[CLI][Doc] Formalize --mm-encoder-tp-mode

docs/configuration/optimization.md

@@ -129,6 +129,51 @@ Data parallelism replicates the entire model across multiple GPU sets and proces
 Data parallelism can be combined with the other parallelism strategies and is set by `data_parallel_size=N`.
 Note that MoE layers will be sharded according to the product of the tensor parallel size and data parallel size.
 
+### Batch-level DP for Multi-Modal Encoders
+
+By default, TP is used to shard the weights of multi-modal encoders just like for language decoders,
+in order to reduce the memory and compute load on each GPU.
+
+However, since the size of multi-modal encoders is very small compared to language decoders,
+there is relatively little gain from TP. On the other hand, TP incurs significant communication
+overhead because of all-reduce being performed after every layer.
+
+Given this, it may be advantageous to instead shard the batched input data using TP, essentially
+performing batch-level DP. This has been shown to improve the throughput by around 10% for
+`tensor_parallel_size=8`. For vision encoders that use hardware-unoptimized Conv3D operations,
+batch-level DP can provide another 40% increase to throughput compared to regular TP.
+
+Nevertheless, since the weights of the multi-modal encoder are replicated across each TP rank,
+there will be a minor increase in memory consumption and may cause OOM if you can barely fit the model already.
+
+You can enable batch-level DP by setting `mm_encoder_tp_mode="data"`, for example:
+
+```python
+from vllm import LLM
+
+llm = LLM(
+    model="Qwen/Qwen2.5-VL-72B-Instruct",
+    # Create two EngineCore instances, one per DP rank
+    data_parallel_size=2,
+    # Within each EngineCore instance:
+    # The vision encoder uses TP=4 (not DP=2) to shard the input data
+    # The language decoder uses TP=4 to shard the weights as usual
+    tensor_parallel_size=4,
+    mm_encoder_tp_mode="data",
+)
+```
+
+!! important
+    Batch-level DP is not to be confused with API request-level DP
+    (which is instead controlled by `data_parallel_size`).
+
+The availablilty of batch-level DP is based on model implementation.
+Currently, the following models support `mm_encoder_tp_mode="data"`:
+
+- Llama4 (<gh-pr:18368>)
+- Qwen2.5-VL (<gh-pr:22742>)
+- Step3 (<gh-pr:22697>)
+
 ## Input Processing
 
 ### Parallel Processing

vllm/config/__init__.py

@@ -248,6 +248,7 @@ def is_init_field(cls: ConfigType, name: str) -> bool:
 ModelDType = Literal["auto", "half", "float16", "bfloat16", "float", "float32"]
 LogprobsMode = Literal["raw_logprobs", "raw_logits", "processed_logprobs",
                        "processed_logits"]
+MMEncoderTPMode = Literal["weights", "data"]
 
 
 @config
@@ -428,6 +429,19 @@ class ModelConfig:
     `mm_processor_cache_gb * (api_server_count + data_parallel_size)`.
 
     Set to `0` to disable this cache completely (not recommended)."""
+    mm_encoder_tp_mode: MMEncoderTPMode = "weights"
+    """Indicates how to optimize multi-modal encoder inference using
+    tensor parallelism (TP).
+
+    - `"weights"`: Within the same vLLM engine, split the weights of
+        each layer across TP ranks. (default TP behavior)
+    - `"data"`: Within the same vLLM engine, split the batched input data
+        across TP ranks to process the data in parallel, while hosting
+        the full weights on each TP rank.
+        This batch-level DP is not to be confused with API request-level
+        DP (which is controlled by `--data-parallel-size`).
+        This is only supported on a per-model basis and falls back to
+        `"weights"` if the encoder does not support DP."""
     override_neuron_config: dict[str, Any] = field(default_factory=dict)
     """Initialize non-default neuron config or override default neuron config
     that are specific to Neuron devices, this argument will be used to
@@ -846,8 +860,10 @@ def _init_multimodal_config(self) -> Optional["MultiModalConfig"]:
                 media_io_kwargs=self.media_io_kwargs,
                 mm_processor_kwargs=self.mm_processor_kwargs,
                 mm_processor_cache_gb=self.mm_processor_cache_gb,
+                mm_encoder_tp_mode=self.mm_encoder_tp_mode,
                 interleave_mm_strings=self.interleave_mm_strings,
-                skip_mm_profiling=self.skip_mm_profiling)
+                skip_mm_profiling=self.skip_mm_profiling,
+            )
 
