Fix vllm:prompt_tokens_total metric calculation (#2869)

tests/conftest.py

@@ -13,12 +13,10 @@ _TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
 _LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")]
 
 
-def _read_prompts(filename: str) -> str:
-    prompts = []
+def _read_prompts(filename: str) -> List[str]:
     with open(filename, "r") as f:
-        prompt = f.readline()
-        prompts.append(prompt)
-    return prompts
+        prompts = f.readlines()
+        return prompts
 
 
 @pytest.fixture
@@ -165,6 +163,7 @@ class VllmRunner:
         model_name: str,
         tokenizer_name: Optional[str] = None,
         dtype: str = "half",
+        disable_log_stats: bool = True,
         tensor_parallel_size: int = 1,
     ) -> None:
         self.model = LLM(
@@ -173,6 +172,7 @@ class VllmRunner:
             trust_remote_code=True,
             dtype=dtype,
             swap_space=0,
+            disable_log_stats=disable_log_stats,
             tensor_parallel_size=tensor_parallel_size,
         )
 

tests/metrics/test_metrics.py

@@ -0,0 +1,33 @@
+import pytest
+import vllm.engine.metrics
+
+MODELS = [
+    "facebook/opt-125m",
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [128])
+def test_metrics(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    vllm_model = vllm_runner(model, dtype=dtype, disable_log_stats=False)
+    tokenizer = vllm_model.model.get_tokenizer()
+    prompt_token_counts = [len(tokenizer.encode(p)) for p in example_prompts]
+    # This test needs at least 2 prompts in a batch of different lengths to verify their token count is correct despite padding.
+    assert len(example_prompts) > 1, "at least 2 prompts are required"
+    assert prompt_token_counts[0] != prompt_token_counts[1], (
+        "prompts of different lengths are required")
+    vllm_prompt_token_count = sum(prompt_token_counts)
+
+    _ = vllm_model.generate_greedy(example_prompts, max_tokens)
+    metric_count = vllm.engine.metrics.counter_prompt_tokens.get_value({})
+
+    assert vllm_prompt_token_count == metric_count, (
+        f"prompt token count: {vllm_prompt_token_count!r}\nmetric: {metric_count!r}"
+    )

vllm/engine/llm_engine.py

@@ -867,7 +867,9 @@ class LLMEngine:
 
             # Number of Tokens.
             if prompt_run:
-                num_prompt_tokens = scheduler_outputs.num_batched_tokens
+                num_prompt_tokens = sum(
+                    len(seq_group.prompt_token_ids)
+                    for seq_group in scheduler_outputs.scheduled_seq_groups)
             else:
                 num_generation_tokens = scheduler_outputs.num_batched_tokens