation="Histogram of time_per_output_token_seconds per request.",
|
|
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,
|
|
5.0,
|
|
7.5,
|
|
10.0,
|
|
20.0,
|
|
40.0,
|
|
80.0,
|
|
],
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_request_time_per_output_token = make_per_engine(
|
|
histogram_request_time_per_output_token, engine_indexes, model_name
|
|
)
|
|
|
|
request_latency_buckets = [
|
|
0.3,
|
|
0.5,
|
|
0.8,
|
|
1.0,
|
|
1.5,
|
|
2.0,
|
|
2.5,
|
|
5.0,
|
|
10.0,
|
|
15.0,
|
|
20.0,
|
|
30.0,
|
|
40.0,
|
|
50.0,
|
|
60.0,
|
|
120.0,
|
|
240.0,
|
|
480.0,
|
|
960.0,
|
|
1920.0,
|
|
7680.0,
|
|
]
|
|
histogram_e2e_time_request = self._histogram_cls(
|
|
name="vllm:e2e_request_latency_seconds",
|
|
documentation="Histogram of e2e request latency in seconds.",
|
|
buckets=request_latency_buckets,
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_e2e_time_request = make_per_engine(
|
|
histogram_e2e_time_request, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_queue_time_request = self._histogram_cls(
|
|
name="vllm:request_queue_time_seconds",
|
|
documentation="Histogram of time spent in WAITING phase for request.",
|
|
buckets=request_latency_buckets,
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_queue_time_request = make_per_engine(
|
|
histogram_queue_time_request, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_inference_time_request = self._histogram_cls(
|
|
name="vllm:request_inference_time_seconds",
|
|
documentation="Histogram of time spent in RUNNING phase for request.",
|
|
buckets=request_latency_buckets,
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_inference_time_request = make_per_engine(
|
|
histogram_inference_time_request, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_prefill_time_request = self._histogram_cls(
|
|
name="vllm:request_prefill_time_seconds",
|
|
documentation="Histogram of time spent in PREFILL phase for request.",
|
|
buckets=request_latency_buckets,
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_prefill_time_request = make_per_engine(
|
|
histogram_prefill_time_request, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_decode_time_request = self._histogram_cls(
|
|
name="vllm:request_decode_time_seconds",
|
|
documentation="Histogram of time spent in DECODE phase for request.",
|
|
buckets=request_latency_buckets,
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_decode_time_request = make_per_engine(
|
|
histogram_decode_time_request, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_prefill_kv_computed_request = self._histogram_cls(
|
|
name="vllm:request_prefill_kv_computed_tokens",
|
|
documentation=(
|
|
"Histogram of new KV tokens computed during prefill "
|
|
"(excluding cached tokens)."
|
|
),
|
|
buckets=build_1_2_5_buckets(max_model_len),
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_prefill_kv_computed_request = make_per_engine(
|
|
histogram_prefill_kv_computed_request, engine_indexes, model_name
|
|
)
|
|
|
|
#
|
|
# KV Cache residency metrics
|
|
#
|
|
if self.kv_cache_metrics_enabled:
|
|
kv_cache_residency_buckets = [
|
|
0.001,
|
|
0.002,
|
|
0.005,
|
|
0.01,
|
|
0.02,
|
|
0.05,
|
|
0.1,
|
|
0.2,
|
|
0.5,
|
|
1,
|
|
2,
|
|
5,
|
|
10,
|
|
20,
|
|
30,
|
|
60,
|
|
120,
|
|
300,
|
|
600,
|
|
1200,
|
|
1800,
|
|
]
|
|
|
|
histogram_kv_block_lifetime = self._histogram_cls(
|
|
name="vllm:kv_block_lifetime_seconds",
|
|
documentation=(
|
|
"Histogram of KV cache block lifetime from allocation to eviction. "
|
|
"Sampled metrics (controlled by --kv-cache-metrics-sample)."
