feat: ec connector handler

components/src/dynamo/vllm/args.py

@@ -61,12 +61,23 @@ class Config:
 
     # multimodal options
     multimodal_processor: bool = False
+    # Emebdding Cache Processor is different from the regular processor
+    # TODO: Have a single processor for all cases and adopting rust based processor
+    ec_processor: bool = False
     multimodal_encode_worker: bool = False
     multimodal_worker: bool = False
     multimodal_decode_worker: bool = False
     enable_multimodal: bool = False
     multimodal_encode_prefill_worker: bool = False
     mm_prompt_template: str = "USER: <image>\n<prompt> ASSISTANT:"
+
+    # vLLM-native encoder worker (ECConnector mode)
+    vllm_native_encoder_worker: bool = False
+    ec_connector_backend: Optional[str] = "ECExampleConnector"
+    ec_storage_path: Optional[str] = None
+    ec_extra_config: Optional[str] = None
+    ec_consumer_mode: bool = False
+
     # dump config to file
     dump_config_to: Optional[str] = None
 
@@ -153,6 +164,11 @@ def parse_args() -> Config:
         action="store_true",
         help="Run as multimodal processor component for handling multimodal requests",
     )
+    parser.add_argument(
+        "--ec-processor",
+        action="store_true",
+        help="Run as ECConnector processor (routes multimodal requests to encoder then PD workers)",
+    )
     parser.add_argument(
         "--multimodal-encode-worker",
         action="store_true",
@@ -191,6 +207,34 @@ def parse_args() -> Config:
             "'USER: <image> please describe the image ASSISTANT:'."
         ),
     )
+    parser.add_argument(
+        "--vllm-native-encoder-worker",
+        action="store_true",
+        help="Run as vLLM-native encoder worker using ECConnector for encoder disaggregation (requires shared storage)",
+    )
+    parser.add_argument(
+        "--ec-connector-backend",
+        type=str,
+        default="ECExampleConnector",
+        help="ECConnector implementation class for encoder disaggregation. Default: ECExampleConnector (disk-based)",
+    )
+    parser.add_argument(
+        "--ec-storage-path",
+        type=str,
+        default=None,
+        help="Storage path for ECConnector (required for ECExampleConnector, optional for other backends)",
+    )
+    parser.add_argument(
+        "--ec-extra-config",
+        type=str,
+        default=None,
+        help="Additional ECConnector configuration as JSON string",
+    )
+    parser.add_argument(
+        "--ec-consumer-mode",
+        action="store_true",
+        help="Configure as ECConnector consumer for receiving encoder embeddings (for PD workers)",
+    )
     parser.add_argument(
         "--store-kv",
         type=str,
@@ -271,27 +315,42 @@ def parse_args() -> Config:
     # Check multimodal role exclusivity
     mm_flags = (
         int(bool(args.multimodal_processor))
+        + int(bool(args.ec_processor))
         + int(bool(args.multimodal_encode_worker))
         + int(bool(args.multimodal_worker))
         + int(bool(args.multimodal_decode_worker))
         + int(bool(args.multimodal_encode_prefill_worker))
+        + int(bool(args.vllm_native_encoder_worker))
     )
     if mm_flags > 1:
         raise ValueError(
-            "Use only one of --multimodal-processor, --multimodal-encode-worker, --multimodal-worker, --multimodal-decode-worker, or --multimodal-encode-prefill-worker"
+            "Use only one of --multimodal-processor, --ec-processor, --multimodal-encode-worker, --multimodal-worker, "
+            "--multimodal-decode-worker, --multimodal-encode-prefill-worker, or --vllm-native-encoder-worker"
         )
 
     if mm_flags == 1 and not args.enable_multimodal:
         raise ValueError("Use --enable-multimodal to enable multimodal processing")
 
+    # Validate vLLM-native encoder worker config
+    if args.vllm_native_encoder_worker:
+        if (
+            args.ec_connector_backend == "ECExampleConnector"
+            and not args.ec_storage_path
+        ):
+            raise ValueError(
+                "--ec-storage-path is required when using ECExampleConnector backend. "
+                "Specify a shared storage path for encoder cache."
+            )
+
     # Set component and endpoint based on worker type
-    if args.multimodal_processor:
+    if args.multimodal_processor or args.ec_processor:
         config.component = "processor"
         config.endpoint = "generate"
-    elif args.multimodal_encode_worker:
-        config.component = "encoder"
-        config.endpoint = "generate"
-    elif args.multimodal_encode_prefill_worker:
+    elif (
+        args.vllm_native_encoder_worker
+        or args.multimodal_encode_worker
+        or args.multimodal_encode_prefill_worker
+    ):
         config.component = "encoder"
         config.endpoint = "generate"
     elif args.multimodal_decode_worker:
@@ -319,12 +378,18 @@ def parse_args() -> Config:
     config.custom_jinja_template = args.custom_jinja_template
     config.dyn_endpoint_types = args.dyn_endpoint_types
     config.multimodal_processor = args.multimodal_processor
+    config.ec_processor = args.ec_processor
     config.multimodal_encode_worker = args.multimodal_encode_worker
     config.multimodal_worker = args.multimodal_worker
     config.multimodal_decode_worker = args.multimodal_decode_worker
     config.multimodal_encode_prefill_worker = args.multimodal_encode_prefill_worker
     config.enable_multimodal = args.enable_multimodal
     config.mm_prompt_template = args.mm_prompt_template
+    config.vllm_native_encoder_worker = args.vllm_native_encoder_worker
+    config.ec_connector_backend = args.ec_connector_backend
+    config.ec_storage_path = args.ec_storage_path
+    config.ec_extra_config = args.ec_extra_config
+    config.ec_consumer_mode = args.ec_consumer_mode
     config.store_kv = args.store_kv
     config.request_plane = args.request_plane
     config.enable_local_indexer = args.enable_local_indexer
@@ -509,7 +574,9 @@ def overwrite_args(config):
             setattr(config.engine_args, key, value)
             logger.debug(f" engine_args.{key} = {value}")
         else:
-            raise ValueError(f"{key} not found in AsyncEngineArgs from vLLM.")
+            logger.debug(
+                f" Skipping engine_args.{key} (not available in this vLLM version)"
+            )
 
