monitor metrics of tokens per step using cudagraph batchsizes

tests/metrics/test_metrics.py

@@ -411,7 +411,7 @@ def log(self, *args, **kwargs):
         logger = _RayPrometheusStatLogger(
             local_interval=0.5,
             labels=dict(model_name=engine.model_config.served_model_name),
-            max_model_len=engine.model_config.max_model_len)
+            vllm_config=engine.vllm_config)
         engine.add_logger("ray", logger)
         for i, prompt in enumerate(example_prompts):
             engine.add_request(

vllm/engine/llm_engine.py

@@ -232,6 +232,7 @@ def __init__(
         use_cached_outputs: bool = False,
     ) -> None:
 
+        self.vllm_config = vllm_config
         self.model_config = vllm_config.model_config
         self.cache_config = vllm_config.cache_config
         self.lora_config = vllm_config.lora_config
@@ -385,13 +386,14 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer:
                 self.stat_loggers = {
                     "logging":
                     LoggingStatLogger(
-                        local_interval=_LOCAL_LOGGING_INTERVAL_SEC),
+                        local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
+                        vllm_config=vllm_config),
                     "prometheus":
                     PrometheusStatLogger(
                         local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
                         labels=dict(
                             model_name=self.model_config.served_model_name),
-                        max_model_len=self.model_config.max_model_len),
+                        vllm_config=vllm_config),
                 }
                 self.stat_loggers["prometheus"].info("cache_config",
                                                      self.cache_config)

vllm/engine/metrics.py

@@ -6,6 +6,7 @@
 import numpy as np
 import prometheus_client
 
+from vllm.config import VllmConfig
 from vllm.engine.metrics_types import (StatLoggerBase, Stats,
                                        SupportsMetricsInfo)
 from vllm.executor.ray_utils import ray
@@ -44,10 +45,12 @@ class Metrics:
     _counter_cls = prometheus_client.Counter
     _histogram_cls = prometheus_client.Histogram
 
-    def __init__(self, labelnames: List[str], max_model_len: int):
+    def __init__(self, labelnames: List[str], vllm_config: VllmConfig):
         # Unregister any existing vLLM collectors (for CI/CD)
         self._unregister_vllm_metrics()
 
+        max_model_len = vllm_config.model_config.max_model_len
+
         # System stats
         #   Scheduler State
         self.gauge_scheduler_running = self._gauge_cls(
@@ -115,11 +118,15 @@ def __init__(self, labelnames: List[str], max_model_len: int):
             name="vllm:tokens_total",
             documentation="Number of prefill plus generation tokens processed.",
             labelnames=labelnames)
+        buckets = [1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8096]
+        if not vllm_config.model_config.enforce_eager:
+            buckets = vllm_config.compilation_config.capture_sizes.copy()
+            buckets.sort()
         self.histogram_iteration_tokens = self._histogram_cls(
             name="vllm:iteration_tokens_total",
             documentation="Histogram of number of tokens per engine_step.",
             labelnames=labelnames,
-            buckets=[1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8096])
+            buckets=buckets)
         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.",
@@ -361,10 +368,10 @@ class RayMetrics(Metrics):
     _histogram_cls: Type[prometheus_client.Histogram] = cast(
         Type[prometheus_client.Histogram], _RayHistogramWrapper)
 
-    def __init__(self, labelnames: List[str], max_model_len: int):
+    def __init__(self, labelnames: List[str], vllm_config: VllmConfig):
         if ray_metrics is None:
             raise ImportError("RayMetrics requires Ray to be installed.")
-        super().__init__(labelnames, max_model_len)
+        super().__init__(labelnames, vllm_config)
 
     def _unregister_vllm_metrics(self) -> None:
         # No-op on purpose
@@ -421,8 +428,8 @@ def get_throughput(tracked_stats: List[int], now: float,
 class LoggingStatLogger(StatLoggerBase):
     """LoggingStatLogger is used in LLMEngine to log to Stdout."""
 
-    def __init__(self, *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
+    def __init__(self, local_interval: float, vllm_config: VllmConfig) -> None:
+        super().__init__(local_interval, vllm_config)
         self.last_prompt_throughput: Optional[float] = None
         self.last_generation_throughput: Optional[float] = None
 
@@ -515,12 +522,12 @@ class PrometheusStatLogger(StatLoggerBase):
     _gauge_cls = prometheus_client.Gauge
 
     def __init__(self, local_interval: float, labels: Dict[str, str],
-                 max_model_len: int) -> None:
-        super().__init__(local_interval)
+                 vllm_config: VllmConfig) -> None:
+        super().__init__(local_interval, vllm_config)
         # Prometheus metrics
         self.labels = labels
         self.metrics = self._metrics_cls(labelnames=list(labels.keys()),
-                                         max_model_len=max_model_len)
+                                         vllm_config=vllm_config)
 
     def _log_gauge(self, gauge, data: Union[int, float]) -> None:
         # Convenience function for logging to gauge.

vllm/engine/metrics_types.py

@@ -16,6 +16,7 @@
 from dataclasses import dataclass
 from typing import Dict, List, Optional, Protocol
 
+from vllm.config import VllmConfig
 from vllm.spec_decode.metrics import SpecDecodeWorkerMetrics
 
 
@@ -77,7 +78,7 @@ def metrics_info(self) -> Dict[str, str]:
 class StatLoggerBase(ABC):
     """Base class for StatLogger."""
 
-    def __init__(self, local_interval: float) -> None:
+    def __init__(self, local_interval: float, vllm_config: VllmConfig) -> None:
         # Tracked stats over current local logging interval.
         self.num_prompt_tokens: List[int] = []
         self.num_generation_tokens: List[int] = []