[Fix] [Qwen3-TTS] Qwen3-TTS streaming chunk-boundary artifacts (#2480)

Signed-off-by: Sy03 <1370724210@qq.com>

tests/model_executor/models/qwen3_tts/test_qwen3_tts_code2wav.py

@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+import torch.nn as nn
+
+from vllm_omni.model_executor.models.qwen3_tts.qwen3_tts_code2wav import Qwen3TTSCode2Wav
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class _FakeDecoder(nn.Module):
+    def __init__(self, total_upsample: int = 4):
+        super().__init__()
+        self.total_upsample = total_upsample
+
+    def chunked_decode(self, codes: torch.Tensor) -> torch.Tensor:
+        frames = codes.shape[-1]
+        wav_len = frames * self.total_upsample + 6
+        wav = torch.arange(wav_len, dtype=torch.float32)
+        return wav.view(1, 1, -1)
+
+
+def _make_model() -> Qwen3TTSCode2Wav:
+    model = Qwen3TTSCode2Wav(
+        vllm_config=SimpleNamespace(
+            model_config=SimpleNamespace(model="unused"),
+            device_config=SimpleNamespace(device=torch.device("cpu")),
+        )
+    )
+    model._decoder = _FakeDecoder()
+    model._num_quantizers = 2
+    model._output_sample_rate = 24000
+    model._total_upsample = 4
+    model._ensure_speech_tokenizer_loaded = lambda: None
+    return model
+
+
+def test_forward_trims_context_on_exact_frame_boundaries():
+    model = _make_model()
+
+    out = model.forward(
+        input_ids=torch.arange(12, dtype=torch.long),
+        runtime_additional_information=[{"left_context_size": 2}],
+    )
+
+    audio = out.multimodal_outputs["model_outputs"][0]
+    expected = torch.arange(8, 24, dtype=torch.float32)
+    torch.testing.assert_close(audio, expected)
+
+
+def test_forward_trims_trailing_padding_without_context():
+    model = _make_model()
+
+    out = model.forward(
+        input_ids=torch.arange(12, dtype=torch.long),
+        runtime_additional_information=[{"left_context_size": 0}],
+    )
+
+    audio = out.multimodal_outputs["model_outputs"][0]
+    expected = torch.arange(24, dtype=torch.float32)
+    torch.testing.assert_close(audio, expected)