# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project """ Unit tests for patch.py """ import asyncio import base64 import json import time from argparse import Namespace from types import SimpleNamespace import pytest from pytest_mock import MockerFixture from vllm.benchmarks.lib.endpoint_request_func import RequestFuncInput from vllm_omni.benchmarks.data_modules.seed_tts_dataset import ( SeedTTSSampleRequest, SeedTTSTextSampleRequest, ) from vllm_omni.benchmarks.patch.patch import ( MixRequestFuncOutput, _add_video_extra_body_to_form, _add_video_reference_to_form, _apply_stage0_token_timings, _apply_video_metrics_from_payload, _attach_seed_tts_to_request_func_input, async_request_openai_chat_omni_completions, async_request_openai_image_edits_omni, async_request_openai_image_generations_omni, async_request_openai_realtime_duplex, should_request_stage_metrics, ) from vllm_omni.clients.duplex import EventCollector pytestmark = [pytest.mark.core_model, pytest.mark.benchmark, pytest.mark.cpu] def _seed_tts_request_func_input() -> RequestFuncInput: return RequestFuncInput( model="test-model", model_name="test-model", prompt="target text", api_url="http://test.com/v1/chat/completions", prompt_len=2, output_len=20, extra_body={"modalities": ["text", "audio"]}, ) def test_seed_tts_chat_request_carries_reference_audio_once() -> None: reference_audio_url = "data:audio/wav;base64,AAAA" sample = SeedTTSSampleRequest( prompt="target text", prompt_len=2, expected_output_len=20, multi_modal_data=None, seed_tts_system_prompt="Clone the supplied voice.", seed_tts_speech_extra={ "ref_audio": reference_audio_url, "ref_text": "reference text", "task_type": "Base", }, ) request_func_input = _seed_tts_request_func_input() _attach_seed_tts_to_request_func_input(sample, request_func_input) assert request_func_input.omni_chat_messages == [ { "role": "system", "content": [{"type": "text", "text": "Clone the supplied voice."}], }, {"role": "user", "content": [{"type": "text", "text": "target text"}]}, ] assert request_func_input.extra_body["ref_audio"] == reference_audio_url serialized_request = json.dumps( {"messages": request_func_input.omni_chat_messages, **request_func_input.extra_body}, ) assert serialized_request.count(reference_audio_url) == 1 def test_seed_tts_text_chat_messages_do_not_add_reference_audio() -> None: sample = SeedTTSTextSampleRequest( prompt="target text", prompt_len=2, expected_output_len=20, multi_modal_data=None, seed_tts_system_prompt="Use the configured voice.", seed_tts_speech_extra=None, ) request_func_input = _seed_tts_request_func_input() _attach_seed_tts_to_request_func_input(sample, request_func_input) assert getattr(request_func_input, "seed_tts_row", False) is True assert request_func_input.omni_chat_messages == [ { "role": "system", "content": [{"type": "text", "text": "Use the configured voice."}], }, {"role": "user", "content": [{"type": "text", "text": "target text"}]}, ] assert "ref_audio" not in request_func_input.extra_body class MockResponse: """Mock aiohttp response for testing""" def __init__(self, status, chunks, delay_between_chunks=0): self.status = status self.reason = "OK" if status == 200 else "Error" self._chunks = chunks self._delay = delay_between_chunks self.content = self async def iter_any(self): for chunk in self._chunks: if self._delay > 0: await asyncio.sleep(self._delay) yield chunk async def __aenter__(self): return self async def __aexit__(self, exc_type, exc_val, exc_tb): pass @pytest.mark.asyncio async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch): class FakeRealtimeClient: last_instance = None def __init__(self, url): assert url == "ws://localhost:8000/v1/realtime?duplex=1" self.events = EventCollector() self.configure_kwargs = None self.sent = [] self.response_count = 0 self.ack_count = 0 type(self).last_instance = self async def __aenter__(self): return self async def __aexit__(self, *_args): return None async def configure(self, model, **kwargs): assert model == "openbmb/MiniCPM-o-4_5" self.configure_kwargs = kwargs async def send(self, event): self.sent.append(event) if event["type"] != "response.create": return self.response_count += 1 response_id = f"resp-{self.response_count}" now = time.monotonic() self.events.add( {"type": "response.created", "response": {"id": response_id}}, received_at_s=now + 0.01, ) self.events.add( { "type": "response.audio_transcript.delta", "response_id": response_id, "delta": f"turn {self.response_count}", }, received_at_s=now + 0.02, ) self.events.add( { "type": "response.audio.delta", "response_id": response_id, "delta": base64.b64encode(b"\x00\x00" * 2400).decode(), "sample_rate_hz": 24_000, "metadata": {"audio_duration_ms": 100}, }, received_at_s=now + 