 
 def get_host_ip() -> str:

components/src/dynamo/vllm/main.py

@@ -30,11 +30,14 @@
 from dynamo.runtime import DistributedRuntime
 from dynamo.runtime.logging import configure_dynamo_logging
 from dynamo.vllm.multimodal_handlers import (
+    ECProcessorHandler,
     EncodeWorkerHandler,
     MultimodalDecodeWorkerHandler,
     MultimodalPDWorkerHandler,
     ProcessorHandler,
+    VLLMEncodeWorkerHandler,
 )
+from dynamo.vllm.multimodal_utils.encode_utils import create_ec_transfer_config
 
 from .args import Config, overwrite_args, parse_args
 from .handlers import DecodeWorkerHandler, PrefillWorkerHandler
@@ -85,7 +88,13 @@ def signal_handler():
         config.model = config.engine_args.model = await fetch_llm(config.model)
 
     # Route to appropriate initialization based on config flags
-    if config.multimodal_processor:
+    if config.vllm_native_encoder_worker:
+        await init_vllm_native_encoder(runtime, config)
+        logger.debug("init_vllm_native_encoder completed")
+    elif config.ec_processor:
+        await init_ec_processor(runtime, config)
+        logger.debug("init_ec_processor completed")
+    elif config.multimodal_processor:
         await init_multimodal_processor(runtime, config)
         logger.debug("init_multimodal_processor completed")
     elif config.multimodal_encode_worker:
@@ -717,7 +726,138 @@ async def init_multimodal_encode_worker(runtime: DistributedRuntime, config: Con
             ),
         )
     except Exception as e:
-        logger.error(f"Failed to serve endpoints: {e}")
+        logger.error(f"Failed to serve encode worker endpoint: {e}")
+        raise
+    finally:
+        handler.cleanup()
+
+
+async def init_vllm_native_encoder(runtime: DistributedRuntime, config: Config):
+    """
+    Initialize vLLM-native encoder worker component (ECConnector mode).
+    In this mode, vLLM handles encoder execution, caching, and storage automatically.
+    """
+    # Create component and endpoint
+    component = runtime.namespace(config.namespace).component(config.component)
+    generate_endpoint = component.endpoint(config.endpoint)
+
+    # Configure ECTransferConfig for producer role
+    instance_id = 0
+    engine_id = f"{config.namespace}.{config.component}.encoder.{instance_id}"
+
+    # Configure encoder with producer role, it will be responsible for creating embeddings and storing them in the shared storage
+    ec_transfer_config = create_ec_transfer_config(
+        engine_id=engine_id,
+        ec_role="ec_producer",
+        ec_connector_backend=config.ec_connector_backend,
+        ec_storage_path=config.ec_storage_path,
+        ec_extra_config=config.ec_extra_config,
+    )
+
+    # Set ECTransferConfig on engine args
+    config.engine_args.ec_transfer_config = ec_transfer_config
+
+    # Setup vLLM engine
+    (
+        engine_client,
+        vllm_config,
+        default_sampling_params,
+        prometheus_temp_dir,
+    ) = setup_vllm_engine(config)
+
+    # Initialize vLLM Native Encoder Worker Handler
+    handler = VLLMEncodeWorkerHandler(
+        runtime,
+        component,
+        engine_client,
+        config,
+    )
+    handler.add_temp_dir(prometheus_temp_dir)
+
+    # 5. No async init needed - vLLM handles everything
+    # await handler.async_init(runtime)  # Not needed for ECConnector mode
+
+    logger.info("Starting to serve vLLM-native encoder endpoint...")
+
+    # 6. Serve endpoint
+    try:
+        await asyncio.gather(
+            generate_endpoint.serve_endpoint(
+                handler.generate, metrics_labels=[("model", config.model)]
+            ),
+        )
+    except Exception as e:
+        logger.error(f"Failed to serve vLLM-native encoder endpoint: {e}")
+        raise
+    finally:
+        handler.cleanup()
+
+
+async def init_ec_processor(runtime: DistributedRuntime, config: Config):
+    """
+    Initialize ECConnector processor component.
+
+    Simple processor that routes multimodal requests using ECConnector pattern:
+    1. Preprocess request (same as regular processor)
+    2. Send multimodal items to encoder workers (stores to shared storage)
+    3. Forward preprocessed request to PD worker (loads from shared storage)
+    4. Stream response back to client
+    """
+    # Create component and endpoint
+    component = runtime.namespace(config.namespace).component(config.component)
+    generate_endpoint = component.endpoint(config.endpoint)
+
+    # Get encoder worker client
+    encoder_client = (
+        await runtime.namespace(config.namespace)
+        .component("encoder")
+        .endpoint("generate")
+        .client()
+    )
+
+    # Get PD worker client
+    pd_client = (
+        await runtime.namespace(config.namespace)
+        .component("backend")
+        .endpoint("generate")
+        .client()
+    )
+
+    # Get prompt template from args (must be passed via environment or command line)
+    mm_prompt_template = config.mm_prompt_template
+
+    # Create EC processor handler (with preprocessing like regular processor)
+    handler = ECProcessorHandler(
+        config.engine_args,
+        encoder_worker_client=encoder_client,
+        pd_worker_client=pd_client,
+        prompt_template=mm_prompt_template,
+    )
+
+    logger.info("Waiting for encoder and PD worker instances...")
+    await encoder_client.wait_for_instances()
+    await pd_client.wait_for_instances()
+
+    # Register the endpoint as entrypoint to a model (same as regular processor)
+    await register_llm(
+        ModelInput.Text,  # Custom processor is used and this type bypasses SDK processor
+        ModelType.Chat,
+        generate_endpoint,
+        config.model,
+        config.served_model_name,
+        kv_cache_block_size=config.engine_args.block_size,
+    )
+
+    logger.info("Starting to serve EC processor endpoint...")
+
+    try:
+        await asyncio.gather(
+            generate_endpoint.serve_endpoint(
+                handler.generate, metrics_labels=[("model", config.model)]
+            ),
+        )
+    except Exception as e:
+        logger.error(f"Failed to serve EC processor endpoint: {e}")
         raise
     finally:
         handler.cleanup()
@@ -732,12 +872,34 @@ async def init_multimodal_worker(runtime: DistributedRuntime, config: Config):
     2. --multimodal-encode-prefill-worker: Handles inline encoding (e.g., Llama 4)
 
