update

vllm/model_executor/models/musicflamingo.py

@@ -0,0 +1,70 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""MusicFlamingo model adapter.
+
+MusicFlamingo shares the AudioFlamingo3 architecture, so we reuse the same
+implementation and multimodal processor, while accepting MusicFlamingo config
+and processor classes when available.
+"""
+
+from collections.abc import Mapping
+
+from transformers.models.audioflamingo3 import (
+    AudioFlamingo3Config,
+    AudioFlamingo3Processor,
+)
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.processing import BaseProcessingInfo
+
+from .audioflamingo3 import (
+    AudioFlamingo3DummyInputsBuilder,
+    AudioFlamingo3ForConditionalGeneration,
+    AudioFlamingo3MultiModalProcessor,
+)
+
+try:
+    # Optional dependency: use MusicFlamingo classes when transformers provides them.
+    from transformers.models.musicflamingo import (
+        MusicFlamingoConfig,
+        MusicFlamingoProcessor,
+    )
+except Exception:  # pragma: no cover - optional dependency
+    MusicFlamingoConfig = None
+    MusicFlamingoProcessor = None
+
+
+class MusicFlamingoProcessingInfo(BaseProcessingInfo):
+    def get_hf_config(self):
+        if MusicFlamingoConfig is None:
+            return self.ctx.get_hf_config(AudioFlamingo3Config)
+        return self.ctx.get_hf_config((MusicFlamingoConfig, AudioFlamingo3Config))
+
+    def get_hf_processor(self, **kwargs: object):
+        if MusicFlamingoProcessor is None:
+            return self.ctx.get_hf_processor(AudioFlamingo3Processor, **kwargs)
+        # Tuple triggers AutoProcessor path and accepts either processor class.
+        return self.ctx.get_hf_processor(
+            (MusicFlamingoProcessor, AudioFlamingo3Processor), **kwargs
+        )
+
+    def get_feature_extractor(self, **kwargs: object):
+        hf_processor = self.get_hf_processor(**kwargs)
+        return hf_processor.feature_extractor
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        return {"audio": None}
+
+
+class MusicFlamingoDummyInputsBuilder(AudioFlamingo3DummyInputsBuilder):
+    pass
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    AudioFlamingo3MultiModalProcessor,
+    info=MusicFlamingoProcessingInfo,
+    dummy_inputs=MusicFlamingoDummyInputsBuilder,
+)
+class MusicFlamingoForConditionalGeneration(AudioFlamingo3ForConditionalGeneration):
+    """MusicFlamingo model for conditional generation."""