0.03, ) self.events.add( { "type": "response.done", "response": { "id": response_id, "metadata": { "vllm_omni": { "stage_metrics": { "0": { "num_tokens_out": 3, "vllm_ttft_ms": 20.0, "vllm_tpot_ms": 10.0, "vllm_itls_ms": [10.0, 10.0], } } } }, }, }, received_at_s=now + 0.04, ) async def acknowledge_playback(self): self.ack_count += 1 async def close_session(self, **_kwargs): return None monkeypatch.setattr( "vllm_omni.benchmarks.patch.patch._RealtimeTTSProbe", FakeRealtimeClient, ) request_input = RequestFuncInput( model="openbmb/MiniCPM-o-4_5", model_name="openbmb/MiniCPM-o-4_5", prompt="hello", api_url="http://localhost:8000/v1/realtime", prompt_len=1, output_len=20, logprobs=None, multi_modal_content=None, ignore_eos=False, extra_body={"save_duplex_request_metrics": True}, ) request_input.seed_tts_speech_extra = {"ref_audio": "data:audio/wav;base64,AAAA"} request_input.seed_tts_system_prompt = "Speak exactly." request_input.seed_tts_turns = tuple( SimpleNamespace(utterance_id=f"utt-{index}", target_text=f"text {index}") for index in range(4) ) output = await async_request_openai_realtime_duplex( request_input, session=None, ) client = FakeRealtimeClient.last_instance assert client.configure_kwargs["native_duplex"] is False assert client.configure_kwargs["extra_body"] == { "ref_audio": "data:audio/wav;base64,AAAA", "return_stage_metrics": True, } assert client.sent == [ event for index in range(4) for event in ( { "type": "conversation.item.create", "item": { "type": "message", "role": "user", "content": [{"type": "input_text", "text": f"text {index}"}], }, }, {"type": "response.create"}, ) ] assert client.ack_count == 4 assert output.success is True assert output.generated_text == "turn 1 turn 2 turn 3 turn 4" assert output.audio_duration == pytest.approx(0.4) assert output.ttft > 0 assert output.audio_ttfp > output.ttft assert output.audio_rtf > 0 assert output.latency > 0 assert output.output_tokens == 12 assert output.itl == [0.01, 0.01] * 4 assert output.text_latency == pytest.approx(output.ttft + 0.08) assert output.tpot_measured is True assert output.tts_turn_pcm_bytes == [b"\x00\x00" * 2400] * 4 assert output.tts_output_pcm_bytes == b"\x00\x00" * 9600 session_id = output.duplex_request_metrics[0]["session_id"] assert len(output.duplex_request_metrics) == 4 for index, metrics in enumerate(output.duplex_request_metrics): assert metrics == { "session_id": session_id, "request_index": index, "utterance_id": f"utt-{index}", "response_id": f"resp-{index + 1}", "source": "client_monotonic_receive", "measurement_origin": { "ttft": "conversation.item.create client send to first non-empty text delta", "ttfp": "conversation.item.create client send to first audio packet", "rtf": "request-start-to-last-audio receive time divided by emitted audio duration", }, "ttft_ms": pytest.approx(20.0, abs=2.0), "ttfp_ms": pytest.approx(30.0, abs=2.0), "rtf": pytest.approx(0.3, abs=0.03), "audio_generation_ms": pytest.approx(30.0, abs=2.0), "audio_duration_ms": 100.0, } assert output.duplex_session_metrics == { "session_id": session_id, "audio_turn_count": 4, "mean_ttft_ms": pytest.approx(20.0, abs=2.0), "mean_ttfp_ms": pytest.approx(30.0, abs=2.0), "mean_rtf": pytest.approx(0.3, abs=0.03), } def test_stage0_token_timings_use_weighted_tpot_when_itls_are_incomplete(): output = MixRequestFuncOutput(ttft=0.1) measured = _apply_stage0_token_timings( output, [ {"output_token_count": 3, "itls_ms": [], "tpot_ms": 10.0}, {"output_token_count": 2, "itls_ms": [], "tpot_ms": 40.0}, ], expected_output_tokens=5, ) assert measured is True assert output.itl == [] assert output.tpot_measured is True assert (output.text_latency - output.ttft) / 4 == pytest.approx(0.02) def test_stage0_zero_itls_fall_back_to_positive_tpot(): output = MixRequestFuncOutput(ttft=0.1) measured = _apply_stage0_token_timings( output, [{"output_token_count": 3, "itls_ms": [0.0, 0.0], "tpot_ms": 25.0}], expected_output_tokens=3, ) assert measured is True assert output.itl == [] assert output.tpot_measured is True assert (output.text_latency - output.ttft) / 2 == pytest.approx(0.025) def test_stage0_token_count_without_timing_is_not_measured(): output = MixRequestFuncOutput(ttft=0.1) measured = _apply_stage0_token_timings( output, [{"output_token_count": 3, "itls_ms": [], "tpot_ms": None}], expected_output_tokens=3, ) assert measured is False assert output.itl == [] assert output.text_latency == output.ttft assert output.tpot_measured is False def create_sse_chunk(data_dict): """Helper to create SSE formatted chunk""" return f"data: {json.dumps(data_dict)}\n\n".encode() def test_chat_text_timing_metrics_request_stage_metrics(): args = Namespace( backend="openai-chat-omni", percentile_metrics="ttft,tpot,itl,e2el", print_stage=False, extra_body={}, ) assert