|
|
),
|
|
buckets=kv_cache_residency_buckets,
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_kv_block_lifetime = make_per_engine(
|
|
histogram_kv_block_lifetime, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_kv_block_idle_before_evict = self._histogram_cls(
|
|
name="vllm:kv_block_idle_before_evict_seconds",
|
|
documentation=(
|
|
"Histogram of idle time before KV cache block eviction. "
|
|
"Sampled metrics (controlled by --kv-cache-metrics-sample)."
|
|
),
|
|
buckets=kv_cache_residency_buckets,
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_kv_block_idle_before_evict = make_per_engine(
|
|
histogram_kv_block_idle_before_evict, engine_indexes, model_name
|
|
)
|
|
|
|
histogram_kv_block_reuse_gap = self._histogram_cls(
|
|
name="vllm:kv_block_reuse_gap_seconds",
|
|
documentation=(
|
|
"Histogram of time gaps between consecutive KV cache block "
|
|
"accesses. Only the most recent accesses are recorded "
|
|
"(ring buffer). Sampled metrics (controlled by "
|
|
"--kv-cache-metrics-sample)."
|
|
),
|
|
buckets=kv_cache_residency_buckets,
|
|
labelnames=labelnames,
|
|
)
|
|
self.histogram_kv_block_reuse_gap = make_per_engine(
|
|
histogram_kv_block_reuse_gap, engine_indexes, model_name
|
|
)
|
|
else:
|
|
self.histogram_kv_block_lifetime = {}
|
|
self.histogram_kv_block_idle_before_evict = {}
|
|
self.histogram_kv_block_reuse_gap = {}
|
|
|
|
#
|
|
# LoRA metrics
|
|
#
|
|
|
|
# TODO: This metric might be incorrect in case of using multiple
|
|
# api_server counts which uses prometheus mp.
|
|
self.gauge_lora_info: Gauge | None = None
|
|
if vllm_config.lora_config is not None:
|
|
if len(self.engine_indexes) > 1:
|
|
logger.warning(
|
|
"vllm:lora_requests_info prometheus metrics may be "
|
|
"incorrect/misleading with data parallel deployments."
|
|
)
|
|
self.labelname_max_lora = "max_lora"
|
|
self.labelname_waiting_lora_adapters = "waiting_lora_adapters"
|
|
self.labelname_running_lora_adapters = "running_lora_adapters"
|
|
self.max_lora = vllm_config.lora_config.max_loras
|
|
self.gauge_lora_info = self._gauge_cls(
|
|
name="vllm:lora_requests_info",
|
|
documentation="Running stats on lora requests.",
|
|
multiprocess_mode="sum",
|
|
labelnames=[
|
|
self.labelname_max_lora,
|
|
self.labelname_waiting_lora_adapters,
|
|
self.labelname_running_lora_adapters,
|
|
],
|
|
)
|
|
|
|
def log_metrics_info(self, type: str, config_obj: SupportsMetricsInfo):
|
|
metrics_info = config_obj.metrics_info()
|
|
metrics_info["engine"] = ""
|
|
|
|
name, documentation = None, None
|
|
if type == "cache_config":
|
|
name = "vllm:cache_config_info"
|
|
documentation = "Information of the LLMEngine CacheConfig"
|
|
assert name is not None, f"Unknown metrics info type {type}"
|
|
|
|
# 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.
|
|
info_gauge = self._gauge_cls(
|
|
name=name,
|
|
documentation=documentation,
|
|
multiprocess_mode="mostrecent",
|
|
labelnames=metrics_info.keys(),
|
|
)
|
|
for engine_index in self.engine_indexes:
|
|
metrics_info = config_obj.metrics_info()
|
|
metrics_info["engine"] = str(engine_index)
|
|
info_gauge.labels(**metrics_info).set(1)
|
|
|
|
def record(
|
|
self,
|
|
scheduler_stats: SchedulerStats | None,
|
|
iteration_stats: IterationStats | None,
|
|
mm_cache_stats: MultiModalCacheStats | None = None,
|
|
engine_idx: int = 0,
|
|
):
|
|
"""Log to prometheus."""
