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convert to use only one prometheus stat logger per async llm

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Signed-off-by: Robert Shaw <robshaw@redhat.com>
This commit is contained in:
Robert Shaw 2025-07-20 13:52:50 +00:00
parent 1e5303a801
commit a69edca369
1 changed files with 177 additions and 103 deletions
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vllm/v1/metrics/loggers.py
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@ -146,107 +146,142 @@ class PrometheusStatLogger(StatLoggerBase):
_histogram_cls = prometheus_client.Histogram _histogram_cls = prometheus_client.Histogram
_spec_decoding_cls = SpecDecodingProm _spec_decoding_cls = SpecDecodingProm
def __init__(self, vllm_config: VllmConfig, engine_index: int = 0): def __init__(self, vllm_config: VllmConfig, engine_indexes: list[int]):
# unregister_vllm_metrics() # unregister_vllm_metrics()
self.vllm_config = vllm_config self.vllm_config = vllm_config
self.engine_index = engine_index self.engine_indexes = [str(idx) for idx in engine_indexes]
# Use this flag to hide metrics that were deprecated in # Use this flag to hide metrics that were deprecated in
# a previous release and which will be removed future # a previous release and which will be removed future
self.show_hidden_metrics = \ self.show_hidden_metrics = \
vllm_config.observability_config.show_hidden_metrics vllm_config.observability_config.show_hidden_metrics
labelnames = ["model_name", "engine"] labelnames = ["model_name", "engine"]
labelvalues = [ model_name = vllm_config.model_config.served_model_name,
vllm_config.model_config.served_model_name,
str(engine_index)
]
max_model_len = vllm_config.model_config.max_model_len max_model_len = vllm_config.model_config.max_model_len
self.spec_decoding_prom = self._spec_decoding_cls( # self.spec_decoding_prom = self._spec_decoding_cls(
vllm_config.speculative_config, labelnames, labelvalues) # vllm_config.speculative_config, labelnames, labelvalues)
# #
# Scheduler state # Scheduler state
# #
self.gauge_scheduler_running = self._gauge_cls( self.gauge_scheduler_running = {
name="vllm:num_requests_running", idx: self._gauge_cls(
documentation="Number of requests in model execution batches.", name="vllm:num_requests_running",
multiprocess_mode="mostrecent", documentation="Number of requests in model execution batches.",
labelnames=labelnames).labels(*labelvalues) multiprocess_mode="mostrecent",
labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.gauge_scheduler_waiting = self._gauge_cls( self.gauge_scheduler_waiting = {
name="vllm:num_requests_waiting", self._gauge_cls(
documentation="Number of requests waiting to be processed.", name="vllm:num_requests_waiting",
multiprocess_mode="mostrecent", documentation="Number of requests waiting to be processed.",
labelnames=labelnames).labels(*labelvalues) multiprocess_mode="mostrecent",
labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
# #
# GPU cache # GPU cache
# #
# Deprecated in 0.9 - Renamed as vllm:kv_cache_usage_perc # Deprecated in 0.9 - Renamed as vllm:kv_cache_usage_perc
# TODO: in 0.10, only enable if show_hidden_metrics=True # TODO: in 0.10, only enable if show_hidden_metrics=True
self.gauge_gpu_cache_usage = self._gauge_cls( self.gauge_gpu_cache_usage = {
name="vllm:gpu_cache_usage_perc", idx: self._gauge_cls(
documentation=( name="vllm:gpu_cache_usage_perc",
"GPU KV-cache usage. 1 means 100 percent usage." documentation=(
"DEPRECATED: Use vllm:kv_cache_usage_perc instead."), "GPU KV-cache usage. 1 means 100 percent usage."