         return None
 
@@ -2516,6 +2532,22 @@ class MultiModalConfig:
     Set to `0` to disable this cache completely (not recommended).
     """
 
+    mm_encoder_tp_mode: MMEncoderTPMode = "weights"
+    """
+    Indicates how to optimize multi-modal encoder inference using
+    tensor parallelism (TP).
+
+    - `"weights"`: Within the same vLLM engine, split the weights of
+        each layer across TP ranks. (default TP behavior)
+    - `"data"`: Within the same vLLM engine, split the batched input data
+        across TP ranks to process the data in parallel, while hosting
+        the full weights on each TP rank.
+        This batch-level DP is not to be confused with API request-level
+        DP (which is controlled by `--data-parallel-size`).
+        This is only supported on a per-model basis and falls back to
+        `"weights"` if the encoder does not support DP.
+    """
+
     interleave_mm_strings: bool = False
     """
     Enable fully interleaved support for multimodal prompts.

vllm/config/parallel.py

@@ -137,10 +137,6 @@ class is dynamically inherited by the worker class. This is used to inject
     rank: int = 0
     """Global rank in distributed setup."""
 
-    enable_multimodal_encoder_data_parallel: bool = False
-    """ Use data parallelism instead of tensor parallelism for vision encoder.
-    Only support LLama4 for now"""
-
     @property
     def world_size_across_dp(self) -> int:
         """world_size_across_dp is TPxPPxDP, it is the size of the world

vllm/engine/arg_utils.py

@@ -27,12 +27,12 @@
                          DeviceConfig, DistributedExecutorBackend,
                          GuidedDecodingBackend, HfOverrides, KVEventsConfig,
                          KVTransferConfig, LoadConfig, LogprobsMode,
-                         LoRAConfig, MambaDType, ModelConfig, ModelDType,
-                         ModelImpl, MultiModalConfig, ObservabilityConfig,
-                         ParallelConfig, PoolerConfig, PrefixCachingHashAlgo,
-                         RunnerOption, SchedulerConfig, SchedulerPolicy,
-                         SpeculativeConfig, TaskOption, TokenizerMode,
-                         VllmConfig, get_attr_docs, get_field)
+                         LoRAConfig, MambaDType, MMEncoderTPMode, ModelConfig,
+                         ModelDType, ModelImpl, MultiModalConfig,
+                         ObservabilityConfig, ParallelConfig, PoolerConfig,
+                         PrefixCachingHashAlgo, RunnerOption, SchedulerConfig,
+                         SchedulerPolicy, SpeculativeConfig, TaskOption,
+                         TokenizerMode, VllmConfig, get_attr_docs, get_field)
 from vllm.logger import init_logger
 from vllm.platforms import CpuArchEnum, current_platform
 from vllm.plugins import load_general_plugins
@@ -351,6 +351,7 @@ class EngineArgs:
         MultiModalConfig.mm_processor_kwargs
     disable_mm_preprocessor_cache: bool = False  # DEPRECATED
     mm_processor_cache_gb: int = MultiModalConfig.mm_processor_cache_gb
+    mm_encoder_tp_mode: MMEncoderTPMode = MultiModalConfig.mm_encoder_tp_mode
     skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
     # LoRA fields
     enable_lora: bool = False
@@ -433,16 +434,14 @@ class EngineArgs:
     use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
     pt_load_map_location: str = LoadConfig.pt_load_map_location
 
