Support Phi-4 Multi-Modal (text + vision only)

.gitignore

@@ -228,5 +228,8 @@ compile_commands.json
 
 1
 
+# Autoenv
+.env.leave
+
 # Rust lib
 Cargo.lock

python/pyproject.toml

@@ -17,6 +17,7 @@ dependencies = ["aiohttp", "requests", "tqdm", "numpy", "IPython", "setproctitle
 
 [project.optional-dependencies]
 runtime_common = [
+    "blobfile==3.0.0",
     "compressed-tensors",
     "datasets",
     "fastapi",
@@ -38,12 +39,12 @@ runtime_common = [
     "python-multipart",
     "pyzmq>=25.1.2",
     "soundfile==0.13.1",
+    "scipy",
     "torchao==0.9.0",
     "transformers==4.51.1",
     "uvicorn",
     "uvloop",
     "xgrammar==0.1.19",
-    "blobfile==3.0.0"
 ]
 
 srt = [

python/sglang/srt/configs/model_config.py

@@ -552,6 +552,7 @@ def is_generation_model(model_architectures: List[str], is_embedding: bool = Fal
     "Qwen2_5_VLForConditionalGeneration",
     "KimiVLForConditionalGeneration",
     "InternVLChatModel",
+    "Phi4MMForCausalLM",
 ]
 
 

python/sglang/srt/conversation.py

@@ -661,6 +661,20 @@ def generate_chat_conv(
     )
 )
 
+# TODO (lifuhuang): Refactor BaseMultimodalProcessor to support the default image token "<|image_{index}|>" in the future.
+register_conv_template(
+    Conversation(
+        name="phi-4-mm",
+        system_message="You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.",
+        system_template="<|system|>{system_message}<|end|>",
+        roles=("<|user|>", "<|assistant|>"),
+        sep_style=SeparatorStyle.NO_COLON_SINGLE,
+        sep="<|end|>",
+        stop_str="<|end|>",
+        image_token="<|endoftext10|>",
+    )
+)
+
 register_conv_template(
     Conversation(
         name="chatml",
@@ -945,3 +959,9 @@ def match_openbmb_minicpm(model_path: str):
 def match_moonshot_kimivl(model_path: str):
     if re.search(r"kimi.*vl", model_path, re.IGNORECASE):
         return "kimi-vl"
+
+
+@register_conv_template_matching_function
+def match_phi_4_mm(model_path: str):
+    if "phi-4-multimodal" in model_path.lower():
+        return "phi-4-mm"

python/sglang/srt/managers/multimodal_processors/phi4mm.py

@@ -0,0 +1,87 @@
+import logging
+from typing import List, Union
+
+from sglang.srt.managers.multimodal_processors.base_processor import (
+    BaseMultimodalProcessor,
+    MultimodalSpecialTokens,
+)
+from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
+from sglang.srt.models.phi4mmvllm import Phi4MMForCausalLM
+
+logger = logging.getLogger(__name__)
+
+_IMAGE_SPECIAL_TOKEN = "<|endoftext10|>"
+_IMAGE_SPECIAL_TOKEN_ID = 200010
+
+
+class Phi4MMImageProcessor(BaseMultimodalProcessor):
+    models = [Phi4MMForCausalLM]
+
+    def __init__(self, hf_config, server_args, _processor):
+        super().__init__(hf_config, server_args, _processor)
+        self.multimodal_tokens = MultimodalSpecialTokens(
+            image_token=_IMAGE_SPECIAL_TOKEN,
+        )
+
+    async def process_mm_data_async(
+        self,
+        image_data: List[Union[str, bytes]],
+        input_text,
+        request_obj,
+        max_req_input_len,
+        **kwargs,
+    ):
+        audio_data = request_obj.audio_data
+
+        if not image_data and not audio_data:
+            return None
+
+        if not isinstance(image_data, list):
+            image_data = [image_data]
+
+        if not isinstance(audio_data, list):
+            audio_data = [audio_data]
+
+        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,
+        )
+        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,
+        )
+
+        input_ids = res["input_ids"].flatten()
+        image_offsets = self.get_mm_items_offset(
+            input_ids=input_ids,
+            mm_token_id=_IMAGE_SPECIAL_TOKEN_ID,
+        )
+
+        items = [
+            MultimodalDataItem(
+                pixel_values=res["input_image_embeds"],
+                image_sizes=res["image_sizes"],
+                image_emb_mask=res["image_attention_mask"],
+                image_offsets=image_offsets,
+                modality=Modality.IMAGE,
+            )
+        ]
+
+        return {
+            "mm_items": items,
+            "input_ids": input_ids.tolist(),
+            "im_token_id": _IMAGE_SPECIAL_TOKEN_ID,
+        }

python/sglang/srt/models/minicpmv.py

@@ -20,6 +20,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only MiniCPM-V model compatible with HuggingFace weights."""
+
 from functools import partial
 from typing import (
     Any,
@@ -386,6 +387,7 @@ def __init__(
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
+        require_post_norm: bool = True,
         prefix: str = "",
     ) -> None:
         super().__init__()
@@ -398,20 +400,35 @@ def __init__(
             quant_config=quant_config,
             prefix=add_prefix("encoder", prefix),
         )
-        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.post_layernorm = (
+            nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+            if require_post_norm
+            else nn.Identity()
+        )
 
     def get_input_embeddings(self) -> nn.Embedding:
         return self.embeddings
 
-    def compute_cu_seqlens(self, tgt_sizes: torch.Tensor) -> torch.Tensor:
-        patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]  # shape: (batch_size,)
+    def compute_cu_seqlens(
+        self,
+        tgt_sizes: Optional[torch.Tensor] = None,
+        atch_attention_mask: Optional[torch.BoolTensor] = None,
+    ) -> torch.Tensor:
+        # shape: (batch_size,)
+        if tgt_sizes is not None:
+            patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
+        else:
+            patch_len = atch_attention_mask[:, :, 0].sum(dim=1) * atch_attention_mask[
+                :, 0, :
+            ].sum(dim=1)
+
         cu_seqlens = torch.cat(
             [
                 torch.tensor([0], device=patch_len.device, dtype=torch.int32),
                 torch.cumsum(patch_len, dim=0, dtype=torch.int32),
             ],
             dim=0,
-        ).to(tgt_sizes.device)
+        ).to(patch_len.device)
         return cu_seqlens
 
     def forward(
@@ -425,7 +442,7 @@ def forward(
             patch_attention_mask=patch_attention_mask,
             tgt_sizes=tgt_sizes,
         )
-        cu_seqlens = self.compute_cu_seqlens(tgt_sizes)
+        cu_seqlens = self.compute_cu_seqlens(tgt_sizes, patch_attention_mask)
         encoder_outputs = self.encoder(
             hidden_states,
             cu_seqlens=cu_seqlens,