feat(metrics): Add prefill KV compute metric excluding cached tokens (#30189)

Signed-off-by: Ziliang Peng <ziliang@character.ai>

tests/v1/metrics/test_stats.py

@@ -1,8 +1,109 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from vllm.v1.metrics.stats import IterationStats
+from vllm.v1.engine import FinishReason
+from vllm.v1.metrics.stats import IterationStats, RequestStateStats
 
 
 def test_iteration_stats_repr():
     iteration_stats = IterationStats()
     assert repr(iteration_stats).startswith("IterationStats(")
+
+
+def test_prefill_kv_computed_with_cache():
+    """Test that prefill KV compute correctly excludes cached tokens."""
+    iteration_stats = IterationStats()
+    req_stats = RequestStateStats(arrival_time=0.0)
+    req_stats.scheduled_ts = 0.1
+    req_stats.first_token_ts = 0.5
+    req_stats.last_token_ts = 5.0
+    req_stats.num_generation_tokens = 50
+
+    # Case 1: With prefix cache (1200 tokens cached)
+    iteration_stats.update_from_finished_request(
+        finish_reason=FinishReason.STOP,
+        num_prompt_tokens=10000,
+        max_tokens_param=100,
+        req_stats=req_stats,
+        num_cached_tokens=1200,
+    )
+
+    finished_req = iteration_stats.finished_requests[0]
+    assert finished_req.num_prompt_tokens == 10000
+    assert finished_req.num_cached_tokens == 1200
+
+    # Verify calculation: prefill KV = prompt tokens - cached tokens
+    prefill_kv_computed = finished_req.num_prompt_tokens - max(
+        finished_req.num_cached_tokens, 0
+    )
+    assert prefill_kv_computed == 8800  # 10000 - 1200
+
+
+def test_prefill_kv_computed_no_cache():
+    """Test prefill KV compute without prefix caching."""
+    iteration_stats = IterationStats()
+    req_stats = RequestStateStats(arrival_time=0.0)
+    req_stats.scheduled_ts = 0.1
+    req_stats.first_token_ts = 0.5
+    req_stats.last_token_ts = 2.0
+    req_stats.num_generation_tokens = 10
+
+    # Case 2: No prefix cache
+    iteration_stats.update_from_finished_request(
+        finish_reason=FinishReason.STOP,
+        num_prompt_tokens=2000,
+        max_tokens_param=100,
+        req_stats=req_stats,
+        num_cached_tokens=0,
+    )
+
+    finished_req = iteration_stats.finished_requests[0]
+    assert finished_req.num_prompt_tokens == 2000
+    assert finished_req.num_cached_tokens == 0
+
+    # Verify calculation: prefill KV = full prompt when no cache
+    prefill_kv_computed = finished_req.num_prompt_tokens - max(
+        finished_req.num_cached_tokens, 0
+    )
+    assert prefill_kv_computed == 2000
+
+
+def test_prefill_kv_computed_edge_cases():
+    """Test edge cases for prefill KV compute calculation."""
+    iteration_stats = IterationStats()
+    req_stats = RequestStateStats(arrival_time=0.0)
+    req_stats.scheduled_ts = 0.1
+    req_stats.first_token_ts = 0.5
+    req_stats.last_token_ts = 1.0
+    req_stats.num_generation_tokens = 1
+
+    # Case 3: Negative num_cached_tokens (shouldn't happen, but handle gracefully)
+    iteration_stats.update_from_finished_request(
+        finish_reason=FinishReason.STOP,
+        num_prompt_tokens=100,
+        max_tokens_param=10,
+        req_stats=req_stats,
+        num_cached_tokens=-1,
+    )
+
+    finished_req = iteration_stats.finished_requests[0]
+    # max() should handle negative values
+    prefill_kv_computed = finished_req.num_prompt_tokens - max(
+        finished_req.num_cached_tokens, 0
+    )
+    assert prefill_kv_computed == 100  # Should treat negative as 0
+
+    # Case 4: All tokens cached (shouldn't happen in practice)
+    iteration_stats2 = IterationStats()
+    iteration_stats2.update_from_finished_request(
+        finish_reason=FinishReason.STOP,
+        num_prompt_tokens=100,
+        max_tokens_param=10,
+        req_stats=req_stats,
+        num_cached_tokens=100,
+    )
+
+    finished_req2 = iteration_stats2.finished_requests[0]
+    prefill_kv_computed2 = finished_req2.num_prompt_tokens - max(
+        finished_req2.num_cached_tokens, 0
+    )
+    assert prefill_kv_computed2 == 0  # All cached, nothing computed

vllm/v1/engine/output_processor.py

@@ -650,6 +650,7 @@ class OutputProcessor:
             ),
             max_tokens_param=req_state.max_tokens_param,
             req_stats=req_state.stats,
+            num_cached_tokens=req_state.num_cached_tokens,
         )
         self.lora_states.request_finished(req_state.request_id, req_state.lora_name)
 

vllm/v1/metrics/loggers.py

@@ -870,6 +870,19 @@ class PrometheusStatLogger(AggregateStatLoggerBase):
             histogram_decode_time_request, engine_indexes, model_name
         )
 
+        histogram_prefill_kv_computed_request = self._histogram_cls(
+            name="vllm:request_prefill_kv_computed_tokens",
+            documentation=(
+                "Histogram of new KV tokens computed during prefill "
+                "(excluding cached tokens)."
+            ),
+            buckets=build_1_2_5_buckets(max_model_len),
+            labelnames=labelnames,
+        )
+        self.histogram_prefill_kv_computed_request = make_per_engine(
+            histogram_prefill_kv_computed_request, engine_indexes, model_name
+        )
+
         #
         # KV Cache residency metrics
         #
@@ -1118,6 +1131,13 @@ class PrometheusStatLogger(AggregateStatLoggerBase):
             self.histogram_decode_time_request[engine_idx].observe(
                 finished_request.decode_time
             )
+            # Calculate prefill KV compute (excludes cached tokens)
+            prefill_kv_computed = finished_request.num_prompt_tokens - max(
+                finished_request.num_cached_tokens, 0
+            )
+            self.histogram_prefill_kv_computed_request[engine_idx].observe(
+                prefill_kv_computed
+            )
             self.histogram_num_prompt_tokens_request[engine_idx].observe(
                 finished_request.num_prompt_tokens
             )

vllm/v1/metrics/stats.py

@@ -224,6 +224,7 @@ class FinishedRequestStats:
     decode_time: float = 0.0
     mean_time_per_output_token: float = 0.0
     is_corrupted: bool = False
+    num_cached_tokens: int = 0
 
 
 class IterationStats:
@@ -330,6 +331,7 @@ class IterationStats:
         num_prompt_tokens: int,
         max_tokens_param: int | None,
         req_stats: RequestStateStats,
+        num_cached_tokens: int = 0,
     ):
         e2e_latency = self._time_since(req_stats.arrival_time)
 
@@ -367,6 +369,7 @@ class IterationStats:
             decode_time=decode_time,
             mean_time_per_output_token=mean_time_per_output_token,
             is_corrupted=req_stats.is_corrupted,
+            num_cached_tokens=num_cached_tokens,
         )
         self.finished_requests.append(finished_req)