Sync from v0.13
This commit is contained in:
437
vllm/v1/metrics/stats.py
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437
vllm/v1/metrics/stats.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import time
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from collections import defaultdict, deque
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from dataclasses import dataclass, field
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from typing import TYPE_CHECKING, Any
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import vllm.envs as envs
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from vllm.compilation.cuda_graph import CUDAGraphStat
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from vllm.v1.spec_decode.metrics import SpecDecodingStats
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if TYPE_CHECKING:
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from vllm.v1.engine import EngineCoreEvent, EngineCoreOutput, FinishReason
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@dataclass
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class BaseCacheStats:
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"""Stores cache hit statistics."""
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reset: bool = False
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"""Whether the cache was reset."""
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requests: int = 0
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"""The number of requests in this update."""
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queries: int = 0
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"""The number of queries in these requests."""
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hits: int = 0
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"""The number of hits in these requests."""
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class CachingMetrics:
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"""Metrics for caching with a hit rate of the most recent N requests.
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Args:
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interval: The number of the most recent requests to aggregate.
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Defaults to 1000.
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"""
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def __init__(self, max_recent_requests: int = 1000) -> None:
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super().__init__()
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self.max_recent_requests = max_recent_requests
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# The current aggregated values.
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self.aggregated_requests = 0
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self.aggregated_query_total = 0
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self.aggregated_query_hit = 0
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# A deque of (requests, queries, hits) for the most recent requests.
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self.query_queue = deque[tuple[int, int, int]]()
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def observe(self, stats: BaseCacheStats):
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"""Observe the prefix caching for a set of requests.
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This function is called with information gathered when new requests
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are being scheduled and are looking for computed blocks.
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When there are more than `max_recent_requests` requests, the oldest set
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of requests are removed from the metrics.
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Args:
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stats: The prefix cache stats.
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"""
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# reset_prefix_cache was invoked before the current update.
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# Reset the metrics before aggregating the current stats.
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if stats.reset:
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self.reset()
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# DO NOT appending empty stats to avoid helpful info get kicked out
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# due to sliding window.
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if stats.requests == 0:
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return
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# Update the metrics.
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self.query_queue.append((stats.requests, stats.queries, stats.hits))
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self.aggregated_requests += stats.requests
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self.aggregated_query_total += stats.queries
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self.aggregated_query_hit += stats.hits
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# Remove the oldest stats until number of requests does not exceed
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# the limit.
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# NOTE: We preserve the latest added stats regardless.
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while (
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len(self.query_queue) > 1
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and self.aggregated_requests > self.max_recent_requests
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):
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old_requests, old_queries, old_hits = self.query_queue.popleft()
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self.aggregated_requests -= old_requests
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self.aggregated_query_total -= old_queries
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self.aggregated_query_hit -= old_hits
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def reset(self):
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"""Reset the metrics."""
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self.aggregated_requests = 0
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self.aggregated_query_total = 0
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self.aggregated_query_hit = 0
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self.query_queue.clear()
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@property
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def empty(self) -> bool:
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"""Return true if no requests have been observed."""
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return self.aggregated_requests == 0
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@property
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def hit_rate(self) -> float:
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"""Calculate the hit rate for the past N requests."""
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if self.aggregated_query_total == 0:
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return 0.0
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return self.aggregated_query_hit / self.aggregated_query_total
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@dataclass
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class PrefixCacheStats(BaseCacheStats):
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"""
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Stores prefix cache hit statistics.
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- `reset`: Whether `reset_prefix_cache` was invoked.
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- `queries`: Refers to the number of tokens that were queried.
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"""
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preempted_requests: int = 0
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"""The number of previously preempted requests in this update."""
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preempted_queries: int = 0
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"""The `queries` number for preempted requests."""
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preempted_hits: int = 0
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"""The `hits` number for preempted requests."""
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def record(self, num_tokens: int, num_hits: int, preempted: bool) -> None:
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"""Aggregate request information into the stats."""
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if preempted:
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# Previously preempted request
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self.preempted_requests += 1
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self.preempted_queries += num_tokens
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self.preempted_hits += num_hits
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else:
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# New request
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self.requests += 1
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self.queries += num_tokens
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self.hits += num_hits
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@dataclass
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class MultiModalCacheStats(BaseCacheStats):
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"""
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Stores multi-modal cache hit statistics.
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- `reset`: Whether `reset_mm_cache` was invoked.
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- `queries`: Refers to the number of multi-modal data items
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that were queried.
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"""
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@dataclass
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class KVCacheEvictionEvent:
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"""Single KV cache block eviction sample."""
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lifetime_seconds: float
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idle_seconds: float
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reuse_gaps_seconds: tuple[float, ...]
