From 4caf7044e052399f07089aa8f586d5bd641f7d53 Mon Sep 17 00:00:00 2001
From: Ronen Schaffer <ronen.schaffer@ibm.com>
Date: Fri, 23 Feb 2024 00:00:12 +0200
Subject: [PATCH] Include tokens from prompt phase in
 `counter_generation_tokens` (#2802)

---
 .buildkite/test-pipeline.yaml |  3 +++
 tests/metrics/test_metrics.py | 34 +++++++++++++++++++++++++++++++++-
 vllm/engine/llm_engine.py     |  3 +++
 3 files changed, 39 insertions(+), 1 deletion(-)

diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml
index a91dcdfaf2ea..efcc4d2d07a1 100644
--- a/.buildkite/test-pipeline.yaml
+++ b/.buildkite/test-pipeline.yaml
@@ -52,6 +52,9 @@ steps:
 - label: LoRA Test
   command: pytest -v -s lora
 
+- label: Metrics Test
+  command: pytest -v -s metrics
+
 - label: Benchmarks
   working_dir: "/vllm-workspace/.buildkite"
   commands:
diff --git a/tests/metrics/test_metrics.py b/tests/metrics/test_metrics.py
index da608a6a18f9..fe09aa8237f2 100644
--- a/tests/metrics/test_metrics.py
+++ b/tests/metrics/test_metrics.py
@@ -9,13 +9,16 @@ MODELS = [
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["float"])
 @pytest.mark.parametrize("max_tokens", [128])
-def test_metrics(
+def test_metric_counter_prompt_tokens(
     vllm_runner,
     example_prompts,
     model: str,
     dtype: str,
     max_tokens: int,
 ) -> None:
+    # Reset metric
+    vllm.engine.metrics.counter_prompt_tokens.set_value({}, 0)
+
     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]
@@ -31,3 +34,32 @@ def test_metrics(
     assert vllm_prompt_token_count == metric_count, (
         f"prompt token count: {vllm_prompt_token_count!r}\nmetric: {metric_count!r}"
     )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+@pytest.mark.parametrize("max_tokens", [128])
+def test_metric_counter_generation_tokens(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    # Reset metric
+    vllm.engine.metrics.counter_generation_tokens.set_value({}, 0)
+
+    vllm_model = vllm_runner(model, dtype=dtype, disable_log_stats=False)
+    vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+    tokenizer = vllm_model.model.get_tokenizer()
+    metric_count = vllm.engine.metrics.counter_generation_tokens.get_value({})
+    vllm_generation_count = 0
+    for i in range(len(example_prompts)):
+        vllm_output_ids, vllm_output_str = vllm_outputs[i]
+        prompt_ids = tokenizer.encode(example_prompts[i])
+        # vllm_output_ids contains both prompt tokens and generation tokens. We're interested only in the count of the generation tokens.
+        vllm_generation_count += len(vllm_output_ids) - len(prompt_ids)
+
+    assert vllm_generation_count == metric_count, (
+        f"generation token count: {vllm_generation_count!r}\nmetric: {metric_count!r}"
+    )
diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py
index f0de40f54db6..81c9281c5541 100644
--- a/vllm/engine/llm_engine.py
+++ b/vllm/engine/llm_engine.py
@@ -872,6 +872,9 @@ class LLMEngine:
                 num_prompt_tokens = sum(
                     len(seq_group.prompt_token_ids)
                     for seq_group in scheduler_outputs.scheduled_seq_groups)
+                num_generation_tokens = sum(
+                    seq_group.num_seqs()
+                    for seq_group in scheduler_outputs.scheduled_seq_groups)
             else:
                 num_generation_tokens = scheduler_outputs.num_batched_tokens