|
|
if scheduler_stats is not None:
|
|
self.gauge_scheduler_running[engine_idx].set(
|
|
scheduler_stats.num_running_reqs
|
|
)
|
|
self.gauge_scheduler_waiting[engine_idx].set(
|
|
scheduler_stats.num_waiting_reqs
|
|
)
|
|
self.gauge_kv_cache_usage[engine_idx].set(scheduler_stats.kv_cache_usage)
|
|
|
|
self.counter_prefix_cache_queries[engine_idx].inc(
|
|
scheduler_stats.prefix_cache_stats.queries
|
|
)
|
|
self.counter_prefix_cache_hits[engine_idx].inc(
|
|
scheduler_stats.prefix_cache_stats.hits
|
|
)
|
|
|
|
if scheduler_stats.connector_prefix_cache_stats is not None:
|
|
self.counter_connector_prefix_cache_queries[engine_idx].inc(
|
|
scheduler_stats.connector_prefix_cache_stats.queries
|
|
)
|
|
self.counter_connector_prefix_cache_hits[engine_idx].inc(
|
|
scheduler_stats.connector_prefix_cache_stats.hits
|
|
)
|
|
|
|
if scheduler_stats.spec_decoding_stats is not None:
|
|
self.spec_decoding_prom.observe(
|
|
scheduler_stats.spec_decoding_stats, engine_idx
|
|
)
|
|
|
|
if scheduler_stats.kv_connector_stats is not None:
|
|
self.kv_connector_prom.observe(
|
|
scheduler_stats.kv_connector_stats, engine_idx
|
|
)
|
|
|
|
if (
|
|
self.kv_cache_metrics_enabled
|
|
and scheduler_stats.kv_cache_eviction_events
|
|
):
|
|
lifetime_hist = self.histogram_kv_block_lifetime[engine_idx]
|
|
idle_hist = self.histogram_kv_block_idle_before_evict[engine_idx]
|
|
reuse_hist = self.histogram_kv_block_reuse_gap[engine_idx]
|
|
|
|
for event in scheduler_stats.kv_cache_eviction_events:
|
|
lifetime_hist.observe(event.lifetime_seconds)
|
|
idle_hist.observe(event.idle_seconds)
|
|
for gap in event.reuse_gaps_seconds:
|
|
reuse_hist.observe(gap)
|
|
|
|
if self.gauge_lora_info is not None:
|
|
running_lora_adapters = ",".join(
|
|
scheduler_stats.running_lora_adapters.keys()
|
|
)
|
|
waiting_lora_adapters = ",".join(
|
|
scheduler_stats.waiting_lora_adapters.keys()
|
|
)
|
|
lora_info_labels = {
|
|
self.labelname_running_lora_adapters: running_lora_adapters,
|
|
self.labelname_waiting_lora_adapters: waiting_lora_adapters,
|
|
self.labelname_max_lora: self.max_lora,
|
|
}
|
|
self.gauge_lora_info.labels(**lora_info_labels).set_to_current_time()
|
|
|
|
if mm_cache_stats is not None:
|
|
self.counter_mm_cache_queries[engine_idx].inc(mm_cache_stats.queries)
|
|
self.counter_mm_cache_hits[engine_idx].inc(mm_cache_stats.hits)
|
|
|
|
if iteration_stats is None:
|
|
return
|
|
if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
|
|
self.counter_corrupted_requests[engine_idx].inc(
|
|
iteration_stats.num_corrupted_reqs
|
|
)
|
|
self.counter_num_preempted_reqs[engine_idx].inc(
|
|
iteration_stats.num_preempted_reqs
|
|
)
|
|
self.counter_prompt_tokens[engine_idx].inc(iteration_stats.num_prompt_tokens)
|
|
self.counter_generation_tokens[engine_idx].inc(
|
|
iteration_stats.num_generation_tokens
|
|
)
|
|
self.histogram_iteration_tokens[engine_idx].observe(
|
|
iteration_stats.num_prompt_tokens + iteration_stats.num_generation_tokens
|
|
)
|
|
|
|
for max_gen_tokens in iteration_stats.max_num_generation_tokens_iter:
|
|
self.histogram_max_num_generation_tokens_request[engine_idx].observe(
