[Perf][MiniCPM-O-4.5]Add CUDA Graph For HiFTGenerator (#5869)

Signed-off-by: boatman <109857087+sphinxkkkbc@users.noreply.github.com>

tests/model_executor/models/step_audio2/test_hift_parity.py

@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from vllm_omni.model_executor.models.cosyvoice3.utils import mel_spectrogram
+from vllm_omni.model_executor.models.step_audio2.step_audio2_token2wav import _build_hift
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+flashcosyvoice_hifigan = pytest.importorskip("flashcosyvoice.modules.hifigan")
+flashcosyvoice_audio = pytest.importorskip("flashcosyvoice.utils.audio")
+
+
+def test_vendored_hift_matches_flashcosyvoice() -> None:
+    reference = flashcosyvoice_hifigan.HiFTGenerator().eval()
+    vendored = _build_hift().eval()
+
+    # Strict loading verifies that the vendored architecture remains compatible
+    # with the Step-Audio2 checkpoint produced for flashcosyvoice.
+    vendored.load_state_dict(reference.state_dict(), strict=True)
+
+    speech_feat = torch.randn(1, 80, 4)
+    cache_source = torch.zeros(1, 1, 0)
+    with torch.inference_mode():
+        torch.manual_seed(0)
+        expected_speech, expected_source = reference(speech_feat, cache_source)
+        torch.manual_seed(0)
+        actual_speech, actual_source = vendored.inference(speech_feat, cache_source)
+
+    torch.testing.assert_close(actual_source, expected_source)
+    torch.testing.assert_close(actual_speech, expected_speech)
+
+
+def test_vendored_mel_spectrogram_matches_flashcosyvoice() -> None:
+    reference = flashcosyvoice_audio.mel_spectrogram
+    vendored = mel_spectrogram
+
+    audio = torch.randn(1, 48000)
+    kwargs = {
+        "n_fft": 1920,
+        "num_mels": 80,
+        "sampling_rate": 24000,
+        "hop_size": 480,
+        "win_size": 1920,
+        "fmin": 0,
+        "fmax": 8000,
+    }
+    with torch.inference_mode():
+        expected_spec = reference(audio, **kwargs)
+        actual_spec = vendored(audio, **kwargs)
+
+    torch.testing.assert_close(actual_spec, expected_spec)