multiprocess_mode="mostrecent", "DEPRECATED: Use vllm:kv_cache_usage_perc instead."),
labelnames=labelnames).labels(*labelvalues) multiprocess_mode="mostrecent",
labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
# Deprecated in 0.9 - Renamed as vllm:prefix_cache_queries # Deprecated in 0.9 - Renamed as vllm:prefix_cache_queries
# TODO: in 0.10, only enable if show_hidden_metrics=True # TODO: in 0.10, only enable if show_hidden_metrics=True
self.counter_gpu_prefix_cache_queries = self._counter_cls( self.counter_gpu_prefix_cache_queries = {
name="vllm:gpu_prefix_cache_queries", idx: self._counter_cls(
documentation= name="vllm:gpu_prefix_cache_queries",
("GPU prefix cache queries, in terms of number of queried tokens." documentation=(
"DEPRECATED: Use vllm:prefix_cache_queries instead."), "GPU prefix cache queries, in terms of number of queried tokens."
labelnames=labelnames).labels(*labelvalues) "DEPRECATED: Use vllm:prefix_cache_queries instead."),
labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
# Deprecated in 0.9 - Renamed as vllm:prefix_cache_hits # Deprecated in 0.9 - Renamed as vllm:prefix_cache_hits
# TODO: in 0.10, only enable if show_hidden_metrics=True # TODO: in 0.10, only enable if show_hidden_metrics=True
self.counter_gpu_prefix_cache_hits = self._counter_cls( self.counter_gpu_prefix_cache_hits = {
name="vllm:gpu_prefix_cache_hits", idx: self._counter_cls(
documentation=( name="vllm:gpu_prefix_cache_hits",
"GPU prefix cache hits, in terms of number of cached tokens." documentation=(
"DEPRECATED: Use vllm:prefix_cache_hits instead."), "GPU prefix cache hits, in terms of number of cached tokens."
labelnames=labelnames).labels(*labelvalues) "DEPRECATED: Use vllm:prefix_cache_hits instead."),
labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.gauge_kv_cache_usage = self._gauge_cls(
self.gauge_kv_cache_usage = {
idx: self._gauge_cls(
name="vllm:kv_cache_usage_perc", name="vllm:kv_cache_usage_perc",
documentation="KV-cache usage. 1 means 100 percent usage.", documentation="KV-cache usage. 1 means 100 percent usage.",
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.counter_prefix_cache_queries = self._counter_cls( self.counter_prefix_cache_queries = {
idx: elf._counter_cls(
name="vllm:prefix_cache_queries", name="vllm:prefix_cache_queries",
documentation=( documentation=(
"Prefix cache queries, in terms of number of queried tokens."), "Prefix cache queries, in terms of number of queried tokens."),
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.counter_prefix_cache_hits = self._counter_cls(
self.counter_prefix_cache_hits = {
idx: self._counter_cls(
name="vllm:prefix_cache_hits", name="vllm:prefix_cache_hits",
documentation=( documentation=(
"Prefix cache hits, in terms of number of cached tokens."), "Prefix cache hits, in terms of number of cached tokens."),
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
# #
# Counters # Counters
# #
self.counter_num_preempted_reqs = self._counter_cls( self.counter_num_preempted_reqs = {
idx: self._counter_cls(
name="vllm:num_preemptions", name="vllm:num_preemptions",
documentation="Cumulative number of preemption from the engine.", documentation="Cumulative number of preemption from the engine.",
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.counter_prompt_tokens = self._counter_cls(
self.counter_prompt_tokens = {
idx: self._counter_cls(
name="vllm:prompt_tokens", name="vllm:prompt_tokens",
documentation="Number of prefill tokens processed.", documentation="Number of prefill tokens processed.",
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.counter_generation_tokens = self._counter_cls(
self.counter_generation_tokens = {
idx: self._counter_cls(
name="vllm:generation_tokens", name="vllm:generation_tokens",
documentation="Number of generation tokens processed.", documentation="Number of generation tokens processed.",
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.counter_request_success: dict[FinishReason, self.counter_request_success: dict[FinishReason,
prometheus_client.Counter] = {} prometheus_client.Counter] = {}
@ -256,30 +291,36 @@ class PrometheusStatLogger(StatLoggerBase):
labelnames=labelnames + ["finished_reason"]) labelnames=labelnames + ["finished_reason"])
for reason in FinishReason: for reason in FinishReason:
self.counter_request_success[ self.counter_request_success[
