[V1][Metrics] Add per-request prompt/generation_tokens histograms (#12516)

Signed-off-by: Mark McLoughlin <markmc@redhat.com>

tests/entrypoints/openai/test_metrics.py

@@ -202,6 +202,12 @@ EXPECTED_METRICS_V1 = [
     "vllm:num_requests_waiting",
     "vllm:prompt_tokens_total",
     "vllm:generation_tokens_total",
+    "vllm:request_prompt_tokens_sum",
+    "vllm:request_prompt_tokens_bucket",
+    "vllm:request_prompt_tokens_count",
+    "vllm:request_generation_tokens_sum",
+    "vllm:request_generation_tokens_bucket",
+    "vllm:request_generation_tokens_count",
 ]
 
 

vllm/v1/engine/async_llm.py

@@ -53,8 +53,7 @@ class AsyncLLM(EngineClient):
         self.log_stats = log_stats
         self.stat_loggers: List[StatLoggerBase] = [
             LoggingStatLogger(),
-            PrometheusStatLogger(labels=dict(
-                model_name=self.model_config.served_model_name)),
+            PrometheusStatLogger(vllm_config.model_config),
         ]
 
         # Tokenizer (+ ensure liveness if running in another process).

vllm/v1/engine/output_processor.py

@@ -8,7 +8,7 @@ from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
 from vllm.v1.engine import EngineCoreOutput, EngineCoreRequest
 from vllm.v1.engine.detokenizer import (DetokenizerOutput,
                                         IncrementalDetokenizer)
-from vllm.v1.metrics.stats import IterationStats
+from vllm.v1.metrics.stats import IterationStats, RequestStateStats
 
 
 @dataclass
@@ -37,6 +37,8 @@ class RequestState:
         self.is_prefilling = True
         self.queue = queue
 
+        self.stats = RequestStateStats()
+
     @classmethod
     def from_new_request(
         cls,
@@ -146,7 +148,8 @@ class OutputProcessor:
             # 1) Compute stats for this iteration.
             iteration_stats.update_from_output(engine_core_output,
                                                req_state.is_prefilling,
-                                               req_state.prompt_len)
+                                               req_state.prompt_len,
+                                               req_state.stats)
             req_state.is_prefilling = False
 
             # 2) Detokenize the token ids into text.
@@ -171,6 +174,10 @@ class OutputProcessor:
                         # detected stop string, abort needed in EngineCore.
                         reqs_to_abort.append(req_id)
 
+                    # Track per-request stats
+                    iteration_stats.update_from_finished_request(
+                        request_output, req_state.stats)
+
         return OutputProcessorOutput(
             request_outputs=request_outputs,
             reqs_to_abort=reqs_to_abort,

vllm/v1/metrics/loggers.py

@@ -1,10 +1,11 @@
 import time
 from abc import ABC, abstractmethod
-from typing import Dict, List
+from typing import List
 
 import numpy as np
 import prometheus_client
 
+from vllm.config import ModelConfig
 from vllm.logger import init_logger
 from vllm.v1.metrics.stats import IterationStats, SchedulerStats
 
@@ -78,14 +79,14 @@ class LoggingStatLogger(StatLoggerBase):
 
 class PrometheusStatLogger(StatLoggerBase):
 
-    def __init__(self, labels: Dict[str, str]):
-        self.labels = labels
-
-        labelnames = self.labels.keys()
-        labelvalues = self.labels.values()
-
+    def __init__(self, model_config: ModelConfig):
         self._unregister_vllm_metrics()
 
+        labelnames = ["model_name"]
+        labelvalues = [model_config.served_model_name]
+
+        max_model_len = model_config.max_model_len
+
         self.gauge_scheduler_running = prometheus_client.Gauge(
             name="vllm:num_requests_running",
             documentation="Number of requests in model execution batches.",
@@ -106,6 +107,20 @@ class PrometheusStatLogger(StatLoggerBase):
             documentation="Number of generation tokens processed.",
             labelnames=labelnames).labels(*labelvalues)
 