|
|
max_gen_tokens
|
|
)
|
|
for n_param in iteration_stats.n_params_iter:
|
|
self.histogram_n_request[engine_idx].observe(n_param)
|
|
for ttft in iteration_stats.time_to_first_tokens_iter:
|
|
self.histogram_time_to_first_token[engine_idx].observe(ttft)
|
|
for itl in iteration_stats.inter_token_latencies_iter:
|
|
self.histogram_inter_token_latency[engine_idx].observe(itl)
|
|
if self.show_hidden_metrics:
|
|
self.histogram_time_per_output_token[engine_idx].observe(itl)
|
|
|
|
for finished_request in iteration_stats.finished_requests:
|
|
self.counter_request_success[finished_request.finish_reason][
|
|
engine_idx
|
|
].inc()
|
|
self.histogram_e2e_time_request[engine_idx].observe(
|
|
finished_request.e2e_latency
|
|
)
|
|
self.histogram_queue_time_request[engine_idx].observe(
|
|
finished_request.queued_time
|
|
)
|
|
self.histogram_prefill_time_request[engine_idx].observe(
|
|
finished_request.prefill_time
|
|
)
|
|
self.histogram_inference_time_request[engine_idx].observe(
|
|
finished_request.inference_time
|
|
)
|
|
self.histogram_decode_time_request[engine_idx].observe(
|
|
finished_request.decode_time
|
|
)
|
|
# Calculate prefill KV compute (excludes cached tokens)
|
|
prefill_kv_computed = finished_request.num_prompt_tokens - max(
|
|
finished_request.num_cached_tokens, 0
|
|
)
|
|
self.histogram_prefill_kv_computed_request[engine_idx].observe(
|
|
prefill_kv_computed
|
|
)
|
|
self.histogram_num_prompt_tokens_request[engine_idx].observe(
|
|
finished_request.num_prompt_tokens
|
|
)
|
|
self.histogram_num_generation_tokens_request[engine_idx].observe(
|
|
finished_request.num_generation_tokens
|
|
)
|
|
self.histogram_request_time_per_output_token[engine_idx].observe(
|
|
finished_request.mean_time_per_output_token
|
|
)
|
|
if finished_request.max_tokens_param:
|
|
self.histogram_max_tokens_request[engine_idx].observe(
|
|
finished_request.max_tokens_param
|
|
)
|
|
|
|
def record_sleep_state(self, sleep: int = 0, level: int = 0):
|
|
awake = 1
|
|
discard_all = 0
|
|
weights_offloaded = 0
|
|
|
|
if sleep == 1:
|
|
awake = 0
|
|
if level == 1:
|
|
weights_offloaded = 1
|
|
elif level == 2:
|
|
discard_all = 1
|
|
|
|
for engine_idx in self.engine_indexes:
|
|
self.gauge_engine_sleep_state["discard_all"][engine_idx].set(discard_all)
|
|
self.gauge_engine_sleep_state["weights_offloaded"][engine_idx].set(
|
|
weights_offloaded
|
|
)
|
|
self.gauge_engine_sleep_state["awake"][engine_idx].set(awake)
|
|
|
|
def log_engine_initialized(self):
|
|
self.log_metrics_info("cache_config", self.vllm_config.cache_config)
|
|
|
|
|
|
PromMetric: TypeAlias = Gauge | Counter | Histogram
|
|
|
|
|
|
def make_per_engine(
|
|
metric: PromMetric, engine_idxs: list[int], model_name: object
|
|
) -> dict[int, PromMetric]:
|
|
return {idx: metric.labels(model_name, str(idx)) for idx in engine_idxs}
|
|
|
|
|
|
def build_buckets(mantissa_lst: list[int], max_value: int) -> list[int]:
|
|
"""
|
|
Builds a list of buckets with increasing powers of 10 multiplied by
|
|
mantissa values until the value exceeds the specified maximum.
