chore: support for agg llama4 mulimodal (#3984)

Signed-off-by: ayushag <ayushag@nvidia.com>

examples/multimodal/launch/agg_llama.sh → components/backends/vllm/launch/agg_multimodal_llama.sh

@@ -11,11 +11,10 @@ MODEL_NAME="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
 python -m dynamo.frontend --http-port=8000 &
 
 # run processor
-python3 components/processor.py --model $MODEL_NAME --prompt-template "<|image|>\n<prompt>" &
-# LLama 4 doesn't support image embedding input, so the prefill worker will also
-# handle image encoding.
-# run EP/D workers
-python3 components/worker.py --model $MODEL_NAME --worker-type encode_prefill --tensor-parallel-size=8 --max-model-len=208960 &
+python -m dynamo.vllm --multimodal-processor --model $MODEL_NAME --mm-prompt-template "<|image|>\n<prompt>" &
+# Llama 4 doesn't support image embedding input, so use encode+prefill worker
+# that handles image encoding inline
+python -m dynamo.vllm --multimodal-encode-prefill-worker --model $MODEL_NAME --tensor-parallel-size=8 --max-model-len=208960 --gpu-memory-utilization 0.80 &
 
 # Wait for all background processes to complete
 wait

components/src/dynamo/vllm/args.py

@@ -69,6 +69,7 @@ class Config:
     multimodal_processor: bool = False
     multimodal_encode_worker: bool = False
     multimodal_worker: bool = False
+    multimodal_encode_prefill_worker: bool = False
     mm_prompt_template: str = "USER: <image>\n<prompt> ASSISTANT:"
     # dump config to file
     dump_config_to: Optional[str] = None
@@ -169,6 +170,11 @@ def parse_args() -> Config:
         action="store_true",
         help="Run as multimodal worker component for LLM inference with multimodal data",
     )
+    parser.add_argument(
+        "--multimodal-encode-prefill-worker",
+        action="store_true",
+        help="Run as unified encode+prefill+decode worker for models requiring integrated image encoding (e.g., Llama 4)",
+    )
     parser.add_argument(
         "--mm-prompt-template",
         type=str,
@@ -212,10 +218,11 @@ def parse_args() -> Config:
         int(bool(args.multimodal_processor))
         + int(bool(args.multimodal_encode_worker))
         + int(bool(args.multimodal_worker))
+        + int(bool(args.multimodal_encode_prefill_worker))
     )
     if mm_flags > 1:
         raise ValueError(
-            "Use only one of --multimodal-processor, --multimodal-encode-worker, or --multimodal-worker"
+            "Use only one of --multimodal-processor, --multimodal-encode-worker, --multimodal-worker, or --multimodal-encode-prefill-worker"
         )
 
     # Set component and endpoint based on worker type
@@ -225,6 +232,9 @@ def parse_args() -> Config:
     elif args.multimodal_encode_worker:
         config.component = "encoder"
         config.endpoint = "generate"
+    elif args.multimodal_encode_prefill_worker:
+        config.component = "encoder"
+        config.endpoint = "generate"
     elif args.multimodal_worker and args.is_prefill_worker:
         config.component = "prefill"
         config.endpoint = "generate"
@@ -248,6 +258,7 @@ def parse_args() -> Config:
     config.multimodal_processor = args.multimodal_processor
     config.multimodal_encode_worker = args.multimodal_encode_worker
     config.multimodal_worker = args.multimodal_worker
+    config.multimodal_encode_prefill_worker = args.multimodal_encode_prefill_worker
     config.mm_prompt_template = args.mm_prompt_template
 
     # Validate custom Jinja template file exists if provided

components/src/dynamo/vllm/main.py

@@ -106,7 +106,7 @@ async def worker(runtime: DistributedRuntime):
     elif config.multimodal_encode_worker:
         await init_multimodal_encode_worker(runtime, config)
         logger.debug("init_multimodal_encode_worker completed")
-    elif config.multimodal_worker:
+    elif config.multimodal_worker or config.multimodal_encode_prefill_worker:
         await init_multimodal_worker(runtime, config)
         logger.debug("init_multimodal_worker completed")
     elif config.is_prefill_worker:
@@ -605,8 +605,15 @@ async def init_multimodal_encode_worker(runtime: DistributedRuntime, config: Con
 
 
 async def init_multimodal_worker(runtime: DistributedRuntime, config: Config):
-    """Initialize multimodal worker component for aggregated or disaggregated mode"""
+    """
+    Initialize multimodal worker component.
+
+    Supports two modes:
+    1. --multimodal-worker: Receives embeddings from separate encoder
+    2. --multimodal-encode-prefill-worker: Handles inline encoding (e.g., Llama 4)
 
+    Both can operate in aggregated (P+D) or disaggregated (P→D) mode.
+    """
     component = runtime.namespace(config.namespace).component(config.component)
     await component.create_service()
 
@@ -615,16 +622,12 @@ async def init_multimodal_worker(runtime: DistributedRuntime, config: Config):
 
     engine_client, vllm_config, default_sampling_params = setup_vllm_engine(config)
 
-    # TODO: Support Disaggregated mode separately
-    client = (
-        await runtime.namespace(config.namespace)
-        .component("backend")
-        .endpoint("generate")
-        .client()
-    )
+    # For aggregated mode, no downstream client is needed
+    # TODO: Implement disaggregated mode with proper decode worker client
+    downstream_client = None
 
     handler = MultimodalPDWorkerHandler(
-        runtime, component, engine_client, config, client
+        runtime, component, engine_client, config, downstream_client
     )
 
     await handler.async_init(runtime)
@@ -637,14 +640,15 @@ async def init_multimodal_worker(runtime: DistributedRuntime, config: Config):
         handler.kv_publisher = kv_publisher
 
     metrics_labels = [("model", config.model)]
-
     try:
         await asyncio.gather(
             generate_endpoint.serve_endpoint(
-                handler.generate, metrics_labels=metrics_labels
+                handler.generate,
+                metrics_labels=metrics_labels,
             ),
             clear_endpoint.serve_endpoint(
-                handler.clear_kv_blocks, metrics_labels=metrics_labels
+                handler.clear_kv_blocks,
+                metrics_labels=metrics_labels,
             ),
         )
     except Exception as e: