Feat: Support audio in Phi4-mm model (#8048)

docs/supported_models/multimodal_language_models.md

@@ -37,5 +37,5 @@ in the GitHub search bar.
 | **Gemma 3 (Multimodal)**   | `google/gemma-3-4b-it`                     | `gemma-it`       | Gemma 3's larger models (4B, 12B, 27B) accept images (each image encoded as 256 tokens) alongside text in a combined 128K-token context.                                                                        |
 | **Kimi-VL** (A3B)          | `moonshotai/Kimi-VL-A3B-Instruct`          | `kimi-vl`        | Kimi-VL is a multimodal model that can understand and generate text from images.                                                                                                                                |
 | **Mistral-Small-3.1-24B**  | `mistralai/Mistral-Small-3.1-24B-Instruct-2503` | `mistral`   | Mistral 3.1 is a multimodal model that can generate text from text or images input. It also supports tool calling and structured output. |
-| **Phi-4-multimodal-instruct**  | `microsoft/Phi-4-multimodal-instruct` | `phi-4-mm`   | Phi-4-multimodal-instruct is the multimodal variant of the Phi-4-mini model, enhanced with LoRA for improved multimodal capabilities. Currently, it supports only text and vision modalities in SGLang. |
+| **Phi-4-multimodal-instruct**  | `microsoft/Phi-4-multimodal-instruct` | `phi-4-mm`   | Phi-4-multimodal-instruct is the multimodal variant of the Phi-4-mini model, enhanced with LoRA for improved multimodal capabilities. It supports text, vision and audio modalities in SGLang. |
 | **MiMo-VL** (7B)           | `XiaomiMiMo/MiMo-VL-7B-RL`                 | `mimo-vl`        | Xiaomi's compact yet powerful vision-language model featuring a native resolution ViT encoder for fine-grained visual details, an MLP projector for cross-modal alignment, and the MiMo-7B language model optimized for complex reasoning tasks. |

python/sglang/srt/conversation.py

@@ -729,6 +729,7 @@ register_conv_template(
         sep="<|end|>",
         stop_str="<|end|>",
         image_token="<|endoftext10|>",
+        audio_token="<|endoftext11|>",
     )
 )
 

python/sglang/srt/managers/schedule_batch.py

@@ -239,6 +239,10 @@ class MultimodalDataItem:
     # For gemma3n
     input_features_mask: Optional[torch.Tensor] = None
 
+    # For phi4-mm
+    image_attention_mask: Optional[torch.Tensor] = None
+    audio_attention_mask: Optional[torch.Tensor] = None
+
     @staticmethod
     def is_empty_list(l):
         if l is None:

python/sglang/srt/models/phi4mm.py

@@ -40,6 +40,7 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.models.idefics2 import Idefics2VisionTransformer
 from sglang.srt.models.llama import LlamaForCausalLM
+from sglang.srt.models.phi4mm_audio import AudioEmbedding
 
 logger = logging.getLogger(__name__)
 
@@ -420,16 +421,49 @@ class Phi4MMForCausalLM(nn.Module):
             model_dir=config._name_or_path,
         )
 
+        if isinstance(config.embd_layer["audio_embd_layer"], dict):
+            embedding_config = {
+                "embedding_cls": config.embd_layer["audio_embd_layer"]["embedding_cls"],
+                **config.embd_layer["audio_embd_layer"],
+            }
+        else:
+            embedding_config = {"embedding_cls": config.embd_layer["embedding_cls"]}
+
+        self.embed_tokens_extend = AudioEmbedding(config, **embedding_config)
+
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         dtype = next(self.vision_encoder.parameters()).dtype
         pixel_values = torch.cat([item.feature for item in items], dim=0).type(dtype)
-        image_attention_mask = torch.cat([item.image_emb_mask for item in items], dim=0)
+        image_attention_mask = torch.cat(
+            [item.image_attention_mask for item in items], dim=0
+        )
         image_sizes = torch.cat([item.image_sizes for item in items], dim=0)
         image_embeds = self.vision_encoder(
             pixel_values, image_sizes, image_attention_mask
         )
         return torch.cat(image_embeds).type(dtype)
 