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@dataclass
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class SchedulerStats:
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"""Stats associated with the scheduler."""
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num_running_reqs: int = 0
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num_waiting_reqs: int = 0
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# These are used for internal DP load-balancing.
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step_counter: int = 0
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current_wave: int = 0
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kv_cache_usage: float = 0.0
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prefix_cache_stats: PrefixCacheStats = field(default_factory=PrefixCacheStats)
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connector_prefix_cache_stats: PrefixCacheStats | None = None
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kv_cache_eviction_events: list[KVCacheEvictionEvent] = field(default_factory=list)
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spec_decoding_stats: SpecDecodingStats | None = None
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kv_connector_stats: dict[str, Any] | None = None
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waiting_lora_adapters: dict[str, int] = field(default_factory=dict)
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running_lora_adapters: dict[str, int] = field(default_factory=dict)
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cudagraph_stats: CUDAGraphStat | None = None
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@dataclass
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class RequestStateStats:
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"""Stats that need to be tracked across delta updates."""
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num_generation_tokens: int = 0
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# This is an engine frontend timestamp (wall-clock)
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arrival_time: float = 0.0
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# These are engine core timestamps (monotonic)
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queued_ts: float = 0.0
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scheduled_ts: float = 0.0
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first_token_ts: float = 0.0
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last_token_ts: float = 0.0
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# first token latency
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first_token_latency: float = 0.0
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# Track if this request is corrupted (NaNs in logits)
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is_corrupted: bool = False
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@dataclass
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class FinishedRequestStats:
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"""Stats associated with a finished request."""
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finish_reason: "FinishReason"
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e2e_latency: float = 0.0
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num_prompt_tokens: int = 0
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num_generation_tokens: int = 0
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max_tokens_param: int | None = None
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queued_time: float = 0.0
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prefill_time: float = 0.0
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inference_time: float = 0.0
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decode_time: float = 0.0
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mean_time_per_output_token: float = 0.0
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is_corrupted: bool = False
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num_cached_tokens: int = 0
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class IterationStats:
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"""Stats associated with a single set of EngineCoreOutputs."""
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def __init__(self):
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self.iteration_timestamp = time.time()
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self.num_generation_tokens = 0
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self.num_prompt_tokens = 0
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self.num_preempted_reqs = 0
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self.finished_requests: list[FinishedRequestStats] = []
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self.max_num_generation_tokens_iter: list[int] = []
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self.n_params_iter: list[int] = []
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self.time_to_first_tokens_iter: list[float] = []
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self.inter_token_latencies_iter: list[float] = []
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self.num_corrupted_reqs: int = 0
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def __repr__(self) -> str:
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field_to_value_str = ", ".join(f"{k}={v}" for k, v in vars(self).items())
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return f"{self.__class__.__name__}({field_to_value_str})"
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def _time_since(self, start: float) -> float:
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"""Calculate an interval relative to this iteration's timestamp."""
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return self.iteration_timestamp - start
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def update_from_output(
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self,
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output: "EngineCoreOutput",
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engine_core_timestamp: float,
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is_prefilling: bool,
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prompt_len: int,
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req_stats: RequestStateStats,
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lora_states: "LoRARequestStates",
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lora_name: str | None,
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):
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num_new_generation_tokens = len(output.new_token_ids)
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self.num_generation_tokens += num_new_generation_tokens
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if is_prefilling:
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self.num_prompt_tokens += prompt_len
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first_token_latency = self._time_since(req_stats.arrival_time)
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self.time_to_first_tokens_iter.append(first_token_latency)
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req_stats.first_token_latency = first_token_latency
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req_stats.num_generation_tokens += num_new_generation_tokens
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# Track if this request is corrupted (only check once per request)
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# Early exit if already marked as corrupted to avoid redundant checks
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if (
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envs.VLLM_COMPUTE_NANS_IN_LOGITS
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and not req_stats.is_corrupted
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and output.num_nans_in_logits > 0
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):
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req_stats.is_corrupted = True
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# Process request-level engine core events
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if output.events is not None:
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self.update_from_events(
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output.request_id,
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output.events,
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is_prefilling,
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req_stats,
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lora_states,
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lora_name,
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)
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# Process the batch-level "new tokens" engine core event
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if is_prefilling:
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req_stats.first_token_ts = engine_core_timestamp
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else:
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itl = engine_core_timestamp - req_stats.last_token_ts
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self.inter_token_latencies_iter.append(itl)
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req_stats.last_token_ts = engine_core_timestamp
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def update_from_events(
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self,
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req_id: str,
