[Model] Interface to enable batch-level DP support (vllm-project#23733)

Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: Cyrus Leung <[email protected]>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

Author: DarkLight1337

docs/configuration/optimization.md

@@ -168,8 +168,11 @@ llm = LLM(
     Batch-level DP is not to be confused with API request-level DP
     (which is instead controlled by `data_parallel_size`).
 
-The availability of batch-level DP is based on model implementation.
-Currently, the following models support `mm_encoder_tp_mode="data"`:
+Batch-level DP needs to be implemented on a per-model basis,
+and enabled by setting `supports_encoder_tp_data = True` in the model class.
+Regardless, you need to set `mm_encoder_tp_mode="data"` in engine arguments to use this feature.
+
+Known supported models:
 
 - Llama4 (<gh-pr:18368>)
 - MiniCPM-V-4 (<gh-pr:23327>)

vllm/config/__init__.py

@@ -872,6 +872,13 @@ def maybe_pull_model_tokenizer_for_s3(self, model: str,
 
     def _init_multimodal_config(self) -> Optional["MultiModalConfig"]:
         if self._model_info.supports_multimodal:
+            if (self.mm_encoder_tp_mode == "data" and
+                    not self._model_info.supports_multimodal_encoder_tp_data):
+                logger.warning_once(
+                    "This model does not support `--mm-encoder-tp-mode data`. "
+                    "Falling back to `--mm-encoder-tp-mode weights`.")
+                self.mm_encoder_tp_mode = "weights"
+
             return MultiModalConfig(
                 limit_per_prompt=self.limit_mm_per_prompt,
                 media_io_kwargs=self.media_io_kwargs,

vllm/model_executor/models/interfaces.py

@@ -52,6 +52,12 @@ class SupportsMultiModal(Protocol):
         MRO of your model class.
     """
 
+    supports_encoder_tp_data: ClassVar[bool] = False
+    """
+    A flag that indicates whether this model supports
+    `multimodal_config.mm_encoder_tp_mode="data"`.
+    """
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
         """
@@ -137,6 +143,11 @@ def supports_multimodal(
     return getattr(model, "supports_multimodal", False)
 
 
+def supports_multimodal_encoder_tp_data(
+        model: Union[type[object], object]) -> bool:
+    return getattr(model, "supports_encoder_tp_data", False)
+
+
 @runtime_checkable
 class SupportsMultiModalWithRawInput(SupportsMultiModal, Protocol):
     """The interface required for all multi-modal models."""

vllm/model_executor/models/minicpmv.py

@@ -1521,6 +1521,8 @@ class MiniCPMV4_0(MiniCPMVBaseModel, SupportsLoRA):
         ],
     }
 
+    supports_encoder_tp_data = True
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__(vllm_config=vllm_config, prefix=prefix)
         assert self.version == (4, 0)

vllm/model_executor/models/mllama4.py

@@ -716,6 +716,8 @@ class Llama4ForConditionalGeneration(nn.Module, SupportsMultiModal,
         "gate_up_proj": ["gate_proj", "up_proj"],
     }
 
+    supports_encoder_tp_data = True
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
         if modality.startswith("image"):

vllm/model_executor/models/qwen2_5_vl.py

@@ -868,6 +868,8 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
             "model.": "language_model.model.",
         })
 
+    supports_encoder_tp_data = True
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
         if modality.startswith("image"):

vllm/model_executor/models/registry.py

@@ -27,8 +27,10 @@
 
 from .interfaces import (has_inner_state, has_noops, is_attention_free,
                          is_hybrid, supports_cross_encoding,
-                         supports_multimodal, supports_multimodal_raw_input,
-                         supports_pp, supports_transcription, supports_v0_only)
+                         supports_multimodal,
+                         supports_multimodal_encoder_tp_data,
+                         supports_multimodal_raw_input, supports_pp,
+                         supports_transcription, supports_v0_only)
 from .interfaces_base import (get_default_pooling_type, is_pooling_model,
                               is_text_generation_model)
 
@@ -324,6 +326,7 @@ class _ModelInfo:
     supports_cross_encoding: bool
     supports_multimodal: bool
     supports_multimodal_raw_input: bool
+    supports_multimodal_encoder_tp_data: bool
     supports_pp: bool
     has_inner_state: bool
     is_attention_free: bool
@@ -343,6 +346,8 @@ def from_model_cls(model: type[nn.Module]) -> "_ModelInfo":
             supports_cross_encoding=supports_cross_encoding(model),
             supports_multimodal=supports_multimodal(model),
             supports_multimodal_raw_input=supports_multimodal_raw_input(model),
+            supports_multimodal_encoder_tp_data=
+            supports_multimodal_encoder_tp_data(model),
             supports_pp=supports_pp(model),
             has_inner_state=has_inner_state(model),
             is_attention_free=is_attention_free(model),

vllm/model_executor/models/step3_vl.py

@@ -867,6 +867,8 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
         "lm_head.": "language_model.lm_head.",
     })
 
+    supports_encoder_tp_data = True
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
         if modality.startswith("image"):