[Frontend][Benchmark] Add duplex performance metrics for OmniInteract and Omni-DuplexEval benchmark (#7242)

tests/engine/test_output_processor.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Regression tests for OmniRequestState multimodal DELTA drain and consolidation guard."""
 
+from dataclasses import dataclass
 from types import SimpleNamespace
 from unittest.mock import MagicMock
 
@@ -38,6 +39,21 @@ _DETOK = MagicMock(
 )
 _LOGPROBS = MagicMock(logprobs=None, cumulative_logprob=None, prompt_logprobs=None)
 
+
+@dataclass
+class _StreamingUpdate:
+    final: bool
+    prompt: object | None
+    prompt_token_ids: list[int]
+    arrival_time: float
+
+
+@dataclass
+class _FinishedRequestMetric:
+    request_id: str
+    mean_time_per_output_token: float
+
+
 _DEFAULT_STATE_KWARGS = dict(
     request_id="r",
     external_req_id="r",
@@ -130,6 +146,119 @@ def test_native_text_metrics_include_segment_generation_token_count(monkeypatch)
     assert processor.pop_native_text_metrics("r")["num_generation_tokens"] == 27
 
 
+def test_native_text_metrics_exclude_cross_segment_gap(monkeypatch):
+    monkeypatch.setattr(VLLMOutputProcessor, "_update_stats_from_output", lambda *args, **kwargs: None)
+    processor = object.__new__(MultimodalOutputProcessor)
+    processor._native_text_metrics_by_request = {}
+    processor.lora_states = {}
+    state = _make_state(RequestOutputKind.DELTA)
+    iteration_stats = MagicMock()
+
+    def update_segment(_output, timestamp, _was_prefilling, native_stats, *_args):
+        native_stats.num_generation_tokens = 2 if timestamp == 10.03 else 1
+        native_stats.first_token_ts = 10.02 if timestamp == 10.03 else timestamp
+        native_stats.last_token_ts = timestamp
+        native_stats.first_token_latency = 0.01
+
+    iteration_stats.update_from_output.side_effect = update_segment
+    processor._update_stats_from_output(
+        state,
+        MagicMock(),
+        10.0,
+        iteration_stats,
+    )
+    first_segment = processor.pop_native_text_metrics("r")
+    assert first_segment["vllm_tpot_ms"] == 0.0
+    assert first_segment["vllm_itls_ms"] == []
+
+    state.apply_streaming_update(
+        _StreamingUpdate(
+            final=False,
+            prompt=None,
+            prompt_token_ids=[],
+            arrival_time=10.01,
+        )
+    )
+    state.is_prefilling = True
+    processor._update_stats_from_output(
+        state,
+        MagicMock(),
+        10.02,
+        iteration_stats,
+    )
+    state.is_prefilling = False
+    processor._update_stats_from_output(
+        state,
+        MagicMock(),
+        10.03,
+        iteration_stats,
+    )
+    second_segment = processor.pop_native_text_metrics("r")
+
+    assert second_segment["num_generation_tokens"] == 2
+    assert second_segment["vllm_itls_ms"] == pytest.approx([10.0])
+    assert second_segment["vllm_tpot_ms"] == pytest.approx(10.0)
+
+
+def test_native_text_tpot_weights_multi_token_engine_output(monkeypatch):
+    monkeypatch.setattr(VLLMOutputProcessor, "_update_stats_from_output", lambda *args, **kwargs: None)
+    processor = object.__new__(MultimodalOutputProcessor)
+    processor._native_text_metrics_by_request = {}
+    processor.lora_states = {}
+    state = _make_state(RequestOutputKind.DELTA)
+    iteration_stats = MagicMock()
+
+    def update_segment(_output, timestamp, _was_prefilling, native_stats, *_args):
+        native_stats.num_generation_tokens = 1 if timestamp == 10.0 else 4
+        native_stats.first_token_ts = 10.0
+        native_stats.last_token_ts = timestamp
+        native_stats.first_token_latency = 0.01
+
+    iteration_stats.update_from_output.side_effect = update_segment
+    state.is_prefilling = True
+    processor._update_stats_from_output(state, MagicMock(), 10.0, iteration_stats)
+    state.is_prefilling = False
+    processor._update_stats_from_output(state, MagicMock(), 10.03, iteration_stats)
+    record = processor.pop_native_text_metrics("r")
+
+    assert record["num_generation_tokens"] == 4
+    assert record["vllm_itls_ms"] == pytest.approx([30.0])
+    assert record["vllm_itl_ms"] == pytest.approx(30.0)
+    assert record["vllm_tpot_ms"] == pytest.approx(10.0)
+    assert "_tpot_elapsed_ms" not in record
+    assert "_tpot_intervals" not in record
+
+
+@pytest.mark.parametrize(
+    ("finished_tpot_s", "expected_tpot_ms"),
+    [(0.0, 19.0), (0.023, 23.0)],
+)
+def test_native_text_tpot_only_accepts_positive_finished_metric(
+    monkeypatch,
+    finished_tpot_s,
+    expected_tpot_ms,
+):
+    monkeypatch.setattr(VLLMOutputProcessor, "_update_stats_from_finished", lambda *args, **kwargs: None)
+    processor = object.__new__(MultimodalOutputProcessor)
+    processor._native_text_metrics_by_request = {"r": {"vllm_tpot_ms": 19.0}}
+    state = _make_state(RequestOutputKind.DELTA)
+    iteration_stats = MagicMock()
+    iteration_stats.finished_requests = [
+        _FinishedRequestMetric(
+            request_id="r",
+            mean_time_per_output_token=finished_tpot_s,
+        )
+    ]
+
+    processor._update_stats_from_finished(
+        state,
+        FinishReason.STOP,
+        iteration_stats,
+    )
+
+    assert processor.pop_native_text_metrics("r")["vllm_tpot_ms"] == expected_tpot_ms
+
+
 def test_delta_drains_output_modality_per_step():
     """DELTA drains the mm_type key (output modality) but preserves hidden-state keys."""
     s = _make_state(RequestOutputKind.DELTA)