-    enable_multimodal_encoder_data_parallel: bool = \
-        ParallelConfig.enable_multimodal_encoder_data_parallel
+    # DEPRECATED
+    enable_multimodal_encoder_data_parallel: bool = False
 
     logits_processors: Optional[list[Union[
         str, type[LogitsProcessor]]]] = ModelConfig.logits_processors
     """Custom logitproc types"""
 
     async_scheduling: bool = SchedulerConfig.async_scheduling
-    # DEPRECATED
-    enable_prompt_adapter: bool = False
 
     kv_sharing_fast_prefill: bool = \
         CacheConfig.kv_sharing_fast_prefill
@@ -677,7 +676,8 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
                                     **parallel_kwargs["worker_extension_cls"])
         parallel_group.add_argument(
             "--enable-multimodal-encoder-data-parallel",
-            **parallel_kwargs["enable_multimodal_encoder_data_parallel"])
+            action="store_true",
+            deprecated=True)
 
         # KV cache arguments
         cache_kwargs = get_kwargs(CacheConfig)
@@ -727,6 +727,8 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         multimodal_group.add_argument("--disable-mm-preprocessor-cache",
                                       action="store_true",
                                       deprecated=True)
+        multimodal_group.add_argument(
+            "--mm-encoder-tp-mode", **multimodal_kwargs["mm_encoder_tp_mode"])
         multimodal_group.add_argument(
             "--interleave-mm-strings",
             **multimodal_kwargs["interleave_mm_strings"])
@@ -901,6 +903,14 @@ def create_model_config(self) -> ModelConfig:
 
             self.mm_processor_cache_gb = envs.VLLM_MM_INPUT_CACHE_GIB
 
+        if self.enable_multimodal_encoder_data_parallel:
+            logger.warning(
+                "--enable-multimodal-encoder-data-parallel` is deprecated "
+                "and will be removed in v0.13. "
+                "Please use `--mm-encoder-tp-mode data` instead.")
+
+            self.mm_encoder_tp_mode = "data"
+
         return ModelConfig(
             model=self.model,
             hf_config_path=self.hf_config_path,
@@ -939,6 +949,7 @@ def create_model_config(self) -> ModelConfig:
             config_format=self.config_format,
             mm_processor_kwargs=self.mm_processor_kwargs,
             mm_processor_cache_gb=self.mm_processor_cache_gb,
+            mm_encoder_tp_mode=self.mm_encoder_tp_mode,
             override_neuron_config=self.override_neuron_config,
             override_pooler_config=self.override_pooler_config,
             logits_processor_pattern=self.logits_processor_pattern,
@@ -1250,8 +1261,6 @@ def create_engine_config(
             distributed_executor_backend=self.distributed_executor_backend,
             worker_cls=self.worker_cls,
             worker_extension_cls=self.worker_extension_cls,
-            enable_multimodal_encoder_data_parallel=self.
-            enable_multimodal_encoder_data_parallel,
         )
 
         if model_config.is_multimodal_model:

vllm/model_executor/models/mllama4.py

@@ -728,8 +728,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
-        self.use_data_parallel = (vllm_config.parallel_config.
-                                  enable_multimodal_encoder_data_parallel)
+        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
+
         self.config = config
         self.quant_config = quant_config
         self.multimodal_config = multimodal_config

vllm/model_executor/models/qwen2_5_vl.py

@@ -877,8 +877,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config: Qwen2_5_VLConfig = vllm_config.model_config.hf_config
         multimodal_config = vllm_config.model_config.multimodal_config
 
-        self.use_data_parallel = (vllm_config.parallel_config.
-                                  enable_multimodal_encoder_data_parallel)
+        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
         self.config = config
         self.multimodal_config = multimodal_config
 

vllm/model_executor/models/step3_vl.py

@@ -882,8 +882,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
 
         self.config = config
         self.multimodal_config = multimodal_config
-        self.use_data_parallel = (vllm_config.parallel_config.
-                                  enable_multimodal_encoder_data_parallel)
+        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
 
         if multimodal_config.get_limit_per_prompt("image"):
             self.vision_model = Step3VisionTransformer(