[WIP][[V1][Metrics] Implement max_num_generation_tokens, request_params_n, and request_params_max_tokens metrics (#14055)

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

tests/entrypoints/openai/test_metrics.py

@@ -239,6 +239,12 @@ EXPECTED_METRICS_V1 = [
     "vllm:request_generation_tokens_sum",
     "vllm:request_generation_tokens_bucket",
     "vllm:request_generation_tokens_count",
+    "vllm:request_params_n_sum",
+    "vllm:request_params_n_bucket",
+    "vllm:request_params_n_count",
+    "vllm:request_params_max_tokens_sum",
+    "vllm:request_params_max_tokens_bucket",
+    "vllm:request_params_max_tokens_count",
     "vllm:time_to_first_token_seconds_sum",
     "vllm:time_to_first_token_seconds_bucket",
     "vllm:time_to_first_token_seconds_count",

vllm/v1/engine/output_processor.py

@@ -36,6 +36,7 @@ class RequestState:
         prompt_token_ids: list[int],
         logprobs_processor: LogprobsProcessor,
         detokenizer: IncrementalDetokenizer,
+        max_tokens_param: Optional[int],
         arrival_time: float,
         queue: Optional[asyncio.Queue[RequestOutput]],
         log_stats: bool,
@@ -50,6 +51,7 @@ class RequestState:
         self.prompt_len = len(prompt_token_ids)
         self.logprobs_processor = logprobs_processor
         self.detokenizer = detokenizer
+        self.max_tokens_param = max_tokens_param
         self.is_prefilling = True
         self.queue = queue
 
@@ -83,6 +85,8 @@ class RequestState:
                 tokenizer=tokenizer,
                 request=request,
             ),
+            max_tokens_param=(request.sampling_params.max_tokens if
+                              request.sampling_params is not None else None),
             arrival_time=request.arrival_time,
             queue=queue,
             log_stats=log_stats,
@@ -198,6 +202,8 @@ class OutputProcessor:
             req_state = self.request_states.pop(request_id, None)
             if req_state is not None:
                 self.lora_states.abort_request(req_state)
+                if req_state.parent_req is not None:
+                    req_state.parent_req.finish_child_request(request_id)
 
     def add_request(
         self,
@@ -310,6 +316,8 @@ class OutputProcessor:
                     # If req not finished in EngineCore, but Detokenizer
                     # detected stop string, abort needed in EngineCore.
                     reqs_to_abort.append(req_id)
+                if req_state.parent_req is not None:
+                    req_state.parent_req.finish_child_request(req_id)
 
                 # Track per-request stats
                 self._update_stats_from_finished(req_state, finish_reason,
@@ -350,5 +358,10 @@ class OutputProcessor:
         iteration_stats.update_from_finished_request(
             finish_reason=finish_reason,
             num_prompt_tokens=len(req_state.prompt_token_ids),
+            max_tokens_param=req_state.max_tokens_param,
             req_stats=req_state.stats)
         self.lora_states.finish_request(req_state)
+
+        ParentRequest.observe_finished_request(
+            req_state.parent_req, iteration_stats,
+            req_state.stats.num_generation_tokens)

vllm/v1/engine/parallel_sampling.py

@@ -6,6 +6,7 @@ from typing import Callable, Optional, Union
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
+from vllm.v1.metrics.stats import IterationStats
 
 
 class ParentRequest:
@@ -18,9 +19,15 @@ class ParentRequest:
     request_id: str
     sampling_params: SamplingParams
 
+    # To track the completion of child requests
+    child_requests: set[str]
+
     # To aggregate child completions when not streaming
     output_aggregator: Optional[RequestOutput]
 
+    # To find the max number of generated tokens across all children
+    max_num_generation_tokens: int
+
     # To efficiently obtain child sampling params
     cached_child_sampling_params: Optional[SamplingParams]
 
@@ -29,7 +36,9 @@ class ParentRequest:
         self.request_id = request_id
         self.sampling_params = sampling_params
 
+        self.child_requests = set()
         self.output_aggregator = None
+        self.max_num_generation_tokens = 0
         self.cached_child_sampling_params = None
 
     @classmethod
@@ -82,8 +91,12 @@ class ParentRequest:
         Returns:
           (request ID, sampling_params) tuple
         """
-        return (f"{index}_{self.request_id}",
+        child_req_id = f"{index}_{self.request_id}"
-                self._get_child_sampling_params(index))
+        self.child_requests.add(child_req_id)
+        return (child_req_id, self._get_child_sampling_params(index))
+
+    def finish_child_request(self, req_id: str):
+        self.child_requests.remove(req_id)
 
     @property
     def n(self) -> int:
@@ -117,3 +130,25 @@ class ParentRequest:
         request_output.outputs = sorted(request_output.outputs,
                                         key=lambda x: x.index)
         return request_output
+
+    def observe_num_generation_tokens(self, num_generation_tokens: int):
+        self.max_num_generation_tokens = max(num_generation_tokens,
+                                             self.max_num_generation_tokens)
+        return self.max_num_generation_tokens
+
+    @staticmethod
+    def observe_finished_request(parent_req: Optional['ParentRequest'],
+                                 iteration_stats: IterationStats,
+                                 num_generation_tokens: int):
+
+        n_param = parent_req.n if parent_req is not None else 1
+
+        if parent_req is not None:
+            num_generation_tokens = parent_req.observe_num_generation_tokens(
+                num_generation_tokens)
+
+        # Child requests finished, we can now record to iteration stats
+        if parent_req is None or not parent_req.child_requests:
+            iteration_stats.max_num_generation_tokens_iter.append(
+                num_generation_tokens)
+            iteration_stats.n_params_iter.append(n_param)

vllm/v1/metrics/loggers.py

@@ -106,6 +106,9 @@ class PrometheusStatLogger(StatLoggerBase):
 
         max_model_len = vllm_config.model_config.max_model_len
 
+        #
+        # Scheduler state
+        #
         self.gauge_scheduler_running = prometheus_client.Gauge(
             name="vllm:num_requests_running",
             documentation="Number of requests in model execution batches.",
@@ -116,6 +119,9 @@ class PrometheusStatLogger(StatLoggerBase):
             documentation="Number of requests waiting to be processed.",
             labelnames=labelnames).labels(*labelvalues)
 