should_request_stage_metrics(args) is True @pytest.mark.asyncio async def test_bundled_first_text_chunk_uses_stage0_token_timings(mocker: MockerFixture): """Engine timings recover TPOT when every text token arrives together.""" request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "ABCD"}}], "modality": "text", "usage": {"prompt_tokens": 10, "completion_tokens": 4, "total_tokens": 14}, "metrics": { "stage_metrics": { "0": { "num_tokens_out": 4, "vllm_itls_ms": [10.0, 11.0, 12.0], "vllm_tpot_ms": 11.0, } } }, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) output = await async_request_openai_chat_omni_completions(request_input, mock_session) assert output.success is True assert output.output_tokens == 4 assert output.itl == pytest.approx([0.010, 0.011, 0.012]) assert output.text_latency - output.ttft == pytest.approx(0.033) assert output.tpot_measured is True @pytest.mark.asyncio async def test_positive_client_text_timings_take_precedence_over_stage0(mocker: MockerFixture): request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "A"}}], "modality": "text", "usage": {"prompt_tokens": 10, "completion_tokens": 1, "total_tokens": 11}, } ), create_sse_chunk( { "choices": [{"delta": {"content": "B"}}], "modality": "text", "usage": {"prompt_tokens": 10, "completion_tokens": 2, "total_tokens": 12}, "metrics": { "stage_metrics": { "0": { "num_tokens_out": 2, "vllm_itls_ms": [1000.0], "vllm_tpot_ms": 1000.0, } } }, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks, delay_between_chunks=0.01) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) output = await async_request_openai_chat_omni_completions(request_input, mock_session) assert output.success is True assert len(output.itl) == 1 assert 0.0 < output.itl[0] < 0.1 # ============================================================================ # output_tokens Tests # ============================================================================ @pytest.mark.asyncio async def test_output_tokens_assigned_with_metrics(mocker: MockerFixture): """Test that output.output_tokens is assigned when metrics are present""" # Arrange request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response chunks with metrics chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Hello"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": " world"}}], "modality": "text", "metrics": {"num_tokens_out": 42, "num_tokens_in": 10}, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert output.output_tokens == 42, "output_tokens should be assigned from metrics" assert output.generated_text == "Hello world" @pytest.mark.asyncio async def test_output_tokens_not_assigned_without_metrics(mocker: MockerFixture): """Test that output.output_tokens defaults to 0 when no metrics present""" # Arrange request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response chunks without metrics chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Hello"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": " world"}}], "modality": "text", } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True # Without server metrics we no longer infer count from SSE chunks because # one chunk may carry multiple tokens. output_tokens stays 0 here. assert output.output_tokens == 0, "output_tokens should be 0 when no server metrics are present" assert output.generated_text == "Hello world" @pytest.mark.asyncio async def test_output_tokens_assigned_multiple_metrics(mocker: MockerFixture): """Test that output.output_tokens is updated with the latest metrics value""" # Arrange request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response chunks with multiple metrics updates chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Hello"}}], "modality": "text", "metrics": {"num_tokens_out": 5}, } ), create_sse_chunk( { "choices": [{"delta": {"content": " world"}}], "modality": "text", "metrics": {"num_tokens_out": 10}, } ), create_sse_chunk( { "choices": [{"delta": {"content": "!"}}], "modality": "text", "metrics": {"num_tokens_out": 15}, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert output.output_tokens == 15, "output_tokens should be updated to the latest value" assert output.generated_text == "Hello world!" @pytest.mark.asyncio async def test_output_tokens_with_audio_and_text(mocker: MockerFixture): """Test output_tokens assignment in mixed audio and text response""" # Arrange request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response chunks with both audio and text, with metrics chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Text response"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": ""}}], "modality": "audio", } ), create_sse_chunk( { "modality": "text", "metrics": {"num_tokens_out": 