[Metrics] Add test for multi-modal cache stats logging (#26588)

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

tests/entrypoints/llm/test_mm_cache_stats.py

@@ -1,10 +1,14 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import logging
+
 import pytest
+import regex as re
 
 from vllm import LLM
 from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+from vllm.v1.metrics import loggers as stat_loggers
 from vllm.v1.metrics.reader import Counter, Metric
 
 from ..openai.test_vision import TEST_IMAGE_ASSETS
@@ -37,12 +41,27 @@ def _get_mm_cache_stats(metrics: list[Metric]):
     return mm_cache_queries, mm_cache_hits
 
 
+def _get_mm_cache_log(llm: LLM, caplog_vllm: pytest.LogCaptureFixture) -> float:
+    caplog_vllm.clear()
+    with caplog_vllm.at_level(logging.INFO, logger=stat_loggers.__name__):
+        llm.llm_engine.do_log_stats()
+
+    assert len(caplog_vllm.records) == 1
+    msg = caplog_vllm.records[0].getMessage()
+
+    assert "MM cache hit rate" in msg
+    match = re.search(r"MM cache hit rate: ([0-9.]+)%", msg)
+    assert match is not None
+    return float(match.group(1))
+
+
 @pytest.mark.parametrize("image_urls", [TEST_IMAGE_ASSETS[:2]], indirect=True)
 @pytest.mark.parametrize("mm_processor_cache_type", ["lru", "shm"])
 def test_mm_cache_stats(
     num_gpus_available,
     image_urls,
     mm_processor_cache_type,
+    caplog_vllm,
 ):
     llm = LLM(
         model="llava-hf/llava-1.5-7b-hf",
@@ -56,12 +75,15 @@ def test_mm_cache_stats(
 
     llm.chat(_make_messages(image_urls[0]))
     assert _get_mm_cache_stats(llm.get_metrics()) == (1, 0)
+    assert _get_mm_cache_log(llm, caplog_vllm) == pytest.approx(0.0)
 
     llm.chat(_make_messages(image_urls[1]))
     assert _get_mm_cache_stats(llm.get_metrics()) == (2, 0)
+    assert _get_mm_cache_log(llm, caplog_vllm) == pytest.approx(0.0)
 
     llm.chat(_make_messages(image_urls[0]))
     assert _get_mm_cache_stats(llm.get_metrics()) == (3, 1)
+    assert _get_mm_cache_log(llm, caplog_vllm) == pytest.approx(33.3)
 
     # NOTE: This only resets hit rate stats in CachingMetrics
     # The raw queries and hits counts remain unaffected
@@ -69,6 +91,8 @@ def test_mm_cache_stats(
 
     llm.chat(_make_messages(image_urls[0]))
     assert _get_mm_cache_stats(llm.get_metrics()) == (4, 1)
+    assert _get_mm_cache_log(llm, caplog_vllm) == pytest.approx(0.0)
 
     llm.chat(_make_messages(image_urls[1]))
     assert _get_mm_cache_stats(llm.get_metrics()) == (5, 1)
+    assert _get_mm_cache_log(llm, caplog_vllm) == pytest.approx(0.0)

vllm/v1/metrics/loggers.py

@@ -60,7 +60,6 @@ class LoggingStatLogger(StatLoggerBase):
         self._reset(time.monotonic())
 
         self.last_scheduler_stats = SchedulerStats()
-        self.last_mm_cache_stats: Optional[MultiModalCacheStats] = None
 
         # Caching metrics. This cannot be reset.
         # TODO: Make the interval configurable.
@@ -115,8 +114,6 @@ class LoggingStatLogger(StatLoggerBase):
         if mm_cache_stats:
             self.mm_caching_metrics.observe(mm_cache_stats)
 
-            self.last_mm_cache_stats = mm_cache_stats
-
     def log(self):
         now = time.monotonic()
         prompt_throughput = self._get_throughput(self.num_prompt_tokens, now)
@@ -157,7 +154,7 @@ class LoggingStatLogger(StatLoggerBase):
             scheduler_stats.kv_cache_usage * 100,
             self.prefix_caching_metrics.hit_rate * 100,
         ]
-        if self.last_mm_cache_stats:
+        if not self.mm_caching_metrics.empty:
             log_parts.append("MM cache hit rate: %.1f%%")
             log_args.append(self.mm_caching_metrics.hit_rate * 100)
 

vllm/v1/metrics/stats.py

@@ -96,6 +96,11 @@ class CachingMetrics:
         self.aggregated_query_hit = 0
         self.query_queue.clear()
 
+    @property
+    def empty(self) -> bool:
+        """Return true if no requests have been observed."""
+        return self.aggregated_requests == 0
+
     @property
     def hit_rate(self) -> float:
         """Calculate the hit rate for the past N requests."""