reason] = counter_request_success_base.labels(*(labelvalues + reason] = {idx: counter_request_success_base.labels(model_name, idx, str(reason)) for idx in self.engine_indexes}
[str(reason)]))
# #
# Histograms of counts # Histograms of counts
# #
self.histogram_num_prompt_tokens_request = \ self.histogram_num_prompt_tokens_request = {
self._histogram_cls( idx: self._histogram_cls(
name="vllm:request_prompt_tokens", name="vllm:request_prompt_tokens",
documentation="Number of prefill tokens processed.", documentation="Number of prefill tokens processed.",
buckets=build_1_2_5_buckets(max_model_len), buckets=build_1_2_5_buckets(max_model_len),
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.histogram_num_generation_tokens_request = \
self._histogram_cls( self.histogram_num_generation_tokens_request = {
idx: self._histogram_cls(
name="vllm:request_generation_tokens", name="vllm:request_generation_tokens",
documentation="Number of generation tokens processed.", documentation="Number of generation tokens processed.",
buckets=build_1_2_5_buckets(max_model_len), buckets=build_1_2_5_buckets(max_model_len),
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
# TODO: This metric might be incorrect in case of using multiple # TODO: This metric might be incorrect in case of using multiple
# api_server counts which uses prometheus mp. # api_server counts which uses prometheus mp.
# See: https://github.com/vllm-project/vllm/pull/18053 # See: https://github.com/vllm-project/vllm/pull/18053
self.histogram_iteration_tokens = \ self.histogram_iteration_tokens = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:iteration_tokens_total", name="vllm:iteration_tokens_total",
documentation="Histogram of number of tokens per engine_step.", documentation="Histogram of number of tokens per engine_step.",
@ -287,34 +328,46 @@ class PrometheusStatLogger(StatLoggerBase):
1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192,
16384 16384
], ],
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.histogram_max_num_generation_tokens_request = \
self._histogram_cls( self.histogram_max_num_generation_tokens_request = {
idx: self._histogram_cls(
name="vllm:request_max_num_generation_tokens", name="vllm:request_max_num_generation_tokens",
documentation= documentation=
"Histogram of maximum number of requested generation tokens.", "Histogram of maximum number of requested generation tokens.",
buckets=build_1_2_5_buckets(max_model_len), buckets=build_1_2_5_buckets(max_model_len),
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.histogram_n_request = \
self.histogram_n_request = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:request_params_n", name="vllm:request_params_n",
documentation="Histogram of the n request parameter.", documentation="Histogram of the n request parameter.",
buckets=[1, 2, 5, 10, 20], buckets=[1, 2, 5, 10, 20],
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.histogram_max_tokens_request = \
self.histogram_max_tokens_request = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:request_params_max_tokens", name="vllm:request_params_max_tokens",
documentation="Histogram of the max_tokens request parameter.", documentation="Histogram of the max_tokens request parameter.",
buckets=build_1_2_5_buckets(max_model_len), buckets=build_1_2_5_buckets(max_model_len),
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
# #
# Histogram of timing intervals # Histogram of timing intervals
# #
self.histogram_time_to_first_token = \ self.histogram_time_to_first_token = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:time_to_first_token_seconds", name="vllm:time_to_first_token_seconds",
documentation="Histogram of time to first token in seconds.", documentation="Histogram of time to first token in seconds.",
@ -323,9 +376,12 @@ class PrometheusStatLogger(StatLoggerBase):
0.75, 1.0, 2.5, 5.0, 7.5, 10.0, 20.0, 40.0, 80.0, 160.0, 0.75, 1.0, 2.5, 5.0, 7.5, 10.0, 20.0, 40.0, 80.0, 160.0,
640.0, 2560.0 640.0, 2560.0
], ],
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
self.histogram_time_per_output_token = \
self.histogram_time_per_output_token = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:time_per_output_token_seconds", name="vllm:time_per_output_token_seconds",
documentation="Histogram of time per output token in seconds.", documentation="Histogram of time per output token in seconds.",