25, "num_tokens_in": 10}, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert output.output_tokens == 25, "output_tokens should be assigned even with audio modality" @pytest.mark.asyncio async def test_output_tokens_with_missing_num_tokens_out(mocker: MockerFixture): """Test that output_tokens defaults to 0 when num_tokens_out is missing in metrics""" # Arrange request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response chunks with metrics but without num_tokens_out chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Hello"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": " world"}}], "modality": "text", "metrics": {"num_tokens_in": 10}, # Missing num_tokens_out } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert output.output_tokens == 0, "output_tokens should be 0 when num_tokens_out is missing" @pytest.mark.asyncio async def test_output_tokens_initialization(): """Test that MixRequestFuncOutput initializes output_tokens correctly""" # Arrange & Act output = MixRequestFuncOutput() # Assert assert hasattr(output, "output_tokens"), "MixRequestFuncOutput should have output_tokens attribute" assert output.output_tokens == 0, "output_tokens should be initialized to 0" # ============================================================================ # text_latency Tests # ============================================================================ class TestTextLatencyAttribute: """Tests for text_latency attribute existence and assignment""" def test_mix_request_func_output_has_text_latency(self): """Test that MixRequestFuncOutput has text_latency attribute""" output = MixRequestFuncOutput() assert hasattr(output, "text_latency"), "MixRequestFuncOutput should have text_latency attribute" def test_text_latency_initial_value(self): """Test that text_latency initializes to a default value""" output = MixRequestFuncOutput() # Check if attribute exists and has a value (should be 0.0 or similar default) text_latency = getattr(output, "text_latency", None) assert text_latency is not None or hasattr(output, "text_latency"), "text_latency attribute should exist" @pytest.mark.asyncio async def test_text_latency_assigned_with_text_response(self, mocker: MockerFixture): """Test that text_latency is assigned when text response is received""" request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response chunks with text modality chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Hello"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": " world"}}], "modality": "text", } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert hasattr(output, "text_latency"), "Output should have text_latency attribute" assert output.text_latency > 0, "text_latency should be greater than 0 for text response" @pytest.mark.asyncio async def test_text_latency_updated_with_multiple_text_chunks(self, mocker: MockerFixture): """Test that text_latency is updated as more text chunks arrive""" request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response chunks with small delays to simulate streaming chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "First"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": " second"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": " third"}}], "modality": "text", } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert hasattr(output, "text_latency"), "Output should have text_latency attribute" assert output.text_latency > 0, "text_latency should accumulate" assert output.generated_text == "First second third" @pytest.mark.asyncio async def test_text_latency_with_only_audio_response(self, mocker: MockerFixture): """Test text_latency behavior when only audio is received""" request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response chunks with only audio modality (no text) chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": ""}}], "modality": "audio", } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert hasattr(output, "text_latency"), "Output should have text_latency attribute even with audio-only" # text_latency should either be 0 or the initial value since no text was processed assert output.text_latency >= 0, "text_latency should be non-negative" @pytest.mark.asyncio async def test_text_latency_not_affected_by_metrics(self, mocker: MockerFixture): """Test that text_latency is independent of metrics data""" request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response with text and metrics chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Response text"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": ""}}], "modality": "text", "metrics": {"num_tokens_out": 100, "num_tokens_in": 20}, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert