[BugFix] Fix metrics error for --num-scheduler-steps > 1 (#8234)

tests/metrics/test_metrics.py

@@ -84,6 +84,45 @@ def test_metric_counter_generation_tokens(
         f"metric: {metric_count!r}")
 
 
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [128, 129])
+@pytest.mark.parametrize("disable_async_output_proc", [True, False])
+def test_metric_counter_generation_tokens_multi_step(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    max_tokens: int,
+    disable_async_output_proc: bool,
+) -> None:
+    num_scheduler_steps = 8
+    with vllm_runner(
+            model,
+            disable_log_stats=False,
+            gpu_memory_utilization=0.4,
+            num_scheduler_steps=num_scheduler_steps,
+            disable_async_output_proc=disable_async_output_proc,
+    ) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+        tokenizer = vllm_model.model.get_tokenizer()
+        stat_logger = vllm_model.model.llm_engine.stat_loggers['prometheus']
+        metric_count = stat_logger.metrics.counter_generation_tokens.labels(
+            **stat_logger.labels)._value.get()
+        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)
+
+    # The multi-step scheduling will continue to execute forward even when
+    # encountering EOS, leading to slightly imprecise metrics.
+    assert abs(vllm_generation_count - metric_count) <\
+        len(example_prompts) * num_scheduler_steps, \
+        (f"generation token count: {vllm_generation_count!r}\n"
+         f"metric: {metric_count!r}")
+
+
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["float"])
 @pytest.mark.parametrize(

vllm/engine/llm_engine.py

@@ -1718,6 +1718,15 @@ class LLMEngine:
                     # TPOTs.
                     latency = seq_group.get_last_latency(now)
                     time_per_output_tokens_iter.append(latency)
+                    if seq_group.state.current_step == 0:
+                        # For async_output_proc, the do_log_stats()
+                        # is called following init_multi_step(), which
+                        # sets the current_step to zero.
+                        actual_num_batched_tokens +=\
+                            seq_group.state.num_steps - 1
+                    else:
+                        actual_num_batched_tokens +=\
+                            seq_group.state.current_step - 1
 
                 # Because of chunked prefill, we can have a single sequence
                 # group that does multiple prompt_runs. To prevent logging