+        self.histogram_num_prompt_tokens_request = \
+            prometheus_client.Histogram(
+                name="vllm:request_prompt_tokens",
+                documentation="Number of prefill tokens processed.",
+                buckets=build_1_2_5_buckets(max_model_len),
+                labelnames=labelnames).labels(*labelvalues)
+
+        self.histogram_num_generation_tokens_request = \
+            prometheus_client.Histogram(
+                name="vllm:request_generation_tokens",
+                documentation="Number of generation tokens processed.",
+                buckets=build_1_2_5_buckets(max_model_len),
+                labelnames=labelnames).labels(*labelvalues)
+
     def log(self, scheduler_stats: SchedulerStats,
             iteration_stats: IterationStats):
         """Log to prometheus."""
@@ -116,9 +131,42 @@ class PrometheusStatLogger(StatLoggerBase):
         self.counter_generation_tokens.inc(
             iteration_stats.num_generation_tokens)
 
+        for finished_request in iteration_stats.finished_requests:
+            self.histogram_num_prompt_tokens_request.observe(
+                finished_request.num_prompt_tokens)
+            self.histogram_num_generation_tokens_request.observe(
+                finished_request.num_generation_tokens)
+
     @staticmethod
     def _unregister_vllm_metrics():
         # Unregister any existing vLLM collectors (for CI/CD
         for collector in list(prometheus_client.REGISTRY._collector_to_names):
             if hasattr(collector, "_name") and "vllm" in collector._name:
                 prometheus_client.REGISTRY.unregister(collector)
+
+
+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)

vllm/v1/metrics/stats.py

@@ -1,7 +1,8 @@
 from dataclasses import dataclass
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, List
 
 if TYPE_CHECKING:
+    from vllm.outputs import RequestOutput
     from vllm.v1.engine import EngineCoreOutput
 
 
@@ -16,6 +17,21 @@ class SchedulerStats:
     # gpu_prefix_cache_hit_rate: float = 0.0
 
 
+@dataclass
+class RequestStateStats:
+    """Stats that need to be tracked across delta updates."""
+
+    num_generation_tokens: int = 0
+
+
+@dataclass
+class FinishedRequestStats:
+    """Stats associated with a finished request."""
+
+    num_prompt_tokens: int = 0
+    num_generation_tokens: int = 0
+
+
 class IterationStats:
     """Stats associated with a single set of EngineCoreOutputs."""
 
@@ -23,17 +39,29 @@ class IterationStats:
         self.log_stats = log_stats
         self.num_generation_tokens = 0
         self.num_prompt_tokens = 0
+        self.finished_requests: List[FinishedRequestStats] = []
 
     def update_from_output(self, output: "EngineCoreOutput",
-                           is_prefilling: bool, prompt_len: int):
+                           is_prefilling: bool, prompt_len: int,
+                           request_state_stats: RequestStateStats):
         if not self.log_stats:
             return
 
-        self.num_generation_tokens += len(output.new_token_ids)
+        num_new_generation_tokens = len(output.new_token_ids)
+
+        self.num_generation_tokens += num_new_generation_tokens
         if is_prefilling:
             # This relies on the invariant that EngineCore does
             # not stream outputs for partially completed prefills
             # (scheduler.update_from_output makes EngineCoreOutput
             # iff num_computed_tokens == num_tokens).
-            assert (len(output.new_token_ids) > 0)
+            assert (num_new_generation_tokens > 0)
             self.num_prompt_tokens += prompt_len
+
+        request_state_stats.num_generation_tokens += num_new_generation_tokens
+
+    def update_from_finished_request(self, request_output: "RequestOutput",
+                                     request_state_stats: RequestStateStats):
+        self.finished_requests.append(
+            FinishedRequestStats(len(request_output.prompt_token_ids),
+                                 request_state_stats.num_generation_tokens))