hasattr(output, "text_latency"), "text_latency should exist" assert output.text_latency > 0, "text_latency should be set for text response" # Verify metrics are also present assert output.output_tokens == 100, "metrics should not affect text_latency" @pytest.mark.asyncio async def test_text_latency_mixed_modalities(self, mocker: MockerFixture): """Test text_latency with mixed text and audio modalities""" request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) # Create response with both text and audio chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Text"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": ""}}], "modality": "audio", } ), create_sse_chunk( { "choices": [{"delta": {"content": " more text"}}], "modality": "text", } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert hasattr(output, "text_latency"), "text_latency should exist with mixed modalities" assert output.text_latency > 0, "text_latency should be set when text is present" assert output.generated_text == "Text more text" @pytest.mark.asyncio async def test_text_latency_value_consistency(self, mocker: MockerFixture): """Test that text_latency matches latency minus ttft relationship""" request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Hello"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": " world"}}], "modality": "text", } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert hasattr(output, "text_latency"), "text_latency should exist" assert hasattr(output, "ttft"), "ttft should exist" assert hasattr(output, "latency"), "latency should exist" # text_latency should be between ttft and total latency assert output.ttft <= output.text_latency <= output.latency, ( "text_latency should be between ttft and total latency" ) # ============================================================================ # prompt_len Tests # ============================================================================ @pytest.mark.asyncio async def test_skips_empty_role_chunk_for_ttft_and_itl(mocker: MockerFixture): """Role/empty text chunks must not count toward TTFT or ITL.""" request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) chunks = [ create_sse_chunk( { "choices": [{"delta": {"role": "assistant", "content": ""}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": "Hello"}}], "modality": "text", "usage": {"prompt_tokens": 10, "completion_tokens": 1, "total_tokens": 11}, } ), create_sse_chunk( { "choices": [{"delta": {"content": " world"}}], "modality": "text", "usage": {"prompt_tokens": 10, "completion_tokens": 2, "total_tokens": 12}, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks, delay_between_chunks=0.01) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) output = await async_request_openai_chat_omni_completions(request_input, mock_session) assert output.success is True assert output.output_tokens == 2 assert len(output.itl) == 1 assert output.ttft > 0 assert output.text_latency - output.ttft == pytest.approx(sum(output.itl), rel=1e-6, abs=1e-6) @pytest.mark.asyncio async def test_bundled_tokens_split_itl_from_usage(mocker: MockerFixture): """Multiple tokens in one SSE chunk should expand ITL via usage deltas.""" request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "A"}}], "modality": "text", "usage": {"prompt_tokens": 10, "completion_tokens": 1, "total_tokens": 11}, } ), create_sse_chunk( { "choices": [{"delta": {"content": "BCD"}}], "modality": "text", "usage": {"prompt_tokens": 10, "completion_tokens": 4, "total_tokens": 14}, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks, delay_between_chunks=0.02) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) output = await async_request_openai_chat_omni_completions(request_input, mock_session) assert output.success is True assert output.output_tokens == 4 assert len(output.itl) == 3 assert output.text_latency - output.ttft == pytest.approx(sum(output.itl), rel=1e-6, abs=1e-6) @pytest.mark.asyncio async def test_output_tokens_prefers_usage_over_metrics(mocker: MockerFixture): request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Hello"}}], "modality": "text", "metrics": {"num_tokens_out": 5}, "usage": {"prompt_tokens": 10, "completion_tokens": 8, "total_tokens": 18}, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) output = await async_request_openai_chat_omni_completions(request_input, mock_session) assert output.success is True assert output.output_tokens == 8 @pytest.mark.asyncio async def test_metrics_only_chunk_updates_output_tokens(mocker: MockerFixture): request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=10, output_len=20, ) chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Text response"}}], "modality": "text", } ), create_sse_chunk( { "modality": "text", "metrics": {"num_tokens_out": 