+        #
+        # GPU cache
+        #
         self.gauge_gpu_cache_usage = prometheus_client.Gauge(
             name="vllm:gpu_cache_usage_perc",
             documentation="GPU KV-cache usage. 1 means 100 percent usage.",
@@ -133,6 +139,9 @@ class PrometheusStatLogger(StatLoggerBase):
             "GPU prefix cache hits, in terms of number of cached blocks.",
             labelnames=labelnames).labels(*labelvalues)
 
+        #
+        # Counters
+        #
         self.counter_num_preempted_reqs = prometheus_client.Counter(
             name="vllm:num_preemptions_total",
             documentation="Cumulative number of preemption from the engine.",
@@ -159,6 +168,9 @@ class PrometheusStatLogger(StatLoggerBase):
                 reason] = counter_request_success_base.labels(*(labelvalues +
                                                                 [str(reason)]))
 
+        #
+        # Histograms of counts
+        #
         self.histogram_num_prompt_tokens_request = \
             prometheus_client.Histogram(
                 name="vllm:request_prompt_tokens",
@@ -180,6 +192,31 @@ class PrometheusStatLogger(StatLoggerBase):
                 buckets=build_cudagraph_buckets(vllm_config),
                 labelnames=labelnames).labels(*labelvalues)
 
+        self.histogram_max_num_generation_tokens_request = \
+            prometheus_client.Histogram(
+                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).labels(*labelvalues)
+
+        self.histogram_n_request = \
+            prometheus_client.Histogram(
+                name="vllm:request_params_n",
+                documentation="Histogram of the n request parameter.",
+                buckets=[1, 2, 5, 10, 20],
+                labelnames=labelnames).labels(*labelvalues)
+
+        self.histogram_max_tokens_request = \
+            prometheus_client.Histogram(
+                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).labels(*labelvalues)
+
+        #
+        # Histogram of timing intervals
+        #
         self.histogram_time_to_first_token = \
             prometheus_client.Histogram(
                 name="vllm:time_to_first_token_seconds",
@@ -239,6 +276,9 @@ class PrometheusStatLogger(StatLoggerBase):
                 buckets=request_latency_buckets,
                 labelnames=labelnames).labels(*labelvalues)
 
+        #
+        # LoRA metrics
+        #
         self.gauge_lora_info: Optional[prometheus_client.Gauge] = None
         if vllm_config.lora_config is not None:
             self.labelname_max_lora = "max_lora"
@@ -255,6 +295,9 @@ class PrometheusStatLogger(StatLoggerBase):
                         self.labelname_running_lora_adapters,
                     ])
 
+        #
+        # Cache config info metric
+        #
         self.log_metrics_info("cache_config", vllm_config.cache_config)
 
     def log_metrics_info(self, type: str, config_obj: SupportsMetricsInfo):
@@ -296,6 +339,11 @@ class PrometheusStatLogger(StatLoggerBase):
             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.observe(
+                max_gen_tokens)
+        for n_param in iteration_stats.n_params_iter:
+            self.histogram_n_request.observe(n_param)
         for ttft in iteration_stats.time_to_first_tokens_iter:
             self.histogram_time_to_first_token.observe(ttft)
         for tpot in iteration_stats.time_per_output_tokens_iter:
@@ -317,6 +365,8 @@ class PrometheusStatLogger(StatLoggerBase):
                 finished_request.num_prompt_tokens)
             self.histogram_num_generation_tokens_request.observe(
                 finished_request.num_generation_tokens)
+            self.histogram_max_tokens_request.observe(
+                finished_request.max_tokens_param)
 
         if self.gauge_lora_info is not None:
             running_lora_adapters = \

vllm/v1/metrics/stats.py

@@ -66,6 +66,7 @@ class FinishedRequestStats:
     e2e_latency: float = 0.0
     num_prompt_tokens: int = 0
     num_generation_tokens: int = 0
+    max_tokens_param: Optional[int] = None
     queued_time: float = 0.0
     prefill_time: float = 0.0
     inference_time: float = 0.0
@@ -81,6 +82,8 @@ class IterationStats:
         self.num_prompt_tokens = 0
         self.num_preempted_reqs = 0
         self.finished_requests: list[FinishedRequestStats] = []
+        self.max_num_generation_tokens_iter: list[int] = []
+        self.n_params_iter: list[int] = []
         self.time_to_first_tokens_iter: list[float] = []
         self.time_per_output_tokens_iter: list[float] = []
         self.waiting_lora_adapters: dict[str, int] = {}
@@ -150,6 +153,7 @@ class IterationStats:
 
     def update_from_finished_request(self, finish_reason: "FinishReason",
                                      num_prompt_tokens: int,
+                                     max_tokens_param: Optional[int],
                                      req_stats: RequestStateStats):
         e2e_latency = self._time_since(req_stats.arrival_time)
 
@@ -173,6 +177,7 @@ class IterationStats:
                                  e2e_latency=e2e_latency,
                                  num_prompt_tokens=num_prompt_tokens,
                                  num_generation_tokens=req_stats.num_generation_tokens,
+                                 max_tokens_param=max_tokens_param,
                                  queued_time=queued_time,
                                  prefill_time=prefill_time,
                                  inference_time=inference_time,