feat: Add base64 and HTTP image URL support to vLLM workers

components/src/dynamo/vllm/handlers.py

@@ -6,7 +6,7 @@
 import os
 from abc import ABC, abstractmethod
 from contextlib import asynccontextmanager
-from typing import Any, AsyncGenerator, Dict
+from typing import Any, AsyncGenerator, Dict, Final
 
 from vllm.inputs import TokensPrompt
 from vllm.sampling_params import SamplingParams
@@ -16,6 +16,13 @@
 from dynamo.runtime.logging import configure_dynamo_logging
 
 from .engine_monitor import VllmEngineMonitor
+from .multimodal_utils.image_loader import ImageLoader
+
+# Multimodal data dictionary keys
+IMAGE_URL_KEY: Final = "image_url"
+VIDEO_URL_KEY: Final = "video_url"
+URL_VARIANT_KEY: Final = "Url"
+DECODED_VARIANT_KEY: Final = "Decoded"
 
 configure_dynamo_logging()
 logger = logging.getLogger(__name__)
@@ -65,6 +72,7 @@ def __init__(self, runtime, component, engine, default_sampling_params):
         self.default_sampling_params = default_sampling_params
         self.kv_publishers: list[ZmqKvEventPublisher] | None = None
         self.engine_monitor = VllmEngineMonitor(runtime, engine)
+        self.image_loader = ImageLoader()
 
     @abstractmethod
     async def generate(self, request, context) -> AsyncGenerator[dict, None]:
@@ -111,6 +119,50 @@ def cleanup(self):
         """Override in subclasses if cleanup is needed."""
         pass
 
+    async def _extract_multimodal_data(
+        self, request: Dict[str, Any]
+    ) -> Dict[str, Any] | None:
+        """
+        Extract and decode multimodal data from PreprocessedRequest.
+        """
+        if "multi_modal_data" not in request or request["multi_modal_data"] is None:
+            return None
+
+        mm_map = request["multi_modal_data"]
+        vllm_mm_data = {}
+
+        # Process image_url entries
+        images = []
+        for item in mm_map.get(IMAGE_URL_KEY, []):
+            if isinstance(item, dict) and URL_VARIANT_KEY in item:
+                url = item[URL_VARIANT_KEY]
+                try:
+                    # ImageLoader supports both data: and http(s): URLs with caching
+                    image = await self.image_loader.load_image(url)
+                    images.append(image)
+                    logger.debug(f"Loaded image from URL: {url[:80]}...")
+                except Exception:
+                    logger.exception(f"Failed to load image from {url[:80]}...")
+                    raise
+            elif isinstance(item, dict) and DECODED_VARIANT_KEY in item:
+                # Decoded support from PRs #3971/#3988 (frontend decoding + NIXL transfer)
+                # Will contain NIXL metadata for direct memory access
+                # TODO: Implement NIXL read when PRs merge
+                logger.warning(
+                    "Decoded multimodal data not yet supported in standard worker"
+                )
+
+        if images:
+            # vLLM expects single image or list
+            vllm_mm_data["image"] = images[0] if len(images) == 1 else images
+            logger.debug(f"Extracted {len(images)} image(s) for multimodal processing")
+
+        # Handle video_url entries (future expansion)
+        if VIDEO_URL_KEY in mm_map:
+            logger.warning("Video multimodal data not yet supported in standard worker")
+
+        return vllm_mm_data if vllm_mm_data else None
+
     async def generate_tokens(
         self, prompt, sampling_params, request_id, data_parallel_rank=None
     ):
@@ -168,7 +220,12 @@ async def generate(self, request, context):
         request_id = context.id()
         logger.debug(f"Decode Request ID: {request_id}")
 
-        prompt = TokensPrompt(prompt_token_ids=request["token_ids"])
+        # Extract and decode multimodal data if present
+        multi_modal_data = await self._extract_multimodal_data(request)
+
+        prompt = TokensPrompt(
+            prompt_token_ids=request["token_ids"], multi_modal_data=multi_modal_data
+        )
 
         # Build sampling params from request
         sampling_params = build_sampling_params(request, self.default_sampling_params)
@@ -210,8 +267,13 @@ async def generate(self, request, context):
         request_id = context.id()
         logger.debug(f"Prefill Request ID: {request_id}")
 