25, "num_tokens_in": 10}, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) output = await async_request_openai_chat_omni_completions(request_input, mock_session) assert output.success is True assert output.output_tokens == 25 @pytest.mark.asyncio async def test_prompt_len_assigned_from_usage(mocker: MockerFixture): # Arrange: request claims prompt_len=100, but server reports 4992 (multimodal). request_input = RequestFuncInput( model="test-model", model_name="test-model", prompt="test prompt", api_url="http://test.com/v1/chat/completions", prompt_len=100, output_len=20, ) chunks = [ create_sse_chunk( { "choices": [{"delta": {"content": "Hello"}}], "modality": "text", } ), create_sse_chunk( { "choices": [{"delta": {"content": " world"}}], "modality": "text", } ), # Final usage chunk emitted because stream_options.include_usage=True. create_sse_chunk( { "choices": [], "usage": {"prompt_tokens": 4992, "completion_tokens": 2, "total_tokens": 4994}, } ), b"data: [DONE]\n\n", ] mock_response = MockResponse(200, chunks) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) # Act output = await async_request_openai_chat_omni_completions(request_input, mock_session) # Assert assert output.success is True assert output.prompt_len == 4992, ( "prompt_len should be overridden by usage.prompt_tokens to reflect the true multimodal input token count" ) def test_video_rtf_prefers_generation_time_over_poll_latency(): """RTF must not grow with client poll_interval overshoot in output.latency.""" payload = { "duration_s": 2.0, "num_frames": 48, "fps": 24.0, "stage_durations": {"stage_0_gen_ms": 4000.0}, } request_body: dict[str, object] = {} short_poll = MixRequestFuncOutput() short_poll.latency = 4.2 # ~generation + small poll overshoot _apply_video_metrics_from_payload(short_poll, payload, request_body) long_poll = MixRequestFuncOutput() long_poll.latency = 8.0 # same job, larger poll_interval_s overshoot _apply_video_metrics_from_payload(long_poll, payload, request_body) assert short_poll.video_generation_time_ms == pytest.approx(4000.0) assert long_poll.video_generation_time_ms == pytest.approx(4000.0) assert short_poll.video_rtf == pytest.approx(2.0) assert long_poll.video_rtf == pytest.approx(2.0) assert short_poll.video_rtf == long_poll.video_rtf def test_video_rtf_falls_back_to_e2e_latency_without_generation_time(): output = MixRequestFuncOutput() output.latency = 6.0 _apply_video_metrics_from_payload( output, {"duration_s": 2.0, "num_frames": 48, "fps": 24.0}, {}, ) assert output.video_generation_time_ms == 0.0 assert output.video_rtf == pytest.approx(3.0) # 1x1 PNG used as a valid b64_json image payload in edit-client tests. _MIN_PNG_B64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==" @pytest.mark.asyncio async def test_image_edits_defaults_to_non_streaming_json(mocker: MockerFixture) -> None: """Single-stage servers reject stream=true; default path must send stream=false + JSON.""" class MockJsonResponse: status = 200 def __init__(self): self._payload = { "created": 1, "data": [ { "b64_json": _MIN_PNG_B64, "stage_durations": {"stage_0_gen_ms": 12.5}, } ], } async def json(self): return self._payload async def text(self): return json.dumps(self._payload) async def __aenter__(self): return self async def __aexit__(self, *_args): return None captured_stream: list[str] = [] real_add_field = None def tracking_add_field(self, name, value=None, **kwargs): if name == "stream": captured_stream.append(str(value)) return real_add_field(self, name, value, **kwargs) import aiohttp real_add_field = aiohttp.FormData.add_field mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=MockJsonResponse()) request = RequestFuncInput( model="single-stage-edit", model_name="single-stage-edit", prompt="make it sunny", api_url="http://test.com/v1/images/edits", prompt_len=4, output_len=1, multi_modal_content=[{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{_MIN_PNG_B64}"}}], extra_body={"num_inference_steps": 4}, ) output = await async_request_openai_image_edits_omni(request, mock_session, pbar=None) assert captured_stream == ["false"] assert output.success is True assert not output.error assert output.image_count == 1 assert output.image_generation_time_ms == pytest.approx(12.5) @pytest.mark.asyncio async def test_image_edits_stream_true_uses_sse_path(mocker: MockerFixture) -> None: """Explicit stream=true keeps the multi-stage SSE client path.""" sse_chunk = ( b'data: {"type":"image","data":[{"b64_json":"' + _MIN_PNG_B64.encode() + b'"}]}\n\n' + b"data: [DONE]\n\n" ) mock_response = MockResponse(200, [sse_chunk]) captured_stream: list[str] = [] import aiohttp real_add_field = aiohttp.FormData.add_field def