+    def get_audio_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        # (e.g. multiple examples) and the second dim is the multi-audio dim
+        # (e.g. multiple audios in the same example)
+        embed_tokens_extend_param = next(self.embed_tokens_extend.parameters())
+        device = embed_tokens_extend_param.device
+        dtype = embed_tokens_extend_param.dtype
+        audio_embeds = [
+            self.embed_tokens_extend(
+                # item.feature: (num_audios_in_a_sequence, T, D)
+                # item.audio_attention_mask: (num_audios_in_a_sequence, T, D) BoolTensor or None
+                audio_features=item.feature.to(device).type(dtype),
+                audio_attention_mask=(
+                    item.audio_attention_mask.to(device)
+                    if item.audio_attention_mask is not None
+                    else None
+                ),
+            )
+            for item in items
+        ]
+        return torch.cat(audio_embeds).type(dtype)
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -443,6 +477,7 @@ class Phi4MMForCausalLM(nn.Module):
             language_model=self.language_model,
             data_embedding_funcs={
                 Modality.IMAGE: self.get_image_feature,
+                Modality.AUDIO: self.get_audio_feature,
             },
             positions=positions,
         )
@@ -464,6 +499,9 @@ class Phi4MMForCausalLM(nn.Module):
             (".self_attn.qkv_proj", ".self_attn.v_proj", "v"),
         ]
         prefix_mapping = {
+            "model.embed_tokens_extend.audio_embed.audio_projection.vision.": "embed_tokens_extend.audio_projection_for_vision.",
+            "model.embed_tokens_extend.audio_embed.audio_projection.speech.": "embed_tokens_extend.audio_projection.",
+            "model.embed_tokens_extend.audio_embed.": "embed_tokens_extend.",
             "model.embed_tokens_extend.image_embed.": "vision_encoder.",
             "model.": "language_model.model.",
         }
@@ -472,7 +510,6 @@ class Phi4MMForCausalLM(nn.Module):
             "img_processor.encoder.layers.26",
             "img_processor.head",
             "img_processor.post_layernorm",
-            "audio",
         ]
 
         def _should_skip(name: str) -> bool:

python/sglang/srt/multimodal/processors/base_processor.py

@@ -158,6 +158,7 @@ class BaseMultimodalProcessor(ABC):
             "pixel_values_videos": Modality.VIDEO,
             "image_sizes": Modality.IMAGE,
             "image_grid_thw": Modality.IMAGE,
+            "image_attention_mask": Modality.IMAGE,
             "image_emb_mask": Modality.IMAGE,
             "image_spatial_crop": Modality.IMAGE,
             "tgt_size": Modality.IMAGE,
@@ -170,6 +171,7 @@ class BaseMultimodalProcessor(ABC):
             "audio_feature_lens": Modality.AUDIO,
             "input_features": Modality.AUDIO,
             "input_features_mask": Modality.AUDIO,
+            "audio_attention_mask": Modality.AUDIO,
             # Video-related attributes
             "video_grid_thw": Modality.VIDEO,
             # Generic attributes that could apply to multiple modalities
@@ -251,7 +253,11 @@ class BaseMultimodalProcessor(ABC):
 
     @staticmethod
     def _load_single_item(
-        data, modality: Modality, frame_count_limit=None, discard_alpha_channel=True
+        data,
+        modality: Modality,
+        frame_count_limit=None,
+        audio_sample_rate: Optional[int] = None,
+        discard_alpha_channel=True,
     ):
         """
         Load a single multimodal data.
@@ -268,7 +274,7 @@ class BaseMultimodalProcessor(ABC):
             elif modality == Modality.VIDEO:
                 return load_video(data, frame_count_limit)
             elif modality == Modality.AUDIO:
-                return load_audio(data)
+                return load_audio(data, audio_sample_rate)
 