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events: list["EngineCoreEvent"],
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is_prefilling: bool,
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req_stats: RequestStateStats,
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lora_states: "LoRARequestStates",
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lora_name: str | None,
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):
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# Avoid circular dependency
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from vllm.v1.engine import EngineCoreEventType
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for event in events:
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if event.type == EngineCoreEventType.QUEUED:
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req_stats.queued_ts = event.timestamp
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lora_states.request_waiting(req_id, lora_name)
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elif event.type == EngineCoreEventType.SCHEDULED:
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if req_stats.scheduled_ts == 0.0: # ignore preemptions
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req_stats.scheduled_ts = event.timestamp
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lora_states.request_running(req_id, lora_name)
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elif event.type == EngineCoreEventType.PREEMPTED:
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self.num_preempted_reqs += 1
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lora_states.request_waiting(req_id, lora_name)
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def update_from_finished_request(
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self,
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finish_reason: "FinishReason",
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num_prompt_tokens: int,
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max_tokens_param: int | None,
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req_stats: RequestStateStats,
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num_cached_tokens: int = 0,
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):
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e2e_latency = self._time_since(req_stats.arrival_time)
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# Queued interval is from first QUEUED event to first SCHEDULED
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queued_time = req_stats.scheduled_ts - req_stats.queued_ts
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# Prefill interval is from first SCHEDULED to first NEW_TOKEN
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# Any preemptions during prefill is included in the interval
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prefill_time = req_stats.first_token_ts - req_stats.scheduled_ts
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# Decode interval is from first NEW_TOKEN to last NEW_TOKEN
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# Any preemptions during decode are included
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decode_time = req_stats.last_token_ts - req_stats.first_token_ts
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# Inference interval is from first SCHEDULED to last NEW_TOKEN
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# Any preemptions during prefill or decode are included
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inference_time = req_stats.last_token_ts - req_stats.scheduled_ts
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# Do not count the token generated by the prefill phase
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mean_time_per_output_token = (
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decode_time / (req_stats.num_generation_tokens - 1)
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if req_stats.num_generation_tokens - 1 > 0
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else 0
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)
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finished_req = FinishedRequestStats(
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finish_reason=finish_reason,
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e2e_latency=e2e_latency,
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num_prompt_tokens=num_prompt_tokens,
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num_generation_tokens=req_stats.num_generation_tokens,
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max_tokens_param=max_tokens_param,
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queued_time=queued_time,
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prefill_time=prefill_time,
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inference_time=inference_time,
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decode_time=decode_time,
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mean_time_per_output_token=mean_time_per_output_token,
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is_corrupted=req_stats.is_corrupted,
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num_cached_tokens=num_cached_tokens,
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)
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self.finished_requests.append(finished_req)
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# Count corrupted requests when they finish (only once per request)
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if req_stats.is_corrupted:
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self.num_corrupted_reqs += 1
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class LoRAStats:
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"""Tracks waiting and running request IDs for a single LoRA."""
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def __init__(self):
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self.waiting: set[str] = set()
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self.running: set[str] = set()
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def update(self, req_id: str, waiting: bool, running: bool):
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assert not (waiting and running)
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if waiting:
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self.waiting.add(req_id)
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else:
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self.waiting.discard(req_id)
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if running:
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self.running.add(req_id)
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else:
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self.running.discard(req_id)
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@property
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def empty(self) -> bool:
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return not (self.waiting or self.running)
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class LoRARequestStates:
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"""A per-LoRA count of running and waiting requests."""
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def __init__(self, log_stats: bool = False):
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self.log_stats = log_stats
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self.requests: defaultdict[str, LoRAStats] = defaultdict(LoRAStats)
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def _request_update(
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self, req_id: str, lora_name: str | None, waiting: bool, running: bool
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):
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if not self.log_stats or lora_name is None:
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return
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lora_stats = self.requests[lora_name]
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lora_stats.update(req_id, waiting, running)
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if lora_stats.empty:
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del self.requests[lora_name]
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def request_waiting(self, req_id: str, lora_name: str | None):
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self._request_update(req_id, lora_name, waiting=True, running=False)
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def request_running(self, req_id: str, lora_name: str | None):
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self._request_update(req_id, lora_name, waiting=False, running=True)
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def request_finished(self, req_id: str, lora_name: str | None):
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self._request_update(req_id, lora_name, waiting=False, running=False)
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def update_scheduler_stats(self, scheduler_stats: SchedulerStats | None):
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if not self.log_stats or scheduler_stats is None:
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return
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for lora_name, stats in self.requests.items():
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scheduler_stats.waiting_lora_adapters[lora_name] = len(stats.waiting)
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scheduler_stats.running_lora_adapters[lora_name] = len(stats.running)
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