|
|
|
|
"""
|
|
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 build_1_2_5_buckets(max_value: int) -> list[int]:
|
|
"""
|
|
Example:
|
|
>>> build_1_2_5_buckets(100)
|
|
[1, 2, 5, 10, 20, 50, 100]
|
|
"""
|
|
return build_buckets([1, 2, 5], max_value)
|
|
|
|
|
|
class StatLoggerManager:
|
|
"""
|
|
StatLoggerManager:
|
|
Logging happens at the level of the EngineCore (per scheduler).
|
|
* DP: >1 EngineCore per AsyncLLM - loggers for each EngineCore.
|
|
* With Local Logger, just make N copies for N EngineCores.
|
|
* With Prometheus, we need a single logger with N "labels"
|
|
|
|
This class abstracts away this implementation detail from
|
|
the AsyncLLM, allowing the AsyncLLM to just call .record()
|
|
and .log() to a simple interface.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
engine_idxs: list[int] | None = None,
|
|
custom_stat_loggers: list[StatLoggerFactory] | None = None,
|
|
enable_default_loggers: bool = True,
|
|
aggregate_engine_logging: bool = False,
|
|
client_count: int = 1,
|
|
):
|
|
self.engine_indexes = engine_idxs if engine_idxs else [0]
|
|
self.stat_loggers: list[AggregateStatLoggerBase] = []
|
|
stat_logger_factories: list[StatLoggerFactory] = []
|
|
if custom_stat_loggers is not None:
|
|
stat_logger_factories.extend(custom_stat_loggers)
|
|
if enable_default_loggers and logger.isEnabledFor(logging.INFO):
|
|
if client_count > 1:
|
|
logger.warning(
|
|
"AsyncLLM created with api_server_count more than 1; "
|
|
"disabling stats logging to avoid incomplete stats."
|
|
)
|
|
else:
|
|
default_logger_factory = (
|
|
AggregatedLoggingStatLogger
|
|
if aggregate_engine_logging
|
|
else LoggingStatLogger
|
|
)
|
|
stat_logger_factories.append(default_logger_factory)
|
|
custom_prometheus_logger: bool = False
|
|
for stat_logger_factory in stat_logger_factories:
|
|
if isinstance(stat_logger_factory, type) and issubclass(
|
|
stat_logger_factory, AggregateStatLoggerBase
|
|
):
|
|
global_stat_logger = stat_logger_factory(
|
|
vllm_config=vllm_config,
|
|
engine_indexes=self.engine_indexes,
|
|
)
|
|
if isinstance(global_stat_logger, PrometheusStatLogger):
|
|
custom_prometheus_logger = True
|
|
else:
|
|
# per engine logger
|
|
global_stat_logger = PerEngineStatLoggerAdapter(
|
|
vllm_config=vllm_config,
|
|
engine_indexes=self.engine_indexes,
|
|
per_engine_stat_logger_factory=stat_logger_factory, # type: ignore[arg-type]
|
|
)
|
|
self.stat_loggers.append(global_stat_logger)
|
|
if not custom_prometheus_logger:
|
|
self.stat_loggers.append(
|
|
PrometheusStatLogger(vllm_config, self.engine_indexes)
|
|
)
|
|
|
|
def record(
|
|
self,
|
|
scheduler_stats: SchedulerStats | None,
|
|
iteration_stats: IterationStats | None,
|
|
mm_cache_stats: MultiModalCacheStats | None = None,
|
|
engine_idx: int | None = None,
|
|
):
|
|
if engine_idx is None:
|
|
engine_idx = 0
|
|
for logger in self.stat_loggers:
|
|
logger.record(
|
|
scheduler_stats,
|
|
iteration_stats,
|
|
mm_cache_stats=mm_cache_stats,
|
|
engine_idx=engine_idx,
|
|
)
|
|
|
|
def record_sleep_state(self, sleep: int = 0, level: int = 0):
|
|
for logger in self.stat_loggers:
|
|
logger.record_sleep_state(sleep, level)
|
|
|
|
def log(self):
|
|
for logger in self.stat_loggers:
|
|
logger.log()
|
|
|
|
def log_engine_initialized(self):
|
|
for agg_logger in self.stat_loggers:
|
|
agg_logger.log_engine_initialized()
|