         except Exception as e:
             raise RuntimeError(f"Error while loading data {data}: {e}")
@@ -282,6 +288,7 @@ class BaseMultimodalProcessor(ABC):
         image_estimated_frames_iter: Optional[iter] = None,
         image_scaling_factor: float = 1.0,
         max_image_frames: int = 30,
+        audio_sample_rate: Optional[int] = None,
     ) -> Tuple[List, List]:
         """
         load multimodal data parallelly using iterators.
@@ -324,6 +331,7 @@ class BaseMultimodalProcessor(ABC):
                         data,
                         modality,
                         frame_count_limit,
+                        audio_sample_rate,
                         discard_alpha_channel,
                     )
                 )
@@ -352,6 +360,7 @@ class BaseMultimodalProcessor(ABC):
         audio_data: Optional[list] = None,
         return_text: Optional[bool] = True,
         discard_alpha_channel: bool = True,
+        audio_sample_rate: Optional[int] = None,
     ) -> BaseMultiModalProcessorOutput:
         """
         Each frame of video/image will be replaced by a single image token
@@ -390,6 +399,7 @@ class BaseMultimodalProcessor(ABC):
             multimodal_tokens=multimodal_tokens,
             data_iterators=data_iterators,
             discard_alpha_channel=discard_alpha_channel,
+            audio_sample_rate=audio_sample_rate,
         )
         task_info_iter = iter(task_info)
         futures_iter = iter(futures)

python/sglang/srt/multimodal/processors/phi4mm.py

 import logging
 from typing import List, Union
 
+from transformers.processing_utils import ProcessorMixin
+
 from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
 from sglang.srt.models.phi4mm import Phi4MMForCausalLM
 from sglang.srt.multimodal.processors.base_processor import (
@@ -10,18 +12,58 @@ from sglang.srt.multimodal.processors.base_processor import (
 
 logger = logging.getLogger(__name__)
 
-_IMAGE_SPECIAL_TOKEN = "<|endoftext10|>"
-_IMAGE_SPECIAL_TOKEN_ID = 200010
+
+# It is an adapter of hf phi4 mm processor to make it work for sglang
+# Ref: https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/processing_phi4mm.py#L693
+class Phi4MMProcessorAdapter(ProcessorMixin):
+    def __init__(self, _processor) -> None:
+        self._processor = _processor
+
+    def __call__(self, **kwargs):
+        result = self._processor(**kwargs)
+
+        # Map HuggingFace output keys to sglang standard keys
+        key_mapping = {
+            "input_image_embeds": "pixel_values",
+            "input_audio_embeds": "audio_features",
+            "audio_embed_sizes": "audio_feature_lens",
+        }
+        for hf_key, sglang_key in key_mapping.items():
+            if hf_key in result:
+                result[sglang_key] = result[hf_key]
+
+        # Filter out None or empty tensors from the result.
+        # This prevents the sglang function base_processor.collect_mm_items_from_processor_output()
+        # from misclassifying audio content as image content, and vice versa.
+        filtered_result = {
+            k: v
+            for k, v in result.items()
+            if v is not None and (not hasattr(v, "numel") or v.numel() > 0)
+        }
+        return filtered_result
 
 
-class Phi4MMImageProcessor(BaseMultimodalProcessor):
+class Phi4MMMultimodalProcessor(BaseMultimodalProcessor):
     models = [Phi4MMForCausalLM]
 
     def __init__(self, hf_config, server_args, _processor):
-        super().__init__(hf_config, server_args, _processor)
+        self.processor = Phi4MMProcessorAdapter(_processor)
+        super().__init__(hf_config, server_args, self.processor)
+
+        # the following CONSTANTS come from hugging-face microsoft/Phi-4-multimodal-instruct's processing_phi4mm.py file
+        # ref: https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/processing_phi4mm.py
+        self.IMAGE_TOKEN = "<|endoftext10|>"
+        self.AUDIO_TOKEN = "<|endoftext11|>"
+        self.IM_TOKEN_ID = 200010
+        self.AUDIO_TOKEN_ID = 200011
+        self.AUDIO_SAMPLE_RATE = 16000
+
         self.multimodal_tokens = MultimodalSpecialTokens(
-            image_token=_IMAGE_SPECIAL_TOKEN,
-        ).build(_processor)
+            image_token=self.IMAGE_TOKEN,
+            image_token_id=self.IM_TOKEN_ID,
+            audio_token=self.AUDIO_TOKEN,
+            audio_token_id=self.AUDIO_TOKEN_ID,
+        ).build(self.processor)
 