+        # Extract and decode multimodal data if present
+        multi_modal_data = await self._extract_multimodal_data(request)
+
         token_ids = request["token_ids"]
-        prompt = TokensPrompt(prompt_token_ids=token_ids)
+        prompt = TokensPrompt(
+            prompt_token_ids=token_ids, multi_modal_data=multi_modal_data
+        )
 
         # Build sampling params from request using shared utility
         sampling_params = build_sampling_params(request, self.default_sampling_params)

examples/backends/vllm/launch/agg_multimodal.sh

@@ -1,13 +1,21 @@
 #!/bin/bash
 # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
+#
+# Aggregated multimodal serving with standard Dynamo preprocessing
+#
+# Architecture: Single-worker PD (Prefill-Decode)
+# - Frontend: Rust OpenAIPreprocessor handles image URLs (HTTP and data:// base64)
+# - Worker: Standard vLLM worker with vision model support
+#
+# For EPD (Encode-Prefill-Decode) architecture with dedicated encoding worker,
+# see agg_multimodal_epd.sh
+
 set -e
 trap 'echo Cleaning up...; kill 0' EXIT
 
 # Default values
-MODEL_NAME="llava-hf/llava-1.5-7b-hf"
-PROMPT_TEMPLATE="USER: <image>\n<prompt> ASSISTANT:"
-PROVIDED_PROMPT_TEMPLATE=""
+MODEL_NAME="Qwen/Qwen2.5-VL-7B-Instruct"
 
 # Parse command line arguments
 while [[ $# -gt 0 ]]; do
@@ -16,15 +24,10 @@ while [[ $# -gt 0 ]]; do
             MODEL_NAME=$2
             shift 2
             ;;
-        --prompt-template)
-            PROVIDED_PROMPT_TEMPLATE=$2
-            shift 2
-            ;;
         -h|--help)
             echo "Usage: $0 [OPTIONS]"
             echo "Options:"
-            echo "  --model <model_name> Specify the model to use (default: $MODEL_NAME)"
-            echo "  --prompt-template <template> Specify the multi-modal prompt template to use. LLaVA 1.5 7B, Qwen2.5-VL, and Phi3V models have predefined templates."
+            echo "  --model <model_name> Specify the VLM model to use (default: $MODEL_NAME)"
             echo "  -h, --help           Show this help message"
             exit 0
             ;;
@@ -36,37 +39,23 @@ while [[ $# -gt 0 ]]; do
     esac
 done
 
-# Set PROMPT_TEMPLATE based on the MODEL_NAME
-if [[ -n "$PROVIDED_PROMPT_TEMPLATE" ]]; then
-    PROMPT_TEMPLATE="$PROVIDED_PROMPT_TEMPLATE"
-elif [[ "$MODEL_NAME" == "llava-hf/llava-1.5-7b-hf" ]]; then
-    PROMPT_TEMPLATE="USER: <image>\n<prompt> ASSISTANT:"
-elif [[ "$MODEL_NAME" == "microsoft/Phi-3.5-vision-instruct" ]]; then
-    PROMPT_TEMPLATE="<|user|>\n<|image_1|>\n<prompt><|end|>\n<|assistant|>\n"
-elif [[ "$MODEL_NAME" == "Qwen/Qwen2.5-VL-7B-Instruct" ]]; then
-    PROMPT_TEMPLATE="<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|><prompt><|im_end|>\n<|im_start|>assistant\n"
-else
-    echo "No multi-modal prompt template is defined for the model: $MODEL_NAME"
-    echo "Please provide a prompt template using --prompt-template option."
-    echo "Example: --prompt-template 'USER: <image>\n<prompt> ASSISTANT:'"
-    exit 1
-fi
-
-# run ingress
+# Start frontend with Rust OpenAIPreprocessor
 python -m dynamo.frontend --http-port=8000 &
 
-# To make Qwen2.5-VL fit in A100 40GB, set the following extra arguments
+# Configure GPU memory optimization for specific models
 EXTRA_ARGS=""
 if [[ "$MODEL_NAME" == "Qwen/Qwen2.5-VL-7B-Instruct" ]]; then
     EXTRA_ARGS="--gpu-memory-utilization 0.85 --max-model-len 2048"
 fi
 