     Both can operate in aggregated (P+D) or disaggregated (P→D) mode.
+
+    When --ec-consumer-mode is enabled, configures as ECConnector consumer
+    to load encoder embeddings from shared storage.
     """
     component = runtime.namespace(config.namespace).component(config.component)
 
     generate_endpoint = component.endpoint(config.endpoint)
     clear_endpoint = component.endpoint("clear_kv_blocks")
 
+    # Configure ECConnector consumer mode if enabled
+    if config.ec_consumer_mode:
+        logger.info("Configuring as ECConnector consumer for encoder embeddings")
+        instance_id = 0
+        engine_id = f"{config.namespace}.{config.component}.backend.{instance_id}"
+
+        # The PD Worker just load the embeddings from the shared storage, so it is a consumer
+        ec_transfer_config = create_ec_transfer_config(
+            engine_id=engine_id,
+            ec_role="ec_consumer",
+            ec_connector_backend=config.ec_connector_backend,
+            ec_storage_path=config.ec_storage_path,
+            ec_extra_config=config.ec_extra_config,
+        )
+
+        # Set ECTransferConfig on engine args
+        config.engine_args.ec_transfer_config = ec_transfer_config
+        logger.info(f"Configured as ECConnector consumer with engine_id={engine_id}")
+
     (
         engine_client,
         vllm_config,

components/src/dynamo/vllm/multimodal_handlers/__init__.py

@@ -1,16 +1,24 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-from dynamo.vllm.multimodal_handlers.encode_worker_handler import EncodeWorkerHandler
-from dynamo.vllm.multimodal_handlers.processor_handler import ProcessorHandler
+from dynamo.vllm.multimodal_handlers.encode_worker_handler import (
+    EncodeWorkerHandler,
+    VLLMEncodeWorkerHandler,
+)
+from dynamo.vllm.multimodal_handlers.processor_handler import (
+    ECProcessorHandler,
+    ProcessorHandler,
+)
 from dynamo.vllm.multimodal_handlers.worker_handler import (
     MultimodalDecodeWorkerHandler,
     MultimodalPDWorkerHandler,
 )
 
 __all__ = [
     "EncodeWorkerHandler",
+    "VLLMEncodeWorkerHandler",
     "ProcessorHandler",
     "MultimodalPDWorkerHandler",
     "MultimodalDecodeWorkerHandler",
+    "ECProcessorHandler",
 ]