@ -333,70 +389,88 @@ class PrometheusStatLogger(StatLoggerBase):
0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 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 0.75, 1.0, 2.5, 5.0, 7.5, 10.0, 20.0, 40.0, 80.0
], ],
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
request_latency_buckets = [ 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, 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 40.0, 50.0, 60.0, 120.0, 240.0, 480.0, 960.0, 1920.0, 7680.0
] ]
self.histogram_e2e_time_request = \ self.histogram_e2e_time_request = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:e2e_request_latency_seconds", name="vllm:e2e_request_latency_seconds",
documentation="Histogram of e2e request latency in seconds.", documentation="Histogram of e2e request latency in seconds.",
buckets=request_latency_buckets, buckets=request_latency_buckets,
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
self.histogram_queue_time_request = \ for idx in self.engine_indexes
}
self.histogram_queue_time_request = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:request_queue_time_seconds", name="vllm:request_queue_time_seconds",
documentation= documentation=
"Histogram of time spent in WAITING phase for request.", "Histogram of time spent in WAITING phase for request.",
buckets=request_latency_buckets, buckets=request_latency_buckets,
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
self.histogram_inference_time_request = \ for idx in self.engine_indexes
}
self.histogram_inference_time_request = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:request_inference_time_seconds", name="vllm:request_inference_time_seconds",
documentation= documentation=
"Histogram of time spent in RUNNING phase for request.", "Histogram of time spent in RUNNING phase for request.",
buckets=request_latency_buckets, buckets=request_latency_buckets,
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
self.histogram_prefill_time_request = \ for idx in self.engine_indexes
}
self.histogram_prefill_time_request = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:request_prefill_time_seconds", name="vllm:request_prefill_time_seconds",
documentation= documentation=
"Histogram of time spent in PREFILL phase for request.", "Histogram of time spent in PREFILL phase for request.",
buckets=request_latency_buckets, buckets=request_latency_buckets,
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
self.histogram_decode_time_request = \ for idx in self.engine_indexes
}
self.histogram_decode_time_request = {idx:
self._histogram_cls( self._histogram_cls(
name="vllm:request_decode_time_seconds", name="vllm:request_decode_time_seconds",
documentation= documentation=
"Histogram of time spent in DECODE phase for request.", "Histogram of time spent in DECODE phase for request.",
buckets=request_latency_buckets, buckets=request_latency_buckets,
labelnames=labelnames).labels(*labelvalues) labelnames=labelnames).labels(model_name, idx)
for idx in self.engine_indexes
}
#
# LoRA metrics
#
# TODO: This metric might be incorrect in case of using multiple # #
# api_server counts which uses prometheus mp. # # 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" # # TODO: This metric might be incorrect in case of using multiple
self.labelname_waiting_lora_adapters = "waiting_lora_adapters" # # api_server counts which uses prometheus mp.
self.labelname_running_lora_adapters = "running_lora_adapters" # self.gauge_lora_info: Optional[prometheus_client.Gauge] = None
self.max_lora = vllm_config.lora_config.max_loras # if vllm_config.lora_config is not None:
self.gauge_lora_info = \ # self.labelname_max_lora = "max_lora"
self._gauge_cls( # self.labelname_waiting_lora_adapters = "waiting_lora_adapters"
name="vllm:lora_requests_info", # self.labelname_running_lora_adapters = "running_lora_adapters"
documentation="Running stats on lora requests.", # self.max_lora = vllm_config.lora_config.max_loras
multiprocess_mode="sum", # self.gauge_lora_info = \
labelnames=[ # self._gauge_cls(
self.labelname_max_lora, # name="vllm:lora_requests_info",
self.labelname_waiting_lora_adapters, # documentation="Running stats on lora requests.",
self.labelname_running_lora_adapters, # 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): def log_metrics_info(self, type: str, config_obj: SupportsMetricsInfo):