tracking_add_field(self, name, value=None, **kwargs): if name == "stream": captured_stream.append(str(value)) return real_add_field(self, name, value, **kwargs) mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field) mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=mock_response) request = RequestFuncInput( model="multi-stage-edit", model_name="multi-stage-edit", prompt="edit", api_url="http://test.com/v1/images/edits", prompt_len=2, output_len=1, multi_modal_content=[{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{_MIN_PNG_B64}"}}], extra_body={"stream": True}, ) output = await async_request_openai_image_edits_omni(request, mock_session, pbar=None) assert captured_stream == ["true"] assert output.success is True assert output.image_count == 1 @pytest.mark.asyncio async def test_image_generations_e2el_includes_json_body_consume(mocker: MockerFixture) -> None: """E2EL must include body transfer/decode, not stop at HTTP headers.""" class SlowJsonResponse: status = 200 def __init__(self): self._payload = { "created": 1, "data": [{"b64_json": _MIN_PNG_B64, "stage_durations": {"stage_0_gen_ms": 1.0}}], } async def json(self): await asyncio.sleep(0.05) return self._payload async def text(self): return json.dumps(self._payload) async def __aenter__(self): return self async def __aexit__(self, *_args): return None mock_session = mocker.AsyncMock() mock_session.post = mocker.MagicMock(return_value=SlowJsonResponse()) request = RequestFuncInput( model="img-gen", model_name="img-gen", prompt="a cat", api_url="http://test.com/v1/images/generations", prompt_len=2, output_len=1, extra_body={}, ) output = await async_request_openai_image_generations_omni(request, mock_session, pbar=None) assert output.success is True assert output.latency >= 0.05 def test_video_local_image_reference_not_forwarded_as_raw_extra_field(tmp_path, mocker: MockerFixture) -> None: """Local image_reference must only be uploaded via dedicated serializer.""" import aiohttp ref_path = tmp_path / "ref.png" ref_path.write_bytes(base64.b64decode(_MIN_PNG_B64)) captured: list[tuple[str, object]] = [] real_add_field = aiohttp.FormData.add_field def tracking_add_field(self, name, value=None, **kwargs): captured.append((str(name), value)) return real_add_field(self, name, value, **kwargs) mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field) form = aiohttp.FormData() extra_body = { "num_inference_steps": 2, "image_reference": str(ref_path), } request_body = {"model": "vid", "prompt": "p", **extra_body} _add_video_extra_body_to_form(form, extra_body, request_body) assert _add_video_reference_to_form(form, extra_body["image_reference"]) is True field_names = [name for name, _ in captured] assert "image_reference" not in field_names assert field_names.count("input_reference") == 1 # Dedicated uploader sends file bytes, not the raw local path string. uploaded = next(value for name, value in captured if name == "input_reference") assert uploaded == base64.b64decode(_MIN_PNG_B64) @pytest.mark.parametrize( "reference", [ {"image_url": "https://example.com/ref.png"}, {"file_id": "file-abc"}, [{"image_url": "https://example.com/a.png"}, {"file_id": "file-xyz"}], ], ) def test_video_structured_image_reference_serialized_to_form(reference: object, mocker: MockerFixture) -> None: """Object-form image_reference must be JSON-serialized, not silently dropped.""" import aiohttp captured: list[tuple[str, object]] = [] real_add_field = aiohttp.FormData.add_field def tracking_add_field(self, name, value=None, **kwargs): captured.append((str(name), value)) return real_add_field(self, name, value, **kwargs) mocker.patch.object(aiohttp.FormData, "add_field", tracking_add_field) form = aiohttp.FormData() extra_body = {"image_reference": reference} request_body = {"model": "vid", "prompt": "p", **extra_body} _add_video_extra_body_to_form(form, extra_body, request_body) assert _add_video_reference_to_form(form, reference) is True field_names = [name for name, _ in captured] # Reserved key must not be double-forwarded as a generic extra field; # exactly one dedicated image_reference field. assert field_names.count("image_reference") == 1 assert "input_reference" not in field_names payload = next(value for name, value in captured if name == "image_reference") assert json.loads(payload) == reference def test_video_unsupported_image_reference_raises() -> None: import aiohttp form = aiohttp.FormData() with pytest.raises(ValueError, match="Unsupported image_reference"): _add_video_reference_to_form(form, {"not_a_supported_key": "x"}) with pytest.raises(ValueError, match="Unsupported image_reference"): _add_video_reference_to_form(form, "/tmp/does-not-exist-ref.png") if __name__ == "__main__": pytest.main([__file__, "-v", "-s"])