-# run processor
-python -m dynamo.vllm --multimodal-processor --model $MODEL_NAME --mm-prompt-template "$PROMPT_TEMPLATE" &
-
-# run E/P/D workers
-CUDA_VISIBLE_DEVICES=0 python -m dynamo.vllm --multimodal-encode-worker --model $MODEL_NAME &
-CUDA_VISIBLE_DEVICES=1 python -m dynamo.vllm --multimodal-worker --model $MODEL_NAME $EXTRA_ARGS &
+# Start vLLM worker with vision model
+# Multimodal data (images) are decoded in the backend worker using ImageLoader
+# --enforce-eager: Quick deployment (remove for production)
+# --connector none: No KV transfer needed for aggregated serving
+DYN_SYSTEM_ENABLED=true DYN_SYSTEM_PORT=8081 \
+    python -m dynamo.vllm --model $MODEL_NAME --enforce-eager --connector none $EXTRA_ARGS
 
 # Wait for all background processes to complete
 wait
+
+

examples/backends/vllm/launch/agg_multimodal_epd.sh

@@ -0,0 +1,83 @@
+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# EPD (Encode-Prefill-Decode) multimodal deployment
+#
+# Architecture: 3-component disaggregation
+# - Processor: Python-based preprocessor (bypasses Rust OpenAIPreprocessor)
+# - Encode Worker: Dedicated vision encoder that extracts image embeddings
+# - PD Worker: Standard prefill/decode worker that receives embeddings via NIXL
+#
+# Benefits: Decouples encoding from inference, enables independent scaling
+# For standard single-worker deployment, see agg_multimodal.sh
+
+set -e
+trap 'echo Cleaning up...; kill 0' EXIT
+
+# Default values
+MODEL_NAME="llava-hf/llava-1.5-7b-hf"
+PROMPT_TEMPLATE="USER: <image>\n<prompt> ASSISTANT:"
+PROVIDED_PROMPT_TEMPLATE=""
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --model)
+            MODEL_NAME=$2
+            shift 2
+            ;;
+        --prompt-template)
+            PROVIDED_PROMPT_TEMPLATE=$2
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --model <model_name> Specify the model to use (default: $MODEL_NAME)"
+            echo "  --prompt-template <template> Specify the multi-modal prompt template to use. LLaVA 1.5 7B, Qwen2.5-VL, and Phi3V models have predefined templates."
+            echo "  -h, --help           Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+# Set PROMPT_TEMPLATE based on the MODEL_NAME
+if [[ -n "$PROVIDED_PROMPT_TEMPLATE" ]]; then
+    PROMPT_TEMPLATE="$PROVIDED_PROMPT_TEMPLATE"
+elif [[ "$MODEL_NAME" == "llava-hf/llava-1.5-7b-hf" ]]; then
+    PROMPT_TEMPLATE="USER: <image>\n<prompt> ASSISTANT:"
+elif [[ "$MODEL_NAME" == "microsoft/Phi-3.5-vision-instruct" ]]; then
+    PROMPT_TEMPLATE="<|user|>\n<|image_1|>\n<prompt><|end|>\n<|assistant|>\n"
+elif [[ "$MODEL_NAME" == "Qwen/Qwen2.5-VL-7B-Instruct" ]]; then
+    PROMPT_TEMPLATE="<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|><prompt><|im_end|>\n<|im_start|>assistant\n"
+else
+    echo "No multi-modal prompt template is defined for the model: $MODEL_NAME"
+    echo "Please provide a prompt template using --prompt-template option."
+    echo "Example: --prompt-template 'USER: <image>\n<prompt> ASSISTANT:'"
+    exit 1
+fi
+
+# Start frontend (HTTP endpoint)
+python -m dynamo.frontend --http-port=8000 &
+
+# To make Qwen2.5-VL fit in A100 40GB, set the following extra arguments
+EXTRA_ARGS=""
+if [[ "$MODEL_NAME" == "Qwen/Qwen2.5-VL-7B-Instruct" ]]; then
+    EXTRA_ARGS="--gpu-memory-utilization 0.85 --max-model-len 2048"
+fi
+
+# Start processor (Python-based preprocessing, handles prompt templating)
+python -m dynamo.vllm --multimodal-processor --model $MODEL_NAME --mm-prompt-template "$PROMPT_TEMPLATE" &
+
+# run E/P/D workers
+CUDA_VISIBLE_DEVICES=0 python -m dynamo.vllm --multimodal-encode-worker --model $MODEL_NAME &
+CUDA_VISIBLE_DEVICES=1 python -m dynamo.vllm --multimodal-worker --model $MODEL_NAME $EXTRA_ARGS &
+
+# Wait for all background processes to complete
+wait