     async def process_mm_data_async(
         self,
@@ -32,46 +74,29 @@ class Phi4MMImageProcessor(BaseMultimodalProcessor):
         max_req_input_len,
         **kwargs,
     ):
-        if audio_data:
-            logger.warning(
-                "Currently SGLang does not support audio data for Phi4MM. We are working on it. You can file an issue to help us prioritize."
-            )
-            audio_data = []
-
         base_output = self.load_mm_data(
             prompt=input_text,
             max_req_input_len=max_req_input_len,
             audio_data=audio_data,
             image_data=image_data,
             multimodal_tokens=self.multimodal_tokens,
+            audio_sample_rate=self.AUDIO_SAMPLE_RATE,
         )
-        if base_output is None:
-            return None
 
-        res = self.process_mm_data(
-            input_text=base_output.input_text,
-            images=base_output.images,
-            audios=base_output.audios,
-        )
+        if base_output.audios is not None:
+            # hugging-face microsoft/Phi-4-multimodal-instruct's processing_phi4mm.py file requires the audio input to be tuple of (audio, sample_rate)
+            # ref: https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/processing_phi4mm.py
+            base_output.audios = [
+                (audio, self.AUDIO_SAMPLE_RATE) for audio in base_output.audios
+            ]
 
-        input_ids = res["input_ids"].flatten()
-        image_offsets = self.get_mm_items_offset(
-            input_ids=input_ids,
-            mm_token_id=_IMAGE_SPECIAL_TOKEN_ID,
+        mm_items, input_ids, _ = self.process_and_combine_mm_data(
+            base_output, self.multimodal_tokens
         )
 
-        items = [
-            MultimodalDataItem(
-                feature=res["input_image_embeds"],
-                image_sizes=res["image_sizes"],
-                image_emb_mask=res["image_attention_mask"],
-                offsets=image_offsets,
-                modality=Modality.IMAGE,
-            )
-        ]
-
         return {
-            "mm_items": items,
             "input_ids": input_ids.tolist(),
-            "im_token_id": _IMAGE_SPECIAL_TOKEN_ID,
+            "mm_items": mm_items,
+            "im_token_id": self.IM_TOKEN_ID,
+            "audio_token_id": self.AUDIO_TOKEN_ID,
         }

python/sglang/srt/utils.py

@@ -691,12 +691,17 @@ def decode_video_base64(video_base64):
         )  # Return an empty array and size tuple if no frames were found
 
 
-def load_audio(audio_file: str, sr: int = 16000, mono: bool = True) -> np.ndarray:
+def load_audio(
+    audio_file: str, sr: Optional[int] = None, mono: bool = True
+) -> np.ndarray:
     # Use soundfile here, since librosa use it under the hood,
     # and librosa will not support audio loading in the future
     import soundfile as sf
     from scipy.signal import resample
 
+    if sr is None:
+        sr = 16000
+
     # Load audio data
     if isinstance(audio_file, bytes):
         audio, original_sr = sf.read(BytesIO(audio_file))

test/srt/test_vision_openai_server_b.py

@@ -200,16 +200,17 @@ class TestPhi4MMServer(TestOpenAIVisionServer):
                 "0.70",
                 "--disable-radix-cache",
                 "--max-loras-per-batch",
-                "1",
+                "2",
                 "--revision",
                 revision,
                 "--lora-paths",
                 f"vision={constants.HF_HUB_CACHE}/models--microsoft--Phi-4-multimodal-instruct/snapshots/{revision}/vision-lora",
+                f"speech={constants.HF_HUB_CACHE}/models--microsoft--Phi-4-multimodal-instruct/snapshots/{revision}/speech-lora",
             ],
         )
         cls.base_url += "/v1"
 
-    def get_request_kwargs(self):
+    def get_vision_request_kwargs(self):
         return {
             "extra_body": {
                 "lora_path": "vision",
@@ -218,8 +219,21 @@ class TestPhi4MMServer(TestOpenAIVisionServer):
             }
         }
 
-    def test_video_chat_completion(self):
-        pass
+    def get_audio_request_kwargs(self):
+        return {
+            "extra_body": {
+                "lora_path": "speech",
+                "top_k": 1,
+                "top_p": 1.0,
+            }
+        }
+
+    def test_audio_chat_completion(self):
+        self._test_audio_speech_completion()
+        # TODO: currently phi4-mm cannot pass this test.
+        # We are investigating this issue.
+        # Response: La ciudad está situada en la costa este de la isla, en la desembocadura del río St. Lawrence.
+        # self._test_audio_ambient_completion()
 
 
 class TestVILAServer(TestOpenAIVisionServer):

test/srt/test_vision_openai_server_common.py

@@ -47,6 +47,12 @@ class TestOpenAIVisionServer(CustomTestCase):
     def tearDownClass(cls):
         kill_process_tree(cls.process.pid)
 
+    def get_audio_request_kwargs(self):
+        return self.get_request_kwargs()
+
+    def get_vision_request_kwargs(self):
+        return self.get_request_kwargs()
+
     def get_request_kwargs(self):
         return {}
 
@@ -71,7 +77,7 @@ class TestOpenAIVisionServer(CustomTestCase):
                 },
             ],
             temperature=0,
-            **(self.get_request_kwargs()),
+            **(self.get_vision_request_kwargs()),
         )
 
         assert response.choices[0].message.role == "assistant"
@@ -134,7 +140,7 @@ class TestOpenAIVisionServer(CustomTestCase):
                 },
             ],
             temperature=0,
-            **(self.get_request_kwargs()),
+            **(self.get_vision_request_kwargs()),
         )
 
         assert response.choices[0].message.role == "assistant"
@@ -177,7 +183,7 @@ class TestOpenAIVisionServer(CustomTestCase):
                 },
             ],
             temperature=0,
-            **(self.get_request_kwargs()),
+            **(self.get_vision_request_kwargs()),
         )
 
         assert response.choices[0].message.role == "assistant"
@@ -333,7 +339,7 @@ class TestOpenAIVisionServer(CustomTestCase):
             temperature=0,
             max_tokens=1024,
             stream=False,
-            **(self.get_request_kwargs()),
+            **(self.get_vision_request_kwargs()),
         )
 
         video_response = response.choices[0].message.content
@@ -376,7 +382,7 @@ class TestOpenAIVisionServer(CustomTestCase):
             + r"""\}"""
         )
 
-        extra_kwargs = self.get_request_kwargs()
+        extra_kwargs = self.get_vision_request_kwargs()
         extra_kwargs.setdefault("extra_body", {})["regex"] = regex
 
         response = client.chat.completions.create(
@@ -443,7 +449,7 @@ class TestOpenAIVisionServer(CustomTestCase):
                 {"role": "user", "content": content},
             ],
             temperature=0,
-            **(self.get_request_kwargs()),
+            **(self.get_vision_request_kwargs()),
         )
 
         assert response.choices[0].message.role == "assistant"
@@ -486,7 +492,7 @@ class TestOpenAIVisionServer(CustomTestCase):
             temperature=0,
             max_tokens=128,
             stream=False,
-            **(self.get_request_kwargs()),
+            **(self.get_audio_request_kwargs()),
         )
 
         audio_response = response.choices[0].message.content
@@ -500,7 +506,7 @@ class TestOpenAIVisionServer(CustomTestCase):
         self.assertIsNotNone(audio_response)
         self.assertGreater(len(audio_response), 0)
 
-        return audio_response
+        return audio_response.lower()
 
     def _test_audio_speech_completion(self):
         # a fragment of Trump's speech